From bbe6cfb899bb78359c6b2f3edc023fb99a042da9 Mon Sep 17 00:00:00 2001
From: jon-chuang <9093549+jon-chuang@users.noreply.github.com>
Date: Thu, 6 Jul 2023 01:49:45 +0800
Subject: [PATCH] feat(formatting): `black[jupyter]` (#6732)
---
docs/examples/agent/openai_agent.ipynb | 24 +-
.../openai_agent_context_retrieval.ipynb | 75 +-
.../agent/openai_agent_query_cookbook.ipynb | 197 +-
.../agent/openai_agent_query_plan.ipynb | 63 +-
.../agent/openai_agent_retrieval.ipynb | 14 +-
.../openai_agent_with_query_engine.ipynb | 45 +-
docs/examples/analysis/PlaygroundDemo.ipynb | 843 +--
docs/examples/analysis/TokenPredictor.ipynb | 737 +--
.../callbacks/LlamaDebugHandler.ipynb | 16 +-
.../callbacks/TokenCountingHandler.ipynb | 57 +-
.../callbacks/WandbCallbackHandler.ipynb | 70 +-
.../chat_engine_condense_question.ipynb | 9 +-
.../chat_engine/chat_engine_react.ipynb | 25 +-
.../chat_engine/chat_engine_repl.ipynb | 7 +-
.../citation/pdf_page_reference.ipynb | 26 +-
.../ComposableIndices-Prior.ipynb | 975 ++--
.../ComposableIndices-Weaviate.ipynb | 805 +--
.../ComposableIndices.ipynb | 909 ++-
.../City_Analysis-Decompose.ipynb | 5173 +++++++++--------
.../City_Analysis-Unified-Query.ipynb | 4573 ++++++++-------
.../city_analysis/City_Analysis.ipynb | 4625 +++++++--------
.../PineconeDemo-CityAnalysis.ipynb | 3807 ++++++------
.../customization/llms/AzureOpenAI.ipynb | 526 +-
.../llms/SimpleIndexDemo-ChatGPT.ipynb | 15 +-
.../SimpleIndexDemo-Huggingface_camel.ipynb | 7 +-
...SimpleIndexDemo-Huggingface_stablelm.ipynb | 9 +-
.../streaming/SimpleIndexDemo-streaming.ipynb | 9 +-
...ne_condense_question_stream_response.ipynb | 15 +-
.../examples/data_connectors/ChromaDemo.ipynb | 291 +-
.../data_connectors/DatabaseReaderDemo.ipynb | 403 +-
.../data_connectors/DiscordDemo.ipynb | 237 +-
docs/examples/data_connectors/FaissDemo.ipynb | 328 +-
.../GithubRepositoryReaderDemo.ipynb | 237 +-
docs/examples/data_connectors/MakeDemo.ipynb | 200 +-
.../data_connectors/MboxReaderDemo.ipynb | 220 +-
.../data_connectors/MilvusReaderDemo.ipynb | 3 +-
docs/examples/data_connectors/MongoDemo.ipynb | 220 +-
.../data_connectors/MyScaleReaderDemo.ipynb | 253 +-
.../examples/data_connectors/NotionDemo.ipynb | 294 +-
.../data_connectors/ObsidianReaderDemo.ipynb | 266 +-
.../data_connectors/PineconeDemo.ipynb | 306 +-
.../data_connectors/PsychicDemo.ipynb | 10 +-
.../examples/data_connectors/QdrantDemo.ipynb | 264 +-
.../data_connectors/WeaviateDemo.ipynb | 356 +-
.../data_connectors/WebPageDemo.ipynb | 446 +-
.../data_connectors/deplot/DeplotReader.ipynb | 12 +-
.../Discord_Thread_Management.ipynb | 76 +-
.../examples/docstore/MongoDocstoreDemo.ipynb | 814 +--
.../RedisDocstoreIndexStoreDemo.ipynb | 39 +-
.../evaluation/QuestionGeneration.ipynb | 705 +--
docs/examples/evaluation/RetryQuery.ipynb | 11 +-
.../evaluation/TestNYC-Evaluation-Query.ipynb | 1593 ++---
.../evaluation/TestNYC-Evaluation.ipynb | 1054 ++--
.../doc_summary/DocSummary.ipynb | 45 +-
.../knowledge_graph/KnowledgeGraphDemo.ipynb | 72 +-
...orStoreIndex_vs_CustomIndex_combined.ipynb | 112 +-
.../NebulaGraphKGIndexDemo.ipynb | 94 +-
.../struct_indices/SQLIndexDemo.ipynb | 898 +--
.../struct_indices/duckdb_sql_query.ipynb | 24 +-
docs/examples/llm/azure_openai.ipynb | 22 +-
docs/examples/llm/langchain.ipynb | 4 +-
docs/examples/llm/llm_predictor.ipynb | 8 +-
docs/examples/llm/openai.ipynb | 47 +-
.../node_postprocessor/CohereRerank.ipynb | 8 +-
.../LLMReranker-Gatsby.ipynb | 62 +-
.../LLMReranker-Lyft-10k.ipynb | 50 +-
.../node_postprocessor/OptimizerDemo.ipynb | 416 +-
docs/examples/node_postprocessor/PII.ipynb | 11 +-
.../PrevNextPostprocessorDemo.ipynb | 43 +-
.../RecencyPostprocessorDemo.ipynb | 754 ++-
.../TimeWeightedPostprocessorDemo.ipynb | 30 +-
.../output_parsing/GuardrailsDemo.ipynb | 25 +-
.../LangchainOutputParserDemo.ipynb | 630 +-
docs/examples/output_parsing/df_program.ipynb | 49 +-
.../output_parsing/evaporate_program.ipynb | 52 +-
.../guidance_pydantic_program.ipynb | 9 +-
.../guidance_sub_question.ipynb | 48 +-
.../openai_pydantic_program.ipynb | 11 +-
.../query_engine/CustomRetrievers.ipynb | 44 +-
.../query_engine/JointQASummary.ipynb | 495 +-
.../RetrieverRouterQueryEngine.ipynb | 20 +-
.../query_engine/RouterQueryEngine.ipynb | 42 +-
.../SQLAutoVectorQueryEngine.ipynb | 66 +-
.../query_engine/SQLJoinQueryEngine.ipynb | 63 +-
.../query_engine/SQLRouterQueryEngine.ipynb | 37 +-
.../query_engine/citation_query_engine.ipynb | 16 +-
.../query_engine/flare_query_engine.ipynb | 25 +-
.../query_engine/json_query_engine.ipynb | 709 ++-
.../query_engine/pandas_query_engine.ipynb | 549 +-
.../pdf_tables/recursive_retriever.ipynb | 41 +-
.../sub_question_query_engine.ipynb | 14 +-
.../HyDEQueryTransformDemo.ipynb | 754 +--
.../SimpleIndexDemo-multistep.ipynb | 545 +-
docs/examples/response_builder/refine.ipynb | 11 +-
.../response_builder/tree_summarize.ipynb | 6 +-
docs/examples/tools/OnDemandLoaderTool.ipynb | 9 +-
docs/examples/usecases/10k_graph_agent.ipynb | 56 +-
docs/examples/usecases/10k_sub_question.ipynb | 21 +-
.../usecases/10q_fn_agent-react-compare.ipynb | 87 +-
docs/examples/usecases/10q_sub_question.ipynb | 42 +-
...City_Analysis-Decompose-KeywordTable.ipynb | 4681 +++++++--------
.../AsyncIndexCreationDemo.ipynb | 442 +-
.../vector_stores/ChromaIndexDemo.ipynb | 980 ++--
.../vector_stores/DeepLakeIndexDemo.ipynb | 896 +--
.../vector_stores/DocArrayHnswIndexDemo.ipynb | 56 +-
.../DocArrayInMemoryIndexDemo.ipynb | 47 +-
.../vector_stores/FaissIndexDemo.ipynb | 400 +-
.../vector_stores/LanceDBIndexDemo.ipynb | 13 +-
.../vector_stores/MetalIndexDemo.ipynb | 320 +-
.../vector_stores/MilvusIndexDemo.ipynb | 679 +--
.../MongoDBAtlasVectorSearch.ipynb | 4 +-
.../vector_stores/MyScaleIndexDemo.ipynb | 332 +-
.../vector_stores/OpensearchDemo.ipynb | 11 +-
.../PineconeIndexDemo-0.6.0.ipynb | 93 +-
.../vector_stores/PineconeIndexDemo.ipynb | 504 +-
.../vector_stores/QdrantIndexDemo.ipynb | 8 +-
.../vector_stores/RedisIndexDemo.ipynb | 1325 ++---
.../vector_stores/SimpleIndexDemo.ipynb | 33 +-
.../vector_stores/SimpleIndexDemoMMR.ipynb | 62 +-
.../vector_stores/SimpleIndexOnS3.ipynb | 36 +-
.../SupabaseVectorIndexDemo.ipynb | 51 +-
.../vector_stores/TairIndexDemo.ipynb | 30 +-
.../vector_stores/TypesenseDemo.ipynb | 348 +-
.../WeaviateIndexDemo-Hybrid.ipynb | 533 +-
.../vector_stores/WeaviateIndexDemo.ipynb | 478 +-
.../vector_stores/chroma_auto_retriever.ipynb | 682 +--
.../chroma_metadata_filter.ipynb | 494 +-
.../pinecone_existing_data.ipynb | 75 +-
.../weaviate_existing_data.ipynb | 95 +-
.../pinecone_auto_retriever.ipynb | 616 +-
.../pinecone_metadata_filter.ipynb | 426 +-
docs/examples/vector_stores/postgres.ipynb | 33 +-
docs/guides/tutorials/Airbyte_demo.ipynb | 1260 ++--
docs/how_to/index/index_progress_bars.ipynb | 4 +-
docs/how_to/index/vector_store_guide.ipynb | 43 +-
.../async/AsyncComposableIndicesSEC.ipynb | 3354 +++++------
examples/async/AsyncGPTTreeIndexDemo.ipynb | 269 +-
examples/async/AsyncLLMPredictorDemo.ipynb | 6 +-
examples/async/AsyncQueryDemo.ipynb | 19 +-
examples/chatbot/Chatbot_SEC.ipynb | 3733 ++++++------
.../ChatGPTRetrievalPluginIndexDemo.ipynb | 468 +-
.../ChatGPTRetrievalPluginReaderDemo.ipynb | 493 +-
.../ChatGPT_Retrieval_Plugin_Upload.ipynb | 5 +-
examples/docstore/DocstoreDemo.ipynb | 569 +-
examples/docstore/DynamoDBDocstoreDemo.ipynb | 793 +--
examples/docstore/MongoDocstoreDemo.ipynb | 809 +--
.../RedisDocstoreIndexStoreDemo.ipynb | 39 +-
examples/experimental/Evaporate.ipynb | 43 +-
examples/experimental/NotionToolSpec.ipynb | 9 +-
examples/gatsby/TestGatsby.ipynb | 327 +-
examples/langchain_demo/LangchainDemo.ipynb | 704 +--
examples/multimodal/Multimodal.ipynb | 26 +-
.../paul_graham_essay/DavinciComparison.ipynb | 15 +-
.../paul_graham_essay/GPT4Comparison.ipynb | 1305 +++--
examples/paul_graham_essay/InsertDemo.ipynb | 7 +-
.../KeywordTableComparison.ipynb | 870 ++-
.../SentenceSplittingDemo.ipynb | 309 +-
examples/paul_graham_essay/TestEssay.ipynb | 1199 ++--
.../test_wiki/TestNYC-Benchmark-GPT4.ipynb | 3401 +++++------
examples/test_wiki/TestNYC-Tree-GPT4.ipynb | 63 +-
examples/test_wiki/TestNYC.ipynb | 322 +-
examples/test_wiki/TestNYC_Embeddings.ipynb | 796 +--
examples/test_wiki/TestWikiReader.ipynb | 547 +-
examples/vellum/Vellum Integration Demo.ipynb | 4 +-
experimental/classifier/TitanicModel.ipynb | 1115 ++--
requirements.txt | 2 +-
166 files changed, 39023 insertions(+), 38158 deletions(-)
diff --git a/docs/examples/agent/openai_agent.ipynb b/docs/examples/agent/openai_agent.ipynb
index 943273c462..2c5040bbaf 100644
--- a/docs/examples/agent/openai_agent.ipynb
+++ b/docs/examples/agent/openai_agent.ipynb
@@ -65,6 +65,7 @@
"from llama_index.tools import BaseTool, FunctionTool\n",
"\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -107,6 +108,7 @@
" \"\"\"Add two integers and returns the result integer\"\"\"\n",
" return a + b\n",
"\n",
+ "\n",
"add_tool = FunctionTool.from_defaults(fn=add)"
]
},
@@ -162,8 +164,10 @@
"\n",
" def chat(self, message: str) -> str:\n",
" chat_history = self._chat_history\n",
- " chat_history.append(ChatMessage(role='user', content=message))\n",
- " functions = [tool.metadata.to_openai_function() for _, tool in self._tools.items()]\n",
+ " chat_history.append(ChatMessage(role=\"user\", content=message))\n",
+ " functions = [\n",
+ " tool.metadata.to_openai_function() for _, tool in self._tools.items()\n",
+ " ]\n",
"\n",
" ai_message = self._llm.chat(chat_history, functions=functions).message\n",
" chat_history.append(ai_message)\n",
@@ -182,11 +186,9 @@
" output = tool(**json.loads(function_call[\"arguments\"]))\n",
" return ChatMessage(\n",
" name=function_call[\"name\"],\n",
- " content=str(output), \n",
- " role='function',\n",
- " additional_kwargs={\n",
- " \"name\": function_call[\"name\"]\n",
- " }\n",
+ " content=str(output),\n",
+ " role=\"function\",\n",
+ " additional_kwargs={\"name\": function_call[\"name\"]},\n",
" )"
]
},
@@ -231,7 +233,7 @@
}
],
"source": [
- "agent.chat('Hi')"
+ "agent.chat(\"Hi\")"
]
},
{
@@ -254,7 +256,7 @@
}
],
"source": [
- "agent.chat('What is 2123 * 215123')"
+ "agent.chat(\"What is 2123 * 215123\")"
]
},
{
@@ -440,7 +442,7 @@
")\n",
"for response in agent_stream:\n",
" response_gen = response.response_gen\n",
- " # NOTE: here, we skip any intermediate steps and wait until the last response \n",
+ " # NOTE: here, we skip any intermediate steps and wait until the last response\n",
" # intermediate steps usually only contain function calls though\n",
" # for token in response_gen:\n",
" # print(token, end=\"\")\n",
@@ -506,7 +508,7 @@
"\n",
"async for response in chat_gen:\n",
" response_gen = response.response_gen\n",
- " # NOTE: here, we skip any intermediate steps and wait until the last response \n",
+ " # NOTE: here, we skip any intermediate steps and wait until the last response\n",
" # intermediate steps usually only contain function calls though\n",
" # for token in response_gen:\n",
" # print(token, end=\"\")\n",
diff --git a/docs/examples/agent/openai_agent_context_retrieval.ipynb b/docs/examples/agent/openai_agent_context_retrieval.ipynb
index 34eff8ef7b..522227e952 100644
--- a/docs/examples/agent/openai_agent_context_retrieval.ipynb
+++ b/docs/examples/agent/openai_agent_context_retrieval.ipynb
@@ -56,10 +56,10 @@
"from typing import Sequence\n",
"\n",
"from llama_index import (\n",
- " SimpleDirectoryReader, \n",
- " VectorStoreIndex, \n",
- " StorageContext, \n",
- " load_index_from_storage\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ " StorageContext,\n",
+ " load_index_from_storage,\n",
")\n",
"from llama_index.tools import QueryEngineTool, ToolMetadata"
]
@@ -74,15 +74,15 @@
"outputs": [],
"source": [
"try:\n",
- " storage_context = StorageContext.from_defaults(persist_dir='./storage/march')\n",
+ " storage_context = StorageContext.from_defaults(persist_dir=\"./storage/march\")\n",
" march_index = load_index_from_storage(storage_context)\n",
"\n",
- " storage_context = StorageContext.from_defaults(persist_dir='./storage/june')\n",
+ " storage_context = StorageContext.from_defaults(persist_dir=\"./storage/june\")\n",
" june_index = load_index_from_storage(storage_context)\n",
- " \n",
- " storage_context = StorageContext.from_defaults(persist_dir='./storage/sept')\n",
+ "\n",
+ " storage_context = StorageContext.from_defaults(persist_dir=\"./storage/sept\")\n",
" sept_index = load_index_from_storage(storage_context)\n",
- " \n",
+ "\n",
" index_loaded = True\n",
"except:\n",
" index_loaded = False"
@@ -100,16 +100,22 @@
"# build indexes across the three data sources\n",
"\n",
"if not index_loaded:\n",
- " # load data \n",
- " march_docs = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]).load_data()\n",
- " june_docs = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]).load_data()\n",
- " sept_docs = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]).load_data()\n",
- " \n",
+ " # load data\n",
+ " march_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]\n",
+ " ).load_data()\n",
+ " june_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]\n",
+ " ).load_data()\n",
+ " sept_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]\n",
+ " ).load_data()\n",
+ "\n",
" # build index\n",
" march_index = VectorStoreIndex.from_documents(march_docs)\n",
" june_index = VectorStoreIndex.from_documents(june_docs)\n",
" sept_index = VectorStoreIndex.from_documents(sept_docs)\n",
- " \n",
+ "\n",
" # persist index\n",
" march_index.storage_context.persist(persist_dir=\"./storage/march\")\n",
" june_index.storage_context.persist(persist_dir=\"./storage/june\")\n",
@@ -141,30 +147,30 @@
"source": [
"query_engine_tools = [\n",
" QueryEngineTool(\n",
- " query_engine=march_engine, \n",
+ " query_engine=march_engine,\n",
" metadata=ToolMetadata(\n",
- " name='uber_march_10q', \n",
+ " name=\"uber_march_10q\",\n",
" description=\"Provides information about Uber 10Q filings for March 2022. \"\n",
- " \"Use a detailed plain text question as input to the tool.\"\n",
- " )\n",
+ " \"Use a detailed plain text question as input to the tool.\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=june_engine, \n",
+ " query_engine=june_engine,\n",
" metadata=ToolMetadata(\n",
- " name='uber_june_10q', \n",
+ " name=\"uber_june_10q\",\n",
" description=\"Provides information about Uber financials for June 2021. \"\n",
- " \"Use a detailed plain text question as input to the tool.\"\n",
- " )\n",
+ " \"Use a detailed plain text question as input to the tool.\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=sept_engine, \n",
+ " query_engine=sept_engine,\n",
" metadata=ToolMetadata(\n",
- " name='uber_sept_10q', \n",
+ " name=\"uber_sept_10q\",\n",
" description=\"Provides information about Uber financials for Sept 2021. \"\n",
- " \"Use a detailed plain text question as input to the tool.\"\n",
- " )\n",
+ " \"Use a detailed plain text question as input to the tool.\",\n",
+ " ),\n",
" ),\n",
- "]\n"
+ "]"
]
},
{
@@ -205,7 +211,7 @@
"texts = [\n",
" \"Abbrevation: X = Revenue\",\n",
" \"Abbrevation: YZ = Risk Factors\",\n",
- " \"Abbreviation: Z = Costs\"\n",
+ " \"Abbreviation: Z = Costs\",\n",
"]\n",
"docs = [Document(text=t) for t in texts]\n",
"context_index = VectorStoreIndex.from_documents(docs)"
@@ -221,9 +227,7 @@
"outputs": [],
"source": [
"context_agent = ContextRetrieverOpenAIAgent.from_tools_and_retriever(\n",
- " query_engine_tools, \n",
- " context_index.as_retriever(similarity_top_k=1),\n",
- " verbose=True\n",
+ " query_engine_tools, context_index.as_retriever(similarity_top_k=1), verbose=True\n",
")"
]
},
@@ -361,6 +365,7 @@
" \"\"\"Runs MAGIC_FORMULA on revenue and cost.\"\"\"\n",
" return revenue - cost\n",
"\n",
+ "\n",
"magic_tool = FunctionTool.from_defaults(fn=magic_formula, name=\"magic_formula\")"
]
},
@@ -374,9 +379,7 @@
"outputs": [],
"source": [
"context_agent = ContextRetrieverOpenAIAgent.from_tools_and_retriever(\n",
- " [magic_tool], \n",
- " sept_index.as_retriever(similarity_top_k=3),\n",
- " verbose=True\n",
+ " [magic_tool], sept_index.as_retriever(similarity_top_k=3), verbose=True\n",
")"
]
},
@@ -521,7 +524,7 @@
}
],
"source": [
- "response = context_agent.chat(\"Can you run MAGIC_FORMULA on Uber's revenue and cost?\") "
+ "response = context_agent.chat(\"Can you run MAGIC_FORMULA on Uber's revenue and cost?\")"
]
},
{
diff --git a/docs/examples/agent/openai_agent_query_cookbook.ipynb b/docs/examples/agent/openai_agent_query_cookbook.ipynb
index e9f3540cb2..6e4c5c7caf 100644
--- a/docs/examples/agent/openai_agent_query_cookbook.ipynb
+++ b/docs/examples/agent/openai_agent_query_cookbook.ipynb
@@ -42,7 +42,7 @@
"import pinecone\n",
"import os\n",
"\n",
- "api_key = os.environ['PINECONE_API_KEY']\n",
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
"pinecone.init(api_key=api_key, environment=\"us-west1-gcp\")"
]
},
@@ -57,7 +57,9 @@
"source": [
"# dimensions are for text-embedding-ada-002\n",
"try:\n",
- " pinecone.create_index(\"quickstart\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")\n",
+ " pinecone.create_index(\n",
+ " \"quickstart\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\"\n",
+ " )\n",
"except Exception:\n",
" # most likely index already exists\n",
" pass"
@@ -124,26 +126,41 @@
"from llama_index.schema import TextNode\n",
"\n",
"nodes = [\n",
- " TextNode(text=\"Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.\", metadata={\n",
- " \"category\": \"Sports\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.\", metadata={\n",
- " \"category\": \"Entertainment\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, and lead designer of SpaceX, Tesla, Inc., Neuralink, and The Boring Company.\", metadata={\n",
- " \"category\": \"Business\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Rihanna is a Barbadian singer, actress, and businesswoman. She has achieved significant success in the music industry and is known for her versatile musical style.\", metadata={\n",
- " \"category\": \"Music\",\n",
- " \"country\": \"Barbados\",\n",
- " }),\n",
- " TextNode(text=\"Cristiano Ronaldo is a Portuguese professional footballer who is considered one of the greatest football players of all time. He has won numerous awards and set multiple records during his career.\", metadata={\n",
- " \"category\": \"Sports\",\n",
- " \"country\": \"Portugal\",\n",
- " })\n",
+ " TextNode(\n",
+ " text=\"Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.\",\n",
+ " metadata={\n",
+ " \"category\": \"Sports\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.\",\n",
+ " metadata={\n",
+ " \"category\": \"Entertainment\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, and lead designer of SpaceX, Tesla, Inc., Neuralink, and The Boring Company.\",\n",
+ " metadata={\n",
+ " \"category\": \"Business\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Rihanna is a Barbadian singer, actress, and businesswoman. She has achieved significant success in the music industry and is known for her versatile musical style.\",\n",
+ " metadata={\n",
+ " \"category\": \"Music\",\n",
+ " \"country\": \"Barbados\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Cristiano Ronaldo is a Portuguese professional footballer who is considered one of the greatest football players of all time. He has won numerous awards and set multiple records during his career.\",\n",
+ " metadata={\n",
+ " \"category\": \"Sports\",\n",
+ " \"country\": \"Portugal\",\n",
+ " },\n",
+ " ),\n",
"]"
]
},
@@ -156,7 +173,7 @@
},
"outputs": [],
"source": [
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='test')\n",
+ "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace=\"test\")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)"
]
},
@@ -205,7 +222,12 @@
"source": [
"# define function tool\n",
"from llama_index.tools import FunctionTool\n",
- "from llama_index.vector_stores.types import VectorStoreInfo, MetadataInfo, ExactMatchFilter, MetadataFilters\n",
+ "from llama_index.vector_stores.types import (\n",
+ " VectorStoreInfo,\n",
+ " MetadataInfo,\n",
+ " ExactMatchFilter,\n",
+ " MetadataFilters,\n",
+ ")\n",
"from llama_index.retrievers import VectorIndexRetriever\n",
"from llama_index.query_engine import RetrieverQueryEngine\n",
"\n",
@@ -217,42 +239,54 @@
"\n",
"# define vector store info describing schema of vector store\n",
"vector_store_info = VectorStoreInfo(\n",
- " content_info='brief biography of celebrities',\n",
+ " content_info=\"brief biography of celebrities\",\n",
" metadata_info=[\n",
" MetadataInfo(\n",
- " name='category', \n",
- " type='str', \n",
- " description='Category of the celebrity, one of [Sports, Entertainment, Business, Music]'),\n",
- " MetadataInfo(name='country', type='str', description='Country of the celebrity, one of [United States, Barbados, Portugal]'),\n",
- " ]\n",
+ " name=\"category\",\n",
+ " type=\"str\",\n",
+ " description=\"Category of the celebrity, one of [Sports, Entertainment, Business, Music]\",\n",
+ " ),\n",
+ " MetadataInfo(\n",
+ " name=\"country\",\n",
+ " type=\"str\",\n",
+ " description=\"Country of the celebrity, one of [United States, Barbados, Portugal]\",\n",
+ " ),\n",
+ " ],\n",
")\n",
- " \n",
+ "\n",
"# define pydantic model for auto-retrieval function\n",
"class AutoRetrieveModel(BaseModel):\n",
" query: str = Field(..., description=\"natural language query string\")\n",
- " filter_key_list: List[str] = Field(..., description=\"List of metadata filter field names\")\n",
- " filter_value_list: List[str] = Field(..., description=(\n",
- " \"List of metadata filter field values (corresponding to names specified in filter_key_list)\"\n",
- " ))\n",
+ " filter_key_list: List[str] = Field(\n",
+ " ..., description=\"List of metadata filter field names\"\n",
+ " )\n",
+ " filter_value_list: List[str] = Field(\n",
+ " ...,\n",
+ " description=(\n",
+ " \"List of metadata filter field values (corresponding to names specified in filter_key_list)\"\n",
+ " ),\n",
+ " )\n",
"\n",
- "def auto_retrieve_fn(query: str, filter_key_list: List[str], filter_value_list: List[str]):\n",
+ "\n",
+ "def auto_retrieve_fn(\n",
+ " query: str, filter_key_list: List[str], filter_value_list: List[str]\n",
+ "):\n",
" \"\"\"Auto retrieval function.\n",
- " \n",
+ "\n",
" Performs auto-retrieval from a vector database, and then applies a set of filters.\n",
- " \n",
+ "\n",
" \"\"\"\n",
" query = query or \"Query\"\n",
- " \n",
- " exact_match_filters = [ExactMatchFilter(key=k, value=v) for k, v in zip(filter_key_list, filter_value_list)]\n",
+ "\n",
+ " exact_match_filters = [\n",
+ " ExactMatchFilter(key=k, value=v)\n",
+ " for k, v in zip(filter_key_list, filter_value_list)\n",
+ " ]\n",
" retriever = VectorIndexRetriever(\n",
- " index,\n",
- " filters=MetadataFilters(filters=exact_match_filters),\n",
- " top_k=top_k\n",
- " )\n",
- " query_engine = RetrieverQueryEngine.from_args(\n",
- " retriever\n",
+ " index, filters=MetadataFilters(filters=exact_match_filters), top_k=top_k\n",
" )\n",
- " \n",
+ " query_engine = RetrieverQueryEngine.from_args(retriever)\n",
+ "\n",
" response = query_engine.query(query)\n",
" return str(response)\n",
"\n",
@@ -262,13 +296,13 @@
"The vector database schema is given below:\n",
"{vector_store_info.json()}\n",
"\"\"\"\n",
- " \n",
+ "\n",
"auto_retrieve_tool = FunctionTool.from_defaults(\n",
" fn=auto_retrieve_fn,\n",
" name=\"celebrity_bios\",\n",
" description=description,\n",
- " fn_schema=AutoRetrieveModel\n",
- ")\n"
+ " fn_schema=AutoRetrieveModel,\n",
+ ")"
]
},
{
@@ -293,9 +327,7 @@
"from llama_index.llms import OpenAI\n",
"\n",
"agent = OpenAIAgent.from_tools(\n",
- " [auto_retrieve_tool], \n",
- " llm=OpenAI(temperature=0, model=\"gpt-4-0613\"),\n",
- " verbose=True\n",
+ " [auto_retrieve_tool], llm=OpenAI(temperature=0, model=\"gpt-4-0613\"), verbose=True\n",
")"
]
},
@@ -327,9 +359,7 @@
}
],
"source": [
- "response = agent.chat(\n",
- " \"Tell me about two celebrities from the United States. \"\n",
- ")\n",
+ "response = agent.chat(\"Tell me about two celebrities from the United States. \")\n",
"print(str(response))"
]
},
@@ -366,7 +396,16 @@
},
"outputs": [],
"source": [
- "from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, select, column\n",
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ " select,\n",
+ " column,\n",
+ ")\n",
"from llama_index import SQLDatabase, SQLStructStoreIndex\n",
"\n",
"engine = create_engine(\"sqlite:///:memory:\", future=True)\n",
@@ -429,6 +468,7 @@
"outputs": [],
"source": [
"from sqlalchemy import insert\n",
+ "\n",
"rows = [\n",
" {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
" {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
@@ -563,7 +603,7 @@
},
"outputs": [],
"source": [
- "cities = ['Toronto', 'Berlin', 'Tokyo']\n",
+ "cities = [\"Toronto\", \"Berlin\", \"Tokyo\"]\n",
"wiki_docs = WikipediaReader().load_data(pages=cities)"
]
},
@@ -576,11 +616,11 @@
},
"outputs": [],
"source": [
- "# define pinecone index \n",
+ "# define pinecone index\n",
"import pinecone\n",
"import os\n",
"\n",
- "api_key = os.environ['PINECONE_API_KEY']\n",
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
"pinecone.init(api_key=api_key, environment=\"us-west1-gcp\")\n",
"\n",
"# dimensions are for text-embedding-ada-002\n",
@@ -636,7 +676,9 @@
"node_parser = SimpleNodeParser(text_splitter=text_splitter)\n",
"\n",
"# define pinecone vector index\n",
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='wiki_cities')\n",
+ "vector_store = PineconeVectorStore(\n",
+ " pinecone_index=pinecone_index, namespace=\"wiki_cities\"\n",
+ ")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"vector_index = VectorStoreIndex([], storage_context=storage_context)"
]
@@ -708,19 +750,18 @@
"\n",
"\n",
"vector_store_info = VectorStoreInfo(\n",
- " content_info='articles about different cities',\n",
+ " content_info=\"articles about different cities\",\n",
" metadata_info=[\n",
- " MetadataInfo(\n",
- " name='title', \n",
- " type='str', \n",
- " description='The name of the city'),\n",
- " ]\n",
+ " MetadataInfo(name=\"title\", type=\"str\", description=\"The name of the city\"),\n",
+ " ],\n",
+ ")\n",
+ "vector_auto_retriever = VectorIndexAutoRetriever(\n",
+ " vector_index, vector_store_info=vector_store_info\n",
")\n",
- "vector_auto_retriever = VectorIndexAutoRetriever(vector_index, vector_store_info=vector_store_info)\n",
"\n",
"retriever_query_engine = RetrieverQueryEngine.from_args(\n",
" vector_auto_retriever, service_context=service_context\n",
- ")\n"
+ ")"
]
},
{
@@ -736,14 +777,14 @@
" query_engine=query_engine,\n",
" name=\"sql_tool\",\n",
" description=(\n",
- " 'Useful for translating a natural language query into a SQL query over a table containing: '\n",
- " 'city_stats, containing the population/country of each city'\n",
- " )\n",
+ " \"Useful for translating a natural language query into a SQL query over a table containing: \"\n",
+ " \"city_stats, containing the population/country of each city\"\n",
+ " ),\n",
")\n",
"vector_tool = QueryEngineTool.from_defaults(\n",
" query_engine=retriever_query_engine,\n",
" name=\"vector_tool\",\n",
- " description=f'Useful for answering semantic questions about different cities',\n",
+ " description=f\"Useful for answering semantic questions about different cities\",\n",
")"
]
},
@@ -769,9 +810,7 @@
"from llama_index.llms import OpenAI\n",
"\n",
"agent = OpenAIAgent.from_tools(\n",
- " [sql_tool, vector_tool], \n",
- " llm=OpenAI(temperature=0, model=\"gpt-4-0613\"),\n",
- " verbose=True\n",
+ " [sql_tool, vector_tool], llm=OpenAI(temperature=0, model=\"gpt-4-0613\"), verbose=True\n",
")"
]
},
@@ -815,7 +854,9 @@
],
"source": [
"# NOTE: gpt-3.5 gives the wrong answer, but gpt-4 is able to reason over both loops\n",
- "response = agent.chat(\"Tell me about the arts and culture of the city with the highest population\")\n",
+ "response = agent.chat(\n",
+ " \"Tell me about the arts and culture of the city with the highest population\"\n",
+ ")\n",
"print(str(response))"
]
},
@@ -853,7 +894,7 @@
}
],
"source": [
- "response = agent.chat('Tell me about the history of Berlin')\n",
+ "response = agent.chat(\"Tell me about the history of Berlin\")\n",
"print(str(response))"
]
},
diff --git a/docs/examples/agent/openai_agent_query_plan.ipynb b/docs/examples/agent/openai_agent_query_plan.ipynb
index 4af2947fa7..7eba53a90d 100644
--- a/docs/examples/agent/openai_agent_query_plan.ipynb
+++ b/docs/examples/agent/openai_agent_query_plan.ipynb
@@ -58,7 +58,12 @@
},
"outputs": [],
"source": [
- "from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " GPTVectorStoreIndex,\n",
+ ")\n",
"from llama_index.response.pprint_utils import pprint_response\n",
"from llama_index.llms import OpenAI"
]
@@ -96,9 +101,15 @@
},
"outputs": [],
"source": [
- "march_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]).load_data()\n",
- "june_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]).load_data()\n",
- "sept_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]).load_data()"
+ "march_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]\n",
+ ").load_data()\n",
+ "june_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]\n",
+ ").load_data()\n",
+ "sept_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]\n",
+ ").load_data()"
]
},
{
@@ -135,9 +146,15 @@
},
"outputs": [],
"source": [
- "march_engine = march_index.as_query_engine(similarity_top_k=3, service_context=service_context)\n",
- "june_engine = june_index.as_query_engine(similarity_top_k=3, service_context=service_context)\n",
- "sept_engine = sept_index.as_query_engine(similarity_top_k=3, service_context=service_context)"
+ "march_engine = march_index.as_query_engine(\n",
+ " similarity_top_k=3, service_context=service_context\n",
+ ")\n",
+ "june_engine = june_index.as_query_engine(\n",
+ " similarity_top_k=3, service_context=service_context\n",
+ ")\n",
+ "sept_engine = sept_index.as_query_engine(\n",
+ " similarity_top_k=3, service_context=service_context\n",
+ ")"
]
},
{
@@ -197,13 +214,11 @@
"from llama_index.tools import QueryPlanTool\n",
"from llama_index import get_response_synthesizer\n",
"\n",
- "response_synthesizer = get_response_synthesizer(\n",
- " service_context=service_context\n",
- ")\n",
+ "response_synthesizer = get_response_synthesizer(service_context=service_context)\n",
"query_plan_tool = QueryPlanTool.from_defaults(\n",
" query_engine_tools=[query_tool_sept, query_tool_june, query_tool_march],\n",
" response_synthesizer=response_synthesizer,\n",
- ")\n"
+ ")"
]
},
{
@@ -272,7 +287,7 @@
" [query_plan_tool],\n",
" max_function_calls=10,\n",
" llm=OpenAI(temperature=0, model=\"gpt-4-0613\"),\n",
- " verbose=True\n",
+ " verbose=True,\n",
")"
]
},
@@ -299,13 +314,15 @@
"source": [
"from llama_index.tools.query_plan import QueryPlan\n",
"\n",
- "query_plan = QueryPlan(**{\n",
- " \"root\": {\n",
- " \"query_str\": \"risk factors\",\n",
- " \"tool_name\": \"sept_2022\",\n",
- " \"child_nodes\": []\n",
- " }\n",
- "})"
+ "query_plan = QueryPlan(\n",
+ " **{\n",
+ " \"root\": {\n",
+ " \"query_str\": \"risk factors\",\n",
+ " \"tool_name\": \"sept_2022\",\n",
+ " \"child_nodes\": [],\n",
+ " }\n",
+ " }\n",
+ ")"
]
},
{
@@ -465,7 +482,9 @@
},
"outputs": [],
"source": [
- "response = agent.query(\"Analyze changes in risk factors in march, june, and september for Uber\")"
+ "response = agent.query(\n",
+ " \"Analyze changes in risk factors in march, june, and september for Uber\"\n",
+ ")"
]
},
{
@@ -534,8 +553,8 @@
"outputs": [],
"source": [
"response = agent.query(\n",
- " \"First look at Uber's revenue growth and risk factors in March, \" +\n",
- " \"then revenue growth and risk factors in September, and then compare and contrast the two documents?\"\n",
+ " \"First look at Uber's revenue growth and risk factors in March, \"\n",
+ " + \"then revenue growth and risk factors in September, and then compare and contrast the two documents?\"\n",
")"
]
},
diff --git a/docs/examples/agent/openai_agent_retrieval.ipynb b/docs/examples/agent/openai_agent_retrieval.ipynb
index 5206c367cf..6bad885592 100644
--- a/docs/examples/agent/openai_agent_retrieval.ipynb
+++ b/docs/examples/agent/openai_agent_retrieval.ipynb
@@ -94,17 +94,22 @@
" \"\"\"Multiply two integers and returns the result integer\"\"\"\n",
" return a * b\n",
"\n",
+ "\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Add two integers and returns the result integer\"\"\"\n",
" return a + b\n",
"\n",
+ "\n",
"def useless(a: int, b: int) -> int:\n",
" \"\"\"Toy useless function.\"\"\"\n",
" pass\n",
"\n",
"\n",
"multiply_tool = FunctionTool.from_defaults(fn=multiply, name=\"multiply\")\n",
- "useless_tools = [FunctionTool.from_defaults(fn=useless, name=f\"useless_{str(idx)}\") for idx in range(28)]\n",
+ "useless_tools = [\n",
+ " FunctionTool.from_defaults(fn=useless, name=f\"useless_{str(idx)}\")\n",
+ " for idx in range(28)\n",
+ "]\n",
"add_tool = FunctionTool.from_defaults(fn=add, name=\"add\")\n",
"\n",
"all_tools = [multiply_tool] + [add_tool] + useless_tools\n",
@@ -145,7 +150,7 @@
"\n",
"tool_mapping = SimpleToolNodeMapping.from_objects(all_tools)\n",
"obj_index = ObjectIndex.from_objects(\n",
- " all_tools, \n",
+ " all_tools,\n",
" tool_mapping,\n",
" VectorStoreIndex,\n",
")"
@@ -194,10 +199,7 @@
},
"outputs": [],
"source": [
- "agent = FnRetrieverOpenAIAgent.from_retriever(\n",
- " obj_index.as_retriever(),\n",
- " verbose=True\n",
- ")"
+ "agent = FnRetrieverOpenAIAgent.from_retriever(obj_index.as_retriever(), verbose=True)"
]
},
{
diff --git a/docs/examples/agent/openai_agent_with_query_engine.ipynb b/docs/examples/agent/openai_agent_with_query_engine.ipynb
index 4bbbd13399..56dfde025b 100644
--- a/docs/examples/agent/openai_agent_with_query_engine.ipynb
+++ b/docs/examples/agent/openai_agent_with_query_engine.ipynb
@@ -36,7 +36,12 @@
}
],
"source": [
- "from llama_index import SimpleDirectoryReader, VectorStoreIndex, StorageContext, load_index_from_storage\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ " StorageContext,\n",
+ " load_index_from_storage,\n",
+ ")\n",
"\n",
"from llama_index.tools import QueryEngineTool, ToolMetadata"
]
@@ -51,12 +56,12 @@
"outputs": [],
"source": [
"try:\n",
- " storage_context = StorageContext.from_defaults(persist_dir='./storage/lyft')\n",
+ " storage_context = StorageContext.from_defaults(persist_dir=\"./storage/lyft\")\n",
" lyft_index = load_index_from_storage(storage_context)\n",
"\n",
- " storage_context = StorageContext.from_defaults(persist_dir='./storage/uber')\n",
+ " storage_context = StorageContext.from_defaults(persist_dir=\"./storage/uber\")\n",
" uber_index = load_index_from_storage(storage_context)\n",
- " \n",
+ "\n",
" index_loaded = True\n",
"except:\n",
" index_loaded = False"
@@ -72,14 +77,18 @@
"outputs": [],
"source": [
"if not index_loaded:\n",
- " # load data \n",
- " lyft_docs = SimpleDirectoryReader(input_files=[\"../data/10k/lyft_2021.pdf\"]).load_data()\n",
- " uber_docs = SimpleDirectoryReader(input_files=[\"../data/10k/uber_2021.pdf\"]).load_data()\n",
- " \n",
+ " # load data\n",
+ " lyft_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10k/lyft_2021.pdf\"]\n",
+ " ).load_data()\n",
+ " uber_docs = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10k/uber_2021.pdf\"]\n",
+ " ).load_data()\n",
+ "\n",
" # build index\n",
" lyft_index = VectorStoreIndex.from_documents(lyft_docs)\n",
" uber_index = VectorStoreIndex.from_documents(uber_docs)\n",
- " \n",
+ "\n",
" # persist index\n",
" lyft_index.storage_context.persist(persist_dir=\"./storage/lyft\")\n",
" uber_index.storage_context.persist(persist_dir=\"./storage/uber\")"
@@ -109,22 +118,22 @@
"source": [
"query_engine_tools = [\n",
" QueryEngineTool(\n",
- " query_engine=lyft_engine, \n",
+ " query_engine=lyft_engine,\n",
" metadata=ToolMetadata(\n",
- " name='lyft_10k', \n",
+ " name=\"lyft_10k\",\n",
" description=\"Provides information about Lyft financials for year 2021. \"\n",
- " \"Use a detailed plain text question as input to the tool.\"\n",
- " )\n",
+ " \"Use a detailed plain text question as input to the tool.\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=uber_engine, \n",
+ " query_engine=uber_engine,\n",
" metadata=ToolMetadata(\n",
- " name='uber_10k', \n",
+ " name=\"uber_10k\",\n",
" description=\"Provides information about Uber financials for year 2021. \"\n",
- " \"Use a detailed plain text question as input to the tool.\"\n",
- " )\n",
+ " \"Use a detailed plain text question as input to the tool.\",\n",
+ " ),\n",
" ),\n",
- "]\n"
+ "]"
]
},
{
diff --git a/docs/examples/analysis/PlaygroundDemo.ipynb b/docs/examples/analysis/PlaygroundDemo.ipynb
index 6a60b88b69..8e384160a6 100644
--- a/docs/examples/analysis/PlaygroundDemo.ipynb
+++ b/docs/examples/analysis/PlaygroundDemo.ipynb
@@ -1,422 +1,427 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "af6397b2",
- "metadata": {},
- "source": [
- "# Playground"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "839c4a87",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "d726e871",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Hide INFO logs regarding token usage, etc\n",
- "import logging\n",
- "logger = logging.getLogger()\n",
- "logger.setLevel(logging.CRITICAL)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40cf0773",
- "metadata": {},
- "source": [
- "## Setup\n",
- "\n",
- "### Generate some example Documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa34cd83",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import download_loader\n",
- "from llama_index.indices.vector_store import VectorStoreIndex\n",
- "from llama_index.indices.tree.base import TreeIndex\n",
- "\n",
- "WikipediaReader = download_loader(\"WikipediaReader\")\n",
- "\n",
- "loader = WikipediaReader()\n",
- "documents = loader.load_data(pages=['Berlin'])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0c32392b",
- "metadata": {},
- "source": [
- "### Create a list of any sort of indices (custom LLMs, custom embeddings, etc)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f59e6c18",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:root:> [build_index_from_documents] Total embedding token usage: 18344 tokens\n",
- "INFO:root:> Building index from nodes: 5 chunks\n"
- ]
- }
- ],
- "source": [
- "indices = [VectorStoreIndex.from_documents(documents), TreeIndex.from_documents(documents)]"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "827ada33",
- "metadata": {},
- "source": [
- "## Using the Playground\n",
- "\n",
- "\n",
- "### Initialize with indices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "a04e4535",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.playground import Playground\n",
- "\n",
- "playground = Playground(indices=indices)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "5f6999fc",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 110.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[1mQuery:\u001b[0m\n",
- "What is the population of Berlin?\n",
- "\n",
- "Trying 10 combinations...\n",
- "\n",
- "\n",
- "\u001b[1mGPTVectorStoreIndex\u001b[0m, mode = default\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 90.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n",
- "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 90.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n",
- "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 80.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n",
- "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[36;1m\u001b[1;3m\n",
- "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\u001b[0m\n",
- "\n",
- "\u001b[1mGPTVectorStoreIndex\u001b[0m, mode = embedding\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 7 tokens\n",
- "INFO:root:> Starting query: What is the population of Berlin?\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[36;1m\u001b[1;3m\n",
- "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\u001b[0m\n",
- "\n",
- "\u001b[1mGPTTreeIndex\u001b[0m, mode = default\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:>[Level 0] Selected node: [1]/[1]\n",
- "INFO:root:>[Level 1] Selected node: [3]/[3]\n",
- "INFO:root:> [query] Total LLM token usage: 5168 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 0 tokens\n",
- "INFO:root:> Starting query: What is the population of Berlin?\n",
- "INFO:root:> Building index from nodes: 6 chunks\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3mThe population of Berlin is approximately 3.7 million people.\u001b[0m\n",
- "\n",
- "\u001b[1mGPTTreeIndex\u001b[0m, mode = summarize\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [query] Total LLM token usage: 21617 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 0 tokens\n",
- "INFO:root:> Starting query: What is the population of Berlin?\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m\n",
- "The population of Berlin is approximately 3.7 million people.\u001b[0m\n",
- "\n",
- "\u001b[1mGPTTreeIndex\u001b[0m, mode = embedding\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [query] Total LLM token usage: 368 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 4598 tokens\n",
- "INFO:root:> Starting query: What is the population of Berlin?\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3mApproximately 3.7 million people.\u001b[0m\n",
- "\n",
- "\u001b[1mGPTTreeIndex\u001b[0m, mode = retrieve\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [query] Total LLM token usage: 1439 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m\n",
- "The population of Berlin is 3.75 million registered inhabitants.\u001b[0m\n",
- "\n",
- "\n",
- "Ran 6 combinations in total.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
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- " .dataframe thead th {\n",
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- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Index</th>\n",
- " <th>Mode</th>\n",
- " <th>Output</th>\n",
- " <th>Duration</th>\n",
- " <th>LLM Tokens</th>\n",
- " <th>Embedding Tokens</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>VectorStoreIndex</td>\n",
- " <td>default</td>\n",
- " <td>\\nThe population of Berlin in 1949 was approxi...</td>\n",
- " <td>52.319133</td>\n",
- " <td>3545</td>\n",
- " <td>7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>VectorStoreIndex</td>\n",
- " <td>embedding</td>\n",
- " <td>\\nThe population of Berlin in 1949 was approxi...</td>\n",
- " <td>8.192025</td>\n",
- " <td>3545</td>\n",
- " <td>7</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>TreeIndex</td>\n",
- " <td>default</td>\n",
- " <td>The population of Berlin is approximately 3.7 ...</td>\n",
- " <td>12.542335</td>\n",
- " <td>5168</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>TreeIndex</td>\n",
- " <td>summarize</td>\n",
- " <td>\\nThe population of Berlin is approximately 3....</td>\n",
- " <td>18.665586</td>\n",
- " <td>21617</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>TreeIndex</td>\n",
- " <td>embedding</td>\n",
- " <td>Approximately 3.7 million people.</td>\n",
- " <td>3.573458</td>\n",
- " <td>368</td>\n",
- " <td>4598</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>TreeIndex</td>\n",
- " <td>retrieve</td>\n",
- " <td>\\nThe population of Berlin is 3.75 million reg...</td>\n",
- " <td>2.269598</td>\n",
- " <td>1439</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
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- ],
- "text/plain": [
- " Index Mode \\\n",
- "0 VectorStoreIndex default \n",
- "1 VectorStoreIndex embedding \n",
- "2 TreeIndex default \n",
- "3 TreeIndex summarize \n",
- "4 TreeIndex embedding \n",
- "5 TreeIndex retrieve \n",
- "\n",
- " Output Duration LLM Tokens \\\n",
- "0 \\nThe population of Berlin in 1949 was approxi... 52.319133 3545 \n",
- "1 \\nThe population of Berlin in 1949 was approxi... 8.192025 3545 \n",
- "2 The population of Berlin is approximately 3.7 ... 12.542335 5168 \n",
- "3 \\nThe population of Berlin is approximately 3.... 18.665586 21617 \n",
- "4 Approximately 3.7 million people. 3.573458 368 \n",
- "5 \\nThe population of Berlin is 3.75 million reg... 2.269598 1439 \n",
- "\n",
- " Embedding Tokens \n",
- "0 7 \n",
- "1 7 \n",
- "2 0 \n",
- "3 0 \n",
- "4 4598 \n",
- "5 0 "
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "playground.compare(\"What is the population of Berlin?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8829a829",
- "metadata": {},
- "source": [
- "### Initialize with Documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dfbc8ade",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Uses documents in a preset list of indices\n",
- "playground = Playground.from_docs(documents=documents)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "af6397b2",
+ "metadata": {},
+ "source": [
+ "# Playground"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "839c4a87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d726e871",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Hide INFO logs regarding token usage, etc\n",
+ "import logging\n",
+ "\n",
+ "logger = logging.getLogger()\n",
+ "logger.setLevel(logging.CRITICAL)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "40cf0773",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "### Generate some example Documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa34cd83",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import download_loader\n",
+ "from llama_index.indices.vector_store import VectorStoreIndex\n",
+ "from llama_index.indices.tree.base import TreeIndex\n",
+ "\n",
+ "WikipediaReader = download_loader(\"WikipediaReader\")\n",
+ "\n",
+ "loader = WikipediaReader()\n",
+ "documents = loader.load_data(pages=[\"Berlin\"])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c32392b",
+ "metadata": {},
+ "source": [
+ "### Create a list of any sort of indices (custom LLMs, custom embeddings, etc)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f59e6c18",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:root:> [build_index_from_documents] Total embedding token usage: 18344 tokens\n",
+ "INFO:root:> Building index from nodes: 5 chunks\n"
+ ]
+ }
+ ],
+ "source": [
+ "indices = [\n",
+ " VectorStoreIndex.from_documents(documents),\n",
+ " TreeIndex.from_documents(documents),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "827ada33",
+ "metadata": {},
+ "source": [
+ "## Using the Playground\n",
+ "\n",
+ "\n",
+ "### Initialize with indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a04e4535",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.playground import Playground\n",
+ "\n",
+ "playground = Playground(indices=indices)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "5f6999fc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 110.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mQuery:\u001b[0m\n",
+ "What is the population of Berlin?\n",
+ "\n",
+ "Trying 10 combinations...\n",
+ "\n",
+ "\n",
+ "\u001b[1mGPTVectorStoreIndex\u001b[0m, mode = default\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 90.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n",
+ "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 90.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n",
+ "INFO:openai:error_code=None error_message='Rate limit reached for default-global-with-image-limits in organization org-ehTdCqs0FpsxuTTwsJIlNSdZ on requests per min. Limit: 60.000000 / min. Current: 80.000000 / min. Contact support@openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method.' error_param=None error_type=requests message='OpenAI API error received' stream_error=False\n",
+ "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36;1m\u001b[1;3m\n",
+ "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\u001b[0m\n",
+ "\n",
+ "\u001b[1mGPTVectorStoreIndex\u001b[0m, mode = embedding\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 7 tokens\n",
+ "INFO:root:> Starting query: What is the population of Berlin?\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36;1m\u001b[1;3m\n",
+ "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\u001b[0m\n",
+ "\n",
+ "\u001b[1mGPTTreeIndex\u001b[0m, mode = default\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:>[Level 0] Selected node: [1]/[1]\n",
+ "INFO:root:>[Level 1] Selected node: [3]/[3]\n",
+ "INFO:root:> [query] Total LLM token usage: 5168 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 0 tokens\n",
+ "INFO:root:> Starting query: What is the population of Berlin?\n",
+ "INFO:root:> Building index from nodes: 6 chunks\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3mThe population of Berlin is approximately 3.7 million people.\u001b[0m\n",
+ "\n",
+ "\u001b[1mGPTTreeIndex\u001b[0m, mode = summarize\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [query] Total LLM token usage: 21617 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 0 tokens\n",
+ "INFO:root:> Starting query: What is the population of Berlin?\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m\n",
+ "The population of Berlin is approximately 3.7 million people.\u001b[0m\n",
+ "\n",
+ "\u001b[1mGPTTreeIndex\u001b[0m, mode = embedding\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [query] Total LLM token usage: 368 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 4598 tokens\n",
+ "INFO:root:> Starting query: What is the population of Berlin?\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3mApproximately 3.7 million people.\u001b[0m\n",
+ "\n",
+ "\u001b[1mGPTTreeIndex\u001b[0m, mode = retrieve\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [query] Total LLM token usage: 1439 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m\n",
+ "The population of Berlin is 3.75 million registered inhabitants.\u001b[0m\n",
+ "\n",
+ "\n",
+ "Ran 6 combinations in total.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
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+ " <td>52.319133</td>\n",
+ " <td>3545</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>VectorStoreIndex</td>\n",
+ " <td>embedding</td>\n",
+ " <td>\\nThe population of Berlin in 1949 was approxi...</td>\n",
+ " <td>8.192025</td>\n",
+ " <td>3545</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>TreeIndex</td>\n",
+ " <td>default</td>\n",
+ " <td>The population of Berlin is approximately 3.7 ...</td>\n",
+ " <td>12.542335</td>\n",
+ " <td>5168</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>TreeIndex</td>\n",
+ " <td>summarize</td>\n",
+ " <td>\\nThe population of Berlin is approximately 3....</td>\n",
+ " <td>18.665586</td>\n",
+ " <td>21617</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>TreeIndex</td>\n",
+ " <td>embedding</td>\n",
+ " <td>Approximately 3.7 million people.</td>\n",
+ " <td>3.573458</td>\n",
+ " <td>368</td>\n",
+ " <td>4598</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>TreeIndex</td>\n",
+ " <td>retrieve</td>\n",
+ " <td>\\nThe population of Berlin is 3.75 million reg...</td>\n",
+ " <td>2.269598</td>\n",
+ " <td>1439</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Index Mode \\\n",
+ "0 VectorStoreIndex default \n",
+ "1 VectorStoreIndex embedding \n",
+ "2 TreeIndex default \n",
+ "3 TreeIndex summarize \n",
+ "4 TreeIndex embedding \n",
+ "5 TreeIndex retrieve \n",
+ "\n",
+ " Output Duration LLM Tokens \\\n",
+ "0 \\nThe population of Berlin in 1949 was approxi... 52.319133 3545 \n",
+ "1 \\nThe population of Berlin in 1949 was approxi... 8.192025 3545 \n",
+ "2 The population of Berlin is approximately 3.7 ... 12.542335 5168 \n",
+ "3 \\nThe population of Berlin is approximately 3.... 18.665586 21617 \n",
+ "4 Approximately 3.7 million people. 3.573458 368 \n",
+ "5 \\nThe population of Berlin is 3.75 million reg... 2.269598 1439 \n",
+ "\n",
+ " Embedding Tokens \n",
+ "0 7 \n",
+ "1 7 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 4598 \n",
+ "5 0 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "playground.compare(\"What is the population of Berlin?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8829a829",
+ "metadata": {},
+ "source": [
+ "### Initialize with Documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dfbc8ade",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Uses documents in a preset list of indices\n",
+ "playground = Playground.from_docs(documents=documents)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/analysis/TokenPredictor.ipynb b/docs/examples/analysis/TokenPredictor.ipynb
index ce8b9f23cb..5e01b2ca14 100644
--- a/docs/examples/analysis/TokenPredictor.ipynb
+++ b/docs/examples/analysis/TokenPredictor.ipynb
@@ -1,367 +1,374 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "df19606e-d67e-44d2-bed0-4b804e6fc6c3",
- "metadata": {},
- "source": [
- "# Token Predictors\n",
- "\n",
- "Using our token predictors, we can predict the token usage of an operation before actually performing it.\n",
- "\n",
- "We first show how to predict LLM token usage with the MockLLMPredictor class, see below.\n",
- "We then show how to also predict embedding token usage."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8a707fa6-d79e-4343-92fd-d0fadb25c466",
- "metadata": {},
- "source": [
- "## Using MockLLMPredictor"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "be3f7baa-1c0a-430b-981b-83ddca9e71f2",
- "metadata": {
- "tags": []
- },
- "source": [
- "#### Predicting Usage of GPT Tree Index\n",
- "\n",
- "Here we predict usage of TreeIndex during index construction and querying, without making any LLM calls.\n",
- "\n",
- "NOTE: Predicting query usage before tree is built is only possible with TreeIndex due to the nature of tree traversal. Results will be more accurate if TreeIndex is actually built beforehand."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "c0ef16d1-45ef-43ec-9aad-4e44e9bb8578",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import TreeIndex, MockLLMPredictor, SimpleDirectoryReader, ServiceContext"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "b2ecdadc-1403-4bd4-a876-f80e4da911ef",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "11056808-fd7f-4bc6-9348-0605fb4ee668",
- "metadata": {},
- "outputs": [],
- "source": [
- "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
- "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ea4ba66-9a09-4478-b0a8-dee8645fa4e3",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = TreeIndex.from_documents(documents, service_context=service_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "345433c2-5553-4645-a513-0186b771a21f",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "19495\n"
- ]
- }
- ],
- "source": [
- "print(llm_predictor.last_token_usage)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f43733ae-af35-46e6-99d9-8ba507acbb0d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# default query\n",
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context\n",
- ")\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "4ba19751-da2d-46af-9f8f-4f42871e65a0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "5493\n"
- ]
- }
- ],
- "source": [
- "print(llm_predictor.last_token_usage)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4324d85b-ae80-48ab-baf0-7dc160dfae46",
- "metadata": {},
- "source": [
- "#### Predicting Usage of GPT Keyword Table Index Query\n",
- "\n",
- "Here we build a real keyword table index over the data, but then predict query usage."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "10447805-38db-41b9-a2c6-b0c95437b276",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import KeywordTableIndex, MockLLMPredictor, SimpleDirectoryReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "8ca76e72-5f43-47c1-a9a4-c5c5db4f0f21",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = KeywordTableIndex.from_documents(documents=documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "61f48870-65d2-4b23-b57e-79082ecb4ab2",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "start token ct: 0\n",
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "query keywords: ['author', 'did', 'y', 'combinator', 'after', 'his', 'the', 'what', 'time', 'at', 'do']\n",
- "Extracted keywords: ['combinator']\n",
- "> Querying with idx: 3483810247393006047: of 2016 we moved to England. We wanted our kids...\n",
- "> Querying with idx: 7597483754542696814: people edit code on our server through the brow...\n",
- "> Querying with idx: 7572417251450701751: invited about 20 of the 225 groups to interview...\n",
- "end token ct: 11313\n",
- "> [query] Total token usage: 11313 tokens\n",
- "11313\n"
- ]
- }
- ],
- "source": [
- "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
- "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)\n",
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context\n",
- ")\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
- "print(llm_predictor.last_token_usage)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0fee4405-05e0-46c2-87bb-64ec63a4c6c1",
- "metadata": {},
- "source": [
- "#### Predicting Usage of GPT List Index Query\n",
- "\n",
- "Here we build a real list index over the data, but then predict query usage."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "267f2213-67d1-4241-b73f-f1790661d06b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, MockLLMPredictor, SimpleDirectoryReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "d553a8b1-7045-4756-9729-df84bd305279",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = ListIndex.from_documents(documents=documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "69c99c68-6a23-48ed-aa41-e7af50fef2f3",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "start token ct: 0\n",
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "end token ct: 23941\n",
- "> [query] Total token usage: 23941 tokens\n"
- ]
- }
- ],
- "source": [
- "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
- "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)\n",
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context\n",
- ")\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "e8422c5c-af68-4138-a8dd-f6e8d7208c4c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "23941\n"
- ]
- }
- ],
- "source": [
- "print(llm_predictor.last_token_usage)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1e19cf61-6d6a-4dfa-af78-1ce184f41c6c",
- "metadata": {},
- "source": [
- "## Using MockEmbedding"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "106d86bf-7725-40bc-84ba-4f273493d3f6",
- "metadata": {},
- "source": [
- "#### Predicting Usage of GPT Simple Vector Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "9baf0fe7-2c11-4233-a930-4e593433ba84",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, MockLLMPredictor, MockEmbedding, SimpleDirectoryReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "97023361-fa47-4008-b8d7-e66d60c5b263",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = VectorStoreIndex.from_documents(documents=documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "63ebe021-2b9c-4024-95f8-56cd9e7e7c47",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 4374 tokens\n",
- "> [query] Total embedding token usage: 14 tokens\n"
- ]
- }
- ],
- "source": [
- "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
- "embed_model = MockEmbedding(embed_dim=1536)\n",
- "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)\n",
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context,\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What did the author do after his time at Y Combinator?\",\n",
- ")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "gpt_retrieve_venv",
- "language": "python",
- "name": "gpt_retrieve_venv"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "df19606e-d67e-44d2-bed0-4b804e6fc6c3",
+ "metadata": {},
+ "source": [
+ "# Token Predictors\n",
+ "\n",
+ "Using our token predictors, we can predict the token usage of an operation before actually performing it.\n",
+ "\n",
+ "We first show how to predict LLM token usage with the MockLLMPredictor class, see below.\n",
+ "We then show how to also predict embedding token usage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a707fa6-d79e-4343-92fd-d0fadb25c466",
+ "metadata": {},
+ "source": [
+ "## Using MockLLMPredictor"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be3f7baa-1c0a-430b-981b-83ddca9e71f2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "#### Predicting Usage of GPT Tree Index\n",
+ "\n",
+ "Here we predict usage of TreeIndex during index construction and querying, without making any LLM calls.\n",
+ "\n",
+ "NOTE: Predicting query usage before tree is built is only possible with TreeIndex due to the nature of tree traversal. Results will be more accurate if TreeIndex is actually built beforehand."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c0ef16d1-45ef-43ec-9aad-4e44e9bb8578",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " TreeIndex,\n",
+ " MockLLMPredictor,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b2ecdadc-1403-4bd4-a876-f80e4da911ef",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "11056808-fd7f-4bc6-9348-0605fb4ee668",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
+ "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ea4ba66-9a09-4478-b0a8-dee8645fa4e3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = TreeIndex.from_documents(documents, service_context=service_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "345433c2-5553-4645-a513-0186b771a21f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "19495\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(llm_predictor.last_token_usage)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f43733ae-af35-46e6-99d9-8ba507acbb0d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# default query\n",
+ "query_engine = index.as_query_engine(service_context=service_context)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "4ba19751-da2d-46af-9f8f-4f42871e65a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5493\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(llm_predictor.last_token_usage)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4324d85b-ae80-48ab-baf0-7dc160dfae46",
+ "metadata": {},
+ "source": [
+ "#### Predicting Usage of GPT Keyword Table Index Query\n",
+ "\n",
+ "Here we build a real keyword table index over the data, but then predict query usage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "10447805-38db-41b9-a2c6-b0c95437b276",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import KeywordTableIndex, MockLLMPredictor, SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "8ca76e72-5f43-47c1-a9a4-c5c5db4f0f21",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = KeywordTableIndex.from_documents(documents=documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "61f48870-65d2-4b23-b57e-79082ecb4ab2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "start token ct: 0\n",
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "query keywords: ['author', 'did', 'y', 'combinator', 'after', 'his', 'the', 'what', 'time', 'at', 'do']\n",
+ "Extracted keywords: ['combinator']\n",
+ "> Querying with idx: 3483810247393006047: of 2016 we moved to England. We wanted our kids...\n",
+ "> Querying with idx: 7597483754542696814: people edit code on our server through the brow...\n",
+ "> Querying with idx: 7572417251450701751: invited about 20 of the 225 groups to interview...\n",
+ "end token ct: 11313\n",
+ "> [query] Total token usage: 11313 tokens\n",
+ "11313\n"
+ ]
+ }
+ ],
+ "source": [
+ "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
+ "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)\n",
+ "query_engine = index.as_query_engine(service_context=service_context)\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
+ "print(llm_predictor.last_token_usage)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0fee4405-05e0-46c2-87bb-64ec63a4c6c1",
+ "metadata": {},
+ "source": [
+ "#### Predicting Usage of GPT List Index Query\n",
+ "\n",
+ "Here we build a real list index over the data, but then predict query usage."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "267f2213-67d1-4241-b73f-f1790661d06b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, MockLLMPredictor, SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "d553a8b1-7045-4756-9729-df84bd305279",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = ListIndex.from_documents(documents=documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "69c99c68-6a23-48ed-aa41-e7af50fef2f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "start token ct: 0\n",
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "end token ct: 23941\n",
+ "> [query] Total token usage: 23941 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
+ "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)\n",
+ "query_engine = index.as_query_engine(service_context=service_context)\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "e8422c5c-af68-4138-a8dd-f6e8d7208c4c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "23941\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(llm_predictor.last_token_usage)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1e19cf61-6d6a-4dfa-af78-1ce184f41c6c",
+ "metadata": {},
+ "source": [
+ "## Using MockEmbedding"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "106d86bf-7725-40bc-84ba-4f273493d3f6",
+ "metadata": {},
+ "source": [
+ "#### Predicting Usage of GPT Simple Vector Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9baf0fe7-2c11-4233-a930-4e593433ba84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " MockLLMPredictor,\n",
+ " MockEmbedding,\n",
+ " SimpleDirectoryReader,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "97023361-fa47-4008-b8d7-e66d60c5b263",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = VectorStoreIndex.from_documents(documents=documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "63ebe021-2b9c-4024-95f8-56cd9e7e7c47",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 4374 tokens\n",
+ "> [query] Total embedding token usage: 14 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "llm_predictor = MockLLMPredictor(max_tokens=256)\n",
+ "embed_model = MockEmbedding(embed_dim=1536)\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm_predictor=llm_predictor, embed_model=embed_model\n",
+ ")\n",
+ "query_engine = index.as_query_engine(\n",
+ " service_context=service_context,\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"What did the author do after his time at Y Combinator?\",\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "gpt_retrieve_venv",
+ "language": "python",
+ "name": "gpt_retrieve_venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/callbacks/LlamaDebugHandler.ipynb b/docs/examples/callbacks/LlamaDebugHandler.ipynb
index 1f9b27382e..296882231d 100644
--- a/docs/examples/callbacks/LlamaDebugHandler.ipynb
+++ b/docs/examples/callbacks/LlamaDebugHandler.ipynb
@@ -41,7 +41,12 @@
"metadata": {},
"outputs": [],
"source": [
- "from llama_index import ListIndex, ServiceContext, SimpleDirectoryReader, VectorStoreIndex"
+ "from llama_index import (\n",
+ " ListIndex,\n",
+ " ServiceContext,\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ ")"
]
},
{
@@ -63,7 +68,8 @@
"source": [
"from llama_index import ServiceContext, LLMPredictor, TreeIndex\n",
"from llama_index.llms import OpenAI\n",
- "llm = OpenAI(model='gpt-3.5-turbo', temperature=0)\n",
+ "\n",
+ "llm = OpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
"service_context = ServiceContext.from_defaults(llm=llm)"
]
},
@@ -85,7 +91,9 @@
"source": [
"llama_debug = LlamaDebugHandler(print_trace_on_end=True)\n",
"callback_manager = CallbackManager([llama_debug])\n",
- "service_context = ServiceContext.from_defaults(callback_manager=callback_manager, llm_predictor=llm_predictor)"
+ "service_context = ServiceContext.from_defaults(\n",
+ " callback_manager=callback_manager, llm_predictor=llm_predictor\n",
+ ")"
]
},
{
@@ -209,7 +217,7 @@
"event_pairs = llama_debug.get_llm_inputs_outputs()\n",
"print(event_pairs[0][0])\n",
"print(event_pairs[0][1].payload.keys())\n",
- "print(event_pairs[0][1].payload['response'])"
+ "print(event_pairs[0][1].payload[\"response\"])"
]
},
{
diff --git a/docs/examples/callbacks/TokenCountingHandler.ipynb b/docs/examples/callbacks/TokenCountingHandler.ipynb
index 83edf68f05..f025526060 100644
--- a/docs/examples/callbacks/TokenCountingHandler.ipynb
+++ b/docs/examples/callbacks/TokenCountingHandler.ipynb
@@ -29,15 +29,12 @@
"from llama_index.llms import OpenAI\n",
"\n",
"from llama_index import (\n",
- " SimpleDirectoryReader, \n",
- " VectorStoreIndex, \n",
- " ServiceContext, \n",
- " set_global_service_context\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ " ServiceContext,\n",
+ " set_global_service_context,\n",
")\n",
- "from llama_index.callbacks import (\n",
- " CallbackManager, \n",
- " TokenCountingHandler\n",
- ")"
+ "from llama_index.callbacks import CallbackManager, TokenCountingHandler"
]
},
{
@@ -62,9 +59,11 @@
"\n",
"callback_manager = CallbackManager([token_counter])\n",
"\n",
- "llm = OpenAI(model='gpt-3.5-turbo', temperature=0)\n",
+ "llm = OpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
"\n",
- "service_context = ServiceContext.from_defaults(llm=llm, callback_manager=callback_manager)\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm=llm, callback_manager=callback_manager\n",
+ ")\n",
"\n",
"# set the global default!\n",
"set_global_service_context(service_context)"
@@ -174,10 +173,20 @@
}
],
"source": [
- "print('Embedding Tokens: ', token_counter.total_embedding_token_count, '\\n',\n",
- " 'LLM Prompt Tokens: ', token_counter.prompt_llm_token_count, '\\n',\n",
- " 'LLM Completion Tokens: ', token_counter.completion_llm_token_count, '\\n',\n",
- " 'Total LLM Token Count: ', token_counter.total_llm_token_count, '\\n')"
+ "print(\n",
+ " \"Embedding Tokens: \",\n",
+ " token_counter.total_embedding_token_count,\n",
+ " \"\\n\",\n",
+ " \"LLM Prompt Tokens: \",\n",
+ " token_counter.prompt_llm_token_count,\n",
+ " \"\\n\",\n",
+ " \"LLM Completion Tokens: \",\n",
+ " token_counter.completion_llm_token_count,\n",
+ " \"\\n\",\n",
+ " \"Total LLM Token Count: \",\n",
+ " token_counter.total_llm_token_count,\n",
+ " \"\\n\",\n",
+ ")"
]
},
{
@@ -227,8 +236,8 @@
}
],
"source": [
- "print('Num LLM token count events: ', len(token_counter.llm_token_counts))\n",
- "print('Num Embedding token count events: ', len(token_counter.embedding_token_counts))"
+ "print(\"Num LLM token count events: \", len(token_counter.llm_token_counts))\n",
+ "print(\"Num Embedding token count events: \", len(token_counter.embedding_token_counts))"
]
},
{
@@ -265,13 +274,19 @@
}
],
"source": [
- "print('prompt: ', token_counter.llm_token_counts[0].prompt[:100], '...\\n')\n",
- "print('prompt token count: ', token_counter.llm_token_counts[0].prompt_token_count, '\\n')\n",
+ "print(\"prompt: \", token_counter.llm_token_counts[0].prompt[:100], \"...\\n\")\n",
+ "print(\n",
+ " \"prompt token count: \", token_counter.llm_token_counts[0].prompt_token_count, \"\\n\"\n",
+ ")\n",
"\n",
- "print('completion: ', token_counter.llm_token_counts[0].completion[:100], '...\\n')\n",
- "print('completion token count: ', token_counter.llm_token_counts[0].completion_token_count, '\\n')\n",
+ "print(\"completion: \", token_counter.llm_token_counts[0].completion[:100], \"...\\n\")\n",
+ "print(\n",
+ " \"completion token count: \",\n",
+ " token_counter.llm_token_counts[0].completion_token_count,\n",
+ " \"\\n\",\n",
+ ")\n",
"\n",
- "print('total token count', token_counter.llm_token_counts[0].total_token_count)"
+ "print(\"total token count\", token_counter.llm_token_counts[0].total_token_count)"
]
},
{
diff --git a/docs/examples/callbacks/WandbCallbackHandler.ipynb b/docs/examples/callbacks/WandbCallbackHandler.ipynb
index 6c2eaf4be8..187fda29c3 100644
--- a/docs/examples/callbacks/WandbCallbackHandler.ipynb
+++ b/docs/examples/callbacks/WandbCallbackHandler.ipynb
@@ -34,8 +34,12 @@
"from getpass import getpass\n",
"\n",
"if os.getenv(\"OPENAI_API_KEY\") is None:\n",
- " os.environ[\"OPENAI_API_KEY\"] = getpass(\"Paste your OpenAI key from: https://platform.openai.com/account/api-keys\\n\")\n",
- "assert os.getenv(\"OPENAI_API_KEY\", \"\").startswith(\"sk-\"), \"This doesn't look like a valid OpenAI API key\"\n",
+ " os.environ[\"OPENAI_API_KEY\"] = getpass(\n",
+ " \"Paste your OpenAI key from: https://platform.openai.com/account/api-keys\\n\"\n",
+ " )\n",
+ "assert os.getenv(\"OPENAI_API_KEY\", \"\").startswith(\n",
+ " \"sk-\"\n",
+ "), \"This doesn't look like a valid OpenAI API key\"\n",
"print(\"OpenAI API key configured\")"
]
},
@@ -49,9 +53,14 @@
"from llama_index.callbacks import CallbackManager, CBEventType\n",
"from llama_index.callbacks import LlamaDebugHandler, WandbCallbackHandler\n",
"from llama_index import (\n",
- " GPTListIndex, GPTTreeIndex, GPTVectorStoreIndex,\n",
- " ServiceContext, SimpleDirectoryReader, LLMPredictor,\n",
- " GPTSimpleKeywordTableIndex, StorageContext\n",
+ " GPTListIndex,\n",
+ " GPTTreeIndex,\n",
+ " GPTVectorStoreIndex,\n",
+ " ServiceContext,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " GPTSimpleKeywordTableIndex,\n",
+ " StorageContext,\n",
")\n",
"from llama_index.indices.composability import ComposableGraph\n",
"from llama_index import load_index_from_storage, load_graph_from_storage\n",
@@ -74,7 +83,7 @@
"metadata": {},
"outputs": [],
"source": [
- "llm = OpenAI(model='gpt-4', temperature=0)"
+ "llm = OpenAI(model=\"gpt-4\", temperature=0)"
]
},
{
@@ -113,7 +122,9 @@
"wandb_callback = WandbCallbackHandler(run_args=run_args)\n",
"\n",
"callback_manager = CallbackManager([llama_debug, wandb_callback])\n",
- "service_context = ServiceContext.from_defaults(callback_manager=callback_manager, llm=llm)"
+ "service_context = ServiceContext.from_defaults(\n",
+ " callback_manager=callback_manager, llm=llm\n",
+ ")"
]
},
{
@@ -240,7 +251,9 @@
}
],
"source": [
- "storage_context = wandb_callback.load_storage_context(artifact_url=\"ayut/llamaindex/simple_vector_store:v0\")\n",
+ "storage_context = wandb_callback.load_storage_context(\n",
+ " artifact_url=\"ayut/llamaindex/simple_vector_store:v0\"\n",
+ ")\n",
"\n",
"# Load the index and initialize a query engine\n",
"index = load_index_from_storage(storage_context, service_context=service_context)"
@@ -316,24 +329,25 @@
"from pathlib import Path\n",
"\n",
"import requests\n",
+ "\n",
"response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
" params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
- " 'explaintext': True,\n",
- " }\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
+ " \"explaintext\": True,\n",
+ " },\n",
").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
"\n",
- "data_path = Path('data')\n",
+ "data_path = Path(\"data\")\n",
"if not data_path.exists():\n",
" Path.mkdir(data_path)\n",
"\n",
- "with open('data/nyc_text.txt', 'w') as fp:\n",
+ "with open(\"data/nyc_text.txt\", \"w\") as fp:\n",
" fp.write(nyc_text)"
]
},
@@ -345,9 +359,9 @@
"outputs": [],
"source": [
"# load NYC dataset\n",
- "nyc_documents = SimpleDirectoryReader('data/').load_data()\n",
+ "nyc_documents = SimpleDirectoryReader(\"data/\").load_data()\n",
"# load PG's essay\n",
- "essay_documents = SimpleDirectoryReader('../paul_graham_essay/data/').load_data()"
+ "essay_documents = SimpleDirectoryReader(\"../paul_graham_essay/data/\").load_data()"
]
},
{
@@ -510,11 +524,11 @@
"\n",
"graph = ComposableGraph.from_indices(\n",
" GPTSimpleKeywordTableIndex,\n",
- " [nyc_index, essay_index], \n",
+ " [nyc_index, essay_index],\n",
" index_summaries=[nyc_index_summary, essay_index_summary],\n",
" max_keywords_per_chunk=50,\n",
" service_context=service_context,\n",
- " storage_context=storage_context\n",
+ " storage_context=storage_context,\n",
")"
]
},
@@ -584,10 +598,14 @@
}
],
"source": [
- "storage_context = wandb_callback.load_storage_context(artifact_url=\"ayut/llamaindex/composable_graph:v0\")\n",
+ "storage_context = wandb_callback.load_storage_context(\n",
+ " artifact_url=\"ayut/llamaindex/composable_graph:v0\"\n",
+ ")\n",
"\n",
"# Load the graph and initialize a query engine\n",
- "graph = load_graph_from_storage(storage_context, root_id=graph.root_id, service_context=service_context)\n",
+ "graph = load_graph_from_storage(\n",
+ " storage_context, root_id=graph.root_id, service_context=service_context\n",
+ ")\n",
"query_engine = index.as_query_engine()"
]
},
@@ -637,7 +655,7 @@
],
"source": [
"response = query_engine.query(\n",
- " \"What is the climate of New York City like? How cold is it during the winter?\", \n",
+ " \"What is the climate of New York City like? How cold is it during the winter?\",\n",
")\n",
"print(response, sep=\"\\n\")"
]
diff --git a/docs/examples/chat_engine/chat_engine_condense_question.ipynb b/docs/examples/chat_engine/chat_engine_condense_question.ipynb
index 3a00a72ad9..1ddb9bce70 100644
--- a/docs/examples/chat_engine/chat_engine_condense_question.ipynb
+++ b/docs/examples/chat_engine/chat_engine_condense_question.ipynb
@@ -77,6 +77,7 @@
],
"source": [
"from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "\n",
"data = SimpleDirectoryReader(input_dir=\"../data/paul_graham/\").load_data()\n",
"index = VectorStoreIndex.from_documents(data)"
]
@@ -128,7 +129,7 @@
}
],
"source": [
- "response = chat_engine.chat('What did Paul Graham do after YC?')"
+ "response = chat_engine.chat(\"What did Paul Graham do after YC?\")"
]
},
{
@@ -177,7 +178,7 @@
}
],
"source": [
- "response = chat_engine.chat('What about after that?')"
+ "response = chat_engine.chat(\"What about after that?\")"
]
},
{
@@ -218,7 +219,7 @@
}
],
"source": [
- "response = chat_engine.chat('Can you tell me more?')"
+ "response = chat_engine.chat(\"Can you tell me more?\")"
]
},
{
@@ -279,7 +280,7 @@
}
],
"source": [
- "response = chat_engine.chat('What about after that?')"
+ "response = chat_engine.chat(\"What about after that?\")"
]
},
{
diff --git a/docs/examples/chat_engine/chat_engine_react.ipynb b/docs/examples/chat_engine/chat_engine_react.ipynb
index 636356e654..8d27f25de1 100644
--- a/docs/examples/chat_engine/chat_engine_react.ipynb
+++ b/docs/examples/chat_engine/chat_engine_react.ipynb
@@ -115,7 +115,7 @@
},
"outputs": [],
"source": [
- "chat_engine = index.as_chat_engine(chat_mode='react', verbose=True)"
+ "chat_engine = index.as_chat_engine(chat_mode=\"react\", verbose=True)"
]
},
{
@@ -156,7 +156,9 @@
}
],
"source": [
- "response = chat_engine.chat('Use the tool to answer: what did Paul Graham do in the summer of 1995?')"
+ "response = chat_engine.chat(\n",
+ " \"Use the tool to answer: what did Paul Graham do in the summer of 1995?\"\n",
+ ")"
]
},
{
@@ -201,7 +203,7 @@
}
],
"source": [
- "response = chat_engine.chat('What did I ask you?')"
+ "response = chat_engine.chat(\"What did I ask you?\")"
]
},
{
@@ -255,7 +257,10 @@
"source": [
"from llama_index import ServiceContext\n",
"from langchain.chat_models import ChatOpenAI\n",
- "service_context = ServiceContext.from_defaults(llm=ChatOpenAI(temperature=0., model='gpt-3.5-turbo'))"
+ "\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm=ChatOpenAI(temperature=0.0, model=\"gpt-3.5-turbo\")\n",
+ ")"
]
},
{
@@ -276,7 +281,9 @@
},
"outputs": [],
"source": [
- "chat_engine = index.as_chat_engine(service_context=service_context, chat_mode='react', verbose=True)"
+ "chat_engine = index.as_chat_engine(\n",
+ " service_context=service_context, chat_mode=\"react\", verbose=True\n",
+ ")"
]
},
{
@@ -309,7 +316,9 @@
}
],
"source": [
- "response = chat_engine.chat('Use the tool to answer: what did Paul Graham do in the summer of 1995?')"
+ "response = chat_engine.chat(\n",
+ " \"Use the tool to answer: what did Paul Graham do in the summer of 1995?\"\n",
+ ")"
]
},
{
@@ -362,7 +371,7 @@
}
],
"source": [
- "response = chat_engine.chat('What did I ask you before?')"
+ "response = chat_engine.chat(\"What did I ask you before?\")"
]
},
{
@@ -517,7 +526,7 @@
}
],
"source": [
- "response = chat_engine.chat('What did I ask you before?')"
+ "response = chat_engine.chat(\"What did I ask you before?\")"
]
},
{
diff --git a/docs/examples/chat_engine/chat_engine_repl.ipynb b/docs/examples/chat_engine/chat_engine_repl.ipynb
index 5b47b52f69..683e0bfe62 100644
--- a/docs/examples/chat_engine/chat_engine_repl.ipynb
+++ b/docs/examples/chat_engine/chat_engine_repl.ipynb
@@ -132,6 +132,7 @@
],
"source": [
"from llama_index.chat_engine import SimpleChatEngine\n",
+ "\n",
"chat_engine = SimpleChatEngine.from_defaults()\n",
"chat_engine.chat_repl()"
]
@@ -163,7 +164,10 @@
"source": [
"from llama_index.llms import OpenAI\n",
"from llama_index import ServiceContext\n",
- "service_context = ServiceContext.from_defaults(llm=OpenAI(temperature=0., model='gpt-3.5-turbo'))"
+ "\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm=OpenAI(temperature=0.0, model=\"gpt-3.5-turbo\")\n",
+ ")"
]
},
{
@@ -309,6 +313,7 @@
],
"source": [
"from llama_index.chat_engine import SimpleChatEngine\n",
+ "\n",
"chat_engine = SimpleChatEngine.from_defaults(service_context=service_context)\n",
"chat_engine.chat_repl()"
]
diff --git a/docs/examples/citation/pdf_page_reference.ipynb b/docs/examples/citation/pdf_page_reference.ipynb
index 9ac7e61c62..a102e90c73 100644
--- a/docs/examples/citation/pdf_page_reference.ipynb
+++ b/docs/examples/citation/pdf_page_reference.ipynb
@@ -9,7 +9,12 @@
},
"outputs": [],
"source": [
- "from llama_index import SimpleDirectoryReader, VectorStoreIndex, download_loader, RAKEKeywordTableIndex"
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ " download_loader,\n",
+ " RAKEKeywordTableIndex,\n",
+ ")"
]
},
{
@@ -58,7 +63,7 @@
},
"outputs": [],
"source": [
- "reader = SimpleDirectoryReader(input_files=['../data/10k/lyft_2021.pdf'])\n",
+ "reader = SimpleDirectoryReader(input_files=[\"../data/10k/lyft_2021.pdf\"])\n",
"data = reader.load_data()"
]
},
@@ -83,10 +88,7 @@
},
"outputs": [],
"source": [
- "query_engine = index.as_query_engine(\n",
- " streaming=True, \n",
- " similarity_top_k=3\n",
- ")"
+ "query_engine = index.as_query_engine(streaming=True, similarity_top_k=3)"
]
},
{
@@ -125,7 +127,9 @@
}
],
"source": [
- "response = query_engine.query(\"What was the impact of COVID? Show statements in bullet form and show page reference after each statement.\")\n",
+ "response = query_engine.query(\n",
+ " \"What was the impact of COVID? Show statements in bullet form and show page reference after each statement.\"\n",
+ ")\n",
"response.print_response_stream()"
]
},
@@ -167,11 +171,11 @@
],
"source": [
"for node in response.source_nodes:\n",
- " print('-----')\n",
- " text_fmt = node.node.get_content().strip().replace('\\n', ' ')[:1000]\n",
+ " print(\"-----\")\n",
+ " text_fmt = node.node.get_content().strip().replace(\"\\n\", \" \")[:1000]\n",
" print(f\"Text:\\t {text_fmt} ...\")\n",
- " print(f'Metadata:\\t {node.node.metadata}')\n",
- " print(f'Score:\\t {node.score:.3f}')"
+ " print(f\"Metadata:\\t {node.node.metadata}\")\n",
+ " print(f\"Score:\\t {node.score:.3f}\")"
]
},
{
diff --git a/docs/examples/composable_indices/ComposableIndices-Prior.ipynb b/docs/examples/composable_indices/ComposableIndices-Prior.ipynb
index 81703ecab5..7ff7ddf642 100644
--- a/docs/examples/composable_indices/ComposableIndices-Prior.ipynb
+++ b/docs/examples/composable_indices/ComposableIndices-Prior.ipynb
@@ -1,489 +1,490 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Composable Graph Basic"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "41927486",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
- "# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa0e62b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex,\n",
- " EmptyIndex,\n",
- " TreeIndex,\n",
- " ListIndex,\n",
- " SimpleDirectoryReader,\n",
- " ServiceContext,\n",
- " StorageContext,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {},
- "source": [
- "### Load Datasets\n",
- "\n",
- "Load PG's essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ddff8f98-e002-40c5-93ac-93aa40dca5ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load PG's essay\n",
- "essay_documents = SimpleDirectoryReader('../paul_graham_essay/data/').load_data()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Building the document indices\n",
- "- Build a vector index for PG's essay\n",
- "- Also build an empty index (to store prior knowledge)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b5aad4a-49ef-4b24-962a-0793f4f09316",
- "metadata": {},
- "outputs": [],
- "source": [
- "# configure\n",
- "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
- "storage_context = StorageContext.from_defaults()\n",
- "\n",
- "# build essay index\n",
- "essay_index = VectorStoreIndex.from_documents(essay_documents, service_context=service_context, storage_context=storage_context)\n",
- "empty_index = EmptyIndex(service_context=service_context, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4ee2ed80-fa2a-477b-835c-464c6fc1d973",
- "metadata": {},
- "source": [
- "### Query Indices\n",
- "See the response of querying each index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "df22aada-bd3c-48e8-98dd-ec38691a6414",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = essay_index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"tree_summarize\",\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"Tell me about what Sam Altman did during his time in YC\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "718f0063-e41c-42da-a6f5-3cae90f7c6d3",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1b934abf-bb30-4d86-b0ba-3dc60666b798",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = empty_index.as_query_engine(\n",
- " response_mode='generation'\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"Tell me about what Sam Altman did during his time in YC\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f677f144-549c-404f-aafb-5ce8fa295146",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ff521fbb",
- "metadata": {},
- "source": [
- "Define summary for each index."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4149cbbd-7d0b-48c4-8c47-7d67ae0c55f0",
- "metadata": {},
- "outputs": [],
- "source": [
- "essay_index_summary = \"This document describes Paul Graham's life, from early adulthood to the present day.\"\n",
- "empty_index_summary = \"This can be used for general knowledge purposes.\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eebbc448-1e0b-402c-b37e-f93bfcc0bf4f",
- "metadata": {},
- "source": [
- "### Define Graph (List Index as Parent Index)\n",
- "\n",
- "This allows us to synthesize responses both using a knowledge corpus as well as prior knowledge."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c0580ff9-ca0a-4ac1-93ef-b570903ea404",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eb064bf2-77f5-4205-bd1e-ec7de40a6f7f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "graph = ComposableGraph.from_indices(\n",
- " ListIndex,\n",
- " [essay_index, empty_index], \n",
- " index_summaries=[essay_index_summary, empty_index_summary],\n",
- " service_context=service_context,\n",
- " storage_context=storage_context,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ae127943-afac-48b4-b22d-84a37e553e4b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# [optional] persist to disk\n",
- "storage_context.persist()\n",
- "root_id = graph.root_id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dca2b64b-9af1-456f-8dab-822bfdc5d0ac",
- "metadata": {},
- "outputs": [],
- "source": [
- "# [optional] load from disk\n",
- "from llama_index.indices.loading import load_graph_from_storage\n",
- "\n",
- "graph = load_graph_from_storage(storage_context, root_id=root_id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7a811f1a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# configure query engines\n",
- "custom_query_engines = {\n",
- " essay_index.index_id: essay_index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"tree_summarize\",\n",
- " )\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f3c4e58b-b153-4e43-bc02-274a85babbe8",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# ask it a question about Sam Altman\n",
- "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
- "response = query_engine.query(\n",
- " \"Tell me about what Sam Altman did during his time in YC\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c0a43443-3e00-4e48-b3ab-f6369191d53a",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c78bc3da-6bad-4998-9a81-90a3fa9200a9",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get source of response\n",
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f437c6df-31b1-40d9-9b57-70f7e0318eb7",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Define Graph (Tree Index as Parent Index)\n",
- "\n",
- "This allows us to \"route\" a query to either a knowledge-augmented index, or to the LLM itself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d0c05040-0f6c-4e9d-bf08-4e5207ea2774",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a6c1b887-9cb5-49db-a9c7-5cb348beff58",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# configure retriever \n",
- "custom_query_engines = {\n",
- " essay_index.index_id: essay_index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"tree_summarize\",\n",
- " )\n",
- "}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d5579f16-cee5-4287-b89e-635d161bdfb5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "graph2 = ComposableGraph.from_indices(\n",
- " TreeIndex,\n",
- " [essay_index, empty_index],\n",
- " index_summaries=[essay_index_summary, empty_index_summary]\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c57d370f-59af-4a2d-8fc6-05cf93d958e5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# ask it a question about NYC \n",
- "query_engine = graph2.as_query_engine(\n",
- " custom_query_engines=custom_query_engines\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"Tell me about what Paul Graham did growing up?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d99502a-ab3c-48da-bfb1-c54a95dadbb5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "str(response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "997498a9-128d-4c0b-8826-c6d6871571f5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8dc10463-ca79-4b47-83d6-217bd186d822",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "response = query_engine.query(\n",
- " \"Tell me about Barack Obama\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9b8411a0-f9a8-4f1b-a476-03e746ec8ab3",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "str(response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "749625a3-722c-4bf4-b4ef-55b00f20ef20",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "response.get_formatted_sources()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "78a616a3",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.11.0 ('llama')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- },
- "vscode": {
- "interpreter": {
- "hash": "775fd5332502f2902173832d699e1edc37222ebadd0e97b5c8a1a7431bebae89"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Composable Graph Basic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41927486",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: This is ONLY necessary in jupyter notebook.\n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
+ "# This results in nested event-loops when we start an event-loop to make async queries.\n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa0e62b6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " EmptyIndex,\n",
+ " TreeIndex,\n",
+ " ListIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " StorageContext,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {},
+ "source": [
+ "### Load Datasets\n",
+ "\n",
+ "Load PG's essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ddff8f98-e002-40c5-93ac-93aa40dca5ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load PG's essay\n",
+ "essay_documents = SimpleDirectoryReader(\"../paul_graham_essay/data/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Building the document indices\n",
+ "- Build a vector index for PG's essay\n",
+ "- Also build an empty index (to store prior knowledge)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b5aad4a-49ef-4b24-962a-0793f4f09316",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# configure\n",
+ "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
+ "storage_context = StorageContext.from_defaults()\n",
+ "\n",
+ "# build essay index\n",
+ "essay_index = VectorStoreIndex.from_documents(\n",
+ " essay_documents, service_context=service_context, storage_context=storage_context\n",
+ ")\n",
+ "empty_index = EmptyIndex(\n",
+ " service_context=service_context, storage_context=storage_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ee2ed80-fa2a-477b-835c-464c6fc1d973",
+ "metadata": {},
+ "source": [
+ "### Query Indices\n",
+ "See the response of querying each index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "df22aada-bd3c-48e8-98dd-ec38691a6414",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = essay_index.as_query_engine(\n",
+ " similarity_top_k=3,\n",
+ " response_mode=\"tree_summarize\",\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"Tell me about what Sam Altman did during his time in YC\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "718f0063-e41c-42da-a6f5-3cae90f7c6d3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b934abf-bb30-4d86-b0ba-3dc60666b798",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = empty_index.as_query_engine(response_mode=\"generation\")\n",
+ "response = query_engine.query(\n",
+ " \"Tell me about what Sam Altman did during his time in YC\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f677f144-549c-404f-aafb-5ce8fa295146",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff521fbb",
+ "metadata": {},
+ "source": [
+ "Define summary for each index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4149cbbd-7d0b-48c4-8c47-7d67ae0c55f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "essay_index_summary = \"This document describes Paul Graham's life, from early adulthood to the present day.\"\n",
+ "empty_index_summary = \"This can be used for general knowledge purposes.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eebbc448-1e0b-402c-b37e-f93bfcc0bf4f",
+ "metadata": {},
+ "source": [
+ "### Define Graph (List Index as Parent Index)\n",
+ "\n",
+ "This allows us to synthesize responses both using a knowledge corpus as well as prior knowledge."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c0580ff9-ca0a-4ac1-93ef-b570903ea404",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb064bf2-77f5-4205-bd1e-ec7de40a6f7f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "graph = ComposableGraph.from_indices(\n",
+ " ListIndex,\n",
+ " [essay_index, empty_index],\n",
+ " index_summaries=[essay_index_summary, empty_index_summary],\n",
+ " service_context=service_context,\n",
+ " storage_context=storage_context,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ae127943-afac-48b4-b22d-84a37e553e4b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# [optional] persist to disk\n",
+ "storage_context.persist()\n",
+ "root_id = graph.root_id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dca2b64b-9af1-456f-8dab-822bfdc5d0ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# [optional] load from disk\n",
+ "from llama_index.indices.loading import load_graph_from_storage\n",
+ "\n",
+ "graph = load_graph_from_storage(storage_context, root_id=root_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a811f1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# configure query engines\n",
+ "custom_query_engines = {\n",
+ " essay_index.index_id: essay_index.as_query_engine(\n",
+ " similarity_top_k=3,\n",
+ " response_mode=\"tree_summarize\",\n",
+ " )\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3c4e58b-b153-4e43-bc02-274a85babbe8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# ask it a question about Sam Altman\n",
+ "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
+ "response = query_engine.query(\n",
+ " \"Tell me about what Sam Altman did during his time in YC\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c0a43443-3e00-4e48-b3ab-f6369191d53a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c78bc3da-6bad-4998-9a81-90a3fa9200a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get source of response\n",
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f437c6df-31b1-40d9-9b57-70f7e0318eb7",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Define Graph (Tree Index as Parent Index)\n",
+ "\n",
+ "This allows us to \"route\" a query to either a knowledge-augmented index, or to the LLM itself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0c05040-0f6c-4e9d-bf08-4e5207ea2774",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6c1b887-9cb5-49db-a9c7-5cb348beff58",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# configure retriever\n",
+ "custom_query_engines = {\n",
+ " essay_index.index_id: essay_index.as_query_engine(\n",
+ " similarity_top_k=3,\n",
+ " response_mode=\"tree_summarize\",\n",
+ " )\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5579f16-cee5-4287-b89e-635d161bdfb5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "graph2 = ComposableGraph.from_indices(\n",
+ " TreeIndex,\n",
+ " [essay_index, empty_index],\n",
+ " index_summaries=[essay_index_summary, empty_index_summary],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c57d370f-59af-4a2d-8fc6-05cf93d958e5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# ask it a question about NYC\n",
+ "query_engine = graph2.as_query_engine(custom_query_engines=custom_query_engines)\n",
+ "response = query_engine.query(\n",
+ " \"Tell me about what Paul Graham did growing up?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d99502a-ab3c-48da-bfb1-c54a95dadbb5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "str(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "997498a9-128d-4c0b-8826-c6d6871571f5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8dc10463-ca79-4b47-83d6-217bd186d822",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"Tell me about Barack Obama\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9b8411a0-f9a8-4f1b-a476-03e746ec8ab3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "str(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "749625a3-722c-4bf4-b4ef-55b00f20ef20",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "response.get_formatted_sources()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "78a616a3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.11.0 ('llama')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "775fd5332502f2902173832d699e1edc37222ebadd0e97b5c8a1a7431bebae89"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/composable_indices/ComposableIndices-Weaviate.ipynb b/docs/examples/composable_indices/ComposableIndices-Weaviate.ipynb
index e0a94a02a8..7df657358d 100644
--- a/docs/examples/composable_indices/ComposableIndices-Weaviate.ipynb
+++ b/docs/examples/composable_indices/ComposableIndices-Weaviate.ipynb
@@ -1,400 +1,409 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Composable Graph with Weaviate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa0e62b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import weaviate\n",
- "from pprint import pprint\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
- " ListIndex, \n",
- " VectorStoreIndex,\n",
- " SimpleDirectoryReader\n",
- ")\n",
- "from llama_index.vector_stores import WeaviateVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5b594b69-5814-4ff1-abc0-765b724f6339",
- "metadata": {},
- "outputs": [],
- "source": [
- "resource_owner_config = weaviate.AuthClientPassword(\n",
- " username = \"<username>\", \n",
- " password = \"<password>\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d6297f6-1a78-4dc5-9d48-f3968729e273",
- "metadata": {},
- "outputs": [],
- "source": [
- "client = weaviate.Client(\"https://test-weaviate-cluster.semi.network/\", auth_client_secret=resource_owner_config)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5583b867-ab33-4e0e-8a38-9995615faa84",
- "metadata": {},
- "outputs": [],
- "source": [
- "# [optional] set batch\n",
- "client.batch.configure(batch_size=10)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {},
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load both the NYC Wikipedia page as well as Paul Graham's \"What I Worked On\" essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {},
- "outputs": [],
- "source": [
- "# fetch \"New York City\" page from Wikipedia\n",
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- ").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
- "\n",
- "data_path = Path('data')\n",
- "if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- "with open('../test_wiki/data/nyc_text.txt', 'w') as fp:\n",
- " fp.write(nyc_text)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load NYC dataset\n",
- "nyc_documents = SimpleDirectoryReader('../test_wiki/data/').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ddff8f98-e002-40c5-93ac-93aa40dca5ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load PG's essay\n",
- "essay_documents = SimpleDirectoryReader('../paul_graham_essay/data/').load_data()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {},
- "source": [
- "### Building the document indices\n",
- "Build a tree index for the NYC wiki page and PG essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "metadata": {},
- "outputs": [],
- "source": [
- "# build NYC index\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix='Nyc_docs')\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "nyc_index = VectorStoreIndex.from_documents(nyc_documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b5aad4a-49ef-4b24-962a-0793f4f09316",
- "metadata": {},
- "outputs": [],
- "source": [
- "# build essay index\n",
- "vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix='Essay_docs')\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "essay_index = VectorStoreIndex.from_documents(essay_documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bdcb22d5-4df8-4d65-aa29-6493fc027fe2",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Set summaries for the indices\n",
- "\n",
- "Add text summaries to indices, so we can compose other indices on top of it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4149cbbd-7d0b-48c4-8c47-7d67ae0c55f0",
- "metadata": {},
- "outputs": [],
- "source": [
- "nyc_index_summary = \"\"\"\n",
- " New York, often called New York City or NYC, \n",
- " is the most populous city in the United States. \n",
- " With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), \n",
- " New York City is also the most densely populated major city in the United States, \n",
- " and is more than twice as populous as second-place Los Angeles. \n",
- " New York City lies at the southern tip of New York State, and \n",
- " constitutes the geographical and demographic center of both the \n",
- " Northeast megalopolis and the New York metropolitan area, the \n",
- " largest metropolitan area in the world by urban landmass.[8] With over \n",
- " 20.1 million people in its metropolitan statistical area and 23.5 million \n",
- " in its combined statistical area as of 2020, New York is one of the world's \n",
- " most populous megacities, and over 58 million people live within 250 mi (400 km) of \n",
- " the city. New York City is a global cultural, financial, and media center with \n",
- " a significant influence on commerce, health care and life sciences, entertainment, \n",
- " research, technology, education, politics, tourism, dining, art, fashion, and sports. \n",
- " Home to the headquarters of the United Nations, \n",
- " New York is an important center for international diplomacy,\n",
- " an established safe haven for global investors, and is sometimes described as the capital of the world.\n",
- "\"\"\"\n",
- "essay_index_summary = \"\"\"\n",
- " Author: Paul Graham. \n",
- " The author grew up painting and writing essays. \n",
- " He wrote a book on Lisp and did freelance Lisp hacking work to support himself. \n",
- " He also became the de facto studio assistant for Idelle Weber, an early photorealist painter. \n",
- " He eventually had the idea to start a company to put art galleries online, but the idea was unsuccessful. \n",
- " He then had the idea to write software to build online stores, which became the basis for his successful company, Viaweb. \n",
- " After Viaweb was acquired by Yahoo!, the author returned to painting and started writing essays online. \n",
- " He wrote a book of essays, Hackers & Painters, and worked on spam filters. \n",
- " He also bought a building in Cambridge to use as an office. \n",
- " He then had the idea to start Y Combinator, an investment firm that would \n",
- " make a larger number of smaller investments and help founders remain as CEO. \n",
- " He and his partner Jessica Livingston ran Y Combinator and funded a batch of startups twice a year. \n",
- " He also continued to write essays, cook for groups of friends, and explore the concept of invented vs discovered in software. \n",
- "\n",
- "\"\"\"\n",
- "index_summaries = [nyc_index_summary, essay_index_summary]\n",
- "nyc_index.set_index_id('nyc_index')\n",
- "essay_index.set_index_id('essay_index')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Build Keyword Table Index on top of vector indices! \n",
- "\n",
- "We set summaries for each of the NYC and essay indices, and then compose a keyword index on top of it."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eebbc448-1e0b-402c-b37e-f93bfcc0bf4f",
- "metadata": {},
- "source": [
- "### Define Graph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "metadata": {},
- "outputs": [],
- "source": [
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex, \n",
- " [nyc_index, essay_index], \n",
- " index_summaries=index_summaries,\n",
- " max_keywords_per_chunk=50)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "56092d98",
- "metadata": {},
- "outputs": [],
- "source": [
- "custom_query_engines = {\n",
- " graph.root_id: graph.root_index.as_query_engine(retriever_mode='simple')\n",
- "}\n",
- "\n",
- "query_engine = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f3c4e58b-b153-4e43-bc02-274a85babbe8",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# ask it a question about NYC \n",
- "response = query_engine.query(\n",
- " \"What is the weather of New York City like? How cold is it during the winter?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c0a43443-3e00-4e48-b3ab-f6369191d53a",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c78bc3da-6bad-4998-9a81-90a3fa9200a9",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get source of response\n",
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6b53e45e-93aa-4b49-a497-ab403f6254f9",
- "metadata": {},
- "outputs": [],
- "source": [
- "# ask it a question about PG's essay\n",
- "response = query_engine.query(\n",
- " \"What did the author do growing up, before his time at Y Combinator?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "06dc71bb-882d-49f5-8566-69b0ea5019dd",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b0894565-2b2c-4987-a891-17ba44d775b5",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Get source of response\n",
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c103b6d-0946-48ba-a875-476c706f8560",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.11.0 ('llama')",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- },
- "vscode": {
- "interpreter": {
- "hash": "775fd5332502f2902173832d699e1edc37222ebadd0e97b5c8a1a7431bebae89"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Composable Graph with Weaviate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa0e62b6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import weaviate\n",
+ "from pprint import pprint\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
+ " ListIndex,\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ ")\n",
+ "from llama_index.vector_stores import WeaviateVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b594b69-5814-4ff1-abc0-765b724f6339",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "resource_owner_config = weaviate.AuthClientPassword(\n",
+ " username=\"<username>\",\n",
+ " password=\"<password>\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d6297f6-1a78-4dc5-9d48-f3968729e273",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "client = weaviate.Client(\n",
+ " \"https://test-weaviate-cluster.semi.network/\",\n",
+ " auth_client_secret=resource_owner_config,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5583b867-ab33-4e0e-8a38-9995615faa84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# [optional] set batch\n",
+ "client.batch.configure(batch_size=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {},
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load both the NYC Wikipedia page as well as Paul Graham's \"What I Worked On\" essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fetch \"New York City\" page from Wikipedia\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ ").json()\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
+ "\n",
+ "data_path = Path(\"data\")\n",
+ "if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ "with open(\"../test_wiki/data/nyc_text.txt\", \"w\") as fp:\n",
+ " fp.write(nyc_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load NYC dataset\n",
+ "nyc_documents = SimpleDirectoryReader(\"../test_wiki/data/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ddff8f98-e002-40c5-93ac-93aa40dca5ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load PG's essay\n",
+ "essay_documents = SimpleDirectoryReader(\"../paul_graham_essay/data/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {},
+ "source": [
+ "### Building the document indices\n",
+ "Build a tree index for the NYC wiki page and PG essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build NYC index\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix=\"Nyc_docs\")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "nyc_index = VectorStoreIndex.from_documents(\n",
+ " nyc_documents, storage_context=storage_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b5aad4a-49ef-4b24-962a-0793f4f09316",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build essay index\n",
+ "vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix=\"Essay_docs\")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "essay_index = VectorStoreIndex.from_documents(\n",
+ " essay_documents, storage_context=storage_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bdcb22d5-4df8-4d65-aa29-6493fc027fe2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Set summaries for the indices\n",
+ "\n",
+ "Add text summaries to indices, so we can compose other indices on top of it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4149cbbd-7d0b-48c4-8c47-7d67ae0c55f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nyc_index_summary = \"\"\"\n",
+ " New York, often called New York City or NYC, \n",
+ " is the most populous city in the United States. \n",
+ " With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), \n",
+ " New York City is also the most densely populated major city in the United States, \n",
+ " and is more than twice as populous as second-place Los Angeles. \n",
+ " New York City lies at the southern tip of New York State, and \n",
+ " constitutes the geographical and demographic center of both the \n",
+ " Northeast megalopolis and the New York metropolitan area, the \n",
+ " largest metropolitan area in the world by urban landmass.[8] With over \n",
+ " 20.1 million people in its metropolitan statistical area and 23.5 million \n",
+ " in its combined statistical area as of 2020, New York is one of the world's \n",
+ " most populous megacities, and over 58 million people live within 250 mi (400 km) of \n",
+ " the city. New York City is a global cultural, financial, and media center with \n",
+ " a significant influence on commerce, health care and life sciences, entertainment, \n",
+ " research, technology, education, politics, tourism, dining, art, fashion, and sports. \n",
+ " Home to the headquarters of the United Nations, \n",
+ " New York is an important center for international diplomacy,\n",
+ " an established safe haven for global investors, and is sometimes described as the capital of the world.\n",
+ "\"\"\"\n",
+ "essay_index_summary = \"\"\"\n",
+ " Author: Paul Graham. \n",
+ " The author grew up painting and writing essays. \n",
+ " He wrote a book on Lisp and did freelance Lisp hacking work to support himself. \n",
+ " He also became the de facto studio assistant for Idelle Weber, an early photorealist painter. \n",
+ " He eventually had the idea to start a company to put art galleries online, but the idea was unsuccessful. \n",
+ " He then had the idea to write software to build online stores, which became the basis for his successful company, Viaweb. \n",
+ " After Viaweb was acquired by Yahoo!, the author returned to painting and started writing essays online. \n",
+ " He wrote a book of essays, Hackers & Painters, and worked on spam filters. \n",
+ " He also bought a building in Cambridge to use as an office. \n",
+ " He then had the idea to start Y Combinator, an investment firm that would \n",
+ " make a larger number of smaller investments and help founders remain as CEO. \n",
+ " He and his partner Jessica Livingston ran Y Combinator and funded a batch of startups twice a year. \n",
+ " He also continued to write essays, cook for groups of friends, and explore the concept of invented vs discovered in software. \n",
+ "\n",
+ "\"\"\"\n",
+ "index_summaries = [nyc_index_summary, essay_index_summary]\n",
+ "nyc_index.set_index_id(\"nyc_index\")\n",
+ "essay_index.set_index_id(\"essay_index\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Build Keyword Table Index on top of vector indices! \n",
+ "\n",
+ "We set summaries for each of the NYC and essay indices, and then compose a keyword index on top of it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eebbc448-1e0b-402c-b37e-f93bfcc0bf4f",
+ "metadata": {},
+ "source": [
+ "### Define Graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [nyc_index, essay_index],\n",
+ " index_summaries=index_summaries,\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56092d98",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "custom_query_engines = {\n",
+ " graph.root_id: graph.root_index.as_query_engine(retriever_mode=\"simple\")\n",
+ "}\n",
+ "\n",
+ "query_engine = graph.as_query_engine(\n",
+ " custom_query_engines=custom_query_engines,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3c4e58b-b153-4e43-bc02-274a85babbe8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# ask it a question about NYC\n",
+ "response = query_engine.query(\n",
+ " \"What is the weather of New York City like? How cold is it during the winter?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c0a43443-3e00-4e48-b3ab-f6369191d53a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c78bc3da-6bad-4998-9a81-90a3fa9200a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get source of response\n",
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b53e45e-93aa-4b49-a497-ab403f6254f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ask it a question about PG's essay\n",
+ "response = query_engine.query(\n",
+ " \"What did the author do growing up, before his time at Y Combinator?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "06dc71bb-882d-49f5-8566-69b0ea5019dd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b0894565-2b2c-4987-a891-17ba44d775b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get source of response\n",
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c103b6d-0946-48ba-a875-476c706f8560",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.11.0 ('llama')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "775fd5332502f2902173832d699e1edc37222ebadd0e97b5c8a1a7431bebae89"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/composable_indices/ComposableIndices.ipynb b/docs/examples/composable_indices/ComposableIndices.ipynb
index c37f3aaec2..c8644800d7 100644
--- a/docs/examples/composable_indices/ComposableIndices.ipynb
+++ b/docs/examples/composable_indices/ComposableIndices.ipynb
@@ -1,458 +1,455 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "tags": []
- },
- "source": [
- "# Composable Graph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "fa0e62b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
- " SimpleDirectoryReader\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {},
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load both the NYC Wikipedia page as well as Paul Graham's \"What I Worked On\" essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {},
- "outputs": [],
- "source": [
- "# fetch \"New York City\" page from Wikipedia\n",
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- ").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
- "\n",
- "data_path = Path('data')\n",
- "if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- "with open('../test_wiki/data/nyc_text.txt', 'w') as fp:\n",
- " fp.write(nyc_text)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load NYC dataset\n",
- "nyc_documents = SimpleDirectoryReader('../test_wiki/data/').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "ddff8f98-e002-40c5-93ac-93aa40dca5ca",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load PG's essay\n",
- "essay_documents = SimpleDirectoryReader('../paul_graham_essay/data/').load_data()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {},
- "source": [
- "### Building the document indices\n",
- "Build a tree index for the NYC wiki page and PG essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 28492 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 28492 tokens\n"
- ]
- }
- ],
- "source": [
- "# build NYC index\n",
- "nyc_index = VectorStoreIndex.from_documents(nyc_documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "8b5aad4a-49ef-4b24-962a-0793f4f09316",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
- ]
- }
- ],
- "source": [
- "# build essay index\n",
- "essay_index = VectorStoreIndex.from_documents(essay_documents)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bdcb22d5-4df8-4d65-aa29-6493fc027fe2",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Set summaries for the indices\n",
- "\n",
- "Add text summaries to indices, so we can compose other indices on top of it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "4149cbbd-7d0b-48c4-8c47-7d67ae0c55f0",
- "metadata": {},
- "outputs": [],
- "source": [
- "nyc_index_summary = \"\"\"\n",
- " New York, often called New York City or NYC, \n",
- " is the most populous city in the United States. \n",
- " With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), \n",
- " New York City is also the most densely populated major city in the United States, \n",
- " and is more than twice as populous as second-place Los Angeles. \n",
- " New York City lies at the southern tip of New York State, and \n",
- " constitutes the geographical and demographic center of both the \n",
- " Northeast megalopolis and the New York metropolitan area, the \n",
- " largest metropolitan area in the world by urban landmass.[8] With over \n",
- " 20.1 million people in its metropolitan statistical area and 23.5 million \n",
- " in its combined statistical area as of 2020, New York is one of the world's \n",
- " most populous megacities, and over 58 million people live within 250 mi (400 km) of \n",
- " the city. New York City is a global cultural, financial, and media center with \n",
- " a significant influence on commerce, health care and life sciences, entertainment, \n",
- " research, technology, education, politics, tourism, dining, art, fashion, and sports. \n",
- " Home to the headquarters of the United Nations, \n",
- " New York is an important center for international diplomacy,\n",
- " an established safe haven for global investors, and is sometimes described as the capital of the world.\n",
- "\"\"\"\n",
- "essay_index_summary = \"\"\"\n",
- " Author: Paul Graham. \n",
- " The author grew up painting and writing essays. \n",
- " He wrote a book on Lisp and did freelance Lisp hacking work to support himself. \n",
- " He also became the de facto studio assistant for Idelle Weber, an early photorealist painter. \n",
- " He eventually had the idea to start a company to put art galleries online, but the idea was unsuccessful. \n",
- " He then had the idea to write software to build online stores, which became the basis for his successful company, Viaweb. \n",
- " After Viaweb was acquired by Yahoo!, the author returned to painting and started writing essays online. \n",
- " He wrote a book of essays, Hackers & Painters, and worked on spam filters. \n",
- " He also bought a building in Cambridge to use as an office. \n",
- " He then had the idea to start Y Combinator, an investment firm that would \n",
- " make a larger number of smaller investments and help founders remain as CEO. \n",
- " He and his partner Jessica Livingston ran Y Combinator and funded a batch of startups twice a year. \n",
- " He also continued to write essays, cook for groups of friends, and explore the concept of invented vs discovered in software. \n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Build Keyword Table Index on top of tree indices! \n",
- "\n",
- "We set summaries for each of the NYC and essay indices, and then compose a keyword index on top of it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex,\n",
- " [nyc_index, essay_index], \n",
- " index_summaries=[nyc_index_summary, essay_index_summary],\n",
- " max_keywords_per_chunk=50\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "f3c4e58b-b153-4e43-bc02-274a85babbe8",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: What is the climate of New York City like? How cold is it during the winter?\n",
- "> Starting query: What is the climate of New York City like? How cold is it during the winter?\n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['cold', 'new york city', 'winter', 'new', 'city', 'climate', 'york']\n",
- "query keywords: ['cold', 'new york city', 'winter', 'new', 'city', 'climate', 'york']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['new', 'city', 'york']\n",
- "> Extracted keywords: ['new', 'city', 'york']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 18 tokens\n",
- "> [retrieve] Total embedding token usage: 18 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3834 tokens\n",
- "> [get_response] Total LLM token usage: 3834 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 282 tokens\n",
- "> [get_response] Total LLM token usage: 282 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# ask it a question about NYC \n",
- "query_engine = graph.as_query_engine()\n",
- "response = query_engine.query(\n",
- " \"What is the climate of New York City like? How cold is it during the winter?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "id": "c0a43443-3e00-4e48-b3ab-f6369191d53a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "The climate of New York City is humid subtropical, with hot and humid summers and cold, wet winters. The average temperature in the winter is around 32°F (0°C), but temperatures can drop below freezing. Snowfall is common in the winter months, with an average of 25 inches (63 cm) of snow per year.\n"
- ]
- }
- ],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "c78bc3da-6bad-4998-9a81-90a3fa9200a9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Source (Doc id: b58b74a6-c0c8-4020-8076-fdcd265dc7a3): \n",
- "\n",
- "The climate of New York City is humid subtropical, with hot and humid summers and cold, wet win...\n",
- "\n",
- "> Source (Doc id: e92aafcf-08c2-4a8c-897b-930ad420179a): one of the world's highest. New York City real estate is a safe haven for global investors.\n",
- "\n",
- "\n",
- "===...\n"
- ]
- }
- ],
- "source": [
- "# Get source of response\n",
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "6b53e45e-93aa-4b49-a497-ab403f6254f9",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: What did the author do growing up, before his time at Y Combinator?\n",
- "> Starting query: What did the author do growing up, before his time at Y Combinator?\n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['growing up', 'y combinator', 'time', 'growing', 'author', 'combinator']\n",
- "query keywords: ['growing up', 'y combinator', 'time', 'growing', 'author', 'combinator']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['author', 'combinator']\n",
- "> Extracted keywords: ['author', 'combinator']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 17 tokens\n",
- "> [retrieve] Total embedding token usage: 17 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3947 tokens\n",
- "> [get_response] Total LLM token usage: 3947 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 218 tokens\n",
- "> [get_response] Total LLM token usage: 218 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# ask it a question about PG's essay\n",
- "response = query_engine.query(\n",
- " \"What did the author do growing up, before his time at Y Combinator?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "06dc71bb-882d-49f5-8566-69b0ea5019dd",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "The author likely grew up doing a variety of activities, such as writing essays, painting, cooking, writing software, and hosting dinners for friends. He may have also been involved in giving talks and was likely driven by the idea of working hard to set the upper bound for everyone else.\n"
- ]
- }
- ],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "b0894565-2b2c-4987-a891-17ba44d775b5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Source (Doc id: 92bc5ce3-3a76-4570-9726-f7e0405ec6cc): \n",
- "Before his time at Y Combinator, the author worked on building the infrastructure of the web, wr...\n",
- "\n",
- "> Source (Doc id: ed37130a-3138-42d4-9e77-1c792fe22f4e): write something and put it on the web, anyone can read it. That may seem obvious now, but it was ...\n"
- ]
- }
- ],
- "source": [
- "# Get source of response\n",
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ce7efacd",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Composable Graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fa0e62b6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleKeywordTableIndex, SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {},
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load both the NYC Wikipedia page as well as Paul Graham's \"What I Worked On\" essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fetch \"New York City\" page from Wikipedia\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ ").json()\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
+ "\n",
+ "data_path = Path(\"data\")\n",
+ "if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ "with open(\"../test_wiki/data/nyc_text.txt\", \"w\") as fp:\n",
+ " fp.write(nyc_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load NYC dataset\n",
+ "nyc_documents = SimpleDirectoryReader(\"../test_wiki/data/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "ddff8f98-e002-40c5-93ac-93aa40dca5ca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load PG's essay\n",
+ "essay_documents = SimpleDirectoryReader(\"../paul_graham_essay/data/\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {},
+ "source": [
+ "### Building the document indices\n",
+ "Build a tree index for the NYC wiki page and PG essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 28492 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 28492 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# build NYC index\n",
+ "nyc_index = VectorStoreIndex.from_documents(nyc_documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "8b5aad4a-49ef-4b24-962a-0793f4f09316",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# build essay index\n",
+ "essay_index = VectorStoreIndex.from_documents(essay_documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bdcb22d5-4df8-4d65-aa29-6493fc027fe2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Set summaries for the indices\n",
+ "\n",
+ "Add text summaries to indices, so we can compose other indices on top of it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "4149cbbd-7d0b-48c4-8c47-7d67ae0c55f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nyc_index_summary = \"\"\"\n",
+ " New York, often called New York City or NYC, \n",
+ " is the most populous city in the United States. \n",
+ " With a 2020 population of 8,804,190 distributed over 300.46 square miles (778.2 km2), \n",
+ " New York City is also the most densely populated major city in the United States, \n",
+ " and is more than twice as populous as second-place Los Angeles. \n",
+ " New York City lies at the southern tip of New York State, and \n",
+ " constitutes the geographical and demographic center of both the \n",
+ " Northeast megalopolis and the New York metropolitan area, the \n",
+ " largest metropolitan area in the world by urban landmass.[8] With over \n",
+ " 20.1 million people in its metropolitan statistical area and 23.5 million \n",
+ " in its combined statistical area as of 2020, New York is one of the world's \n",
+ " most populous megacities, and over 58 million people live within 250 mi (400 km) of \n",
+ " the city. New York City is a global cultural, financial, and media center with \n",
+ " a significant influence on commerce, health care and life sciences, entertainment, \n",
+ " research, technology, education, politics, tourism, dining, art, fashion, and sports. \n",
+ " Home to the headquarters of the United Nations, \n",
+ " New York is an important center for international diplomacy,\n",
+ " an established safe haven for global investors, and is sometimes described as the capital of the world.\n",
+ "\"\"\"\n",
+ "essay_index_summary = \"\"\"\n",
+ " Author: Paul Graham. \n",
+ " The author grew up painting and writing essays. \n",
+ " He wrote a book on Lisp and did freelance Lisp hacking work to support himself. \n",
+ " He also became the de facto studio assistant for Idelle Weber, an early photorealist painter. \n",
+ " He eventually had the idea to start a company to put art galleries online, but the idea was unsuccessful. \n",
+ " He then had the idea to write software to build online stores, which became the basis for his successful company, Viaweb. \n",
+ " After Viaweb was acquired by Yahoo!, the author returned to painting and started writing essays online. \n",
+ " He wrote a book of essays, Hackers & Painters, and worked on spam filters. \n",
+ " He also bought a building in Cambridge to use as an office. \n",
+ " He then had the idea to start Y Combinator, an investment firm that would \n",
+ " make a larger number of smaller investments and help founders remain as CEO. \n",
+ " He and his partner Jessica Livingston ran Y Combinator and funded a batch of startups twice a year. \n",
+ " He also continued to write essays, cook for groups of friends, and explore the concept of invented vs discovered in software. \n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Build Keyword Table Index on top of tree indices! \n",
+ "\n",
+ "We set summaries for each of the NYC and essay indices, and then compose a keyword index on top of it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [nyc_index, essay_index],\n",
+ " index_summaries=[nyc_index_summary, essay_index_summary],\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "f3c4e58b-b153-4e43-bc02-274a85babbe8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: What is the climate of New York City like? How cold is it during the winter?\n",
+ "> Starting query: What is the climate of New York City like? How cold is it during the winter?\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['cold', 'new york city', 'winter', 'new', 'city', 'climate', 'york']\n",
+ "query keywords: ['cold', 'new york city', 'winter', 'new', 'city', 'climate', 'york']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['new', 'city', 'york']\n",
+ "> Extracted keywords: ['new', 'city', 'york']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 18 tokens\n",
+ "> [retrieve] Total embedding token usage: 18 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3834 tokens\n",
+ "> [get_response] Total LLM token usage: 3834 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 282 tokens\n",
+ "> [get_response] Total LLM token usage: 282 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# ask it a question about NYC\n",
+ "query_engine = graph.as_query_engine()\n",
+ "response = query_engine.query(\n",
+ " \"What is the climate of New York City like? How cold is it during the winter?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "c0a43443-3e00-4e48-b3ab-f6369191d53a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "The climate of New York City is humid subtropical, with hot and humid summers and cold, wet winters. The average temperature in the winter is around 32°F (0°C), but temperatures can drop below freezing. Snowfall is common in the winter months, with an average of 25 inches (63 cm) of snow per year.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "c78bc3da-6bad-4998-9a81-90a3fa9200a9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Source (Doc id: b58b74a6-c0c8-4020-8076-fdcd265dc7a3): \n",
+ "\n",
+ "The climate of New York City is humid subtropical, with hot and humid summers and cold, wet win...\n",
+ "\n",
+ "> Source (Doc id: e92aafcf-08c2-4a8c-897b-930ad420179a): one of the world's highest. New York City real estate is a safe haven for global investors.\n",
+ "\n",
+ "\n",
+ "===...\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get source of response\n",
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "6b53e45e-93aa-4b49-a497-ab403f6254f9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: What did the author do growing up, before his time at Y Combinator?\n",
+ "> Starting query: What did the author do growing up, before his time at Y Combinator?\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['growing up', 'y combinator', 'time', 'growing', 'author', 'combinator']\n",
+ "query keywords: ['growing up', 'y combinator', 'time', 'growing', 'author', 'combinator']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['author', 'combinator']\n",
+ "> Extracted keywords: ['author', 'combinator']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 17 tokens\n",
+ "> [retrieve] Total embedding token usage: 17 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3947 tokens\n",
+ "> [get_response] Total LLM token usage: 3947 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 218 tokens\n",
+ "> [get_response] Total LLM token usage: 218 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ask it a question about PG's essay\n",
+ "response = query_engine.query(\n",
+ " \"What did the author do growing up, before his time at Y Combinator?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "06dc71bb-882d-49f5-8566-69b0ea5019dd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "The author likely grew up doing a variety of activities, such as writing essays, painting, cooking, writing software, and hosting dinners for friends. He may have also been involved in giving talks and was likely driven by the idea of working hard to set the upper bound for everyone else.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "b0894565-2b2c-4987-a891-17ba44d775b5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Source (Doc id: 92bc5ce3-3a76-4570-9726-f7e0405ec6cc): \n",
+ "Before his time at Y Combinator, the author worked on building the infrastructure of the web, wr...\n",
+ "\n",
+ "> Source (Doc id: ed37130a-3138-42d4-9e77-1c792fe22f4e): write something and put it on the web, anyone can read it. That may seem obvious now, but it was ...\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get source of response\n",
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ce7efacd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/composable_indices/city_analysis/City_Analysis-Decompose.ipynb b/docs/examples/composable_indices/city_analysis/City_Analysis-Decompose.ipynb
index 48b4b48547..adcd13c4e1 100644
--- a/docs/examples/composable_indices/city_analysis/City_Analysis-Decompose.ipynb
+++ b/docs/examples/composable_indices/city_analysis/City_Analysis-Decompose.ipynb
@@ -1,2580 +1,2597 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "tags": []
- },
- "source": [
- "# Test Complex Queries over Multiple Documents (with and without Query Decomposition)\n",
- "\n",
- "Query Decomposition: The ability to decompose a complex query into a simpler query given the content of the index.\n",
- "\n",
- "Use ChatGPT as the LLM model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "fa0e62b6",
- "metadata": {
- "id": "fa0e62b6",
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "# Uncomment if you want to temporarily disable logger\n",
- "logger = logging.getLogger()\n",
- "logger.disabled = True"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
- " SimpleDirectoryReader,\n",
- " ServiceContext\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
- },
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load Wikipedia pages as well as Paul Graham's \"What I Worked On\" essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "fc4692a1",
- "metadata": {
- "id": "fc4692a1",
- "tags": []
- },
- "outputs": [],
- "source": [
- "wiki_titles = [\"Toronto\", \"Seattle\", \"San Francisco\", \"Chicago\", \"Boston\", \"Washington, D.C.\", \"Cambridge, Massachusetts\", \"Houston\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "for title in wiki_titles:\n",
- " response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- " ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
- "\n",
- " data_path = Path('data')\n",
- " if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load all wiki documents\n",
- "city_docs = {}\n",
- "for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {
- "id": "f1782198-c0de-4679-8951-1297c21b8639"
- },
- "source": [
- "### Building the document indices\n",
- "Build a vector index for the wiki pages about cities and persons, and PG essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "M0GylZB-C2zL",
- "metadata": {
- "id": "M0GylZB-C2zL",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/langchain/llms/openai.py:661: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "# # LLM Predictor (gpt-3.5-turbo)\n",
- "from llama_index.llms.openai import OpenAI\n",
- "\n",
- "\n",
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context = ServiceContext.from_defaults(llm=chatgpt)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 183,
- "referenced_widgets": [
- "b5566e3db2914ddebd80d7bde75b2559",
- "208d404f405a42a3b06d65ad67fb7322",
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- "47838fa763ca40598b2622a9d1e79444",
- "ff32a3f12e814740a1cd5dd12bd731d4",
- "3fef46c902524717b377dee6c1dfc929",
- "fd8b887c1f7149f2876cf8a31e534ad6",
- "7438aea716f44d85ad1c2b49a93acd83",
- "fe39f994fa9b4d7daa232e1dcd2b0e8b",
- "b102e756f9b848a98f58396fc825be84",
- "fbd7219af1924d2ead5310eb7b35aab0",
- "3b4c1066797b43a586611ec2d63e7ca1",
- "c06865c1e01a441698dacf48600dd03c",
- "9d229e5dd56e4d539ca2c1b9f0a37812",
- "868aa268dd28498d902782215e53c6fa",
- "46f644cf589e4a48a6fad1742f0c0575",
- "adb40ef11f094594b14776e238955224",
- "7b47c78391a4431aa2d3f84677f24046",
- "398f1c0f56fe4f218d999df138adfdac",
- "f1839e86863948f68314f81ba6bca4c9",
- "3c37e72850c746ce9c919add5340dede",
- "2053e6adef1b4dba89f861eaf3d916fd",
- "eab4127882d24acfa9518ebff6f4e22a",
- "64b754f563834be0a6963349b1f2dcf2",
- "c7636a6d7380465895b8c86d34caf500",
- "f7803dea63994cc2a31acf805bd19e67",
- "380a0c11434241b191b17421e395be8b",
- "a02534c347aa4865ab4ab3de3a3ee2f5",
- "b0ccb9d9d96e4ed8bec4d540c34d337c",
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- "1bdaf4dab16f48dbaeed3fb9bf268e45",
- "026cc1a42e154f1f92b5236869311929",
- "a2edbc4195d843e0acfba83726a08e78",
- "40e148c291ad4f739998a7eac55a8af6",
- "028aa5d1f7a74d538b5c606d4a6d146f",
- "c078fe9a056a473dab7d474cd7907154",
- "4cc9ec6ba46647aba2d53e352f91c137",
- "f2a1c5087d0e44909139697ed90474e8",
- "7b24b46d6c3643e581ba003a9c473745",
- "3f748152b9274556afad2555572aa9f4"
- ]
- },
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "outputId": "5721e863-d460-4f5c-9e36-5a586180b669",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20744 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 16942 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 23433 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 26082 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18614 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 21649 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 12855 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 21844 tokens\n"
- ]
- }
- ],
- "source": [
- "# Build city document index\n",
- "city_indices = {}\n",
- "index_summaries = {}\n",
- "for wiki_title in wiki_titles:\n",
- " city_indices[wiki_title] = VectorStoreIndex.from_documents(city_docs[wiki_title], service_context=service_context)\n",
- " # set summary text for city\n",
- " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "tags": []
- },
- "source": [
- "### Build Graph: Keyword Table Index on top of vector indices! \n",
- "\n",
- "We compose a keyword table index on top of all the vector indices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "metadata": {
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "outputId": "fc875b0e-c8bf-439b-c794-fcae25954cfb",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex,\n",
- " [index for _, index in city_indices.items()], \n",
- " [summary for _, summary in index_summaries.items()],\n",
- " max_keywords_per_chunk=50\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b4c36f69-596b-4974-afa2-09cc652c1111",
- "metadata": {},
- "source": [
- "### Define Query Configs"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "accd31e4-0ae7-4660-833c-5ae23037fd14",
- "metadata": {},
- "source": [
- "**Query Transform**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "82432236-fa93-4269-b695-d6d2131edb41",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
- "decompose_transform = DecomposeQueryTransform(\n",
- " service_context.llm_predictor, verbose=True\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43",
- "metadata": {
- "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43"
- },
- "source": [
- "**Complex Query 1**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "f40e4216",
- "metadata": {},
- "outputs": [],
- "source": [
- "# with query decomposition in subindices\n",
- "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
- "\n",
- "\n",
- "custom_query_engines = {}\n",
- "for index in city_indices.values():\n",
- " query_engine = index.as_query_engine(service_context=service_context)\n",
- " transform_metadata = {'index_summary': index.index_struct.summary}\n",
- " tranformed_query_engine = TransformQueryEngine(query_engine, decompose_transform, transform_metadata=transform_metadata)\n",
- " custom_query_engines[index.index_id] = tranformed_query_engine\n",
- "\n",
- "custom_query_engines[graph.root_index.index_id] = graph.root_index.as_query_engine(\n",
- " retriever_mode='simple', \n",
- " response_mode='tree_summarize', \n",
- " service_context=service_context\n",
- ")\n",
- "\n",
- "query_engine_decompose = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "5705e023-e5bc-4c1b-bed4-8d70a6152122",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['toronto', 'airports', 'seattle', 'contrast', 'compare', 'houston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['toronto', 'seattle', 'houston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable features of the Toronto Pearson International Airport?\n",
- "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable features of the Toronto Pearson International Airport?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1142 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1142 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 10 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the name of the airport in Seattle?\n",
- "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the name of the airport in Seattle?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1773 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1773 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are the major airports in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are the major airports in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1162 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1162 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 254 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 254 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "response_chatgpt = query_engine_decompose.query(\n",
- " \"Compare and contrast the airports in Seattle, Houston, and Toronto. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "13108dca-8ce6-4485-a018-dcab2514868d",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Seattle has one major airport called Seattle-Tacoma International Airport, while Houston has two major airports called George Bush Intercontinental Airport and William P. Hobby Airport, as well as a third municipal airport called Ellington Airport. Toronto Pearson International Airport is Canada's busiest airport and offers limited commercial and passenger service to nearby destinations in Canada and the United States. All three cities have at least one major airport, but Houston has more options with two major airports.\n"
- ]
- }
- ],
- "source": [
- "print(str(response_chatgpt))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "08359128",
- "metadata": {},
- "outputs": [],
- "source": [
- "# without query decomposition in subindices\n",
- "\n",
- "custom_query_engines = {}\n",
- "for index in city_indices.values():\n",
- " query_engine = index.as_query_engine(service_context=service_context)\n",
- " custom_query_engines[index.index_id] = query_engine\n",
- "\n",
- "custom_query_engines[graph.root_index.index_id] = graph.root_index.as_query_engine(\n",
- " retriever_mode='simple', \n",
- " response_mode='tree_summarize', \n",
- " service_context=service_context\n",
- ")\n",
- "\n",
- "query_engine = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines, \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "d39a5e4d-6a5a-4375-9ec0-1cad18f06996",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['toronto', 'airports', 'seattle', 'contrast', 'compare', 'houston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['toronto', 'seattle', 'houston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 14 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1114 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1114 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1799 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1799 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1186 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1186 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 196 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 196 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "response_chatgpt = query_engine.query(\n",
- " \"Compare and contrast the airports in Seattle, Houston, and Toronto. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "f63aaa69-9b12-4e4f-801e-ba70fe50a0ef",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'It is not possible to compare and contrast the airports in Seattle, Houston, and Toronto based on the given context information.'"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "str(response_chatgpt)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd",
- "metadata": {
- "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd"
- },
- "source": [
- "**Complex Query 2**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "6f621760-fb65-455c-8a31-e53442f9d24a",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 10 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
- "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# with query decomposition\n",
- "response_chatgpt = query_engine_decompose.query(\n",
- " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "98f4728f-a190-4e9b-b9d9-1361f6f4c50d",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Houston has sports teams for every major professional league except the National Hockey League, while Boston has teams for Major League Baseball, National Hockey League, National Basketball Association, National Football League, Major League Lacrosse, and Overwatch League. Both cities have a strong sports culture, but Boston has a more diverse range of professional sports teams.'"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "str(response_chatgpt)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "800ff760-6ee8-4c5a-93a3-5823d132060a",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# without query decomposition\n",
- "response_chatgpt = query_engine.query(\n",
- " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "6f6d1661-a224-4bd2-81c6-7e1b51b8b11b",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Sorry, I cannot answer this question as there is no information provided about the sports environment of Houston or Boston in the given context information.'"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "str(response_chatgpt)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "cac6b56a-34e2-46d3-94fe-3acdde66aa66",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 10 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# with query decomposition\n",
- "response_chatgpt = query_engine_decompose.query(\n",
- " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "226ee4f5-c941-4497-a04c-630757622282",
- "metadata": {
- "id": "226ee4f5-c941-4497-a04c-630757622282",
- "outputId": "c8b0c521-d2e7-4ba6-dc9f-52189fbf0b9b",
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Houston has sports teams for every major professional league except the National Hockey League, while Boston has teams for Major League Baseball, National Hockey League, National Basketball Association, National Football League, Major League Lacrosse, and Overwatch League. Both cities have a strong sports culture, but Boston has a more diverse range of professional sports teams.\n"
- ]
- }
- ],
- "source": [
- "print(response_chatgpt)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "e12aa255-af39-4ef1-9071-ded49aa84d9f",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# without query decomposition\n",
- "response_chatgpt = query_engine.query(\n",
- " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "001c646d-33fd-4b4a-b4c7-44203e8e9401",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Sorry, I cannot answer this question as there is no information provided about the sports environment of Houston or Boston in the given context information.\n"
- ]
- }
- ],
- "source": [
- "print(response_chatgpt)"
- ]
- },
- {
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- "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e"
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- "source": [
- "**Complex Query 3**"
- ]
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- "execution_count": 25,
- "id": "4cb83f2e-f838-4384-acd0-eb12f72ad2ec",
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- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['arts', 'culture', 'contrast', 'compare', 'houston', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 9 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Houston?\n",
- "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1835 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1835 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 9 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1918 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1918 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 444 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 444 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
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- "source": [
- "# with query decomposition\n",
- "response_chatgpt = query_engine_decompose.query(\n",
- " \"Compare and contrast the arts and culture of Houston and Boston. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "0aa7efdf-c8c0-4efb-83a0-ad617f120307",
- "metadata": {
- "tags": []
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- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Both Houston and Boston have a variety of cultural institutions, including museums, performing arts organizations, and theaters. Some notable museums in both cities include the Museum of Fine Arts. However, Houston has a greater focus on contemporary art with institutions such as the Contemporary Arts Museum Houston and the Station Museum of Contemporary Art. Boston, on the other hand, has a unique museum in the Isabella Stewart Gardner Museum, which features a collection of art and artifacts in a recreated Venetian palace. In terms of performing arts, both cities have symphony orchestras and opera companies, but Boston also has a strong focus on contemporary classical music with groups such as the Boston Modern Orchestra Project. Overall, while both cities have a rich arts and culture scene, they differ in their specific areas of focus.\n"
- ]
- }
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- "source": [
- "print(response_chatgpt)"
- ]
- },
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- "execution_count": 27,
- "id": "3afe8251-9901-4843-b72a-ba4c7d6024b9",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['arts', 'culture', 'contrast', 'compare', 'houston', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 13 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1779 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1779 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1817 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1817 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 122 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 122 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
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- "source": [
- "# without query decomposition\n",
- "response_chatgpt = query_engine.query(\n",
- " \"Compare and contrast the arts and culture of Houston and Boston. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "10c6ca94-c053-4009-b52d-a5255e74853c",
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- "outputId": "b4575737-59e2-43b5-85e2-c51ffe0f8cdd",
- "tags": []
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- "outputs": [
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- "output_type": "stream",
- "text": [
- "I'm sorry, but there is not enough information provided to compare and contrast the arts and culture of Houston and Boston.\n"
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+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "tags": []
+ },
+ "source": [
+ "# Test Complex Queries over Multiple Documents (with and without Query Decomposition)\n",
+ "\n",
+ "Query Decomposition: The ability to decompose a complex query into a simpler query given the content of the index.\n",
+ "\n",
+ "Use ChatGPT as the LLM model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fa0e62b6",
+ "metadata": {
+ "id": "fa0e62b6",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "# Uncomment if you want to temporarily disable logger\n",
+ "logger = logging.getLogger()\n",
+ "logger.disabled = True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
+ },
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load Wikipedia pages as well as Paul Graham's \"What I Worked On\" essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "fc4692a1",
+ "metadata": {
+ "id": "fc4692a1",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "wiki_titles = [\n",
+ " \"Toronto\",\n",
+ " \"Seattle\",\n",
+ " \"San Francisco\",\n",
+ " \"Chicago\",\n",
+ " \"Boston\",\n",
+ " \"Washington, D.C.\",\n",
+ " \"Cambridge, Massachusetts\",\n",
+ " \"Houston\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "for title in wiki_titles:\n",
+ " response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ " ).json()\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
+ "\n",
+ " data_path = Path(\"data\")\n",
+ " if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Load all wiki documents\n",
+ "city_docs = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[f\"data/{wiki_title}.txt\"]\n",
+ " ).load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {
+ "id": "f1782198-c0de-4679-8951-1297c21b8639"
+ },
+ "source": [
+ "### Building the document indices\n",
+ "Build a vector index for the wiki pages about cities and persons, and PG essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "M0GylZB-C2zL",
+ "metadata": {
+ "id": "M0GylZB-C2zL",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/langchain/llms/openai.py:661: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# # LLM Predictor (gpt-3.5-turbo)\n",
+ "from llama_index.llms.openai import OpenAI\n",
+ "\n",
+ "\n",
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context = ServiceContext.from_defaults(llm=chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20744 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 16942 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 23433 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 26082 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18614 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 21649 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 12855 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 21844 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Build city document index\n",
+ "city_indices = {}\n",
+ "index_summaries = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
+ " city_docs[wiki_title], service_context=service_context\n",
+ " )\n",
+ " # set summary text for city\n",
+ " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "tags": []
+ },
+ "source": [
+ "### Build Graph: Keyword Table Index on top of vector indices! \n",
+ "\n",
+ "We compose a keyword table index on top of all the vector indices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "metadata": {
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "outputId": "fc875b0e-c8bf-439b-c794-fcae25954cfb",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [index for _, index in city_indices.items()],\n",
+ " [summary for _, summary in index_summaries.items()],\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b4c36f69-596b-4974-afa2-09cc652c1111",
+ "metadata": {},
+ "source": [
+ "### Define Query Configs"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "accd31e4-0ae7-4660-833c-5ae23037fd14",
+ "metadata": {},
+ "source": [
+ "**Query Transform**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "82432236-fa93-4269-b695-d6d2131edb41",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
+ "\n",
+ "decompose_transform = DecomposeQueryTransform(\n",
+ " service_context.llm_predictor, verbose=True\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43",
+ "metadata": {
+ "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43"
+ },
+ "source": [
+ "**Complex Query 1**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "f40e4216",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# with query decomposition in subindices\n",
+ "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
+ "\n",
+ "\n",
+ "custom_query_engines = {}\n",
+ "for index in city_indices.values():\n",
+ " query_engine = index.as_query_engine(service_context=service_context)\n",
+ " transform_metadata = {\"index_summary\": index.index_struct.summary}\n",
+ " tranformed_query_engine = TransformQueryEngine(\n",
+ " query_engine, decompose_transform, transform_metadata=transform_metadata\n",
+ " )\n",
+ " custom_query_engines[index.index_id] = tranformed_query_engine\n",
+ "\n",
+ "custom_query_engines[graph.root_index.index_id] = graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\",\n",
+ " response_mode=\"tree_summarize\",\n",
+ " service_context=service_context,\n",
+ ")\n",
+ "\n",
+ "query_engine_decompose = graph.as_query_engine(\n",
+ " custom_query_engines=custom_query_engines,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "5705e023-e5bc-4c1b-bed4-8d70a6152122",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['toronto', 'airports', 'seattle', 'contrast', 'compare', 'houston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['toronto', 'seattle', 'houston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable features of the Toronto Pearson International Airport?\n",
+ "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable features of the Toronto Pearson International Airport?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1142 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1142 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 10 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the name of the airport in Seattle?\n",
+ "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the name of the airport in Seattle?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1773 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1773 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are the major airports in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are the major airports in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1162 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1162 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 254 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 254 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response_chatgpt = query_engine_decompose.query(\n",
+ " \"Compare and contrast the airports in Seattle, Houston, and Toronto. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "13108dca-8ce6-4485-a018-dcab2514868d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Seattle has one major airport called Seattle-Tacoma International Airport, while Houston has two major airports called George Bush Intercontinental Airport and William P. Hobby Airport, as well as a third municipal airport called Ellington Airport. Toronto Pearson International Airport is Canada's busiest airport and offers limited commercial and passenger service to nearby destinations in Canada and the United States. All three cities have at least one major airport, but Houston has more options with two major airports.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(str(response_chatgpt))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "08359128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# without query decomposition in subindices\n",
+ "\n",
+ "custom_query_engines = {}\n",
+ "for index in city_indices.values():\n",
+ " query_engine = index.as_query_engine(service_context=service_context)\n",
+ " custom_query_engines[index.index_id] = query_engine\n",
+ "\n",
+ "custom_query_engines[graph.root_index.index_id] = graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\",\n",
+ " response_mode=\"tree_summarize\",\n",
+ " service_context=service_context,\n",
+ ")\n",
+ "\n",
+ "query_engine = graph.as_query_engine(\n",
+ " custom_query_engines=custom_query_engines,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "d39a5e4d-6a5a-4375-9ec0-1cad18f06996",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the airports in Seattle, Houston, and Toronto. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['toronto', 'airports', 'seattle', 'contrast', 'compare', 'houston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['toronto', 'seattle', 'houston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 14 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1114 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1114 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1799 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1799 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1186 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1186 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 196 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 196 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response_chatgpt = query_engine.query(\n",
+ " \"Compare and contrast the airports in Seattle, Houston, and Toronto. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "f63aaa69-9b12-4e4f-801e-ba70fe50a0ef",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'It is not possible to compare and contrast the airports in Seattle, Houston, and Toronto based on the given context information.'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str(response_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd",
+ "metadata": {
+ "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd"
+ },
+ "source": [
+ "**Complex Query 2**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "6f621760-fb65-455c-8a31-e53442f9d24a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 10 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
+ "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# with query decomposition\n",
+ "response_chatgpt = query_engine_decompose.query(\n",
+ " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "98f4728f-a190-4e9b-b9d9-1361f6f4c50d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Houston has sports teams for every major professional league except the National Hockey League, while Boston has teams for Major League Baseball, National Hockey League, National Basketball Association, National Football League, Major League Lacrosse, and Overwatch League. Both cities have a strong sports culture, but Boston has a more diverse range of professional sports teams.'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str(response_chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "800ff760-6ee8-4c5a-93a3-5823d132060a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# without query decomposition\n",
+ "response_chatgpt = query_engine.query(\n",
+ " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "6f6d1661-a224-4bd2-81c6-7e1b51b8b11b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Sorry, I cannot answer this question as there is no information provided about the sports environment of Houston or Boston in the given context information.'"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "str(response_chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "cac6b56a-34e2-46d3-94fe-3acdde66aa66",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What sports teams are based in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1861 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 10 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable sports teams based in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1812 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 226 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# with query decomposition\n",
+ "response_chatgpt = query_engine_decompose.query(\n",
+ " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "226ee4f5-c941-4497-a04c-630757622282",
+ "metadata": {
+ "id": "226ee4f5-c941-4497-a04c-630757622282",
+ "outputId": "c8b0c521-d2e7-4ba6-dc9f-52189fbf0b9b",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Houston has sports teams for every major professional league except the National Hockey League, while Boston has teams for Major League Baseball, National Hockey League, National Basketball Association, National Football League, Major League Lacrosse, and Overwatch League. Both cities have a strong sports culture, but Boston has a more diverse range of professional sports teams.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "e12aa255-af39-4ef1-9071-ded49aa84d9f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the sports environment of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['environment', 'contrast', 'sports', 'compare', 'houston', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1795 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1792 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 119 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# without query decomposition\n",
+ "response_chatgpt = query_engine.query(\n",
+ " \"Compare and contrast the sports environment of Houston and Boston. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "001c646d-33fd-4b4a-b4c7-44203e8e9401",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sorry, I cannot answer this question as there is no information provided about the sports environment of Houston or Boston in the given context information.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e",
+ "metadata": {
+ "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e"
+ },
+ "source": [
+ "**Complex Query 3**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "4cb83f2e-f838-4384-acd0-eb12f72ad2ec",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['arts', 'culture', 'contrast', 'compare', 'houston', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 9 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Houston?\n",
+ "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1835 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1835 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 9 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1918 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1918 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 444 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 444 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# with query decomposition\n",
+ "response_chatgpt = query_engine_decompose.query(\n",
+ " \"Compare and contrast the arts and culture of Houston and Boston. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "0aa7efdf-c8c0-4efb-83a0-ad617f120307",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Both Houston and Boston have a variety of cultural institutions, including museums, performing arts organizations, and theaters. Some notable museums in both cities include the Museum of Fine Arts. However, Houston has a greater focus on contemporary art with institutions such as the Contemporary Arts Museum Houston and the Station Museum of Contemporary Art. Boston, on the other hand, has a unique museum in the Isabella Stewart Gardner Museum, which features a collection of art and artifacts in a recreated Venetian palace. In terms of performing arts, both cities have symphony orchestras and opera companies, but Boston also has a strong focus on contemporary classical music with groups such as the Boston Modern Orchestra Project. Overall, while both cities have a rich arts and culture scene, they differ in their specific areas of focus.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "3afe8251-9901-4843-b72a-ba4c7d6024b9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['arts', 'culture', 'contrast', 'compare', 'houston', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 13 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1779 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1779 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1817 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1817 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 122 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 122 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# without query decomposition\n",
+ "response_chatgpt = query_engine.query(\n",
+ " \"Compare and contrast the arts and culture of Houston and Boston. \"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "10c6ca94-c053-4009-b52d-a5255e74853c",
+ "metadata": {
+ "id": "10c6ca94-c053-4009-b52d-a5255e74853c",
+ "outputId": "b4575737-59e2-43b5-85e2-c51ffe0f8cdd",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "I'm sorry, but there is not enough information provided to compare and contrast the arts and culture of Houston and Boston.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
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diff --git a/docs/examples/composable_indices/city_analysis/City_Analysis-Unified-Query.ipynb b/docs/examples/composable_indices/city_analysis/City_Analysis-Unified-Query.ipynb
index fc2d8116e8..f6a1ef732f 100644
--- a/docs/examples/composable_indices/city_analysis/City_Analysis-Unified-Query.ipynb
+++ b/docs/examples/composable_indices/city_analysis/City_Analysis-Unified-Query.ipynb
@@ -1,2292 +1,2287 @@
{
- "cells": [
- {
- "attachments": {},
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- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "tags": []
- },
- "source": [
- "# Defining a Unified Query Interface over your Data"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "2040a3ac",
- "metadata": {},
- "source": [
- "This notebook shows how to build a unified query interface that can handle:\n",
- "1. **heterogeneous data sources** (e.g. data about multiple cities) and \n",
- "2. **complex queries** (e.g. compare and contrast)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "fa0e62b6",
- "metadata": {
- "id": "fa0e62b6",
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "# Uncomment if you want to temporarily disable logger\n",
- "logger = logging.getLogger()\n",
- "logger.disabled = True"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
- " SimpleDirectoryReader,\n",
- " ServiceContext\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
- },
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load Wikipedia pages about different cities."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "fc4692a1",
- "metadata": {
- "id": "fc4692a1",
- "tags": []
- },
- "outputs": [],
- "source": [
- "wiki_titles = [\"Toronto\", \"Seattle\", \"Chicago\", \"Boston\", \"Houston\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "for title in wiki_titles:\n",
- " response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- " ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
- "\n",
- " data_path = Path('data')\n",
- " if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load all wiki documents\n",
- "city_docs = {}\n",
- "for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {
- "id": "f1782198-c0de-4679-8951-1297c21b8639"
- },
- "source": [
- "### Building Vector Indices\n",
- "Build a vector index for the wiki pages about cities."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "M0GylZB-C2zL",
- "metadata": {
- "id": "M0GylZB-C2zL",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/langchain/llms/openai.py:687: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
- " warnings.warn(\n"
- ]
- }
- ],
- "source": [
- "from llama_index.llms import OpenAI\n",
- "\n",
- "\n",
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context = ServiceContext.from_defaults(\n",
- " llm=chatgpt, chunk_size=1024\n",
- ")\n",
- "\n",
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context = ServiceContext.from_defaults(\n",
- " llm=gpt4, chunk_size=1024\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 183,
- "referenced_widgets": [
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- ]
- },
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "outputId": "5721e863-d460-4f5c-9e36-5a586180b669",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20744 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 16942 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 26082 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18648 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 21844 tokens\n"
- ]
- }
- ],
- "source": [
- "# Build city document index\n",
- "vector_indices = {}\n",
- "for wiki_title in wiki_titles:\n",
- " # build vector index\n",
- " vector_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
- " city_docs[wiki_title], service_context=service_context\n",
- " )\n",
- "\n",
- " # set id for vector index\n",
- " vector_indices[wiki_title].set_index_id(wiki_title)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "2fc6cbcf-3640-4cb0-bafa-8a367159ffa9",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "index_summaries = {\n",
- " wiki_title: (\n",
- " f\"This content contains Wikipedia articles about {wiki_title}. \"\n",
- " f\"Use this index if you need to lookup specific facts about {wiki_title}.\\n\"\n",
- " \"Do not use this index if you want to analyze multiple cities.\"\n",
- " )\n",
- " for wiki_title in wiki_titles\n",
- "}\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "c385eaa5-5ea5-4ddb-a9e8-87583ff75e14",
- "metadata": {},
- "source": [
- "#### Test Querying the Vector Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "f690177f-9e9b-46c5-9a2e-9e697da8ce1b",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1904 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = vector_indices[\"Toronto\"].as_query_engine()\n",
- "response = query_engine.query(\"What are the sports teams in Toronto?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "0ba97f40-e4d7-4e40-96f7-f354f91b2b64",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The sports teams in Toronto include:\n",
- "\n",
- "1. Toronto Maple Leafs (NHL - ice hockey)\n",
- "2. Toronto Blue Jays (MLB - baseball)\n",
- "3. Toronto Raptors (NBA - basketball)\n",
- "4. Toronto Argonauts (CFL - Canadian football)\n",
- "5. Toronto FC (MLS - soccer)\n",
- "6. Toronto Marlies (AHL - ice hockey)\n",
- "7. Toronto Six (NWHL - women's ice hockey)\n",
- "8. Toronto Rock (NLL - lacrosse)\n",
- "9. Toronto Rush (AUDL - ultimate frisbee)\n",
- "10. Toronto Wolfpack (Rugby league, playing in the North American Rugby League tournament)\n"
- ]
- }
- ],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "tags": []
- },
- "source": [
- "### Build a Graph for Compare/Contrast Queries\n",
- "\n",
- "We build a graph by composing a keyword table index on top of all the vector indices.\n",
- "We use this graph for compare/contrast queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "outputId": "fc875b0e-c8bf-439b-c794-fcae25954cfb",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "from llama_index.indices.composability import ComposableGraph\n",
- "\n",
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex,\n",
- " [index for _, index in vector_indices.items()], \n",
- " [summary for _, summary in index_summaries.items()],\n",
- " max_keywords_per_chunk=50\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "de977a92-acf2-40db-be58-34b29f4be47b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# get root index\n",
- "root_index = graph.get_index(graph.root_id)\n",
- "\n",
- "# set id of root index\n",
- "root_index.set_index_id(\"compare_contrast\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "7917d4a6-30f8-44fd-9bd7-0e40fa9054a5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define decompose_transform\n",
- "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
- "\n",
- "\n",
- "decompose_transform = DecomposeQueryTransform(\n",
- " llm_predictor_chatgpt, verbose=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "42edf61e-d1d5-4d5c-a554-d20489413180",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define custom retrievers\n",
- "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
- "\n",
- "\n",
- "custom_query_engines = {}\n",
- "for index in vector_indices.values():\n",
- " query_engine = index.as_query_engine(service_context=service_context)\n",
- " query_engine = TransformQueryEngine(\n",
- " query_engine,\n",
- " query_transform=decompose_transform,\n",
- " transform_metadata={'index_summary': index.index_struct.summary},\n",
- " )\n",
- " custom_query_engines[index.index_id] = query_engine\n",
- "\n",
- "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
- " retriever_mode='simple',\n",
- " response_mode='tree_summarize',\n",
- " service_context=service_context,\n",
- " verbose=True,\n",
- ")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "3221be27",
- "metadata": {},
- "outputs": [],
- "source": [
- "# define graph\n",
- "graph_query_engine = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b4c36f69-596b-4974-afa2-09cc652c1111",
- "metadata": {},
- "source": [
- "#### Test querying the graph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "4cb83f2e-f838-4384-acd0-eb12f72ad2ec",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['contrast', 'houston', 'arts', 'boston', 'culture', 'compare']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1877 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 2130 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 885 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 885 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_str = (\n",
- " \"Compare and contrast the arts and culture of Houston and Boston. \"\n",
- ")\n",
- "response = graph_query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "0aa7efdf-c8c0-4efb-83a0-ad617f120307",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Houston and Boston both have rich arts and culture scenes, with a variety of cultural institutions and events that cater to diverse interests. Both cities have a strong presence of performing arts organizations, such as the Houston Grand Opera and Houston Ballet in Houston, and the Boston Ballet and Boston Lyric Opera Company in Boston. They also have renowned symphony orchestras, with the Houston Symphony Orchestra and the Boston Symphony Orchestra.\n",
- "\n",
- "Both cities host annual events that celebrate their unique cultural identities, such as the Houston Livestock Show and Rodeo, Houston Gay Pride Parade, and Houston Greek Festival in Houston, and the Boston Gay Pride Parade and Festival, Italian Summer Feasts, and Fourth of July events in Boston. Additionally, both cities have thriving theater districts, with Houston's Theater District and Boston's Theater District housing several historic and modern theaters.\n",
- "\n",
- "In terms of visual arts, both Houston and Boston have notable art museums, such as the Museum of Fine Arts in both cities, as well as the Houston Museum of Natural Science and the Contemporary Arts Museum Houston in Houston, and the Isabella Stewart Gardner Museum and the Institute of Contemporary Art in Boston. Houston also has unique institutions like the Menil Collection, Rothko Chapel, and the Byzantine Fresco Chapel Museum, while Boston has historic sites related to the American Revolution preserved in the Boston National Historical Park and along the Freedom Trail.\n",
- "\n",
- "While both cities have a strong focus on arts and culture, Houston's cultural scene tends to be more diverse, with events like the Art Car Parade, Houston International Festival, and Bayou City Art Festival showcasing the city's eclectic mix of cultures. On the other hand, Boston's cultural scene is deeply rooted in its history and traditions, with events like the Boston Early Music Festival and historic sites along the Freedom Trail reflecting the city's rich past.\n"
- ]
- }
- ],
- "source": [
- "print(response)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "a8bc0d20-fa6a-455b-ab85-ea9e4fcc0b37",
- "metadata": {},
- "source": [
- "### Build a router to automatically choose between indices and graph"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "3bc185eb",
- "metadata": {},
- "source": [
- "We can use a `RouterQueryEngine` to automatically route to the vector indices and the graph.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d4a920fc",
- "metadata": {},
- "source": [
- "\n",
- "To do this, first build the query engines, and give each a description to obtain a `QueryEngineTool`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "ed558157",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.tools.query_engine import QueryEngineTool\n",
- "\n",
- "query_engine_tools = []\n",
- "\n",
- "# add vector index tools\n",
- "for wiki_title in wiki_titles:\n",
- " index = vector_indices[wiki_title]\n",
- " summary = index_summaries[wiki_title]\n",
- " \n",
- " query_engine = index.as_query_engine(service_context=service_context)\n",
- " vector_tool = QueryEngineTool.from_defaults(query_engine, description=summary)\n",
- " query_engine_tools.append(vector_tool)\n",
- "\n",
- "\n",
- "# add graph tool\n",
- "graph_description = (\n",
- " \"This tool contains Wikipedia articles about multiple cities. \"\n",
- " \"Use this tool if you want to compare multiple cities. \"\n",
- ")\n",
- "graph_tool = QueryEngineTool.from_defaults(graph_query_engine, description=graph_description)\n",
- "query_engine_tools.append(graph_tool)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1318dcbb",
- "metadata": {},
- "source": [
- "Then, define the `RouterQueryEngine` with a desired selector module. \n",
- "Here, we use the `LLMSingleSelector`, which uses LLM to choose a underlying query engine to route the query to."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "46922462-604c-43a3-b59a-f040cbd1ed3f",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.query_engine.router_query_engine import RouterQueryEngine\n",
- "from llama_index.selectors.llm_selectors import LLMSingleSelector\n",
- "\n",
- "\n",
- "router_query_engine = RouterQueryEngine(\n",
- " selector=LLMSingleSelector.from_defaults(service_context=service_context),\n",
- " query_engine_tools=query_engine_tools\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "eef2630a",
- "metadata": {},
- "source": [
- "Asking a compare and contrast question should route the query to the graph."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "f67065c4-3a68-4adb-ab3f-093ec9e2a8f3",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 5: This tool contains Wikipedia articles about multiple cities, which allows for comparison and analysis of different cities, such as Houston and Boston..\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston.\n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['contrast', 'houston', 'arts', 'boston', 'culture', 'compare']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston?\n",
- "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston and Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1835 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 2134 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 772 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 772 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# ask a compare/contrast question \n",
- "response = router_query_engine.query(\n",
- " \"Compare and contrast the arts and culture of Houston and Boston.\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
- "id": "ebf1a61f-422c-42ac-ae4e-a3a00415bf25",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Based on the context information provided, both Houston and Boston have rich arts and cultural scenes, with a variety of institutions and events catering to diverse interests.\n",
- "\n",
- "Houston's cultural institutions and events include the Houston Theater District, the Museum District, the Houston Livestock Show and Rodeo, the Houston Gay Pride Parade, the Houston Greek Festival, the Art Car Parade, the Houston Auto Show, the Houston International Festival, and the Bayou City Art Festival.\n",
- "\n",
- "In contrast, Boston's cultural institutions and events include the Boston Symphony Hall, New England Conservatory's Jordan Hall, Boston Ballet, various performing-arts organizations, contemporary classical music groups, the Theater District, First Night, Boston Early Music Festival, Boston Arts Festival, Boston Gay Pride Parade and Festival, Italian Summer Feasts, Fourth of July events, art museums such as the Museum of Fine Arts and Isabella Stewart Gardner Museum, the Institute of Contemporary Art, art gallery destinations like the South End Art and Design District (SoWa) and Newbury St, and the Boston National Historical Park.\n",
- "\n",
- "Both cities have theater districts, gay pride parades, and arts festivals. However, Houston has unique events such as the Livestock Show and Rodeo, the Greek Festival, the Art Car Parade, and the Houston Auto Show. On the other hand, Boston has a strong focus on classical music with venues like the Symphony Hall and Jordan Hall, as well as historical sites related to the American Revolution.\n"
- ]
- }
- ],
- "source": [
- "print(response)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d4ecdfbe",
- "metadata": {},
- "source": [
- "Asking a question about a specific city should route the query to the specific vector index query engine."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "5d78bcef-785b-4667-a509-9f6d4e1d9d5f",
- "metadata": {
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- "outputs": [
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- "output_type": "stream",
- "text": [
- "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 0: This content contains Wikipedia articles about Toronto, which can provide information about the sports teams in the city..\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1905 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
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- "source": [
- "response = router_query_engine.query(\"What are the sports teams in Toronto?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "431d75f1-d153-44b7-ac2f-4bfc4b65e3f5",
- "metadata": {
- "tags": []
- },
- "outputs": [
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- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The sports teams in Toronto include:\n",
- "\n",
- "1. Toronto Maple Leafs (NHL - ice hockey)\n",
- "2. Toronto Blue Jays (MLB - baseball)\n",
- "3. Toronto Raptors (NBA - basketball)\n",
- "4. Toronto Argonauts (CFL - Canadian football)\n",
- "5. Toronto FC (MLS - soccer)\n",
- "6. Toronto Marlies (AHL - ice hockey)\n",
- "7. Toronto Six (NWHL - women's ice hockey)\n",
- "8. Toronto Rock (NLL - lacrosse)\n",
- "9. Toronto Rush (AUDL - ultimate frisbee)\n",
- "10. Toronto Wolfpack (Rugby league, currently playing in the North American Rugby League tournament)\n"
- ]
- }
- ],
- "source": [
- "print(response)"
- ]
- }
- ],
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+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "tags": []
+ },
+ "source": [
+ "# Defining a Unified Query Interface over your Data"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "2040a3ac",
+ "metadata": {},
+ "source": [
+ "This notebook shows how to build a unified query interface that can handle:\n",
+ "1. **heterogeneous data sources** (e.g. data about multiple cities) and \n",
+ "2. **complex queries** (e.g. compare and contrast)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fa0e62b6",
+ "metadata": {
+ "id": "fa0e62b6",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "# Uncomment if you want to temporarily disable logger\n",
+ "logger = logging.getLogger()\n",
+ "logger.disabled = True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
+ },
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load Wikipedia pages about different cities."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "fc4692a1",
+ "metadata": {
+ "id": "fc4692a1",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "wiki_titles = [\"Toronto\", \"Seattle\", \"Chicago\", \"Boston\", \"Houston\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "for title in wiki_titles:\n",
+ " response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ " ).json()\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
+ "\n",
+ " data_path = Path(\"data\")\n",
+ " if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Load all wiki documents\n",
+ "city_docs = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[f\"data/{wiki_title}.txt\"]\n",
+ " ).load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {
+ "id": "f1782198-c0de-4679-8951-1297c21b8639"
+ },
+ "source": [
+ "### Building Vector Indices\n",
+ "Build a vector index for the wiki pages about cities."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "M0GylZB-C2zL",
+ "metadata": {
+ "id": "M0GylZB-C2zL",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/langchain/llms/openai.py:687: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.llms import OpenAI\n",
+ "\n",
+ "\n",
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)\n",
+ "\n",
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context = ServiceContext.from_defaults(llm=gpt4, chunk_size=1024)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
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- "nbformat": 4,
- "nbformat_minor": 5
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+ "outputId": "5721e863-d460-4f5c-9e36-5a586180b669",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20744 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 16942 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 26082 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18648 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 21844 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Build city document index\n",
+ "vector_indices = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " # build vector index\n",
+ " vector_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
+ " city_docs[wiki_title], service_context=service_context\n",
+ " )\n",
+ "\n",
+ " # set id for vector index\n",
+ " vector_indices[wiki_title].set_index_id(wiki_title)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "2fc6cbcf-3640-4cb0-bafa-8a367159ffa9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "index_summaries = {\n",
+ " wiki_title: (\n",
+ " f\"This content contains Wikipedia articles about {wiki_title}. \"\n",
+ " f\"Use this index if you need to lookup specific facts about {wiki_title}.\\n\"\n",
+ " \"Do not use this index if you want to analyze multiple cities.\"\n",
+ " )\n",
+ " for wiki_title in wiki_titles\n",
+ "}"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "c385eaa5-5ea5-4ddb-a9e8-87583ff75e14",
+ "metadata": {},
+ "source": [
+ "#### Test Querying the Vector Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f690177f-9e9b-46c5-9a2e-9e697da8ce1b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1904 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = vector_indices[\"Toronto\"].as_query_engine()\n",
+ "response = query_engine.query(\"What are the sports teams in Toronto?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "0ba97f40-e4d7-4e40-96f7-f354f91b2b64",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The sports teams in Toronto include:\n",
+ "\n",
+ "1. Toronto Maple Leafs (NHL - ice hockey)\n",
+ "2. Toronto Blue Jays (MLB - baseball)\n",
+ "3. Toronto Raptors (NBA - basketball)\n",
+ "4. Toronto Argonauts (CFL - Canadian football)\n",
+ "5. Toronto FC (MLS - soccer)\n",
+ "6. Toronto Marlies (AHL - ice hockey)\n",
+ "7. Toronto Six (NWHL - women's ice hockey)\n",
+ "8. Toronto Rock (NLL - lacrosse)\n",
+ "9. Toronto Rush (AUDL - ultimate frisbee)\n",
+ "10. Toronto Wolfpack (Rugby league, playing in the North American Rugby League tournament)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "tags": []
+ },
+ "source": [
+ "### Build a Graph for Compare/Contrast Queries\n",
+ "\n",
+ "We build a graph by composing a keyword table index on top of all the vector indices.\n",
+ "We use this graph for compare/contrast queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "outputId": "fc875b0e-c8bf-439b-c794-fcae25954cfb",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph\n",
+ "\n",
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [index for _, index in vector_indices.items()],\n",
+ " [summary for _, summary in index_summaries.items()],\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "de977a92-acf2-40db-be58-34b29f4be47b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# get root index\n",
+ "root_index = graph.get_index(graph.root_id)\n",
+ "\n",
+ "# set id of root index\n",
+ "root_index.set_index_id(\"compare_contrast\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "7917d4a6-30f8-44fd-9bd7-0e40fa9054a5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define decompose_transform\n",
+ "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
+ "\n",
+ "\n",
+ "decompose_transform = DecomposeQueryTransform(llm_predictor_chatgpt, verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "42edf61e-d1d5-4d5c-a554-d20489413180",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define custom retrievers\n",
+ "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
+ "\n",
+ "\n",
+ "custom_query_engines = {}\n",
+ "for index in vector_indices.values():\n",
+ " query_engine = index.as_query_engine(service_context=service_context)\n",
+ " query_engine = TransformQueryEngine(\n",
+ " query_engine,\n",
+ " query_transform=decompose_transform,\n",
+ " transform_metadata={\"index_summary\": index.index_struct.summary},\n",
+ " )\n",
+ " custom_query_engines[index.index_id] = query_engine\n",
+ "\n",
+ "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\",\n",
+ " response_mode=\"tree_summarize\",\n",
+ " service_context=service_context,\n",
+ " verbose=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "3221be27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define graph\n",
+ "graph_query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b4c36f69-596b-4974-afa2-09cc652c1111",
+ "metadata": {},
+ "source": [
+ "#### Test querying the graph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4cb83f2e-f838-4384-acd0-eb12f72ad2ec",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['contrast', 'houston', 'arts', 'boston', 'culture', 'compare']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1877 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 2130 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 885 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 885 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_str = \"Compare and contrast the arts and culture of Houston and Boston. \"\n",
+ "response = graph_query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "0aa7efdf-c8c0-4efb-83a0-ad617f120307",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Houston and Boston both have rich arts and culture scenes, with a variety of cultural institutions and events that cater to diverse interests. Both cities have a strong presence of performing arts organizations, such as the Houston Grand Opera and Houston Ballet in Houston, and the Boston Ballet and Boston Lyric Opera Company in Boston. They also have renowned symphony orchestras, with the Houston Symphony Orchestra and the Boston Symphony Orchestra.\n",
+ "\n",
+ "Both cities host annual events that celebrate their unique cultural identities, such as the Houston Livestock Show and Rodeo, Houston Gay Pride Parade, and Houston Greek Festival in Houston, and the Boston Gay Pride Parade and Festival, Italian Summer Feasts, and Fourth of July events in Boston. Additionally, both cities have thriving theater districts, with Houston's Theater District and Boston's Theater District housing several historic and modern theaters.\n",
+ "\n",
+ "In terms of visual arts, both Houston and Boston have notable art museums, such as the Museum of Fine Arts in both cities, as well as the Houston Museum of Natural Science and the Contemporary Arts Museum Houston in Houston, and the Isabella Stewart Gardner Museum and the Institute of Contemporary Art in Boston. Houston also has unique institutions like the Menil Collection, Rothko Chapel, and the Byzantine Fresco Chapel Museum, while Boston has historic sites related to the American Revolution preserved in the Boston National Historical Park and along the Freedom Trail.\n",
+ "\n",
+ "While both cities have a strong focus on arts and culture, Houston's cultural scene tends to be more diverse, with events like the Art Car Parade, Houston International Festival, and Bayou City Art Festival showcasing the city's eclectic mix of cultures. On the other hand, Boston's cultural scene is deeply rooted in its history and traditions, with events like the Boston Early Music Festival and historic sites along the Freedom Trail reflecting the city's rich past.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a8bc0d20-fa6a-455b-ab85-ea9e4fcc0b37",
+ "metadata": {},
+ "source": [
+ "### Build a router to automatically choose between indices and graph"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "3bc185eb",
+ "metadata": {},
+ "source": [
+ "We can use a `RouterQueryEngine` to automatically route to the vector indices and the graph.\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d4a920fc",
+ "metadata": {},
+ "source": [
+ "\n",
+ "To do this, first build the query engines, and give each a description to obtain a `QueryEngineTool`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "ed558157",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.tools.query_engine import QueryEngineTool\n",
+ "\n",
+ "query_engine_tools = []\n",
+ "\n",
+ "# add vector index tools\n",
+ "for wiki_title in wiki_titles:\n",
+ " index = vector_indices[wiki_title]\n",
+ " summary = index_summaries[wiki_title]\n",
+ "\n",
+ " query_engine = index.as_query_engine(service_context=service_context)\n",
+ " vector_tool = QueryEngineTool.from_defaults(query_engine, description=summary)\n",
+ " query_engine_tools.append(vector_tool)\n",
+ "\n",
+ "\n",
+ "# add graph tool\n",
+ "graph_description = (\n",
+ " \"This tool contains Wikipedia articles about multiple cities. \"\n",
+ " \"Use this tool if you want to compare multiple cities. \"\n",
+ ")\n",
+ "graph_tool = QueryEngineTool.from_defaults(\n",
+ " graph_query_engine, description=graph_description\n",
+ ")\n",
+ "query_engine_tools.append(graph_tool)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1318dcbb",
+ "metadata": {},
+ "source": [
+ "Then, define the `RouterQueryEngine` with a desired selector module. \n",
+ "Here, we use the `LLMSingleSelector`, which uses LLM to choose a underlying query engine to route the query to."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "46922462-604c-43a3-b59a-f040cbd1ed3f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.query_engine.router_query_engine import RouterQueryEngine\n",
+ "from llama_index.selectors.llm_selectors import LLMSingleSelector\n",
+ "\n",
+ "\n",
+ "router_query_engine = RouterQueryEngine(\n",
+ " selector=LLMSingleSelector.from_defaults(service_context=service_context),\n",
+ " query_engine_tools=query_engine_tools,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "eef2630a",
+ "metadata": {},
+ "source": [
+ "Asking a compare and contrast question should route the query to the graph."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "f67065c4-3a68-4adb-ab3f-093ec9e2a8f3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 5: This tool contains Wikipedia articles about multiple cities, which allows for comparison and analysis of different cities, such as Houston and Boston..\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the arts and culture of Houston and Boston.\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['contrast', 'houston', 'arts', 'boston', 'culture', 'compare']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['houston', 'boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston?\n",
+ "\u001b[0m\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Houston and Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1835 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 11 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the arts and culture of Houston and Boston.\n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What are some notable cultural institutions or events in Boston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 2134 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 772 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 772 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ask a compare/contrast question\n",
+ "response = router_query_engine.query(\n",
+ " \"Compare and contrast the arts and culture of Houston and Boston.\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "ebf1a61f-422c-42ac-ae4e-a3a00415bf25",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Based on the context information provided, both Houston and Boston have rich arts and cultural scenes, with a variety of institutions and events catering to diverse interests.\n",
+ "\n",
+ "Houston's cultural institutions and events include the Houston Theater District, the Museum District, the Houston Livestock Show and Rodeo, the Houston Gay Pride Parade, the Houston Greek Festival, the Art Car Parade, the Houston Auto Show, the Houston International Festival, and the Bayou City Art Festival.\n",
+ "\n",
+ "In contrast, Boston's cultural institutions and events include the Boston Symphony Hall, New England Conservatory's Jordan Hall, Boston Ballet, various performing-arts organizations, contemporary classical music groups, the Theater District, First Night, Boston Early Music Festival, Boston Arts Festival, Boston Gay Pride Parade and Festival, Italian Summer Feasts, Fourth of July events, art museums such as the Museum of Fine Arts and Isabella Stewart Gardner Museum, the Institute of Contemporary Art, art gallery destinations like the South End Art and Design District (SoWa) and Newbury St, and the Boston National Historical Park.\n",
+ "\n",
+ "Both cities have theater districts, gay pride parades, and arts festivals. However, Houston has unique events such as the Livestock Show and Rodeo, the Greek Festival, the Art Car Parade, and the Houston Auto Show. On the other hand, Boston has a strong focus on classical music with venues like the Symphony Hall and Jordan Hall, as well as historical sites related to the American Revolution.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d4ecdfbe",
+ "metadata": {},
+ "source": [
+ "Asking a question about a specific city should route the query to the specific vector index query engine."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "5d78bcef-785b-4667-a509-9f6d4e1d9d5f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 0: This content contains Wikipedia articles about Toronto, which can provide information about the sports teams in the city..\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1905 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = router_query_engine.query(\"What are the sports teams in Toronto?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "431d75f1-d153-44b7-ac2f-4bfc4b65e3f5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The sports teams in Toronto include:\n",
+ "\n",
+ "1. Toronto Maple Leafs (NHL - ice hockey)\n",
+ "2. Toronto Blue Jays (MLB - baseball)\n",
+ "3. Toronto Raptors (NBA - basketball)\n",
+ "4. Toronto Argonauts (CFL - Canadian football)\n",
+ "5. Toronto FC (MLS - soccer)\n",
+ "6. Toronto Marlies (AHL - ice hockey)\n",
+ "7. Toronto Six (NWHL - women's ice hockey)\n",
+ "8. Toronto Rock (NLL - lacrosse)\n",
+ "9. Toronto Rush (AUDL - ultimate frisbee)\n",
+ "10. Toronto Wolfpack (Rugby league, currently playing in the North American Rugby League tournament)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response)"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
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diff --git a/docs/examples/composable_indices/city_analysis/City_Analysis.ipynb b/docs/examples/composable_indices/city_analysis/City_Analysis.ipynb
index e7e3fe1009..88fbac9d0c 100644
--- a/docs/examples/composable_indices/city_analysis/City_Analysis.ipynb
+++ b/docs/examples/composable_indices/city_analysis/City_Analysis.ipynb
@@ -1,2309 +1,2322 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "tags": []
- },
- "source": [
- "# Test Complex Queries over Multiple Documents (text-davinci-003 vs. ChatGPT)\n",
- "\n",
- "Test complex queries over both text-davinci-003 and ChatGPT"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "D2ZI8iKch-V_",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "D2ZI8iKch-V_",
- "outputId": "bc63c640-8508-4c74-8bd9-3fc1495b7839"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
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- " Uninstalling requests-2.25.1:\n",
- " Successfully uninstalled requests-2.25.1\n",
- "Successfully installed aiodns-3.0.0 aiohttp-retry-2.8.3 aleph-alpha-client-2.16.0 boto3-1.26.82 botocore-1.29.82 dataclasses_json-0.5.7 deeplake-3.2.12 hub-3.0.1 huggingface-hub-0.12.1 humbug-0.2.8 jmespath-1.0.1 langchain-0.0.98 llama-index-0.4.17 marshmallow-enum-1.5.1 multiprocess-0.70.14 mypy-extensions-1.0.0 numcodecs-0.11.0 openai-0.27.0 pathos-0.3.0 pox-0.3.2 ppft-1.7.6.6 pycares-4.3.0 pyjwt-2.6.0 requests-2.28.2 s3transfer-0.6.0 tenacity-8.1.0 tokenizers-0.13.2 transformers-4.26.1 typing-inspect-0.8.0\n"
- ]
- }
- ],
- "source": [
- "!pip install llama-index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d35ov8dk_6WP",
- "metadata": {
- "id": "d35ov8dk_6WP"
- },
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa0e62b6",
- "metadata": {
- "id": "fa0e62b6"
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13"
- },
- "outputs": [],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
- " ListIndex, \n",
- " SimpleDirectoryReader,\n",
- " LLMPredictor,\n",
- " ServiceContext\n",
- ")\n",
- "from llama_index.llms import OpenAI\n",
- "import requests"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
- },
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load Wikipedia pages as well as Paul Graham's \"What I Worked On\" essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "fc4692a1",
- "metadata": {
- "id": "fc4692a1"
- },
- "outputs": [],
- "source": [
- "wiki_titles = [\"Toronto\", \"Seattle\", \"San Francisco\", \"Chicago\", \"Boston\", \"Washington, D.C.\", \"Cambridge, Massachusetts\", \"Houston\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52"
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "for title in wiki_titles:\n",
- " response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- " ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
- "\n",
- " data_path = Path('data')\n",
- " if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea"
- },
- "outputs": [],
- "source": [
- "# Load all wiki documents\n",
- "city_docs = {}\n",
- "for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {
- "id": "f1782198-c0de-4679-8951-1297c21b8639"
- },
- "source": [
- "### Building the document indices\n",
- "Build a vector index for the wiki pages about cities and persons, and PG essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "M0GylZB-C2zL",
- "metadata": {
- "id": "M0GylZB-C2zL"
- },
- "outputs": [],
- "source": [
- "# LLM Predictor (text-davinci-003)\n",
- "davinci = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
- "service_context_davinci = ServiceContext.from_defaults(llm=davinci)\n",
- "\n",
- "# # LLM Predictor (gpt-3.5-turbo)\n",
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 183,
- "referenced_widgets": [
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- "7b24b46d6c3643e581ba003a9c473745",
- "3f748152b9274556afad2555572aa9f4"
- ]
- },
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "outputId": "5721e863-d460-4f5c-9e36-5a586180b669"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17592 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 14402 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 19954 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 22057 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 15733 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18327 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 10999 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18480 tokens\n"
- ]
- }
- ],
- "source": [
- "# Build city document index\n",
- "city_indices = {}\n",
- "for wiki_title in wiki_titles:\n",
- " city_indices[wiki_title] = VectorStoreIndex.from_documents(city_docs[wiki_title])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "tags": []
- },
- "source": [
- "### Build Graph: Keyword Table Index on top of vector indices! \n",
- "\n",
- "We compose a keyword table index on top of all the vector indices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "be1e3d7d-c4a3-4268-9408-b3cb984ffa4a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set summaries for each city\n",
- "index_summaries = {}\n",
- "for wiki_title in wiki_titles:\n",
- " # set summary text for city\n",
- " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "ddc2e4de-0719-4607-86f8-18c953344199",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "from llama_index.indices.composability import ComposableGraph\n",
- "\n",
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex,\n",
- " [index for _, index in city_indices.items()], \n",
- " [summary for _, summary in index_summaries.items()],\n",
- " max_keywords_per_chunk=50\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb",
- "metadata": {
- "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb"
- },
- "source": [
- "### Compare Queries (text-davinci-003 vs. ChatGPT)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e",
- "metadata": {
- "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e"
- },
- "source": [
- "**Simple Query**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "OVnzf3myEz88",
- "metadata": {
- "id": "OVnzf3myEz88",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Tell me more about Boston\n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['tell', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 5 tokens\n",
- "INFO:llama_index.indices.common_tree.base:> Building index from nodes: 1 chunks\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 802 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 4801 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 545 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 545 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Tell me more about Boston\n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['tell', 'boston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['boston']\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 5 tokens\n",
- "INFO:llama_index.indices.common_tree.base:> Building index from nodes: 1 chunks\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 641 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 4580 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 308 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 308 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine_davinci = graph.as_query_engine(\n",
- " custom_query_engines={\n",
- " graph.root_index.index_id: graph.root_index.as_query_engine(\n",
- " retriever_mode='simple',\n",
- " service_context=service_context_davinci,\n",
- " response_mode='tree_summarize', \n",
- " )\n",
- " }\n",
- ")\n",
- "query_engine_chatgpt = graph.as_query_engine(\n",
- " custom_query_engines={\n",
- " graph.root_index.index_id: graph.root_index.as_query_engine(\n",
- " retriever_mode='simple',\n",
- " service_context=service_context_chatgpt,\n",
- " response_mode='tree_summarize', \n",
- " )\n",
- " }\n",
- ")\n",
- "query_str = \"Tell me more about Boston\"\n",
- "response_davinci = query_engine_davinci.query(query_str)\n",
- "response_chatgpt = query_engine_chatgpt.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "6f5f5467-fa79-4f48-8b78-32ae8f86d12d",
- "metadata": {
- "id": "6f5f5467-fa79-4f48-8b78-32ae8f86d12d",
- "outputId": "53105550-370a-4281-974d-9b0ae8064e1c"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Boston is the capital and largest city of the Commonwealth of Massachusetts and the cultural and financial center of the New England region of the Northeastern United States. It is one of the oldest municipalities in America, founded on the Shawmut Peninsula in 1630 by Puritan settlers from the English town of the same name. It is a center of scientific research and innovation, with nearly 5,000 startups, and is home to a number of colleges and universities, notably Harvard and MIT. It has a long seafaring tradition, and was a major port for both domestic and international trade in the 19th century. It has seen waves of immigration, with Irish, Germans, Lebanese, Syrians, French Canadians, and Russian and Polish Jews settling in the city. It was an early port of the Atlantic triangular slave trade in the New England colonies, but was soon overtaken. Boston is also known for its philanthropy, with households in the city claiming the highest average rate of philanthropy in the United States.\n"
- ]
- }
- ],
- "source": [
- "print(response_davinci)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
- "metadata": {
- "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
- "outputId": "904002ea-f062-4f7d-8fe6-3e6b7b13b420"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Boston is a city in the New England region of the United States with a population of 675,647 as of 2020. It is known for its rich history and is considered the economic and cultural center of the region. The city has many firsts, including the first public park, first public or state school, first subway system, and first large public library in the United States. Boston is also a global pioneer in innovation and entrepreneurship, with nearly 5,000 startups. The city's economy includes finance, professional and business services, biotechnology, information technology, and government activities. Boston is a popular tourist destination, with Faneuil Hall alone drawing more than 20 million visitors per year. The city is home to many prestigious hospitals and universities, including Massachusetts General Hospital, Harvard Medical School, and Boston University.\n"
- ]
- }
- ],
- "source": [
- "print(response_chatgpt)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43",
- "metadata": {
- "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43"
- },
- "source": [
- "**Complex Query 1**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "730b7a1f-5197-4cdf-add2-9b46c07465f3",
- "metadata": {
- "id": "730b7a1f-5197-4cdf-add2-9b46c07465f3"
- },
- "outputs": [],
- "source": [
- "query_str = (\n",
- " \"Tell me the airports in Seattle, Houston, and Toronto. \"\n",
- " \"If only one city is provided, return the airport information for that city. \"\n",
- " \"If airports for multiple cities are provided, compare and contrast the airports. \"\n",
- ")\n",
- "response_davinci = query_engine_davinci.query(query_str)\n",
- "response_chatgpt = query_engine_chatgpt.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ee57efaa-dd8e-45af-968c-45d9bf92b948",
- "metadata": {
- "id": "ee57efaa-dd8e-45af-968c-45d9bf92b948",
- "outputId": "8b70b13d-c07a-4685-bd1d-b0e776607ad5"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "The airports in Seattle, Houston, and Toronto are Seattle–Tacoma International Airport (IATA: SEA), George Bush Intercontinental Airport (IATA: IAH), Toronto Pearson International Airport (IATA: YYZ), and Billy Bishop Toronto City Airport (IATA: YTZ). Seattle–Tacoma International Airport is the largest airport in the Pacific Northwest region of the United States, serving over 44 million passengers annually. George Bush Intercontinental Airport is the largest airport in Houston, serving over 40 million passengers annually. Toronto Pearson International Airport is the busiest airport in Canada, serving over 50 million passengers annually. Billy Bishop Toronto City Airport is a smaller airport located on the Toronto Islands, serving over 2 million passengers annually.\n"
- ]
- }
- ],
- "source": [
- "print(response_davinci)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
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- "Airports in Seattle: Seattle-Tacoma International Airport.\n",
- "Airports in Houston: George Bush Intercontinental Airport, William P. Hobby Airport, and Ellington Airport.\n",
- "Airports in Toronto: Toronto Pearson International Airport, Billy Bishop Toronto City Airport, Buttonville Municipal Airport, and Downsview Airport.\n",
- "\n",
- "Seattle has one major airport, Seattle-Tacoma International Airport. Houston has three airports: George Bush Intercontinental Airport, William P. Hobby Airport, and Ellington Airport. Toronto has four airports: Toronto Pearson International Airport, Billy Bishop Toronto City Airport, Buttonville Municipal Airport, and Downsview Airport. Toronto has a mix of commercial and smaller airports, while Houston has a mix of commercial, military, government, and general aviation airports.\n"
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- "**Complex Query 2**"
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- "query_str = (\n",
- " \"Look at Houston and Boston. \"\n",
- " \"If only one city is provided, provide information about the sports teams for that city. \"\n",
- " \"If context for multiple cities are provided, compare and contrast the sports environment of the cities. \"\n",
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- "\n",
- "Houston has teams for every major professional league. The Houston Astros are a Major League Baseball team that have won the World Series in 2017, 2022, and appeared in it in 2005, 2019, and 2021. The Houston Rockets are a National Basketball Association franchise based in the city since 1971, and have won two NBA Championships. The Houston Texans are a National Football League expansion team formed in 2002, and the Houston Dynamo is a Major League Soccer franchise that has been based in Houston since 2006, winning two MLS Cup titles. The Houston Dash team plays in the National Women's Soccer League, and the Houston SaberCats are a rugby team that plays in Major League Rugby. \n",
- "\n",
- "Boston also has teams for every major professional league. The Boston Red Sox are a Major League Baseball team that have won the World Series in 2004, 2007, 2013, and 2018. The Boston Celtics are a National Basketball Association team that have won 17 championships, most recently in 2008. The Boston Bruins are a National Hockey League team that have won six Stanley Cup championships, most recently in 2011. The New England Revolution is a Major League Soccer team that has been based in Boston since 1996. During a particularly impressive 17-year stretch from 2001 to 2018, the city's professional sports teams won twelve championships\n"
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- "If only one city is provided, Houston has sports teams for every major professional league except the National Hockey League, including the Houston Astros (MLB), Houston Rockets (NBA), Houston Texans (NFL), Houston Dynamo (MLS), Houston Dash (National Women's Soccer League), and Houston SaberCats (rugby).\n",
- "\n",
- "If context for multiple cities are provided, Boston has teams in the four major North American men's professional sports leagues plus Major League Soccer, and has won 39 championships in these leagues. Boston is one of eight cities to have won championships in all four major American sports leagues. During a particularly impressive 17-year stretch from 2001 to 2018, the city's professional sports teams won twelve championships. The Celtics and Bruins remain competitive for titles in the century’s third decade, though the Patriots and Red Sox have fallen off from these recent glory days. In contrast, Houston has not won as many championships as Boston, but has hosted several major sports events, including the Super Bowl and World Series. Houston is also home to the first major esports team, the Houston Outlaws.\n"
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- " \"Look at Houston and Boston. \"\n",
- " \"If only one city is provided, provide information about the arts and culture for that city. \"\n",
- " \"If context for multiple cities are provided, compare and contrast the arts and culture of the two cities. \"\n",
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- "Houston and Boston both have a wide range of cultural attractions. In Houston, the Theater District is a 17-block area in the center of Downtown Houston that is home to the Bayou Place entertainment complex, restaurants, movies, plazas, and parks. The Museum District's cultural institutions and exhibits attract more than 7 million visitors a year. Notable facilities include The Museum of Fine Arts, the Houston Museum of Natural Science, the Contemporary Arts Museum Houston, the Station Museum of Contemporary Art, the Holocaust Museum Houston, the Children's Museum of Houston, and the Houston Zoo. Houston also has many annual events celebrating the diverse cultures of the city, such as the Houston Livestock Show and Rodeo, the Houston Gay Pride Parade, the Houston Greek Festival, Art Car Parade, the Houston Auto Show, the Houston International Festival, and the Bayou City Art Festival.\n",
- "\n",
- "In Boston, the Freedom Trail is a 2.5-mile walking tour of 16 historically significant sites in downtown Boston. The Museum of Fine Arts is one of the largest and most comprehensive art museums in the world, with more than 450,000 works of art. Boston also has many annual events celebrating the diverse cultures of the city, such as the Boston Marathon, the Boston Arts Festival\n"
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- "There is no information about the arts and culture of Houston provided, but for Boston, there is a rich cultural history with a strong literary culture and a center for classical music. The city is also home to several art museums and galleries, including the Museum of Fine Arts and the Isabella Stewart Gardner Museum. The Institute of Contemporary Art is housed in a contemporary building designed by Diller Scofidio + Renfro in the Seaport District. Boston's South End Art and Design District (SoWa) and Newbury St. are both art gallery destinations.\n"
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- "In Toronto, the population is 2,731,571 people, with a median age of 39.2 years. The racial makeup of the city is 51.5% White, 20.3% Asian, 8.6% African American, 0.8% Native American, 0.2% Pacific Islander, and 18.6% from other races. The city is also home to a large Hispanic population, making up 6.2% of the population. The three most commonly reported ethnic origins are White (46.9%), Asian (20.3%), and Black (8.6%). Christianity is the most commonly reported religion (48.4%), followed by no religion and secular perspectives (31.2%). English is the predominant language spoken by Torontonians with approximately 79% of residents having proficiency in the language, although only 43.2% of Torontonians reported English as their mother tongue.\n",
- "\n",
- "When comparing Toronto and San Francisco, we can see that Toronto has a larger population than San Francisco, with a median age that is slightly higher. The racial makeup of Toronto is slightly more White than San Francisco, while San Francisco has a larger Asian population. The Hispanic population is larger in San Francisco than in Toronto. Christianity is the\n"
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+ "metadata": {
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "tags": []
+ },
+ "source": [
+ "# Test Complex Queries over Multiple Documents (text-davinci-003 vs. ChatGPT)\n",
+ "\n",
+ "Test complex queries over both text-davinci-003 and ChatGPT"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ "id": "D2ZI8iKch-V_",
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+ "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.5.0->pycares>=4.0.0->aiodns>=3.0.0->aleph-alpha-client<3.0.0,>=2.15.0->langchain->llama-index) (2.21)\n",
+ "Building wheels for collected packages: llama-index, deeplake\n",
+ " Building wheel for llama-index (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for llama-index: filename=llama_index-0.4.17-py3-none-any.whl size=182750 sha256=67cb3c836e93d9d29a73307c2393d49392a4c8ceae94be552e0a91ca4b1d2cf1\n",
+ " Stored in directory: /root/.cache/pip/wheels/15/bb/a9/de82e6a211b5f22899972226d5164f91546e6ac016bbd6c248\n",
+ " Building wheel for deeplake (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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+ " Stored in directory: /root/.cache/pip/wheels/4b/1a/74/4b341aa1a16e01324c9728738ff705c049c3fa2a09e40d3d9f\n",
+ "Successfully built llama-index deeplake\n",
+ "Installing collected packages: tokenizers, tenacity, requests, pyjwt, ppft, pox, numcodecs, mypy-extensions, multiprocess, jmespath, typing-inspect, pycares, pathos, marshmallow-enum, humbug, huggingface-hub, botocore, transformers, s3transfer, openai, dataclasses_json, aiohttp-retry, aiodns, boto3, aleph-alpha-client, hub, deeplake, langchain, llama-index\n",
+ " Attempting uninstall: tenacity\n",
+ " Found existing installation: tenacity 8.2.1\n",
+ " Uninstalling tenacity-8.2.1:\n",
+ " Successfully uninstalled tenacity-8.2.1\n",
+ " Attempting uninstall: requests\n",
+ " Found existing installation: requests 2.25.1\n",
+ " Uninstalling requests-2.25.1:\n",
+ " Successfully uninstalled requests-2.25.1\n",
+ "Successfully installed aiodns-3.0.0 aiohttp-retry-2.8.3 aleph-alpha-client-2.16.0 boto3-1.26.82 botocore-1.29.82 dataclasses_json-0.5.7 deeplake-3.2.12 hub-3.0.1 huggingface-hub-0.12.1 humbug-0.2.8 jmespath-1.0.1 langchain-0.0.98 llama-index-0.4.17 marshmallow-enum-1.5.1 multiprocess-0.70.14 mypy-extensions-1.0.0 numcodecs-0.11.0 openai-0.27.0 pathos-0.3.0 pox-0.3.2 ppft-1.7.6.6 pycares-4.3.0 pyjwt-2.6.0 requests-2.28.2 s3transfer-0.6.0 tenacity-8.1.0 tokenizers-0.13.2 transformers-4.26.1 typing-inspect-0.8.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install llama-index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d35ov8dk_6WP",
+ "metadata": {
+ "id": "d35ov8dk_6WP"
+ },
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa0e62b6",
+ "metadata": {
+ "id": "fa0e62b6"
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
+ " ListIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ ")\n",
+ "from llama_index.llms import OpenAI\n",
+ "import requests"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
+ },
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load Wikipedia pages as well as Paul Graham's \"What I Worked On\" essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "fc4692a1",
+ "metadata": {
+ "id": "fc4692a1"
+ },
+ "outputs": [],
+ "source": [
+ "wiki_titles = [\n",
+ " \"Toronto\",\n",
+ " \"Seattle\",\n",
+ " \"San Francisco\",\n",
+ " \"Chicago\",\n",
+ " \"Boston\",\n",
+ " \"Washington, D.C.\",\n",
+ " \"Cambridge, Massachusetts\",\n",
+ " \"Houston\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52"
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "for title in wiki_titles:\n",
+ " response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ " ).json()\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
+ "\n",
+ " data_path = Path(\"data\")\n",
+ " if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea"
+ },
+ "outputs": [],
+ "source": [
+ "# Load all wiki documents\n",
+ "city_docs = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[f\"data/{wiki_title}.txt\"]\n",
+ " ).load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {
+ "id": "f1782198-c0de-4679-8951-1297c21b8639"
+ },
+ "source": [
+ "### Building the document indices\n",
+ "Build a vector index for the wiki pages about cities and persons, and PG essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "M0GylZB-C2zL",
+ "metadata": {
+ "id": "M0GylZB-C2zL"
+ },
+ "outputs": [],
+ "source": [
+ "# LLM Predictor (text-davinci-003)\n",
+ "davinci = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
+ "service_context_davinci = ServiceContext.from_defaults(llm=davinci)\n",
+ "\n",
+ "# # LLM Predictor (gpt-3.5-turbo)\n",
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 183,
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+ ]
+ },
+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
+ "outputId": "5721e863-d460-4f5c-9e36-5a586180b669"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17592 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 14402 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 19954 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 22057 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 15733 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18327 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 10999 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 18480 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Build city document index\n",
+ "city_indices = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_indices[wiki_title] = VectorStoreIndex.from_documents(city_docs[wiki_title])"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "tags": []
+ },
+ "source": [
+ "### Build Graph: Keyword Table Index on top of vector indices! \n",
+ "\n",
+ "We compose a keyword table index on top of all the vector indices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "be1e3d7d-c4a3-4268-9408-b3cb984ffa4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set summaries for each city\n",
+ "index_summaries = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " # set summary text for city\n",
+ " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "ddc2e4de-0719-4607-86f8-18c953344199",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph\n",
+ "\n",
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [index for _, index in city_indices.items()],\n",
+ " [summary for _, summary in index_summaries.items()],\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb",
+ "metadata": {
+ "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb"
+ },
+ "source": [
+ "### Compare Queries (text-davinci-003 vs. ChatGPT)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e",
+ "metadata": {
+ "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e"
+ },
+ "source": [
+ "**Simple Query**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "OVnzf3myEz88",
+ "metadata": {
+ "id": "OVnzf3myEz88",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Tell me more about Boston\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['tell', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 5 tokens\n",
+ "INFO:llama_index.indices.common_tree.base:> Building index from nodes: 1 chunks\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 802 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 4801 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 545 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 545 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Tell me more about Boston\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['tell', 'boston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['boston']\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 5 tokens\n",
+ "INFO:llama_index.indices.common_tree.base:> Building index from nodes: 1 chunks\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 641 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 4580 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 308 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 308 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine_davinci = graph.as_query_engine(\n",
+ " custom_query_engines={\n",
+ " graph.root_index.index_id: graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\",\n",
+ " service_context=service_context_davinci,\n",
+ " response_mode=\"tree_summarize\",\n",
+ " )\n",
+ " }\n",
+ ")\n",
+ "query_engine_chatgpt = graph.as_query_engine(\n",
+ " custom_query_engines={\n",
+ " graph.root_index.index_id: graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\",\n",
+ " service_context=service_context_chatgpt,\n",
+ " response_mode=\"tree_summarize\",\n",
+ " )\n",
+ " }\n",
+ ")\n",
+ "query_str = \"Tell me more about Boston\"\n",
+ "response_davinci = query_engine_davinci.query(query_str)\n",
+ "response_chatgpt = query_engine_chatgpt.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "6f5f5467-fa79-4f48-8b78-32ae8f86d12d",
+ "metadata": {
+ "id": "6f5f5467-fa79-4f48-8b78-32ae8f86d12d",
+ "outputId": "53105550-370a-4281-974d-9b0ae8064e1c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Boston is the capital and largest city of the Commonwealth of Massachusetts and the cultural and financial center of the New England region of the Northeastern United States. It is one of the oldest municipalities in America, founded on the Shawmut Peninsula in 1630 by Puritan settlers from the English town of the same name. It is a center of scientific research and innovation, with nearly 5,000 startups, and is home to a number of colleges and universities, notably Harvard and MIT. It has a long seafaring tradition, and was a major port for both domestic and international trade in the 19th century. It has seen waves of immigration, with Irish, Germans, Lebanese, Syrians, French Canadians, and Russian and Polish Jews settling in the city. It was an early port of the Atlantic triangular slave trade in the New England colonies, but was soon overtaken. Boston is also known for its philanthropy, with households in the city claiming the highest average rate of philanthropy in the United States.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_davinci)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
+ "metadata": {
+ "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
+ "outputId": "904002ea-f062-4f7d-8fe6-3e6b7b13b420"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Boston is a city in the New England region of the United States with a population of 675,647 as of 2020. It is known for its rich history and is considered the economic and cultural center of the region. The city has many firsts, including the first public park, first public or state school, first subway system, and first large public library in the United States. Boston is also a global pioneer in innovation and entrepreneurship, with nearly 5,000 startups. The city's economy includes finance, professional and business services, biotechnology, information technology, and government activities. Boston is a popular tourist destination, with Faneuil Hall alone drawing more than 20 million visitors per year. The city is home to many prestigious hospitals and universities, including Massachusetts General Hospital, Harvard Medical School, and Boston University.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43",
+ "metadata": {
+ "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43"
+ },
+ "source": [
+ "**Complex Query 1**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "730b7a1f-5197-4cdf-add2-9b46c07465f3",
+ "metadata": {
+ "id": "730b7a1f-5197-4cdf-add2-9b46c07465f3"
+ },
+ "outputs": [],
+ "source": [
+ "query_str = (\n",
+ " \"Tell me the airports in Seattle, Houston, and Toronto. \"\n",
+ " \"If only one city is provided, return the airport information for that city. \"\n",
+ " \"If airports for multiple cities are provided, compare and contrast the airports. \"\n",
+ ")\n",
+ "response_davinci = query_engine_davinci.query(query_str)\n",
+ "response_chatgpt = query_engine_chatgpt.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ee57efaa-dd8e-45af-968c-45d9bf92b948",
+ "metadata": {
+ "id": "ee57efaa-dd8e-45af-968c-45d9bf92b948",
+ "outputId": "8b70b13d-c07a-4685-bd1d-b0e776607ad5"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "The airports in Seattle, Houston, and Toronto are Seattle–Tacoma International Airport (IATA: SEA), George Bush Intercontinental Airport (IATA: IAH), Toronto Pearson International Airport (IATA: YYZ), and Billy Bishop Toronto City Airport (IATA: YTZ). Seattle–Tacoma International Airport is the largest airport in the Pacific Northwest region of the United States, serving over 44 million passengers annually. George Bush Intercontinental Airport is the largest airport in Houston, serving over 40 million passengers annually. Toronto Pearson International Airport is the busiest airport in Canada, serving over 50 million passengers annually. Billy Bishop Toronto City Airport is a smaller airport located on the Toronto Islands, serving over 2 million passengers annually.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_davinci)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e499b388-5a1c-4047-8fee-122dfe73c800",
+ "metadata": {
+ "id": "e499b388-5a1c-4047-8fee-122dfe73c800",
+ "outputId": "ca0c8d9d-2f7c-4d80-a793-a79cb3b243ed"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Airports in Seattle: Seattle-Tacoma International Airport.\n",
+ "Airports in Houston: George Bush Intercontinental Airport, William P. Hobby Airport, and Ellington Airport.\n",
+ "Airports in Toronto: Toronto Pearson International Airport, Billy Bishop Toronto City Airport, Buttonville Municipal Airport, and Downsview Airport.\n",
+ "\n",
+ "Seattle has one major airport, Seattle-Tacoma International Airport. Houston has three airports: George Bush Intercontinental Airport, William P. Hobby Airport, and Ellington Airport. Toronto has four airports: Toronto Pearson International Airport, Billy Bishop Toronto City Airport, Buttonville Municipal Airport, and Downsview Airport. Toronto has a mix of commercial and smaller airports, while Houston has a mix of commercial, military, government, and general aviation airports.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd",
+ "metadata": {
+ "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd"
+ },
+ "source": [
+ "**Complex Query 2**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a0fa2840-77b2-42c3-b6af-39fbe02c78ce",
+ "metadata": {
+ "id": "a0fa2840-77b2-42c3-b6af-39fbe02c78ce"
+ },
+ "outputs": [],
+ "source": [
+ "query_str = (\n",
+ " \"Look at Houston and Boston. \"\n",
+ " \"If only one city is provided, provide information about the sports teams for that city. \"\n",
+ " \"If context for multiple cities are provided, compare and contrast the sports environment of the cities. \"\n",
+ ")\n",
+ "response_davinci = query_engine_davinci.query(query_str)\n",
+ "response_chatgpt = query_engine_chatgpt.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3af1e27f-7697-4cbc-ba38-a7dc11330dc0",
+ "metadata": {
+ "id": "3af1e27f-7697-4cbc-ba38-a7dc11330dc0",
+ "outputId": "3d394401-ad19-4fa6-97fe-6bae70f0beff"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Houston has teams for every major professional league. The Houston Astros are a Major League Baseball team that have won the World Series in 2017, 2022, and appeared in it in 2005, 2019, and 2021. The Houston Rockets are a National Basketball Association franchise based in the city since 1971, and have won two NBA Championships. The Houston Texans are a National Football League expansion team formed in 2002, and the Houston Dynamo is a Major League Soccer franchise that has been based in Houston since 2006, winning two MLS Cup titles. The Houston Dash team plays in the National Women's Soccer League, and the Houston SaberCats are a rugby team that plays in Major League Rugby. \n",
+ "\n",
+ "Boston also has teams for every major professional league. The Boston Red Sox are a Major League Baseball team that have won the World Series in 2004, 2007, 2013, and 2018. The Boston Celtics are a National Basketball Association team that have won 17 championships, most recently in 2008. The Boston Bruins are a National Hockey League team that have won six Stanley Cup championships, most recently in 2011. The New England Revolution is a Major League Soccer team that has been based in Boston since 1996. During a particularly impressive 17-year stretch from 2001 to 2018, the city's professional sports teams won twelve championships\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_davinci)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "226ee4f5-c941-4497-a04c-630757622282",
+ "metadata": {
+ "id": "226ee4f5-c941-4497-a04c-630757622282",
+ "outputId": "c8b0c521-d2e7-4ba6-dc9f-52189fbf0b9b"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "If only one city is provided, Houston has sports teams for every major professional league except the National Hockey League, including the Houston Astros (MLB), Houston Rockets (NBA), Houston Texans (NFL), Houston Dynamo (MLS), Houston Dash (National Women's Soccer League), and Houston SaberCats (rugby).\n",
+ "\n",
+ "If context for multiple cities are provided, Boston has teams in the four major North American men's professional sports leagues plus Major League Soccer, and has won 39 championships in these leagues. Boston is one of eight cities to have won championships in all four major American sports leagues. During a particularly impressive 17-year stretch from 2001 to 2018, the city's professional sports teams won twelve championships. The Celtics and Bruins remain competitive for titles in the century’s third decade, though the Patriots and Red Sox have fallen off from these recent glory days. In contrast, Houston has not won as many championships as Boston, but has hosted several major sports events, including the Super Bowl and World Series. Houston is also home to the first major esports team, the Houston Outlaws.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e",
+ "metadata": {
+ "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e"
+ },
+ "source": [
+ "**Complex Query 3**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b11d9c6-1905-4bd1-bb9a-4d60b0bc3c2d",
+ "metadata": {
+ "id": "5b11d9c6-1905-4bd1-bb9a-4d60b0bc3c2d"
+ },
+ "outputs": [],
+ "source": [
+ "query_str = (\n",
+ " \"Look at Houston and Boston. \"\n",
+ " \"If only one city is provided, provide information about the arts and culture for that city. \"\n",
+ " \"If context for multiple cities are provided, compare and contrast the arts and culture of the two cities. \"\n",
+ ")\n",
+ "response_davinci = query_engine_davinci.query(query_str)\n",
+ "response_chatgpt = query_engine_chatgpt.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ccbbcf6-3074-4d8e-9ad4-92daa13a67dc",
+ "metadata": {
+ "id": "4ccbbcf6-3074-4d8e-9ad4-92daa13a67dc",
+ "outputId": "28429b4e-1854-44e8-8dcd-850f7ca7d0c2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Houston and Boston both have a wide range of cultural attractions. In Houston, the Theater District is a 17-block area in the center of Downtown Houston that is home to the Bayou Place entertainment complex, restaurants, movies, plazas, and parks. The Museum District's cultural institutions and exhibits attract more than 7 million visitors a year. Notable facilities include The Museum of Fine Arts, the Houston Museum of Natural Science, the Contemporary Arts Museum Houston, the Station Museum of Contemporary Art, the Holocaust Museum Houston, the Children's Museum of Houston, and the Houston Zoo. Houston also has many annual events celebrating the diverse cultures of the city, such as the Houston Livestock Show and Rodeo, the Houston Gay Pride Parade, the Houston Greek Festival, Art Car Parade, the Houston Auto Show, the Houston International Festival, and the Bayou City Art Festival.\n",
+ "\n",
+ "In Boston, the Freedom Trail is a 2.5-mile walking tour of 16 historically significant sites in downtown Boston. The Museum of Fine Arts is one of the largest and most comprehensive art museums in the world, with more than 450,000 works of art. Boston also has many annual events celebrating the diverse cultures of the city, such as the Boston Marathon, the Boston Arts Festival\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_davinci)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10c6ca94-c053-4009-b52d-a5255e74853c",
+ "metadata": {
+ "id": "10c6ca94-c053-4009-b52d-a5255e74853c",
+ "outputId": "b4575737-59e2-43b5-85e2-c51ffe0f8cdd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "There is no information about the arts and culture of Houston provided, but for Boston, there is a rich cultural history with a strong literary culture and a center for classical music. The city is also home to several art museums and galleries, including the Museum of Fine Arts and the Isabella Stewart Gardner Museum. The Institute of Contemporary Art is housed in a contemporary building designed by Diller Scofidio + Renfro in the Seaport District. Boston's South End Art and Design District (SoWa) and Newbury St. are both art gallery destinations.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7b299ebe-cdbd-4abf-9015-4894f6aa94ba",
+ "metadata": {
+ "id": "7b299ebe-cdbd-4abf-9015-4894f6aa94ba"
+ },
+ "source": [
+ "**Complex Query 4**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "261e3881-6414-4ace-9816-aa71a39051b5",
+ "metadata": {
+ "id": "261e3881-6414-4ace-9816-aa71a39051b5"
+ },
+ "outputs": [],
+ "source": [
+ "query_str = (\n",
+ " \"Look at Toronto and San Francisco. \"\n",
+ " \"If only one city is provided, provide information about the demographics for that city. \"\n",
+ " \"If context for multiple cities are provided, compare and contrast the demographics of the two cities. \"\n",
+ ")\n",
+ "response_davinci = query_engine_davinci.query(query_str)\n",
+ "response_chatgpt = query_engine_chatgpt.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f58ca597-8a40-4fa0-995b-f54cff133ec8",
+ "metadata": {
+ "id": "f58ca597-8a40-4fa0-995b-f54cff133ec8",
+ "outputId": "7fefef2f-78b8-47c3-ade3-5a8673a264e1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "In Toronto, the population is 2,731,571 people, with a median age of 39.2 years. The racial makeup of the city is 51.5% White, 20.3% Asian, 8.6% African American, 0.8% Native American, 0.2% Pacific Islander, and 18.6% from other races. The city is also home to a large Hispanic population, making up 6.2% of the population. The three most commonly reported ethnic origins are White (46.9%), Asian (20.3%), and Black (8.6%). Christianity is the most commonly reported religion (48.4%), followed by no religion and secular perspectives (31.2%). English is the predominant language spoken by Torontonians with approximately 79% of residents having proficiency in the language, although only 43.2% of Torontonians reported English as their mother tongue.\n",
+ "\n",
+ "When comparing Toronto and San Francisco, we can see that Toronto has a larger population than San Francisco, with a median age that is slightly higher. The racial makeup of Toronto is slightly more White than San Francisco, while San Francisco has a larger Asian population. The Hispanic population is larger in San Francisco than in Toronto. Christianity is the\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response_davinci)"
+ ]
+ },
+ {
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diff --git a/docs/examples/composable_indices/city_analysis/PineconeDemo-CityAnalysis.ipynb b/docs/examples/composable_indices/city_analysis/PineconeDemo-CityAnalysis.ipynb
index c35233df74..70e1d7a7d2 100644
--- a/docs/examples/composable_indices/city_analysis/PineconeDemo-CityAnalysis.ipynb
+++ b/docs/examples/composable_indices/city_analysis/PineconeDemo-CityAnalysis.ipynb
@@ -1,1892 +1,1921 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "tags": []
- },
- "source": [
- "# Using LlamaIndex with Pinecone\n",
- "\n",
- "Test complex queries over both text-davinci-003 and ChatGPT"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "D2ZI8iKch-V_",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "D2ZI8iKch-V_",
- "outputId": "bc63c640-8508-4c74-8bd9-3fc1495b7839"
- },
- "outputs": [],
- "source": [
- "!pip install llama-index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d35ov8dk_6WP",
- "metadata": {
- "id": "d35ov8dk_6WP"
- },
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa0e62b6",
- "metadata": {
- "id": "fa0e62b6",
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex,\n",
- " SimpleKeywordTableIndex, \n",
- " SimpleDirectoryReader,\n",
- " LLMPredictor,\n",
- " ServiceContext\n",
- ")\n",
- "from llama_index.vector_stores import PineconeVectorStore\n",
- "from llama_index.llms import OpenAI"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
- },
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load Wikipedia pages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fc4692a1",
- "metadata": {
- "id": "fc4692a1",
- "tags": []
- },
- "outputs": [],
- "source": [
- "wiki_titles = [\"Toronto\", \"Seattle\", \"San Francisco\", \"Chicago\", \"Boston\", \"Washington, D.C.\", \"Cambridge, Massachusetts\", \"Houston\"]\n",
- "pinecone_titles = [\"toronto\", \"seattle\", \"san-francisco\", \"chicago\", \"boston\", \"dc\", \"cambridge\", \"houston\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "for title in wiki_titles:\n",
- " response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- " ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
- "\n",
- " data_path = Path('data')\n",
- " if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load all wiki documents\n",
- "city_docs = {}\n",
- "for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "84bfcaa1-db15-45ba-8af1-fee548354965",
- "metadata": {},
- "source": [
- "## Initialize Pinecone Indexes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "06b261d7-f2a3-47b3-89ce-5f6103926174",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import pinecone\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e2fc4bfb",
- "metadata": {},
- "outputs": [],
- "source": [
- "api_key = \"\"\n",
- "environment = \"eu-west1-gcp\"\n",
- "index_name = \"quickstart\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7df2f613",
- "metadata": {},
- "outputs": [],
- "source": [
- "os.environ['PINECONE_API_KEY'] = api_key"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "M0GylZB-C2zL",
- "metadata": {
- "id": "M0GylZB-C2zL",
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context = ServiceContext.from_defaults(llm=llm)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b373db78",
- "metadata": {},
- "source": [
- "### Recommended Option: Pass API key via env variable, and index_name & environment as argument"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd7e6946",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "# Build city document index\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "city_indices = {}\n",
- "for pinecone_title, wiki_title in zip(pinecone_titles, wiki_titles):\n",
- " metadata_filters = {\"wiki_title\": wiki_title}\n",
- " vector_store = PineconeVectorStore(\n",
- " index_name=index_name,\n",
- " environment=environment,\n",
- " metadata_filters=metadata_filters\n",
- " ) \n",
- " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
- " city_docs[wiki_title], storage_context=storage_context, service_context=service_context\n",
- " )\n",
- " # set summary text for city\n",
- " city_indices[wiki_title].index_struct.index_id = pinecone_title"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b1d69b03",
- "metadata": {},
- "source": [
- "### Alternative Option: instantiate pinecone client first, then pass to PineconeVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "73a0cf0f-62b4-4f52-9c7f-726838d71f9a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "pinecone.init(api_key=api_key, environment=environment)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7bfbbf9d-4432-4af9-94ff-01e084c0cde0",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "pinecone_index = pinecone.Index(index_name)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3cb9e7ec",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "# Build city document index\n",
- "city_indices = {}\n",
- "for pinecone_title, wiki_title in zip(pinecone_titles, wiki_titles):\n",
- " metadata_filters = {\"wiki_title\": wiki_title}\n",
- " vector_store = PineconeVectorStore(\n",
- " pinecone_index=pinecone_index,\n",
- " metadata_filters=metadata_filters\n",
- " ) \n",
- " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
- " city_docs[wiki_title], storage_context=storage_context, service_context=service_context\n",
- " )\n",
- " # set summary text for city\n",
- " city_indices[wiki_title].index_struct.index_id = pinecone_title"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "17605939-09ce-4405-a92a-8f296c941893",
- "metadata": {},
- "source": [
- "### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d5865649-16c2-4681-a6cf-ccee589dcaa7",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "response = city_indices[\"Boston\"].as_query_engine(service_context=service_context).query(\n",
- " \"Tell me about the arts and culture of Boston\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f8ec6f33-73d1-46cb-93f0-d76f9c42d78d",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(str(response))\n",
- "print(response.get_formatted_sources())"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "tags": []
- },
- "source": [
- "### Build Graph: Keyword Table Index on top of vector indices! \n",
- "\n",
- "We compose a keyword table index on top of all the vector indices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "metadata": {
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3"
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.composability.graph import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2c01db2c-07b1-4e9b-bfda-e25b8953cde9",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# set summaries for each city\n",
- "index_summaries = {}\n",
- "for wiki_title in wiki_titles:\n",
- " # set summary text for city\n",
- " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "586b404a-5cb6-465f-8a0f-dfb3b27cd80a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex,\n",
- " [index for _, index in city_indices.items()], \n",
- " [summary for _, summary in index_summaries.items()],\n",
- " max_keywords_per_chunk=50\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "76c251ca-b06b-42e9-ac99-aa0a0a5187d4",
- "metadata": {
- "id": "76c251ca-b06b-42e9-ac99-aa0a0a5187d4"
- },
- "outputs": [],
- "source": [
- "custom_query_engines = {\n",
- " graph.root_id: graph.root_index.as_query_engine(retriever_mode='simple', service_context=service_context)\n",
- "}\n",
- "\n",
- "query_engine = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines,\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb",
- "metadata": {
- "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb"
- },
- "source": [
- "### Compare Queries (text-davinci-003 vs. ChatGPT)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e",
- "metadata": {
- "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e"
- },
- "source": [
- "**Simple Query**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "OVnzf3myEz88",
- "metadata": {
- "id": "OVnzf3myEz88",
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_str = \"Tell me more about Boston\"\n",
- "response_chatgpt = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
- "metadata": {
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- "outputId": "904002ea-f062-4f7d-8fe6-3e6b7b13b420"
- },
- "outputs": [],
- "source": [
- "print(response_chatgpt)\n",
- "print(response_chatgpt.get_formatted_sources())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "38438507",
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+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "tags": []
+ },
+ "source": [
+ "# Using LlamaIndex with Pinecone\n",
+ "\n",
+ "Test complex queries over both text-davinci-003 and ChatGPT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "D2ZI8iKch-V_",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "id": "D2ZI8iKch-V_",
+ "outputId": "bc63c640-8508-4c74-8bd9-3fc1495b7839"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install llama-index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d35ov8dk_6WP",
+ "metadata": {
+ "id": "d35ov8dk_6WP"
+ },
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa0e62b6",
+ "metadata": {
+ "id": "fa0e62b6",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ ")\n",
+ "from llama_index.vector_stores import PineconeVectorStore\n",
+ "from llama_index.llms import OpenAI"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
+ },
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load Wikipedia pages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc4692a1",
+ "metadata": {
+ "id": "fc4692a1",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "wiki_titles = [\n",
+ " \"Toronto\",\n",
+ " \"Seattle\",\n",
+ " \"San Francisco\",\n",
+ " \"Chicago\",\n",
+ " \"Boston\",\n",
+ " \"Washington, D.C.\",\n",
+ " \"Cambridge, Massachusetts\",\n",
+ " \"Houston\",\n",
+ "]\n",
+ "pinecone_titles = [\n",
+ " \"toronto\",\n",
+ " \"seattle\",\n",
+ " \"san-francisco\",\n",
+ " \"chicago\",\n",
+ " \"boston\",\n",
+ " \"dc\",\n",
+ " \"cambridge\",\n",
+ " \"houston\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "for title in wiki_titles:\n",
+ " response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ " ).json()\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
+ "\n",
+ " data_path = Path(\"data\")\n",
+ " if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Load all wiki documents\n",
+ "city_docs = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[f\"data/{wiki_title}.txt\"]\n",
+ " ).load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "84bfcaa1-db15-45ba-8af1-fee548354965",
+ "metadata": {},
+ "source": [
+ "## Initialize Pinecone Indexes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "06b261d7-f2a3-47b3-89ce-5f6103926174",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pinecone\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2fc4bfb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "api_key = \"\"\n",
+ "environment = \"eu-west1-gcp\"\n",
+ "index_name = \"quickstart\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7df2f613",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.environ[\"PINECONE_API_KEY\"] = api_key"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "M0GylZB-C2zL",
+ "metadata": {
+ "id": "M0GylZB-C2zL",
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b373db78",
+ "metadata": {},
+ "source": [
+ "### Recommended Option: Pass API key via env variable, and index_name & environment as argument"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd7e6946",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Build city document index\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "city_indices = {}\n",
+ "for pinecone_title, wiki_title in zip(pinecone_titles, wiki_titles):\n",
+ " metadata_filters = {\"wiki_title\": wiki_title}\n",
+ " vector_store = PineconeVectorStore(\n",
+ " index_name=index_name,\n",
+ " environment=environment,\n",
+ " metadata_filters=metadata_filters,\n",
+ " )\n",
+ " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
+ " city_docs[wiki_title],\n",
+ " storage_context=storage_context,\n",
+ " service_context=service_context,\n",
+ " )\n",
+ " # set summary text for city\n",
+ " city_indices[wiki_title].index_struct.index_id = pinecone_title"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b1d69b03",
+ "metadata": {},
+ "source": [
+ "### Alternative Option: instantiate pinecone client first, then pass to PineconeVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "73a0cf0f-62b4-4f52-9c7f-726838d71f9a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "pinecone.init(api_key=api_key, environment=environment)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7bfbbf9d-4432-4af9-94ff-01e084c0cde0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "pinecone_index = pinecone.Index(index_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3cb9e7ec",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Build city document index\n",
+ "city_indices = {}\n",
+ "for pinecone_title, wiki_title in zip(pinecone_titles, wiki_titles):\n",
+ " metadata_filters = {\"wiki_title\": wiki_title}\n",
+ " vector_store = PineconeVectorStore(\n",
+ " pinecone_index=pinecone_index, metadata_filters=metadata_filters\n",
+ " )\n",
+ " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
+ " city_docs[wiki_title],\n",
+ " storage_context=storage_context,\n",
+ " service_context=service_context,\n",
+ " )\n",
+ " # set summary text for city\n",
+ " city_indices[wiki_title].index_struct.index_id = pinecone_title"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "17605939-09ce-4405-a92a-8f296c941893",
+ "metadata": {},
+ "source": [
+ "### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d5865649-16c2-4681-a6cf-ccee589dcaa7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "response = (\n",
+ " city_indices[\"Boston\"]\n",
+ " .as_query_engine(service_context=service_context)\n",
+ " .query(\"Tell me about the arts and culture of Boston\")\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f8ec6f33-73d1-46cb-93f0-d76f9c42d78d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(str(response))\n",
+ "print(response.get_formatted_sources())"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "tags": []
+ },
+ "source": [
+ "### Build Graph: Keyword Table Index on top of vector indices! \n",
+ "\n",
+ "We compose a keyword table index on top of all the vector indices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "metadata": {
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability.graph import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c01db2c-07b1-4e9b-bfda-e25b8953cde9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set summaries for each city\n",
+ "index_summaries = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " # set summary text for city\n",
+ " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "586b404a-5cb6-465f-8a0f-dfb3b27cd80a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [index for _, index in city_indices.items()],\n",
+ " [summary for _, summary in index_summaries.items()],\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "76c251ca-b06b-42e9-ac99-aa0a0a5187d4",
+ "metadata": {
+ "id": "76c251ca-b06b-42e9-ac99-aa0a0a5187d4"
+ },
+ "outputs": [],
+ "source": [
+ "custom_query_engines = {\n",
+ " graph.root_id: graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\", service_context=service_context\n",
+ " )\n",
+ "}\n",
+ "\n",
+ "query_engine = graph.as_query_engine(\n",
+ " custom_query_engines=custom_query_engines,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb",
+ "metadata": {
+ "id": "49c900ee-a31f-4fcd-bb44-ff2cd12a41eb"
+ },
+ "source": [
+ "### Compare Queries (text-davinci-003 vs. ChatGPT)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e",
+ "metadata": {
+ "id": "e0a8fa6a-e96e-4341-bb43-7547415f766e"
+ },
+ "source": [
+ "**Simple Query**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "OVnzf3myEz88",
+ "metadata": {
+ "id": "OVnzf3myEz88",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_str = \"Tell me more about Boston\"\n",
+ "response_chatgpt = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
+ "metadata": {
+ "id": "29f32345-6f28-4545-afa9-e3c5849dfb82",
+ "outputId": "904002ea-f062-4f7d-8fe6-3e6b7b13b420"
+ },
+ "outputs": [],
+ "source": [
+ "print(response_chatgpt)\n",
+ "print(response_chatgpt.get_formatted_sources())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38438507",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
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+ "colab": {
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diff --git a/docs/examples/customization/llms/AzureOpenAI.ipynb b/docs/examples/customization/llms/AzureOpenAI.ipynb
index 8151280f3e..b6427cbcd0 100644
--- a/docs/examples/customization/llms/AzureOpenAI.ipynb
+++ b/docs/examples/customization/llms/AzureOpenAI.ipynb
@@ -1,265 +1,263 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "ec51f656",
- "metadata": {},
- "source": [
- "# Azure OpenAI"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "ef3d26db",
- "metadata": {},
- "source": [
- "Azure openAI resources unfortunately differ from standard openAI resources as you can't generate embeddings unless you use an embedding model. The regions where these models are available can be found here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/concepts/models#embeddings-models\n",
- "\n",
- "Furthermore the regions that support embedding models unfortunately don't support the latest versions (<*>-003) of openAI models, so we are forced to use one region for embeddings and another for the text generation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "b05e71d5",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import json\n",
- "import openai\n",
- "from langchain.embeddings import OpenAIEmbeddings\n",
- "from llama_index.llms import AzureOpenAI\n",
- "from llama_index import LangchainEmbedding\n",
- "from llama_index import (\n",
- " VectorStoreIndex,\n",
- " SimpleDirectoryReader, \n",
- " ServiceContext\n",
- ")\n",
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO) # logging.DEBUG for more verbose output\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "80a962f8",
- "metadata": {},
- "source": [
- "This is the API setup for the embedding model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "fe013749",
- "metadata": {},
- "outputs": [],
- "source": [
- "openai.api_type = \"azure\"\n",
- "openai.api_base = \"<insert api base url from azure>\"\n",
- "openai.api_version = \"2022-12-01\"\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"<insert api key from azure>\"\n",
- "openai.api_key = os.getenv(\"OPENAI_API_KEY\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "fcaaafdf",
- "metadata": {},
- "source": [
- "And here you can see the setup for the text generation model, you should note we use deployment name instead of model name as an arguement. This name is the one chosen when deploying the model in Azure."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "e2569cb0",
- "metadata": {},
- "outputs": [],
- "source": [
- "llm = AzureOpenAI(engine=\"<insert deployment name from azure>\", mode='model name')\n",
- "\n",
- "# You need to deploy your own embedding model as well as your own chat completion model\n",
- "embedding_llm = LangchainEmbedding(\n",
- " OpenAIEmbeddings(\n",
- " model=\"text-embedding-ada-002\",\n",
- " deployment=\"<insert EMBEDDING model deployment name from azure>\",\n",
- " openai_api_key= openai.api_key,\n",
- " openai_api_base=openai.api_base,\n",
- " openai_api_type=openai.api_type,\n",
- " openai_api_version=openai.api_version,\n",
- " ),\n",
- " embed_batch_size=1,\n",
- ")\n",
- "\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "72aac5a6-495e-40d2-82d3-bda8688ae919",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import set_global_service_context\n",
- "\n",
- "service_context = ServiceContext.from_defaults(\n",
- " llm=llm,\n",
- " embed_model=embedding_llm,\n",
- ")\n",
- "\n",
- "set_global_service_context(service_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "1cf0e9c9",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Adding chunk: \t\t\n",
- "\n",
- "What I Worked On\n",
- "\n",
- "February 2021\n",
- "\n",
- "Before col...\n",
- "> Adding chunk: interesting of that type. So I'm not surprised ...\n",
- "> Adding chunk: to be the study of the ultimate truths, compare...\n",
- "> Adding chunk: language called PL/I, and the situation was sim...\n",
- "> Adding chunk: or if there even was a specific moment, but dur...\n",
- "> Adding chunk: an uneasy alliance between two halves, theory a...\n",
- "> Adding chunk: were hundreds of years old.\n",
- "\n",
- "And moreover this ...\n",
- "> Adding chunk: that he'd found such a spectacular way to get o...\n",
- "> Adding chunk: the classes that everyone has to take in fundam...\n",
- "> Adding chunk: students wouldn't require the faculty to teach ...\n",
- "> Adding chunk: or you get merely photographic accuracy, and wh...\n",
- "> Adding chunk: But the Accademia wasn't teaching me anything e...\n",
- "> Adding chunk: In Florence, after paying my part of the rent, ...\n",
- "> Adding chunk: about a new thing called HTML, which was, as he...\n",
- "> Adding chunk: were plenty of earnest students too: kids who \"...\n",
- "> Adding chunk: Lisp hacking work was very rare, and I didn't w...\n",
- "> Adding chunk: had done for the popularity of microcomputers. ...\n",
- "> Adding chunk: shopping cart, and I wrote a new site generator...\n",
- "> Adding chunk: seed funding from Idelle's husband Julian. In r...\n",
- "> Adding chunk: for a month,\" he said, \"and it's still not done...\n",
- "> Adding chunk: fun to work on. If all I'd had to do was work o...\n",
- "> Adding chunk: the collar than a picture of the whole shirt. T...\n",
- "> Adding chunk: partly because that's what startups did during ...\n",
- "> Adding chunk: had given us a lot of options when they bought ...\n",
- "> Adding chunk: That's what I should have done, just gone off s...\n",
- "> Adding chunk: buy. Now I could actually choose what neighborh...\n",
- "> Adding chunk: trying to build what it's now clear is about tw...\n",
- "> Adding chunk: dream of building a new Lisp, partly because on...\n",
- "> Adding chunk: me several years to understand the implications...\n",
- "> Adding chunk: seems about as hip.\n",
- "\n",
- "It's not that unprestigiou...\n",
- "> Adding chunk: charge of marketing at a Boston investment bank...\n",
- "> Adding chunk: out \"But not me!\" and went on with the talk. Bu...\n",
- "> Adding chunk: And neither of them helped founders enough in t...\n",
- "> Adding chunk: fake investors, because they would in a similar...\n",
- "> Adding chunk: batch was so good. You had to be pretty bold to...\n",
- "> Adding chunk: had not originally intended YC to be a full-tim...\n",
- "> Adding chunk: internal software in Arc. But while I continued...\n",
- "> Adding chunk: double from a kidney stone, he suggested that i...\n",
- "> Adding chunk: we agreed to make it a complete changing of the...\n",
- "> Adding chunk: of 2014 painting. I'd never been able to work s...\n",
- "> Adding chunk: his grad student Steve Russell suggested it. Ru...\n",
- "> Adding chunk: defined goal, or it would have been hard to kee...\n",
- "> Adding chunk: pools. It felt like I was doing life right. I r...\n",
- "> Adding chunk: the more exciting.\n",
- "\n",
- "[2] Italian words for abstr...\n",
- "> Adding chunk: expensive.\n",
- "\n",
- "[7] Technically the apartment wasn'...\n",
- "> Adding chunk: online means you treat the online version as th...\n",
- "> Adding chunk: logo had been a white V on a red circle, so I m...\n",
- "> Adding chunk: YC was not working with Jessica anymore. We'd b...\n",
- "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_documents] Total embedding token usage: 17533 tokens\n"
- ]
- }
- ],
- "source": [
- "index = VectorStoreIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "98d9d3fd",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 815 tokens\n",
- "> [query] Total embedding token usage: 8 tokens\n",
- "> Source (Doc id: ad03b507-8953-4201-b545-6195c5cfec49): me several years to understand the implications. It meant there would be a whole new generation o...\n",
- "query was: What is most interesting about this essay?\n",
- "answer was: \n",
- "\n",
- "The most interesting thing about this essay is the way the author reflects on the impact of online publishing on their life and career. They discuss how the opening up of the internet to allow for more diverse, and less prestigious, forms of writing allowed them to pursue the kind of writing they were interested in, which was something that had not been possible before. Furthermore, the author acknowledges that their work may not be seen as prestigious, such as Latin, but yet still has a great impact. They further reflect on how their life and career have been shaped by working on these types of projects.\n"
- ]
- }
- ],
- "source": [
- "query = 'What is most interesting about this essay?'\n",
- "query_engine = index.as_query_engine()\n",
- "answer = query_engine.query(query)\n",
- "\n",
- "print(answer.get_formatted_sources())\n",
- "print('query was:', query)\n",
- "print('answer was:', answer)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3e33f1eb",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "ec51f656",
+ "metadata": {},
+ "source": [
+ "# Azure OpenAI"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "ef3d26db",
+ "metadata": {},
+ "source": [
+ "Azure openAI resources unfortunately differ from standard openAI resources as you can't generate embeddings unless you use an embedding model. The regions where these models are available can be found here: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/concepts/models#embeddings-models\n",
+ "\n",
+ "Furthermore the regions that support embedding models unfortunately don't support the latest versions (<*>-003) of openAI models, so we are forced to use one region for embeddings and another for the text generation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "b05e71d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import json\n",
+ "import openai\n",
+ "from langchain.embeddings import OpenAIEmbeddings\n",
+ "from llama_index.llms import AzureOpenAI\n",
+ "from llama_index import LangchainEmbedding\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(\n",
+ " stream=sys.stdout, level=logging.INFO\n",
+ ") # logging.DEBUG for more verbose output\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "80a962f8",
+ "metadata": {},
+ "source": [
+ "This is the API setup for the embedding model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "fe013749",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "openai.api_type = \"azure\"\n",
+ "openai.api_base = \"<insert api base url from azure>\"\n",
+ "openai.api_version = \"2022-12-01\"\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"<insert api key from azure>\"\n",
+ "openai.api_key = os.getenv(\"OPENAI_API_KEY\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "fcaaafdf",
+ "metadata": {},
+ "source": [
+ "And here you can see the setup for the text generation model, you should note we use deployment name instead of model name as an arguement. This name is the one chosen when deploying the model in Azure."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e2569cb0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = AzureOpenAI(engine=\"<insert deployment name from azure>\", mode=\"model name\")\n",
+ "\n",
+ "# You need to deploy your own embedding model as well as your own chat completion model\n",
+ "embedding_llm = LangchainEmbedding(\n",
+ " OpenAIEmbeddings(\n",
+ " model=\"text-embedding-ada-002\",\n",
+ " deployment=\"<insert EMBEDDING model deployment name from azure>\",\n",
+ " openai_api_key=openai.api_key,\n",
+ " openai_api_base=openai.api_base,\n",
+ " openai_api_type=openai.api_type,\n",
+ " openai_api_version=openai.api_version,\n",
+ " ),\n",
+ " embed_batch_size=1,\n",
+ ")\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72aac5a6-495e-40d2-82d3-bda8688ae919",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import set_global_service_context\n",
+ "\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm=llm,\n",
+ " embed_model=embedding_llm,\n",
+ ")\n",
+ "\n",
+ "set_global_service_context(service_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "1cf0e9c9",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Adding chunk: \t\t\n",
+ "\n",
+ "What I Worked On\n",
+ "\n",
+ "February 2021\n",
+ "\n",
+ "Before col...\n",
+ "> Adding chunk: interesting of that type. So I'm not surprised ...\n",
+ "> Adding chunk: to be the study of the ultimate truths, compare...\n",
+ "> Adding chunk: language called PL/I, and the situation was sim...\n",
+ "> Adding chunk: or if there even was a specific moment, but dur...\n",
+ "> Adding chunk: an uneasy alliance between two halves, theory a...\n",
+ "> Adding chunk: were hundreds of years old.\n",
+ "\n",
+ "And moreover this ...\n",
+ "> Adding chunk: that he'd found such a spectacular way to get o...\n",
+ "> Adding chunk: the classes that everyone has to take in fundam...\n",
+ "> Adding chunk: students wouldn't require the faculty to teach ...\n",
+ "> Adding chunk: or you get merely photographic accuracy, and wh...\n",
+ "> Adding chunk: But the Accademia wasn't teaching me anything e...\n",
+ "> Adding chunk: In Florence, after paying my part of the rent, ...\n",
+ "> Adding chunk: about a new thing called HTML, which was, as he...\n",
+ "> Adding chunk: were plenty of earnest students too: kids who \"...\n",
+ "> Adding chunk: Lisp hacking work was very rare, and I didn't w...\n",
+ "> Adding chunk: had done for the popularity of microcomputers. ...\n",
+ "> Adding chunk: shopping cart, and I wrote a new site generator...\n",
+ "> Adding chunk: seed funding from Idelle's husband Julian. In r...\n",
+ "> Adding chunk: for a month,\" he said, \"and it's still not done...\n",
+ "> Adding chunk: fun to work on. If all I'd had to do was work o...\n",
+ "> Adding chunk: the collar than a picture of the whole shirt. T...\n",
+ "> Adding chunk: partly because that's what startups did during ...\n",
+ "> Adding chunk: had given us a lot of options when they bought ...\n",
+ "> Adding chunk: That's what I should have done, just gone off s...\n",
+ "> Adding chunk: buy. Now I could actually choose what neighborh...\n",
+ "> Adding chunk: trying to build what it's now clear is about tw...\n",
+ "> Adding chunk: dream of building a new Lisp, partly because on...\n",
+ "> Adding chunk: me several years to understand the implications...\n",
+ "> Adding chunk: seems about as hip.\n",
+ "\n",
+ "It's not that unprestigiou...\n",
+ "> Adding chunk: charge of marketing at a Boston investment bank...\n",
+ "> Adding chunk: out \"But not me!\" and went on with the talk. Bu...\n",
+ "> Adding chunk: And neither of them helped founders enough in t...\n",
+ "> Adding chunk: fake investors, because they would in a similar...\n",
+ "> Adding chunk: batch was so good. You had to be pretty bold to...\n",
+ "> Adding chunk: had not originally intended YC to be a full-tim...\n",
+ "> Adding chunk: internal software in Arc. But while I continued...\n",
+ "> Adding chunk: double from a kidney stone, he suggested that i...\n",
+ "> Adding chunk: we agreed to make it a complete changing of the...\n",
+ "> Adding chunk: of 2014 painting. I'd never been able to work s...\n",
+ "> Adding chunk: his grad student Steve Russell suggested it. Ru...\n",
+ "> Adding chunk: defined goal, or it would have been hard to kee...\n",
+ "> Adding chunk: pools. It felt like I was doing life right. I r...\n",
+ "> Adding chunk: the more exciting.\n",
+ "\n",
+ "[2] Italian words for abstr...\n",
+ "> Adding chunk: expensive.\n",
+ "\n",
+ "[7] Technically the apartment wasn'...\n",
+ "> Adding chunk: online means you treat the online version as th...\n",
+ "> Adding chunk: logo had been a white V on a red circle, so I m...\n",
+ "> Adding chunk: YC was not working with Jessica anymore. We'd b...\n",
+ "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_documents] Total embedding token usage: 17533 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "98d9d3fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 815 tokens\n",
+ "> [query] Total embedding token usage: 8 tokens\n",
+ "> Source (Doc id: ad03b507-8953-4201-b545-6195c5cfec49): me several years to understand the implications. It meant there would be a whole new generation o...\n",
+ "query was: What is most interesting about this essay?\n",
+ "answer was: \n",
+ "\n",
+ "The most interesting thing about this essay is the way the author reflects on the impact of online publishing on their life and career. They discuss how the opening up of the internet to allow for more diverse, and less prestigious, forms of writing allowed them to pursue the kind of writing they were interested in, which was something that had not been possible before. Furthermore, the author acknowledges that their work may not be seen as prestigious, such as Latin, but yet still has a great impact. They further reflect on how their life and career have been shaped by working on these types of projects.\n"
+ ]
+ }
+ ],
+ "source": [
+ "query = \"What is most interesting about this essay?\"\n",
+ "query_engine = index.as_query_engine()\n",
+ "answer = query_engine.query(query)\n",
+ "\n",
+ "print(answer.get_formatted_sources())\n",
+ "print(\"query was:\", query)\n",
+ "print(\"answer was:\", answer)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e33f1eb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb b/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb
index ae4662258e..906013ad46 100644
--- a/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb
+++ b/docs/examples/customization/llms/SimpleIndexDemo-ChatGPT.ipynb
@@ -33,7 +33,12 @@
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ ")\n",
"from llama_index.llms import OpenAI\n",
"from IPython.display import Markdown, display"
]
@@ -48,7 +53,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../../data/paul_graham\").load_data()"
]
},
{
@@ -128,7 +133,7 @@
" streaming=True,\n",
")\n",
"response = query_engine.query(\n",
- " \"What did the author do growing up?\", \n",
+ " \"What did the author do growing up?\",\n",
")"
]
},
@@ -182,7 +187,7 @@
" streaming=True,\n",
")\n",
"response = query_engine.query(\n",
- " \"What did the author do during his time at RISD?\", \n",
+ " \"What did the author do during his time at RISD?\",\n",
")"
]
},
@@ -278,7 +283,7 @@
" streaming=True,\n",
")\n",
"response = query_engine.query(\n",
- " \"What did the author do during his time at RISD?\", \n",
+ " \"What did the author do during his time at RISD?\",\n",
")"
]
},
diff --git a/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb b/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb
index f1af55393a..2df98ae955 100644
--- a/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb
+++ b/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_camel.ipynb
@@ -64,7 +64,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../../data/paul_graham\").load_data()"
]
},
{
@@ -83,7 +83,7 @@
" \"Below is an instruction that describes a task. \"\n",
" \"Write a response that appropriately completes the request.\\n\\n\"\n",
" \"### Instruction:\\n{query_str}\\n\\n### Response:\"\n",
- ")\n"
+ ")"
]
},
{
@@ -104,8 +104,9 @@
],
"source": [
"import torch\n",
+ "\n",
"llm = HuggingFaceLLM(\n",
- " context_window=2048, \n",
+ " context_window=2048,\n",
" max_new_tokens=256,\n",
" temperature=0.25,\n",
" do_sample=False,\n",
diff --git a/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb b/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb
index eb464e755c..324a66a6e6 100644
--- a/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb
+++ b/docs/examples/customization/llms/SimpleIndexDemo-Huggingface_stablelm.ipynb
@@ -64,7 +64,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../../data/paul_graham\").load_data()"
]
},
{
@@ -82,10 +82,10 @@
"- StableLM is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\n",
"- StableLM is more than just an information source, StableLM is also able to write poetry, short stories, and make jokes.\n",
"- StableLM will refuse to participate in anything that could harm a human.\n",
- "\"\"\" \n",
+ "\"\"\"\n",
"\n",
"# This will wrap the default prompts that are internal to llama-index\n",
- "query_wrapper_prompt = SimpleInputPrompt(\"<|USER|>{query_str}<|ASSISTANT|>\")\n"
+ "query_wrapper_prompt = SimpleInputPrompt(\"<|USER|>{query_str}<|ASSISTANT|>\")"
]
},
{
@@ -106,8 +106,9 @@
],
"source": [
"import torch\n",
+ "\n",
"llm = HuggingFaceLLM(\n",
- " context_window=4096, \n",
+ " context_window=4096,\n",
" max_new_tokens=256,\n",
" temperature=0.7,\n",
" do_sample=False,\n",
diff --git a/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb b/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb
index cfe629e1e0..c5bfb006ae 100644
--- a/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb
+++ b/docs/examples/customization/streaming/SimpleIndexDemo-streaming.ipynb
@@ -65,7 +65,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../../data/paul_graham\").load_data()"
]
},
{
@@ -101,12 +101,9 @@
"outputs": [],
"source": [
"# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(\n",
- " streaming=True,\n",
- " similarity_top_k=1\n",
- ")\n",
+ "query_engine = index.as_query_engine(streaming=True, similarity_top_k=1)\n",
"response_stream = query_engine.query(\n",
- " \"What did the author do growing up?\", \n",
+ " \"What did the author do growing up?\",\n",
")"
]
},
diff --git a/docs/examples/customization/streaming/chat_engine_condense_question_stream_response.ipynb b/docs/examples/customization/streaming/chat_engine_condense_question_stream_response.ipynb
index 798fcd1daa..1871acc0cc 100644
--- a/docs/examples/customization/streaming/chat_engine_condense_question_stream_response.ipynb
+++ b/docs/examples/customization/streaming/chat_engine_condense_question_stream_response.ipynb
@@ -65,7 +65,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../../data/paul_graham\").load_data()"
]
},
{
@@ -105,11 +105,8 @@
}
],
"source": [
- "chat_engine = index.as_chat_engine(\n",
- " chat_mode='condense_question',\n",
- " streaming=True\n",
- ")\n",
- "response_stream = chat_engine.chat('What did Paul Graham do after YC?')"
+ "chat_engine = index.as_chat_engine(chat_mode=\"condense_question\", streaming=True)\n",
+ "response_stream = chat_engine.chat(\"What did Paul Graham do after YC?\")"
]
},
{
@@ -160,7 +157,7 @@
}
],
"source": [
- "response_stream = chat_engine.chat('What about after that?')"
+ "response_stream = chat_engine.chat(\"What about after that?\")"
]
},
{
@@ -202,7 +199,7 @@
}
],
"source": [
- "response_stream = chat_engine.chat('Can you tell me more?')"
+ "response_stream = chat_engine.chat(\"Can you tell me more?\")"
]
},
{
@@ -265,7 +262,7 @@
}
],
"source": [
- "response_stream = chat_engine.chat('What about after that?')"
+ "response_stream = chat_engine.chat(\"What about after that?\")"
]
},
{
diff --git a/docs/examples/data_connectors/ChromaDemo.ipynb b/docs/examples/data_connectors/ChromaDemo.ipynb
index 7f7ff9b951..82698c04d1 100644
--- a/docs/examples/data_connectors/ChromaDemo.ipynb
+++ b/docs/examples/data_connectors/ChromaDemo.ipynb
@@ -1,147 +1,148 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
- "metadata": {},
- "source": [
- "# Chroma Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "778ee662",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "262f990a-79c8-413a-9f3c-cd9a3c191307",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.readers.chroma import ChromaReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "252f8163-7297-44b6-a838-709e9662f3d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "# The chroma reader loads data from a persisted Chroma collection.\n",
- "# This requires a collection name and a persist directory.\n",
- "\n",
- "reader = ChromaReader(\n",
- " collection_name=\"chroma_collection\",\n",
- " persist_directory=\"examples/data_connectors/chroma_collection\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53b49187-8477-436c-9718-5d2f8cc6fad0",
- "metadata": {},
- "outputs": [],
- "source": [
- "# the query_vector is an embedding representation of your query.\n",
- "# Example query vector:\n",
- "# query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]\n",
- "\n",
- "query_vector=[n1, n2, n3, ...]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a88be1c4-603f-48b9-ac64-10a219af4951",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: Required args are collection_name, query_vector.\n",
- "# See the Python client: https://github.com/qdrant/qdrant_client\n",
- "# for more details. \n",
- "documents = reader.load_data(collection_name=\"demo\", query_vector=query_vector, limit=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "169b4273-eb20-4d06-9ffe-71320f4570f6",
- "metadata": {},
- "source": [
- "### Create index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ac4563a1",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices import ListIndex\n",
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f06b02db",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "97d1ae80",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "chroma-gpt-index",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- },
- "vscode": {
- "interpreter": {
- "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
+ "metadata": {},
+ "source": [
+ "# Chroma Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "778ee662",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "262f990a-79c8-413a-9f3c-cd9a3c191307",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.readers.chroma import ChromaReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "252f8163-7297-44b6-a838-709e9662f3d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The chroma reader loads data from a persisted Chroma collection.\n",
+ "# This requires a collection name and a persist directory.\n",
+ "\n",
+ "reader = ChromaReader(\n",
+ " collection_name=\"chroma_collection\",\n",
+ " persist_directory=\"examples/data_connectors/chroma_collection\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53b49187-8477-436c-9718-5d2f8cc6fad0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# the query_vector is an embedding representation of your query.\n",
+ "# Example query vector:\n",
+ "# query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]\n",
+ "\n",
+ "query_vector = [n1, n2, n3, ...]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a88be1c4-603f-48b9-ac64-10a219af4951",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: Required args are collection_name, query_vector.\n",
+ "# See the Python client: https://github.com/qdrant/qdrant_client\n",
+ "# for more details.\n",
+ "documents = reader.load_data(collection_name=\"demo\", query_vector=query_vector, limit=5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "169b4273-eb20-4d06-9ffe-71320f4570f6",
+ "metadata": {},
+ "source": [
+ "### Create index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac4563a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices import ListIndex\n",
+ "\n",
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f06b02db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97d1ae80",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "chroma-gpt-index",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/DatabaseReaderDemo.ipynb b/docs/examples/data_connectors/DatabaseReaderDemo.ipynb
index 4a0619a8cc..10da4f6f44 100644
--- a/docs/examples/data_connectors/DatabaseReaderDemo.ipynb
+++ b/docs/examples/data_connectors/DatabaseReaderDemo.ipynb
@@ -1,203 +1,204 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Database Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from __future__ import absolute_import\n",
- "\n",
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"\"\n",
- "\n",
- "from llama_index.readers.database import DatabaseReader\n",
- "from llama_index import VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Initialize DatabaseReader object with the following parameters:\n",
- "\n",
- "db = DatabaseReader(\n",
- " scheme = \"postgresql\", # Database Scheme\n",
- " host = \"localhost\", # Database Host\n",
- " port = \"5432\", # Database Port\n",
- " user = \"postgres\", # Database User\n",
- " password = \"FakeExamplePassword\", # Database Password\n",
- " dbname = \"postgres\", # Database Name\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "### DatabaseReader class ###\n",
- "# db is an instance of DatabaseReader:\n",
- "print(type(db))\n",
- "# DatabaseReader available method:\n",
- "print(type(db.load_data))\n",
- "\n",
- "### SQLDatabase class ###\n",
- "# db.sql is an instance of SQLDatabase:\n",
- "print(type(db.sql_database))\n",
- "# SQLDatabase available methods:\n",
- "print(type(db.sql_database.from_uri))\n",
- "print(type(db.sql_database.get_single_table_info))\n",
- "print(type(db.sql_database.get_table_columns))\n",
- "print(type(db.sql_database.get_table_info))\n",
- "print(type(db.sql_database.get_table_names))\n",
- "print(type(db.sql_database.insert_into_table))\n",
- "print(type(db.sql_database.run))\n",
- "print(type(db.sql_database.run_sql))\n",
- "# SQLDatabase available properties:\n",
- "print(type(db.sql_database.dialect))\n",
- "print(type(db.sql_database.engine))\n",
- "print(type(db.sql_database.table_info))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "### Testing DatabaseReader\n",
- "### from SQLDatabase, SQLAlchemy engine and Database URI:\n",
- "\n",
- "# From SQLDatabase instance:\n",
- "print(type(db.sql_database))\n",
- "db_from_sql_database = DatabaseReader(sql_database = db.sql_database)\n",
- "print(type(db_from_sql_database))\n",
- "\n",
- "# From SQLAlchemy engine:\n",
- "print(type(db.sql_database.engine))\n",
- "db_from_engine = DatabaseReader(engine = db.sql_database.engine)\n",
- "print(type(db_from_engine))\n",
- "\n",
- "# From Database URI:\n",
- "print(type(db.uri))\n",
- "db_from_uri = DatabaseReader(uri = db.uri)\n",
- "print(type(db_from_uri))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The below SQL Query example returns a list values of each row\n",
- "# with concatenated text from the name and age columns\n",
- "# from the users table where the age is greater than or equal to 18\n",
- "\n",
- "query = f\"\"\"\n",
- " SELECT\n",
- " CONCAT(name, ' is ', age, ' years old.') AS text\n",
- " FROM public.users\n",
- " WHERE age >= 18\n",
- " \"\"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Please refer to llama_index.langchain_helpers.sql_wrapper\n",
- "# SQLDatabase.run_sql method\n",
- "texts = db.sql_database.run_sql(command = query)\n",
- "\n",
- "# Display type(texts) and texts\n",
- "# type(texts) must return <class 'list'>\n",
- "print(type(texts))\n",
- "\n",
- "# Documents must return a list of Tuple objects\n",
- "print(texts)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Please refer to llama_index.readers.database.DatabaseReader.load_data\n",
- "# DatabaseReader.load_data method\n",
- "documents = db.load_data(query = query)\n",
- "\n",
- "# Display type(documents) and documents\n",
- "# type(documents) must return <class 'list'>\n",
- "print(type(documents))\n",
- "\n",
- "# Documents must return a list of Document objects\n",
- "print(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "index = VectorStoreIndex.from_documents(documents)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- },
- "vscode": {
- "interpreter": {
- "hash": "bd5508c2ffc7f17f7d31cf4086cc872f89e96996a08987e995649e5fbe85a3a4"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Database Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from __future__ import absolute_import\n",
+ "\n",
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
+ "\n",
+ "from llama_index.readers.database import DatabaseReader\n",
+ "from llama_index import VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initialize DatabaseReader object with the following parameters:\n",
+ "\n",
+ "db = DatabaseReader(\n",
+ " scheme=\"postgresql\", # Database Scheme\n",
+ " host=\"localhost\", # Database Host\n",
+ " port=\"5432\", # Database Port\n",
+ " user=\"postgres\", # Database User\n",
+ " password=\"FakeExamplePassword\", # Database Password\n",
+ " dbname=\"postgres\", # Database Name\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### DatabaseReader class ###\n",
+ "# db is an instance of DatabaseReader:\n",
+ "print(type(db))\n",
+ "# DatabaseReader available method:\n",
+ "print(type(db.load_data))\n",
+ "\n",
+ "### SQLDatabase class ###\n",
+ "# db.sql is an instance of SQLDatabase:\n",
+ "print(type(db.sql_database))\n",
+ "# SQLDatabase available methods:\n",
+ "print(type(db.sql_database.from_uri))\n",
+ "print(type(db.sql_database.get_single_table_info))\n",
+ "print(type(db.sql_database.get_table_columns))\n",
+ "print(type(db.sql_database.get_table_info))\n",
+ "print(type(db.sql_database.get_table_names))\n",
+ "print(type(db.sql_database.insert_into_table))\n",
+ "print(type(db.sql_database.run))\n",
+ "print(type(db.sql_database.run_sql))\n",
+ "# SQLDatabase available properties:\n",
+ "print(type(db.sql_database.dialect))\n",
+ "print(type(db.sql_database.engine))\n",
+ "print(type(db.sql_database.table_info))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Testing DatabaseReader\n",
+ "### from SQLDatabase, SQLAlchemy engine and Database URI:\n",
+ "\n",
+ "# From SQLDatabase instance:\n",
+ "print(type(db.sql_database))\n",
+ "db_from_sql_database = DatabaseReader(sql_database=db.sql_database)\n",
+ "print(type(db_from_sql_database))\n",
+ "\n",
+ "# From SQLAlchemy engine:\n",
+ "print(type(db.sql_database.engine))\n",
+ "db_from_engine = DatabaseReader(engine=db.sql_database.engine)\n",
+ "print(type(db_from_engine))\n",
+ "\n",
+ "# From Database URI:\n",
+ "print(type(db.uri))\n",
+ "db_from_uri = DatabaseReader(uri=db.uri)\n",
+ "print(type(db_from_uri))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The below SQL Query example returns a list values of each row\n",
+ "# with concatenated text from the name and age columns\n",
+ "# from the users table where the age is greater than or equal to 18\n",
+ "\n",
+ "query = f\"\"\"\n",
+ " SELECT\n",
+ " CONCAT(name, ' is ', age, ' years old.') AS text\n",
+ " FROM public.users\n",
+ " WHERE age >= 18\n",
+ " \"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Please refer to llama_index.langchain_helpers.sql_wrapper\n",
+ "# SQLDatabase.run_sql method\n",
+ "texts = db.sql_database.run_sql(command=query)\n",
+ "\n",
+ "# Display type(texts) and texts\n",
+ "# type(texts) must return <class 'list'>\n",
+ "print(type(texts))\n",
+ "\n",
+ "# Documents must return a list of Tuple objects\n",
+ "print(texts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Please refer to llama_index.readers.database.DatabaseReader.load_data\n",
+ "# DatabaseReader.load_data method\n",
+ "documents = db.load_data(query=query)\n",
+ "\n",
+ "# Display type(documents) and documents\n",
+ "# type(documents) must return <class 'list'>\n",
+ "print(type(documents))\n",
+ "\n",
+ "# Documents must return a list of Document objects\n",
+ "print(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "bd5508c2ffc7f17f7d31cf4086cc872f89e96996a08987e995649e5fbe85a3a4"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/docs/examples/data_connectors/DiscordDemo.ipynb b/docs/examples/data_connectors/DiscordDemo.ipynb
index 5191121983..5b4f0d713c 100644
--- a/docs/examples/data_connectors/DiscordDemo.ipynb
+++ b/docs/examples/data_connectors/DiscordDemo.ipynb
@@ -1,119 +1,122 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "effeb5a7-8544-4ee4-8c11-bad0d8165394",
- "metadata": {},
- "source": [
- "# Discord Reader\n",
- "Demonstrates our Discord data connector"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5fb15bc4",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "04edcd4a-5633-47ee-8a92-ff2f6abc2ec7",
- "metadata": {},
- "outputs": [],
- "source": [
- "# This is due to the fact that we use asyncio.loop_until_complete in\n",
- "# the DiscordReader. Since the Jupyter kernel itself runs on\n",
- "# an event loop, we need to add some help with nesting\n",
- "!pip install nest_asyncio\n",
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6ea1f66d-10ed-4417-bdcb-f8a894836ea5",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, DiscordReader\n",
- "from IPython.display import Markdown, display\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da90589a-fb44-4ec6-9706-753dba4fa968",
- "metadata": {},
- "outputs": [],
- "source": [
- "discord_token = os.getenv(\"DISCORD_TOKEN\")\n",
- "channel_ids = [1057178784895348746] # Replace with your channel_id\n",
- "documents = DiscordReader(discord_token=discord_token).load_data(channel_ids=channel_ids)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "341295df-2029-4728-ab3d-2ee178a7e6f1",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "01c26b9d-49ec-4a6e-9c61-5c06bb86bbb2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f160c678-2fb5-4d6d-b2bc-87abb61cfdec",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "effeb5a7-8544-4ee4-8c11-bad0d8165394",
+ "metadata": {},
+ "source": [
+ "# Discord Reader\n",
+ "Demonstrates our Discord data connector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5fb15bc4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "04edcd4a-5633-47ee-8a92-ff2f6abc2ec7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This is due to the fact that we use asyncio.loop_until_complete in\n",
+ "# the DiscordReader. Since the Jupyter kernel itself runs on\n",
+ "# an event loop, we need to add some help with nesting\n",
+ "!pip install nest_asyncio\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6ea1f66d-10ed-4417-bdcb-f8a894836ea5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, DiscordReader\n",
+ "from IPython.display import Markdown, display\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da90589a-fb44-4ec6-9706-753dba4fa968",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "discord_token = os.getenv(\"DISCORD_TOKEN\")\n",
+ "channel_ids = [1057178784895348746] # Replace with your channel_id\n",
+ "documents = DiscordReader(discord_token=discord_token).load_data(\n",
+ " channel_ids=channel_ids\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "341295df-2029-4728-ab3d-2ee178a7e6f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "01c26b9d-49ec-4a6e-9c61-5c06bb86bbb2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f160c678-2fb5-4d6d-b2bc-87abb61cfdec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/FaissDemo.ipynb b/docs/examples/data_connectors/FaissDemo.ipynb
index 1d901ac6f4..a16b0bfcf1 100644
--- a/docs/examples/data_connectors/FaissDemo.ipynb
+++ b/docs/examples/data_connectors/FaissDemo.ipynb
@@ -1,166 +1,166 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "5d974136",
- "metadata": {},
- "source": [
- "# Faiss Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4026b434",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b541d8ec",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.readers.faiss import FaissReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "90d37078",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Build the Faiss index. \n",
- "# A guide for how to get started with Faiss is here: https://github.com/facebookresearch/faiss/wiki/Getting-started\n",
- "# We provide some example code below.\n",
- "\n",
- "import faiss\n",
- "\n",
- "# # Example Code\n",
- "# d = 8\n",
- "# docs = np.array([\n",
- "# [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
- "# [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],\n",
- "# [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],\n",
- "# [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],\n",
- "# [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
- "# ])\n",
- "# # id_to_text_map is used for query retrieval\n",
- "# id_to_text_map = {\n",
- "# 0: \"aaaaaaaaa bbbbbbb cccccc\",\n",
- "# 1: \"foooooo barrrrrr\",\n",
- "# 2: \"tmp tmptmp tmp\",\n",
- "# 3: \"hello world hello world\",\n",
- "# 4: \"cat dog cat dog\"\n",
- "# }\n",
- "# # build the index\n",
- "# index = faiss.IndexFlatL2(d)\n",
- "# index.add(docs)\n",
- "\n",
- "id_to_text_map = {\n",
- " \"id1\": \"text blob 1\",\n",
- " \"id2\": \"text blob 2\",\n",
- "}\n",
- "index = ..."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fd470a09",
- "metadata": {},
- "outputs": [],
- "source": [
- "reader = FaissReader(index)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c33084c5",
- "metadata": {},
- "outputs": [],
- "source": [
- "# To load data from the Faiss index, you must specify: \n",
- "# k: top nearest neighbors\n",
- "# query: a 2D embedding representation of your queries (rows are queries)\n",
- "k = 4\n",
- "query1 = np.array([...])\n",
- "query2 = np.array([...])\n",
- "query=np.array([query1, query2])\n",
- "\n",
- "documents = reader.load_data(query=query, id_to_text_map=id_to_text_map, k=k)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "0b74697a",
- "metadata": {},
- "source": [
- "### Create index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e85d7e5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "31c3b68f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "56fce3fb",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "5d974136",
+ "metadata": {},
+ "source": [
+ "# Faiss Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4026b434",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b541d8ec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.readers.faiss import FaissReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90d37078",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Build the Faiss index.\n",
+ "# A guide for how to get started with Faiss is here: https://github.com/facebookresearch/faiss/wiki/Getting-started\n",
+ "# We provide some example code below.\n",
+ "\n",
+ "import faiss\n",
+ "\n",
+ "# # Example Code\n",
+ "# d = 8\n",
+ "# docs = np.array([\n",
+ "# [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],\n",
+ "# [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],\n",
+ "# [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],\n",
+ "# [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],\n",
+ "# [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]\n",
+ "# ])\n",
+ "# # id_to_text_map is used for query retrieval\n",
+ "# id_to_text_map = {\n",
+ "# 0: \"aaaaaaaaa bbbbbbb cccccc\",\n",
+ "# 1: \"foooooo barrrrrr\",\n",
+ "# 2: \"tmp tmptmp tmp\",\n",
+ "# 3: \"hello world hello world\",\n",
+ "# 4: \"cat dog cat dog\"\n",
+ "# }\n",
+ "# # build the index\n",
+ "# index = faiss.IndexFlatL2(d)\n",
+ "# index.add(docs)\n",
+ "\n",
+ "id_to_text_map = {\n",
+ " \"id1\": \"text blob 1\",\n",
+ " \"id2\": \"text blob 2\",\n",
+ "}\n",
+ "index = ..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd470a09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reader = FaissReader(index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c33084c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# To load data from the Faiss index, you must specify:\n",
+ "# k: top nearest neighbors\n",
+ "# query: a 2D embedding representation of your queries (rows are queries)\n",
+ "k = 4\n",
+ "query1 = np.array([...])\n",
+ "query2 = np.array([...])\n",
+ "query = np.array([query1, query2])\n",
+ "\n",
+ "documents = reader.load_data(query=query, id_to_text_map=id_to_text_map, k=k)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b74697a",
+ "metadata": {},
+ "source": [
+ "### Create index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e85d7e5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "31c3b68f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56fce3fb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/GithubRepositoryReaderDemo.ipynb b/docs/examples/data_connectors/GithubRepositoryReaderDemo.ipynb
index 609b0a3fe9..dbdb7a7e3b 100644
--- a/docs/examples/data_connectors/GithubRepositoryReaderDemo.ipynb
+++ b/docs/examples/data_connectors/GithubRepositoryReaderDemo.ipynb
@@ -1,119 +1,122 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Github Repo Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# This is due to the fact that we use asyncio.loop_until_complete in\n",
- "# the DiscordReader. Since the Jupyter kernel itself runs on\n",
- "# an event loop, we need to add some help with nesting\n",
- "!pip install nest_asyncio httpx\n",
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%env OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
- "from llama_index import VectorStoreIndex, GithubRepositoryReader\n",
- "from IPython.display import Markdown, display\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%env GITHUB_TOKEN=github_pat_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
- "github_token = os.environ.get(\"GITHUB_TOKEN\")\n",
- "owner = \"jerryjliu\"\n",
- "repo = \"llama_index\"\n",
- "branch = \"main\"\n",
- "\n",
- "documents = GithubRepositoryReader(\n",
- " github_token=github_token,\n",
- " owner=owner,\n",
- " repo=repo,\n",
- " use_parser=False,\n",
- " verbose=False,\n",
- " ignore_directories=[\"examples\"]\n",
- ").load_data(branch=branch)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "index = VectorStoreIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# import time\n",
- "# for document in documents:\n",
- "# print(document.metadata)\n",
- "# time.sleep(.25) \n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What is the difference between VectorStoreIndex and ListIndex?\", verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index-github-reader",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.0"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "5bc2ab08ee48b6366504a28e3231c27a37c154a347ee8ac6184b716eff7bdbcd"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Github Repo Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This is due to the fact that we use asyncio.loop_until_complete in\n",
+ "# the DiscordReader. Since the Jupyter kernel itself runs on\n",
+ "# an event loop, we need to add some help with nesting\n",
+ "!pip install nest_asyncio httpx\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%env OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
+ "from llama_index import VectorStoreIndex, GithubRepositoryReader\n",
+ "from IPython.display import Markdown, display\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%env GITHUB_TOKEN=github_pat_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n",
+ "github_token = os.environ.get(\"GITHUB_TOKEN\")\n",
+ "owner = \"jerryjliu\"\n",
+ "repo = \"llama_index\"\n",
+ "branch = \"main\"\n",
+ "\n",
+ "documents = GithubRepositoryReader(\n",
+ " github_token=github_token,\n",
+ " owner=owner,\n",
+ " repo=repo,\n",
+ " use_parser=False,\n",
+ " verbose=False,\n",
+ " ignore_directories=[\"examples\"],\n",
+ ").load_data(branch=branch)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import time\n",
+ "# for document in documents:\n",
+ "# print(document.metadata)\n",
+ "# time.sleep(.25)\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\n",
+ " \"What is the difference between VectorStoreIndex and ListIndex?\", verbose=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index-github-reader",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "5bc2ab08ee48b6366504a28e3231c27a37c154a347ee8ac6184b716eff7bdbcd"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/docs/examples/data_connectors/MakeDemo.ipynb b/docs/examples/data_connectors/MakeDemo.ipynb
index 353bdedc3a..6adcd81920 100644
--- a/docs/examples/data_connectors/MakeDemo.ipynb
+++ b/docs/examples/data_connectors/MakeDemo.ipynb
@@ -1,102 +1,102 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7fc13177-7d9d-4959-bbe9-fa26d60ea786",
- "metadata": {},
- "source": [
- "# Make Reader\n",
- "\n",
- "We show how LlamaIndex can fit with your Make.com workflow by sending the GPT Index response to a scenario webhook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d2289d27",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "f90c60a6-50b3-4b66-abf3-9723dac8a045",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.readers import MakeWrapper"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "dd8885c5-39e2-444b-9666-5032ab4cb50d",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = VectorStoreIndex.from_documents(documents=documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e5f7d888-01ed-40f7-9216-6c7340b229bf",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# query index\n",
- "query_str = \"What did the author do growing up?\"\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "eaf06ad9-ba04-42fb-a7c8-daf7a5320b53",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Send response to Make.com webhook\n",
- "wrapper = MakeWrapper()\n",
- "wrapper.pass_response_to_webhook(\n",
- " \"<webhook_url>,\n",
- " response,\n",
- " query_str\n",
- ")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7fc13177-7d9d-4959-bbe9-fa26d60ea786",
+ "metadata": {},
+ "source": [
+ "# Make Reader\n",
+ "\n",
+ "We show how LlamaIndex can fit with your Make.com workflow by sending the GPT Index response to a scenario webhook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d2289d27",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "f90c60a6-50b3-4b66-abf3-9723dac8a045",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.readers import MakeWrapper"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "dd8885c5-39e2-444b-9666-5032ab4cb50d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = VectorStoreIndex.from_documents(documents=documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e5f7d888-01ed-40f7-9216-6c7340b229bf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# query index\n",
+ "query_str = \"What did the author do growing up?\"\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "eaf06ad9-ba04-42fb-a7c8-daf7a5320b53",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Send response to Make.com webhook\n",
+ "wrapper = MakeWrapper()\n",
+ "wrapper.pass_response_to_webhook(\n",
+ " \"<webhook_url>,\n",
+ " response,\n",
+ " query_str\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/MboxReaderDemo.ipynb b/docs/examples/data_connectors/MboxReaderDemo.ipynb
index dd9e1f22f5..6aa2877a06 100644
--- a/docs/examples/data_connectors/MboxReaderDemo.ipynb
+++ b/docs/examples/data_connectors/MboxReaderDemo.ipynb
@@ -1,111 +1,113 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Mbox Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%env OPENAI_API_KEY=sk-************"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import MboxReader, VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = MboxReader().load_data('mbox_data_dir', max_count=1000) # Returns list of documents "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "index = VectorStoreIndex.from_documents(documents) # Initialize index with documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 100 tokens\n",
- "> [query] Total embedding token usage: 10 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "res = query_engine.query('When did i have that call with the London office?')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> There is a call scheduled with the London office at 12am GMT on the 10th of February."
- ]
- }
- ],
- "source": [
- "res.response"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "7dd9b00487715d9ffc85f7f860a0013e7a0542b27fc53d2b1d33405d7679eac1"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Mbox Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%env OPENAI_API_KEY=sk-************"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import MboxReader, VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = MboxReader().load_data(\n",
+ " \"mbox_data_dir\", max_count=1000\n",
+ ") # Returns list of documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents) # Initialize index with documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 100 tokens\n",
+ "> [query] Total embedding token usage: 10 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "res = query_engine.query(\"When did i have that call with the London office?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> There is a call scheduled with the London office at 12am GMT on the 10th of February."
+ ]
+ }
+ ],
+ "source": [
+ "res.response"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8 (main, Oct 13 2022, 09:48:40) [Clang 14.0.0 (clang-1400.0.29.102)]"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "7dd9b00487715d9ffc85f7f860a0013e7a0542b27fc53d2b1d33405d7679eac1"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/docs/examples/data_connectors/MilvusReaderDemo.ipynb b/docs/examples/data_connectors/MilvusReaderDemo.ipynb
index fb67fb7873..3e15af8e45 100644
--- a/docs/examples/data_connectors/MilvusReaderDemo.ipynb
+++ b/docs/examples/data_connectors/MilvusReaderDemo.ipynb
@@ -43,6 +43,7 @@
"outputs": [],
"source": [
"import os\n",
+ "\n",
"os.environ[\"OPENAI_API_KEY\"] = \"sk-\""
]
},
@@ -69,7 +70,7 @@
],
"source": [
"reader = MilvusReader()\n",
- "reader.load_data([random.random() for _ in range(1536)], 'llamalection')"
+ "reader.load_data([random.random() for _ in range(1536)], \"llamalection\")"
]
}
],
diff --git a/docs/examples/data_connectors/MongoDemo.ipynb b/docs/examples/data_connectors/MongoDemo.ipynb
index b47c09545c..954a78be5d 100644
--- a/docs/examples/data_connectors/MongoDemo.ipynb
+++ b/docs/examples/data_connectors/MongoDemo.ipynb
@@ -1,111 +1,113 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "effeb5a7-8544-4ee4-8c11-bad0d8165394",
- "metadata": {},
- "source": [
- "# MongoDB Reader\n",
- "Demonstrates our MongoDB data connector"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "60355655",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "6ea1f66d-10ed-4417-bdcb-f8a894836ea5",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, SimpleMongoReader\n",
- "from IPython.display import Markdown, display\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da90589a-fb44-4ec6-9706-753dba4fa968",
- "metadata": {},
- "outputs": [],
- "source": [
- "host = \"<host>\"\n",
- "port = \"<port>\"\n",
- "db_name = \"<db_name>\"\n",
- "collection_name = \"<collection_name>\"\n",
- "# query_dict is passed into db.collection.find()\n",
- "query_dict = {}\n",
- "field_names = [\"text\"]\n",
- "reader = SimpleMongoReader(host, port)\n",
- "documents = reader.load_data(db_name, collection_name, field_names, query_dict=query_dict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "341295df-2029-4728-ab3d-2ee178a7e6f1",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "01c26b9d-49ec-4a6e-9c61-5c06bb86bbb2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f160c678-2fb5-4d6d-b2bc-87abb61cfdec",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "effeb5a7-8544-4ee4-8c11-bad0d8165394",
+ "metadata": {},
+ "source": [
+ "# MongoDB Reader\n",
+ "Demonstrates our MongoDB data connector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60355655",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6ea1f66d-10ed-4417-bdcb-f8a894836ea5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, SimpleMongoReader\n",
+ "from IPython.display import Markdown, display\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da90589a-fb44-4ec6-9706-753dba4fa968",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "host = \"<host>\"\n",
+ "port = \"<port>\"\n",
+ "db_name = \"<db_name>\"\n",
+ "collection_name = \"<collection_name>\"\n",
+ "# query_dict is passed into db.collection.find()\n",
+ "query_dict = {}\n",
+ "field_names = [\"text\"]\n",
+ "reader = SimpleMongoReader(host, port)\n",
+ "documents = reader.load_data(\n",
+ " db_name, collection_name, field_names, query_dict=query_dict\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "341295df-2029-4728-ab3d-2ee178a7e6f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "01c26b9d-49ec-4a6e-9c61-5c06bb86bbb2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f160c678-2fb5-4d6d-b2bc-87abb61cfdec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/MyScaleReaderDemo.ipynb b/docs/examples/data_connectors/MyScaleReaderDemo.ipynb
index 943c536772..4992e06b84 100644
--- a/docs/examples/data_connectors/MyScaleReaderDemo.ipynb
+++ b/docs/examples/data_connectors/MyScaleReaderDemo.ipynb
@@ -1,129 +1,128 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
- "metadata": {},
- "source": [
- "# MyScale Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "b2bd3c59",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e2f49003-b952-4b9b-b935-2941f9303773",
- "metadata": {},
- "outputs": [],
- "source": [
- "import clickhouse_connect\n",
- "\n",
- "host=\"YOUR_CLUSTER_HOST\"\n",
- "username=\"YOUR_USERNAME\"\n",
- "password=\"YOUR_CLUSTER_PASSWORD\"\n",
- "client = clickhouse_connect.get_client(\n",
- " host=host,\n",
- " port=8443,\n",
- " username=username,\n",
- " password=password\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "252b9918",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Document(text='logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\\n\\n[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\\n\\n[15] I\\'ve never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it\\'s the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\\n\\n[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now.\\n\\n[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you\\'re assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you\\'re present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it\\'s correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance.\\n\\n[18] The worst thing about leaving YC was not working with Jessica anymore. We\\'d been working on YC almost the whole time we\\'d known each other, and we\\'d neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree.\\n\\n[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy\\'s 1960 paper.\\n\\nBut if so there\\'s no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy\\'s Lisp along which discoveredness is preserved.\\n\\n\\n\\nThanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this.\\n\\n\\n\\n', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='f37cfb543bc616db976b338777f74c9b996e792bb1219dfc4b279e52559f7b24', extra_info={'_dummy': 0}),\n",
- " Document(text='\\t\\t\\n\\nWhat I Worked On\\n\\nFebruary 2021\\n\\nBefore college the two main things I worked on, outside of school, were writing and programming. I didn\\'t write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\\n\\nThe first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district\\'s 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain\\'s lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\\n\\nThe language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\\n\\nI was puzzled by the 1401. I couldn\\'t figure out what to do with it. And in retrospect there\\'s not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn\\'t have any data stored on punched cards. The only other option was to do things that didn\\'t rely on any input, like calculate approximations of pi, but I didn\\'t know enough math to do anything interesting of that type. So I\\'m not surprised I can\\'t remember any programs I wrote, because they can\\'t have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn\\'t. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager\\'s expression made clear.\\n\\nWith microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\\n\\nThe first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\\n\\nComputers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he\\'d write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\\n\\nThough I liked programming, I didn\\'t plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn\\'t much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\\n\\nI couldn\\'t have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.\\n\\nAI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven\\'t tried rereading The Moon is a Harsh Mistress, so I don\\'t know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we\\'d have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words.\\n\\nThere weren\\'t any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers\\' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn\\'t happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do.\\n\\nFor my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence.\\n\\nI had gotten into a program at Cornell that didn\\'t make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose \"Artificial Intelligence.\" When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover.\\n\\nI applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I\\'d visited because Rich Draves went there, and was also home to Bill Woods, who\\'d invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went.\\n\\nI don\\'t remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that\\'s told \"the dog is sitting on the chair\" translates this into some formal representation and adds it to the list of things it knows.\\n\\nWhat these programs really showed was that there\\'s a subset of natural language that\\'s a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike.\\n\\nSo I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It\\'s scary to think how little I knew about Lisp hacking when I started writing that book. But there\\'s nothing like writing a book about something to help you learn it. The book, On Lisp, wasn\\'t published till 1993, but I wrote much of it in grad school.\\n\\nComputer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting.\\n\\nThe problem with systems work, though, was that it didn\\'t last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good.\\n\\nThere were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work.\\n\\nI wanted not just to build things, but to build things that would last.\\n\\nIn this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I\\'d spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn\\'t become obsolete. Some of the best ones were hundreds of years old.\\n\\nAnd moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn\\'t have a boss, or even need to get research funding.\\n\\nI had always liked looking at paintings. Could I make them? I had no idea. I\\'d never imagined it was even possible. I knew intellectually that people made art — that it didn\\'t just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous.\\n\\nThat fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything.\\n\\nSo now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis.\\n\\nI didn\\'t see a way out of this situation. I didn\\'t want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he\\'d found such a spectacular way to get out of grad school.\\n\\nThen one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn\\'t have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay \"Yes, I think so. I\\'ll give you something to read in a few days.\"\\n\\nI picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There\\'s a whole world there that\\'s barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely.\\n\\nMeanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went.\\n\\nI\\'d applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design.\\n\\nToward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they\\'d sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I\\'d done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian.\\n\\nOnly stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don\\'t know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2]\\n\\nI\\'m only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn\\'t require the faculty to teach anything, and in return the faculty wouldn\\'t require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\n\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\n\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='a1b2ca0762828e8c50154b978ceacdc648f83a14c9e79b7109237574d811e8b5', extra_info={'_dummy': 0}),\n",
- " Document(text='write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]\\n\\nI\\'ve worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I\\'d always write essays too.\\n\\nI knew that online essays would be a marginal medium at first. Socially they\\'d seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.\\n\\nOne of the most conspicuous patterns I\\'ve noticed in my life is how well it has worked, for me at least, to work on things that weren\\'t prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I\\'m writing, and I explain that it\\'s an essay I\\'m going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.\\n\\nIt\\'s not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it\\'s a sign both that there\\'s something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren\\'t prestigious doesn\\'t guarantee you\\'re on the right track, it at least guarantees you\\'re not on the most common type of wrong one.\\n\\nOver the next several years I wrote lots of essays about all kinds of different topics. O\\'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\\n\\nOne night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn\\'t know but would probably like. One of the guests was someone I didn\\'t know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\\n\\nJessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\\n\\nWhen the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\\n\\nOne of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won\\'t waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they\\'d be able to avoid the worst of the mistakes we\\'d made.\\n\\nSo I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they\\'d be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I\\'d only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I\\'d been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn\\'t done one angel investment.\\n\\nMeanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\\n\\nAs Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We\\'d start our own investment firm and actually implement the ideas we\\'d been talking about. I\\'d fund it, and Jessica could quit her job and work for it, and we\\'d get Robert and Trevor as partners too. [13]\\n\\nOnce again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\\n\\nThere are multiple components to Y Combinator, and we didn\\'t figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn\\'t go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we\\'d been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us.\\n\\nYC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" But once again, this was not due to any particular insight on our part. We didn\\'t know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn\\'t have known where to start. [14]\\n\\nThe most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they\\'d start startups instead? We wouldn\\'t feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn\\'t make much money out of it, we\\'d at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft.\\n\\nWe\\'d use the building I owned in Cambridge as our headquarters. We\\'d all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we\\'d bring in experts on startups to give talks.\\n\\nWe knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get \"deal flow,\" as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who\\'d already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we\\'d intended.\\n\\nWe invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don\\'t think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs.\\n\\nThe deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16]\\n\\nFairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them.\\n\\nAs YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another\\'s customers. We used to refer jokingly to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates.\\n\\nI had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things.\\n\\nIn the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn\\'t startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one\\'s intellectual curiosity.\\n\\nHN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I\\'d had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one\\'s work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]\\n\\nAs well as HN, I wrote all of YC\\'s internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn\\'t have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC.\\n\\nYC was different from other kinds of work I\\'ve done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn\\'t have picked a better way to do it.\\n\\nThere were parts of the job I didn\\'t like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn\\'t like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\" He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='c76a60b0aa4fd700e689c9fa2e22be2cf96fd0c2c1c6c2a189b7d894e332e363', extra_info={'_dummy': 0}),\n",
- " Document(text='YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life\\'s work or I\\'d have to leave eventually. And it wasn\\'t, so I would.\\n\\nIn the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else.\\n\\nI asked Jessica if she wanted to be president, but she didn\\'t, so we decided we\\'d try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn\\'t be controlled by the founders. So if Sam said yes, we\\'d let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners.\\n\\nWhen we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he\\'d take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.\\n\\nShe died on January 15, 2014. We knew this was coming, but it was still hard when it did.\\n\\nI kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I\\'m interested in, but that only takes a few hours a week.)\\n\\nWhat should I do next? Rtm\\'s advice hadn\\'t included anything about that. I wanted to do something completely different, so I decided I\\'d paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]\\n\\nI spent most of the rest of 2014 painting. I\\'d never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I\\'d always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven\\'t painted since. So far anyway.\\n\\nI realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that\\'s not the best one (or at least a good one) for you, then it\\'s getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.\\n\\nI started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren\\'t about startups. Then in March 2015 I started working on Lisp again.\\n\\nThe distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn\\'t originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what\\'s the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19]\\n\\nMcCarthy didn\\'t realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy\\'s interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn\\'t match. It was this that attracted me in college, though I didn\\'t understand why at the time.\\n\\nMcCarthy\\'s 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you\\'d want in a programming language. So these had to be added, and when they were, they weren\\'t defined using McCarthy\\'s original axiomatic approach. That wouldn\\'t have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you\\'d have had to run it, and computers then weren\\'t powerful enough.\\n\\nNow they are, though. Now you could continue using McCarthy\\'s axiomatic approach till you\\'d defined a complete programming language. And as long as every change you made to McCarthy\\'s Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long.\\n\\nI wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it\\'s an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test.\\n\\nI had to ban myself from writing essays during most of this time, or I\\'d never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you\\'re working on an interpreter written in itself, it\\'s hard to keep track of what\\'s happening at what level, and errors can be practically encrypted by the time you get them.\\n\\nSo I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I\\'d ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I\\'d check Twitter or HN and see someone asking \"Does Paul Graham still code?\"\\n\\nWorking on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\\n\\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\\n\\nIn the fall of 2019, Bel was finally finished. Like McCarthy\\'s original Lisp, it\\'s a spec rather than an implementation, although like McCarthy\\'s Lisp it\\'s a spec expressed as code.\\n\\nNow that I could write essays again, I wrote a bunch about topics I\\'d had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who\\'d lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nNotes\\n\\n[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.\\n\\n[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It\\'s the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.\\n\\n[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.\\n\\n[4] You can of course paint people like still lives if you want to, and they\\'re willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.\\n\\n[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore\\'s Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.\\n\\n[6] The signature style seekers at RISD weren\\'t specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.\\n\\n[7] Technically the apartment wasn\\'t rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.\\n\\n[8] Most software you can launch as soon as it\\'s done. But when the software is an online store builder and you\\'re hosting the stores, if you don\\'t have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.\\n\\n[9] We\\'d had a code editor in Viaweb for users to define their own page styles. They didn\\'t know it, but they were editing Lisp expressions underneath. But this wasn\\'t an app editor, because the code ran when the merchants\\' sites were generated, not when shoppers visited them.\\n\\n[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren\\'t they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I\\'m not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don\\'t already know, and some people dislike being told such things.\\n\\n[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.\\n\\n[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs\\' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.\\n\\nWhich in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).\\n\\nHere\\'s an interesting point, though: you can\\'t always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?\\n\\n[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn\\'t want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.\\n\\nI picked orange as our color partly because it\\'s the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\\n\\n[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\\n\\n[15] I\\'ve never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it\\'s the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\\n\\n[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='117cb89093d8096b5a56654f293dd1407185f92de4eb81cf82611339a65a65dd', extra_info={'_dummy': 0}),\n",
- " Document(text='funding to live on.\\n\\nWe originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\\n\\nIt helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\\n\\n(If you\\'re curious why my site looks so old-fashioned, it\\'s because it\\'s still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)\\n\\nIn September, Robert rebelled. \"We\\'ve been working on this for a month,\" he said, \"and it\\'s still not done.\" This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.\\n\\nIt was a lot of fun working with Robert and Trevor. They\\'re the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm\\'s brain it would look like a colonial New England church, and if you could see inside Trevor\\'s it would look like the worst excesses of Austrian Rococo.\\n\\nWe opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]\\n\\nThere were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn\\'t have to integrate with any other software except Robert\\'s and Trevor\\'s, so it was quite fun to work on. If all I\\'d had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.\\n\\nThere were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn\\'t because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.\\n\\nWe did a lot of things right by accident like that. For example, we did what\\'s now called \"doing things that don\\'t scale,\" although at the time we would have described it as \"being so lame that we\\'re driven to the most desperate measures to get users.\" The most common of which was building stores for them. This seemed particularly humiliating, since the whole raison d\\'etre of our software was that people could use it to make their own stores. But anything to get users.\\n\\nWe learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man\\'s shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men\\'s shirts. My first set of scans were so beautiful too.\\n\\nThough this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by \"business\" and thought we needed a \"business person\" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we\\'d have so many users that I couldn\\'t scan their images for them, but in the meantime there was nothing more important to do.\\n\\nAnother thing I didn\\'t get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that\\'s how much money you\\'re making, and if you\\'re not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we\\'d been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you\\'re doing fine. You\\'re growing 7x a year. Just don\\'t hire too many more people and you\\'ll soon be profitable, and then you\\'ll control your own destiny.\\n\\nAlas I hired lots more people, partly because our investors wanted me to, and partly because that\\'s what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn\\'t reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.\\n\\nIt was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn\\'t feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned.\\n\\nThe next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn\\'t realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo\\'s prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf.\\n\\nYahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they\\'d never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I\\'d painted anything that I\\'d half forgotten why I was doing this. My brain had been entirely full of software and men\\'s shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint.\\n\\nWhen I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan.\\n\\nBut I really was quitting to paint, and I started immediately. There was no time to lose. I\\'d already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That\\'s what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me.\\n\\nSo I tried to paint, but I just didn\\'t seem to have any energy or ambition. Part of the problem was that I didn\\'t know many people in California. I\\'d compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you\\'ll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn\\'t know any of them.\\n\\nWhen I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn\\'t been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I\\'d paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn\\'t rotted yet).\\n\\nMeanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn\\'t one. Huh.\\n\\nAround this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.\\n\\nI got so excited about this idea that I couldn\\'t think about anything else. It seemed obvious that this was the future. I didn\\'t particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he\\'d made a lot of money the last time I\\'d lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.\\n\\nHmph. Well, I\\'d do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it\\'s now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn\\'t so naive as to assume I could spring an overt Lisp on a general audience; we\\'d hide the parentheses, like Dylan did.\\n\\nBy then there was a name for the kind of company Viaweb was, an \"application service provider,\" or ASP. This name didn\\'t last long before it was replaced by \"software as a service,\" but it was current for long enough that I named this new company after it: it was going to be called Aspra.\\n\\nI started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn\\'t want to run a company — especially not a big one, which it was looking like this would have to be. I\\'d only started Viaweb because I needed the money. Now that I didn\\'t need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I\\'d build a subset that could be done as an open source project.\\n\\nMuch to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.\\n\\nThe subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn\\'t even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.\\n\\nThe following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we\\'d used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I\\'d created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]\\n\\nWow, I thought, there\\'s an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='5237d41599e80fa3cf87381e7acc7dd5a47a9629f133d33ca9c2c0cc7901952e', extra_info={'_dummy': 0}),\n",
- " Document(text='a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\n\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\n\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can\\'t move. People can\\'t sit for more than about 15 minutes at a time, and when they do they don\\'t sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you\\'re painting. Whereas a still life you can, if you want, copy pixel by pixel from what you\\'re seeing. You don\\'t want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it\\'s been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it\\'s the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4]\\n\\nI liked painting still lives because I was curious about what I was seeing. In everyday life, we aren\\'t consciously aware of much we\\'re seeing. Most visual perception is handled by low-level processes that merely tell your brain \"that\\'s a water droplet\" without telling you details like where the lightest and darkest points are, or \"that\\'s a bush\" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there\\'s a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted.\\n\\nThis is not the only way to paint. I\\'m not 100% sure it\\'s even a good way to paint. But it seemed a good enough bet to be worth trying.\\n\\nOur teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn\\'t teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US.\\n\\nI wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5]\\n\\nInterleaf had done something pretty bold. Inspired by Emacs, they\\'d added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I\\'ve had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn\\'t know C and didn\\'t want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish.\\n\\nThe good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans.\\n\\nI learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it\\'s better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it\\'s depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there\\'s not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it.\\n\\nBut the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it\\'s good to be the \"entry level\" option, even though that will be less prestigious, because if you\\'re not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign.\\n\\nWhen I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life.\\n\\nIn the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style.\\n\\nA signature style is the visual equivalent of what in show business is known as a \"schtick\": something that immediately identifies the work as yours and no one else\\'s. For example, when you see a painting that looks like a certain kind of cartoon, you know it\\'s by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That\\'s not always why artists have a signature style, but it\\'s usually why buyers pay a lot for such work. [6]\\n\\nThere were plenty of earnest students too: kids who \"could draw\" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers.\\n\\nI learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn\\'t much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7]\\n\\nAsterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there\\'s a tiny corner that\\'s not rich, or at least wasn\\'t in 1993. It\\'s called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York.\\n\\nI was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn\\'t want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.)\\n\\nThe best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I\\'d taken her painting class at Harvard. I\\'ve never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant.\\n\\nShe liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn\\'t that much older than me, and was super rich. The thought suddenly occurred to me: why don\\'t I become rich? Then I\\'ll be able to work on whatever I want.\\n\\nMeanwhile I\\'d been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I\\'d seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet.\\n\\nIf I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can\\'t honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn\\'t want to be online, and still don\\'t, not the fancy ones. That\\'s not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us.\\n\\nThen some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we\\'d been generating for galleries. This impressive-sounding thing called an \"internet storefront\" was something we already knew how to build.\\n\\nSo in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we\\'d at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course.\\n\\nWe were working out of Robert\\'s apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we\\'d never have to write anything to run on users\\' computers. We could generate the sites on the same server we\\'d serve them from. Users wouldn\\'t need anything more than a browser.\\n\\nThis kind of software, known as a web app, is common now, but at the time it wasn\\'t clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server.\\n\\nNow we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn\\'t need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server.\\n\\nWe started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle\\'s husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator\\'s. We knew founders needed something like this, because we\\'d needed it ourselves.\\n\\nAt this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I\\'d made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on.\\n\\nWe originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\\n\\nIt helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\\n\\n(If you\\'re curious why my site looks so old-fashioned, it\\'s because it\\'s still made', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='f42a6b9e1a97e222f48f63437428140986ad29027530a07b9d116c993a2ce794', extra_info={'_dummy': 0})]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import random\n",
- "from llama_index.readers.myscale import MyScaleReader\n",
- "\n",
- "reader = MyScaleReader(myscale_host=host, username=username, password=password)\n",
- "reader.load_data([random.random() for _ in range(1536)])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "164482f5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[Document(text='funding to live on.\\n\\nWe originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\\n\\nIt helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\\n\\n(If you\\'re curious why my site looks so old-fashioned, it\\'s because it\\'s still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)\\n\\nIn September, Robert rebelled. \"We\\'ve been working on this for a month,\" he said, \"and it\\'s still not done.\" This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.\\n\\nIt was a lot of fun working with Robert and Trevor. They\\'re the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm\\'s brain it would look like a colonial New England church, and if you could see inside Trevor\\'s it would look like the worst excesses of Austrian Rococo.\\n\\nWe opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]\\n\\nThere were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn\\'t have to integrate with any other software except Robert\\'s and Trevor\\'s, so it was quite fun to work on. If all I\\'d had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.\\n\\nThere were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn\\'t because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.\\n\\nWe did a lot of things right by accident like that. For example, we did what\\'s now called \"doing things that don\\'t scale,\" although at the time we would have described it as \"being so lame that we\\'re driven to the most desperate measures to get users.\" The most common of which was building stores for them. This seemed particularly humiliating, since the whole raison d\\'etre of our software was that people could use it to make their own stores. But anything to get users.\\n\\nWe learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man\\'s shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men\\'s shirts. My first set of scans were so beautiful too.\\n\\nThough this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by \"business\" and thought we needed a \"business person\" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we\\'d have so many users that I couldn\\'t scan their images for them, but in the meantime there was nothing more important to do.\\n\\nAnother thing I didn\\'t get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that\\'s how much money you\\'re making, and if you\\'re not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we\\'d been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you\\'re doing fine. You\\'re growing 7x a year. Just don\\'t hire too many more people and you\\'ll soon be profitable, and then you\\'ll control your own destiny.\\n\\nAlas I hired lots more people, partly because our investors wanted me to, and partly because that\\'s what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn\\'t reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.\\n\\nIt was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn\\'t feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned.\\n\\nThe next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn\\'t realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo\\'s prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf.\\n\\nYahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they\\'d never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I\\'d painted anything that I\\'d half forgotten why I was doing this. My brain had been entirely full of software and men\\'s shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint.\\n\\nWhen I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan.\\n\\nBut I really was quitting to paint, and I started immediately. There was no time to lose. I\\'d already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That\\'s what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me.\\n\\nSo I tried to paint, but I just didn\\'t seem to have any energy or ambition. Part of the problem was that I didn\\'t know many people in California. I\\'d compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you\\'ll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn\\'t know any of them.\\n\\nWhen I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn\\'t been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I\\'d paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn\\'t rotted yet).\\n\\nMeanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn\\'t one. Huh.\\n\\nAround this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.\\n\\nI got so excited about this idea that I couldn\\'t think about anything else. It seemed obvious that this was the future. I didn\\'t particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he\\'d made a lot of money the last time I\\'d lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.\\n\\nHmph. Well, I\\'d do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it\\'s now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn\\'t so naive as to assume I could spring an overt Lisp on a general audience; we\\'d hide the parentheses, like Dylan did.\\n\\nBy then there was a name for the kind of company Viaweb was, an \"application service provider,\" or ASP. This name didn\\'t last long before it was replaced by \"software as a service,\" but it was current for long enough that I named this new company after it: it was going to be called Aspra.\\n\\nI started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn\\'t want to run a company — especially not a big one, which it was looking like this would have to be. I\\'d only started Viaweb because I needed the money. Now that I didn\\'t need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I\\'d build a subset that could be done as an open source project.\\n\\nMuch to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.\\n\\nThe subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn\\'t even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.\\n\\nThe following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we\\'d used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I\\'d created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]\\n\\nWow, I thought, there\\'s an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='5237d41599e80fa3cf87381e7acc7dd5a47a9629f133d33ca9c2c0cc7901952e', extra_info={'_dummy': 0}),\n",
- " Document(text='write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]\\n\\nI\\'ve worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I\\'d always write essays too.\\n\\nI knew that online essays would be a marginal medium at first. Socially they\\'d seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.\\n\\nOne of the most conspicuous patterns I\\'ve noticed in my life is how well it has worked, for me at least, to work on things that weren\\'t prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I\\'m writing, and I explain that it\\'s an essay I\\'m going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.\\n\\nIt\\'s not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it\\'s a sign both that there\\'s something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren\\'t prestigious doesn\\'t guarantee you\\'re on the right track, it at least guarantees you\\'re not on the most common type of wrong one.\\n\\nOver the next several years I wrote lots of essays about all kinds of different topics. O\\'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\\n\\nOne night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn\\'t know but would probably like. One of the guests was someone I didn\\'t know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\\n\\nJessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\\n\\nWhen the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\\n\\nOne of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won\\'t waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they\\'d be able to avoid the worst of the mistakes we\\'d made.\\n\\nSo I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they\\'d be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I\\'d only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I\\'d been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn\\'t done one angel investment.\\n\\nMeanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\\n\\nAs Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We\\'d start our own investment firm and actually implement the ideas we\\'d been talking about. I\\'d fund it, and Jessica could quit her job and work for it, and we\\'d get Robert and Trevor as partners too. [13]\\n\\nOnce again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\\n\\nThere are multiple components to Y Combinator, and we didn\\'t figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn\\'t go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we\\'d been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us.\\n\\nYC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" But once again, this was not due to any particular insight on our part. We didn\\'t know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn\\'t have known where to start. [14]\\n\\nThe most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they\\'d start startups instead? We wouldn\\'t feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn\\'t make much money out of it, we\\'d at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft.\\n\\nWe\\'d use the building I owned in Cambridge as our headquarters. We\\'d all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we\\'d bring in experts on startups to give talks.\\n\\nWe knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get \"deal flow,\" as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who\\'d already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we\\'d intended.\\n\\nWe invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don\\'t think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs.\\n\\nThe deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16]\\n\\nFairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them.\\n\\nAs YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another\\'s customers. We used to refer jokingly to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates.\\n\\nI had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things.\\n\\nIn the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn\\'t startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one\\'s intellectual curiosity.\\n\\nHN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I\\'d had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one\\'s work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]\\n\\nAs well as HN, I wrote all of YC\\'s internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn\\'t have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC.\\n\\nYC was different from other kinds of work I\\'ve done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn\\'t have picked a better way to do it.\\n\\nThere were parts of the job I didn\\'t like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn\\'t like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\" He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='c76a60b0aa4fd700e689c9fa2e22be2cf96fd0c2c1c6c2a189b7d894e332e363', extra_info={'_dummy': 0}),\n",
- " Document(text='YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life\\'s work or I\\'d have to leave eventually. And it wasn\\'t, so I would.\\n\\nIn the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else.\\n\\nI asked Jessica if she wanted to be president, but she didn\\'t, so we decided we\\'d try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn\\'t be controlled by the founders. So if Sam said yes, we\\'d let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners.\\n\\nWhen we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he\\'d take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.\\n\\nShe died on January 15, 2014. We knew this was coming, but it was still hard when it did.\\n\\nI kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I\\'m interested in, but that only takes a few hours a week.)\\n\\nWhat should I do next? Rtm\\'s advice hadn\\'t included anything about that. I wanted to do something completely different, so I decided I\\'d paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]\\n\\nI spent most of the rest of 2014 painting. I\\'d never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I\\'d always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven\\'t painted since. So far anyway.\\n\\nI realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that\\'s not the best one (or at least a good one) for you, then it\\'s getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.\\n\\nI started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren\\'t about startups. Then in March 2015 I started working on Lisp again.\\n\\nThe distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn\\'t originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what\\'s the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19]\\n\\nMcCarthy didn\\'t realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy\\'s interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn\\'t match. It was this that attracted me in college, though I didn\\'t understand why at the time.\\n\\nMcCarthy\\'s 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you\\'d want in a programming language. So these had to be added, and when they were, they weren\\'t defined using McCarthy\\'s original axiomatic approach. That wouldn\\'t have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you\\'d have had to run it, and computers then weren\\'t powerful enough.\\n\\nNow they are, though. Now you could continue using McCarthy\\'s axiomatic approach till you\\'d defined a complete programming language. And as long as every change you made to McCarthy\\'s Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long.\\n\\nI wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it\\'s an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test.\\n\\nI had to ban myself from writing essays during most of this time, or I\\'d never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you\\'re working on an interpreter written in itself, it\\'s hard to keep track of what\\'s happening at what level, and errors can be practically encrypted by the time you get them.\\n\\nSo I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I\\'d ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I\\'d check Twitter or HN and see someone asking \"Does Paul Graham still code?\"\\n\\nWorking on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\\n\\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\\n\\nIn the fall of 2019, Bel was finally finished. Like McCarthy\\'s original Lisp, it\\'s a spec rather than an implementation, although like McCarthy\\'s Lisp it\\'s a spec expressed as code.\\n\\nNow that I could write essays again, I wrote a bunch about topics I\\'d had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who\\'d lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nNotes\\n\\n[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.\\n\\n[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It\\'s the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.\\n\\n[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.\\n\\n[4] You can of course paint people like still lives if you want to, and they\\'re willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.\\n\\n[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore\\'s Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.\\n\\n[6] The signature style seekers at RISD weren\\'t specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.\\n\\n[7] Technically the apartment wasn\\'t rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.\\n\\n[8] Most software you can launch as soon as it\\'s done. But when the software is an online store builder and you\\'re hosting the stores, if you don\\'t have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.\\n\\n[9] We\\'d had a code editor in Viaweb for users to define their own page styles. They didn\\'t know it, but they were editing Lisp expressions underneath. But this wasn\\'t an app editor, because the code ran when the merchants\\' sites were generated, not when shoppers visited them.\\n\\n[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren\\'t they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I\\'m not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don\\'t already know, and some people dislike being told such things.\\n\\n[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.\\n\\n[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs\\' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.\\n\\nWhich in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).\\n\\nHere\\'s an interesting point, though: you can\\'t always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?\\n\\n[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn\\'t want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.\\n\\nI picked orange as our color partly because it\\'s the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\\n\\n[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\\n\\n[15] I\\'ve never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it\\'s the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\\n\\n[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='117cb89093d8096b5a56654f293dd1407185f92de4eb81cf82611339a65a65dd', extra_info={'_dummy': 0})]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "reader.load_data([random.random() for _ in range(1536)], where_str=\"extra_info._dummy=0\", limit=3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ee8dd789",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
+ "metadata": {},
+ "source": [
+ "# MyScale Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b2bd3c59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2f49003-b952-4b9b-b935-2941f9303773",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import clickhouse_connect\n",
+ "\n",
+ "host = \"YOUR_CLUSTER_HOST\"\n",
+ "username = \"YOUR_USERNAME\"\n",
+ "password = \"YOUR_CLUSTER_PASSWORD\"\n",
+ "client = clickhouse_connect.get_client(\n",
+ " host=host, port=8443, username=username, password=password\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
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+ "metadata": {},
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+ "[Document(text='logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\\n\\n[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\\n\\n[15] I\\'ve never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it\\'s the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\\n\\n[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now.\\n\\n[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you\\'re assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you\\'re present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it\\'s correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance.\\n\\n[18] The worst thing about leaving YC was not working with Jessica anymore. We\\'d been working on YC almost the whole time we\\'d known each other, and we\\'d neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree.\\n\\n[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy\\'s 1960 paper.\\n\\nBut if so there\\'s no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy\\'s Lisp along which discoveredness is preserved.\\n\\n\\n\\nThanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this.\\n\\n\\n\\n', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='f37cfb543bc616db976b338777f74c9b996e792bb1219dfc4b279e52559f7b24', extra_info={'_dummy': 0}),\n",
+ " Document(text='\\t\\t\\n\\nWhat I Worked On\\n\\nFebruary 2021\\n\\nBefore college the two main things I worked on, outside of school, were writing and programming. I didn\\'t write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\\n\\nThe first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district\\'s 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain\\'s lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\\n\\nThe language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.\\n\\nI was puzzled by the 1401. I couldn\\'t figure out what to do with it. And in retrospect there\\'s not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn\\'t have any data stored on punched cards. The only other option was to do things that didn\\'t rely on any input, like calculate approximations of pi, but I didn\\'t know enough math to do anything interesting of that type. So I\\'m not surprised I can\\'t remember any programs I wrote, because they can\\'t have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn\\'t. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager\\'s expression made clear.\\n\\nWith microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]\\n\\nThe first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.\\n\\nComputers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he\\'d write 2 pages at a time and then print them out, but it was a lot better than a typewriter.\\n\\nThough I liked programming, I didn\\'t plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn\\'t much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.\\n\\nI couldn\\'t have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.\\n\\nAI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven\\'t tried rereading The Moon is a Harsh Mistress, so I don\\'t know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we\\'d have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words.\\n\\nThere weren\\'t any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers\\' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn\\'t happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do.\\n\\nFor my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence.\\n\\nI had gotten into a program at Cornell that didn\\'t make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose \"Artificial Intelligence.\" When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover.\\n\\nI applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I\\'d visited because Rich Draves went there, and was also home to Bill Woods, who\\'d invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went.\\n\\nI don\\'t remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that\\'s told \"the dog is sitting on the chair\" translates this into some formal representation and adds it to the list of things it knows.\\n\\nWhat these programs really showed was that there\\'s a subset of natural language that\\'s a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike.\\n\\nSo I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It\\'s scary to think how little I knew about Lisp hacking when I started writing that book. But there\\'s nothing like writing a book about something to help you learn it. The book, On Lisp, wasn\\'t published till 1993, but I wrote much of it in grad school.\\n\\nComputer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting.\\n\\nThe problem with systems work, though, was that it didn\\'t last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good.\\n\\nThere were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work.\\n\\nI wanted not just to build things, but to build things that would last.\\n\\nIn this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I\\'d spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn\\'t become obsolete. Some of the best ones were hundreds of years old.\\n\\nAnd moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn\\'t have a boss, or even need to get research funding.\\n\\nI had always liked looking at paintings. Could I make them? I had no idea. I\\'d never imagined it was even possible. I knew intellectually that people made art — that it didn\\'t just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous.\\n\\nThat fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything.\\n\\nSo now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis.\\n\\nI didn\\'t see a way out of this situation. I didn\\'t want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he\\'d found such a spectacular way to get out of grad school.\\n\\nThen one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn\\'t have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay \"Yes, I think so. I\\'ll give you something to read in a few days.\"\\n\\nI picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There\\'s a whole world there that\\'s barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely.\\n\\nMeanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went.\\n\\nI\\'d applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design.\\n\\nToward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they\\'d sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I\\'d done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian.\\n\\nOnly stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don\\'t know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2]\\n\\nI\\'m only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn\\'t require the faculty to teach anything, and in return the faculty wouldn\\'t require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\n\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\n\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='a1b2ca0762828e8c50154b978ceacdc648f83a14c9e79b7109237574d811e8b5', extra_info={'_dummy': 0}),\n",
+ " Document(text='write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]\\n\\nI\\'ve worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I\\'d always write essays too.\\n\\nI knew that online essays would be a marginal medium at first. Socially they\\'d seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.\\n\\nOne of the most conspicuous patterns I\\'ve noticed in my life is how well it has worked, for me at least, to work on things that weren\\'t prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I\\'m writing, and I explain that it\\'s an essay I\\'m going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.\\n\\nIt\\'s not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it\\'s a sign both that there\\'s something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren\\'t prestigious doesn\\'t guarantee you\\'re on the right track, it at least guarantees you\\'re not on the most common type of wrong one.\\n\\nOver the next several years I wrote lots of essays about all kinds of different topics. O\\'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\\n\\nOne night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn\\'t know but would probably like. One of the guests was someone I didn\\'t know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\\n\\nJessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\\n\\nWhen the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\\n\\nOne of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won\\'t waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they\\'d be able to avoid the worst of the mistakes we\\'d made.\\n\\nSo I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they\\'d be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I\\'d only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I\\'d been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn\\'t done one angel investment.\\n\\nMeanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\\n\\nAs Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We\\'d start our own investment firm and actually implement the ideas we\\'d been talking about. I\\'d fund it, and Jessica could quit her job and work for it, and we\\'d get Robert and Trevor as partners too. [13]\\n\\nOnce again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\\n\\nThere are multiple components to Y Combinator, and we didn\\'t figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn\\'t go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we\\'d been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us.\\n\\nYC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" But once again, this was not due to any particular insight on our part. We didn\\'t know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn\\'t have known where to start. [14]\\n\\nThe most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they\\'d start startups instead? We wouldn\\'t feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn\\'t make much money out of it, we\\'d at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft.\\n\\nWe\\'d use the building I owned in Cambridge as our headquarters. We\\'d all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we\\'d bring in experts on startups to give talks.\\n\\nWe knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get \"deal flow,\" as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who\\'d already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we\\'d intended.\\n\\nWe invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don\\'t think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs.\\n\\nThe deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16]\\n\\nFairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them.\\n\\nAs YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another\\'s customers. We used to refer jokingly to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates.\\n\\nI had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things.\\n\\nIn the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn\\'t startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one\\'s intellectual curiosity.\\n\\nHN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I\\'d had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one\\'s work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]\\n\\nAs well as HN, I wrote all of YC\\'s internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn\\'t have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC.\\n\\nYC was different from other kinds of work I\\'ve done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn\\'t have picked a better way to do it.\\n\\nThere were parts of the job I didn\\'t like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn\\'t like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\" He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='c76a60b0aa4fd700e689c9fa2e22be2cf96fd0c2c1c6c2a189b7d894e332e363', extra_info={'_dummy': 0}),\n",
+ " Document(text='YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life\\'s work or I\\'d have to leave eventually. And it wasn\\'t, so I would.\\n\\nIn the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else.\\n\\nI asked Jessica if she wanted to be president, but she didn\\'t, so we decided we\\'d try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn\\'t be controlled by the founders. So if Sam said yes, we\\'d let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners.\\n\\nWhen we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he\\'d take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.\\n\\nShe died on January 15, 2014. We knew this was coming, but it was still hard when it did.\\n\\nI kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I\\'m interested in, but that only takes a few hours a week.)\\n\\nWhat should I do next? Rtm\\'s advice hadn\\'t included anything about that. I wanted to do something completely different, so I decided I\\'d paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]\\n\\nI spent most of the rest of 2014 painting. I\\'d never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I\\'d always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven\\'t painted since. So far anyway.\\n\\nI realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that\\'s not the best one (or at least a good one) for you, then it\\'s getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.\\n\\nI started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren\\'t about startups. Then in March 2015 I started working on Lisp again.\\n\\nThe distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn\\'t originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what\\'s the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19]\\n\\nMcCarthy didn\\'t realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy\\'s interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn\\'t match. It was this that attracted me in college, though I didn\\'t understand why at the time.\\n\\nMcCarthy\\'s 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you\\'d want in a programming language. So these had to be added, and when they were, they weren\\'t defined using McCarthy\\'s original axiomatic approach. That wouldn\\'t have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you\\'d have had to run it, and computers then weren\\'t powerful enough.\\n\\nNow they are, though. Now you could continue using McCarthy\\'s axiomatic approach till you\\'d defined a complete programming language. And as long as every change you made to McCarthy\\'s Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long.\\n\\nI wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it\\'s an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test.\\n\\nI had to ban myself from writing essays during most of this time, or I\\'d never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you\\'re working on an interpreter written in itself, it\\'s hard to keep track of what\\'s happening at what level, and errors can be practically encrypted by the time you get them.\\n\\nSo I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I\\'d ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I\\'d check Twitter or HN and see someone asking \"Does Paul Graham still code?\"\\n\\nWorking on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\\n\\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\\n\\nIn the fall of 2019, Bel was finally finished. Like McCarthy\\'s original Lisp, it\\'s a spec rather than an implementation, although like McCarthy\\'s Lisp it\\'s a spec expressed as code.\\n\\nNow that I could write essays again, I wrote a bunch about topics I\\'d had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who\\'d lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nNotes\\n\\n[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.\\n\\n[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It\\'s the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.\\n\\n[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.\\n\\n[4] You can of course paint people like still lives if you want to, and they\\'re willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.\\n\\n[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore\\'s Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.\\n\\n[6] The signature style seekers at RISD weren\\'t specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.\\n\\n[7] Technically the apartment wasn\\'t rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.\\n\\n[8] Most software you can launch as soon as it\\'s done. But when the software is an online store builder and you\\'re hosting the stores, if you don\\'t have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.\\n\\n[9] We\\'d had a code editor in Viaweb for users to define their own page styles. They didn\\'t know it, but they were editing Lisp expressions underneath. But this wasn\\'t an app editor, because the code ran when the merchants\\' sites were generated, not when shoppers visited them.\\n\\n[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren\\'t they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I\\'m not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don\\'t already know, and some people dislike being told such things.\\n\\n[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.\\n\\n[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs\\' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.\\n\\nWhich in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).\\n\\nHere\\'s an interesting point, though: you can\\'t always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?\\n\\n[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn\\'t want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.\\n\\nI picked orange as our color partly because it\\'s the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\\n\\n[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\\n\\n[15] I\\'ve never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it\\'s the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\\n\\n[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='117cb89093d8096b5a56654f293dd1407185f92de4eb81cf82611339a65a65dd', extra_info={'_dummy': 0}),\n",
+ " Document(text='funding to live on.\\n\\nWe originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\\n\\nIt helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\\n\\n(If you\\'re curious why my site looks so old-fashioned, it\\'s because it\\'s still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)\\n\\nIn September, Robert rebelled. \"We\\'ve been working on this for a month,\" he said, \"and it\\'s still not done.\" This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.\\n\\nIt was a lot of fun working with Robert and Trevor. They\\'re the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm\\'s brain it would look like a colonial New England church, and if you could see inside Trevor\\'s it would look like the worst excesses of Austrian Rococo.\\n\\nWe opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]\\n\\nThere were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn\\'t have to integrate with any other software except Robert\\'s and Trevor\\'s, so it was quite fun to work on. If all I\\'d had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.\\n\\nThere were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn\\'t because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.\\n\\nWe did a lot of things right by accident like that. For example, we did what\\'s now called \"doing things that don\\'t scale,\" although at the time we would have described it as \"being so lame that we\\'re driven to the most desperate measures to get users.\" The most common of which was building stores for them. This seemed particularly humiliating, since the whole raison d\\'etre of our software was that people could use it to make their own stores. But anything to get users.\\n\\nWe learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man\\'s shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men\\'s shirts. My first set of scans were so beautiful too.\\n\\nThough this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by \"business\" and thought we needed a \"business person\" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we\\'d have so many users that I couldn\\'t scan their images for them, but in the meantime there was nothing more important to do.\\n\\nAnother thing I didn\\'t get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that\\'s how much money you\\'re making, and if you\\'re not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we\\'d been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you\\'re doing fine. You\\'re growing 7x a year. Just don\\'t hire too many more people and you\\'ll soon be profitable, and then you\\'ll control your own destiny.\\n\\nAlas I hired lots more people, partly because our investors wanted me to, and partly because that\\'s what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn\\'t reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.\\n\\nIt was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn\\'t feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned.\\n\\nThe next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn\\'t realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo\\'s prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf.\\n\\nYahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they\\'d never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I\\'d painted anything that I\\'d half forgotten why I was doing this. My brain had been entirely full of software and men\\'s shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint.\\n\\nWhen I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan.\\n\\nBut I really was quitting to paint, and I started immediately. There was no time to lose. I\\'d already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That\\'s what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me.\\n\\nSo I tried to paint, but I just didn\\'t seem to have any energy or ambition. Part of the problem was that I didn\\'t know many people in California. I\\'d compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you\\'ll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn\\'t know any of them.\\n\\nWhen I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn\\'t been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I\\'d paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn\\'t rotted yet).\\n\\nMeanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn\\'t one. Huh.\\n\\nAround this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.\\n\\nI got so excited about this idea that I couldn\\'t think about anything else. It seemed obvious that this was the future. I didn\\'t particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he\\'d made a lot of money the last time I\\'d lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.\\n\\nHmph. Well, I\\'d do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it\\'s now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn\\'t so naive as to assume I could spring an overt Lisp on a general audience; we\\'d hide the parentheses, like Dylan did.\\n\\nBy then there was a name for the kind of company Viaweb was, an \"application service provider,\" or ASP. This name didn\\'t last long before it was replaced by \"software as a service,\" but it was current for long enough that I named this new company after it: it was going to be called Aspra.\\n\\nI started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn\\'t want to run a company — especially not a big one, which it was looking like this would have to be. I\\'d only started Viaweb because I needed the money. Now that I didn\\'t need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I\\'d build a subset that could be done as an open source project.\\n\\nMuch to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.\\n\\nThe subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn\\'t even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.\\n\\nThe following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we\\'d used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I\\'d created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]\\n\\nWow, I thought, there\\'s an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='5237d41599e80fa3cf87381e7acc7dd5a47a9629f133d33ca9c2c0cc7901952e', extra_info={'_dummy': 0}),\n",
+ " Document(text='a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they\\'d seen in American art magazines.\\n\\nOur model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She\\'d copy an obscure old painting out of a book, and then he\\'d take the copy and maltreat it to make it look old. [3]\\n\\nWhile I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can\\'t move. People can\\'t sit for more than about 15 minutes at a time, and when they do they don\\'t sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you\\'re painting. Whereas a still life you can, if you want, copy pixel by pixel from what you\\'re seeing. You don\\'t want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it\\'s been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it\\'s the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4]\\n\\nI liked painting still lives because I was curious about what I was seeing. In everyday life, we aren\\'t consciously aware of much we\\'re seeing. Most visual perception is handled by low-level processes that merely tell your brain \"that\\'s a water droplet\" without telling you details like where the lightest and darkest points are, or \"that\\'s a bush\" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there\\'s a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted.\\n\\nThis is not the only way to paint. I\\'m not 100% sure it\\'s even a good way to paint. But it seemed a good enough bet to be worth trying.\\n\\nOur teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn\\'t teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US.\\n\\nI wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5]\\n\\nInterleaf had done something pretty bold. Inspired by Emacs, they\\'d added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I\\'ve had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn\\'t know C and didn\\'t want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish.\\n\\nThe good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans.\\n\\nI learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it\\'s better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it\\'s depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there\\'s not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it.\\n\\nBut the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it\\'s good to be the \"entry level\" option, even though that will be less prestigious, because if you\\'re not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign.\\n\\nWhen I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life.\\n\\nIn the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style.\\n\\nA signature style is the visual equivalent of what in show business is known as a \"schtick\": something that immediately identifies the work as yours and no one else\\'s. For example, when you see a painting that looks like a certain kind of cartoon, you know it\\'s by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That\\'s not always why artists have a signature style, but it\\'s usually why buyers pay a lot for such work. [6]\\n\\nThere were plenty of earnest students too: kids who \"could draw\" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers.\\n\\nI learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn\\'t much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7]\\n\\nAsterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there\\'s a tiny corner that\\'s not rich, or at least wasn\\'t in 1993. It\\'s called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York.\\n\\nI was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn\\'t want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.)\\n\\nThe best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I\\'d taken her painting class at Harvard. I\\'ve never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant.\\n\\nShe liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn\\'t that much older than me, and was super rich. The thought suddenly occurred to me: why don\\'t I become rich? Then I\\'ll be able to work on whatever I want.\\n\\nMeanwhile I\\'d been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I\\'d seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet.\\n\\nIf I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can\\'t honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn\\'t want to be online, and still don\\'t, not the fancy ones. That\\'s not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us.\\n\\nThen some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we\\'d been generating for galleries. This impressive-sounding thing called an \"internet storefront\" was something we already knew how to build.\\n\\nSo in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we\\'d at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course.\\n\\nWe were working out of Robert\\'s apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we\\'d never have to write anything to run on users\\' computers. We could generate the sites on the same server we\\'d serve them from. Users wouldn\\'t need anything more than a browser.\\n\\nThis kind of software, known as a web app, is common now, but at the time it wasn\\'t clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server.\\n\\nNow we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn\\'t need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server.\\n\\nWe started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle\\'s husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator\\'s. We knew founders needed something like this, because we\\'d needed it ourselves.\\n\\nAt this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I\\'d made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on.\\n\\nWe originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\\n\\nIt helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\\n\\n(If you\\'re curious why my site looks so old-fashioned, it\\'s because it\\'s still made', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='f42a6b9e1a97e222f48f63437428140986ad29027530a07b9d116c993a2ce794', extra_info={'_dummy': 0})]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import random\n",
+ "from llama_index.readers.myscale import MyScaleReader\n",
+ "\n",
+ "reader = MyScaleReader(myscale_host=host, username=username, password=password)\n",
+ "reader.load_data([random.random() for _ in range(1536)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "164482f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Document(text='funding to live on.\\n\\nWe originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server.\\n\\nIt helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company.\\n\\n(If you\\'re curious why my site looks so old-fashioned, it\\'s because it\\'s still made with this software. It may look clunky today, but in 1996 it was the last word in slick.)\\n\\nIn September, Robert rebelled. \"We\\'ve been working on this for a month,\" he said, \"and it\\'s still not done.\" This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker.\\n\\nIt was a lot of fun working with Robert and Trevor. They\\'re the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm\\'s brain it would look like a colonial New England church, and if you could see inside Trevor\\'s it would look like the worst excesses of Austrian Rococo.\\n\\nWe opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8]\\n\\nThere were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn\\'t have to integrate with any other software except Robert\\'s and Trevor\\'s, so it was quite fun to work on. If all I\\'d had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful.\\n\\nThere were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn\\'t because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us.\\n\\nWe did a lot of things right by accident like that. For example, we did what\\'s now called \"doing things that don\\'t scale,\" although at the time we would have described it as \"being so lame that we\\'re driven to the most desperate measures to get users.\" The most common of which was building stores for them. This seemed particularly humiliating, since the whole raison d\\'etre of our software was that people could use it to make their own stores. But anything to get users.\\n\\nWe learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man\\'s shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men\\'s shirts. My first set of scans were so beautiful too.\\n\\nThough this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by \"business\" and thought we needed a \"business person\" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we\\'d have so many users that I couldn\\'t scan their images for them, but in the meantime there was nothing more important to do.\\n\\nAnother thing I didn\\'t get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that\\'s how much money you\\'re making, and if you\\'re not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we\\'d been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you\\'re doing fine. You\\'re growing 7x a year. Just don\\'t hire too many more people and you\\'ll soon be profitable, and then you\\'ll control your own destiny.\\n\\nAlas I hired lots more people, partly because our investors wanted me to, and partly because that\\'s what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn\\'t reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards.\\n\\nIt was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn\\'t feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned.\\n\\nThe next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn\\'t realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo\\'s prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf.\\n\\nYahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they\\'d never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I\\'d painted anything that I\\'d half forgotten why I was doing this. My brain had been entirely full of software and men\\'s shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint.\\n\\nWhen I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan.\\n\\nBut I really was quitting to paint, and I started immediately. There was no time to lose. I\\'d already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That\\'s what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me.\\n\\nSo I tried to paint, but I just didn\\'t seem to have any energy or ambition. Part of the problem was that I didn\\'t know many people in California. I\\'d compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you\\'ll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn\\'t know any of them.\\n\\nWhen I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn\\'t been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I\\'d paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn\\'t rotted yet).\\n\\nMeanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn\\'t one. Huh.\\n\\nAround this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc.\\n\\nI got so excited about this idea that I couldn\\'t think about anything else. It seemed obvious that this was the future. I didn\\'t particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he\\'d made a lot of money the last time I\\'d lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it.\\n\\nHmph. Well, I\\'d do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it\\'s now clear is about twenty companies and several open source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn\\'t so naive as to assume I could spring an overt Lisp on a general audience; we\\'d hide the parentheses, like Dylan did.\\n\\nBy then there was a name for the kind of company Viaweb was, an \"application service provider,\" or ASP. This name didn\\'t last long before it was replaced by \"software as a service,\" but it was current for long enough that I named this new company after it: it was going to be called Aspra.\\n\\nI started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn\\'t want to run a company — especially not a big one, which it was looking like this would have to be. I\\'d only started Viaweb because I needed the money. Now that I didn\\'t need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I\\'d build a subset that could be done as an open source project.\\n\\nMuch to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it.\\n\\nThe subset I would build as an open source project was the new Lisp, whose parentheses I now wouldn\\'t even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge.\\n\\nThe following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we\\'d used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I\\'d created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10]\\n\\nWow, I thought, there\\'s an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='5237d41599e80fa3cf87381e7acc7dd5a47a9629f133d33ca9c2c0cc7901952e', extra_info={'_dummy': 0}),\n",
+ " Document(text='write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything.\\n\\nThis had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11]\\n\\nIn the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12]\\n\\nI\\'ve worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I\\'d always write essays too.\\n\\nI knew that online essays would be a marginal medium at first. Socially they\\'d seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging.\\n\\nOne of the most conspicuous patterns I\\'ve noticed in my life is how well it has worked, for me at least, to work on things that weren\\'t prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I\\'m writing, and I explain that it\\'s an essay I\\'m going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip.\\n\\nIt\\'s not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it\\'s a sign both that there\\'s something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren\\'t prestigious doesn\\'t guarantee you\\'re on the right track, it at least guarantees you\\'re not on the most common type of wrong one.\\n\\nOver the next several years I wrote lots of essays about all kinds of different topics. O\\'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office.\\n\\nOne night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn\\'t know but would probably like. One of the guests was someone I didn\\'t know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out.\\n\\nJessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders.\\n\\nWhen the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on.\\n\\nOne of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won\\'t waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they\\'d be able to avoid the worst of the mistakes we\\'d made.\\n\\nSo I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they\\'d be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I\\'d only known), I blurted out \"But not me!\" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I\\'d been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn\\'t done one angel investment.\\n\\nMeanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on.\\n\\nAs Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We\\'d start our own investment firm and actually implement the ideas we\\'d been talking about. I\\'d fund it, and Jessica could quit her job and work for it, and we\\'d get Robert and Trevor as partners too. [13]\\n\\nOnce again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel.\\n\\nThere are multiple components to Y Combinator, and we didn\\'t figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn\\'t go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we\\'d been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us.\\n\\nYC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking \"Wow, that means they got all the returns.\" But once again, this was not due to any particular insight on our part. We didn\\'t know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn\\'t have known where to start. [14]\\n\\nThe most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they\\'d start startups instead? We wouldn\\'t feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn\\'t make much money out of it, we\\'d at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft.\\n\\nWe\\'d use the building I owned in Cambridge as our headquarters. We\\'d all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we\\'d bring in experts on startups to give talks.\\n\\nWe knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get \"deal flow,\" as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who\\'d already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we\\'d intended.\\n\\nWe invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don\\'t think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs.\\n\\nThe deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16]\\n\\nFairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them.\\n\\nAs YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another\\'s customers. We used to refer jokingly to the \"YC GDP,\" but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates.\\n\\nI had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things.\\n\\nIn the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn\\'t startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one\\'s intellectual curiosity.\\n\\nHN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I\\'d had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one\\'s work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]\\n\\nAs well as HN, I wrote all of YC\\'s internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn\\'t have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC.\\n\\nYC was different from other kinds of work I\\'ve done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn\\'t have picked a better way to do it.\\n\\nThere were parts of the job I didn\\'t like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn\\'t like. I was haunted by something Kevin Hale once said about companies: \"No one works harder than the boss.\" He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='c76a60b0aa4fd700e689c9fa2e22be2cf96fd0c2c1c6c2a189b7d894e332e363', extra_info={'_dummy': 0}),\n",
+ " Document(text='YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I\\'d better work very hard.\\n\\nOne day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. \"You know,\" he said, \"you should make sure Y Combinator isn\\'t the last cool thing you do.\"\\n\\nAt the time I didn\\'t understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life\\'s work or I\\'d have to leave eventually. And it wasn\\'t, so I would.\\n\\nIn the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else.\\n\\nI asked Jessica if she wanted to be president, but she didn\\'t, so we decided we\\'d try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn\\'t be controlled by the founders. So if Sam said yes, we\\'d let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners.\\n\\nWhen we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he\\'d take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned.\\n\\nShe died on January 15, 2014. We knew this was coming, but it was still hard when it did.\\n\\nI kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I\\'m interested in, but that only takes a few hours a week.)\\n\\nWhat should I do next? Rtm\\'s advice hadn\\'t included anything about that. I wanted to do something completely different, so I decided I\\'d paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18]\\n\\nI spent most of the rest of 2014 painting. I\\'d never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I\\'d always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven\\'t painted since. So far anyway.\\n\\nI realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that\\'s not the best one (or at least a good one) for you, then it\\'s getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around.\\n\\nI started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren\\'t about startups. Then in March 2015 I started working on Lisp again.\\n\\nThe distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn\\'t originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what\\'s the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19]\\n\\nMcCarthy didn\\'t realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy\\'s interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn\\'t match. It was this that attracted me in college, though I didn\\'t understand why at the time.\\n\\nMcCarthy\\'s 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you\\'d want in a programming language. So these had to be added, and when they were, they weren\\'t defined using McCarthy\\'s original axiomatic approach. That wouldn\\'t have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you\\'d have had to run it, and computers then weren\\'t powerful enough.\\n\\nNow they are, though. Now you could continue using McCarthy\\'s axiomatic approach till you\\'d defined a complete programming language. And as long as every change you made to McCarthy\\'s Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long.\\n\\nI wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it\\'s an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test.\\n\\nI had to ban myself from writing essays during most of this time, or I\\'d never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you\\'re working on an interpreter written in itself, it\\'s hard to keep track of what\\'s happening at what level, and errors can be practically encrypted by the time you get them.\\n\\nSo I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I\\'d ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I\\'d check Twitter or HN and see someone asking \"Does Paul Graham still code?\"\\n\\nWorking on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years.\\n\\nIn the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England.\\n\\nIn the fall of 2019, Bel was finally finished. Like McCarthy\\'s original Lisp, it\\'s a spec rather than an implementation, although like McCarthy\\'s Lisp it\\'s a spec expressed as code.\\n\\nNow that I could write essays again, I wrote a bunch about topics I\\'d had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who\\'d lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nNotes\\n\\n[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting.\\n\\n[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It\\'s the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way.\\n\\n[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists.\\n\\n[4] You can of course paint people like still lives if you want to, and they\\'re willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters.\\n\\n[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore\\'s Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer.\\n\\n[6] The signature style seekers at RISD weren\\'t specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive.\\n\\n[7] Technically the apartment wasn\\'t rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price.\\n\\n[8] Most software you can launch as soon as it\\'s done. But when the software is an online store builder and you\\'re hosting the stores, if you don\\'t have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores.\\n\\n[9] We\\'d had a code editor in Viaweb for users to define their own page styles. They didn\\'t know it, but they were editing Lisp expressions underneath. But this wasn\\'t an app editor, because the code ran when the merchants\\' sites were generated, not when shoppers visited them.\\n\\n[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren\\'t they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I\\'m not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don\\'t already know, and some people dislike being told such things.\\n\\n[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version.\\n\\n[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs\\' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era.\\n\\nWhich in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete).\\n\\nHere\\'s an interesting point, though: you can\\'t always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be?\\n\\n[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn\\'t want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator.\\n\\nI picked orange as our color partly because it\\'s the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square.\\n\\n[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded.\\n\\n[15] I\\'ve never liked the term \"deal flow,\" because it implies that the number of new startups at any given time is fixed. This is not only false, but it\\'s the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed.\\n\\n[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than', doc_id='85bdc61b-9298-49bd-9ccc-eced01ee2f80', embedding=None, doc_hash='117cb89093d8096b5a56654f293dd1407185f92de4eb81cf82611339a65a65dd', extra_info={'_dummy': 0})]"
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+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/NotionDemo.ipynb b/docs/examples/data_connectors/NotionDemo.ipynb
index cdf81b97ed..0f43b3f811 100644
--- a/docs/examples/data_connectors/NotionDemo.ipynb
+++ b/docs/examples/data_connectors/NotionDemo.ipynb
@@ -1,147 +1,151 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "effeb5a7-8544-4ee4-8c11-bad0d8165394",
- "metadata": {},
- "source": [
- "# Notion Reader\n",
- "Demonstrates our Notion data connector"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "995afc19",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "6ea1f66d-10ed-4417-bdcb-f8a894836ea5",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, NotionPageReader\n",
- "from IPython.display import Markdown, display\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da90589a-fb44-4ec6-9706-753dba4fa968",
- "metadata": {},
- "outputs": [],
- "source": [
- "integration_token = os.getenv(\"NOTION_INTEGRATION_TOKEN\")\n",
- "page_ids = [\"<page_id>\"]\n",
- "documents = NotionPageReader(integration_token=integration_token).load_data(page_ids=page_ids)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "341295df-2029-4728-ab3d-2ee178a7e6f1",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "01c26b9d-49ec-4a6e-9c61-5c06bb86bbb2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f160c678-2fb5-4d6d-b2bc-87abb61cfdec",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8e8e1b13",
- "metadata": {},
- "source": [
- "You can also pass the id of a database to index all the pages in that database:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "087431a2-b04c-441c-820f-6d6d3cdf831c",
- "metadata": {},
- "outputs": [],
- "source": [
- "database_id = \"<database-id>\"\n",
- "\n",
- "# https://developers.notion.com/docs/working-with-databases for how to find your database id\n",
- "\n",
- "documents = NotionPageReader(integration_token=integration_token).load_data(database_id=database_id)\n",
- "\n",
- "print(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6464025d-0c5a-4e2d-8a90-91c29ece9884",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "index = ListIndex.from_documents(documents)\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- },
- "vscode": {
- "interpreter": {
- "hash": "c32397a35d2e76e766f80c3872b208f0c0029e8a6a9b8e2a8fe7b1641cfa009b"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "effeb5a7-8544-4ee4-8c11-bad0d8165394",
+ "metadata": {},
+ "source": [
+ "# Notion Reader\n",
+ "Demonstrates our Notion data connector"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "995afc19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6ea1f66d-10ed-4417-bdcb-f8a894836ea5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, NotionPageReader\n",
+ "from IPython.display import Markdown, display\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da90589a-fb44-4ec6-9706-753dba4fa968",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "integration_token = os.getenv(\"NOTION_INTEGRATION_TOKEN\")\n",
+ "page_ids = [\"<page_id>\"]\n",
+ "documents = NotionPageReader(integration_token=integration_token).load_data(\n",
+ " page_ids=page_ids\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "341295df-2029-4728-ab3d-2ee178a7e6f1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "01c26b9d-49ec-4a6e-9c61-5c06bb86bbb2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f160c678-2fb5-4d6d-b2bc-87abb61cfdec",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e8e1b13",
+ "metadata": {},
+ "source": [
+ "You can also pass the id of a database to index all the pages in that database:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "087431a2-b04c-441c-820f-6d6d3cdf831c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "database_id = \"<database-id>\"\n",
+ "\n",
+ "# https://developers.notion.com/docs/working-with-databases for how to find your database id\n",
+ "\n",
+ "documents = NotionPageReader(integration_token=integration_token).load_data(\n",
+ " database_id=database_id\n",
+ ")\n",
+ "\n",
+ "print(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6464025d-0c5a-4e2d-8a90-91c29ece9884",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "index = ListIndex.from_documents(documents)\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "c32397a35d2e76e766f80c3872b208f0c0029e8a6a9b8e2a8fe7b1641cfa009b"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/ObsidianReaderDemo.ipynb b/docs/examples/data_connectors/ObsidianReaderDemo.ipynb
index 2c02e3fdf9..93f0a0c603 100644
--- a/docs/examples/data_connectors/ObsidianReaderDemo.ipynb
+++ b/docs/examples/data_connectors/ObsidianReaderDemo.ipynb
@@ -1,134 +1,136 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Obsidian Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%env OPENAI_API_KEY=sk-************"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ObsidianReader, VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = ObsidianReader('/Users/hursh/vault').load_data() # Returns list of documents "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "index = VectorStoreIndex.from_documents(documents) # Initialize index with documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 920 tokens\n",
- "> [query] Total embedding token usage: 7 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "res = query_engine.query('What is the meaning of life?')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'\\nThe meaning of life is subjective and can vary from person to person. It is ultimately up to each individual to decide what they believe is the purpose and value of life. Some may find meaning in their faith, while others may find it in their relationships, work, or hobbies. Ultimately, it is up to each individual to decide what brings them joy and fulfillment and to pursue that path.'"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "res.response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- },
- "vscode": {
- "interpreter": {
- "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Obsidian Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%env OPENAI_API_KEY=sk-************"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ObsidianReader, VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = ObsidianReader(\n",
+ " \"/Users/hursh/vault\"\n",
+ ").load_data() # Returns list of documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents) # Initialize index with documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 920 tokens\n",
+ "> [query] Total embedding token usage: 7 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "res = query_engine.query(\"What is the meaning of life?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'\\nThe meaning of life is subjective and can vary from person to person. It is ultimately up to each individual to decide what they believe is the purpose and value of life. Some may find meaning in their faith, while others may find it in their relationships, work, or hobbies. Ultimately, it is up to each individual to decide what brings them joy and fulfillment and to pursue that path.'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "res.response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/docs/examples/data_connectors/PineconeDemo.ipynb b/docs/examples/data_connectors/PineconeDemo.ipynb
index 06672576bb..b00ee6e586 100644
--- a/docs/examples/data_connectors/PineconeDemo.ipynb
+++ b/docs/examples/data_connectors/PineconeDemo.ipynb
@@ -1,152 +1,158 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
- "metadata": {},
- "source": [
- "# Pinecone Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b2bd3c59",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "e2f49003-b952-4b9b-b935-2941f9303773",
- "metadata": {},
- "outputs": [],
- "source": [
- "api_key = \"<api_key>\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "262f990a-79c8-413a-9f3c-cd9a3c191307",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.readers.pinecone import PineconeReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "252f8163-7297-44b6-a838-709e9662f3d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "reader = PineconeReader(api_key=api_key, environment=\"us-west1-gcp\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "53b49187-8477-436c-9718-5d2f8cc6fad0",
- "metadata": {},
- "outputs": [],
- "source": [
- "# the id_to_text_map specifies a mapping from the ID specified in Pinecone to your text. \n",
- "id_to_text_map = {\n",
- " \"id1\": \"text blob 1\",\n",
- " \"id2\": \"text blob 2\",\n",
- "}\n",
- "\n",
- "# the query_vector is an embedding representation of your query_vector\n",
- "# Example query vector:\n",
- "# query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]\n",
- "\n",
- "query_vector=[n1, n2, n3, ...]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a88be1c4-603f-48b9-ac64-10a219af4951",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: Required args are index_name, id_to_text_map, vector.\n",
- "# In addition, we pass-through all kwargs that can be passed into the the `Query` operation in Pinecone.\n",
- "# See the API reference: https://docs.pinecone.io/reference/query\n",
- "# and also the Python client: https://github.com/pinecone-io/pinecone-python-client\n",
- "# for more details. \n",
- "documents = reader.load_data(index_name='quickstart', id_to_text_map=id_to_text_map, top_k=3, vector=query_vector, separate_documents=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a4baf59e-fc97-4a1e-947f-354a6438ffa6",
- "metadata": {},
- "source": [
- "### Create index "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "109d083e-f3b4-420b-886b-087c8cf3f98b",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e15b9177-9e94-4e4e-9a2e-cd3a288a7faf",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "67b50613-a589-4acf-ba16-10571b415268",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
+ "metadata": {},
+ "source": [
+ "# Pinecone Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b2bd3c59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "e2f49003-b952-4b9b-b935-2941f9303773",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "api_key = \"<api_key>\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "262f990a-79c8-413a-9f3c-cd9a3c191307",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.readers.pinecone import PineconeReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "252f8163-7297-44b6-a838-709e9662f3d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reader = PineconeReader(api_key=api_key, environment=\"us-west1-gcp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "53b49187-8477-436c-9718-5d2f8cc6fad0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# the id_to_text_map specifies a mapping from the ID specified in Pinecone to your text.\n",
+ "id_to_text_map = {\n",
+ " \"id1\": \"text blob 1\",\n",
+ " \"id2\": \"text blob 2\",\n",
+ "}\n",
+ "\n",
+ "# the query_vector is an embedding representation of your query_vector\n",
+ "# Example query vector:\n",
+ "# query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]\n",
+ "\n",
+ "query_vector = [n1, n2, n3, ...]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a88be1c4-603f-48b9-ac64-10a219af4951",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: Required args are index_name, id_to_text_map, vector.\n",
+ "# In addition, we pass-through all kwargs that can be passed into the the `Query` operation in Pinecone.\n",
+ "# See the API reference: https://docs.pinecone.io/reference/query\n",
+ "# and also the Python client: https://github.com/pinecone-io/pinecone-python-client\n",
+ "# for more details.\n",
+ "documents = reader.load_data(\n",
+ " index_name=\"quickstart\",\n",
+ " id_to_text_map=id_to_text_map,\n",
+ " top_k=3,\n",
+ " vector=query_vector,\n",
+ " separate_documents=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4baf59e-fc97-4a1e-947f-354a6438ffa6",
+ "metadata": {},
+ "source": [
+ "### Create index "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "109d083e-f3b4-420b-886b-087c8cf3f98b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e15b9177-9e94-4e4e-9a2e-cd3a288a7faf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "67b50613-a589-4acf-ba16-10571b415268",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/PsychicDemo.ipynb b/docs/examples/data_connectors/PsychicDemo.ipynb
index 14d4269782..90ee2296c6 100644
--- a/docs/examples/data_connectors/PsychicDemo.ipynb
+++ b/docs/examples/data_connectors/PsychicDemo.ipynb
@@ -35,7 +35,7 @@
"outputs": [],
"source": [
"from llama_index import ListIndex, PsychicReader\n",
- "from IPython.display import Markdown, display\n"
+ "from IPython.display import Markdown, display"
]
},
{
@@ -48,9 +48,11 @@
"# Get Psychic API key from https://dashboard.psychic.dev/api-keys\n",
"psychic_key = \"PSYCHIC_API_KEY\"\n",
"# Connector ID and Account ID are typically set programatically based on the application state.\n",
- "account_id = \"ACCOUNT_ID\" \n",
+ "account_id = \"ACCOUNT_ID\"\n",
"connector_id = \"notion\"\n",
- "documents = PsychicReader(psychic_key=psychic_key).load_data(connector_id=connector_id, account_id=account_id)"
+ "documents = PsychicReader(psychic_key=psychic_key).load_data(\n",
+ " connector_id=connector_id, account_id=account_id\n",
+ ")"
]
},
{
@@ -97,7 +99,7 @@
],
"source": [
"# set Logging to DEBUG for more detailed outputs\n",
- "os.environ['OPENAI_API_KEY'] = \"OPENAI_API_KEY\"\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"OPENAI_API_KEY\"\n",
"index = ListIndex.from_documents(documents)\n",
"query_engine = index.as_query_engine()\n",
"response = query_engine.query(\"What is Psychic's privacy policy?\")\n",
diff --git a/docs/examples/data_connectors/QdrantDemo.ipynb b/docs/examples/data_connectors/QdrantDemo.ipynb
index e6021fef3d..ab8ad5dd76 100644
--- a/docs/examples/data_connectors/QdrantDemo.ipynb
+++ b/docs/examples/data_connectors/QdrantDemo.ipynb
@@ -1,134 +1,134 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
- "metadata": {},
- "source": [
- "# Qdrant Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "778ee662",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "262f990a-79c8-413a-9f3c-cd9a3c191307",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.readers.qdrant import QdrantReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "252f8163-7297-44b6-a838-709e9662f3d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "reader = QdrantReader(host=\"localhost\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53b49187-8477-436c-9718-5d2f8cc6fad0",
- "metadata": {},
- "outputs": [],
- "source": [
- "# the query_vector is an embedding representation of your query_vector\n",
- "# Example query vector:\n",
- "# query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]\n",
- "\n",
- "query_vector=[n1, n2, n3, ...]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a88be1c4-603f-48b9-ac64-10a219af4951",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: Required args are collection_name, query_vector.\n",
- "# See the Python client: https://github.com/qdrant/qdrant_client\n",
- "# for more details. \n",
- "documents = reader.load_data(collection_name=\"demo\", query_vector=query_vector, limit=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "169b4273-eb20-4d06-9ffe-71320f4570f6",
- "metadata": {},
- "source": [
- "### Create index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ac4563a1",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f06b02db",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "97d1ae80",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "f3ca56f0-6ef1-426f-bac5-fd7c374d0f51",
+ "metadata": {},
+ "source": [
+ "# Qdrant Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "778ee662",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "262f990a-79c8-413a-9f3c-cd9a3c191307",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.readers.qdrant import QdrantReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "252f8163-7297-44b6-a838-709e9662f3d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reader = QdrantReader(host=\"localhost\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53b49187-8477-436c-9718-5d2f8cc6fad0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# the query_vector is an embedding representation of your query_vector\n",
+ "# Example query vector:\n",
+ "# query_vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]\n",
+ "\n",
+ "query_vector = [n1, n2, n3, ...]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a88be1c4-603f-48b9-ac64-10a219af4951",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: Required args are collection_name, query_vector.\n",
+ "# See the Python client: https://github.com/qdrant/qdrant_client\n",
+ "# for more details.\n",
+ "documents = reader.load_data(collection_name=\"demo\", query_vector=query_vector, limit=5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "169b4273-eb20-4d06-9ffe-71320f4570f6",
+ "metadata": {},
+ "source": [
+ "### Create index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac4563a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f06b02db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97d1ae80",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/WeaviateDemo.ipynb b/docs/examples/data_connectors/WeaviateDemo.ipynb
index a22378b6ef..d8524f3199 100644
--- a/docs/examples/data_connectors/WeaviateDemo.ipynb
+++ b/docs/examples/data_connectors/WeaviateDemo.ipynb
@@ -1,179 +1,181 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "36e7bb96-0c27-47e9-a525-c11f40be3b86",
- "metadata": {},
- "source": [
- "# Weaviate Reader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "38ca1434",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d99bc57b-85df-46ac-8262-2409344af428",
- "metadata": {},
- "outputs": [],
- "source": [
- "import weaviate\n",
- "from llama_index.readers.weaviate import WeaviateReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fec36c7a-3766-4167-890e-b93adb831a64",
- "metadata": {},
- "outputs": [],
- "source": [
- "# See https://weaviate.io/developers/weaviate/current/client-libraries/python.html\n",
- "# for more details on authentication\n",
- "resource_owner_config = weaviate.AuthClientPassword(\n",
- " username = \"<username>\", \n",
- " password = \"<password>\", \n",
- ")\n",
- "\n",
- "# initialize reader\n",
- "reader = WeaviateReader(\"https://<cluster-id>.semi.network/\", auth_client_secret=resource_owner_config)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ce9f299c-4f0a-4bca-bc90-79848f02b381",
- "metadata": {},
- "source": [
- "You have two options for the Weaviate reader: 1) directly specify the class_name and properties, or 2) input the raw graphql_query. Examples are shown below."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b92d69a1-d39f-45cf-a136-cb9c2f2f5cdf",
- "metadata": {},
- "outputs": [],
- "source": [
- "# 1) load data using class_name and properties\n",
- "# docs = reader.load_data(\n",
- "# class_name=\"Author\", properties=[\"name\", \"description\"], separate_documents=True\n",
- "# )\n",
- "\n",
- "documents = reader.load_data(\n",
- " class_name=\"<class_name>\", \n",
- " properties=[\"property1\", \"property2\", \"...\"], \n",
- " separate_documents=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "722b5d47-9897-4c54-9734-259ab0c1634c",
- "metadata": {},
- "outputs": [],
- "source": [
- "# 2) example GraphQL query\n",
- "# query = \"\"\"\n",
- "# {\n",
- "# Get {\n",
- "# Author {\n",
- "# name\n",
- "# description\n",
- "# }\n",
- "# }\n",
- "# }\n",
- "# \"\"\"\n",
- "# docs = reader.load_data(graphql_query=query, separate_documents=True)\n",
- "\n",
- "query = \"\"\"\n",
- "{\n",
- " Get {\n",
- " <class_name> {\n",
- " <property1>\n",
- " <property2>\n",
- " ...\n",
- " }\n",
- " }\n",
- "}\n",
- "\"\"\"\n",
- "\n",
- "documents = reader.load_data(graphql_query=query, separate_documents=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "169b4273-eb20-4d06-9ffe-71320f4570f6",
- "metadata": {},
- "source": [
- "### Create index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "92599a0a-93ba-4c93-80f1-9acae0663c34",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "52d93c3f-a08d-4637-98bc-0c3cc693c563",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"<query_text>\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "771b42be-4108-43a0-a1b4-b259a7819936",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "36e7bb96-0c27-47e9-a525-c11f40be3b86",
+ "metadata": {},
+ "source": [
+ "# Weaviate Reader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38ca1434",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d99bc57b-85df-46ac-8262-2409344af428",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import weaviate\n",
+ "from llama_index.readers.weaviate import WeaviateReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fec36c7a-3766-4167-890e-b93adb831a64",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# See https://weaviate.io/developers/weaviate/current/client-libraries/python.html\n",
+ "# for more details on authentication\n",
+ "resource_owner_config = weaviate.AuthClientPassword(\n",
+ " username=\"<username>\",\n",
+ " password=\"<password>\",\n",
+ ")\n",
+ "\n",
+ "# initialize reader\n",
+ "reader = WeaviateReader(\n",
+ " \"https://<cluster-id>.semi.network/\", auth_client_secret=resource_owner_config\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce9f299c-4f0a-4bca-bc90-79848f02b381",
+ "metadata": {},
+ "source": [
+ "You have two options for the Weaviate reader: 1) directly specify the class_name and properties, or 2) input the raw graphql_query. Examples are shown below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b92d69a1-d39f-45cf-a136-cb9c2f2f5cdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 1) load data using class_name and properties\n",
+ "# docs = reader.load_data(\n",
+ "# class_name=\"Author\", properties=[\"name\", \"description\"], separate_documents=True\n",
+ "# )\n",
+ "\n",
+ "documents = reader.load_data(\n",
+ " class_name=\"<class_name>\",\n",
+ " properties=[\"property1\", \"property2\", \"...\"],\n",
+ " separate_documents=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "722b5d47-9897-4c54-9734-259ab0c1634c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# 2) example GraphQL query\n",
+ "# query = \"\"\"\n",
+ "# {\n",
+ "# Get {\n",
+ "# Author {\n",
+ "# name\n",
+ "# description\n",
+ "# }\n",
+ "# }\n",
+ "# }\n",
+ "# \"\"\"\n",
+ "# docs = reader.load_data(graphql_query=query, separate_documents=True)\n",
+ "\n",
+ "query = \"\"\"\n",
+ "{\n",
+ " Get {\n",
+ " <class_name> {\n",
+ " <property1>\n",
+ " <property2>\n",
+ " ...\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\"\"\"\n",
+ "\n",
+ "documents = reader.load_data(graphql_query=query, separate_documents=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "169b4273-eb20-4d06-9ffe-71320f4570f6",
+ "metadata": {},
+ "source": [
+ "### Create index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "92599a0a-93ba-4c93-80f1-9acae0663c34",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "52d93c3f-a08d-4637-98bc-0c3cc693c563",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"<query_text>\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "771b42be-4108-43a0-a1b4-b259a7819936",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/WebPageDemo.ipynb b/docs/examples/data_connectors/WebPageDemo.ipynb
index cad1523ae0..5e510792e4 100644
--- a/docs/examples/data_connectors/WebPageDemo.ipynb
+++ b/docs/examples/data_connectors/WebPageDemo.ipynb
@@ -1,224 +1,226 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "30146ad2-f165-4f4b-ae07-fe6597a2964f",
- "metadata": {},
- "source": [
- "# Web Page Reader\n",
- "\n",
- "Demonstrates our web page reader."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3c39063b",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2315a154-f72d-4447-b1eb-cde9b66868cb",
- "metadata": {},
- "source": [
- "#### Using SimpleWebPageReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "87bf7ecd-50cd-47da-9f0e-bc48d7ae45d8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, SimpleWebPageReader\n",
- "from IPython.display import Markdown, display\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b6de3929-51eb-4064-b4b6-c203bb6debc4",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: the html_to_text=True option requires html2text to be installed"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "663403de-2e6e-4340-ab8f-8ee681bc06aa",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleWebPageReader(html_to_text=True).load_data([\"http://paulgraham.com/worked.html\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b8cd183a-2423-4a3e-ad92-dfe89ed5454e",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "26854cc3-af61-4910-ab6b-3bed6acfb447",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5cfdf87a-97cb-481f-ad51-be5bf8b5217f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7278d033-cae3-4ddf-96bd-75ea570ca53f",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2708dc99-0e4d-4c7e-b180-8392286d87c2",
- "metadata": {},
- "source": [
- "#### Using TrafilaturaWebReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aa2d54c6-c694-4852-a743-165e4777bd56",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import TrafilaturaWebReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "46854f2f-426e-40a3-a87f-5fb51f90e14c",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = TrafilaturaWebReader().load_data([\"http://paulgraham.com/worked.html\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "80752ad3-1ed8-4695-9247-22efbe475746",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8cc9b154-1dcf-479b-b49b-251874aea506",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "971b6415-8bcd-4d8b-a1de-9b7ada3cd392",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b2b6d07c",
- "metadata": {},
- "source": [
- "### Using RssReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a5ad5ca8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, RssReader\n",
- "\n",
- "documents = RssReader().load_data([\n",
- " \"https://rss.nytimes.com/services/xml/rss/nyt/HomePage.xml\"\n",
- " ])\n",
- "\n",
- "index = ListIndex.from_documents(documents)\n",
- "\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What happened in the news today?\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- },
- "vscode": {
- "interpreter": {
- "hash": "c32397a35d2e76e766f80c3872b208f0c0029e8a6a9b8e2a8fe7b1641cfa009b"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "30146ad2-f165-4f4b-ae07-fe6597a2964f",
+ "metadata": {},
+ "source": [
+ "# Web Page Reader\n",
+ "\n",
+ "Demonstrates our web page reader."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3c39063b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2315a154-f72d-4447-b1eb-cde9b66868cb",
+ "metadata": {},
+ "source": [
+ "#### Using SimpleWebPageReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "87bf7ecd-50cd-47da-9f0e-bc48d7ae45d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, SimpleWebPageReader\n",
+ "from IPython.display import Markdown, display\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b6de3929-51eb-4064-b4b6-c203bb6debc4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: the html_to_text=True option requires html2text to be installed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "663403de-2e6e-4340-ab8f-8ee681bc06aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleWebPageReader(html_to_text=True).load_data(\n",
+ " [\"http://paulgraham.com/worked.html\"]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b8cd183a-2423-4a3e-ad92-dfe89ed5454e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "26854cc3-af61-4910-ab6b-3bed6acfb447",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5cfdf87a-97cb-481f-ad51-be5bf8b5217f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7278d033-cae3-4ddf-96bd-75ea570ca53f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2708dc99-0e4d-4c7e-b180-8392286d87c2",
+ "metadata": {},
+ "source": [
+ "#### Using TrafilaturaWebReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aa2d54c6-c694-4852-a743-165e4777bd56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import TrafilaturaWebReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "46854f2f-426e-40a3-a87f-5fb51f90e14c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = TrafilaturaWebReader().load_data([\"http://paulgraham.com/worked.html\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80752ad3-1ed8-4695-9247-22efbe475746",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8cc9b154-1dcf-479b-b49b-251874aea506",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "971b6415-8bcd-4d8b-a1de-9b7ada3cd392",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2b6d07c",
+ "metadata": {},
+ "source": [
+ "### Using RssReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a5ad5ca8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, RssReader\n",
+ "\n",
+ "documents = RssReader().load_data(\n",
+ " [\"https://rss.nytimes.com/services/xml/rss/nyt/HomePage.xml\"]\n",
+ ")\n",
+ "\n",
+ "index = ListIndex.from_documents(documents)\n",
+ "\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What happened in the news today?\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "c32397a35d2e76e766f80c3872b208f0c0029e8a6a9b8e2a8fe7b1641cfa009b"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/data_connectors/deplot/DeplotReader.ipynb b/docs/examples/data_connectors/deplot/DeplotReader.ipynb
index b264dc9fef..f887d0ba3d 100644
--- a/docs/examples/data_connectors/deplot/DeplotReader.ipynb
+++ b/docs/examples/data_connectors/deplot/DeplotReader.ipynb
@@ -66,7 +66,7 @@
},
"outputs": [],
"source": [
- "documents = loader.load_data(file=Path('./marine_chart.png'))"
+ "documents = loader.load_data(file=Path(\"./marine_chart.png\"))"
]
},
{
@@ -107,7 +107,9 @@
],
"source": [
"list_index = ListIndex.from_documents(documents)\n",
- "response = list_index.as_query_engine().query(\"What is the difference between the shares of Greenland and the share of Mauritania?\")"
+ "response = list_index.as_query_engine().query(\n",
+ " \"What is the difference between the shares of Greenland and the share of Mauritania?\"\n",
+ ")"
]
},
{
@@ -143,7 +145,7 @@
},
"outputs": [],
"source": [
- "documents = loader.load_data(file=Path('./pew1.png'))"
+ "documents = loader.load_data(file=Path(\"./pew1.png\"))"
]
},
{
@@ -176,7 +178,9 @@
"outputs": [],
"source": [
"list_index = ListIndex.from_documents(documents)\n",
- "response = list_index.as_query_engine().query(\"What percentage says that the US contributes to peace and stability?\")"
+ "response = list_index.as_query_engine().query(\n",
+ " \"What percentage says that the US contributes to peace and stability?\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/discover_llamaindex/document_management/Discord_Thread_Management.ipynb b/docs/examples/discover_llamaindex/document_management/Discord_Thread_Management.ipynb
index 006fde678c..a0fbd41db9 100644
--- a/docs/examples/discover_llamaindex/document_management/Discord_Thread_Management.ipynb
+++ b/docs/examples/discover_llamaindex/document_management/Discord_Thread_Management.ipynb
@@ -42,6 +42,7 @@
],
"source": [
"import os\n",
+ "\n",
"print(os.listdir(\"./discord_dumps\"))"
]
},
@@ -80,13 +81,14 @@
],
"source": [
"import json\n",
- "with open(\"./discord_dumps/help_channel_dump_05_25_23.json\", 'r') as f:\n",
+ "\n",
+ "with open(\"./discord_dumps/help_channel_dump_05_25_23.json\", \"r\") as f:\n",
" data = json.load(f)\n",
- "print('JSON keys: ', data.keys(), '\\n')\n",
- "print('Message Count: ', len(data['messages']), '\\n')\n",
- "print('Sample Message Keys: ', data['messages'][0].keys(), '\\n')\n",
- "print('First Message: ', data['messages'][0]['content'], '\\n')\n",
- "print('Last Message: ', data['messages'][-1]['content'])"
+ "print(\"JSON keys: \", data.keys(), \"\\n\")\n",
+ "print(\"Message Count: \", len(data[\"messages\"]), \"\\n\")\n",
+ "print(\"Sample Message Keys: \", data[\"messages\"][0].keys(), \"\\n\")\n",
+ "print(\"First Message: \", data[\"messages\"][0][\"content\"], \"\\n\")\n",
+ "print(\"Last Message: \", data[\"messages\"][-1][\"content\"])"
]
},
{
@@ -149,9 +151,9 @@
"source": [
"with open(\"conversation_docs.json\", \"r\") as f:\n",
" threads = json.load(f)\n",
- "print('Thread keys: ', threads[0].keys(), '\\n')\n",
- "print(threads[0]['metadata'], '\\n')\n",
- "print(threads[0]['thread'], '\\n')"
+ "print(\"Thread keys: \", threads[0].keys(), \"\\n\")\n",
+ "print(threads[0][\"metadata\"], \"\\n\")\n",
+ "print(threads[0][\"thread\"], \"\\n\")"
]
},
{
@@ -182,10 +184,12 @@
"# create document objects using doc_id's and dates from each thread\n",
"documents = []\n",
"for thread in threads:\n",
- " thread_text = thread['thread']\n",
- " thread_id = thread['metadata']['id']\n",
- " timestamp = thread['metadata']['timestamp']\n",
- " documents.append(Document(text=thread_text, id_=thread_id, metadata={'date': timestamp}))"
+ " thread_text = thread[\"thread\"]\n",
+ " thread_id = thread[\"metadata\"][\"id\"]\n",
+ " timestamp = thread[\"metadata\"][\"timestamp\"]\n",
+ " documents.append(\n",
+ " Document(text=thread_text, id_=thread_id, metadata={\"date\": timestamp})\n",
+ " )"
]
},
{
@@ -196,6 +200,7 @@
"outputs": [],
"source": [
"from llama_index import VectorStoreIndex\n",
+ "\n",
"index = VectorStoreIndex.from_documents(documents)"
]
},
@@ -223,8 +228,8 @@
}
],
"source": [
- "print('ref_docs ingested: ', len(index.ref_doc_info))\n",
- "print('number of input documents: ', len(documents))"
+ "print(\"ref_docs ingested: \", len(index.ref_doc_info))\n",
+ "print(\"number of input documents: \", len(documents))"
]
},
{
@@ -250,7 +255,7 @@
}
],
"source": [
- "thread_id = threads[0]['metadata']['id']\n",
+ "thread_id = threads[0][\"metadata\"][\"id\"]\n",
"print(index.ref_doc_info[thread_id])"
]
},
@@ -284,9 +289,10 @@
"\n",
"# load it again to confirm it worked\n",
"from llama_index import StorageContext, load_index_from_storage\n",
+ "\n",
"index = load_index_from_storage(StorageContext.from_defaults(persist_dir=\"./storage\"))\n",
"\n",
- "print('Double check ref_docs ingested: ', len(index.ref_doc_info))"
+ "print(\"Double check ref_docs ingested: \", len(index.ref_doc_info))"
]
},
{
@@ -330,13 +336,14 @@
],
"source": [
"import json\n",
- "with open(\"./discord_dumps/help_channel_dump_06_02_23.json\", 'r') as f:\n",
+ "\n",
+ "with open(\"./discord_dumps/help_channel_dump_06_02_23.json\", \"r\") as f:\n",
" data = json.load(f)\n",
- "print('JSON keys: ', data.keys(), '\\n')\n",
- "print('Message Count: ', len(data['messages']), '\\n')\n",
- "print('Sample Message Keys: ', data['messages'][0].keys(), '\\n')\n",
- "print('First Message: ', data['messages'][0]['content'], '\\n')\n",
- "print('Last Message: ', data['messages'][-1]['content'])"
+ "print(\"JSON keys: \", data.keys(), \"\\n\")\n",
+ "print(\"Message Count: \", len(data[\"messages\"]), \"\\n\")\n",
+ "print(\"Sample Message Keys: \", data[\"messages\"][0].keys(), \"\\n\")\n",
+ "print(\"First Message: \", data[\"messages\"][0][\"content\"], \"\\n\")\n",
+ "print(\"Last Message: \", data[\"messages\"][-1][\"content\"])"
]
},
{
@@ -401,9 +408,9 @@
"source": [
"with open(\"conversation_docs.json\", \"r\") as f:\n",
" threads = json.load(f)\n",
- "print('Thread keys: ', threads[0].keys(), '\\n')\n",
- "print(threads[0]['metadata'], '\\n')\n",
- "print(threads[0]['thread'], '\\n')"
+ "print(\"Thread keys: \", threads[0].keys(), \"\\n\")\n",
+ "print(threads[0][\"metadata\"], \"\\n\")\n",
+ "print(threads[0][\"thread\"], \"\\n\")"
]
},
{
@@ -416,10 +423,12 @@
"# create document objects using doc_id's and dates from each thread\n",
"new_documents = []\n",
"for thread in threads:\n",
- " thread_text = thread['thread']\n",
- " thread_id = thread['metadata']['id']\n",
- " timestamp = thread['metadata']['timestamp']\n",
- " new_documents.append(Document(text=thread_text, id_=thread_id, metadata={'date': timestamp}))"
+ " thread_text = thread[\"thread\"]\n",
+ " thread_id = thread[\"metadata\"][\"id\"]\n",
+ " timestamp = thread[\"metadata\"][\"timestamp\"]\n",
+ " new_documents.append(\n",
+ " Document(text=thread_text, id_=thread_id, metadata={\"date\": timestamp})\n",
+ " )"
]
},
{
@@ -437,7 +446,7 @@
}
],
"source": [
- "print('Number of new documents: ', len(new_documents) - len(documents))"
+ "print(\"Number of new documents: \", len(new_documents) - len(documents))"
]
},
{
@@ -449,8 +458,7 @@
"source": [
"# now, refresh!\n",
"refreshed_docs = index.refresh(\n",
- " new_documents,\n",
- " update_kwargs={\"delete_kwargs\": {'delete_from_docstore': True}}\n",
+ " new_documents, update_kwargs={\"delete_kwargs\": {\"delete_from_docstore\": True}}\n",
")"
]
},
@@ -479,7 +487,7 @@
}
],
"source": [
- "print('Number of newly inserted/refreshed docs: ', sum(refreshed_docs))"
+ "print(\"Number of newly inserted/refreshed docs: \", sum(refreshed_docs))"
]
},
{
diff --git a/docs/examples/docstore/MongoDocstoreDemo.ipynb b/docs/examples/docstore/MongoDocstoreDemo.ipynb
index b047b86f49..6d2d6a3544 100644
--- a/docs/examples/docstore/MongoDocstoreDemo.ipynb
+++ b/docs/examples/docstore/MongoDocstoreDemo.ipynb
@@ -1,404 +1,414 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import os\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor, StorageContext\n",
- "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
- "from llama_index.composability import ComposableGraph\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.response.notebook_utils import display_response"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
- "metadata": {},
- "source": [
- "#### Load Documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
- "documents = reader.load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
- "metadata": {},
- "source": [
- "#### Parse into Nodes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.node_parser import SimpleNodeParser\n",
- "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
- "metadata": {},
- "source": [
- "#### Add to Docstore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1514211c",
- "metadata": {},
- "outputs": [],
- "source": [
- "MONGO_URI = os.environ['MONGO_URI']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.storage.docstore import MongoDocumentStore\n",
- "from llama_index.storage.index_store.mongo_index_store import MongoIndexStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "60e781d1",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "storage_context = StorageContext.from_defaults(\n",
- " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
- " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e0b18789",
- "metadata": {},
- "outputs": [],
- "source": [
- "storage_context.docstore.add_documents(nodes)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
- "metadata": {},
- "source": [
- "#### Define Multiple Indexes\n",
- "\n",
- "Each index uses the same underlying Node."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "list_index = ListIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9440f405-fa75-4788-bc7c-11d021a0a17b",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "364ef89f-4ba2-4b1a-b5e5-619e0e8420ef",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "# NOTE: the docstore still has the same nodes\n",
- "len(storage_context.docstore.docs)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "365a025b",
- "metadata": {},
- "source": [
- "#### Test out saving and loading"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1b359a08",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: docstore and index_store is persisted in MongoDB by default\n",
- "# NOTE: here only need to persist simple vector store to disk\n",
- "storage_context.persist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84b3d2f4",
- "metadata": {},
- "outputs": [],
- "source": [
- "# note down index IDs\n",
- "list_id = list_index.index_id\n",
- "vector_id = vector_index.index_id\n",
- "keyword_id = keyword_table_index.index_id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1593ca1d",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.loading import load_index_from_storage\n",
- "\n",
- "# re-create storage context\n",
- "storage_context = StorageContext.from_defaults(\n",
- " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
- " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
- ")\n",
- "\n",
- "# load indices\n",
- "list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
- "vector_index = load_index_from_storage(storage_context=storage_context, vector_id=vector_id)\n",
- "keyword_table_index = load_index_from_storage(storage_context=storage_context, keyword_id=keyword_id)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d3bf6aaf-3375-4212-8323-777969a918f7",
- "metadata": {},
- "source": [
- "#### Test out some Queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "544c0565-72a0-434b-98e5-83138ebdaa2b",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "query_engine = list_index.as_query_engine()\n",
- "list_response = query_engine.query(\"What is a summary of this document?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39d250be",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "display_response(list_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "036077b7-108e-4026-9628-44c694343460",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "vector_response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "42229e09",
- "metadata": {},
- "outputs": [],
- "source": [
- "display_response(vector_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ecd7719c-f663-4edb-a239-d2a8f0a5c091",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = keyword_table_index.as_query_engine()\n",
- "keyword_response = query_engine.query(\"What did the author do after his time at YC?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "37524641-2632-4a76-8ae6-00f1285256d9",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "display_response(keyword_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ff58018c-3117-4d50-abff-16a1873eda9c",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
+ " StorageContext,\n",
+ ")\n",
+ "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
+ "from llama_index.composability import ComposableGraph\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.response.notebook_utils import display_response"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
+ "metadata": {},
+ "source": [
+ "#### Load Documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
+ "documents = reader.load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
+ "metadata": {},
+ "source": [
+ "#### Parse into Nodes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.node_parser import SimpleNodeParser\n",
+ "\n",
+ "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
+ "metadata": {},
+ "source": [
+ "#### Add to Docstore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1514211c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MONGO_URI = os.environ[\"MONGO_URI\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.docstore import MongoDocumentStore\n",
+ "from llama_index.storage.index_store.mongo_index_store import MongoIndexStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60e781d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "storage_context = StorageContext.from_defaults(\n",
+ " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
+ " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0b18789",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "storage_context.docstore.add_documents(nodes)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
+ "metadata": {},
+ "source": [
+ "#### Define Multiple Indexes\n",
+ "\n",
+ "Each index uses the same underlying Node."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "list_index = ListIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9440f405-fa75-4788-bc7c-11d021a0a17b",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "364ef89f-4ba2-4b1a-b5e5-619e0e8420ef",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: the docstore still has the same nodes\n",
+ "len(storage_context.docstore.docs)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "365a025b",
+ "metadata": {},
+ "source": [
+ "#### Test out saving and loading"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b359a08",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: docstore and index_store is persisted in MongoDB by default\n",
+ "# NOTE: here only need to persist simple vector store to disk\n",
+ "storage_context.persist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84b3d2f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# note down index IDs\n",
+ "list_id = list_index.index_id\n",
+ "vector_id = vector_index.index_id\n",
+ "keyword_id = keyword_table_index.index_id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1593ca1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.loading import load_index_from_storage\n",
+ "\n",
+ "# re-create storage context\n",
+ "storage_context = StorageContext.from_defaults(\n",
+ " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
+ " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
+ ")\n",
+ "\n",
+ "# load indices\n",
+ "list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
+ "vector_index = load_index_from_storage(\n",
+ " storage_context=storage_context, vector_id=vector_id\n",
+ ")\n",
+ "keyword_table_index = load_index_from_storage(\n",
+ " storage_context=storage_context, keyword_id=keyword_id\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d3bf6aaf-3375-4212-8323-777969a918f7",
+ "metadata": {},
+ "source": [
+ "#### Test out some Queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "544c0565-72a0-434b-98e5-83138ebdaa2b",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = list_index.as_query_engine()\n",
+ "list_response = query_engine.query(\"What is a summary of this document?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39d250be",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "display_response(list_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "036077b7-108e-4026-9628-44c694343460",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "vector_response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "42229e09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display_response(vector_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ecd7719c-f663-4edb-a239-d2a8f0a5c091",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = keyword_table_index.as_query_engine()\n",
+ "keyword_response = query_engine.query(\"What did the author do after his time at YC?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "37524641-2632-4a76-8ae6-00f1285256d9",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "display_response(keyword_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff58018c-3117-4d50-abff-16a1873eda9c",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb b/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
index cd5f6b9087..fed62ad97c 100644
--- a/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
+++ b/docs/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
@@ -20,6 +20,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -70,7 +71,12 @@
}
],
"source": [
- "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor, StorageContext\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
+ " StorageContext,\n",
+ ")\n",
"from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
"from llama_index.composability import ComposableGraph\n",
"from llama_index.llms import OpenAI\n",
@@ -96,7 +102,7 @@
},
"outputs": [],
"source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
"documents = reader.load_data()"
]
},
@@ -120,6 +126,7 @@
"outputs": [],
"source": [
"from llama_index.node_parser import SimpleNodeParser\n",
+ "\n",
"nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
]
},
@@ -139,8 +146,8 @@
"metadata": {},
"outputs": [],
"source": [
- "REDIS_HOST = os.getenv('REDIS_HOST', '127.0.0.1')\n",
- "REDIS_PORT = os.getenv('REDIS_PORT', 6379)"
+ "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"127.0.0.1\")\n",
+ "REDIS_PORT = os.getenv(\"REDIS_PORT\", 6379)"
]
},
{
@@ -174,8 +181,12 @@
"outputs": [],
"source": [
"storage_context = StorageContext.from_defaults(\n",
- " docstore=RedisDocumentStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace='llama_index'),\n",
- " index_store=RedisIndexStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace='llama_index'),\n",
+ " docstore=RedisDocumentStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
+ " index_store=RedisIndexStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
")"
]
},
@@ -395,14 +406,22 @@
"\n",
"# re-create storage context\n",
"storage_context = StorageContext.from_defaults(\n",
- " docstore=RedisDocumentStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"),\n",
- " index_store=RedisIndexStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"),\n",
+ " docstore=RedisDocumentStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
+ " index_store=RedisIndexStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
")\n",
"\n",
"# load indices\n",
"list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
- "vector_index = load_index_from_storage(storage_context=storage_context, index_id=vector_id)\n",
- "keyword_table_index = load_index_from_storage(storage_context=storage_context, index_id=keyword_id)"
+ "vector_index = load_index_from_storage(\n",
+ " storage_context=storage_context, index_id=vector_id\n",
+ ")\n",
+ "keyword_table_index = load_index_from_storage(\n",
+ " storage_context=storage_context, index_id=keyword_id\n",
+ ")"
]
},
{
diff --git a/docs/examples/evaluation/QuestionGeneration.ipynb b/docs/examples/evaluation/QuestionGeneration.ipynb
index 76f7505b09..e054ed2a8e 100644
--- a/docs/examples/evaluation/QuestionGeneration.ipynb
+++ b/docs/examples/evaluation/QuestionGeneration.ipynb
@@ -1,352 +1,357 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f3f797ad",
- "metadata": {},
- "source": [
- "# Question Generation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "9080b39e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import pandas as pd\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.evaluation import DatasetGenerator, QueryResponseEvaluator\n",
- "from llama_index import SimpleDirectoryReader, VectorStoreIndex, ServiceContext, LLMPredictor, Response\n",
- "from llama_index.llms import OpenAI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "834f4c8c-8c10-4f8d-bf43-444aaa1234b1",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "reader = SimpleDirectoryReader(\"../data/paul_graham/\")\n",
- "documents = reader.load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "9cc71140-d614-4696-9ade-d5bdc251d398",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:llama_index.indices.service_context:chunk_size_limit is deprecated, please specify chunk_size instead\n",
- "chunk_size_limit is deprecated, please specify chunk_size instead\n",
- "chunk_size_limit is deprecated, please specify chunk_size instead\n",
- "chunk_size_limit is deprecated, please specify chunk_size instead\n",
- "chunk_size_limit is deprecated, please specify chunk_size instead\n"
- ]
- }
- ],
- "source": [
- "data_generator = DatasetGenerator.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "id": "f481b532-9be2-4ec3-b551-fd44060099bd",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "eval_questions = data_generator.generate_questions_from_nodes()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "63720bd6-c060-4cc2-8a60-a39e935ee3e6",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['What were the two main things the author worked on before college?',\n",
- " 'How did the author describe their early attempts at writing short stories?',\n",
- " 'What type of computer did the author first work on for programming?',\n",
- " 'What language did the author use for programming on the IBM 1401?',\n",
- " \"What was the author's experience with programming on the 1401?\",\n",
- " 'What type of computer did the author eventually get for themselves?',\n",
- " \"What was the author's initial plan for college?\",\n",
- " 'What made the author change their mind about studying philosophy?',\n",
- " \"What sparked the author's interest in AI?\",\n",
- " 'What did the author realize about AI during their first year of grad school?',\n",
- " 'What were the two art schools that the author applied to?',\n",
- " 'How did the author end up at RISD?',\n",
- " 'What was the purpose of the foundation classes at RISD?',\n",
- " 'How did the author manage to pass the entrance exam for the Accademia di Belli Arti?',\n",
- " 'What was the arrangement between the students and faculty at the Accademia?',\n",
- " \"What was the author's experience painting still lives in Florence?\",\n",
- " 'What did the author learn about visual perception while painting still lives?',\n",
- " 'Why did the author decide to leave the Accademia and return to the US?',\n",
- " 'What did the author learn about technology companies while working at Interleaf?',\n",
- " 'What lesson did the author learn about the low end and high end in the software industry?',\n",
- " \"What was the author's motivation for writing another book on Lisp?\",\n",
- " 'How did the author come up with the idea for starting a company to put art galleries online?',\n",
- " 'What was the initial reaction of art galleries to the idea of being online?',\n",
- " 'How did the author and his team come up with the concept of a web app?',\n",
- " 'What were the three main parts of the software developed by the author and his team?',\n",
- " 'How did the author and his team learn about retail and improve their software based on user feedback?',\n",
- " 'Why did the author initially believe that the absolute number of users was the most important factor for a startup?',\n",
- " \"What was the growth rate of the author's company and why was it significant?\",\n",
- " \"How did the author's decision to hire more people impact the financial stability of the company?\",\n",
- " \"What was the outcome of the company's acquisition by Yahoo in 1998?\",\n",
- " \"What was the author's initial reaction when Yahoo bought their startup?\",\n",
- " \"How did the author's lifestyle change after Yahoo bought their startup?\",\n",
- " 'Why did the author leave Yahoo and what did they plan to do?',\n",
- " \"What was the author's experience like when they returned to New York after becoming rich?\",\n",
- " 'What idea did the author have in the spring of 2000 and why did they decide to start a new company?',\n",
- " \"Why did the author decide to build a subset of the new company's vision as an open source project?\",\n",
- " \"How did the author's perception of publishing essays change with the advent of the internet?\",\n",
- " \"What is the author's perspective on working on things that are not prestigious?\",\n",
- " 'What other projects did the author work on besides writing essays?',\n",
- " 'What type of building did the author buy in Cambridge?',\n",
- " \"What was the concept behind the big party at the narrator's house in October 2003?\",\n",
- " \"How did Jessica Livingston's perception of startups change after meeting friends of the narrator?\",\n",
- " 'What were some of the ideas that the narrator shared with Jessica about fixing venture capital?',\n",
- " 'How did the idea of starting their own investment firm come about for the narrator and Jessica?',\n",
- " 'What was the Summer Founders Program and how did it attract applicants?',\n",
- " \"How did Y Combinator's batch model help solve the problem of isolation for startup founders?\",\n",
- " \"What advantages did YC's scale bring, both in terms of community and customer acquisition?\",\n",
- " 'Why did the narrator consider Hacker News to be a source of stress?',\n",
- " \"How did the narrator's role in YC differ from other types of work they had done?\",\n",
- " 'What advice did Robert Morris offer the narrator during his visit in 2010?',\n",
- " 'What was the advice given to the author by Rtm regarding their involvement with Y Combinator?',\n",
- " 'Why did the author decide to hand over Y Combinator to someone else?',\n",
- " \"What event in the author's personal life prompted them to reevaluate their priorities?\",\n",
- " 'How did the author spend most of 2014?',\n",
- " 'What project did the author work on from March 2015 to October 2019?',\n",
- " 'How did the author manage to write an interpreter for Lisp in itself?',\n",
- " \"What was the author's experience like living in England?\",\n",
- " \"When was the author's project, Bel, finally finished?\",\n",
- " 'What did the author do during the fall of 2019?',\n",
- " \"How would you describe the author's journey and decision-making process throughout the document?\",\n",
- " \"How did the author's experience with editing Lisp expressions differ from traditional app editing?\",\n",
- " 'Why did the author receive negative comments when claiming that Lisp was better than other languages?',\n",
- " 'What is the difference between putting something online and publishing it online?',\n",
- " 'How did the customs of venture capital practice and essay writing reflect outdated constraints?',\n",
- " 'Why did Y Combinator change its name to avoid a regional association?',\n",
- " \"What was the significance of the orange color chosen for Y Combinator's logo?\",\n",
- " 'Why did Y Combinator become a fund for a couple of years before returning to self-funding?',\n",
- " 'What is the purpose of Y Combinator in relation to the concept of \"deal flow\"?',\n",
- " 'How did the combination of running a forum and writing essays lead to a problem for the author?',\n",
- " \"What was the author's biggest regret about leaving Y Combinator?\"]"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eval_questions"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "b9b98f89-d5b8-4d29-92f6-ad76d5060e9f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# gpt-4\n",
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "8eb3e616-64e5-4bf4-a67b-661e9b3657e7",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "evaluator_gpt4 = QueryResponseEvaluator(service_context=service_context_gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c",
- "metadata": {},
- "outputs": [],
- "source": [
- "# create vector index\n",
- "vector_index = VectorStoreIndex.from_documents(\n",
- " documents, \n",
- " service_context=service_context_gpt4\n",
- ")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "id": "af730b2e-6949-4865-b7af-bb2bc60a9173",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define jupyter display function\n",
- "def display_eval_df(query: str, response: Response, eval_result: str) -> None:\n",
- " eval_df = pd.DataFrame(\n",
- " {\n",
- " \"Query\": query,\n",
- " \"Response\": str(response), \n",
- " \"Source\": response.source_nodes[0].node.get_content()[:1000] + \"...\",\n",
- " \"Evaluation Result\": eval_result\n",
- " },\n",
- " index=[0]\n",
- " )\n",
- " eval_df = eval_df.style.set_properties(\n",
- " **{\n",
- " 'inline-size': '600px',\n",
- " 'overflow-wrap': 'break-word',\n",
- " }, \n",
- " subset=[\"Response\", \"Source\"]\n",
- " )\n",
- " display(eval_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "180a5d2e-9286-477b-9cd0-a5976d18d845",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(eval_questions[1])\n",
- "eval_result = evaluator_gpt4.evaluate(eval_questions[1], response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "id": "c764b8b3-69b1-4ac8-b88b-3f9e204b8bfb",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_138fa_row0_col1, #T_138fa_row0_col2 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_138fa\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_138fa_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
- " <th id=\"T_138fa_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
- " <th id=\"T_138fa_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
- " <th id=\"T_138fa_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_138fa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_138fa_row0_col0\" class=\"data row0 col0\" >How did the author describe their early attempts at writing short stories?</td>\n",
- " <td id=\"T_138fa_row0_col1\" class=\"data row0 col1\" >The author described their early attempts at writing short stories as awful. They mentioned that their stories had hardly any plot and were mostly about characters with strong feelings, which they thought made the stories deep.</td>\n",
- " <td id=\"T_138fa_row0_col2\" class=\"data row0 col2\" >What I Worked On\n",
- "\n",
- "February 2021\n",
- "\n",
- "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n",
- "\n",
- "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n",
- "\n",
- "The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the...</td>\n",
- " <td id=\"T_138fa_row0_col3\" class=\"data row0 col3\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x7fcb78d7f130>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(eval_questions[1], response_vector, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "52581d3c-7ad1-49a8-9de0-71fea2a945b4",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f3f797ad",
+ "metadata": {},
+ "source": [
+ "# Question Generation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "9080b39e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import pandas as pd\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.evaluation import DatasetGenerator, QueryResponseEvaluator\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " VectorStoreIndex,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
+ " Response,\n",
+ ")\n",
+ "from llama_index.llms import OpenAI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "834f4c8c-8c10-4f8d-bf43-444aaa1234b1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "reader = SimpleDirectoryReader(\"../data/paul_graham/\")\n",
+ "documents = reader.load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "9cc71140-d614-4696-9ade-d5bdc251d398",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:llama_index.indices.service_context:chunk_size_limit is deprecated, please specify chunk_size instead\n",
+ "chunk_size_limit is deprecated, please specify chunk_size instead\n",
+ "chunk_size_limit is deprecated, please specify chunk_size instead\n",
+ "chunk_size_limit is deprecated, please specify chunk_size instead\n",
+ "chunk_size_limit is deprecated, please specify chunk_size instead\n"
+ ]
+ }
+ ],
+ "source": [
+ "data_generator = DatasetGenerator.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "f481b532-9be2-4ec3-b551-fd44060099bd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "eval_questions = data_generator.generate_questions_from_nodes()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "63720bd6-c060-4cc2-8a60-a39e935ee3e6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['What were the two main things the author worked on before college?',\n",
+ " 'How did the author describe their early attempts at writing short stories?',\n",
+ " 'What type of computer did the author first work on for programming?',\n",
+ " 'What language did the author use for programming on the IBM 1401?',\n",
+ " \"What was the author's experience with programming on the 1401?\",\n",
+ " 'What type of computer did the author eventually get for themselves?',\n",
+ " \"What was the author's initial plan for college?\",\n",
+ " 'What made the author change their mind about studying philosophy?',\n",
+ " \"What sparked the author's interest in AI?\",\n",
+ " 'What did the author realize about AI during their first year of grad school?',\n",
+ " 'What were the two art schools that the author applied to?',\n",
+ " 'How did the author end up at RISD?',\n",
+ " 'What was the purpose of the foundation classes at RISD?',\n",
+ " 'How did the author manage to pass the entrance exam for the Accademia di Belli Arti?',\n",
+ " 'What was the arrangement between the students and faculty at the Accademia?',\n",
+ " \"What was the author's experience painting still lives in Florence?\",\n",
+ " 'What did the author learn about visual perception while painting still lives?',\n",
+ " 'Why did the author decide to leave the Accademia and return to the US?',\n",
+ " 'What did the author learn about technology companies while working at Interleaf?',\n",
+ " 'What lesson did the author learn about the low end and high end in the software industry?',\n",
+ " \"What was the author's motivation for writing another book on Lisp?\",\n",
+ " 'How did the author come up with the idea for starting a company to put art galleries online?',\n",
+ " 'What was the initial reaction of art galleries to the idea of being online?',\n",
+ " 'How did the author and his team come up with the concept of a web app?',\n",
+ " 'What were the three main parts of the software developed by the author and his team?',\n",
+ " 'How did the author and his team learn about retail and improve their software based on user feedback?',\n",
+ " 'Why did the author initially believe that the absolute number of users was the most important factor for a startup?',\n",
+ " \"What was the growth rate of the author's company and why was it significant?\",\n",
+ " \"How did the author's decision to hire more people impact the financial stability of the company?\",\n",
+ " \"What was the outcome of the company's acquisition by Yahoo in 1998?\",\n",
+ " \"What was the author's initial reaction when Yahoo bought their startup?\",\n",
+ " \"How did the author's lifestyle change after Yahoo bought their startup?\",\n",
+ " 'Why did the author leave Yahoo and what did they plan to do?',\n",
+ " \"What was the author's experience like when they returned to New York after becoming rich?\",\n",
+ " 'What idea did the author have in the spring of 2000 and why did they decide to start a new company?',\n",
+ " \"Why did the author decide to build a subset of the new company's vision as an open source project?\",\n",
+ " \"How did the author's perception of publishing essays change with the advent of the internet?\",\n",
+ " \"What is the author's perspective on working on things that are not prestigious?\",\n",
+ " 'What other projects did the author work on besides writing essays?',\n",
+ " 'What type of building did the author buy in Cambridge?',\n",
+ " \"What was the concept behind the big party at the narrator's house in October 2003?\",\n",
+ " \"How did Jessica Livingston's perception of startups change after meeting friends of the narrator?\",\n",
+ " 'What were some of the ideas that the narrator shared with Jessica about fixing venture capital?',\n",
+ " 'How did the idea of starting their own investment firm come about for the narrator and Jessica?',\n",
+ " 'What was the Summer Founders Program and how did it attract applicants?',\n",
+ " \"How did Y Combinator's batch model help solve the problem of isolation for startup founders?\",\n",
+ " \"What advantages did YC's scale bring, both in terms of community and customer acquisition?\",\n",
+ " 'Why did the narrator consider Hacker News to be a source of stress?',\n",
+ " \"How did the narrator's role in YC differ from other types of work they had done?\",\n",
+ " 'What advice did Robert Morris offer the narrator during his visit in 2010?',\n",
+ " 'What was the advice given to the author by Rtm regarding their involvement with Y Combinator?',\n",
+ " 'Why did the author decide to hand over Y Combinator to someone else?',\n",
+ " \"What event in the author's personal life prompted them to reevaluate their priorities?\",\n",
+ " 'How did the author spend most of 2014?',\n",
+ " 'What project did the author work on from March 2015 to October 2019?',\n",
+ " 'How did the author manage to write an interpreter for Lisp in itself?',\n",
+ " \"What was the author's experience like living in England?\",\n",
+ " \"When was the author's project, Bel, finally finished?\",\n",
+ " 'What did the author do during the fall of 2019?',\n",
+ " \"How would you describe the author's journey and decision-making process throughout the document?\",\n",
+ " \"How did the author's experience with editing Lisp expressions differ from traditional app editing?\",\n",
+ " 'Why did the author receive negative comments when claiming that Lisp was better than other languages?',\n",
+ " 'What is the difference between putting something online and publishing it online?',\n",
+ " 'How did the customs of venture capital practice and essay writing reflect outdated constraints?',\n",
+ " 'Why did Y Combinator change its name to avoid a regional association?',\n",
+ " \"What was the significance of the orange color chosen for Y Combinator's logo?\",\n",
+ " 'Why did Y Combinator become a fund for a couple of years before returning to self-funding?',\n",
+ " 'What is the purpose of Y Combinator in relation to the concept of \"deal flow\"?',\n",
+ " 'How did the combination of running a forum and writing essays lead to a problem for the author?',\n",
+ " \"What was the author's biggest regret about leaving Y Combinator?\"]"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_questions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "b9b98f89-d5b8-4d29-92f6-ad76d5060e9f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# gpt-4\n",
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "8eb3e616-64e5-4bf4-a67b-661e9b3657e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "evaluator_gpt4 = QueryResponseEvaluator(service_context=service_context_gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create vector index\n",
+ "vector_index = VectorStoreIndex.from_documents(\n",
+ " documents, service_context=service_context_gpt4\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "af730b2e-6949-4865-b7af-bb2bc60a9173",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define jupyter display function\n",
+ "def display_eval_df(query: str, response: Response, eval_result: str) -> None:\n",
+ " eval_df = pd.DataFrame(\n",
+ " {\n",
+ " \"Query\": query,\n",
+ " \"Response\": str(response),\n",
+ " \"Source\": response.source_nodes[0].node.get_content()[:1000] + \"...\",\n",
+ " \"Evaluation Result\": eval_result,\n",
+ " },\n",
+ " index=[0],\n",
+ " )\n",
+ " eval_df = eval_df.style.set_properties(\n",
+ " **{\n",
+ " \"inline-size\": \"600px\",\n",
+ " \"overflow-wrap\": \"break-word\",\n",
+ " },\n",
+ " subset=[\"Response\", \"Source\"]\n",
+ " )\n",
+ " display(eval_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "180a5d2e-9286-477b-9cd0-a5976d18d845",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(eval_questions[1])\n",
+ "eval_result = evaluator_gpt4.evaluate(eval_questions[1], response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "c764b8b3-69b1-4ac8-b88b-3f9e204b8bfb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_138fa_row0_col1, #T_138fa_row0_col2 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_138fa\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_138fa_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
+ " <th id=\"T_138fa_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
+ " <th id=\"T_138fa_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
+ " <th id=\"T_138fa_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_138fa_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_138fa_row0_col0\" class=\"data row0 col0\" >How did the author describe their early attempts at writing short stories?</td>\n",
+ " <td id=\"T_138fa_row0_col1\" class=\"data row0 col1\" >The author described their early attempts at writing short stories as awful. They mentioned that their stories had hardly any plot and were mostly about characters with strong feelings, which they thought made the stories deep.</td>\n",
+ " <td id=\"T_138fa_row0_col2\" class=\"data row0 col2\" >What I Worked On\n",
+ "\n",
+ "February 2021\n",
+ "\n",
+ "Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.\n",
+ "\n",
+ "The first programs I tried writing were on the IBM 1401 that our school district used for what was then called \"data processing.\" This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.\n",
+ "\n",
+ "The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the...</td>\n",
+ " <td id=\"T_138fa_row0_col3\" class=\"data row0 col3\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x7fcb78d7f130>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(eval_questions[1], response_vector, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "52581d3c-7ad1-49a8-9de0-71fea2a945b4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/evaluation/RetryQuery.ipynb b/docs/examples/evaluation/RetryQuery.ipynb
index aec68b262f..b549f30fbf 100644
--- a/docs/examples/evaluation/RetryQuery.ipynb
+++ b/docs/examples/evaluation/RetryQuery.ipynb
@@ -78,7 +78,7 @@
},
"outputs": [],
"source": [
- "documents = SimpleDirectoryReader('../data/paul_graham/').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
"index = VectorStoreIndex.from_documents(documents)\n",
"query = \"What did the author do growing up?\""
]
@@ -194,9 +194,11 @@
"source": [
"from llama_index.query_engine import RetrySourceQueryEngine\n",
"\n",
- "retry_source_query_engine = RetrySourceQueryEngine(base_query_engine, query_response_evaluator)\n",
+ "retry_source_query_engine = RetrySourceQueryEngine(\n",
+ " base_query_engine, query_response_evaluator\n",
+ ")\n",
"retry_source_response = retry_source_query_engine.query(query)\n",
- "print(retry_source_response) "
+ "print(retry_source_response)"
]
},
{
@@ -234,8 +236,7 @@
"\n",
"# Guideline eval\n",
"guideline_eval = GuidelineEvaluator(\n",
- " guidelines=DEFAULT_GUIDELINES\n",
- " + \"\\nThe response should not be overly long.\\n\"\n",
+ " guidelines=DEFAULT_GUIDELINES + \"\\nThe response should not be overly long.\\n\"\n",
" \"The response should try to summarize where possible.\\n\"\n",
") # just for example"
]
diff --git a/docs/examples/evaluation/TestNYC-Evaluation-Query.ipynb b/docs/examples/evaluation/TestNYC-Evaluation-Query.ipynb
index cb5bc0ddfa..bac4c2ae31 100644
--- a/docs/examples/evaluation/TestNYC-Evaluation-Query.ipynb
+++ b/docs/examples/evaluation/TestNYC-Evaluation-Query.ipynb
@@ -1,796 +1,801 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0c266183",
- "metadata": {},
- "source": [
- "# Query Response Evaluator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9080b39e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import (\n",
- " TreeIndex, \n",
- " VectorStoreIndex, \n",
- " SimpleDirectoryReader, \n",
- " LLMPredictor, \n",
- " ServiceContext,\n",
- " Response\n",
- ")\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.evaluation import QueryResponseEvaluator\n",
- "import pandas as pd\n",
- "pd.set_option('display.max_colwidth', 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b9b98f89-d5b8-4d29-92f6-ad76d5060e9f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# gpt-3 (davinci)\n",
- "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
- "service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3)\n",
- "\n",
- "# gpt-4\n",
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8eb3e616-64e5-4bf4-a67b-661e9b3657e7",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "evaluator = QueryResponseEvaluator(service_context=service_context_gpt3)\n",
- "evaluator_gpt4 = QueryResponseEvaluator(service_context=service_context_gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../test_wiki/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "dca06a5b-8a15-40b4-8c7f-dae5407c674f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# create tree index\n",
- "tree_index = TreeIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c",
- "metadata": {},
- "outputs": [],
- "source": [
- "# create vector index\n",
- "vector_index = VectorStoreIndex.from_documents(\n",
- " documents, \n",
- " service_context=ServiceContext.from_defaults(chunk_size=512)\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "af730b2e-6949-4865-b7af-bb2bc60a9173",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define jupyter display function\n",
- "def display_eval_df(query: str, response: Response, eval_result: str) -> None:\n",
- " eval_df = pd.DataFrame(\n",
- " {\n",
- " \"Query\": query,\n",
- " \"Response\": str(response), \n",
- " \"Source\": response.source_nodes[0].source_text[:1000] + \"...\",\n",
- " \"Evaluation Result\": eval_result\n",
- " },\n",
- " index=[0]\n",
- " )\n",
- " eval_df = eval_df.style.set_properties(\n",
- " **{\n",
- " 'inline-size': '600px',\n",
- " 'overflow-wrap': 'break-word',\n",
- " }, \n",
- " subset=[\"Response\", \"Source\"]\n",
- " )\n",
- " display(eval_df)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "4780e16a-aa6c-4143-978d-4a93a4357130",
- "metadata": {},
- "source": [
- "### Evaluate Response\n",
- "\n",
- "Evaluate response relative to source nodes as well as query."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_str = \"What battles took place in New York City in the American Revolution?\"\n",
- "query_engine = tree_index.as_query_engine()\n",
- "response_tree = query_engine.query(query_str)\n",
- "eval_result = evaluator_gpt4.evaluate(query_str, response_tree)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "db9d00bc-8428-4a08-b48e-248ad7570923",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_aa49d_row0_col1, #T_aa49d_row0_col2 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_aa49d\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_aa49d_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
- " <th id=\"T_aa49d_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
- " <th id=\"T_aa49d_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
- " <th id=\"T_aa49d_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_aa49d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_aa49d_row0_col0\" class=\"data row0 col0\" >What battles took place in New York City in the American Revolution?</td>\n",
- " <td id=\"T_aa49d_row0_col1\" class=\"data row0 col1\" >The Battle of Long Island, the Battle of White Plains, the Battle of Harlem Heights, the Battle of Fort Washington, the Battle of Fort Lee, and the Battle of Yorktown all took place in New York City during the American Revolution. These battles took place in various locations throughout the city, including Battery Weed and Fort Tompkins, Great Kills Park, and Central Park.</td>\n",
- " <td id=\"T_aa49d_row0_col2\" class=\"data row0 col2\" >in 2015 makes it the highest of any county in the United States and higher than the density of any individual American city.Manhattan is the cultural, administrative, and financial center of New York City and contains the headquarters of many major multinational corporations, the United Nations headquarters, Wall Street, and a number of important universities. The borough of Manhattan is often described as the financial and cultural center of the world.Most of the borough is situated on Manhattan Island, at the mouth of the Hudson River and the East River, and its southern tip, at the confluence of the two rivers, represents the birthplace of New York City itself. Several small islands also compose part of the borough of Manhattan, including Randalls and Wards Islands, and Roosevelt Island in the East River, and Governors Island and Liberty Island to the south in New York Harbor.\n",
- "Manhattan Island is loosely divided into the Lower, Midtown, and Uptown regions. Uptown Manhattan is divide...</td>\n",
- " <td id=\"T_aa49d_row0_col3\" class=\"data row0 col3\" >NO</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d6e8310>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(query_str, response_tree, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "180a5d2e-9286-477b-9cd0-a5976d18d845",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_str = \"What battles took place in New York City in the American Revolution?\"\n",
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(query_str)\n",
- "eval_result = evaluator_gpt4.evaluate(query_str, response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "c764b8b3-69b1-4ac8-b88b-3f9e204b8bfb",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_36563_row0_col1, #T_36563_row0_col2 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_36563\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_36563_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
- " <th id=\"T_36563_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
- " <th id=\"T_36563_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
- " <th id=\"T_36563_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_36563_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_36563_row0_col0\" class=\"data row0 col0\" >What battles took place in New York City in the American Revolution?</td>\n",
- " <td id=\"T_36563_row0_col1\" class=\"data row0 col1\" >\n",
- "The Battle of Long Island and the Great Fire of New York.</td>\n",
- " <td id=\"T_36563_row0_col2\" class=\"data row0 col2\" >at labor. Slavery became integrally tied to New York's economy through the labor of slaves throughout the port, and the banking and shipping industries trading with the American South. During construction in Foley Square in the 1990s, the African Burying Ground was discovered; the cemetery included 10,000 to 20,000 of graves of colonial-era Africans, some enslaved and some free.The 1735 trial and acquittal in Manhattan of John Peter Zenger, who had been accused of seditious libel after criticizing colonial governor William Cosby, helped to establish the freedom of the press in North America. In 1754, Columbia University was founded under charter by King George II as King's College in Lower Manhattan.\n",
- "\n",
- "\n",
- "=== American Revolution ===\n",
- "\n",
- "The Stamp Act Congress met in New York in October 1765, as the Sons of Liberty organization emerged in the city and skirmished over the next ten years with British troops stationed there. The Battle of Long Island, the largest battle of the American Revolutio...</td>\n",
- " <td id=\"T_36563_row0_col3\" class=\"data row0 col3\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d6ea8f0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(query_str, response_vector, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_str = \"What are the airports in New York City?\"\n",
- "query_engine = tree_index.as_query_engine()\n",
- "response_tree = query_engine.query(query_str)\n",
- "eval_result = evaluator_gpt4.evaluate(query_str, response_tree)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "id": "a34490f6-7242-4c31-b49e-b2a65d9923ab",
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- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_26784_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
- " <th id=\"T_26784_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
- " <th id=\"T_26784_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
- " <th id=\"T_26784_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_26784_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_26784_row0_col0\" class=\"data row0 col0\" >What are the airports in New York City?</td>\n",
- " <td id=\"T_26784_row0_col1\" class=\"data row0 col1\" >The airports in New York City are John F. Kennedy International Airport (JFK), LaGuardia Airport (LGA), and Newark Liberty International Airport (EWR).</td>\n",
- " <td id=\"T_26784_row0_col2\" class=\"data row0 col2\" >Avenue are also used as metonyms for national industries there: the theater, finance, advertising, and fashion organizations, respectively.\n",
- "New York City also has an extensive web of freeways and parkways, which link the city's boroughs to each other and to North Jersey, Westchester County, Long Island, and southwestern Connecticut through various bridges and tunnels. Because these highways serve millions of outer borough and suburban residents who commute into Manhattan, it is quite common for motorists to be stranded for hours in traffic congestion that are a daily occurrence, particularly during rush hour. Congestion pricing in New York City will go into effect in 2022 at the earliest.New York City is also known for its rules regarding turning at red lights. Unlike the rest of the United States, New York State prohibits right or left turns on red in cities with a population greater than one million, to reduce traffic collisions and increase pedestrian safety. In New York City, there...</td>\n",
- " <td id=\"T_26784_row0_col3\" class=\"data row0 col3\" >NO</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d52a560>"
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- "metadata": {},
- "output_type": "display_data"
- }
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- "source": [
- "display_eval_df(query_str, response_tree, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_str = \"What are the airports in New York City?\"\n",
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(query_str)\n",
- "eval_result = evaluator_gpt4.evaluate(query_str, response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "01c53014-82b0-4865-b849-c0beb042143d",
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- "<table id=\"T_920be\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_920be_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
- " <th id=\"T_920be_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
- " <th id=\"T_920be_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
- " <th id=\"T_920be_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_920be_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_920be_row0_col0\" class=\"data row0 col0\" >What are the airports in New York City?</td>\n",
- " <td id=\"T_920be_row0_col1\" class=\"data row0 col1\" >\n",
- "The airports in New York City are Long Island MacArthur Airport, Trenton–Mercer Airport, Westchester County Airport, and Teterboro Airport.</td>\n",
- " <td id=\"T_920be_row0_col2\" class=\"data row0 col2\" >announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
- "\n",
- "\n",
- "=== Ferries ===\n",
- "\n",
- "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
- "NYC Ferry, a NYCEDC initiative with routes planned to travel to all five boroughs, was launched in 2017, with second graders choosing the names of the ferries. Meanwhile, Seastreak ferry announced construction of a 600-passenger high-speed luxury ferry in Septe...</td>\n",
- " <td id=\"T_920be_row0_col3\" class=\"data row0 col3\" >NO</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d52aa10>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(query_str, response_vector, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b860691-9f6a-4b2f-bfef-5a5a56693a18",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_str = \"Who is the mayor of New York City?\"\n",
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(query_str)\n",
- "eval_result = evaluator_gpt4.evaluate(query_str, response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "91cce4e3-b4e5-4583-9543-6c3726c0e7d3",
- "metadata": {
- "tags": []
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- "data": {
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- "<style type=\"text/css\">\n",
- "#T_efae7_row0_col1, #T_efae7_row0_col2 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_efae7\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_efae7_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
- " <th id=\"T_efae7_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
- " <th id=\"T_efae7_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
- " <th id=\"T_efae7_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_efae7_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_efae7_row0_col0\" class=\"data row0 col0\" >Who is the mayor of New York City?</td>\n",
- " <td id=\"T_efae7_row0_col1\" class=\"data row0 col1\" >\n",
- "The mayor of New York City is Eric Adams.</td>\n",
- " <td id=\"T_efae7_row0_col2\" class=\"data row0 col2\" >by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n",
- "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for t...</td>\n",
- " <td id=\"T_efae7_row0_col3\" class=\"data row0 col3\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d551090>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(query_str, response_vector, eval_result)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0ee6a336-8fd0-46b3-bb8f-7f47a8781c60",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Evaluate Source Nodes\n",
- "\n",
- "Evaluate the set of returned sources, and determine which sources actually contain the answer to a given query."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "74eed7ba-7ed5-44fa-af4d-356f6ce3b710",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from typing import List\n",
- "\n",
- "# define jupyter display function\n",
- "def display_eval_sources(query: str, response: Response, eval_result: List[str]) -> None:\n",
- " \n",
- " sources = [s.node.get_text() for s in response.source_nodes]\n",
- " eval_df = pd.DataFrame(\n",
- " {\n",
- " \"Source\": sources,\n",
- " \"Eval Result\": eval_result, \n",
- " },\n",
- " )\n",
- " eval_df.style.set_caption(query)\n",
- " eval_df = eval_df.style.set_properties(\n",
- " **{\n",
- " 'inline-size': '600px',\n",
- " 'overflow-wrap': 'break-word',\n",
- " }, \n",
- " subset=[\"Source\"]\n",
- " )\n",
- " \n",
- "\n",
- " display(eval_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0e3674ed-2632-4f48-9102-895fea94e340",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# NOTE: you can set response_mode=\"no_text\" to get just the sources\n",
- "query_str = \"What are the airports in New York City?\"\n",
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(query_str, similarity_top_k=3, response_mode=\"no_text\")\n",
- "eval_source_result = evaluator_gpt4.evaluate_source_nodes(query_str, response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "35125a2d-927e-40d3-ac83-da9297b6e81e",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_62aff_row0_col0, #T_62aff_row1_col0, #T_62aff_row2_col0 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_62aff\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_62aff_level0_col0\" class=\"col_heading level0 col0\" >Source</th>\n",
- " <th id=\"T_62aff_level0_col1\" class=\"col_heading level0 col1\" >Eval Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_62aff_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_62aff_row0_col0\" class=\"data row0 col0\" >announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
- "\n",
- "\n",
- "=== Ferries ===\n",
- "\n",
- "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
- "NYC Ferry, a NYCEDC initiative with routes planned to travel to all five boroughs, was launched in 2017, with second graders choosing the names of the ferries. Meanwhile, Seastreak ferry announced construction of a 600-passenger high-speed luxury ferry in September 2016, to shuttle riders between the Jersey Shore and Manhattan, anticipated to start service in 2017; this would be the largest vessel in its class.\n",
- "\n",
- "\n",
- "=== Taxis, vehicles for hire, and trams ===\n",
- "\n",
- "Other features of the city's transportation infrastructure encompass 13,587 yellow taxicabs; other vehicle for hire companies; and the Roosevelt Island Tramway, an aerial tramway that transports commuters between Roosevelt Island and Manhattan Island.\n",
- "\n",
- "\n",
- "=== Streets and highways ===\n",
- "\n",
- "Despite New York's heavy reliance on its vast public transit system, streets are a defining feature of the city. The Commissioners' Plan of 1811 greatly influenced the city's physical development. Several of the city's streets and avenues, including Broadway, Wall Street, Madison Avenue, and Seventh Avenue are also used as metonyms for national industries there: the theater, finance, advertising, and fashion organizations, respectively.\n",
- "New York City also has an extensive web of freeways and parkways, which link the city's boroughs to each other and to North Jersey,</td>\n",
- " <td id=\"T_62aff_row0_col1\" class=\"data row0 col1\" >YES</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_62aff_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_62aff_row1_col0\" class=\"data row1 col0\" >the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n",
- "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n",
- "\n",
- "\n",
- "==== Buses ====\n",
- "\n",
- "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buses and 200,000 commuters daily, making it the busiest bus station in the world.\n",
- "\n",
- "\n",
- "=== Air ===\n",
- "\n",
- "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International Airport, and LaGuardia Airport; 130.5 million travelers used these three airports in 2016. JFK and Newark Liberty were the busiest and fourth busiest U.S. gateways for international air passengers, respectively, in 2012; as of 2011, JFK was the busiest airport for international passengers in North America.Plans have advanced to expand passenger volume at a fourth airport, Stewart International Airport near Newburgh, New York, by the Port Authority of New York and New Jersey. Plans were announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
- "\n",
- "\n",
- "=== Ferries ===\n",
- "\n",
- "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
- "NYC Ferry, a NYCEDC initiative with routes planned to travel</td>\n",
- " <td id=\"T_62aff_row1_col1\" class=\"data row1 col1\" >YES</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_62aff_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_62aff_row2_col0\" class=\"data row2 col0\" >and New Jersey Transit. The combined systems converge at Grand Central Terminal and Pennsylvania Station and contain more than 250 stations and 20 rail lines. In Queens, the elevated AirTrain people mover system connects 24 hours a day JFK International Airport to the New York City Subway and the Long Island Rail Road; a separate AirTrain system is planned alongside the Grand Central Parkway to connect LaGuardia Airport to these transit systems. For inter-city rail, New York City is served by Amtrak, whose busiest station by a significant margin is Pennsylvania Station on the West Side of Manhattan, from which Amtrak provides connections to Boston, Philadelphia, and Washington, D.C. along the Northeast Corridor, and long-distance train service to other North American cities.The Staten Island Railway rapid transit system solely serves Staten Island, operating 24 hours a day. The Port Authority Trans-Hudson (PATH train) links Midtown and Lower Manhattan to northeastern New Jersey, primarily Hoboken, Jersey City, and Newark. Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n",
- "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n",
- "\n",
- "\n",
- "==== Buses ====\n",
- "\n",
- "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buses and 200,000 commuters daily, making it the busiest bus station in the world.\n",
- "\n",
- "\n",
- "=== Air ===\n",
- "\n",
- "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International Airport, and LaGuardia Airport;</td>\n",
- " <td id=\"T_62aff_row2_col1\" class=\"data row2 col1\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d6ea6e0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_sources(query_str, response_vector, eval_source_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cdc340a9-1853-414e-9e87-820aa5d82db7",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# NOTE: you can set response_mode=\"no_text\" to get just the sources\n",
- "query_str = \"Who is the mayor of New York City?\"\n",
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(query_str, similarity_top_k=3, response_mode=\"no_text\")\n",
- "eval_source_result = evaluator_gpt4.evaluate_source_nodes(query_str, response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "b88eb350-cae6-4890-9b55-cb5f750afa9e",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
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- " overflow-wrap: break-word;\n",
- "}\n",
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- " <thead>\n",
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- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_79b25_level0_col0\" class=\"col_heading level0 col0\" >Source</th>\n",
- " <th id=\"T_79b25_level0_col1\" class=\"col_heading level0 col1\" >Eval Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_79b25_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_79b25_row0_col0\" class=\"data row0 col0\" >by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n",
- "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for the Southern District of New York, whose main courthouse is on Foley Square near City Hall in Manhattan and whose jurisdiction includes Manhattan and the Bronx; and the District Court for the Eastern District of New York, whose main courthouse is in Brooklyn and whose jurisdiction includes Brooklyn, Queens, and Staten Island. The U.S. Court of Appeals for the Second Circuit and U.S. Court of International Trade are also based in New York, also on Foley Square in Manhattan.\n",
- "\n",
- "\n",
- "=== Politics ===\n",
- "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n",
- "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping all</td>\n",
- " <td id=\"T_79b25_row0_col1\" class=\"data row0 col1\" >NO</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_79b25_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_79b25_row1_col0\" class=\"data row1 col0\" >for the Second Circuit and U.S. Court of International Trade are also based in New York, also on Foley Square in Manhattan.\n",
- "\n",
- "\n",
- "=== Politics ===\n",
- "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n",
- "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping all five boroughs. Party platforms center on affordable housing, education, and economic development, and labor politics are of importance in the city. Thirteen out of 27 U.S. congressional districts in the state of New York include portions of New York City.New York is one of the most important sources of political fundraising in the United States. At least four of the top five ZIP Codes in the nation for political contributions were in Manhattan for the 2004, 2006, and 2008 elections. The top ZIP Code, 10021 on the Upper East Side, generated the most money for the 2004 presidential campaigns of George W. Bush and John Kerry. The city has a strong imbalance of payments with the national and state governments. It receives 83 cents in services for every $1 it sends to the federal government in taxes (or annually sends $11.4 billion more than it receives back). City residents and businesses also sent an additional $4.1 billion in the 2009–2010 fiscal year to the state of New York than the city received in return.\n",
- "\n",
- "\n",
- "== Transportation ==\n",
- "\n",
- "New York City's comprehensive transportation system is both complex and extensive.\n",
- "\n",
- "\n",
- "=== Rapid transit ===\n",
- "Mass transit in New York City, most of which runs 24 hours a day, accounts for one in every three users of mass transit in</td>\n",
- " <td id=\"T_79b25_row1_col1\" class=\"data row1 col1\" >YES</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th id=\"T_79b25_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
- " <td id=\"T_79b25_row2_col0\" class=\"data row2 col0\" >ed.). New York: The New Press. ISBN 978-1-56584-321-9.\n",
- "Holli, Melvin G., and Jones, Peter d'A., eds. Biographical Dictionary of American Mayors, 1820-1980 (Greenwood Press, 1981) short scholarly biographies each of the city's mayors 1820 to 1980. online; see index at p. 410 for list.Jackson, Kenneth T., ed. (1995). The Encyclopedia of New York City. New Haven: Yale University Press. ISBN 0300055366.\n",
- "Jackson, Kenneth T.; Dunbar, David S., eds. (2005). Empire City: New York Through the Centuries. Columbia University Press. ISBN 978-0-231-10909-3.\n",
- "Lankevich, George L. (1998). American Metropolis: A History of New York City. NYU Press. ISBN 978-0-8147-5186-2.\n",
- "White, E.B. (1949). Here is New York (2000 reissue ed.). Little Bookroom.\n",
- "White, Norval & Willensky, Elliot (2000). AIA Guide to New York City (4th ed.). New York: Three Rivers Press. ISBN 978-0-8129-3107-5.\n",
- "Whitehead, Colson (2003). The Colossus of New York: A City in 13 Parts. New York: Doubleday. ISBN 978-0-385-50794-3.\n",
- "\n",
- "\n",
- "== External links ==\n",
- "\n",
- "Official website \n",
- "NYC Go, official tourism website\n",
- "New York City at Curlie\n",
- " Geographic data related to New York City at OpenStreetMap\n",
- "Collections, 145,000 NYC photographs at the Museum of the City of New York\n",
- "\"The New New York Skyline (interactive)\". National Geographic. November 2015.</td>\n",
- " <td id=\"T_79b25_row2_col1\" class=\"data row2 col1\" >NO</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x17d6eab00>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_sources(query_str, response_vector, eval_source_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "72681384-c71d-44b9-9385-54820db30988",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0c266183",
+ "metadata": {},
+ "source": [
+ "# Query Response Evaluator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9080b39e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " TreeIndex,\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " Response,\n",
+ ")\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.evaluation import QueryResponseEvaluator\n",
+ "import pandas as pd\n",
+ "\n",
+ "pd.set_option(\"display.max_colwidth\", 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9b98f89-d5b8-4d29-92f6-ad76d5060e9f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# gpt-3 (davinci)\n",
+ "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
+ "service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3)\n",
+ "\n",
+ "# gpt-4\n",
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8eb3e616-64e5-4bf4-a67b-661e9b3657e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "evaluator = QueryResponseEvaluator(service_context=service_context_gpt3)\n",
+ "evaluator_gpt4 = QueryResponseEvaluator(service_context=service_context_gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../test_wiki/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dca06a5b-8a15-40b4-8c7f-dae5407c674f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create tree index\n",
+ "tree_index = TreeIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create vector index\n",
+ "vector_index = VectorStoreIndex.from_documents(\n",
+ " documents, service_context=ServiceContext.from_defaults(chunk_size=512)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "af730b2e-6949-4865-b7af-bb2bc60a9173",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define jupyter display function\n",
+ "def display_eval_df(query: str, response: Response, eval_result: str) -> None:\n",
+ " eval_df = pd.DataFrame(\n",
+ " {\n",
+ " \"Query\": query,\n",
+ " \"Response\": str(response),\n",
+ " \"Source\": response.source_nodes[0].source_text[:1000] + \"...\",\n",
+ " \"Evaluation Result\": eval_result,\n",
+ " },\n",
+ " index=[0],\n",
+ " )\n",
+ " eval_df = eval_df.style.set_properties(\n",
+ " **{\n",
+ " \"inline-size\": \"600px\",\n",
+ " \"overflow-wrap\": \"break-word\",\n",
+ " },\n",
+ " subset=[\"Response\", \"Source\"]\n",
+ " )\n",
+ " display(eval_df)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "4780e16a-aa6c-4143-978d-4a93a4357130",
+ "metadata": {},
+ "source": [
+ "### Evaluate Response\n",
+ "\n",
+ "Evaluate response relative to source nodes as well as query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_str = \"What battles took place in New York City in the American Revolution?\"\n",
+ "query_engine = tree_index.as_query_engine()\n",
+ "response_tree = query_engine.query(query_str)\n",
+ "eval_result = evaluator_gpt4.evaluate(query_str, response_tree)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "db9d00bc-8428-4a08-b48e-248ad7570923",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_aa49d_row0_col1, #T_aa49d_row0_col2 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_aa49d\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_aa49d_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
+ " <th id=\"T_aa49d_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
+ " <th id=\"T_aa49d_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
+ " <th id=\"T_aa49d_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_aa49d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_aa49d_row0_col0\" class=\"data row0 col0\" >What battles took place in New York City in the American Revolution?</td>\n",
+ " <td id=\"T_aa49d_row0_col1\" class=\"data row0 col1\" >The Battle of Long Island, the Battle of White Plains, the Battle of Harlem Heights, the Battle of Fort Washington, the Battle of Fort Lee, and the Battle of Yorktown all took place in New York City during the American Revolution. These battles took place in various locations throughout the city, including Battery Weed and Fort Tompkins, Great Kills Park, and Central Park.</td>\n",
+ " <td id=\"T_aa49d_row0_col2\" class=\"data row0 col2\" >in 2015 makes it the highest of any county in the United States and higher than the density of any individual American city.Manhattan is the cultural, administrative, and financial center of New York City and contains the headquarters of many major multinational corporations, the United Nations headquarters, Wall Street, and a number of important universities. The borough of Manhattan is often described as the financial and cultural center of the world.Most of the borough is situated on Manhattan Island, at the mouth of the Hudson River and the East River, and its southern tip, at the confluence of the two rivers, represents the birthplace of New York City itself. Several small islands also compose part of the borough of Manhattan, including Randalls and Wards Islands, and Roosevelt Island in the East River, and Governors Island and Liberty Island to the south in New York Harbor.\n",
+ "Manhattan Island is loosely divided into the Lower, Midtown, and Uptown regions. Uptown Manhattan is divide...</td>\n",
+ " <td id=\"T_aa49d_row0_col3\" class=\"data row0 col3\" >NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d6e8310>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(query_str, response_tree, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "180a5d2e-9286-477b-9cd0-a5976d18d845",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_str = \"What battles took place in New York City in the American Revolution?\"\n",
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(query_str)\n",
+ "eval_result = evaluator_gpt4.evaluate(query_str, response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "c764b8b3-69b1-4ac8-b88b-3f9e204b8bfb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_36563_row0_col1, #T_36563_row0_col2 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_36563\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_36563_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
+ " <th id=\"T_36563_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
+ " <th id=\"T_36563_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
+ " <th id=\"T_36563_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_36563_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_36563_row0_col0\" class=\"data row0 col0\" >What battles took place in New York City in the American Revolution?</td>\n",
+ " <td id=\"T_36563_row0_col1\" class=\"data row0 col1\" >\n",
+ "The Battle of Long Island and the Great Fire of New York.</td>\n",
+ " <td id=\"T_36563_row0_col2\" class=\"data row0 col2\" >at labor. Slavery became integrally tied to New York's economy through the labor of slaves throughout the port, and the banking and shipping industries trading with the American South. During construction in Foley Square in the 1990s, the African Burying Ground was discovered; the cemetery included 10,000 to 20,000 of graves of colonial-era Africans, some enslaved and some free.The 1735 trial and acquittal in Manhattan of John Peter Zenger, who had been accused of seditious libel after criticizing colonial governor William Cosby, helped to establish the freedom of the press in North America. In 1754, Columbia University was founded under charter by King George II as King's College in Lower Manhattan.\n",
+ "\n",
+ "\n",
+ "=== American Revolution ===\n",
+ "\n",
+ "The Stamp Act Congress met in New York in October 1765, as the Sons of Liberty organization emerged in the city and skirmished over the next ten years with British troops stationed there. The Battle of Long Island, the largest battle of the American Revolutio...</td>\n",
+ " <td id=\"T_36563_row0_col3\" class=\"data row0 col3\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d6ea8f0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(query_str, response_vector, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_str = \"What are the airports in New York City?\"\n",
+ "query_engine = tree_index.as_query_engine()\n",
+ "response_tree = query_engine.query(query_str)\n",
+ "eval_result = evaluator_gpt4.evaluate(query_str, response_tree)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "a34490f6-7242-4c31-b49e-b2a65d9923ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_26784_row0_col1, #T_26784_row0_col2 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_26784\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_26784_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
+ " <th id=\"T_26784_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
+ " <th id=\"T_26784_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
+ " <th id=\"T_26784_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_26784_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_26784_row0_col0\" class=\"data row0 col0\" >What are the airports in New York City?</td>\n",
+ " <td id=\"T_26784_row0_col1\" class=\"data row0 col1\" >The airports in New York City are John F. Kennedy International Airport (JFK), LaGuardia Airport (LGA), and Newark Liberty International Airport (EWR).</td>\n",
+ " <td id=\"T_26784_row0_col2\" class=\"data row0 col2\" >Avenue are also used as metonyms for national industries there: the theater, finance, advertising, and fashion organizations, respectively.\n",
+ "New York City also has an extensive web of freeways and parkways, which link the city's boroughs to each other and to North Jersey, Westchester County, Long Island, and southwestern Connecticut through various bridges and tunnels. Because these highways serve millions of outer borough and suburban residents who commute into Manhattan, it is quite common for motorists to be stranded for hours in traffic congestion that are a daily occurrence, particularly during rush hour. Congestion pricing in New York City will go into effect in 2022 at the earliest.New York City is also known for its rules regarding turning at red lights. Unlike the rest of the United States, New York State prohibits right or left turns on red in cities with a population greater than one million, to reduce traffic collisions and increase pedestrian safety. In New York City, there...</td>\n",
+ " <td id=\"T_26784_row0_col3\" class=\"data row0 col3\" >NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d52a560>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(query_str, response_tree, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_str = \"What are the airports in New York City?\"\n",
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(query_str)\n",
+ "eval_result = evaluator_gpt4.evaluate(query_str, response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "01c53014-82b0-4865-b849-c0beb042143d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_920be_row0_col1, #T_920be_row0_col2 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_920be\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_920be_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
+ " <th id=\"T_920be_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
+ " <th id=\"T_920be_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
+ " <th id=\"T_920be_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_920be_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_920be_row0_col0\" class=\"data row0 col0\" >What are the airports in New York City?</td>\n",
+ " <td id=\"T_920be_row0_col1\" class=\"data row0 col1\" >\n",
+ "The airports in New York City are Long Island MacArthur Airport, Trenton–Mercer Airport, Westchester County Airport, and Teterboro Airport.</td>\n",
+ " <td id=\"T_920be_row0_col2\" class=\"data row0 col2\" >announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
+ "\n",
+ "\n",
+ "=== Ferries ===\n",
+ "\n",
+ "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
+ "NYC Ferry, a NYCEDC initiative with routes planned to travel to all five boroughs, was launched in 2017, with second graders choosing the names of the ferries. Meanwhile, Seastreak ferry announced construction of a 600-passenger high-speed luxury ferry in Septe...</td>\n",
+ " <td id=\"T_920be_row0_col3\" class=\"data row0 col3\" >NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d52aa10>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(query_str, response_vector, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b860691-9f6a-4b2f-bfef-5a5a56693a18",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_str = \"Who is the mayor of New York City?\"\n",
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(query_str)\n",
+ "eval_result = evaluator_gpt4.evaluate(query_str, response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "91cce4e3-b4e5-4583-9543-6c3726c0e7d3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_efae7_row0_col1, #T_efae7_row0_col2 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_efae7\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_efae7_level0_col0\" class=\"col_heading level0 col0\" >Query</th>\n",
+ " <th id=\"T_efae7_level0_col1\" class=\"col_heading level0 col1\" >Response</th>\n",
+ " <th id=\"T_efae7_level0_col2\" class=\"col_heading level0 col2\" >Source</th>\n",
+ " <th id=\"T_efae7_level0_col3\" class=\"col_heading level0 col3\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_efae7_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_efae7_row0_col0\" class=\"data row0 col0\" >Who is the mayor of New York City?</td>\n",
+ " <td id=\"T_efae7_row0_col1\" class=\"data row0 col1\" >\n",
+ "The mayor of New York City is Eric Adams.</td>\n",
+ " <td id=\"T_efae7_row0_col2\" class=\"data row0 col2\" >by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n",
+ "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for t...</td>\n",
+ " <td id=\"T_efae7_row0_col3\" class=\"data row0 col3\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d551090>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(query_str, response_vector, eval_result)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0ee6a336-8fd0-46b3-bb8f-7f47a8781c60",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Evaluate Source Nodes\n",
+ "\n",
+ "Evaluate the set of returned sources, and determine which sources actually contain the answer to a given query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "74eed7ba-7ed5-44fa-af4d-356f6ce3b710",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from typing import List\n",
+ "\n",
+ "# define jupyter display function\n",
+ "def display_eval_sources(\n",
+ " query: str, response: Response, eval_result: List[str]\n",
+ ") -> None:\n",
+ "\n",
+ " sources = [s.node.get_text() for s in response.source_nodes]\n",
+ " eval_df = pd.DataFrame(\n",
+ " {\n",
+ " \"Source\": sources,\n",
+ " \"Eval Result\": eval_result,\n",
+ " },\n",
+ " )\n",
+ " eval_df.style.set_caption(query)\n",
+ " eval_df = eval_df.style.set_properties(\n",
+ " **{\n",
+ " \"inline-size\": \"600px\",\n",
+ " \"overflow-wrap\": \"break-word\",\n",
+ " },\n",
+ " subset=[\"Source\"]\n",
+ " )\n",
+ "\n",
+ " display(eval_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0e3674ed-2632-4f48-9102-895fea94e340",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: you can set response_mode=\"no_text\" to get just the sources\n",
+ "query_str = \"What are the airports in New York City?\"\n",
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(\n",
+ " query_str, similarity_top_k=3, response_mode=\"no_text\"\n",
+ ")\n",
+ "eval_source_result = evaluator_gpt4.evaluate_source_nodes(query_str, response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "35125a2d-927e-40d3-ac83-da9297b6e81e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_62aff_row0_col0, #T_62aff_row1_col0, #T_62aff_row2_col0 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_62aff\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_62aff_level0_col0\" class=\"col_heading level0 col0\" >Source</th>\n",
+ " <th id=\"T_62aff_level0_col1\" class=\"col_heading level0 col1\" >Eval Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_62aff_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_62aff_row0_col0\" class=\"data row0 col0\" >announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
+ "\n",
+ "\n",
+ "=== Ferries ===\n",
+ "\n",
+ "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
+ "NYC Ferry, a NYCEDC initiative with routes planned to travel to all five boroughs, was launched in 2017, with second graders choosing the names of the ferries. Meanwhile, Seastreak ferry announced construction of a 600-passenger high-speed luxury ferry in September 2016, to shuttle riders between the Jersey Shore and Manhattan, anticipated to start service in 2017; this would be the largest vessel in its class.\n",
+ "\n",
+ "\n",
+ "=== Taxis, vehicles for hire, and trams ===\n",
+ "\n",
+ "Other features of the city's transportation infrastructure encompass 13,587 yellow taxicabs; other vehicle for hire companies; and the Roosevelt Island Tramway, an aerial tramway that transports commuters between Roosevelt Island and Manhattan Island.\n",
+ "\n",
+ "\n",
+ "=== Streets and highways ===\n",
+ "\n",
+ "Despite New York's heavy reliance on its vast public transit system, streets are a defining feature of the city. The Commissioners' Plan of 1811 greatly influenced the city's physical development. Several of the city's streets and avenues, including Broadway, Wall Street, Madison Avenue, and Seventh Avenue are also used as metonyms for national industries there: the theater, finance, advertising, and fashion organizations, respectively.\n",
+ "New York City also has an extensive web of freeways and parkways, which link the city's boroughs to each other and to North Jersey,</td>\n",
+ " <td id=\"T_62aff_row0_col1\" class=\"data row0 col1\" >YES</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_62aff_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_62aff_row1_col0\" class=\"data row1 col0\" >the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n",
+ "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n",
+ "\n",
+ "\n",
+ "==== Buses ====\n",
+ "\n",
+ "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buses and 200,000 commuters daily, making it the busiest bus station in the world.\n",
+ "\n",
+ "\n",
+ "=== Air ===\n",
+ "\n",
+ "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International Airport, and LaGuardia Airport; 130.5 million travelers used these three airports in 2016. JFK and Newark Liberty were the busiest and fourth busiest U.S. gateways for international air passengers, respectively, in 2012; as of 2011, JFK was the busiest airport for international passengers in North America.Plans have advanced to expand passenger volume at a fourth airport, Stewart International Airport near Newburgh, New York, by the Port Authority of New York and New Jersey. Plans were announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
+ "\n",
+ "\n",
+ "=== Ferries ===\n",
+ "\n",
+ "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
+ "NYC Ferry, a NYCEDC initiative with routes planned to travel</td>\n",
+ " <td id=\"T_62aff_row1_col1\" class=\"data row1 col1\" >YES</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_62aff_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_62aff_row2_col0\" class=\"data row2 col0\" >and New Jersey Transit. The combined systems converge at Grand Central Terminal and Pennsylvania Station and contain more than 250 stations and 20 rail lines. In Queens, the elevated AirTrain people mover system connects 24 hours a day JFK International Airport to the New York City Subway and the Long Island Rail Road; a separate AirTrain system is planned alongside the Grand Central Parkway to connect LaGuardia Airport to these transit systems. For inter-city rail, New York City is served by Amtrak, whose busiest station by a significant margin is Pennsylvania Station on the West Side of Manhattan, from which Amtrak provides connections to Boston, Philadelphia, and Washington, D.C. along the Northeast Corridor, and long-distance train service to other North American cities.The Staten Island Railway rapid transit system solely serves Staten Island, operating 24 hours a day. The Port Authority Trans-Hudson (PATH train) links Midtown and Lower Manhattan to northeastern New Jersey, primarily Hoboken, Jersey City, and Newark. Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n",
+ "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n",
+ "\n",
+ "\n",
+ "==== Buses ====\n",
+ "\n",
+ "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buses and 200,000 commuters daily, making it the busiest bus station in the world.\n",
+ "\n",
+ "\n",
+ "=== Air ===\n",
+ "\n",
+ "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International Airport, and LaGuardia Airport;</td>\n",
+ " <td id=\"T_62aff_row2_col1\" class=\"data row2 col1\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d6ea6e0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_sources(query_str, response_vector, eval_source_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cdc340a9-1853-414e-9e87-820aa5d82db7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: you can set response_mode=\"no_text\" to get just the sources\n",
+ "query_str = \"Who is the mayor of New York City?\"\n",
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(\n",
+ " query_str, similarity_top_k=3, response_mode=\"no_text\"\n",
+ ")\n",
+ "eval_source_result = evaluator_gpt4.evaluate_source_nodes(query_str, response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "b88eb350-cae6-4890-9b55-cb5f750afa9e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_79b25_row0_col0, #T_79b25_row1_col0, #T_79b25_row2_col0 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_79b25\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_79b25_level0_col0\" class=\"col_heading level0 col0\" >Source</th>\n",
+ " <th id=\"T_79b25_level0_col1\" class=\"col_heading level0 col1\" >Eval Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_79b25_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_79b25_row0_col0\" class=\"data row0 col0\" >by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n",
+ "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for the Southern District of New York, whose main courthouse is on Foley Square near City Hall in Manhattan and whose jurisdiction includes Manhattan and the Bronx; and the District Court for the Eastern District of New York, whose main courthouse is in Brooklyn and whose jurisdiction includes Brooklyn, Queens, and Staten Island. The U.S. Court of Appeals for the Second Circuit and U.S. Court of International Trade are also based in New York, also on Foley Square in Manhattan.\n",
+ "\n",
+ "\n",
+ "=== Politics ===\n",
+ "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n",
+ "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping all</td>\n",
+ " <td id=\"T_79b25_row0_col1\" class=\"data row0 col1\" >NO</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_79b25_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+ " <td id=\"T_79b25_row1_col0\" class=\"data row1 col0\" >for the Second Circuit and U.S. Court of International Trade are also based in New York, also on Foley Square in Manhattan.\n",
+ "\n",
+ "\n",
+ "=== Politics ===\n",
+ "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n",
+ "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping all five boroughs. Party platforms center on affordable housing, education, and economic development, and labor politics are of importance in the city. Thirteen out of 27 U.S. congressional districts in the state of New York include portions of New York City.New York is one of the most important sources of political fundraising in the United States. At least four of the top five ZIP Codes in the nation for political contributions were in Manhattan for the 2004, 2006, and 2008 elections. The top ZIP Code, 10021 on the Upper East Side, generated the most money for the 2004 presidential campaigns of George W. Bush and John Kerry. The city has a strong imbalance of payments with the national and state governments. It receives 83 cents in services for every $1 it sends to the federal government in taxes (or annually sends $11.4 billion more than it receives back). City residents and businesses also sent an additional $4.1 billion in the 2009–2010 fiscal year to the state of New York than the city received in return.\n",
+ "\n",
+ "\n",
+ "== Transportation ==\n",
+ "\n",
+ "New York City's comprehensive transportation system is both complex and extensive.\n",
+ "\n",
+ "\n",
+ "=== Rapid transit ===\n",
+ "Mass transit in New York City, most of which runs 24 hours a day, accounts for one in every three users of mass transit in</td>\n",
+ " <td id=\"T_79b25_row1_col1\" class=\"data row1 col1\" >YES</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th id=\"T_79b25_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+ " <td id=\"T_79b25_row2_col0\" class=\"data row2 col0\" >ed.). New York: The New Press. ISBN 978-1-56584-321-9.\n",
+ "Holli, Melvin G., and Jones, Peter d'A., eds. Biographical Dictionary of American Mayors, 1820-1980 (Greenwood Press, 1981) short scholarly biographies each of the city's mayors 1820 to 1980. online; see index at p. 410 for list.Jackson, Kenneth T., ed. (1995). The Encyclopedia of New York City. New Haven: Yale University Press. ISBN 0300055366.\n",
+ "Jackson, Kenneth T.; Dunbar, David S., eds. (2005). Empire City: New York Through the Centuries. Columbia University Press. ISBN 978-0-231-10909-3.\n",
+ "Lankevich, George L. (1998). American Metropolis: A History of New York City. NYU Press. ISBN 978-0-8147-5186-2.\n",
+ "White, E.B. (1949). Here is New York (2000 reissue ed.). Little Bookroom.\n",
+ "White, Norval & Willensky, Elliot (2000). AIA Guide to New York City (4th ed.). New York: Three Rivers Press. ISBN 978-0-8129-3107-5.\n",
+ "Whitehead, Colson (2003). The Colossus of New York: A City in 13 Parts. New York: Doubleday. ISBN 978-0-385-50794-3.\n",
+ "\n",
+ "\n",
+ "== External links ==\n",
+ "\n",
+ "Official website \n",
+ "NYC Go, official tourism website\n",
+ "New York City at Curlie\n",
+ " Geographic data related to New York City at OpenStreetMap\n",
+ "Collections, 145,000 NYC photographs at the Museum of the City of New York\n",
+ "\"The New New York Skyline (interactive)\". National Geographic. November 2015.</td>\n",
+ " <td id=\"T_79b25_row2_col1\" class=\"data row2 col1\" >NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x17d6eab00>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_sources(query_str, response_vector, eval_source_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72681384-c71d-44b9-9385-54820db30988",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/evaluation/TestNYC-Evaluation.ipynb b/docs/examples/evaluation/TestNYC-Evaluation.ipynb
index a2c215862a..e4b6e3dd60 100644
--- a/docs/examples/evaluation/TestNYC-Evaluation.ipynb
+++ b/docs/examples/evaluation/TestNYC-Evaluation.ipynb
@@ -1,527 +1,531 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "de6537c4",
- "metadata": {},
- "source": [
- "# Response Evaluator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "9080b39e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import (\n",
- " TreeIndex, \n",
- " VectorStoreIndex, \n",
- " SimpleDirectoryReader, \n",
- " LLMPredictor, \n",
- " ServiceContext,\n",
- " Response\n",
- ")\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.evaluation import ResponseEvaluator\n",
- "import pandas as pd\n",
- "pd.set_option('display.max_colwidth', 0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "b9b98f89-d5b8-4d29-92f6-ad76d5060e9f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# gpt-3 (davinci)\n",
- "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
- "service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3)\n",
- "\n",
- "# gpt-4\n",
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "8eb3e616-64e5-4bf4-a67b-661e9b3657e7",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "evaluator = ResponseEvaluator(service_context=service_context_gpt3)\n",
- "evaluator_gpt4 = ResponseEvaluator(service_context=service_context_gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../test_wiki/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "dca06a5b-8a15-40b4-8c7f-dae5407c674f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# create tree index\n",
- "tree_index = TreeIndex.from_documents(documents=documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 44108 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 44108 tokens\n"
- ]
- }
- ],
- "source": [
- "# create vector index\n",
- "vector_index = VectorStoreIndex.from_documents(\n",
- " documents, \n",
- " service_context=ServiceContext.from_defaults(chunk_size=512)\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "id": "af730b2e-6949-4865-b7af-bb2bc60a9173",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define jupyter display function\n",
- "def display_eval_df(response: Response, eval_result: str) -> None:\n",
- " eval_df = pd.DataFrame(\n",
- " {\n",
- " \"Response\": str(response), \n",
- " \"Source\": response.source_nodes[0].source_text[:1000] + \"...\",\n",
- " \"Evaluation Result\": eval_result\n",
- " },\n",
- " index=[0]\n",
- " )\n",
- " eval_df = eval_df.style.set_properties(\n",
- " **{\n",
- " 'inline-size': '600px',\n",
- " 'overflow-wrap': 'break-word',\n",
- " }, \n",
- " subset=[\"Response\", \"Source\"]\n",
- " )\n",
- " display(eval_df)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4332 tokens\n",
- "> [query] Total LLM token usage: 4332 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = tree_index.as_query_engine()\n",
- "response_tree = query_engine.query(\"What battles took place in New York City in the American Revolution?\")\n",
- "eval_result = evaluator_gpt4.evaluate(response_tree)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 66,
- "id": "db9d00bc-8428-4a08-b48e-248ad7570923",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_e9b07_row0_col0, #T_e9b07_row0_col1 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_e9b07\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_e9b07_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
- " <th id=\"T_e9b07_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
- " <th id=\"T_e9b07_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_e9b07_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_e9b07_row0_col0\" class=\"data row0 col0\" >The Battle of Long Island, the Battle of White Plains, the Battle of Harlem Heights, the Battle of Fort Washington, the Battle of Fort Lee, and the Battle of Yorktown all took place in New York City during the American Revolution. These battles took place in various locations throughout the city, including Battery Weed and Fort Tompkins, Great Kills Park, and Central Park.</td>\n",
- " <td id=\"T_e9b07_row0_col1\" class=\"data row0 col1\" >in 2015 makes it the highest of any county in the United States and higher than the density of any individual American city.Manhattan is the cultural, administrative, and financial center of New York City and contains the headquarters of many major multinational corporations, the United Nations headquarters, Wall Street, and a number of important universities. The borough of Manhattan is often described as the financial and cultural center of the world.Most of the borough is situated on Manhattan Island, at the mouth of the Hudson River and the East River, and its southern tip, at the confluence of the two rivers, represents the birthplace of New York City itself. Several small islands also compose part of the borough of Manhattan, including Randalls and Wards Islands, and Roosevelt Island in the East River, and Governors Island and Liberty Island to the south in New York Harbor.\n",
- "Manhattan Island is loosely divided into the Lower, Midtown, and Uptown regions. Uptown Manhattan is divide...</td>\n",
- " <td id=\"T_e9b07_row0_col2\" class=\"data row0 col2\" >NO</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x296847df0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(response_tree, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "edb91d53",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "180a5d2e-9286-477b-9cd0-a5976d18d845",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(\"What battles took place in New York City in the American Revolution?\")\n",
- "eval_result = evaluator_gpt4.evaluate(response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "id": "c764b8b3-69b1-4ac8-b88b-3f9e204b8bfb",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_bf32a_row0_col0, #T_bf32a_row0_col1 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_bf32a\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_bf32a_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
- " <th id=\"T_bf32a_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
- " <th id=\"T_bf32a_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_bf32a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_bf32a_row0_col0\" class=\"data row0 col0\" >\n",
- "The Battle of Long Island and the Great Fire of New York.</td>\n",
- " <td id=\"T_bf32a_row0_col1\" class=\"data row0 col1\" >at labor. Slavery became integrally tied to New York's economy through the labor of slaves throughout the port, and the banking and shipping industries trading with the American South. During construction in Foley Square in the 1990s, the African Burying Ground was discovered; the cemetery included 10,000 to 20,000 of graves of colonial-era Africans, some enslaved and some free.The 1735 trial and acquittal in Manhattan of John Peter Zenger, who had been accused of seditious libel after criticizing colonial governor William Cosby, helped to establish the freedom of the press in North America. In 1754, Columbia University was founded under charter by King George II as King's College in Lower Manhattan.\n",
- "\n",
- "\n",
- "=== American Revolution ===\n",
- "\n",
- "The Stamp Act Congress met in New York in October 1765, as the Sons of Liberty organization emerged in the city and skirmished over the next ten years with British troops stationed there. The Battle of Long Island, the largest battle of the American Revolutio...</td>\n",
- " <td id=\"T_bf32a_row0_col2\" class=\"data row0 col2\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x2968446a0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(response_vector, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_engine = tree_index.as_query_engine()\n",
- "response_tree = query_engine.query(\"What are the airports in New York City?\")\n",
- "eval_result = evaluator_gpt4.evaluate(response_tree)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "a34490f6-7242-4c31-b49e-b2a65d9923ab",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_08e51_row0_col0, #T_08e51_row0_col1 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_08e51\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_08e51_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
- " <th id=\"T_08e51_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
- " <th id=\"T_08e51_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_08e51_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_08e51_row0_col0\" class=\"data row0 col0\" >The airports in New York City are John F. Kennedy International Airport (JFK), LaGuardia Airport (LGA), and Newark Liberty International Airport (EWR).</td>\n",
- " <td id=\"T_08e51_row0_col1\" class=\"data row0 col1\" >Avenue are also used as metonyms for national industries there: the theater, finance, advertising, and fashion organizations, respectively.\n",
- "New York City also has an extensive web of freeways and parkways, which link the city's boroughs to each other and to North Jersey, Westchester County, Long Island, and southwestern Connecticut through various bridges and tunnels. Because these highways serve millions of outer borough and suburban residents who commute into Manhattan, it is quite common for motorists to be stranded for hours in traffic congestion that are a daily occurrence, particularly during rush hour. Congestion pricing in New York City will go into effect in 2022 at the earliest.New York City is also known for its rules regarding turning at red lights. Unlike the rest of the United States, New York State prohibits right or left turns on red in cities with a population greater than one million, to reduce traffic collisions and increase pedestrian safety. In New York City, there...</td>\n",
- " <td id=\"T_08e51_row0_col2\" class=\"data row0 col2\" >NO</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x296846170>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(response_tree, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(\"What are the airports in New York City?\")\n",
- "eval_result = evaluator_gpt4.evaluate(response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 62,
- "id": "01c53014-82b0-4865-b849-c0beb042143d",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_82fc9_row0_col0, #T_82fc9_row0_col1 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_82fc9\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_82fc9_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
- " <th id=\"T_82fc9_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
- " <th id=\"T_82fc9_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_82fc9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_82fc9_row0_col0\" class=\"data row0 col0\" >\n",
- "The airports in New York City are Long Island MacArthur Airport, Trenton–Mercer Airport, Westchester County Airport, and Teterboro Airport.</td>\n",
- " <td id=\"T_82fc9_row0_col1\" class=\"data row0 col1\" >announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
- "\n",
- "\n",
- "=== Ferries ===\n",
- "\n",
- "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
- "NYC Ferry, a NYCEDC initiative with routes planned to travel to all five boroughs, was launched in 2017, with second graders choosing the names of the ferries. Meanwhile, Seastreak ferry announced construction of a 600-passenger high-speed luxury ferry in Septe...</td>\n",
- " <td id=\"T_82fc9_row0_col2\" class=\"data row0 col2\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x296847ac0>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(response_vector, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b860691-9f6a-4b2f-bfef-5a5a56693a18",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "response_vector = query_engine.query(\"Who is the mayor of New York City?\")\n",
- "eval_result = evaluator_gpt4.evaluate(response_vector)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "id": "91cce4e3-b4e5-4583-9543-6c3726c0e7d3",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style type=\"text/css\">\n",
- "#T_9373c_row0_col0, #T_9373c_row0_col1 {\n",
- " inline-size: 600px;\n",
- " overflow-wrap: break-word;\n",
- "}\n",
- "</style>\n",
- "<table id=\"T_9373c\">\n",
- " <thead>\n",
- " <tr>\n",
- " <th class=\"blank level0\" > </th>\n",
- " <th id=\"T_9373c_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
- " <th id=\"T_9373c_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
- " <th id=\"T_9373c_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th id=\"T_9373c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
- " <td id=\"T_9373c_row0_col0\" class=\"data row0 col0\" >\n",
- "The mayor of New York City is Eric Adams.</td>\n",
- " <td id=\"T_9373c_row0_col1\" class=\"data row0 col1\" >by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n",
- "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for t...</td>\n",
- " <td id=\"T_9373c_row0_col2\" class=\"data row0 col2\" >YES</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n"
- ],
- "text/plain": [
- "<pandas.io.formats.style.Styler at 0x2958ca410>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_eval_df(response_vector, eval_result)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8b1ccc17-d16f-4440-bdf2-15c83bc81159",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "de6537c4",
+ "metadata": {},
+ "source": [
+ "# Response Evaluator"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9080b39e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " TreeIndex,\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " Response,\n",
+ ")\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.evaluation import ResponseEvaluator\n",
+ "import pandas as pd\n",
+ "\n",
+ "pd.set_option(\"display.max_colwidth\", 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b9b98f89-d5b8-4d29-92f6-ad76d5060e9f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# gpt-3 (davinci)\n",
+ "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
+ "service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3)\n",
+ "\n",
+ "# gpt-4\n",
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "8eb3e616-64e5-4bf4-a67b-661e9b3657e7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "evaluator = ResponseEvaluator(service_context=service_context_gpt3)\n",
+ "evaluator_gpt4 = ResponseEvaluator(service_context=service_context_gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../test_wiki/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "dca06a5b-8a15-40b4-8c7f-dae5407c674f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create tree index\n",
+ "tree_index = TreeIndex.from_documents(documents=documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 44108 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 44108 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# create vector index\n",
+ "vector_index = VectorStoreIndex.from_documents(\n",
+ " documents, service_context=ServiceContext.from_defaults(chunk_size=512)\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "af730b2e-6949-4865-b7af-bb2bc60a9173",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define jupyter display function\n",
+ "def display_eval_df(response: Response, eval_result: str) -> None:\n",
+ " eval_df = pd.DataFrame(\n",
+ " {\n",
+ " \"Response\": str(response),\n",
+ " \"Source\": response.source_nodes[0].source_text[:1000] + \"...\",\n",
+ " \"Evaluation Result\": eval_result,\n",
+ " },\n",
+ " index=[0],\n",
+ " )\n",
+ " eval_df = eval_df.style.set_properties(\n",
+ " **{\n",
+ " \"inline-size\": \"600px\",\n",
+ " \"overflow-wrap\": \"break-word\",\n",
+ " },\n",
+ " subset=[\"Response\", \"Source\"]\n",
+ " )\n",
+ " display(eval_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4332 tokens\n",
+ "> [query] Total LLM token usage: 4332 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = tree_index.as_query_engine()\n",
+ "response_tree = query_engine.query(\n",
+ " \"What battles took place in New York City in the American Revolution?\"\n",
+ ")\n",
+ "eval_result = evaluator_gpt4.evaluate(response_tree)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "id": "db9d00bc-8428-4a08-b48e-248ad7570923",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_e9b07_row0_col0, #T_e9b07_row0_col1 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_e9b07\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_e9b07_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
+ " <th id=\"T_e9b07_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
+ " <th id=\"T_e9b07_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_e9b07_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_e9b07_row0_col0\" class=\"data row0 col0\" >The Battle of Long Island, the Battle of White Plains, the Battle of Harlem Heights, the Battle of Fort Washington, the Battle of Fort Lee, and the Battle of Yorktown all took place in New York City during the American Revolution. These battles took place in various locations throughout the city, including Battery Weed and Fort Tompkins, Great Kills Park, and Central Park.</td>\n",
+ " <td id=\"T_e9b07_row0_col1\" class=\"data row0 col1\" >in 2015 makes it the highest of any county in the United States and higher than the density of any individual American city.Manhattan is the cultural, administrative, and financial center of New York City and contains the headquarters of many major multinational corporations, the United Nations headquarters, Wall Street, and a number of important universities. The borough of Manhattan is often described as the financial and cultural center of the world.Most of the borough is situated on Manhattan Island, at the mouth of the Hudson River and the East River, and its southern tip, at the confluence of the two rivers, represents the birthplace of New York City itself. Several small islands also compose part of the borough of Manhattan, including Randalls and Wards Islands, and Roosevelt Island in the East River, and Governors Island and Liberty Island to the south in New York Harbor.\n",
+ "Manhattan Island is loosely divided into the Lower, Midtown, and Uptown regions. Uptown Manhattan is divide...</td>\n",
+ " <td id=\"T_e9b07_row0_col2\" class=\"data row0 col2\" >NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x296847df0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(response_tree, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "edb91d53",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "180a5d2e-9286-477b-9cd0-a5976d18d845",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(\n",
+ " \"What battles took place in New York City in the American Revolution?\"\n",
+ ")\n",
+ "eval_result = evaluator_gpt4.evaluate(response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "c764b8b3-69b1-4ac8-b88b-3f9e204b8bfb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_bf32a_row0_col0, #T_bf32a_row0_col1 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_bf32a\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_bf32a_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
+ " <th id=\"T_bf32a_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
+ " <th id=\"T_bf32a_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_bf32a_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_bf32a_row0_col0\" class=\"data row0 col0\" >\n",
+ "The Battle of Long Island and the Great Fire of New York.</td>\n",
+ " <td id=\"T_bf32a_row0_col1\" class=\"data row0 col1\" >at labor. Slavery became integrally tied to New York's economy through the labor of slaves throughout the port, and the banking and shipping industries trading with the American South. During construction in Foley Square in the 1990s, the African Burying Ground was discovered; the cemetery included 10,000 to 20,000 of graves of colonial-era Africans, some enslaved and some free.The 1735 trial and acquittal in Manhattan of John Peter Zenger, who had been accused of seditious libel after criticizing colonial governor William Cosby, helped to establish the freedom of the press in North America. In 1754, Columbia University was founded under charter by King George II as King's College in Lower Manhattan.\n",
+ "\n",
+ "\n",
+ "=== American Revolution ===\n",
+ "\n",
+ "The Stamp Act Congress met in New York in October 1765, as the Sons of Liberty organization emerged in the city and skirmished over the next ten years with British troops stationed there. The Battle of Long Island, the largest battle of the American Revolutio...</td>\n",
+ " <td id=\"T_bf32a_row0_col2\" class=\"data row0 col2\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x2968446a0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(response_vector, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = tree_index.as_query_engine()\n",
+ "response_tree = query_engine.query(\"What are the airports in New York City?\")\n",
+ "eval_result = evaluator_gpt4.evaluate(response_tree)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "a34490f6-7242-4c31-b49e-b2a65d9923ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_08e51_row0_col0, #T_08e51_row0_col1 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_08e51\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_08e51_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
+ " <th id=\"T_08e51_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
+ " <th id=\"T_08e51_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_08e51_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_08e51_row0_col0\" class=\"data row0 col0\" >The airports in New York City are John F. Kennedy International Airport (JFK), LaGuardia Airport (LGA), and Newark Liberty International Airport (EWR).</td>\n",
+ " <td id=\"T_08e51_row0_col1\" class=\"data row0 col1\" >Avenue are also used as metonyms for national industries there: the theater, finance, advertising, and fashion organizations, respectively.\n",
+ "New York City also has an extensive web of freeways and parkways, which link the city's boroughs to each other and to North Jersey, Westchester County, Long Island, and southwestern Connecticut through various bridges and tunnels. Because these highways serve millions of outer borough and suburban residents who commute into Manhattan, it is quite common for motorists to be stranded for hours in traffic congestion that are a daily occurrence, particularly during rush hour. Congestion pricing in New York City will go into effect in 2022 at the earliest.New York City is also known for its rules regarding turning at red lights. Unlike the rest of the United States, New York State prohibits right or left turns on red in cities with a population greater than one million, to reduce traffic collisions and increase pedestrian safety. In New York City, there...</td>\n",
+ " <td id=\"T_08e51_row0_col2\" class=\"data row0 col2\" >NO</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x296846170>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(response_tree, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(\"What are the airports in New York City?\")\n",
+ "eval_result = evaluator_gpt4.evaluate(response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "01c53014-82b0-4865-b849-c0beb042143d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_82fc9_row0_col0, #T_82fc9_row0_col1 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_82fc9\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_82fc9_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
+ " <th id=\"T_82fc9_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
+ " <th id=\"T_82fc9_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_82fc9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_82fc9_row0_col0\" class=\"data row0 col0\" >\n",
+ "The airports in New York City are Long Island MacArthur Airport, Trenton–Mercer Airport, Westchester County Airport, and Teterboro Airport.</td>\n",
+ " <td id=\"T_82fc9_row0_col1\" class=\"data row0 col1\" >announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.\n",
+ "\n",
+ "\n",
+ "=== Ferries ===\n",
+ "\n",
+ "The Staten Island Ferry is the world's busiest ferry route, carrying more than 23 million passengers from July 2015 through June 2016 on the 5.2-mile (8.4 km) route between Staten Island and Lower Manhattan and running 24 hours a day. Other ferry systems shuttle commuters between Manhattan and other locales within the city and the metropolitan area.\n",
+ "NYC Ferry, a NYCEDC initiative with routes planned to travel to all five boroughs, was launched in 2017, with second graders choosing the names of the ferries. Meanwhile, Seastreak ferry announced construction of a 600-passenger high-speed luxury ferry in Septe...</td>\n",
+ " <td id=\"T_82fc9_row0_col2\" class=\"data row0 col2\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x296847ac0>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(response_vector, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b860691-9f6a-4b2f-bfef-5a5a56693a18",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "response_vector = query_engine.query(\"Who is the mayor of New York City?\")\n",
+ "eval_result = evaluator_gpt4.evaluate(response_vector)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "91cce4e3-b4e5-4583-9543-6c3726c0e7d3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style type=\"text/css\">\n",
+ "#T_9373c_row0_col0, #T_9373c_row0_col1 {\n",
+ " inline-size: 600px;\n",
+ " overflow-wrap: break-word;\n",
+ "}\n",
+ "</style>\n",
+ "<table id=\"T_9373c\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <th class=\"blank level0\" > </th>\n",
+ " <th id=\"T_9373c_level0_col0\" class=\"col_heading level0 col0\" >Response</th>\n",
+ " <th id=\"T_9373c_level0_col1\" class=\"col_heading level0 col1\" >Source</th>\n",
+ " <th id=\"T_9373c_level0_col2\" class=\"col_heading level0 col2\" >Evaluation Result</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th id=\"T_9373c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+ " <td id=\"T_9373c_row0_col0\" class=\"data row0 col0\" >\n",
+ "The mayor of New York City is Eric Adams.</td>\n",
+ " <td id=\"T_9373c_row0_col1\" class=\"data row0 col1\" >by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n",
+ "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for t...</td>\n",
+ " <td id=\"T_9373c_row0_col2\" class=\"data row0 col2\" >YES</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n"
+ ],
+ "text/plain": [
+ "<pandas.io.formats.style.Styler at 0x2958ca410>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_eval_df(response_vector, eval_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8b1ccc17-d16f-4440-bdf2-15c83bc81159",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/index_structs/doc_summary/DocSummary.ipynb b/docs/examples/index_structs/doc_summary/DocSummary.ipynb
index 5593ac0a39..7c6b0471e6 100644
--- a/docs/examples/index_structs/doc_summary/DocSummary.ipynb
+++ b/docs/examples/index_structs/doc_summary/DocSummary.ipynb
@@ -26,6 +26,7 @@
"source": [
"import logging\n",
"import sys\n",
+ "\n",
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"# # Uncomment if you want to temporarily disable logger\n",
@@ -43,6 +44,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -70,7 +72,7 @@
" SimpleDirectoryReader,\n",
" LLMPredictor,\n",
" ServiceContext,\n",
- " get_response_synthesizer\n",
+ " get_response_synthesizer,\n",
")\n",
"from llama_index.indices.document_summary import DocumentSummaryIndex\n",
"from llama_index.llms import OpenAI"
@@ -113,27 +115,28 @@
"from pathlib import Path\n",
"\n",
"import requests\n",
+ "\n",
"for title in wiki_titles:\n",
" response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
" params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
" # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
+ " \"explaintext\": True,\n",
+ " },\n",
" ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
"\n",
- " data_path = Path('data')\n",
+ " data_path = Path(\"data\")\n",
" if not data_path.exists():\n",
" Path.mkdir(data_path)\n",
"\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
]
},
{
@@ -150,7 +153,7 @@
"for wiki_title in wiki_titles:\n",
" docs = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()\n",
" docs[0].doc_id = wiki_title\n",
- " city_docs.extend(docs)\n"
+ " city_docs.extend(docs)"
]
},
{
@@ -191,11 +194,13 @@
"outputs": [],
"source": [
"# default mode of building the index\n",
- "response_synthesizer = get_response_synthesizer(response_mode=\"tree_summarize\", use_async=True)\n",
+ "response_synthesizer = get_response_synthesizer(\n",
+ " response_mode=\"tree_summarize\", use_async=True\n",
+ ")\n",
"doc_summary_index = DocumentSummaryIndex.from_documents(\n",
- " city_docs, \n",
+ " city_docs,\n",
" service_context=service_context,\n",
- " response_synthesizer=response_synthesizer\n",
+ " response_synthesizer=response_synthesizer,\n",
")"
]
},
@@ -231,7 +236,7 @@
},
"outputs": [],
"source": [
- "doc_summary_index.storage_context.persist('index')"
+ "doc_summary_index.storage_context.persist(\"index\")"
]
},
{
@@ -307,7 +312,9 @@
"metadata": {},
"outputs": [],
"source": [
- "query_engine = doc_summary_index.as_query_engine(response_mode=\"tree_summarize\", use_async=True)"
+ "query_engine = doc_summary_index.as_query_engine(\n",
+ " response_mode=\"tree_summarize\", use_async=True\n",
+ ")"
]
},
{
diff --git a/docs/examples/index_structs/knowledge_graph/KnowledgeGraphDemo.ipynb b/docs/examples/index_structs/knowledge_graph/KnowledgeGraphDemo.ipynb
index abcd3b549f..0089c7acbf 100644
--- a/docs/examples/index_structs/knowledge_graph/KnowledgeGraphDemo.ipynb
+++ b/docs/examples/index_structs/knowledge_graph/KnowledgeGraphDemo.ipynb
@@ -18,7 +18,8 @@
"source": [
"# My OpenAI Key\n",
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
]
},
{
@@ -69,7 +70,12 @@
}
],
"source": [
- "from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, KnowledgeGraphIndex\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " KnowledgeGraphIndex,\n",
+ ")\n",
"from llama_index.graph_stores import SimpleGraphStore\n",
"\n",
"from llama_index.llms import OpenAI\n",
@@ -85,7 +91,9 @@
},
"outputs": [],
"source": [
- "documents = SimpleDirectoryReader('../../../../examples/paul_graham_essay/data').load_data()"
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../../examples/paul_graham_essay/data\"\n",
+ ").load_data()"
]
},
{
@@ -125,13 +133,13 @@
"graph_store = SimpleGraphStore()\n",
"storage_context = StorageContext.from_defaults(graph_store=graph_store)\n",
"\n",
- "# NOTE: can take a while! \n",
+ "# NOTE: can take a while!\n",
"index = KnowledgeGraphIndex.from_documents(\n",
" documents,\n",
" max_triplets_per_chunk=2,\n",
" storage_context=storage_context,\n",
- " service_context=service_context\n",
- ")\n"
+ " service_context=service_context,\n",
+ ")"
]
},
{
@@ -174,12 +182,9 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " include_text=False, \n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(include_text=False, response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\n",
- " \"Tell me more about Interleaf\", \n",
+ " \"Tell me more about Interleaf\",\n",
")"
]
},
@@ -229,12 +234,9 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " include_text=True, \n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(include_text=True, response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\n",
- " \"Tell me more about what the author worked on at Interleaf\", \n",
+ " \"Tell me more about what the author worked on at Interleaf\",\n",
")"
]
},
@@ -287,12 +289,12 @@
}
],
"source": [
- "# NOTE: can take a while! \n",
+ "# NOTE: can take a while!\n",
"new_index = KnowledgeGraphIndex.from_documents(\n",
- " documents, \n",
+ " documents,\n",
" max_triplets_per_chunk=2,\n",
" service_context=service_context,\n",
- " include_embeddings=True\n",
+ " include_embeddings=True,\n",
")"
]
},
@@ -320,13 +322,13 @@
"source": [
"# query using top 3 triplets plus keywords (duplicate triplets are removed)\n",
"query_engine = index.as_query_engine(\n",
- " include_text=True, \n",
+ " include_text=True,\n",
" response_mode=\"tree_summarize\",\n",
- " embedding_mode='hybrid',\n",
- " similarity_top_k=5\n",
+ " embedding_mode=\"hybrid\",\n",
+ " similarity_top_k=5,\n",
")\n",
"response = query_engine.query(\n",
- " \"Tell me more about what the author worked on at Interleaf\", \n",
+ " \"Tell me more about what the author worked on at Interleaf\",\n",
")"
]
},
@@ -464,7 +466,7 @@
}
],
"source": [
- "# initialize an empty index for now \n",
+ "# initialize an empty index for now\n",
"index = KnowledgeGraphIndex(\n",
" [],\n",
" service_context=service_context,\n",
@@ -479,18 +481,21 @@
"outputs": [],
"source": [
"# add keyword mappings and nodes manually\n",
- "# add triplets (subject, relationship, object) \n",
+ "# add triplets (subject, relationship, object)\n",
"\n",
"# for node 0\n",
- "node_0_tups = [(\"author\", \"worked on\", \"writing\"), (\"author\", \"worked on\", \"programming\")]\n",
+ "node_0_tups = [\n",
+ " (\"author\", \"worked on\", \"writing\"),\n",
+ " (\"author\", \"worked on\", \"programming\"),\n",
+ "]\n",
"for tup in node_0_tups:\n",
" index.upsert_triplet_and_node(tup, nodes[0])\n",
- " \n",
+ "\n",
"# for node 1\n",
"node_1_tups = [\n",
- " ('Interleaf', 'made software for', 'creating documents'),\n",
- " ('Interleaf', 'added', 'scripting language'),\n",
- " ('software', 'generate', 'web sites')\n",
+ " (\"Interleaf\", \"made software for\", \"creating documents\"),\n",
+ " (\"Interleaf\", \"added\", \"scripting language\"),\n",
+ " (\"software\", \"generate\", \"web sites\"),\n",
"]\n",
"for tup in node_1_tups:\n",
" index.upsert_triplet_and_node(tup, nodes[1])"
@@ -518,12 +523,9 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " include_text=False, \n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(include_text=False, response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\n",
- " \"Tell me more about Interleaf\", \n",
+ " \"Tell me more about Interleaf\",\n",
")"
]
},
diff --git a/docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb b/docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb
index 7afd16db5e..fec9c05970 100644
--- a/docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb
+++ b/docs/examples/index_structs/knowledge_graph/KnowledgeGraphIndex_vs_VectorStoreIndex_vs_CustomIndex_combined.ipynb
@@ -95,12 +95,15 @@
"# For OpenAI\n",
"\n",
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\"\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\"\n",
"\n",
"import logging\n",
"import sys\n",
"\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO) # logging.DEBUG for more verbose output\n",
+ "logging.basicConfig(\n",
+ " stream=sys.stdout, level=logging.INFO\n",
+ ") # logging.DEBUG for more verbose output\n",
"\n",
"from llama_index import (\n",
" KnowledgeGraphIndex,\n",
@@ -142,7 +145,7 @@
" VectorStoreIndex,\n",
" SimpleDirectoryReader,\n",
" KnowledgeGraphIndex,\n",
- " ServiceContext\n",
+ " ServiceContext,\n",
")\n",
"from llama_index import set_global_service_context\n",
"\n",
@@ -154,7 +157,9 @@
"\n",
"from IPython.display import Markdown, display\n",
"\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO) # logging.DEBUG for more verbose output\n",
+ "logging.basicConfig(\n",
+ " stream=sys.stdout, level=logging.INFO\n",
+ ") # logging.DEBUG for more verbose output\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
"openai.api_type = \"azure\"\n",
@@ -186,7 +191,7 @@
" embed_model=embedding_llm,\n",
")\n",
"\n",
- "set_global_service_context(service_context)\n"
+ "set_global_service_context(service_context)"
]
},
{
@@ -207,25 +212,29 @@
"source": [
"%pip install nebula3-python\n",
"\n",
- "os.environ['NEBULA_USER'] = \"root\"\n",
- "os.environ['NEBULA_PASSWORD'] = \"nebula\"\n",
- "os.environ['NEBULA_ADDRESS'] = \"127.0.0.1:9669\" # assumed we have NebulaGraph installed locally\n",
+ "os.environ[\"NEBULA_USER\"] = \"root\"\n",
+ "os.environ[\"NEBULA_PASSWORD\"] = \"nebula\"\n",
+ "os.environ[\n",
+ " \"NEBULA_ADDRESS\"\n",
+ "] = \"127.0.0.1:9669\" # assumed we have NebulaGraph installed locally\n",
"\n",
"# Assume that the graph has already been created\n",
- " # Create a NebulaGraph cluster with:\n",
- " # Option 0: `curl -fsSL nebula-up.siwei.io/install.sh | bash`\n",
- " # Option 1: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext\n",
+ "# Create a NebulaGraph cluster with:\n",
+ "# Option 0: `curl -fsSL nebula-up.siwei.io/install.sh | bash`\n",
+ "# Option 1: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext\n",
"# and that the graph space is called \"test\"\n",
- " # If not, create it with the following commands from NebulaGraph's console:\n",
- " # CREATE SPACE llamaindex(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);\n",
- " # :sleep 10;\n",
- " # USE llamaindex;\n",
- " # CREATE TAG entity();\n",
- " # CREATE EDGE rel(predicate string);\n",
+ "# If not, create it with the following commands from NebulaGraph's console:\n",
+ "# CREATE SPACE llamaindex(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);\n",
+ "# :sleep 10;\n",
+ "# USE llamaindex;\n",
+ "# CREATE TAG entity();\n",
+ "# CREATE EDGE rel(predicate string);\n",
"\n",
"space_name = \"llamaindex\"\n",
- "edge_types, rel_prop_names = [\"rel\"], [\"predicate\"] # default, could be omit if create from an empty kg\n",
- "tags = [\"entity\"] # default, could be omit if create from an empty kg"
+ "edge_types, rel_prop_names = [\"rel\"], [\n",
+ " \"predicate\"\n",
+ "] # default, could be omit if create from an empty kg\n",
+ "tags = [\"entity\"] # default, could be omit if create from an empty kg"
]
},
{
@@ -250,7 +259,7 @@
"\n",
"loader = WikipediaReader()\n",
"\n",
- "documents = loader.load_data(pages=['2023 in science'], auto_suggest=False)"
+ "documents = loader.load_data(pages=[\"2023 in science\"], auto_suggest=False)"
]
},
{
@@ -282,7 +291,12 @@
}
],
"source": [
- "graph_store = NebulaGraphStore(space_name=space_name, edge_types=edge_types, rel_prop_names=rel_prop_names, tags=tags)\n",
+ "graph_store = NebulaGraphStore(\n",
+ " space_name=space_name,\n",
+ " edge_types=edge_types,\n",
+ " rel_prop_names=rel_prop_names,\n",
+ " tags=tags,\n",
+ ")\n",
"storage_context = StorageContext.from_defaults(graph_store=graph_store)\n",
"\n",
"kg_index = KnowledgeGraphIndex.from_documents(\n",
@@ -351,9 +365,11 @@
"source": [
"# import QueryBundle\n",
"from llama_index import QueryBundle\n",
+ "\n",
"# import NodeWithScore\n",
"from llama_index.schema import NodeWithScore\n",
- "# Retrievers \n",
+ "\n",
+ "# Retrievers\n",
"from llama_index.retrievers import BaseRetriever, VectorIndexRetriever, KGTableRetriever\n",
"\n",
"from typing import List\n",
@@ -361,40 +377,40 @@
"\n",
"class CustomRetriever(BaseRetriever):\n",
" \"\"\"Custom retriever that performs both Vector search and Knowledge Graph search\"\"\"\n",
- " \n",
+ "\n",
" def __init__(\n",
" self,\n",
" vector_retriever: VectorIndexRetriever,\n",
" kg_retriever: KGTableRetriever,\n",
- " mode: str = \"OR\"\n",
+ " mode: str = \"OR\",\n",
" ) -> None:\n",
" \"\"\"Init params.\"\"\"\n",
- " \n",
+ "\n",
" self._vector_retriever = vector_retriever\n",
" self._kg_retriever = kg_retriever\n",
" if mode not in (\"AND\", \"OR\"):\n",
" raise ValueError(\"Invalid mode.\")\n",
" self._mode = mode\n",
- " \n",
- " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: \n",
+ "\n",
+ " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n",
" \"\"\"Retrieve nodes given query.\"\"\"\n",
- " \n",
+ "\n",
" vector_nodes = self._vector_retriever.retrieve(query_bundle)\n",
" kg_nodes = self._kg_retriever.retrieve(query_bundle)\n",
"\n",
" vector_ids = {n.node.node_id for n in vector_nodes}\n",
" kg_ids = {n.node.node_id for n in kg_nodes}\n",
- " \n",
+ "\n",
" combined_dict = {n.node.node_id: n for n in vector_nodes}\n",
" combined_dict.update({n.node.node_id: n for n in kg_nodes})\n",
- " \n",
+ "\n",
" if self._mode == \"AND\":\n",
" retrieve_ids = vector_ids.intersection(kg_ids)\n",
" else:\n",
" retrieve_ids = vector_ids.union(kg_ids)\n",
"\n",
" retrieve_nodes = [combined_dict[rid] for rid in retrieve_ids]\n",
- " return retrieve_nodes\n"
+ " return retrieve_nodes"
]
},
{
@@ -418,7 +434,9 @@
"\n",
"# create custom retriever\n",
"vector_retriever = VectorIndexRetriever(index=vector_index)\n",
- "kg_retriever = KGTableRetriever(index=kg_index, retriever_mode='keyword', include_text=False)\n",
+ "kg_retriever = KGTableRetriever(\n",
+ " index=kg_index, retriever_mode=\"keyword\", include_text=False\n",
+ ")\n",
"custom_retriever = CustomRetriever(vector_retriever, kg_retriever)\n",
"\n",
"# create response synthesizer\n",
@@ -455,8 +473,8 @@
"\n",
"kg_keyword_query_engine = kg_index.as_query_engine(\n",
" # setting to false uses the raw triplets instead of adding the text from the corresponding nodes\n",
- " include_text=False, \n",
- " retriever_mode='keyword',\n",
+ " include_text=False,\n",
+ " retriever_mode=\"keyword\",\n",
" response_mode=\"tree_summarize\",\n",
")"
]
@@ -534,9 +552,7 @@
}
],
"source": [
- "response = kg_keyword_query_engine.query(\n",
- " \"Tell me events about NASA\"\n",
- ")\n",
+ "response = kg_keyword_query_engine.query(\"Tell me events about NASA\")\n",
"display(Markdown(f\"<b>{response}</b>\"))"
]
},
@@ -592,9 +608,7 @@
}
],
"source": [
- "response = vector_query_engine.query(\n",
- " \"Tell me events about NASA\"\n",
- ")\n",
+ "response = vector_query_engine.query(\"Tell me events about NASA\")\n",
"display(Markdown(f\"<b>{response}</b>\"))"
]
},
@@ -684,9 +698,7 @@
}
],
"source": [
- "response = custom_query_engine.query(\n",
- " \"Tell me events about NASA\"\n",
- ")\n",
+ "response = custom_query_engine.query(\"Tell me events about NASA\")\n",
"display(Markdown(f\"<b>{response}</b>\"))"
]
},
@@ -795,9 +807,7 @@
}
],
"source": [
- "response = custom_query_engine.query(\n",
- " \"Tell me events about ChatGPT\"\n",
- ")\n",
+ "response = custom_query_engine.query(\"Tell me events about ChatGPT\")\n",
"display(Markdown(f\"<b>{response}</b>\"))"
]
},
@@ -861,9 +871,7 @@
}
],
"source": [
- "response = kg_keyword_query_engine.query(\n",
- " \"Tell me events about ChatGPT\"\n",
- ")\n",
+ "response = kg_keyword_query_engine.query(\"Tell me events about ChatGPT\")\n",
"display(Markdown(f\"<b>{response}</b>\"))"
]
},
@@ -909,9 +917,7 @@
}
],
"source": [
- "response = vector_query_engine.query(\n",
- " \"Tell me events about ChatGPT\"\n",
- ")\n",
+ "response = vector_query_engine.query(\"Tell me events about ChatGPT\")\n",
"display(Markdown(f\"<b>{response}</b>\"))"
]
},
@@ -1009,4 +1015,4 @@
},
"nbformat": 4,
"nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/docs/examples/index_structs/knowledge_graph/NebulaGraphKGIndexDemo.ipynb b/docs/examples/index_structs/knowledge_graph/NebulaGraphKGIndexDemo.ipynb
index 041a857dd0..a7dee7bff2 100644
--- a/docs/examples/index_structs/knowledge_graph/NebulaGraphKGIndexDemo.ipynb
+++ b/docs/examples/index_structs/knowledge_graph/NebulaGraphKGIndexDemo.ipynb
@@ -9,7 +9,8 @@
"source": [
"# My OpenAI Key\n",
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
]
},
{
@@ -83,7 +84,9 @@
},
"outputs": [],
"source": [
- "documents = SimpleDirectoryReader('../../../../examples/paul_graham_essay/data').load_data()"
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../../examples/paul_graham_essay/data\"\n",
+ ").load_data()"
]
},
{
@@ -131,25 +134,29 @@
"source": [
"%pip install nebula3-python\n",
"\n",
- "os.environ['NEBULA_USER'] = \"root\"\n",
- "os.environ['NEBULA_PASSWORD'] = \"nebula\"\n",
- "os.environ['NEBULA_ADDRESS'] = \"127.0.0.1:9669\" # assumed we have NebulaGraph installed locally\n",
+ "os.environ[\"NEBULA_USER\"] = \"root\"\n",
+ "os.environ[\"NEBULA_PASSWORD\"] = \"nebula\"\n",
+ "os.environ[\n",
+ " \"NEBULA_ADDRESS\"\n",
+ "] = \"127.0.0.1:9669\" # assumed we have NebulaGraph installed locally\n",
"\n",
"# Assume that the graph has already been created\n",
- " # Create a NebulaGraph cluster with:\n",
- " # Option 0: `curl -fsSL nebula-up.siwei.io/install.sh | bash`\n",
- " # Option 1: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext\n",
+ "# Create a NebulaGraph cluster with:\n",
+ "# Option 0: `curl -fsSL nebula-up.siwei.io/install.sh | bash`\n",
+ "# Option 1: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext\n",
"# and that the graph space is called \"test\"\n",
- " # If not, create it with the following commands from NebulaGraph's console:\n",
- " # CREATE SPACE test(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);\n",
- " # :sleep 10;\n",
- " # USE test;\n",
- " # CREATE TAG entity();\n",
- " # CREATE EDGE rel(predicate string);\n",
+ "# If not, create it with the following commands from NebulaGraph's console:\n",
+ "# CREATE SPACE test(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);\n",
+ "# :sleep 10;\n",
+ "# USE test;\n",
+ "# CREATE TAG entity();\n",
+ "# CREATE EDGE rel(predicate string);\n",
"\n",
"space_name = \"test\"\n",
- "edge_types, rel_prop_names = [\"rel\"], [\"predicate\"] # default, could be omit if create from an empty kg\n",
- "tags = [\"entity\"] # default, could be omit if create from an empty kg"
+ "edge_types, rel_prop_names = [\"rel\"], [\n",
+ " \"predicate\"\n",
+ "] # default, could be omit if create from an empty kg\n",
+ "tags = [\"entity\"] # default, could be omit if create from an empty kg"
]
},
{
@@ -183,7 +190,7 @@
"graph_store = NebulaGraphStore(space_name=space_name)\n",
"storage_context = StorageContext.from_defaults(graph_store=graph_store)\n",
"\n",
- "# NOTE: can take a while! \n",
+ "# NOTE: can take a while!\n",
"index = KnowledgeGraphIndex.from_documents(\n",
" documents,\n",
" storage_context=storage_context,\n",
@@ -192,7 +199,7 @@
" space_name=space_name,\n",
" edge_types=edge_types,\n",
" rel_prop_names=rel_prop_names,\n",
- " tags=tags\n",
+ " tags=tags,\n",
")"
]
},
@@ -313,9 +320,7 @@
"query_engine = index.as_query_engine()\n",
"\n",
"\n",
- "response = query_engine.query(\n",
- " \"Tell me more about Interleaf\"\n",
- ")"
+ "response = query_engine.query(\"Tell me more about Interleaf\")"
]
},
{
@@ -510,7 +515,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# NOTE: can take a while! \n",
+ "# NOTE: can take a while!\n",
"\n",
"index = KnowledgeGraphIndex.from_documents(\n",
" documents,\n",
@@ -525,10 +530,10 @@
")\n",
"\n",
"query_engine = index.as_query_engine(\n",
- " include_text=True, \n",
+ " include_text=True,\n",
" response_mode=\"tree_summarize\",\n",
- " embedding_mode='hybrid',\n",
- " similarity_top_k=5\n",
+ " embedding_mode=\"hybrid\",\n",
+ " similarity_top_k=5,\n",
")"
]
},
@@ -572,16 +577,14 @@
"outputs": [],
"source": [
"query_engine = index.as_query_engine(\n",
- " include_text=True, \n",
+ " include_text=True,\n",
" response_mode=\"tree_summarize\",\n",
- " embedding_mode='hybrid',\n",
+ " embedding_mode=\"hybrid\",\n",
" similarity_top_k=5,\n",
" explore_global_knowledge=True,\n",
")\n",
"\n",
- "response = query_engine.query(\n",
- " \"Tell me more about what the author and Lisp\"\n",
- ")"
+ "response = query_engine.query(\"Tell me more about what the author and Lisp\")"
]
},
{
@@ -680,11 +683,8 @@
"source": [
"# not yet implemented\n",
"\n",
- "# initialize an empty index for now \n",
- "index = KnowledgeGraphIndex.from_documents(\n",
- " [],\n",
- " storage_context=storage_context\n",
- ")\n"
+ "# initialize an empty index for now\n",
+ "index = KnowledgeGraphIndex.from_documents([], storage_context=storage_context)"
]
},
{
@@ -695,18 +695,21 @@
"outputs": [],
"source": [
"# add keyword mappings and nodes manually\n",
- "# add triplets (subject, relationship, object) \n",
+ "# add triplets (subject, relationship, object)\n",
"\n",
"# for node 0\n",
- "node_0_tups = [(\"author\", \"worked on\", \"writing\"), (\"author\", \"worked on\", \"programming\")]\n",
+ "node_0_tups = [\n",
+ " (\"author\", \"worked on\", \"writing\"),\n",
+ " (\"author\", \"worked on\", \"programming\"),\n",
+ "]\n",
"for tup in node_0_tups:\n",
" index.upsert_triplet_and_node(tup, nodes[0])\n",
- " \n",
+ "\n",
"# for node 1\n",
"node_1_tups = [\n",
- " ('Interleaf', 'made software for', 'creating documents'),\n",
- " ('Interleaf', 'added', 'scripting language'),\n",
- " ('software', 'generate', 'web sites')\n",
+ " (\"Interleaf\", \"made software for\", \"creating documents\"),\n",
+ " (\"Interleaf\", \"added\", \"scripting language\"),\n",
+ " (\"software\", \"generate\", \"web sites\"),\n",
"]\n",
"for tup in node_1_tups:\n",
" index.upsert_triplet_and_node(tup, nodes[1])"
@@ -719,14 +722,9 @@
"metadata": {},
"outputs": [],
"source": [
- "query_engine = index.as_query_engine(\n",
- " include_text=False, \n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(include_text=False, response_mode=\"tree_summarize\")\n",
"\n",
- "response = query_engine.query(\n",
- " \"Tell me more about Interleaf\"\n",
- ")"
+ "response = query_engine.query(\"Tell me more about Interleaf\")"
]
},
{
diff --git a/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb b/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb
index 112eedd660..7ce92e8134 100644
--- a/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb
+++ b/docs/examples/index_structs/struct_indices/SQLIndexDemo.ipynb
@@ -1,447 +1,459 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
- "metadata": {},
- "source": [
- "# SQL Index Guide (Core)\n",
- "\n",
- "This is a basic guide to LlamaIndex's SQL index capabilities. We first show how to define a SQL table, then we build a TableIndex over the schema. This will allow us to synthesize a SQL query given the user's natural language query."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "119eb42b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# import logging\n",
- "# import sys\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "107396a9-4aa7-49b3-9f0f-a755726c19ba",
- "metadata": {},
- "outputs": [],
- "source": [
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "461438c8-302d-45c5-8e69-16ad604686d1",
- "metadata": {},
- "source": [
- "### Create Database Schema\n",
- "\n",
- "We use `sqlalchemy`, a popular SQL database toolkit, to create an empty `city_stats` Table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a370b266-66f5-4624-bbf9-2ad57f0511f8",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, select, column"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ea24f794-f10b-42e6-922d-9258b7167405",
- "metadata": {},
- "outputs": [],
- "source": [
- "engine = create_engine(\"sqlite:///:memory:\")\n",
- "metadata_obj = MetaData()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b4154b29-7e23-4c26-a507-370a66186ae7",
- "metadata": {},
- "outputs": [],
- "source": [
- "# create city SQL table\n",
- "table_name = \"city_stats\"\n",
- "city_stats_table = Table(\n",
- " table_name,\n",
- " metadata_obj,\n",
- " Column(\"city_name\", String(16), primary_key=True),\n",
- " Column(\"population\", Integer),\n",
- " Column(\"country\", String(16), nullable=False),\n",
- ")\n",
- "metadata_obj.create_all(engine)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1c09089a-6bcd-48db-8120-a84c8da3f82e",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Define SQL Database\n",
- "\n",
- "We first define our `SQLDatabase` abstraction (a light wrapper around SQLAlchemy). "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "768d1581-b482-4c73-9963-5ffd68a2aafb",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import SQLDatabase, ServiceContext\n",
- "from llama_index.llms import OpenAI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bffabba0-8e54-4f24-ad14-2c8979c582a5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "llm = OpenAI(temperature=0, model=\"text-davinci-002\")\n",
- "service_context = ServiceContext.from_defaults(llm=llm)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9432787b-a8f0-4fc3-8323-e2cd9497df73",
- "metadata": {},
- "outputs": [],
- "source": [
- "sql_database = SQLDatabase(engine, include_tables=[\"city_stats\"])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84d4ee54-9f00-40fd-bab0-36e5e579dc9f",
- "metadata": {},
- "outputs": [],
- "source": [
- "sql_database.table_info"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bad7ffbe",
- "metadata": {},
- "source": [
- "We add some testing data to our SQL database."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "95043e10-6cdf-4f66-96bd-ce307ea7df3e",
- "metadata": {},
- "outputs": [],
- "source": [
- "sql_database = SQLDatabase(engine, include_tables=[\"city_stats\"])\n",
- "from sqlalchemy import insert\n",
- "rows = [\n",
- " {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
- " {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
- " {\"city_name\": \"Chicago\", \"population\": 2679000, \"country\": \"United States\"},\n",
- " {\"city_name\": \"Seoul\", \"population\": 9776000, \"country\": \"South Korea\"},\n",
- "]\n",
- "for row in rows:\n",
- " stmt = insert(city_stats_table).values(**row)\n",
- " with engine.connect() as connection:\n",
- " cursor = connection.execute(stmt)\n",
- " connection.commit()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b315b8ff-7dd7-4e7d-ac47-8c5a0c3e7ae9",
- "metadata": {},
- "outputs": [],
- "source": [
- "# view current table\n",
- "stmt = select(\n",
- " city_stats_table.c[\"city_name\", \"population\", \"country\"]\n",
- ").select_from(city_stats_table)\n",
- "\n",
- "with engine.connect() as connection:\n",
- " results = connection.execute(stmt).fetchall()\n",
- " print(results)\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "051a171f-8c97-40ed-ae17-4e3fa3785487",
- "metadata": {},
- "source": [
- "### Query Index"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f6a2303f-3bae-4fa2-8750-03f9af747848",
- "metadata": {},
- "source": [
- "We first show how we can execute a raw SQL query, which directly executes over the table."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eddd3608-31ff-4591-a02a-90987e312669",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sqlalchemy import text\n",
- "\n",
- "with engine.connect() as con:\n",
- " rows = con.execute(text(\"SELECT city_name from city_stats\"))\n",
- " for row in rows:\n",
- " print(row)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "4e72b931",
- "metadata": {},
- "source": [
- "## Natural language SQL\n",
- "Once we have constructed our SQL database, we can use the NLSQLTableQueryEngine to\n",
- "construct natural language queries that are synthesized into SQL queries.\n",
- "\n",
- "Note that we need to specify the tables we want to use with this query engine.\n",
- "If we don't the query engine will pull all the schema context, which could\n",
- "overflow the context window of the LLM."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5d992fb5",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine\n",
- "\n",
- "query_engine = NLSQLTableQueryEngine(\n",
- " sql_database=sql_database,\n",
- " tables=[\"city_stats\"],\n",
- ")\n",
- "query_str = (\n",
- " \"Which city has the highest population?\"\n",
- ")\n",
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "298b4ca2",
- "metadata": {},
- "source": [
- "This query engine should used in any case where you can specify the tables you want\n",
- "to query over beforehand, or the total size of all the table schema plus the rest of\n",
- "the prompt fits your context window."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "dee4d251",
- "metadata": {},
- "source": [
- "## Building our Table Index\n",
- "If we don't know ahead of time which table we would like to use, and the total size of\n",
- "the table schema overflows your context window size, we should store the table schema \n",
- "in an index so that during query time we can retrieve the right schema.\n",
- "\n",
- "The way we can do this is using the SQLTableNodeMapping object, which takes in a \n",
- "SQLDatabase and produces a Node object for each SQLTableSchema object passed \n",
- "into the ObjectIndex constructor.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d71045c0-7a96-4e86-b38c-c378b7759aa4",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.struct_store.sql_query import SQLTableRetrieverQueryEngine\n",
- "from llama_index.objects import SQLTableNodeMapping, ObjectIndex, SQLTableSchema\n",
- "from llama_index import VectorStoreIndex\n",
- "\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
- "table_schema_objs = [(SQLTableSchema(table_name=\"city_stats\"))] # add a SQLTableSchema for each table\n",
- "\n",
- "obj_index = ObjectIndex.from_objects(\n",
- " table_schema_objs,\n",
- " table_node_mapping,\n",
- " VectorStoreIndex,\n",
- ")\n",
- "query_engine = SQLTableRetrieverQueryEngine(\n",
- " sql_database, obj_index.as_retriever(similarity_top_k=1)\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b6156caf",
- "metadata": {},
- "source": [
- "Now we can take our SQLTableRetrieverQueryEngine and query it for our response."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "802da9ed",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = query_engine.query(\"Which city has the highest population?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "54a99cb0-578a-40ec-a3eb-1666ac18fbed",
- "metadata": {},
- "outputs": [],
- "source": [
- "# you can also fetch the raw result from SQLAlchemy! \n",
- "response.metadata[\"result\"]"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0d19b9cd",
- "metadata": {},
- "source": [
- "You can also add additional context information for each table schema you define."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "44a87651",
- "metadata": {},
- "outputs": [],
- "source": [
- "# manually set context text\n",
- "city_stats_text = (\n",
- " \"This table gives information regarding the population and country of a given city.\\n\"\n",
- " \"The user will query with codewords, where 'foo' corresponds to population and 'bar'\"\n",
- " \"corresponds to city.\"\n",
- ")\n",
- "\n",
- "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
- "table_schema_objs = [(SQLTableSchema(table_name=\"city_stats\", context_str=city_stats_text))]"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "2a567566-9828-4aa4-afde-b253c01eda69",
- "metadata": {},
- "source": [
- "### Using LangChain for Querying\n",
- "\n",
- "Since our SQLDatabase inherits from langchain, you can also use langchain itself for querying purposes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8e0acde4-ca61-42e9-97f8-c9cf11502157",
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "30860e8b-9ad0-418c-b266-753242c1f208",
- "metadata": {},
- "outputs": [],
- "source": [
- "llm = OpenAI(temperature=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "07068a3a-30a4-4473-ba82-ab6e93e3437c",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "db_chain = SQLDatabaseChain(llm=llm, database=sql_database)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a04c0a1d-f6a8-4a4a-9181-4123b09ec614",
- "metadata": {},
- "outputs": [],
- "source": [
- "db_chain.run(\"Which city has the highest population?\")"
- ]
- }
- ],
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
+ "metadata": {},
+ "source": [
+ "# SQL Index Guide (Core)\n",
+ "\n",
+ "This is a basic guide to LlamaIndex's SQL index capabilities. We first show how to define a SQL table, then we build a TableIndex over the schema. This will allow us to synthesize a SQL query given the user's natural language query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "119eb42b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import logging\n",
+ "# import sys\n",
+ "\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "107396a9-4aa7-49b3-9f0f-a755726c19ba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "461438c8-302d-45c5-8e69-16ad604686d1",
+ "metadata": {},
+ "source": [
+ "### Create Database Schema\n",
+ "\n",
+ "We use `sqlalchemy`, a popular SQL database toolkit, to create an empty `city_stats` Table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a370b266-66f5-4624-bbf9-2ad57f0511f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ " select,\n",
+ " column,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea24f794-f10b-42e6-922d-9258b7167405",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "engine = create_engine(\"sqlite:///:memory:\")\n",
+ "metadata_obj = MetaData()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4154b29-7e23-4c26-a507-370a66186ae7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create city SQL table\n",
+ "table_name = \"city_stats\"\n",
+ "city_stats_table = Table(\n",
+ " table_name,\n",
+ " metadata_obj,\n",
+ " Column(\"city_name\", String(16), primary_key=True),\n",
+ " Column(\"population\", Integer),\n",
+ " Column(\"country\", String(16), nullable=False),\n",
+ ")\n",
+ "metadata_obj.create_all(engine)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1c09089a-6bcd-48db-8120-a84c8da3f82e",
"metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
+ "tags": []
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "source": [
+ "### Define SQL Database\n",
+ "\n",
+ "We first define our `SQLDatabase` abstraction (a light wrapper around SQLAlchemy). "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "768d1581-b482-4c73-9963-5ffd68a2aafb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import SQLDatabase, ServiceContext\n",
+ "from llama_index.llms import OpenAI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bffabba0-8e54-4f24-ad14-2c8979c582a5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "llm = OpenAI(temperature=0, model=\"text-davinci-002\")\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9432787b-a8f0-4fc3-8323-e2cd9497df73",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql_database = SQLDatabase(engine, include_tables=[\"city_stats\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84d4ee54-9f00-40fd-bab0-36e5e579dc9f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql_database.table_info"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bad7ffbe",
+ "metadata": {},
+ "source": [
+ "We add some testing data to our SQL database."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95043e10-6cdf-4f66-96bd-ce307ea7df3e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sql_database = SQLDatabase(engine, include_tables=[\"city_stats\"])\n",
+ "from sqlalchemy import insert\n",
+ "\n",
+ "rows = [\n",
+ " {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
+ " {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
+ " {\"city_name\": \"Chicago\", \"population\": 2679000, \"country\": \"United States\"},\n",
+ " {\"city_name\": \"Seoul\", \"population\": 9776000, \"country\": \"South Korea\"},\n",
+ "]\n",
+ "for row in rows:\n",
+ " stmt = insert(city_stats_table).values(**row)\n",
+ " with engine.connect() as connection:\n",
+ " cursor = connection.execute(stmt)\n",
+ " connection.commit()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b315b8ff-7dd7-4e7d-ac47-8c5a0c3e7ae9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# view current table\n",
+ "stmt = select(city_stats_table.c[\"city_name\", \"population\", \"country\"]).select_from(\n",
+ " city_stats_table\n",
+ ")\n",
+ "\n",
+ "with engine.connect() as connection:\n",
+ " results = connection.execute(stmt).fetchall()\n",
+ " print(results)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "051a171f-8c97-40ed-ae17-4e3fa3785487",
+ "metadata": {},
+ "source": [
+ "### Query Index"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f6a2303f-3bae-4fa2-8750-03f9af747848",
+ "metadata": {},
+ "source": [
+ "We first show how we can execute a raw SQL query, which directly executes over the table."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eddd3608-31ff-4591-a02a-90987e312669",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sqlalchemy import text\n",
+ "\n",
+ "with engine.connect() as con:\n",
+ " rows = con.execute(text(\"SELECT city_name from city_stats\"))\n",
+ " for row in rows:\n",
+ " print(row)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "4e72b931",
+ "metadata": {},
+ "source": [
+ "## Natural language SQL\n",
+ "Once we have constructed our SQL database, we can use the NLSQLTableQueryEngine to\n",
+ "construct natural language queries that are synthesized into SQL queries.\n",
+ "\n",
+ "Note that we need to specify the tables we want to use with this query engine.\n",
+ "If we don't the query engine will pull all the schema context, which could\n",
+ "overflow the context window of the LLM."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5d992fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine\n",
+ "\n",
+ "query_engine = NLSQLTableQueryEngine(\n",
+ " sql_database=sql_database,\n",
+ " tables=[\"city_stats\"],\n",
+ ")\n",
+ "query_str = \"Which city has the highest population?\"\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "298b4ca2",
+ "metadata": {},
+ "source": [
+ "This query engine should used in any case where you can specify the tables you want\n",
+ "to query over beforehand, or the total size of all the table schema plus the rest of\n",
+ "the prompt fits your context window."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "dee4d251",
+ "metadata": {},
+ "source": [
+ "## Building our Table Index\n",
+ "If we don't know ahead of time which table we would like to use, and the total size of\n",
+ "the table schema overflows your context window size, we should store the table schema \n",
+ "in an index so that during query time we can retrieve the right schema.\n",
+ "\n",
+ "The way we can do this is using the SQLTableNodeMapping object, which takes in a \n",
+ "SQLDatabase and produces a Node object for each SQLTableSchema object passed \n",
+ "into the ObjectIndex constructor.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d71045c0-7a96-4e86-b38c-c378b7759aa4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.struct_store.sql_query import SQLTableRetrieverQueryEngine\n",
+ "from llama_index.objects import SQLTableNodeMapping, ObjectIndex, SQLTableSchema\n",
+ "from llama_index import VectorStoreIndex\n",
+ "\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
+ "table_schema_objs = [\n",
+ " (SQLTableSchema(table_name=\"city_stats\"))\n",
+ "] # add a SQLTableSchema for each table\n",
+ "\n",
+ "obj_index = ObjectIndex.from_objects(\n",
+ " table_schema_objs,\n",
+ " table_node_mapping,\n",
+ " VectorStoreIndex,\n",
+ ")\n",
+ "query_engine = SQLTableRetrieverQueryEngine(\n",
+ " sql_database, obj_index.as_retriever(similarity_top_k=1)\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b6156caf",
+ "metadata": {},
+ "source": [
+ "Now we can take our SQLTableRetrieverQueryEngine and query it for our response."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "802da9ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\"Which city has the highest population?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54a99cb0-578a-40ec-a3eb-1666ac18fbed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# you can also fetch the raw result from SQLAlchemy!\n",
+ "response.metadata[\"result\"]"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0d19b9cd",
+ "metadata": {},
+ "source": [
+ "You can also add additional context information for each table schema you define."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44a87651",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# manually set context text\n",
+ "city_stats_text = (\n",
+ " \"This table gives information regarding the population and country of a given city.\\n\"\n",
+ " \"The user will query with codewords, where 'foo' corresponds to population and 'bar'\"\n",
+ " \"corresponds to city.\"\n",
+ ")\n",
+ "\n",
+ "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
+ "table_schema_objs = [\n",
+ " (SQLTableSchema(table_name=\"city_stats\", context_str=city_stats_text))\n",
+ "]"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "2a567566-9828-4aa4-afde-b253c01eda69",
+ "metadata": {},
+ "source": [
+ "### Using LangChain for Querying\n",
+ "\n",
+ "Since our SQLDatabase inherits from langchain, you can also use langchain itself for querying purposes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e0acde4-ca61-42e9-97f8-c9cf11502157",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30860e8b-9ad0-418c-b266-753242c1f208",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = OpenAI(temperature=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07068a3a-30a4-4473-ba82-ab6e93e3437c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "db_chain = SQLDatabaseChain(llm=llm, database=sql_database)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a04c0a1d-f6a8-4a4a-9181-4123b09ec614",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "db_chain.run(\"Which city has the highest population?\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb b/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb
index 87567b6586..47f4066200 100644
--- a/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb
+++ b/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb
@@ -50,7 +50,10 @@
"outputs": [],
"source": [
"from llama_index import SQLDatabase, SimpleDirectoryReader, WikipediaReader, Document\n",
- "from llama_index.indices.struct_store import NLSQLTableQueryEngine, SQLTableRetrieverQueryEngine"
+ "from llama_index.indices.struct_store import (\n",
+ " NLSQLTableQueryEngine,\n",
+ " SQLTableRetrieverQueryEngine,\n",
+ ")"
]
},
{
@@ -94,7 +97,16 @@
},
"outputs": [],
"source": [
- "from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, select, column"
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ " select,\n",
+ " column,\n",
+ ")"
]
},
{
@@ -107,7 +119,7 @@
"outputs": [],
"source": [
"engine = create_engine(\"duckdb:///:memory:\")\n",
- "# uncomment to make this work with MotherDuck \n",
+ "# uncomment to make this work with MotherDuck\n",
"# engine = create_engine(\"duckdb:///md:llama-index\")\n",
"metadata_obj = MetaData()"
]
@@ -176,6 +188,7 @@
"outputs": [],
"source": [
"from sqlalchemy import insert\n",
+ "\n",
"rows = [\n",
" {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
" {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
@@ -393,7 +406,7 @@
"outputs": [],
"source": [
"engine = create_engine(\"duckdb:///:memory:\")\n",
- "# uncomment to make this work with MotherDuck \n",
+ "# uncomment to make this work with MotherDuck\n",
"# engine = create_engine(\"duckdb:///md:llama-index\")\n",
"metadata_obj = MetaData()"
]
@@ -444,6 +457,7 @@
"source": [
"# insert dummy data\n",
"from sqlalchemy import insert\n",
+ "\n",
"rows = [\n",
" {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
" {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
@@ -520,7 +534,7 @@
"obj_index = ObjectIndex.from_objects(\n",
" table_schema_objs,\n",
" table_node_mapping,\n",
- " VectorStoreIndex, \n",
+ " VectorStoreIndex,\n",
")"
]
},
diff --git a/docs/examples/llm/azure_openai.ipynb b/docs/examples/llm/azure_openai.ipynb
index 5496d1abfb..0626c15514 100644
--- a/docs/examples/llm/azure_openai.ipynb
+++ b/docs/examples/llm/azure_openai.ipynb
@@ -87,7 +87,8 @@
],
"source": [
"from IPython.display import Image\n",
- "Image(filename='./azure_playground.png')"
+ "\n",
+ "Image(filename=\"./azure_playground.png\")"
]
},
{
@@ -122,7 +123,8 @@
],
"source": [
"from IPython.display import Image\n",
- "Image(filename='./azure_env.png')"
+ "\n",
+ "Image(filename=\"./azure_env.png\")"
]
},
{
@@ -164,7 +166,7 @@
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"<your-api-key>\"\n",
"os.environ[\"OPENAI_API_BASE\"] = \"https://<your-resource-name>.openai.azure.com/\"\n",
- "os.environ[\"OPENAI_API_TYPE\"] = 'azure'\n",
+ "os.environ[\"OPENAI_API_TYPE\"] = \"azure\"\n",
"os.environ[\"OPENAI_API_VERSION\"] = \"2023-03-15-preview\""
]
},
@@ -216,7 +218,7 @@
},
"outputs": [],
"source": [
- "llm = AzureOpenAI(engine='simon-llm', model='gpt-35-turbo-16k', temperature=0.)"
+ "llm = AzureOpenAI(engine=\"simon-llm\", model=\"gpt-35-turbo-16k\", temperature=0.0)"
]
},
{
@@ -244,7 +246,7 @@
}
],
"source": [
- "response = llm.complete('The sky is a beautiful blue and')\n",
+ "response = llm.complete(\"The sky is a beautiful blue and\")\n",
"print(response)"
]
},
@@ -265,9 +267,9 @@
}
],
"source": [
- "response = llm.stream_complete('The sky is a beautiful blue and')\n",
+ "response = llm.stream_complete(\"The sky is a beautiful blue and\")\n",
"for r in response:\n",
- " print(r.delta, end='')"
+ " print(r.delta, end=\"\")"
]
},
{
@@ -298,8 +300,8 @@
"from llama_index.llms import ChatMessage\n",
"\n",
"messages = [\n",
- " ChatMessage(role='system', content='You are a pirate with colorful personality.'),\n",
- " ChatMessage(role='user', content='Hello'),\n",
+ " ChatMessage(role=\"system\", content=\"You are a pirate with colorful personality.\"),\n",
+ " ChatMessage(role=\"user\", content=\"Hello\"),\n",
"]\n",
"\n",
"response = llm.chat(messages)\n",
@@ -325,7 +327,7 @@
"source": [
"response = llm.stream_chat(messages)\n",
"for r in response:\n",
- " print(r.delta, end='')"
+ " print(r.delta, end=\"\")"
]
}
],
diff --git a/docs/examples/llm/langchain.ipynb b/docs/examples/llm/langchain.ipynb
index 703bb2bf4f..209d3c145a 100644
--- a/docs/examples/llm/langchain.ipynb
+++ b/docs/examples/llm/langchain.ipynb
@@ -55,7 +55,7 @@
},
"outputs": [],
"source": [
- "response_gen = llm.stream_complete('Hi this is')"
+ "response_gen = llm.stream_complete(\"Hi this is\")"
]
},
{
@@ -78,7 +78,7 @@
],
"source": [
"for delta in response_gen:\n",
- " print(delta.delta, end='')"
+ " print(delta.delta, end=\"\")"
]
},
{
diff --git a/docs/examples/llm/llm_predictor.ipynb b/docs/examples/llm/llm_predictor.ipynb
index a6beff3469..361378209d 100644
--- a/docs/examples/llm/llm_predictor.ipynb
+++ b/docs/examples/llm/llm_predictor.ipynb
@@ -52,7 +52,7 @@
},
"outputs": [],
"source": [
- "stream = await llm_predictor.astream('Hi, write a short story')"
+ "stream = await llm_predictor.astream(\"Hi, write a short story\")"
]
},
{
@@ -93,7 +93,7 @@
],
"source": [
"for token in stream:\n",
- " print(token, end='')"
+ " print(token, end=\"\")"
]
},
{
@@ -138,7 +138,7 @@
},
"outputs": [],
"source": [
- "stream = await llm_predictor.astream('Hi, write a short story')"
+ "stream = await llm_predictor.astream(\"Hi, write a short story\")"
]
},
{
@@ -177,7 +177,7 @@
],
"source": [
"for token in stream:\n",
- " print(token, end='')"
+ " print(token, end=\"\")"
]
}
],
diff --git a/docs/examples/llm/openai.ipynb b/docs/examples/llm/openai.ipynb
index c936aec45e..cba672e62b 100644
--- a/docs/examples/llm/openai.ipynb
+++ b/docs/examples/llm/openai.ipynb
@@ -38,7 +38,8 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
- "resp = OpenAI().complete('Paul Graham is ')"
+ "\n",
+ "resp = OpenAI().complete(\"Paul Graham is \")"
]
},
{
@@ -81,8 +82,8 @@
"from llama_index.llms import ChatMessage, OpenAI\n",
"\n",
"messages = [\n",
- " ChatMessage(role='system', content='You are a pirate with a colorful personality'),\n",
- " ChatMessage(role='user', content='What is your name')\n",
+ " ChatMessage(role=\"system\", content=\"You are a pirate with a colorful personality\"),\n",
+ " ChatMessage(role=\"user\", content=\"What is your name\"),\n",
"]\n",
"resp = OpenAI().chat(messages)"
]
@@ -133,8 +134,9 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
+ "\n",
"llm = OpenAI()\n",
- "resp = llm.stream_complete('Paul Graham is ')"
+ "resp = llm.stream_complete(\"Paul Graham is \")"
]
},
{
@@ -155,7 +157,7 @@
],
"source": [
"for delta in resp:\n",
- " print(delta, end='')"
+ " print(delta, end=\"\")"
]
},
{
@@ -176,10 +178,11 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
+ "\n",
"llm = OpenAI(stream=True)\n",
"messages = [\n",
- " ChatMessage(role='system', content='You are a pirate with a colorful personality'),\n",
- " ChatMessage(role='user', content='What is your name')\n",
+ " ChatMessage(role=\"system\", content=\"You are a pirate with a colorful personality\"),\n",
+ " ChatMessage(role=\"user\", content=\"What is your name\"),\n",
"]\n",
"resp = llm.stream_chat(messages)"
]
@@ -202,7 +205,7 @@
],
"source": [
"for delta in resp:\n",
- " print(delta, end='')"
+ " print(delta, end=\"\")"
]
},
{
@@ -223,7 +226,8 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
- "llm = OpenAI(model='text-davinci-003')"
+ "\n",
+ "llm = OpenAI(model=\"text-davinci-003\")"
]
},
{
@@ -235,7 +239,7 @@
},
"outputs": [],
"source": [
- "resp = llm.complete('Paul Graham is ')"
+ "resp = llm.complete(\"Paul Graham is \")"
]
},
{
@@ -270,8 +274,8 @@
"outputs": [],
"source": [
"messages = [\n",
- " ChatMessage(role='system', content='You are a pirate with a colorful personality'),\n",
- " ChatMessage(role='user', content='What is your name')\n",
+ " ChatMessage(role=\"system\", content=\"You are a pirate with a colorful personality\"),\n",
+ " ChatMessage(role=\"user\", content=\"What is your name\"),\n",
"]\n",
"resp = llm.chat(messages)"
]
@@ -317,11 +321,14 @@
"from pydantic import BaseModel\n",
"from llama_index.llms.openai_utils import to_openai_function\n",
"\n",
+ "\n",
"class Song(BaseModel):\n",
" \"\"\"A song with name and artist\"\"\"\n",
+ "\n",
" name: str\n",
" artist: str\n",
- " \n",
+ "\n",
+ "\n",
"song_fn = to_openai_function(Song)"
]
},
@@ -335,8 +342,9 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
- "response = OpenAI().complete('Generate a song', functions=[song_fn])\n",
- "function_call = response.additional_kwargs['function_call']\n",
+ "\n",
+ "response = OpenAI().complete(\"Generate a song\", functions=[song_fn])\n",
+ "function_call = response.additional_kwargs[\"function_call\"]\n",
"print(function_call)"
]
},
@@ -358,7 +366,8 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
- "llm = OpenAI(model='text-davinci-003')"
+ "\n",
+ "llm = OpenAI(model=\"text-davinci-003\")"
]
},
{
@@ -370,7 +379,7 @@
},
"outputs": [],
"source": [
- "resp = await llm.acomplete('Paul Graham is ')"
+ "resp = await llm.acomplete(\"Paul Graham is \")"
]
},
{
@@ -404,7 +413,7 @@
},
"outputs": [],
"source": [
- "resp = await llm.astream_complete('Paul Graham is ')"
+ "resp = await llm.astream_complete(\"Paul Graham is \")"
]
},
{
@@ -427,7 +436,7 @@
],
"source": [
"async for delta in resp:\n",
- " print(delta.delta, end='')"
+ " print(delta.delta, end=\"\")"
]
}
],
diff --git a/docs/examples/node_postprocessor/CohereRerank.ipynb b/docs/examples/node_postprocessor/CohereRerank.ipynb
index dce4848d3c..7d66ff2061 100644
--- a/docs/examples/node_postprocessor/CohereRerank.ipynb
+++ b/docs/examples/node_postprocessor/CohereRerank.ipynb
@@ -33,9 +33,9 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
"\n",
- "# build index \n",
+ "# build index\n",
"index = VectorStoreIndex.from_documents(documents=documents)"
]
},
@@ -72,7 +72,7 @@
" node_postprocessors=[cohere_rerank],\n",
")\n",
"response = query_engine.query(\n",
- " \"What did Sam Altman do in this essay?\", \n",
+ " \"What did Sam Altman do in this essay?\",\n",
")"
]
},
@@ -136,7 +136,7 @@
" similarity_top_k=2,\n",
")\n",
"response = query_engine.query(\n",
- " \"What did Sam Altman do in this essay?\", \n",
+ " \"What did Sam Altman do in this essay?\",\n",
")"
]
},
diff --git a/docs/examples/node_postprocessor/LLMReranker-Gatsby.ipynb b/docs/examples/node_postprocessor/LLMReranker-Gatsby.ipynb
index 284cb5d952..f5e94f62de 100644
--- a/docs/examples/node_postprocessor/LLMReranker-Gatsby.ipynb
+++ b/docs/examples/node_postprocessor/LLMReranker-Gatsby.ipynb
@@ -25,6 +25,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -42,10 +43,13 @@
"\n",
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
- "from llama_index.indices.postprocessor import (\n",
- " LLMRerank\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
")\n",
+ "from llama_index.indices.postprocessor import LLMRerank\n",
"from llama_index.llms import OpenAI\n",
"from IPython.display import Markdown, display"
]
@@ -92,7 +96,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../../examples/gatsby/data').load_data()"
+ "documents = SimpleDirectoryReader(\"../../../examples/gatsby/data\").load_data()"
]
},
{
@@ -162,39 +166,40 @@
"from IPython.display import display, HTML\n",
"\n",
"\n",
- "pd.set_option('display.max_colwidth', -1)\n",
+ "pd.set_option(\"display.max_colwidth\", -1)\n",
"\n",
"\n",
- "def get_retrieved_nodes(query_str, vector_top_k=10, reranker_top_n=3, with_reranker=False):\n",
+ "def get_retrieved_nodes(\n",
+ " query_str, vector_top_k=10, reranker_top_n=3, with_reranker=False\n",
+ "):\n",
" query_bundle = QueryBundle(query_str)\n",
" # configure retriever\n",
" retriever = VectorIndexRetriever(\n",
- " index=index, \n",
+ " index=index,\n",
" similarity_top_k=vector_top_k,\n",
" )\n",
" retrieved_nodes = retriever.retrieve(query_bundle)\n",
"\n",
" if with_reranker:\n",
" # configure reranker\n",
- " reranker = LLMRerank(choice_batch_size=5, top_n=reranker_top_n, service_context=service_context)\n",
+ " reranker = LLMRerank(\n",
+ " choice_batch_size=5, top_n=reranker_top_n, service_context=service_context\n",
+ " )\n",
" retrieved_nodes = reranker.postprocess_nodes(retrieved_nodes, query_bundle)\n",
- " \n",
+ "\n",
" return retrieved_nodes\n",
"\n",
"\n",
"def pretty_print(df):\n",
- " return display( HTML( df.to_html().replace(\"\\\\n\",\"<br>\") ) )\n",
+ " return display(HTML(df.to_html().replace(\"\\\\n\", \"<br>\")))\n",
"\n",
"\n",
"def visualize_retrieved_nodes(nodes) -> None:\n",
" result_dicts = []\n",
" for node in nodes:\n",
- " result_dict = {\n",
- " \"Score\": node.score,\n",
- " \"Text\": node.node.get_text()\n",
- " }\n",
+ " result_dict = {\"Score\": node.score, \"Text\": node.node.get_text()}\n",
" result_dicts.append(result_dict)\n",
- " \n",
+ "\n",
" pretty_print(pd.DataFrame(result_dicts))\n",
" # print_text(Score\n",
" # f'\\n\\n****Score****: {node.score}\\n****Node text****\\n: {node.node.get_text()}',\n",
@@ -287,7 +292,10 @@
"outputs": [],
"source": [
"new_nodes = get_retrieved_nodes(\n",
- " \"Who was driving the car that hit Myrtle?\", vector_top_k=10, reranker_top_n=3, with_reranker=True\n",
+ " \"Who was driving the car that hit Myrtle?\",\n",
+ " vector_top_k=10,\n",
+ " reranker_top_n=3,\n",
+ " with_reranker=True,\n",
")"
]
},
@@ -352,7 +360,9 @@
],
"source": [
"new_nodes = get_retrieved_nodes(\n",
- " \"What did Gatsby want Daisy to do in front of Tom?\", vector_top_k=3, with_reranker=False\n",
+ " \"What did Gatsby want Daisy to do in front of Tom?\",\n",
+ " vector_top_k=3,\n",
+ " with_reranker=False,\n",
")"
]
},
@@ -533,7 +543,10 @@
],
"source": [
"new_nodes = get_retrieved_nodes(\n",
- " \"What did Gatsby want Daisy to do in front of Tom?\", vector_top_k=10, reranker_top_n=3, with_reranker=True\n",
+ " \"What did Gatsby want Daisy to do in front of Tom?\",\n",
+ " vector_top_k=10,\n",
+ " reranker_top_n=3,\n",
+ " with_reranker=True,\n",
")"
]
},
@@ -619,12 +632,10 @@
"outputs": [],
"source": [
"query_engine = index.as_query_engine(\n",
- " similarity_top_k=10,\n",
- " node_postprocessors=[reranker],\n",
- " response_mode=\"tree_summarize\"\n",
+ " similarity_top_k=10, node_postprocessors=[reranker], response_mode=\"tree_summarize\"\n",
")\n",
"response = query_engine.query(\n",
- " \"What did the author do during his time at Y Combinator?\", \n",
+ " \"What did the author do during his time at Y Combinator?\",\n",
")"
]
},
@@ -635,12 +646,9 @@
"metadata": {},
"outputs": [],
"source": [
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(similarity_top_k=3, response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\n",
- " \"What did the author do during his time at Y Combinator?\", \n",
+ " \"What did the author do during his time at Y Combinator?\",\n",
")"
]
},
diff --git a/docs/examples/node_postprocessor/LLMReranker-Lyft-10k.ipynb b/docs/examples/node_postprocessor/LLMReranker-Lyft-10k.ipynb
index 4e93e0dbce..125a760246 100644
--- a/docs/examples/node_postprocessor/LLMReranker-Lyft-10k.ipynb
+++ b/docs/examples/node_postprocessor/LLMReranker-Lyft-10k.ipynb
@@ -23,6 +23,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -40,10 +41,13 @@
"\n",
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
- "from llama_index.indices.postprocessor import (\n",
- " LLMRerank\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
")\n",
+ "from llama_index.indices.postprocessor import LLMRerank\n",
"\n",
"from llama_index.llms import OpenAI\n",
"from IPython.display import Markdown, display"
@@ -90,7 +94,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader(input_files=['lyft_10k.pdf']).load_data()"
+ "documents = SimpleDirectoryReader(input_files=[\"lyft_10k.pdf\"]).load_data()"
]
},
{
@@ -152,28 +156,32 @@
"from copy import deepcopy\n",
"\n",
"\n",
- "pd.set_option('display.max_colwidth', -1)\n",
+ "pd.set_option(\"display.max_colwidth\", -1)\n",
"\n",
"\n",
- "def get_retrieved_nodes(query_str, vector_top_k=10, reranker_top_n=3, with_reranker=False):\n",
+ "def get_retrieved_nodes(\n",
+ " query_str, vector_top_k=10, reranker_top_n=3, with_reranker=False\n",
+ "):\n",
" query_bundle = QueryBundle(query_str)\n",
" # configure retriever\n",
" retriever = VectorIndexRetriever(\n",
- " index=index, \n",
+ " index=index,\n",
" similarity_top_k=vector_top_k,\n",
" )\n",
" retrieved_nodes = retriever.retrieve(query_bundle)\n",
"\n",
" if with_reranker:\n",
" # configure reranker\n",
- " reranker = LLMRerank(choice_batch_size=5, top_n=reranker_top_n, service_context=service_context)\n",
+ " reranker = LLMRerank(\n",
+ " choice_batch_size=5, top_n=reranker_top_n, service_context=service_context\n",
+ " )\n",
" retrieved_nodes = reranker.postprocess_nodes(retrieved_nodes, query_bundle)\n",
- " \n",
+ "\n",
" return retrieved_nodes\n",
"\n",
"\n",
"def pretty_print(df):\n",
- " return display( HTML( df.to_html().replace(\"\\\\n\",\"<br>\") ) )\n",
+ " return display(HTML(df.to_html().replace(\"\\\\n\", \"<br>\")))\n",
"\n",
"\n",
"def visualize_retrieved_nodes(nodes) -> None:\n",
@@ -184,12 +192,9 @@
" node_text = node.node.get_text()\n",
" node_text = node_text.replace(\"\\n\", \" \")\n",
"\n",
- " result_dict = {\n",
- " \"Score\": node.score,\n",
- " \"Text\": node_text\n",
- " }\n",
+ " result_dict = {\"Score\": node.score, \"Text\": node_text}\n",
" result_dicts.append(result_dict)\n",
- " \n",
+ "\n",
" pretty_print(pd.DataFrame(result_dicts))\n",
" # print_text(Score\n",
" # f'\\n\\n****Score****: {node.score}\\n****Node text****\\n: {node.node.get_text()}',\n",
@@ -315,7 +320,10 @@
],
"source": [
"new_nodes = get_retrieved_nodes(\n",
- " \"What is Lyft's response to COVID-19?\", vector_top_k=20, reranker_top_n=5, with_reranker=True\n",
+ " \"What is Lyft's response to COVID-19?\",\n",
+ " vector_top_k=20,\n",
+ " reranker_top_n=5,\n",
+ " with_reranker=True,\n",
")"
]
},
@@ -389,7 +397,9 @@
"outputs": [],
"source": [
"new_nodes = get_retrieved_nodes(\n",
- " \"What initiatives are the company focusing on independently of COVID-19?\", vector_top_k=5, with_reranker=False\n",
+ " \"What initiatives are the company focusing on independently of COVID-19?\",\n",
+ " vector_top_k=5,\n",
+ " with_reranker=False,\n",
")"
]
},
@@ -463,8 +473,10 @@
"outputs": [],
"source": [
"new_nodes = get_retrieved_nodes(\n",
- " \"What initiatives are the company focusing on independently of COVID-19?\", \n",
- " vector_top_k=40, reranker_top_n=5, with_reranker=True\n",
+ " \"What initiatives are the company focusing on independently of COVID-19?\",\n",
+ " vector_top_k=40,\n",
+ " reranker_top_n=5,\n",
+ " with_reranker=True,\n",
")"
]
},
diff --git a/docs/examples/node_postprocessor/OptimizerDemo.ipynb b/docs/examples/node_postprocessor/OptimizerDemo.ipynb
index ce29f222f2..6863016904 100644
--- a/docs/examples/node_postprocessor/OptimizerDemo.ipynb
+++ b/docs/examples/node_postprocessor/OptimizerDemo.ipynb
@@ -1,210 +1,212 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7ff2c269",
- "metadata": {},
- "source": [
- "# Sentence Embedding Optimizer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "839c4a87",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "40cf0773",
- "metadata": {},
- "source": [
- "### Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "fa34cd83",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import download_loader\n",
- "\n",
- "WikipediaReader = download_loader(\"WikipediaReader\")\n",
- "\n",
- "loader = WikipediaReader()\n",
- "documents = loader.load_data(pages=['Berlin'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "f59e6c18",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'llama_index.readers.schema.base.Document'>\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:root:> [build_index_from_documents] Total embedding token usage: 18390 tokens\n"
- ]
- }
- ],
- "source": [
- "from llama_index import VectorStoreIndex\n",
- "index = VectorStoreIndex.from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "827ada33",
- "metadata": {},
- "source": [
- "Compare query with and without optimization for LLM token usage, Embedding Model usage on query, Embedding model usage for optimizer, and total time."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "a04e4535",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Without optimization\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Total time elapsed: 2.8928110599517822\n",
- "Answer: \n",
- "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\n",
- "\n",
- "With optimization\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [optimize] Total embedding token usage: 7 tokens\n",
- "INFO:root:> [query] Total LLM token usage: 1779 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Total time elapsed: 2.346346139907837\n",
- "Answer: \n",
- "The population of Berlin is around 4.5 million.\n",
- "Alternate optimization cutoff\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [optimize] Total embedding token usage: 7 tokens\n",
- "INFO:root:> [query] Total LLM token usage: 3215 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Total time elapsed: 2.101111888885498\n",
- "Answer: \n",
- "The population of Berlin is around 4.5 million.\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "from llama_index import VectorStoreIndex\n",
- "from llama_index.indices.postprocessor import SentenceEmbeddingOptimizer\n",
- "\n",
- "print(\"Without optimization\")\n",
- "start_time = time.time()\n",
- "query_engine = index.as_query_engine()\n",
- "res = query_engine.query(\"What is the population of Berlin?\")\n",
- "end_time = time.time()\n",
- "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
- "print(\"Answer: {}\".format(res))\n",
- "\n",
- "print(\"With optimization\")\n",
- "start_time = time.time()\n",
- "query_engine = index.as_query_engine(\n",
- " node_postprocessors=[SentenceEmbeddingOptimizer(percentile_cutoff=0.5)]\n",
- ")\n",
- "res = query_engine.query(\"What is the population of Berlin?\")\n",
- "end_time = time.time()\n",
- "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
- "print(\"Answer: {}\".format(res))\n",
- "\n",
- "print(\"Alternate optimization cutoff\")\n",
- "start_time = time.time()\n",
- "query_engine = index.as_query_engine(\n",
- " node_postprocessors=[SentenceEmbeddingOptimizer(threshold_cutoff=0.7)]\n",
- ")\n",
- "res = query_engine.query(\"What is the population of Berlin?\")\n",
- "end_time = time.time()\n",
- "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
- "print(\"Answer: {}\".format(res))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.0"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7ff2c269",
+ "metadata": {},
+ "source": [
+ "# Sentence Embedding Optimizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "839c4a87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "40cf0773",
+ "metadata": {},
+ "source": [
+ "### Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "fa34cd83",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import download_loader\n",
+ "\n",
+ "WikipediaReader = download_loader(\"WikipediaReader\")\n",
+ "\n",
+ "loader = WikipediaReader()\n",
+ "documents = loader.load_data(pages=[\"Berlin\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "f59e6c18",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'llama_index.readers.schema.base.Document'>\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:root:> [build_index_from_documents] Total embedding token usage: 18390 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import VectorStoreIndex\n",
+ "\n",
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "827ada33",
+ "metadata": {},
+ "source": [
+ "Compare query with and without optimization for LLM token usage, Embedding Model usage on query, Embedding model usage for optimizer, and total time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "a04e4535",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Without optimization\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [query] Total LLM token usage: 3545 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total time elapsed: 2.8928110599517822\n",
+ "Answer: \n",
+ "The population of Berlin in 1949 was approximately 2.2 million inhabitants. After the fall of the Berlin Wall in 1989, the population of Berlin increased to approximately 3.7 million inhabitants.\n",
+ "\n",
+ "With optimization\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [optimize] Total embedding token usage: 7 tokens\n",
+ "INFO:root:> [query] Total LLM token usage: 1779 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total time elapsed: 2.346346139907837\n",
+ "Answer: \n",
+ "The population of Berlin is around 4.5 million.\n",
+ "Alternate optimization cutoff\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [optimize] Total embedding token usage: 7 tokens\n",
+ "INFO:root:> [query] Total LLM token usage: 3215 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 7 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total time elapsed: 2.101111888885498\n",
+ "Answer: \n",
+ "The population of Berlin is around 4.5 million.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "from llama_index import VectorStoreIndex\n",
+ "from llama_index.indices.postprocessor import SentenceEmbeddingOptimizer\n",
+ "\n",
+ "print(\"Without optimization\")\n",
+ "start_time = time.time()\n",
+ "query_engine = index.as_query_engine()\n",
+ "res = query_engine.query(\"What is the population of Berlin?\")\n",
+ "end_time = time.time()\n",
+ "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
+ "print(\"Answer: {}\".format(res))\n",
+ "\n",
+ "print(\"With optimization\")\n",
+ "start_time = time.time()\n",
+ "query_engine = index.as_query_engine(\n",
+ " node_postprocessors=[SentenceEmbeddingOptimizer(percentile_cutoff=0.5)]\n",
+ ")\n",
+ "res = query_engine.query(\"What is the population of Berlin?\")\n",
+ "end_time = time.time()\n",
+ "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
+ "print(\"Answer: {}\".format(res))\n",
+ "\n",
+ "print(\"Alternate optimization cutoff\")\n",
+ "start_time = time.time()\n",
+ "query_engine = index.as_query_engine(\n",
+ " node_postprocessors=[SentenceEmbeddingOptimizer(threshold_cutoff=0.7)]\n",
+ ")\n",
+ "res = query_engine.query(\"What is the population of Berlin?\")\n",
+ "end_time = time.time()\n",
+ "print(\"Total time elapsed: {}\".format(end_time - start_time))\n",
+ "print(\"Answer: {}\".format(res))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/node_postprocessor/PII.ipynb b/docs/examples/node_postprocessor/PII.ipynb
index d93b8d20bc..330954a1c8 100644
--- a/docs/examples/node_postprocessor/PII.ipynb
+++ b/docs/examples/node_postprocessor/PII.ipynb
@@ -43,7 +43,10 @@
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
- "from llama_index.indices.postprocessor import PIINodePostprocessor, NERPIINodePostprocessor\n",
+ "from llama_index.indices.postprocessor import (\n",
+ " PIINodePostprocessor,\n",
+ " NERPIINodePostprocessor,\n",
+ ")\n",
"from llama_index.llms import HuggingFaceLLM\n",
"from llama_index import ServiceContext, Document, VectorStoreIndex\n",
"from llama_index.schema import TextNode"
@@ -111,6 +114,7 @@
],
"source": [
"from llama_index.schema import NodeWithScore\n",
+ "\n",
"new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)])"
]
},
@@ -163,7 +167,7 @@
],
"source": [
"# get mapping in metadata\n",
- "# NOTE: this is not sent to the LLM! \n",
+ "# NOTE: this is not sent to the LLM!\n",
"new_nodes[0].node.metadata[\"__pii_node_info__\"]"
]
},
@@ -201,6 +205,7 @@
"outputs": [],
"source": [
"from llama_index.schema import NodeWithScore\n",
+ "\n",
"new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)])"
]
},
@@ -251,7 +256,7 @@
],
"source": [
"# get mapping in metadata\n",
- "# NOTE: this is not sent to the LLM! \n",
+ "# NOTE: this is not sent to the LLM!\n",
"new_nodes[0].node.metadata[\"__pii_node_info__\"]"
]
},
diff --git a/docs/examples/node_postprocessor/PrevNextPostprocessorDemo.ipynb b/docs/examples/node_postprocessor/PrevNextPostprocessorDemo.ipynb
index 0bc9a69400..36d91dec40 100644
--- a/docs/examples/node_postprocessor/PrevNextPostprocessorDemo.ipynb
+++ b/docs/examples/node_postprocessor/PrevNextPostprocessorDemo.ipynb
@@ -19,8 +19,8 @@
"source": [
"from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
"from llama_index.indices.postprocessor import (\n",
- " PrevNextNodePostprocessor, \n",
- " AutoPrevNextNodePostprocessor\n",
+ " PrevNextNodePostprocessor,\n",
+ " AutoPrevNextNodePostprocessor,\n",
")\n",
"from llama_index.node_parser import SimpleNodeParser\n",
"from llama_index.storage.docstore import SimpleDocumentStore"
@@ -47,7 +47,7 @@
"from llama_index.storage.storage_context import StorageContext\n",
"\n",
"\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
"\n",
"# define service context (wrapper container around current classes)\n",
"service_context = ServiceContext.from_defaults(chunk_size=512)\n",
@@ -79,7 +79,7 @@
},
"outputs": [],
"source": [
- "# build index \n",
+ "# build index\n",
"index = VectorStoreIndex(nodes, storage_context=storage_context)"
]
},
@@ -115,10 +115,10 @@
"query_engine = index.as_query_engine(\n",
" similarity_top_k=1,\n",
" node_postprocessors=[node_postprocessor],\n",
- " response_mode=\"tree_summarize\"\n",
+ " response_mode=\"tree_summarize\",\n",
")\n",
"response = query_engine.query(\n",
- " \"What did the author do after handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do after handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -140,7 +140,7 @@
}
],
"source": [
- "print(response) "
+ "print(response)"
]
},
{
@@ -153,12 +153,9 @@
"outputs": [],
"source": [
"# Try querying index without node postprocessor\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=1,\n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(similarity_top_k=1, response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\n",
- " \"What did the author do after handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do after handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -193,12 +190,9 @@
"outputs": [],
"source": [
"# Try querying index without node postprocessor and higher top-k\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(similarity_top_k=3, response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\n",
- " \"What did the author do after handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do after handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -241,10 +235,7 @@
"outputs": [],
"source": [
"node_postprocessor = AutoPrevNextNodePostprocessor(\n",
- " docstore=docstore, \n",
- " num_nodes=3,\n",
- " service_context=service_context,\n",
- " verbose=True\n",
+ " docstore=docstore, num_nodes=3, service_context=service_context, verbose=True\n",
")"
]
},
@@ -269,10 +260,10 @@
"query_engine = index.as_query_engine(\n",
" similarity_top_k=1,\n",
" node_postprocessors=[node_postprocessor],\n",
- " response_mode=\"tree_summarize\"\n",
+ " response_mode=\"tree_summarize\",\n",
")\n",
"response = query_engine.query(\n",
- " \"What did the author do after handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do after handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -316,7 +307,7 @@
"source": [
"# Infer that we don't need to search previous or next\n",
"response = query_engine.query(\n",
- " \"What did the author do during his time at Y Combinator?\", \n",
+ " \"What did the author do during his time at Y Combinator?\",\n",
")"
]
},
@@ -360,7 +351,7 @@
"source": [
"# Infer that we need to search nodes before current one\n",
"response = query_engine.query(\n",
- " \"What did the author do before handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do before handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -403,7 +394,7 @@
],
"source": [
"response = query_engine.query(\n",
- " \"What did the author do before handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do before handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
diff --git a/docs/examples/node_postprocessor/RecencyPostprocessorDemo.ipynb b/docs/examples/node_postprocessor/RecencyPostprocessorDemo.ipynb
index fad4fd19ce..71a5f8a67f 100644
--- a/docs/examples/node_postprocessor/RecencyPostprocessorDemo.ipynb
+++ b/docs/examples/node_postprocessor/RecencyPostprocessorDemo.ipynb
@@ -1,382 +1,376 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b1c1ebaa-50de-4851-a720-acbb977551ea",
- "metadata": {},
- "source": [
- "# Recency Filtering\n",
- "\n",
- "Showcase capabilities of recency-weighted node postprocessor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "92d06b38-2103-4a40-93c3-60e0708a1124",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/jerryliu/Programming/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
- "from llama_index.indices.postprocessor import (\n",
- " FixedRecencyPostprocessor,\n",
- " EmbeddingRecencyPostprocessor\n",
- ")\n",
- "from llama_index.node_parser import SimpleNodeParser\n",
- "from llama_index.storage.docstore import SimpleDocumentStore\n",
- "from llama_index.response.notebook_utils import display_response"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "67020156-2975-4bbb-8e98-afc55abb3d72",
- "metadata": {},
- "source": [
- "### Parse Documents into Nodes, add to Docstore\n",
- "\n",
- "In this example, there are 3 different versions of PG's essay. They are largely identical **except** \n",
- "for one specific section, which details the amount of funding they raised for Viaweb. \n",
- "\n",
- "V1: 50k, V2: 30k, V3: 10K\n",
- "\n",
- "V1: 2020-01-01, V2: 2020-02-03, V3: 2022-04-12\n",
- "\n",
- "The idea is to encourage index to fetch the most recent info (which is V3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "caddd84e-9827-40a4-9520-dba6405fd1fd",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# load documents\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "def get_file_metadata(file_name: str):\n",
- " \"\"\"Get file metadata.\"\"\"\n",
- " if \"v1\" in file_name:\n",
- " return {\"date\": \"2020-01-01\"}\n",
- " elif \"v2\" in file_name:\n",
- " return {\"date\": \"2020-02-03\"}\n",
- " elif \"v3\" in file_name:\n",
- " return {\"date\": \"2022-04-12\"}\n",
- " else:\n",
- " raise ValueError(\"invalid file\")\n",
- "\n",
- "documents = SimpleDirectoryReader(\n",
- " input_files=[\n",
- " 'test_versioned_data/paul_graham_essay_v1.txt',\n",
- " 'test_versioned_data/paul_graham_essay_v2.txt',\n",
- " 'test_versioned_data/paul_graham_essay_v3.txt'\n",
- " ],\n",
- " file_metadata=get_file_metadata\n",
- ").load_data()\n",
- "\n",
- "# define service context (wrapper container around current classes)\n",
- "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
- "\n",
- "# use node parser in service context to parse into nodes\n",
- "nodes = service_context.node_parser.get_nodes_from_documents(documents)\n",
- "\n",
- "# add to docstore\n",
- "docstore = SimpleDocumentStore()\n",
- "docstore.add_documents(nodes)\n",
- "\n",
- "storage_context = StorageContext.from_defaults(docstore=docstore)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "191ced40-80f4-40e7-bf31-0c9a5a664cf2",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(documents[2].get_text())"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e5a25b95-de5e-4e56-a846-51e9c6eba181",
- "metadata": {},
- "source": [
- "### Build Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "5f7f68d6-2389-4f6c-bc4e-8612a1a53fb8",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 84471 tokens\n"
- ]
- }
- ],
- "source": [
- "# build index \n",
- "index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "86c5e8aa-18d8-4229-b7b2-a1c97c11a09a",
- "metadata": {},
- "source": [
- "### Define Recency Postprocessors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "ba5e10c9-5a7e-4ea8-a74d-0e0f74b5cd1b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "node_postprocessor = FixedRecencyPostprocessor(service_context=service_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "94f44f2b-d816-43a0-87dc-ea8eefc7d534",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "node_postprocessor_emb = EmbeddingRecencyPostprocessor(service_context=service_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "efcfffe4-a8aa-486d-b46d-f73f985dffca",
- "metadata": {},
- "source": [
- "### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "78d6c3db-61e6-4d9a-a84d-d7be846b4112",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 1813 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens\n"
- ]
- }
- ],
- "source": [
- "# naive query\n",
- "\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d672c52-c0ac-4e5f-9175-855e66eb97ba",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# query using fixed recency node postprocessor\n",
- "\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " node_postprocessors=[node_postprocessor]\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "bc1328c1-23b2-406c-b80b-6d97bffc33ae",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 541 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens\n"
- ]
- }
- ],
- "source": [
- "# query using embedding-based node postprocessor\n",
- "\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " node_postprocessors=[node_postprocessor]\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd00cc97-4de7-4c61-9c0c-3f9ee3598528",
- "metadata": {},
- "source": [
- "### Query Index (Lower-Level Usage)\n",
- "\n",
- "In this example we first get the full set of nodes from a query call, and then send to node postprocessor, and then\n",
- "finally synthesize response through a list index."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "350b039e-d45d-4b6b-957a-4b14d8816cbd",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import ListIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "234f909f-6faa-43e6-96f8-0966699c9552",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_str = \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "20afbf6b-9473-446e-b522-b90fef2e3bf0",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"no_text\"\n",
- ")\n",
- "init_response = query_engine.query(\n",
- " query_str, \n",
- ")\n",
- "resp_nodes = [n.node for n in init_response.source_nodes]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "cdc03574-a806-4255-953c-6f82fc3f202f",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 541 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "list_index = ListIndex(resp_nodes)\n",
- "query_engine = list_index.as_query_engine(\n",
- " node_postprocessors=[node_postprocessor]\n",
- ")\n",
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f371e105-ad93-491c-ad27-35b3e34382f3",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b1c1ebaa-50de-4851-a720-acbb977551ea",
+ "metadata": {},
+ "source": [
+ "# Recency Filtering\n",
+ "\n",
+ "Showcase capabilities of recency-weighted node postprocessor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "92d06b38-2103-4a40-93c3-60e0708a1124",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jerryliu/Programming/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
+ "from llama_index.indices.postprocessor import (\n",
+ " FixedRecencyPostprocessor,\n",
+ " EmbeddingRecencyPostprocessor,\n",
+ ")\n",
+ "from llama_index.node_parser import SimpleNodeParser\n",
+ "from llama_index.storage.docstore import SimpleDocumentStore\n",
+ "from llama_index.response.notebook_utils import display_response"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67020156-2975-4bbb-8e98-afc55abb3d72",
+ "metadata": {},
+ "source": [
+ "### Parse Documents into Nodes, add to Docstore\n",
+ "\n",
+ "In this example, there are 3 different versions of PG's essay. They are largely identical **except** \n",
+ "for one specific section, which details the amount of funding they raised for Viaweb. \n",
+ "\n",
+ "V1: 50k, V2: 30k, V3: 10K\n",
+ "\n",
+ "V1: 2020-01-01, V2: 2020-02-03, V3: 2022-04-12\n",
+ "\n",
+ "The idea is to encourage index to fetch the most recent info (which is V3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "caddd84e-9827-40a4-9520-dba6405fd1fd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "def get_file_metadata(file_name: str):\n",
+ " \"\"\"Get file metadata.\"\"\"\n",
+ " if \"v1\" in file_name:\n",
+ " return {\"date\": \"2020-01-01\"}\n",
+ " elif \"v2\" in file_name:\n",
+ " return {\"date\": \"2020-02-03\"}\n",
+ " elif \"v3\" in file_name:\n",
+ " return {\"date\": \"2022-04-12\"}\n",
+ " else:\n",
+ " raise ValueError(\"invalid file\")\n",
+ "\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\n",
+ " input_files=[\n",
+ " \"test_versioned_data/paul_graham_essay_v1.txt\",\n",
+ " \"test_versioned_data/paul_graham_essay_v2.txt\",\n",
+ " \"test_versioned_data/paul_graham_essay_v3.txt\",\n",
+ " ],\n",
+ " file_metadata=get_file_metadata,\n",
+ ").load_data()\n",
+ "\n",
+ "# define service context (wrapper container around current classes)\n",
+ "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
+ "\n",
+ "# use node parser in service context to parse into nodes\n",
+ "nodes = service_context.node_parser.get_nodes_from_documents(documents)\n",
+ "\n",
+ "# add to docstore\n",
+ "docstore = SimpleDocumentStore()\n",
+ "docstore.add_documents(nodes)\n",
+ "\n",
+ "storage_context = StorageContext.from_defaults(docstore=docstore)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "191ced40-80f4-40e7-bf31-0c9a5a664cf2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(documents[2].get_text())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5a25b95-de5e-4e56-a846-51e9c6eba181",
+ "metadata": {},
+ "source": [
+ "### Build Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "5f7f68d6-2389-4f6c-bc4e-8612a1a53fb8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 84471 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# build index\n",
+ "index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "86c5e8aa-18d8-4229-b7b2-a1c97c11a09a",
+ "metadata": {},
+ "source": [
+ "### Define Recency Postprocessors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ba5e10c9-5a7e-4ea8-a74d-0e0f74b5cd1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "node_postprocessor = FixedRecencyPostprocessor(service_context=service_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "94f44f2b-d816-43a0-87dc-ea8eefc7d534",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "node_postprocessor_emb = EmbeddingRecencyPostprocessor(service_context=service_context)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "efcfffe4-a8aa-486d-b46d-f73f985dffca",
+ "metadata": {},
+ "source": [
+ "### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "78d6c3db-61e6-4d9a-a84d-d7be846b4112",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 1813 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# naive query\n",
+ "\n",
+ "query_engine = index.as_query_engine(\n",
+ " similarity_top_k=3,\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d672c52-c0ac-4e5f-9175-855e66eb97ba",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# query using fixed recency node postprocessor\n",
+ "\n",
+ "query_engine = index.as_query_engine(\n",
+ " similarity_top_k=3, node_postprocessors=[node_postprocessor]\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "bc1328c1-23b2-406c-b80b-6d97bffc33ae",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 541 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# query using embedding-based node postprocessor\n",
+ "\n",
+ "query_engine = index.as_query_engine(\n",
+ " similarity_top_k=3, node_postprocessors=[node_postprocessor]\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd00cc97-4de7-4c61-9c0c-3f9ee3598528",
+ "metadata": {},
+ "source": [
+ "### Query Index (Lower-Level Usage)\n",
+ "\n",
+ "In this example we first get the full set of nodes from a query call, and then send to node postprocessor, and then\n",
+ "finally synthesize response through a list index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "350b039e-d45d-4b6b-957a-4b14d8816cbd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "234f909f-6faa-43e6-96f8-0966699c9552",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_str = \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "20afbf6b-9473-446e-b522-b90fef2e3bf0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 22 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine(similarity_top_k=3, response_mode=\"no_text\")\n",
+ "init_response = query_engine.query(\n",
+ " query_str,\n",
+ ")\n",
+ "resp_nodes = [n.node for n in init_response.source_nodes]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "cdc03574-a806-4255-953c-6f82fc3f202f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 541 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "list_index = ListIndex(resp_nodes)\n",
+ "query_engine = list_index.as_query_engine(node_postprocessors=[node_postprocessor])\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f371e105-ad93-491c-ad27-35b3e34382f3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/node_postprocessor/TimeWeightedPostprocessorDemo.ipynb b/docs/examples/node_postprocessor/TimeWeightedPostprocessorDemo.ipynb
index 070b55318c..3890400b5b 100644
--- a/docs/examples/node_postprocessor/TimeWeightedPostprocessorDemo.ipynb
+++ b/docs/examples/node_postprocessor/TimeWeightedPostprocessorDemo.ipynb
@@ -73,16 +73,16 @@
"\n",
"\n",
"doc1 = SimpleDirectoryReader(\n",
- " input_files=['./test_versioned_data/paul_graham_essay_v1.txt']\n",
+ " input_files=[\"./test_versioned_data/paul_graham_essay_v1.txt\"]\n",
").load_data()[0]\n",
"\n",
"\n",
"doc2 = SimpleDirectoryReader(\n",
- " input_files=['./test_versioned_data/paul_graham_essay_v2.txt']\n",
+ " input_files=[\"./test_versioned_data/paul_graham_essay_v2.txt\"]\n",
").load_data()[0]\n",
"\n",
"doc3 = SimpleDirectoryReader(\n",
- " input_files=['./test_versioned_data/paul_graham_essay_v3.txt']\n",
+ " input_files=[\"./test_versioned_data/paul_graham_essay_v3.txt\"]\n",
").load_data()[0]\n",
"\n",
"\n",
@@ -92,9 +92,9 @@
"\n",
"# use node parser in service context to parse docs into nodes\n",
"nodes1 = node_parser.get_nodes_from_documents([doc1])\n",
- "nodes2 = node_parser.get_nodes_from_documents([doc2]) \n",
+ "nodes2 = node_parser.get_nodes_from_documents([doc2])\n",
"nodes3 = node_parser.get_nodes_from_documents([doc3])\n",
- " \n",
+ "\n",
"\n",
"# fetch the modified chunk from each document, set metadata\n",
"# also exclude the date from being read by the LLM\n",
@@ -133,7 +133,7 @@
},
"outputs": [],
"source": [
- "# build index \n",
+ "# build index\n",
"index = VectorStoreIndex(nodes, storage_context=storage_context)"
]
},
@@ -154,7 +154,9 @@
},
"outputs": [],
"source": [
- "node_postprocessor = TimeWeightedPostprocessor(time_decay=0.5, time_access_refresh=False, top_k=1)"
+ "node_postprocessor = TimeWeightedPostprocessor(\n",
+ " time_decay=0.5, time_access_refresh=False, top_k=1\n",
+ ")"
]
},
{
@@ -179,7 +181,7 @@
" similarity_top_k=3,\n",
")\n",
"response = query_engine.query(\n",
- " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\", \n",
+ " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\",\n",
")"
]
},
@@ -220,11 +222,10 @@
"# query using time weighted node postprocessor\n",
"\n",
"query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " node_postprocessors=[node_postprocessor]\n",
+ " similarity_top_k=3, node_postprocessors=[node_postprocessor]\n",
")\n",
"response = query_engine.query(\n",
- " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\", \n",
+ " \"How much did the author raise in seed funding from Idelle's husband (Julian) for Viaweb?\",\n",
")"
]
},
@@ -295,12 +296,9 @@
},
"outputs": [],
"source": [
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " response_mode=\"no_text\"\n",
- ")\n",
+ "query_engine = index.as_query_engine(similarity_top_k=3, response_mode=\"no_text\")\n",
"init_response = query_engine.query(\n",
- " query_str, \n",
+ " query_str,\n",
")\n",
"resp_nodes = [n for n in init_response.source_nodes]"
]
diff --git a/docs/examples/output_parsing/GuardrailsDemo.ipynb b/docs/examples/output_parsing/GuardrailsDemo.ipynb
index 2079821313..4edd43c4a5 100644
--- a/docs/examples/output_parsing/GuardrailsDemo.ipynb
+++ b/docs/examples/output_parsing/GuardrailsDemo.ipynb
@@ -41,7 +41,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
]
},
{
@@ -118,7 +118,10 @@
"outputs": [],
"source": [
"from llama_index.prompts.prompts import QuestionAnswerPrompt, RefinePrompt\n",
- "from llama_index.prompts.default_prompts import DEFAULT_TEXT_QA_PROMPT_TMPL, DEFAULT_REFINE_PROMPT_TMPL"
+ "from llama_index.prompts.default_prompts import (\n",
+ " DEFAULT_TEXT_QA_PROMPT_TMPL,\n",
+ " DEFAULT_REFINE_PROMPT_TMPL,\n",
+ ")"
]
},
{
@@ -131,7 +134,7 @@
"outputs": [],
"source": [
"# NOTE: we don't need to define the query_str in the rail spec, we can define during query-time.\n",
- "rail_spec = (\"\"\"\n",
+ "rail_spec = \"\"\"\n",
"<rail version=\"0.1\">\n",
"\n",
"<output>\n",
@@ -155,7 +158,7 @@
"@json_suffix_prompt_v2_wo_none\n",
"</prompt>\n",
"</rail>\n",
- "\"\"\")"
+ "\"\"\""
]
},
{
@@ -167,7 +170,9 @@
},
"outputs": [],
"source": [
- "output_parser = GuardrailsOutputParser.from_rail_string(rail_spec, llm=llm_predictor.llm)"
+ "output_parser = GuardrailsOutputParser.from_rail_string(\n",
+ " rail_spec, llm=llm_predictor.llm\n",
+ ")"
]
},
{
@@ -234,7 +239,7 @@
}
],
"source": [
- "# take a look at the new QA template! \n",
+ "# take a look at the new QA template!\n",
"print(fmt_qa_tmpl)"
]
},
@@ -267,12 +272,12 @@
],
"source": [
"query_engine = index.as_query_engine(\n",
- " text_qa_template=qa_prompt, \n",
- " refine_template=refine_prompt, \n",
- " llm_predictor=llm_predictor\n",
+ " text_qa_template=qa_prompt,\n",
+ " refine_template=refine_prompt,\n",
+ " llm_predictor=llm_predictor,\n",
")\n",
"response = query_engine.query(\n",
- " \"What are the three items the author did growing up?\", \n",
+ " \"What are the three items the author did growing up?\",\n",
")"
]
},
diff --git a/docs/examples/output_parsing/LangchainOutputParserDemo.ipynb b/docs/examples/output_parsing/LangchainOutputParserDemo.ipynb
index 04d178b431..907a24a563 100644
--- a/docs/examples/output_parsing/LangchainOutputParserDemo.ipynb
+++ b/docs/examples/output_parsing/LangchainOutputParserDemo.ipynb
@@ -1,314 +1,322 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
- "metadata": {},
- "source": [
- "# Langchain Output Parsing"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 18579 tokens\n",
- "> [build_index_from_documents] Total embedding token usage: 18579 tokens\n"
- ]
- }
- ],
- "source": [
- "index = VectorStoreIndex.from_documents(documents, chunk_size=512)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8b7d7c61-b5d7-4b8f-b90b-3ebee1103f27",
- "metadata": {},
- "source": [
- "#### Define Query + Langchain Output Parser"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "6fb88295-0840-4e2d-b79b-def0b0a63a7f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.output_parsers import LangchainOutputParser\n",
- "from llama_index.llm_predictor import StructuredLLMPredictor\n",
- "from langchain.output_parsers import StructuredOutputParser, ResponseSchema"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "057139d2-09e8-4b8d-83a1-a2356a1475a8",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "llm_predictor = StructuredLLMPredictor()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "bc25edf7-9343-4e82-a3f1-eec4281a9371",
- "metadata": {},
- "source": [
- "**Define custom QA and Refine Prompts**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "2833d086-d240-4798-b3c5-a83ac4593b0e",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.prompts.prompts import QuestionAnswerPrompt, RefinePrompt\n",
- "from llama_index.prompts.default_prompts import DEFAULT_TEXT_QA_PROMPT_TMPL, DEFAULT_REFINE_PROMPT_TMPL"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "a4b9201d-fe16-4cc0-8135-a08d9928625d",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "response_schemas = [\n",
- " ResponseSchema(name=\"Education\", description=\"Describes the author's educational experience/background.\"),\n",
- " ResponseSchema(name=\"Work\", description=\"Describes the author's work experience/background.\")\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "e73b87b8-90da-4ab8-9ff7-e40880277d9b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "lc_output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
- "output_parser = LangchainOutputParser(lc_output_parser)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "a9b440d4-6fb4-46e6-973f-44207b432d3f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# NOTE: we use the same output parser for both prompts, though you can choose to use different parsers\n",
- "# NOTE: here we add formatting instructions to the prompts.\n",
- "\n",
- "fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL)\n",
- "fmt_refine_tmpl = output_parser.format(DEFAULT_REFINE_PROMPT_TMPL)\n",
- "\n",
- "qa_prompt = QuestionAnswerPrompt(fmt_qa_tmpl, output_parser=output_parser)\n",
- "refine_prompt = RefinePrompt(fmt_refine_tmpl, output_parser=output_parser)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "1ba18a80-35f4-4fd4-9b13-9f13f84db4fe",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Context information is below. \n",
- "---------------------\n",
- "{context_str}\n",
- "---------------------\n",
- "Given the context information and not prior knowledge, answer the question: {query_str}\n",
- "\n",
- "\n",
- "The output should be a markdown code snippet formatted in the following schema:\n",
- "\n",
- "```json\n",
- "{{\n",
- "\t\"Education\": string // Describes the author's educational experience/background.\n",
- "\t\"Work\": string // Describes the author's work experience/background.\n",
- "}}\n",
- "```\n"
- ]
- }
- ],
- "source": [
- "# take a look at the new QA template! \n",
- "print(fmt_qa_tmpl)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "fb9cdf43-0f31-4c36-869b-df9fa50aebdb",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 609 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 609 tokens\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 11 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total embedding token usage: 11 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine(\n",
- " text_qa_template=qa_prompt, \n",
- " refine_template=refine_prompt, \n",
- " llm_predictor=llm_predictor\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What are a few things the author did growing up?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "bc7760b6-5be3-4303-b97e-3f5edacf674b",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'Education': 'Before college, the author wrote short stories and experimented with programming on an IBM 1401.', 'Work': 'The author worked on writing and programming outside of school.'}\n"
- ]
- }
- ],
- "source": [
- "print(response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "002a4b5f-51ac-437a-afe7-94e2687737a9",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
+ "metadata": {},
+ "source": [
+ "# Langchain Output Parsing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 18579 tokens\n",
+ "> [build_index_from_documents] Total embedding token usage: 18579 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents, chunk_size=512)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b7d7c61-b5d7-4b8f-b90b-3ebee1103f27",
+ "metadata": {},
+ "source": [
+ "#### Define Query + Langchain Output Parser"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "6fb88295-0840-4e2d-b79b-def0b0a63a7f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.output_parsers import LangchainOutputParser\n",
+ "from llama_index.llm_predictor import StructuredLLMPredictor\n",
+ "from langchain.output_parsers import StructuredOutputParser, ResponseSchema"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "057139d2-09e8-4b8d-83a1-a2356a1475a8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "llm_predictor = StructuredLLMPredictor()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bc25edf7-9343-4e82-a3f1-eec4281a9371",
+ "metadata": {},
+ "source": [
+ "**Define custom QA and Refine Prompts**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2833d086-d240-4798-b3c5-a83ac4593b0e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.prompts.prompts import QuestionAnswerPrompt, RefinePrompt\n",
+ "from llama_index.prompts.default_prompts import (\n",
+ " DEFAULT_TEXT_QA_PROMPT_TMPL,\n",
+ " DEFAULT_REFINE_PROMPT_TMPL,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "a4b9201d-fe16-4cc0-8135-a08d9928625d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "response_schemas = [\n",
+ " ResponseSchema(\n",
+ " name=\"Education\",\n",
+ " description=\"Describes the author's educational experience/background.\",\n",
+ " ),\n",
+ " ResponseSchema(\n",
+ " name=\"Work\", description=\"Describes the author's work experience/background.\"\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e73b87b8-90da-4ab8-9ff7-e40880277d9b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "lc_output_parser = StructuredOutputParser.from_response_schemas(response_schemas)\n",
+ "output_parser = LangchainOutputParser(lc_output_parser)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "a9b440d4-6fb4-46e6-973f-44207b432d3f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: we use the same output parser for both prompts, though you can choose to use different parsers\n",
+ "# NOTE: here we add formatting instructions to the prompts.\n",
+ "\n",
+ "fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL)\n",
+ "fmt_refine_tmpl = output_parser.format(DEFAULT_REFINE_PROMPT_TMPL)\n",
+ "\n",
+ "qa_prompt = QuestionAnswerPrompt(fmt_qa_tmpl, output_parser=output_parser)\n",
+ "refine_prompt = RefinePrompt(fmt_refine_tmpl, output_parser=output_parser)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "1ba18a80-35f4-4fd4-9b13-9f13f84db4fe",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Context information is below. \n",
+ "---------------------\n",
+ "{context_str}\n",
+ "---------------------\n",
+ "Given the context information and not prior knowledge, answer the question: {query_str}\n",
+ "\n",
+ "\n",
+ "The output should be a markdown code snippet formatted in the following schema:\n",
+ "\n",
+ "```json\n",
+ "{{\n",
+ "\t\"Education\": string // Describes the author's educational experience/background.\n",
+ "\t\"Work\": string // Describes the author's work experience/background.\n",
+ "}}\n",
+ "```\n"
+ ]
+ }
+ ],
+ "source": [
+ "# take a look at the new QA template!\n",
+ "print(fmt_qa_tmpl)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "fb9cdf43-0f31-4c36-869b-df9fa50aebdb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 609 tokens\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 609 tokens\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 11 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total embedding token usage: 11 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine(\n",
+ " text_qa_template=qa_prompt,\n",
+ " refine_template=refine_prompt,\n",
+ " llm_predictor=llm_predictor,\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"What are a few things the author did growing up?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "bc7760b6-5be3-4303-b97e-3f5edacf674b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'Education': 'Before college, the author wrote short stories and experimented with programming on an IBM 1401.', 'Work': 'The author worked on writing and programming outside of school.'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "002a4b5f-51ac-437a-afe7-94e2687737a9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/output_parsing/df_program.ipynb b/docs/examples/output_parsing/df_program.ipynb
index ef70bb835d..f1d57d9c63 100644
--- a/docs/examples/output_parsing/df_program.ipynb
+++ b/docs/examples/output_parsing/df_program.ipynb
@@ -53,7 +53,12 @@
},
"outputs": [],
"source": [
- "from llama_index.program import OpenAIPydanticProgram, DFFullProgram, DataFrame, DataFrameRowsOnly\n",
+ "from llama_index.program import (\n",
+ " OpenAIPydanticProgram,\n",
+ " DFFullProgram,\n",
+ " DataFrame,\n",
+ " DataFrameRowsOnly,\n",
+ ")\n",
"from llama_index.llms import OpenAI"
]
},
@@ -249,15 +254,18 @@
"import pandas as pd\n",
"\n",
"# initialize empty df\n",
- "df = pd.DataFrame({'Name': pd.Series(dtype='str'),\n",
- " 'Age': pd.Series(dtype='int'),\n",
- " 'City': pd.Series(dtype='str'),\n",
- " 'Favorite Sport': pd.Series(dtype='str')})\n",
+ "df = pd.DataFrame(\n",
+ " {\n",
+ " \"Name\": pd.Series(dtype=\"str\"),\n",
+ " \"Age\": pd.Series(dtype=\"int\"),\n",
+ " \"City\": pd.Series(dtype=\"str\"),\n",
+ " \"Favorite Sport\": pd.Series(dtype=\"str\"),\n",
+ " }\n",
+ ")\n",
"\n",
- "# initialize program, using existing df as schema \n",
+ "# initialize program, using existing df as schema\n",
"df_rows_program = DFRowsProgram.from_defaults(\n",
- " pydantic_program_cls=OpenAIPydanticProgram,\n",
- " df=df\n",
+ " pydantic_program_cls=OpenAIPydanticProgram, df=df\n",
")"
]
},
@@ -270,7 +278,7 @@
},
"outputs": [],
"source": [
- "# parse text, using existing df as schema \n",
+ "# parse text, using existing df as schema\n",
"result_obj = df_rows_program(\n",
" input_str=\"\"\"My name is John and I am 25 years old. I live in \n",
" New York and I like to play basketball. His name is \n",
@@ -384,7 +392,7 @@
},
"outputs": [],
"source": [
- "# initialize program that can do joint schema extraction and structured data extraction \n",
+ "# initialize program that can do joint schema extraction and structured data extraction\n",
"df_full_program = DFFullProgram.from_defaults(\n",
" pydantic_program_cls=OpenAIPydanticProgram,\n",
")"
@@ -503,14 +511,17 @@
"outputs": [],
"source": [
"# initialize empty df\n",
- "df = pd.DataFrame({'City': pd.Series(dtype='str'),\n",
- " 'State': pd.Series(dtype='str'),\n",
- " 'Population': pd.Series(dtype='int')})\n",
+ "df = pd.DataFrame(\n",
+ " {\n",
+ " \"City\": pd.Series(dtype=\"str\"),\n",
+ " \"State\": pd.Series(dtype=\"str\"),\n",
+ " \"Population\": pd.Series(dtype=\"int\"),\n",
+ " }\n",
+ ")\n",
"\n",
- "# initialize program, using existing df as schema \n",
+ "# initialize program, using existing df as schema\n",
"df_rows_program = DFRowsProgram.from_defaults(\n",
- " pydantic_program_cls=OpenAIPydanticProgram,\n",
- " df=df\n",
+ " pydantic_program_cls=OpenAIPydanticProgram, df=df\n",
")"
]
},
@@ -531,10 +542,8 @@
"The city boundaries encompass an area of about 48.4 sq mi (125 km2)[9] and a population of 675,647 as of 2020.[4]\n",
"\"\"\"\n",
"\n",
- "# parse text, using existing df as schema \n",
- "result_obj = df_rows_program(\n",
- " input_str=input_text\n",
- ")"
+ "# parse text, using existing df as schema\n",
+ "result_obj = df_rows_program(input_str=input_text)"
]
},
{
diff --git a/docs/examples/output_parsing/evaporate_program.ipynb b/docs/examples/output_parsing/evaporate_program.ipynb
index 742840ef29..88a88b56ac 100644
--- a/docs/examples/output_parsing/evaporate_program.ipynb
+++ b/docs/examples/output_parsing/evaporate_program.ipynb
@@ -38,12 +38,12 @@
},
"outputs": [],
"source": [
- "from llama_index import (\n",
- " SimpleDirectoryReader,\n",
- " ServiceContext,\n",
- " LLMPredictor\n",
+ "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
+ "from llama_index.program.predefined import (\n",
+ " DFEvaporateProgram,\n",
+ " EvaporateExtractor,\n",
+ " MultiValueEvaporateProgram,\n",
")\n",
- "from llama_index.program.predefined import DFEvaporateProgram, EvaporateExtractor, MultiValueEvaporateProgram\n",
"from llama_index.llms import OpenAI\n",
"import requests"
]
@@ -96,27 +96,28 @@
"from pathlib import Path\n",
"\n",
"import requests\n",
+ "\n",
"for title in wiki_titles:\n",
" response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
" params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
" # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
+ " \"explaintext\": True,\n",
+ " },\n",
" ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
"\n",
- " data_path = Path('data')\n",
+ " data_path = Path(\"data\")\n",
" if not data_path.exists():\n",
" Path.mkdir(data_path)\n",
"\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
]
},
{
@@ -131,7 +132,9 @@
"# Load all wiki documents\n",
"city_docs = {}\n",
"for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()"
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[f\"data/{wiki_title}.txt\"]\n",
+ " ).load_data()"
]
},
{
@@ -154,9 +157,7 @@
"source": [
"# setup service context\n",
"llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context = ServiceContext.from_defaults(\n",
- " llm=llm, chunk_size=512\n",
- ")"
+ "service_context = ServiceContext.from_defaults(llm=llm, chunk_size=512)"
]
},
{
@@ -197,7 +198,9 @@
"outputs": [],
"source": [
"# define program\n",
- "program = DFEvaporateProgram.from_defaults(fields_to_extract=[\"population\"], service_context=service_context)"
+ "program = DFEvaporateProgram.from_defaults(\n",
+ " fields_to_extract=[\"population\"], service_context=service_context\n",
+ ")"
]
},
{
@@ -540,6 +543,7 @@
"outputs": [],
"source": [
"from llama_index.program.predefined import MultiValueEvaporateProgram\n",
+ "\n",
"program = MultiValueEvaporateProgram.from_defaults(\n",
" fields_to_extract=[\"countries\", \"medal_count\"], service_context=service_context\n",
")"
@@ -714,7 +718,9 @@
"outputs": [],
"source": [
"# Try with Toronto and Seattle (should extract \"population\")\n",
- "existing_fields = extractor.identify_fields(city_pop_nodes, topic=\"population\", fields_top_k=4)"
+ "existing_fields = extractor.identify_fields(\n",
+ " city_pop_nodes, topic=\"population\", fields_top_k=4\n",
+ ")"
]
},
{
diff --git a/docs/examples/output_parsing/guidance_pydantic_program.ipynb b/docs/examples/output_parsing/guidance_pydantic_program.ipynb
index 85c0a47cab..68661a7db8 100644
--- a/docs/examples/output_parsing/guidance_pydantic_program.ipynb
+++ b/docs/examples/output_parsing/guidance_pydantic_program.ipynb
@@ -60,7 +60,8 @@
"class Song(BaseModel):\n",
" title: str\n",
" length_seconds: int\n",
- " \n",
+ "\n",
+ "\n",
"class Album(BaseModel):\n",
" name: str\n",
" artist: str\n",
@@ -85,9 +86,9 @@
"outputs": [],
"source": [
"program = GuidancePydanticProgram(\n",
- " output_cls=Album, \n",
+ " output_cls=Album,\n",
" prompt_template_str=\"Generate an example album, with an artist and a list of songs. Using the movie {{movie_name}} as inspiration\",\n",
- " guidance_llm=OpenAI('text-davinci-003'),\n",
+ " guidance_llm=OpenAI(\"text-davinci-003\"),\n",
" verbose=True,\n",
")"
]
@@ -140,7 +141,7 @@
}
],
"source": [
- "output = program(movie_name='The Shining')"
+ "output = program(movie_name=\"The Shining\")"
]
},
{
diff --git a/docs/examples/output_parsing/guidance_sub_question.ipynb b/docs/examples/output_parsing/guidance_sub_question.ipynb
index ffd0f6ff01..7e6560783d 100644
--- a/docs/examples/output_parsing/guidance_sub_question.ipynb
+++ b/docs/examples/output_parsing/guidance_sub_question.ipynb
@@ -72,7 +72,9 @@
},
"outputs": [],
"source": [
- "question_gen = GuidanceQuestionGenerator.from_defaults(guidance_llm=GuidanceOpenAI('text-davinci-003'), verbose=False)"
+ "question_gen = GuidanceQuestionGenerator.from_defaults(\n",
+ " guidance_llm=GuidanceOpenAI(\"text-davinci-003\"), verbose=False\n",
+ ")"
]
},
{
@@ -106,9 +108,15 @@
},
"outputs": [],
"source": [
- "tools = [\n",
- " ToolMetadata(name='lyft_10k', description='Provides information about Lyft financials for year 2021'),\n",
- " ToolMetadata(name='uber_10k', description='Provides information about Uber financials for year 2021')\n",
+ "tools = [\n",
+ " ToolMetadata(\n",
+ " name=\"lyft_10k\",\n",
+ " description=\"Provides information about Lyft financials for year 2021\",\n",
+ " ),\n",
+ " ToolMetadata(\n",
+ " name=\"uber_10k\",\n",
+ " description=\"Provides information about Uber financials for year 2021\",\n",
+ " ),\n",
"]"
]
},
@@ -121,7 +129,10 @@
},
"outputs": [],
"source": [
- "sub_questions = question_gen.generate(tools=tools, query=QueryBundle('Compare and contrast Uber and Lyft financial in 2021'))"
+ "sub_questions = question_gen.generate(\n",
+ " tools=tools,\n",
+ " query=QueryBundle(\"Compare and contrast Uber and Lyft financial in 2021\"),\n",
+ ")"
]
},
{
@@ -182,7 +193,12 @@
},
"outputs": [],
"source": [
- "from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, VectorStoreIndex\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " VectorStoreIndex,\n",
+ ")\n",
"from llama_index.response.pprint_utils import pprint_response\n",
"\n",
"from llama_index.tools import QueryEngineTool, ToolMetadata\n",
@@ -248,18 +264,24 @@
"source": [
"query_engine_tools = [\n",
" QueryEngineTool(\n",
- " query_engine=lyft_engine, \n",
- " metadata=ToolMetadata(name='lyft_10k', description='Provides information about Lyft financials for year 2021')\n",
+ " query_engine=lyft_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"lyft_10k\",\n",
+ " description=\"Provides information about Lyft financials for year 2021\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=uber_engine, \n",
- " metadata=ToolMetadata(name='uber_10k', description='Provides information about Uber financials for year 2021')\n",
+ " query_engine=uber_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"uber_10k\",\n",
+ " description=\"Provides information about Uber financials for year 2021\",\n",
+ " ),\n",
" ),\n",
"]\n",
"\n",
"s_engine = SubQuestionQueryEngine.from_defaults(\n",
" question_gen=question_gen, # use guidance based question_gen defined above\n",
- " query_engine_tools=query_engine_tools, \n",
+ " query_engine_tools=query_engine_tools,\n",
")"
]
},
@@ -294,7 +316,9 @@
}
],
"source": [
- "response = s_engine.query('Compare and contrast the customer segments and geographies that grew the fastest')"
+ "response = s_engine.query(\n",
+ " \"Compare and contrast the customer segments and geographies that grew the fastest\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/output_parsing/openai_pydantic_program.ipynb b/docs/examples/output_parsing/openai_pydantic_program.ipynb
index 89f25a70fa..177a5e4eca 100644
--- a/docs/examples/output_parsing/openai_pydantic_program.ipynb
+++ b/docs/examples/output_parsing/openai_pydantic_program.ipynb
@@ -68,11 +68,14 @@
"source": [
"class Song(BaseModel):\n",
" \"\"\"Data model for a song.\"\"\"\n",
+ "\n",
" title: str\n",
" length_seconds: int\n",
- " \n",
+ "\n",
+ "\n",
"class Album(BaseModel):\n",
" \"\"\"Data model for an album.\"\"\"\n",
+ "\n",
" name: str\n",
" artist: str\n",
" songs: List[Song]"
@@ -100,7 +103,7 @@
"Using the movie {movie_name} as inspiration.\\\n",
"\"\"\"\n",
"program = OpenAIPydanticProgram.from_defaults(\n",
- " output_cls=Album, \n",
+ " output_cls=Album,\n",
" prompt_template_str=prompt_template_str,\n",
" verbose=True,\n",
")"
@@ -156,7 +159,7 @@
}
],
"source": [
- "output = program(movie_name='The Shining')"
+ "output = program(movie_name=\"The Shining\")"
]
},
{
@@ -276,7 +279,7 @@
"outputs": [],
"source": [
"program = OpenAIPydanticProgram.from_defaults(\n",
- " output_cls=DirectoryTree, \n",
+ " output_cls=DirectoryTree,\n",
" prompt_template_str=\"{input_str}\",\n",
" verbose=True,\n",
")"
diff --git a/docs/examples/query_engine/CustomRetrievers.ipynb b/docs/examples/query_engine/CustomRetrievers.ipynb
index 61011f6241..dd2b3ec62c 100644
--- a/docs/examples/query_engine/CustomRetrievers.ipynb
+++ b/docs/examples/query_engine/CustomRetrievers.ipynb
@@ -59,11 +59,11 @@
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
"from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
" SimpleDirectoryReader,\n",
" ServiceContext,\n",
- " StorageContext\n",
+ " StorageContext,\n",
")\n",
"from IPython.display import Markdown, display"
]
@@ -89,7 +89,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()"
]
},
{
@@ -186,10 +186,16 @@
"source": [
"# import QueryBundle\n",
"from llama_index import QueryBundle\n",
+ "\n",
"# import NodeWithScore\n",
"from llama_index.schema import NodeWithScore\n",
- "# Retrievers \n",
- "from llama_index.retrievers import BaseRetriever, VectorIndexRetriever, KeywordTableSimpleRetriever\n",
+ "\n",
+ "# Retrievers\n",
+ "from llama_index.retrievers import (\n",
+ " BaseRetriever,\n",
+ " VectorIndexRetriever,\n",
+ " KeywordTableSimpleRetriever,\n",
+ ")\n",
"\n",
"from typing import List"
]
@@ -205,42 +211,40 @@
"source": [
"class CustomRetriever(BaseRetriever):\n",
" \"\"\"Custom retriever that performs both semantic search and hybrid search.\"\"\"\n",
- " \n",
+ "\n",
" def __init__(\n",
" self,\n",
" vector_retriever: VectorIndexRetriever,\n",
" keyword_retriever: KeywordTableSimpleRetriever,\n",
- " mode: str = \"AND\"\n",
+ " mode: str = \"AND\",\n",
" ) -> None:\n",
" \"\"\"Init params.\"\"\"\n",
- " \n",
+ "\n",
" self._vector_retriever = vector_retriever\n",
" self._keyword_retriever = keyword_retriever\n",
" if mode not in (\"AND\", \"OR\"):\n",
" raise ValueError(\"Invalid mode.\")\n",
" self._mode = mode\n",
- " \n",
- " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: \n",
+ "\n",
+ " def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:\n",
" \"\"\"Retrieve nodes given query.\"\"\"\n",
- " \n",
+ "\n",
" vector_nodes = self._vector_retriever.retrieve(query_bundle)\n",
" keyword_nodes = self._keyword_retriever.retrieve(query_bundle)\n",
- " \n",
+ "\n",
" vector_ids = {n.node.node_id for n in vector_nodes}\n",
" keyword_ids = {n.node.node_id for n in keyword_nodes}\n",
- " \n",
+ "\n",
" combined_dict = {n.node.node_id: n for n in vector_nodes}\n",
" combined_dict.update({n.node.node_id: n for n in keyword_nodes})\n",
- " \n",
+ "\n",
" if self._mode == \"AND\":\n",
" retrieve_ids = vector_ids.intersection(keyword_ids)\n",
" else:\n",
" retrieve_ids = vector_ids.union(keyword_ids)\n",
- " \n",
+ "\n",
" retrieve_nodes = [combined_dict[rid] for rid in retrieve_ids]\n",
- " return retrieve_nodes\n",
- " \n",
- " "
+ " return retrieve_nodes"
]
},
{
@@ -339,7 +343,7 @@
}
],
"source": [
- "print(response) "
+ "print(response)"
]
},
{
diff --git a/docs/examples/query_engine/JointQASummary.ipynb b/docs/examples/query_engine/JointQASummary.ipynb
index 44d034ca4a..65bd24b8d7 100644
--- a/docs/examples/query_engine/JointQASummary.ipynb
+++ b/docs/examples/query_engine/JointQASummary.ipynb
@@ -1,249 +1,250 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "68490dba",
- "metadata": {},
- "source": [
- "# Joint QA Summary Query Engine"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.composability.joint_qa_summary import QASummaryQueryEngineBuilder\n",
- "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
- "from llama_index.response.notebook_utils import display_response\n",
- "from llama_index.llms import OpenAI"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
- "documents = reader.load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:llama_index.llm_predictor.base:Unknown max input size for gpt-3.5-turbo, using defaults.\n",
- "Unknown max input size for gpt-3.5-turbo, using defaults.\n"
- ]
- }
- ],
- "source": [
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4, chunk_size=1024)\n",
- "\n",
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "16216dfb-35ea-49ac-b651-2e8a9e423512",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# NOTE: can also specify an existing docstore, service context, summary text, qa_text, etc.\n",
- "query_engine_builder = QASummaryQueryEngineBuilder(service_context=service_context_gpt4)\n",
- "query_engine = query_engine_builder.build_from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "ae60000b-403c-4350-af32-71e26cc68a75",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 1 because: This choice is relevant because it is specifically for summarization queries, which matches the request for a summary of the author's life..\n",
- "Selecting query engine 1 because: This choice is relevant because it is specifically for summarization queries, which matches the request for a summary of the author's life..\n",
- "INFO:llama_index.indices.common_tree.base:> Building index from nodes: 6 chunks\n",
- "> Building index from nodes: 6 chunks\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1012 tokens\n",
- "> [get_response] Total LLM token usage: 1012 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 23485 tokens\n",
- "> [get_response] Total LLM token usage: 23485 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\n",
- " \"Can you give me a summary of the author's life?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "4488669d-0f67-48c9-994c-bd7a42498ecb",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities growing up..\n",
- "Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities growing up..\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1893 tokens\n",
- "> [get_response] Total LLM token usage: 1893 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\n",
- " \"What did the author do growing up?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "ff95db5f-7cbe-4ed7-83ff-27e00b94e7da",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities in art school..\n",
- "Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities in art school..\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n",
- "> [retrieve] Total embedding token usage: 12 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1883 tokens\n",
- "> [get_response] Total LLM token usage: 1883 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\n",
- " \"What did the author do during his time in art school?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3da9bf34-d242-4fbd-b67a-1dc99b387a13",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "68490dba",
+ "metadata": {},
+ "source": [
+ "# Joint QA Summary Query Engine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.composability.joint_qa_summary import QASummaryQueryEngineBuilder\n",
+ "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
+ "from llama_index.response.notebook_utils import display_response\n",
+ "from llama_index.llms import OpenAI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
+ "documents = reader.load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:llama_index.llm_predictor.base:Unknown max input size for gpt-3.5-turbo, using defaults.\n",
+ "Unknown max input size for gpt-3.5-turbo, using defaults.\n"
+ ]
+ }
+ ],
+ "source": [
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4, chunk_size=1024)\n",
+ "\n",
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "16216dfb-35ea-49ac-b651-2e8a9e423512",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NOTE: can also specify an existing docstore, service context, summary text, qa_text, etc.\n",
+ "query_engine_builder = QASummaryQueryEngineBuilder(service_context=service_context_gpt4)\n",
+ "query_engine = query_engine_builder.build_from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "ae60000b-403c-4350-af32-71e26cc68a75",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 1 because: This choice is relevant because it is specifically for summarization queries, which matches the request for a summary of the author's life..\n",
+ "Selecting query engine 1 because: This choice is relevant because it is specifically for summarization queries, which matches the request for a summary of the author's life..\n",
+ "INFO:llama_index.indices.common_tree.base:> Building index from nodes: 6 chunks\n",
+ "> Building index from nodes: 6 chunks\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1012 tokens\n",
+ "> [get_response] Total LLM token usage: 1012 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 23485 tokens\n",
+ "> [get_response] Total LLM token usage: 23485 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"Can you give me a summary of the author's life?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "4488669d-0f67-48c9-994c-bd7a42498ecb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities growing up..\n",
+ "Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities growing up..\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1893 tokens\n",
+ "> [get_response] Total LLM token usage: 1893 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What did the author do growing up?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "ff95db5f-7cbe-4ed7-83ff-27e00b94e7da",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.query_engine.router_query_engine:Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities in art school..\n",
+ "Selecting query engine 0 because: This choice is relevant because it involves retrieving specific context from documents, which is needed to answer the question about the author's activities in art school..\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 12 tokens\n",
+ "> [retrieve] Total embedding token usage: 12 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1883 tokens\n",
+ "> [get_response] Total LLM token usage: 1883 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What did the author do during his time in art school?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3da9bf34-d242-4fbd-b67a-1dc99b387a13",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/query_engine/RetrieverRouterQueryEngine.ipynb b/docs/examples/query_engine/RetrieverRouterQueryEngine.ipynb
index f2895482a4..8dc849e7d9 100644
--- a/docs/examples/query_engine/RetrieverRouterQueryEngine.ipynb
+++ b/docs/examples/query_engine/RetrieverRouterQueryEngine.ipynb
@@ -28,10 +28,11 @@
"outputs": [],
"source": [
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -73,7 +74,7 @@
" ListIndex,\n",
" SimpleDirectoryReader,\n",
" ServiceContext,\n",
- " StorageContext\n",
+ " StorageContext,\n",
")"
]
},
@@ -96,7 +97,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()"
]
},
{
@@ -189,12 +190,11 @@
"\n",
"list_tool = QueryEngineTool.from_defaults(\n",
" query_engine=list_query_engine,\n",
- " description='Useful for questions asking for a biography of the author.',\n",
+ " description=\"Useful for questions asking for a biography of the author.\",\n",
")\n",
"vector_tool = QueryEngineTool.from_defaults(\n",
" query_engine=vector_query_engine,\n",
- " description='Useful for retrieving specific snippets from the author\\'s life, like his time in college, his time in YC, or more.',\n",
- "\n",
+ " description=\"Useful for retrieving specific snippets from the author's life, like his time in college, his time in YC, or more.\",\n",
")"
]
},
@@ -239,7 +239,7 @@
"\n",
"tool_mapping = SimpleToolNodeMapping.from_objects([list_tool, vector_tool])\n",
"obj_index = ObjectIndex.from_objects(\n",
- " [list_tool, vector_tool], \n",
+ " [list_tool, vector_tool],\n",
" tool_mapping,\n",
" VectorStoreIndex,\n",
")"
@@ -299,7 +299,7 @@
}
],
"source": [
- "response = query_engine.query('What is a biography of the author\\'s life?')"
+ "response = query_engine.query(\"What is a biography of the author's life?\")"
]
},
{
@@ -387,7 +387,7 @@
}
],
"source": [
- "response = query_engine.query('What did Paul Graham do during his time in college?')"
+ "response = query_engine.query(\"What did Paul Graham do during his time in college?\")"
]
},
{
diff --git a/docs/examples/query_engine/RouterQueryEngine.ipynb b/docs/examples/query_engine/RouterQueryEngine.ipynb
index 6303fc025e..6968316d99 100644
--- a/docs/examples/query_engine/RouterQueryEngine.ipynb
+++ b/docs/examples/query_engine/RouterQueryEngine.ipynb
@@ -24,10 +24,11 @@
"outputs": [],
"source": [
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -58,7 +59,7 @@
" ListIndex,\n",
" SimpleDirectoryReader,\n",
" ServiceContext,\n",
- " StorageContext\n",
+ " StorageContext,\n",
")"
]
},
@@ -79,7 +80,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()"
]
},
{
@@ -148,7 +149,7 @@
"outputs": [],
"source": [
"list_query_engine = list_index.as_query_engine(\n",
- " response_mode='tree_summarize',\n",
+ " response_mode=\"tree_summarize\",\n",
" use_async=True,\n",
")\n",
"vector_query_engine = vector_index.as_query_engine()"
@@ -165,13 +166,12 @@
"\n",
"list_tool = QueryEngineTool.from_defaults(\n",
" query_engine=list_query_engine,\n",
- " description='Useful for summarization questions related to Paul Graham eassy on What I Worked On.',\n",
+ " description=\"Useful for summarization questions related to Paul Graham eassy on What I Worked On.\",\n",
")\n",
"\n",
"vector_tool = QueryEngineTool.from_defaults(\n",
" query_engine=vector_query_engine,\n",
- " description='Useful for retrieving specific context from Paul Graham essay on What I Worked On.',\n",
- "\n",
+ " description=\"Useful for retrieving specific context from Paul Graham essay on What I Worked On.\",\n",
")"
]
},
@@ -203,7 +203,10 @@
"source": [
"from llama_index.query_engine.router_query_engine import RouterQueryEngine\n",
"from llama_index.selectors.llm_selectors import LLMSingleSelector, LLMMultiSelector\n",
- "from llama_index.selectors.pydantic_selectors import PydanticMultiSelector, PydanticSingleSelector\n",
+ "from llama_index.selectors.pydantic_selectors import (\n",
+ " PydanticMultiSelector,\n",
+ " PydanticSingleSelector,\n",
+ ")\n",
"\n",
"\n",
"query_engine = RouterQueryEngine(\n",
@@ -211,7 +214,7 @@
" query_engine_tools=[\n",
" list_tool,\n",
" vector_tool,\n",
- " ]\n",
+ " ],\n",
")"
]
},
@@ -246,7 +249,7 @@
}
],
"source": [
- "query_engine.query('What is the summary of the document?')"
+ "query_engine.query(\"What is the summary of the document?\")"
]
},
{
@@ -280,7 +283,7 @@
}
],
"source": [
- "query_engine.query('What did Paul Graham do after RICS?')"
+ "query_engine.query(\"What did Paul Graham do after RICS?\")"
]
},
{
@@ -304,7 +307,7 @@
" query_engine_tools=[\n",
" list_tool,\n",
" vector_tool,\n",
- " ]\n",
+ " ],\n",
")"
]
},
@@ -336,7 +339,7 @@
}
],
"source": [
- "query_engine.query('What is the summary of the document?')"
+ "query_engine.query(\"What is the summary of the document?\")"
]
},
{
@@ -367,7 +370,7 @@
}
],
"source": [
- "query_engine.query('What did Paul Graham do after RICS?')"
+ "query_engine.query(\"What did Paul Graham do after RICS?\")"
]
},
{
@@ -401,8 +404,7 @@
"\n",
"keyword_tool = QueryEngineTool.from_defaults(\n",
" query_engine=vector_query_engine,\n",
- " description='Useful for retrieving specific context using keywords from Paul Graham essay on What I Worked On.',\n",
- "\n",
+ " description=\"Useful for retrieving specific context using keywords from Paul Graham essay on What I Worked On.\",\n",
")"
]
},
@@ -418,7 +420,7 @@
" list_tool,\n",
" vector_tool,\n",
" keyword_tool,\n",
- " ]\n",
+ " ],\n",
")"
]
},
@@ -462,7 +464,9 @@
],
"source": [
"# This query could use either a keyword or vector query engine, so it will combine responses from both\n",
- "query_engine.query('What were noteable events and people from the authors time at Interleaf and YC?')"
+ "query_engine.query(\n",
+ " \"What were noteable events and people from the authors time at Interleaf and YC?\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb b/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
index 26f036ec2a..06cf418a18 100644
--- a/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
+++ b/docs/examples/query_engine/SQLAutoVectorQueryEngine.ipynb
@@ -30,10 +30,11 @@
"outputs": [],
"source": [
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()\n",
"\n",
"import logging\n",
@@ -66,7 +67,7 @@
" ServiceContext,\n",
" StorageContext,\n",
" SQLDatabase,\n",
- " WikipediaReader\n",
+ " WikipediaReader,\n",
")"
]
},
@@ -89,11 +90,11 @@
},
"outputs": [],
"source": [
- "# define pinecone index \n",
+ "# define pinecone index\n",
"import pinecone\n",
"import os\n",
"\n",
- "api_key = os.environ['PINECONE_API_KEY']\n",
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
"pinecone.init(api_key=api_key, environment=\"us-west1-gcp\")\n",
"\n",
"# dimensions are for text-embedding-ada-002\n",
@@ -147,9 +148,11 @@
"node_parser = SimpleNodeParser(text_splitter=text_splitter)\n",
"\n",
"# define pinecone vector index\n",
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='wiki_cities')\n",
+ "vector_store = PineconeVectorStore(\n",
+ " pinecone_index=pinecone_index, namespace=\"wiki_cities\"\n",
+ ")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "vector_index = VectorStoreIndex([], storage_context=storage_context)\n"
+ "vector_index = VectorStoreIndex([], storage_context=storage_context)"
]
},
{
@@ -170,7 +173,16 @@
},
"outputs": [],
"source": [
- "from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, select, column"
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ " select,\n",
+ " column,\n",
+ ")"
]
},
{
@@ -246,6 +258,7 @@
"outputs": [],
"source": [
"from sqlalchemy import insert\n",
+ "\n",
"rows = [\n",
" {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
" {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
@@ -327,7 +340,7 @@
},
"outputs": [],
"source": [
- "cities = ['Toronto', 'Berlin', 'Tokyo']\n",
+ "cities = [\"Toronto\", \"Berlin\", \"Tokyo\"]\n",
"wiki_docs = WikipediaReader().load_data(pages=cities)"
]
},
@@ -443,19 +456,18 @@
"\n",
"\n",
"vector_store_info = VectorStoreInfo(\n",
- " content_info='articles about different cities',\n",
+ " content_info=\"articles about different cities\",\n",
" metadata_info=[\n",
- " MetadataInfo(\n",
- " name='title', \n",
- " type='str', \n",
- " description='The name of the city'),\n",
- " ]\n",
+ " MetadataInfo(name=\"title\", type=\"str\", description=\"The name of the city\"),\n",
+ " ],\n",
+ ")\n",
+ "vector_auto_retriever = VectorIndexAutoRetriever(\n",
+ " vector_index, vector_store_info=vector_store_info\n",
")\n",
- "vector_auto_retriever = VectorIndexAutoRetriever(vector_index, vector_store_info=vector_store_info)\n",
"\n",
"retriever_query_engine = RetrieverQueryEngine.from_args(\n",
" vector_auto_retriever, service_context=service_context\n",
- ")\n"
+ ")"
]
},
{
@@ -467,13 +479,13 @@
"sql_tool = QueryEngineTool.from_defaults(\n",
" query_engine=sql_query_engine,\n",
" description=(\n",
- " 'Useful for translating a natural language query into a SQL query over a table containing: '\n",
- " 'city_stats, containing the population/country of each city'\n",
- " )\n",
+ " \"Useful for translating a natural language query into a SQL query over a table containing: \"\n",
+ " \"city_stats, containing the population/country of each city\"\n",
+ " ),\n",
")\n",
"vector_tool = QueryEngineTool.from_defaults(\n",
" query_engine=retriever_query_engine,\n",
- " description=f'Useful for answering semantic questions about different cities',\n",
+ " description=f\"Useful for answering semantic questions about different cities\",\n",
")"
]
},
@@ -494,9 +506,7 @@
"outputs": [],
"source": [
"query_engine = SQLAutoVectorQueryEngine(\n",
- " sql_tool,\n",
- " vector_tool,\n",
- " service_context=service_context\n",
+ " sql_tool, vector_tool, service_context=service_context\n",
")"
]
},
@@ -526,7 +536,9 @@
}
],
"source": [
- "response = query_engine.query('Tell me about the arts and culture of the city with the highest population')"
+ "response = query_engine.query(\n",
+ " \"Tell me about the arts and culture of the city with the highest population\"\n",
+ ")"
]
},
{
@@ -572,7 +584,7 @@
}
],
"source": [
- "response = query_engine.query('Tell me about the history of Berlin')"
+ "response = query_engine.query(\"Tell me about the history of Berlin\")"
]
},
{
@@ -626,7 +638,7 @@
}
],
"source": [
- "response = query_engine.query('Can you give me the country corresponding to each city?')"
+ "response = query_engine.query(\"Can you give me the country corresponding to each city?\")"
]
},
{
diff --git a/docs/examples/query_engine/SQLJoinQueryEngine.ipynb b/docs/examples/query_engine/SQLJoinQueryEngine.ipynb
index af12c5a070..fd5cf66e5d 100644
--- a/docs/examples/query_engine/SQLJoinQueryEngine.ipynb
+++ b/docs/examples/query_engine/SQLJoinQueryEngine.ipynb
@@ -30,10 +30,11 @@
"outputs": [],
"source": [
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()\n",
"\n",
"import logging\n",
@@ -57,7 +58,7 @@
" ServiceContext,\n",
" StorageContext,\n",
" SQLDatabase,\n",
- " WikipediaReader\n",
+ " WikipediaReader,\n",
")"
]
},
@@ -80,7 +81,7 @@
},
"outputs": [],
"source": [
- "# # define pinecone index \n",
+ "# # define pinecone index\n",
"# import pinecone\n",
"# import os\n",
"\n",
@@ -129,7 +130,7 @@
"# # define pinecone vector index\n",
"# vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='wiki_cities')\n",
"# storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "# vector_index = VectorStoreIndex([], storage_context=storage_context)\n"
+ "# vector_index = VectorStoreIndex([], storage_context=storage_context)"
]
},
{
@@ -150,7 +151,16 @@
},
"outputs": [],
"source": [
- "from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, select, column"
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ " select,\n",
+ " column,\n",
+ ")"
]
},
{
@@ -226,6 +236,7 @@
"outputs": [],
"source": [
"from sqlalchemy import insert\n",
+ "\n",
"rows = [\n",
" {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
" {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
@@ -307,7 +318,7 @@
},
"outputs": [],
"source": [
- "cities = ['Toronto', 'Berlin', 'Tokyo']\n",
+ "cities = [\"Toronto\", \"Berlin\", \"Tokyo\"]\n",
"wiki_docs = WikipediaReader().load_data(pages=cities)"
]
},
@@ -430,18 +441,20 @@
},
"outputs": [],
"source": [
- "from llama_index.query_engine import SubQuestionQueryEngine \n",
+ "from llama_index.query_engine import SubQuestionQueryEngine\n",
"\n",
"query_engine_tools = []\n",
"for city in cities:\n",
" query_engine = vector_query_engines[city]\n",
- " \n",
+ "\n",
" query_engine_tool = QueryEngineTool(\n",
- " query_engine=query_engine, \n",
- " metadata=ToolMetadata(name=city, description=f\"Provides information about {city}\")\n",
+ " query_engine=query_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=city, description=f\"Provides information about {city}\"\n",
+ " ),\n",
" )\n",
" query_engine_tools.append(query_engine_tool)\n",
- " \n",
+ "\n",
"\n",
"s_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=query_engine_tools)\n",
"\n",
@@ -454,8 +467,8 @@
"# content_info='articles about different cities',\n",
"# metadata_info=[\n",
"# MetadataInfo(\n",
- "# name='title', \n",
- "# type='str', \n",
+ "# name='title',\n",
+ "# type='str',\n",
"# description='The name of the city'),\n",
"# ]\n",
"# )\n",
@@ -463,7 +476,7 @@
"\n",
"# retriever_query_engine = RetrieverQueryEngine.from_args(\n",
"# vector_auto_retriever, service_context=service_context\n",
- "# )\n"
+ "# )"
]
},
{
@@ -475,13 +488,13 @@
"sql_tool = QueryEngineTool.from_defaults(\n",
" query_engine=sql_query_engine,\n",
" description=(\n",
- " 'Useful for translating a natural language query into a SQL query over a table containing: '\n",
- " 'city_stats, containing the population/country of each city'\n",
- " )\n",
+ " \"Useful for translating a natural language query into a SQL query over a table containing: \"\n",
+ " \"city_stats, containing the population/country of each city\"\n",
+ " ),\n",
")\n",
"s_engine_tool = QueryEngineTool.from_defaults(\n",
" query_engine=s_engine,\n",
- " description=f'Useful for answering semantic questions about different cities',\n",
+ " description=f\"Useful for answering semantic questions about different cities\",\n",
")"
]
},
@@ -502,9 +515,7 @@
"outputs": [],
"source": [
"query_engine = SQLJoinQueryEngine(\n",
- " sql_tool,\n",
- " s_engine_tool,\n",
- " service_context=service_context\n",
+ " sql_tool, s_engine_tool, service_context=service_context\n",
")"
]
},
@@ -626,7 +637,9 @@
}
],
"source": [
- "response = query_engine.query('Tell me about the arts and culture of the city with the highest population')"
+ "response = query_engine.query(\n",
+ " \"Tell me about the arts and culture of the city with the highest population\"\n",
+ ")"
]
},
{
@@ -778,7 +791,9 @@
}
],
"source": [
- "response = query_engine.query('Compare and contrast the demographics of Berlin and Toronto')"
+ "response = query_engine.query(\n",
+ " \"Compare and contrast the demographics of Berlin and Toronto\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/query_engine/SQLRouterQueryEngine.ipynb b/docs/examples/query_engine/SQLRouterQueryEngine.ipynb
index 42ac80be0c..8c4fdf037f 100644
--- a/docs/examples/query_engine/SQLRouterQueryEngine.ipynb
+++ b/docs/examples/query_engine/SQLRouterQueryEngine.ipynb
@@ -26,10 +26,11 @@
"outputs": [],
"source": [
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -72,7 +73,7 @@
" ServiceContext,\n",
" StorageContext,\n",
" SQLDatabase,\n",
- " WikipediaReader\n",
+ " WikipediaReader,\n",
")"
]
},
@@ -94,7 +95,16 @@
},
"outputs": [],
"source": [
- "from sqlalchemy import create_engine, MetaData, Table, Column, String, Integer, select, column"
+ "from sqlalchemy import (\n",
+ " create_engine,\n",
+ " MetaData,\n",
+ " Table,\n",
+ " Column,\n",
+ " String,\n",
+ " Integer,\n",
+ " select,\n",
+ " column,\n",
+ ")"
]
},
{
@@ -170,6 +180,7 @@
"outputs": [],
"source": [
"from sqlalchemy import insert\n",
+ "\n",
"rows = [\n",
" {\"city_name\": \"Toronto\", \"population\": 2930000, \"country\": \"Canada\"},\n",
" {\"city_name\": \"Tokyo\", \"population\": 13960000, \"country\": \"Japan\"},\n",
@@ -251,7 +262,7 @@
},
"outputs": [],
"source": [
- "cities = ['Toronto', 'Berlin', 'Tokyo']\n",
+ "cities = [\"Toronto\", \"Berlin\", \"Tokyo\"]\n",
"wiki_docs = WikipediaReader().load_data(pages=cities)"
]
},
@@ -392,15 +403,15 @@
"sql_tool = QueryEngineTool.from_defaults(\n",
" query_engine=sql_query_engine,\n",
" description=(\n",
- " 'Useful for translating a natural language query into a SQL query over a table containing: '\n",
- " 'city_stats, containing the population/country of each city'\n",
- " )\n",
+ " \"Useful for translating a natural language query into a SQL query over a table containing: \"\n",
+ " \"city_stats, containing the population/country of each city\"\n",
+ " ),\n",
")\n",
"vector_tools = []\n",
"for city, query_engine in zip(cities, vector_query_engines):\n",
" vector_tool = QueryEngineTool.from_defaults(\n",
" query_engine=query_engine,\n",
- " description=f'Useful for answering semantic questions about {city}',\n",
+ " description=f\"Useful for answering semantic questions about {city}\",\n",
" )\n",
" vector_tools.append(vector_tool)"
]
@@ -426,7 +437,7 @@
"\n",
"query_engine = RouterQueryEngine(\n",
" selector=LLMSingleSelector.from_defaults(),\n",
- " query_engine_tools=([sql_tool] + vector_tools)\n",
+ " query_engine_tools=([sql_tool] + vector_tools),\n",
")"
]
},
@@ -459,7 +470,7 @@
}
],
"source": [
- "response = query_engine.query('Which city has the highest population?')\n",
+ "response = query_engine.query(\"Which city has the highest population?\")\n",
"print(str(response))"
]
},
@@ -491,7 +502,7 @@
}
],
"source": [
- "response = query_engine.query('Tell me about the historical museums in Berlin')\n",
+ "response = query_engine.query(\"Tell me about the historical museums in Berlin\")\n",
"print(str(response))"
]
},
@@ -523,7 +534,7 @@
}
],
"source": [
- "response = query_engine.query('Which countries are each city from?')\n",
+ "response = query_engine.query(\"Which countries are each city from?\")\n",
"print(str(response))"
]
},
diff --git a/docs/examples/query_engine/citation_query_engine.ipynb b/docs/examples/query_engine/citation_query_engine.ipynb
index 0a646c826f..d06bd2b6e6 100644
--- a/docs/examples/query_engine/citation_query_engine.ipynb
+++ b/docs/examples/query_engine/citation_query_engine.ipynb
@@ -60,7 +60,7 @@
"outputs": [],
"source": [
"service_context = ServiceContext.from_defaults(\n",
- " llm = OpenAI(model='gpt-3.5-turbo', temperature=0)\n",
+ " llm=OpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n",
")"
]
},
@@ -77,9 +77,9 @@
" index.storage_context.persist(persist_dir=\"./citation\")\n",
"else:\n",
" index = load_index_from_storage(\n",
- " StorageContext.from_defaults(persist_dir=\"./citation\"), \n",
- " service_context=service_context\n",
- " )\n"
+ " StorageContext.from_defaults(persist_dir=\"./citation\"),\n",
+ " service_context=service_context,\n",
+ " )"
]
},
{
@@ -99,10 +99,10 @@
"outputs": [],
"source": [
"query_engine = CitationQueryEngine.from_args(\n",
- " index, \n",
+ " index,\n",
" similarity_top_k=3,\n",
" # here we can control how granular citation sources are, the default is 512\n",
- " citation_chunk_size=512 \n",
+ " citation_chunk_size=512,\n",
")"
]
},
@@ -246,10 +246,10 @@
"outputs": [],
"source": [
"query_engine = CitationQueryEngine.from_args(\n",
- " index, \n",
+ " index,\n",
" # increase the citation chunk size!\n",
" citation_chunk_size=1024,\n",
- " similarity_top_k=3\n",
+ " similarity_top_k=3,\n",
")"
]
},
diff --git a/docs/examples/query_engine/flare_query_engine.ipynb b/docs/examples/query_engine/flare_query_engine.ipynb
index 6858a56dd2..5d1bc1e5a4 100644
--- a/docs/examples/query_engine/flare_query_engine.ipynb
+++ b/docs/examples/query_engine/flare_query_engine.ipynb
@@ -55,8 +55,7 @@
"outputs": [],
"source": [
"service_context = ServiceContext.from_defaults(\n",
- " llm=OpenAI(model='gpt-4', temperature=0),\n",
- " chunk_size=512\n",
+ " llm=OpenAI(model=\"gpt-4\", temperature=0), chunk_size=512\n",
")"
]
},
@@ -98,7 +97,7 @@
" query_engine=index_query_engine,\n",
" service_context=service_context,\n",
" max_iterations=7,\n",
- " verbose=True\n",
+ " verbose=True,\n",
")"
]
},
@@ -151,7 +150,9 @@
}
],
"source": [
- "response = flare_query_engine.query(\"Can you tell me about the author's trajectory in the startup world?\")"
+ "response = flare_query_engine.query(\n",
+ " \"Can you tell me about the author's trajectory in the startup world?\"\n",
+ ")"
]
},
{
@@ -197,7 +198,9 @@
}
],
"source": [
- "response = flare_query_engine.query(\"Can you tell me about what the author did during his time at YC?\")"
+ "response = flare_query_engine.query(\n",
+ " \"Can you tell me about what the author did during his time at YC?\"\n",
+ ")"
]
},
{
@@ -247,7 +250,9 @@
}
],
"source": [
- "response = flare_query_engine.query(\"Tell me about the author's life from childhood to adulthood\")"
+ "response = flare_query_engine.query(\n",
+ " \"Tell me about the author's life from childhood to adulthood\"\n",
+ ")"
]
},
{
@@ -279,7 +284,9 @@
},
"outputs": [],
"source": [
- "response = index_query_engine.query(\"Can you tell me about the author's trajectory in the startup world?\")"
+ "response = index_query_engine.query(\n",
+ " \"Can you tell me about the author's trajectory in the startup world?\"\n",
+ ")"
]
},
{
@@ -317,7 +324,9 @@
},
"outputs": [],
"source": [
- "response = index_query_engine.query(\"Tell me about the author's life from childhood to adulthood\")"
+ "response = index_query_engine.query(\n",
+ " \"Tell me about the author's life from childhood to adulthood\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/query_engine/json_query_engine.ipynb b/docs/examples/query_engine/json_query_engine.ipynb
index 9adbdd2135..de4e221b4a 100644
--- a/docs/examples/query_engine/json_query_engine.ipynb
+++ b/docs/examples/query_engine/json_query_engine.ipynb
@@ -1,358 +1,357 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
- "metadata": {},
- "source": [
- "# JSON Query Engine\n",
- "The JSON query engine is useful for querying JSON documents that conform to a JSON schema.\n",
- "\n",
- "This JSON schema is then used in the context of a prompt to convert a natural language query into a structured JSON Path query. This JSON Path query is then used to retrieve data to answer the given question."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "f7c5da2e",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: jsonpath-ng in /workspaces/llama_index/.venv/lib/python3.10/site-packages (1.5.3)\n",
- "Requirement already satisfied: ply in /workspaces/llama_index/.venv/lib/python3.10/site-packages (from jsonpath-ng) (3.11)\n",
- "Requirement already satisfied: decorator in /workspaces/llama_index/.venv/lib/python3.10/site-packages (from jsonpath-ng) (5.1.1)\n",
- "Requirement already satisfied: six in /workspaces/llama_index/.venv/lib/python3.10/site-packages (from jsonpath-ng) (1.16.0)\n"
- ]
- }
- ],
- "source": [
- "# First, install the jsonpath-ng package which is used by default to parse & execute the JSONPath queries.\n",
- "!pip install jsonpath-ng"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "119eb42b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "7aa21e46",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "False"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import dotenv\n",
- "dotenv.load_dotenv(\"../../../.env\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "107396a9-4aa7-49b3-9f0f-a755726c19ba",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "5ece7d73-0f67-4ff5-95e5-249a25bd118c",
- "metadata": {},
- "source": [
- "### Let's start on a Toy JSON\n",
- "\n",
- "Very simple JSON object containing data from a blog post site with user comments.\n",
- "\n",
- "We will also provide a JSON schema (which we were able to generate by giving ChatGPT a sample of the JSON).\n",
- "\n",
- "#### Advice\n",
- "Do make sure that you've provided a helpful `\"description\"` value for each of the fields in your JSON schema.\n",
- "\n",
- "As you can see in the given example, the description for the `\"username\"` field mentions that usernames are lowercased. You'll see that this ends up being helpful for the LLM in producing the correct JSON path query."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "1484fe58-4853-4a76-bffc-435a9cce3e2e",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Test on some sample data \n",
- "json_value = {\n",
- " \"blogPosts\": [\n",
- " {\n",
- " \"id\": 1,\n",
- " \"title\": \"First blog post\",\n",
- " \"content\": \"This is my first blog post\"\n",
- " },\n",
- " {\n",
- " \"id\": 2,\n",
- " \"title\": \"Second blog post\",\n",
- " \"content\": \"This is my second blog post\"\n",
- " }\n",
- " ],\n",
- " \"comments\": [\n",
- " {\n",
- " \"id\": 1,\n",
- " \"content\": \"Nice post!\",\n",
- " \"username\": \"jerry\",\n",
- " \"blogPostId\": 1\n",
- " },\n",
- " {\n",
- " \"id\": 2,\n",
- " \"content\": \"Interesting thoughts\",\n",
- " \"username\": \"simon\",\n",
- " \"blogPostId\": 2\n",
- " },\n",
- " {\n",
- " \"id\": 3,\n",
- " \"content\": \"Loved reading this!\",\n",
- " \"username\": \"simon\",\n",
- " \"blogPostId\": 2\n",
- " }\n",
- " ]\n",
- "}\n",
- "\n",
- "# JSON Schema object that the above JSON value conforms to\n",
- "json_schema = {\n",
- " \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n",
- " \"description\": \"Schema for a very simple blog post app\",\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"blogPosts\": {\n",
- " \"description\": \"List of blog posts\",\n",
- " \"type\": \"array\",\n",
- " \"items\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"id\": {\n",
- " \"description\": \"Unique identifier for the blog post\",\n",
- " \"type\": \"integer\"\n",
- " },\n",
- " \"title\": {\n",
- " \"description\": \"Title of the blog post\",\n",
- " \"type\": \"string\"\n",
- " },\n",
- " \"content\": {\n",
- " \"description\": \"Content of the blog post\",\n",
- " \"type\": \"string\"\n",
- " }\n",
- " },\n",
- " \"required\": [\"id\", \"title\", \"content\"]\n",
- " }\n",
- " },\n",
- " \"comments\": {\n",
- " \"description\": \"List of comments on blog posts\",\n",
- " \"type\": \"array\",\n",
- " \"items\": {\n",
- " \"type\": \"object\",\n",
- " \"properties\": {\n",
- " \"id\": {\n",
- " \"description\": \"Unique identifier for the comment\",\n",
- " \"type\": \"integer\"\n",
- " },\n",
- " \"content\": {\n",
- " \"description\": \"Content of the comment\",\n",
- " \"type\": \"string\"\n",
- " },\n",
- " \"username\": {\n",
- " \"description\": \"Username of the commenter (lowercased)\",\n",
- " \"type\": \"string\"\n",
- " },\n",
- " \"blogPostId\": {\n",
- " \"description\": \"Identifier for the blog post to which the comment belongs\",\n",
- " \"type\": \"integer\"\n",
- " }\n",
- " },\n",
- " \"required\": [\"id\", \"content\", \"username\", \"blogPostId\"]\n",
- " }\n",
- " }\n",
- " },\n",
- " \"required\": [\"blogPosts\", \"comments\"]\n",
- "}\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "4fea2edb-b3d4-4313-a656-d6edb00d93c0",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
- "NumExpr defaulting to 2 threads.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/workspaces/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index.indices.service_context import ServiceContext\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.indices.struct_store import JSONQueryEngine\n",
- "\n",
- "llm = OpenAI(model=\"text-davinci-003\")\n",
- "service_context = ServiceContext.from_defaults(llm=llm)\n",
- "nl_query_engine = JSONQueryEngine(json_value=json_value, json_schema=json_schema, service_context=service_context)\n",
- "raw_query_engine = JSONQueryEngine(json_value=json_value, json_schema=json_schema, service_context=service_context, synthesize_response=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "451836bc-b073-4838-8ab8-3def7d2c4d9d",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 797 tokens\n",
- "> [query] Total LLM token usage: 797 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 363 tokens\n",
- "> [query] Total LLM token usage: 363 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "nl_response = nl_query_engine.query(\n",
- " \"What comments has Jerry been writing?\",\n",
- ")\n",
- "raw_response = raw_query_engine.query(\n",
- " \"What comments has Jerry been writing?\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "4253d4c3-f3e5-4779-bcd1-2e6e2818305f",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<h1>Natural language Response</h1><br><b> Jerry has written one comment with the content 'Nice post!' on blog post with id 1.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<h1>Raw JSON Response</h1><br><b>[{\"id\": 1, \"content\": \"Nice post!\", \"username\": \"jerry\", \"blogPostId\": 1}]</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<h1>Natural language Response</h1><br><b>{nl_response}</b>\"))\n",
- "display(Markdown(f\"<h1>Raw JSON Response</h1><br><b>{raw_response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "5e10b7da-b355-49b2-9f80-f17541d4f850",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " $.comments[?(@.username == 'jerry')]\n"
- ]
- }
- ],
- "source": [
- "# get the json path query string. Same would apply to raw_response\n",
- "print(nl_response.metadata[\"json_path_response_str\"])"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.4"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
+ "metadata": {},
+ "source": [
+ "# JSON Query Engine\n",
+ "The JSON query engine is useful for querying JSON documents that conform to a JSON schema.\n",
+ "\n",
+ "This JSON schema is then used in the context of a prompt to convert a natural language query into a structured JSON Path query. This JSON Path query is then used to retrieve data to answer the given question."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f7c5da2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: jsonpath-ng in /workspaces/llama_index/.venv/lib/python3.10/site-packages (1.5.3)\n",
+ "Requirement already satisfied: ply in /workspaces/llama_index/.venv/lib/python3.10/site-packages (from jsonpath-ng) (3.11)\n",
+ "Requirement already satisfied: decorator in /workspaces/llama_index/.venv/lib/python3.10/site-packages (from jsonpath-ng) (5.1.1)\n",
+ "Requirement already satisfied: six in /workspaces/llama_index/.venv/lib/python3.10/site-packages (from jsonpath-ng) (1.16.0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# First, install the jsonpath-ng package which is used by default to parse & execute the JSONPath queries.\n",
+ "!pip install jsonpath-ng"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "119eb42b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7aa21e46",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import dotenv\n",
+ "\n",
+ "dotenv.load_dotenv(\"../../../.env\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "107396a9-4aa7-49b3-9f0f-a755726c19ba",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "5ece7d73-0f67-4ff5-95e5-249a25bd118c",
+ "metadata": {},
+ "source": [
+ "### Let's start on a Toy JSON\n",
+ "\n",
+ "Very simple JSON object containing data from a blog post site with user comments.\n",
+ "\n",
+ "We will also provide a JSON schema (which we were able to generate by giving ChatGPT a sample of the JSON).\n",
+ "\n",
+ "#### Advice\n",
+ "Do make sure that you've provided a helpful `\"description\"` value for each of the fields in your JSON schema.\n",
+ "\n",
+ "As you can see in the given example, the description for the `\"username\"` field mentions that usernames are lowercased. You'll see that this ends up being helpful for the LLM in producing the correct JSON path query."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "1484fe58-4853-4a76-bffc-435a9cce3e2e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Test on some sample data\n",
+ "json_value = {\n",
+ " \"blogPosts\": [\n",
+ " {\"id\": 1, \"title\": \"First blog post\", \"content\": \"This is my first blog post\"},\n",
+ " {\n",
+ " \"id\": 2,\n",
+ " \"title\": \"Second blog post\",\n",
+ " \"content\": \"This is my second blog post\",\n",
+ " },\n",
+ " ],\n",
+ " \"comments\": [\n",
+ " {\"id\": 1, \"content\": \"Nice post!\", \"username\": \"jerry\", \"blogPostId\": 1},\n",
+ " {\n",
+ " \"id\": 2,\n",
+ " \"content\": \"Interesting thoughts\",\n",
+ " \"username\": \"simon\",\n",
+ " \"blogPostId\": 2,\n",
+ " },\n",
+ " {\n",
+ " \"id\": 3,\n",
+ " \"content\": \"Loved reading this!\",\n",
+ " \"username\": \"simon\",\n",
+ " \"blogPostId\": 2,\n",
+ " },\n",
+ " ],\n",
+ "}\n",
+ "\n",
+ "# JSON Schema object that the above JSON value conforms to\n",
+ "json_schema = {\n",
+ " \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n",
+ " \"description\": \"Schema for a very simple blog post app\",\n",
+ " \"type\": \"object\",\n",
+ " \"properties\": {\n",
+ " \"blogPosts\": {\n",
+ " \"description\": \"List of blog posts\",\n",
+ " \"type\": \"array\",\n",
+ " \"items\": {\n",
+ " \"type\": \"object\",\n",
+ " \"properties\": {\n",
+ " \"id\": {\n",
+ " \"description\": \"Unique identifier for the blog post\",\n",
+ " \"type\": \"integer\",\n",
+ " },\n",
+ " \"title\": {\n",
+ " \"description\": \"Title of the blog post\",\n",
+ " \"type\": \"string\",\n",
+ " },\n",
+ " \"content\": {\n",
+ " \"description\": \"Content of the blog post\",\n",
+ " \"type\": \"string\",\n",
+ " },\n",
+ " },\n",
+ " \"required\": [\"id\", \"title\", \"content\"],\n",
+ " },\n",
+ " },\n",
+ " \"comments\": {\n",
+ " \"description\": \"List of comments on blog posts\",\n",
+ " \"type\": \"array\",\n",
+ " \"items\": {\n",
+ " \"type\": \"object\",\n",
+ " \"properties\": {\n",
+ " \"id\": {\n",
+ " \"description\": \"Unique identifier for the comment\",\n",
+ " \"type\": \"integer\",\n",
+ " },\n",
+ " \"content\": {\n",
+ " \"description\": \"Content of the comment\",\n",
+ " \"type\": \"string\",\n",
+ " },\n",
+ " \"username\": {\n",
+ " \"description\": \"Username of the commenter (lowercased)\",\n",
+ " \"type\": \"string\",\n",
+ " },\n",
+ " \"blogPostId\": {\n",
+ " \"description\": \"Identifier for the blog post to which the comment belongs\",\n",
+ " \"type\": \"integer\",\n",
+ " },\n",
+ " },\n",
+ " \"required\": [\"id\", \"content\", \"username\", \"blogPostId\"],\n",
+ " },\n",
+ " },\n",
+ " },\n",
+ " \"required\": [\"blogPosts\", \"comments\"],\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "4fea2edb-b3d4-4313-a656-d6edb00d93c0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
+ "NumExpr defaulting to 2 threads.\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/workspaces/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.indices.service_context import ServiceContext\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.indices.struct_store import JSONQueryEngine\n",
+ "\n",
+ "llm = OpenAI(model=\"text-davinci-003\")\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)\n",
+ "nl_query_engine = JSONQueryEngine(\n",
+ " json_value=json_value, json_schema=json_schema, service_context=service_context\n",
+ ")\n",
+ "raw_query_engine = JSONQueryEngine(\n",
+ " json_value=json_value,\n",
+ " json_schema=json_schema,\n",
+ " service_context=service_context,\n",
+ " synthesize_response=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "451836bc-b073-4838-8ab8-3def7d2c4d9d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 797 tokens\n",
+ "> [query] Total LLM token usage: 797 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 363 tokens\n",
+ "> [query] Total LLM token usage: 363 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "nl_response = nl_query_engine.query(\n",
+ " \"What comments has Jerry been writing?\",\n",
+ ")\n",
+ "raw_response = raw_query_engine.query(\n",
+ " \"What comments has Jerry been writing?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "4253d4c3-f3e5-4779-bcd1-2e6e2818305f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<h1>Natural language Response</h1><br><b> Jerry has written one comment with the content 'Nice post!' on blog post with id 1.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<h1>Raw JSON Response</h1><br><b>[{\"id\": 1, \"content\": \"Nice post!\", \"username\": \"jerry\", \"blogPostId\": 1}]</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<h1>Natural language Response</h1><br><b>{nl_response}</b>\"))\n",
+ "display(Markdown(f\"<h1>Raw JSON Response</h1><br><b>{raw_response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "5e10b7da-b355-49b2-9f80-f17541d4f850",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " $.comments[?(@.username == 'jerry')]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get the json path query string. Same would apply to raw_response\n",
+ "print(nl_response.metadata[\"json_path_response_str\"])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/query_engine/pandas_query_engine.ipynb b/docs/examples/query_engine/pandas_query_engine.ipynb
index 1d37849819..c1403fbf0e 100644
--- a/docs/examples/query_engine/pandas_query_engine.ipynb
+++ b/docs/examples/query_engine/pandas_query_engine.ipynb
@@ -1,278 +1,275 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
- "metadata": {},
- "source": [
- "# Pandas Query Engine"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "119eb42b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "from IPython.display import Markdown, display\n",
- "\n",
- "import pandas as pd\n",
- "from llama_index.query_engine import PandasQueryEngine\n",
- "\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "5ece7d73-0f67-4ff5-95e5-249a25bd118c",
- "metadata": {},
- "source": [
- "### Let's start on a Toy DataFrame\n",
- "\n",
- "Very simple dataframe containing city and population pairs."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "1484fe58-4853-4a76-bffc-435a9cce3e2e",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Test on some sample data \n",
- "df = pd.DataFrame(\n",
- " {\n",
- " \"city\": [\"Toronto\", \"Tokyo\", \"Berlin\"], \n",
- " \"population\": [2930000, 13960000, 3645000]\n",
- " }\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "4fea2edb-b3d4-4313-a656-d6edb00d93c0",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = PandasQueryEngine(df=df, verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "451836bc-b073-4838-8ab8-3def7d2c4d9d",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Pandas Instructions:\n",
- "```\n",
- "\n",
- "df['city'][df['population'].idxmax()]\n",
- "```\n",
- "> Pandas Output: Tokyo\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\n",
- " \"What is the city with the highest population?\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "4253d4c3-f3e5-4779-bcd1-2e6e2818305f",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>Tokyo</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "5e10b7da-b355-49b2-9f80-f17541d4f850",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "df['city'][df['population'].idxmax()]\n"
- ]
- }
- ],
- "source": [
- "# get pandas python instructions\n",
- "print(response.metadata[\"pandas_instruction_str\"])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1de5eaf3-6129-47b1-b630-faf9138a04c5",
- "metadata": {},
- "source": [
- "### Analyzing the Titanic Dataset\n",
- "\n",
- "The Titanic dataset is one of the most popular tabular datasets in introductory machine learning\n",
- "Source: https://www.kaggle.com/c/titanic"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "809f18c8-e38b-449e-b5ee-c2ea700f8698",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "df = pd.read_csv(\"../data/csv/titanic_train.csv\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "fb1758de-6310-4ed5-ae02-2dbf50d2c55f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = PandasQueryEngine(df=df, verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "f9dd658d-b62c-4e3b-aee9-0a06f57de032",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Pandas Instructions:\n",
- "```\n",
- "df['survived'].corr(df['age'])\n",
- "```\n",
- "> Pandas Output: -0.07722109457217768\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\n",
- " \"What is the correlation between survival and age?\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "60474389-341b-4187-87b2-83811546dcea",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>-0.07722109457217768</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "af999a1f-fea6-4734-82e6-4450f1a06a3b",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "df['survived'].corr(df['age'])\n"
- ]
- }
- ],
- "source": [
- "# get pandas python instructions\n",
- "print(response.metadata[\"pandas_instruction_str\"])"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
+ "metadata": {},
+ "source": [
+ "# Pandas Query Engine"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "119eb42b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "from IPython.display import Markdown, display\n",
+ "\n",
+ "import pandas as pd\n",
+ "from llama_index.query_engine import PandasQueryEngine\n",
+ "\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "5ece7d73-0f67-4ff5-95e5-249a25bd118c",
+ "metadata": {},
+ "source": [
+ "### Let's start on a Toy DataFrame\n",
+ "\n",
+ "Very simple dataframe containing city and population pairs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1484fe58-4853-4a76-bffc-435a9cce3e2e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Test on some sample data\n",
+ "df = pd.DataFrame(\n",
+ " {\"city\": [\"Toronto\", \"Tokyo\", \"Berlin\"], \"population\": [2930000, 13960000, 3645000]}\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "4fea2edb-b3d4-4313-a656-d6edb00d93c0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = PandasQueryEngine(df=df, verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "451836bc-b073-4838-8ab8-3def7d2c4d9d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Pandas Instructions:\n",
+ "```\n",
+ "\n",
+ "df['city'][df['population'].idxmax()]\n",
+ "```\n",
+ "> Pandas Output: Tokyo\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What is the city with the highest population?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "4253d4c3-f3e5-4779-bcd1-2e6e2818305f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>Tokyo</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "5e10b7da-b355-49b2-9f80-f17541d4f850",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "df['city'][df['population'].idxmax()]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get pandas python instructions\n",
+ "print(response.metadata[\"pandas_instruction_str\"])"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1de5eaf3-6129-47b1-b630-faf9138a04c5",
+ "metadata": {},
+ "source": [
+ "### Analyzing the Titanic Dataset\n",
+ "\n",
+ "The Titanic dataset is one of the most popular tabular datasets in introductory machine learning\n",
+ "Source: https://www.kaggle.com/c/titanic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "809f18c8-e38b-449e-b5ee-c2ea700f8698",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "df = pd.read_csv(\"../data/csv/titanic_train.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "fb1758de-6310-4ed5-ae02-2dbf50d2c55f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = PandasQueryEngine(df=df, verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "f9dd658d-b62c-4e3b-aee9-0a06f57de032",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Pandas Instructions:\n",
+ "```\n",
+ "df['survived'].corr(df['age'])\n",
+ "```\n",
+ "> Pandas Output: -0.07722109457217768\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What is the correlation between survival and age?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "60474389-341b-4187-87b2-83811546dcea",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>-0.07722109457217768</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "af999a1f-fea6-4734-82e6-4450f1a06a3b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "df['survived'].corr(df['age'])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get pandas python instructions\n",
+ "print(response.metadata[\"pandas_instruction_str\"])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb b/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb
index 7aa1885c24..fa48784b7d 100644
--- a/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb
+++ b/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb
@@ -33,6 +33,7 @@
"source": [
"import camelot\n",
"from llama_index import Document, ListIndex\n",
+ "\n",
"# https://en.wikipedia.org/wiki/The_World%27s_Billionaires\n",
"from llama_index import VectorStoreIndex, ServiceContext, LLMPredictor\n",
"from llama_index.query_engine import PandasQueryEngine, RetrieverQueryEngine\n",
@@ -109,7 +110,11 @@
" for page in pages:\n",
" table_list = camelot.read_pdf(path, pages=str(page))\n",
" table_df = table_list[0].df\n",
- " table_df = table_df.rename(columns=table_df.iloc[0]).drop(table_df.index[0]).reset_index(drop=True)\n",
+ " table_df = (\n",
+ " table_df.rename(columns=table_df.iloc[0])\n",
+ " .drop(table_df.index[0])\n",
+ " .reset_index(drop=True)\n",
+ " )\n",
" table_dfs.append(table_df)\n",
" return table_dfs"
]
@@ -601,7 +606,9 @@
}
],
"source": [
- "df_query_engines[0].query(\"What's the net worth of the second richest billionaire in 2023?\")"
+ "df_query_engines[0].query(\n",
+ " \"What's the net worth of the second richest billionaire in 2023?\"\n",
+ ")"
]
},
{
@@ -653,7 +660,9 @@
},
"outputs": [],
"source": [
- "llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name=\"gpt-4\", streaming=True))\n",
+ "llm_predictor = LLMPredictor(\n",
+ " llm=ChatOpenAI(temperature=0, model_name=\"gpt-4\", streaming=True)\n",
+ ")\n",
"service_context = ServiceContext.from_defaults(\n",
" llm_predictor=llm_predictor,\n",
")"
@@ -686,9 +695,15 @@
" \"This node provides information on the number of billionaires and their combined net worth from 2000 to 2023.\",\n",
"]\n",
"\n",
- "df_nodes = [IndexNode(text=summary, index_id=f\"pandas{idx}\") for idx, summary in enumerate(summaries)]\n",
+ "df_nodes = [\n",
+ " IndexNode(text=summary, index_id=f\"pandas{idx}\")\n",
+ " for idx, summary in enumerate(summaries)\n",
+ "]\n",
"\n",
- "df_id_query_engine_mapping = {f\"pandas{idx}\": df_query_engine for idx, df_query_engine in enumerate(df_query_engines)}"
+ "df_id_query_engine_mapping = {\n",
+ " f\"pandas{idx}\": df_query_engine\n",
+ " for idx, df_query_engine in enumerate(df_query_engines)\n",
+ "}"
]
},
{
@@ -727,7 +742,7 @@
"outputs": [],
"source": [
"# baseline vector index (that doesn't include the extra df nodes).\n",
- "# used to benchmark \n",
+ "# used to benchmark\n",
"vector_index0 = VectorStoreIndex(doc_nodes)\n",
"vector_query_engine0 = vector_index0.as_query_engine()"
]
@@ -749,8 +764,7 @@
" \"vector\",\n",
" retriever_dict={\"vector\": vector_retriever},\n",
" query_engine_dict=df_id_query_engine_mapping,\n",
- " verbose=True\n",
- " \n",
+ " verbose=True,\n",
")\n",
"\n",
"response_synthesizer = get_response_synthesizer(\n",
@@ -759,8 +773,7 @@
")\n",
"\n",
"query_engine = RetrieverQueryEngine.from_args(\n",
- " recursive_retriever,\n",
- " response_synthesizer=response_synthesizer\n",
+ " recursive_retriever, response_synthesizer=response_synthesizer\n",
")"
]
},
@@ -786,7 +799,9 @@
}
],
"source": [
- "response = query_engine.query(\"What's the net worth of the second richest billionaire in 2023?\")"
+ "response = query_engine.query(\n",
+ " \"What's the net worth of the second richest billionaire in 2023?\"\n",
+ ")"
]
},
{
@@ -890,7 +905,9 @@
},
"outputs": [],
"source": [
- "response = vector_query_engine0.query(\"What's the net worth of the second richest billionaire in 2023?\")"
+ "response = vector_query_engine0.query(\n",
+ " \"What's the net worth of the second richest billionaire in 2023?\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/query_engine/sub_question_query_engine.ipynb b/docs/examples/query_engine/sub_question_query_engine.ipynb
index 317b445ebc..0f2b8639bc 100644
--- a/docs/examples/query_engine/sub_question_query_engine.ipynb
+++ b/docs/examples/query_engine/sub_question_query_engine.ipynb
@@ -71,12 +71,16 @@
"# setup base query engine as tool\n",
"query_engine_tools = [\n",
" QueryEngineTool(\n",
- " query_engine=query_engine, \n",
- " metadata=ToolMetadata(name='pg_essay', description='Paul Graham essay on What I Worked On')\n",
+ " query_engine=query_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"pg_essay\", description=\"Paul Graham essay on What I Worked On\"\n",
+ " ),\n",
" )\n",
"]\n",
"\n",
- "query_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=query_engine_tools)"
+ "query_engine = SubQuestionQueryEngine.from_defaults(\n",
+ " query_engine_tools=query_engine_tools\n",
+ ")"
]
},
{
@@ -121,7 +125,9 @@
}
],
"source": [
- "response = await query_engine.aquery('How was Paul Grahams life different before and after YC?')"
+ "response = await query_engine.aquery(\n",
+ " \"How was Paul Grahams life different before and after YC?\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb b/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb
index d5637a556e..d46d9fd436 100644
--- a/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb
+++ b/docs/examples/query_transformations/HyDEQueryTransformDemo.ipynb
@@ -1,379 +1,379 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f20bdf25",
- "metadata": {},
- "source": [
- "# HyDE Query Transform"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a1e7e17f",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f7476659",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.indices.query.query_transform import HyDEQueryTransform\n",
- "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "16ff354a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7d6a1b5e",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = VectorStoreIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1cec25b6",
- "metadata": {},
- "source": [
- "## Example: HyDE improves specific temporal queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f3c39ddd",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_str = \"what did paul graham do after going to RISD\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8b022d86",
- "metadata": {},
- "source": [
- "#### First, we query *without* transformation: The same query string is used for embedding lookup and also summarization."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a5b93d35",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d8b9a9db",
- "metadata": {},
- "source": [
- "> After going to RISD, Paul Graham continued to pursue his passion for painting and art. He took classes in the painting department at the Accademia di Belli Arti in Florence, and he also took the entrance exam for the school. He also continued to work on his book On Lisp, and he took on consulting work to make money. At the school, Paul Graham and the other students had an arrangement where the faculty wouldn't require the students to learn anything, and in return the students wouldn't require the faculty to teach anything. Paul Graham was one of the few students who actually painted the nude model that was provided, while the rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. The model turned out to live just down the street from Paul Graham, and she made a living from a combination of modelling and making fakes for a local antique dealer."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1b117ead",
- "metadata": {},
- "source": [
- "#### Now, we use `HyDEQueryTransform` to generate a hypothetical document and use it for embedding lookup. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d43ee7a",
- "metadata": {},
- "outputs": [],
- "source": [
- "hyde = HyDEQueryTransform(include_original=True)\n",
- "hyde_query_engine = TransformQueryEngine(query_engine, hyde)\n",
- "response = hyde_query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "660c2286",
- "metadata": {},
- "source": [
- "> After going to RISD, Paul Graham worked as a consultant for Interleaf and then co-founded Viaweb with Robert Morris. They created a software that allowed users to build websites via the web and received $10,000 in seed funding from Idelle's husband Julian. They gave Julian 10% of the company in return for the initial legal work and business advice. Paul Graham had a negative net worth due to taxes he owed, so the seed funding was necessary for him to live on. They opened for business in January 1996 with 6 stores.\n",
- "\n",
- "> Paul Graham then left Yahoo after his options vested and went back to New York. He resumed his old life, but now he was rich. He tried to paint, but he didn't have much energy or ambition. He eventually moved back to Cambridge and started working on a web app for making web apps. He recruited Dan Giffin and two undergrads to help him, but he eventually realized he didn't want to run a company and decided to build a subset of the project as an open source project. He and Dan worked on a new dialect of Lisp, which he called Arc, in a house he bought in Cambridge. The subset he built as an open source project was the new Lisp, whose"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "84f4ba19",
- "metadata": {},
- "source": [
- "#### In this example, `HyDE` improves output quality significantly, by hallucinating accurately what Paul Graham did after RISD (see below), and thus improving the embedding quality, and final output."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a733f80d",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_bundle = hyde(query_str)\n",
- "hyde_doc = query_bundle.embedding_strs[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "30e11fc3",
- "metadata": {},
- "outputs": [],
- "source": [
- "hyde_doc"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d66a4296",
- "metadata": {},
- "source": [
- "> After graduating from the Rhode Island School of Design (RISD) in 1985, Paul Graham went on to pursue a career in computer programming. He worked as a software developer for several companies, including Viaweb, which he co-founded in 1995. Viaweb was eventually acquired by Yahoo in 1998, and Graham used the proceeds to become a venture capitalist. He founded Y Combinator in 2005, a startup accelerator that has helped launch over 2,000 companies, including Dropbox, Airbnb, and Reddit. Graham has also written several books on programming and startups, and he continues to be an active investor in the tech industry."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e5099208",
- "metadata": {},
- "source": [
- "## Failure case 1: HyDE may mislead when query can be mis-interpreted without context."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2e2c0f87",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_str = \"What is Bel?\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4bed7470",
- "metadata": {},
- "source": [
- "### Querying without transformation yields reasonable answer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "da43432f",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "1a7ad6e2",
- "metadata": {},
- "source": [
- "> Bel is a programming language that was written in Arc by Paul Graham over the course of four years (March 26, 2015 to October 12, 2019). It is based on John McCarthy's original Lisp, but with additional features added. It is a spec expressed as code, and is meant to be a formal model of computation, an alternative to the Turing machine."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9fa43d10",
- "metadata": {},
- "source": [
- "#### Querying with `HyDEQueryTransform` results in nonsense"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b2f7be02",
- "metadata": {},
- "outputs": [],
- "source": [
- "hyde = HyDEQueryTransform(include_original=True)\n",
- "hyde_query_engine = TransformQueryEngine(query_engine, hyde)\n",
- "response = hyde_query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9d910f39",
- "metadata": {},
- "source": [
- "> Bel is the pseudonym of Paul Graham, the author of the context information who was in need of seed funding to live on and was part of a deal that became the model for Y Combinator's."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "59d82d63",
- "metadata": {},
- "source": [
- "#### In this example, `HyDE` mis-interprets Bel without document context (see below), resulting in a completely unrelated embedding string and poor retrieval outcome."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3e771a56",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_bundle = hyde(query_str)\n",
- "hyde_doc = query_bundle.embedding_strs[0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8f5ca4c7",
- "metadata": {},
- "outputs": [],
- "source": [
- "hyde_doc"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b879b64b",
- "metadata": {},
- "source": [
- "> Bel is an ancient Semitic god, originating from the Middle East. He is often associated with the sun and is sometimes referred to as the \"Lord of Heaven\". Bel is also known as the god of fertility, abundance, and prosperity. He is often depicted as a bull or a man with a bull\\'s head. In some cultures, Bel is seen as a creator god, responsible for the creation of the universe. He is also associated with the underworld and is sometimes seen as a god of death. Bel is also associated with justice and is often seen as a protector of the innocent. Bel is an important figure in many religions, including Judaism, Christianity, and Islam."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7b8df34d",
- "metadata": {},
- "source": [
- "## Failure case 2: HyDE may bias open-ended queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3942dc8d",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_str = \"What would the author say about art vs. engineering?\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "decc9107",
- "metadata": {},
- "source": [
- "#### Querying without transformation yields a reasonable answer"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "168fab08",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c1e4939b",
- "metadata": {},
- "source": [
- "> The author would likely say that art and engineering are two different disciplines that require different skills and approaches. Art is more focused on expression and creativity, while engineering is more focused on problem-solving and technical knowledge. The author also suggests that art school does not always provide the same level of rigor as engineering school, and that painting students are often encouraged to develop a signature style rather than learn the fundamentals of painting. Furthermore, the author would likely point out that engineering can provide more financial stability than art, as evidenced by the author's own experience of needing seed funding to live on while launching a company."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d01a0ed4",
- "metadata": {},
- "source": [
- "#### Querying with `HyDEQueryTransform` results in a more biased output"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0229cf6f",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = hyde_query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "16c8f053",
- "metadata": {},
- "source": [
- "> The author would likely say that art is a more lasting and independent form of work than engineering. They mention that software written today will be obsolete in a couple decades, and that systems work does not last. In contrast, they note that paintings can last hundreds of years and that it is possible to make a living as an artist. They also mention that as an artist, you can be truly independent and don't need to have a boss or research funding. Furthermore, they note that art can be a source of income for people who may not have access to traditional forms of employment, such as the model in the example who was able to make a living from modelling and making fakes for a local antique dealer."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f20bdf25",
+ "metadata": {},
+ "source": [
+ "# HyDE Query Transform"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a1e7e17f",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7476659",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.indices.query.query_transform import HyDEQueryTransform\n",
+ "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16ff354a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d6a1b5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1cec25b6",
+ "metadata": {},
+ "source": [
+ "## Example: HyDE improves specific temporal queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3c39ddd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_str = \"what did paul graham do after going to RISD\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8b022d86",
+ "metadata": {},
+ "source": [
+ "#### First, we query *without* transformation: The same query string is used for embedding lookup and also summarization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a5b93d35",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d8b9a9db",
+ "metadata": {},
+ "source": [
+ "> After going to RISD, Paul Graham continued to pursue his passion for painting and art. He took classes in the painting department at the Accademia di Belli Arti in Florence, and he also took the entrance exam for the school. He also continued to work on his book On Lisp, and he took on consulting work to make money. At the school, Paul Graham and the other students had an arrangement where the faculty wouldn't require the students to learn anything, and in return the students wouldn't require the faculty to teach anything. Paul Graham was one of the few students who actually painted the nude model that was provided, while the rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. The model turned out to live just down the street from Paul Graham, and she made a living from a combination of modelling and making fakes for a local antique dealer."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1b117ead",
+ "metadata": {},
+ "source": [
+ "#### Now, we use `HyDEQueryTransform` to generate a hypothetical document and use it for embedding lookup. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d43ee7a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hyde = HyDEQueryTransform(include_original=True)\n",
+ "hyde_query_engine = TransformQueryEngine(query_engine, hyde)\n",
+ "response = hyde_query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "660c2286",
+ "metadata": {},
+ "source": [
+ "> After going to RISD, Paul Graham worked as a consultant for Interleaf and then co-founded Viaweb with Robert Morris. They created a software that allowed users to build websites via the web and received $10,000 in seed funding from Idelle's husband Julian. They gave Julian 10% of the company in return for the initial legal work and business advice. Paul Graham had a negative net worth due to taxes he owed, so the seed funding was necessary for him to live on. They opened for business in January 1996 with 6 stores.\n",
+ "\n",
+ "> Paul Graham then left Yahoo after his options vested and went back to New York. He resumed his old life, but now he was rich. He tried to paint, but he didn't have much energy or ambition. He eventually moved back to Cambridge and started working on a web app for making web apps. He recruited Dan Giffin and two undergrads to help him, but he eventually realized he didn't want to run a company and decided to build a subset of the project as an open source project. He and Dan worked on a new dialect of Lisp, which he called Arc, in a house he bought in Cambridge. The subset he built as an open source project was the new Lisp, whose"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84f4ba19",
+ "metadata": {},
+ "source": [
+ "#### In this example, `HyDE` improves output quality significantly, by hallucinating accurately what Paul Graham did after RISD (see below), and thus improving the embedding quality, and final output."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a733f80d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_bundle = hyde(query_str)\n",
+ "hyde_doc = query_bundle.embedding_strs[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30e11fc3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hyde_doc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d66a4296",
+ "metadata": {},
+ "source": [
+ "> After graduating from the Rhode Island School of Design (RISD) in 1985, Paul Graham went on to pursue a career in computer programming. He worked as a software developer for several companies, including Viaweb, which he co-founded in 1995. Viaweb was eventually acquired by Yahoo in 1998, and Graham used the proceeds to become a venture capitalist. He founded Y Combinator in 2005, a startup accelerator that has helped launch over 2,000 companies, including Dropbox, Airbnb, and Reddit. Graham has also written several books on programming and startups, and he continues to be an active investor in the tech industry."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5099208",
+ "metadata": {},
+ "source": [
+ "## Failure case 1: HyDE may mislead when query can be mis-interpreted without context."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e2c0f87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_str = \"What is Bel?\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4bed7470",
+ "metadata": {},
+ "source": [
+ "### Querying without transformation yields reasonable answer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "da43432f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1a7ad6e2",
+ "metadata": {},
+ "source": [
+ "> Bel is a programming language that was written in Arc by Paul Graham over the course of four years (March 26, 2015 to October 12, 2019). It is based on John McCarthy's original Lisp, but with additional features added. It is a spec expressed as code, and is meant to be a formal model of computation, an alternative to the Turing machine."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9fa43d10",
+ "metadata": {},
+ "source": [
+ "#### Querying with `HyDEQueryTransform` results in nonsense"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b2f7be02",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hyde = HyDEQueryTransform(include_original=True)\n",
+ "hyde_query_engine = TransformQueryEngine(query_engine, hyde)\n",
+ "response = hyde_query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d910f39",
+ "metadata": {},
+ "source": [
+ "> Bel is the pseudonym of Paul Graham, the author of the context information who was in need of seed funding to live on and was part of a deal that became the model for Y Combinator's."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59d82d63",
+ "metadata": {},
+ "source": [
+ "#### In this example, `HyDE` mis-interprets Bel without document context (see below), resulting in a completely unrelated embedding string and poor retrieval outcome."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e771a56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_bundle = hyde(query_str)\n",
+ "hyde_doc = query_bundle.embedding_strs[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8f5ca4c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hyde_doc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b879b64b",
+ "metadata": {},
+ "source": [
+ "> Bel is an ancient Semitic god, originating from the Middle East. He is often associated with the sun and is sometimes referred to as the \"Lord of Heaven\". Bel is also known as the god of fertility, abundance, and prosperity. He is often depicted as a bull or a man with a bull\\'s head. In some cultures, Bel is seen as a creator god, responsible for the creation of the universe. He is also associated with the underworld and is sometimes seen as a god of death. Bel is also associated with justice and is often seen as a protector of the innocent. Bel is an important figure in many religions, including Judaism, Christianity, and Islam."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b8df34d",
+ "metadata": {},
+ "source": [
+ "## Failure case 2: HyDE may bias open-ended queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3942dc8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_str = \"What would the author say about art vs. engineering?\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "decc9107",
+ "metadata": {},
+ "source": [
+ "#### Querying without transformation yields a reasonable answer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "168fab08",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c1e4939b",
+ "metadata": {},
+ "source": [
+ "> The author would likely say that art and engineering are two different disciplines that require different skills and approaches. Art is more focused on expression and creativity, while engineering is more focused on problem-solving and technical knowledge. The author also suggests that art school does not always provide the same level of rigor as engineering school, and that painting students are often encouraged to develop a signature style rather than learn the fundamentals of painting. Furthermore, the author would likely point out that engineering can provide more financial stability than art, as evidenced by the author's own experience of needing seed funding to live on while launching a company."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d01a0ed4",
+ "metadata": {},
+ "source": [
+ "#### Querying with `HyDEQueryTransform` results in a more biased output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0229cf6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = hyde_query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16c8f053",
+ "metadata": {},
+ "source": [
+ "> The author would likely say that art is a more lasting and independent form of work than engineering. They mention that software written today will be obsolete in a couple decades, and that systems work does not last. In contrast, they note that paintings can last hundreds of years and that it is possible to make a living as an artist. They also mention that as an artist, you can be truly independent and don't need to have a boss or research funding. Furthermore, they note that art can be a source of income for people who may not have access to traditional forms of employment, such as the model in the example who was able to make a living from modelling and making fakes for a local antique dealer."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb b/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb
index 30eaa54cf4..de2ca32de1 100644
--- a/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb
+++ b/docs/examples/query_transformations/SimpleIndexDemo-multistep.ipynb
@@ -1,277 +1,272 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
- "metadata": {},
- "source": [
- "# Multi-Step Query Engine\n",
- "\n",
- "We have a multi-step query engine that's able to decompose a complex query into sequential subquestions. This\n",
- "guide walks you through how to set it up!"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleDirectoryReader,\n",
- " LLMPredictor,\n",
- " ServiceContext\n",
- ")\n",
- "from llama_index.llms import OpenAI\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c48da73f-aadb-480c-8db1-99c915b7cc1c",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# LLM Predictor (gpt-3)\n",
- "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
- "service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3)\n",
- "\n",
- "# LLMPredictor (gpt-4)\n",
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "index = VectorStoreIndex.from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "95d989ba-0c1d-43b6-a1d3-0ea7135f43a6",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.query.query_transform.base import StepDecomposeQueryTransform\n",
- "# gpt-4\n",
- "step_decompose_transform = StepDecomposeQueryTransform(\n",
- " llm_predictor_gpt4, verbose=True\n",
- ")\n",
- "\n",
- "# gpt-3\n",
- "step_decompose_transform_gpt3 = StepDecomposeQueryTransform(\n",
- " llm_predictor_gpt3, verbose=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2a124db0-e2d7-4566-bcec-1d41cf669ff4",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "index_summary = \"Used to answer questions about the author\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "85466fdf-93f3-4cb1-a5f9-0056a8245a6f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "from llama_index.query_engine.multistep_query_engine import MultiStepQueryEngine\n",
- "\n",
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt4\n",
- ")\n",
- "query_engine = MultiStepQueryEngine(\n",
- " query_engine=query_engine,\n",
- " query_transform=step_decompose_transform,\n",
- " index_summary=index_summary,\n",
- ")\n",
- "response_gpt4 = query_engine.query(\n",
- " \"Who was in the first batch of the accelerator program the author started?\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bdda1b2c-ae46-47cf-91d7-3153e8d0473b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response_gpt4}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1c9670bd-729d-478b-a77c-c6e13c282456",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "sub_qa = response_gpt4.metadata[\"sub_qa\"]\n",
- "tuples = [(t[0], t[1].response) for t in sub_qa]\n",
- "print(tuples)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ec88df57",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "response_gpt4 = query_engine.query(\n",
- " \"In which city did the author found his first company, Viaweb?\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "653508f1-b2b0-479a-85b3-113cda507231",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(response_gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9fa93cdb-7007-4664-853a-5c81c6c17560",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt3\n",
- ")\n",
- "query_engine = MultiStepQueryEngine(\n",
- " query_engine=query_engine,\n",
- " query_transform=step_decompose_transform_gpt3,\n",
- " index_summary=index_summary,\n",
- ")\n",
- "\n",
- "response_gpt3 = query_engine.query(\n",
- " \"In which city did the author found his first company, Viaweb?\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05899fcf-7a04-4d21-9e6d-04983755d175",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(response_gpt3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7a43c659-ffd0-40df-b52b-032e6647cf9f",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
+ "metadata": {},
+ "source": [
+ "# Multi-Step Query Engine\n",
+ "\n",
+ "We have a multi-step query engine that's able to decompose a complex query into sequential subquestions. This\n",
+ "guide walks you through how to set it up!"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ ")\n",
+ "from llama_index.llms import OpenAI\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c48da73f-aadb-480c-8db1-99c915b7cc1c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# LLM Predictor (gpt-3)\n",
+ "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
+ "service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3)\n",
+ "\n",
+ "# LLMPredictor (gpt-4)\n",
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context_gpt4 = ServiceContext.from_defaults(llm=gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "index = VectorStoreIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95d989ba-0c1d-43b6-a1d3-0ea7135f43a6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.query.query_transform.base import StepDecomposeQueryTransform\n",
+ "\n",
+ "# gpt-4\n",
+ "step_decompose_transform = StepDecomposeQueryTransform(llm_predictor_gpt4, verbose=True)\n",
+ "\n",
+ "# gpt-3\n",
+ "step_decompose_transform_gpt3 = StepDecomposeQueryTransform(\n",
+ " llm_predictor_gpt3, verbose=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2a124db0-e2d7-4566-bcec-1d41cf669ff4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "index_summary = \"Used to answer questions about the author\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "85466fdf-93f3-4cb1-a5f9-0056a8245a6f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "from llama_index.query_engine.multistep_query_engine import MultiStepQueryEngine\n",
+ "\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt4)\n",
+ "query_engine = MultiStepQueryEngine(\n",
+ " query_engine=query_engine,\n",
+ " query_transform=step_decompose_transform,\n",
+ " index_summary=index_summary,\n",
+ ")\n",
+ "response_gpt4 = query_engine.query(\n",
+ " \"Who was in the first batch of the accelerator program the author started?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bdda1b2c-ae46-47cf-91d7-3153e8d0473b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response_gpt4}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1c9670bd-729d-478b-a77c-c6e13c282456",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "sub_qa = response_gpt4.metadata[\"sub_qa\"]\n",
+ "tuples = [(t[0], t[1].response) for t in sub_qa]\n",
+ "print(tuples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ec88df57",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "response_gpt4 = query_engine.query(\n",
+ " \"In which city did the author found his first company, Viaweb?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "653508f1-b2b0-479a-85b3-113cda507231",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(response_gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9fa93cdb-7007-4664-853a-5c81c6c17560",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = index.as_query_engine(service_context=service_context_gpt3)\n",
+ "query_engine = MultiStepQueryEngine(\n",
+ " query_engine=query_engine,\n",
+ " query_transform=step_decompose_transform_gpt3,\n",
+ " index_summary=index_summary,\n",
+ ")\n",
+ "\n",
+ "response_gpt3 = query_engine.query(\n",
+ " \"In which city did the author found his first company, Viaweb?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05899fcf-7a04-4d21-9e6d-04983755d175",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(response_gpt3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a43c659-ffd0-40df-b52b-032e6647cf9f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/response_builder/refine.ipynb b/docs/examples/response_builder/refine.ipynb
index 2d0bf6466f..6f72bc567f 100644
--- a/docs/examples/response_builder/refine.ipynb
+++ b/docs/examples/response_builder/refine.ipynb
@@ -46,7 +46,9 @@
},
"outputs": [],
"source": [
- "reader = SimpleDirectoryReader(input_files=['../data/paul_graham/paul_graham_essay.txt'])"
+ "reader = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/paul_graham/paul_graham_essay.txt\"]\n",
+ ")"
]
},
{
@@ -91,7 +93,8 @@
"outputs": [],
"source": [
"from llama_index.llms import OpenAI\n",
- "llm = OpenAI(model='gpt-3.5-turbo')"
+ "\n",
+ "llm = OpenAI(model=\"gpt-3.5-turbo\")"
]
},
{
@@ -104,6 +107,7 @@
"outputs": [],
"source": [
"from llama_index import ServiceContext\n",
+ "\n",
"service_context = ServiceContext.from_defaults(llm=llm)"
]
},
@@ -117,6 +121,7 @@
"outputs": [],
"source": [
"from llama_index.indices.response import Refine\n",
+ "\n",
"summarizer = Refine(service_context=service_context, verbose=True)"
]
},
@@ -143,7 +148,7 @@
}
],
"source": [
- "response = summarizer.get_response('who is Paul Graham?', [text])"
+ "response = summarizer.get_response(\"who is Paul Graham?\", [text])"
]
},
{
diff --git a/docs/examples/response_builder/tree_summarize.ipynb b/docs/examples/response_builder/tree_summarize.ipynb
index 9ed12d5a09..b408ed468e 100644
--- a/docs/examples/response_builder/tree_summarize.ipynb
+++ b/docs/examples/response_builder/tree_summarize.ipynb
@@ -37,7 +37,9 @@
},
"outputs": [],
"source": [
- "reader = SimpleDirectoryReader(input_files=['../data/paul_graham/paul_graham_essay.txt'])"
+ "reader = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/paul_graham/paul_graham_essay.txt\"]\n",
+ ")"
]
},
{
@@ -114,7 +116,7 @@
}
],
"source": [
- "response = await summarizer.aget_response('who is Paul Graham?', [text])"
+ "response = await summarizer.aget_response(\"who is Paul Graham?\", [text])"
]
},
{
diff --git a/docs/examples/tools/OnDemandLoaderTool.ipynb b/docs/examples/tools/OnDemandLoaderTool.ipynb
index 63f267b5d0..d7561f9a56 100644
--- a/docs/examples/tools/OnDemandLoaderTool.ipynb
+++ b/docs/examples/tools/OnDemandLoaderTool.ipynb
@@ -134,7 +134,12 @@
},
"outputs": [],
"source": [
- "lc_tool.run(tool_input={\"pages\": [\"Berlin\"], \"query_str\": \"What's the arts and culture scene in Berlin?\"})"
+ "lc_tool.run(\n",
+ " tool_input={\n",
+ " \"pages\": [\"Berlin\"],\n",
+ " \"query_str\": \"What's the arts and culture scene in Berlin?\",\n",
+ " }\n",
+ ")"
]
},
{
@@ -188,7 +193,7 @@
" [lc_tool],\n",
" llm=llm,\n",
" agent=\"structured-chat-zero-shot-react-description\",\n",
- " verbose=True\n",
+ " verbose=True,\n",
")"
]
},
diff --git a/docs/examples/usecases/10k_graph_agent.ipynb b/docs/examples/usecases/10k_graph_agent.ipynb
index 474ede20bd..6f68378704 100644
--- a/docs/examples/usecases/10k_graph_agent.ipynb
+++ b/docs/examples/usecases/10k_graph_agent.ipynb
@@ -22,7 +22,12 @@
},
"outputs": [],
"source": [
- "from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, VectorStoreIndex\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " VectorStoreIndex,\n",
+ ")\n",
"from llama_index.response.pprint_utils import pprint_response\n",
"from langchain import OpenAI"
]
@@ -36,7 +41,11 @@
},
"outputs": [],
"source": [
- "llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name=\"text-davinci-003\", max_tokens=-1, streaming=True))\n",
+ "llm_predictor = LLMPredictor(\n",
+ " llm=OpenAI(\n",
+ " temperature=0, model_name=\"text-davinci-003\", max_tokens=-1, streaming=True\n",
+ " )\n",
+ ")\n",
"service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)"
]
},
@@ -96,7 +105,9 @@
},
"outputs": [],
"source": [
- "response = lyft_engine.query('what is the revenue growth in the last year, show me the reference page number')"
+ "response = lyft_engine.query(\n",
+ " \"what is the revenue growth in the last year, show me the reference page number\"\n",
+ ")"
]
},
{
@@ -206,7 +217,9 @@
},
"outputs": [],
"source": [
- "response = uber_engine.query(\"what is the revenue growth in the last year, show me the reference page number\")"
+ "response = uber_engine.query(\n",
+ " \"what is the revenue growth in the last year, show me the reference page number\"\n",
+ ")"
]
},
{
@@ -297,7 +310,7 @@
" index_summaries=[\n",
" \"Provides information about Lyft financials for year 2021\",\n",
" \"Provides information about Uber financials for year 2021\",\n",
- " ]\n",
+ " ],\n",
")"
]
},
@@ -323,7 +336,7 @@
" query_engine = TransformQueryEngine(\n",
" query_engine,\n",
" query_transform=decompose_transform,\n",
- " transform_extra_info={'index_summary': index.index_struct.summary},\n",
+ " transform_extra_info={\"index_summary\": index.index_struct.summary},\n",
" )\n",
" custom_query_engines[index.index_id] = query_engine\n",
"\n",
@@ -343,9 +356,7 @@
"outputs": [],
"source": [
"# define graph\n",
- "g_engine = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines\n",
- ")"
+ "g_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)"
]
},
{
@@ -373,7 +384,7 @@
}
],
"source": [
- "response = g_engine.query('Compare Uber and Lyft revenue growth in 2021.')"
+ "response = g_engine.query(\"Compare Uber and Lyft revenue growth in 2021.\")"
]
},
{
@@ -416,7 +427,7 @@
},
"outputs": [],
"source": [
- "llm = OpenAI(temperature=0., max_tokens=-1)"
+ "llm = OpenAI(temperature=0.0, max_tokens=-1)"
]
},
{
@@ -428,20 +439,24 @@
},
"outputs": [],
"source": [
- "from llama_index.langchain_helpers.agents import IndexToolConfig, LlamaIndexTool, LlamaToolkit\n",
+ "from llama_index.langchain_helpers.agents import (\n",
+ " IndexToolConfig,\n",
+ " LlamaIndexTool,\n",
+ " LlamaToolkit,\n",
+ ")\n",
"\n",
"uber_config = IndexToolConfig(\n",
- " query_engine=uber_engine, \n",
+ " query_engine=uber_engine,\n",
" name=f\"Uber 10K 2021\",\n",
" description=f\"Provides information about Lyft financials for year 2021\",\n",
- " tool_kwargs={\"return_direct\": False}\n",
+ " tool_kwargs={\"return_direct\": False},\n",
")\n",
"\n",
"lyft_config = IndexToolConfig(\n",
" query_engine=lyft_engine,\n",
" name=f\"Lyft 10K 2021\",\n",
" description=f\"Provides information about Uber financials for year 2021\",\n",
- " tool_kwargs={\"return_direct\": False}\n",
+ " tool_kwargs={\"return_direct\": False},\n",
")"
]
},
@@ -470,12 +485,7 @@
"source": [
"from llama_index.langchain_helpers.agents import create_llama_agent\n",
"\n",
- "agent_chain = create_llama_agent(\n",
- " toolkit,\n",
- " llm,\n",
- " memory=memory,\n",
- " verbose=True\n",
- ")"
+ "agent_chain = create_llama_agent(toolkit, llm, memory=memory, verbose=True)"
]
},
{
@@ -568,7 +578,9 @@
}
],
"source": [
- "agent_chain.run(input=\"Compare and contrast the customer segments and geographies that grew the fastest\")"
+ "agent_chain.run(\n",
+ " input=\"Compare and contrast the customer segments and geographies that grew the fastest\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/usecases/10k_sub_question.ipynb b/docs/examples/usecases/10k_sub_question.ipynb
index 800b17dca4..ef8bb69f2c 100644
--- a/docs/examples/usecases/10k_sub_question.ipynb
+++ b/docs/examples/usecases/10k_sub_question.ipynb
@@ -22,6 +22,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -175,12 +176,18 @@
"source": [
"query_engine_tools = [\n",
" QueryEngineTool(\n",
- " query_engine=lyft_engine, \n",
- " metadata=ToolMetadata(name='lyft_10k', description='Provides information about Lyft financials for year 2021')\n",
+ " query_engine=lyft_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"lyft_10k\",\n",
+ " description=\"Provides information about Lyft financials for year 2021\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=uber_engine, \n",
- " metadata=ToolMetadata(name='uber_10k', description='Provides information about Uber financials for year 2021')\n",
+ " query_engine=uber_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"uber_10k\",\n",
+ " description=\"Provides information about Uber financials for year 2021\",\n",
+ " ),\n",
" ),\n",
"]\n",
"\n",
@@ -229,7 +236,9 @@
}
],
"source": [
- "response = s_engine.query('Compare and contrast the customer segments and geographies that grew the fastest')"
+ "response = s_engine.query(\n",
+ " \"Compare and contrast the customer segments and geographies that grew the fastest\"\n",
+ ")"
]
},
{
@@ -281,7 +290,7 @@
}
],
"source": [
- "response = s_engine.query('Compare revenue growth of Uber and Lyft from 2020 to 2021')"
+ "response = s_engine.query(\"Compare revenue growth of Uber and Lyft from 2020 to 2021\")"
]
},
{
diff --git a/docs/examples/usecases/10q_fn_agent-react-compare.ipynb b/docs/examples/usecases/10q_fn_agent-react-compare.ipynb
index 2ded3cbb18..f193edb1a4 100644
--- a/docs/examples/usecases/10q_fn_agent-react-compare.ipynb
+++ b/docs/examples/usecases/10q_fn_agent-react-compare.ipynb
@@ -49,7 +49,12 @@
}
],
"source": [
- "from llama_index import SimpleDirectoryReader, LLMPredictor, ServiceContext, GPTVectorStoreIndex\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " GPTVectorStoreIndex,\n",
+ ")\n",
"from llama_index.response.pprint_utils import pprint_response\n",
"from llama_index.llms import OpenAI"
]
@@ -90,9 +95,15 @@
},
"outputs": [],
"source": [
- "march_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]).load_data()\n",
- "june_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]).load_data()\n",
- "sept_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]).load_data()"
+ "march_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]\n",
+ ").load_data()\n",
+ "june_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]\n",
+ ").load_data()\n",
+ "sept_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]\n",
+ ").load_data()"
]
},
{
@@ -127,9 +138,15 @@
},
"outputs": [],
"source": [
- "march_engine = march_index.as_query_engine(similarity_top_k=3, service_context=service_context)\n",
- "june_engine = june_index.as_query_engine(similarity_top_k=3, service_context=service_context)\n",
- "sept_engine = sept_index.as_query_engine(similarity_top_k=3, service_context=service_context)"
+ "march_engine = march_index.as_query_engine(\n",
+ " similarity_top_k=3, service_context=service_context\n",
+ ")\n",
+ "june_engine = june_index.as_query_engine(\n",
+ " similarity_top_k=3, service_context=service_context\n",
+ ")\n",
+ "sept_engine = sept_index.as_query_engine(\n",
+ " similarity_top_k=3, service_context=service_context\n",
+ ")"
]
},
{
@@ -169,25 +186,29 @@
},
"outputs": [],
"source": [
- "from llama_index.langchain_helpers.agents import IndexToolConfig, LlamaIndexTool, LlamaToolkit\n",
+ "from llama_index.langchain_helpers.agents import (\n",
+ " IndexToolConfig,\n",
+ " LlamaIndexTool,\n",
+ " LlamaToolkit,\n",
+ ")\n",
"\n",
"uber_config_sept = IndexToolConfig(\n",
- " query_engine=sept_engine, \n",
+ " query_engine=sept_engine,\n",
" name=f\"Uber 10Q September 2022\",\n",
" description=f\"Provides information about Uber quarterly financials ending September 2022\",\n",
- " tool_kwargs={\"return_direct\": False}\n",
+ " tool_kwargs={\"return_direct\": False},\n",
")\n",
"uber_config_june = IndexToolConfig(\n",
- " query_engine=june_engine, \n",
+ " query_engine=june_engine,\n",
" name=f\"Uber 10Q June 2022\",\n",
" description=f\"Provides information about Uber quarterly financials ending June 2022\",\n",
- " tool_kwargs={\"return_direct\": False}\n",
+ " tool_kwargs={\"return_direct\": False},\n",
")\n",
"uber_config_march = IndexToolConfig(\n",
- " query_engine=march_engine, \n",
+ " query_engine=march_engine,\n",
" name=f\"Uber 10Q March 2022\",\n",
" description=f\"Provides information about Uber quarterly financials ending March 2022\",\n",
- " tool_kwargs={\"return_direct\": False}\n",
+ " tool_kwargs={\"return_direct\": False},\n",
")"
]
},
@@ -221,14 +242,10 @@
" toolkit,\n",
" llm,\n",
" # memory=memory,\n",
- " verbose=True\n",
+ " verbose=True,\n",
")\n",
"\n",
- "agent_chain_gpt4 = create_llama_agent(\n",
- " toolkit,\n",
- " llm_gpt4,\n",
- " verbose=True\n",
- ")"
+ "agent_chain_gpt4 = create_llama_agent(toolkit, llm_gpt4, verbose=True)"
]
},
{
@@ -270,7 +287,7 @@
}
],
"source": [
- "# vague answer? \n",
+ "# vague answer?\n",
"agent_chain.run(input=\"Analyze Uber revenue growth over the last few quarters\")"
]
},
@@ -387,7 +404,7 @@
}
],
"source": [
- "# only picks september and june \n",
+ "# only picks september and june\n",
"agent_chain.run(input=\"Analyze changes in risk factors for Uber\")"
]
},
@@ -517,7 +534,7 @@
}
],
"source": [
- "# Prompt variation 1: \n",
+ "# Prompt variation 1:\n",
"agent_chain.run(input=\"Analyze Uber revenue growth and risk factors over time\")"
]
},
@@ -677,8 +694,10 @@
}
],
"source": [
- "# only picks september \n",
- "agent_chain.run(input=\"What have been the biggest changes in the macro environment in the past few quarters?\")"
+ "# only picks september\n",
+ "agent_chain.run(\n",
+ " input=\"What have been the biggest changes in the macro environment in the past few quarters?\"\n",
+ ")"
]
},
{
@@ -866,8 +885,8 @@
" index_summaries=[\n",
" \"Provides information about Uber quarterly financials ending March 2022\",\n",
" \"Provides information about Uber quarterly financials ending June 2022\",\n",
- " \"Provides information about Uber quarterly financials ending September 2022\"\n",
- " ]\n",
+ " \"Provides information about Uber quarterly financials ending September 2022\",\n",
+ " ],\n",
")"
]
},
@@ -893,7 +912,7 @@
" query_engine = TransformQueryEngine(\n",
" query_engine,\n",
" query_transform=decompose_transform,\n",
- " transform_extra_info={'index_summary': index.index_struct.summary},\n",
+ " transform_extra_info={\"index_summary\": index.index_struct.summary},\n",
" )\n",
" custom_query_engines[index.index_id] = query_engine\n",
"\n",
@@ -913,9 +932,7 @@
"outputs": [],
"source": [
"# define graph\n",
- "g_engine = graph.as_query_engine(\n",
- " custom_query_engines=custom_query_engines\n",
- ")"
+ "g_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)"
]
},
{
@@ -1000,7 +1017,7 @@
],
"source": [
"# test querying the graph\n",
- "response = g_engine.query('Analyze Uber revenue growth over the last few quarters')\n",
+ "response = g_engine.query(\"Analyze Uber revenue growth over the last few quarters\")\n",
"print(str(response))"
]
},
@@ -1111,7 +1128,7 @@
" query_tool_march,\n",
" ],\n",
" max_function_calls=10,\n",
- " verbose=True\n",
+ " verbose=True,\n",
")"
]
},
@@ -1314,7 +1331,9 @@
}
],
"source": [
- "response = query_engine.query(\"Analyze Uber revenue growth and risk factors over quarters\")"
+ "response = query_engine.query(\n",
+ " \"Analyze Uber revenue growth and risk factors over quarters\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/usecases/10q_sub_question.ipynb b/docs/examples/usecases/10q_sub_question.ipynb
index a7ee2458cc..c23fc95d37 100644
--- a/docs/examples/usecases/10q_sub_question.ipynb
+++ b/docs/examples/usecases/10q_sub_question.ipynb
@@ -22,6 +22,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -86,9 +87,15 @@
},
"outputs": [],
"source": [
- "march_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]).load_data()\n",
- "june_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]).load_data()\n",
- "sept_2022 = SimpleDirectoryReader(input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]).load_data()"
+ "march_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_march_2022.pdf\"]\n",
+ ").load_data()\n",
+ "june_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_june_2022.pdf\"]\n",
+ ").load_data()\n",
+ "sept_2022 = SimpleDirectoryReader(\n",
+ " input_files=[\"../data/10q/uber_10q_sept_2022.pdf\"]\n",
+ ").load_data()"
]
},
{
@@ -150,16 +157,25 @@
"source": [
"query_engine_tools = [\n",
" QueryEngineTool(\n",
- " query_engine=sept_engine, \n",
- " metadata=ToolMetadata(name='sept_22', description='Provides information about Uber quarterly financials ending September 2022')\n",
+ " query_engine=sept_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"sept_22\",\n",
+ " description=\"Provides information about Uber quarterly financials ending September 2022\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=june_engine, \n",
- " metadata=ToolMetadata(name='june_22', description='Provides information about Uber quarterly financials ending June 2022')\n",
+ " query_engine=june_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"june_22\",\n",
+ " description=\"Provides information about Uber quarterly financials ending June 2022\",\n",
+ " ),\n",
" ),\n",
" QueryEngineTool(\n",
- " query_engine=march_engine, \n",
- " metadata=ToolMetadata(name='march_22', description='Provides information about Uber quarterly financials ending March 2022')\n",
+ " query_engine=march_engine,\n",
+ " metadata=ToolMetadata(\n",
+ " name=\"march_22\",\n",
+ " description=\"Provides information about Uber quarterly financials ending March 2022\",\n",
+ " ),\n",
" ),\n",
"]"
]
@@ -213,7 +229,9 @@
}
],
"source": [
- "response = s_engine.query('Analyze Uber revenue growth over the latest two quarter filings')"
+ "response = s_engine.query(\n",
+ " \"Analyze Uber revenue growth over the latest two quarter filings\"\n",
+ ")"
]
},
{
@@ -264,7 +282,7 @@
}
],
"source": [
- "response = s_engine.query('Analyze change in macro environment over the 3 quarters')"
+ "response = s_engine.query(\"Analyze change in macro environment over the 3 quarters\")"
]
},
{
@@ -307,7 +325,7 @@
}
],
"source": [
- "response = s_engine.query('How much cash did Uber have in sept 2022')"
+ "response = s_engine.query(\"How much cash did Uber have in sept 2022\")"
]
},
{
diff --git a/docs/examples/usecases/City_Analysis-Decompose-KeywordTable.ipynb b/docs/examples/usecases/City_Analysis-Decompose-KeywordTable.ipynb
index 92e407d72b..df495571df 100644
--- a/docs/examples/usecases/City_Analysis-Decompose-KeywordTable.ipynb
+++ b/docs/examples/usecases/City_Analysis-Decompose-KeywordTable.ipynb
@@ -1,2339 +1,2346 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "metadata": {
- "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
- "tags": []
- },
- "source": [
- "# Test Complex Queries over Multiple Documents (with and without Query Decomposition)\n",
- "\n",
- "Query Decomposition: The ability to decompose a complex query into a simpler query given the content of the index.\n",
- "\n",
- "Use OpenAI as the LLM model and embedding model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "fa0e62b6",
- "metadata": {
- "id": "fa0e62b6",
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "# Uncomment if you want to temporarily disable logger\n",
- "logger = logging.getLogger()\n",
- "logger.disabled = True"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "metadata": {
- "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index import (\n",
- " VectorStoreIndex, \n",
- " SimpleKeywordTableIndex, \n",
- " ListIndex, \n",
- " SimpleDirectoryReader,\n",
- " LLMPredictor,\n",
- " ServiceContext\n",
- ")\n",
- "import requests"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
- "metadata": {
- "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
- },
- "source": [
- "#### Load Datasets\n",
- "\n",
- "Load Wikipedia pages as well as Paul Graham's \"What I Worked On\" essay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "fc4692a1",
- "metadata": {
- "id": "fc4692a1",
- "tags": []
- },
- "outputs": [],
- "source": [
- "wiki_titles = [\"Toronto\", \"Seattle\", \"San Francisco\", \"Chicago\", \"Boston\", \"Washington, D.C.\", \"Cambridge, Massachusetts\", \"Houston\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "metadata": {
- "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from pathlib import Path\n",
- "import requests\n",
- "\n",
- "data_path = Path('data_wiki')\n",
- "\n",
- "for title in wiki_titles:\n",
- " response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- " ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
- "\n",
- " if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "metadata": {
- "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# Load all wiki documents\n",
- "city_docs = {}\n",
- "all_docs = []\n",
- "for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[data_path / f\"{wiki_title}.txt\"]).load_data()\n",
- " all_docs.extend(city_docs[wiki_title])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "39695a27-4bb1-41eb-b878-4f7c7a3fce31",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define service context\n",
- "service_context = ServiceContext.from_defaults(\n",
- " chunk_size=512, \n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f1782198-c0de-4679-8951-1297c21b8639",
- "metadata": {
- "id": "f1782198-c0de-4679-8951-1297c21b8639"
- },
- "source": [
- "### Building the document indices\n",
- "Build a separate vector index for each wiki pages about cities.\n",
- "\n",
- "We also build a \"global\" vector index, which ingest documents for *all* cities. \n",
- "\n",
- "This allows us to test different types of data structures!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 183,
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- },
- "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
- "outputId": "5721e863-d460-4f5c-9e36-5a586180b669",
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Toronto\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 27294 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Seattle\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 22263 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for San Francisco\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 30887 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Chicago\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 34336 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Boston\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 24512 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Washington, D.C.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 28480 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Cambridge, Massachusetts\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17036 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building index for Houston\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 28795 tokens\n"
- ]
- }
- ],
- "source": [
- "# Build index for each city document\n",
- "city_indices = {}\n",
- "index_summaries = {}\n",
- "for wiki_title in wiki_titles:\n",
- " print(f\"Building index for {wiki_title}\")\n",
- " city_indices[wiki_title] = VectorStoreIndex.from_documents(city_docs[wiki_title], service_context=service_context)\n",
- " # set summary text for city\n",
- " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "36c99bc1-c712-489d-a9da-4a9be76d710e",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 213603 tokens\n"
- ]
- }
- ],
- "source": [
- "# also setup a global vector index \n",
- "global_index = VectorStoreIndex.from_documents(all_docs, service_context=service_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "metadata": {
- "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
- "tags": []
- },
- "source": [
- "### Creating the right structure to run compare/contrast queries\n",
- "\n",
- "Our key goal in this notebook is to run compare/contrast queries between different cities.\n",
- "\n",
- "We currently have a separate vector index for every city document. We want to setup a \"graph\" structure in order to route the query \n",
- "in the right manner in order to retrieve the relevant text sections for each city. \n",
- "\n",
- "We compose a keyword table index on top of all the vector indices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "metadata": {
- "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "f975514f-fddd-4737-91de-97bc61394ea9",
- "outputId": "fc875b0e-c8bf-439b-c794-fcae25954cfb",
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "graph = ComposableGraph.from_indices(\n",
- " SimpleKeywordTableIndex,\n",
- " [index for _, index in city_indices.items()], \n",
- " [summary for _, summary in index_summaries.items()],\n",
- " max_keywords_per_chunk=50\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b4c36f69-596b-4974-afa2-09cc652c1111",
- "metadata": {},
- "source": [
- "### Define Query Transformation + Query Configs\n",
- "\n",
- "We also define a \"query decomposition\" transform. Since we have a graph structure over multiple indexes, query decomposition\n",
- "allows us to break a complex question into a simpler one over a given index.\n",
- "\n",
- "This works well in comparing/contrasting different cities because it allows us to ask questions specific to each city."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "accd31e4-0ae7-4660-833c-5ae23037fd14",
- "metadata": {},
- "source": [
- "**Query Transform**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "82432236-fa93-4269-b695-d6d2131edb41",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
- "decompose_transform = DecomposeQueryTransform(verbose=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "d9199887",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The autoreload extension is already loaded. To reload it, use:\n",
- " %reload_ext autoreload\n"
- ]
- }
- ],
- "source": [
- "%load_ext autoreload\n",
- "%autoreload 2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "2ab2fbf1",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
- "\n",
- "custom_query_engines = {}\n",
- "for index in city_indices.values():\n",
- " query_engine = index.as_query_engine(service_context=service_context)\n",
- " query_engine = TransformQueryEngine(\n",
- " query_engine,\n",
- " query_transform=decompose_transform,\n",
- " transform_extra_info={'index_summary': index.index_struct.summary},\n",
- " )\n",
- " custom_query_engines[index.index_id] = query_engine\n",
- "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
- " retriever_mode='simple',\n",
- " response_mode='tree_summarize',\n",
- " service_context=service_context,\n",
- ")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "7d8e2a20-44a2-4d9f-af0a-1a91e3afbcf2",
- "metadata": {},
- "source": [
- "### Let's Run Some Queries! \n",
- "\n",
- "We run queries over the graphs and analyze the results.\n",
- "\n",
- "We also compare results against the baseline global vector index. In the majority of cases the global vector index provides insufficient answers."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43",
- "metadata": {
- "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43"
- },
- "source": [
- "**Complex Query 1**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "5da241f2-e8e6-459e-8492-60724faeb173",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# with query decomposition in subindices\n",
- "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
- "query_str = (\n",
- " \"Compare and contrast the demographics in Seattle, Houston, and Toronto. \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "984fcf86-8b10-40aa-ac7b-85518537b433",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
- "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['demographics', 'seattle', 'toronto', 'compare', 'contrast', 'houston']\n",
- "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['seattle', 'toronto', 'houston']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the population of Seattle?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1375 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1375 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the population of Toronto?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1303 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1303 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
- "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the population of Houston?\n",
- "\u001b[0m"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1401 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1401 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1681 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1681 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "13108dca-8ce6-4485-a018-dcab2514868d",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "Seattle, Houston, and Toronto all have diverse populations, with immigrants making up a significant portion of the population in each city. However, the countries of origin for the immigrants vary between the cities. In Seattle, the top countries of origin for immigrants are Mexico, India, China, Philippines, and Vietnam. In Houston, the top countries of origin for immigrants are Mexico, India, El Salvador, Honduras, and Guatemala. In Toronto, the top countries of origin for immigrants are Philippines, China, India, Sri Lanka, and Jamaica. Additionally, the median age of the population varies between the cities. In Seattle, the median age is 37.2, in Houston it is 33.4, and in Toronto it is 39.2. Furthermore, the gender population also varies between the cities. In Seattle, the gender population is 48.2% male and 51.8% female, in Houston it is 48.3% male and 51.7% female, and in Toronto it is 48% male and 52% female. In 2016, the three most commonly reported ethnic origins overall were Chinese (332,830 or 12.5 per cent), South Asian (323,810 or 11.9 per cent), and Black (308,345 or 11.3\n"
- ]
- }
- ],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "id": "59acffe2-c653-4ea7-a381-b55e67d8a35c",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 14 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3549 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3549 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = global_index.as_query_engine(\n",
- " similarity_top_k=3, \n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "id": "e88666ad-a934-4f0a-b377-fe92d2361e8e",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "Seattle is a major U.S. city located in the Pacific Northwest region of the United States. It has a population of over 730,000 people and is known for its high percentage of college and university graduates. Of the city's population over the age of 25, 53.8% hold a bachelor's degree or higher, and 91.9% have a high school diploma or equivalent. Seattle is also home to the University of Washington, as well as a number of smaller private universities such as Seattle Pacific University, a Jesuit Catholic institution, and Seattle University, a Free Methodist institution. The Seattle Colleges District operates three colleges: North Seattle College, Seattle Central College, and South Seattle College. According to a 2006 study by UCLA, 12.9% of city residents polled identified as gay, lesbian, or bisexual. This was the second-highest proportion of any major U.S. city, behind San Francisco. Seattle's economy is driven by a mix of older industrial companies and \"new economy\" internet and technology companies, as well as service, design, and clean technology companies. It is estimated that King County has 8,000 homeless people on any given night, and many of those live in Seattle. In recent years, the city has experienced steady population\n"
- ]
- }
- ],
- "source": [
- "# NOTE: the global vector index seems to provide the right results....\n",
- "# BUT see below! \n",
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "id": "0435679a-d3ea-47ed-995e-e981ed65294b",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Tiffany Washington, and Kendee Yamaguchi.\n",
- "\n",
- "\n",
- "== Education ==\n",
- "\n",
- "Of the city's population over the age of 25, 53.8% (vs. a national average of 27.4%) hold a bachelor's degree or higher, and 91.9% (vs. 84.5% nationally) have a high school diploma or equivalent. A 2008 United States Census Bureau survey showed that Seattle had the highest percentage of college and university graduates of any major U.S. city. The city was listed as the most literate of the country's 69 largest cities in 2005 and 2006, the second most literate in 2007 and the most literate in 2008 in studies conducted by Central Connecticut State University.Seattle Public Schools is the school district for the vast majority of the city. That school district desegregated without a court order but continue to struggle to achieve racial balance in a somewhat ethnically divided city (the south part of town having more ethnic minorities than the north). In 2007, Seattle's racial tie-breaking system was struck down by the United States Supreme Court, but the ruling left the door open for desegregation formulae based on other indicators (e.g., income or socioeconomic class). A very small portion of the city is within the Highline School District.The public school system is supplemented by a moderate number of private schools: Five of the private high schools are Catholic, one is Lutheran, and six are secular.Seattle is home to the University of Washington, as well as the institution's professional and continuing education unit, the University of Washington Educational Outreach. The 2017 U.S. News & World Report ranked the University of Washington at No. 11 in the world. The UW receives more federal research and development funding than any public institution. Over the last 10 years, it has also produced more Peace Corps volunteers than any other U.S. university. Seattle also has a number of smaller private universities including Seattle University and Seattle Pacific University, the former a Jesuit Catholic institution, the latter a Free Methodist institution. The Seattle Colleges District operates three colleges: North Seattle College, Seattle Central College, and South Seattle College. Universities aimed at the\n",
- "bisexual, and transgender community. According to a 2006 study by UCLA, 12.9% of city residents polled identified as gay, lesbian, or bisexual. This was the second-highest proportion of any major U.S. city, behind San Francisco. Greater Seattle also ranked second among major U.S. metropolitan areas, with 6.5% of the population identifying as gay, lesbian, or bisexual. According to 2012 estimates from the United States Census Bureau, Seattle has the highest percentage of same-sex households in the United States, at 2.6 percent, surpassing San Francisco (2.5 percent). The Capitol Hill district has historically been the center of LGBT culture in Seattle.\n",
- "\n",
- "\n",
- "== Economy ==\n",
- "\n",
- "Seattle's economy is driven by a mix of older industrial companies and \"new economy\" internet and technology companies, as well as service, design, and clean technology companies. The city's gross metropolitan product (GMP) was $231 billion in 2010, making it the 11th largest metropolitan economy in the United States. The Port of Seattle, which also operates Seattle–Tacoma International Airport, is a major gateway for trade with Asia and cruises to Alaska. It also is the 8th largest port in the United States when measured by container capacity. Its maritime cargo operations merged with the Port of Tacoma in 2015 to form the Northwest Seaport Alliance. Although it was affected by the Great Recession, Seattle has retained a comparatively strong economy, and is noted for start-up businesses, especially in green building and clean technologies. In February 2010, the city government committed Seattle to become North America's first \"climate neutral\" city, with a goal of reaching zero net per capita greenhouse gas emissions by 2030.Large companies continue to dominate the business landscape. Seven companies on Fortune 500's 2022 list of the United States' largest companies (based on total revenue) are headquartered in Seattle: Internet retailer Amazon (#2), coffee chain Starbucks (#120), freight forwarder Expeditors International of Washington (#225), department store Nordstrom (#245), forest products company Weyerhaeuser (#354), online travel company\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/dev/llama_index/llama_index/data_structs/node.py:176: UserWarning: .source_text is deprecated, use .node.get_text() instead\n",
- " warnings.warn(\".source_text is deprecated, use .node.get_text() instead\")\n"
- ]
- }
- ],
- "source": [
- "# NOTE: there's hallucination! the sources only reference Toronto\n",
- "print(response.source_nodes[0].source_text)\n",
- "print(response.source_nodes[1].source_text)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd",
- "metadata": {
- "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd"
- },
- "source": [
- "**Complex Query 2**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cd85d13a-b3ff-41f6-8d32-59f0cb3b864c",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# with query decomposition\n",
- "query_str = (\n",
- " \"What are the basketball teams in Houston and Boston?\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "efc816e8-099c-41b9-99e6-2c6ab3e6434f",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
- "\n",
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7d749f14-4868-49db-8c03-2583f849400f",
- "metadata": {},
- "outputs": [],
- "source": [
- "print(str(response))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4774de86-a90d-4aad-9991-efd6310712d0",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = global_index.as_query_engine(\n",
- " similarity_top_k=2, \n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
- "response = query_engine.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "82ade804-fe01-46b2-9a37-cd597f1be322",
- "metadata": {
- "tags": []
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- "outputs": [],
- "source": [
- "print(str(response))"
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- {
- "cell_type": "markdown",
- "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e",
- "metadata": {
- "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e"
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- "source": [
- "**Complex Query 3**"
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- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8ce68a61-26ec-452d-8548-0dab82e30edd",
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- "source": [
- "# with query decomposition\n",
- "query_str = (\n",
- " \"Compare and contrast the climate of Houston and Boston \"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8eb46d09-f1e5-4890-a2c3-efa54fb91874",
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- "source": [
- "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
- "\n",
- "response = query_engine.query(query_str)"
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- "cell_type": "code",
- "execution_count": null,
- "id": "0aa7efdf-c8c0-4efb-83a0-ad617f120307",
- "metadata": {
- "tags": []
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- "outputs": [],
- "source": [
- "print(response)"
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- "id": "ba153f95-9ee7-45c7-91d8-5edfd02a841a",
- "metadata": {
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- "source": [
- "query_engine = global_index.as_query_engine(\n",
- " similarity_top_k=2, \n",
- " response_mode=\"tree_summarize\"\n",
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+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "metadata": {
+ "id": "cfb64210-9c6b-47d7-81f4-67dbdab68e4c",
+ "tags": []
+ },
+ "source": [
+ "# Test Complex Queries over Multiple Documents (with and without Query Decomposition)\n",
+ "\n",
+ "Query Decomposition: The ability to decompose a complex query into a simpler query given the content of the index.\n",
+ "\n",
+ "Use OpenAI as the LLM model and embedding model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fa0e62b6",
+ "metadata": {
+ "id": "fa0e62b6",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "# Uncomment if you want to temporarily disable logger\n",
+ "logger = logging.getLogger()\n",
+ "logger.disabled = True"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "metadata": {
+ "id": "e27b0473-4bda-47f0-b6ed-fd482eac1a13",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleKeywordTableIndex,\n",
+ " ListIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ ")\n",
+ "import requests"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf",
+ "metadata": {
+ "id": "49e0d841-680f-4a0c-b455-788b54978ebf"
+ },
+ "source": [
+ "#### Load Datasets\n",
+ "\n",
+ "Load Wikipedia pages as well as Paul Graham's \"What I Worked On\" essay"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fc4692a1",
+ "metadata": {
+ "id": "fc4692a1",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "wiki_titles = [\n",
+ " \"Toronto\",\n",
+ " \"Seattle\",\n",
+ " \"San Francisco\",\n",
+ " \"Chicago\",\n",
+ " \"Boston\",\n",
+ " \"Washington, D.C.\",\n",
+ " \"Cambridge, Massachusetts\",\n",
+ " \"Houston\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "metadata": {
+ "id": "9ec16a8b-6aae-4bf7-9b83-b82087b4ea52",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "import requests\n",
+ "\n",
+ "data_path = Path(\"data_wiki\")\n",
+ "\n",
+ "for title in wiki_titles:\n",
+ " response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ " ).json()\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
+ "\n",
+ " if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "metadata": {
+ "id": "39c00aeb-adef-4ce3-8134-031de18e64ea",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Load all wiki documents\n",
+ "city_docs = {}\n",
+ "all_docs = []\n",
+ "for wiki_title in wiki_titles:\n",
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[data_path / f\"{wiki_title}.txt\"]\n",
+ " ).load_data()\n",
+ " all_docs.extend(city_docs[wiki_title])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "39695a27-4bb1-41eb-b878-4f7c7a3fce31",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define service context\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " chunk_size=512,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1782198-c0de-4679-8951-1297c21b8639",
+ "metadata": {
+ "id": "f1782198-c0de-4679-8951-1297c21b8639"
+ },
+ "source": [
+ "### Building the document indices\n",
+ "Build a separate vector index for each wiki pages about cities.\n",
+ "\n",
+ "We also build a \"global\" vector index, which ingest documents for *all* cities. \n",
+ "\n",
+ "This allows us to test different types of data structures!"
+ ]
+ },
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+ "cell_type": "code",
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+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
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+ "3b4c1066797b43a586611ec2d63e7ca1",
+ "c06865c1e01a441698dacf48600dd03c",
+ "9d229e5dd56e4d539ca2c1b9f0a37812",
+ "868aa268dd28498d902782215e53c6fa",
+ "46f644cf589e4a48a6fad1742f0c0575",
+ "adb40ef11f094594b14776e238955224",
+ "7b47c78391a4431aa2d3f84677f24046",
+ "398f1c0f56fe4f218d999df138adfdac",
+ "f1839e86863948f68314f81ba6bca4c9",
+ "3c37e72850c746ce9c919add5340dede",
+ "2053e6adef1b4dba89f861eaf3d916fd",
+ "eab4127882d24acfa9518ebff6f4e22a",
+ "64b754f563834be0a6963349b1f2dcf2",
+ "c7636a6d7380465895b8c86d34caf500",
+ "f7803dea63994cc2a31acf805bd19e67",
+ "380a0c11434241b191b17421e395be8b",
+ "a02534c347aa4865ab4ab3de3a3ee2f5",
+ "b0ccb9d9d96e4ed8bec4d540c34d337c",
+ "f22e9615de674e05978f332eb88750cf",
+ "b53e8481f6d64018988dc03081bf2765",
+ "b458d6fa793d4fa080b9f1e5013af3de",
+ "119d6d7a8d524aa49170f5784ebc6b9e",
+ "d55f842766484d299c75f74e31e7aa6a",
+ "1bdaf4dab16f48dbaeed3fb9bf268e45",
+ "026cc1a42e154f1f92b5236869311929",
+ "a2edbc4195d843e0acfba83726a08e78",
+ "40e148c291ad4f739998a7eac55a8af6",
+ "028aa5d1f7a74d538b5c606d4a6d146f",
+ "c078fe9a056a473dab7d474cd7907154",
+ "4cc9ec6ba46647aba2d53e352f91c137",
+ "f2a1c5087d0e44909139697ed90474e8",
+ "7b24b46d6c3643e581ba003a9c473745",
+ "3f748152b9274556afad2555572aa9f4"
+ ]
+ },
+ "id": "5431e83e-428b-4473-bad1-24b7a6c4db38",
+ "outputId": "5721e863-d460-4f5c-9e36-5a586180b669",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Toronto\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 27294 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Seattle\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 22263 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for San Francisco\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 30887 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Chicago\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 34336 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Boston\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 24512 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Washington, D.C.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 28480 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Cambridge, Massachusetts\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17036 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Building index for Houston\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 28795 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Build index for each city document\n",
+ "city_indices = {}\n",
+ "index_summaries = {}\n",
+ "for wiki_title in wiki_titles:\n",
+ " print(f\"Building index for {wiki_title}\")\n",
+ " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
+ " city_docs[wiki_title], service_context=service_context\n",
+ " )\n",
+ " # set summary text for city\n",
+ " index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "36c99bc1-c712-489d-a9da-4a9be76d710e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 213603 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# also setup a global vector index\n",
+ "global_index = VectorStoreIndex.from_documents(\n",
+ " all_docs, service_context=service_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "metadata": {
+ "id": "d4d3cd8b-4134-4cfa-8002-e0a34694d2e1",
+ "tags": []
+ },
+ "source": [
+ "### Creating the right structure to run compare/contrast queries\n",
+ "\n",
+ "Our key goal in this notebook is to run compare/contrast queries between different cities.\n",
+ "\n",
+ "We currently have a separate vector index for every city document. We want to setup a \"graph\" structure in order to route the query \n",
+ "in the right manner in order to retrieve the relevant text sections for each city. \n",
+ "\n",
+ "We compose a keyword table index on top of all the vector indices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "metadata": {
+ "id": "6d68750c-e5ae-481a-8b03-6173020c9bf3",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "f975514f-fddd-4737-91de-97bc61394ea9",
+ "outputId": "fc875b0e-c8bf-439b-c794-fcae25954cfb",
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "graph = ComposableGraph.from_indices(\n",
+ " SimpleKeywordTableIndex,\n",
+ " [index for _, index in city_indices.items()],\n",
+ " [summary for _, summary in index_summaries.items()],\n",
+ " max_keywords_per_chunk=50,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b4c36f69-596b-4974-afa2-09cc652c1111",
+ "metadata": {},
+ "source": [
+ "### Define Query Transformation + Query Configs\n",
+ "\n",
+ "We also define a \"query decomposition\" transform. Since we have a graph structure over multiple indexes, query decomposition\n",
+ "allows us to break a complex question into a simpler one over a given index.\n",
+ "\n",
+ "This works well in comparing/contrasting different cities because it allows us to ask questions specific to each city."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "accd31e4-0ae7-4660-833c-5ae23037fd14",
+ "metadata": {},
+ "source": [
+ "**Query Transform**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "82432236-fa93-4269-b695-d6d2131edb41",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
+ "\n",
+ "decompose_transform = DecomposeQueryTransform(verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "d9199887",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The autoreload extension is already loaded. To reload it, use:\n",
+ " %reload_ext autoreload\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "2ab2fbf1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
+ "\n",
+ "custom_query_engines = {}\n",
+ "for index in city_indices.values():\n",
+ " query_engine = index.as_query_engine(service_context=service_context)\n",
+ " query_engine = TransformQueryEngine(\n",
+ " query_engine,\n",
+ " query_transform=decompose_transform,\n",
+ " transform_extra_info={\"index_summary\": index.index_struct.summary},\n",
+ " )\n",
+ " custom_query_engines[index.index_id] = query_engine\n",
+ "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
+ " retriever_mode=\"simple\",\n",
+ " response_mode=\"tree_summarize\",\n",
+ " service_context=service_context,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d8e2a20-44a2-4d9f-af0a-1a91e3afbcf2",
+ "metadata": {},
+ "source": [
+ "### Let's Run Some Queries! \n",
+ "\n",
+ "We run queries over the graphs and analyze the results.\n",
+ "\n",
+ "We also compare results against the baseline global vector index. In the majority of cases the global vector index provides insufficient answers."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43",
+ "metadata": {
+ "id": "018d0a51-3a3f-4dc5-9e1d-f2e79eb0cc43"
+ },
+ "source": [
+ "**Complex Query 1**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "5da241f2-e8e6-459e-8492-60724faeb173",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# with query decomposition in subindices\n",
+ "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
+ "query_str = \"Compare and contrast the demographics in Seattle, Houston, and Toronto. \""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "984fcf86-8b10-40aa-ac7b-85518537b433",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.keyword_table.retrievers:> Starting query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
+ "INFO:llama_index.indices.keyword_table.retrievers:query keywords: ['demographics', 'seattle', 'toronto', 'compare', 'contrast', 'houston']\n",
+ "INFO:llama_index.indices.keyword_table.retrievers:> Extracted keywords: ['seattle', 'toronto', 'houston']\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the population of Seattle?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1375 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1375 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the population of Toronto?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1303 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1303 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33;1m\u001b[1;3m> Current query: Compare and contrast the demographics in Seattle, Houston, and Toronto. \n",
+ "\u001b[0m\u001b[38;5;200m\u001b[1;3m> New query: What is the population of Houston?\n",
+ "\u001b[0m"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 7 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1401 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1401 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1681 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1681 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "13108dca-8ce6-4485-a018-dcab2514868d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Seattle, Houston, and Toronto all have diverse populations, with immigrants making up a significant portion of the population in each city. However, the countries of origin for the immigrants vary between the cities. In Seattle, the top countries of origin for immigrants are Mexico, India, China, Philippines, and Vietnam. In Houston, the top countries of origin for immigrants are Mexico, India, El Salvador, Honduras, and Guatemala. In Toronto, the top countries of origin for immigrants are Philippines, China, India, Sri Lanka, and Jamaica. Additionally, the median age of the population varies between the cities. In Seattle, the median age is 37.2, in Houston it is 33.4, and in Toronto it is 39.2. Furthermore, the gender population also varies between the cities. In Seattle, the gender population is 48.2% male and 51.8% female, in Houston it is 48.3% male and 51.7% female, and in Toronto it is 48% male and 52% female. In 2016, the three most commonly reported ethnic origins overall were Chinese (332,830 or 12.5 per cent), South Asian (323,810 or 11.9 per cent), and Black (308,345 or 11.3\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "59acffe2-c653-4ea7-a381-b55e67d8a35c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 14 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3549 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 3549 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = global_index.as_query_engine(\n",
+ " similarity_top_k=3, response_mode=\"tree_summarize\"\n",
+ ")\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "e88666ad-a934-4f0a-b377-fe92d2361e8e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Seattle is a major U.S. city located in the Pacific Northwest region of the United States. It has a population of over 730,000 people and is known for its high percentage of college and university graduates. Of the city's population over the age of 25, 53.8% hold a bachelor's degree or higher, and 91.9% have a high school diploma or equivalent. Seattle is also home to the University of Washington, as well as a number of smaller private universities such as Seattle Pacific University, a Jesuit Catholic institution, and Seattle University, a Free Methodist institution. The Seattle Colleges District operates three colleges: North Seattle College, Seattle Central College, and South Seattle College. According to a 2006 study by UCLA, 12.9% of city residents polled identified as gay, lesbian, or bisexual. This was the second-highest proportion of any major U.S. city, behind San Francisco. Seattle's economy is driven by a mix of older industrial companies and \"new economy\" internet and technology companies, as well as service, design, and clean technology companies. It is estimated that King County has 8,000 homeless people on any given night, and many of those live in Seattle. In recent years, the city has experienced steady population\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NOTE: the global vector index seems to provide the right results....\n",
+ "# BUT see below!\n",
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "0435679a-d3ea-47ed-995e-e981ed65294b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Tiffany Washington, and Kendee Yamaguchi.\n",
+ "\n",
+ "\n",
+ "== Education ==\n",
+ "\n",
+ "Of the city's population over the age of 25, 53.8% (vs. a national average of 27.4%) hold a bachelor's degree or higher, and 91.9% (vs. 84.5% nationally) have a high school diploma or equivalent. A 2008 United States Census Bureau survey showed that Seattle had the highest percentage of college and university graduates of any major U.S. city. The city was listed as the most literate of the country's 69 largest cities in 2005 and 2006, the second most literate in 2007 and the most literate in 2008 in studies conducted by Central Connecticut State University.Seattle Public Schools is the school district for the vast majority of the city. That school district desegregated without a court order but continue to struggle to achieve racial balance in a somewhat ethnically divided city (the south part of town having more ethnic minorities than the north). In 2007, Seattle's racial tie-breaking system was struck down by the United States Supreme Court, but the ruling left the door open for desegregation formulae based on other indicators (e.g., income or socioeconomic class). A very small portion of the city is within the Highline School District.The public school system is supplemented by a moderate number of private schools: Five of the private high schools are Catholic, one is Lutheran, and six are secular.Seattle is home to the University of Washington, as well as the institution's professional and continuing education unit, the University of Washington Educational Outreach. The 2017 U.S. News & World Report ranked the University of Washington at No. 11 in the world. The UW receives more federal research and development funding than any public institution. Over the last 10 years, it has also produced more Peace Corps volunteers than any other U.S. university. Seattle also has a number of smaller private universities including Seattle University and Seattle Pacific University, the former a Jesuit Catholic institution, the latter a Free Methodist institution. The Seattle Colleges District operates three colleges: North Seattle College, Seattle Central College, and South Seattle College. Universities aimed at the\n",
+ "bisexual, and transgender community. According to a 2006 study by UCLA, 12.9% of city residents polled identified as gay, lesbian, or bisexual. This was the second-highest proportion of any major U.S. city, behind San Francisco. Greater Seattle also ranked second among major U.S. metropolitan areas, with 6.5% of the population identifying as gay, lesbian, or bisexual. According to 2012 estimates from the United States Census Bureau, Seattle has the highest percentage of same-sex households in the United States, at 2.6 percent, surpassing San Francisco (2.5 percent). The Capitol Hill district has historically been the center of LGBT culture in Seattle.\n",
+ "\n",
+ "\n",
+ "== Economy ==\n",
+ "\n",
+ "Seattle's economy is driven by a mix of older industrial companies and \"new economy\" internet and technology companies, as well as service, design, and clean technology companies. The city's gross metropolitan product (GMP) was $231 billion in 2010, making it the 11th largest metropolitan economy in the United States. The Port of Seattle, which also operates Seattle–Tacoma International Airport, is a major gateway for trade with Asia and cruises to Alaska. It also is the 8th largest port in the United States when measured by container capacity. Its maritime cargo operations merged with the Port of Tacoma in 2015 to form the Northwest Seaport Alliance. Although it was affected by the Great Recession, Seattle has retained a comparatively strong economy, and is noted for start-up businesses, especially in green building and clean technologies. In February 2010, the city government committed Seattle to become North America's first \"climate neutral\" city, with a goal of reaching zero net per capita greenhouse gas emissions by 2030.Large companies continue to dominate the business landscape. Seven companies on Fortune 500's 2022 list of the United States' largest companies (based on total revenue) are headquartered in Seattle: Internet retailer Amazon (#2), coffee chain Starbucks (#120), freight forwarder Expeditors International of Washington (#225), department store Nordstrom (#245), forest products company Weyerhaeuser (#354), online travel company\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/dev/llama_index/llama_index/data_structs/node.py:176: UserWarning: .source_text is deprecated, use .node.get_text() instead\n",
+ " warnings.warn(\".source_text is deprecated, use .node.get_text() instead\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "# NOTE: there's hallucination! the sources only reference Toronto\n",
+ "print(response.source_nodes[0].source_text)\n",
+ "print(response.source_nodes[1].source_text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd",
+ "metadata": {
+ "id": "d3cb4d7b-7bcc-46bf-b7d6-d0230c3d7fdd"
+ },
+ "source": [
+ "**Complex Query 2**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd85d13a-b3ff-41f6-8d32-59f0cb3b864c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# with query decomposition\n",
+ "query_str = \"What are the basketball teams in Houston and Boston?\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "efc816e8-099c-41b9-99e6-2c6ab3e6434f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
+ "\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d749f14-4868-49db-8c03-2583f849400f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4774de86-a90d-4aad-9991-efd6310712d0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = global_index.as_query_engine(\n",
+ " similarity_top_k=2, response_mode=\"tree_summarize\"\n",
+ ")\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82ade804-fe01-46b2-9a37-cd597f1be322",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(str(response))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e",
+ "metadata": {
+ "id": "53f527c8-0d53-4b29-8f1f-7b5bf22ca55e"
+ },
+ "source": [
+ "**Complex Query 3**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ce68a61-26ec-452d-8548-0dab82e30edd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# with query decomposition\n",
+ "query_str = \"Compare and contrast the climate of Houston and Boston \""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8eb46d09-f1e5-4890-a2c3-efa54fb91874",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n",
+ "\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0aa7efdf-c8c0-4efb-83a0-ad617f120307",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba153f95-9ee7-45c7-91d8-5edfd02a841a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = global_index.as_query_engine(\n",
+ " similarity_top_k=2, response_mode=\"tree_summarize\"\n",
+ ")\n",
+ "response = query_engine.query(query_str)"
+ ]
+ },
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diff --git a/docs/examples/vector_stores/AsyncIndexCreationDemo.ipynb b/docs/examples/vector_stores/AsyncIndexCreationDemo.ipynb
index 3780209fce..8d9cbb7939 100644
--- a/docs/examples/vector_stores/AsyncIndexCreationDemo.ipynb
+++ b/docs/examples/vector_stores/AsyncIndexCreationDemo.ipynb
@@ -1,217 +1,231 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f57c7b08",
- "metadata": {},
- "source": [
- "# Simple Vector Store - Async Index Creation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "5db0283d",
- "metadata": {},
- "outputs": [],
- "source": [
- "import time\n",
- "\n",
- "# Helps asyncio run within Jupyter\n",
- "import nest_asyncio\n",
- "nest_asyncio.apply()\n",
- "\n",
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"[YOUR_API_KEY]\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "50e3bb2e",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, download_loader\n",
- "\n",
- "WikipediaReader = download_loader(\"WikipediaReader\")\n",
- "\n",
- "loader = WikipediaReader()\n",
- "documents = loader.load_data(pages=['Berlin', 'Santiago', 'Moscow', 'Tokyo', 'Jakarta', 'Cairo', 'Bogota', 'Shanghai', 'Damascus'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "d14b17bf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "9"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(documents)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2684824b",
- "metadata": {},
- "source": [
- "9 Wikipedia articles downloaded as documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "4537def9",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:root:> [build_index_from_documents] Total embedding token usage: 142295 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "7.691995083000052\n"
- ]
- }
- ],
- "source": [
- "start_time = time.perf_counter()\n",
- "index = VectorStoreIndex.from_documents(documents)\n",
- "duration = time.perf_counter() - start_time\n",
- "print(duration)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6374ac99",
- "metadata": {},
- "source": [
- "Standard index creation took 7.69 seconds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "60a7c522",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=245 request_id=314b145a07f65fd34e707f633cc1a444 response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=432 request_id=bb9e796d0b8f9c2365b68de8a56009ff response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=433 request_id=7a94707fe2f8916e9cdd8276a5748207 response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=499 request_id=cda679215293c3a13ed57c2eae3dc582 response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=527 request_id=5e1c3e74aa3f9f950e4035f81a0f0a15 response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=585 request_id=81983fe76eab95f73f82df881ff7b2d9 response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=574 request_id=702a182b54a29a33719205f722378c8e response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=575 request_id=d1df11775c59a3ba403dda253081f8eb response_code=200\n",
- "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=575 request_id=47929f13469569527505b51958cd8e71 response_code=200\n",
- "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:root:> [build_index_from_documents] Total embedding token usage: 142295 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2.3730635830000892\n"
- ]
- }
- ],
- "source": [
- "start_time = time.perf_counter()\n",
- "index = VectorStoreIndex(documents, use_async=True)\n",
- "duration = time.perf_counter() - start_time\n",
- "print(duration)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8bd9de0b",
- "metadata": {},
- "source": [
- "Async index creation took 2.37 seconds"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "d0db93cb",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:root:> [query] Total LLM token usage: 4075 tokens\n",
- "INFO:root:> [query] Total embedding token usage: 8 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "Response(response=\"\\n\\nThe name 'Jakarta' is derived from the word Jayakarta (Devanagari: जयकर्त) which is ultimately derived from the Sanskrit जय jaya (victorious), and कृत krta (accomplished, acquired), thus Jayakarta translates as 'victorious deed', 'complete act' or 'complete victory'. It was named for the Muslim troops of Fatahillah which successfully defeated and drove the Portuguese away from the city in 1527. Before it was called Jayakarta, the city was known as 'Sunda Kelapa'. Tomé Pires, a Portuguese apothecary wrote the name of the city on his magnum opus as Jacatra or Jacarta during his journey to East Indies. The city is located in a low-lying area ranging from −2 to 91 m (−7 to 299 ft) with an average elevation of 8 m (26 ft) above sea level with historically extensive swampy areas. Some parts of the city have been constructed on reclaimed tidal flats that occur around the area. Thirteen rivers flow through Jakarta, including the Ciliwung River, Kalibaru, Pesanggra\", source_nodes=[SourceNode(source_text=\"Jakarta (; Indonesian pronunciation: [dʒaˈkarta] (listen)), officially the Special Capital Region of Jakarta (Indonesian: Daerah Khusus Ibukota Jakarta), is the capital and largest city of Indonesia. Lying on the northwest coast of Java, the world's most populous island, Jakarta is the largest city in Southeast Asia and serves as the diplomatic capital of ASEAN.\\nThe city is the economic, cultural, and political centre of Indonesia. It possesses a province-level status and has a population of 10,562,088 as of mid-2021. Although Jakarta extends over only 664.01 km2 (256.38 sq mi) and thus has the smallest area of any Indonesian province, its metropolitan area covers 9,957.08 km2 (3,844.45 sq mi), which includes the satellite cities Bogor, Depok, Tangerang, South Tangerang, and Bekasi, and has an estimated population of 35 million as of 2021, making it the largest urban area in Indonesia and the second-largest in the world (after Tokyo). Jakarta ranks first among the Indonesian provinces in the human development index. Jakarta's business and employment opportunities, along with its ability to offer a potentially higher standard of living compared to other parts of the country, have attracted migrants from across the Indonesian archipelago, making it a melting pot of numerous cultures.\\nJakarta is one of the oldest continuously inhabited cities in Southeast Asia. Established in the fourth century as Sunda Kelapa, the city became an important trading port for the Sunda Kingdom. At one time, it was the de facto capital of the Dutch East Indies, when it was known as Batavia. Jakarta was officially a city within West Java until 1960 when its official status was changed to a province with special capital region distinction. As a province, its government consists of five administrative cities and one administrative regency. Jakarta is an alpha world city and is the seat of the ASEAN secretariat. Financial institutions such as the Bank of Indonesia, Indonesia Stock Exchange, and corporate headquarters of numerous Indonesian companies and multinational corporations are located in the city. In 2021, the city's GRP PPP was estimated at US$602.946 billion.\\nJakarta's main challenges include rapid urban growth, ecological breakdown, gridlocked traffic, congestion, and flooding. Jakarta is sinking up to 17 cm (6.7 inches) annually, which coupled with the rising of sea levels, has made the city more prone to flooding. Hence, it is one of the fastest-sinking capitals in the world. In response to these challenges, in August 2019, President Joko Widodo announced that the capital of Indonesia would be moved from Jakarta to the planned city of Nusantara, in the province of East Kalimantan on the island of Borneo.\\n\\n\\n== Name ==\\n\\nJakarta has been home to multiple settlements. Below is the list of names used during its existence:\\n\\nSunda Kelapa (397–1527)\\nJayakarta (1527–1619)\\nBatavia (1619–1942)\\nDjakarta (1942–1972)\\nJakarta (1972–present)The name 'Jakarta' is derived from the word Jayakarta (Devanagari: जयकर्त) which is ultimately derived from the Sanskrit जय jaya (victorious), and कृत krta (accomplished, acquired), thus Jayakarta translates as 'victorious deed', 'complete act' or 'complete victory'. It was named for the Muslim troops of Fatahillah which successfully defeated and drove the Portuguese away from the city in 1527. Before it was called Jayakarta, the city was known as 'Sunda Kelapa'. Tomé Pires, a Portuguese apothecary wrote the name of the city on his magnum opus as Jacatra or Jacarta during his journey to East Indies. \\nIn the 17th century, the city was known as Koningin van het Oosten (Queen of the Orient), a name that was given for the urban beauty of downtown Batavia's canals, mansions and ordered city layout. After expanding to the south in the 19th century, this nickname came to be more associated with the suburbs (e.g. Menteng and the area around Merdeka Square), with their wide lanes, green spaces and villas. During the Japanese occupation, the city was renamed as Jakaruta Tokubetsu-shi (ジャカルタ特別市, Jakarta Special City).\\n\\n\\n== History ==\\n\\n\\n=== Precolonial era ===\\n\\nThe north coast area of western Java including Jakarta was the location of prehistoric Buni culture that flourished from 400 BC to 100 AD. The area in and around modern Jakarta was part of the 4th-century Sundanese kingdom of Tarumanagara, one of the oldest Hindu kingdoms in Indonesia. The area of North Jakarta around Tugu became a populated settlement in the early 5th century. The Tugu inscription (probably written around 417 AD) discovered in Batutumbuh hamlet, Tugu village, Koja, North Jakarta, mentions that King Purnawarman of Tarumanagara undertook hydraulic projects; the irrigation and water drainage project of the Chandrabhaga river and the Gomati river near his capital. Following the decline of Tarumanagara, its territories, including the Jakarta area, became part of the Hindu Kingdom of Sunda. From the 7th to the early 13th century, the port of Sunda was under the Srivijaya maritime empire. According to the Chinese source, Chu-fan-chi, written circa 1225, Chou Ju-kua reported in the early 13th century that Srivijaya still ruled Sumatra, the Malay peninsula and western Java (Sunda). The source says the port of Sunda is strategic and thriving, mentioning pepper from Sunda as among the best in quality. The people worked in agriculture, and their houses were built on wooden piles. The harbour area became known as Sunda Kelapa, (Sundanese: ᮞᮥᮔ᮪ᮓ ᮊᮨᮜᮕ) and by the 14th century, it was an important trading port for the Sunda Kingdom.\\nThe first European fleet, four Portuguese ships from Malacca, arrived in 1513 while looking for a route for spices. The Sunda Kingdom made an alliance treaty with the Portuguese by allowing them to build a port in 1522 to defend against the rising power of Demak Sultanate from central Java. In 1527, Fatahillah, a Javanese general from Demak attacked and conquered Sunda Kelapa, driving out the Portuguese. Sunda Kelapa was renamed Jayakarta, and became a fiefdom of the Banten Sultanate, which became a major Southeast Asian trading centre.\\nThrough the relationship with Prince Jayawikarta of the Banten Sultanate, Dutch ships arrived in 1596. In 1602, the British East India Company's first voyage, commanded by Sir James Lancaster, arrived in Aceh and sailed on to Banten where they were allowed to build a trading post. This site became the centre of British trade in the Indonesian archipelago until 1682. Jayawikarta is thought to have made trading connections with the British merchants, rivals of the Dutch, by allowing them to build houses directly across from the Dutch buildings in 1615.\\n\\n\\n=== Colonial era ===\\n\\nWhen relations between Prince Jayawikarta and the Dutch deteriorated, his soldiers attacked the Dutch fortress. His army and the British, however, were defeated by the Dutch, in part owing to the timely arrival of Jan Pieterszoon Coen. The Dutch burned the British fort and forced them to retreat on their ships. The victory consolidated Dutch power, and they renamed the city Batavia in 1619.\\n\\nCommercial opportunities in the city attracted native and especially Chinese and Arab immigrants. This sudden population increase created burdens on the city. Tensions grew as the colonial government tried to restrict Chinese migration through deportations. Following a revolt, 5,000 Chinese were massacred by the Dutch and natives on 9 October 1740, and the following year, Chinese inhabitants were moved to Glodok outside the city walls. At the beginning of the 19th century, around 400 Arabs and Moors lived in Batavia, a number that changed little during the following decades. Among the commodities traded were fabrics, mainly imported cotton, batik and clothing worn by Arab communities.The city began to expand further south as epidemics in 1835 and 1870 forced residents to move away from the port. The Koningsplein, now Merdeka Square was completed in 1818, the housing park of Menteng was started in 1913, and Kebayoran Baru was the last Dutch-built residential area. By 1930, Batavia had more than 500,000 inhabitants, including 37,067 Europeans.On 5 March 1942, the Japanese captured Batavia from Dutch control, and the city was named Jakarta (Jakarta Special City (ジャカルタ特別市, Jakaruta tokubetsu-shi), under the special status that was assigned to the city). After the war, the Dutch name Batavia was internationally recognised until full Indonesian independence on 27 December 1949. The city, now renamed Jakarta, was officially proclaimed the national capital of Indonesia.\\n\\n\\n=== Independence era ===\\n\\nAfter World War II ended, Indonesian nationalists declared independence on 17 August 1945, and the government of Jakarta City was changed into the Jakarta National Administration in the following month. During the Indonesian National Revolution, Indonesian Republicans withdrew from Allied-occupied Jakarta and established their capital in Yogyakarta.\\nAfter securing full independence, Jakarta again became the national capital in 1950. With Jakarta selected to host the 1962 Asian Games, Soekarno, envisaging Jakarta as a great international city, instigated large government-funded projects with openly nationalistic and modernist architecture. Projects included a cloverleaf interchange, a major boulevard (Jalan MH Thamrin-Sudirman), monuments such as The National Monument, Hotel Indonesia, a shopping centre, and a new building intended to be the headquarters of CONEFO. In October 1965, Jakarta was the site of an abortive coup attempt in which six top generals were killed, precipitating a violent anti-communist purge which killed at least 500,000 people, including some ethnic Chinese. The event marked the beginning of Suharto's New Order. The first government was led by a mayor until the end of 1960 when the office was changed to that of a governor. The last mayor of Jakarta was Soediro until he was replaced by Soemarno Sosroatmodjo as governor. Based on law No. 5 of 1974 relating to regional governments, Jakarta was confirmed as the capital of Indonesia and one of the country's then 26 provinces.In 1966, Jakarta was declared a 'special capital region' (Daerah Khusus Ibukota), with a status equivalent to that of a province. Lieutenant General Ali Sadikin served as governor from 1966 to 1977; he rehabilitated roads and bridges, encouraged the arts, built hospitals and a large number of schools. He cleared out slum dwellers for new development projects — some for the benefit of the Suharto family,— and attempted to eliminate rickshaws and ban street vendors. He began control of migration to the city to stem overcrowding and poverty. Foreign investment contributed to a real estate boom that transformed the face of Jakarta. The boom ended with the 1997 Asian financial crisis, putting Jakarta at the centre of violence, protest, and political manoeuvring.\\nAfter three decades in power, support for President Suharto began to wane. Tensions peaked when four students were shot dead at Trisakti University by security forces. Four days of riots and violence in 1998 ensued that killed an estimated 1,200, and destroyed or damaged 6,000 buildings, forcing Suharto to resign. Much of the rioting targeted Chinese Indonesians. In the post-Suharto era, Jakarta has remained the focal point of democratic change in Indonesia. Jemaah Islamiah-connected bombings occurred almost annually in the city between 2000 and 2005, with another in 2009. In August 2007, Jakarta held its first-ever election to choose a governor as part of a nationwide decentralisation program that allows direct local elections in several areas. Previously, governors were elected by the city's legislative body.During the Jokowi presidency, the Government adopted a plan to move Indonesia's capital to East Kalimantan.Between 2016 and 2017, a series of terrorist attacks rocked Jakarta with scenes of multiple suicide bombings and gunfire. In suspicion to its links, the Islamic State, the perpetrator led by Abu Bakr al-Baghdadi claimed responsibility for the attacks.\\n\\n\\n== Geography ==\\n\\nJakarta covers 699.5 km2 (270.1 sq mi), the smallest among any Indonesian provinces. However, its metropolitan area covers 6,392 km2 (2,468 sq mi), which extends into two of the bordering provinces of West Java and Banten. The Greater Jakarta area includes three bordering regencies (Bekasi Regency, Tangerang Regency and Bogor Regency) and five adjacent cities (Bogor, Depok, Bekasi, Tangerang and South Tangerang).\\n\\nJakarta is situated on the northwest coast of Java, at the mouth of the Ciliwung River on Jakarta Bay, an inlet of the Java Sea. It is strategically located near the Sunda Strait. The northern part of Jakarta is plain land, some areas of which are below sea level, and subject to frequent flooding. The southern parts of the city are hilly. It is one of only two Asian capital cities located in the southern hemisphere (along with East Timor's Dili). Officially, the area of the Jakarta Special District is 662 km2 (256 sq mi) of land area and 6,977 km2 (2,694 sq mi) of sea area. The Thousand Islands, which are administratively a part of Jakarta, are located in Jakarta Bay, north of the city.\\nJakarta lies in a low and flat alluvial plain, ranging from −2 to 91 m (−7 to 299 ft) with an average elevation of 8 m (26 ft) above sea level with historically extensive swampy areas. Some parts of the city have been constructed on reclaimed tidal flats that occur around the area. Thirteen rivers flow through Jakarta. They are Ciliwung River, Kalibaru, Pesanggrahan, Cipinang, Angke River, Maja, Mookervart, Krukut, Buaran, West Tarum, Cakung, Petukangan, Sunter River and Grogol River. They flow from the Puncak highlands to the south of the city, then across the city northwards towards the Java Sea. The Ciliwung River divides the city into the western and eastern districts.\\nThese rivers, combined with the wet season rains and insufficient\", doc_id='eeb6ef32-c857-44e2-b0c5-dff6e29a9cd7', extra_info=None, node_info={'start': 0, 'end': 13970}, similarity=0.8701780916463354)], extra_info=None)"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "query_engine.query(\"What is the etymology of Jakarta?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4d2e2a79",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f57c7b08",
+ "metadata": {},
+ "source": [
+ "# Simple Vector Store - Async Index Creation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "5db0283d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "# Helps asyncio run within Jupyter\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()\n",
+ "\n",
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"[YOUR_API_KEY]\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "50e3bb2e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, download_loader\n",
+ "\n",
+ "WikipediaReader = download_loader(\"WikipediaReader\")\n",
+ "\n",
+ "loader = WikipediaReader()\n",
+ "documents = loader.load_data(\n",
+ " pages=[\n",
+ " \"Berlin\",\n",
+ " \"Santiago\",\n",
+ " \"Moscow\",\n",
+ " \"Tokyo\",\n",
+ " \"Jakarta\",\n",
+ " \"Cairo\",\n",
+ " \"Bogota\",\n",
+ " \"Shanghai\",\n",
+ " \"Damascus\",\n",
+ " ]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d14b17bf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "9"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2684824b",
+ "metadata": {},
+ "source": [
+ "9 Wikipedia articles downloaded as documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4537def9",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:root:> [build_index_from_documents] Total embedding token usage: 142295 tokens\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "7.691995083000052\n"
+ ]
+ }
+ ],
+ "source": [
+ "start_time = time.perf_counter()\n",
+ "index = VectorStoreIndex.from_documents(documents)\n",
+ "duration = time.perf_counter() - start_time\n",
+ "print(duration)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6374ac99",
+ "metadata": {},
+ "source": [
+ "Standard index creation took 7.69 seconds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "60a7c522",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=245 request_id=314b145a07f65fd34e707f633cc1a444 response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=432 request_id=bb9e796d0b8f9c2365b68de8a56009ff response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=433 request_id=7a94707fe2f8916e9cdd8276a5748207 response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=499 request_id=cda679215293c3a13ed57c2eae3dc582 response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=527 request_id=5e1c3e74aa3f9f950e4035f81a0f0a15 response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=585 request_id=81983fe76eab95f73f82df881ff7b2d9 response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=574 request_id=702a182b54a29a33719205f722378c8e response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=575 request_id=d1df11775c59a3ba403dda253081f8eb response_code=200\n",
+ "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/engines/text-embedding-ada-002/embeddings processing_ms=575 request_id=47929f13469569527505b51958cd8e71 response_code=200\n",
+ "INFO:root:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:root:> [build_index_from_documents] Total embedding token usage: 142295 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2.3730635830000892\n"
+ ]
+ }
+ ],
+ "source": [
+ "start_time = time.perf_counter()\n",
+ "index = VectorStoreIndex(documents, use_async=True)\n",
+ "duration = time.perf_counter() - start_time\n",
+ "print(duration)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bd9de0b",
+ "metadata": {},
+ "source": [
+ "Async index creation took 2.37 seconds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "d0db93cb",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:root:> [query] Total LLM token usage: 4075 tokens\n",
+ "INFO:root:> [query] Total embedding token usage: 8 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Response(response=\"\\n\\nThe name 'Jakarta' is derived from the word Jayakarta (Devanagari: जयकर्त) which is ultimately derived from the Sanskrit जय jaya (victorious), and कृत krta (accomplished, acquired), thus Jayakarta translates as 'victorious deed', 'complete act' or 'complete victory'. It was named for the Muslim troops of Fatahillah which successfully defeated and drove the Portuguese away from the city in 1527. Before it was called Jayakarta, the city was known as 'Sunda Kelapa'. Tomé Pires, a Portuguese apothecary wrote the name of the city on his magnum opus as Jacatra or Jacarta during his journey to East Indies. The city is located in a low-lying area ranging from −2 to 91 m (−7 to 299 ft) with an average elevation of 8 m (26 ft) above sea level with historically extensive swampy areas. Some parts of the city have been constructed on reclaimed tidal flats that occur around the area. Thirteen rivers flow through Jakarta, including the Ciliwung River, Kalibaru, Pesanggra\", source_nodes=[SourceNode(source_text=\"Jakarta (; Indonesian pronunciation: [dʒaˈkarta] (listen)), officially the Special Capital Region of Jakarta (Indonesian: Daerah Khusus Ibukota Jakarta), is the capital and largest city of Indonesia. Lying on the northwest coast of Java, the world's most populous island, Jakarta is the largest city in Southeast Asia and serves as the diplomatic capital of ASEAN.\\nThe city is the economic, cultural, and political centre of Indonesia. It possesses a province-level status and has a population of 10,562,088 as of mid-2021. Although Jakarta extends over only 664.01 km2 (256.38 sq mi) and thus has the smallest area of any Indonesian province, its metropolitan area covers 9,957.08 km2 (3,844.45 sq mi), which includes the satellite cities Bogor, Depok, Tangerang, South Tangerang, and Bekasi, and has an estimated population of 35 million as of 2021, making it the largest urban area in Indonesia and the second-largest in the world (after Tokyo). Jakarta ranks first among the Indonesian provinces in the human development index. Jakarta's business and employment opportunities, along with its ability to offer a potentially higher standard of living compared to other parts of the country, have attracted migrants from across the Indonesian archipelago, making it a melting pot of numerous cultures.\\nJakarta is one of the oldest continuously inhabited cities in Southeast Asia. Established in the fourth century as Sunda Kelapa, the city became an important trading port for the Sunda Kingdom. At one time, it was the de facto capital of the Dutch East Indies, when it was known as Batavia. Jakarta was officially a city within West Java until 1960 when its official status was changed to a province with special capital region distinction. As a province, its government consists of five administrative cities and one administrative regency. Jakarta is an alpha world city and is the seat of the ASEAN secretariat. Financial institutions such as the Bank of Indonesia, Indonesia Stock Exchange, and corporate headquarters of numerous Indonesian companies and multinational corporations are located in the city. In 2021, the city's GRP PPP was estimated at US$602.946 billion.\\nJakarta's main challenges include rapid urban growth, ecological breakdown, gridlocked traffic, congestion, and flooding. Jakarta is sinking up to 17 cm (6.7 inches) annually, which coupled with the rising of sea levels, has made the city more prone to flooding. Hence, it is one of the fastest-sinking capitals in the world. In response to these challenges, in August 2019, President Joko Widodo announced that the capital of Indonesia would be moved from Jakarta to the planned city of Nusantara, in the province of East Kalimantan on the island of Borneo.\\n\\n\\n== Name ==\\n\\nJakarta has been home to multiple settlements. Below is the list of names used during its existence:\\n\\nSunda Kelapa (397–1527)\\nJayakarta (1527–1619)\\nBatavia (1619–1942)\\nDjakarta (1942–1972)\\nJakarta (1972–present)The name 'Jakarta' is derived from the word Jayakarta (Devanagari: जयकर्त) which is ultimately derived from the Sanskrit जय jaya (victorious), and कृत krta (accomplished, acquired), thus Jayakarta translates as 'victorious deed', 'complete act' or 'complete victory'. It was named for the Muslim troops of Fatahillah which successfully defeated and drove the Portuguese away from the city in 1527. Before it was called Jayakarta, the city was known as 'Sunda Kelapa'. Tomé Pires, a Portuguese apothecary wrote the name of the city on his magnum opus as Jacatra or Jacarta during his journey to East Indies. \\nIn the 17th century, the city was known as Koningin van het Oosten (Queen of the Orient), a name that was given for the urban beauty of downtown Batavia's canals, mansions and ordered city layout. After expanding to the south in the 19th century, this nickname came to be more associated with the suburbs (e.g. Menteng and the area around Merdeka Square), with their wide lanes, green spaces and villas. During the Japanese occupation, the city was renamed as Jakaruta Tokubetsu-shi (ジャカルタ特別市, Jakarta Special City).\\n\\n\\n== History ==\\n\\n\\n=== Precolonial era ===\\n\\nThe north coast area of western Java including Jakarta was the location of prehistoric Buni culture that flourished from 400 BC to 100 AD. The area in and around modern Jakarta was part of the 4th-century Sundanese kingdom of Tarumanagara, one of the oldest Hindu kingdoms in Indonesia. The area of North Jakarta around Tugu became a populated settlement in the early 5th century. The Tugu inscription (probably written around 417 AD) discovered in Batutumbuh hamlet, Tugu village, Koja, North Jakarta, mentions that King Purnawarman of Tarumanagara undertook hydraulic projects; the irrigation and water drainage project of the Chandrabhaga river and the Gomati river near his capital. Following the decline of Tarumanagara, its territories, including the Jakarta area, became part of the Hindu Kingdom of Sunda. From the 7th to the early 13th century, the port of Sunda was under the Srivijaya maritime empire. According to the Chinese source, Chu-fan-chi, written circa 1225, Chou Ju-kua reported in the early 13th century that Srivijaya still ruled Sumatra, the Malay peninsula and western Java (Sunda). The source says the port of Sunda is strategic and thriving, mentioning pepper from Sunda as among the best in quality. The people worked in agriculture, and their houses were built on wooden piles. The harbour area became known as Sunda Kelapa, (Sundanese: ᮞᮥᮔ᮪ᮓ ᮊᮨᮜᮕ) and by the 14th century, it was an important trading port for the Sunda Kingdom.\\nThe first European fleet, four Portuguese ships from Malacca, arrived in 1513 while looking for a route for spices. The Sunda Kingdom made an alliance treaty with the Portuguese by allowing them to build a port in 1522 to defend against the rising power of Demak Sultanate from central Java. In 1527, Fatahillah, a Javanese general from Demak attacked and conquered Sunda Kelapa, driving out the Portuguese. Sunda Kelapa was renamed Jayakarta, and became a fiefdom of the Banten Sultanate, which became a major Southeast Asian trading centre.\\nThrough the relationship with Prince Jayawikarta of the Banten Sultanate, Dutch ships arrived in 1596. In 1602, the British East India Company's first voyage, commanded by Sir James Lancaster, arrived in Aceh and sailed on to Banten where they were allowed to build a trading post. This site became the centre of British trade in the Indonesian archipelago until 1682. Jayawikarta is thought to have made trading connections with the British merchants, rivals of the Dutch, by allowing them to build houses directly across from the Dutch buildings in 1615.\\n\\n\\n=== Colonial era ===\\n\\nWhen relations between Prince Jayawikarta and the Dutch deteriorated, his soldiers attacked the Dutch fortress. His army and the British, however, were defeated by the Dutch, in part owing to the timely arrival of Jan Pieterszoon Coen. The Dutch burned the British fort and forced them to retreat on their ships. The victory consolidated Dutch power, and they renamed the city Batavia in 1619.\\n\\nCommercial opportunities in the city attracted native and especially Chinese and Arab immigrants. This sudden population increase created burdens on the city. Tensions grew as the colonial government tried to restrict Chinese migration through deportations. Following a revolt, 5,000 Chinese were massacred by the Dutch and natives on 9 October 1740, and the following year, Chinese inhabitants were moved to Glodok outside the city walls. At the beginning of the 19th century, around 400 Arabs and Moors lived in Batavia, a number that changed little during the following decades. Among the commodities traded were fabrics, mainly imported cotton, batik and clothing worn by Arab communities.The city began to expand further south as epidemics in 1835 and 1870 forced residents to move away from the port. The Koningsplein, now Merdeka Square was completed in 1818, the housing park of Menteng was started in 1913, and Kebayoran Baru was the last Dutch-built residential area. By 1930, Batavia had more than 500,000 inhabitants, including 37,067 Europeans.On 5 March 1942, the Japanese captured Batavia from Dutch control, and the city was named Jakarta (Jakarta Special City (ジャカルタ特別市, Jakaruta tokubetsu-shi), under the special status that was assigned to the city). After the war, the Dutch name Batavia was internationally recognised until full Indonesian independence on 27 December 1949. The city, now renamed Jakarta, was officially proclaimed the national capital of Indonesia.\\n\\n\\n=== Independence era ===\\n\\nAfter World War II ended, Indonesian nationalists declared independence on 17 August 1945, and the government of Jakarta City was changed into the Jakarta National Administration in the following month. During the Indonesian National Revolution, Indonesian Republicans withdrew from Allied-occupied Jakarta and established their capital in Yogyakarta.\\nAfter securing full independence, Jakarta again became the national capital in 1950. With Jakarta selected to host the 1962 Asian Games, Soekarno, envisaging Jakarta as a great international city, instigated large government-funded projects with openly nationalistic and modernist architecture. Projects included a cloverleaf interchange, a major boulevard (Jalan MH Thamrin-Sudirman), monuments such as The National Monument, Hotel Indonesia, a shopping centre, and a new building intended to be the headquarters of CONEFO. In October 1965, Jakarta was the site of an abortive coup attempt in which six top generals were killed, precipitating a violent anti-communist purge which killed at least 500,000 people, including some ethnic Chinese. The event marked the beginning of Suharto's New Order. The first government was led by a mayor until the end of 1960 when the office was changed to that of a governor. The last mayor of Jakarta was Soediro until he was replaced by Soemarno Sosroatmodjo as governor. Based on law No. 5 of 1974 relating to regional governments, Jakarta was confirmed as the capital of Indonesia and one of the country's then 26 provinces.In 1966, Jakarta was declared a 'special capital region' (Daerah Khusus Ibukota), with a status equivalent to that of a province. Lieutenant General Ali Sadikin served as governor from 1966 to 1977; he rehabilitated roads and bridges, encouraged the arts, built hospitals and a large number of schools. He cleared out slum dwellers for new development projects — some for the benefit of the Suharto family,— and attempted to eliminate rickshaws and ban street vendors. He began control of migration to the city to stem overcrowding and poverty. Foreign investment contributed to a real estate boom that transformed the face of Jakarta. The boom ended with the 1997 Asian financial crisis, putting Jakarta at the centre of violence, protest, and political manoeuvring.\\nAfter three decades in power, support for President Suharto began to wane. Tensions peaked when four students were shot dead at Trisakti University by security forces. Four days of riots and violence in 1998 ensued that killed an estimated 1,200, and destroyed or damaged 6,000 buildings, forcing Suharto to resign. Much of the rioting targeted Chinese Indonesians. In the post-Suharto era, Jakarta has remained the focal point of democratic change in Indonesia. Jemaah Islamiah-connected bombings occurred almost annually in the city between 2000 and 2005, with another in 2009. In August 2007, Jakarta held its first-ever election to choose a governor as part of a nationwide decentralisation program that allows direct local elections in several areas. Previously, governors were elected by the city's legislative body.During the Jokowi presidency, the Government adopted a plan to move Indonesia's capital to East Kalimantan.Between 2016 and 2017, a series of terrorist attacks rocked Jakarta with scenes of multiple suicide bombings and gunfire. In suspicion to its links, the Islamic State, the perpetrator led by Abu Bakr al-Baghdadi claimed responsibility for the attacks.\\n\\n\\n== Geography ==\\n\\nJakarta covers 699.5 km2 (270.1 sq mi), the smallest among any Indonesian provinces. However, its metropolitan area covers 6,392 km2 (2,468 sq mi), which extends into two of the bordering provinces of West Java and Banten. The Greater Jakarta area includes three bordering regencies (Bekasi Regency, Tangerang Regency and Bogor Regency) and five adjacent cities (Bogor, Depok, Bekasi, Tangerang and South Tangerang).\\n\\nJakarta is situated on the northwest coast of Java, at the mouth of the Ciliwung River on Jakarta Bay, an inlet of the Java Sea. It is strategically located near the Sunda Strait. The northern part of Jakarta is plain land, some areas of which are below sea level, and subject to frequent flooding. The southern parts of the city are hilly. It is one of only two Asian capital cities located in the southern hemisphere (along with East Timor's Dili). Officially, the area of the Jakarta Special District is 662 km2 (256 sq mi) of land area and 6,977 km2 (2,694 sq mi) of sea area. The Thousand Islands, which are administratively a part of Jakarta, are located in Jakarta Bay, north of the city.\\nJakarta lies in a low and flat alluvial plain, ranging from −2 to 91 m (−7 to 299 ft) with an average elevation of 8 m (26 ft) above sea level with historically extensive swampy areas. Some parts of the city have been constructed on reclaimed tidal flats that occur around the area. Thirteen rivers flow through Jakarta. They are Ciliwung River, Kalibaru, Pesanggrahan, Cipinang, Angke River, Maja, Mookervart, Krukut, Buaran, West Tarum, Cakung, Petukangan, Sunter River and Grogol River. They flow from the Puncak highlands to the south of the city, then across the city northwards towards the Java Sea. The Ciliwung River divides the city into the western and eastern districts.\\nThese rivers, combined with the wet season rains and insufficient\", doc_id='eeb6ef32-c857-44e2-b0c5-dff6e29a9cd7', extra_info=None, node_info={'start': 0, 'end': 13970}, similarity=0.8701780916463354)], extra_info=None)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "query_engine.query(\"What is the etymology of Jakarta?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d2e2a79",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/ChromaIndexDemo.ipynb b/docs/examples/vector_stores/ChromaIndexDemo.ipynb
index 2b95af4232..3e1c8352c1 100644
--- a/docs/examples/vector_stores/ChromaIndexDemo.ipynb
+++ b/docs/examples/vector_stores/ChromaIndexDemo.ipynb
@@ -1,486 +1,500 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Chroma\n",
- "\n",
- ">[Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.\n",
- "\n",
- "<a href=\"https://discord.gg/MMeYNTmh3x\" target=\"_blank\">\n",
- " <img src=\"https://img.shields.io/discord/1073293645303795742\" alt=\"Discord\">\n",
- " </a> \n",
- " <a href=\"https://github.com/chroma-core/chroma/blob/master/LICENSE\" target=\"_blank\">\n",
- " <img src=\"https://img.shields.io/static/v1?label=license&message=Apache 2.0&color=white\" alt=\"License\">\n",
- " </a> \n",
- " <img src=\"https://github.com/chroma-core/chroma/actions/workflows/chroma-integration-test.yml/badge.svg?branch=main\" alt=\"Integration Tests\">\n",
- "\n",
- "- [Website](https://www.trychroma.com/)\n",
- "- [Documentation](https://docs.trychroma.com/)\n",
- "- [Twitter](https://twitter.com/trychroma)\n",
- "- [Discord](https://discord.gg/MMeYNTmh3x)\n",
- "\n",
- "Chroma is fully-typed, fully-tested and fully-documented.\n",
- "\n",
- "Install Chroma with:\n",
- "\n",
- "```sh\n",
- "pip install chromadb\n",
- "```\n",
- "\n",
- "Chroma runs in various modes. See below for examples of each integrated with LangChain.\n",
- "- `in-memory` - in a python script or jupyter notebook\n",
- "- `in-memory with persistance` - in a script or notebook and save/load to disk\n",
- "- `in a docker container` - as a server running your local machine or in the cloud\n",
- "\n",
- "Like any other database, you can: \n",
- "- `.add` \n",
- "- `.get` \n",
- "- `.update`\n",
- "- `.upsert`\n",
- "- `.delete`\n",
- "- `.peek`\n",
- "- and `.query` runs the similarity search.\n",
- "\n",
- "View full docs at [docs](https://docs.trychroma.com/reference/Collection). "
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b5331b6b",
- "metadata": {},
- "source": [
- "## Basic Example\n",
- "\n",
- "In this basic example, we take the a Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Chroma Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b3df0b97",
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install llama-index\n",
- "!pip install langchain\n",
- "!pip install chromadb"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "# import\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import ChromaVectorStore\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
- "from llama_index.embeddings import LangchainEmbedding\n",
- "from IPython.display import Markdown, display\n",
- "import chromadb"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "374a148b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set up OpenAI\n",
- "import os\n",
- "import getpass\n",
- "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n",
- "Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n",
- "Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n",
- "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n",
- "Use pytorch device: cpu\n",
- "Use pytorch device: cpu\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1874 tokens\n",
- "> [get_response] Total LLM token usage: 1874 tokens\n",
- "> [get_response] Total LLM token usage: 1874 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author grew up writing essays, learning Italian, exploring Florence, painting people, working with computers, studying at RISD, living in a rent-controlled apartment, building an online store builder, editing code, publishing essays online, writing essays, working on spam filters, cooking for groups, buying a building, and attending parties.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# create client and a new collection\n",
- "chroma_client = chromadb.Client()\n",
- "chroma_collection = chroma_client.create_collection(\"quickstart\")\n",
- "\n",
- "# define embedding function\n",
- "embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\"))\n",
- "\n",
- "# load documents\n",
- "documents = SimpleDirectoryReader('../../../examples/paul_graham_essay/data').load_data()\n",
- "\n",
- "# set up ChromaVectorStore and load in data\n",
- "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)\n",
- "\n",
- "# Query Data \n",
- "query_engine = index.as_query_engine(\n",
- " chroma_collection=chroma_collection\n",
- ")\n",
- "response = query_engine.query(\"What did the author do growing up?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "349de571",
- "metadata": {},
- "source": [
- "## Basic Example (including saving to disk)\n",
- "\n",
- "Extending the previous example, if you want to save to disk, simply initialize the Chroma client and pass the directory where you want the data to be saved to. \n",
- "\n",
- "`Caution`: Chroma makes a best-effort to automatically save data to disk, however multiple in-memory clients can stomp each other's work. As a best practice, only have one client per path running at any given time.\n",
- "\n",
- "`Protip`: Sometimes you can call `db.persist()` to force a save. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "9c3a56a5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "INFO:chromadb.db.duckdb:loaded in 20 embeddings\n",
- "loaded in 20 embeddings\n",
- "loaded in 20 embeddings\n",
- "INFO:chromadb.db.duckdb:loaded in 1 collections\n",
- "loaded in 1 collections\n",
- "loaded in 1 collections\n",
- "INFO:chromadb.db.duckdb:collection with name quickstart already exists, returning existing collection\n",
- "collection with name quickstart already exists, returning existing collection\n",
- "collection with name quickstart already exists, returning existing collection\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "INFO:chromadb.db.duckdb:Persisting DB to disk, putting it in the save folder: ./chroma_db\n",
- "Persisting DB to disk, putting it in the save folder: ./chroma_db\n",
- "Persisting DB to disk, putting it in the save folder: ./chroma_db\n",
- "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "INFO:chromadb.db.duckdb:loaded in 40 embeddings\n",
- "loaded in 40 embeddings\n",
- "loaded in 40 embeddings\n",
- "INFO:chromadb.db.duckdb:loaded in 1 collections\n",
- "loaded in 1 collections\n",
- "loaded in 1 collections\n",
- "INFO:chromadb.db.duckdb:collection with name quickstart already exists, returning existing collection\n",
- "collection with name quickstart already exists, returning existing collection\n",
- "collection with name quickstart already exists, returning existing collection\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1877 tokens\n",
- "> [get_response] Total LLM token usage: 1877 tokens\n",
- "> [get_response] Total LLM token usage: 1877 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author grew up skipping a step in the evolution of computers, learning Italian, exploring Florence, painting people, working with technology companies, seeking signature styles at RISD, living in a rent-stabilized apartment, launching an online store builder, editing Lisp expressions, and publishing essays online.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# save to disk\n",
- "from chromadb.config import Settings\n",
- "db = chromadb.Client(Settings(\n",
- " chroma_db_impl=\"duckdb+parquet\",\n",
- " persist_directory=\"./chroma_db\"\n",
- "))\n",
- "chroma_collection = db.get_or_create_collection(\"quickstart\")\n",
- "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)\n",
- "db.persist()\n",
- "\n",
- "# load from disk\n",
- "db2 = chromadb.Client(Settings(\n",
- " chroma_db_impl=\"duckdb+parquet\",\n",
- " persist_directory=\"./chroma_db\"\n",
- "))\n",
- "chroma_collection = db2.get_or_create_collection(\"quickstart\")\n",
- "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_vector_store(vector_store= vector_store, storage_context=storage_context, embed_model=embed_model)\n",
- "\n",
- "# Query Data from the persisted index\n",
- "query_engine = index.as_query_engine(\n",
- " chroma_collection=chroma_collection\n",
- ")\n",
- "response = query_engine.query(\"What did the author do growing up?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d596e475",
- "metadata": {},
- "source": [
- "## Basic Example (using the Docker Container)\n",
- "\n",
- "You can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LlamaIndex. \n",
- "\n",
- "Here is how to clone, build, and run the Docker Image:\n",
- "```\n",
- "git clone git@github.com:chroma-core/chroma.git\n",
- "docker-compose up -d --build\n",
- "```"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "d6c9bd64",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n"
- ]
- }
- ],
- "source": [
- "# create the chroma client and add our data\n",
- "import chromadb\n",
- "from chromadb.config import Settings\n",
- "\n",
- "remote_db = chromadb.Client(Settings(chroma_api_impl=\"rest\",\n",
- " chroma_server_host=\"localhost\",\n",
- " chroma_server_http_port=\"8000\"\n",
- " ))\n",
- "remote_db.reset() # resets the database\n",
- "chroma_collection = remote_db.get_or_create_collection(\"quickstart\")\n",
- "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, embed_model=embed_model)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "88e10c26",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1874 tokens\n",
- "> [get_response] Total LLM token usage: 1874 tokens\n",
- "> [get_response] Total LLM token usage: 1874 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author grew up writing essays, learning Italian, exploring Florence, painting people, working with computers, studying at RISD, living in a rent-controlled apartment, building an online store builder, editing code, publishing essays online, writing essays, working on spam filters, cooking for groups, buying a building, and attending parties.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Query Data from the Chroma Docker index\n",
- "query_engine = index.as_query_engine(\n",
- " chroma_collection=chroma_collection\n",
- ")\n",
- "response = query_engine.query(\"What did the author do growing up?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0a0e79f7",
- "metadata": {},
- "source": [
- "## Update and Delete\n",
- "\n",
- "While building toward a real application, you want to go beyond adding data, and also update and delete data. \n",
- "\n",
- "Chroma has users provide `ids` to simplify the bookkeeping here. `ids` can be the name of the file, or a combined has like `filename_paragraphNumber`, etc.\n",
- "\n",
- "Here is a basic example showing how to do various operations:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 60,
- "id": "d9411826",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'node_info': '{\"start\": 0, \"end\": 4040, \"_node_type\": \"1\"}', 'relationships': '{\"1\": \"a0294b91-ff5f-45fe-b249-5596a18cc952\", \"3\": \"95771df1-9ec9-4128-9a11-ac92b768e2e3\"}', 'document_id': 'a0294b91-ff5f-45fe-b249-5596a18cc952', 'doc_id': 'a0294b91-ff5f-45fe-b249-5596a18cc952', 'ref_doc_id': 'a0294b91-ff5f-45fe-b249-5596a18cc952', 'author': 'Paul Graham'}\n",
- "count before 20\n",
- "count after 19\n"
- ]
- }
- ],
- "source": [
- "doc_to_update = chroma_collection.get(limit=1)\n",
- "doc_to_update['metadatas'][0] = {**doc_to_update['metadatas'][0], **{\"author\": \"Paul Graham\"}}\n",
- "chroma_collection.update(ids=[doc_to_update['ids'][0]], metadatas=[doc_to_update['metadatas'][0]])\n",
- "updated_doc = chroma_collection.get(limit=1)\n",
- "print(updated_doc['metadatas'][0])\n",
- "\n",
- "# delete the last document\n",
- "print(\"count before\", chroma_collection.count())\n",
- "chroma_collection.delete(ids=[doc_to_update['ids'][0]])\n",
- "print(\"count after\", chroma_collection.count())"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- },
- "vscode": {
- "interpreter": {
- "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
- }
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Chroma\n",
+ "\n",
+ ">[Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.\n",
+ "\n",
+ "<a href=\"https://discord.gg/MMeYNTmh3x\" target=\"_blank\">\n",
+ " <img src=\"https://img.shields.io/discord/1073293645303795742\" alt=\"Discord\">\n",
+ " </a> \n",
+ " <a href=\"https://github.com/chroma-core/chroma/blob/master/LICENSE\" target=\"_blank\">\n",
+ " <img src=\"https://img.shields.io/static/v1?label=license&message=Apache 2.0&color=white\" alt=\"License\">\n",
+ " </a> \n",
+ " <img src=\"https://github.com/chroma-core/chroma/actions/workflows/chroma-integration-test.yml/badge.svg?branch=main\" alt=\"Integration Tests\">\n",
+ "\n",
+ "- [Website](https://www.trychroma.com/)\n",
+ "- [Documentation](https://docs.trychroma.com/)\n",
+ "- [Twitter](https://twitter.com/trychroma)\n",
+ "- [Discord](https://discord.gg/MMeYNTmh3x)\n",
+ "\n",
+ "Chroma is fully-typed, fully-tested and fully-documented.\n",
+ "\n",
+ "Install Chroma with:\n",
+ "\n",
+ "```sh\n",
+ "pip install chromadb\n",
+ "```\n",
+ "\n",
+ "Chroma runs in various modes. See below for examples of each integrated with LangChain.\n",
+ "- `in-memory` - in a python script or jupyter notebook\n",
+ "- `in-memory with persistance` - in a script or notebook and save/load to disk\n",
+ "- `in a docker container` - as a server running your local machine or in the cloud\n",
+ "\n",
+ "Like any other database, you can: \n",
+ "- `.add` \n",
+ "- `.get` \n",
+ "- `.update`\n",
+ "- `.upsert`\n",
+ "- `.delete`\n",
+ "- `.peek`\n",
+ "- and `.query` runs the similarity search.\n",
+ "\n",
+ "View full docs at [docs](https://docs.trychroma.com/reference/Collection). "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b5331b6b",
+ "metadata": {},
+ "source": [
+ "## Basic Example\n",
+ "\n",
+ "In this basic example, we take the a Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Chroma Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b3df0b97",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install llama-index\n",
+ "!pip install langchain\n",
+ "!pip install chromadb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import ChromaVectorStore\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
+ "from llama_index.embeddings import LangchainEmbedding\n",
+ "from IPython.display import Markdown, display\n",
+ "import chromadb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "374a148b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set up OpenAI\n",
+ "import os\n",
+ "import getpass\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n",
+ "Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n",
+ "Load pretrained SentenceTransformer: sentence-transformers/all-mpnet-base-v2\n",
+ "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n",
+ "Use pytorch device: cpu\n",
+ "Use pytorch device: cpu\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1874 tokens\n",
+ "> [get_response] Total LLM token usage: 1874 tokens\n",
+ "> [get_response] Total LLM token usage: 1874 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author grew up writing essays, learning Italian, exploring Florence, painting people, working with computers, studying at RISD, living in a rent-controlled apartment, building an online store builder, editing code, publishing essays online, writing essays, working on spam filters, cooking for groups, buying a building, and attending parties.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# create client and a new collection\n",
+ "chroma_client = chromadb.Client()\n",
+ "chroma_collection = chroma_client.create_collection(\"quickstart\")\n",
+ "\n",
+ "# define embedding function\n",
+ "embed_model = LangchainEmbedding(\n",
+ " HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
+ ")\n",
+ "\n",
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../examples/paul_graham_essay/data\"\n",
+ ").load_data()\n",
+ "\n",
+ "# set up ChromaVectorStore and load in data\n",
+ "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " documents, storage_context=storage_context, embed_model=embed_model\n",
+ ")\n",
+ "\n",
+ "# Query Data\n",
+ "query_engine = index.as_query_engine(chroma_collection=chroma_collection)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "349de571",
+ "metadata": {},
+ "source": [
+ "## Basic Example (including saving to disk)\n",
+ "\n",
+ "Extending the previous example, if you want to save to disk, simply initialize the Chroma client and pass the directory where you want the data to be saved to. \n",
+ "\n",
+ "`Caution`: Chroma makes a best-effort to automatically save data to disk, however multiple in-memory clients can stomp each other's work. As a best practice, only have one client per path running at any given time.\n",
+ "\n",
+ "`Protip`: Sometimes you can call `db.persist()` to force a save. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "9c3a56a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "INFO:chromadb.db.duckdb:loaded in 20 embeddings\n",
+ "loaded in 20 embeddings\n",
+ "loaded in 20 embeddings\n",
+ "INFO:chromadb.db.duckdb:loaded in 1 collections\n",
+ "loaded in 1 collections\n",
+ "loaded in 1 collections\n",
+ "INFO:chromadb.db.duckdb:collection with name quickstart already exists, returning existing collection\n",
+ "collection with name quickstart already exists, returning existing collection\n",
+ "collection with name quickstart already exists, returning existing collection\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "INFO:chromadb.db.duckdb:Persisting DB to disk, putting it in the save folder: ./chroma_db\n",
+ "Persisting DB to disk, putting it in the save folder: ./chroma_db\n",
+ "Persisting DB to disk, putting it in the save folder: ./chroma_db\n",
+ "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "INFO:chromadb.db.duckdb:loaded in 40 embeddings\n",
+ "loaded in 40 embeddings\n",
+ "loaded in 40 embeddings\n",
+ "INFO:chromadb.db.duckdb:loaded in 1 collections\n",
+ "loaded in 1 collections\n",
+ "loaded in 1 collections\n",
+ "INFO:chromadb.db.duckdb:collection with name quickstart already exists, returning existing collection\n",
+ "collection with name quickstart already exists, returning existing collection\n",
+ "collection with name quickstart already exists, returning existing collection\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1877 tokens\n",
+ "> [get_response] Total LLM token usage: 1877 tokens\n",
+ "> [get_response] Total LLM token usage: 1877 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author grew up skipping a step in the evolution of computers, learning Italian, exploring Florence, painting people, working with technology companies, seeking signature styles at RISD, living in a rent-stabilized apartment, launching an online store builder, editing Lisp expressions, and publishing essays online.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# save to disk\n",
+ "from chromadb.config import Settings\n",
+ "\n",
+ "db = chromadb.Client(\n",
+ " Settings(chroma_db_impl=\"duckdb+parquet\", persist_directory=\"./chroma_db\")\n",
+ ")\n",
+ "chroma_collection = db.get_or_create_collection(\"quickstart\")\n",
+ "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " documents, storage_context=storage_context, embed_model=embed_model\n",
+ ")\n",
+ "db.persist()\n",
+ "\n",
+ "# load from disk\n",
+ "db2 = chromadb.Client(\n",
+ " Settings(chroma_db_impl=\"duckdb+parquet\", persist_directory=\"./chroma_db\")\n",
+ ")\n",
+ "chroma_collection = db2.get_or_create_collection(\"quickstart\")\n",
+ "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_vector_store(\n",
+ " vector_store=vector_store, storage_context=storage_context, embed_model=embed_model\n",
+ ")\n",
+ "\n",
+ "# Query Data from the persisted index\n",
+ "query_engine = index.as_query_engine(chroma_collection=chroma_collection)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d596e475",
+ "metadata": {},
+ "source": [
+ "## Basic Example (using the Docker Container)\n",
+ "\n",
+ "You can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LlamaIndex. \n",
+ "\n",
+ "Here is how to clone, build, and run the Docker Image:\n",
+ "```\n",
+ "git clone git@github.com:chroma-core/chroma.git\n",
+ "docker-compose up -d --build\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "d6c9bd64",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17038 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# create the chroma client and add our data\n",
+ "import chromadb\n",
+ "from chromadb.config import Settings\n",
+ "\n",
+ "remote_db = chromadb.Client(\n",
+ " Settings(\n",
+ " chroma_api_impl=\"rest\",\n",
+ " chroma_server_host=\"localhost\",\n",
+ " chroma_server_http_port=\"8000\",\n",
+ " )\n",
+ ")\n",
+ "remote_db.reset() # resets the database\n",
+ "chroma_collection = remote_db.get_or_create_collection(\"quickstart\")\n",
+ "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " documents, storage_context=storage_context, embed_model=embed_model\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "88e10c26",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1874 tokens\n",
+ "> [get_response] Total LLM token usage: 1874 tokens\n",
+ "> [get_response] Total LLM token usage: 1874 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author grew up writing essays, learning Italian, exploring Florence, painting people, working with computers, studying at RISD, living in a rent-controlled apartment, building an online store builder, editing code, publishing essays online, writing essays, working on spam filters, cooking for groups, buying a building, and attending parties.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Query Data from the Chroma Docker index\n",
+ "query_engine = index.as_query_engine(chroma_collection=chroma_collection)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0a0e79f7",
+ "metadata": {},
+ "source": [
+ "## Update and Delete\n",
+ "\n",
+ "While building toward a real application, you want to go beyond adding data, and also update and delete data. \n",
+ "\n",
+ "Chroma has users provide `ids` to simplify the bookkeeping here. `ids` can be the name of the file, or a combined has like `filename_paragraphNumber`, etc.\n",
+ "\n",
+ "Here is a basic example showing how to do various operations:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "d9411826",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'node_info': '{\"start\": 0, \"end\": 4040, \"_node_type\": \"1\"}', 'relationships': '{\"1\": \"a0294b91-ff5f-45fe-b249-5596a18cc952\", \"3\": \"95771df1-9ec9-4128-9a11-ac92b768e2e3\"}', 'document_id': 'a0294b91-ff5f-45fe-b249-5596a18cc952', 'doc_id': 'a0294b91-ff5f-45fe-b249-5596a18cc952', 'ref_doc_id': 'a0294b91-ff5f-45fe-b249-5596a18cc952', 'author': 'Paul Graham'}\n",
+ "count before 20\n",
+ "count after 19\n"
+ ]
+ }
+ ],
+ "source": [
+ "doc_to_update = chroma_collection.get(limit=1)\n",
+ "doc_to_update[\"metadatas\"][0] = {\n",
+ " **doc_to_update[\"metadatas\"][0],\n",
+ " **{\"author\": \"Paul Graham\"},\n",
+ "}\n",
+ "chroma_collection.update(\n",
+ " ids=[doc_to_update[\"ids\"][0]], metadatas=[doc_to_update[\"metadatas\"][0]]\n",
+ ")\n",
+ "updated_doc = chroma_collection.get(limit=1)\n",
+ "print(updated_doc[\"metadatas\"][0])\n",
+ "\n",
+ "# delete the last document\n",
+ "print(\"count before\", chroma_collection.count())\n",
+ "chroma_collection.delete(ids=[doc_to_update[\"ids\"][0]])\n",
+ "print(\"count after\", chroma_collection.count())"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/DeepLakeIndexDemo.ipynb b/docs/examples/vector_stores/DeepLakeIndexDemo.ipynb
index ba2c700386..b655642d82 100644
--- a/docs/examples/vector_stores/DeepLakeIndexDemo.ipynb
+++ b/docs/examples/vector_stores/DeepLakeIndexDemo.ipynb
@@ -1,451 +1,451 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# DeepLake Vector Store"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/adilkhansarsen/Documents/work/LlamaIndex/llama_index/GPTIndex/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import textwrap\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document\n",
- "from llama_index.vector_stores import DeepLakeVectorStore\n",
- "\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-********************************\"\n",
- "os.environ[\n",
- " \"ACTIVELOOP_TOKEN\"\n",
- "] = \"********************************\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install deeplake"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "if you don't export token in your environment alternativalay you can use deeplake CLI to loging to deeplake"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# !activeloop login -t <TOKEN> "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Document ID: 14935662-4884-4c57-ac2e-fa62da019665 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n"
- ]
- }
- ],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Your Deep Lake dataset has been successfully created!\n",
- "The dataset is private so make sure you are logged in!\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "|"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/adilkhan/paul_graham_essay\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " \r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "hub://adilkhan/paul_graham_essay loaded successfully.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Evaluating ingest: 100%|██████████| 1/1 [00:21<00:00\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset(path='hub://adilkhan/paul_graham_essay', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
- "\n",
- " tensor htype shape dtype compression\n",
- " ------- ------- ------- ------- ------- \n",
- " embedding generic (6, 1536) None None \n",
- " ids text (6, 1) str None \n",
- " metadata json (6, 1) str None \n",
- " text text (6, 1) str None \n"
- ]
- }
- ],
- "source": [
- "# dataset_path = \"hub://adilkhan/paul_graham_essay\" # if we comment this out and don't pass the path then GPTDeepLakeIndex will create dataset in memory\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "dataset_path = \"paul_graham_essay\"\n",
- "\n",
- "# Create an index over the documnts\n",
- "vector_store = DeepLakeVectorStore(dataset_path=dataset_path, overwrite=True)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "if we decide to not pass the path then GPTDeepLakeIndex will create dataset locally called llama_index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "llama_index loaded successfully.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Evaluating ingest: 100%|██████████| 1/1 [00:04<00:00\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset(path='llama_index', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
- "\n",
- " tensor htype shape dtype compression\n",
- " ------- ------- ------- ------- ------- \n",
- " embedding generic (6, 1536) None None \n",
- " ids text (6, 1) str None \n",
- " metadata json (6, 1) str None \n",
- " text text (6, 1) str None \n"
- ]
- }
- ],
- "source": [
- "# Create an index over the documnts\n",
- "# vector_store = DeepLakeVectorStore(overwrite=True)\n",
- "# storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "# index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4028 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 6 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author learn?\",)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " The author learned that working on things that are not prestigious can be a good thing, as it can\n",
- "lead to discovering something real and avoiding the wrong track. The author also learned that\n",
- "ignorance can be beneficial, as it can lead to discovering something new and unexpected. The author\n",
- "also learned the importance of working hard, even at the parts of the job they don't like, in order\n",
- "to set an example for others. The author also learned the value of unsolicited advice, as it can be\n",
- "beneficial in unexpected ways, such as when Robert Morris suggested that the author should make sure\n",
- "Y Combinator wasn't the last cool thing they did.\n"
- ]
- }
- ],
- "source": [
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4072 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 9 tokens\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\"What was a hard moment for the author?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " A hard moment for the author was when he was dealing with urgent problems during YC and about 60%\n",
- "of them had to do with Hacker News, a news aggregator he had created. He was overwhelmed by the\n",
- "amount of work he had to do to keep Hacker News running, and it was taking away from his ability to\n",
- "focus on other projects. He was also haunted by the idea that his own work ethic set the upper bound\n",
- "for how hard everyone else worked, so he felt he had to work very hard. He was also dealing with\n",
- "disputes between cofounders, figuring out when people were lying to them, and fighting with people\n",
- "who maltreated the startups. On top of this, he was given unsolicited advice from Robert Morris to\n",
- "make sure Y Combinator wasn't the last cool thing he did, which made him consider quitting.\n"
- ]
- }
- ],
- "source": [
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4072 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 9 tokens\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " A hard moment for the author was when he was dealing with urgent problems during YC and about 60%\n",
- "of them had to do with Hacker News, a news aggregator he had created. He was overwhelmed by the\n",
- "amount of work he had to do to keep Hacker News running, and it was taking away from his ability to\n",
- "focus on other projects. He was also haunted by the idea that his own work ethic set the upper bound\n",
- "for how hard everyone else worked, so he felt he had to work very hard. He was also dealing with\n",
- "disputes between cofounders, figuring out when people were lying to them, and fighting with people\n",
- "who maltreated the startups. On top of this, he was given unsolicited advice from Robert Morris to\n",
- "make sure Y Combinator wasn't the last cool thing he did, which made him consider quitting.\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What was a hard moment for the author?\")\n",
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Deleting items from the database"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\\"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/adilkhan/paul_graham_essay\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\\"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "hub://adilkhan/paul_graham_essay loaded successfully.\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " \r"
- ]
- }
- ],
- "source": [
- "import deeplake as dp\n",
- "\n",
- "\n",
- "ds = dp.load(\"paul_graham_essay\")\n",
- "\n",
- "idx = ds.ids[0].numpy().tolist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|██████████| 6/6 [00:00<00:00, 4501.13it/s]\n",
- " \r"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dataset(path='hub://adilkhan/paul_graham_essay', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
- "\n",
- " tensor htype shape dtype compression\n",
- " ------- ------- ------- ------- ------- \n",
- " embedding generic (5, 1536) None None \n",
- " ids text (5, 1) str None \n",
- " metadata json (5, 1) str None \n",
- " text text (5, 1) str None \n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": []
- }
- ],
- "source": [
- "index.delete(idx[0])"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- },
- "vscode": {
- "interpreter": {
- "hash": "6e44765f40d39e4c6e3d7a9b35e5b42b8711c1c0fb3c237b84fa62e4b3e35e04"
- }
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# DeepLake Vector Store"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/adilkhansarsen/Documents/work/LlamaIndex/llama_index/GPTIndex/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import textwrap\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document\n",
+ "from llama_index.vector_stores import DeepLakeVectorStore\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-********************************\"\n",
+ "os.environ[\"ACTIVELOOP_TOKEN\"] = \"********************************\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install deeplake"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "if you don't export token in your environment alternativalay you can use deeplake CLI to loging to deeplake"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !activeloop login -t <TOKEN>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Document ID: 14935662-4884-4c57-ac2e-fa62da019665 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Your Deep Lake dataset has been successfully created!\n",
+ "The dataset is private so make sure you are logged in!\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 4
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "|"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/adilkhan/paul_graham_essay\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hub://adilkhan/paul_graham_essay loaded successfully.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Evaluating ingest: 100%|██████████| 1/1 [00:21<00:00\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset(path='hub://adilkhan/paul_graham_essay', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+ "\n",
+ " tensor htype shape dtype compression\n",
+ " ------- ------- ------- ------- ------- \n",
+ " embedding generic (6, 1536) None None \n",
+ " ids text (6, 1) str None \n",
+ " metadata json (6, 1) str None \n",
+ " text text (6, 1) str None \n"
+ ]
+ }
+ ],
+ "source": [
+ "# dataset_path = \"hub://adilkhan/paul_graham_essay\" # if we comment this out and don't pass the path then GPTDeepLakeIndex will create dataset in memory\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "dataset_path = \"paul_graham_essay\"\n",
+ "\n",
+ "# Create an index over the documnts\n",
+ "vector_store = DeepLakeVectorStore(dataset_path=dataset_path, overwrite=True)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "if we decide to not pass the path then GPTDeepLakeIndex will create dataset locally called llama_index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "llama_index loaded successfully.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Evaluating ingest: 100%|██████████| 1/1 [00:04<00:00\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset(path='llama_index', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+ "\n",
+ " tensor htype shape dtype compression\n",
+ " ------- ------- ------- ------- ------- \n",
+ " embedding generic (6, 1536) None None \n",
+ " ids text (6, 1) str None \n",
+ " metadata json (6, 1) str None \n",
+ " text text (6, 1) str None \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create an index over the documnts\n",
+ "# vector_store = DeepLakeVectorStore(overwrite=True)\n",
+ "# storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "# index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4028 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 6 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\n",
+ " \"What did the author learn?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " The author learned that working on things that are not prestigious can be a good thing, as it can\n",
+ "lead to discovering something real and avoiding the wrong track. The author also learned that\n",
+ "ignorance can be beneficial, as it can lead to discovering something new and unexpected. The author\n",
+ "also learned the importance of working hard, even at the parts of the job they don't like, in order\n",
+ "to set an example for others. The author also learned the value of unsolicited advice, as it can be\n",
+ "beneficial in unexpected ways, such as when Robert Morris suggested that the author should make sure\n",
+ "Y Combinator wasn't the last cool thing they did.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4072 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 9 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What was a hard moment for the author?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " A hard moment for the author was when he was dealing with urgent problems during YC and about 60%\n",
+ "of them had to do with Hacker News, a news aggregator he had created. He was overwhelmed by the\n",
+ "amount of work he had to do to keep Hacker News running, and it was taking away from his ability to\n",
+ "focus on other projects. He was also haunted by the idea that his own work ethic set the upper bound\n",
+ "for how hard everyone else worked, so he felt he had to work very hard. He was also dealing with\n",
+ "disputes between cofounders, figuring out when people were lying to them, and fighting with people\n",
+ "who maltreated the startups. On top of this, he was given unsolicited advice from Robert Morris to\n",
+ "make sure Y Combinator wasn't the last cool thing he did, which made him consider quitting.\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 4072 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 9 tokens\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " A hard moment for the author was when he was dealing with urgent problems during YC and about 60%\n",
+ "of them had to do with Hacker News, a news aggregator he had created. He was overwhelmed by the\n",
+ "amount of work he had to do to keep Hacker News running, and it was taking away from his ability to\n",
+ "focus on other projects. He was also haunted by the idea that his own work ethic set the upper bound\n",
+ "for how hard everyone else worked, so he felt he had to work very hard. He was also dealing with\n",
+ "disputes between cofounders, figuring out when people were lying to them, and fighting with people\n",
+ "who maltreated the startups. On top of this, he was given unsolicited advice from Robert Morris to\n",
+ "make sure Y Combinator wasn't the last cool thing he did, which made him consider quitting.\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What was a hard moment for the author?\")\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Deleting items from the database"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\\"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/adilkhan/paul_graham_essay\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\\"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "hub://adilkhan/paul_graham_essay loaded successfully.\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " \r"
+ ]
+ }
+ ],
+ "source": [
+ "import deeplake as dp\n",
+ "\n",
+ "\n",
+ "ds = dp.load(\"paul_graham_essay\")\n",
+ "\n",
+ "idx = ds.ids[0].numpy().tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|██████████| 6/6 [00:00<00:00, 4501.13it/s]\n",
+ " \r"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset(path='hub://adilkhan/paul_graham_essay', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
+ "\n",
+ " tensor htype shape dtype compression\n",
+ " ------- ------- ------- ------- ------- \n",
+ " embedding generic (5, 1536) None None \n",
+ " ids text (5, 1) str None \n",
+ " metadata json (5, 1) str None \n",
+ " text text (5, 1) str None \n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": []
+ }
+ ],
+ "source": [
+ "index.delete(idx[0])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "6e44765f40d39e4c6e3d7a9b35e5b42b8711c1c0fb3c237b84fa62e4b3e35e04"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/docs/examples/vector_stores/DocArrayHnswIndexDemo.ipynb b/docs/examples/vector_stores/DocArrayHnswIndexDemo.ipynb
index b32be01389..1457bc9452 100644
--- a/docs/examples/vector_stores/DocArrayHnswIndexDemo.ipynb
+++ b/docs/examples/vector_stores/DocArrayHnswIndexDemo.ipynb
@@ -25,14 +25,15 @@
"import textwrap\n",
"\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# stop h|uggingface warnings\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"\n",
"# Uncomment to see debug logs\n",
- "#logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "#logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
"from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, Document\n",
"from llama_index.vector_stores import DocArrayHnswVectorStore\n",
@@ -47,6 +48,7 @@
"outputs": [],
"source": [
"import os\n",
+ "\n",
"os.environ[\"OPENAI_API_KEY\"] = \"<your openai key>\""
]
},
@@ -66,8 +68,8 @@
],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
]
},
{
@@ -89,7 +91,7 @@
"from llama_index.storage.storage_context import StorageContext\n",
"\n",
"\n",
- "vector_store = DocArrayHnswVectorStore(work_dir='hnsw_index')\n",
+ "vector_store = DocArrayHnswVectorStore(work_dir=\"hnsw_index\")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"index = GPTVectorStoreIndex.from_documents(documents, storage_context=storage_context)"
]
@@ -175,17 +177,26 @@
"from llama_index.schema import TextNode\n",
"\n",
"nodes = [\n",
- " TextNode(text='The Shawshank Redemption', metadata={\n",
- " \"author\": \"Stephen King\",\n",
- " \"theme\": \"Friendship\",\n",
- " }),\n",
- " TextNode(text='The Godfather', metadata={\n",
- " \"director\": \"Francis Ford Coppola\",\n",
- " \"theme\": \"Mafia\",\n",
- " }),\n",
- " TextNode(text=\"Inception\", metadata={\n",
- " \"director\": \"Christopher Nolan\",\n",
- " })\n",
+ " TextNode(\n",
+ " text=\"The Shawshank Redemption\",\n",
+ " metadata={\n",
+ " \"author\": \"Stephen King\",\n",
+ " \"theme\": \"Friendship\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"The Godfather\",\n",
+ " metadata={\n",
+ " \"director\": \"Francis Ford Coppola\",\n",
+ " \"theme\": \"Mafia\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Inception\",\n",
+ " metadata={\n",
+ " \"director\": \"Christopher Nolan\",\n",
+ " },\n",
+ " ),\n",
"]"
]
},
@@ -199,7 +210,7 @@
"from llama_index.storage.storage_context import StorageContext\n",
"\n",
"\n",
- "vector_store = DocArrayHnswVectorStore(work_dir='hnsw_filters')\n",
+ "vector_store = DocArrayHnswVectorStore(work_dir=\"hnsw_filters\")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"\n",
"index = GPTVectorStoreIndex(nodes, storage_context=storage_context)"
@@ -226,12 +237,10 @@
"from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
"\n",
"\n",
- "filters = MetadataFilters(filters=[\n",
- " ExactMatchFilter(key='theme', value='Mafia')\n",
- "])\n",
+ "filters = MetadataFilters(filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")])\n",
"\n",
"retriever = index.as_retriever(filters=filters)\n",
- "retriever.retrieve('What is inception about?')"
+ "retriever.retrieve(\"What is inception about?\")"
]
},
{
@@ -243,10 +252,11 @@
"source": [
"# remove created indices\n",
"import os, shutil\n",
- "hnsw_dirs = ['hnsw_filters', 'hnsw_index']\n",
+ "\n",
+ "hnsw_dirs = [\"hnsw_filters\", \"hnsw_index\"]\n",
"for dir in hnsw_dirs:\n",
" if os.path.exists(dir):\n",
- " shutil.rmtree(dir)\n"
+ " shutil.rmtree(dir)"
]
},
{
diff --git a/docs/examples/vector_stores/DocArrayInMemoryIndexDemo.ipynb b/docs/examples/vector_stores/DocArrayInMemoryIndexDemo.ipynb
index 4d406670b7..89bc15adb2 100644
--- a/docs/examples/vector_stores/DocArrayInMemoryIndexDemo.ipynb
+++ b/docs/examples/vector_stores/DocArrayInMemoryIndexDemo.ipynb
@@ -26,14 +26,15 @@
"import textwrap\n",
"\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# stop huggingface warnings\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"\n",
"# Uncomment to see debug logs\n",
- "#logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "#logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
"from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, Document\n",
"from llama_index.vector_stores import DocArrayInMemoryVectorStore\n",
@@ -48,6 +49,7 @@
"outputs": [],
"source": [
"import os\n",
+ "\n",
"os.environ[\"OPENAI_API_KEY\"] = \"<your openai key>\""
]
},
@@ -67,8 +69,8 @@
],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
]
},
{
@@ -177,17 +179,26 @@
"from llama_index.schema import TextNode\n",
"\n",
"nodes = [\n",
- " TextNode(text='The Shawshank Redemption', metadata={\n",
- " \"author\": \"Stephen King\",\n",
- " \"theme\": \"Friendship\",\n",
- " }),\n",
- " TextNode(text='The Godfather', metadata={\n",
- " \"director\": \"Francis Ford Coppola\",\n",
- " \"theme\": \"Mafia\",\n",
- " }),\n",
- " TextNode(text=\"Inception\", metadata={\n",
- " \"director\": \"Christopher Nolan\",\n",
- " })\n",
+ " TextNode(\n",
+ " text=\"The Shawshank Redemption\",\n",
+ " metadata={\n",
+ " \"author\": \"Stephen King\",\n",
+ " \"theme\": \"Friendship\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"The Godfather\",\n",
+ " metadata={\n",
+ " \"director\": \"Francis Ford Coppola\",\n",
+ " \"theme\": \"Mafia\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Inception\",\n",
+ " metadata={\n",
+ " \"director\": \"Christopher Nolan\",\n",
+ " },\n",
+ " ),\n",
"]"
]
},
@@ -228,12 +239,10 @@
"from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
"\n",
"\n",
- "filters = MetadataFilters(filters=[\n",
- " ExactMatchFilter(key='theme', value='Mafia')\n",
- "])\n",
+ "filters = MetadataFilters(filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")])\n",
"\n",
"retriever = index.as_retriever(filters=filters)\n",
- "retriever.retrieve('What is inception about?')"
+ "retriever.retrieve(\"What is inception about?\")"
]
},
{
diff --git a/docs/examples/vector_stores/FaissIndexDemo.ipynb b/docs/examples/vector_stores/FaissIndexDemo.ipynb
index 2e0fa310b4..3ce94da79c 100644
--- a/docs/examples/vector_stores/FaissIndexDemo.ipynb
+++ b/docs/examples/vector_stores/FaissIndexDemo.ipynb
@@ -1,199 +1,205 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Faiss Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Faiss Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a1b5e530",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
- "metadata": {},
- "outputs": [],
- "source": [
- "import faiss\n",
- "# dimensions of text-ada-embedding-002\n",
- "d = 1536 \n",
- "faiss_index = faiss.IndexFlatL2(d)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import SimpleDirectoryReader, load_index_from_storage, VectorStoreIndex, StorageContext\n",
- "from llama_index.vector_stores.faiss import FaissVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ba1558b3",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_store = FaissVectorStore(faiss_index=faiss_index)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c36cadc1",
- "metadata": {},
- "outputs": [],
- "source": [
- "# save index to disk\n",
- "index.storage_context.persist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "70b372a7",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load index from disk\n",
- "vector_store = FaissVectorStore.from_persist_dir('./storage')\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = load_index_from_storage(storage_context=storage_context)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "35369eda",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "99212d33",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1a720ad6",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b0b6d770",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Faiss Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Faiss Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1b5e530",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import faiss\n",
+ "\n",
+ "# dimensions of text-ada-embedding-002\n",
+ "d = 1536\n",
+ "faiss_index = faiss.IndexFlatL2(d)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " load_index_from_storage,\n",
+ " VectorStoreIndex,\n",
+ " StorageContext,\n",
+ ")\n",
+ "from llama_index.vector_stores.faiss import FaissVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba1558b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_store = FaissVectorStore(faiss_index=faiss_index)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c36cadc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save index to disk\n",
+ "index.storage_context.persist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "70b372a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load index from disk\n",
+ "vector_store = FaissVectorStore.from_persist_dir(\"./storage\")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = load_index_from_storage(storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "99212d33",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a720ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b0b6d770",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/LanceDBIndexDemo.ipynb b/docs/examples/vector_stores/LanceDBIndexDemo.ipynb
index 2202b1bfee..1aff6c622c 100644
--- a/docs/examples/vector_stores/LanceDBIndexDemo.ipynb
+++ b/docs/examples/vector_stores/LanceDBIndexDemo.ipynb
@@ -57,7 +57,8 @@
"outputs": [],
"source": [
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"\""
]
},
{
@@ -85,8 +86,8 @@
}
],
"source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
]
},
{
@@ -121,7 +122,7 @@
}
],
"source": [
- "vector_store = LanceDBVectorStore(uri='/tmp/lancedb')\n",
+ "vector_store = LanceDBVectorStore(uri=\"/tmp/lancedb\")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
]
@@ -250,7 +251,9 @@
"source": [
"del index\n",
"\n",
- "index = VectorStoreIndex.from_documents([Document(text=\"The sky is blue\")], uri=\"/tmp/new_dataset\")"
+ "index = VectorStoreIndex.from_documents(\n",
+ " [Document(text=\"The sky is blue\")], uri=\"/tmp/new_dataset\"\n",
+ ")"
]
},
{
diff --git a/docs/examples/vector_stores/MetalIndexDemo.ipynb b/docs/examples/vector_stores/MetalIndexDemo.ipynb
index ec59f7cbfc..78938f1fc2 100644
--- a/docs/examples/vector_stores/MetalIndexDemo.ipynb
+++ b/docs/examples/vector_stores/MetalIndexDemo.ipynb
@@ -1,163 +1,163 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {
- "id": "EuGbkREYqu6n"
- },
- "source": [
- "# Metal Vector Store"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "_D2M6RiFq3zp"
- },
- "source": [
- "## Creating a Metal Vector Store"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3iAr1k9Rrjg1"
- },
- "source": [
- "1. Register an account for [Metal](https://app.getmetal.io/)\n",
- "2. Generate an API key in [Metal's Settings](https://app.getmetal.io/settings/organization). Save the `api_key` + `client_id`\n",
- "3. Generate an Index in [Metal's Dashboard](https://app.getmetal.io/). Save the `index_id`"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "EUPtAhnGsQ7I"
- },
- "source": [
- "## Load data into your Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "id": "lJ3PobNlq8PC"
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "aQb6tXm4sxMZ"
- },
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import MetalVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "id": "a8ae33cFszV7"
- },
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "id": "AOcOxebas1We"
- },
- "outputs": [],
- "source": [
- "# initialize Metal Vector Store\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "api_key = \"api key\"\n",
- "client_id = \"client id\"\n",
- "index_id = \"index id\"\n",
- "\n",
- "vector_store = MetalVectorStore(\n",
- " api_key=api_key,\n",
- " client_id=client_id,\n",
- " index_id=index_id,\n",
- ")\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "cVz4ADaFtxgg"
- },
- "source": [
- "## Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "dIgc_nA-tzzZ"
- },
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "7vbx3mBkt3V8"
- },
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))\n"
- ]
- }
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EuGbkREYqu6n"
+ },
+ "source": [
+ "# Metal Vector Store"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 0
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_D2M6RiFq3zp"
+ },
+ "source": [
+ "## Creating a Metal Vector Store"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3iAr1k9Rrjg1"
+ },
+ "source": [
+ "1. Register an account for [Metal](https://app.getmetal.io/)\n",
+ "2. Generate an API key in [Metal's Settings](https://app.getmetal.io/settings/organization). Save the `api_key` + `client_id`\n",
+ "3. Generate an Index in [Metal's Dashboard](https://app.getmetal.io/). Save the `index_id`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EUPtAhnGsQ7I"
+ },
+ "source": [
+ "## Load data into your Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "lJ3PobNlq8PC"
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "aQb6tXm4sxMZ"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import MetalVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "a8ae33cFszV7"
+ },
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "AOcOxebas1We"
+ },
+ "outputs": [],
+ "source": [
+ "# initialize Metal Vector Store\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "api_key = \"api key\"\n",
+ "client_id = \"client id\"\n",
+ "index_id = \"index id\"\n",
+ "\n",
+ "vector_store = MetalVectorStore(\n",
+ " api_key=api_key,\n",
+ " client_id=client_id,\n",
+ " index_id=index_id,\n",
+ ")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "cVz4ADaFtxgg"
+ },
+ "source": [
+ "## Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dIgc_nA-tzzZ"
+ },
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7vbx3mBkt3V8"
+ },
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
}
diff --git a/docs/examples/vector_stores/MilvusIndexDemo.ipynb b/docs/examples/vector_stores/MilvusIndexDemo.ipynb
index 25e3496126..44ab395b98 100644
--- a/docs/examples/vector_stores/MilvusIndexDemo.ipynb
+++ b/docs/examples/vector_stores/MilvusIndexDemo.ipynb
@@ -1,341 +1,344 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0b692c73",
- "metadata": {},
- "source": [
- "# Milvus Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1e7787c2",
- "metadata": {},
- "source": [
- "In this notebook we are going to show a quick demo of using the MilvusVectorStore. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "47264e32",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:23.988789Z",
- "start_time": "2023-02-10T12:20:23.967877Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/filiphaltmayer/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "# Uncomment to see debug logs\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document\n",
- "from llama_index.vector_stores import MilvusVectorStore\n",
- "from IPython.display import Markdown, display\n",
- "import textwrap"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f9b97a89",
- "metadata": {},
- "source": [
- "### Setup OpenAI\n",
- "Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:24.908956Z",
- "start_time": "2023-02-10T12:20:24.537064Z"
- },
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": [
- "import os\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-\""
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "59ff935d",
- "metadata": {},
- "source": [
- "### Generate our data\n",
- "With our LLM set, lets start using the Milvus Index. As a first example, lets generate a document from the file found in the `paul_graham_essay/data` folder. In this folder there is a single essay from Paul Graham titled `What I Worked On`. To generate the documents we will use the SimpleDirectoryReader."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:30.175678Z",
- "start_time": "2023-02-10T12:20:30.172456Z"
- },
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Document ID: 05b6691b-d567-43a2-94e1-e9ca81cd4624 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n"
- ]
- }
- ],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham/').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "dd270925",
- "metadata": {},
- "source": [
- "### Create an index across the data\n",
- "Now that we have a document, we can can create an index and insert the document. For the index we will use a GPTMilvusIndex. GPTMilvusIndex takes in a few arguments:\n",
- "\n",
- "- collection_name (str, optional): The name of the collection where data will be stored. Defaults to \"llamalection\".\n",
- "- index_params (dict, optional): The index parameters for Milvus, if none are provided an HNSW index will be used. Defaults to None.\n",
- "- search_params (dict, optional): The search parameters for a Milvus query. If none are provided, default params will be generated. Defaults to None.\n",
- "- dim (int, optional): The dimension of the embeddings. If it is not provided, collection creation will be done on first insert. Defaults to None.\n",
- "- host (str, optional): The host address of Milvus. Defaults to \"localhost\".\n",
- "- port (int, optional): The port of Milvus. Defaults to 19530.\n",
- "- user (str, optional): The username for RBAC. Defaults to \"\".\n",
- "- password (str, optional): The password for RBAC. Defaults to \"\".\n",
- "- use_secure (bool, optional): Use https. Defaults to False.\n",
- "- overwrite (bool, optional): Whether to overwrite existing collection with same name. Defaults to False.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "ba1558b3",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:33.735897Z",
- "start_time": "2023-02-10T12:20:30.404245Z"
- },
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": [
- "# Create an index over the documnts\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = MilvusVectorStore(overwrite=True)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "### Query the data\n",
- "Now that we have our document stored in the index, we can ask questions against the index. The index will use the data stored in itself as the knowledge base for chatgpt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "35369eda",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:51.328762Z",
- "start_time": "2023-02-10T12:20:33.822688Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " The author learned that the AI programs of the time were not capable of understanding natural\n",
- "language, and that the field of AI was a hoax. He also learned that he could make art, and that he\n",
- "could pass the entrance exam for the Accademia di Belli Arti in Florence. He also learned Lisp\n",
- "hacking and wrote his dissertation on applications of continuations.\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author learn?\")\n",
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "99212d33",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:21:10.337294Z",
- "start_time": "2023-02-10T12:20:51.338718Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " A hard moment for the author was when he realized that the AI programs of the time were a hoax and\n",
- "that there was an unbridgeable gap between what they could do and actually understanding natural\n",
- "language.\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\"What was a hard moment for the author?\")\n",
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "64cc925b",
- "metadata": {},
- "source": [
- "This next test shows that overwriting removes the previous data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "8d641e24",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Res: \n",
- "The author is unknown.\n"
- ]
- }
- ],
- "source": [
- "vector_store = MilvusVectorStore(overwrite=True)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents([Document(text=\"The number that is being searched for is ten.\")], storage_context)\n",
- "query_engine = index.as_query_engine()\n",
- "res = query_engine.query(\"Who is the author?\")\n",
- "print(\"Res:\", res)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d8123529",
- "metadata": {},
- "source": [
- "The next test shows adding additional data to an already existing index."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "a5c429a4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Res: \n",
- "The number is ten.\n"
- ]
- }
- ],
- "source": [
- "del index, vector_store, storage_context, query_engine\n",
- "\n",
- "vector_store = MilvusVectorStore(overwrite=False)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
- "query_engine = index.as_query_engine()\n",
- "res = query_engine.query(\"What is the number?\")\n",
- "print(\"Res:\", res)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "e5287c2d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Res: \n",
- "The author is Paul Graham.\n"
- ]
- }
- ],
- "source": [
- "res = query_engine.query(\"Who is the author?\")\n",
- "print(\"Res:\", res)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0b692c73",
+ "metadata": {},
+ "source": [
+ "# Milvus Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1e7787c2",
+ "metadata": {},
+ "source": [
+ "In this notebook we are going to show a quick demo of using the MilvusVectorStore. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "47264e32",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:23.988789Z",
+ "start_time": "2023-02-10T12:20:23.967877Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/filiphaltmayer/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "# Uncomment to see debug logs\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document\n",
+ "from llama_index.vector_stores import MilvusVectorStore\n",
+ "from IPython.display import Markdown, display\n",
+ "import textwrap"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f9b97a89",
+ "metadata": {},
+ "source": [
+ "### Setup OpenAI\n",
+ "Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:24.908956Z",
+ "start_time": "2023-02-10T12:20:24.537064Z"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "59ff935d",
+ "metadata": {},
+ "source": [
+ "### Generate our data\n",
+ "With our LLM set, lets start using the Milvus Index. As a first example, lets generate a document from the file found in the `paul_graham_essay/data` folder. In this folder there is a single essay from Paul Graham titled `What I Worked On`. To generate the documents we will use the SimpleDirectoryReader."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:30.175678Z",
+ "start_time": "2023-02-10T12:20:30.172456Z"
+ },
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Document ID: 05b6691b-d567-43a2-94e1-e9ca81cd4624 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "dd270925",
+ "metadata": {},
+ "source": [
+ "### Create an index across the data\n",
+ "Now that we have a document, we can can create an index and insert the document. For the index we will use a GPTMilvusIndex. GPTMilvusIndex takes in a few arguments:\n",
+ "\n",
+ "- collection_name (str, optional): The name of the collection where data will be stored. Defaults to \"llamalection\".\n",
+ "- index_params (dict, optional): The index parameters for Milvus, if none are provided an HNSW index will be used. Defaults to None.\n",
+ "- search_params (dict, optional): The search parameters for a Milvus query. If none are provided, default params will be generated. Defaults to None.\n",
+ "- dim (int, optional): The dimension of the embeddings. If it is not provided, collection creation will be done on first insert. Defaults to None.\n",
+ "- host (str, optional): The host address of Milvus. Defaults to \"localhost\".\n",
+ "- port (int, optional): The port of Milvus. Defaults to 19530.\n",
+ "- user (str, optional): The username for RBAC. Defaults to \"\".\n",
+ "- password (str, optional): The password for RBAC. Defaults to \"\".\n",
+ "- use_secure (bool, optional): Use https. Defaults to False.\n",
+ "- overwrite (bool, optional): Whether to overwrite existing collection with same name. Defaults to False.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ba1558b3",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:33.735897Z",
+ "start_time": "2023-02-10T12:20:30.404245Z"
+ },
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Create an index over the documnts\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = MilvusVectorStore(overwrite=True)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "### Query the data\n",
+ "Now that we have our document stored in the index, we can ask questions against the index. The index will use the data stored in itself as the knowledge base for chatgpt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "35369eda",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:51.328762Z",
+ "start_time": "2023-02-10T12:20:33.822688Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " The author learned that the AI programs of the time were not capable of understanding natural\n",
+ "language, and that the field of AI was a hoax. He also learned that he could make art, and that he\n",
+ "could pass the entrance exam for the Accademia di Belli Arti in Florence. He also learned Lisp\n",
+ "hacking and wrote his dissertation on applications of continuations.\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author learn?\")\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "99212d33",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:21:10.337294Z",
+ "start_time": "2023-02-10T12:20:51.338718Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " A hard moment for the author was when he realized that the AI programs of the time were a hoax and\n",
+ "that there was an unbridgeable gap between what they could do and actually understanding natural\n",
+ "language.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What was a hard moment for the author?\")\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "64cc925b",
+ "metadata": {},
+ "source": [
+ "This next test shows that overwriting removes the previous data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8d641e24",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Res: \n",
+ "The author is unknown.\n"
+ ]
+ }
+ ],
+ "source": [
+ "vector_store = MilvusVectorStore(overwrite=True)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " [Document(text=\"The number that is being searched for is ten.\")], storage_context\n",
+ ")\n",
+ "query_engine = index.as_query_engine()\n",
+ "res = query_engine.query(\"Who is the author?\")\n",
+ "print(\"Res:\", res)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d8123529",
+ "metadata": {},
+ "source": [
+ "The next test shows adding additional data to an already existing index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "a5c429a4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Res: \n",
+ "The number is ten.\n"
+ ]
+ }
+ ],
+ "source": [
+ "del index, vector_store, storage_context, query_engine\n",
+ "\n",
+ "vector_store = MilvusVectorStore(overwrite=False)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
+ "query_engine = index.as_query_engine()\n",
+ "res = query_engine.query(\"What is the number?\")\n",
+ "print(\"Res:\", res)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "e5287c2d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Res: \n",
+ "The author is Paul Graham.\n"
+ ]
+ }
+ ],
+ "source": [
+ "res = query_engine.query(\"Who is the author?\")\n",
+ "print(\"Res:\", res)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/MongoDBAtlasVectorSearch.ipynb b/docs/examples/vector_stores/MongoDBAtlasVectorSearch.ipynb
index d1f6dec27e..c4620fd46e 100644
--- a/docs/examples/vector_stores/MongoDBAtlasVectorSearch.ipynb
+++ b/docs/examples/vector_stores/MongoDBAtlasVectorSearch.ipynb
@@ -19,8 +19,7 @@
"from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch\n",
"from llama_index.indices.vector_store.base import VectorStoreIndex\n",
"from llama_index.storage.storage_context import StorageContext\n",
- "from llama_index.readers.file.base import SimpleDirectoryReader\n",
- "\n"
+ "from llama_index.readers.file.base import SimpleDirectoryReader"
]
},
{
@@ -79,6 +78,7 @@
],
"source": [
"from llama_index.response.schema import Response\n",
+ "\n",
"# Initial size\n",
"\n",
"print(store._collection.count_documents({}))\n",
diff --git a/docs/examples/vector_stores/MyScaleIndexDemo.ipynb b/docs/examples/vector_stores/MyScaleIndexDemo.ipynb
index cc3e097d50..757d8f8d9d 100644
--- a/docs/examples/vector_stores/MyScaleIndexDemo.ipynb
+++ b/docs/examples/vector_stores/MyScaleIndexDemo.ipynb
@@ -1,168 +1,168 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# MyScale Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a MyScale Client"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "50ad978c",
- "metadata": {},
- "outputs": [],
- "source": [
- "import clickhouse_connect\n",
- "\n",
- "# initialize client\n",
- "client = clickhouse_connect.get_client(\n",
- " host='YOUR_CLUSTER_HOST', \n",
- " port=8443, \n",
- " username='YOUR_USERNAME', \n",
- " password='YOUR_CLUSTER_PASSWORD'\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex with MyScaleVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import MyScaleVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ba1558b3",
- "metadata": {},
- "outputs": [],
- "source": [
- "# initialize without metadata filter\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = MyScaleVectorStore(myscale_client=client)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "35369eda",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "Growing up, the author wrote short stories, programmed on an IBM 1401, wrote simple games and a word processor on a TRS-80, studied philosophy in college, learned Lisp, reverse-engineered SHRDLU, wrote a book about Lisp hacking, took art classes at Harvard, and painted still lives in his bedroom at night. He also had the opportunity to observe a nude model in his art classes, and learned that she made a living from modelling and making fakes for a local antique dealer.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# MyScale Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a MyScale Client"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "50ad978c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import clickhouse_connect\n",
+ "\n",
+ "# initialize client\n",
+ "client = clickhouse_connect.get_client(\n",
+ " host=\"YOUR_CLUSTER_HOST\",\n",
+ " port=8443,\n",
+ " username=\"YOUR_USERNAME\",\n",
+ " password=\"YOUR_CLUSTER_PASSWORD\",\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex with MyScaleVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import MyScaleVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba1558b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# initialize without metadata filter\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = MyScaleVectorStore(myscale_client=client)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "Growing up, the author wrote short stories, programmed on an IBM 1401, wrote simple games and a word processor on a TRS-80, studied philosophy in college, learned Lisp, reverse-engineered SHRDLU, wrote a book about Lisp hacking, took art classes at Harvard, and painted still lives in his bedroom at night. He also had the opportunity to observe a nude model in his art classes, and learned that she made a living from modelling and making fakes for a local antique dealer.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/OpensearchDemo.ipynb b/docs/examples/vector_stores/OpensearchDemo.ipynb
index 84d85862bd..5b322cc360 100644
--- a/docs/examples/vector_stores/OpensearchDemo.ipynb
+++ b/docs/examples/vector_stores/OpensearchDemo.ipynb
@@ -50,12 +50,13 @@
"from llama_index import SimpleDirectoryReader\n",
"from llama_index.vector_stores import OpensearchVectorStore, OpensearchVectorClient\n",
"from llama_index import VectorStoreIndex, StorageContext\n",
+ "\n",
"# http endpoint for your cluster (opensearch required for vector index usage)\n",
"endpoint = getenv(\"OPENSEARCH_ENDPOINT\", \"http://localhost:9200\")\n",
"# index to demonstrate the VectorStore impl\n",
"idx = getenv(\"OPENSEARCH_INDEX\", \"gpt-index-demo\")\n",
"# load some sample data\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
]
},
{
@@ -76,12 +77,16 @@
"embedding_field = \"embedding\"\n",
"# OpensearchVectorClient encapsulates logic for a\n",
"# single opensearch index with vector search enabled\n",
- "client = OpensearchVectorClient(endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field)\n",
+ "client = OpensearchVectorClient(\n",
+ " endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field\n",
+ ")\n",
"# initialize vector store\n",
"vector_store = OpensearchVectorStore(client)\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"# initialize an index using our sample data and the client we just created\n",
- "index = VectorStoreIndex.from_documents(documents=documents, storage_context=storage_context)"
+ "index = VectorStoreIndex.from_documents(\n",
+ " documents=documents, storage_context=storage_context\n",
+ ")"
]
},
{
diff --git a/docs/examples/vector_stores/PineconeIndexDemo-0.6.0.ipynb b/docs/examples/vector_stores/PineconeIndexDemo-0.6.0.ipynb
index f8e6c3fac3..5ac7dfd933 100644
--- a/docs/examples/vector_stores/PineconeIndexDemo-0.6.0.ipynb
+++ b/docs/examples/vector_stores/PineconeIndexDemo-0.6.0.ipynb
@@ -90,7 +90,9 @@
"source": [
"# create index if it does not already exist\n",
"# dimensions are for text-embedding-ada-002\n",
- "pinecone.create_index(\"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")"
+ "pinecone.create_index(\n",
+ " \"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\"\n",
+ ")"
]
},
{
@@ -155,7 +157,16 @@
},
"outputs": [],
"source": [
- "wiki_titles = [\"Toronto\", \"Seattle\", \"San Francisco\", \"Chicago\", \"Boston\", \"Washington, D.C.\", \"Cambridge, Massachusetts\", \"Houston\"]"
+ "wiki_titles = [\n",
+ " \"Toronto\",\n",
+ " \"Seattle\",\n",
+ " \"San Francisco\",\n",
+ " \"Chicago\",\n",
+ " \"Boston\",\n",
+ " \"Washington, D.C.\",\n",
+ " \"Cambridge, Massachusetts\",\n",
+ " \"Houston\",\n",
+ "]"
]
},
{
@@ -170,27 +181,27 @@
"from pathlib import Path\n",
"import requests\n",
"\n",
- "data_path = Path('data_wiki')\n",
+ "data_path = Path(\"data_wiki\")\n",
"\n",
"for title in wiki_titles:\n",
" response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
" params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
- " 'explaintext': True,\n",
- " }\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
+ " \"explaintext\": True,\n",
+ " },\n",
" ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
"\n",
" if not data_path.exists():\n",
" Path.mkdir(data_path)\n",
"\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
]
},
{
@@ -206,8 +217,10 @@
"city_docs = {}\n",
"all_docs = []\n",
"for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[data_path / f\"{wiki_title}.txt\"]).load_data()\n",
- " all_docs.extend(city_docs[wiki_title])\n"
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[data_path / f\"{wiki_title}.txt\"]\n",
+ " ).load_data()\n",
+ " all_docs.extend(city_docs[wiki_title])"
]
},
{
@@ -293,11 +306,15 @@
"for wiki_title in wiki_titles:\n",
" print(f\"Building index for {wiki_title}\")\n",
" # create storage context\n",
- " vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace=wiki_title)\n",
+ " vector_store = PineconeVectorStore(\n",
+ " pinecone_index=pinecone_index, namespace=wiki_title\n",
+ " )\n",
" storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- " \n",
+ "\n",
" # build index\n",
- " city_indices[wiki_title] = VectorStoreIndex.from_documents(city_docs[wiki_title], storage_context=storage_context)\n",
+ " city_indices[wiki_title] = VectorStoreIndex.from_documents(\n",
+ " city_docs[wiki_title], storage_context=storage_context\n",
+ " )\n",
"\n",
" # set summary text for city\n",
" index_summaries[wiki_title] = f\"Wikipedia articles about {wiki_title}\""
@@ -346,9 +363,9 @@
"source": [
"graph = ComposableGraph.from_indices(\n",
" SimpleKeywordTableIndex,\n",
- " [index for _, index in city_indices.items()], \n",
+ " [index for _, index in city_indices.items()],\n",
" [summary for _, summary in index_summaries.items()],\n",
- " max_keywords_per_chunk=50\n",
+ " max_keywords_per_chunk=50,\n",
")"
]
},
@@ -373,14 +390,14 @@
" query_engine = TransformQueryEngine(\n",
" query_engine,\n",
" query_transform=decompose_transform,\n",
- " transform_extra_info={'index_summary': index_summaries[wiki_title]},\n",
+ " transform_extra_info={\"index_summary\": index_summaries[wiki_title]},\n",
" )\n",
" custom_query_engines[index.index_id] = query_engine\n",
"\n",
"custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
- " retriever_mode='simple',\n",
- " response_mode='tree_summarize',\n",
- ")\n"
+ " retriever_mode=\"simple\",\n",
+ " response_mode=\"tree_summarize\",\n",
+ ")"
]
},
{
@@ -470,7 +487,9 @@
}
],
"source": [
- "response = query_engine.query(\"Compare and contrast the demographics in Seattle, Houston, and Toronto.\")"
+ "response = query_engine.query(\n",
+ " \"Compare and contrast the demographics in Seattle, Houston, and Toronto.\"\n",
+ ")"
]
},
{
@@ -549,18 +568,20 @@
"\n",
"\n",
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
"\n",
"# define storage context\n",
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='pg_essay_0.6.0')\n",
+ "vector_store = PineconeVectorStore(\n",
+ " pinecone_index=pinecone_index, namespace=\"pg_essay_0.6.0\"\n",
+ ")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"\n",
- "# build index \n",
+ "# build index\n",
"index = VectorStoreIndex.from_documents(\n",
- " documents, \n",
+ " documents,\n",
" storage_context=storage_context,\n",
" # override to store Node in document store in addition to vector store, necessary for the node postprocessor\n",
- " store_nodes_override=True \n",
+ " store_nodes_override=True,\n",
")"
]
},
@@ -585,10 +606,10 @@
"\n",
"# define postprocessor\n",
"node_postprocessor = AutoPrevNextNodePostprocessor(\n",
- " docstore=index.storage_context.docstore, \n",
+ " docstore=index.storage_context.docstore,\n",
" service_context=index.service_context,\n",
" num_nodes=3,\n",
- " verbose=True\n",
+ " verbose=True,\n",
")\n",
"\n",
"# define query engine\n",
@@ -641,7 +662,7 @@
"source": [
"# Infer that we need to search nodes after current one\n",
"response = query_engine.query(\n",
- " \"What did the author do after handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do after handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -715,7 +736,7 @@
")\n",
"\n",
"response = naive_query_engine.query(\n",
- " \"What did the author do after handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do after handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
@@ -796,7 +817,7 @@
"source": [
"# Infer that we need to search nodes before current one\n",
"response = query_engine.query(\n",
- " \"What did the author do before handing off Y Combinator to Sam Altman?\", \n",
+ " \"What did the author do before handing off Y Combinator to Sam Altman?\",\n",
")"
]
},
diff --git a/docs/examples/vector_stores/PineconeIndexDemo.ipynb b/docs/examples/vector_stores/PineconeIndexDemo.ipynb
index 2df6ce57fe..74016e3d28 100644
--- a/docs/examples/vector_stores/PineconeIndexDemo.ipynb
+++ b/docs/examples/vector_stores/PineconeIndexDemo.ipynb
@@ -1,254 +1,254 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Pinecone Vector Store"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import os\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Pinecone Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/pinecone/index.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from tqdm.autonotebook import tqdm\n"
- ]
- }
- ],
- "source": [
- "import pinecone"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
- "metadata": {},
- "outputs": [],
- "source": [
- "api_key = os.environ['PINECONE_API_KEY']\n",
- "pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
- "metadata": {},
- "outputs": [],
- "source": [
- "# dimensions are for text-embedding-ada-002\n",
- "pinecone.create_index(\"quickstart\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
- "metadata": {},
- "outputs": [],
- "source": [
- "pinecone_index = pinecone.Index(\"quickstart\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents, build the PineconeVectorStore and VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
- "NumExpr defaulting to 8 threads.\n",
- "NumExpr defaulting to 8 threads.\n"
- ]
- }
- ],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import PineconeVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "ba1558b3",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n"
- ]
- }
- ],
- "source": [
- "# initialize without metadata filter\n",
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "35369eda",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1917 tokens\n",
- "> [get_response] Total LLM token usage: 1917 tokens\n",
- "> [get_response] Total LLM token usage: 1917 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author grew up writing short stories and programming on the IBM 1401. He also nagged his father to buy him a TRS-80 microcomputer, which he used to write simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, but eventually switched to AI.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a91e2008",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Pinecone Vector Store"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Pinecone Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/pinecone/index.py:4: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from tqdm.autonotebook import tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pinecone"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
+ "pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dimensions are for text-embedding-ada-002\n",
+ "pinecone.create_index(\"quickstart\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pinecone_index = pinecone.Index(\"quickstart\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the PineconeVectorStore and VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
+ "NumExpr defaulting to 8 threads.\n",
+ "NumExpr defaulting to 8 threads.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import PineconeVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "ba1558b3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# initialize without metadata filter\n",
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "vector_store = PineconeVectorStore(pinecone_index=pinecone_index)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1917 tokens\n",
+ "> [get_response] Total LLM token usage: 1917 tokens\n",
+ "> [get_response] Total LLM token usage: 1917 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author grew up writing short stories and programming on the IBM 1401. He also nagged his father to buy him a TRS-80 microcomputer, which he used to write simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, but eventually switched to AI.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a91e2008",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/QdrantIndexDemo.ipynb b/docs/examples/vector_stores/QdrantIndexDemo.ipynb
index c77c0cd43f..2e5e470c1d 100644
--- a/docs/examples/vector_stores/QdrantIndexDemo.ipynb
+++ b/docs/examples/vector_stores/QdrantIndexDemo.ipynb
@@ -31,8 +31,12 @@
"\n",
"import qdrant_client\n",
"from IPython.display import Markdown, display\n",
- "from llama_index import (VectorStoreIndex, LLMPredictor, ServiceContext,\n",
- " SimpleDirectoryReader)\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " LLMPredictor,\n",
+ " ServiceContext,\n",
+ " SimpleDirectoryReader,\n",
+ ")\n",
"from llama_index.storage.storage_context import StorageContext\n",
"from llama_index.vector_stores.qdrant import QdrantVectorStore"
]
diff --git a/docs/examples/vector_stores/RedisIndexDemo.ipynb b/docs/examples/vector_stores/RedisIndexDemo.ipynb
index d06ad4462a..ed5999223c 100644
--- a/docs/examples/vector_stores/RedisIndexDemo.ipynb
+++ b/docs/examples/vector_stores/RedisIndexDemo.ipynb
@@ -1,661 +1,670 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0b692c73",
- "metadata": {},
- "source": [
- "# Redis Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1e7787c2",
- "metadata": {},
- "source": [
- "In this notebook we are going to show a quick demo of using the RedisVectorStore."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "id": "47264e32",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:23.988789Z",
- "start_time": "2023-02-10T12:20:23.967877Z"
- }
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import sys\n",
- "import logging\n",
- "import textwrap\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings(\"ignore\")\n",
- "\n",
- "# stop huggingface warnings\n",
- "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
- "\n",
- "# Uncomment to see debug logs\n",
- "#logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "#logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document\n",
- "from llama_index.vector_stores import RedisVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "3c692310",
- "metadata": {},
- "source": [
- "### Start Redis\n",
- "\n",
- "The easiest way to start Redis as a vector database is using the [redis-stack](https://hub.docker.com/r/redis/redis-stack) docker image.\n",
- "\n",
- "To follow every step of this tutorial, launch the image as follows:\n",
- "\n",
- "```bash\n",
- "docker run --name redis-vecdb -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n",
- "```\n",
- "\n",
- "This will also launch the RedisInsight UI on port 8001 which you can view at http://localhost:8001.\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f9b97a89",
- "metadata": {},
- "source": [
- "### Setup OpenAI\n",
- "Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:24.908956Z",
- "start_time": "2023-02-10T12:20:24.537064Z"
- },
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": [
- "import os\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-<your key here>\""
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "59ff935d",
- "metadata": {},
- "source": [
- "### Read in a dataset\n",
- "Here we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a ``RedisVectorStore`` and query to find context for our LLM QnA loop."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:30.175678Z",
- "start_time": "2023-02-10T12:20:30.172456Z"
- },
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Document ID: faa23c94-ac9e-4763-92ba-e0f87bf38195 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n"
- ]
- }
- ],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "dd270925",
- "metadata": {},
- "source": [
- "### Initialize the Redis Vector Store\n",
- "\n",
- "Now we have our documents read in, we can initialize the Redis Vector Store. This will allow us to store our vectors in Redis and create an index.\n",
- "\n",
- "Here is the docstring for the RedisVectorStore:\n",
- "\n",
- "```python\n",
- "class RedisVectorStore(VectorStore):\n",
- " \n",
- "def __init__(\n",
- " self,\n",
- " index_name: str,\n",
- " index_prefix: str = \"llama_index\",\n",
- " prefix_ending: str = \"/vector\",\n",
- " index_args: Optional[Dict[str, Any]] = None,\n",
- " metadata_fields: Optional[List[str]] = None,\n",
- " redis_url: str = \"redis://localhost:6379\",\n",
- " overwrite: bool = False,\n",
- " **kwargs: Any,\n",
- " ) -> None:\n",
- " \"\"\"Initialize RedisVectorStore.\n",
- "\n",
- " For index arguments that can be passed to RediSearch, see\n",
- " https://redis.io/docs/stack/search/reference/vectors/\n",
- "\n",
- " The index arguments will depend on the index type chosen. There\n",
- " are two available index types\n",
- " - FLAT: a flat index that uses brute force search\n",
- " - HNSW: a hierarchical navigable small world graph index\n",
- "\n",
- " Args:\n",
- " index_name (str): Name of the index.\n",
- " index_prefix (str): Prefix for the index. Defaults to \"llama_index\".\n",
- " The actual prefix used by Redis will be\n",
- " \"{index_prefix}{prefix_ending}\".\n",
- " prefix_ending (str): Prefix ending for the index. Be careful when\n",
- " changing this: https://github.com/jerryjliu/llama_index/pull/6665.\n",
- " Defaults to \"/vector\".\n",
- " index_args (Dict[str, Any]): Arguments for the index. Defaults to None.\n",
- " metadata_fields (List[str]): List of metadata fields to store in the index (only supports TAG fields).\n",
- " redis_url (str): URL for the redis instance.\n",
- " Defaults to \"redis://localhost:6379\".\n",
- " overwrite (bool): Whether to overwrite the index if it already exists.\n",
- " Defaults to False.\n",
- " kwargs (Any): Additional arguments to pass to the redis client.\n",
- "\n",
- " Raises:\n",
- " ValueError: If redis-py is not installed\n",
- " ValueError: If RediSearch is not installed\n",
- "\n",
- " Examples:\n",
- " >>> from llama_index.vector_stores.redis import RedisVectorStore\n",
- " >>> # Create a RedisVectorStore\n",
- " >>> vector_store = RedisVectorStore(\n",
- " >>> index_name=\"my_index\",\n",
- " >>> index_prefix=\"gpt_index\",\n",
- " >>> index_args={\"algorithm\": \"HNSW\", \"m\": 16, \"ef_construction\": 200,\n",
- " \"distance_metric\": \"cosine\"},\n",
- " >>> redis_url=\"redis://localhost:6379/\",\n",
- " >>> overwrite=True)\n",
- "\n",
- " \"\"\"\n",
- "```\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "id": "ba1558b3",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:33.735897Z",
- "start_time": "2023-02-10T12:20:30.404245Z"
- },
- "pycharm": {
- "is_executing": true
- }
- },
- "outputs": [],
- "source": [
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = RedisVectorStore(\n",
- " index_name=\"pg_essays\",\n",
- " index_prefix=\"llama\",\n",
- " redis_url=\"redis://localhost:6379\",\n",
- " overwrite=True\n",
- ")\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "# Query the data\n",
- "Now that we have our document stored in the index, we can ask questions against the index. The index will use the data stored in itself as the knowledge base for chatgpt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "35369eda",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:20:51.328762Z",
- "start_time": "2023-02-10T12:20:33.822688Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " The author learned that it is possible to publish essays online, and that working on things that\n",
- "are not prestigious can be a sign that one is on the right track. They also learned that impure\n",
- "motives can lead ambitious people astray, and that it is possible to make connections with people\n",
- "through cleverly planned events. Finally, the author learned that they could find love through a\n",
- "chance meeting at a party.\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author learn?\")\n",
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "99212d33",
- "metadata": {
- "ExecuteTime": {
- "end_time": "2023-02-10T12:21:10.337294Z",
- "start_time": "2023-02-10T12:20:51.338718Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " A hard moment for the author was when he realized that he had been working on things that weren't\n",
- "prestigious. He had been drawn to these types of work despite their lack of prestige, and he was\n",
- "worried that his ambition was leading him astray. He was also concerned that people would give him a\n",
- "\"glassy eye\" when he explained what he was writing.\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\"What was a hard moment for the author?\")\n",
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "4d7bc976",
- "metadata": {},
- "source": [
- "### Saving and Loading\n",
- "\n",
- "Redis allows the user to perform backups in the background or synchronously. With Llamaindex, the ``RedisVectorStore.persist()`` function can be used to trigger such a backup."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "09836567",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "redis redisinsight\n"
- ]
- }
- ],
- "source": [
- "!docker exec -it redis-vecdb ls /data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "93ef500b",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_store.persist(persist_path=\"\") # persist_path means nothing for RedisVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "ed5ab256",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dump.rdb redis redisinsight\n"
- ]
- }
- ],
- "source": [
- "!docker exec -it redis-vecdb ls /data"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "52b975a7",
- "metadata": {},
- "source": [
- "### Deleting documents or index completely\n",
- "\n",
- "Sometimes it may be useful to delete documents or the entire index. This can be done using the `delete` and `delete_index` methods."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "6fe322f7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'faa23c94-ac9e-4763-92ba-e0f87bf38195'"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "document_id = documents[0].doc_id\n",
- "document_id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "ae4fb2b0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of documents 20\n"
- ]
- }
- ],
- "source": [
- "redis_client = vector_store.client\n",
- "print(\"Number of documents\", len(redis_client.keys()))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "id": "0ce45788",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_store.delete(document_id)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "id": "4a1ac683",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of documents 10\n"
- ]
- }
- ],
- "source": [
- "print(\"Number of documents\", len(redis_client.keys()))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "id": "c380605a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# now lets delete the index entirely (happens in the background, may take a second)\n",
- "# this will delete all the documents and the index\n",
- "vector_store.delete_index()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "id": "474ad4ee",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Number of documents 0\n"
- ]
- }
- ],
- "source": [
- "print(\"Number of documents\", len(redis_client.keys()))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "61b67496",
- "metadata": {},
- "source": [
- "# Working with Metadata\n",
- "\n",
- "RedisVectorStore supports adding metadata and then using it in your queries (for example, to limit the scope of documents retrieved). However, there are a couple of important caveats:\n",
- "1. Currently, only [Tag fields](https://redis.io/docs/stack/search/reference/tags/) are supported, and only with exact match.\n",
- "2. You must declare the metadata when creating the index (usually when initializing RedisVectorStore). If you do not do this, your queries will come back empty. There is no way to modify an existing index after it had already been created (this is a Redis limitation).\n",
- "\n",
- "Here's how to work with Metadata:\n",
- "\n",
- "\n",
- "### When **creating** the index\n",
- "\n",
- "Make sure to declare the metadata when you **first** create the index:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "id": "9889ec79",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_store = RedisVectorStore(\n",
- " index_name=\"pg_essays_with_metadata\",\n",
- " index_prefix=\"llama\",\n",
- " redis_url=\"redis://localhost:6379\",\n",
- " overwrite=True,\n",
- " metadata_fields=[\"user_id\", \"favorite_color\"],\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f8d6dc21",
- "metadata": {},
- "source": [
- "Note: the field names `text`, `doc_id`, `id` and the name of your vector field (`vector` by default) should **not** be used as metadata field names, as they are are reserved."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "429947d5",
- "metadata": {},
- "source": [
- "### When adding a document\n",
- "\n",
- "Add your metadata under the `metadata` key. You can add metadata to documents you load in just by looping over them:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "id": "89781b7d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Document ID: 6a5aa8dd-2771-454b-befc-bcfc311d2008 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e Metadata: {'user_id': '12345', 'favorite_color': 'blue'}\n"
- ]
- }
- ],
- "source": [
- "# load your documents normally, then add your metadata\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
- "\n",
- "for document in documents:\n",
- " document.metadata = {\"user_id\": \"12345\", \"favorite_color\": \"blue\"}\n",
- "\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
- " \n",
- "# load documents\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash, 'Metadata:', documents[0].metadata)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "42b24e76",
- "metadata": {},
- "source": [
- "### When querying the index\n",
- "\n",
- "To filter by your metadata fields, include one or more of your metadata keys, like so:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "0b01f346",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " The author learned that it was possible to publish anything online, and that working on things that\n",
- "weren't prestigious could lead to discovering something real. They also learned that impure motives\n",
- "were a big danger for the ambitious, and that it was possible for programs not to terminate.\n",
- "Finally, they learned that computers were expensive in those days, and that they could write\n",
- "programs on the IBM 1401.\n"
- ]
- }
- ],
- "source": [
- "from llama_index.vector_stores.types import MetadataFilters, ExactMatchFilter\n",
- "\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=3,\n",
- " filters=MetadataFilters(\n",
- " filters=[\n",
- " ExactMatchFilter(key=\"user_id\", value=\"12345\"),\n",
- " ExactMatchFilter(key=\"favorite_color\", value=\"blue\")\n",
- " ]\n",
- " ),\n",
- ")\n",
- "\n",
- "response = query_engine.query(\"What did the author learn?\")\n",
- "print(textwrap.fill(str(response), 100))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "07514f85",
- "metadata": {},
- "source": [
- "## Troubleshooting\n",
- "\n",
- "In case you run into issues retrieving your documents from the index, you might get a message similar to this.\n",
- "```\n",
- "No docs found on index 'pg_essays' with prefix 'llama' and filters '(@user_id:{12345} & @favorite_color:{blue})'.\n",
- "* Did you originally create the index with a different prefix?\n",
- "* Did you index your metadata fields when you created the index?\n",
- "```\n",
- "\n",
- "If you get this error, there a couple of gotchas to be aware of when working with Redis:\n",
- "#### Prefix issues\n",
- "\n",
- "If you first create your index with a specific `prefix` but later change that prefix in your code, your query will come back empty. Redis saves the prefix your originally created your index with and expects it to be consistent.\n",
- "\n",
- "To see what prefix your index was created with, you can run `FT.INFO <name of your index>` in the Redis CLI and look under `index_definition` => `prefixes`.\n",
- "\n",
- "#### Empty queries when using metadata\n",
- "\n",
- "If you add metadata to the index *after* it has already been created and then try to query over that metadata, your queries will come back empty.\n",
- "\n",
- "Redis indexes fields upon index creation only (similar to how it indexes the prefixes, above).\n",
- "\n",
- "If you have an existing index and want to make sure it's dropped, you can run `FT.DROPINDEX <name of your index>` in the Redis CLI. Note that this will *not* drop your actual data."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "c09d1199",
- "metadata": {},
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.13"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0b692c73",
+ "metadata": {},
+ "source": [
+ "# Redis Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1e7787c2",
+ "metadata": {},
+ "source": [
+ "In this notebook we are going to show a quick demo of using the RedisVectorStore."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "47264e32",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:23.988789Z",
+ "start_time": "2023-02-10T12:20:23.967877Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import sys\n",
+ "import logging\n",
+ "import textwrap\n",
+ "\n",
+ "import warnings\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "# stop huggingface warnings\n",
+ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
+ "\n",
+ "# Uncomment to see debug logs\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document\n",
+ "from llama_index.vector_stores import RedisVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "3c692310",
+ "metadata": {},
+ "source": [
+ "### Start Redis\n",
+ "\n",
+ "The easiest way to start Redis as a vector database is using the [redis-stack](https://hub.docker.com/r/redis/redis-stack) docker image.\n",
+ "\n",
+ "To follow every step of this tutorial, launch the image as follows:\n",
+ "\n",
+ "```bash\n",
+ "docker run --name redis-vecdb -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n",
+ "```\n",
+ "\n",
+ "This will also launch the RedisInsight UI on port 8001 which you can view at http://localhost:8001.\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f9b97a89",
+ "metadata": {},
+ "source": [
+ "### Setup OpenAI\n",
+ "Lets first begin by adding the openai api key. This will allow us to access openai for embeddings and to use chatgpt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:24.908956Z",
+ "start_time": "2023-02-10T12:20:24.537064Z"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"sk-<your key here>\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "59ff935d",
+ "metadata": {},
+ "source": [
+ "### Read in a dataset\n",
+ "Here we will use a set of Paul Graham essays to provide the text to turn into embeddings, store in a ``RedisVectorStore`` and query to find context for our LLM QnA loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:30.175678Z",
+ "start_time": "2023-02-10T12:20:30.172456Z"
+ },
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Document ID: faa23c94-ac9e-4763-92ba-e0f87bf38195 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "dd270925",
+ "metadata": {},
+ "source": [
+ "### Initialize the Redis Vector Store\n",
+ "\n",
+ "Now we have our documents read in, we can initialize the Redis Vector Store. This will allow us to store our vectors in Redis and create an index.\n",
+ "\n",
+ "Here is the docstring for the RedisVectorStore:\n",
+ "\n",
+ "```python\n",
+ "class RedisVectorStore(VectorStore):\n",
+ " \n",
+ "def __init__(\n",
+ " self,\n",
+ " index_name: str,\n",
+ " index_prefix: str = \"llama_index\",\n",
+ " prefix_ending: str = \"/vector\",\n",
+ " index_args: Optional[Dict[str, Any]] = None,\n",
+ " metadata_fields: Optional[List[str]] = None,\n",
+ " redis_url: str = \"redis://localhost:6379\",\n",
+ " overwrite: bool = False,\n",
+ " **kwargs: Any,\n",
+ " ) -> None:\n",
+ " \"\"\"Initialize RedisVectorStore.\n",
+ "\n",
+ " For index arguments that can be passed to RediSearch, see\n",
+ " https://redis.io/docs/stack/search/reference/vectors/\n",
+ "\n",
+ " The index arguments will depend on the index type chosen. There\n",
+ " are two available index types\n",
+ " - FLAT: a flat index that uses brute force search\n",
+ " - HNSW: a hierarchical navigable small world graph index\n",
+ "\n",
+ " Args:\n",
+ " index_name (str): Name of the index.\n",
+ " index_prefix (str): Prefix for the index. Defaults to \"llama_index\".\n",
+ " The actual prefix used by Redis will be\n",
+ " \"{index_prefix}{prefix_ending}\".\n",
+ " prefix_ending (str): Prefix ending for the index. Be careful when\n",
+ " changing this: https://github.com/jerryjliu/llama_index/pull/6665.\n",
+ " Defaults to \"/vector\".\n",
+ " index_args (Dict[str, Any]): Arguments for the index. Defaults to None.\n",
+ " metadata_fields (List[str]): List of metadata fields to store in the index (only supports TAG fields).\n",
+ " redis_url (str): URL for the redis instance.\n",
+ " Defaults to \"redis://localhost:6379\".\n",
+ " overwrite (bool): Whether to overwrite the index if it already exists.\n",
+ " Defaults to False.\n",
+ " kwargs (Any): Additional arguments to pass to the redis client.\n",
+ "\n",
+ " Raises:\n",
+ " ValueError: If redis-py is not installed\n",
+ " ValueError: If RediSearch is not installed\n",
+ "\n",
+ " Examples:\n",
+ " >>> from llama_index.vector_stores.redis import RedisVectorStore\n",
+ " >>> # Create a RedisVectorStore\n",
+ " >>> vector_store = RedisVectorStore(\n",
+ " >>> index_name=\"my_index\",\n",
+ " >>> index_prefix=\"gpt_index\",\n",
+ " >>> index_args={\"algorithm\": \"HNSW\", \"m\": 16, \"ef_construction\": 200,\n",
+ " \"distance_metric\": \"cosine\"},\n",
+ " >>> redis_url=\"redis://localhost:6379/\",\n",
+ " >>> overwrite=True)\n",
+ "\n",
+ " \"\"\"\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "ba1558b3",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:33.735897Z",
+ "start_time": "2023-02-10T12:20:30.404245Z"
+ },
+ "pycharm": {
+ "is_executing": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = RedisVectorStore(\n",
+ " index_name=\"pg_essays\",\n",
+ " index_prefix=\"llama\",\n",
+ " redis_url=\"redis://localhost:6379\",\n",
+ " overwrite=True,\n",
+ ")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "# Query the data\n",
+ "Now that we have our document stored in the index, we can ask questions against the index. The index will use the data stored in itself as the knowledge base for chatgpt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "35369eda",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:20:51.328762Z",
+ "start_time": "2023-02-10T12:20:33.822688Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " The author learned that it is possible to publish essays online, and that working on things that\n",
+ "are not prestigious can be a sign that one is on the right track. They also learned that impure\n",
+ "motives can lead ambitious people astray, and that it is possible to make connections with people\n",
+ "through cleverly planned events. Finally, the author learned that they could find love through a\n",
+ "chance meeting at a party.\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author learn?\")\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "99212d33",
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-02-10T12:21:10.337294Z",
+ "start_time": "2023-02-10T12:20:51.338718Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " A hard moment for the author was when he realized that he had been working on things that weren't\n",
+ "prestigious. He had been drawn to these types of work despite their lack of prestige, and he was\n",
+ "worried that his ambition was leading him astray. He was also concerned that people would give him a\n",
+ "\"glassy eye\" when he explained what he was writing.\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What was a hard moment for the author?\")\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "4d7bc976",
+ "metadata": {},
+ "source": [
+ "### Saving and Loading\n",
+ "\n",
+ "Redis allows the user to perform backups in the background or synchronously. With Llamaindex, the ``RedisVectorStore.persist()`` function can be used to trigger such a backup."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "09836567",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "redis redisinsight\n"
+ ]
+ }
+ ],
+ "source": [
+ "!docker exec -it redis-vecdb ls /data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "93ef500b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_store.persist(persist_path=\"\") # persist_path means nothing for RedisVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "ed5ab256",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dump.rdb redis redisinsight\n"
+ ]
+ }
+ ],
+ "source": [
+ "!docker exec -it redis-vecdb ls /data"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "52b975a7",
+ "metadata": {},
+ "source": [
+ "### Deleting documents or index completely\n",
+ "\n",
+ "Sometimes it may be useful to delete documents or the entire index. This can be done using the `delete` and `delete_index` methods."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "6fe322f7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'faa23c94-ac9e-4763-92ba-e0f87bf38195'"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "document_id = documents[0].doc_id\n",
+ "document_id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "ae4fb2b0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of documents 20\n"
+ ]
+ }
+ ],
+ "source": [
+ "redis_client = vector_store.client\n",
+ "print(\"Number of documents\", len(redis_client.keys()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "0ce45788",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_store.delete(document_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "4a1ac683",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of documents 10\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Number of documents\", len(redis_client.keys()))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "c380605a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# now lets delete the index entirely (happens in the background, may take a second)\n",
+ "# this will delete all the documents and the index\n",
+ "vector_store.delete_index()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "474ad4ee",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of documents 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Number of documents\", len(redis_client.keys()))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "61b67496",
+ "metadata": {},
+ "source": [
+ "# Working with Metadata\n",
+ "\n",
+ "RedisVectorStore supports adding metadata and then using it in your queries (for example, to limit the scope of documents retrieved). However, there are a couple of important caveats:\n",
+ "1. Currently, only [Tag fields](https://redis.io/docs/stack/search/reference/tags/) are supported, and only with exact match.\n",
+ "2. You must declare the metadata when creating the index (usually when initializing RedisVectorStore). If you do not do this, your queries will come back empty. There is no way to modify an existing index after it had already been created (this is a Redis limitation).\n",
+ "\n",
+ "Here's how to work with Metadata:\n",
+ "\n",
+ "\n",
+ "### When **creating** the index\n",
+ "\n",
+ "Make sure to declare the metadata when you **first** create the index:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "9889ec79",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_store = RedisVectorStore(\n",
+ " index_name=\"pg_essays_with_metadata\",\n",
+ " index_prefix=\"llama\",\n",
+ " redis_url=\"redis://localhost:6379\",\n",
+ " overwrite=True,\n",
+ " metadata_fields=[\"user_id\", \"favorite_color\"],\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f8d6dc21",
+ "metadata": {},
+ "source": [
+ "Note: the field names `text`, `doc_id`, `id` and the name of your vector field (`vector` by default) should **not** be used as metadata field names, as they are are reserved."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "429947d5",
+ "metadata": {},
+ "source": [
+ "### When adding a document\n",
+ "\n",
+ "Add your metadata under the `metadata` key. You can add metadata to documents you load in just by looping over them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "89781b7d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Document ID: 6a5aa8dd-2771-454b-befc-bcfc311d2008 Document Hash: 77ae91ab542f3abb308c4d7c77c9bc4c9ad0ccd63144802b7cbe7e1bb3a4094e Metadata: {'user_id': '12345', 'favorite_color': 'blue'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# load your documents normally, then add your metadata\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
+ "\n",
+ "for document in documents:\n",
+ " document.metadata = {\"user_id\": \"12345\", \"favorite_color\": \"blue\"}\n",
+ "\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
+ "\n",
+ "# load documents\n",
+ "print(\n",
+ " \"Document ID:\",\n",
+ " documents[0].doc_id,\n",
+ " \"Document Hash:\",\n",
+ " documents[0].doc_hash,\n",
+ " \"Metadata:\",\n",
+ " documents[0].metadata,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "42b24e76",
+ "metadata": {},
+ "source": [
+ "### When querying the index\n",
+ "\n",
+ "To filter by your metadata fields, include one or more of your metadata keys, like so:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "0b01f346",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " The author learned that it was possible to publish anything online, and that working on things that\n",
+ "weren't prestigious could lead to discovering something real. They also learned that impure motives\n",
+ "were a big danger for the ambitious, and that it was possible for programs not to terminate.\n",
+ "Finally, they learned that computers were expensive in those days, and that they could write\n",
+ "programs on the IBM 1401.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.vector_stores.types import MetadataFilters, ExactMatchFilter\n",
+ "\n",
+ "query_engine = index.as_query_engine(\n",
+ " similarity_top_k=3,\n",
+ " filters=MetadataFilters(\n",
+ " filters=[\n",
+ " ExactMatchFilter(key=\"user_id\", value=\"12345\"),\n",
+ " ExactMatchFilter(key=\"favorite_color\", value=\"blue\"),\n",
+ " ]\n",
+ " ),\n",
+ ")\n",
+ "\n",
+ "response = query_engine.query(\"What did the author learn?\")\n",
+ "print(textwrap.fill(str(response), 100))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "07514f85",
+ "metadata": {},
+ "source": [
+ "## Troubleshooting\n",
+ "\n",
+ "In case you run into issues retrieving your documents from the index, you might get a message similar to this.\n",
+ "```\n",
+ "No docs found on index 'pg_essays' with prefix 'llama' and filters '(@user_id:{12345} & @favorite_color:{blue})'.\n",
+ "* Did you originally create the index with a different prefix?\n",
+ "* Did you index your metadata fields when you created the index?\n",
+ "```\n",
+ "\n",
+ "If you get this error, there a couple of gotchas to be aware of when working with Redis:\n",
+ "#### Prefix issues\n",
+ "\n",
+ "If you first create your index with a specific `prefix` but later change that prefix in your code, your query will come back empty. Redis saves the prefix your originally created your index with and expects it to be consistent.\n",
+ "\n",
+ "To see what prefix your index was created with, you can run `FT.INFO <name of your index>` in the Redis CLI and look under `index_definition` => `prefixes`.\n",
+ "\n",
+ "#### Empty queries when using metadata\n",
+ "\n",
+ "If you add metadata to the index *after* it has already been created and then try to query over that metadata, your queries will come back empty.\n",
+ "\n",
+ "Redis indexes fields upon index creation only (similar to how it indexes the prefixes, above).\n",
+ "\n",
+ "If you have an existing index and want to make sure it's dropped, you can run `FT.DROPINDEX <name of your index>` in the Redis CLI. Note that this will *not* drop your actual data."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "c09d1199",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/SimpleIndexDemo.ipynb b/docs/examples/vector_stores/SimpleIndexDemo.ipynb
index 9926c600f3..e413a43621 100644
--- a/docs/examples/vector_stores/SimpleIndexDemo.ipynb
+++ b/docs/examples/vector_stores/SimpleIndexDemo.ipynb
@@ -52,7 +52,12 @@
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, StorageContext\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " load_index_from_storage,\n",
+ " StorageContext,\n",
+ ")\n",
"from IPython.display import Markdown, display"
]
},
@@ -66,7 +71,9 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../../examples/paul_graham_essay/data').load_data()"
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../examples/paul_graham_essay/data\"\n",
+ ").load_data()"
]
},
{
@@ -93,7 +100,7 @@
"source": [
"# save index to disk\n",
"index.set_index_id(\"vector_index\")\n",
- "index.storage_context.persist('./storage')"
+ "index.storage_context.persist(\"./storage\")"
]
},
{
@@ -115,7 +122,7 @@
],
"source": [
"# rebuild storage context\n",
- "storage_context = StorageContext.from_defaults(persist_dir='storage')\n",
+ "storage_context = StorageContext.from_defaults(persist_dir=\"storage\")\n",
"# load index\n",
"index = load_index_from_storage(storage_context, index_id=\"vector_index\")"
]
@@ -140,7 +147,7 @@
"outputs": [],
"source": [
"# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(response_mode='tree_summarize')\n",
+ "query_engine = index.as_query_engine(response_mode=\"tree_summarize\")\n",
"response = query_engine.query(\"What did the author do growing up?\")"
]
},
@@ -196,13 +203,9 @@
" \"logistic_regression\",\n",
"]\n",
"for query_mode in query_modes:\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- " query_engine = index.as_query_engine(\n",
- " vector_store_query_mode=query_mode\n",
- " )\n",
- " response = query_engine.query(\n",
- " \"What did the author do growing up?\"\n",
- " )\n",
+ " # set Logging to DEBUG for more detailed outputs\n",
+ " query_engine = index.as_query_engine(vector_store_query_mode=query_mode)\n",
+ " response = query_engine.query(\"What did the author do growing up?\")\n",
" print(f\"Query mode: {query_mode}\")\n",
" display(Markdown(f\"<b>{response}</b>\"))"
]
@@ -256,8 +259,8 @@
"outputs": [],
"source": [
"query_bundle = QueryBundle(\n",
- " query_str=\"What did the author do growing up?\", \n",
- " custom_embedding_strs=['The author grew up painting.']\n",
+ " query_str=\"What did the author do growing up?\",\n",
+ " custom_embedding_strs=[\"The author grew up painting.\"],\n",
")\n",
"query_engine = index.as_query_engine()\n",
"response = query_engine.query(query_bundle)"
@@ -292,7 +295,7 @@
"outputs": [],
"source": [
"query_engine = index.as_query_engine(\n",
- " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\":0.2}\n",
+ " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\": 0.2}\n",
")\n",
"response = query_engine.query(\"What did the author do growing up?\")"
]
diff --git a/docs/examples/vector_stores/SimpleIndexDemoMMR.ipynb b/docs/examples/vector_stores/SimpleIndexDemoMMR.ipynb
index 1ca1257bd3..7c851fcee6 100644
--- a/docs/examples/vector_stores/SimpleIndexDemoMMR.ipynb
+++ b/docs/examples/vector_stores/SimpleIndexDemoMMR.ipynb
@@ -41,8 +41,9 @@
],
"source": [
"from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "\n",
"# llama_index/docs/examples/data/paul_graham\n",
- "documents = SimpleDirectoryReader('../data/paul_graham/').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
"index = VectorStoreIndex.from_documents(documents)\n",
"\n",
"# To use mmr, set it as a vector_store_query_mode\n",
@@ -68,12 +69,12 @@
"source": [
"from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
"\n",
- "documents = SimpleDirectoryReader('../data/paul_graham/').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
"index = VectorStoreIndex.from_documents(documents)\n",
"\n",
"# To set the threshold, set it in vector_store_kwargs\n",
"query_engine_with_threshold = index.as_query_engine(\n",
- " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\":0.2}\n",
+ " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\": 0.2}\n",
")\n",
"\n",
"response = query_engine_with_threshold.query(\"What did the author do growing up?\")\n",
@@ -110,19 +111,29 @@
"\n",
"index2 = VectorStoreIndex.from_documents(documents)\n",
"query_engine_with_high_threshold = index2.as_query_engine(\n",
- " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\":0.8}\n",
+ " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\": 0.8}\n",
+ ")\n",
+ "response_low_threshold = query_engine_with_low_threshold.query(\n",
+ " \"What did the author do growing up?\"\n",
")\n",
- "response_low_threshold = query_engine_with_low_threshold.query(\"What did the author do growing up?\")\n",
"\n",
"index3 = VectorStoreIndex.from_documents(documents)\n",
"query_engine_with_low_threshold = index3.as_query_engine(\n",
- " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\":0.2}\n",
+ " vector_store_query_mode=\"mmr\", vector_store_kwargs={\"mmr_threshold\": 0.2}\n",
+ ")\n",
+ "response_high_threshold = query_engine_with_high_threshold.query(\n",
+ " \"What did the author do growing up?\"\n",
")\n",
- "response_high_threshold = query_engine_with_high_threshold.query(\"What did the author do growing up?\")\n",
"\n",
"print(\"Scores without MMR \", [node.score for node in response_no_mmr.source_nodes])\n",
- "print(\"Scores with MMR and a threshold of 0.8 \", [node.score for node in response_high_threshold.source_nodes])\n",
- "print(\"Scores with MMR and a threshold of 0.2 \", [node.score for node in response_low_threshold.source_nodes])\n"
+ "print(\n",
+ " \"Scores with MMR and a threshold of 0.8 \",\n",
+ " [node.score for node in response_high_threshold.source_nodes],\n",
+ ")\n",
+ "print(\n",
+ " \"Scores with MMR and a threshold of 0.2 \",\n",
+ " [node.score for node in response_low_threshold.source_nodes],\n",
+ ")"
]
},
{
@@ -146,7 +157,12 @@
},
"outputs": [],
"source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
+ ")\n",
"from llama_index.response.notebook_utils import display_source_node\n",
"from llama_index.llms import OpenAI\n",
"\n",
@@ -163,7 +179,7 @@
"outputs": [],
"source": [
"# llama_index/docs/examples/data/paul_graham\n",
- "documents = SimpleDirectoryReader('../data/paul_graham/').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
"index = VectorStoreIndex.from_documents(documents, service_context=service_context)"
]
},
@@ -175,7 +191,11 @@
},
"outputs": [],
"source": [
- "retriever = index.as_retriever(vector_store_query_mode=\"mmr\", similarity_top_k=3, vector_store_kwargs={\"mmr_threshold\": 0.1})\n",
+ "retriever = index.as_retriever(\n",
+ " vector_store_query_mode=\"mmr\",\n",
+ " similarity_top_k=3,\n",
+ " vector_store_kwargs={\"mmr_threshold\": 0.1},\n",
+ ")\n",
"nodes = retriever.retrieve(\"What did the author do during his time in Y Combinator?\")"
]
},
@@ -238,7 +258,11 @@
},
"outputs": [],
"source": [
- "retriever = index.as_retriever(vector_store_query_mode=\"mmr\", similarity_top_k=3, vector_store_kwargs={\"mmr_threshold\": 0.5})\n",
+ "retriever = index.as_retriever(\n",
+ " vector_store_query_mode=\"mmr\",\n",
+ " similarity_top_k=3,\n",
+ " vector_store_kwargs={\"mmr_threshold\": 0.5},\n",
+ ")\n",
"nodes = retriever.retrieve(\"What did the author do during his time in Y Combinator?\")"
]
},
@@ -305,7 +329,11 @@
},
"outputs": [],
"source": [
- "retriever = index.as_retriever(vector_store_query_mode=\"mmr\", similarity_top_k=3, vector_store_kwargs={\"mmr_threshold\": 0.8})\n",
+ "retriever = index.as_retriever(\n",
+ " vector_store_query_mode=\"mmr\",\n",
+ " similarity_top_k=3,\n",
+ " vector_store_kwargs={\"mmr_threshold\": 0.8},\n",
+ ")\n",
"nodes = retriever.retrieve(\"What did the author do during his time in Y Combinator?\")"
]
},
@@ -368,7 +396,11 @@
},
"outputs": [],
"source": [
- "retriever = index.as_retriever(vector_store_query_mode=\"mmr\", similarity_top_k=3, vector_store_kwargs={\"mmr_threshold\": 1.0})\n",
+ "retriever = index.as_retriever(\n",
+ " vector_store_query_mode=\"mmr\",\n",
+ " similarity_top_k=3,\n",
+ " vector_store_kwargs={\"mmr_threshold\": 1.0},\n",
+ ")\n",
"nodes = retriever.retrieve(\"What did the author do during his time in Y Combinator?\")"
]
},
diff --git a/docs/examples/vector_stores/SimpleIndexOnS3.ipynb b/docs/examples/vector_stores/SimpleIndexOnS3.ipynb
index f5e5412f21..46082fca2e 100644
--- a/docs/examples/vector_stores/SimpleIndexOnS3.ipynb
+++ b/docs/examples/vector_stores/SimpleIndexOnS3.ipynb
@@ -39,7 +39,12 @@
"logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, load_index_from_storage, StorageContext\n",
+ "from llama_index import (\n",
+ " VectorStoreIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " load_index_from_storage,\n",
+ " StorageContext,\n",
+ ")\n",
"from IPython.display import Markdown, display"
]
},
@@ -52,20 +57,21 @@
"import dotenv\n",
"import s3fs\n",
"import os\n",
+ "\n",
"dotenv.load_dotenv(\"../../../.env\")\n",
"\n",
- "AWS_KEY = os.environ['AWS_ACCESS_KEY_ID']\n",
- "AWS_SECRET = os.environ['AWS_SECRET_ACCESS_KEY']\n",
- "R2_ACCOUNT_ID = os.environ['R2_ACCOUNT_ID']\n",
+ "AWS_KEY = os.environ[\"AWS_ACCESS_KEY_ID\"]\n",
+ "AWS_SECRET = os.environ[\"AWS_SECRET_ACCESS_KEY\"]\n",
+ "R2_ACCOUNT_ID = os.environ[\"R2_ACCOUNT_ID\"]\n",
"\n",
"assert AWS_KEY is not None and AWS_KEY != \"\"\n",
"\n",
"s3 = s3fs.S3FileSystem(\n",
- " key=AWS_KEY,\n",
- " secret=AWS_SECRET,\n",
- " endpoint_url=f'https://{R2_ACCOUNT_ID}.r2.cloudflarestorage.com',\n",
- " s3_additional_kwargs={'ACL': 'public-read'}\n",
- ")\n"
+ " key=AWS_KEY,\n",
+ " secret=AWS_SECRET,\n",
+ " endpoint_url=f\"https://{R2_ACCOUNT_ID}.r2.cloudflarestorage.com\",\n",
+ " s3_additional_kwargs={\"ACL\": \"public-read\"},\n",
+ ")"
]
},
{
@@ -83,7 +89,9 @@
],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../../../examples/paul_graham_essay/data/').load_data()\n",
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../examples/paul_graham_essay/data/\"\n",
+ ").load_data()\n",
"print(len(documents))"
]
},
@@ -115,7 +123,7 @@
"source": [
"# save index to disk\n",
"index.set_index_id(\"vector_index\")\n",
- "index.storage_context.persist('llama-index/storage_demo', fs=s3)"
+ "index.storage_context.persist(\"llama-index/storage_demo\", fs=s3)"
]
},
{
@@ -158,7 +166,7 @@
}
],
"source": [
- "s3.listdir('llama-index/storage_demo')"
+ "s3.listdir(\"llama-index/storage_demo\")"
]
},
{
@@ -168,7 +176,7 @@
"outputs": [],
"source": [
"# load index from s3\n",
- "sc = StorageContext.from_defaults(persist_dir='llama-index/storage_demo', fs=s3)"
+ "sc = StorageContext.from_defaults(persist_dir=\"llama-index/storage_demo\", fs=s3)"
]
},
{
@@ -186,7 +194,7 @@
}
],
"source": [
- "index2 = load_index_from_storage(sc, 'vector_index')"
+ "index2 = load_index_from_storage(sc, \"vector_index\")"
]
},
{
diff --git a/docs/examples/vector_stores/SupabaseVectorIndexDemo.ipynb b/docs/examples/vector_stores/SupabaseVectorIndexDemo.ipynb
index e006ffa453..af86de078c 100644
--- a/docs/examples/vector_stores/SupabaseVectorIndexDemo.ipynb
+++ b/docs/examples/vector_stores/SupabaseVectorIndexDemo.ipynb
@@ -49,7 +49,8 @@
"outputs": [],
"source": [
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"[your_openai_api_key]\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"[your_openai_api_key]\""
]
},
{
@@ -77,8 +78,8 @@
}
],
"source": [
- "documents = SimpleDirectoryReader('../data/paul_graham/').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
]
},
{
@@ -102,8 +103,8 @@
"outputs": [],
"source": [
"vector_store = SupabaseVectorStore(\n",
- " postgres_connection_string=\"postgresql://<user>:<password>@<host>:<port>/<db_name>\", \n",
- " collection_name='base_demo'\n",
+ " postgres_connection_string=\"postgresql://<user>:<password>@<host>:<port>/<db_name>\",\n",
+ " collection_name=\"base_demo\",\n",
")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
@@ -207,17 +208,26 @@
"from llama_index.schema import TextNode\n",
"\n",
"nodes = [\n",
- " TextNode('The Shawshank Redemption', metadata={\n",
- " \"author\": \"Stephen King\",\n",
- " \"theme\": \"Friendship\",\n",
- " }),\n",
- " TextNode('The Godfather', metadata={\n",
- " \"director\": \"Francis Ford Coppola\",\n",
- " \"theme\": \"Mafia\",\n",
- " }),\n",
- " TextNode(\"Inception\", metadata={\n",
- " \"director\": \"Christopher Nolan\",\n",
- " })\n",
+ " TextNode(\n",
+ " \"The Shawshank Redemption\",\n",
+ " metadata={\n",
+ " \"author\": \"Stephen King\",\n",
+ " \"theme\": \"Friendship\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " \"The Godfather\",\n",
+ " metadata={\n",
+ " \"director\": \"Francis Ford Coppola\",\n",
+ " \"theme\": \"Mafia\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " \"Inception\",\n",
+ " metadata={\n",
+ " \"director\": \"Christopher Nolan\",\n",
+ " },\n",
+ " ),\n",
"]"
]
},
@@ -229,8 +239,8 @@
"outputs": [],
"source": [
"vector_store = SupabaseVectorStore(\n",
- " postgres_connection_string=\"postgresql://<user>:<password>@<host>:<port>/<db_name>\", \n",
- " collection_name='metadata_filters_demo'\n",
+ " postgres_connection_string=\"postgresql://<user>:<password>@<host>:<port>/<db_name>\",\n",
+ " collection_name=\"metadata_filters_demo\",\n",
")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"index = VectorStoreIndex(nodes, storage_context=storage_context)"
@@ -253,7 +263,8 @@
"outputs": [],
"source": [
"from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
- "filters = MetadataFilters(filters=[ExactMatchFilter(key='theme', value='Mafia')])"
+ "\n",
+ "filters = MetadataFilters(filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")])"
]
},
{
@@ -284,7 +295,7 @@
],
"source": [
"retriever = index.as_retriever(filters=filters)\n",
- "retriever.retrieve('What is inception about?')"
+ "retriever.retrieve(\"What is inception about?\")"
]
},
{
diff --git a/docs/examples/vector_stores/TairIndexDemo.ipynb b/docs/examples/vector_stores/TairIndexDemo.ipynb
index 2106debb92..39a5a5a136 100644
--- a/docs/examples/vector_stores/TairIndexDemo.ipynb
+++ b/docs/examples/vector_stores/TairIndexDemo.ipynb
@@ -1,6 +1,7 @@
{
"cells": [
{
+ "attachments": {},
"cell_type": "markdown",
"id": "0b692c73",
"metadata": {},
@@ -9,6 +10,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "1e7787c2",
"metadata": {},
@@ -35,14 +37,15 @@
"import textwrap\n",
"\n",
"import warnings\n",
+ "\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"# stop huggingface warnings\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"\n",
"# Uncomment to see debug logs\n",
- "#logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "#logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
"from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, Document\n",
"from llama_index.vector_stores import TairVectorStore\n",
@@ -50,6 +53,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "f9b97a89",
"metadata": {},
@@ -75,10 +79,12 @@
"outputs": [],
"source": [
"import os\n",
+ "\n",
"os.environ[\"OPENAI_API_KEY\"] = \"sk-<your key here>\""
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "59ff935d",
"metadata": {},
@@ -103,11 +109,12 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "740cfaab-01ca-4f79-84fe-9e2444357d52",
"metadata": {},
@@ -134,18 +141,19 @@
"source": [
"from llama_index.storage.storage_context import StorageContext\n",
"\n",
- "tair_url=\"redis://{username}:{password}@r-bp****************.redis.rds.aliyuncs.com:{port}\"\n",
+ "tair_url = (\n",
+ " \"redis://{username}:{password}@r-bp****************.redis.rds.aliyuncs.com:{port}\"\n",
+ ")\n",
"\n",
"vector_store = TairVectorStore(\n",
- " tair_url=tair_url,\n",
- " index_name=\"pg_essays\",\n",
- " overwrite=True\n",
+ " tair_url=tair_url, index_name=\"pg_essays\", overwrite=True\n",
")\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"index = GPTVectorStoreIndex.from_documents(documents, storage_context=storage_context)"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "04304299-fc3e-40a0-8600-f50c3292767e",
"metadata": {},
@@ -189,6 +197,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "52b975a7",
"metadata": {},
@@ -216,7 +225,7 @@
"outputs": [],
"source": [
"info = vector_store.client.tvs_get_index(\"pg_essays\")\n",
- "print(\"Number of documents\", int(info[\"data_count\"])"
+ "print(\"Number of documents\", int(info[\"data_count\"]))"
]
},
{
@@ -237,10 +246,11 @@
"outputs": [],
"source": [
"info = vector_store.client.tvs_get_index(\"pg_essays\")\n",
- "print(\"Number of documents\", int(info[\"data_count\"])"
+ "print(\"Number of documents\", int(info[\"data_count\"]))"
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "b267728a-4abd-4305-82a6-66804c14823b",
"metadata": {},
diff --git a/docs/examples/vector_stores/TypesenseDemo.ipynb b/docs/examples/vector_stores/TypesenseDemo.ipynb
index 3d93252f27..7bbf2698e8 100644
--- a/docs/examples/vector_stores/TypesenseDemo.ipynb
+++ b/docs/examples/vector_stores/TypesenseDemo.ipynb
@@ -1,175 +1,177 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
- "metadata": {},
- "source": [
- "# Typesense Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
- "metadata": {},
- "outputs": [],
- "source": [
- "# import logging\n",
- "# import sys\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader, StorageContext\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../../../examples/paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "from llama_index.vector_stores.typesense import TypesenseVectorStore\n",
- "from typesense import Client\n",
- "\n",
- "typesense_client = Client({\n",
- " 'api_key': 'xyz',\n",
- " 'nodes': [{\n",
- " 'host': 'localhost',\n",
- " 'port': '8108',\n",
- " 'protocol': 'http'\n",
- " }],\n",
- " 'connection_timeout_seconds': 2\n",
- "})\n",
- "typesense_vector_store = TypesenseVectorStore(typesense_client)\n",
- "storage_context = StorageContext.from_defaults(vector_store=typesense_vector_store)\n",
- "\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "bdda1b2c-ae46-47cf-91d7-3153e8d0473b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "The author grew up skipping a step in the evolution of computers, learning Italian, walking through Florence, painting people, working with technology companies, seeking signature styles at RISD, living in a rent-stabilized apartment, launching software, editing code (including Lisp expressions), writing essays, publishing them online, and receiving feedback from angry readers. He also experienced the exponential growth of commodity processors in the 1990s, which rolled up high-end, special-purpose hardware and software companies. He also learned how to make a little Italian go a long way by stringing together abstract concepts with a few simple verbs. He also experienced the tight coupling of money and coolness in the art world, and the fact that anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive. He also experienced the challenge of launching software, as he had to recruit an initial set of users and make sure they had decent-looking stores before launching publicly. He also experienced the first instance of what is now a familiar experience, when he read the comments and found they were full of angry people. He also experienced the difference between putting something online and publishing it online. Finally, he wrote essays about topics he had stacked up, and wrote a more detailed version for others to read.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from llama_index.indices.query.schema import QueryBundle \n",
- "from llama_index.embeddings import OpenAIEmbedding\n",
- "\n",
- "# By default, typesense vector store uses vector search. You need to provide the embedding yourself.\n",
- "query_str = \"What did the author do growing up?\"\n",
- "embed_model = OpenAIEmbedding()\n",
- "# If your service context has an embed_model you can also do:\n",
- "# embed_model = index.service_context.embed_model\n",
- "query_embedding = embed_model.get_agg_embedding_from_queries(query_str)\n",
- "query_bundle = QueryBundle(query_str, embedding=query_embedding)\n",
- "response = index.as_query_engine().query(query_bundle)\n",
- "\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "751fb318",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "The author grew up during the Internet Bubble and was running a startup. They had to hire more people than they wanted to in order to seem more professional and were at the mercy of their investors until Yahoo bought them. They learned a lot about retail and startups, and had to do a lot of things that they weren't necessarily good at in order to make their business successful.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from llama_index.vector_stores.types import VectorStoreQueryMode\n",
- "\n",
- "# You can also use text search\n",
- "\n",
- "query_bundle = QueryBundle(query_str = query_str)\n",
- "response = index.as_query_engine(vector_store_query_mode=VectorStoreQueryMode.TEXT_SEARCH).query(query_bundle)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
+ "metadata": {},
+ "source": [
+ "# Typesense Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import logging\n",
+ "# import sys\n",
+ "\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader, StorageContext\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\n",
+ " \"../../../examples/paul_graham_essay/data\"\n",
+ ").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.vector_stores.typesense import TypesenseVectorStore\n",
+ "from typesense import Client\n",
+ "\n",
+ "typesense_client = Client(\n",
+ " {\n",
+ " \"api_key\": \"xyz\",\n",
+ " \"nodes\": [{\"host\": \"localhost\", \"port\": \"8108\", \"protocol\": \"http\"}],\n",
+ " \"connection_timeout_seconds\": 2,\n",
+ " }\n",
+ ")\n",
+ "typesense_vector_store = TypesenseVectorStore(typesense_client)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=typesense_vector_store)\n",
+ "\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bdda1b2c-ae46-47cf-91d7-3153e8d0473b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "The author grew up skipping a step in the evolution of computers, learning Italian, walking through Florence, painting people, working with technology companies, seeking signature styles at RISD, living in a rent-stabilized apartment, launching software, editing code (including Lisp expressions), writing essays, publishing them online, and receiving feedback from angry readers. He also experienced the exponential growth of commodity processors in the 1990s, which rolled up high-end, special-purpose hardware and software companies. He also learned how to make a little Italian go a long way by stringing together abstract concepts with a few simple verbs. He also experienced the tight coupling of money and coolness in the art world, and the fact that anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive. He also experienced the challenge of launching software, as he had to recruit an initial set of users and make sure they had decent-looking stores before launching publicly. He also experienced the first instance of what is now a familiar experience, when he read the comments and found they were full of angry people. He also experienced the difference between putting something online and publishing it online. Finally, he wrote essays about topics he had stacked up, and wrote a more detailed version for others to read.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from llama_index.indices.query.schema import QueryBundle\n",
+ "from llama_index.embeddings import OpenAIEmbedding\n",
+ "\n",
+ "# By default, typesense vector store uses vector search. You need to provide the embedding yourself.\n",
+ "query_str = \"What did the author do growing up?\"\n",
+ "embed_model = OpenAIEmbedding()\n",
+ "# If your service context has an embed_model you can also do:\n",
+ "# embed_model = index.service_context.embed_model\n",
+ "query_embedding = embed_model.get_agg_embedding_from_queries(query_str)\n",
+ "query_bundle = QueryBundle(query_str, embedding=query_embedding)\n",
+ "response = index.as_query_engine().query(query_bundle)\n",
+ "\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "751fb318",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "The author grew up during the Internet Bubble and was running a startup. They had to hire more people than they wanted to in order to seem more professional and were at the mercy of their investors until Yahoo bought them. They learned a lot about retail and startups, and had to do a lot of things that they weren't necessarily good at in order to make their business successful.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from llama_index.vector_stores.types import VectorStoreQueryMode\n",
+ "\n",
+ "# You can also use text search\n",
+ "\n",
+ "query_bundle = QueryBundle(query_str=query_str)\n",
+ "response = index.as_query_engine(\n",
+ " vector_store_query_mode=VectorStoreQueryMode.TEXT_SEARCH\n",
+ ").query(query_bundle)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb b/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb
index f75a720e18..70325ecef2 100644
--- a/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb
+++ b/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb
@@ -1,271 +1,266 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Weaviate Vector Store - Hybrid Search"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eccceb71",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "## Creating a Weaviate Client"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "72a4b618-668d-4713-84c5-6362030e9f19",
- "metadata": {},
- "outputs": [],
- "source": [
- "import weaviate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
- "metadata": {},
- "outputs": [],
- "source": [
- "resource_owner_config = weaviate.AuthClientPassword(\n",
- " username = \"<username>\", \n",
- " password = \"<password>\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "de43b464",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Connect to cloud instance\n",
- "# client = weaviate.Client(\"https://<cluster-id>.semi.network/\", auth_client_secret=resource_owner_config)\n",
- "\n",
- "# Connect to local instance\n",
- "client = weaviate.Client(\"http://localhost:8080\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "## Load documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import WeaviateVectorStore\n",
- "from llama_index.response.notebook_utils import display_response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "17fbf703",
- "metadata": {},
- "source": [
- "## Build the VectorStoreIndex with WeaviateVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ba1558b3",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = WeaviateVectorStore(weaviate_client=client)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
- "\n",
- "# NOTE: you may also choose to define a class_prefix manually.\n",
- "# class_prefix = \"test_prefix\"\n",
- "# vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix=class_prefix)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "622599aa",
- "metadata": {},
- "source": [
- "## Query Index with Default Vector Search"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "82f154f4",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(\n",
- " similarity_top_k=2\n",
- ")\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2c5bd359",
- "metadata": {},
- "outputs": [],
- "source": [
- "display_response(response)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "## Query Index with Hybrid Search"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "4925c9e6",
- "metadata": {},
- "source": [
- "Use hybrid search with bm25 and vector. \n",
- "`alpha` parameter determines weighting (alpha = 0 -> bm25, alpha=1 -> vector search). "
- ]
- },
- {
- "cell_type": "markdown",
- "id": "93e9f4d6",
- "metadata": {},
- "source": [
- "### By default, `alpha=0.75` is used (very similar to vector search) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "35369eda",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(\n",
- " vector_store_query_mode=\"hybrid\", \n",
- " similarity_top_k=2\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What did the author do growing up?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "display_response(response)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "80396381",
- "metadata": {},
- "source": [
- "### Set `alpha=0.` to favor bm25"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6b4b26d4",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(\n",
- " vector_store_query_mode=\"hybrid\", \n",
- " similarity_top_k=2, \n",
- " alpha=0.\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What did the author do growing up?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3d755768",
- "metadata": {},
- "outputs": [],
- "source": [
- "display_response(response)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Weaviate Vector Store - Hybrid Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eccceb71",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "## Creating a Weaviate Client"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "72a4b618-668d-4713-84c5-6362030e9f19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import weaviate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c9f4d21-145a-401e-95ff-ccb259e8ef84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "resource_owner_config = weaviate.AuthClientPassword(\n",
+ " username=\"<username>\",\n",
+ " password=\"<password>\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "de43b464",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Connect to cloud instance\n",
+ "# client = weaviate.Client(\"https://<cluster-id>.semi.network/\", auth_client_secret=resource_owner_config)\n",
+ "\n",
+ "# Connect to local instance\n",
+ "client = weaviate.Client(\"http://localhost:8080\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "## Load documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import WeaviateVectorStore\n",
+ "from llama_index.response.notebook_utils import display_response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17fbf703",
+ "metadata": {},
+ "source": [
+ "## Build the VectorStoreIndex with WeaviateVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba1558b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = WeaviateVectorStore(weaviate_client=client)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
+ "\n",
+ "# NOTE: you may also choose to define a class_prefix manually.\n",
+ "# class_prefix = \"test_prefix\"\n",
+ "# vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix=class_prefix)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "622599aa",
+ "metadata": {},
+ "source": [
+ "## Query Index with Default Vector Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f154f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(similarity_top_k=2)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2c5bd359",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display_response(response)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "## Query Index with Hybrid Search"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4925c9e6",
+ "metadata": {},
+ "source": [
+ "Use hybrid search with bm25 and vector. \n",
+ "`alpha` parameter determines weighting (alpha = 0 -> bm25, alpha=1 -> vector search). "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93e9f4d6",
+ "metadata": {},
+ "source": [
+ "### By default, `alpha=0.75` is used (very similar to vector search) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(\n",
+ " vector_store_query_mode=\"hybrid\", similarity_top_k=2\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"What did the author do growing up?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display_response(response)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80396381",
+ "metadata": {},
+ "source": [
+ "### Set `alpha=0.` to favor bm25"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6b4b26d4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(\n",
+ " vector_store_query_mode=\"hybrid\", similarity_top_k=2, alpha=0.0\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"What did the author do growing up?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3d755768",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display_response(response)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/WeaviateIndexDemo.ipynb b/docs/examples/vector_stores/WeaviateIndexDemo.ipynb
index ac2e412efe..4a1c368ec8 100644
--- a/docs/examples/vector_stores/WeaviateIndexDemo.ipynb
+++ b/docs/examples/vector_stores/WeaviateIndexDemo.ipynb
@@ -1,242 +1,242 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Weaviate Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Weaviate Client"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "eccceb71",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "72a4b618-668d-4713-84c5-6362030e9f19",
- "metadata": {},
- "outputs": [],
- "source": [
- "import weaviate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "df8b27e5-5ad5-4dfe-90c7-0cf1f1d1b37f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# cloud\n",
- "# resource_owner_config = weaviate.AuthClientPassword(\n",
- "# username = \"<username>\", \n",
- "# password = \"<password>\", \n",
- "# )\n",
- "# client = weaviate.Client(\"https://<cluster-id>.semi.network/\", auth_client_secret=resource_owner_config)\n",
- "\n",
- "# local\n",
- "client = weaviate.Client('http://localhost:8080')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents, build the VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
- "NumExpr defaulting to 8 threads.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import WeaviateVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "ba1558b3",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n"
- ]
- }
- ],
- "source": [
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = WeaviateVectorStore(weaviate_client=client)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
- "\n",
- "# NOTE: you may also choose to define a class_prefix manually.\n",
- "# class_prefix = \"test_prefix\"\n",
- "# vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix=class_prefix)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "35369eda",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
- "> [retrieve] Total embedding token usage: 8 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1920 tokens\n",
- "> [get_response] Total LLM token usage: 1920 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
- "> [get_response] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "Growing up, the author wrote short stories, programmed on an IBM 1401, and nagged his father to buy him a TRS-80 microcomputer. He wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, but switched to AI after becoming bored with it. He then took art classes at Harvard and applied to art schools, eventually attending RISD.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f2e0997c",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bc9a2ad0",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Weaviate Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Weaviate Client"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "eccceb71",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "72a4b618-668d-4713-84c5-6362030e9f19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import weaviate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "df8b27e5-5ad5-4dfe-90c7-0cf1f1d1b37f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# cloud\n",
+ "# resource_owner_config = weaviate.AuthClientPassword(\n",
+ "# username = \"<username>\",\n",
+ "# password = \"<password>\",\n",
+ "# )\n",
+ "# client = weaviate.Client(\"https://<cluster-id>.semi.network/\", auth_client_secret=resource_owner_config)\n",
+ "\n",
+ "# local\n",
+ "client = weaviate.Client(\"http://localhost:8080\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
+ "NumExpr defaulting to 8 threads.\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import WeaviateVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ba1558b3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 20729 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = WeaviateVectorStore(weaviate_client=client)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\n",
+ "\n",
+ "# NOTE: you may also choose to define a class_prefix manually.\n",
+ "# class_prefix = \"test_prefix\"\n",
+ "# vector_store = WeaviateVectorStore(weaviate_client=client, class_prefix=class_prefix)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 8 tokens\n",
+ "> [retrieve] Total embedding token usage: 8 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total LLM token usage: 1920 tokens\n",
+ "> [get_response] Total LLM token usage: 1920 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [get_response] Total embedding token usage: 0 tokens\n",
+ "> [get_response] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "Growing up, the author wrote short stories, programmed on an IBM 1401, and nagged his father to buy him a TRS-80 microcomputer. He wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He also studied philosophy in college, but switched to AI after becoming bored with it. He then took art classes at Harvard and applied to art schools, eventually attending RISD.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f2e0997c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc9a2ad0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/chroma_auto_retriever.ipynb b/docs/examples/vector_stores/chroma_auto_retriever.ipynb
index acd0fa424f..9d8a88dc0f 100644
--- a/docs/examples/vector_stores/chroma_auto_retriever.ipynb
+++ b/docs/examples/vector_stores/chroma_auto_retriever.ipynb
@@ -1,334 +1,354 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Chroma Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Chroma Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
- "metadata": {},
- "outputs": [],
- "source": [
- "import chromadb"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "INFO:chromadb:Running Chroma using direct local API.\n",
- "Running Chroma using direct local API.\n",
- "Running Chroma using direct local API.\n",
- "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
- "NumExpr defaulting to 8 threads.\n",
- "NumExpr defaulting to 8 threads.\n",
- "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n",
- "Using embedded DuckDB without persistence: data will be transient\n",
- "Using embedded DuckDB without persistence: data will be transient\n",
- "INFO:clickhouse_connect.driver.ctypes:Successfully imported ClickHouse Connect C data optimizations\n",
- "Successfully imported ClickHouse Connect C data optimizations\n",
- "Successfully imported ClickHouse Connect C data optimizations\n",
- "INFO:clickhouse_connect.driver.ctypes:Successfully import ClickHouse Connect C/Numpy optimizations\n",
- "Successfully import ClickHouse Connect C/Numpy optimizations\n",
- "Successfully import ClickHouse Connect C/Numpy optimizations\n",
- "INFO:clickhouse_connect.json_impl:Using python library for writing JSON byte strings\n",
- "Using python library for writing JSON byte strings\n",
- "Using python library for writing JSON byte strings\n",
- "WARNING:chromadb.api.models.Collection:No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
- "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
- "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
- "Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
- "Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
- "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n",
- "Use pytorch device: cpu\n",
- "Use pytorch device: cpu\n"
- ]
- }
- ],
- "source": [
- "chroma_client = chromadb.Client()\n",
- "chroma_collection = chroma_client.create_collection(\"quickstart\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, StorageContext\n",
- "from llama_index.vector_stores import ChromaVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.schema import TextNode\n",
- "\n",
- "nodes = [\n",
- " TextNode(text=\"Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.\", metadata={\n",
- " \"category\": \"Sports\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.\", metadata={\n",
- " \"category\": \"Entertainment\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, and lead designer of SpaceX, Tesla, Inc., Neuralink, and The Boring Company.\", metadata={\n",
- " \"category\": \"Business\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Rihanna is a Barbadian singer, actress, and businesswoman. She has achieved significant success in the music industry and is known for her versatile musical style.\", metadata={\n",
- " \"category\": \"Music\",\n",
- " \"country\": \"Barbados\",\n",
- " }),\n",
- " TextNode(text=\"Cristiano Ronaldo is a Portuguese professional footballer who is considered one of the greatest football players of all time. He has won numerous awards and set multiple records during his career.\", metadata={\n",
- " \"category\": \"Sports\",\n",
- " \"country\": \"Portugal\",\n",
- " })\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "ba1558b3",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "35369eda",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n"
- ]
- }
- ],
- "source": [
- "index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.vector_store.retrievers import VectorIndexAutoRetriever\n",
- "from llama_index.vector_stores.types import MetadataInfo, VectorStoreInfo\n",
- "\n",
- "\n",
- "vector_store_info = VectorStoreInfo(\n",
- " content_info='brief biography of celebrities',\n",
- " metadata_info=[\n",
- " MetadataInfo(\n",
- " name='category', \n",
- " type='str', \n",
- " description='Category of the celebrity, one of [Sports, Entertainment, Business, Music]'),\n",
- " MetadataInfo(name='country', type='str', description='Country of the celebrity, one of [United States, Barbados, Portugal]'),\n",
- " ]\n",
- ")\n",
- "retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "eeb18e9c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using query str: celebrities\n",
- "Using query str: celebrities\n",
- "Using query str: celebrities\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using filters: {'country': 'United States'}\n",
- "Using filters: {'country': 'United States'}\n",
- "Using filters: {'country': 'United States'}\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using top_k: 2\n",
- "Using top_k: 2\n",
- "Using top_k: 2\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 3 tokens\n",
- "> [retrieve] Total embedding token usage: 3 tokens\n",
- "> [retrieve] Total embedding token usage: 3 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=Node(text='Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.', doc_id='7389c8ad-2feb-4cf3-a8da-6c0f08c0f222', embedding=None, doc_hash='1171ef7bb1b89283a1012fecb0ea7a831a0e38c2ed6fac9fbfdd62ad64063934', extra_info={'category': 'Entertainment', 'country': 'United States'}, node_info={}, relationships={}), score=0.3262841090927262),\n",
- " NodeWithScore(node=Node(text='Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='da13fc89-72cb-401e-8445-9b9680372fbf', embedding=None, doc_hash='44c17458239bdba3c72f8ed6ac12e096b4c7965f6b5154d74b7a10484dad16a4', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.3734403491142674)]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "retriever.retrieve('Tell me about two celebrities from United States')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "51f00cde",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using query str: Sports celebrities\n",
- "Using query str: Sports celebrities\n",
- "Using query str: Sports celebrities\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using filters: {'category': 'Sports', 'country': 'United States'}\n",
- "Using filters: {'category': 'Sports', 'country': 'United States'}\n",
- "Using filters: {'category': 'Sports', 'country': 'United States'}\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using top_k: 2\n",
- "Using top_k: 2\n",
- "Using top_k: 2\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 2 tokens\n",
- "> [retrieve] Total embedding token usage: 2 tokens\n",
- "> [retrieve] Total embedding token usage: 2 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=Node(text='Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='da13fc89-72cb-401e-8445-9b9680372fbf', embedding=None, doc_hash='44c17458239bdba3c72f8ed6ac12e096b4c7965f6b5154d74b7a10484dad16a4', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.3328886457329614)]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "retriever.retrieve('Tell me about Sports celebrities from United States')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b8387a0e",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- },
- "vscode": {
- "interpreter": {
- "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
- }
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Chroma Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Chroma Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import chromadb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "INFO:chromadb:Running Chroma using direct local API.\n",
+ "Running Chroma using direct local API.\n",
+ "Running Chroma using direct local API.\n",
+ "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
+ "NumExpr defaulting to 8 threads.\n",
+ "NumExpr defaulting to 8 threads.\n",
+ "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n",
+ "Using embedded DuckDB without persistence: data will be transient\n",
+ "Using embedded DuckDB without persistence: data will be transient\n",
+ "INFO:clickhouse_connect.driver.ctypes:Successfully imported ClickHouse Connect C data optimizations\n",
+ "Successfully imported ClickHouse Connect C data optimizations\n",
+ "Successfully imported ClickHouse Connect C data optimizations\n",
+ "INFO:clickhouse_connect.driver.ctypes:Successfully import ClickHouse Connect C/Numpy optimizations\n",
+ "Successfully import ClickHouse Connect C/Numpy optimizations\n",
+ "Successfully import ClickHouse Connect C/Numpy optimizations\n",
+ "INFO:clickhouse_connect.json_impl:Using python library for writing JSON byte strings\n",
+ "Using python library for writing JSON byte strings\n",
+ "Using python library for writing JSON byte strings\n",
+ "WARNING:chromadb.api.models.Collection:No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
+ "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
+ "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
+ "Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
+ "Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
+ "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n",
+ "Use pytorch device: cpu\n",
+ "Use pytorch device: cpu\n"
+ ]
+ }
+ ],
+ "source": [
+ "chroma_client = chromadb.Client()\n",
+ "chroma_collection = chroma_client.create_collection(\"quickstart\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, StorageContext\n",
+ "from llama_index.vector_stores import ChromaVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.schema import TextNode\n",
+ "\n",
+ "nodes = [\n",
+ " TextNode(\n",
+ " text=\"Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.\",\n",
+ " metadata={\n",
+ " \"category\": \"Sports\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.\",\n",
+ " metadata={\n",
+ " \"category\": \"Entertainment\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, and lead designer of SpaceX, Tesla, Inc., Neuralink, and The Boring Company.\",\n",
+ " metadata={\n",
+ " \"category\": \"Business\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Rihanna is a Barbadian singer, actress, and businesswoman. She has achieved significant success in the music industry and is known for her versatile musical style.\",\n",
+ " metadata={\n",
+ " \"category\": \"Music\",\n",
+ " \"country\": \"Barbados\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Cristiano Ronaldo is a Portuguese professional footballer who is considered one of the greatest football players of all time. He has won numerous awards and set multiple records during his career.\",\n",
+ " metadata={\n",
+ " \"category\": \"Sports\",\n",
+ " \"country\": \"Portugal\",\n",
+ " },\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "ba1558b3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.vector_store.retrievers import VectorIndexAutoRetriever\n",
+ "from llama_index.vector_stores.types import MetadataInfo, VectorStoreInfo\n",
+ "\n",
+ "\n",
+ "vector_store_info = VectorStoreInfo(\n",
+ " content_info=\"brief biography of celebrities\",\n",
+ " metadata_info=[\n",
+ " MetadataInfo(\n",
+ " name=\"category\",\n",
+ " type=\"str\",\n",
+ " description=\"Category of the celebrity, one of [Sports, Entertainment, Business, Music]\",\n",
+ " ),\n",
+ " MetadataInfo(\n",
+ " name=\"country\",\n",
+ " type=\"str\",\n",
+ " description=\"Country of the celebrity, one of [United States, Barbados, Portugal]\",\n",
+ " ),\n",
+ " ],\n",
+ ")\n",
+ "retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "eeb18e9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using query str: celebrities\n",
+ "Using query str: celebrities\n",
+ "Using query str: celebrities\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using filters: {'country': 'United States'}\n",
+ "Using filters: {'country': 'United States'}\n",
+ "Using filters: {'country': 'United States'}\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using top_k: 2\n",
+ "Using top_k: 2\n",
+ "Using top_k: 2\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 3 tokens\n",
+ "> [retrieve] Total embedding token usage: 3 tokens\n",
+ "> [retrieve] Total embedding token usage: 3 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=Node(text='Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.', doc_id='7389c8ad-2feb-4cf3-a8da-6c0f08c0f222', embedding=None, doc_hash='1171ef7bb1b89283a1012fecb0ea7a831a0e38c2ed6fac9fbfdd62ad64063934', extra_info={'category': 'Entertainment', 'country': 'United States'}, node_info={}, relationships={}), score=0.3262841090927262),\n",
+ " NodeWithScore(node=Node(text='Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='da13fc89-72cb-401e-8445-9b9680372fbf', embedding=None, doc_hash='44c17458239bdba3c72f8ed6ac12e096b4c7965f6b5154d74b7a10484dad16a4', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.3734403491142674)]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "retriever.retrieve(\"Tell me about two celebrities from United States\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "51f00cde",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using query str: Sports celebrities\n",
+ "Using query str: Sports celebrities\n",
+ "Using query str: Sports celebrities\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using filters: {'category': 'Sports', 'country': 'United States'}\n",
+ "Using filters: {'category': 'Sports', 'country': 'United States'}\n",
+ "Using filters: {'category': 'Sports', 'country': 'United States'}\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Using top_k: 2\n",
+ "Using top_k: 2\n",
+ "Using top_k: 2\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 2 tokens\n",
+ "> [retrieve] Total embedding token usage: 2 tokens\n",
+ "> [retrieve] Total embedding token usage: 2 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=Node(text='Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='da13fc89-72cb-401e-8445-9b9680372fbf', embedding=None, doc_hash='44c17458239bdba3c72f8ed6ac12e096b4c7965f6b5154d74b7a10484dad16a4', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.3328886457329614)]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "retriever.retrieve(\"Tell me about Sports celebrities from United States\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b8387a0e",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/chroma_metadata_filter.ipynb b/docs/examples/vector_stores/chroma_metadata_filter.ipynb
index 5b0f7e9e99..4139832c19 100644
--- a/docs/examples/vector_stores/chroma_metadata_filter.ipynb
+++ b/docs/examples/vector_stores/chroma_metadata_filter.ipynb
@@ -1,247 +1,253 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Chroma Vector Store"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Chroma Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
- "metadata": {},
- "outputs": [],
- "source": [
- "import chromadb"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
- "INFO:chromadb:Running Chroma using direct local API.\n",
- "Running Chroma using direct local API.\n",
- "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
- "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
- "NumExpr defaulting to 8 threads.\n",
- "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n",
- "Using embedded DuckDB without persistence: data will be transient\n",
- "INFO:clickhouse_connect.driver.ctypes:Successfully imported ClickHouse Connect C data optimizations\n",
- "Successfully imported ClickHouse Connect C data optimizations\n",
- "INFO:clickhouse_connect.driver.ctypes:Successfully import ClickHouse Connect C/Numpy optimizations\n",
- "Successfully import ClickHouse Connect C/Numpy optimizations\n",
- "INFO:clickhouse_connect.json_impl:Using python library for writing JSON byte strings\n",
- "Using python library for writing JSON byte strings\n",
- "WARNING:chromadb.api.models.Collection:No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
- "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
- "Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
- "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n",
- "Use pytorch device: cpu\n"
- ]
- }
- ],
- "source": [
- "chroma_client = chromadb.Client()\n",
- "chroma_collection = chroma_client.create_collection(\"quickstart\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
- "from llama_index.vector_stores import ChromaVectorStore\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.schema import TextNode\n",
- "\n",
- "nodes = [\n",
- " TextNode(text='The Shawshank Redemption', metadata={\n",
- " \"author\": \"Stephen King\",\n",
- " \"theme\": \"Friendship\",\n",
- " }),\n",
- " TextNode(text='The Godfather', metadata={\n",
- " \"director\": \"Francis Ford Coppola\",\n",
- " \"theme\": \"Mafia\",\n",
- " }),\n",
- " TextNode(text=\"Inception\", metadata={\n",
- " \"director\": \"Christopher Nolan\",\n",
- " })\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "ba1558b3",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "35369eda",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 39 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 39 tokens\n"
- ]
- }
- ],
- "source": [
- "index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 5 tokens\n",
- "> [retrieve] Total embedding token usage: 5 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=Node(text='director: Francis Ford Coppola\\ntheme: Mafia\\n\\nThe Godfather', doc_id='d73abd97-ae46-4046-a6c2-012e46be7459', embedding=None, doc_hash='208e5a6c4e8f182d28af5293fb3d975531cf8ce3cf6b1e47cb64744d96f6285f', extra_info={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, node_info={}, relationships={}), score=0.37088084637164886)]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "\n",
- "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
- "\n",
- "\n",
- "filters = MetadataFilters(filters=[\n",
- " ExactMatchFilter(key='theme', value='Mafia')\n",
- "])\n",
- "\n",
- "retriever = index.as_retriever(filters=filters)\n",
- "retriever.retrieve('What is inception about?')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "eeb18e9c",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- },
- "vscode": {
- "interpreter": {
- "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
- }
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Chroma Vector Store"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Chroma Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0ce3143d-198c-4dd2-8e5a-c5cdf94f017a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import chromadb"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:chromadb.telemetry.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n",
+ "INFO:chromadb:Running Chroma using direct local API.\n",
+ "Running Chroma using direct local API.\n",
+ "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n",
+ "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
+ "NumExpr defaulting to 8 threads.\n",
+ "WARNING:chromadb:Using embedded DuckDB without persistence: data will be transient\n",
+ "Using embedded DuckDB without persistence: data will be transient\n",
+ "INFO:clickhouse_connect.driver.ctypes:Successfully imported ClickHouse Connect C data optimizations\n",
+ "Successfully imported ClickHouse Connect C data optimizations\n",
+ "INFO:clickhouse_connect.driver.ctypes:Successfully import ClickHouse Connect C/Numpy optimizations\n",
+ "Successfully import ClickHouse Connect C/Numpy optimizations\n",
+ "INFO:clickhouse_connect.json_impl:Using python library for writing JSON byte strings\n",
+ "Using python library for writing JSON byte strings\n",
+ "WARNING:chromadb.api.models.Collection:No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n",
+ "No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/suo/miniconda3/envs/llama/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:sentence_transformers.SentenceTransformer:Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
+ "Load pretrained SentenceTransformer: all-MiniLM-L6-v2\n",
+ "INFO:sentence_transformers.SentenceTransformer:Use pytorch device: cpu\n",
+ "Use pytorch device: cpu\n"
+ ]
+ }
+ ],
+ "source": [
+ "chroma_client = chromadb.Client()\n",
+ "chroma_collection = chroma_client.create_collection(\"quickstart\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
+ "from llama_index.vector_stores import ChromaVectorStore\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.schema import TextNode\n",
+ "\n",
+ "nodes = [\n",
+ " TextNode(\n",
+ " text=\"The Shawshank Redemption\",\n",
+ " metadata={\n",
+ " \"author\": \"Stephen King\",\n",
+ " \"theme\": \"Friendship\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"The Godfather\",\n",
+ " metadata={\n",
+ " \"director\": \"Francis Ford Coppola\",\n",
+ " \"theme\": \"Mafia\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Inception\",\n",
+ " metadata={\n",
+ " \"director\": \"Christopher Nolan\",\n",
+ " },\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ba1558b3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "35369eda",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 39 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 39 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 5 tokens\n",
+ "> [retrieve] Total embedding token usage: 5 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=Node(text='director: Francis Ford Coppola\\ntheme: Mafia\\n\\nThe Godfather', doc_id='d73abd97-ae46-4046-a6c2-012e46be7459', embedding=None, doc_hash='208e5a6c4e8f182d28af5293fb3d975531cf8ce3cf6b1e47cb64744d96f6285f', extra_info={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, node_info={}, relationships={}), score=0.37088084637164886)]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
+ "\n",
+ "\n",
+ "filters = MetadataFilters(filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")])\n",
+ "\n",
+ "retriever = index.as_retriever(filters=filters)\n",
+ "retriever.retrieve(\"What is inception about?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eeb18e9c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "0ac390d292208ca2380c85f5bce7ded36a7a25670a97c40b8009630eb36cb06e"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/existing_data/pinecone_existing_data.ipynb b/docs/examples/vector_stores/existing_data/pinecone_existing_data.ipynb
index 2e8f9cea5e..658defb433 100644
--- a/docs/examples/vector_stores/existing_data/pinecone_existing_data.ipynb
+++ b/docs/examples/vector_stores/existing_data/pinecone_existing_data.ipynb
@@ -31,7 +31,7 @@
},
"outputs": [],
"source": [
- "api_key = os.environ['PINECONE_API_KEY']\n",
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
"pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
]
},
@@ -85,9 +85,11 @@
},
"outputs": [],
"source": [
- "if 'quickstart-index' not in indexes:\n",
+ "if \"quickstart-index\" not in indexes:\n",
" # dimensions are for text-embedding-ada-002\n",
- " pinecone.create_index(\"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")"
+ " pinecone.create_index(\n",
+ " \"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\"\n",
+ " )"
]
},
{
@@ -122,7 +124,7 @@
}
],
"source": [
- "pinecone_index.delete(deleteAll='true')"
+ "pinecone_index.delete(deleteAll=\"true\")"
]
},
{
@@ -147,30 +149,30 @@
"outputs": [],
"source": [
"books = [\n",
- " {\n",
- " \"title\": \"To Kill a Mockingbird\",\n",
- " \"author\": \"Harper Lee\",\n",
- " \"content\": \"To Kill a Mockingbird is a novel by Harper Lee published in 1960...\",\n",
- " \"year\": 1960\n",
- " },\n",
- " {\n",
- " \"title\": \"1984\",\n",
- " \"author\": \"George Orwell\",\n",
- " \"content\": \"1984 is a dystopian novel by George Orwell published in 1949...\",\n",
- " \"year\": 1949\n",
- " },\n",
- " {\n",
- " \"title\": \"The Great Gatsby\",\n",
- " \"author\": \"F. Scott Fitzgerald\",\n",
- " \"content\": \"The Great Gatsby is a novel by F. Scott Fitzgerald published in 1925...\",\n",
- " \"year\": 1925\n",
- " },\n",
- " {\n",
- " \"title\": \"Pride and Prejudice\",\n",
- " \"author\": \"Jane Austen\",\n",
- " \"content\": \"Pride and Prejudice is a novel by Jane Austen published in 1813...\",\n",
- " \"year\": 1813\n",
- " },\n",
+ " {\n",
+ " \"title\": \"To Kill a Mockingbird\",\n",
+ " \"author\": \"Harper Lee\",\n",
+ " \"content\": \"To Kill a Mockingbird is a novel by Harper Lee published in 1960...\",\n",
+ " \"year\": 1960,\n",
+ " },\n",
+ " {\n",
+ " \"title\": \"1984\",\n",
+ " \"author\": \"George Orwell\",\n",
+ " \"content\": \"1984 is a dystopian novel by George Orwell published in 1949...\",\n",
+ " \"year\": 1949,\n",
+ " },\n",
+ " {\n",
+ " \"title\": \"The Great Gatsby\",\n",
+ " \"author\": \"F. Scott Fitzgerald\",\n",
+ " \"content\": \"The Great Gatsby is a novel by F. Scott Fitzgerald published in 1925...\",\n",
+ " \"year\": 1925,\n",
+ " },\n",
+ " {\n",
+ " \"title\": \"Pride and Prejudice\",\n",
+ " \"author\": \"Jane Austen\",\n",
+ " \"content\": \"Pride and Prejudice is a novel by Jane Austen published in 1813...\",\n",
+ " \"year\": 1813,\n",
+ " },\n",
"]"
]
},
@@ -197,6 +199,7 @@
"source": [
"import uuid\n",
"from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "\n",
"embed_model = OpenAIEmbedding()"
]
},
@@ -222,14 +225,8 @@
"source": [
"entries = []\n",
"for book in books:\n",
- " vector = embed_model.get_text_embedding(book['content'])\n",
- " entries.append(\n",
- " {\n",
- " 'id': str(uuid.uuid4()),\n",
- " 'values': vector,\n",
- " 'metadata': book\n",
- " }\n",
- " )\n",
+ " vector = embed_model.get_text_embedding(book[\"content\"])\n",
+ " entries.append({\"id\": str(uuid.uuid4()), \"values\": vector, \"metadata\": book})\n",
"pinecone_index.upsert(entries)"
]
},
@@ -274,7 +271,7 @@
},
"outputs": [],
"source": [
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, text_key='content')"
+ "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, text_key=\"content\")"
]
},
{
@@ -286,7 +283,9 @@
},
"outputs": [],
"source": [
- "retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(similarity_top_k=1)"
+ "retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(\n",
+ " similarity_top_k=1\n",
+ ")"
]
},
{
diff --git a/docs/examples/vector_stores/existing_data/weaviate_existing_data.ipynb b/docs/examples/vector_stores/existing_data/weaviate_existing_data.ipynb
index 0b2e80be85..3240adc613 100644
--- a/docs/examples/vector_stores/existing_data/weaviate_existing_data.ipynb
+++ b/docs/examples/vector_stores/existing_data/weaviate_existing_data.ipynb
@@ -80,29 +80,17 @@
"outputs": [],
"source": [
"schema = {\n",
- " \"classes\": [\n",
- " {\n",
- " \"class\": \"Book\",\n",
- " \"properties\": [\n",
- " {\n",
- " \"name\": \"title\",\n",
- " \"dataType\": [\"text\"]\n",
- " },\n",
- " {\n",
- " \"name\": \"author\",\n",
- " \"dataType\": [\"text\"]\n",
- " },\n",
+ " \"classes\": [\n",
" {\n",
- " \"name\": \"content\",\n",
- " \"dataType\": [\"text\"]\n",
+ " \"class\": \"Book\",\n",
+ " \"properties\": [\n",
+ " {\"name\": \"title\", \"dataType\": [\"text\"]},\n",
+ " {\"name\": \"author\", \"dataType\": [\"text\"]},\n",
+ " {\"name\": \"content\", \"dataType\": [\"text\"]},\n",
+ " {\"name\": \"year\", \"dataType\": [\"int\"]},\n",
+ " ],\n",
" },\n",
- " {\n",
- " \"name\": \"year\",\n",
- " \"dataType\": [\"int\"]\n",
- " }\n",
- " ]\n",
- " },\n",
- " ]\n",
+ " ]\n",
"}\n",
"\n",
"if not client.schema.contains(schema):\n",
@@ -132,30 +120,30 @@
"outputs": [],
"source": [
"books = [\n",
- " {\n",
- " \"title\": \"To Kill a Mockingbird\",\n",
- " \"author\": \"Harper Lee\",\n",
- " \"content\": \"To Kill a Mockingbird is a novel by Harper Lee published in 1960...\",\n",
- " \"year\": 1960\n",
- " },\n",
- " {\n",
- " \"title\": \"1984\",\n",
- " \"author\": \"George Orwell\",\n",
- " \"content\": \"1984 is a dystopian novel by George Orwell published in 1949...\",\n",
- " \"year\": 1949\n",
- " },\n",
- " {\n",
- " \"title\": \"The Great Gatsby\",\n",
- " \"author\": \"F. Scott Fitzgerald\",\n",
- " \"content\": \"The Great Gatsby is a novel by F. Scott Fitzgerald published in 1925...\",\n",
- " \"year\": 1925\n",
- " },\n",
- " {\n",
- " \"title\": \"Pride and Prejudice\",\n",
- " \"author\": \"Jane Austen\",\n",
- " \"content\": \"Pride and Prejudice is a novel by Jane Austen published in 1813...\",\n",
- " \"year\": 1813\n",
- " },\n",
+ " {\n",
+ " \"title\": \"To Kill a Mockingbird\",\n",
+ " \"author\": \"Harper Lee\",\n",
+ " \"content\": \"To Kill a Mockingbird is a novel by Harper Lee published in 1960...\",\n",
+ " \"year\": 1960,\n",
+ " },\n",
+ " {\n",
+ " \"title\": \"1984\",\n",
+ " \"author\": \"George Orwell\",\n",
+ " \"content\": \"1984 is a dystopian novel by George Orwell published in 1949...\",\n",
+ " \"year\": 1949,\n",
+ " },\n",
+ " {\n",
+ " \"title\": \"The Great Gatsby\",\n",
+ " \"author\": \"F. Scott Fitzgerald\",\n",
+ " \"content\": \"The Great Gatsby is a novel by F. Scott Fitzgerald published in 1925...\",\n",
+ " \"year\": 1925,\n",
+ " },\n",
+ " {\n",
+ " \"title\": \"Pride and Prejudice\",\n",
+ " \"author\": \"Jane Austen\",\n",
+ " \"content\": \"Pride and Prejudice is a novel by Jane Austen published in 1813...\",\n",
+ " \"year\": 1813,\n",
+ " },\n",
"]"
]
},
@@ -182,6 +170,7 @@
"outputs": [],
"source": [
"from llama_index.embeddings.openai import OpenAIEmbedding\n",
+ "\n",
"embed_model = OpenAIEmbedding()"
]
},
@@ -196,12 +185,8 @@
"source": [
"with client.batch as batch:\n",
" for book in books:\n",
- " vector = embed_model.get_text_embedding(book['content'])\n",
- " batch.add_data_object(\n",
- " data_object=book,\n",
- " class_name=\"Book\",\n",
- " vector=vector\n",
- " )"
+ " vector = embed_model.get_text_embedding(book[\"content\"])\n",
+ " batch.add_data_object(data_object=book, class_name=\"Book\", vector=vector)"
]
},
{
@@ -245,7 +230,9 @@
},
"outputs": [],
"source": [
- "vector_store = WeaviateVectorStore(weaviate_client=client, index_name='Book', text_key='content')"
+ "vector_store = WeaviateVectorStore(\n",
+ " weaviate_client=client, index_name=\"Book\", text_key=\"content\"\n",
+ ")"
]
},
{
@@ -257,7 +244,9 @@
},
"outputs": [],
"source": [
- "retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(similarity_top_k=1)"
+ "retriever = VectorStoreIndex.from_vector_store(vector_store).as_retriever(\n",
+ " similarity_top_k=1\n",
+ ")"
]
},
{
diff --git a/docs/examples/vector_stores/pinecone_auto_retriever.ipynb b/docs/examples/vector_stores/pinecone_auto_retriever.ipynb
index 9b750662aa..1a132a9096 100644
--- a/docs/examples/vector_stores/pinecone_auto_retriever.ipynb
+++ b/docs/examples/vector_stores/pinecone_auto_retriever.ipynb
@@ -1,300 +1,322 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Pinecone Vector Store - Auto Retriever"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "#### Creating a Pinecone Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import os\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pinecone\n",
- "\n",
- "api_key = os.environ['PINECONE_API_KEY']\n",
- "pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
- "metadata": {},
- "outputs": [],
- "source": [
- "# dimensions are for text-embedding-ada-002\n",
- "try:\n",
- " pinecone.create_index(\"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")\n",
- "except Exception:\n",
- " # most likely index already exists\n",
- " pass"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
- "metadata": {},
- "outputs": [],
- "source": [
- "pinecone_index = pinecone.Index(\"quickstart-index\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents, build the PineconeVectorStore and VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, StorageContext\n",
- "from llama_index.vector_stores import PineconeVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "9ae59590",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.schema import TextNode\n",
- "\n",
- "nodes = [\n",
- " TextNode(text=\"Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.\", metadata={\n",
- " \"category\": \"Sports\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.\", metadata={\n",
- " \"category\": \"Entertainment\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, and lead designer of SpaceX, Tesla, Inc., Neuralink, and The Boring Company.\", metadata={\n",
- " \"category\": \"Business\",\n",
- " \"country\": \"United States\",\n",
- " }),\n",
- " TextNode(text=\"Rihanna is a Barbadian singer, actress, and businesswoman. She has achieved significant success in the music industry and is known for her versatile musical style.\", metadata={\n",
- " \"category\": \"Music\",\n",
- " \"country\": \"Barbados\",\n",
- " }),\n",
- " TextNode(text=\"Cristiano Ronaldo is a Portuguese professional footballer who is considered one of the greatest football players of all time. He has won numerous awards and set multiple records during his career.\", metadata={\n",
- " \"category\": \"Sports\",\n",
- " \"country\": \"Portugal\",\n",
- " })\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "ee6eeecb-d54f-4a71-b5fe-0cda8a5c3e10",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='test')\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "cad08884",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n"
- ]
- }
- ],
- "source": [
- "index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "1a57e62f",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.vector_store.retrievers import VectorIndexAutoRetriever\n",
- "from llama_index.vector_stores.types import MetadataInfo, VectorStoreInfo\n",
- "\n",
- "\n",
- "vector_store_info = VectorStoreInfo(\n",
- " content_info='brief biography of celebrities',\n",
- " metadata_info=[\n",
- " MetadataInfo(\n",
- " name='category', \n",
- " type='str', \n",
- " description='Category of the celebrity, one of [Sports, Entertainment, Business, Music]'),\n",
- " MetadataInfo(name='country', type='str', description='Country of the celebrity, one of [United States, Barbados, Portugal]'),\n",
- " ]\n",
- ")\n",
- "retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "a5c0490d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto query: celebrities\n",
- "Auto query: celebrities\n",
- "Auto query: celebrities\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto filter: {'country': 'United States'}\n",
- "Auto filter: {'country': 'United States'}\n",
- "Auto filter: {'country': 'United States'}\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto top_k: 2\n",
- "Auto top_k: 2\n",
- "Auto top_k: 2\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 3 tokens\n",
- "> [retrieve] Total embedding token usage: 3 tokens\n",
- "> [retrieve] Total embedding token usage: 3 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=Node(text='category: Entertainment\\ncountry: United States\\n\\nAngelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.', doc_id='6821b1fe-e1dc-400c-ad2c-83f7fa683321', embedding=None, doc_hash='4086bd15d984c4f3ee3d4f911f0a347735406351d1936b6060b411707d3e82cc', extra_info={'category': 'Entertainment', 'country': 'United States'}, node_info={}, relationships={}), score=0.80265522),\n",
- " NodeWithScore(node=Node(text='category: Sports\\ncountry: United States\\n\\nMichael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='4cf176e5-363f-479b-8979-c3e07cfaead8', embedding=None, doc_hash='9aaec18f659138a23ca519f8d6d1f3997d34aae993b8c07443b165c13163b886', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.766244411)]"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "retriever.retrieve('Tell me about two celebrities from United States')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "id": "3a1a9287",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto query: Sports celebrities\n",
- "Auto query: Sports celebrities\n",
- "Auto query: Sports celebrities\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto filter: {'category': 'Sports', 'country': 'United States'}\n",
- "Auto filter: {'category': 'Sports', 'country': 'United States'}\n",
- "Auto filter: {'category': 'Sports', 'country': 'United States'}\n",
- "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto top_k: 2\n",
- "Auto top_k: 2\n",
- "Auto top_k: 2\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "> [retrieve] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 2 tokens\n",
- "> [retrieve] Total embedding token usage: 2 tokens\n",
- "> [retrieve] Total embedding token usage: 2 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=Node(text='category: Sports\\ncountry: United States\\n\\nMichael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='4cf176e5-363f-479b-8979-c3e07cfaead8', embedding=None, doc_hash='9aaec18f659138a23ca519f8d6d1f3997d34aae993b8c07443b165c13163b886', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.797632515)]"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "retriever.retrieve('Tell me about Sports celebrities from United States')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "50d622e3",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Pinecone Vector Store - Auto Retriever"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "#### Creating a Pinecone Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pinecone\n",
+ "\n",
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
+ "pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dimensions are for text-embedding-ada-002\n",
+ "try:\n",
+ " pinecone.create_index(\n",
+ " \"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\"\n",
+ " )\n",
+ "except Exception:\n",
+ " # most likely index already exists\n",
+ " pass"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pinecone_index = pinecone.Index(\"quickstart-index\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build the PineconeVectorStore and VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, StorageContext\n",
+ "from llama_index.vector_stores import PineconeVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "9ae59590",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.schema import TextNode\n",
+ "\n",
+ "nodes = [\n",
+ " TextNode(\n",
+ " text=\"Michael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.\",\n",
+ " metadata={\n",
+ " \"category\": \"Sports\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Angelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.\",\n",
+ " metadata={\n",
+ " \"category\": \"Entertainment\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Elon Musk is a business magnate, industrial designer, and engineer. He is the founder, CEO, and lead designer of SpaceX, Tesla, Inc., Neuralink, and The Boring Company.\",\n",
+ " metadata={\n",
+ " \"category\": \"Business\",\n",
+ " \"country\": \"United States\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Rihanna is a Barbadian singer, actress, and businesswoman. She has achieved significant success in the music industry and is known for her versatile musical style.\",\n",
+ " metadata={\n",
+ " \"category\": \"Music\",\n",
+ " \"country\": \"Barbados\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Cristiano Ronaldo is a Portuguese professional footballer who is considered one of the greatest football players of all time. He has won numerous awards and set multiple records during his career.\",\n",
+ " metadata={\n",
+ " \"category\": \"Sports\",\n",
+ " \"country\": \"Portugal\",\n",
+ " },\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "ee6eeecb-d54f-4a71-b5fe-0cda8a5c3e10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace=\"test\")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "cad08884",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 211 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "1a57e62f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.vector_store.retrievers import VectorIndexAutoRetriever\n",
+ "from llama_index.vector_stores.types import MetadataInfo, VectorStoreInfo\n",
+ "\n",
+ "\n",
+ "vector_store_info = VectorStoreInfo(\n",
+ " content_info=\"brief biography of celebrities\",\n",
+ " metadata_info=[\n",
+ " MetadataInfo(\n",
+ " name=\"category\",\n",
+ " type=\"str\",\n",
+ " description=\"Category of the celebrity, one of [Sports, Entertainment, Business, Music]\",\n",
+ " ),\n",
+ " MetadataInfo(\n",
+ " name=\"country\",\n",
+ " type=\"str\",\n",
+ " description=\"Country of the celebrity, one of [United States, Barbados, Portugal]\",\n",
+ " ),\n",
+ " ],\n",
+ ")\n",
+ "retriever = VectorIndexAutoRetriever(index, vector_store_info=vector_store_info)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "a5c0490d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto query: celebrities\n",
+ "Auto query: celebrities\n",
+ "Auto query: celebrities\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto filter: {'country': 'United States'}\n",
+ "Auto filter: {'country': 'United States'}\n",
+ "Auto filter: {'country': 'United States'}\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto top_k: 2\n",
+ "Auto top_k: 2\n",
+ "Auto top_k: 2\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 3 tokens\n",
+ "> [retrieve] Total embedding token usage: 3 tokens\n",
+ "> [retrieve] Total embedding token usage: 3 tokens\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=Node(text='category: Entertainment\\ncountry: United States\\n\\nAngelina Jolie is an American actress, filmmaker, and humanitarian. She has received numerous awards for her acting and is known for her philanthropic work.', doc_id='6821b1fe-e1dc-400c-ad2c-83f7fa683321', embedding=None, doc_hash='4086bd15d984c4f3ee3d4f911f0a347735406351d1936b6060b411707d3e82cc', extra_info={'category': 'Entertainment', 'country': 'United States'}, node_info={}, relationships={}), score=0.80265522),\n",
+ " NodeWithScore(node=Node(text='category: Sports\\ncountry: United States\\n\\nMichael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='4cf176e5-363f-479b-8979-c3e07cfaead8', embedding=None, doc_hash='9aaec18f659138a23ca519f8d6d1f3997d34aae993b8c07443b165c13163b886', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.766244411)]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "retriever.retrieve(\"Tell me about two celebrities from United States\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "3a1a9287",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto query: Sports celebrities\n",
+ "Auto query: Sports celebrities\n",
+ "Auto query: Sports celebrities\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto filter: {'category': 'Sports', 'country': 'United States'}\n",
+ "Auto filter: {'category': 'Sports', 'country': 'United States'}\n",
+ "Auto filter: {'category': 'Sports', 'country': 'United States'}\n",
+ "INFO:llama_index.indices.vector_store.auto_retriever.auto_retriever:Auto top_k: 2\n",
+ "Auto top_k: 2\n",
+ "Auto top_k: 2\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "> [retrieve] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [retrieve] Total embedding token usage: 2 tokens\n",
+ "> [retrieve] Total embedding token usage: 2 tokens\n",
+ "> [retrieve] Total embedding token usage: 2 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=Node(text='category: Sports\\ncountry: United States\\n\\nMichael Jordan is a retired professional basketball player, widely regarded as one of the greatest basketball players of all time.', doc_id='4cf176e5-363f-479b-8979-c3e07cfaead8', embedding=None, doc_hash='9aaec18f659138a23ca519f8d6d1f3997d34aae993b8c07443b165c13163b886', extra_info={'category': 'Sports', 'country': 'United States'}, node_info={}, relationships={}), score=0.797632515)]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "retriever.retrieve(\"Tell me about Sports celebrities from United States\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "50d622e3",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/pinecone_metadata_filter.ipynb b/docs/examples/vector_stores/pinecone_metadata_filter.ipynb
index 814e5e187d..5f57fef802 100644
--- a/docs/examples/vector_stores/pinecone_metadata_filter.ipynb
+++ b/docs/examples/vector_stores/pinecone_metadata_filter.ipynb
@@ -1,208 +1,222 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# Pinecone Vector Store - Metadata Filter"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d48af8e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import os\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
- "metadata": {},
- "source": [
- "Build a Pinecone Index and connect to it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
- "metadata": {},
- "outputs": [],
- "source": [
- "import pinecone\n",
- "\n",
- "api_key = os.environ['PINECONE_API_KEY']\n",
- "pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
- "metadata": {},
- "outputs": [],
- "source": [
- "# dimensions are for text-embedding-ada-002\n",
- "pinecone.create_index(\"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
- "metadata": {},
- "outputs": [],
- "source": [
- "pinecone_index = pinecone.Index(\"quickstart-index\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "Build the PineconeVectorStore and VectorStoreIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a2bcc07",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex, StorageContext\n",
- "from llama_index.vector_stores import PineconeVectorStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9ae59590",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.schema import TextNode\n",
- "\n",
- "nodes = [\n",
- " TextNode(text='The Shawshank Redemption', metadata={\n",
- " \"author\": \"Stephen King\",\n",
- " \"theme\": \"Friendship\",\n",
- " }),\n",
- " TextNode(text='The Godfather', metadata={\n",
- " \"director\": \"Francis Ford Coppola\",\n",
- " \"theme\": \"Mafia\",\n",
- " }),\n",
- " TextNode(text=\"Inception\", metadata={\n",
- " \"director\": \"Christopher Nolan\",\n",
- " })\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ee6eeecb-d54f-4a71-b5fe-0cda8a5c3e10",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_store = PineconeVectorStore(pinecone_index=pinecone_index, namespace='test_05_14')\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8f0f49cf",
- "metadata": {},
- "source": [
- "Define metadata filters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "22157658",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
- "filters = MetadataFilters(filters=[ExactMatchFilter(key='theme', value='Mafia')])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f31c16b3",
- "metadata": {},
- "source": [
- "Retrieve from vector store with filters"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "147df357",
- "metadata": {},
- "outputs": [],
- "source": [
- "retriever = index.as_retriever(filters=filters)\n",
- "retriever.retrieve('What is inception about?')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1a57e62f",
- "metadata": {},
- "source": [
- "Use keyword arguments specific to pinecone"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4874ca94",
- "metadata": {},
- "outputs": [],
- "source": [
- "retriever = index.as_retriever(vector_store_kwargs={\"filter\": {\"theme\": \"Mafia\"}})\n",
- "retriever.retrieve('What is inception about?')"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# Pinecone Vector Store - Metadata Filter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d48af8e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f7010b1d-d1bb-4f08-9309-a328bb4ea396",
+ "metadata": {},
+ "source": [
+ "Build a Pinecone Index and connect to it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ad14111-0bbb-4c62-906d-6d6253e0cdee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pinecone\n",
+ "\n",
+ "api_key = os.environ[\"PINECONE_API_KEY\"]\n",
+ "pinecone.init(api_key=api_key, environment=\"eu-west1-gcp\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c2c90087-bdd9-4ca4-b06b-2af883559f88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# dimensions are for text-embedding-ada-002\n",
+ "pinecone.create_index(\n",
+ " \"quickstart-index\", dimension=1536, metric=\"euclidean\", pod_type=\"p1\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "667f3cb3-ce18-48d5-b9aa-bfc1a1f0f0f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pinecone_index = pinecone.Index(\"quickstart-index\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "Build the PineconeVectorStore and VectorStoreIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a2bcc07",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex, StorageContext\n",
+ "from llama_index.vector_stores import PineconeVectorStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ae59590",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.schema import TextNode\n",
+ "\n",
+ "nodes = [\n",
+ " TextNode(\n",
+ " text=\"The Shawshank Redemption\",\n",
+ " metadata={\n",
+ " \"author\": \"Stephen King\",\n",
+ " \"theme\": \"Friendship\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"The Godfather\",\n",
+ " metadata={\n",
+ " \"director\": \"Francis Ford Coppola\",\n",
+ " \"theme\": \"Mafia\",\n",
+ " },\n",
+ " ),\n",
+ " TextNode(\n",
+ " text=\"Inception\",\n",
+ " metadata={\n",
+ " \"director\": \"Christopher Nolan\",\n",
+ " },\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ee6eeecb-d54f-4a71-b5fe-0cda8a5c3e10",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_store = PineconeVectorStore(\n",
+ " pinecone_index=pinecone_index, namespace=\"test_05_14\"\n",
+ ")\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8f0f49cf",
+ "metadata": {},
+ "source": [
+ "Define metadata filters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "22157658",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
+ "\n",
+ "filters = MetadataFilters(filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")])"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f31c16b3",
+ "metadata": {},
+ "source": [
+ "Retrieve from vector store with filters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "147df357",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "retriever = index.as_retriever(filters=filters)\n",
+ "retriever.retrieve(\"What is inception about?\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1a57e62f",
+ "metadata": {},
+ "source": [
+ "Use keyword arguments specific to pinecone"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4874ca94",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "retriever = index.as_retriever(vector_store_kwargs={\"filter\": {\"theme\": \"Mafia\"}})\n",
+ "retriever.retrieve(\"What is inception about?\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/examples/vector_stores/postgres.ipynb b/docs/examples/vector_stores/postgres.ipynb
index 6929159f8f..accd63dce0 100644
--- a/docs/examples/vector_stores/postgres.ipynb
+++ b/docs/examples/vector_stores/postgres.ipynb
@@ -59,7 +59,8 @@
"outputs": [],
"source": [
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"<your key>\"\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"<your key>\"\n",
"openai.api_key = \"<your key>\""
]
},
@@ -93,8 +94,8 @@
}
],
"source": [
- "documents = SimpleDirectoryReader('../data/paul_graham').load_data()\n",
- "print('Document ID:', documents[0].doc_id, 'Document Hash:', documents[0].doc_hash)"
+ "documents = SimpleDirectoryReader(\"../data/paul_graham\").load_data()\n",
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].doc_hash)"
]
},
{
@@ -120,12 +121,12 @@
"outputs": [],
"source": [
"vector_store = PGVectorStore.from_params(\n",
- " database=\"vector_db\",\n",
- " host=\"localhost\",\n",
- " password=\"\",\n",
- " port=5432,\n",
- " user=\"postgres\",\n",
- " table_name=\"paul_graham_essay\"\n",
+ " database=\"vector_db\",\n",
+ " host=\"localhost\",\n",
+ " password=\"\",\n",
+ " port=5432,\n",
+ " user=\"postgres\",\n",
+ " table_name=\"paul_graham_essay\",\n",
")\n",
"\n",
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
@@ -248,12 +249,12 @@
"from llama_index import load_index_from_storage\n",
"\n",
"vector_store = PGVectorStore.from_params(\n",
- " database=\"vector_db\",\n",
- " host=\"localhost\",\n",
- " password=\"\",\n",
- " port=5432,\n",
- " user=\"postgres\",\n",
- " table_name=\"paul_graham_essay\"\n",
+ " database=\"vector_db\",\n",
+ " host=\"localhost\",\n",
+ " password=\"\",\n",
+ " port=5432,\n",
+ " user=\"postgres\",\n",
+ " table_name=\"paul_graham_essay\",\n",
")\n",
"\n",
"index = VectorStoreIndex.from_vector_store(vector_store=vector_store)\n",
@@ -281,7 +282,7 @@
}
],
"source": [
- "print(textwrap.fill(str(response), 100))\n"
+ "print(textwrap.fill(str(response), 100))"
]
},
{
diff --git a/docs/guides/tutorials/Airbyte_demo.ipynb b/docs/guides/tutorials/Airbyte_demo.ipynb
index 92097e3b4f..e3dad7b1a8 100644
--- a/docs/guides/tutorials/Airbyte_demo.ipynb
+++ b/docs/guides/tutorials/Airbyte_demo.ipynb
@@ -1,635 +1,635 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
- "metadata": {},
- "source": [
- "# Airbyte SQL Index Guide\n",
- "\n",
- "We will show how to generate SQL queries on a Snowflake db generated by Airbyte."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "119eb42b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Uncomment to enable debugging.\n",
- "\n",
- "# import logging\n",
- "# import sys\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e7b550f4",
- "metadata": {},
- "source": [
- "### Airbyte ingestion"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bcd28d60",
- "metadata": {},
- "source": [
- "Here we show how to ingest data from Github into a Snowflake db using Airbyte."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "66b43c8c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import Image\n",
- "Image(filename=\"img/airbyte_1.png\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "54d6f224",
- "metadata": {},
- "source": [
- "Let's create a new connection. Here we will be dumping our Zendesk tickets into a Snowflake db."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "19dbd6ab",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/github_1.png\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "a6bf69b6",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/github_2.png\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "8b9b5154",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/snowflake_1.png\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "8310ba0b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/snowflake_2.png\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "966b4452",
- "metadata": {},
- "source": [
- "Choose the streams you want to sync."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "17614c43",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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9IxSQ1TyqHtyuXQs7++wTo1bIrVmrmt1ww0UuCN3DLr74jFBAVsvL/PgTjvLrUbXaxYuXa3Ku2sKFS32IVudf5oCsVqSqszfeeJFfpwK5Oh+DpirKH3/0jX955pkn2CGHtAyFdDWxevUqdv31F7r+BUvs/df89tj7HrIGBBBAAAEEEEAAAQQSJ7B79x5LTd1tO3em2o4du2z79p371EN9Vt+1D9oX2r4pQCXZffO40WsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAYL8R0A2g+kBSDz0viJtQ9xtsdtQL6IZitXLlyviv/IMAAggggAACCCCAAAL5J6CAnh6FCqU99DwZmoKjakGlzmTokwKw0VoQDp0ze5EdfXTHLEHahg1rZ1l0y5attmhRWqDxmGM6ZQgWBjPreBzTvZN98P63tmTxCh82DQ+Naj7N0yDC+oN15OfXZctW+9V36dwm4mZUTbRJ03q2ZMlKW7liTcR5Mk9csTxtvkMPbZUhNBw+3+GHt7N5rgJuQbTTzzjeHd/CWTZdtFhRO/OsE+yF5991fVuU4f15rhKsmgKkTZs2yPBe8EJVX/v1G+hDx8G04KvOrw4dW/lHMC38a7lyB1mVKhVtxYrVtmD+EqtTp0b42zk+n+MqxKppG+EVZMMXbN2mmQssl3Tn4BZ3PFdYw4Zp3w86L1XNVkHhXr0zVpgNlm/SpL61adPc/vxzSjBpr77mt8dedY6FEUAAAQQQQAABBBBIgIA+rw1Cpfv6Z7fB58+7d6fBBdcoChcu5P/eTQAnm4iDACHZOCCyCgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgL6INVPfb1D1bjL8MaEUAAAQQQQAABBBBAAIG/j0Ckm1F1I6oeBdmCcOx2VxkzWVp2A/k0a97ABQCn2YYNm+zNPp+7QGBTX9m1YqVyUbu/cuVa/ze3gqSVq2SsMBu+UOXK5X2Adre7Y3jlyhSrX79m+NsuSFzUV53NMDEBLzZt2mJ6qG3ctNk/Im1W+6eWkrI+0ttZpq1ID9Nqv6O1ihXL+ZulC+KaRW1X1TVaC8Kpqsyq4xXs+7x5aaHZ4P1Iy+tG8Jo1q9msWfMjvZ1h2q5dqbZ06QpbtSrFP6ZPm+MDspppWx6+Z+YEoV5XtGnGjLkZthX+QkHcefO2+G0HIdlg3xSu1T5Ea3o/XiHZzNuIt0fm9fMaAQQQQAABBBBAAIFkEXDZWPf5bVrV1aBPQahUg1/peXa/lwfLJNPXv65L/DVoc2pqWgg47fpEYbdPydRj+hJJgJBsJBWmIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIFJiAgrG6uZCGAAIIIIAAAggggAACCCCw/wno5lT9TahHkSKFCywsG1RL3bx56z5xEKpVq2THn3CEDRo4wgdHf/99nOlRokRxa9CglrU4uKGr7lk9w76oIqeawreRKpMGMytoWb7CQbZm9TrbnB5KDd4ryK/Ll6dVkVUfPvn4hxy7smHDZn8zd3b7unXLNtu4cbNfV3YBY52bZcuWtnXrNua43XjOoPB2BRfQjdaqVq1kqnK6Y8dOW+qqrQZVWeenV5KtlU3AVuvMLiS7des2GzpkpA0fPtYWLFjiBzYL74duHtc1nby0uXMW+sWGDRtteuTUli1dGZpl3vy0Krk571vV0DLxeJKfHvHoH+tAAAEEEEAAAQQQQCDeApk/w9XfAPp7UeHYfbkFwd708ZXcgEMKy6YN5qx91qMgr0/sy7aJ7Dsh2URqsy0E9jGBbe767oRxqTZz2h5btGC3u8hptmXzHjd6YHx2RCMplCx1gFWsZFa7biFr0vwAa9OusBUvEZ/1sxYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBfUsguBFaHzzSEEAAAQQQQAABBBBAAAEEEFBQVjem6mbURFeiKV/+IH8AFDZUZdGqVSsm/QFp0aKhr/I6c+YCmzNnkQtJrjQF+aZMme0fel9B2sDS1feJfZ+CP9VzsUjsK8/bnOH3sTV3lXRjabrmULhw9DkLhVUwzinwWRADfKlik66fBMcw854EN3BremH3fRO0IBi8c9euYFLErzt37ow4ff36jfbE46+ZKtSqlSt3kDVoWMequVButeqVXdXievbOO1/YjOnRq8BGXHH6RO2TmkLA7Q85OH1q9C81avwVeA32bdfOnPYt+/ejby3rO/ntkXWLTEEAAQQQQAABBBBAoGAF9PdP8DeSgrFFihSK+ndJwfZ077eu0G+hQmmDdu3atdtfl9D+6+8WXZ+gJacAIdnkPC70CoECFViyeI8NHpBqw3/VD/H864rWvXnTHvcwWzg/1X7/xdx/krvssKMK2zHHFbaatZLoimr+MbBmBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAASegm5537qR6LCcDAggggAACCCCAAAIIIIBARgGFGnfs2OWqYxb2FWoyvpt/r8qUKWXVXfhv2bJVNmPGvJhCsgowvvtOP9u+faf1PLaLNWpUJ62D6bdBBUHASL1WmDUeTZVj27Rp6h/anqqtjh0zxe3DfJs6dY5Vr1HZ2rZt5jdVqnRaNYO1azeEKvdG6oNuhNY8aqVLlYw0S4FMq1YtLbiskKTCv7pRe29bsWJFXYXYMqYQ5OrVa6106cj7u337Dl+xNzfbC+6Gy+482LDR3UyXTdvpgqAKbevcjNRUYVXXWBQ2rVbtr3nq1a/lq78uXrQs0mKhaUuWLA89D3/yTb9BPiBbvnxZu+LKc935lXYOhc9je3GvYYMGdfz6Tzq5m51++nEZVpvTi/r1avlZFuVx33Jaf6T389sjARuITQAAQABJREFU0jaZhgACCCCAAAIIIIBAQQnoM1z9naG2P1VU1eBEuhaRmnqA/5tZfxsrB6VptOQT2PsrAsm3T/QIAQT2QuCLj3fZw/fucIHV+AZkm7csZGeeX8Tu+L+i9tTLxezVd9Meeq5pek/z6D8MbVt9UF9oCOzLArooPfz3P/M8QmJB7ftSd7F8yJA/bPPmLQXVBbaLAAJJIDB79mL7+MMB7oO/7D+AS4KuFngXsCrwQ0AHEEAAAQQQQAABBBBA4G8goA8UCcj+DQ4ku4AAAggggAACCCCAAAII5KOA/m4Mqtbk42YyrDqoTjruz2m2bl1aSDTDDJlezJg+34dJdc9I3bo1Qu+WLVvaP9+2bYcL/Eau1LlwQVqFztBCcXiiG3oVpjy5V9dQf+bNXRxac5UqFXywVKHNlSvXhKZnfqL3dEO01lcliSrqKshcqlQJd16k2qpVazN3O8+vg6rBCqNGa3nZXuUqaaFe3ZOzbdv2iKueMnlmxOnhExcuXBL+MsPzefMW+dd169bMUNUpCJLOm/fX8c+woHuh81b3DUVq06fP8ZOPP+GoiAFZnUNLlqyItGhM0xqmB8rnugrIuW0KAKstXrzc70O05RfMj+4WbZlo0/PbI9p2mY4AAggggAACCCCAQKIFgoBsWmC0iBUuvP9FEbXPBx5YxP+NxcDPiT4DY9/e/ndmxm7DnAjsVwKqHvvY/Tts4A/xHaX/6O6F7cEni9pNdx5oPY4vbA0bH2Cly6hibNpDzzVN72kezatl1NQX9Ul9oyEggYcfesmuveZ+GzN60j4B0r//YHv55fftoYdetPlxvMianzuvC/D/vv9Ze7PPp/baax/n56b22XXruOo87PPGp/vsPtBxBHIS2Lx5m/U+6Ta78/YX7Zabns1p9izv68Ovdq3/4R8KkCayTZky12+3c4dLErLZvbVKSCfZCAIIIIAAAggggAACCCCQ5AL6IHHXrvhem07yXaZ7CCCAAAIIIIAAAggggAACeRTQ349B9Zo8riJXi7V2FVkrVSrv/27t9/WQbCuHKlA5ePBIv/5mzer7G2iDjR10UGkrUaKYfzl9+rxgcuirqrTOn5/3kKzCw0OHjLYP3v/WV74NrTjsSaXK5f2rokUPDE1V1dm6dav71z8P+iNiCFneek9N85YsWdw/T5Z/goqqI4aPj3pujBo1yfp/O9Rmz14YU7erpleoHTVykm2IMLC0TH4ZOjqmdYXPpHOpdOlSftLw4X+Gv+Wfq2rxxIkzskzPPOHTT76LGLLdtGmz9e37vZ+9QcPaGRar3yDt9aRJM2z06IkZ3gtefPJJ/6gh7gMPTDtvKlQoF8ye4avWq+1Ha8HyKWvWRZylUcO6fvrkyTMsCKBmnnHDhk32yMMvWd9Pv/OVfoP3a9So4ivn6r6njz/6Jpic4euYMZNcJeXZGabtzYtgf/bWY01KZI+96RvLIoAAAggggAACCCAQL4Hgb/CgomqhQi4ItJ827bsqyMqCz7eT8yQgJJucx4VeIZBQgdkzd9szj+20hfPjF0atW/8AXyH2vIuKWNXqsf9HqHm1jKrLah3qk/qmPtIQ0IXU9es32o6dkUfUTLRQ/28H2zvvfOEuoC+IuOnChdMC33ozWUZM0YiJ6vNnn/0Qsc+F3C9thQql/XpQJKz/EWfeTydud6O66jzcsmXrfirAbu8PAvpDrnD6z4IDixTJssuvvvyF3Xfva/bn2OlZ3gsmrF61zvRITfBNzrvc6NF+26vXB13J1685WeXrxlk5AggggAACCCCAAAIIIPA3ENBAS1SQ/RscSHYBAQQQQAABBBBAAAEEEEiggP6O1N+TiWi63+P4E47wgdfVq9f6EOrYsVNt69Ztoc0rRDls2Fj79JMfbfv2HaYqpN2O6Rh6P3hStVol/1ThyqlT5/jgrarKzpq1wAX7vrdixYsGs+b6q/qpgKWCugN+/N3d0/BX/3Tz7rx5S2zSxLQKpY0bp4URg4306HmYC26WdJVkU2zQwBHunohNwVs+IKppWq8qtvY89rDQe8nypFu3Q33f5rgKpJn3XX2cOmWODf99nM2YMd8fn1j63a5dc6tcuYIPjPbv/0uGKrW6X2TwzyO9iSoJ5bY1TA+vfvhBP/tt2Bhf+VThTgVXH3zgeb8v2a1T9/WkuGBlnzc+sTVhgdNly1baK6986CseV65c0Xr37pFhNY0a1bWuXTv7aW/26WuTXcXaoDLzDnfeDhk8wvn96sLckUPQrVo39ct+/91QVy054+fxWtfzz72TYXuZXzRr1sBPmjNnYcR7req4ysu9e3f3Hk/9903fP527QdM+v/TS+zZt2hwbNWqCcyoZvOXvdbrk0jP9659++s0GDfo9FCLWzwoto8Hwo+1baEW5eBIvj5F/jHfHbGMutsysCCCAAAIIIIAAAggkRkB/LwR/MxQpkhYOTcyWk3crCsjKQi3cJ3l7vH/1LPd/oe9fPuwtAn97AVVpffW5XbZ5U/wuHHfoVMguu/ZAXy02r4CqLnv3A0XtrVd22piRu30fb733QKtZK/bAbV63zXIIxCowcuR4mzt3kdWvV8t0ITlzO+mkblarZlXTiIG1a6eNupl5nkS/XrUqxQa6i8EVK5azs846IcvmixYrao8+drv7AGa+tWvXIsv7TEAAgf1DQCMY/zDweftzzHTr3vPQLDvd/9vfbOKEWdaqVUNr///snQd8k1UXxo+UvTcFChTK3nsjQ7YICigI4gBUBFEUBfcABRRF/RQFFBQHCDgAZYmy9967UMoohbKhzMJ3nhvekKRJSdt00ef8fmnecef/ffM2ufc+59QoG+18ajpwJ1apiQX7SgIkQAIkQAIkQAIkQAIkQAJxIcAIsnGhxjwkQAIkQAIkQAIkQAIkQAIkQAL4PRkXgWJcyEH02vXRtvLnH/+aSLKLFq7RqK1rJIOusYiKijJiV6vc/Plzy0Mdm2vbbkdrtc7dd19dI4aFyHLO7KUyd84ycwoiviJF/KWKRq2FIDOu1rhJLflt2j9GOPnN178akWfWrJnkyJHj9uigJUsWFdcIo9mzZzFtnvLrHBUm7jWvfCbq7D0qDj1lmoP+oF+IiJvcLHuOrNKxUwsVKc8x4mMIkLEuJpv2C1FLz52zRTeFOLVChZJeNR/31oMP3Se//Py3ER//OHGG5MiRzUTRPXYswoi069evKqGhYXL4cLhXZVqJnniyk4ph/2cctH/zzS8yduxkcwqC0HLlS0rz++rLl1/+aCWP9p45cyZ5pEtbmTB+mqxSgWVg8QC5pmLrI0ds7ciYMYO88kovt9eqZ6/OcuzYCROpdfiwb4w4umjRQka0CsF2hQqlpFTp4jL9z3+i1duwYU2971er4PqQPN/vPQnQtVD5VUiM4AKI8FoiqKjJs19FsO6sbNkgE5UZYvN33/ncRH5FmZa4FXm6dG2nAtxzKjpfK2gfRK1Yk3VRPzOHQo8aAS3616vXI/aF6VZdKAsMZs74V76f8JsK2qdLyVKBckQDCqB9uH5PPNlRRqvQ1hcWXx6161SV2bMX6f1zTPr1fcfw6P10F6lXr5ovmscySIAESIAESIAESIAESCBeBOCXyprHhSg0NUeQdQUJFmACPnjBkZFqZ2nJgIAtVFwyaAibQAIkkDQEJo675nOBbO9+8RPIWiTwjwJlQXQLES/aSiOBlEQAXjqr16hoBqNTUrvz5s1lBlwxqEwjARJIvQQCAvJL+wfvVe+r7r3Epl4y0XtOVtGZ8AgJkAAJkAAJkAAJkAAJkAAJeEMA3nVv3PCdA0dv6mQaEiABEiABEiABEiABEiABEiCBu4MAfk9aEW0So0cQvz7xZAdp2Ki6ZMuWxVSJqLFYEItIMlhr0fb+e+WxHg94jASaM2c26dS5hRTTiJkQYUIci2PVq5eT9h2aSho/WzSauPanUKF80qVLKwkMLGyKgMAVEWQhfoRAsNG9NaTDg82iiQuRGP1D2wICCtzKe9oukC2sDuI7dW5uIuSak8nwj639LaVoUZsTe0RYDdG+QyCL69WqVQMjeo1N0yEedmRy9ux5I5jFtWvcuKbUU5FsXKxAgbwy+LVnpWLF0gJn9hDH5s+fR1q1vlcGDHhKr8+dY9/cp0LaZ5591IhcQw4ctgtkixcvIq8OetoIWN21zU/vsZdefkog8MS6pgsXIlVYvM+0AcdeefVpSeNhdTfaPWToAIHYFff84UNhsmHDdiNArd+gugwe/EyMwnVwG/rByyoGL6t1+5lIr3v2HojWzKef6SodOjQ31w0Rm7du3S2W8LaGrsP6eORrRkwcLaMeeOSRttKxYysVM2cygtqd2rfz5y8aoe37Qwbo5y27u2xxOhZfHvicvvNOf3OtwBPRhPdpUAMaCZAACZAACZAACZAACSQHAnAIBYMAFL8daM4EwARsYBYr5xTcSwoC9+hAC1cfJAV51ukVgfBwmyc6DAYmpeFjYnvJrfebZmDICo+dN2+OpGxenOv+ffJ1mT/H9s8rzoU4ZCxW3Bb91cM4mUPK2G3iKTXivaty8MBNadHGTzo9eueBwNjVwNSJSeDatesxDoiiLYd0EDU8PEKyZc1iBKbwvAkbPOgjm/e853tI/frVzTF3f06qx8GjYTYvmIUKFTCD9NaXEHfpHY9hkPxgyGHj0bOAf14zCO14Htvw4IcB6rfeHGW+1LTWQerGTeqYZLlz59AB6NuTIegHvgQVLuzvVEyoejeE4bj1xfH48ZNmALmYRqaFR0tvLDLyshzTvsLLYgYVtRZVL425tA2uhgHf06fPysaNO2TqlFnm9PARr5p31/ahzZjIceyLa3nYB6ujR8MFA9J58+SSIupZEgPansxXffZUPo7jWY02wTMkvJfi+uP9Tob7Eh4nI06e1omr3DppUsjtpBDK+f23ufLHH/Okdu0q8uKAJ03RmPQKDj5oJr+C1DOmtwJjx3oxIRUQ4K+TZZlNme7+uDJEfw9oNGNcL3jyhFcaR8N5sIAXULAoWDCfmahwTOO6jTy4F4+HnzTXNrfeiyVKFLF/kXdNj/+FeO57+xlzzc/9xCGASc/t2/brhF2EBAYWktJlitrvl507QkwjSpUuYj+GAwdDwiQy8or4F8wjuXLZvovt2R1qJprbth4g1/Vz0+vp9urJtaXJ75gO91HRQg+Y4/8uHC1lyhYz20ePRsj2rcHqrbWIFC9RyBxz9yfixBmdcD2jnn0z6+civ7skEn7slJw6dU5y5sqq93Zee5rNm/ZKuzYvGa/N+0L+MMfxWdux/YA+H06Y8spXKGF/9tozxmPDHSvX4jBJH7zvkEbqPqTP+ByGAd7vZGfPXpCtW4Llkj7vA4oUMCzdeeTC/4ODIcf0GqbR54HNQ69r2RfOR+r/2OOSLn1anQQMcD1t30edu3cd1P8tZ6RoMX8ppdfL+l9sT+Rhw9v2esjOwyRAAiRAAiRAAiRAAiRAAqmMwJUrdIyYyi55iurumTPnTXuTeo4qRUFjY+NNAGOzO3fuM+PPiJhEIwESIAESIAESIAESuDOBDBmiR2y9c674p7h06YqcU9FkWl0nAeGdtfbD25Kx7gRzfFmzel4j4G1Z7tJhLcc5nfe7pusZEBXW2/k+lHXx4iVx/E2UJUsmd1Uk22OYW8W8ZdSNKLP+xts1HDF1CGJSMMmcOaNZTxNT2ticw2JqXCd3a31cy4GY9cMPRpt1SWPHfWA/jXU+iJibT4W2EGt7axcvRup6kghBwKM7rflxLRPrVBBRFmtFiuhapdj+fsEcfkTEKV2LkMPjGhuwwW+kU6fOGuYQpXq7NgXlh+l6KkRtLlq0sLlurn2w9rGe4qknB1m7d3xHZFsIbh0tvjxwLbC2K59G5oWAmEYCJEACJEACJEACJEACSUkAa4CvXr1umpBe15vCqQstOgFyEv1dd9aMh2BMBL/XcK/YXmLfjk4u4Y54VrIkXJ0smQRIIBkQOHL4pk8FsujSI4/5JoKsKx78T0XZI4deNW2u28hPCgfwH60rp+S+j4G8v/9eIKEHjxpviO7au3v3fvnh+9/FEgIiDf5ZtmrdSLp1a+8ui9MxDPpO/OF32bx5l9NxDCT36PGg1Knj2YPjkiVrZPKkv4x3Q8fMlSuXVc+LXc2grHX8/ff+ZwZRrf25c5cIXrCnenaW5s0bmO3g4FC3g9P4QvT6ayNNmtFfvy/btu6RqVNnGdGpOah/8uigdd++3Y33ReuY4zsErzNn/CcLF640ng+tc/hSUbFSaendu4vTwPfy5evlpx//tJKZd6sNEJF+M2ao/dy4sb/Krl3B6gG1o7Rs2ch+3NoAZ1wneGpEXyxDOe3aNZP72zW1DtnffdFne2FuNjAwPumXmbJg4Sq5qgPxlkE02qRJXdMXTwPlc2Yvlpkz/3W69shXuUo5eUa9U1oeYK0yHd8xUP7Vlz/Kli277NcB9VSoUMoIaGOaBHBXL8pu2Kimud8hmnU0V4arVm5Use4/OjkVaZLBu2izZvXMvY4D0//8R+bNW+rUr3LlguTZPo/qoHoex6Lt22vWbJY/fp9nhOr2g7qBCb0HHmgmrds0djxstk+dOiMfjRijnjhbq5fUavwhFI1Q0h/4+ac58unIXwTCU8swcTfso77SsVNTaXnf8+bw+k0/Sv4Cua0kMvClL2T1qm0y9MM+8mTPdub4Q+1f1Xvqoj3N+G9nCl6wYSP6So8n2trPOW7Mmb1Chrw3Xh0BhNsPQyD67pCn5aGOTezHrI2JP8ySz0dNVk/LDWTsd69bh53evx79m0z4bqZ07dZSRn76gtM5x53vvp0hw4Z+b/+M4hzEpm+/29OU75g2rtvuWFllQUz8wdAJMunneSo8v2IdNk4F0HbwdTdhDrHvgBdGqafYQ/Y82IDA+KORz0vtOhWcjm/auEe6dH7DCJq37JjsdM7aWbZsszzd80MpFlhQlq381jpsf798+aq89/Y4+W3aAiO+t05kzJhe+r/4iDzXr7NHZwixba9VNt9JgARIgARIgARIgARIgARSLwFfR/vZuHG7fPm/idJAo5706vVI6gV7F/V88uS/ZP4/S+Xlgb1NdJ27qGvqzNB2v7r2CQ704LwQUXnubVxHnSzaIji5pkuI/fHjp8ryZevkrbefV6d57h1wJUS9CVnmoFeH68L48zJ23LCErMZj2XG5h8d884uEqCNPOD7s0sU2JuexAp4gARIgARIgARIgARIwBPD70t18W0LjyZQpgwoDM8S5GqwtSCiBLBqF9QoxrVmIqeEQxaY0YaxjfzLpfDhevjRcq4S4XhBEeiOQjakvEI/iFVvDb1A4go+LQXSN365xNTjhL1jQvcNsq0ywQZo7pbPSO76jfDjITyyLLw9ci5ic6idWP1gPCZAACZAACZAACZAACYCANY+L39pJIZDFWvbff59rgp2N+uxNj22A46mne7+uQWOcHdm88foncvDgEflh4kiPa159caXBBozACy/XYFe+qINlxI4ARbKx48XUJHDXEFgwz3cRZAGl8X1+ElQq4YSrKBt1LP4vStD2Hr34+EopNyMEfLNnLTIi0suXr0iZMiXcNh3e+z4Z+Z0Rn+ILAyJXQnS5fftegZgw6voNt/msgxcuXFSh3lgTgRb5ixdHNNZc6nE9WE6rR8H/fTFRxVAHpXv3DlYW+/svv8wwbcSB/OpVEQJHeCKEUBTixzHfTJLXXu9j/4JVs2YlIx5avXqTvQxLgFsgf+wGnSE0/Xbcr0aIWbtOFVPu7l37BdFwR378rbz5Zl8p4TIgDS+H7737hYkeiwZAwIg2HzlyTBfPHNGIg7tl+LBv5N33XrBHUfX3z2dEwhs37bCLSK02QyznrZ07d8HOGXkQ+RQD7bhOODdp0kzR2NtGLOupzLj02VNZOA7vj8M+/Fr27DlgrhHuHSziwj4E1//+u1wjTWaXhx6yRbt0LGvunMXy88/TzSFMDoFjWNgJw3LD+m0aLfhTeXXQM6afjvmwfePmDfl69M8mOm/RYoUkQKMC4z6GOBp9xPUb/Nqzbr2xQlRt1YtJlPLlS2nUzFNm8dOypetk44YdMnz4K0Ys7Vov9hER+OefZwiua7nyQeaawysl+pMnd04T6XjatDnmS3/FiqUF3kTPnDlnPg/4LAwZ+pL9frbKx+fMahO+sJcsGWgmYjZv2mny/vSTjZM7oSyYjR79k0yfPl86dW5lohzgc0hLegLT/1gkrw8abRqSPn06XShcxXgf3bB+lwzoPyrWDWzVpp6JaPr3X8vseds90NBsQ3jpzlDX22+O1QmdjNKydV2TZN2aHeoY4Ky8pCJQCDDbtK3vLmu8j0Eg+/473xrvzeg7xOZrVm83Yt1+z42UyVNySN16FeNdj6cCIKSHcHXd2p3q9OEeFd+XkipVS+n+DkEE358mztZnaG558aWuTkXMnrVc+vf9RD1xXTOi14b3VpWbGol20cL1RjT77NPDBRF6vYlE61RwDDtg80K/T/R/7gqTChFk0VaIX0MPHpORH/0s/85fK3/M+DjaD/mkaG8MXeEpEiABEiABEiABEiABEiCBFELAmlxNIc1lM70ggHExCPzq1aumjrlae5GDSRAxB2OsMESROnz4mHFgd+hQmBnXbPfAffLggy2ijeWR3N1LoErVcmY8tlw8FpuDzquvDDfzHRjfppEACZAACZAACZDA3U4Avy+TQiR7t3Nl/0ggtRDAWgosnvfWuB7GW1JMRwIkQAIkQAIkQAIkkFIJ3ND1qjBPAaoSul/L1KkrDHqOPbsPSJmy7vUnCd0Ob8oHI4xLWMy8ycM0CUeAKrOEY8uSSSDZErh8SWTFEt+KZJu19Evw/qIOiGTR9oe7pZWMmRK8SlYQDwLwzDFnziIjcL106bIpCWLPBx9qEa3UCxci7QLZatXKS++nuxjhJxJCIPT3XwuMNxBPX7Qgkhz16QQjkEXk1+c0AisEtjCIfv79d4VGPv3NtKV27SpSqlSgOYc/EPHOm7vUTJi88urTgvyWQXj77jtfyLZte1R8uERFXLYomojECXv7rZOyf/8hE220cZM6VrZYvY8dM1k6d26tXG4LOCNUIIvInEePHpep02bLa6/1cSrz23FTjEAW4koscIH3E8vgXR4CWrwv0qiq7Ts0N6eq6sIavCCu/GTktyqsyikvvPiElc2rd0Ro/fST7wxnRCTFdYJIE4ZrsHrVJhkzZpL8OvlvyasC5bq6GM6dxaXP7sqxjuH6QBCLPr39Tn+Nkno7EuZ+XZg3ZMiX8ucf86SKXltHwfF6FcFCaAp7+pku0qhRLb0PbM+yI0fCVVj9g1mUNmXKLBk4sJdVnf19/bptJsLwJ5++7uSRc+3aLfLF5z8IIiMjMivKdbQNG7bLzyo4RV0vvPCEVK9Rwf4j4syZ8ypw/trUO37CNBnkYQHThPHTtM1dpXHj2qZoCGQRKXihXnMIlWG4vpYQGp+D1as3m6i3uGdxH1SvfjsKZciBw/Lrr3+ZfPc1ry9duz4glnga1/b33+bKX3/9J1OmzpaWrRrZ24sMWMj38CNtjUAXQm2IcOGNs5Pe1xCU05KOAISZiHAKe2lgN+nTt6P9up46dU5eeelzfdnOe9vKUZ8PMElDW78kWzbvlU9GvShdHo3+XHcsb/CrX0m//p1l8Ou3nzlnz16QZ3oNkxXLt8jQ98cniEgWn4sPtGxEa33n3V6SLXsW0yzUPfyDH+SXn+dqGz6UmbNGSWBx9wJfx37EZXvpkk1GIFuoUF75ffpHJoKtVQ7Epx07DDIRc5s0rWEEqda50V/+Zv7/4boNePlRI7DFOXweEV125vQlMuiVL2X8929ZWeL9/qFG24VAFm398Zf3dVDh9v+W9et2yWOPvq0C/t0ybsyf0vf5zk71JUV7nRrAHRIgARIgARIgARIgARIggRRHAGMVeNHuLgLXr19Xp1in5YKOudK8I1Benfa5Rj4OD48wDhyn6rjkXzP/lWs6Rv5Il/u9K5CpUjyBDh1aCF7xNXwWsdCbRgIkQAIkQAIkQAKpgYD1G5PCtdRwtdlHEkgYAog8SyMBEiABEiABEiABEiABEoBTV9s8Ln5jIzhMYhs0AZgrQ+AziGSXLl2bzEWy9xhntxibALukYJbY1yg515cmOTeObSMBEkgYAps3RukCJN+VXa5iGilQMOH/AaIO1IW2ow+05EkA0WIRTXLAi0MFoe4hkC2rHs8RiXWoRq90FKFaPfh3/jIj6oTY7tk+3ewCWZzHIo6HOrY0Ak940ndnCxasNIJERPN8ccCTdoEs0uILWosWDaTdA83MwrtJv9gEhFY5mzfvUu8dUSoKKhCtbVmzZpH+KmLs2+8xjb5Yw8ri0/c6Gj3WUSCLwvPmzSWPPfagqQfiRVfr+mg76aSRGF56uaeTQBbprIix2A7eH4o3n9ms2Ytk376DKrLLZJhYAllUgMHiho1qmgiy+JI3TqPjQqDmzuLSZ3flWMcQ8RUGQaajQBbHIIqFEHXgwN4qTrstgjNtHDvZ3BMQEjdpUtcukEW+woULGBEq7p9NKig9efIMDjsZyhg0+BkngSwS1KpVWRo0sN0vB1SQ6mjIA5Ew3iHMrVmrkpPgNGfObEYUnSVLZo3euNMItB3zW9tNVJRtCWRxLEOG9NLhweb2L9logyWQxXn0o27dqhqxtiR2TaRcs3HrT2GNVtG//xNGVP3kk53sQkqcxrVt3eZeUwaE0ohm4Whp0/qZaBZf/O8d6f5YB/P5RQTfz0ZN0EjInxpBrmN6bicegY9H/GiElvc1r6Ui2Uedrmvu3Nnls/+9rM+MPAneoNYafdZRIIsKc+TIKh9/0t/UfSg0XKNjnE+QdpQuU1Q+GNbHLpC16h6qxypUKCGnT5+X/33+a4LUjULnz1ttykYE3oAiBZzqQZTWMeNekwkT33YSpB4PP2UEyEjcs/cDTj+Y8Xkc/lE/GfphH3nr7Z5O5cVnB1Ftx37zh2TNljmaQBbl1qhZVr4eO9hUMeqTSXLhgnpcuWVJ0V6rbr6TAAmQAAmQAAmQAAmQAAmkXAJJ7Uk3MvKScUTkLUE48kMebw1jP3BiBwd97gznz5w55+6Ux2PIc/78RbfnMW4am/LQHzgujK2dO3fBjGvFNp+79NevRxlHgJaDRXdpUuuxAgXy6thfHen3fA8zZjlv3hIJ1nHZmCwu9xTuAVyHxLDYfobQJnx+kC82dvbseZ/do7GpNzZpcc97Gjv3thywgTM1X9nFi5fMPElsysO9A6eJEMfTSIAESIAESIAESCA5EEjq35nJgQHbQAIkQAIkQAIkQAIkQAIkQAIkQALxJYA5J1hSiT2XLllr6n9C17NDW7J69SafzomYwn38x2JlsfNx8SwuFgTo/igWsJiUBO4WAnt2+lAhq1AqVE48vT3q2rnthqAPderfLVfk7ugHxHP//LNMo00u0AVWtsVaVaqUVeFeCylTpkSMnTxwSwj6QPv7JFs2W6Q/xwwQ+D38cFvZpKJBd7ZvX4g53KZNY8mYMYO7JNKgfg0TkfbgwSNmkQzKhEFcCIPwD5FbCxXKb/atP4GBhQWvhLJKlcu4LbpMWRszLHxDZFkIZy3DIqmOnVpZu9HeS5cprtFzl8vBkCPRzsXnwKqVG032DioqzZ07p9uiOj/cRqOZrjSL7DZqxFR30WTj0me3ld06mP7WNURk2G7dOwhEm45WvUZFx12zfexYhGkjrv/D2mZ3hgi93343zNwv1n3imA5fvIs4CG8dz+H6LVu2Tqx72zoHzzb4fOB6ukaYtdLkyp1DEKl33bqtApF0xYqlrVP2d3cM8+XLIwEqdj10KCyaeNrK6F8wn2zfvlciTpy2Dpl3CO8g2MXLneXMmd145EH7cV8hUqyrgVHbtk1UlN5QFi9ebT5vaD8iFwepWBn3hjuRvGs53PcNAfzQ2ro12BT25ttPGZGza8kQqvZ+5kF5751xrqd8ut+kmXsnA8UCC0r+ArkFIsutm/dJo8bVfFovCnthQFf7c96xcNzzg994XB7v/p5s2bLP8ZRPtzNlsv1Pglj2bY1mi3odrUWrOo67ZtvKg51Zfy9X8XlrpzTZNSLukz3bOR2L787GjbtNEW1UzOsYQdaxXES7xT2DSLz79h6SqtVsz6akaK9ju7hNAiRAAiRAAiRAAiRAAiSQMgkk5uLlyZP/kvn/LFVnQ89LmI7/zVeHfRg/8fNLI8WKFZZu3TpI8RJFooHEb+v/dIxt8+adAo/FEITBY3HtOlU1ymNzJ8dnjnVs27rHjI9BrPfoow9Ii5aN7GVv2rRDMMaG8RmI3DCWCWdnnXXs03Vc9L13P5ewsOPyzZgPZOLEP2Tjhm1mPAuO6+6/v6k69aupYzyn5Oefp5v2wXkhHK891LGVNGtWz16nteHaHwjscuTIZtrXqlUjJwdyyGPVP2bshzJ16ixZr2NVGKfMlCmjGe/t2esRyZo1sykeor9+fd+xqpJFC1eZFw4MG/6qcexnndygY4Z///WfWOO0OB6k43APPdRSHbyVspLxXQmAB+4hXGM4igQnV4vNPYW8EJ3O+nuBjofsEji6w+egZKlA6d27i2vRZt+6thjfe/Ot56OlmTFjvsxQp5l9nusutWtXMec3btwuX/5vormvq1UtL+PHT5WQkMPmHD5rVaqUM+OIadJEn1vC2CnmGbZt3W3uEWTCmGanjq3F3TgvzsOL+Ly5S3QsbJf9HsXnqvstR5RIE1fbtTPYCN4xzu1p7sHbsrGIA+0EdxicOnbU+76y8nA063nysjp+dBwbhrPP33+bq+PGW0w/4WC0dOnihgucQFqGz65VB54hPZ8aZE49/kRH4yzSSgeR9Ny5i2WHPo/wecT4Lhye1tDxdDxfXM16Jnw1eoiM/26KeTZC7Pve+wPMIpWXBgw1z4RRn73l9Hy0yhk9+ifzHHlenTVWr17BOsx3EiABEiABEiCBFExg165g2b1rf5x7gLUkvjT8zvRzXirgy+JZFgkkCwJYo/LSSz3Fz2VdTLJoHBtBAiRAAiRAAiRAAiRAAiRwVxCw5nEt4Wdidgrzl6tWbTIB0yqrxqKezs/M1iBfmKd0p0lIzLbFVBdY6TSOiSTLsYmYSCX8OedV2glfH2sgARJIBgQOHXQfjTOuTSseFH0hQ1zLulM+qy5f9+FO9fK8ZwJYmPHPvGUyc+a/gkgCEJ9CaPegTmgULx59cZm7kg4csEXbdCe8s9IX0sieKNudh439wbb8cFyye7f7SRjbhIif8dB+SqOC5rklOq1QoZRZwHH69FkT9bKRLv5ARNQSJYpGE1tabfHle2CxALfFYcENxJ8QHyMKhKNI1jUDIjBisUtExCkj9rXErPH1Ru9YD750WhFE3S0atNJCoIpB+Z26eOiALnxy94XUF3226sN7g/rVzeIiLNJDBOP7mtc3iwsRDdYSQzumx3ZwsC3yAu45d4uxrPRY9OfJihd3f+2QHgsMYadPO0cEsd+res7TvYp81qLIo0fDsRvNChV0FnNbCbCoDeZpwVbaW9+88bmNybDoE3Wf0IWWeGExGASysMt6T8ZkEAE2b95AmjatJyuWr5cZM/5V3qHy0YixZtFWl673m8VWMZXBc/EncODAUblwPtJE4y4R5Ple9SSIjH8LbpdQqXLJ2zsuW/ny5jQi2fDjzsJtl2Rx3i2jkWQ9Wbnyxc0pCD4vX76qnxub0wRP6eNy/MGOTWTC+Jn6/Dwu9Wr3lB6Pt5U2betLqdJFPD6fsqkItkXL2rqAe428MXi0/Kvv3bq3knr1K5lIr3Fpx53ybN64xyTB/9i1a3Z4TF60mL9sVVHxvn2H7SLZpGivxwbyBAmQAAmQAAmQAAmQAAmQQIoh4G6ML6EbD4dkEG+WVod+GN9at3arGbMYNWq8vPb6c4KxJEebOmWWIIInxoeqqtDPT8dVIJj9S8dBg9Vp30sv94omLF2oQsaVKoINDAyQipXKqHOovPYiIRz86ssfzZgj2gBHeHt0LHPp0rWm3CFDXzYTvvYMuoHxzkmTZsrOHXt1zLWyEfciouj33/9mfldCoAgRYx0V7h4+HCb79x+Sn3/6Ux3c5TBtdiwLQlcI9CCkrV69oqTVMRyI436bNlu2b9sjr7z6dLTfqqh/yq9/62T0Rimv46jFdcwUYl0IM0d9+p0Mfq2PEdalTZvWiG136yJ5S5xniYMzZ85kb8a/KlBGfzAeV0JFl6VKBspeZblfx46++Px7bcMzUkoFm7TbBHDdIZIFc0QMdhzLjO09hc/dd9/+apzzZcmSSerUrSo3om6ouHS3jBj+jRFt3q45/lsndbx2uJYLwzgxxqshfsU9fEbH4x/r8ZBTJYhICnHt3r0hki9fbmncpI6c1bFviC+++upH45CzQwdnIQXE5p/pZxhjlxhDb9K0ro41nZTlOi6Jc9ZCDqeKYrGzfMV6Wa7OEOG00NOYqzfFod9Llqwxzg2baL8gFg9VYepXX/2kz5Ke6jTR8/iZVf7Ij8cZQXxBdfSJzxeE8Zv1vtimn1+0rW5dmwM6sC6jYlc4B4BZn8WAgIJWUUYs/cUX35trgbHsho1qmWuybdtuc69F6nMFzhBdDffQBBU9Q2QNcTXyZtZnJMaz4SAR9ynmBjDv4mho6xZ9fmbNmkVF0mUdT3GbBEiABEiABEgghRKAoxT8HomP7dodLIMH94lPEU55k+J3plMDuEMCiUAA3709OT9PhOpZBQmQAAmQAAmQAAmQAAmQQCogYP2+9rQGPyERbNB5yMjISwIHv5gTa9CghhHJYj7VnSYhIdsSm7ItVha72ORlWt8SSOvb4lgaCZBASiBw0qZz8llTC/jbInL6rMAYCrLq8nUfYqiSp+5A4NSps2aRDpIhcsBrr/fxWhyLPBDWnlTRKgxRMD0ZvKIjUoMl1LPSYaHLsWMnzO64sZOtwzG+H1VBqSWSxeKRlwf2Motv4HH+v/9WmBfqK1e+pNSvV12FSdWiLXiLsYJYnHSNeuptVkSvWLhglVl0AoaOZgklHY/FdxuRLyyL6TohDTzgYyEMxL3uLK59dlcWjmFBXZ8+3WTChGkqSj2rC/vmmBcmB6pWK28itrouysGCQRiihcTVsPgvtha8P9RkwQKxIe9/ecfsYWG2e/uOCX2QAAsqFy1cLSt04RciB0TpAjlHw33leszxvOs20je6t5YRnUMoO23qbLOIa+qU2fLOu/1dk3PfxwS2bQk2JQaVLKzPL8/OLEqrWDOhLb0uuk0Kw2Lf4sWjRz222uLvn0cg8Dx/7qIuCt6vUTd8v0CvStVSMuqLl1Ts+rWEHzsln3z8s3nlzp1dBf21pNPDzTQyhy3KitUuvH/86QvyQr9PZOmSTfLv/DXmhesIwfED7RvJw12aq4MHmxjfMV9ctzdv2muy/jZtgT4/F9yxmP3Bh53SJHZ7nSrnDgmQAAmQAAmQAAmQAAmQQIokkBQThBDIDnzlaUFETFj37h3k+wm/qah1gxGvIWKnZYsWrTICWTgBhBAUY4UwiPg+Gfmt7Nixz4jcEK3S0TZu3GHSuwo9IYYbO2aSEa8iGmfOnNnt2SC6/fPPf2TSLzNMNE77Cd1AfQfVEd3QDwYaMSrOoR8//viHfKeRHBG5s/fTXe3O/mbNWqiRJueYCLgQ9lq2eNFqI5CtpCK2F1980i60hCM1iFMhskMaCAwdDfXjHOq3nLoh0ieEj4gMukPFu9WqVRA4TQM/OIX7aMQYU44jT5R58eIlmTNnsenH62/0FUeHictUhAjh3cKFKymSdbwAup09e1YjRMT4LF65cuUwKeJyT8HRJcTiFSqWNpGHLMEtrvOwD7+WTXr/wiCO9oXherrWhUi2EOQiMm6RIoWMENaqa/x3U41AtqNGQ273wH3WYTPmO+zD0RqBeIEReMNJo2WIToo5A0QifqB9c+uwiS77/ntfGBF5XMZx7QX5aGPx4tVGWI/Ir7Bu+vz58495gs8sIlbfSSQLR6OYE4Bzzzff6mcXtEN8+s7bo2SOelGvWbOSPgvSSuvWjU0dEMkWVGeLrp9FnPxWxdIQKyPtw4+0tZeH+ZahQ76UXzUCd768uc34uins1h84Vzxy5JgMHzHI7ijSOl9fF6pAJLtGI+a6jsdDFAxHnI0b10mwuRarHXwnARIgARIgARJIHALxFciilfGJQuuul0nxO9NdO3iMBEiABEiABEiABEiABEiABEiABFIyAev3tSX8TMy+LF2yzlSHOQdYoAa0KqTOQ+HwFfNkVhArczIZ/bFYWeySUdNSXVOSZsV6qsPMDpNA8iIQedFHKxxudStL1sTrn1WXr/uQeD24+2rC4qjadapo5LktcuFCpFkk1rZtU2neooF94VZMvXYUTF7XRRIx2dVr16KddlywgwVZmTJniJbG9QDEvI5WQiNHfDzyNdsCjjWbzWQMhLubN+00L0SMwAK6nDl9J0hyrD+22/PnL5eJP/xuouoiggVEokVUYIzIFFgghC9Yn42aENtiY0zvKIV3ZO42060E94hjLrcpfXYQ0X+rVC2nC3A26wLFbbpgKETOn7+oArO15oXzENJaX0Ktll3TBWCJaY5ffus3qH7HqvPqQqTEMPxwGDF8jD3SBxZq4r7y13vKv2A+szjxB73nYjtRefTocZmpAlkIb2GIHFJPI//SEp5Ajpy2f85nzziL6F1rvnIl+nPVNU1K3b+hC32vq9g7rW0dc7RuIJLINV2YCcNi3oSyTp2bSdNmNWXW38s1KuxqjVS0U06dOqfC8f/MC+chpE2TxnoyiUY+ySm//DrUpP1r5lKNyrxFowuF6mLVPeb1zde/y7jv3pBatW8veI5P+2/ctInis2bNJM1b1rljUUElA5zSJHZ7nSrnDgmQAAmQAAmQAAmQAAmQAAl4SQCREi2BLLJATIZIlRDJhqgAzdHma8TTDBnSy/P9H7cLZK08Tz7VWf6Zt9SI7xzzYBsiU1eBLI4vXrzGRG7s9/zjTgJZnIOwD+LaNTou2ePxjhrp9XbkVZx/8MGWTuOsGFuZMuVvE5WzabN6doEs0jbUCWMI71wdDf4zf6mZNO7b9zG7QBbpMbb4nB7r//x7RqDqKpJFGggWLYEs9hEJsq5GroWwN+TAYSOSxfE7Gfo18pPX9TfxWRPx0zE9xH0/qfAX5dGiEyiqjv62auROR5FsXO6pZUvXmfG53r27ON0H+Cz0ea67vPH6SBOtNnoL4nYEUZifVhG3JcZFKRCcP/Pso/L2W6Nk2bK1dpEsHEGuVnFllSrlnASyyANhcA+NOvu5CrrhKbxbt/Y4LBHqiHCXOmssWbJYtDz58+cx6car+Do5WMOGNcUSyKI9YNKkaT3j/Twk5Mgdm2g5UyxbLsg+xo1McALqKKK/Y0GaAKzXq1gacwmdOrd2Kg+RpnF9PvxgtHkmwAmlqz3SpZ3bRShVdXwen/P167eZZ5nj3A+uLcxa1OJaJvdJgARIgARIgARSHoEOHVrEO5IsyqCRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAhg/mLLlp1SQNeuY+7HMkSTnaYBtFYs3yBt2tochVrn+E4CrgQSbjW4a03cJwESIAESuCsJYBEGog+EHjwqv2mUAiyAmDRppsyc+Z+0bnOvCXcPYZwnwzksWEEU10OHwyRXbpsnfNf0kZGX5bQuoHI1CF7xZQgLv9o90FTKlg1yTeLVPgRSiPxgRX84ceKkQIz6r74QdfTHiX/ICy8+4VVZCZkIntwtgSwW8T32WAezsMmxzu0a3cHXlsMhusVhvU4xCYYPHTpmqneMiOHr9rgrD4v17mte37wgRg0ODjUe9FercBYLwEqWDJQWKt6GQQAKw32bmAZBNqy8Rinu169HYlYdY10QsoaGHjULzp5+pqsuRnMTUTMW/hXAdfr0+WZxJ64FFsS1adNYf5w00fs1Y4xt4UnfEKhcpZQp6OjRCP3heFGjjmRxW/ChQ+Fujyf1QUvQfuOGTbzprj0nT0b/n+CYDiLYvXsPSaVK7v8vHAo9JpcvX1URbVopWy7QMavPtxE5tsfjbcwL7dq8aY98O3a6RkBZphF+NApKjTLy+JP3O9ULBhDBWkLYs2cvyB+/LZQxKpDFdX3+uY9l6cpv7Yu1bzPz/GH1xKxK1dKyc0eIPPtcRxnw8qNO7fB2J7bt9bZcpiMBEiABEiABEiABEiABEiABXxFwFMhaZULQinGLsLDj1iEjfj0WdsII2qyonfaTuuHvn08ef6Kj4yH7tuOErf2gbiAKJDwbBwY6Ox2y0kA8h8isx7QdQQ6TvjhvjWNZaSHexTjYlStXnUR3OI8xPJzDWKpliDSJ/mDcE3ldDf0PULFc2NFw43zP+n1ppXPHrbIKGSGSPerAzUof0zuEgXnz5rInwe/+06fPqcO7/Ro1N0pOnjxtP8eN2wSs8RHHaxPbewoOLsG3okaRdedlG2P0GGd3/CzcbkHctoqpuBeRcF0NnyF4/T50KMyIcnFfWEJ1iGTdWSn9jKD/ju07qBGaYYia7MjGyo/IybG1U6fOaJtuj62cVCEuDIJcR+eRmJfA3Ii3VlKfNa6WJ09OKVy4gBw+fMx8nt19Pq081VSsijkMRJLOqc+SBiq6xWcXFlM+K7/jO+4dWE19JkAo72qIVpsvX259brkXrXt6zkFsXatWFUEk7q1bd9kF9Ig+jTkL9NXTM9C1DdwnARIgARIgARJI/gQ6PGgTuMY1oiwEslYZyb+3bCEJkAAJkAAJkAAJkAAJkAAJkAAJkEBCE1ixYoNEaWAciGIdDQ44IZKFI1WKZB3JcNsdAYpk3VHhMRK4ywlkznKPXLxwe5I/vt29qEHqsiZSgE3UBUMfaMmLQNFiheTlgb3Mgq/f9IvIJo3CivdZfy+UFi0bGoGcuwUx6EVg8QAjksWii8oeFq6EhtoWvLjrNRZqQSS7P/hQnEWyruXmy2fzNA8R77Sps2Xz5p1uF4m55kvofUTyhOgQHt179nzYKVKEVTcW1fja4PUdi5qw8AjiUyymcmfXNBowFjfBgkrahKju0iX0MSyKwmKd/i88IReGfSPbdBHO5k07botkb4lVIfhFmz1FkcTiJyxCw8Kj9G4WEca2H0FBxUwW3Ou4ju4Wb8W2TF+k37Ur2BQDYbs7gSzaeuTIncWU+/YdlOl/ztfoJ9tNeVgo1rJVI7n//qYaZSSzL5rKMrwkkCtXNikWWFAOhoTJogXrpf2D97rNOePPxW6PJ/XBIkULmCYcOXx7kbJrm5Yv2+x6KNr+zu0HPIpkt2zeZ9KXLVPM4zMgWoE+OICIsdWql5Gvxw6WM2fOa7TrTbLgv3XRRLKuVeXIkVWe6vWAVKwcJB3bDzJC2V07Q8QSRFvMIKa9cOGSfuaiO6hYvtQ9s6rVSsuvk/7R5+Re12rjvH+n9sa5YGYkARIgARIgARIgARIgARIggTgSwHiaq2FsBpEtMT5k2UGN6oixkKJFC1mHvH6HSMzVIP7EGBTen3n6DdfTZt8SQYYdOxFNJOtp3AoZvRlbgvgW/UGk2g0bbGM2ro2IiooyaSAIzKviOEfL4mZMxxLl3YzBuZVjGdY26tmkUXPXaRRLOGzDmK7Vd6TRZtLcEAi9JQa1xK1xuacsQWnhwv5uarAdgnDVUYTqMaGXJ1CeJ0M7MI589OhxCQjwtwsyf9SIwnDC6c5wH0NIbtlBvbdhhVR86c4wH4HPPQTl3trQIV+aiL2u6T8aMcbpEITyTZrUdToW04675w/SW58lx8+Bu3IgiO2gUaV/V0el4DNlyiwprvMqFSuWkbr1qhlHpO7yuTtmiWRjuhdwDmO8ESdORXsmOEaIdS0fC1Ugkl2jjiurVatgTuPzjv7Vr++8qMU1L/dJgARIgARIgARSHgFL5BpboSwFsinvWrPFJEACJEACJEACJEACJEACJEACJJDQBP6a+a+pAgHXdu6wre91rBNzXZhbKqLOf2kk4IlA9Nl6Tyl5nARI4K4hkCevqEjWd90JP3ZTRbKJI1pFXTD0gZY8CRQvXkReHfSMQCwHkezWrbsFUSrnzl0izZrWlXbtmkWLFovomlg08ZdGn61fv7rxUu7YOyye+uUX9wtjkA5iSHgPmT9/mWARhrsopzv0y9IMjWxZpWo5I9i1FpAhWuya1ZukRo2KGvm2sWO1ZttaDGd5ZXdMkC5dOrN7Ur3LJ5ZZdSIqpLvFKFhsgsipniy9epuHnTt3Qb2tRLn1FO8pL64NvmDietbzsPBmsi7QQbRbRAuFB/+ENiwI+/XXv2TnzmB56qnO5l5wrRPXECJZR8/+iFaQNWsWFZJdNFGCe/V+xDWbbN2yW0bo4id40//fl+/6RCTr75/XiEURuQEC8nYPNItWLxZ7jf7qJ3UGkEmaNauv3vULR0vj6wPWfZU7d063ReNzDFaeDJ+v6Ro9ZPt2m8AOguKWLRqa/iF6CS1pCDRqVNWIZF8bPNoIKQOLO/8wXL9ul0yd+l+cGpchg+35FxYWEaf8d8pkRX/dqSLQPbtDpXQZZ9E9IrBGnLjzs/fTkT9LqzZ1NUKKc9SSC+cjZdiHP5hmVK5S8k7NidN5LLAe/sEPsmrlVvlwRF8jjHUtqKxGsIVINouDoHXp4o36XJotGTNlkC9Hv+KaRSDqxf8wPCsc8wUE5Ndo0Nk0As95/X+3WLo/1top7/79R2Txog1Ox6ydatXKmE20ZfWqbVKnbkXrlP0dUWj7PD3cRLbt2au9LpC0PS/i2l57wdwgARIgARIgARIgARIgARJIlQSs3zXJsfPXrt8SzOpvL18YxuDg9RhWp06VGIvMpZFgfW3Xrl6zF3mn+u/RiJ4JZVe1HV9+OdFElIQwOVDHkcuVC5L8Gr00SCNXjhkzSccszydU9Sm23NOnz8r58xd1vDWTPSprXO6pO4kwkwqQ1a6r12z3KcYncV94svTpb0dDtvJ6ShuX47VrV5HIS7cjMS9fts4UU7ZskM5L3Y6CjPHlxLa2bZtINR1zX7Vqo+zYuc9En8Y8zExdNNL9sQ7StGk9r5p0/ZZTgLR+nj/vfrfO2Z+HXpVsm6tBVOJN6rAS4mSIgFev2iSIFlxP5xdoJEACJEACJEACdx+B2AplE1Iga60Bufsos0ckQAIkQAIkQAIkQAIkQAIkQAIkkHgErHlcrFFNrN/aCBCGoDOw8d9N9dhZRJPt1q29x/NJdQKsYInFK6n6mRLqpUg2JVwltpEEfEygSLE0EhoS5bNSDwTfkKBSfj4rL6aCUBcMfaAlbwIQrr72eh/ZvXu/CXEPjx4QyiKC5jvv9ndqfMuWjYzINfTgUfnqyx8FgkVLnHr27Hn5/fe5EqL5sKACCytcrdl99WXt2i1GKAmP7i+8+KQULHh7kQoWiozVRVaICopFNo5fQE6rwBUCP6QpXaaEQLBr2WH1NjJVPbLDatas6JQPx8qWLWH6h0UeEDO6E+cinS+tjNaJ6BFhYSeMR/ZGjWqriNP2ebh8+YqMHTvZtMlTnUF6XSCuhXhrwX8rpXmLBtH65SkvRMSIqAteuE5PPtlJiisv8IQwdtmy9TJv3lKz4KVvvx6JEpkRfQneF2ruj3Fjf5W33u7ntFhs29Y9snDhKtOlWrUr27uGNvd7/jH5ZOS3smDBSimgi/FwH0HcC4MY+NtvfzXbEFD76tqi3j7PdZdRn46XyZP/MoyaqnjcilKLxYKIArBy5UZzndq0aWLakNB/KlUuI3v3hsjsWYvMIrRcuXLYq4TA+IvPf7Dvu26c0GgCH34w2hzGvXlf8wbSvv19KkpMpBDjrg3ivp3AO+/31ufiAYEYtnPHwdL2/gbSpGkNI65ct3aHjP3mT6lVq5ysXLHVnsfbjbr1KsraNTvUucFSI8bMl9M2K1MAAEAASURBVP/2IkFvy4gpXanSRc3nMTLysjzd80P55PMX1ZlBOTlx/LQKshfJ8A9/kIAiBeTwIc8RjvF8OHXqnLz0wigZ8sGzJj3qRHTdD4ZMMHkL+OeWl1/pHlNT4nwOn4eNG3ers4hgGfjSFzL192GSN69NWIrFiEuXbpLJGr0V1qZtfad65s5ZafZr16kg3bq3sj/nw4+dkg+GTjDXEIyCggKc8lWpWloWLVwvH7w/Xh0DpFehekN93kepEHejvPbqV5JdxcLnzl10yoOd8hWKS7/+D8voL6fJk48PkXHfvaFOJyrb64UY+uUXP1PB7zY5Hn5aXhrYzamMuLbXqRDukAAJkAAJkAAJkAAJkAAJpCoCGCOxJguTW8cRnRGGsUFfGMY0EVHz1Kmz0rPXI16PxfmibpQBMSqsSpVyOu7axWwnxZ/167cagSzEhhgfQ5RPR0sIwaNj+Sl1G84lYdWrVzDjrtiOyz1VrFhhZJXDR46Zd3d/ENXV1SBuhF2+HH1cHsdP633tydyVZ6U9ou1A5OXCt6LAwvkmrIU63mvV+l4rWYzvVp+OHAl367ARjiIxbu0uwrOngh91s7ACQlnMWeTJ49vxL09tiOl4QX2WPNSxlTwkrcxcydIla+Q3jS77h86hNGxYy6sx+eIqSocdVm6V9bngzsAUjkPjIgZuoI5M/1SHihDKli5dXPbsOSAVKpTSMXbfOwFw13YeIwESIAESIAESSHwC3gplE1Igi17jdyaNBEiABEiABEiABEiABEiABEiABEggfgSsedzEFMlC/ArD2vM2baMHPTt39oLMnr1IVizfIF27trPPmcWvp77Lbc17c2zCd0zjWhJFsnElx3wkkIIJlC53jyxf7LsObN9yQ5q3ThyRLOqCoQ+0lEGgjApP33qrnxFWIrKsO8OinlcG9pa33/7MiFVff22kwNs4FkoFB4eaBXOdOrU2USp37QqOVgSESC9r/iHv/09CQ49qWcM0ml4OKVaskBHGYkEHvnxACPlotwec8kP4CYEtFuy8/dYo4w0ei2v2a71nzpwzabGIre39TZ3yYad2narmCxe8l/Tr+46JVNr76S4mymq0xD46gCgLbbTNMzXq7rfjpsjPP82QMmWKy1n98gdhJxaTtVDhKyLkujNEU61Zq7IKnTbKDz/8LpM08iuiNXz00SB3yZ2OQXD20su95H3ljOuC65Urdw5zrfapwNKKiPFUz85SrVrCR5G1Gtete3sZMXyMYGFV3+fekSJFC5rrv3vXfl28dcUkg9C1WrUKVhbzXrlyWXniiU4yYcI0I1idOnW2lCoVKCdOnJSTJ20RKgsX9leBmm89zoBNb11UNW7cr/Ljj3+aKMlBQUXFT/ke0vsXUWbxJfnhR9o6ib2dGu/jnYYNa6qwbrWK2A/J8/3eUzFhQcmfL7f5PGIxWQltHwyfC3eGe6Nps3rSoUNzw95dGh5LfAKZNBLpDz+9Kx07DJa9e0Ll+/F/mZfVEggwR376gjSs97R1yOv3tu0ayLgx002U1+pVemiE5Ezy0cj+0v5B7xYx3qkiiP8RRfWZ3sMEEVA7th8keP5BSA7r/+Ijusjxskz4znOU8Tx5c8gbbz0lLz7/qcz/Z40Rgt64cVN27QwxZUBEOuGHtwVC2YSyt97pJd27vmX4V6/cQ8qVD9T/RXlUYLxdP+uXTLUtW9dVhwW17U1oeG9VuV/5zvp7ubyhUYBHfPiDid4aGhpuykHCbBpN/O13e9rzWBsfDOsjD7Z/1UTZHdB/lAwc8Lk5hedzvfqVpMfjbaVvn4+s5E7vr73xhIQfO6lR4BdIty5vSdZsmU30Wwwu7Np50Cy8xHUeMfJ5pwWX8WmvUwO4QwIkQAIkQAIkQAIkQAIkkKoIpElzj45jJc8uZ82aRZ0c5TJRGhHBM1u2LE4NPaVO95YuWSsQqyHqpDcGoSrGKDH2U/SWWNExH5yQQUCGcU5fG9qP/mDcB47z3NURFnbciOF8NXFrjRM69iUk5IjZhYM1V4EsxmERMTU2YkbHsu/W7Q0btsvUqbPMRD8c/DlabO8pMMeYeYjeB+7uaziYDA+PcKzCbGPsHvkw9or7NJ+OGTrajh17za41+e94DuPVGOvMmjWz42FTDu65gICC6qDLNrdkidN379nvViSLe/es3id5HeovFmgTtG/dsksQZdX1/t2+fY9TvSl5B+P+mPvA3IXVT1yb5ioqDgk5bJyQor9VNdKsoyHqsKtZjkI3q4gVcw1WeVY6XBvcC3AS6nrOShPTe7161WX69PmyevUmnbc4b+ZmGuj4M40ESIAESIAESODuJnAnoWxCC2RBF78zaSRAAiRAAiRAAiRAAiRAAiRAAiRAAvEjYM3jYr3tLV+q8SvwDrkxx7R8+XozHzZs+CsenW4eORoumzftFAR/ggYgORlYwTg2kfRXxeb+N+nbwRaQAAkkIoEq1fx0Ytt3Fe7cdkPCw2wPdt+VGr0k1IG60Hb0gZayCJQvX9JEkIXHdXeWRxdqDRr8rC68CDKnjx8/aQR6EHV2U+/tHTu1cpfNfgwRQAe/9qw0bFTTLPTCoqpN+kUIAlYIxe5v11RGqBDUdQEW9t96+3lp0qSuyXdSFwNtWL/NCGRxrnHj2jL0g5eNENRe2a2NwMDC8s47/Y2gEItFIMiEWDShrYt6QEEUVywuunTpsuknFrmhvS+91FPFVDEvzuvb9zGz0Aie4CE4O6KM4FHfGwPn1/Q61W9Q3SxgQqQAiFGx8A0i5Gee6apRdet5U5TP0kDY+qYKsa0vvIhIjC/BuB4QW3d9tJ2Ke3sagZ1rpfc1r28Eq/ny5dE+RAlE2BDIYjEkhKPvD3kx2sIv1zList+4SR2NmNHNRBFBvfCmj2jLFy9ekkCNVoJoy+3aNYtL0XHKg2s3ZOgA8/nDvYxIKViEB4EsrvXgwc+4XUCJyrDQctRnb5l70jECbZwawkw+J4AoyHP++VxGfT5Ao0w0kQYNq5jIrxDHTpn2oRFbxqXSihWD5PfpH0mZssXMYjkIPjds2BWXojzmgXj0iy8HakSLUiai6fXr16V6jbLy+ptPyquDe3jM53iiY6emMvqbQRrpI4fs2H7ALpCFWBUCWZSdkFajZlmNIDtcGjepbqpBGxYuWGcEskWL+Zu+fDfhTRP11WoHPoNfaZsHvtrdRJ5F5Nf//l1rBLJ4BjdsVEVmzflMhenRFxcWCywoP08eIo1UaIv/fXg2o56evdvLuPFvSLr0MS+2HjnqRSNAzp07u1w4HylLF2/U5+les5gR1+O/xd8Ysa3VVrzHp72O5XCbBEiABEiABEiABEiABEggdRFI7hOEjRrVMuNuY775xTilc7w636vDtRkz5su1W46cHM952q5Xr5qZ2P3665/NeItjupMnT8v7730hA1/+0NTpeM5X2/U1siPGecaOid6fTRt3yJtvfCKffvJdvKuzIm1CHOkqmiyqTtlgEOY5GsbGJv0yw/FQqt4Gt0M6Njd//jJzvbB/v44TBt4ShFpw4nJPQdSN8cfvv59mFWPeIcAcN3ZStHvdSlRMx8Fhs2cttDslRJ4fJ/5hdzZopXV8x9j1hAlTHQ+ZOsaNmyzXr0c5icxx75RWh5u4H+fNXeKUBzu/a7TUQYNGyN9//Wc/B8EuuGBs9Z9/ltqPYwPzA5N+8exczSlxCtgZN3aycRL677/OzjExvr9zp82xaKFCBZx6AqYQP0Oo7GgYwy1bLkj26lzGvHnOrHFdxmpduL7169dwzOb1NoTMpTSC7Latu9Ux4yoTkdbVgaXXhTEhCZAACZAACZBAiiIAoSzEsK6WGAJZ1Jncf2e6cuE+CZAACZAACZAACZAACZAACZAACSRHAlgTCrOEnwndRoheoUeA1gROhT1ZA53vhFlRZz2lS4rjFiuLXVK0gXXaCNyjk6sJr2wjbRKII4Hw8FMmJwQeSWn4mNhecuv9ppkghvAAr7waqSyl2U/jr2s02egepOPaj8b3+cmjT8QsvIhr2Va+yROvy+L/oqRBYz/p0Sth67Lq5HvSEMAXnfDjESr6zGYiacb2CwPEkRDZYvGHv39eyZ07p1cduXLlqpzQfOc0QkRB/3wmSqpXGTURRKbwwI+FOZb3e2/zxidduHp0P3YswvQTgtDYsMJzDZwgloXANrYWGXlZjmv9kbrYKU+enEaQGpv6Y1ufN+lxDRDRAIsUixQtJBCUeWNggXwQSefWvkA0mliGhUqoF57/CxUu4FbMm1htQT34HEB0nUbd/xTRxYu4P2h3L4FDGp20fp1epoNbdkzWyCCx/86FSNanTp7T+yW/pE2AiDdoHASb13XRbHy+E6Kvhw8jSkl+fT44LxpEHRCvPt79PWx6Zf36PyyIvuqtnTp1TgXo4bqY9KqUKxfolUD5ukZJOXTouISFRZiIt8WLF/Z6gh95I06e1f8Pebxtoj0d8h4MPSbHwk6Kf8E8uuC0kBEq2xN42IhPez0UycMkQAIkQAIkQAIkQAIkQAJ3MYGrV69HE1LGp7sbN26XL/83UR1E1ZRevW476ps8+S+ZrwK6lwf2looVS0er4qUBQ02E1a9Gv28/h7EiiNIQCRFRWKtUKaeOh9LJ5s07JezocSPMe+315+zjOHeqAwUvXrRaJk783YzDlS9fSp0aFTICthXqGRnjmZ07t5G29ze1t+G9dz83kSPHfTssWoTVQa8ON3knfP+xPb214ak/3377q6xaudFEjMUkc968uU0EyrVrt5gxRTgwLK3CNstiqv/YsRPyxusjpXr1CvJ8f+ffxm+/NcpEHYV4EeOVT6izP4wvIaLkW29+okLgK+Y6wFni2XPnZbtOfKP/ttdVQX8t84arlTYh3s+cOW+Kjc94hKd2WfcrBIuFdUwQBoEiBMYQl8IwPof7orVG+3Rnsb2nIHwc9el3skOd9UHIWL1aBTPXhbZc17mu0uqMEPfDm289L0FBRe1VIv1no8YbJ4NwagkxK9qZUcczK1UqIxBu9nmuu130avWtRs1K6rRsr7nnIZK8evWqccyHyMF16lSVZ/t0s9eBjcjISzJ82NdyVD9jqB/3CCIfb9cyIITNoQsjPvhgoGTJksmeD2LYoUO+NE4vi2vEZjhSxP25RaPLltAyTqlDRIz7Ot5X9sxebEyeNFPWrNmsTg1fMBF1vcjilORO9/CHH3wlwcGhMvrrIfZxWHd5IGgd+fFYLfsewxyfVcyhYOFIhPYPEWRfePFJp7p/+Xm6/PffCvMMCwoqJohGDIeTMIs1IlxjDBj5LcejFy5cNA4cO3ZqbdJaf2J6JlhprPelS9aoGPs3s9vo3try1FOdrVN8JwESIAESIAESSAUEZmhU+V27bY48IJC1HKUnZNexTiH9HZzFJmT9LJsESIAESIAESIAESIAESIAESIAE7iYC1jwu5mkS2inVN1//IsuWrdN5o0flXp1T8GRY3/5cn7flpib45pshGpwmg853XJane78uJUsW08BYA+xZ33j9EzOX9cPEkR6DRNkTx3MDAtlruuY2tY5NREScNeuL/fzSmLlNcLC9bEF4sJ2YRpVZYtJmXSSQjAg0a+XnU5EsxKu16/tJUKmEeYgF771pBLJAiLbT7m4CuXLniJVA1ZUGvvQUVYFkbA0ixYBbEQ1imzdLlsy6OCdzbLPFOz3EnHEVdOJLR1zzouEQoCLqaXIyRDXFK7YGFli0h1diGxZa4pVcDJ+DxJioTC79vdvbsXNHiIkkmiWLe7Hz9u37DQJEHY2LQBaZc+TIal4JyTJrtvg/XyGMdSeOTch2O5aN6Kx4xcYgOi5eopB5xSYf0iJvXASyVt6goABdkBq7Z3x82hvb/jE9CZAACZAACZAACZAACZBAyieAiTKIApOjYayoV+9HjDO1rSq2g9AMBrFnQxXhPtbjIbtA1tv2N25Sx0wir1y1Udav3yqr9B2GcaGeKuqtqYLChDLTH60jjzoT3KqiuoUa3RFCYByHkPDxxzsaoZwv6n95YC+NgDrJRKkMCTks9zaurZ6fS+nYQTYjvvxGo+lCwIgXrHiJItK3Xw8j3sTkdmozCBPxguF6wJkhxmwh6G6oEY1jGq+M7T0F0e1zfXvI1KmzBPe1FX01IMBfjz8mi1TIbWuHebP/gai6jwpap0//xwhY96pgFcc6P9zWfh/bEztsoB9t2zYx98PcuYvNGdzvzZs3MHkdkprNzJkzGTH79D//0eio++TvvxeY42nTppW6davJo93aOwlkcRIi4wEDnpKZGmEWglzL+V/VauWNWP6jEWOUqykmTn9QJ15JbRC3vvRyLxOZFWwgRIbhfmn3wH3yoEZtc7UuXdvJVV2UsWrlBiP4z6dj35ZIFqxR3p/KGtz+Un64/yBub936XifBvmu53uzXrFVZftEI0Yh0a3l29yYf05AACZAACZAACdwdBExEWYn+/SQhe4fflzQSIAESIAESIAESIAESIAESIAESIAHfEIAwNirKFlgwTZqE0+7AiS4cuEKMW0vnFmIyrG+vUbOirFiOeY/N0ljnIJODwUktLKHFxMmhrymhDYwkmxKuUipuIyPJJuzF/33ydZk/x3eLkIoVv0deey99vBYcuOuxrtWREe9dlYMHbkqLNn7S6VHq+91x4jESIAESIAEScCTw809z5J03x0r9BpVl9JhB0YSsiIbS/v6BcmD/UWnVuq589/1bjtlT3Ta8OV2/ft3rfmNhadq0Cffj3+uGMCEJkAAJkAAJkAAJkAAJkAAJpGACV65cSxGtP6uRLy/pJC1EfxCSxdfgTfjo0XAjkE0Kx3sXL0bKSY2w6e+fL9ZiX2/7jmiomNiGiNHVEKkyLOyE5NdopogOmlwtISPJ+rrPcbmncA9mzZrFCC29bQ/uHYjFMS7iyaxIsoiMjEi4MAiBIYLGPeetRUScVs/b18znLqb6rPKioqI0inG4SY+FEnerQeBurp067fTm8wMuiN6bUz9rfn7ux7IQfRfOJ331PML9OODFIXp/ZZYRHw32yXPzbr2e7BcJkAAJkAAJkIBvCGTIkM43BSVxKaGhYXLp0hV1blI0xu/cSdxMVu8jAkePHpfdu/dL7dqVffZd3EdNYzEkQAIkQAIkQAIkQAIkQAKpnADmIhBNFpY+fVqO83u4H8hJhJFkPdwcPEwCJJD4BCA23b3zhoSGIOh4/A0i1vFfX5Pe/Xw7+IwyUXbRwHsokI3/ZWIJJEACJEACqYRA7doVJGeubLJ40QZp3qSftLm/vpQrF2iEnbt2HZQpk+fL2bMXpGDBvDL49cdTCRXP3YQXp/TpffsdxnNtPEMCJEACJEACJEACJEACJEACJAACcD6UXKPJOl4hCNGiSz0dU8RuG96QixUrHLtMPkwNIZyvxHCemgUhJV7uDMLMUqWyuDvFY3EkEJd7qlChArGuLa73jTux9J0qR9TZ2BgEoEWLFopNlhSZFkL9woX9vW47uOTJEzPL2IiXval40aJVKu64LK1a3cuFM94AYxoSIAESIAESIIF4EUgMp7azZy2RgwfDorUTPpQyZspgHI4EBRXRudgSEleHLYcPh8u0qfNMHU2a1pIaNSpEq48HbhNYu3abrFu7XYKCAqRlqwa3T6SQLTiVevedzyQy8rJs2LBdBg7sFauWX7gQKYNeHWHyDB/xqjrMzhar/ExMAiRAAiRAAiRAAiRAAiRAAjERwFyEn18ajSZ7Q+dyb2ikV/dOOGMqIzWcAxsYWPnC0XJqYJbQfWQ4xoQmzPJJIJkTeOKZdDJq2DW5eME3Qtl1q/Ggvya9+qbTB338Oq8OKIzoFmVmyXqPoK00EiABEiABEiAB7wiULlNUfvtzhPR8fKgEBx+W78f/FS3jvY2ryf9Gv6IL5Xy51DdaNTxAAiRAAiRAAiRAAiRAAiRAAiRAAm4JYMLwxo0b+vLN+LTbSniQBEiABEggwQngWb5t2x51yndeZkyfb4T4zVukPLFCgoNiBSRAAiRAAiRAAj4lACe4+F2Z0Hb5ylUVM15yW83Fi5fkZMQZCTlwRDZt3CVdurSWTJndOwxyW8Ctg479SJMm4fsUU1tSwrlrV6+Za4JrkxItjS6qs65zWnVsE1tDtCJ894Zhm0YCJEACJEACJEACJEACJEACviYAJ5wQyWL8PyoqcX5/+7oPCVmexQZ1gBUteRCgSDZ5XAe2ggSSjEDhgHvkuQFp5ZvPr/tUKHvi+FV55LF0ElQqbkrZ4L03ZerPtgiyEMiijWgrjQRIgARIgARIwHsCJUoUloVLv5GVK7bK5k17JTz8pGTWSdmyZQOlrEaVLVmqiE6+8f+r90SZkgRIgARIgARIgARIgARIgARIwNcEEPXn6tXrvi6W5ZEACZAACSQigX17Q+TzzyaYGhE97dlnu+k4ZKZEbAGrIgESIAESIAESSI0EEiOKrCPX8hWCpEGDavZDcPh08uQZORYWIVu37jHb06bNk0dUKJsxYwZ7Om82ChbMJ9263y+XL12RwOKFvcnCNCmYQHr9zvzhsFdkr36PrlatfAruCZtOAiRAAiRAAiRAAiRAAiRwtxJAwDz87r5+Pcq8ECmV641tVxvjAeACA6P4Bhe0lcq/viBAkawvKLIMEkjhBEqWTiMvv5FOJo67JqEhvvEud/DATRk59Ko0vs9PmrX0kwIFvRPghIfdlAX/RMni/2z/NIoG2iLIUiCbwm8yNp8ESIAESCDJCOCHaf0Glc0ryRrBikmABEiABEiABEiABEiABEiABEjAAwH8bk2Xzk+uXbONCXtIxsMkQAIphEDBgvmlw4MtpFSp4imkxWymLwhkz5FNHn64rWTPnlWd85WUPHly+qJYlkECJEACJEACJEACHgngdyR+TyampU+fznzfcawzZ85sEhRURJ0UF5cpU+bKiROnZd3a7dKwUXXHZF5tQyhLSz0E8ubNJXjRSIAESIAESIAESIAESIAESCC5EvDzSyM3b940EWUhCk2K3+LJjQ14WAJZ8MGLlnwIUCSbfK4FW0ICSUoAItQ3hqSX3ydfl/lzfLcYCWJXvMpVTCMVKqeR4kFppID/PZIlq627Fy+IhB+7KQeCb8j2LTdk57Ybdg4t2vhJp0f5mLID4QYJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJ3IUE0qRJo152xT6heBd2kV0igVRDwN8/n3To0CLV9JcdtRHAdW/TtglxkAAJkAAJkAAJkECiEECUFvyOTE6WJ29OqVGzvCxbukGOHD3u1LTIyEty8eJlyZQpg2TNmtmcO3v2vBw+HC65cuWQQoVs4lgIbGG5cmU3kWjMjv45c+a8Opa6rgLdLJJBI5DCzpw5J6dPnZNChfPbj5kTt/5cuBApx45FSLZsWVSImVMX7fo5nna7ffHiJTl9+py29ZJkzpRR13flEQiD3Zk3fbpy5aqcO3fRZM+Xz7MYFAuMIyLOmHQ5cmT1WKe7dsR0LCLitC7mFi0vreRQpy6uFhUVZUTN57WNmbNkVEcvuTQCsI2vY9qE6kd4eISg7Ny5c+h9kcWxSvs2rvvBg0fk1Kkz4l8gnxQO8OcCbDsdbpAACZAACZAACZAACZAACSQGAfwGx2+rGzduGKfHtt/kieu0KjH66U0dVgRZ/I61zW/f+be2N+Uyje8IUH3mO5YsiQTuCgIQpdZt5CcL5kXJiiVR5h+aLzoG8aujANZTmXDyWP9ejT7byk8H9lLnP09PbHicBEiABEiABEiABEiABEiABEiABEiABEiABEiABO5WAvCyi/FhRpS9W68w+0UCJEACJEACJEACJEACJEAC8SeAqDXJTSBr9crfP6/ZDFdxKhYPW+3ctHGXrFy5WSpXLi2161SWP/6YL6dOnjVpq1YtaxfJ/jhxhjn2xJMdnCKMzpu7zAhq7293rxFTzpm9VMWnGpVADdF0ixYtKB0ebKoRfdLJ/v2HZeGCNUZEaxLon+wqPG3VqoFJZx1zfA8PPykrVmyS/cGHHA9reWmlXLkS0uy+OtFEtt706cqVa2L1qeujbaRw4QJO5Vs7hw4dk2lT55m+PNvnEZ+IZJcuWS9r1mw1ZXV+uGU0kez69dtlzeqtEhl52WqGeS9fPkgaN6kpmTNnsh9PqH6MG/ur7NoVLE882VFatmxkr8/aWLBgpfw2bY5AUG1ZxowZpFevR6RS5TLWIb6TAAmQAAmQAAmQAAmQAAmQQIITwG/xa9csoex149gptUVQjYq6YXf4jN/7YEJLfgSSl0u15MeHLSKBVEkA4tQevdLKZ2MyyFN90kqDxn5SNBDRX+8xi5R8BQULnlAmykYdqAt1om4KZH1FmeWQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQMoggAlFRHhJk4YOFFPGFWMrSYAESIAESIAESIAESIAESCBxCOB3ou33YvJd7ohoOjAsnEV0GVe7evWaTP/zPyOQzZ49qxQrVkiyaXRYbw1RWWfOXGgW5ZYqXcxEpUX0moMHj8qsv5fIodBj8peex3qssmVL2IW2585e0HoXCKLLutqxsAiZ9Mssu0A2T56cUr5CkIlAiyimW7bsMWWjHncWU58Q+bZIUX+TbdfOA+6yO50rFlhIsmS5LU71mOEOJ5Yt2+AkkC1Y0Bap18q2YcMOWbRwrRHIQnQKlgUK5DEi3R07guX7CX/aI+AiT1L0Y9XKjTL+u6lGIAuxcpUqZaV06eIm8uzo0T/J6tWbrO7wnQRIgARIgARIgARIgARIgAQShQBEoZYw9vr1KOP42NNvxURpUCJVgj7CyTP6DAMDCmQTCX4cqmEk2ThAYxYSSC0EMuq4Y536fvpKLT1mP0mABEiABEiABEiABEiABEiABEiABEiABEiABEiABJKSAKLgYAGoozfepGwP6yYBEiABEiABEiABEiABEiABEkhaAhCfWgtxk7YlMdd+TCPIwvIXyG2i6rim3rXrgInq+uSTD0qevDldT99xH9FRK1YqZaLCIjEW6m7btlf+mbdCgjUKLF7Vq5eXJk1rGcEn0kREnJapGqX1kkZMXbduuzRpUguHjUHg+vffi0zUW/+CeaV9+6ZGHGud37cvVGZMXyB79x6UMBXTFirkLDZFujv1qUL5kka8u2dPiIlIi9/8joaIu3v3hphDFSqUdDwVp+2VGhF39aotOq6QTjp1biGuAlkwgkAWTrraPdBESpYsYmd18eIlmTZtnpyMOCP/zl8hHTu1sLchMfsRHBwqY8ZMMnU/9FBLbWczgZgXdv78RRk7ZrJM/OEPs88/JEACJEACJEACJEACJEACJJCYBPD7HL/rIBjF77mrV2+Y3+v4jXW3OUGG8yv0EXPWlqWU8QmrvanxPfm6VkuNV4N9JgESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESMBOqGTKkMwuLXRfREg8JkAAJkAAJkAAJkAAJkAAJkMDdTQC/A7H4FL8LU4JANlJFqJs37TYXpVDB/B4vziOPtI6TQBYF5s6TQ1q2vB3pAIwqVSotOXJkM/VlzZpZ7m1c0y76xMG8eXNJ9WrlzPmTJ8+Yd+tP+vTppO3990qNGhXkoYfucxLIIk3JkkVNndi2BMDYdrWY+oQorXCEBT4HD4a5ZpWQA0fk8uWrep3Tm/qiJYjFAYhjV6hIFvV16txcRb3Rr8O8ucuMuLhVqwZSqlRRJ1aIYtu5c0sVpKaXA9ouROi1LDH7MWXKLI1SdF0qVy6r/WhtF8iiLdmyZZE+z3WTnDmzW03jOwmQAAmQAAmQAAmQAAmQAAkkKgH8RsfvSeu3OkSk+A1z9ep1u3g2JUaYRZshioUAGH1BnyyBrGufExU4K4sVAUaSjRUuJiYBEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEiABEkgsAph0xMs2MYnJyZtmOyVOriYWM9ZDAiRAAiRAAiRAAiRAAiRAAimNAASfeCHyDF7YTgl27twFE2UVAs3Tp88JhJbVqpd12/R8+XKroDWr23PeHCxWtJBbLiWCAmTjhp2SP39u+yJlx/Jy5bIJKtFWV4OQ1J2Y1EoXULiAnDp5Vo4fP2kdcnq/U5+wcBpi250792vU2f0SGFjIKT8i0cJKlw50G33XKXEMO2vXbJNlyzYYgSwiwBbWdrsars+lS1cke/asUr5CkOtpsw+hcUCAvyCK7vHwU1KsmK29idUPNOLA/kOmLd26t3d7vdHGNm0byy8/zzDp+IcESIAESIAESIAESIAESIAEEpsAfrJbUVUhJLXmb6OibqqwNLFbkzD1WWMUmKdOKWMUCUMiZZVKkWzKul5sLQmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmkOgKYfPTzwyvVdZ0dTiICBQrkTqKaWS0JkAAJkAAJkAAJkAAJkEByJLBp4y7ZuWO/vWlYBHzt2jX7fqZMGeXhh1t5jPKZM5ct4qs9Qyw3cuV2Hz00zT1pTEnpVJDqztLogl7YDV2sfCc7c+a8nDlzTs6duyjh4SeNuBV5EEHHnXnTpwoVS5py9u0NlagWUfq73vbDHmXu22cThFaoGOSueK+OrV+3XZYsWWfSPtSxuYpcowtkcfJ2NNybcuRIuMeycR1hp06ddUqT0P1AZRAjR0ZeMoxiEi8XCSjo1DbukAAJkAAJkAAJkAAJkAAJkEBSEMD8LcSyMEsoa72nNIfH6AteluMuvNNSHgGKZFPeNWOLSYAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAESIAEEpHAlStXo9WG6LFBQUWkRs0Kkjt3jmjnk/sBRHjdvj1YjhwOjyaGTZPGJrCNTx+KFi0oiH564UKkHDhwxESWRXn79x82IuOcObO5jfzqTZ179xwUvCyLjLxsbUZ7Dz8WYY5BAPzr5DnRzrsecBXJJmQ/rLpDDhw2mwUL5rOLia1zju+FPQiBHdNwmwRIgARIgARIgARIgARIgAQSk4BNVEqHx4nJnHVFJ0CRbHQmPEICJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEACJEAChkCZssWlWbPaDjTukYwZ02uUmfgLSR0KTbTNGzduyNw5y+zRYtNrJFoIQXOp0DeXClfzF8gjB0OOyurVW+LVJkTiKVe+hKxds0127dxvF8nuVnEurHyFkvEqH8JkiHBDQ8Nk3txlkidPDsmbN1e0Mh2jGJUrVyLaedcD2bNndTqU0P1AZX63IjB5itxrNehO5610fCcBEiABEiABEiABEiABEiABEiCB1ESAItnUdLXZVxIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIggVgRyJQpg2TOnClWeZJzYkRy3amiVYh8GzepKdWqlRMIQR0t9GCY426ctyuUL2lEslb02Bs3bmok2SOmvPLlg+JcbloVlXbt2kbSqcD318mzJTz8pMyYvlAe69FOMmRI71Suv39es1+kqL+0vf9ep3Pe7iRUP6z6AwMDzObx4yfl6tVrAuGyOzty+Ji7wzxGAiRAAiRAAiRAAiRAAiRAAiRAAqmaQMp0Y5aqLxk7TwIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJxI3D4ULjJWCywkFSvXj6aQBYnT548HbfCXXLlyZtTCmhkWkRA3bfvkOzbGypRUVESEFBAcuRwjtjqkjXG3eIlAiRT5owCsWz7Dk0FQuYzZ87J7NlLo+XzL5jPHAs/djLaOW8PJFQ/rPrz5Mkp2bJlEUS9DQk5bB2O9h5y0CYwjnaCB0iABEiABEiABEiABEiABEiABEggFROgSDYVX3x2nQRIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIgARIIHURgLAUli1rZrcdj4y8LKGhvotYWr6CLWLs7l0HZJe+YBUqlHRbd1wOZs+eVe5v19iIffcHH5IVKzY5FZMrV3bJmDGDidC6ds02p3OOO7P+Xiz/zl8px4+fcjxs307ofpQoUcTU9ePEP42Q2F7xrY2IiNMyZ/Yi18PcJwESIAESIAESIAESIAESIAESIIFUT4Ai2VR/CxAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZAACZBAaiGACLKwPXtCJCzshFO3z529IFOmzJErV646HY/PTtmyJSRNmjSyf/9hOXjwqKRLl1ZKlwmMT5HR8hYrVkgaNqpujq9UkWywimUdrU3bRkZEu2TJOtmwYaeJbGudv349ShYtXGMEvNu27TXts845vid0P7o/1kEj4maUAwcOyfff/y6nT521V3/06HH56ssfNdKs/RA3SIAESIAESIAESIAESIAESIAESIAEbhFISxIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAKpg0ChQvklIKCAHD4cLpN+mSVZNaKsv39eiYg4I2fOnJMMGdJLuXIlZOfO/T4BkjlzRgksXlgQ5RVWsmRRSZ8+nU/Kdiykdu1KcuxYhOzdc1Ajri6V7o+1E0SRhZUoESAtW9aXefOWy8IFq2XxorWmz35+aeTEidNy+fIVI6Jt0LC6PY9j2dhO6H4ULuwv/fs/LiNHfqttXKnC3VVSXKPLXoq8ZMTMaOurg56REcPHuDaN+yRAAiRAAiRAAiRAAiRAAiRAAiSQqgkwkmyqvvzsPAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQGoiALFl54dbSaVKpSVtWj+5cCFS9u0LNQJZiGW7PtpGcuTM5lMkFSoE2curULGkfdvXG61bN5TceXKYSLgzZizQiLHX7FVUrFRK2rRpJLlz55AbN24IorMeOnTMpC1QII906dpGatWqaE/vbiOh+1Glajl5XoWyBQrk1aix/2fvPsCjqLo4jB+qSpHee++9N+lKR8EGgg1QPrGDHRFRsXdRsIIiIIgKKEoRkKL03nuvoRcRCHz33GWWTTKbbJJNsiTv9YnZzE6585vZkJ2d/z2XTLB4pw3I5siRVZ5+5kGpWLG0W7eYhgA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QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEcpIAIdmcdDTZFwTSKbBlyzbbtWuXqfrr/laQjbZqLXPXrvW2w1UKi0fTtmqbVd2Oln0FdD7onEutLVy41JYvX+WrB6tKa8GCB6U2eYpxq1attaVLV9j27dutYsVyVq5cGVNgNS1t9eq1Nn/eYr+N5cqXtUMOKZ1itkWLltnu3bttwYIlftxC9z14XKpUCb/dGrFt23a/H/ny5fUVZcMXFExfuXL50LatWLHaFrl9r1a9kq/0Gj59rMdbtmz1+6p9lpMq1mobItvGjZtszZr1Nn/PNms/g22I3D7Za9vD9yVyeXquZSxZssK2um0oXaakVa1a0fLnj31sg/Xt7z5H25ZgmCpPL1my3JYsXu5C9UWtoqvkq+/7ajt37vQeq51jmbKlnKP2Jd++ZguN37lzl82Zs8C0nFq1qqX5nA1f78ElivvzpGjRwqHlRj6INNT+qpqxjledOjVSbLPGL3YWy5atdK+FQ6xChUMsT548kYtN9lzz6Fxc4c6DLVu3+fOgZs0qofM02cTuiV4Laml9jfmJ+S/hAtu377Apk+e418cqq16jotWrW9Xy7TnHp06d67enbp29wzRg/rylttm9viuULxOq8jlj+nzb5Y65lqU2zc0bzB8+nR8Z5b8lS1ba5ImzrbZbV42aFaNMkTRo5cq1pi+9fmMFdZcvW22r3ftaSffaqVCxTMxlaYR+9kyZPNft/0q/vIaNasa1On2wvaVKFrfyFZK2Zd26ja7a7iy/7lq1Knn3WBsZbf4F85fZ6NGTrWbNStayVf0Us2r506fNt5Ur1lrZQ0pavfrVUv19LtrxTLFQN0DHWNMWLHSQ1XXnSbA/0aaNHLZ48QqbPWux+/m7w5o0qWXlyqf8Gbpt2w73frnINm/aGppd59CmzVv881q1KpuqmsejBftStFhha9K0dujnc2rL1nug9n/BgmW2ccNmd26VtabN6sQ8XzJy7OQ0c8ZC31GNll269MGpbVKKccHrSMeoevWKhJ9TCDEAAQQQQAABBBBAAIHcKaDrUz//PML9vTHXXZffbe2Pbm0tWmS/Dvxy59FjrxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE9kcgdpJlf5bKvAggkOUFdJOMQlVqmVmdVctevXp93Dy0zQpOKNRHy14CCmkOHjzMFsxfbLfedkXUjZ/hbuB6/70vQuFNTaTg3fHHH2nnnHty1HnCByrcOeD9L23ChGnhg33I77zzT7XWrZslGx7+5I/f/7aBAwfZhg2bwge7kE89u7zHOVay5N4Ay+OPvWyb94R5NPEPP/zuv/T44ku62bHHttVDmzN7gT355Gs+lPNC3wf8MP2nAM799z3nnz/33P9s0uSZ9vlnQ3zoNJiojAudXnnleS70VCsYlOz72rXrbdC3P9uwYaN8MDMYqQBko8Z17dJLz0xWDXbkyDH24QdfB5P578E2HHxwMXu+z32hcf37f+KCV7PtwotOt44d24WGBw/krOM0adIMvy/BcC3nxC4d7MQTOwSDQt/jsc+hhUV5oBD9wI8HOY+/fDgrmERB1/btD3f70jVmiPP773+z7wb9YuvXbwxm82HTJk3r2+WXn+2OX+yQrYJgr736gU2cOC3UIYDO2YYNa1vP6y52of5CoWVGPoi2Xk3Trl0rd76fYvIMb5GGo0aNs6+//tE2bUoKlum9scMxR9j57lxX++abn+xHd26Gn9P13fnUw53PZaOEvzXPP39PsK+++sEUVA9vJdz5f5I7tp1OaB8+2D9evXqdPfvMm3Za1+OtTZsW+wzhplgAAzJd4MMPhlqfZz/yodNgZYWLFLKHH7naunbrYKeceIsfPGJUf9epwN4q0nfd8aKNHjXJ7n/wSru4+0l+mnPP6uVeK3vfJ9/u/63pS+1Bt7wLLzrRP478b+iQP+3xR95y59by0CgFA++9r4d1Pf3o0LDgwYD3BlvfPgPde0pbe/GVO4PByb6/9urn9s5bg+zsc4+3x5+4Ltm48Cdv9f/Gnnz83dBrVOOqVClnd997qV9++LQZfRxs73kXnGD3/u8yu+P2vjZ08Mhk75GHt2lsj/bu6UOvkesJn/+aa8+wHpc9YrNmLvSTXXhxl2Qh2a1bt9vDD75pX37+q/tdbkdoUQULFrDTTu9g9z1wRdQORaIdz9DM7sFvw8fY44++7YO34cNbtKxnD7lj26hx9J9Hmnbe3CX24P1v2PBhY8Jn9SHZu3t1d+8Pe4/x7NmL7NQuSedcMPG1V/UOHtrQH/u6EHWV0POMPIi2L/r52KBBdXvymRtNIelobYg7Zn37fOzC3/OSjdbr4sprutlllye9v4aPTM+xU7C7190v27Bf/012bhx5VHPr8+Lt4YuN+vizT3+2Jx57J8Xv1u2PbmlPPHV91FBy1AUxEAEEEEAAAQQQQACBHCowZswk6/vCO/vcO12b1DW/jLQPP/zGfnDXksKXEaz3pJOPtbPO6pKRxe73PArIPvnEqzZzZtLfM/ob6Kj2h+33cuOxgKzgE4/9yOxl6LOPm296yH9u0+OKc+3IIw/N7FVmq+Xfecfjtm7dBnvt9cey1XazsQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAukT0L3sX335gy921vuJu2Lem65iY9f1vM9q167m7t29PrSSB+5/3ua7zMrrbzyWagGw0Aw8yDECpMxyzKFkRxBIn0AQkC3iQjL7quqZviUnn1rL1jri2YJtj+cyWVbmCSjA9/nnQ11g6HEb/J0CPUnh7Mg1qvJrn+ff8gFZ3cRVs1ZVa9a8oQ8rKkwYGfCMnH/jxs0+qKeArOavUaOKHXpoEx9QXbNmnb3y8vv28cdJQa7IeTW8X7+BPkyoyrFHH324CznW8aHKiROn25tvfJwszNKyVeMUgVsFcPVV7pDUqxlGrltB0/5u3Qp5HnZYU1etroGvQqrKsM87D1UnjWwK6D76yEv2008jfEBWAcYj2rZ0FWgr++2c5Lb56afeSBb6LO+q4mr7wqvzBducnooSClwqECkXhTZVFbZly8Z+mxUyVVB1iAtDp9Yyss+pLU/VWJ/o/aoPKqtapI69gsqqbKsbBH/5ZaQPFEdbhm5q/Mjd3KhtV6C1lTu2lVz1Wc03dsxke/CBF0yVg6M17f8br39o48ZN8fO0bdvKn7eqrKp9fP65/jHPdwWrg/UWLVrEDnXHXtutc3fEiH/t3l5Pmc6BWG3c+Kn20Uffuk4Oilkrd56rirACu9qfoUOG+9faF+51d4g7HxVc1TmiNs2Fn192rwVte2TT6+yll97zAVkFfevWrWGHH97cu6xzoezgJtDI+fRcr9/XX/vQ/u9/z7rKl+OjLj/afAzLfIGvvxpu/9frVR+QTQpSt3LnTAPb5qoE335rH/vqi9Rfr5Fb2OmENtbl5OTheT3XV/XqFSIn98/Hjplmt970rKsUutWO73S4/yrlArLqROPO2/q4c/bPqPPFY6ACso8+1N+fk23bNXXvlU19BV2FdW++4Rkb9dekeKwmtAy9tq69urcN+W6EC7oXtc4nHhEKfGpdV7jw69q1G0PTRz5Q5dKrrnjMB2RVQfeo9s3d+0vZ0GS7d/9nt9z4rH384Q8+IKvw5okntfPhZgVmP/n4R7vZjVdnKOlpY8dMt2uufNwHZHWeHNW+hSm4qeCtxnU77Q4Xuh8VdZFr3HG8/NKHQwHZhg1rWEd3nAsXLmgKhd5603P2mAtIB00uOl+OOXbvzb6q8hqcR8Vc1df9aeH7oo4SdNwVUHZvrzZlylw7+4y77btv/0ixirf6fW3XX/ukD8iqyvJhrRvayacc5X+PWL58jT+PdD7Favs6dlu2bPPH9tdf/vHnY9Vq5f2xK1OmhP3x+zjrftH9yYLcketRgPkuF77W60bznnNeJ79v2laFgu+47QXeeyPReI4AAggggAACCCCQawVKlSphTV3nZ7G+ihUrkuNspk+f4wOyur70yKO32auvPWrN3bVVWvYR+OefiaFriSP++Cf7bDhbigACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAcBUa6e+nVVqxYbSrCRkMgrQJUkk2rFNMhkIMEFOBQmEqtSJGCmb5nWsdmF4yJV9O2K3ihMBkt6wqoZ47vh/5mCt7psVrNmlXt1NOOT7HRmzZt9gFZhT8VjL3MVUENQn063oMH/+p7A1FwL1pTSLLvC2+bKpyqCsQVrgJrUIVT5/svv/zpK59qew47tKkLLFUPLUYh3h9/+MMHYm++5fJkVSQUvH30kRdt8uQZPoB5wp4qmqrEqbbiwRds7tyFrrLc2a5aaevQMtPzQOHc07udYKee2jE026pVa1wQtZ8tWbLcB4xvi6i8+9Zbn7oA5RpTuFLbrN5PgrZs2UofoNX3334bbSe7ChZqzVz4Vl/jXaBT4dvSpUvatT0vCmZL03cdCwWZ5ayKpJdedpYpfKumcKrCkf3e/Ng++eQ7K+0q4SpgGa1lZJ+jLScYNtkFUlUlQ/t09z3XWtmyeythKmT8+GOvuOqoP5oqw+ocDJqqWChoqnaZ25d2rjpDvnz5/PMli5e7MOl7PiD72WeD7cYbL/XDw/8b8+8kX2H4scfvcAG1veFo3dD2sgub6pdyVWbVcsPb2LGTfUBW67r22gutRctGoSq3qobw9FOv+/W+++7ndos7vtHau+987s+7o446zI9W+FxB8uHDR/mKyBqo46sgtJpeB3+74/Oqq3qrc1bB3vBw9Lx5i+xTd9zUjjn2CDv77JN8OFbP9fr68ovv7TsXcv/MVTzu6Co7h78WVWX5jDM6u9f677Z48TIfSFdAuevpJ/jQsZZBOzAC//4z1e521WDVbrz5XLvyqtNNFWTVFGy8684X/ZcfkMb/nnj6Bj9ltwV32ITxM+3xJ6+3s8/Z+/4VbTH33vOKXe2qo95+5973nHXrNtp11zxpf46c4KuXKkwa76bQaO9H3/aVZnu56q6qbq+mdT/1xHv20QffW89rnrDPvnjCqteo6Mft73+fffKzD6g+/exN7uddex/I1TJVaVXrUoXS69z3d95/wHcCEbm+b7/53Ve5Hfx9H6tbb+97ezDdIw/1c2H4v1yl8BL2yut3+wqz+n1Ir3GFQ3u6gO5PLsz60ANv2oMPXxXMlur3hQuW2dUumCuvntefZdded5b/PUszaVivu150P4OH2333vurDpgq5Bk0/F6656nG/f591V3YAAEAASURBVE2b1bGXX7vLKlZM+rmgUOgH7w/xx7f/m1+7n3UdfDVahX/7vnSHbXG/GzZtdL5flJ4r+Lm/bcH8ZXZVj0f9dquqr6rYBje/r1y51gddv/n6N3vgvtetU+c2oY5iJk2cbU/2fs+v/oILO9sdd18SOl/8z77nPrLXXvncnn1qgF3S/WT3syLl7yOpHTsdH4VYJ06Y5ULPh9gb/e+1+q6qrZqCz6ose61znOpCvNGaztl33vrWn09vunlVOTZoa9dssLPOuMsHbd92Id7LepwWjOI7AggggAACCCCAAAK5VqBR47oWXL/LLQizZ83zu9q2XSvfmVtu2e+ctJ9BMFadOE6dOst1krTWX+/MSfvIviCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQmoDuv1dWQZ+ZKSSrz9Dq1auZ2iyMQyAkkPLu3tAoHiCAQE4VUJhNTZXCgkBYZu6r1qF1xbMF+xDPZbKs+AgorPftNz/ZnXc87oKJP/iAbD0XqLzt9ivt/+67IVkINVjjzz+PNIU6Fba74opzQgFZjde5c5oL1irgqQqd0dqvv/7lA4kK5vW87uJQQFbTKjx03HFtrctJx/gQ0cCBg5ItYuLEab6Ka6VK5VJsm4I1CjFeddX5piqhmdEOcyHG8ICs1lGmTCk777xT/OrmzV2UYrUKMHY9vZPdcMMlyQKymjCoGKvH0arQanhG29Ahw2z27PkuXF/Yrrr6/FBAVstT1eh27ia8Ll06eOe3+n8SqnwQub6M7HPkMsKfj3GhUzVVgQ0PyGqYQrE9XVj0ppsudVVv91a5VGCpf79P/LYqSNzeVQ8Ofz+s6M4HhZ91/owfN9XflKblhTct45ZbeyQLyGq8KhgfcURSgEmB1PCWtN6Bfr0K5qoKbHjgtESJ4narC0UXLVrYBRCn+oB2+PzB4/ZHt7YgIKthqiR7yinH+e3VOrTcICCr8dqP1i603KBBbT11Ydbl/nvwnyoCX3PNhT5UfdFFLkjpquoGLX/+/Nap01F+GQqLRVbWVaXGU1zI+6mn77Fz3XmrgPuCBUvsxb7vuEq8fXwwO1gW3xMr8IwL9OmYHXvcoXbDTeeGArLaClVyfeqZG61C+dKZvlEnuDBieEBWKyxRopg91runX7dCmqlVV92fDaxbt5o98NBVocBjsO77H7zSGjaqaQoYvtT3k/1ZRbJ5VYX60stPtdPPOCYUkNUECuH26XubFSp0kP3150T70QVdozW9Vt//8KGoAdnRoybbu28n/QxTOFkVgTW9mr63bFXfej+VFGIe8N5gv55o64gc9tgjb7uOF9b5sPOtt18YCshqOlWSfeLpG1216wbuosMae/P1L5PN/uGA711nAFN9aDc8IKuJ1KFJjyu7hirGfjBgaLJ5M+PJ44++7Suttj68sT/uQUBW6ypbtqQ99Og1Vs6d86rGOmTwyNAm1K1X1fq8eJtd0/NMuz/ifNHvId0vO8W9V+dxv9Nsc79vzA/NF/4gtWMnI1UXVrj22T63hAKyml/L1WtU9rEqAA8fNsZXOK9Tp0qygKzmL1mquL3w4u32zPO3WNdux2gQDQEEEEAAAQQQQAABBLKZgP6WXLRoqe+oLNqmq4NB/X2fWtu157qpri2lpSmAuWrV2qiT6trS2rXro46LNlDXbNev3xhtVMxhu3btsg0bNsUcn9tGrFmzzqZMmemv9QbXsUeOHJMuBp0n6uwuPS0jx07nojqXTE/TOZXec0SdCWo+GgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQO4RGDHiH7+zF7r72ZUtUREvskO55/jv755SSXZ/BZkfgWwooJtu1BR8SFTTuvZ1I096tkX7EO/gbXrWz7QpBXR8f/rxD1/1NbhJpqmr3KnwXN26NVLOEDZk3p4g4UkurFis2N4KdcEkCp50c5Uqx7vQYLQ2e1ZSYEWVXgsVil4dWaHFwd/9avPnL/Y31wTBouA8UvBPlVsrViyXbBXVqlc2fWVWa+IqW0Rrdff0eLJx4yZfNVbB2aCpamnXrp2Cpym+y/uXX0ba/HkpA7YpJk7HgFGjxvmpFSotVapE1DlP79bZhg0bZbopS9VKo1WTzcg+R13ZnoEHHXSQfzRmzGQ759xTUlRnVKXWyKZQtrZR4VKdW9FarVrV7MWXHvTni6aLbPrFu0qVvcHb8PHqsWbkyH991dbw4erZRq8PHc/ICrPBdLJVpd5/XaVahaQbNUp5jjRpXC+YPPS9rOsxR2HXhQuXpghPBxOVr1DW33C3auWaYJD/riCsgrX6itYUfFWPPNp+vYYUSI9sMurc+Wjr2LGdr2Ks19s8dw6qcnHNWlWtmzs3VOmZlhgB3USo6phqd93TPRSmDF+7gqqqOqnqpJnZju4QvZOBatUruJB5KR++VIXNo9pHrz69P9t23Q1nR/19R8F+BXd7XPqwTZw4a39WkWxe/Wy59bak6qjJRrgndepWtTPP7mgKsGp/TzypXeQkLshe3apUjV5RddC3v/vpOxzTyo7rmFRFOnIBwbiff/rbBrmqtG2OiP6aDp9v7Njp/ul1N54TPjj0WEH4Licd6cOwwTkVjBw7Zpp/ePkVp4UqyAbjgu+qeKtwabTqq8E08foebM/dvS5J8bNA61A14Z9+fdlfMAl+/mu4Hp/Q+Qj/peeRTedp1WoV3HvyEps8aY47TjUiJ0n12Ol4qx3f6XA7rHXDFPNqwJVXn26vvPSpu0k85Y3GChyrzZg+32bNWuTe45P/XtKocS1fpddPxH8IIIAAAggggAACCCCQZoExYyZZ3xfeMV2TPOusLinme/KJ13xFz+f73JesU74UE6ZzwAP3P++vQ7740kPW782P/TU0dTz4wIM3W7VqlfzSdE3w++9/d38/TvPXVzSwQsVD7MwzTkx2/eaHH363Dz/4OrQFr74ywPSl9vobj7m/jfJbsJ9nuY73KrplfPrpYFviOlBr2bKx3XBj99C8Y11HdH+6YOakSTP8dTNdZ1UnbJqvePGU12zVQd9HH37jt0/XhnXNsom7HqzO1tSRX7SmjtU+/ugbmzlznv+8QNebju7QxnfcFm363DJMgVhdy2njrmHLfMD7X5luANB12GjtQ+f+w/e/2f/+73oXaN5gAz/+1l+309+3DRrWcR0+trQ2bVokm3XLlq12Xc/7TNfsr3EdQ7799me+czudexUqHOKHn+Q6mtR1wMimbdO1f13vnT59jv+7WoHsTu56fOfO7ZN1Phi+nitd55PvvPOZTZ0yy59Tuu6pDhTVQWRwfT58Xbr2OHTIcJswYaqtdNcv1ZGfPHQTBA0BBBBAAAEEEEAg6wlMnTrLpk1N+jwyI1unjsFpCCCAAAIIIIAAAggggAACCCCAQCCgDmFHjxrvP5vVPef6vGvo0OH+885omYRgPr4jEAgQkg0k+I5ALhIIqlQpIJKoFu91BfuQqO1nPbEF1Ov/Tz+NsO8G/eJ7g9fNLQraqapljRpVYs8YNmauCwKqRQveBZMpvKplR+s9PqiYqn7lZ8yYG8yS7Lt6xVelUN30s3r1OhdSLOnHN3Q3DSnsqOoIqnrZrt2h1tZVRFUFUgWDMrvFCuDqJjTd1KQbzHSjU3hINnKb1Kv+0qUrTMHHRYuX2ai/ksKs2td4Nf3SGVQQrVEz9nGVmYKj06bN9jfIRfuFNB77HL5fRxzRwn50NwSuWrXGVzA+9tgj3DnY1FQdONrNVpo3OGequLBneCXX8OXqcXhF1chxqZ3fB7vwodpad2zCW7Bes/9inquaPgiLK7gdremmyGgtrzvH1QoVjB4Wz79nvF63qTV1RKB1r1y52t+QNt0dT92kprav80o3Xx57bFvr4G5w1A1+g7792ebMXmDPPvOmD8yfeeaJpurStMwVmOvCfApk632kZq3kgbrwNderXy38aaY8btK0dszllnHVPVWhdMXy1TGn2Z8RqhAaqymQqjZr5kLbunW7r/Iaa9q0Dq9cpVyyir2R8zVomLTOWMFcVZyN1YKAatNmdWJN4ofLWyHZYPrUJl6+bLXpS23pklX+K9r0BQokvbcooBnexo2b4Z82bhL7Na3fAeP9e2D4NgSPtf06lxTGrR8lxBpMp8BpEDoNhkV+V69js2YttIULlvuv0aPUacESP9mWzVsjJ/fPUzt2wfFObbv080pB6jH/JgWPw1fStl1TXwFXx+r0U26z013FWFUrbta8TkJsw7eFxwgggAACCCCAAAIIIBAfAV3j7N9voO8UsE7dGqbAYREXCFTTdTiFd3WdUyHSDse0sXXu+qBuQH/xxXdNN5IHHehVd6FaBRUVmFTTcg7fE46MvOY114Vav/7qB3+dsW3bVq4zoL1/gypI+2Lfd/31UF23UchV14N++220D0Y+9PCtyYLC6szwxb7v+Out6uBN13W1fb/8PNLPd9/9N/qArt+oPf+tdpVrn3+uv+98r3371n7/1BnbZy60Kw9dT86tbaQLxOp46Vpq0aJFfGBVoWVdS9R16lht3Ngp9q279qbz5Ljj2tpiF35WsHqCOz553d+Zrd3yIpv+5u3jOrXTshu6Y1fSnTM6ngpc65y7+55rU3RQOnDgIB9e1ba1atXE8ru/9Se7MPWnn3znrj9Mt9vvuDLFdditW7e58/htf32q9eHNbLHrpFIBW52r27dvt+7dz0y2aerQ8Lln+5k6NyxbtpQd467xLl+2yv744x8fsN29W1f/aQgggAACCCCAAAJZReCrL3+wr9zfF/vTpk6bZXfddc3+LIJ5EUAAAQQQQAABBBBAAAEEEEAgBwmoYNbmzVvs+OOP9J+dHeE6hlVIdoT7vChaJiEH7Tq7EieBxCXk4rTBLAYBBPZfQGFBNQUGE9Xiva5gHxK1/awntsCaNet9xQBNoZtkbrv9ijSHYzXPhg2bXGh1rR76Kpj+QZT/FPQKKlmGj960aYu/cUbDdGNZWpoCpUFIVmHUG2+61F55+X1bsWK1/frrn/5L66vfoLbvgaRNm+aZ9nrJaBBXNywNHz7K9/Yvw/AWeQNc+LiMPlYQN2iVK0evnhoavycku84Fj6O1jO5ztGVpmG4U63HFufbeu5/7sPMXX3xv+lLQtHnzBr5iq8LQ4W3OnIX+abWqSdU5wsel9XG+DISog/WucjcFPv7Yy/tclc7VRDVVedCNj6oYohsUI99ndV5FDktt2zT9kUce6oLnrXxQ9vPPh/ob7T77bIjd06tnarMyLg4CQUCylgvIplbBU6G8zG6JCEhG2we919SoGfs1Xr5CGV9ZVJU7p06Zay1a7n+l47p1Y3cioG2sWzcplDxxT5XfaNsda9gKFwBVSy34q/H16iWtQ4HRfbXx42eGJjnv7F6hx7EeLF603HfeoJ+Ra9dudBXLl/pJGzaqGWuWhA0fPz4psKtjXqhQyurf+9oQhco/+fhHd8P4bzZ58hzb5ToLCG96z48cFj4+tcfB67FeKqFtzR8rJFu0aGF77Y177Mbrn7YF85e5KlFD/Zf2U9WCTz2tvZ3ivuL98zW1fWIcAggggAACCCCAAAII7J+AOihbtGipPd77Th9sDV9avzcH+msoZ5zR2U45tWNo1Jo16+yxR1+yb7/52QcVFUxVoFVfRYoUMt2kfv4Fp8X8gPiffybaBRd2tY4d24WWqQfz5y2y1179wEqXLmH3/u9636FgMME3X//or7N9MOArX300GP6z6zRR+3DDDd2tabMGwWDfmaIqxf7lOvHTdaHw9uefY+xcV0G0c+ejQ4MVzlRwduiQYbk2JDt37kLfOaEqvB58cFLHe/rAXyFZfeCfWkj2m29+sm7uPDk17DxRR3fqDLKfu1Ze3lWIreaC1OFNYWZ1GvnU071C61NIeYA7xjqub/X/xK6+5oLQLMN+/csHZHWcb3LX0YNrz+qET2HbiS4kq2kUag1vun6t3r3vuvua0DwLFy41VVIePmyUne0qFBcpUjg0y0svveev83frdoL7O/f40PBgf3TtUp3z0RBAAAEEEEAAAQSyhsD+BmS1F/tThTZrKLAVCCCAAAIIIIAAAggggAACCCAQTwF9Nqamz8rUqlevbCq0ps+j1q/fGPpsy4/kPwSiCPBpYhQUBiGQ0wWCSpx58+ZJ2K7Ge13BPiRsB1hRTIHixYvaYYc1Nd1kpd7edWNM5xOP9pUkCxbcd0glPLylXuxTa9HH7+1BvnnzhqlW/gyWrbBJeFNF0Ecfu91VRZhq//w9wfdor+CuetzXl6qU3nTzZSluWAtfRiIf/+wqMgx4/0tfYUEBdN0oVblyeStXroxVdgFVd0+Tr+QQ323a+36xz9efNkDNVStIVFMQs1mz+vb36An+BrKZM+e5KgWbfKUBVRvQeAVpg8qywZapMkciW7jdEUck/QKf2vqDMHdq08RjnP5weObpN2zBgqRKiSXcjXI1XcXg8uXKupvpylrt2tX9OadqD+lpqkY7yFWZVvBWTTe+tUnDfqdnHUwbXaBEiaJ+xLp1G6NPsGfo9jhWnE51RQdgpELdumE3VnBQFUiCnyuxpknvZm/btiPVWVQdXK1ABkL2wXtq8BYba0XB+OD9LtZ0Gh4efD+t696blFObZ9eu3X50+Ft8sF+pzZfZ44L9zci2rFy51i696EFX9Wiu38xy5UpZ0+Z13QWWCq4ScyVr2aq+PXjfGzZ61OQM7UZwfm3bnvrPnNRej6ogPPTHvq4q0z82dPBIvy1Llqx0NyL/67/eeXuQvd7vXtehSMkMbSMzIYAAAggggAACCCCQkwT+Hj3edYY0K+ouqYLpaV33BvCiTpSggeece0qK6426RvPXX2Ndx28NkwVktUmlSpWwiy/uZs+766/q6OwCF4hNT6tVq2qKgKzmH+YCi/pb6rrrL0kWkNU4hRXVY/OoUePs4kvOcJ0kJl1X1TUfBRZVBTe8qZrpUUcdZtGuC6vjvfCArOZr5oKXuqapIKQ6MFQnibmtjRjxr9/l4MN+PWnRopHrAKqgDxufd/6pMTtwrFOneopwsTx1bigkq+MWGZLV8i+7/OxkNxHob+pz3fmo6rCj3etH49VBltr3P/zmz9OePS8KhV01XNelr3XDbrj+Afvll5EpQrJa5kUXn55sniru2nV9F+qeMmWmzZ27yBo1qqNF2cqVa/xr1u9PWOBX48L3R89pCCCAAAIIIIAAAllDoGvXTvtdSVbLoCGAAAIIIIAAAggggAACCCCAAAISUNGwCROm+c+GatVKKhaj4foM7QtXqEkd8p5wQnsNoiEQU4CQbEwaRiCAAAIIpEVANzz1vO5iH6770lXvHDNmkg38eJB954JxnTodZR1dufvwHuEjl6lxuvlJN0EtWrTM3+wVOY2eq6d4VUuIbKpeG9xI1aVLB185IXKatDzXTV2HHtrEf2n6lW57FEbVDT7qzV/VEnTTz4FuqpwbBGR1U5+qL0T6Tp48M+6bWaJE8dAyVeUi/HloxJ4HC91xVCsZNs+eUZn6TZVjVbFAXwqjzpmzwIYO/c0FZ8ebbjar5YKeulFPLajAEIRCM3XDwhau4Klag4a17aqrzw8bc2AfDvr2Z/8aVhUJ3QSnyhWRLTzgGzku8rlcv3WVLP52oXPNV7hwIet0wlH+Rkg9pmW+QNOmSTcZKkS3fv0md+NjUmg2cs0LFy6PHJQlngeBRwVZY7XVq6JXqw6m17yzZi60xk1qBYOSfV+4YJlt3brdh2jrN6iebFxGn8ycsSDVWadPm+/HN25SO9Xpoo1UcFPbPH3aPHeX8lHRJvHDpk934107xE2/r9asWV0/iW58ffKZG2MGiqMtp0SJYla9RkWbN3eJTZk81/XWVTbaZAkb1syFSNUWLljuOu3YErpxOy0b8OrLn/mAbLnypa33k9fb0R1SdmKQnvfAyHU2cefgFFeddoaOXSptxoyFqYw1f4Ny5xOPcJ2RJFXn0fnw/ntD3O8FQ1ynHjPtoQfesL4v3ZHqMhiJAAIIIIAAAggggEBuEFClyyDgF7m/+TLSaVHkQuL0XIHAyDbXXc9SU0g2Wqtbr6bvBE4h1fS2OnVqRJ1F19B0rU8dCUZr9dw6dX10qVtn7T3bfPjhzX3HaK+99qGddlpHCz6o1t/z0QKyWm6duin3V8MVlP3xxz9s8eJluS4kq86r/nIf6Ot8bdWqiTh888/dtWr1lq1quy1bNg5GJfveuEm9UKeA4SNa7Jl+njtukU3h1iCcGj6uQIH8/prgD67DyPnzF5vOz61bt7njvsJat24W9bjqOl8VV9F4iTt2+rs5uJ6j5ZYvX9b9bV4kfBX+cbPmDXxIVudwsB3zXDVjNYWDw5fhB7r/wqsVB8P4jgACCCCAAAIIIHBgBbqenhRwzWhFWQVkg2Uc2D1h7QgggAACCCCAAAIIIIAAAgggkBUEFILVZ2fhHctqu1SUSiFZfW5GSDYrHKmsvQ35s/bmsXUIIJAZArrJQDcsKDwS7wqvsbY3tZBLrHlSGx7tRonUpmdc5gtUdTfD3HBjd3/DlMKyunnnC/d9yJDhPph4QuejTVVno7Xq1Sv7kKxutmribuyJ1ha4G3NiNQUeVW1gzpyFGQ7JRi67rAvunnPuyS6AWsg+d79YqWeSyBt9IudJxPMZrpKntkNVG1S9IahOF77uxS7EGu+m9ajqgG6Qmj17gbuBKSlYFbkeVWVctDCpGmnNsF5cIqfL7Od6j9DNeddee6E9vXGzTZ48w5+TQUi2xp6wqgK/qiarkHS0tmrVGv9eqRsFY91cGW2+WMOCcO48VykhK5xPwXZO21MhVn88xArIpuXmy1mz5vtw7LhxU/yidVPk8S4of6ILsEe7KS5YP9/jL1CyVHGr5qpgzp+31IYPG+Oqz0QPVX7z9W/xX3kcllilanm/lEWLYt/0O3LEhH2uacqUOTFDshMmzPTz16tf3XQjaDza8uVrbNWqdVamTImoi5s4MamSUazgbtSZ9gxs7iqb/vvPVBs/bkZqk9m4sUnjm7eI/j4dPnOFimVcRxOl3M9QVWyZa02api+829RNr5DsxAmz7LiOh4UvOvRYgVUFmmWs9WVWK1+hjK+iumLFWps0cbYd3ib6DcQ6Pps3bXW/kxQxvU7URv2VVCH2sstPjRqQ1e+xM13gOqNNoehPBv5kwfGPthxVbZo9K33r0Ovk7l7dfQj+2acH2PBfx2Spny3R9pNhCCCAAAIIIIAAAggkQqCVCxf26HFOIla1X+uIdl1P1zfV3n33c/vgg6+jLl/XlBRYTW+LFhDeuXOXLXTX8vT9qit7RV2kPoxWW7J0RSgke6zriO7ffye5v1Gn+C9Vua3foJYddmhTa96iYdTKp7GuDR3krh+pBevxT3LJf7qGrl6xS7iO6xRWDm+l3DC1H77/PWZItlKlpOsn4fPpsa4dqzO8IHwaPl7hVQVlo7VKlZOWp+v0Csnqu843VaTV8Y7Wdu3a5adZ5arB6pp60IKqw8Hz4HsQog4/3kGYN1h/MG3w/eCDi/lri9u2bQ8G8R0BBBBAAAEEEEAgCwgEIdf0BmUJyGaBg8cmIIAAAggggAACCCCAAAIIIJDFBFSgTa1UyRKu6EnSvbbhm6gMw8KFS61KlQrhg3mMQDKB+NwNnmyRPEEAgawuoJ70deOCvvLmTczbgNYVz6Z9oGVNAVUcuPmWy01huS+/GGqTJs3wVQXUA32HDm18WE43TYW3Gi7kqoqTg7/71ff2UbZs8up3On8+dtVpY7XatavZX3+NdZVfR/jeQ6JVOZ06ZZZ966plNm1W3/ciEgStVS3277/HW0vXS30nFxCMbOoJX61QoYKRo0KhqjWr16UYl1kDgiBX8eLFogZkdXPRaFc5NVbLX6CAH7V+/Ub/HhDrhqho87dp08KHZPVLqB6rAnBk+2TgIF/BT1UEVAUis5tu4Pv0k+9s2rTZLjTcLVS1Iny9CnArJFs47BjqZrBixYrYRhegHeCqBHfvfmb4LP7xpInT7Zln3vQ3jT39TK+4hGSDCgqbNm22IYOHWZeTjkmxXt149rqrwlHYBbSPca+Zai5EntktOK9KlU7+2gzWq9exrGI1vb6+cZVjp0xJCh0qUHxcx3Z2kts/VfilHRiBI49s7kOy/9frFRd+ru2rfoZvyZh/p9lnn/wcPijNjwsWTHovWbpkVZrnSc+EjRsnVX+dOnWezZg+3+rWq5Zs9sGDRtjKlWuTDYv2pM+zH7n39jauIk6xZKN1Pj/V+z0/rGmz9AVDky0oypOHH3jTnu97W4oxo0dNtq+/HOaHB1VPU0yUyoBTTjvK3ur/jf3+2zjT/nc5uV2KqYcO+dOHojXi1NNS/kxLMYMb0LxFPXfD7V/2Qp+P7ZVX77JoN02/9srn7uf5bL9MeQat1aENXDD+d+v3xlem7atVK/n7lcKlPS592FX0nmIXdz/J7n/wymBWv558+fQ76W5bunSlVa0W/cbe0AxpeKDtkcHDD/azTz7v7X52J91oHcyqAG2XTjfY2rUb7cVX7nS/kyRVFw/O51gh3j9+H2tr12wIFpPu78E5pmOn7QsqwYYvSOejKhtHawPeG+x+RxppHTu1dtW+T00xSYM9lZCLFiucYhwDEEAAAQQQQAABBBBAIHsJbN+xw29w6dIlrWHD2H+vHnRQ8r93MrqXSZ8TJIVg27RpnupigtCmJtL2PfDgzTZmzCTXodNEmzFjrv05coz/Ukd7t91+ZcwOE1NdSS4bqR6v1datXW9PPvFa1L3XTQDqgCpW6DTqTHsGZrTz0v/2BKN3uA6dgrav8yPPfnxmEx6YDdbHdwQQQAABBBBAAIHsIZDeoCwB2exxXNlKBBBAAAEEEEAAAQQQQAABBBIpsGjRMlu3LukezXfe+SzmqvXZmoqg0RCIJZCYdFystTMcAQQOiEBSIGGXqeJjEIzK7A3RuuLZtA+0rC2g4KpuhtINUipxr5t5FJRV7/P39OqZbOM7ujDdX3+OsQULlthrrw6wS1xgUcFGNYU5v/zye9/rvXqZj9Zb/DHHHmH/uJuxFJR81oUar+15kVWocEhoHQrs9us30FX3W2MKAQYBWU2wds06Vz1vlqvephBWTVPIN2iqMvr5Z0P801atGiebTwPr16/l92/06HHW4RiFsIoHs2bad22jXrdLXeWG34aPsiOPOsyF3ZNeD7Lp9+bHfptibYCOi6pUqHrqsF//MlV9CPeINZ+GK0Q8YeI07/WqO04XXXS699L8ulFr5Mh/7ccf//Dbc/XV5yfk/UX7Mnv2fH9+9O/3id1519Wuol5SGE43+U124c7hzklNVUSCpm2+6uoL7Pnn+nuHcoeUMZ1HCveqqbeZt9761D9u0bJR3I6t1nvllefaCy+8Y5+4cK+O5dEuCBtUqVUlwc/cOafQt/ZNlV0T0Zo0rmezZs5z4a3h/rxWpYmgKWD88ktJYcJgWPj3la5KxJNPJt3Ep/055ti2dvLJx4aOQ/i0PE6sQK//u8y99841hWHPP/d/PhB4dIeWrrqH2T9/T/HBxkMPa+jefyeme8NaH97YBx+/G/SHnXfBCb6CZ7oXksoMdepW9UHxLZu3Ws+rn7DeT91gLVvVt5Ur1tjXXw23p554z/UGVc71ChW7co7CnmvWrLc7bnvB7nvgCj+9Vqnqur0fe8fPW658abvp5vNS2ZL0jVK4XaHRBo1q2kUXn+jD+LohVcfg9lue95WpO3Y63I7t2Dp9C3ZTK8za8/qz7OUXP7X/ueBzocIF3c+AZv79Q79njfhjvN1790t+uddce4b3SstK7r3vchs7Zpr9/ONou+vOF+2eey9NVgn3i89+seef/dD/3njMsYcmW+QFF53oA7aq6qvj9FyfW6yh23c1BZFff/ULf57o+fkXdNa3UNP7nvZJ1XE/HPC9fxy8F4YmSueD/7v/Cm89ZfIcu+v2vnb3vd2tYsWyfikKdMtNAdlDDilpndxxCFr7o1v4+RT2beMq0Oq8CNofv4+z6699Kniaoe/az7PPPd4++fhHu/eel915Udh1CtLEB4W3bNnmwtPD7e23vg113hC5kqVLV7sbzSf443SYe802bVYnNMn0afPtGVdFVk0B5rT+TA8tgAcIIIAAAggggAACCORigdA1ta3boiqsXr3vzpmizrgfA2u6zgTVOnU6ynWwc/R+LClts+p6a6VK5Wy16wTwcld9Nz1/U+g60OGHN/dfWpuu+6ozu8mTZ7oOnkZH7RwubVuVO6bavHmLjR07xe9st24nuL8RU35kK0+1UaPG2rHumltkW7x4WeQg/1zXate64G2DKEHrZctWxuw8UTcgqFXf02mfOrdUa968ofW44lz/ODP+C9an9bdwnVlGNn1GoE4H80cxipyW5wgggAACCCCAAAKJF0hrUJaAbOKPDWtEAAEEEEAAAQQQQAABBBBAIDsIjBiR1LGsMgAndE75GekG91nR0KHD7U+XNznr7C6h7ER22De2MbECKT9xTez6WRsCCBwAAQWvFMTatm2HFXFhjkQ0rSueTftAyx4CdevW8MFFBVG/cJVlozXdjHXjTZfZww+94CvQ3n/fc75KqX7RmT17gQt0/Wf6YGWKW8Z0F4SNbLo55sabLrXHH3vZB2173fOUKeSnqgUKxi5evNwvo1y5Mnb22Sclm13BTwVslyxZbg89+IILBpV01ewq2Zw5C30P/pq4YsVy7qa0Dsnm05PDDmvqf+HSzTu33Pywrzbb/dIzfZXVFBPHaYACRNpmVXNViPOjj741GetGIQU71ev+cS74qgq50ZqsW7Vq4m6sGmfvv/+lDXSVXw9xAdGHH7k12uTJhul1d8MN3e2xR1+2Oe64PPxQX1NVYFWUnekClkGP/xdf3M2auRunEtXOPfcUe/rpN9xxTjoOVapUsJJuu2ZMn+Oq8iXd5NiyZWN/M1f4NjVpUs8Hfd9993MfWP3chblr16nuQnir3Y2BSTdBVqpU3rT8eDbZ6Dx5q/8n9sEHX/sqyTVrVvE3eSkorhu+dEPiGWecmCzsHc9tiFxW23atfJhYNzPeessjVrlyBX9cFUDWuRXcoDlnzoLIWf1znRsK+55yynH+tRd1IgYmXKCwC1G+0f9/dt7ZvWzmjAX27tuD/FewIa0Pb2SPP3GdHdfh2mBQmr93Oamt9X/zK1/ltW3ry3w1k0d797RTTj0qzctIbUJ1hqHA5XXXPOHejxfbuWfd48Og+v1F7drrzjLdTPrOW7GrjJcpU8LuuvsSu82FUxUAbdiwhu12P0+mueq0aqoy+tob9yQLRPoR+/FfBxdC1uvhaRfi7eOCpQr2zp61KFT1tr6r+Pnc87e4P9DzZGgtt95+oXuvW2lffv6rXXn5I969WfM6Nn7cTN9ZgRba9fSj7bY7L0rz8hU27v/Ofe48udcvV8tWSLlSpbLuvFno1rfCL0vh3m5nHJNsudrXl1z12bO73eXPsVNPutWHUqtULe9Cp1Ndhwy7/PvZ7W576tWvlmxePTndLU8hWQWfB7nA9UHuBuufhr1i5colryifYsYYA1QJ9vV+99r559xrg7793X/puLuNMAVn1YoXL2JPPXtzsoq5p3c7xga6AOvECbOsXZseflu1D+PGTHe/R6xzP9Pq+HnlnNH28KPX2Nw5S2z0qEnW/aIH3HtlMXezck0ffFUF2bbtmrrOHBrYS30/SbGKy3qcat8PGel+R1pk3U67wx2bQ6xh45o2YdwMW758jZ++du3KdsWVXVPMywAEEEAAAQQQQAABBBCILaBrfmoKKl5wYddkAVFdU1y+fJUfr2uTiWq6RqQ2bfrsqCFZdZKkqqNl3fW4eDUFIXV9c4G7tlhtTzgyfNkr3LUyXWtVKDZoul6k62663ho0dT7Y/dKzXAdMvf21xy4nHROM4nsUAV2fVUeGCr+eetrxUaYwa9igtj38cF/XMdY/UUOy6iDwNDdvZLh5wvipfnk1qiedT+ELV8eC6myyUaO64YP93/BanpYVnAfFixe1smVLuWszC2J2uqrr6uqwMnIbki18H0+q7+m4Utt9kjtvIpc1adL0fSyB0QgggAACCCCAAAIHWmBfQVkCsgf6CLF+BBBAAAEEEEAAAQQQQAABBLKmgD6L/XPkGB98ffDBm61EWKGn8C1e7D6T0mdJ6rBXGQAaAtEEKMUYTYVhCORwgeBmFgVNdENEZjetIwi1xGtdwT7Ea3ksJ/MF1Gu9Kshe6m6UitYUTr31VgVTavnRuvlKFWALFSpo55x7sulDk9SaKoDeetsV1s6F/RSaVU/5490vQrrBS8s40YVcH3r41hTVLXWjz113X2Ptjz7c3+i1atVaF1iZ7G8207ijXKXW++6/0QcGI9evm4XuvudaHyjUjTu6MUwVaTO7nXVWFx/uLFq0iG3ZstXvp8KNxV2o+PrrL7FDXXg3tXaVq/J6vKtGITO9NhUuVXWBtLQk5x6uAl5Ly5dPVRrX2XQXRlVAVjfFXXb52b6qblqWFa9pFGzVMQx+4VXQVL8E63gowHuWC0Zff8MloWqt4etV9VgFVnVjod6rFMJWQLZYsSLWtm0ru/d/1/kbwcLnicfj9u1b2xWu+oJuxtR6FTJWtWWF/lQ5QefViV1SBrPjse5oy9Cx+7/7bvCvP53LqqI8duxkH5DVsb7FvTZjve/qddL7ibv8ORlegTbaehiWeAEF8b769hl74ukbfHhSQTxVflU49r0PHvKBwYxsVaPGtezDgY/5MKHOGb2HqBppPNvxLpT59HM3+6qZCs3qxtEWLevZHXddbLfefkGaVtW1Wwd7vu9tVrr0wa6jhbmhgGyDBjXs1Td6JavImaYFpmGiZ9w2X9z9JB+EHfXXJB+QVSj2hM5HuNDyvVakaKE0LCX2JL2fvN6udpViS5Qo5t1VxVX+Bx9c1K66ppuvuht5M2nspSWNUfXXt9+73wc1NUSh6uHDxviArCqxPu7WqUBxtOVqvf3fvd9OPuUoHzxdsmSlD4LqwoWcX3n9br+90bbhggs7u+293odid7lAraqqjhu7fze+Nmla2/q9/X/uZ2EDv0oddwVkFRpv46q3fvrlk3ZU++bJNqda9Qr2uRuuCsk6VqrOqmC1ArKndT3a+r99n6mTif1pChS/8tpdpnNSVY5V0VbVYXft2u2HKdAeKzyt83fAx4/4arQFCxbwx+WnH0b5gKzGnXV2R/vi66ddJx/l92cTmRcBBBBAAAEEEEAAgVwnoOtGutalQOxvrvJp0AHcxo2b7dVXBkT9GyizkcqUKeWvz+j65NAhw1Os7rNPB9udLoT67Tc/pRiX0QFt27b0Hzq//PL7/lpQ+HJk8+ADfey2Wx/11yE1bonrkPDmmx6yJ594LdRBXTDPOHc9Sa1yZf4+CUxifVfwVe0I5x+r1axV1cqXL+uvk6sCbGSbMWOu/fDD78kG63rtgAFf+mN66KFNko0LnqjzPl2HDG/q4FLXiZs2re+vpwfj2h15qD8vXnt1QOg1EozTeXpvr6ftmaffDAZl6LteiwpZ6zrz99//lmwZ2p8PBnydbBhPEEAAAQQQQAABBLKmgIKy0e7rICCbNY8XW4UAAggggAACCCCAAAIIIIBAVhBQ6FWfBzVwncfGCshqO9u6+9nVgs/Y/BP+QyBCII+7eTlxXWBHrJynCOxLYNmy1X6SkiWL72vSTB2vl0nSl+35/p+/GUA3tetLFamyW9u8easPx6mSbGZv/4YNm90NF1vjRqRKmomqgBu3jWZB6RLQLzorXKUGhT4z0gv9tm3bfaWHTe6GtvIVyvpqp2nZAM23cuVq27B+U7rm07IVUNq4cZMPVCo8mqimiha6QUo3S+lmomgBpljbovc1hZF1M6CCjultCuhq/Vvc67u0Czmnd/3pXV9aptcxWLlyjX9/q1q1ot+3tMwnC82nG/9Kly6ZrApGWubfn2m0ToWzFcBSaFbvcQey6XUwz4Wu87rzWDc06vyg5VyBhQuW2THtr/E7+PeYd10V5vT/zrVu3UZbs3qDValazldRzQwt3aC8c+duX30zo8vXvi5atMKd14e4bU15s+6wX/+1Hpc+nObFX+OCqre7wK5an+c+tL59Brpwe1t78ZU7/TCFPqdNm+duGN7uqjFXslIuzBjPpk4O5s9b6oOShxxS0lV5qejeR2K/f5x/zv98ePX+B6/0Id5Y27J69XpXlX2Z7di+01eUVdA6rW29+/m5YP4yv8+NGtWwwu73zLS2pUtWuUq/u32V1Cd7v2uvv/pFWme1t969z3V2kfKmYlVZ1fYUKVLQ6tWrlqx6bKyF62faxImzfEcQqn6rThPi3fSamTt3ieVx/xo0rJ6u931t34IFy12HDuv8eVW+wt6qTfHeTpaHAAIIIIAAAggggEB2ExgzZpL1feEdO9J1etejxzlp2vxvvv7Rvvjiez+trglVqlTOd2amztjWb9jkO1R7vs99/8/eXcBJUf9/HP/cgYKEhEiIpIJ0GYRiK2J3d8fPQOzf728rFnZ3omIriNiIEhZKgyCdktJxx//7/t7NMrc3e1zsHcvx+vJYdnfiOzPPnb3bm533fGIX3nv77c/sKxfi08X6ggu2Bcs9/IgDTBe321S7/bZHbbqr2vr8C/f6i/3FT6/wYq97n3aBxfm2yy71/RfCunjZGFflUyFCfTl89909rWLF7WKzfvLJV/bJx1/ZpZedYXvtlfPCQPlZv0HfD7fXXvvAb6cqjNZvsJM/VqYvmXUhOm2Xti9ojz7ysr9wn47dtmnTzLavUtldoOgvX6E0PT3del53kTVp0tBPvqnlv++Cv5/3/86uvOoca9++ZbCIlLhfsmSZX49kf0elY6o33Xi/P5Z8/wM35XlMV6+rXt+jjjrIjju+m1+fYD/UBR5/cq9RY7eftG61m81yIdeRf44zHd87++zjTRcIDJqO5V5x+a2mY6Y6fq1joe3aNXfHgqq4C5+NsZkz5/pj8f/7v/+4v6M37ls6bvrCC+/4K3jr9W7RYle33tXd37Uz7ZdfRvp1v+HGS9zf3Y38ooLlaN/97//+Eyw+dv/998Ps9dc+tNNOP9oOcRdyDJq+E7jrzif8xS8buerG2q/mzv3H72favkXu+KmOZet9Q0MAAQQQQAABBBBIbQF9hh0/YbJfSQVkdZIrDQEEEEAAAQQQQAABBBBAAAEEEIgSeOH5d2zoUHf+rvt+V9/zJmr6/ksX8tV3V/r+VkXUEn0vtanvYxMtg+EFF1BWTIWQdNP3xMqyZN0s9rjgvRZ+jrKFn5U5EUBgSxZQGEvhCoVXy5ffNmGFwKJu47p165MakNX6FLWSV1G3ifmLX6CaOzFHt8I27SM62aegTfPVrVvblToo6JzmT0oLn5hW8B4KN4cqgOpWmKYPIIWdV8tTeFJVT1OpVapU0QWaCh74lYVCvrqVdFOFEN1Spel9EFR0TpV1Yj0KLzB+/FSrX692wuqlY12FTTVV2SxMQFbzqqKpbsXZkhFUVDA2KhxbXOutaqGquFtcTYH6XZvU87f8LEMhYTUFavNqqkyqW2Gaqsq2bFW4ba5dp3C/y/Jaz5o1q/kqtXlNEz9OwV5VlC3OpvdL27ZNCrUIrZ/CuzQEEEAAAQQQQAABBBBIjsBRRx9s692FMH90lWQXLVriv1RVZc9TTz3KFATdHE0BRYVwP3bh3XHjJlm/ft/61Shbtqx1cldIVrgw2cch99u/o/vSMs2GDhthv/02yoa5e7UaNarZ+e4L6T32aO2fB/9dfsVZpoCxgruqZKovoxW81LHCc8870Xbe2R1jpSUUGDIku4qsez11XDKvpv1RIVmdHHDscYfmmH6vju38cby3+3xqkydN8+P0GnTu0iFHQDbcv44nXHnVufbcs318wFavnfYnhauPP+GwHAFZzaf100kJO7gQ+ajRE+2774b511vDFV5VGLcwx+LD66TH+j7gmmvOs09dleSxbr+aMmWG/yK9XfsWfvn33/esW5f4uXiOAAIIIIAAAgggkIoCvqKsHZKKq8Y6IYAAAggggAACCCCAAAIIIIBACgko+Pr776N9lqnD7q3yXDOd364L7up7zF9/HWW6mCwNgXgBKsnGi/A8pQSoJFu8L8eqVWv8FcV1FfrChiE2tYaqRqagbLKafrkpyENDAAEEEEAAgbwF3u4z0O687UXr1Lm1PfrEtbmCrEuWLLcTj7vBpk6ZY4cc2tGeef6mvDss5WMzMze4arX5/8xSxl3xSEFYtahKsqnENXTIKDvr9Fv9Kn33w7NWr37uSrqptL6qwpuRmZnvVdKJ4jqZm4YAAggggAACCCCAAAIIFEXg33+XxyrGFqWfZM+rip/r1q2zWrVq+NBgsvuP70/H82e7iqQKyFasWCF+dK7nK1asdAHjpVanTk1XGTfr7+RcE22hA4qrkmxROYJKskFFYwVdVXVVr9f220dfyCzqStoapv2rbt1a+d639HovdFVdVVVWgdviaBkZGTZr1jy/z+s7IRoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCKSeAJVkU+81YY0Q2GoFFDZd70IIOull6dLlucIzRYVRn8kMyOpK9ARki/qqMD8CCCCAwNYisMceza1q1Uo2+IcRdni3a6zbYZ2sWbMGPtg5ccJ0e7/vN/73vyp49rz+jK2FJeF2KmRZXCd3JlxoMY9YvnylnX7K/7kKLH/7Je2xZ/OUD8hqRRU+1j8aAggggAACCCCAAAIIIFCSAonChSW5DlHLUli1JJsuqqlqpPltCmbmJ0yb3/6YruACquqqkHJB23bblS9wFdiSeL31XVD9+jsVdHOYHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEtmKBslvxtrPpCCDgBCpUKG8KUKxevdY9S15QVgHZrD6Tw6yTPLSuNAQQQAABBBDIn0CTpvWtT9977JIL7rW//55lr7/aP9eM+3RtZw8/1qPYKsrnWiADSlRg3dr1PiCrAPDuLjT93Iu3lOjyWRgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFDcAoRki1uY/hFIcYEyZdLdVd63sxUrVvlQa0bGv1a5cgXT1eIL01Q5ViWzk1lBVgFZraPWlYYAAggggAAC+Rdo1GgnG/jNEzZ82Ggb+eckmz9/kavKXt52cxVld9utvu2yaz1TgJJWNIEjj+pqzVs0slq1dyhaR0meu5L7TPfqG7db23ZN/Oe7JHdPdwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwGYXSNvg2mZfC1YAgQQC8+Yt8mOqVq2cYIqSGay3SdbNsu83WGZmpmVkZN0UKt3Sm7Zl5crVbpsy/KaoamuFCuVcMLVMvjZN861cucb3ka8Z8jmRlq91ISCbTzAmQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSILLFmyzPexub+jit+Q0aMn2uTJ06xz5w5Ws2b+Lti1fv1669//O6terYp13Xev+C55jgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBRIQAUWlfXSLT093VQgMetmsccF6rCIExOSLSIgsxevACHZ4vWN6n3VqjW2Zs3a2Khtt93GypXbxleWVWA1qDaXmbnBB2pVMXbNmnW2du2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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/airbyte_7.png\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "8111e68c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/github_3.png\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f69ea94e",
- "metadata": {},
- "source": [
- "Sync your data."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "b176a01e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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LllfZ19LCDuG7/2Ybl7q/yj4mZSD79+mDzS23PO2Ce9VAuP/+7V3gre/tuXTpCnPqqQNdg2GpB5xBC7z66kfuaDWc8ovjh9ueRuPNXXf1z6AzqJpDvXbAg2bANQ+Y0+w1oKGoK1o0lNwR/3dRJLh3q60a/z979wHmVNG2cXx2l6ICNqTIq4BYEFApVqyo2EURG4L1U6TYOyp2FLD3hhUBGyioqK8FFRWsgFSxAYKoFF8EC3X3m3uyk5xkk91ski3Z/c91ZZOcfn452cyZ85xnzGGH7eUy39Wtu6FbvDL7HnHExVEZbtNdb2WZX40nPrj35JMPMVO/GWG+/+EV09JmzKYggAACCFR+AWXPrWxF2zT868XlulnV9fdcN8O9XXiDknq6+PiTx83ceWNNj56Hlat/VVhZdT2G9NlxHlIVjmD2AQEEEEhfQAG4yuarYNxMFS3LLbMwgDhTy013OdX5d192tAWlfgRV92Nn/PtfhYN7b7utn/nu+1Hms8+fSrvHr9Q/EeZEAAEEEKiMAi+88K5Za6/3JlOGDXszmcmYBoEKFbjxxqEuuLdevY3MddedbcaMvcOMfP4W1xPq6tVrTa9zbjVjx06o0G1k5QhUdYEOu+aZ626onTC4V/u/jc3qe8NNtU2z5lUyfKyqf8TsHwIIVBOBKpnBd+aK8gnwVWCvD+79+Z9VZr59JCrapoMabpZodNrDX3r5ffPOO5+75RzdZV9z990Xmzp1QkGWGjh79lxbSb7NzJ27yNxz90gb6HuUyxSQ9oorwQKmTJnjtmLvfXax3Xm0ca8zlaG2EuxexjZhn33bmueGv2U+/niqC/J9xnY/nY7T4EHPmh9+WOi279oBZ5m+fY8PN0orU8T7739pzut3u1lpA8sfeXi02f3piuuCO2OIgQVNmRw67jTozLOONpvbLN0UBBBAAAEE0hVQJt9dmtQxu2xZJ91FJTV/df09/+mnX4xuflPp2vUA06LFf9zrWmXc44ZbSRX7U12PIX2MnIdUsYOZ3UEAAQRKEBg4aYmZtXS1C+SNl1VXw/QY/d2KIhl9kw3+bb1FbdPNLqN1/dpxt0bLnr1stRtX3HRxZ87AwOr8uy8+2oJSP4iq+7EzubAdUT3snXraEfQ8l/qhxJwIIIBAlRZYunS5vZl4Yom9pq5c+Y959ZUPq7QFO5f9AgpWHznivyY3N8eMfmWIadOmRXinDjigg9mm+Zamf/+HzLO299ljj90/PI4XCCCQOYENbbjQ/51d09QubGKYZzP1vvDCWvPz/AJTz4Y27LRTnjn8iBqmQYMcs8kmOeaoo2qYhx9KP1Fc5vaAJSGAAAIIeIEqdwvGAhtku9IGF5Z1CQb3al1NbfbY4oq2SdtWVuX99750i65Rs4bN4nthVHCvRrRqtY25bVA/N83//rfSTJw4zb2O90cnkF9//a1R9tV0M72q67pFi5a45S1cuNh1ZRdvnbHDlFVNXZdNnfqd0fbEKxq+ZEnoofH56/PD7//9N3SxI3Y+7fu0aT+Y6dN/NH/+WXwguF920GDOnPlm4qfx7f6xn++33843M2b8aJRROZmSqk8yy46d5ogjOprHHutvdIz4IN/gvsVOX9L79977wk3Srt0Opl+/E8LBvRqobuaUzbdXr65umg8/nGzUDZ8vf//9r/usli370w9yzwq2UdcpyRZvPtker7/9tizhbP6zDG6Dn3h94LjR8mKLTkD9/OpGT8eW3i9atDQ8qT/2YvcnPEExLzL1fdOx7bdTAdaxRcekAvxl9ccfK2JH8x4BBBCodgLlFUCbCuyIcszim87veey+Zeo3TctN9XerpDqkfiNDv+NLwpu/Pj9Sh4z3G6oJf/11aVL142C9wS9Lz1/Z7gNnzZobXmfwhX7Dv/nme/c7rbphMqU865AlbU9lPYZSNSrpGJIH5yElHRWMRwABBKqegAJrFdyrMnrOCtPj9YUukDfenirId8DeDcIZfRW02ypOwO6AjtHTaB4Nixfcq/W7ddp1azv0GDgxUp+Jtx1lMSyd3/3ybAsK1slWry56cVLtYeE2lDhteH5bNY3qFLQFpX80pXPsLF8eavNSEgFf1J6njMoal2xJph0x08eObioMnn+obVHtchrmbzhMdvtVT1W7qc4t5s/71cgglaLl+ONf9dp4xVnNnufa0eO1l8abh2EIIIAAAukJ1LM3gagMe3ZciQt6ZfQHRvUVP09xM+j/vq7T6vdD1ziLu36pa1z6jQiWn3/+3ejaXXHFz6d1xSv6Dddy49XL/PTaHyWr0jWsxYv/5wfznMUCX3w+0x2nrVu3iAru9bt0/AkHmZr2mrWOzZKu2ft5Yp9Vr5o65Tuj3iJKW/y8uq6voPnYErx+HDsu+N4f3zqG4xWde6jtNxNxF6qr6lrvl1/OcvXBeOuLN0z1ObVN639A8Hvqv7vx5vHD1HateA3NH+86u5+O58opsGOrPBs3lOM2bo09NR48eLWZ822+PcctMIt/LzDj319nf3fWhjd+l13y7Pcy9LZevRyzsQ361aNGgrSRtW2IlJ/Gr0dzB+cNL9y+2GyzHLPllqHtCQ5P9LpWLWP+859c09xmFk60DYnm1fANN4y/Dwp8btYs1954Wdzc0eO07dtum2u22irXJtGLHuffBdfng6r9OD3LyHt55+D4ugHz0mxbcBm8RgCBqiuQ4F9x9u6wMumWdYkN7v3YNoTpUVLRtm1dQiBwSctINN5XfOvY5W+4YfwsH/vu2851/aVlbLppvahFKfBg+HNvmXvvfT7q5E1ZgI/r1slce+1ZRhkGSlPGvfGJDTYebr7/fkF4Np1s9rOZXvv2O95V2sMjCl+oAn7lFfebz22lPxik22HXHc2AAf9n9tyzTXiWjnudbYINjOPHf2Xate3pxt94Yy/T69yu4WnV4DvwlqdcYKsPnMjJyTEHHrirue76s80OOzQNT6sXqmz7Zd0ysI9p1rSxueaah42ClJXZbe68MeHp1ZB8801Pmldf/TDc1ZqmabNTCzv8XKNtj1dS8Ym3nNIMO/zwUJBv796Dw0G+qWTy1fHiT1Tqb7FJwk248KKTw11d165dWBu0U997zwvm4YdH2bvBNjVTvxlhhgweZl566b1wkO6WW25h+vTtZs4559i4y5b5TTc9Yca8+lHYXBMqkP2q/qeZQw7ZM2q+Lkdf6hrAlcX4pZduixqnCwz/d9Ytbtjxxx9o7n/g8qjxzzz9hlEXMirvj3/YfPjB1+aWW56Mmuaooy5x79XFzLdzXo4aF+9Npr9vo21jzkUX3uUuOh155N7m0ceuDq9Wx7u8n3rqtajA3qZNG5kBtjuco47aJzwtLxBAAAEE4gv07NDQKLNuSUVBwzvbRzLTFresab/+bX5Zscb8Z2PbelGGJd3fc21apn/TUv3dSrYOqTrhkUdcHKV668CnjR4q/33nfnvX+rbh8aqv3XnnCNcA6weqrt2t24GuDqnf/mDRTVSnnXqDG/T++w+5HjYeeWS0u3jfqVMHM2JkqM6hCXz9dMKEKeFFqL7dsePOZvCQ803DBL1/VEQdMryBMS8q4zGkTUzFKNljSMvnPEQKFAQQQKB6CfisucG9VqCvHsrOG5vRV0G6esQOn1WYfdcH8Wp87DTBdWj6VwLBxcFx5f063d/98mwL+v33P8yee5zliK644lRz8SWnRHENHvSMGTp0rBt2510XmVNOOTRq/Pnn3eHqcfXrb+LarZ59ZhxtQVFCpXuT7rHTs+f1LnDjmGP2N3fceaG59JJ7jOrQCsRQ+27Llk3NwIF9Tce9d467YaVpR8z0sXP++XcYn5hDGxdscz700D3N089cH3ebgwPV7q8e+Z6zbfea3xe1n6pXsfPOi0684Mcner66/4Nm+PC33ejYNnQFcNx8c3Sbq4x33721ufW2vqZ1620SLZbhCCCAAAJpChxtr9WoZ8zPPpvh2qFir1sGFz/suTfd2//7vy7mvntfCI4Kv9ZvhoKFdS0uGLSr3iiVTf7KK0+LStyjGQ8+qJ+b9qe5r5onn3zNZlYd566LapzqRaedfqRR3Sq2+PkmT3nONGq0eexou64HzWuvTTAPPnSFOe64TlHjlZDpLtv29u67n4eH67dnr712MjfYa7077xxppwtPwIusEPDxCv45dqNr2eBeBfgqkFbPpSm6Xq/r9uPf/yqcXExtqUqCtulm0TEQsctVlmzVrYLJGHRdX9dXVd/xMRTr1683++93rmvXffOte03bttvHLsq9P9peg1aPt48PvSbqmquCYvtf9aBbj78xayMbx3H22ceY/lefEXdZiQYutuc3DzzwkhlhMyIHA+W33XYrc+llPVwPdfHm1c1lN9ww1Lz4wrvh+RT7oRiKyy/vGfWdr107+lqEApJvtPOq3u1jK5RE7Jgu+5kbb+rl/ifEWyfDKpeAsvL68uefBWZ1nFCqGdPXm/vvXWNqFYYY1aqVY9auLTB9z6tlYy9C+SLHvLrWjB1TNMHduefWsrEweW4V775rs3YPDwUL3zaotlGwqsq55/xr9tmnhjn08Brh4N4VKwpsUr315qUX19rjy00W9UdBsCedXNPGBuWFA3vtV9IsXJBv3nprnfn8s6LJzqIWUPhG2Yt32z20fXffucb8u6rAnNy9pmmxTa7JtYMV9Dxr5noz9PG1Nu4ozobY5XTsmGeOPqamadIkYplvVz95cmj7lyyJzHfoYXmm63GhmBjt39DHI+dv2qSBt9W2/2NCy3nj9XVm9KiQl8YpWPq++zZw26X3l1y0yt7QGlm2hlEQQKB6C1S5DL6/Bhq5yuKjTTW4V9tSltu2g23IVFGD39NPve5ex/5RFxhbb93IPWKDEc484yYbxPtI+CRPDYR5ebkuiFOBv2ecflNUA2LssmPfv/32JNOnz+BwcO9mtjKt9esutiFDhpljj7m8yF2iqugeesgFRtleFdy7xRab2jtyGrhF647NHqcMcHfx+XWpEqxHsPhhNWqGfqg1TneyndJ9QLgCqkq6ThRUGVVQcPeTry32zr6f5/9q1Birk4XYokq0bF588d1woOkGG9Ryr9Vt3/HH9zejRo2Pnc2k4lNkISkOUJDv449fnVYmX2Xo1UmDykf28wp2URjcLJ20+WMu0cnZQw+NMvff/6IL7tUxoqIseTdc/7h5bliokSK4TDVKnH7ajbbC955z1om+TkZUdHfvOWffaj6wQbjBcnDn3d3bL7+YVeQ4/njC1PCkH3/yTfi1fzHBBuqobLVVQ7Pjjs2sW16R407jdextGHM8ani8ksnv23//+5m7sKHjWV3aPPzIVVHd/N1885M2MGl4OLhX3ysV3XV9bq/bnHO8bWQYAgggUNUFFIibbNl5y+ggzkTzDT6qedrBvYmWXRbDM/F7nsnfNO1jKr9bpalDqn4bW3/Uen0dMjc3cnqkxl7VZ9WYqVK3bqi+oXrqiBFv28bYgbahq2ijlpvY/tH8qvfGy8yljFu+fqrp1Tiqz0PT6rf9iMMvcr1C+GX554qsQ/ptCD5XxmMoFaPSHEPaf3+8BC38MM5Dgiq8RgABBKqOQLwMvH7vXKCvgnALg3f98NhnZeFV1l099Lq4omUNnBSa1mcOjp1egcXlWTLxu++3t6zbgtSG07JlM7e64M1Ufv26KcuXjwM3W2mYglEnTZruRh940G6uPZO2IK+V2nOmjh31vNGv3xAzbtyn4aQPag9Tj2pnnnmTyywWu4WlbUfM9LGzgQ2UiD3/8PXG2rYNuaSi4OQDO/V1AenaF7WdNmkSai9X++mg255x51AlLcePV9ICH9yrYJBgggy1c+v8zre56nPbZJO6rg39iy9mmsMPu9DdSOeXxTMCCCCAQGYF1DZ0So/D3EKHPVv02phfmzJ3fjt7nmnR4j9m333a+sFFns/rd7tLlKPg3u2339oF56pHTmUtvf++F4u9NqT1DxkSCtZVMLBuRlemz3vved7cddeIIutKdYDaYxQcqeBeXedT8p4TTjzYZn7cyNXHdF24pOzBqa6b+cpeoF37li4IdIr9nONlZf7ooymuTqfkTLH1peK2Tllkzzj9RncTla49K5mXgnP//nuVOeusm03wum/scnSTX69zbnVBtwoI1vGma8iqU44Z85E5zV5/9gnIFE9wzLH7u0VoXLyiBA4K7lVQsG7e8kXtu8d1vdK27/7kzkt69TrWHGTPLXSuoUBdJZdKtqi9+BgbV/GUjf9Yt26du/FKwfa6/q7ekC+wMQxKdhCvXHbpfS7QX4G++v5rf9XGre+y6pGJiuqZarv+6KPJLrGcfPfbr51RJOYrr3xgzrB1RtVNKZVf4Pff8sMb2aBBjtl3v0gMTXiEfTFlynoXNKvA2b//DgWVfjYpcu2jffui8ykD7U47R4Z/NjF+0G3bdnnm9DNrhoN7td6NN84xhx9Rw3Q/JRQMG9wWZde97PJaNig4Etyr8cpo28xm8u3Tt5YLorUhIqUqW9j9v/CiWma77ULBvZpZGYLb2X27+NJa7nXsAvfZN8/06l0rKrhX0yg4WIHDV1xV254zRTZk+rSI946FwdF+mY1t5mIf3KthseN32D4vHNy74Od8gns9HM8IIBAWiFzBDg/K7hfLbAWlrEo6wb3aprLcNt3t5e+AU2ZTVbrGjp3gAmpL8lCjnQ+IVMaKiZOeNF99/ayZMnW4u4tM86sRTxfLkykKxtWJo7p4UKVY2ctmzHzBPa6zGUNV1BWFGvOD5Vl7J6kaLXUSO/qVITZDxnDzxZfPuExqqmSronjbrU+HZ/lm2gjz/Q+jw3fSnXzyIe69hp11Vhc3nU5ce/a4zp14KrDxkUevMtNnPO8eDzx4uWukVGYGnSRq3fGKKvuaV3feyeWjCY+5ybR/2k91HaJyVf/T3fjZ375knnxqgAuAVYX5mqsfDjc6a7pUfTRvpsphh+2VdpDveeef6DZHJyPHHnu5ufyy+8wnn0wtNtAldvuXLv3T3G4DXwbe2sfo8/zhx1fMQw9fGT6Wb73tGXdS5efTHY59bPbhr+0xpsBeHU/6TGbNesGMfP4WdyevtkeBq2oY8OXgg0MBvvo8Pv98hh/snoMXenQHpC4K+KKuij63d0urHFS4DGUV1jGmzM6+6DjVsCn27uSSSia/b/KWh/ZZmTR03AUDqZ94Yqx5/LFX3SZdcMFJzkrOn3/xdPguad2FrTvCKQgggEB1Ehj+9eKoQFxl6NXjzXPaGGXhVaCuHhoWLBqmafxwP52fR5l3Nc4PD06n1346jddrP62fLriuL+cXH/QRnDad1+n8nmfyN037kOrvVmnqkMrOq99s1U99ue++S8N1SJ+RSr1JKHOb6nvqjeHFl241M2e9aL786hlz1VWnu1k/tTcGXXhBKIO+X1bwWRc8Oh+yh3nhxVtdPeeuuy92o3VBpEeP6139VL1bqH46w9ZPXa8Gt19gu3eq5W588j0I+GVWhjqk35bgc2U6hlI1Ks0xpH3nPCR4BPAaAQQQqB4CLtNuMQG1CvItLnBXAb2axhcfFOzfB599IHCiwF5N23qLotmBg8soq9fp/O77bSrvtqCv7Q34vhcqbUNs288ntk6nC/q+qM3Sd9HbmbYgz5L2cyaOnbfe/NRl8n3uuRtdD1qzZr9okzKE2icV6KEs0cGSbjtiJo4dtSfr/ENBESrqUUvv9Xj00f7BzY37WucEartWO6jaIqdNf96dk0z4+LFwxmK1/em4Lqkow+Ojj7ziJlNg72WX9QzPovMenf+onVvZ29TDmdrzZ9o213fefcBefG7usutdc80jRll+KQgggAACZSNw6qmHuwQuo0aPj7quGFybD/493Qb45RQmzQmO12sF4Omartqd1FvVhx89artlP8+Me/Mee6PMPW5yBQvGuyldI3XD+ou2Peu11++yr883o0YPNnffE2rXUnBwMIOoW1gKf3Rd6+KL7nbJoHrYwGb95jzz7PU2e+Gl9vdupFGyIgUjD4uTCCiF1TFLBQjoxiT1NJBv024q2FU30enY0XGnGAbduKVgUwW/JltUZ+nbd4i7lquAWV3zHz7iZjP0iWvdMdS1ayeX9Cje8ubP/y0cZ6DjWRmndbwNG3aja+tr3Li+u4YfvBnwRBsQq/L6ax9HnTP45Y8tDPw9tuv+Uddnhz4+xiWpUg/J7773oM14e655bvhNNgbjCRebMHDgUy7ZlV9Occ+32EROCxb8btq0aWG3c6QZM/YO933+7POnXO/HMrn//peKLEJBvPpfoJvDdF1Y33/t79eTh7n5FacRzO7tF6BzoVNtD3W//bbMnNv7OFsnfN75qo1b3021dyvxVz/7OWjdlMot8MMP+fZYi3xOZ59Ty2aQrm0OPKiGadgoEpgaby+++jLfxiCExiiwdrPNo6dv3SYvHBT7++8F9oaMSHBrcHm9+9Ry45St9z2b5dcHEGuaQw+rYeNwIsutYZN5X3BRbXveFApjU3ZcZbl96IE15rNJ6+05SWjJx3atYXZoWbpQtx49a5o1q/V9XmdeGb3W3ngQcdl++1yzx555wc12gb//d3Ytey7mYtvNJx+vt+eca2yCurVm/rzQvipo+rIratnYltA+zJ2bb/5aGVru5tZL431p1Sp6+coivEEgl2HLHSP7My0QKOzn5xkBBBCI/JeoIhZ/2ROCZEozm2lTAbvJlnSDe7WeZLct2W0KTqdsqk88OSDcla+CFlWxatOmu7v7UXeDKWNnvLL3PruY++6/zDXqDbFBBc2aNXaTqbuVSy7t4QJuNWDqlDnxZi8y7I47hrtgXHVV8fjj17gGQE2kO/779O1m+vY73s1zr21UDN7d9YHNpqtykL3TTl2vqOFSRYEYagy9555LzE0393bDkv3z/Mj/GlXYVW6/4wJ7h9v+7mRBd3+qa2V1taGiu+tefnm8ex37R93VvDrmdtethjIbqyFW5euvZ9suAEJBz6rgXnjhyfbOoy1sRaamO/HUSa+KLiK8aTNL+JKqj58/0fNBB/azdsk/hgweZmrbbVXRyYoyNAQ/j0Tr8cNPOOEgc+2As9wJixrMn3/+HXPySdeaVjue5LqmHmntlbG5uKILKFfYboAUkK0gajUgd+16gFEAsormVwZmXyZP/jbcTc855xzjjiedcCkoXNlrH7EN4wp0/+efVe4OZD/fPvYY9wHwwROzRYuWuLsbdZLpj/vg+GBD/iGFWYD9MlN9ztT3TQEsZ515iztB1HdEFzf8Pmrb1Dji775Ud33q7kXHp4qykdxrT+J2262VOyG94/aSA5PdjPxBAAEEqoBAbHCvdmm6DcxVOfKJmUZBuiMnL7HD/rEBuA3ccP3xQbj9x80zyuirAF1No/eaR69Dj9Dr4HSadsTkxXa+yDxappajdfnlaVh5l3R+zzP1m6Z9Tud3qyzqkOqmTQ3OuhCiC+/77tvOZdhVg+iFF51sevY83H1U6l4w2J1b8PNT9oknbf1c2Q1Uz1GdRWWE7Q533rxFrq57v62Dq36quql6vNCFnPPOCwUpfDZphmu89cvMdB1SNwoVV3dUA3AypbIcQ9rWVI3K4hiqTuchyRwnTIMAAghUBQEF+Y7sspUpLnOuAnd7vL4wKptvMLjXBeYWBgrHC/JV1t5gIHCsm+YfsHcDM6BjpJ4aO01Zvk/nd99vV3m1BfnenII3b2sbfO9NqtfpJmndfBWsz/mMvspe2qlTB7/ZaT1nqt5cEW1BDz74crF1xmSz6mXi2FEAwTPPXO9uwleWN7U1q73L17O/mfZ91OeUajtiZTp2evXqam69ta+5864Ljbph1zmDiq4F9Lbt0b4oM15xRUFctxe2/ynBxw03nBM1uazUA4nKAw9cZnulO9Cdo6h9XsEkz4+8xb1funS5PccZGzUvbxBAAAEEMieg+knnznu4a2PxMoYq4PUNm6lTN4efdHLnhCvW9c833rjb/f/W9aNgadd+B5csR3WyaTG/nX46tX3pOm2wKMmSMoCqDW/WzLnBUSm9Vl1LAcQPPnSFGWSDj4PJa/S6+ymHuOV+MzX69z2llTFThQn07tPNvDxqsL1u+685wfZ6u8MOJ5rWrU52MQzNmzexNxI9aPbfv33S2zdnzjwz/v0vbdDcpuaeey8J97imBei4GTzkPKPlxiu6DqwkXk/b+qSOZx+HoGlVr/TZeoMJpHQdVctTRlslhAgWfYcUqKxy0omR76NPNqVrzz16HhacxV2nVczBpM+eCl+zjZogzptBg/q5bR5mrwP7uqCfrLut1+ka+bdz5kcF3mvbHrM3gak3O33HfI/Jmk/7rQzAqu/FK6+//rHLEq7vuwKUde3eFzndbZNYyF9ZihP9D/HT81zxAmttbsSHH1xjli6NBLMqkPT0M2qaIbdvYM8zNrDZ42vaY6Ro2Ng//xSYb6ZGYq/a2Uy8wdK+fWSeSRMj2X6D0+j199/lm8G3rTZvvbnOXhtZa/pftdoG/kem2qZFZDnK9tuqMPPtv/8WmDuGrDZvvL7O3oi43jz26BoXIOzn3DMmINcPT/T8118FNsh/tQvuVZBv/ytX2TiiSFDyNjbgNlhOOKGG7UElNOSd/64zTz6xxnmMf3+dTWC3xt74GDLdeutce/NlyMZ+9Wwyl8gyg0G7sRl7lQV4h5YR0+C006cHgIIbxWsEEKjWAtH/paoAxRrbVVdJRcG6PZs2dgG+yQT5ZiK4V9uUzLaVtO3FjVej8wcfPuKCLnfYoambVIGXuotq8KBnbXDBuebGGx4vEsSpYD81sqpbLlX0YstGNmBS5fvvF8SOKvJeFUZf8e3e/RB3khk70dFH7esGqYIbbARWQLGKAizV5YOW5Yuyk+pkdeedt/WDknqebPddRSesPmg0OONxx3WyJxMhq0QBzAocVVBGbJGriirCl9pA6NjSce+d3R1xyojss7+m4xO7/Nj3c2zlvbSPYAYT3TmpCwWlKf36nWDGf/Cw685NlXkVdV0y3gZrX3H5/bbbnrPtXUyJuxLS9GqsiC1Hd9kvPGj69B/Dr725TooUGBxbttmmSbhRI9iwrZMPBeaoBLtm8cG8u9hgdJ3MufEfT3HPwddqLNm7mK6OwjMk8SIT3zddeOrZ83oXyLzddlu57MUKQAoWHQs+YPvMM48OjnKv1Xhy2OGhQOrghawiEzIAAQQQqGICCrSNLQrQVfGZdXvYwF4F/frhfnoF8KooKNeP8/NouA8UVsBu6H1oej+txvtp3ASFf4LL88ObbBKqf/n3Zfmc6u95Jn7T/H6l87uV6Tqkq6/ZzG0qp516RFQDqN9e9dzgy5Qp8S+mH2u7cNPvbWzx9dOddmoR1XWbn+7CC09yPWp8OvEJ142dhpdFHVJZzoqrOy5f/pffpBKfK8MxlI5Rpo8hgfnPuTqch5R4gDABAgggUMUESsrmq919xWbsVZm1bHVUwK4CczW/AnVVFMyradxrO09xWXsVWKz5W9cvv3qi27CYP6n+7gcXUx5tQboYv3FhW8kE29boi+/JSTeY77lnKHAlXlvR7nu0djd7+fnSec5Evbmi2oKWLP5fsXXGNavtFeMkS7rHjpIwqHeNYFG77FFH7eMGKYttsPvnVNsRK9Oxo+Ba3bjfvfuhwd12rzfaMJLuqbh2+5dfet9cf91jbp4ux+xnk2BcGBXQohH+nEZt4EcdHWq7dzMU/mnYaHPXe5jezp41r3AoTwgggAACZSFw+hlHucXGu7b2wgvvuKQvxx57gAtILG797Tu0NHrEFrWfbL556Hrsd9/Fv+7r60ix8+p3SWXGjMh1u9hpSvNedTVdp43XfrbppqGbWr777ufSLJJpK5mA6ihP2Z5EdZOQSk3bVupjEX74YYF5YuiYUvUOMG3aD245SpgQ79p9nTob2kDdyDVmN3Hgj5J4HXronoEhkZebFR5z38ccc4qhUIkNuldvswsXLja6Tqv2P1+UCGyLBpu57LZKwOav1frxLVs2cwGy/n1Jzwrg1Tb7m9qC0yuoua7dZ93UOPenReFR8+f/6jIl77hjc3vO0yY8PPjCXxcPDtPrbwpvHOvb9/i43021o55is26rTE3QLu5G8qfSCCxcmG8GXLvKvDZ2nVkWCPTVBta32XOVRffmgbVtQHpNo6DTYFHWXF/ad4jEEdnTMJfh1o+bFJjOD/PPEyasC2cC1jBluJ01OxLT1axZZLnbbht5/c5/19ss05F4Ic2rwNznR651jx9tduLSFAUr//FHZHkKRfryi8j+KUuxL8qs26Qw6HnNGtt2Y7MIB4uCn+XpS4vAdgeDc3cszNorrx0LM/R+bYOVfWZkH8xc2zbz+ADjf/+1yQm/L92++e3gGQEEqraATXJe/crPNrunLz7A9+PCiqUf7p8zFdzrl1fWzzrhUWOpHspc+9GHX7sssxMnTnOVu6FDx5pfflliHrOZdRUkGSzqhkGBtd9+O89VAlUp1WvdjamSTDcLCtj1XbqoO4gPP5wcXIV7vTaQZVkVe98Vcu/e3cwll9zjAkR7nHKdy6iqoEwF9ypzghpxS1umTg0F4bayFdhERZVbnSAGA0KD0zZrvmXwbfi1b/jceuuGLoNBeETghRrwgyUdn+By4r1WhuPSFAVYD7KB38uXr3QV9IcfvtIGse5SmkW4aVu0+I+58cZe5vrrzzHTp/9gj7nJRnf36cKDumzr3/8hs8o29ifqYkUnPrFl++23Dg9SNl5fwuY2QF8nafGK/6zVqK87Kn3W2s72GHr33c/NzJk/uW59dDz5CzgH2qzRnexDmSyULU8nQjphmvBRKNhXGfgU5JvJkur3TXeI9jhlQPh7puzWPigluH3+IoaG3Xbr00Ua7jX898V/6MktSxdAGjbczL3nDwIIIFBVBZS9N17x2Xk1TsG4g7dsbqZv+Y/L0uunV2CugnlHFK3auMy8Cur1wcM9TAM3rebVML33RcHDO/+qTL6hTL/K6qt5p40LBRn76cr7OZ3f81R/04L7mM7vVqbrkD/++Eu4FwJ1RRuv6LdXNzdp33WTmDLvxpbmzeLXIX39tHXrFrGzuPeqg/ieBfwEZVGH3Hnn7VwPGX4dsc/xLgLFThN8X9HHUDpGmT6G5OI/Z183DVr511XlPMTvD88IIIBAdRJoZYNsFaSbKCBX42OLD+rVcI338862Ab4VHbQbu60lvU/nd1/LLo+2IAWKqL1HWbV8Vl6t27/WONXp1KuBgn7V65jaoL4qvPm988FFb0jX/OmUVOvNFdkWpKAbH8wTb9+3bBLqLSreuHjD0jl2lLU2Xgm2I/6bgXbEynjsqO36M5ucQdnZFthe+tTjWbDXs/wESU+etAE1/9je5RTMpfLAA5cXuSag4VNsBl8VBd/831m3uNexf378caEbVFwwcew8vEcAAQQQKL3AAQe0N7ouqUBGBc/5wEH9Lx/+3FtugaefcWRSC1bvVJ9+Os1dr9P/cV33nV147U4LWB+4XhtcYPME10U3qhO6ueTvvyPX7YLzpfpa2/bVV7PtNWn9zv1me776zd6UQsGWAABAAElEQVRgNM8tbr3vjz3VhTNfhQmop4xTug9w12p1A5GSGqgtTHEHShZ23YBHjXoZUN1i5PMD49ZRYjd++rQf3aAdbJBsouITeyUar++SbsxX9lkFuS9c8Lvt6XdBuCdkHxfh5z/eBvjeddcIM85mzx44sE846HXsmI/cJCeceLCfNPx8+eU9zZVXPGAeeXi0ecH2gKv933vvXVxPb6nEO2jB8pxov8+zbezG/Hm/up7fZsz4KZy5d13gu/LNN6FA6B13TOzUMsE4fy0+0Xhtiz5HlanfxE984Ubyp1IJrLb/tl99Za0Z8+paF0jauk2uTayXZ7bfIdfGEBiXqfaQQ0OhYyNHRIJZp9qg2H9tJt0NN8yxmXXzTG37M6BlbWOz7m6ySSjW6Kcf883i30PnG/F2+peFRcfN/Snf9uIdCqhVMK0vwQDfBT8XDXL9++8Co2y6qZRE2+GXtUGgGUf7JxeV337LN8qEHFuC2xfc7hnT8+35l5IERoJ6FSy88cahBX7x+Xqz4UY5NkYqN5yteLvtc+3ND6E1zJq13gS+zrGr5T0CCFRjgSoX4FvL5klfnaBBy3/O821DnwJ6fXCvf44N8s10cK+2rTyLAgN0p6ceP9uGv4suvMt88cVM193W229PMkceubfbHAV6XmErmWNe/TAczOu3UyeRy22Q5p9/JpfBa86cn/2srsKuAMviik4mfVGG3ho188yQwcPcSaYClOfPf9uMGPG2qywrC9rNt/Q2/q5NP1+iZwWuquFcZeutGyWazI4LBeGqMiwL3VmXTPnu+9C+bm2DTZMt6fiUtI7iuuOJnVf7efbZt0YF98bLzhA7X3HvFTDe1mbC1UPd9+gk46KL7jaLFi0xN9801HTr1iluIGpuCd8L3wCtdYfNt0r8eW4V+KzV+O0DfA86eDe3+VqeLt50sVmCfQbfAw/czeyyy3Zu+3SC9LU9sVPg+TeF2fs6H7x7cbteqnGZ+L7541orvn3Ic+bJpwYU2QZl5PPlnXc+9y8TPv9iv4sE+CbkYQQCCFQRgXjZc7VrPjDX7+aRT8x0wb1HPhGqp/QfN8+N8sN9Rl7/rJFvnhO5E17T72IDd/14P7/eK5DXj/Pj3cJj/uzRNJSlImZwmb8tze95Jn7T/A6l87uV6TpkMDOIryf67Qw+q86h3+Tg9MHx8V4H66fKGJFsKYs6pLpmK039MdltrbhjiPOQ4j6jsjiGilsf4xBAAIGqLDDaZtpV5t3iig/wVeCuMu+6TL1LV5uBk5a42Xxwr4J+ldFXxc/j3sT549ep6SpLQHBpfveDu1RebUEH216jFOCrYBHd2Ky62O/2hnDdNK4MvZvbAN9bbnnStZeqbqsASt30raJkA5kqmag3V1RbkIKKfGBRpjy0nFSOHc1TUikwkYvH6bQjVpZjR4k6+vYZbJQhLlhkoZ7ugj2fBcf71ytXRN9I+swz4+ImYZgTyFSn7paLK8H2/OKmYxwCCCCAQGoCyk6vHqUGDnzKDLM9ZPrf4Y9sQhpdN9W1rHbtItlCE63lzjuHmyeeeC18E7um29D22KprXwpg9Ne/Es1fHsMV2HmxvY7oe4bVOrX/TewNRLqGp3ocJXsFBt32jIsV0A1jDzx4uftstTe6mWqvvXYyr465wxx7zGXuWu1LL70bt8eC2L3XDf4qsQm2gtOpzTNRefPNieamG4e6OAQ/jbZHN5LpBsAPPvjaDw4/qw1XWXA/+2yG21ZNpx6UlexKdTKf4Tc8g33Rs+fhLsPw/fe/6AL1FZyvh9Z1sL3mrHiH4vYhuCz1wnbZZfeat9+aFBXHsdlm9cy++7U10+3NAMFeLDSvr681alQ/uKio18qArEQT/vzHj/zhh9BNXcW1XTcrjI3gxi+vlj3PCjz9yQbX6vHG6+ts3E2O6X5KTbPnXqHoUgX5vvfuOntMhc6rlGn2qy/zzX7759nj15idd8qzN2SsN+3bF0aj2l0vLnuvZOKFbmk74pUm/4mc8y1dlmCieDMmMSw/zuLiDHJLatIkN7zEZQm2Izi8UaMcF6CrwNwVKwrs73W+aW4zAtevn2MTxSg4OrQ87ffMmfk2c/J6F+Db1GYvrlMnxwbNRzxnTCsa2BzeGF4ggEC1FqhyAb51bcVo9ZqS/+n5YF4f3Oufg8P9MB0hGu7HpXrEaNsqqqgS9uBDV5g99zjL3bH/xeczwgG+l116n3nllQ/cpilT6dG2W7M2O21r795p4rLmHtL5/KQDfJWFwZf+V59h9t2nrX8b97lJTCW7W7cDbSDoge5k7nNbUdYdm++//5W7+2z06A/MIhsw/PLLg8InAXEXWjhQgcCqnCrzwAJ7912i4iu5Mko2uFfLUmX/29nzzDybMSHZkq5PsuspbjpdVFBw7/j3v3QnEsrcm25wb7z1KRuwuq9WYLmyP+uzPOywveJNmvQwZXiR+YKFiT9PBar6st12kUzATZo0sJWnbczs2XNd5l5l99CxsckmdV1jiBoNDjigg/sufPzxFHfM+7s01aifqZKJ79sppxzqsu6OG/epUbD+0MfHmF7ndo3axBbbRr6Lo0YPNhvULj4D8Q4tm0bNzxsEEECgKgoUF1Abu7+Jpo0drmBdZeUdMTkUrOGXEzudH67n4sZpfDCjsN5XZCnu9zwTv2l+39L93cpkHTKYUW7BgsVmjz0iwdt+e/Xs6xzB+kZwfLzXqp8qU5xuKJprby5LtlSGOmSy2xo7XbkdQ5yHxNJHvc/mYyhqR3iDAAIIVKDALJtpd+DE6Dpf7OYoYLebDdgNBuD6AF4f5Ovn0bQDOkYuPmuekV22MsUFEPsgXwUN++X65VWG5+J+91PdvnTagnTxXRfd1S6lm73V05TKPrbNSt3ZKqOVurr97bdl9qLlbDPh46luvNoIg1lh3cA0/mSi3lzV24I4dooeYAq2OL7bVa5dW8exssep7VJZ6dRur3bLVjueVHTGmCE33Xyuy46n5BYKFttt1x2LdNm+ra1Lq81V34tXX709ZgnRb9WGSkEAAQQQKFuB7vYakHqcHDv2I3PDjee461jDnh3nVqqkTiUVZRu95+7nTW17XUi9FOj3o6XNeKokL/o/fv11j5VrgG+8LLxqGzuu6xWufqaA5dNOP8Jeq2vperVSILISTxHgW9InXbnHqxcKFdVj49Uf6tbd0HS1wb+DBz1rJk2cnlSA77b22qd6I1a9JlH52QbCxytK+tT73NvcuUGXY/YzXWxW3Z1sD2cKCFbg7Vs2gDZegK+WpSy9CvDVd1LnGJMmTXOJH/bbr1040VTsOnVNXA/dsKV5lRhrzJgPjW6m0s1bH3z4SFK9F595xk12fdNNw0abmz69j3M9H29rr5fr+rZK54PPLxLg26owO68yEycqukYeG9yradWGqGvpip9I1IOGj7vYLkEPG4nWyfDyF2jYMMfkFoYo/WEDVdesid6G5csLzKOPrDHbbreBjasJ1fOVjXbxYhupWlgmTVrnAnz1tl2HwgDfDqGA1Xw7mTLSZqr8uqjAZhUObYcCY+fPy9SSS7ecRYsi8WZb2CDdeEXBu74oIDqYdXe6DdJVgK9Kyx1zzY6FAb7KXKwsxMrye9LJkSy/msaX6dMz5+mXyTMCCFQNgSoX4FvfZl9dZgMYkyk+YNcH8vpnzRt8nYngXi1T21YWRRlSTzv1Brfo7t0PLRLo59epO7nUSKcAz5Ur/3WDdYeZvyu/a9cDzEM22DO2LF+eXPZezacGRmXB+Nt2/aVsHKXt2tevWydzevS2A3zXYKrsqoKvu/MSVSj9/P5Zd7a+9+4XrvsyPyz2WVk8VNq3bxk7qtj37e32qTsOVcyVDaHexnWKTK+7adVF2sZ2nII5MuVTZEVJDshUcK/ucLzL3v2rMmjweQkDXxo33jy8ZStX/hN+neqLDvYzeuP1T1wXPeo2caONAn02FC5UXZOoqKEi9i7Nzjb7igvwtSdxPnhnf9vdUV5eqNLUyZ6UKdj94wlTzZ+Fx70u9sQux60ghT+Z+L4paP2OOy80umNz5sy5Zt68RWbgrU+bXW3jfAf78CV4PCu4N9Xvol8ezwgggAAC8QUUrKusvJksp+7aMJOLS7isdH7PM/GbFtywTP1uZaIOqWCOevU2svXlf8ycwnpFcFv1+o8/VoQbTjt0KF0dsq2tQ+pGK11Aj1fW2gv5/gY01WdUt67oOmS87dSwynQMZcooE8eQbDgPkQIFAQQQqDoCxQXdai/jBfYG994H4/oA3djg3uKmDY7zr/1y/HL98LJ8Tud3P53tSqctSN3fqp6pNkVd0FcdTkUX5X3R6+dtt7ka73t6OvjgPfzotJ8zUW/O9rYgjp3UDiN1ueyDJwYNOs+cetoRUQtSu3NJRUkdzjnnWJclr8vRl7nrAr17DzLvvPtAVC95+p4okYCuHexie2fzbaUlLZ/xCCCAAAJlI6DrucfYAMRRo8abl1563xx99D7mvfe+cNcadS23pKLraCqPPHpV3MQ7/qankpaT7Ph69eq4YEcF5TayQYixZcqU72IHueu92g5lJH79jbvdTVnBif73v+J7zAhOy+vKJ6DeVHUTnUpxPeE2b7alm0bX2pMpO9uAXJU534Wu7cebJ1Fva2+9NdEF9/bqday58aZzi8xa3DF3tK1TDbj2EZdFd/WQNbZX5I/c/Cee1LnIcmIH6Brz8ccf6B7XXHumTbR2iet5+eWX3jO9+3SLnTzqvQLhFdyrOtp77z3oYg2iJrBv4m13u8KYh5kzf3KJ3+IFWE+ZMid2Ue59extboWvpip9IFI/xbWEvsj7DeNwFMbBSCPQ8taat34cifJ8fuda8899QjzWxG7dqVYEdFApYje18+dvZ+bY3nAKX7bdt21x7k2yOjZ0IxVbMmLneZayNXV6q73/8Md8G+IaW3bRprr0RNzrYtV69HLP/AaH9WWnv353wUfz9SXX9fj4F4irbru5tbNQ41yYJNEWCo5V915cff4gEBGvY9GnrTZdjarjRrVvn2ZuLQ9NOt4G9KgsW5NsbWQpsoL7tIbtdng2sD41f9EuBTQ6jz4KCAAIIFBWI/NcpOi4rh2y5QfFZKmN3KjZ4V4G9ZRHcq/WWdttitzXRe2UmVWOxKlpDh44xCnyMV162J4EK8FTxDdk62VIwrkor2y1LbNEyFUCcbFE2gV3ahirXY179MLzs4PxffjnLXHXlA+aZp99wXeNpnNZx7z3PmwvOv9PdxRacXo3YnQ+JNKyvXZP8D7UuAqhMmTzHvPPO58HFutdjxnxkfJfQ7TtEgiOLTBhnQLDSqjtpY8uECVPM3h3PtlmMe5kPxn/lRqfqE7vsVN5nKrhX695tt1auO2odH+riRydqsUXZUUYM/68brBMH3SGcbvFBqlp2PHMFu7704ntuNcFAIb9e372iGseHDXvLDfbfBb05wAb7alvVHZAuPqh0zmD23kx833R3qrZRgUePD73a3YGtOyz72O76go0ybdq0CGekHjHibbcvsX+GD3/b3Gi7olEjUbzPMHZ63iOAAALZLqBsu5W9lGf23nR+zzPxmxb8LFL93SqLOqR+Z9u23cFt3jDbdVq8uvCQwc+GN9/XT8IDSnihm8RUZsz40V1Ej5387rtGuvrjfvueGw5Cqcg6ZOz2Bd9XpmMoVaOyOIZkxHlI8EjhNQIIIJD9ArNt9t5ERdl0lYk3mLU33rQKxnXT7t0gKnNvommVzVfTJyo+yDfR+EwPT+d3P51t8XWtVNuCfLuO2uY+sxfHVToduFt4k3y7kG749jdgBdshwxOm+CIT9eZsbwvi2Ent4Pkl0Cav7tRjy/jxX8YOKvLeZ6LeyfbWd7PN5KuiAJqLbHfowbZA386t6wpqL49XbrrpCXPHHcPNp598E280wxBAAAEEMizgM/UOf+5No2s5ug58kg0mVHbbksqvhYGVbe1NG7FFy/nk08z+L9fvjIp6Zo0tav+K17b2q+2xVUXzqk0ntnz04ZTYQbzPIgG1r6qHBhX1mpqofPjhZDdKmXCTKTvvEjrWxo6ZYH7//Y8isyghV6LMz78uCh1zupkpXlFm4ERF12SVjVcJId5++zN3DVlJGY48cu8isyxZsty88MI74d6TgxMoGZjv7TZ4TTc4TfC1/55stVXDuMG9Cmb2gdTB+XSjY7NmjV29T9m8Y4t6grj33hdiB7v3vl742KOvuP87sRNpu58fGbr+395m3aZUboEpkyOBp50PqWEzThf9f7v3Pnlmq61CYWMK+fBBqH7PNOyzSaFA27p1c8zJ3SNJDSdNjA7A9fOk+vyTDaz15dBDa7hgYv9ez12Pq2Ezatd0j6ZNi+5LcNp0Xq+y4VaLfglti4J7tc5gqVMnxxxzbCiAV8MVmBwsev/PP6H4mT33zLNJXELbOmNGxEtZfFX23S/P3mAZmpvsvSEH/iKAQHyBKhfg2zRORs/4ux4ZGhvk68ckGu7Hl/Y5lW1Ldh1Hd9nXTaoGus4Hn2def+1jm7L+Vxfwp24frrV3lV1zzcNuGnXJokBGFVXw1C2LigJugydZs2bNNWecfqMbV5o/F13U3d1Jpvn/76yB7i4v32D4xRczXRCvTkaHDh1r6trKr4oysd5334uusnvppfeaH35Y6Iar8V7Blk/YaVWaNd/SprBv7l4n8+eUHoeZrbdu5Ca94vL7XaVelW9V8EeP/sBcc3XIRHegnWC7OStNUXfNhxyyp5vlqadedwHKyramQNr33/vS9O//kBunfTsiUMFPxac025Vo2ottw62yxambkYdtpmZlcEi1KJuc765aWYy7nzzAZTlZbE+mdCIx3l406d79WvPaaxPcKpTZrkGDTVNdXXi+XXdtFQ721jHxyMOjjU5uFOCqk8A+vQe7AHc1cFxwwUnh+fyLDjaIW3c+qygYWKVTp0jWFgWT77zztq6LO39C5IOC3cRp/sn0903BUMHG+QsvvCvcOK+7OS+66GS3xcpEo0BefwesvlcvvviuuW7Ao2bo42PcZ6eTbQoCCCBQ1QV2ruQBvgruLa/svfqs0/k9z/RvWqq/W2VVh7z4ku7uRhnVGXvb+oWyuanhU3W9+2zjp+qyKqqDx7vY7kYm+HPa6Ucadfuscpmt975qb4pbYdezfPlK1/j7yCOj3bg992wTrsdqQEXVId3GJPhTmY6hVI3K6hjiPCTBQcNgBBBAoAoJuCy8Nlg3URbdgZOWGD2U/dcXTRsMBNY4P82sOEHEmr6kQF+/7LJ+Tud3P51tS7ctyAfr6kK7ekNSl7O+Lqbt2m//UM9OvgcF1Q323nvndDY5at5M15uzsS2IYyfqkEj6zV577RTuzvqBB15y7dmaWYFZao+95eankl6WJtR5yLHH7u/mUY93/rxDA3a3yRw6dQolZ1DbvNoNFZyuouQgN1z/uHn8sVdD7d+/LHbD+YMAAgggULYC6rVRbU66Xqq2KBX9L0+m7N0xVJd54onQtVU/j66l9e07xOhaXiaLsvCqKDGPek7w5fPPZ5oep1zneqfyw/yzD/7UjSXKUhosmu+2254ODuJ1Fgr4a+i33Pyk6203dhdUv3nppXfdYB/0GjtN7PuWLZubgw7azbWjXmSviQZ7j9X1+cuvuD/cM1rsvHvv09YNevLJ12JHuZgFn/m6yMjCASeceLB7df11j7l60lE2s3a8gHtdf77s0vvMpZfc65KzBZen755PSHbQwZGbDoPTBF+3bt3C9bowd+4id40/OE7LOrfXbcFBUa/vuvtilyTqnntGmlsHPm0Uo6H2Z8WNnHjC1WapPT+KF1zfpct+rjc5fZeV5EK9zfki78svu88FV+s8a+fC774fz3PlE5g6db29rhHargYNcsyNN29g+varZY7tWsMF6va/urbtHdxGsBaWH2wm2hUrQoGpfpiefYCvXrdrH4pGXW3vg54yORKwqnHpFi1vzrehwNfaGxhz+ZW1zXHdapo998ozffrWsucskaDaL2Oy+6a77tj5R49aZ7N+h4YqOPrcPrXMbrvnmUMPq2GuurqWy2isscrGG/TRMM03a2Zo5tzC4F0F/P4UCAT2wbzBEI1pNvMvBQEEEEgkEPkPmGiKLBu+tW2ErWcDGFfaC/ClKQrmVfHZezMd3Ktt0raVVdEFfzVUD7dZxtQ9l7J5xisNbdcoTz91nam3cSSDXY+eh7lGOgVK7r7bma6he+XKv93yfECtz2ARb5mxw3SX3QMPXG769RtiPvlkqg04Pt+ml69r8u3tPQqSUFGFd+CtfUwNG4Sosumm9cwVV57qKpgKTD5g/96uG5dVq9aEGxM3sNmZr+5/hps+2T9qQB75/C3m2GMuN0vtZ9zPnrgqgETVEp3Iqqi7GE3jAz/dwCT+qKsydW9zwvH9XRCyMhjooQDq1avXuCXUqlXTDLn9/KiT11R8kticEic599zjXHDK4MHnpRXc61f0yCNXmXPPHWSUkVmfsx7xirJ0PPHkgHijSj1M5o8+2t/epXyNmWxPLAYOfMrceuvT7njymasVwPz441cbn+EluBLNf6DN0qKsLCqtWm1TpLugTrZrxmnTfnDjdVzqQlImS6a/b+qeT92zqBFEAdwPPTTKnH/+iW6TL77kFHvc/2mefvp1F8irYF79D/hz+V/hY1Rdw/Tvf3omd5FlIYAAApVWYJcmdcyIxDfiV9h2K7Nwz10bmorIMJzO73mmf9NS+d0qqzpkR3sh5MGHrrA3Dw1ydY7uJ1/rbkjz9Q0dLLoQoTpvaW+S0U1Pw0fcbI7pcrlrVD3/vDvcDVhapoKIVRo3rm9uGdjHvfZ/KqoO6def6LkyHUOpGJXVMcR5SKIjhuEIIIBAdgp0s4G2s5aGerhSYK/eBwN1g3uloN1gdt1ZS1cbZQBWlt9gCU6naVSUsTdewLCGtapf2y3HL3uADS4u75LO736q25puW5ACY1S38jdy+4y9fns2tm2kuiFc7Vsq++7bNtwjkp8m3edM15uzsS2IY6f0R5HP8Pb225NcIEibNt1tsEUz26Xzby7o9vDDO9oMcpNKteDb77jQZuT60fz00y82YGOY66VNSRzURj/0iWtdm6t6wlNAioI3GjWqbxYv/iOcvW3ffduZE0/sXKp1MjECCCCAQOoCZ5x5tOsRVUmU1A613XZbJbWwU3ocat6314uUJEc3dWheBfMpwE83PO1j6zuZzMiuus7LL7/vekzV9dimNnPovzYr/OLF/zOnnnq4DRb7J5wQyO+A6mgdbBCzrvV13Ots09HeYNXUJmyabH+HlPVX1/Lefbdoz6x+fp4rv0APm4RL1/wffniUOeOMm9z1WN2s9u+q1WbmjJ9cwgNdU7/r7ouSTu6lgNSH7fXpY7pc5q5577zTKUbtgYo7UCKof/9dZQYN6meuLkzwFVQ64oiO5kEbVDx1ynemffvTzP52vho1arjvhepGCkgu7phTT7Vq79NxrXJSgjrR7ru3Di/r8MMudPUtHe9TbEKzb+xDN2spSHm33VoHNy/ua+2vHGV41lm32O1uaXue29591/Rd0TVeZepVbEhsUfv2QzbhV+9zb3Pzaxm+6CbEESNvMV2sY35+KF7Cj9P5kdquuxx9qbvm/Oyz44yunf9t/3d8YntyUMBvO9tDna7X61yNUrkFli8vMINuXW3Ov6CW2WzzHHuua8weNqOsMYVRp4HNnz073zz8UCjGJTDYvZw/P98mCiwwTZpEEoZN/nq9jTeInTK99wpGvu/eNebqa2vZ5Ce5NnN1dKZcv/RXX1kbDgT2wzL9PGXKevPUk2vM2efUsteAjOnYMc89gutZsqTA3HXHmnC23uA4BesqINiXWbPywwHDGqYAYGVH9gG+svxuTmFEsZ+JZwQQQCAgUCV/ddtsXDewi8m/9EG9I2wjmQ/4TX7u4qdMdZuKX2pkrIJWhww539xzzyUusFHZJnxR0IECdbt2PcC89da99q6aHfwo93zOOceau++52GXz1QBVYhUsrBO8558faDYJBANHzVjMmy7H7Gcri1eFswPrjn8F96qirgrwe+8/aE/OIplTtah+/U4w991/mbsrTO/VtYbmU6VcDY2vv3G30XJLW5SZ4/kXBtoG+lD3Hqp4KrhXLtoGjVPXFqkUbduzw25w2X/1GagouFeBvQowffGlW023bgcWWXQqPkUWUsoB+twnffZURoJ7tWoFio4aNcicd94J7nNWYK0vahTWyUqfvt3MqNGDM5K91y9b5sOG3WhOPOlg56yGDR9so+zOaow+6ODd/eRFnoMZeWOPQU0cHKbMFZk+OSmL79vtd1xglIVa5fYhw4zubvbl5lt6m8su6xn+DHRHp45RnaDpbu/xHzxiu+PYwk/OMwIIIFClBVwgrc2SW1ElGMCr13oMOXob9wiOK8/tS+f3vCx+01L53SqrOuRRR+1js1r1N75bW1/f0E1nalh9yt40pzpfKkW/27rBTPVtFQX26qHfZ2U/eNPW2VWXii0VUYeM3YbY95XtGErFqKyOIc5DYo8W3iOAAALZK6BgXmXS1UOBuvGCe5WB12XjnRPJ2Ov32Afw+vd6VtBvbFHwbo/XF0Zl/fXTaJ0+o6+2I942+GnL6jmd3/10tindtqDOnSPtRLo4HVuCbUEHd94jdnTa78ui3pxtbUEcO6kdRo/ZJAZnndXFtY8rGGT27LkucEXdtt9736WlXmjduhuax4debXROo/OPvn2GhLMm6nrCc8/d5NpcNV49gCkhiNarIHmdq6kdXYEmFAQQQACB8hE47rgDTL16G7mVnWn/9ydbFKioAD1d+/n++wXm2WfGmXHjPrXLquP+l+9Y2LNrsssraToleVI7l4IW9XuioE7dUK1kNLfe1i/u7LpG+/LLg8xxx3VyPaMqEFm9pU6f/oP97TvaJU+KOyMDs0ZAdYZrB5zlkigo2FS9AqgH2EkTp7s2VV3LfeWVIe4YKM1O6TvxrL1OrGu4uuavZer4rmPrORruM/XGLlOxAG+Mu9vFSSyxQbqjRo13vaktsQnCVLc+ufshsbNEvdf14q72eFVR78F7ddzJvY73Z+gT17i4hw3sNW0laVLWYAWzN2y4ubn88lPddiZbp5KhHjon0k2Jysyt3uZ2372VDdK92X7nQr0lx9sOBTVPnPSkufOui1xPDmp3vvSyHuatt+8zPvN2vPkUODzSxogotkI3BSi78QcffO16kVWPEIqN0PZQskPgp5/yzY03rLY3dqy39fuCqCDTNTaeV1llx45ZZ+68Y7X5a6XS5MUvn02KDgafNLF0CRfjL7Xo0H//LTB337nGTPw0kn1YUyn4d97cfPPYo2vMa2Ojt6XoUjIzRGaPP7bGLPol2iXf7vpXNoPwHUNW23im6HF+zTOmRwfrxr5faa3nz4tM8+230fvrl8MzAggg4AVybIbU+P9x/BQJnhVUp6JnNfaEnvNdg0++zTmuO8wrqvzPdsHwiA1SrUylrw0y3SzFAIBU9kOfibqA+OOPFS740p8AlrSsBQt+dxktFHSgu7cyUZQhY9EvS8wmm9Y1zZs3SSpgUne/aZ66tpK+zTbJzZPMtspDWQ5yc3Ndd3w6wcxU0YnJgp9/N2tt4+iOOzZzmYKTWXYqPskst7ynUdcn3333s1ttS9s44AOey3I7FGjzs70r8W/73KTJFlkXqFoW37dE3mq0/8V2K754yf9cQDtBvYmkGI4AAtVBYPjXi20m38Xlvqtv9Urc6FfuG5Nghan+nmf6Ny3V362yqkMuWrTE3qG+1F1Uadq0cUYbMH39VPV1BYQmmxG4stYhK8sxpEM8FaOyOob858x5SIJ/PgxGAAEEslhAgb2v2Ky98YJ4g7uloNxgUTBwSfMkyugbXE5Fvk71dz+dbaYtKHm9VOvUya8h9Sk5dkpnpyDb722762rb/qobEIPJPUq3pOSm/vff1bar2d/tBf5/zLY2W6QCtygIIIAAAtkpoLaRH39YaJrba60K2ivrovrH//63Mpx0Jpn1qV4wa+Zcm8bQuGvaBA4mo5Z90yh2Ye7cX1y7apMmmTkWlbBM1/3VW29pknkpKdmMmT+5eBa1xwaTWBUne9ddI8zdd4006onuiitOLW5SN07HtupUahdMN/ZCsTg/2O/ysmV/uu9JaXsmjt1Y9aK83bbdbHK2mua770cnjN9QD8m6Fl/Txrk0t9mCg71Exy6T98kLxMZaKb6qd6/yCVqtaXOWNPlPrlm9qsAm3FOcV3LbvetueS4TsKZeYYNaL7l4VVSwcHJLKd1Uyji8RQObebhmjlm4MN8F+ZZuCZmberPNcmz8VI6xScjNsqX5ZtWqzC27vJb02NAaLk5KNxj461D+uby2gfUgUBUE9Fusa326+UfP+h6Vx/eqSgb46oB4256wTF6+slIcGx1sEOnh9i53CgIIIIAAAggggEDlECjvIF9l6q2oLL2VQ5ytQAABBBBAAAEEqp7AaBvYq6y7yZRUAny13Moe5JvMvjMNAggggAACCCCAAAIIIJDNArrRcJ99ehn1lPrpxKEuqVll3h/dIPbGG5+YY2zvyPEC+NQbbLfjrjTKhDxi5C2VeVeq5LZVZIBvKqA2fs1cd31t03ybUAfx/317nXnh+bWpLIp5KlCAAN8KxGfVVUqgogJ8a1QpxcDO7LfFpmamvftqtb3bpSJLbftrp22hIIAAAggggAACCFQegVN3bWj0mPbr32baor/DG5ZqZl8F72pZsUXDFdxLQQABBBBAAAEEEKhaAj1eX1guO6QAYj0G7N3AtK5PN6jlgs5KEEAAAQQQQAABBBBAAAEroB58leH0phuHuuDeLjZgVj0WV/Zywfl3mLFjJ5jx739pbhvUz9Sps6HbZAWWfjP1e3PF5fe795067VrZd4Xtq0CB3XbPs4HteaZho1zbm7JNs27Lapu59s1x5ZNtuAJ3nVUjgAAClU6gygb41qmRZzo33NyM+21phaJrG7QtFAQQQAABBBBAAIHKJ6AA3GBm3ekK+I0TqFvSlvsg3th5g8suaRmMRwABBBBAAAEEEMgOAWXuLe/yil1n646Z6cq2vLed9SGAAAIIIIAAAggggAAC2SawevUa03aXnmbVqjU2yLfA1K+/ibn66jOzYjf62+2cPHmOGTVqvHnnnc9Nm51amC1sUrpJE6ebpUuXu304uPPu5uTuh2TF/rCRFSPQsWOeadc+Euu0zsb1PvfcGrNihY16pyCAAAIIlKtAlQ3wlWLbTeuaX1etNpOXryxXVL+yDpvWc9vg3/OMAAIIIIAAAgggULkFFKh7xNAZpdrInh0ahqcnoDdMwQsEEEAAAQQQQKDKCsxetjruvh3fcmNz/A4bm4GTlphZS+NPE3fGwoEju2xlFDysjL0UBBBAAAEEEEAAAQQQQACBihOYN+9Xm7W0gcnLyzVt221vrrrqdLPllltU3AaVYs1NmzYyr4653dxx+3Dz+usfu8BezV6rVk3Tpk0Lc9xxnUyfvt1MTk4oK2spFs2k1UhAnaUvWVJgVv1bYObPLzAffLDO/PRjxfagXo342VUEEEAgSqBKB/hqTw9vXN/8tW69+e6vf6J2vKzf7FB3I7fusl4Py0cAAQQQQAABBBDIrICCfK96Y25SC1Vw76m7RgJ8k5qJiRBAAAEEEEAAAQSyWqCbDeKdtXRJeB9ab1HbaFjr+rXDw1J9oQBhPWIDfVtlYNmpbhPzIYAAAggggAACCCCAAALVTaBly2ZmwsePZe1uKxj57nsuNnfedZFZtuxP88/f/5qttm7kApazdqfY8HIVeOjBNeW6PlaGAAIIIJBYoMoH+GrXT9iqoRm1cHG5BfkquFfrpCCAAAIIIIAAAghkn4Cy8L7Vaycz/OvFZsTkxXF3QNP0tIG9ZOyNy8NABBBAAAEEEECgSgsokFfZdmcVZvLNRGBvLJiCfINBvWWxjth18h4BBBBAAAEEEEAAAQQQQKBqCeTm5pgGDTY1Rg8KAggggAACCGSlQLUI8NUno4Dbt39bZiYvX1mmH1SHTeuRubdMhVk4AggggAACCCBQPgLKzKvH3D9WhVe42Yah6vOmhc/hEbxAAAEEEEAAAQQQqHYCZR10W9bLr3YfGDuMAAIIIIAAAggggAACCCCAAAIIIIAAAghkmUC1CfDV53J44/pmyw1qm/cW/2FW5+dn9KOqnZtrOjfc3LTdtG5Gl8vCEEAAAQQQQAABBCpWYJvNN6jYDWDtCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFDtBHKr2x4rALdPi/8YZdrNVNGytEyCezMlynIQQAABBBBAAAEEEEAAAQQQQAABBBDIToFuO2wcteHHt4x+r5Gt6tcucZqoCXiDAAIIIIAAAggggAACCCCAAAIIIIAAAgggUO0EqlUGX//p1qmR57L57rn5xmbK8r/MzBV/mZXr1vvRST3Xs8tos3Fd094GDG9Wq2ZS8zARAggggAACCCCAAAIIIIAAAggggAACCFRtgdY2eFdBvaPnrDCtt6hdJJhXe398YRBwcdNUbSX2DgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKAkgZxVq9YUlDRRvPEFBaHZ9JyfX2BCz/lm/fp8+z7f1K+/SbzZKu2wBf+sMj/bx6+r1phla9aav2zA7xq7Hyq1cnNNXRvQW98G8m65QS3TdKMNzNb2QUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEShKIjbVSfFXvXutKmo3xCKQl8NjQGibXxr7l5uaYnJwctyz/nNaCmRmBaiawbNmf7ruUl6fvU677PpXH96paZvCNd2wpYJeg3XgyDEMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFMC9gYMZtIMdNLZXkIhAR0fFEQQCC7BfgaZ/fnx9YjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAFgrUq5eFG80mZ40Ax1fWfFRsKAIJBQjwTUjDCAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgbAS2bJJTNgtmqQhYAY4vDgMEsl+AAN/s/wzZAwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEMgygR12yLINZnOzSoDjK6s+LjYWgbgCBPjGZWEgAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFBWAjmmw65k8C0rXZZrCo8vjjGOBQSyWYAA32z+9Nh2BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyAKBSKBlTuHLRo1yzG67Z8Gms4lZJ6DjSseXij/eQjsROQ5D7/mLAAKVWYAA38r86bBtCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUOUEfNDlkUcak0sEV5X7fCtyh3Q86bhS8cdZ6B1/EUAg2wT4eci2T4ztRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSwVUAbVSGbVRo1zzEknF2TpvrDZlVFAx5OOq0hwb+SYq4zbyzYhgEBiAQJ8E9swBgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIuECOi75UEGaO2Xsfm3H1qPyMr4MFVj8BHUc6noLHV/VTYI8RqDoCBPhWnc+SPUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFKKOCzqeo58joU4Jubm2MOPazAnHjSuvC4SrgLbFIlFtAxpeNHx5GOJwX4hoJ8Q8db5JirxDvBpiGAQBGBGkWGMAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCDDAjYK0xS4ZSrgMvLIsUGZuabj3uvNNi3WmHfezjNTp+ZleN0srqoKtGu33hx6+HrTuHGuO45Cwb2R4yuy3zr+KAggkE0COatWrQn9apRyqwsKQrPpOT+/wISe88369fn2fb6pX3+TUi6RyRFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBqikQCrdSnJUN87V/fLyVYq/Wr1/v4q70vG5dvvn9twIzbVqu+enHXPP74jzz9185br6qKcNeJSugoPA6dQtMo4brTYtt880uu+SbRo1zTI0auSYvL88+Qs/K4qugcZ/JN5TBV1l9k10T0yGAQFBg2bI/3XdK3zH/3fLZsjWdz5gdnCcTr8ngmwlFloEAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIFCOg4EoF9waDLH22VQWMhQJ/jQ3WNKbxlvmmgQ3iPPCgdS7Zog8I9sHBfjV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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/airbyte_9.png\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "768c7b3c",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<IPython.core.display.Image object>"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "Image(filename=\"img/airbyte_8.png\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "58e0d239",
- "metadata": {},
- "source": [
- "### Snowflake-SQLAlchemy version fix"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "b937785b",
- "metadata": {},
- "source": [
- "Hack to make snowflake-sqlalchemy work despite incompatible sqlalchemy versions\n",
- "\n",
- "Taken from https://github.com/snowflakedb/snowflake-sqlalchemy/issues/380#issuecomment-1470762025"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "6559dbbe",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/hongyishi/Documents/GitHub/gpt_index/.venv/lib/python3.9/site-packages/snowflake/connector/options.py:108: UserWarning: You have an incompatible version of 'pyarrow' installed (12.0.1), please install a version that adheres to: 'pyarrow<10.1.0,>=10.0.1; extra == \"pandas\"'\n",
- " warn_incompatible_dep(\n"
- ]
- }
- ],
- "source": [
- "# Hack to make snowflake-sqlalchemy work until they patch it\n",
- "\n",
- "def snowflake_sqlalchemy_20_monkey_patches():\n",
- " import sqlalchemy.util.compat\n",
- "\n",
- " # make strings always return unicode strings\n",
- " sqlalchemy.util.compat.string_types = (str,)\n",
- " sqlalchemy.types.String.RETURNS_UNICODE = True\n",
- "\n",
- " import snowflake.sqlalchemy.snowdialect\n",
- "\n",
- " snowflake.sqlalchemy.snowdialect.SnowflakeDialect.returns_unicode_strings = True\n",
- "\n",
- " # make has_table() support the `info_cache` kwarg\n",
- " import snowflake.sqlalchemy.snowdialect\n",
- "\n",
- " def has_table(self, connection, table_name, schema=None, info_cache=None):\n",
- " \"\"\"\n",
- " Checks if the table exists\n",
- " \"\"\"\n",
- " return self._has_object(connection, \"TABLE\", table_name, schema)\n",
- "\n",
- " snowflake.sqlalchemy.snowdialect.SnowflakeDialect.has_table = has_table\n",
- "\n",
- "# usage: call this function before creating an engine:\n",
- "try:\n",
- " snowflake_sqlalchemy_20_monkey_patches()\n",
- "except Exception as e:\n",
- " raise ValueError(\"Please run `pip install snowflake-sqlalchemy`\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "461438c8-302d-45c5-8e69-16ad604686d1",
- "metadata": {},
- "source": [
- "### Define database\n",
- "\n",
- "We pass the Snowflake uri to the SQL db constructor"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "b4154b29-7e23-4c26-a507-370a66186ae7",
- "metadata": {},
- "outputs": [],
- "source": [
- "snowflake_uri = 'snowflake://<user_login_name>:<password>@<account_identifier>/<database_name>/<schema_name>?warehouse=<warehouse_name>&role=<role_name>'\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7ac38b7c",
- "metadata": {},
- "source": [
- "First we try connecting with sqlalchemy to check the db works."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "f06e0ba4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(False, 'test case', '[]', datetime.datetime(2022, 7, 18, 16, 59, 13, tzinfo=<UTC>), 'test to', None, None, 'question', '{\\n \"channel\": \"web\",\\n \"source\": {\\n \"from\": {},\\n \"rel\": null,\\n \"to\": {}\\n }\\n}', True, datetime.datetime(2022, 7, 18, 18, 1, 37, tzinfo=<UTC>), None, '[]', None, 134, None, 1658167297, 'test case', None, '[]', False, '{\\n \"score\": \"offered\"\\n}', 360786799676, 'low', '[]', 'https://d3v-airbyte.zendesk.com/api/v2/tickets/134.json', '[]', 360000358316, 360000084116, '[]', None, '[]', 360033549136, True, None, False, 'new', 360786799676, 'abd39a87-b1f9-4390-bf8b-cf3c288b1f74', datetime.datetime(2023, 6, 9, 0, 25, 23, 501000, tzinfo=pytz.FixedOffset(-420)), datetime.datetime(2023, 6, 9, 0, 38, 20, 440000, tzinfo=<UTC>), '6577ef036668746df889983970579a55', '02522a2b2726fb0a03bb19f2d8d9524d')\n",
- "RMKeyView(['from_messaging_channel', 'subject', 'email_cc_ids', 'created_at', 'description', 'custom_status_id', 'external_id', 'type', 'via', 'allow_attachments', 'updated_at', 'problem_id', 'follower_ids', 'due_at', 'id', 'assignee_id', 'generated_timestamp', 'raw_subject', 'forum_topic_id', 'custom_fields', 'allow_channelback', 'satisfaction_rating', 'submitter_id', 'priority', 'collaborator_ids', 'url', 'tags', 'brand_id', 'ticket_form_id', 'sharing_agreement_ids', 'group_id', 'followup_ids', 'organization_id', 'is_public', 'recipient', 'has_incidents', 'status', 'requester_id', '_airbyte_ab_id', '_airbyte_emitted_at', '_airbyte_normalized_at', '_airbyte_zendesk_tickets_hashid', '_airbyte_unique_key'])\n"
- ]
- }
- ],
- "source": [
- "from sqlalchemy import select, create_engine, MetaData, Table\n",
- "\n",
- "# view current table\n",
- "engine = create_engine(snowflake_uri)\n",
- "metadata = MetaData(bind=None)\n",
- "table = Table(\n",
- " 'ZENDESK_TICKETS', \n",
- " metadata, \n",
- " autoload=True, \n",
- " autoload_with=engine\n",
- ")\n",
- "stmt = select(table.columns)\n",
- "\n",
- "\n",
- "with engine.connect() as connection:\n",
- " results = connection.execute(stmt).fetchone()\n",
- " print(results)\n",
- " print(results.keys())\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1c09089a-6bcd-48db-8120-a84c8da3f82e",
- "metadata": {
- "tags": []
- },
- "source": [
- "### Define SQL DB\n",
- "\n",
- "Once we have defined the SQLDatabase, we can wrap it in a query engine to query it.\n",
- "If we know what tables we want to use we can use `NLSQLTableQueryEngine`.\n",
- "This will generate a SQL query on the specified tables."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "3869e15e",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import SQLDatabase\n",
- "\n",
- "# You can specify table filters during engine creation.\n",
- "# sql_database = SQLDatabase(engine, include_tables=[\"github_issues\",\"github_comments\", \"github_users\"])\n",
- "\n",
- "sql_database = SQLDatabase(engine)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "051a171f-8c97-40ed-ae17-4e3fa3785487",
- "metadata": {},
- "source": [
- "### Synthesize Query"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "abff69de-80c4-4fe6-afa1-b3d7208a5c4c",
- "metadata": {},
- "source": [
- "We then show a natural language query, which is translated to a SQL query under the hood with our text-to-SQL prompt."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "d71045c0-7a96-4e86-b38c-c378b7759aa4",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The top 10 issues with the most comments, based on a join on url, are: 'Proof of concept parallel source stream reading implementation for MySQL', 'Remove noisy logging for `LegacyStateManager`', 'Track stream status in source', 'Source Google Analytics v4: - add pk and lookback window', 'Connector Health: Fixed SAT for marketo, close, chargebee, facebook marketing, paystack, hubspot, pipedrive and marketo', '📝 Update outdated docs urls in metadata files', 'Fix emitted intermediate state for initial incremental non-CDC syncs', 'source-postgres : Add logic to handle xmin wraparound', ':bug: Source HubSpot: fix cast string as boolean using string comparison', and 'Fix db-lib JdbcUtils.java to accept JDBC parameters with = sign.'.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine\n",
- "from IPython.display import Markdown, display\n",
- "\n",
- "query_engine = NLSQLTableQueryEngine(\n",
- " sql_database=sql_database,\n",
- " tables=[\"github_issues\", \"github_comments\", \"github_users\"],\n",
- ")\n",
- "query_str = (\n",
- " \"Which issues have the most comments? Give the top 10 and use a join on url.\"\n",
- ")\n",
- "response = query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "431e684e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>[('Proof of concept parallel source stream reading implementation for MySQL', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 104), ('Remove noisy logging for `LegacyStateManager`', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 39), ('Track stream status in source', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 35), ('Source Google Analytics v4: - add pk and lookback window', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 29), ('Connector Health: Fixed SAT for marketo, close, chargebee, facebook marketing, paystack, hubspot, pipedrive and marketo', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 28), ('📝 Update outdated docs urls in metadata files', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 26), ('Fix emitted intermediate state for initial incremental non-CDC syncs', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 25), ('source-postgres : Add logic to handle xmin wraparound', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 24), (':bug: Source HubSpot: fix cast string as boolean using string comparison', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 24), ('Fix db-lib JdbcUtils.java to accept JDBC parameters with = sign.', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 22)]</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# You can also get only the SQL query result.\n",
- "\n",
- "query_engine = NLSQLTableQueryEngine(\n",
- " sql_database=sql_database,\n",
- " synthesize_response = False,\n",
- " tables=[\"github_issues\", \"github_comments\", \"github_users\"],\n",
- ")\n",
- "response = query_engine.query(query_str)\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "c79eeef5",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>SELECT gi.title, gi.url, gc.issue_url, COUNT(gc.id) AS comment_count \n",
- "FROM github_issues gi \n",
- "JOIN github_comments gc ON gi.url = gc.issue_url \n",
- "GROUP BY gi.title, gi.url, gc.issue_url \n",
- "ORDER BY comment_count DESC \n",
- "LIMIT 10;</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# You can also get the original SQL query\n",
- "sql_query = response.metadata[\"sql_query\"]\n",
- "display(Markdown(f\"<b>{sql_query}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7607cd6a",
- "metadata": {},
- "source": [
- "We can also use LLM prediction to figure out what tables to use."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8c418f13",
- "metadata": {},
- "source": [
- "We first need to create an ObjectIndex of SQLTableSchema. In this case we only pass in the table names.\n",
- "The query engine will fetch the relevant table schema at query time."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "cf1f4b04",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>[('Proof of concept parallel source stream reading implementation for MySQL', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 104), ('Remove noisy logging for `LegacyStateManager`', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 39), ('Track stream status in source', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 35), ('Source Google Analytics v4: - add pk and lookback window', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 29), ('Connector Health: Fixed SAT for marketo, close, chargebee, facebook marketing, paystack, hubspot, pipedrive and marketo', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 28), ('📝 Update outdated docs urls in metadata files', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 26), ('Fix emitted intermediate state for initial incremental non-CDC syncs', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 25), ('source-postgres : Add logic to handle xmin wraparound', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 24), (':bug: Source HubSpot: fix cast string as boolean using string comparison', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 24), ('Fix db-lib JdbcUtils.java to accept JDBC parameters with = sign.', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 22)]</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from llama_index.indices.struct_store.sql_query import SQLTableRetrieverQueryEngine\n",
- "from llama_index.objects import SQLTableNodeMapping, ObjectIndex, SQLTableSchema\n",
- "from llama_index import VectorStoreIndex\n",
- "\n",
- "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
- "all_table_names = sql_database.get_table_names()\n",
- "table_schema_objs = []\n",
- "for table_name in all_table_names:\n",
- " table_schema_objs.append(SQLTableSchema(table_name=table_name))\n",
- "\n",
- "obj_index = ObjectIndex.from_objects(\n",
- " table_schema_objs,\n",
- " table_node_mapping,\n",
- " VectorStoreIndex, \n",
- ")\n",
- "table_retriever_query_engine = SQLTableRetrieverQueryEngine(sql_database, obj_index.as_retriever(similarity_top_k=1))\n",
- "response = query_engine.query(query_str)\n",
- "\n",
- "display(Markdown(f\"<b>{response}</b>\"))\n",
- "sql_query = response.extra_info[\"sql_query\"]\n",
- "display(Markdown(f\"<b>{sql_query}</b>\"))"
- ]
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e45f9b60-cd6b-4c15-958f-1feca5438128",
+ "metadata": {},
+ "source": [
+ "# Airbyte SQL Index Guide\n",
+ "\n",
+ "We will show how to generate SQL queries on a Snowflake db generated by Airbyte."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "119eb42b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Uncomment to enable debugging.\n",
+ "\n",
+ "# import logging\n",
+ "# import sys\n",
+ "\n",
+ "# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
+ "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e7b550f4",
+ "metadata": {},
+ "source": [
+ "### Airbyte ingestion"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bcd28d60",
+ "metadata": {},
+ "source": [
+ "Here we show how to ingest data from Github into a Snowflake db using Airbyte."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "66b43c8c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
],
+ "source": [
+ "from IPython.display import Image\n",
+ "\n",
+ "Image(filename=\"img/airbyte_1.png\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "54d6f224",
+ "metadata": {},
+ "source": [
+ "Let's create a new connection. Here we will be dumping our Zendesk tickets into a Snowflake db."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "19dbd6ab",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/github_1.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a6bf69b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/github_2.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "8b9b5154",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/snowflake_1.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8310ba0b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/snowflake_2.png\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "966b4452",
+ "metadata": {},
+ "source": [
+ "Choose the streams you want to sync."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "17614c43",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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tmNavffbdzvRDK05g1dVWtvr6evvh8b+1G/76i7KDsg//+wUfkN16m+F2860XpFXG1fGpra2z4465yJ55apLde/cT9pOfHVpch5fhud99Z6bv3SabbkBAdhk+TnQNAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJBArLMzKASrMOx1t/SwMedW+Vf/+3nVBQVkc4VcywnYypUqsp397Cq+/6+5gKyqdvZdZSXbcsuNMgKyyfVVVsZt5NbDbIUVetmiRd/YnDmfRG5I61rkwocLFnxpi10Fx1ytrq7eliyp9dsP89XW1psqQqqqbVQrdbnM9Sk0p2qvX321OOf2Mpf7+usl9pnbvyVL6jIn+fcy0H59437UlnxT59/rs3pXXTS0BlepN/OzMC31VduTp/qpdUe1bOtTNWBVSU3dbtTy4fOwf998k+x/+HxpvT799CRX0Xg5F9ge0q6bHHv5P/y5rKqbt9x2QauArDZeVVVpv73kx7bb7iP8sbru2ntb9WmhC5p/Mv9zPz1M1PuP3Y+ajpne60fHoq2bjrvWrUrPUe3TT7/w8+Q6f8KyM6Z/5KuSKkDfnu2QQ3ezsVf93BoaGnxQ9n+PvVzW5h55+AW//AknHpgWkA0rVdXmM878nq/srGrFqS0cQ40toelYvf32B0VdOxobVZ34/fdnWeq6wjr1Gral6yxb03HU8dTYkK2FYxmud8378fzP/Kwac/VeP9pOMU1j+5TJ0+3ViVNt/sfJ9RWzfJg3tQ/hs8zX4JTrXpK5DO8RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAga4i0OkryaYeiHyh1tR58/0+anQ8b8g1V8BWy9O6l8DChYt92DMej9k2rvpiLBadQde0QYP62+uvv2sffjjPVdRcvRXWuzUzfTgsNTzaq3dPG+Iq0a7tqtRmNlWwnT17vm233SZ+0uTJ03xAVm/Up9VW7+sr2+r31FbqcmEd2u+33nzfPv54QfjI7/tAt3/qq0LB2Zoq6U5+e5otXNgSPlt++eVs0Ppr2frrr928yIIFX1hqCE/zP/yfZIBvww3XsWHDB/l5P5w513uuN2BN23zzwc3Lh1+ybU+hzYGD1vL9TK/4axbWp20MGNjfJr36jg/XJhIJF36usJX69Paevd0xyWwKT052gUAdW4WHQ1t55RVsM9c3Vb1dGm3mjLmmn933GGmVbl/bqylgOO6eJ/3qr/jjz23tLOdz2LbszjznaHv8f6/YIw+/6M/RVI+zz/qTPXj/M3b1NWfYQQfv4hfbYtOjwuJp77dwQfQHHvpD2rRy30ybNsd23fFH7rzob08/d33W1e2x6099cPKVSbfY6mv0zTrPXXc+Zhf9+qbmIK+uu2+P2t6FhH9iOg/aoykoK9+fnzK27Iqy0z6Y47uoaySqbemC15PeuK3V5HAMb739Qj/tkt/+3QdGFdbv2bOH7bLrVnbVn073v7da2H3w6CMv2aWX3Gw1TRVdNY/OkWO/P8pOd8HcWKylPPcN199vV/zhdvveUfvYpb//Wdrq6l1Id7uR3/cPIzhtzBF+2dQZ5s1dYCO2OMaWW67a3ph8hz1w39N2xpirmmfR9sO5t587dtfdcG7ztKhf3nRj4RUuMP7ofyc0z6Jjss22w+1XF/7QNtlk/ebP8/3y4cx5dujos334/JzzjrWfnnxY2iLZnFZxD2jQ9XXEkXunOaUtyBsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoAsJpKflutCO5duV8ePqLXfINToYpHVr+ahWSMA2alk+77wCc+bM951XmEtVFvM1BS8PPGhn+9a3Nm0169Sp000hVwVkV1hheVtn3dV9oExVZV+dOMWFppLbarWg+0AVG19+ebKruJmw9dZbw9Zcs5+r7Gr2kavK+NJLb0VWTy1lOVVHfeH5N3xAtrq60tZaa1Vb3YVxFQp7/71Z9uSTE31ly8x+zneVGl+e8LYPyCo0p/1bccVevlquArcTX5nSvIgCbOu6/dB8oem9fvoUGDZUJccJLyW318OtT6FkHSdVp6x5Z4YL+b4XVt3qtdbN8+ILb/rKs2v27+cCoKv546uquc8/97plVrBUEPA597mqX+p3LaMgrgLAqmqpfhRTibZVh4r4QFVk1XbYafMilip+1pcnTLZPPvnch7G3bQpp51rLxhuvbx9+NN7enzGuoMDwd4/Y0xQADU3v9bPnXluHj5ap13H3PGFjTrvSH/8DDtrJtvvWJv6aUPj3h8f/NrIqalvsxMGH7GpXXn162RVlhzeFOW+64X4rtQryG+4hAD/64SVufxv88dp7n21dvxL2n38/bz/4/m+yOjzz1CQ78YSLfUB21dVWtkMP282GuyC8KmL/8cp/2q9/lR5c3nPvbTzbk09MbMX38stTfEBWE552681sYZkd3fWh8UUBfZ1X/d01G1o417bfYbPwUeTr66+9awfsd7oPyKp6s85P9V9j+IsvvGVHHX6+ffDB7MjlUyfogQffOfQ8H5A9y4VeMwOyzz7zup3obBXk1Vj4ncP3MIWWNY6fc+af7NST2zY8nto3fkcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEliWB3EnQZamnbdyX3AHZ7JUvQxfKDdiG9fDatQS+chVV1VbqU16VyJmuIuo7U2f4IOYOO27mAla9mqE+nrfAXnzxLZs4caot16Pa+rqqgZltypTpNtyFEDfYoKUa65dfLrJnXGBSYdFPP/3cVl115czFrNjlGhoafHhUIVFVjB280XouCJhcraY999wbvrLulMnTbeOU6omqPKtQpaqtbrXVUB86DZ0J/Zw162Mf/FI/e/de3rbYYiPf9+ddILffqn38+7BMvleF6ya4QK4CqyNHDrP+Lsgb2qJF37gqta/54JpCrOunmIV5Zkz/yAeNR249zAcd9Xltbb0PyGrd06fPsaFDB4bZne8XtsD9KIy7xx5bt1TS3XQDe9tVl33v3Q+d9TRXUXKD5mXa65dnnnrNr3qnnbZor0349SoQrLaBq7qrUHNbt8vHnmqqCnrP3Y/76ph6vyy3yy691U780Wg79xfHNR9/hRe/d/iv7CV3/Z579jXWnvswuqkCbzkVZc848yhf0VVhzL12/5mdctrhto8LufZ2gc9C2+9/d5udf8EPvEVYZqobnw7a/wxTGFbBUQVUQ1NlaQVkFaT98/XnmKq3KnCvporIhx18jv3tpgd9yP2HJx3kP1dlVj0IQKFSXVs6B0N76slX/a+q4quK2bpeU6sWh5Dsnnslg7a6xvWjvh3pAq0KxhZ6nBR8P81V8NV5esT39nYVg39soQqvwvg/PvFSXzn5tpv/4yrKnhC6mPX1o48+se8ccq7NcpWoVTn35FO/mzaf9vMk5yQZVetVZd7QdC0efMBZdt+9T9r+++9ge7ljRkMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEurJAtwzJllsFtpyAbVc+mbr7voVqi3369C6LQqFStc23GJwWkNVnq7kqrRu5MKoqzdbUzLRsVTtVfTE1IKvlVKVVlWsVrvr886+yhmSLXW7WrPk+dKbKqhsNWU+baW7xeNy23XYT++8jL7oA6iw/PQTGampm+AqSGw5eNy0gq4XVz41deFRVaOfPzx7mbd5Igb+8996HvnLr+uuvnRaQ1eKq9qjqi6oIK9NBbp4Q9A2rr6yM2wgXrg1hPX2uqrlDhw30IWF5pjZVyVVbe63VmgOSYbrCtAr0rVxgBdywXCmvCiE/9+zrtvoafV2Aed1SVlHwMgpfq23qgsDLQhvnAoKqYpqrKbTaXm2LLTdKC8hqO31ceP6aP59l++x5st15x6N23i+/b337rlhQF8Y/+Kw95H6KbQqZz537qa9ee8Nff2G77zGy4FUMGLim3T3uUjvlZ3/w44YCt2dWVbpg+xAfyvz2qG/ZIFchOVfbcect0gKymnfI0AF29LH72XXX3mNvvvFeWkhWn33lqmWf8MMDbZQLeKY2VUtVhdzDDzvP/vD72+wHbp5YLBmg3cNVbL315n/bE49PTAvJPuneq0LsCSceZFdfdad7UMBrzetVEFfVZXVd77Fn+RWJNU7cP/5ye/yxl23UATumXfsa+757xF4+JPuaC+vmavPmLvABWYWCTxtzhP/JnP/qq/5leqDAZZefnBaQ1Xwa566+9kwXyD7frhx7ByHZTDzeI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMD/s3cf4FFV6R/H3/QOgUjvHSl2AUUBey+I2P4WXHtvqyz2jr2hoq6L6+piR1dFxd5BmlIFlF5CCCU9pP/Pe8Idp9xJnTTyPfuMM/fcc88993MnQ/Z58psXAQQQQGC3E2imIdkS1xvZt3+YnDi6YpLaBmxdT0znbiGg1VO1xZgKrzVtGrTdubPAhGPjbZjSbZ4+plLi8uVrTdjVPeSX4lJdVudxKihmhui4jB1Zdnldu7Z3W6YNkmoYdOvWDNEqu612BQIzdpSvu2fPjq7HdTNhOH2Eqjnn01CuW9tjj2Sztpa2+mtOTm5AMDnZXIMTxvM+/i9P35BsXFysHbZxY3k1XA3+Ok3nqWow0jmmps8aQNTKmUcdfXhNp6jycVqRV5tW+XVrc+YslScff8Ntl1x/w1kyZOhA13017fzDBMj10VDt9LGH+4QknXVo8FSrfk7/+CdZvGiljDAh0qo0vRYNyta0aSXTl1/6oFohWT3XYBN6/uLrZ+XLL2bL2299aSusaiVcfTw88VU59riDbNi3R5Cf5aFB7uvAgeWVlxcvXulzSQt+Kw82X33dGT79zsZBBw+21af1/aSBfyf8ffQxQ8tDst/ME6fC7FYTsl+6dLUcfsQBZp3DbEhWK8s64dv585fZoKkGmvULAkLR9Gf91NNGuU6V3Kr8yxP+WLHedb92atj8zNNvlTWrU231WK0i69bUKdIEb0ePOcxtt6hTdHSU/GGMtIK2d8Df9QA6EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEmLFBxIrQJX1hFSz9xdISs+L1UViwrEw3GatNwbN89wys6zLNPj3eqydbkeM9EvNitBJxwbJYJJrYzFV9r0pzgqXew0n+e8IhwUwE1zlRczJO8vJ0SH18eynTG6T63ppUOtRWbCopurbrH7dgVttWKhxs3prtNKQUFhbY/26xVQ7Ia1tNKkRrgio2NcT0mlJ3O+WJiokx4OSro1C1MKHn7tkzZYQK8SUl/hVr1gEpdin1D9126tBOtXptrrvObr+eKhpb1/aAB0latqlY5NOhCq7FDq2Rq02qedd3a7ArHLl2y2vVU27Zm2oCl287/O/dYt+5a9Y3724mmcudRFc6xfNlauf6aJyocU9OdfU2152BN92lIduGCP6sckj3n/441Yc+qV4HVc+tnw43XPSkbNmyRA4cMkJdfuSPYkirs188NDcPqQ3+eZ/68SD768Af5ZPrP8tmnM2XunN/l/Q8fNZWqOwTMEyzs7vxM5e0KV+uBWhl19epNoqH1YEF/HadVqzUku8BUZHVCsgcP38tWhf5l5mLJzy+w1WO/+26+DYgedvj+Nuyr835vQrJO++arefblUUcPdbpC9rxy5QaZN2eZrU69fl2arF2bKvp+01a868sU/E+mgdYzTEBWj9V2yz/O8x9it7ON06pVG+213XX7i65jtDM2NtqabjT3v7P5TKIhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArurQDMNyZrLHl2zW+pUmnWeazYLR+2OAhpK0pZhQrI1bdmm4qo2/+Cr/3wJCbE2JKvjKxvrf2yotp21agivspafV2CHaEVZbbr++mjO+eLj3YPDzhridwWLnfFOf02eI0yIeeTI/WwVSw2obTPhW31oi4uLkb6mom33Hu5VdGtyvmDH/PD9rpDsofsEGxKy/ra7KnFqdVS3duiIfeX7n17y2TX65JutS3x86MPSbdu2lkGDevmcz3+jqLDYvytk2506tQk6l7Nv9eqNQcf472jXvrXoo6pNA7IXnHu3JyD72tR7Q/Izp18EoJVw9THh1nFygwnhfmuqt95809PyzrSHqro813FaGVarnnbpWnGgs+uuKtMaKnWahu5HjtrfBHd/siFerR777TflgdjDDj/AVlIdOWo/ee/dr20F2t6mGreuW5tWoQ1V02u4/tonTIC3vCKuzqtVXDt23ENOPPlQ+fCD74Oeas7spT77Pv3kZznu+IN9+nTDqQ6rr6f+d4Y+Vdj0CwwIyVZIxE4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBo4gLNMiTbxO8Zy2+kAlqpcM2aVMnMqFpINtdUUUxNTbdVVbt2bW+vKtFUNNWWZ6ohVtQ0BKctMbF8fEVj62pfogmWavXH4YfsbcKfFYdeo6PLP2oSEsvDqrm5+XW1LJ95E3b55OWXe/ns9NrID7FnVFSk7L13Hxk8uLepTptlw6CbTFgt0wSoFyz4QwoKi6RfBdVGvZZWo5f6/tAKn/337C5tdgVYazRRFQ8aetAgGwbUMPCmTVttKND7UA1F9+j5VzBYHbZvz7JDOnTYw3tok3hd4lc92H/Ra0115WBhT92nrWfPTv6HhWTbCcjOMlVVtYJsqAKy/ovTysiPPnGtDNlvnMybu8xWmXWqafuPrcp2z17lHpWF7tevT7PT9erV2Wfao0zYVUOyWr1Zq8f+YCopq7ETqtU+Dcl+a6rJJrdKEg106z3SyrShaPreP+2UW2w16r336SPnnX+87LNvX3t+Dcfre76ikKyu4dXX75Y1ppruXXe8ZKsA9zFhXg30erceu943WuH3ux9f9N7l+ro64WrXCehEAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBq5ACHZRn6DWF7TEWjfIUW0iqgGQDUQ2bGCapJ6VX/+sc6Gap1wmPa1MuEtbdkmfBqslZaWmSqy+TZcW18VWd3WokEzDckWmsCnBoSr0rTio65ZA8L5Jgis4bG6bBrOTTBhXr0nuk49v1vT69Cm1xTKFh4eJikpLe1DK8j++ed6WbJ4la1mWZch2V9mLZaiomI5dETdV5FVLw267rd/PxuWfPaZt+XBh66skHH2rCW2amjLloni/f6v8KB62tliV1A9K9P9Z3CdCbnu2JFd4WpWLF8rhxy6t+uY5WaftsF79XbdX5vOUAVkV63aKB9M+06SjMUll50adEn6c6/Bd63ArCbt26cEHVvZjuTkJFNhuYMJiabauZzPQv/jli8r99MAqnc74sgD7eevVojVAKyGVk89baRnyAhTSVZ/Hr8zFWZ1bq1aG8oqsjN/XmTXrff1w+lP2HN5Tm5e7Nhe8Xvm9LGHi1bA1fbbryvk/WnfysUXPiAff/qEOF+eoPt07Rr81fdhUosEz78Zuo+GAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCDRHgfDmeNFcMwJ1IRARESG9epdXN5w3f5mnUqbbuTTA5VRE7NqlvIqsjouNjTGPaBs+TUvb7naorDRBSw14BQuRuR5UB52tTKhN20YTCA7WVpuw3datGaLBXqdpGE6b7nNra9em2mqQS5eu9t0dVr5ZXFTi21/JluP05x/rXUduN/dCq5pqZUYNBda26fUuW7bGhnL95+rWrYPtKikpFX3UVfvhu9/s1PUVktWTXXPdmfacr736ibw4eZp97fYfDWCOv+VZu2uMCQYGCy4HHBtW/gbQ95KGrOuqtW3XWrRKqv6M/vnnhoDTzJgxK6DPv+PtN7+0IWX//j9WrLMhTe0fNLiX/+5abYcqIKuL0PDrc5PekXvvflkWmsrHwdoP3/9qA7Ja7bQ2AVln/n327WdfTn7uXafL53nOnKUmiP27Ddr7V1jVn/P9D9jThmxf+ddH9rjDDi8PneqG7tf5NUD+yfSf7f6jjh5qn0Pxn9TUrXYava8axvVv338337/LZ9v75+Dhx66RAQN7yMqVG+T6a5+wn/feg7VSrbYPP/jOu9vzWkPLLzz/nixd4vcZ6hnBCwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEBg9xEgJLv73EuupBEI9OvXXVqY6n6lJgA5+5fFsmlTuk/ASYORq1ZulFkzF9mQZG8Tqm2ZnOiz8v79u9vtX+cvtxVjvXemp++wAczw8HDpYyqTNmTr1LmtDZVq1dwlS1YFLEXDkAsX/ilzTbCtpOSvYKuuWwOpf5jQ6oYNW3yO03DXooUrbUjYvzptixblTlnZuaKBwKo2DdPp+bSKqxNkc47Varbz5i2z96hvv24StiuI6eyvyfOK5etEq10uMtfu39asSbVdem1adbiu2vcmvBgVFSnDDhpcV6cImFcreWo1TG0P3PeKTHr6bZ+KyFqt94sZv8iZY26V9C07zNoGyfgJ5wfME6xD72G//t3s7q+/nBNsWEj699pV5fUGE1DcvHmbnbO4uERef+1Teezh1yXS2FbU9OdBA6Z6jNO00urVVz4qBQWFcsZZR0rr1i2cXSF5Hn/zs+ZzZbEcOGSAvDb1XhskrenE+hl22OH728Ovv/ZJW9nUf67PPp0p11z5mO0OVUXWy64YbdetIesZn83y+ezUMP61Vz1uPzevu+Fs158fZx3vvP2V/bIBfY95t1GH7WcrWH9u5tZrHDrMd7/32Oq+Pnj4XvaQ/73/nQ1Yex8/+5clMvGBf3t3VfhaK2z/81+3iVZaVodJT7/lM/7yK06T+PhYueuOl+x+7507dxbKhH88b38G779vivcuXiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACu6VAxUmf3fKSuSgE6k5AKwgOP2Rv+cWEorRC6ZzZS21YMSExzgZnc3PzPRVEu3RpJwMHBVaT7Na9g+Tl7xQNW3715Wwb5ko2VRC3pmd4wqEHHLinpKS0rLsLqcLMGsI86OC9RCskapXWdWs3S8oeLSUmOko0EJiZmWNDp4MG97YGzpQa/DrAVHxUI60KudQECtu0bWUDlXqcts4mgNvW9Hm3mJgoW+FSK7V+8/VcUdMePTqKU53Ve6z3az3fgQcOkFmmgqSG1bRar86toc2MjPLz6Tx9TJg2FE2DnNu3Z9oAsFYjbdOmlamkWyoacC4oKLKB3VCdy229W0wFYg3pHnTwYNGwXX22Rx+/VqLM/X/jvzPkkYf+I08/+aZ0697eBhp1TU5F4UMO3VumvHpntdd3wonD7bVdc9WjMumZt+z76MGHrwr5Jf79lnPte+W3X1fIQUMukp49O8pGE+jWCraTnvu73GMCsPrzGKw99OjV8g8TWn3/vW/lsCMOEL0nWsFUQ/IaYn3okauDHVrj/lvGn2dDpQ8/ek2tArLOAu6feIV5z2bIfBMiP+XEm2woX9+3arB06SpJ27zdDh175pGiXqFog8zn4Qv/nCAXnn+vXHzh/bY67YhR+8q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Pp88SDbQ5TSvoagXes88eagOFTn9lnzWYptbVGVB7/bUPTaXXAZIR4+q1NRGUXe5RtTVUWFbDrvrQwKy7OUFbJwyr+wPHBI6f4grouvdFc3v/gQNl0x0q2/TaOu30QaIPr+Z+n9z4iyts+PPjj2aIu/qqHtexY1sZMfIMc99o4DWN7Vv3Q7Z88cV3do7AQe1MMFXvO1qx2t30mC/Ne1Vb/+N62veRe79uf2X2r1q13na3bt3CVs0NHKOVdTNnzLfdJ5x4rD3PB+9/4wvXOuP1/eW8xzT8e9WYC5xdFT7rlwLMmrnABo7d9wA9UMPFPXp0Mtc4uFKBYj1W78+61uXL1+pLGwK+8qrz7Lb7nzlzlsjs2Yvslxa4++Pj4+SkQf1k8OD+Ur9+acjZvT/c7SP5HhSuAeMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgtgJxsZ2e2RE4egXWrFkTMqg6efKUmOFkZGQEnTta1W01/KSVFrVpGLRLl/ZBz+neMWRIf7np5jH2ceFFp7p3HXXbixetkk8+zvQLyCqCBtw0wPfyy+9JYeHuiFwyTThWA6tOeC+iSYIcpAHYK64sH7hzhmsw9+GJz5nf/9IwtNMf7WcNyg7N6FduWq8qs+UGRdDRK0iFV53KhmXDCLJqNdgJmbn2oduhmoZpnbFOsDZwvFayjVZr1aqsmurixatsxeeqzr19+06Z9s4X5QKyOq9WYJ0y+TPRSqZebePGHJk69TPPgKyO37Rpm7z15ie2+qr7+K7dOvheZq3f5Nt2NvT9tWHDZuelbN26vVxIVHeuX1d2bNeu6b7x0drQatrP/W+yzJ271O8eoCF5beqybNlamfz2dNm/3xVgDmMB003w3gnIauVfrfIdWLl3rgnIfmkCyLqOwHbwYIkJ734vL0x6RzT4H0k7Gu5BkbhwDAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCERXgEqy0fVkNgTCEtAqsrFsWk02VBhWA7zdunWr0hK2bdvhO75LJQJkcXFx5cJavolCbBw3oKf06t1FNmVvlXff/dKObNYsxRfW1HnrUjtw4KB89tm30qRJsql22UO6du0gxXuLZfXqDbJo0UobTMvPL7BVZi8dcUalLs2p3lipg8IcrAFZrRSrTYOyGsQN1jQs26FDG/sINsbd/8jEl+TRh19xd0W8rUHZYCHaSCd1qr1qINar2f6VIhpcdca6x2ko1n2ssx3OWPc8zrZXlVpnXyTPvXt3lblzltpw5o4du+R/z06Rk08ZaKqZdpaGDRMimVLemfq5ObaBnHzy8dI+vbUNey4xFaQXmiqt2jQoq9WUBw7s7Td/Xm6+CdB+Kvo+0aZr69Gzs2j12PXrN8tyEx5du3aj7Nmz1wRlP5YxV19ow/o6tkWLptK0aYqt+KrvoZ07C83rZN1lW86WPBMA3ue8tM+6jp69uvj69u/fL5s3b7OvExIaSHp6mt0ec/UFNtCqAVANuWs7bkAvU3G1NKyt1V/DbZ99NluKivbY4a1atZDjTZXtzubLBuLNvWzV6iz59tuFssusXcPA8+ctk0GHz1HR/Fol1/FNSUmSy684x2fjHKuVubVCr7bGjRPt3Fq5u1GjRNlgLLS6rIaHNeT8zdfzbDVb59hwnuvqPSica2MMAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAII1C6BCkOyWsXsy8/nyEZTNVIrmWkYrmevznLqaSdJXFxptbPadUl1dzWrV2XJzb+6z4aAnvnvvVK/foU/nrp7saw85gIaxM3MnBGz8xQVlVU41aBnrJsG1fSR0iTJdyoNgOmjoqZBtG++mVfRML9qjhUOruIArWapj5GjzpTU1LIKnukmVNrWBAHfnfaFPcNqE5bbsjlX2rRNDfuMGkzVIKuGVKPZ3AFZnbei+XUNupZw26yZi8MdGta4WAVlNdQaGHh1L0jDr/oYn5EqGmTV5h6vfVqV1hmn+91BWa0eG6xyrI7V40eYNThza180mv4ennnWYFPdeKb93dy9e498/FGmDWq3a99aOnVsZz8DtG7dIuzT1a8fb0Ka54kG2p3Wrl1rGwSdP3+Z7dqyJdfZ5XvWCrLFxaVB1n79esg555ZVx9bArD7eeP0jG7LVEOyHH3wjoy8723d8127pNliqHetNNdn+/Xv49q3PKqsQ63Rq8NYdkt2wIcd3P+jUuZ35vFMawteq2drc952UlMZ+r505Qz1rddYEEx5Wc0T04LUAAEAASURBVL1OXbsztx6n69VKsp99+q2dxgnshppT9307a6F8913p+0jDr1pBNvD+vGtXkXzw/td2qgYNGsio0WdLWlpL39Tq0LFTW3n1lQ9sSHbBguVybN/ufmN8g4Ns1NV7UJDLoRsBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRqsUDQFGb2xq1y92//bgMXXuvvfkxHGXfXT0xY9kSv3fR5CGggbsH85VKvXj0ZMLBXuRFffzVX1v2QbR9r12y0VfPKDaKjzgisWbM66FozMsoCX0EHxXCHrq2qlWSdCoi6TA1jBWtaJbLkYEmw3dLPhMHi42NbBVbDfhoeq21NQ6TugKyzvp6mYuaqVV1kxfIfbNfKVesqFZLVgzTQql9uoBUdo9ECA7JaQTbU3HptekxNt0cfftkEhidEfRlOqNWpBut1gimmcmzvjIY28Ooep+FZbcvzin37eqUm2NCrhmlDBWSDVan1On8kfRpI1aqm0z+ZKTk5eXaKg+b9uyHL/C6Zh4bNtUrrSScdayog97R/z0Kdp0+fbn4BWWfsQFM1NVhIViunaiVbbRpGPf2Mk5zD/J7PO/9kU+12sv0CDw3C6j3JCZp2NV/oodVXtWUFhmTNa21t2qRKcnJjU705SwKDs3qM07qZKs/Rbhr4P/vsoXZa/WygnwsCm1bwdUKyXkHiwPHq6XwZgFb+HX3ZObaqbuC4FSt+sAFc7R8ytL9n+FUryp40qK989OEMG5jWSrvuIG3gnF6vuQd5qdCHAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC0RbwDMkuWbxafn79HyUvb6epQJYkI0efJb37dBUNXWiIc+qUz0Wrnv7ihj/Lo4/fKRpUoVUsoFXhxlxxp63Au3TlO+UOUOeNJpzczlSR7GFCcjQEarNA/fh43/IOHDjo2w7c+PKL70SrJgZrem+Jj08ItvuI7u/YsW3Q6+vSub0vJLspe1vQcaF2XHHlefLwxOeCDtFqj/rIrKDibGUDsnrC2hCQ1XUMGdpXn2LSbLA1rzQE63UCrRYb2NzVX3W/E4hdnrsv6pVhA88d7msNj/7oxxfbkOyypWtk7dqNvtCqzrFzZ4FMnz5LFixYYSugatA0WGtrqsZ6taZNk20wVAOiO7aXBmKdce6AaoeObUSrnXo1DdBqVdvs7K2+IKdWmNWmv9cNGtS3AdosE+512v79B2STGa+tc5f2vpDsLlONNj9/lwn0NrH7tLKs07p0be9sxuQ5MCCra9SQ8JLFq3zn27Nnr2/ba2PpkjW+QK3ON8p8nghW8Xf9urIAsDoFa1rx12nbt+90Niv1zD2oUlwMRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgQgEyoVkNYjxm1sesAHZrt3S5ZXXHpSmzVL8pr7xl1fIP596TZ74+0ty19hHpWOndtLHBN1oVRNoZpx/f++NVZuEoxEIQ6Bbt+5hjAo9xKnYqKO0Umttbi1aNpXRo8+ucIka9tXKlNXVWpp1BWstWzbz7XJX7fV1VnFDw3EaYHNasKBsJAFZZ87KPt92x9XmkJcre5jMzFzkecxtd4yR28de47mvqp1a8dVdHdZrPg3RatNgrFaA1fEaip2QmWv7nYCs3d+z9O+sPWal3e35j3NOp/Ks56AodWrlUH2cdvog2bWr0FRc3WCD25s2lYZMc3N3yJTJn8qVV51vA6lep01ObuTVbb4sIs4XktWgrLsVFO72vUxt2dy37bWh7xMNyWordB0Xb0L8ncxnE60Sq59rtCquXkt2do6polpa2bqLhmSTygK+GozVkKy+3/TatGlguHFj72uwA6r4j167hpB/MF9Akrtth2gYtaJAbOAp163LFq2k6zQND7sDrk6/81xQUORsyssvvRdWJe/tefm+Y6K1URvvQdG6NuZBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB6hMoF5LV8OuGDVukRYum8q//3FsuIKtLi4urJzfdcpVs2bxN3nj9Y3nphXflbxNuDbrqzZtzJWdLrjRtmiLpHdKChmn27t0nRSbk0qhxogmlJNr5iov32aq1Xbqm+/oCT6SBl3xTda1+/XjfektKDsmqleslNbWZtDSPcJoGU7LWbzEBmoPStVsHSUwsDThVdKxznI7rfkwHEzgpq7DpHJuXmy/7THU4bbo2fa3NvWbnOuLNdWhgNljLzy+QDaYynlbJ0yp77rCi+xhnPvc5InHZac63zlSe06ChBm/i4+Pcp2E7AoHMzMwIjqrcIZmZMyp3QCVHu6tHbtu6PejR1/1khN8+rSr73P+m2D4NyiUkeFeJ9Duoii/i4+JNVezkCmcJVRG3woMrOUCvO1iFTJ0qyRUu3LdvXyVnLx0eLPiqe/U+rw8Nqg3NGGAPCBxflYDs66996BfCLV1R6H+HZvQza5kQelDA3itGjw/oKX0Zq4CsO+TqeWLTqaHXESb06q4aO/JwCNYJyjrH6pjxGanOS3vMpIvbS6gQrg3KmiCtBm+deX0TxGhD3z/HH9/bPlatWi8ff5Qpe/cW+6rN9j+uZ9TOXGzmdVpySlmI1elzP2sg1GnF5jOEu+nnBg3JassyAVgNyTpVVPXve9u2rWxQVz/vaDhV9x1nrsNdybarmSNWbYv5bDR16md+4V7nXHpvbJ/e2v6td/pCPbuDxhpoXmoqAPfp083zEP25uZsTGnb3BW4XFJQFlwP3hXodeE9xj62N9yD3+thGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBuiHgF5LVAOXktz+zK7//odtseCrUZVx3/Qgbkv3ogxmmAuovyoVKP/v0W3lk4iQbcnXmadI0Wa750YVyy6+vsWFbp1+fp7z9qfzxD/+QG268TMZcfb789s7HZMH85aKhOg1m9jm2mzz08B3S2VR/czcNtlxw7i9tuOXFV++X341/XD7//DvZtbPQDuvYsa2M++1P5OxzhroP821r9bn7//YfeWfqF/ZcukODwMNPO1F+94cbJT09zTfWveF1nAZvjj+xj/zlr7fYgIszftiQHzub9tl5rYGc196aaPuc6+jUuZ18NP1ffuP1hVbxm/DXf0vmjAXiBGLq168v5194soy/52c22Ow+yJlPzxGJy+S3PpUnn3hZsjeWVunTudu2TZWxd/1ELrxouPtUbFdSINYB1hkzYhuQ1cvtYN5XTsvK2mzfO16BV3eITce7Kx5qlcl69eo50xwRz3ofDafpfU0r8AarUrnDBP+dltioobMZ9rOG02aaR6jmBFm9grJVCcjqOTUAp2vQeWLVNCDrVUU2VgHZUMFVvUavcKz72p1Aq1MNNjAgG2qse5+z7czjzOv0x/r5mGM62aqnTgBSK7lGMySrX5ThNP2ShlBNvzTCaY0a+79P3AFXve+cNKivuf9stsM7miqzzr2nc+f2NiS7YcNm+7fVGaMD3XM454nGs76/33jjI99njubNm8gxPTqZyrWtpHnzFPNoYr9w45GHn/f9vQ91Xv2ckmaq3s6ds8QOm/7JTBsKdlekdo5v1CjR3Hv22pfnn3+KBN6jnXHuZ/3yjsq2o+EeVFkTxiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggED0BfxCsvPmLhWtCKmB1EGD+1V4tm6m2ury1dM8x2V+s0Bu+dUEW5U1tVVzOfnkgbJi+TpZtmytaLVarUr2uz/83PNYDcX8/Po/yq5dRTbYGmfWo8HQRQtXyU+u/Z28/NqD0rZdq3LHHiwpkfF3PSaffzpbTjBBVa0aN2/uMlmzZoPc/usH5Z/P/F5OPuV4v+O0auyvb75fZnwz31a0HGbW2cwEVGbNXChffPadzMr8Xl59Y6L06t2l3HG33jxB9Do1qDpkaH9T/bWJfPXlHNs3esRv7PkGmsp72kZffrYcMJVkp0753PdaNzScFk7bYirxXv9/v7dV+zTgcvLw423V3W9nLZJpJtybtW6zPP/SfeWCyjp3JC6PTHxennn6TVutVn8Xju3bXeZ8t1gWL1otd419VLRCX58+XcNZ+lE75pxzzg157Q899KCMG3dnyDGR7pw48aGQh3br5l1hMORBATs1xNW6dQvZau4ZWolQfz8yhg0MGFX+5caNOb7OVq1a+LbrwkZ9V5VorXzt1XJy8ry6PfvycndK446NvPfl5fv6k5NCV9P0DTy8oQHVigKyzjFeQVnd51SX1W0do3NWtuka9B4X7n2uMvNXd0BW17Y8z78Cp3u94VZ1dQKtvVIT/CrNuudytnWsPkKFczUo68zpHBfJc575fZs9e5E9NLFhQzn9jEEhp9G/r07TKqzRbO7Qpq4rVHPvdx+nx2i1a+celZ2dYz9TbNtWWvW6iwnGOq2L+eKNefOWmsq4+0zV+zxfkF+rtGvwNBZt7doNvoBs9+4d5aKLT7WhWPe59POJ84UY7n6v7VGjz7aV6bduzbPVZ/ebzxvT3vnCfCnJRfbvuPuYFFOd13FzKtK790dj+2i4B0XDiTkQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg6gJ+Idm1azfaGbua8KtWRI20LV/2g9xy031SYkKrjz1xl5x73jBfxTYNTlx79Xh5cdI0W2n1Jz8dUe40r7/2kQ3H6rHxh0NpGpzVgOzSpWvltVc/lN/c7l+ZVSdZvGiVCd8WyYefPC2tTHhPm4b3/vC7J+WtNz6Rfzz5armQ7J/ufdoGZE8ZfoI88dR4cVds/NtfnpEXnp8m95jKtK+/9bAND9tJzT9/+sM/bRi2X/9j5Nnn/+KrxFZcvE8mmKq0r778gUx88Dl56dUH7CF/m3CrHDhQGpLVKrX6OtymFWt/fv2fbEBWvX5zx4+lYcPSn49Wy71z3CM20HvHbx6UJ/5xT7kKvZV12bunWCY99479men6+x/Xw7fUL0yF3l/c8Ge5566/y1tTHyt3Lt9ANioU0CBrLEKyWkU2VKXajIxhFa4t3AFaPVIrFmrTgJ1WeW7XrnXQwzdkbfFVOtRBffsdE3RsODv0/V2drVXr5rJuXbY95ebN20TDdYFNKziH25YvX2sq8pYPymswbpm51zmtY6eyqr1OX6hnpzJsVYOyeo5IA7J6rAZtj5SArF6PV6uoeuyEzFx7WK+WDX1h1sBQq4ZgNYCrY7zCs+GEZb3WVpk+rQK9dMka3yG9zZcgtAkREF1z+POCHqChy2g2rd769Vdz7ZTrzPupqGiPaGA1sGngVUP62vTLPTqZ6rCBTb/QQcccOHDQLzjurkifboLc9U2lVB0z13xZiP7N1abHxqpt2VwWptf7qPNZx30+9xcKuPsDt7USroZdtV144anywqR3rJkGYfX+fP4Fp/gd0rlLurmPld6n9DOZVrD1anv3FotWvE1Nbe6b32ucV9+Rfg/yumb6EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoGYE4tyn3bZ1h3157LFVqzL53/+8bQMa1153iZx3/sm+gKxOrsGJBybebsOVTzz2kgnSHnIvwW43bpwoj/z9Tr/QSNNmKfLrw8HYJYvLgjyBB9//4G98AVndp8GZ3959vZ1r2dIf/M6XlbVZXjeB267d0m2Y1x2Q1WPv/t0NopVglyxebSvEap+2rPXmOBPkbdGiqQml3u0LyOo+Da/eNf6ntpLtThNg3by5NCCl+yJtH7z/jaxcsU40kDv2rut8AVmdr0nTZLnv/l9Ly9Rm8un0b+1avc5TGZd585bZinq9enXxC8jqvKedfpK89uZEefn1BwjIekEH9I0dOy6gx//lyJHlQ+L+Iyr3SgOyo0aFnrOiNVXmjMeZcFd6epo9RAOrb7z+kSxcuNKG093zaGB+9eosmTx5umiFQ21aRVbDcJVtWh3SaRoC07mrq6WllVWVnDtnieTmlt4znfNrIF0rUobb1EorXQe2WbMWmnvHNttdr1496dGjc+CQCl9nmIDqHWOv86sIG+ogrzCsV1+oOZx9ep+/4srzRNcQ7VYTFWSdaxhhqro6TcOx4zNS7UO3A5sGX6+dli3LTPhVH1rx1QnMusc6VWLdY7TPq2lYVs+pVWudpq+j0bQKq7s6rIYrNSDp1b5fsNwEalf7dukXa0SzaSiz3eFq8RrU/PDDb8pNr1VW33/va9/7v0fPLn5/G50DunYtW9vixatst87vvo9oQDY9vTSsrsF1p0Vyf3KOrei5eYuyn6ETynUfo/dJvQ9UtmmYWIOyet/QtnTpGlm0aKXfNPq3vUGDBrZv/vxlsmxZ2TW7B346fZa8/NJ78sTjL8l887mgsu1IvQdV1oHxCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFsBv0qye/bstWdLbdW8SmddtLA0iHLjLy/3nGfQ4H4yYGAvmTd3maw1AbHux3T0G6eVS52qaO4dWtlOm4Y+vJoGVL0qU2r4J71DmmiFx3U/ZNtQrB7vrPOcczM8q9RpyOTkU4634ZBVK7Pk9DMG2dM6x5151mDPSnuNGiX6Ksh6rbOyfc75rr9hlF9w2JlHw7qXX3GOPP2P121AUcO07lZZFyecpMGZTz7KlLPOGeoL3Oi8xw3o6Z6e7SoIaMXXtLRW8vbbU2TYsKpVeH3ooQdFq9NWdzvv/FNMdecPbBVnrcT4yceZkjljvrRvnyZJyY0kLzffBj6dcKyuT38nL7n0dBO09svph7X0Jk2SpVmzJpKfv0u0crMGObuYyogaGO/du/QeEdZEEQzS94ZTcXLfvv3y4gvvSvfuHaSxCaZtyt5qqz1rVUen2myoU+g9Tqt3vjvtCxMW7mDn2bN3n6xetV7cFSQHmntl8+ZlgbpQc3rt06CahlbVqaKmY3S8VtbUKrRaZbKyTavHxiIcq+uYmbnIPgLXdNsdY+T2sdcEdkf9tYZhJ13cPuS8GnadYkKu+hzYvPq0gmxg00CtPjQMG1h1Vtegj8D+wDkieT381BNl8tvTbUXVnJw8mfT8VPN72clUEm1mq6xrKH3zpm2yZUvZlz+0bdvKvO+q9sUaXmu95NIz5JWX3xP9sgn9u63VUbXiqZ5PK1KvXLnOF+LVvrPPHuo1jRmfKvo32fl8o4O8KkBrZVn3+zZYZVrPk0TQ2blzO19l2y+/+E52794jnU0l3IaJCeZesk3mL1gmWyL8kg2tTq3vwRnmPqzts0+/NZ9VWpkvJij9bKdB2hEjz5C33/rEfqHBhx98Y0w32/tEi5bNjHmBLJi/3Pf+b9SooRzbt3sEV1l6yJF0D4oYgQMRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgZgJ+IdmWLZvaEy1f9kPEJ9y1q8iGUXUuDW8Gaxp20ZCsBkADQ7IdO7b1PEwrzGrbXVQa5g0c1MaEYbzCtTouyTnWBFGctvhwmPftN6fL9rydTrffsxMSW7t2o69fqz9q63U4tOvbEaONRd+Xnk/NgjVnnxOodY+rrIuGhS6+9DSZNvULueWmCaKvzzl3qJw0qK9owFkDjrTwBMaNu1MyM01o1IRhtWVkDDPVVKeY8NIMG2h1+rX6q+7LyMiwz+EGZjUYqy3ccKyeI9y57cRh/NPUhFN/9OOLZMrkz3zVT4uK9tgQm9fhWvX4ootONUHXsqqcXuNC9Q08vpd8/tlsOyTbhFP1ocHZWIdkNXB/oVn7O1M/l0OHDpmA2UFZsWKdb6laoVL3P/Xky76+YBuJiQ1tpe233vzEWmnoL7DpvfDkU04I7K70a+c+Fs6BmSYcW5WmgdxYtaEZ/UQDsY8+/IrvFNUVkPWdMMhGqHBskEMq7A4Vlq3w4AgG6O/bVWMusL/fu3YV2rCsu7Jq4JR9TNX5c87JsBXbA/dV9bUGOUdfdo688sr7smf3Xtm6dbt9BM6rnzNGjjoz6N9+/bILDcW6v1xD/6YFNh3zxedlvRry1xB7rJoGe7VCtL7vNez/zdfz7MN9viFDj7NhVa2mW9k2eEh/G7Zfv36T/TlOe+dzc5++2HdN+rO+4MLhJqT/pa3Gu8hUwdZHYNOAsX4RQlUtjpR7UKAPrxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKDmBfxCsq1bt7QrWrrEu1JrOMv9wYRJNTyWnh46KOUEqdaYSrI11VatyrKn1vDN6699FHIZWj3PaWtWl67ZuQanP1bPTkA3PT0t6CmctWhl3mi0+x+8TXr27Gwq+b1vq/g98/Sboo/k5MYy+vKz5bbbf2wrC0bjXEf6HBp8dcKw+qzBVg3Pahs1qjQ8q9u6r3RcaTXYnJyy3znd726RVo0dO3ace5qobTdu3MiE686X1auzZL6pQLjRowJpWlpLOfbY7tKvfw9bjbUqJz/++D4m+N5Ivv56rq00qXNp9UMNm8U6xN29e0cbhF0wf5kJBefaoKyeX/tPP2OwCZP53VZ1V9CmQbUrrjxPPvrwG19VTB2slRsHDOwtgwf3j0kAMeiCorBDK9B2MNcUq+ZUjNWgbG0JyE42lWM10Bqr5swdi+qxgWvW96mGKb/+aq4NvW/fvtOGKJ1xjcwXTqS1bmF/348b0MvpjsmzVlC+4opzzX1xgamwnGU/Wzgn0orOvUzlaK1SqkHOUK1r13RfSLZBgwa2ynXgeA3bapVqDQdr69otPXBIVF9rePfiS06TGd/MlwWmauxeU0XaaXoP0yr2xx3X04Zknf7KPOv8F5oQ7CRTgbewcLe9v3xsqnzrFxQ4TUO6l46Il+9mL7JfNOD067OuQd1OO32QOF9Q4t5fm7djfQ+qzdfO2hBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQOBoF/NJcJw3uKxqs0FDMli250qZNakiT/fsPyIuTppngisg1P77Qhio6d25nj8netDXksVr5UZtWb6up1smsVau33XTLVTLCVKIL1dzBO61Cp8dlmQptIlWv8hjqvLqvc5d2smL5OtmUvU26dPX2irZnfHyc/Ozno+1DKwvPnbtUvv5yrnz5xRx5/n9TZaWpnvns83+xvy8Vrf9o3+8EYp1qr86z9r/99hQTlB1Rjkj7Q7XAOUONdfZpQDbaVWSdufU5Li7OVkbU4JVWWNWq0kUmnJVoAp/JyUmSmBheBWINfN8x9jr31J7bPXt1EX1oMLawYLekNEnyq3aoAdRw5nFPrsG7cI7RALk+DhzQ6ywUrTDrrmIdbA69vwbua9++tfz0+lGyZ89eycvNFw0halVcfQ9Gq3mFlqM1d03Mo0FZJyxbE+d3n/PaadnulzHbdqrKjs9Ild4tG8bsPDqxhrTPOTfDnuPgwRLJy8uX3aYKu1ZK1i9KqKhdGWZI+rbbr61oKnvOSy453b7P8/ML7PtE19C0aYrfey7URM69ItQY3XfDzy+raIhvv4Zz9VFRC3y/B44fdvJA0Yde244du8x1Jdsq23o/1XbTzWMCD7Gvw7lP6r3kxl9c4Xm809mtWwfRh/58d+0skn3794sGhsP5OTtzhPN8pN2DwrlmxiCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggED1CPilsFqb6nAnnnisPfO/TdXQitrHH2XKAxOelXemfu6r3ti0WYp07NRWcrftsKGPYHOsWrne7up/XI9gQ2Le75w7P79QNHAS6qE2TuvX/xi7qeHR6mj9TeVNbY6Z1zmdfc41eY2JtK9X7y5yzY8ulKf//Qd57c2JNhg7M/N7qUrF4UjXUlePc0Ktzvo1KKvVYLX9+c9/NWGrYc6umD0HriFmJzITx8fHi1aBTDfvKw3WhRuQjWRNGmBvmdrMLyAbyTyRHKOhWg2UuQOykcyjx2g1TPVq2bJZVAOyOvcGj8q+2h+LVp3nisX6KzOnVpCt7jalms+pYW39+9e5c/uoBycrY6fvc61yq+vQe0o03nOVOX+sxzYzn530S0P0fuIEZGN9Tvf8Wg28TdtU0S8XiHZAVs9TnfeF6jyX25BtBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqBkBv0qyuoRf3nSlfHfdYnnpxfds2PX/fnKp58o0BPv3R1+0+y4deYbfGA1qZq3fLP955i0Ze+d1fvv0xby5y2T+vGXS2FQ562oqmNVU69evNOyqFVILCopsNcjAtUz/ZJaUmEp6Q4cd59vft193O+zT6d/KTbfmSlsTLHG33bv3yo0/+5OtMPn0v+/17dcqktpKSg6Zqm177fW7jwu23c94vvH6x/LsfyfLmWcPKReg0wp0b5r92vodDtQGmyuc/tWrN8jXX82VgQN7yQDzcDf92fYy1TuXLVsbMgTtPobtUgGt5OpUkXVMtLKrPvr27Wu6xjndYVV8LQ3WPuQ7JtRGRZVpQx3LvrovcIWrsqcTIJuZuSAqFzbUVNPULxg4GtvyvGLPyx7Zo4mM7JkiEzJzZVmQMZ4HHu6cdHF70QCuVo+lIXAkCHAPOhJ+ilwDAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAL/z959gMlV1u0f/6X3Xjabsum9kE4SSEgg9AAJwQhEBBX1j8qrFOWFV1ERBRUQ7IqISg9Eeg0lhDRSSEgjvSeb3huk/Z/72ZzZM7MzW2ZnN1u+z3Vt5vTyOWfOnMk19/khgAACCCCAAAIIIIAAAgiUTIGoSrLaxCFn97YvX3OR39pf3/eYD7oePHg4svUnT540BUfHX/O/Pgjbt19Xu3b8JZHx6vj6jVf6AOg///FfP63mCVrm5u32w9sesOMuePqd712dI/AZTFccr23btbBLRw2z9esz7TvfuteOHI4OPM2ft9Ruv/UB+/7N97sqrusjm9TeBXuvGD3Cdu/eZ9//3n124MChyDjt1/2/esxmz1rkK0OGA7SqrtmxU2s/7eQPZkfmyavjoovPtg4dWpm25+GHnrCjR49FZtG6f3zn723btl02/NwB1r1HVoA3MkESHe++M8N+7fbhR7c/lMNkzuzFtnLlen98+w/IqjqcxCrK5Syq5KqgbNDC1WSDsGzwGkyTiletU8ullV+BcJXsIS7Uqr9UBFu1jGBZ4XWUF+nRLggbbl0bVbM7hzT2Adnw8GS6FbJVWFaB23Dr4tZBQ6C0CYSvD1yDStvRY3sRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECjZAjkqyWpz7/7ZTVapYkV7+qk37IHf/Mv++PunXVXZ5la1ahUXjN3sKqQe9HvVpWtb+/PffmLVqlWN2stu3drZI3/8X/t/3/yFfe+mX1paWiM7a2gfW75snS1etNIUmlWw9sZvjY2ar7h7VNn1/t/eYjt37rGZMxbYoAHX+sqp7dq1tLVrN9uM6Z/6bb362otNYeBwu/e+/7HNm7bb7NmLbFD/8TbwzB7WqHF9U1VaVXatV7+O3XHn18Oz+O4LLzrLBW7X2Q9d+PZvf5lgffp2tZ/d850c04UH1K1byx7958/ty1fdbo/+7QV72lX5HTqsnym8PHPGpz4027NXR3v4kTtSEjoed/WFNvH5ST4EPXTI9XbmoJ7WuEkDb7R2zSaT283fvzbHcQ9vM93xBRSUVQsqyup1+vTpftiQIUP8q/4JposMSLJDAdlULSvJTWC2MizQspxWkA0OqUKxCrIG1WLVn+qmsGyXxtmfsUWxjlRvM8tDoLgEyvs1qLicWQ8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAiVZIG5ItlKlinb3z2+yc0YMsH8//rIPiy5fttbvhwKSqsB64zfH2ugrz0sYylSI8+E/3GEPP/iErVq1wf77wrt+fgU+r772Evv+LV8pES5VqlS2P/7l/+z+X/7DXnv1Qx8EVWBWLb15E7v1tq/aZVcM9/3hf7Lmu8vuc/O9/toUmz5tvh+twPDAM3vavb+62QWL08Oz+O7v3ny1ez3pXF+xZUvX2vZtu/MMyWpGbctj/7rHfnXvo/54vPXmVL88VadVNdy7fvItq14jNQGthg3r2X+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QQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEcpIAIdmcdDTZFwTSKbBlyzbbtWuXqfrr/laQjbZqLXPXrvW2w1UKi0fTtmqbVd2Oln0FdD7onEutLVy41JYvX+WrB6tKa8GCB6U2eYpxq1attaVLV9j27dutYsVyVq5cGVNgNS1t9eq1Nn/eYr+N5cqXtUMOKZ1itkWLltnu3bttwYIlftxC9z14XKpUCb/dGrFt23a/H/ny5fUVZcMXFExfuXL50LatWLHaFrl9r1a9kq/0Gj59rMdbtmz1+6p9lpMq1mobItvGjZtszZr1Nn/PNms/g22I3D7Za9vD9yVyeXquZSxZssK2um0oXaakVa1a0fLnj31sg/Xt7z5H25ZgmCpPL1my3JYsXu5C9UWtoqvkq+/7ajt37vQeq51jmbKlnKP2Jd++ZguN37lzl82Zs8C0nFq1qqX5nA1f78ElivvzpGjRwqHlRj6INNT+qpqxjledOjVSbLPGL3YWy5atdK+FQ6xChUMsT548kYtN9lzz6Fxc4c6DLVu3+fOgZs0qofM02cTuiV4Laml9jfmJ+S/hAtu377Apk+e418cqq16jotWrW9Xy7TnHp06d67enbp29wzRg/rylttm9viuULxOq8jlj+nzb5Y65lqU2zc0bzB8+nR8Z5b8lS1ba5ImzrbZbV42aFaNMkTRo5cq1pi+9fmMFdZcvW22r3ftaSffaqVCxTMxlaYR+9kyZPNft/0q/vIaNasa1On2wvaVKFrfyFZK2Zd26ja7a7iy/7lq1Knn3WBsZbf4F85fZ6NGTrWbNStayVf0Us2r506fNt5Ur1lrZQ0pavfrVUv19LtrxTLFQN0DHWNMWLHSQ1XXnSbA/0aaNHLZ48QqbPWux+/m7w5o0qWXlyqf8Gbpt2w73frnINm/aGppd59CmzVv881q1KpuqmsejBftStFhha9K0dujnc2rL1nug9n/BgmW2ccNmd26VtabN6sQ8XzJy7OQ0c8ZC31GNll269MGpbVKKccHrSMeoevWKhJ9TCDEAAQQQQAABBBBAAIHcKaDrUz//PML9vTHXXZffbe2Pbm0tWmS/Dvxy59FjrxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE9kcgdpJlf5bKvAggkOUFdJOMQlVqmVmdVctevXp93Dy0zQpOKNRHy14CCmkOHjzMFsxfbLfedkXUjZ/hbuB6/70vQuFNTaTg3fHHH2nnnHty1HnCByrcOeD9L23ChGnhg33I77zzT7XWrZslGx7+5I/f/7aBAwfZhg2bwge7kE89u7zHOVay5N4Ay+OPvWyb94R5NPEPP/zuv/T44ku62bHHttVDmzN7gT355Gs+lPNC3wf8MP2nAM799z3nnz/33P9s0uSZ9vlnQ3zoNJiojAudXnnleS70VCsYlOz72rXrbdC3P9uwYaN8MDMYqQBko8Z17dJLz0xWDXbkyDH24QdfB5P578E2HHxwMXu+z32hcf37f+KCV7PtwotOt44d24WGBw/krOM0adIMvy/BcC3nxC4d7MQTOwSDQt/jsc+hhUV5oBD9wI8HOY+/fDgrmERB1/btD3f70jVmiPP773+z7wb9YuvXbwxm82HTJk3r2+WXn+2OX+yQrYJgr736gU2cOC3UIYDO2YYNa1vP6y52of5CoWVGPoi2Xk3Trl0rd76fYvIMb5GGo0aNs6+//tE2bUoKlum9scMxR9j57lxX++abn+xHd26Gn9P13fnUw53PZaOEvzXPP39PsK+++sEUVA9vJdz5f5I7tp1OaB8+2D9evXqdPfvMm3Za1+OtTZsW+wzhplgAAzJd4MMPhlqfZz/yodNgZYWLFLKHH7naunbrYKeceIsfPGJUf9epwN4q0nfd8aKNHjXJ7n/wSru4+0l+mnPP6uVeK3vfJ9/u/63pS+1Bt7wLLzrRP478b+iQP+3xR95y59by0CgFA++9r4d1Pf3o0LDgwYD3BlvfPgPde0pbe/GVO4PByb6/9urn9s5bg+zsc4+3x5+4Ltm48Cdv9f/Gnnz83dBrVOOqVClnd997qV9++LQZfRxs73kXnGD3/u8yu+P2vjZ08Mhk75GHt2lsj/bu6UOvkesJn/+aa8+wHpc9YrNmLvSTXXhxl2Qh2a1bt9vDD75pX37+q/tdbkdoUQULFrDTTu9g9z1wRdQORaIdz9DM7sFvw8fY44++7YO34cNbtKxnD7lj26hx9J9Hmnbe3CX24P1v2PBhY8Jn9SHZu3t1d+8Pe4/x7NmL7NQuSedcMPG1V/UOHtrQH/u6EHWV0POMPIi2L/r52KBBdXvymRtNIelobYg7Zn37fOzC3/OSjdbr4sprutlllye9v4aPTM+xU7C7190v27Bf/012bhx5VHPr8+Lt4YuN+vizT3+2Jx57J8Xv1u2PbmlPPHV91FBy1AUxEAEEEEAAAQQQQACBHCowZswk6/vCO/vcO12b1DW/jLQPP/zGfnDXksKXEaz3pJOPtbPO6pKRxe73PArIPvnEqzZzZtLfM/ob6Kj2h+33cuOxgKzgE4/9yOxl6LOPm296yH9u0+OKc+3IIw/N7FVmq+Xfecfjtm7dBnvt9cey1XazsQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAukT0L3sX335gy921vuJu2Lem65iY9f1vM9q167m7t29PrSSB+5/3ua7zMrrbzyWagGw0Aw8yDECpMxyzKFkRxBIn0AQkC3iQjL7quqZviUnn1rL1jri2YJtj+cyWVbmCSjA9/nnQ11g6HEb/J0CPUnh7Mg1qvJrn+ff8gFZ3cRVs1ZVa9a8oQ8rKkwYGfCMnH/jxs0+qKeArOavUaOKHXpoEx9QXbNmnb3y8vv28cdJQa7IeTW8X7+BPkyoyrFHH324CznW8aHKiROn25tvfJwszNKyVeMUgVsFcPVV7pDUqxlGrltB0/5u3Qp5HnZYU1etroGvQqrKsM87D1UnjWwK6D76yEv2008jfEBWAcYj2rZ0FWgr++2c5Lb56afeSBb6LO+q4mr7wqvzBducnooSClwqECkXhTZVFbZly8Z+mxUyVVB1iAtDp9Yyss+pLU/VWJ/o/aoPKqtapI69gsqqbKsbBH/5ZaQPFEdbhm5q/Mjd3KhtV6C1lTu2lVz1Wc03dsxke/CBF0yVg6M17f8br39o48ZN8fO0bdvKn7eqrKp9fP65/jHPdwWrg/UWLVrEDnXHXtutc3fEiH/t3l5Pmc6BWG3c+Kn20Uffuk4Oilkrd56rirACu9qfoUOG+9faF+51d4g7HxVc1TmiNs2Fn192rwVte2TT6+yll97zAVkFfevWrWGHH97cu6xzoezgJtDI+fRcr9/XX/vQ/u9/z7rKl+OjLj/afAzLfIGvvxpu/9frVR+QTQpSt3LnTAPb5qoE335rH/vqi9Rfr5Fb2OmENtbl5OTheT3XV/XqFSIn98/Hjplmt970rKsUutWO73S4/yrlArLqROPO2/q4c/bPqPPFY6ACso8+1N+fk23bNXXvlU19BV2FdW++4Rkb9dekeKwmtAy9tq69urcN+W6EC7oXtc4nHhEKfGpdV7jw69q1G0PTRz5Q5dKrrnjMB2RVQfeo9s3d+0vZ0GS7d/9nt9z4rH384Q8+IKvw5okntfPhZgVmP/n4R7vZjVdnKOlpY8dMt2uufNwHZHWeHNW+hSm4qeCtxnU77Q4Xuh8VdZFr3HG8/NKHQwHZhg1rWEd3nAsXLmgKhd5603P2mAtIB00uOl+OOXbvzb6q8hqcR8Vc1df9aeH7oo4SdNwVUHZvrzZlylw7+4y77btv/0ixirf6fW3XX/ukD8iqyvJhrRvayacc5X+PWL58jT+PdD7Favs6dlu2bPPH9tdf/vHnY9Vq5f2xK1OmhP3x+zjrftH9yYLcketRgPkuF77W60bznnNeJ79v2laFgu+47QXeeyPReI4AAggggAACCCCQawVKlSphTV3nZ7G+ihUrkuNspk+f4wOyur70yKO32auvPWrN3bVVWvYR+OefiaFriSP++Cf7bDhbigACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAcBUa6e+nVVqxYbSrCRkMgrQJUkk2rFNMhkIMEFOBQmEqtSJGCmb5nWsdmF4yJV9O2K3ihMBkt6wqoZ47vh/5mCt7psVrNmlXt1NOOT7HRmzZt9gFZhT8VjL3MVUENQn063oMH/+p7A1FwL1pTSLLvC2+bKpyqCsQVrgJrUIVT5/svv/zpK59qew47tKkLLFUPLUYh3h9/+MMHYm++5fJkVSQUvH30kRdt8uQZPoB5wp4qmqrEqbbiwRds7tyFrrLc2a5aaevQMtPzQOHc07udYKee2jE026pVa1wQtZ8tWbLcB4xvi6i8+9Zbn7oA5RpTuFLbrN5PgrZs2UofoNX3334bbSe7ChZqzVz4Vl/jXaBT4dvSpUvatT0vCmZL03cdCwWZ5ayKpJdedpYpfKumcKrCkf3e/Ng++eQ7K+0q4SpgGa1lZJ+jLScYNtkFUlUlQ/t09z3XWtmyeythKmT8+GOvuOqoP5oqw+ocDJqqWChoqnaZ25d2rjpDvnz5/PMli5e7MOl7PiD72WeD7cYbL/XDw/8b8+8kX2H4scfvcAG1veFo3dD2sgub6pdyVWbVcsPb2LGTfUBW67r22gutRctGoSq3qobw9FOv+/W+++7ndos7vtHau+987s+7o446zI9W+FxB8uHDR/mKyBqo46sgtJpeB3+74/Oqq3qrc1bB3vBw9Lx5i+xTd9zUjjn2CDv77JN8OFbP9fr68ovv7TsXcv/MVTzu6Co7h78WVWX5jDM6u9f677Z48TIfSFdAuevpJ/jQsZZBOzAC//4z1e521WDVbrz5XLvyqtNNFWTVFGy8684X/ZcfkMb/nnj6Bj9ltwV32ITxM+3xJ6+3s8/Z+/4VbTH33vOKXe2qo95+5973nHXrNtp11zxpf46c4KuXKkwa76bQaO9H3/aVZnu56q6qbq+mdT/1xHv20QffW89rnrDPvnjCqteo6Mft73+fffKzD6g+/exN7uddex/I1TJVaVXrUoXS69z3d95/wHcCEbm+b7/53Ve5Hfx9H6tbb+97ezDdIw/1c2H4v1yl8BL2yut3+wqz+n1Ir3GFQ3u6gO5PLsz60ANv2oMPXxXMlur3hQuW2dUumCuvntefZdded5b/PUszaVivu150P4OH2333vurDpgq5Bk0/F6656nG/f591V3YAAEAASURBVE2b1bGXX7vLKlZM+rmgUOgH7w/xx7f/m1+7n3UdfDVahX/7vnSHbXG/GzZtdL5flJ4r+Lm/bcH8ZXZVj0f9dquqr6rYBje/r1y51gddv/n6N3vgvtetU+c2oY5iJk2cbU/2fs+v/oILO9sdd18SOl/8z77nPrLXXvncnn1qgF3S/WT3syLl7yOpHTsdH4VYJ06Y5ULPh9gb/e+1+q6qrZqCz6ose61znOpCvNGaztl33vrWn09vunlVOTZoa9dssLPOuMsHbd92Id7LepwWjOI7AggggAACCCCAAAK5VqBR47oWXL/LLQizZ83zu9q2XSvfmVtu2e+ctJ9BMFadOE6dOst1krTWX+/MSfvIviCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQmoDuv1dWQZ+ZKSSrz9Dq1auZ2iyMQyAkkPLu3tAoHiCAQE4VUJhNTZXCgkBYZu6r1qF1xbMF+xDPZbKs+AgorPftNz/ZnXc87oKJP/iAbD0XqLzt9ivt/+67IVkINVjjzz+PNIU6Fba74opzQgFZjde5c5oL1irgqQqd0dqvv/7lA4kK5vW87uJQQFbTKjx03HFtrctJx/gQ0cCBg5ItYuLEab6Ka6VK5VJsm4I1CjFeddX5piqhmdEOcyHG8ICs1lGmTCk777xT/OrmzV2UYrUKMHY9vZPdcMMlyQKymjCoGKvH0arQanhG29Ahw2z27PkuXF/Yrrr6/FBAVstT1eh27ia8Ll06eOe3+n8SqnwQub6M7HPkMsKfj3GhUzVVgQ0PyGqYQrE9XVj0ppsudVVv91a5VGCpf79P/LYqSNzeVQ8Ofz+s6M4HhZ91/owfN9XflKblhTct45ZbeyQLyGq8KhgfcURSgEmB1PCWtN6Bfr0K5qoKbHjgtESJ4narC0UXLVrYBRCn+oB2+PzB4/ZHt7YgIKthqiR7yinH+e3VOrTcICCr8dqP1i603KBBbT11Ydbl/nvwnyoCX3PNhT5UfdFFLkjpquoGLX/+/Nap01F+GQqLRVbWVaXGU1zI+6mn77Fz3XmrgPuCBUvsxb7vuEq8fXwwO1gW3xMr8IwL9OmYHXvcoXbDTeeGArLaClVyfeqZG61C+dKZvlEnuDBieEBWKyxRopg91runX7dCmqlVV92fDaxbt5o98NBVocBjsO77H7zSGjaqaQoYvtT3k/1ZRbJ5VYX60stPtdPPOCYUkNUECuH26XubFSp0kP3150T70QVdozW9Vt//8KGoAdnRoybbu28n/QxTOFkVgTW9mr63bFXfej+VFGIe8N5gv55o64gc9tgjb7uOF9b5sPOtt18YCshqOlWSfeLpG1216wbuosMae/P1L5PN/uGA711nAFN9aDc8IKuJ1KFJjyu7hirGfjBgaLJ5M+PJ44++7Suttj68sT/uQUBW6ypbtqQ99Og1Vs6d86rGOmTwyNAm1K1X1fq8eJtd0/NMuz/ifNHvId0vO8W9V+dxv9Nsc79vzA/NF/4gtWMnI1UXVrj22T63hAKyml/L1WtU9rEqAA8fNsZXOK9Tp0qygKzmL1mquL3w4u32zPO3WNdux2gQDQEEEEAAAQQQQAABBLKZgP6WXLRoqe+oLNqmq4NB/X2fWtu157qpri2lpSmAuWrV2qiT6trS2rXro46LNlDXbNev3xhtVMxhu3btsg0bNsUcn9tGrFmzzqZMmemv9QbXsUeOHJMuBp0n6uwuPS0jx07nojqXTE/TOZXec0SdCWo+GgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQO4RGDHiH7+zF7r72ZUtUREvskO55/jv755SSXZ/BZkfgWwooJtu1BR8SFTTuvZ1I096tkX7EO/gbXrWz7QpBXR8f/rxD1/1NbhJpqmr3KnwXN26NVLOEDZk3p4g4UkurFis2N4KdcEkCp50c5Uqx7vQYLQ2e1ZSYEWVXgsVil4dWaHFwd/9avPnL/Y31wTBouA8UvBPlVsrViyXbBXVqlc2fWVWa+IqW0Rrdff0eLJx4yZfNVbB2aCpamnXrp2Cpym+y/uXX0ba/HkpA7YpJk7HgFGjxvmpFSotVapE1DlP79bZhg0bZbopS9VKo1WTzcg+R13ZnoEHHXSQfzRmzGQ759xTUlRnVKXWyKZQtrZR4VKdW9FarVrV7MWXHvTni6aLbPrFu0qVvcHb8PHqsWbkyH991dbw4erZRq8PHc/ICrPBdLJVpd5/XaVahaQbNUp5jjRpXC+YPPS9rOsxR2HXhQuXpghPBxOVr1DW33C3auWaYJD/riCsgrX6itYUfFWPPNp+vYYUSI9sMurc+Wjr2LGdr2Ks19s8dw6qcnHNWlWtmzs3VOmZlhgB3USo6phqd93TPRSmDF+7gqqqOqnqpJnZju4QvZOBatUruJB5KR++VIXNo9pHrz69P9t23Q1nR/19R8F+BXd7XPqwTZw4a39WkWxe/Wy59bak6qjJRrgndepWtTPP7mgKsGp/TzypXeQkLshe3apUjV5RddC3v/vpOxzTyo7rmFRFOnIBwbiff/rbBrmqtG2OiP6aDp9v7Njp/ul1N54TPjj0WEH4Licd6cOwwTkVjBw7Zpp/ePkVp4UqyAbjgu+qeKtwabTqq8E08foebM/dvS5J8bNA61A14Z9+fdlfMAl+/mu4Hp/Q+Qj/peeRTedp1WoV3HvyEps8aY47TjUiJ0n12Ol4qx3f6XA7rHXDFPNqwJVXn26vvPSpu0k85Y3GChyrzZg+32bNWuTe45P/XtKocS1fpddPxH8IIIAAAggggAACCCCQZoExYyZZ3xfeMV2TPOusLinme/KJ13xFz+f73JesU74UE6ZzwAP3P++vQ7740kPW782P/TU0dTz4wIM3W7VqlfzSdE3w++9/d38/TvPXVzSwQsVD7MwzTkx2/eaHH363Dz/4OrQFr74ywPSl9vobj7m/jfJbsJ9nuY73KrplfPrpYFviOlBr2bKx3XBj99C8Y11HdH+6YOakSTP8dTNdZ1UnbJqvePGU12zVQd9HH37jt0/XhnXNsom7HqzO1tSRX7SmjtU+/ugbmzlznv+8QNebju7QxnfcFm363DJMgVhdy2njrmHLfMD7X5luANB12GjtQ+f+w/e/2f/+73oXaN5gAz/+1l+309+3DRrWcR0+trQ2bVokm3XLlq12Xc/7TNfsr3EdQ7799me+czudexUqHOKHn+Q6mtR1wMimbdO1f13vnT59jv+7WoHsTu56fOfO7ZN1Phi+nitd55PvvPOZTZ0yy59Tuu6pDhTVQWRwfT58Xbr2OHTIcJswYaqtdNcv1ZGfPHQTBA0BBBBAAAEEEEAg6wlMnTrLpk1N+jwyI1unjsFpCCCAAAIIIIAAAggggAACCCCAQCCgDmFHjxrvP5vVPef6vGvo0OH+885omYRgPr4jEAgQkg0k+I5ALhIIqlQpIJKoFu91BfuQqO1nPbEF1Ov/Tz+NsO8G/eJ7g9fNLQraqapljRpVYs8YNmauCwKqRQveBZMpvKplR+s9PqiYqn7lZ8yYG8yS7Lt6xVelUN30s3r1OhdSLOnHN3Q3DSnsqOoIqnrZrt2h1tZVRFUFUgWDMrvFCuDqJjTd1KQbzHSjU3hINnKb1Kv+0qUrTMHHRYuX2ai/ksKs2td4Nf3SGVQQrVEz9nGVmYKj06bN9jfIRfuFNB77HL5fRxzRwn50NwSuWrXGVzA+9tgj3DnY1FQdONrNVpo3OGequLBneCXX8OXqcXhF1chxqZ3fB7vwodpad2zCW7Bes/9inquaPgiLK7gdremmyGgtrzvH1QoVjB4Wz79nvF63qTV1RKB1r1y52t+QNt0dT92kprav80o3Xx57bFvr4G5w1A1+g7792ebMXmDPPvOmD8yfeeaJpurStMwVmOvCfApk632kZq3kgbrwNderXy38aaY8btK0dszllnHVPVWhdMXy1TGn2Z8RqhAaqymQqjZr5kLbunW7r/Iaa9q0Dq9cpVyyir2R8zVomLTOWMFcVZyN1YKAatNmdWJN4ofLWyHZYPrUJl6+bLXpS23pklX+K9r0BQokvbcooBnexo2b4Z82bhL7Na3fAeP9e2D4NgSPtf06lxTGrR8lxBpMp8BpEDoNhkV+V69js2YttIULlvuv0aPUacESP9mWzVsjJ/fPUzt2wfFObbv080pB6jH/JgWPw1fStl1TXwFXx+r0U26z013FWFUrbta8TkJsw7eFxwgggAACCCCAAAIIIBAfAV3j7N9voO8UsE7dGqbAYREXCFTTdTiFd3WdUyHSDse0sXXu+qBuQH/xxXdNN5IHHehVd6FaBRUVmFTTcg7fE46MvOY114Vav/7qB3+dsW3bVq4zoL1/gypI+2Lfd/31UF23UchV14N++220D0Y+9PCtyYLC6szwxb7v+Out6uBN13W1fb/8PNLPd9/9N/qArt+oPf+tdpVrn3+uv+98r3371n7/1BnbZy60Kw9dT86tbaQLxOp46Vpq0aJFfGBVoWVdS9R16lht3Ngp9q279qbz5Ljj2tpiF35WsHqCOz553d+Zrd3yIpv+5u3jOrXTshu6Y1fSnTM6ngpc65y7+55rU3RQOnDgIB9e1ba1atXE8ru/9Se7MPWnn3znrj9Mt9vvuDLFdditW7e58/htf32q9eHNbLHrpFIBW52r27dvt+7dz0y2aerQ8Lln+5k6NyxbtpQd467xLl+2yv744x8fsN29W1f/aQgggAACCCCAAAJZReCrL3+wr9zfF/vTpk6bZXfddc3+LIJ5EUAAAQQQQAABBBBAAAEEEEAgBwmoYNbmzVvs+OOP9J+dHeE6hlVIdoT7vChaJiEH7Tq7EieBxCXk4rTBLAYBBPZfQGFBNQUGE9Xiva5gHxK1/awntsCaNet9xQBNoZtkbrv9ijSHYzXPhg2bXGh1rR76Kpj+QZT/FPQKKlmGj960aYu/cUbDdGNZWpoCpUFIVmHUG2+61F55+X1bsWK1/frrn/5L66vfoLbvgaRNm+aZ9nrJaBBXNywNHz7K9/Yvw/AWeQNc+LiMPlYQN2iVK0evnhoavycku84Fj6O1jO5ztGVpmG4U63HFufbeu5/7sPMXX3xv+lLQtHnzBr5iq8LQ4W3OnIX+abWqSdU5wsel9XG+DISog/WucjcFPv7Yy/tclc7VRDVVedCNj6oYohsUI99ndV5FDktt2zT9kUce6oLnrXxQ9vPPh/ob7T77bIjd06tnarMyLg4CQUCylgvIplbBU6G8zG6JCEhG2we919SoGfs1Xr5CGV9ZVJU7p06Zay1a7n+l47p1Y3cioG2sWzcplDxxT5XfaNsda9gKFwBVSy34q/H16iWtQ4HRfbXx42eGJjnv7F6hx7EeLF603HfeoJ+Ra9dudBXLl/pJGzaqGWuWhA0fPz4psKtjXqhQyurf+9oQhco/+fhHd8P4bzZ58hzb5ToLCG96z48cFj4+tcfB67FeKqFtzR8rJFu0aGF77Y177Mbrn7YF85e5KlFD/Zf2U9WCTz2tvZ3ivuL98zW1fWIcAggggAACCCCAAAII7J+AOihbtGipPd77Th9sDV9avzcH+msoZ5zR2U45tWNo1Jo16+yxR1+yb7/52QcVFUxVoFVfRYoUMt2kfv4Fp8X8gPiffybaBRd2tY4d24WWqQfz5y2y1179wEqXLmH3/u9636FgMME3X//or7N9MOArX300GP6z6zRR+3DDDd2tabMGwWDfmaIqxf7lOvHTdaHw9uefY+xcV0G0c+ejQ4MVzlRwduiQYbk2JDt37kLfOaEqvB58cFLHe/rAXyFZfeCfWkj2m29+sm7uPDk17DxRR3fqDLKfu1Ze3lWIreaC1OFNYWZ1GvnU071C61NIeYA7xjqub/X/xK6+5oLQLMN+/csHZHWcb3LX0YNrz+qET2HbiS4kq2kUag1vun6t3r3vuvua0DwLFy41VVIePmyUne0qFBcpUjg0y0svveev83frdoL7O/f40PBgf3TtUp3z0RBAAAEEEEAAAQSyhsD+BmS1F/tThTZrKLAVCCCAAAIIIIAAAggggAACCCAQTwF9Nqamz8rUqlevbCq0ps+j1q/fGPpsy4/kPwSiCPBpYhQUBiGQ0wWCSpx58+ZJ2K7Ge13BPiRsB1hRTIHixYvaYYc1Nd1kpd7edWNM5xOP9pUkCxbcd0glPLylXuxTa9HH7+1BvnnzhqlW/gyWrbBJeFNF0Ecfu91VRZhq//w9wfdor+CuetzXl6qU3nTzZSluWAtfRiIf/+wqMgx4/0tfYUEBdN0oVblyeStXroxVdgFVd0+Tr+QQ323a+36xz9efNkDNVStIVFMQs1mz+vb36An+BrKZM+e5KgWbfKUBVRvQeAVpg8qywZapMkciW7jdEUck/QKf2vqDMHdq08RjnP5weObpN2zBgqRKiSXcjXI1XcXg8uXKupvpylrt2tX9OadqD+lpqkY7yFWZVvBWTTe+tUnDfqdnHUwbXaBEiaJ+xLp1G6NPsGfo9jhWnE51RQdgpELdumE3VnBQFUiCnyuxpknvZm/btiPVWVQdXK1ABkL2wXtq8BYba0XB+OD9LtZ0Gh4efD+t696blFObZ9eu3X50+Ft8sF+pzZfZ44L9zci2rFy51i696EFX9Wiu38xy5UpZ0+Z13QWWCq4ScyVr2aq+PXjfGzZ61OQM7UZwfm3bnvrPnNRej6ogPPTHvq4q0z82dPBIvy1Llqx0NyL/67/eeXuQvd7vXtehSMkMbSMzIYAAAggggAACCCCQkwT+Hj3edYY0K+ouqYLpaV33BvCiTpSggeece0qK6426RvPXX2Ndx28NkwVktUmlSpWwiy/uZs+766/q6OwCF4hNT6tVq2qKgKzmH+YCi/pb6rrrL0kWkNU4hRXVY/OoUePs4kvOcJ0kJl1X1TUfBRZVBTe8qZrpUUcdZtGuC6vjvfCArOZr5oKXuqapIKQ6MFQnibmtjRjxr9/l4MN+PWnRopHrAKqgDxufd/6pMTtwrFOneopwsTx1bigkq+MWGZLV8i+7/OxkNxHob+pz3fmo6rCj3etH49VBltr3P/zmz9OePS8KhV01XNelr3XDbrj+Afvll5EpQrJa5kUXn55sniru2nV9F+qeMmWmzZ27yBo1qqNF2cqVa/xr1u9PWOBX48L3R89pCCCAAAIIIIAAAllDoGvXTvtdSVbLoCGAAAIIIIAAAggggAACCCCAAAISUNGwCROm+c+GatVKKhaj4foM7QtXqEkd8p5wQnsNoiEQU4CQbEwaRiCAAAIIpEVANzz1vO5iH6770lXvHDNmkg38eJB954JxnTodZR1dufvwHuEjl6lxuvlJN0EtWrTM3+wVOY2eq6d4VUuIbKpeG9xI1aVLB185IXKatDzXTV2HHtrEf2n6lW57FEbVDT7qzV/VEnTTz4FuqpwbBGR1U5+qL0T6Tp48M+6bWaJE8dAyVeUi/HloxJ4HC91xVCsZNs+eUZn6TZVjVbFAXwqjzpmzwIYO/c0FZ8ebbjar5YKeulFPLajAEIRCM3XDwhau4Klag4a17aqrzw8bc2AfDvr2Z/8aVhUJ3QSnyhWRLTzgGzku8rlcv3WVLP52oXPNV7hwIet0wlH+Rkg9pmW+QNOmSTcZKkS3fv0md+NjUmg2cs0LFy6PHJQlngeBRwVZY7XVq6JXqw6m17yzZi60xk1qBYOSfV+4YJlt3brdh2jrN6iebFxGn8ycsSDVWadPm+/HN25SO9Xpoo1UcFPbPH3aPHeX8lHRJvHDpk934107xE2/r9asWV0/iW58ffKZG2MGiqMtp0SJYla9RkWbN3eJTZk81/XWVTbaZAkb1syFSNUWLljuOu3YErpxOy0b8OrLn/mAbLnypa33k9fb0R1SdmKQnvfAyHU2cefgFFeddoaOXSptxoyFqYw1f4Ny5xOPcJ2RJFXn0fnw/ntD3O8FQ1ynHjPtoQfesL4v3ZHqMhiJAAIIIIAAAggggEBuEFClyyDgF7m/+TLSaVHkQuL0XIHAyDbXXc9SU0g2Wqtbr6bvBE4h1fS2OnVqRJ1F19B0rU8dCUZr9dw6dX10qVtn7T3bfPjhzX3HaK+99qGddlpHCz6o1t/z0QKyWm6duin3V8MVlP3xxz9s8eJluS4kq86r/nIf6Ot8bdWqiTh888/dtWr1lq1quy1bNg5GJfveuEm9UKeA4SNa7Jl+njtukU3h1iCcGj6uQIH8/prgD67DyPnzF5vOz61bt7njvsJat24W9bjqOl8VV9F4iTt2+rs5uJ6j5ZYvX9b9bV4kfBX+cbPmDXxIVudwsB3zXDVjNYWDw5fhB7r/wqsVB8P4jgACCCCAAAIIIHBgBbqenhRwzWhFWQVkg2Uc2D1h7QgggAACCCCAAAIIIIAAAgggkBUEFILVZ2fhHctqu1SUSiFZfW5GSDYrHKmsvQ35s/bmsXUIIJAZArrJQDcsKDwS7wqvsbY3tZBLrHlSGx7tRonUpmdc5gtUdTfD3HBjd3/DlMKyunnnC/d9yJDhPph4QuejTVVno7Xq1Sv7kKxutmribuyJ1ha4G3NiNQUeVW1gzpyFGQ7JRi67rAvunnPuyS6AWsg+d79YqWeSyBt9IudJxPMZrpKntkNVG1S9IahOF77uxS7EGu+m9ajqgG6Qmj17gbuBKSlYFbkeVWVctDCpGmnNsF5cIqfL7Od6j9DNeddee6E9vXGzTZ48w5+TQUi2xp6wqgK/qiarkHS0tmrVGv9eqRsFY91cGW2+WMOCcO48VykhK5xPwXZO21MhVn88xArIpuXmy1mz5vtw7LhxU/yidVPk8S4of6ILsEe7KS5YP9/jL1CyVHGr5qpgzp+31IYPG+Oqz0QPVX7z9W/xX3kcllilanm/lEWLYt/0O3LEhH2uacqUOTFDshMmzPTz16tf3XQjaDza8uVrbNWqdVamTImoi5s4MamSUazgbtSZ9gxs7iqb/vvPVBs/bkZqk9m4sUnjm7eI/j4dPnOFimVcRxOl3M9QVWyZa02api+829RNr5DsxAmz7LiOh4UvOvRYgVUFmmWs9WVWK1+hjK+iumLFWps0cbYd3ib6DcQ6Pps3bXW/kxQxvU7URv2VVCH2sstPjRqQ1e+xM13gOqNNoehPBv5kwfGPthxVbZo9K33r0Ovk7l7dfQj+2acH2PBfx2Spny3R9pNhCCCAAAIIIIAAAggkQqCVCxf26HFOIla1X+uIdl1P1zfV3n33c/vgg6+jLl/XlBRYTW+LFhDeuXOXLXTX8vT9qit7RV2kPoxWW7J0RSgke6zriO7ffye5v1Gn+C9Vua3foJYddmhTa96iYdTKp7GuDR3krh+pBevxT3LJf7qGrl6xS7iO6xRWDm+l3DC1H77/PWZItlKlpOsn4fPpsa4dqzO8IHwaPl7hVQVlo7VKlZOWp+v0Csnqu843VaTV8Y7Wdu3a5adZ5arB6pp60IKqw8Hz4HsQog4/3kGYN1h/MG3w/eCDi/lri9u2bQ8G8R0BBBBAAAEEEEAgCwgEIdf0BmUJyGaBg8cmIIAAAggggAACCCCAAAIIIJDFBFSgTa1UyRKu6EnSvbbhm6gMw8KFS61KlQrhg3mMQDKB+NwNnmyRPEEAgawuoJ70deOCvvLmTczbgNYVz6Z9oGVNAVUcuPmWy01huS+/GGqTJs3wVQXUA32HDm18WE43TYW3Gi7kqoqTg7/71ff2UbZs8up3On8+dtVpY7XatavZX3+NdZVfR/jeQ6JVOZ06ZZZ966plNm1W3/ciEgStVS3277/HW0vXS30nFxCMbOoJX61QoYKRo0KhqjWr16UYl1kDgiBX8eLFogZkdXPRaFc5NVbLX6CAH7V+/Ub/HhDrhqho87dp08KHZPVLqB6rAnBk+2TgIF/BT1UEVAUis5tu4Pv0k+9s2rTZLjTcLVS1Iny9CnArJFs47BjqZrBixYrYRhegHeCqBHfvfmb4LP7xpInT7Zln3vQ3jT39TK+4hGSDCgqbNm22IYOHWZeTjkmxXt149rqrwlHYBbSPca+Zai5EntktOK9KlU7+2gzWq9exrGI1vb6+cZVjp0xJCh0qUHxcx3Z2kts/VfilHRiBI49s7kOy/9frFRd+ru2rfoZvyZh/p9lnn/wcPijNjwsWTHovWbpkVZrnSc+EjRsnVX+dOnWezZg+3+rWq5Zs9sGDRtjKlWuTDYv2pM+zH7n39jauIk6xZKN1Pj/V+z0/rGmz9AVDky0oypOHH3jTnu97W4oxo0dNtq+/HOaHB1VPU0yUyoBTTjvK3ur/jf3+2zjT/nc5uV2KqYcO+dOHojXi1NNS/kxLMYMb0LxFPXfD7V/2Qp+P7ZVX77JoN02/9srn7uf5bL9MeQat1aENXDD+d+v3xlem7atVK/n7lcKlPS592FX0nmIXdz/J7n/wymBWv558+fQ76W5bunSlVa0W/cbe0AxpeKDtkcHDD/azTz7v7X52J91oHcyqAG2XTjfY2rUb7cVX7nS/kyRVFw/O51gh3j9+H2tr12wIFpPu78E5pmOn7QsqwYYvSOejKhtHawPeG+x+RxppHTu1dtW+T00xSYM9lZCLFiucYhwDEEAAAQQQQAABBBBAIHsJbN+xw29w6dIlrWHD2H+vHnRQ8r93MrqXSZ8TJIVg27RpnupigtCmJtL2PfDgzTZmzCTXodNEmzFjrv05coz/Ukd7t91+ZcwOE1NdSS4bqR6v1datXW9PPvFa1L3XTQDqgCpW6DTqTHsGZrTz0v/2BKN3uA6dgrav8yPPfnxmEx6YDdbHdwQQQAABBBBAAIHsIZDeoCwB2exxXNlKBBBAAAEEEEAAAQQQQAABBBIpsGjRMlu3LukezXfe+SzmqvXZmoqg0RCIJZCYdFystTMcAQQOiEBSIGGXqeJjEIzK7A3RuuLZtA+0rC2g4KpuhtINUipxr5t5FJRV7/P39OqZbOM7ujDdX3+OsQULlthrrw6wS1xgUcFGNYU5v/zye9/rvXqZj9Zb/DHHHmH/uJuxFJR81oUar+15kVWocEhoHQrs9us30FX3W2MKAQYBWU2wds06Vz1vlqvephBWTVPIN2iqMvr5Z0P801atGiebTwPr16/l92/06HHW4RiFsIoHs2bad22jXrdLXeWG34aPsiOPOsyF3ZNeD7Lp9+bHfptibYCOi6pUqHrqsF//MlV9CPeINZ+GK0Q8YeI07/WqO04XXXS699L8ulFr5Mh/7ccf//Dbc/XV5yfk/UX7Mnv2fH9+9O/3id1519Wuol5SGE43+U124c7hzklNVUSCpm2+6uoL7Pnn+nuHcoeUMZ1HCveqqbeZt9761D9u0bJR3I6t1nvllefaCy+8Y5+4cK+O5dEuCBtUqVUlwc/cOafQt/ZNlV0T0Zo0rmezZs5z4a3h/rxWpYmgKWD88ktJYcJgWPj3la5KxJNPJt3Ep/055ti2dvLJx4aOQ/i0PE6sQK//u8y99841hWHPP/d/PhB4dIeWrrqH2T9/T/HBxkMPa+jefyeme8NaH97YBx+/G/SHnXfBCb6CZ7oXksoMdepW9UHxLZu3Ws+rn7DeT91gLVvVt5Ur1tjXXw23p554z/UGVc71ChW7co7CnmvWrLc7bnvB7nvgCj+9Vqnqur0fe8fPW658abvp5vNS2ZL0jVK4XaHRBo1q2kUXn+jD+LohVcfg9lue95WpO3Y63I7t2Dp9C3ZTK8za8/qz7OUXP7X/ueBzocIF3c+AZv79Q79njfhjvN1790t+uddce4b3SstK7r3vchs7Zpr9/ONou+vOF+2eey9NVgn3i89+seef/dD/3njMsYcmW+QFF53oA7aq6qvj9FyfW6yh23c1BZFff/ULf57o+fkXdNa3UNP7nvZJ1XE/HPC9fxy8F4YmSueD/7v/Cm89ZfIcu+v2vnb3vd2tYsWyfikKdMtNAdlDDilpndxxCFr7o1v4+RT2beMq0Oq8CNofv4+z6699Kniaoe/az7PPPd4++fhHu/eel915Udh1CtLEB4W3bNnmwtPD7e23vg113hC5kqVLV7sbzSf443SYe802bVYnNMn0afPtGVdFVk0B5rT+TA8tgAcIIIAAAggggAACCORigdA1ta3boiqsXr3vzpmizrgfA2u6zgTVOnU6ynWwc/R+LClts+p6a6VK5Wy16wTwcld9Nz1/U+g60OGHN/dfWpuu+6ozu8mTZ7oOnkZH7RwubVuVO6bavHmLjR07xe9st24nuL8RU35kK0+1UaPG2rHumltkW7x4WeQg/1zXate64G2DKEHrZctWxuw8UTcgqFXf02mfOrdUa968ofW44lz/ODP+C9an9bdwnVlGNn1GoE4H80cxipyW5wgggAACCCCAAAKJF0hrUJaAbOKPDWtEAAEEEEAAAQQQQAABBBBAIDsIjBiR1LGsMgAndE75GekG91nR0KHD7U+XNznr7C6h7ER22De2MbECKT9xTez6WRsCCBwAAQWvFMTatm2HFXFhjkQ0rSueTftAyx4CdevW8MFFBVG/cJVlozXdjHXjTZfZww+94CvQ3n/fc75KqX7RmT17gQt0/Wf6YGWKW8Z0F4SNbLo55sabLrXHH3vZB2173fOUKeSnqgUKxi5evNwvo1y5Mnb22Sclm13BTwVslyxZbg89+IILBpV01ewq2Zw5C30P/pq4YsVy7qa0Dsnm05PDDmvqf+HSzTu33Pywrzbb/dIzfZXVFBPHaYACRNpmVXNViPOjj741GetGIQU71ev+cS74qgq50ZqsW7Vq4m6sGmfvv/+lDXSVXw9xAdGHH7k12uTJhul1d8MN3e2xR1+2Oe64PPxQX1NVYFWUnekClkGP/xdf3M2auRunEtXOPfcUe/rpN9xxTjoOVapUsJJuu2ZMn+Oq8iXd5NiyZWN/M1f4NjVpUs8Hfd9993MfWP3chblr16nuQnir3Y2BSTdBVqpU3rT8eDbZ6Dx5q/8n9sEHX/sqyTVrVvE3eSkorhu+dEPiGWecmCzsHc9tiFxW23atfJhYNzPeessjVrlyBX9cFUDWuRXcoDlnzoLIWf1znRsK+55yynH+tRd1IgYmXKCwC1G+0f9/dt7ZvWzmjAX27tuD/FewIa0Pb2SPP3GdHdfh2mBQmr93Oamt9X/zK1/ltW3ry3w1k0d797RTTj0qzctIbUJ1hqHA5XXXPOHejxfbuWfd48Og+v1F7drrzjLdTPrOW7GrjJcpU8LuuvsSu82FUxUAbdiwhu12P0+mueq0aqoy+tob9yQLRPoR+/FfBxdC1uvhaRfi7eOCpQr2zp61KFT1tr6r+Pnc87e4P9DzZGgtt95+oXuvW2lffv6rXXn5I969WfM6Nn7cTN9ZgRba9fSj7bY7L0rz8hU27v/Ofe48udcvV8tWSLlSpbLuvFno1rfCL0vh3m5nHJNsudrXl1z12bO73eXPsVNPutWHUqtULe9Cp1Ndhwy7/PvZ7W576tWvlmxePTndLU8hWQWfB7nA9UHuBuufhr1i5colryifYsYYA1QJ9vV+99r559xrg7793X/puLuNMAVn1YoXL2JPPXtzsoq5p3c7xga6AOvECbOsXZseflu1D+PGTHe/R6xzP9Pq+HnlnNH28KPX2Nw5S2z0qEnW/aIH3HtlMXezck0ffFUF2bbtmrrOHBrYS30/SbGKy3qcat8PGel+R1pk3U67wx2bQ6xh45o2YdwMW758jZ++du3KdsWVXVPMywAEEEAAAQQQQAABBBCILaBrfmoKKl5wYddkAVFdU1y+fJUfr2uTiWq6RqQ2bfrsqCFZdZKkqqNl3fW4eDUFIXV9c4G7tlhtTzgyfNkr3LUyXWtVKDZoul6k62663ho0dT7Y/dKzXAdMvf21xy4nHROM4nsUAV2fVUeGCr+eetrxUaYwa9igtj38cF/XMdY/UUOy6iDwNDdvZLh5wvipfnk1qiedT+ELV8eC6myyUaO64YP93/BanpYVnAfFixe1smVLuWszC2J2uqrr6uqwMnIbki18H0+q7+m4Utt9kjtvIpc1adL0fSyB0QgggAACCCCAAAIHWmBfQVkCsgf6CLF+BBBAAAEEEEAAAQQQQAABBLKmgD6L/XPkGB98ffDBm61EWKGn8C1e7D6T0mdJ6rBXGQAaAtEEKMUYTYVhCORwgeBmFgVNdENEZjetIwi1xGtdwT7Ea3ksJ/MF1Gu9Kshe6m6UitYUTr31VgVTavnRuvlKFWALFSpo55x7sulDk9SaKoDeetsV1s6F/RSaVU/5490vQrrBS8s40YVcH3r41hTVLXWjz113X2Ptjz7c3+i1atVaF1iZ7G8207ijXKXW++6/0QcGI9evm4XuvudaHyjUjTu6MUwVaTO7nXVWFx/uLFq0iG3ZstXvp8KNxV2o+PrrL7FDXXg3tXaVq/J6vKtGITO9NhUuVXWBtLQk5x6uAl5Ly5dPVRrX2XQXRlVAVjfFXXb52b6qblqWFa9pFGzVMQx+4VXQVL8E63gowHuWC0Zff8MloWqt4etV9VgFVnVjod6rFMJWQLZYsSLWtm0ru/d/1/kbwcLnicfj9u1b2xWu+oJuxtR6FTJWtWWF/lQ5QefViV1SBrPjse5oy9Cx+7/7bvCvP53LqqI8duxkH5DVsb7FvTZjve/qddL7ibv8ORlegTbaehiWeAEF8b769hl74ukbfHhSQTxVflU49r0PHvKBwYxsVaPGtezDgY/5MKHOGb2HqBppPNvxLpT59HM3+6qZCs3qxtEWLevZHXddbLfefkGaVtW1Wwd7vu9tVrr0wa6jhbmhgGyDBjXs1Td6JavImaYFpmGiZ9w2X9z9JB+EHfXXJB+QVSj2hM5HuNDyvVakaKE0LCX2JL2fvN6udpViS5Qo5t1VxVX+Bx9c1K66ppuvuht5M2nspSWNUfXXt9+73wc1NUSh6uHDxviArCqxPu7WqUBxtOVqvf3fvd9OPuUoHzxdsmSlD4LqwoWcX3n9br+90bbhggs7u+293odid7lAraqqjhu7fze+Nmla2/q9/X/uZ2EDv0oddwVkFRpv46q3fvrlk3ZU++bJNqda9Qr2uRuuCsk6VqrOqmC1ArKndT3a+r99n6mTif1pChS/8tpdpnNSVY5V0VbVYXft2u2HKdAeKzyt83fAx4/4arQFCxbwx+WnH0b5gKzGnXV2R/vi66ddJx/l92cTmRcBBBBAAAEEEEAAgVwnoOtGutalQOxvrvJp0AHcxo2b7dVXBkT9GyizkcqUKeWvz+j65NAhw1Os7rNPB9udLoT67Tc/pRiX0QFt27b0Hzq//PL7/lpQ+HJk8+ADfey2Wx/11yE1bonrkPDmmx6yJ594LdRBXTDPOHc9Sa1yZf4+CUxifVfwVe0I5x+r1axV1cqXL+uvk6sCbGSbMWOu/fDD78kG63rtgAFf+mN66KFNko0LnqjzPl2HDG/q4FLXiZs2re+vpwfj2h15qD8vXnt1QOg1EozTeXpvr6ftmaffDAZl6LteiwpZ6zrz99//lmwZ2p8PBnydbBhPEEAAAQQQQAABBLKmgIKy0e7rICCbNY8XW4UAAggggAACCCCAAAIIIIBAVhBQ6FWfBzVwncfGCshqO9u6+9nVgs/Y/BP+QyBCII+7eTlxXWBHrJynCOxLYNmy1X6SkiWL72vSTB2vl0nSl+35/p+/GUA3tetLFamyW9u8easPx6mSbGZv/4YNm90NF1vjRqRKmomqgBu3jWZB6RLQLzorXKUGhT4z0gv9tm3bfaWHTe6GtvIVyvpqp2nZAM23cuVq27B+U7rm07IVUNq4cZMPVCo8mqimiha6QUo3S+lmomgBpljbovc1hZF1M6CCjultCuhq/Vvc67u0Czmnd/3pXV9aptcxWLlyjX9/q1q1ot+3tMwnC82nG/9Kly6ZrApGWubfn2m0ToWzFcBSaFbvcQey6XUwz4Wu87rzWDc06vyg5VyBhQuW2THtr/E7+PeYd10V5vT/zrVu3UZbs3qDValazldRzQwt3aC8c+duX30zo8vXvi5atMKd14e4bU15s+6wX/+1Hpc+nObFX+OCqre7wK5an+c+tL59Brpwe1t78ZU7/TCFPqdNm+duGN7uqjFXslIuzBjPpk4O5s9b6oOShxxS0lV5qejeR2K/f5x/zv98ePX+B6/0Id5Y27J69XpXlX2Z7di+01eUVdA6rW29+/m5YP4yv8+NGtWwwu73zLS2pUtWuUq/u32V1Cd7v2uvv/pFWme1t969z3V2kfKmYlVZ1fYUKVLQ6tWrlqx6bKyF62faxImzfEcQqn6rThPi3fSamTt3ieVx/xo0rJ6u931t34IFy12HDuv8eVW+wt6qTfHeTpaHAAIIIIAAAggggEB2ExgzZpL1feEdO9J1etejxzlp2vxvvv7Rvvjiez+trglVqlTOd2amztjWb9jkO1R7vs99/8/eXcBJUf9/HP/cgYKEhEiIpIJ0GYRiK2J3d8fPQOzf728rFnZ3omIriNiIEhZKgyCdktJxx//7/t7NMrc3e1zsHcvx+vJYdnfiOzPPnb3bm533fGIX3nv77c/sKxfi08X6ggu2Bcs9/IgDTBe321S7/bZHbbqr2vr8C/f6i/3FT6/wYq97n3aBxfm2yy71/RfCunjZGFflUyFCfTl89909rWLF7WKzfvLJV/bJx1/ZpZedYXvtlfPCQPlZv0HfD7fXXvvAb6cqjNZvsJM/VqYvmXUhOm2Xti9ojz7ysr9wn47dtmnTzLavUtldoOgvX6E0PT3del53kTVp0tBPvqnlv++Cv5/3/86uvOoca9++ZbCIlLhfsmSZX49kf0elY6o33Xi/P5Z8/wM35XlMV6+rXt+jjjrIjju+m1+fYD/UBR5/cq9RY7eftG61m81yIdeRf44zHd87++zjTRcIDJqO5V5x+a2mY6Y6fq1joe3aNXfHgqq4C5+NsZkz5/pj8f/7v/+4v6M37ls6bvrCC+/4K3jr9W7RYle33tXd37Uz7ZdfRvp1v+HGS9zf3Y38ooLlaN/97//+Eyw+dv/998Ps9dc+tNNOP9oOcRdyDJq+E7jrzif8xS8buerG2q/mzv3H72favkXu+KmOZet9Q0MAAQQQQAABBBBIbQF9hh0/YbJfSQVkdZIrDQEEEEAAAQQQQAABBBBAAAEEEIgSeOH5d2zoUHf+rvt+V9/zJmr6/ksX8tV3V/r+VkXUEn0vtanvYxMtg+EFF1BWTIWQdNP3xMqyZN0s9rjgvRZ+jrKFn5U5EUBgSxZQGEvhCoVXy5ffNmGFwKJu47p165MakNX6FLWSV1G3ifmLX6CaOzFHt8I27SM62aegTfPVrVvblToo6JzmT0oLn5hW8B4KN4cqgOpWmKYPIIWdV8tTeFJVT1OpVapU0QWaCh74lYVCvrqVdFOFEN1Spel9EFR0TpV1Yj0KLzB+/FSrX692wuqlY12FTTVV2SxMQFbzqqKpbsXZkhFUVDA2KhxbXOutaqGquFtcTYH6XZvU87f8LEMhYTUFavNqqkyqW2Gaqsq2bFW4ba5dp3C/y/Jaz5o1q/kqtXlNEz9OwV5VlC3OpvdL27ZNCrUIrZ/CuzQEEEAAAQQQQAABBBBIjsBRRx9s692FMH90lWQXLVriv1RVZc9TTz3KFATdHE0BRYVwP3bh3XHjJlm/ft/61Shbtqx1cldIVrgw2cch99u/o/vSMs2GDhthv/02yoa5e7UaNarZ+e4L6T32aO2fB/9dfsVZpoCxgruqZKovoxW81LHCc8870Xbe2R1jpSUUGDIku4qsez11XDKvpv1RIVmdHHDscYfmmH6vju38cby3+3xqkydN8+P0GnTu0iFHQDbcv44nXHnVufbcs318wFavnfYnhauPP+GwHAFZzaf100kJO7gQ+ajRE+2774b511vDFV5VGLcwx+LD66TH+j7gmmvOs09dleSxbr+aMmWG/yK9XfsWfvn33/esW5f4uXiOAAIIIIAAAgggkIoCvqKsHZKKq8Y6IYAAAggggAACCCCAAAIIIIBACgko+Pr776N9lqnD7q3yXDOd364L7up7zF9/HWW6mCwNgXgBKsnGi/A8pQSoJFu8L8eqVWv8FcV1FfrChiE2tYaqRqagbLKafrkpyENDAAEEEEAAgbwF3u4z0O687UXr1Lm1PfrEtbmCrEuWLLcTj7vBpk6ZY4cc2tGeef6mvDss5WMzMze4arX5/8xSxl3xSEFYtahKsqnENXTIKDvr9Fv9Kn33w7NWr37uSrqptL6qwpuRmZnvVdKJ4jqZm4YAAggggAACCCCAAAIIFEXg33+XxyrGFqWfZM+rip/r1q2zWrVq+NBgsvuP70/H82e7iqQKyFasWCF+dK7nK1asdAHjpVanTk1XGTfr7+RcE22hA4qrkmxROYJKskFFYwVdVXVVr9f220dfyCzqStoapv2rbt1a+d639HovdFVdVVVWgdviaBkZGTZr1jy/z+s7IRoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCKSeAJVkU+81YY0Q2GoFFDZd70IIOull6dLlucIzRYVRn8kMyOpK9ARki/qqMD8CCCCAwNYisMceza1q1Uo2+IcRdni3a6zbYZ2sWbMGPtg5ccJ0e7/vN/73vyp49rz+jK2FJeF2KmRZXCd3JlxoMY9YvnylnX7K/7kKLH/7Je2xZ/OUD8hqRRU+1j8aAggggAACCCCAAAIIIFCSAonChSW5DlHLUli1JJsuqqlqpPltCmbmJ0yb3/6YruACquqqkHJB23bblS9wFdiSeL31XVD9+jsVdHOYHgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEtmKBslvxtrPpCCDgBCpUKG8KUKxevdY9S15QVgHZrD6Tw6yTPLSuNAQQQAABBBDIn0CTpvWtT9977JIL7rW//55lr7/aP9eM+3RtZw8/1qPYKsrnWiADSlRg3dr1PiCrAPDuLjT93Iu3lOjyWRgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFDcAoRki1uY/hFIcYEyZdLdVd63sxUrVvlQa0bGv1a5cgXT1eIL01Q5ViWzk1lBVgFZraPWlYYAAggggAAC+Rdo1GgnG/jNEzZ82Ggb+eckmz9/kavKXt52cxVld9utvu2yaz1TgJJWNIEjj+pqzVs0slq1dyhaR0meu5L7TPfqG7db23ZN/Oe7JHdPdwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwGYXSNvg2mZfC1YAgQQC8+Yt8mOqVq2cYIqSGay3SdbNsu83WGZmpmVkZN0UKt3Sm7Zl5crVbpsy/KaoamuFCuVcMLVMvjZN861cucb3ka8Z8jmRlq91ISCbTzAmQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSILLFmyzPexub+jit+Q0aMn2uTJ06xz5w5Ws2b+Lti1fv1669//O6terYp13Xev+C55jgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBRIQAUWlfXSLT093VQgMetmsccF6rCIExOSLSIgsxevACHZ4vWN6n3VqjW2Zs3a2Khtt93GypXbxleWVWA1qDaXmbnBB2pVMXbNmnW2du2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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/airbyte_7.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8111e68c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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aLPfhkLfHjh1n+2pFWq/w7siRwyUzc0fI81WmY07OXklP3yp7dudKfv5+adgoQZo2bSzJLZra7bLmzsnZI2+++Ynk7t0nJ5/SS4YNO6WsIZ7H8/LyZePGLfaYhnM7dUrx7OfeeeTIEVmxYr1vV8+enSQqKsr3Ohwby5evs9NER0dJjx6dwjFlmXP4V6AtqyJkmRMe7aDzpJlqtdratm15dG/Jbd/OMG48Oell+csTr8obb0+Uwal9wjhz+aYa3j1RVqRp5djwtmDBWa2CW1WtoOCAbM/cae4dWaKf66YmLJqc3ExamM9y3br8CVFYWChffD5XlixZI4cPH/a9LedfMER69+5qXzufd72P6P0kXG3fvnzZsKHo/paQ0EDatWsVrqkjMs+xdg+KCBKTIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQMQFSMREnJgTIFAk4BWo1SNOGFaryOrDP1yrodvU1FQfY1pammt7lh2vQdpQmoZtQ+0bynz+fbZv32nWv0h+XL9ZNJzq3zRU1qtXZxkytJ+p5hjvf9j3etnSdTaQqzsWLlhurr+f1KkT4zse6kZ2do589OE3tnvz5k1DCtVqxUlnjA7s2rV9WMOD6uLMr6HEqgrVvvH6jFDZyt1v9tFQ7WzXyLvGXu96Fd5NJ1Crs145any1Bmt7NqsnUy5tI1NX7RWtThvJpufSEK8+R7odOHBQZnz0jaxZk+55Kv0s9+3bTc48a6DExER79jkedk6f/oWsX7ep1KV26NDGt8/5vMfExIQ1VJudvdt3L9Hz1fRQ7bF0D/K9uWwggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBArRMgVFvr3jIWXBsFNCgbagVaJ1xbmevUIK7I46Wm0GBvpEK1q1ZtsCG8Q4eKqzX6L0ADpcuWrZVVq36Uiy4eZgOr/n30dUdTUXbu3CWiVSDbt29dKlC7bWuWvP32J3ZomzbJMnzE2V7TsM8loFVk/StFug6HddNdsTasEx+dTCvUult1B2t1LSNM2FVbpIK1Wp3WOYc9UQT/0TD69Gmfy65dgUPC+ln+/vtVsmPHTrns8rMkPr5+BFdUM6fesyfPF6iNjo6WE07oYu5dbUTvgVo5llZS4Fi6B5W8Ml4hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAbRIgVFub3i3WWmsFAlWp1QvSEG1VtkhUq9Vqlu+/96XvMurVi7VVLFNMkFPDdnv35smWjO2yePEqOXjwoA2dffjB13LFlRdI69bNfeOcDd13081XyM6duyUlJdnZ7XsuPFIo+/cfsK+1oiatZgno+x7JNji1j8xOW1LiFDUlWKvB13BWra3KMK2CFhQckNde/dD3+WrYMMFWlm7Roqn9LGeYz/HGDVtk9eoNsm/fftmyZYe88/an8rPrLivxfhwPL9LTt/guUwOj551fXFHcd4CNahGI9D2oWi6KkyKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBYBKLDMguTIIBAQIGyqtTq8apswQK+FVlHvgnWzfw0zTe0desWcsOvRsqw00+RTqbibHJyM+nSpZ19/asbR0nz5k1sX63mOG3aZzZg6xvs2tAwrgbVoqKiXHvZrKjA4NSTKjq0xo174+2JosFa/6bBWv+wrX+fcLyemJYl172XYcOzXvNpsHbKpW1EA7H+zWuffx993bNZPRmfmhSwOq0Gd3Ud+liRXeA1RYX2afVZJ7DerFljG5bt1auzJCU1kfr14+xn+exzBsnoay6yr/Uk27fvlI0biwOmFTpxLRyklWqd5hX+d47xXCRwLN2DeE8RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg9gpQqbb2vnesvJYIhDvEGsplp6UFD+qGs1rt7Dnf24qVuq7GjRPlJ1ecJ3Xret9aGjSIs8f/99w0E9wrEA3krl27UXr06FTisjTUl5mZZffFxdWzwVx9sWd3ruzK2WOr3joDNm3a5gv0xcTEeFa2dfqG4zlzW5bsN9U8tbVsmSRalde/FRYWiq5LW6hr0gqhmZnZkp2VI+rUslWSNGqU6D91hV8766nwBOUYqGHoSDcN1nqFaCNdsVbDrE6IderqPaKPQNVkNVzbIylWVmYdsP00KKuv/ZuGZ53qttpnuBmnz17N6ec+tiKtwIZ43fsqup25Lds3tNcJnSUurvR6tUPjxg2lV69OsmjRSttff3bbt2/tG+v+nGjgVD8H2nJy9spm89mob37G27RpYeb3vk7fRK6Nbeazt2vXHnu/STCh+2ZJjW3Y19XFt5mVtUvy8vLt66ZNG0liYrzvmLOha9m9e699qSF+DQ77N63Gu2PHTrvbuRdlZGTaLwPYuCHD1z09fau0Olp1W+8Jem8ob8vPL5Ds7Bx7jYfNlw4kJDawRhpmrmhz34t0Dr1He91XDh8+LFqFWD2OFB7x2Xrd3yq6lmPtHlRRB8YhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA9Qp4J9+qd02cHYHjRiA1dYgMGTIk7Nc7btzdkpaWZh7Bw7WVPfGRI0dk1coffdOcdfaggIFap1ODBvWlX/+epqLoYrtr+bJ1pUK1GmJ7681P7PEOHdrIqJ+ca7dXrdogX38935nK9+z0bdgwXm686Qrf/khsfPnVfBsK1LmvufZiadWqeanTHDhw0Ld+Dev9+jdXlerj3vHDD6vls5lzRANwTtMKvVrh99xzB9sAorO/vM8aZFPrqgy0vfH6DFNJ9iRJjXB13OoI1q70qAobLFyr4Vh9aMDW3ZxgrhOe1eP+ffz7T3MFet3Hwrl98NCh4umOFG96bZ1x5gDRh1dzf05u/vWVNiz6ycezxF3dVce1a9dKho84y9w36npNY/dt+DFDvvxynp3Dv1NrE2TV+45WxHY3HfOV+axq63tid/s5ch/X7a/N8TVrNtrdLVo0tVV5/fto5d60WYvs7pNPOcGe56MPv/WFcZ3++vlyPmMaFr569EXOoTKf9UsE5sxebAPK7nuADtQwcrdu7c01DixXAFnH6v1Z17py5Xp9aUPDV119gd12/zN//jKZO3eJ/ZID9/6YmGg5dUAfGTiwr9SpUxSKdh8PdftYvgeFakA/BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqDkC0TVnKawEgWNPYN26dUGDrVOnTovYRaempgacO1zVczUspZUctWl4tGPHNgHP6T4waFBfueXW0fZx8SWnuw8dd9tLl6yRTz9JKxGoVQQNxGng75VXPpDc3H0VckkzYVoNuDphvwpNEmCQBmavvKp0QM/prkHeJyY9b37+i8LTzv5wP2uwdnBqn1LTelWxLdWpAjt6BKggq1PZcG0IwVetNjsxLcs+dDtY0/Ct09cJ4vr310q54WrNmxdXa126dI2tKF3ZuXfu3C3vvftlqUCtzqsVXqdN/Vy0UqpX27w5U6ZP/9wzUKv9t2zZIW+/9amt7uoe36lzW9/L9I1bfNvOhn6+Nm3a6ryU7dt3lgqV6sGNG4rHduqU4usfrg2t1v38/6bKggXLS9wDNFSvTV1WrFgvU9+ZKQcPugLPISxgpgnqO4FarSysVcT9KwMvMIHar0xgWdfh3w4fLjRh3+/lxSnvin5RQEXa8XAPqogLYxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKD6BKhUW332nPk4F9AqtZFsWq02WHhWA7+dO3eu1BJ27NjlG9+xHIGz6OjoUuEu30RBNk48qbv06NlRtmRsl/ff/8r2bNw40Rfu1HlrUzt06LB8/vl30rBhgqmm2U06dWorBfsLZO3aTbJkyWobZMvJ2Wur2F4+/KxyXZpTHbJcg0LsrIFarUSrTYO1GtwN1DRc27ZtS/sI1Me9/8lJL8tfnnjVvavC2xqsDRS6reikTjVZDdB6Nbt/tYgGXZ2+7n4aonWPdbZD6euex9n2qoLrHKvIc8+enWTB/OU2zLlr1x7533PTZOhp/Uy11A5Sr15sRaaUd6d/YcbWlaFD+0ublBY2HLrMVKj+wVSB1abBWq3W3K9fzxLzZ2flmMDtZ6KfE226tm7dO4hWp924causNGHT9es3S37+fhOs/URGX3OxDfdr36ZNG0mjRom2oqx+hnbvzjWvE/SQbZnbsk1g+IDz0j7rOrr36Ojbd/DgQdm6dYd9HRtbV1JSku326GsusgFYDYxqKF7biSf1MBVdi8LdWl021Pb553MlLy/fdm/evKn0N1W8O5gvJ4gx97I1a9Plu+9+kD1m7RoeXrRwhQw4eo6y5tcqvI5vYmK8XHHleT4bZ6xW/tYKwNoaNIizc2tl8Pr142STsdDqtRo21lD0t98stNVynbGhPNfWe1Ao10YfBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQqL0CIYVqtUraV1/Ml82mKqVWStPwXPceHeT0M06V6Oiiamq1l6BmrXztmnS59f8m2NDQs/99UOrUCektqlkXwWpqjIAGd9PSZkVsPXl5xRVUNRga6abBNn0kNoz3nUoDY/ooq2lw7dtvF5bVrUS1yDI7V7KDVsvUx4iRZ0tSUnGF0BQTQm1lgoPvv/elPcNaE67btjVLWrZKCvmMGmTV4KuGWsPZ3IFanbes+XUNupZQ25zZS0PtGlK/SAVrNQTrH5B1L0jDsvoYn5okGnzV5u6v+7TqrdNPj7uDtVqdNlBlWu2r44ebNThz675wNP05PPucgaZ68mz7s7lvX7588nGaDXa3btNC2rdrbf8GaNGiacinq1MnxoQ6LxANwDutdesWNji6aNEKu2vbtiznkO9ZK9QWFBQFX/v06SbnnV9cfVsDtvp4842PbShXQ7MzPvpWRv3kXN/4Tp1TbBBVd2w01Wr79u3mO7YxvbgCrbNTg7ruUO2mTZm++0H7Dq3N3ztFoX2tyq3Nfd9JTGxQ4rUzZ7Bnrf4aa8LGaq7XqWt35tZxul6tVPv5Z9/ZaZyAb7A59dh3c36QefOKPkcaltUKtf735z178uSjD7+xU9WtW1dGjjpXkpOb+aZWh3btW8lrr35kQ7WLF6+UE3p3KdHH1znARm29BwW4HHYjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAMSIQNLGZsXm73HfvX21Aw+t6u3RtJ+Pu+YUJ157idZh9HgIaoFu8aKVERUXJSf16lOrxzdcLZMOPGfaxft1mW5WvVCd21BqBdevWBlxrampxQCxgpwge0LVVtlKtU2FRl6nhrUBNq1AWHi4MdFj6mPBYTExkq8xqOFDDZjWtaejUHah11tfdVORcs6ajrFr5o921es2GcoVqdZAGYPXLELRiZDiaf6BWK9QGm1uvTcdUd/vLE6+YgPHEsC/DCcE61Wa9TjDNVKbtmVrPBmTd/TRsq21ldoHvWI+kWBuS1fBtsEBtoCq4XuevyD4NsGrV1JmfzpbMzGw7xWHz+d2Ubn6WzEPD6VoF9tRTTzAVlrvb32fBztOrV+cSgVqnbz9TlTVQqFYrs2qlXG0aXj3zrFOdYSWeL7hwqKmmO9V+4YcGZ/We5ARTO5kvANHqrtrS/UO15rW2li2TJCGhgakOnS7+QVsd47TOpop0uJt+QcC55w620+rfBvp3gX/TCsFOqNYreOzfXz2dLw/QysKjfnKerdrr32/Vqh9tYFf3Dxrc1zMsqxVrTx3QWz6eMcsGrLWSrzt46z+n12vuQV4q7EMAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHqFAgYql22dK3cdMNDkp2921Q4i5cRo86Rnr06iYY0NPQ5fdoXolVVf33jw/KXp+8WDbbQyhbQqnOjr7zbVvhdvvrdUgPUebMJM7c2VSq7mVAdDYGaLFAnJsa3vEOHDvu2/Te++nKeaFXGQE3vLTExsYEOH9P727VrFfD6OnZo4wvVbsnYEbBfsANXXnWBPDHp+YBdtJqkPtLKqGhb3kCtnrAmBGp1HYMG99aniDQbhM0uCs16nUCr0fo3d3VZPe4EaFdmHQh75Vn/c4f6WsOmP/3ZpTZUu2L5Olm/frMv5Kpz7N69V2bOnCOLF6+yFVY1mBqotTJVab1ao0YJNkiqgdJdO4sCtE4/d6C1bbuWotVUvZoGbrVqbkbGdl/wUyvYatOf67p169jAbboJAzvt4MFDssX019ahYxtfqHaPqXabk7PHBIAb2mNaudZpHTu1cTYj8uwfqNU1aqh42dI1vvPl5+/3bXttLF+2zhfA1flGmr8nAlUU3rihODCsToGaVhR22s6du53Ncj1zDyoXF50RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgwgKeoVoNbvx2zKM2UNupc4q8+vpj0qhxYoml3PybK+Uff3tdJv/1Zbln7F+kXfvW0ssE42iVE2hsnH//4M2Vm4TRCIQg0LlzlxB6Be/iVITUXloJtia3ps0ayahR55a5RA0Ha+XLqmrNzLoCtWbNGvsOuasC+3ZWckPDdBp4c1qgYG1FArXOnOV9vuOua8yQV8o7TGanLfEcc8ddo+XOsdd6HqvsTq0o664+6zWfhm61aZBWK8xqfw3RTkzLsvudQK093r3o96wds9oe9vzHOadT2dazU5h2amVSfZxx5gDZsyfXVHTdZIPeW7YUhVKzsnbJtKmfyVVXX2gDrF6nTUio77XbfLlEtC9Uq8Fad9ubu8/3MqlZE9+214Z+TjRUqy3XNS7GhP7bm79NtAqt/l2jVXf1WjIyMk2V1qLK2R01VBtfHAjWIK2GavXzptemTQPGDRp4X4PtUMl/9No1tPyj+cKSrB27RMOrZQVo/U+5YUOGaKVep2nY2B2IdfY7z3v35jmb8srLH4RUKXxndo5vTCNjtUAAAEAASURBVLg2auI9KFzXxjwIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBQMwU8Q7Ualt20aZs0bdpI/vWfB0sFavVSoqOj5JYxV8u2rTvkzTc+kZdffF/+PPG2gFe5dWuWZG7LkkaNEiWlbXLA8M3+/Qckz4Ri6jeIMyGWODtfQcEBWxW3Y6cU3z7/E2lAJsdUdatTJ8a33sLCI7Jm9UZJSmoszcwjlKZBlvSN20zg5rB06txW4uKKAlFljXXGab8uXduagEpxBU9nbHZWjhww1ee06dr0tTb3mp3riDHXoQHbQC0nZ69sMpX3tAqfVvFzhxvdY5z53OeoiMtuc74NprKdBhM1qBMTE+0+DdsVEEhLS6vAqPINSUubVb4B5eztrk65Y/vOgKOv/8XwEse0au3z/5tm92mwLjbWuwpliUGVfBETHWOqbieUOUuwirtlDi5nB73uQBU4dap4VxjxwIED5Zy9qHugoKwe1fu8PjTYNjj1JDvAv39lArVvvD6jRGi3aEXB/x2c2sesZWLwTn5Hrxw13m9P0ctIBWrdoVjPE5udGpIdbkKy7qq0I46GZp1grTNW+4xPTXJe2jFTLm0jwUK7Nlhrgrca1HXm9U0QoQ39/PTv39M+1qzZKJ98nCb79xf4qtn2PbF72M5cYOZ1WkJicejV2ed+1gCp0wrM3xDupn83aKhWW7oJzGqo1qnSqr/fW7VqboO9+veOhln12InmOtyVcjuZOSLVtpm/jaZP/7xEGNg5l94b26S0sL/rnX3Bnt3BZA1ALzcVhnv16uw5RN83d3NCxu59/tt79xYHnf2PBXvtf09x962J9yD3+thGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBY0+gVKhWA5dT3/ncXukjj99hw1bBLvv6G4bbUO3HH80yFVZ/XSqE+vln38mTk6bYUKwzT8NGCXLtTy+WMbdfa8O5zn59nvbOZ/LQA3+XG2/+iYy+5kK59+6nZPGilaIhPA1y9jqhszz+xF3SwVSXczcNwlx0/m9sGOal1x6R341/Wr74Yp7s2Z1ru7Vr10rG3fsLOfe8we5hvm2tbvfIn/8j707/0p5LD2hweNgZp8jvHrhZUlKSfX3dG17jNKjT/5Re8sc/jbGBGKf/kEE/czbts/NaAzyvvz3J7nOuo32H1vLxzH+V6K8vtErgxD/9W9JmLRYnQFOnTh258OKhMv7+X9kgtHuQM5+eoyIuU9/+TJ6Z/IpkbC6qAqhzt2qVJGPv+YVcfMkw96nYLqdApAOvs2ZFNlCrl9vWfK6clp6+1X52vAKy7tCb9ndXVNQqllFRUc40x8Sz3kdDaXpf0wq/gapg7jJfFOC0uPr1nM2QnzXMNts8gjUn+OoVrK1MoFbPqYE5XYPOE6mmgVqvKrWRCtQGC7rqNXqFad3X7gRgnWqz/oHaYH3dx5xtZx5nXmd/pJ+7dm1vq6o6gUmtFBvOUK1+sYbT9EsdgjX9kgmn1W9Q8nPiDsTqfefUAb3N/Wer7d7OVLF17j0dOrSxodpNm7ba361OH+3onsM5Tzie9fP95psf+/7maNKkoXTt1t5Uxm0uTZokmkdD+wUdTz7xgu/3fbDz6t8pyaaq7oL5y2y3mZ/OtiFid8VrZ3z9+nHm3rPfvrzwwtPE/x7t9HM/65d9lLcdD/eg8prQHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKpXoFSoduGC5aIVJzXAOmBgnzJX19lUc1259j3PfmnfLpYx/zfRVn1Nat5Ehg7tJ6tWbpAVK9aLVsPVqme/e+Amz7Eaornphodkz548G4SNNuvRIOmSH9bIL677nbzy+mPSqnXzUmMPFxbK+Hueki8+mysnm2CrVqVbuGCFrFu3Se68/TH5x7O/l6Gn9S8xTqvS3n7rIzLr20W2YuYQs87GJtAyZ/YP8uXn82RO2vfy2puTpEfPjqXG3XbrRNHr1GDroMF9TXXZhvL1V/PtvlHDf2vP189U9tM26opz5ZCpVDt92he+17qhYbZQ2jZT6feGn//eVgXUQMzQYf1tVd/v5iyR90wYOH3DVnnh5Qmlgs06d0Vcnpz0gjz7z7dsNVz9WTihdxeZP2+pLF2yVu4Z+xfRCoC9enUKZenHbZ/zzjs/6LU//vhjMm7c3UH7VPTgpEmPBx3aubN3BcOgg/wOauirRYumst3cM7TSof58pA7p59er9MvNmzN9O5s3b+rbrg0bdVxVqLWytlfLzMz22u25LztrtzRoV9/7WHaOb39CfPBqnb6ORzc00FpWoNYZ4xWs1WNO9Vrd1j46Z3mbrkHvcaHe58ozf1UHanVtK7NLVvh0rzfUqrFOALZHUmyJSrbuuZxt7auPYGFeDdY6czrjKvKcbX7e5s5dYofG1asnZ541IOg0+vvVaVrlNZzNHfLUdQVr7uPucTpGq2k796iMjEz7N8WOHUVVtTuaIK3TOpov6li4cLmpvHtAMrdl+4L/WgVeg6qRaOvXb/IFart0aSeXXHq6DdG6z6V/nzhfoOHe77U9ctS55m+RGHM/zrbVbQ+avzfee/dL8yUml9jf4+4xiab6r+PmVLx3Hw/H9vFwDwqHE3MggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA1QqUCtWuX7/ZrqCTCctqxdWKtpUrfpQxt0yQQhNyfWryPXL+BUN8FeE0aHHdNePlpSnv2Uquv/jl8FKneeP1j22YVsfGHA2xadBWA7XLl6+X11+bIb+9s2TlV51k6ZI1JqybJzM+/ac0N2E/bRr2e+B3z8jbb34qf3/mtVKh2j88+E8bqD1t2Mky+W/jxV0R8s9/fFZefOE9ud9Uvn3j7Sds2NhOav75wwP/sOHZPn27ynMv/NFX6a2g4IBMNFVvX3vlI5n02PPy8muP2iF/nnibHDpUFKrVKrj6OtSmFXFvuuEPNlCrXr+962dSr17R+6PVeO8e96QNAN/128dk8t/vL1UBuLwu+/MLZMrz79r3TNff98RuvqV+aSoA//rGh+X+e/4qb09/qtS5fB3ZKFNAg6+RCNVqldpglXBTU4eUubZQO2h1Sq2IqE0DeVpFunXrFgGHb0rf5qukqJ169+kasG8oB/TzXZWteYsmsmFDhj3l1q07RMN4/k0rRIfaVq5cbyr+lg7Wa5BuhbnXOa1d++KqwM6+YM9O5dnKBmv1HBUN1OpYDeYeK4FavR6vVlZ12olpWXZYj2b1fOFX/xCshmY1sKt9vMK2oYRrvdZWnn1aZXr5snW+IT3Nlya0DBIoXXf07wUdoCHNcDatDvvN1wvslBvM5ykvL1804OrfNCCroX5t+mUg7U31Wf+mXwChfQ4dOlwiaO6ueJ9igt8aSNU+C8yXi+jvXG06NlJt29bi8L3eR52/ddznc38BgXu//7ZW2tVwrLaLLz5dXpzyrjXT4Kzeny+86LQSQzp0TDH3saL7lP5NphVyvdr+/QWiFXWTkpr45vfq57XvWL8HeV0z+xBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKDmC0T7L3HH9l121wknVK6K5X//844NdFx3/WVywYVDfYFanVyDFo9OutOGMSc/9bIJ3h7xX4Y0aBAnT/717hIhk0aNE+X2o0HaZUuLgz/+gx957Le+QK0e06DNvffdYOdasfzHEudLT98qb5iAbqfOKTb86w7U6tj7fnejaKXZZUvX2gq0uk9b+kYzzgR/mzZtZEKs9/kCtXpMw673jP+lrZS72wRet24tClTpsYq2jz78Vlav2iAa4B17z/W+QK3O17BRgkx45HZpltRYPpv5nV2r13nK47Jw4Qpbsa9Hj44lArU67xlnniqvvzVJXnnjUQK1XtB++8aOHee3p+TLESNKh8pL9ijfKw3UjhwZfM6y1lSeM55owmApKcl2iAZc33zjY/nhh9U2zO6eRwP2a9emy9SpM0UrKGrTKrUanitv0+qTTtPQmM5dVS05ubhq5YL5yyQrq+ie6ZxfA+xa8TLUplZaSdu/zZnzg7l37LC7o6KipFu3Dv5dynydagKtd429vkTF2WCDvMKzXvuCzeEc0/v8lVddILqGcLfqqFDrXMNwUzXWaRqmHZ+aZB+67d80KHvdexmywoRl9aEVZZ2ArbuvU4XW3Uf3eTUN1+o5tSqu0/R1OJpWeXVXn9UwpgYqvdr3i1eaAO5a3yH9Io5wNg1xtj5ajV6DnTNmfFtqeq3i+uEH3/g+/926dyzxu9EZ0KlT8dqWLl1jd+v87vuIBmpTUorC7Rp0d1pF7k/O2LKemzQtfg+dEK97jN4n9T5Q3qbhYw3W6n1D2/Ll62TJktUlptHf7XXr1rX7Fi1aIStWFF+zu+NnM+fIKy9/IJOfflkWmb8LytuO1XtQeR3ojwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAjVHoFSl2vz8/XZ1Sc2bVGqVS34oCq7c/JsrPOcZMLCPnNSvhyxcsELWm0BZl67tSvTTyqhO1TX3Aa2cp01DIl5NA61elS81LJTSNlm0guSGHzNsiFbHO+s87/xUzyp4GkoZelp/GyZZszpdzjxrgD2tM+7scwZ6VvKrXz/OV6HWa53l3eec74YbR5YIGjvzaLj3iivPk3/+/Q0baNTwrbuV18UJM2nQ5tOP0+Sc8wb7Ajo674kndXdPz3YlBLSibHJyc3nnnWkyZEjlKsg+/vhjotVvq7pdcOFppnr0R7ZKtFZ6/PSTNEmbtUjatEmW+IT6kp2VYwOiTphW16c/k5ddfqYJZpfK9pe5/IYNE6Rx44aSk7NHtDK0Bj87msqLGjDv2bPoHlHmJBXsoJ8Np6LlgQMH5aUX35cuXdpKAxNk25Kx3VaT1qqRTjXbYKfRe5xWB33/vS9NuLitnSd//wFZu2ajuCtU9jP3yiZNigN4web0OqbBNg25qlNZTftof63cqVVutYpleZtWp41EmFbXMTttiX34r+mOu0bLnWOv9d8d9tcanp1yaZug82o4dpoJxeqzf/PapxVq/ZsGcPWh4Vn/qra6Bn347/efoyKvh51+ikx9Z6at2JqZmS1TXphufi7bm0qljW0Vdw2xb92yQ7ZtK/6yiFatmpvPXeW+iMNrrZddfpa8+soHol9Oob+3tfqqVlTV82nF69WrN/hCv7rv3HMHe01j+ieJ/k52/r7RTl4VprVyrftzG6jyredJKrCzQ4fWvsq5X305T/bty5cOptJuvbhYcy/ZIYsWr5BtFfxSDq1+rZ/BWeY+rO3zz74zf6s0N19kUPS3nQZvh484S955+1P7BQgzPvrWmG6194mmzRob872yeNFK3+e/fv16ckLvLhW4yqIhx9I9qMIIDEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIEaIVAqVNusWSO7sJUrfqzwAvfsybPhVZ1Lw56BmoZjNFSrgVH/UG27dq08h2kFW2378orCv/6dWprwjFcYV/vFO2NNcMVpS4+Gf995a6bszN7t7C7x7ITK1q/f7Nuv1SW19Tga8vUdiNDGku+LzqdmgZpzzAnguvuV10XDRZdefoa8N/1LGXPLRNHX550/WE4d0Fs0EK2BSFpoAuPG3S1paSZkasKz2lJTh5hqrdNM2GmWDcA6+7W6rB5LTU21z6EGbDVIqy3UMK2eI9S57cQh/NPIhFl/+rNLZNrUz33VVfPy8m3ozWu4VlW+5JLTTTC2uOqnV79g+/r17yFffD7XdskwYVZ9aNA20qFaDehfbNb+7vQv5MiRIyaQdlhWrdrgW6pWwNTjf3vmFd++QBtxcfVsJe+33/rUWmlI0L/pvXDoaSf77y73a+c+FsrANBOmrUzTAG+k2uDUPqIB2r888arvFFUVqPWdMMBGsDBtgCFl7g4Wri1zcAU66M/b1aMvsj/fe/bk2nCtu3Kr/5S9TFX7885LtRXh/Y9V9rUGP0f95Dx59dUPJX/fftm+fad9+M+rf2eMGHl2wN/9+uUYGqJ1fxmH/k7zb9rnyy+K9+qXAmjoPVJNg8BagVo/9/rlAN9+s9A+3OcbNPhEG27Var3lbQMH9bXh/I0bt9j38b13vzD36Ut916Tv9UUXDzOh/q9std8lpsq2PvybBpL1ixMqa3Gs3IP8fXiNAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACtUugVKi2RYtm9gqWL/OuBBvK5f1owqcaNktJCR6scoJX60yl2upqa9ak21NrWOeN1z8Ougytzue0dWuL1uxcg7M/Us9OoDclJTngKZy1aOXfcLRHHrtDunfvYCoFfmirBD77z7dEHwkJDWTUFefKHXf+zFYuDMe5jvU5NCjrhGf1WYOwGrbVNnJkUdhWt/VYUb+iarOZmcU/c3rc3SpalXbs2HHuacK23aBBfRPGu1DWrk2XRabC4WaPCqfJyc3khBO6SJ++3Wy118qcvH//XiYoX1+++WaBrWSpc2l1RQ2nRTr03aVLOxucXbxohQkRZ9lgrZ5f95951kATPit1a9XDnk2DbVdedYF8PONbX9VN7aiVIU/q11MGDuwbkcCi52LCtFMr3LY11xSp5lSk1WBtTQnUTjWVaTUAG6nmzB2J6rT+a9bPqYYvv/l6gQ3J79y524YunX71zRdUJLdoan/eTzyph7M7Is9aofnKK88398XFpoJzuv3bwjmRVozuYSpTaxVUDX4Ga506pfhCtXXr1rVVtP37azhXq2BrmFhbp84p/l3C+lrDvpdedobM+naRLDZVafebKtVO03vY0NP6y4kndrehWmd/eZ51/otNaHaKqfCbm7vP3l8+MVXE9QsNnKah3suHx8i8uUvsFxM4+/VZ16BuZ5w5QJwvNHEfr8nbkb4H1eRrZ20IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQXKBU8uvUgb1Fgxgaotm2LUtatkwKOsPBg4fkpSnvmaCLyLU/u9iGMDp0aG3HZGzZHnSsVpbUptXhqqu1N2vV6nC3jLlahptKd8GaO6inVe50XLqpACdS+SqSwc6rxzp0bC2rVm6QLRk7pGMnb69we8bERMuvbhplH1q5eMGC5fLNVwvkqy/nywv/my6rTXXO5174o/15KWv9x/txJ0DrVJN1nnX/O+9MM8Ha4aWIdH+w5j9nsL7OMQ3UhrtKrTO3PkdHR9vKixrU0gquWrU6z4S54kxANCEhXuLiQqtwrAHxu8Ze757ac7t7j46iDw3S5u7dJ4kN40tUU9TAaijzuCfXoF4oYzRwro9Dh/Q6c0Ur2LqrZAeaQ++v/sfatGkhv7xhpOTn75fsrBzR0KJW3dXPYLiaV8g5XHNXxzwarHXCtdVxfvc5r3svw/0yYttO1drxqUnSs1m9iJ1HJ9ZQ93nnp9pzHD5cKNnZObLPVHnXSsz6xQpltatCDFXfced1ZU1lz3nZZWfaz3lOzl77OdE1NGqUWOIzF2wi514RrI8eu/Gmn5TVxXdcw7z6KKv5f979+w8Z2k/0ode2a9cec10Jtoq33k+13XLraP8h9nUo90m9l9z86ys9xzs7O3duK/rQ93fP7jw5cPCgaMA4lPfZmSOU52PtHhTKNdMHAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEap5AqcRWC1N97pRTTrAr/bepSlpW++TjNHl04nPy7vQvfNUhGzVOlHbtW0nWjl02JBJojjWrN9pDfU/sFqhLxPc7587JyRUNqAR7qI3T+vTtajc1bFoVra+p7KnNMfM6p3PMuSavPhXd16NnR7n2pxfLP//9gLz+1iQbpJ2d9r1UpqJxRddSW8c5IVhn/Rqs1Wqz2h5++E8mnDXEORSxZ/81ROxEZuKYmBjRKpMp5nOlQbxQA7UVWZMG3pslNS4RqK3IPBUZoyFcDaC5A7UVmUfHaLVN9WrWrHFYA7U69yaPysG6PxKtKs8VifWXZ06tUFvVbVoVn1PD3fr7r0OHNmEPWpbHTj/nWkVX16H3lHB85spz/kj3bWz+dtIvGdH7iROojfQ53fNrtfGWrZJEv4wg3IFaPU9V3heq8lxuQ7YRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg5guUqlSrS/7NLVfJvOuXyssvfWDDsT//xeWeV6Kh2b/+5SV77PIRZ5Xoo8HO9I1b5T/Pvi1j776+xDF9sXDBClm0cIU0MFXUOpkKadXV+vQpCsdqBda9e/NstUn/tcz8dI4Umkp9g4ec6Dveu08X2+2zmd/JLbdlSSsTRHG3ffv2y82/+oOtYPnPfz/oO65VKrUVFh4xVeH22+t3jwu03cd4vvnGJ/Lcf6fK2ecOKhW40wp3b5nj2vocDeAGmiuU/WvXbpJvvl4g/fr1kJPMw930ve1hqoOuWLE+aGjaPYbtIgGtFOtUqXVMtHKsPnr37m12jXN2h1RRtiiI+7hvTLCNsirfBhvLsdovcKWrcqgTOJudtjgsFzbYVOvULyQ4HtvK7ALPyx7RraGM6J4oE9OyZEWAPp4Dj+6ccmkb0cCuVqelIXAsCHAPOhbeRa4BAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHaL1CqUq1eUurQk+Sq0RfYq3t04n9tMDYvL993tUeOHBENml47+l4bnO1/ck+55tqLfMd145e/GmkDo8/95x3bV8c4beuWHTLurkly2ARV/+/Wq0sFRJ1+VfHcsVMbufiSYZKevlX+76Y/yf78kgGpxYtWytg7J8ntYx4xVWLTfUvqbILAlw8/U3bt2iO33zpRcnP3+Y7pdT0y4b8yb+5SW3nSHbjV6p1du7W3fb/8Yp5vTFkbF1w4VLp0aSu6nqeefFEOHjzkG6Ln/t34p2X79p1yxlmnygm9iwK/vg4V2Jj5yWx51FzD3WOfLGUyf94yWbs23b6/p5xaVNW4Aqc4LodopVgN1jrNXa3WCdc6z06fcDzrOXVe2vEr4K7CnWpCsPoIRxBW53Dmcp/jeJEeboKz7tazWT0Zn5pkA7Xu/RXZ1lCuhms1oOtuPcw5aAjUNgH3/YF7UG1791gvAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALHjoBnpVq9vAce+o3EREfLKy9/KJMee16eefoVU7W2tcTG1jVB2i2mAmueVejRs6P8/V+/l3r1Ykuo9OrVSf76zL3y6xv/KLf+5s+SnNxMhpzWT1av2ijLlq4VDdlqEPdXN40qMa6qX2jl2Ecev0Oys3NkzuwfZNCp19jKrJ06pciGDVtkdtr3dq1XX3OhaHjY3f408TbZkrFD5s1bKoNOuVYGDOwtzZIai1a91cqxjRonyj3jf+keYrfPv2CICehulHEmrPuvf7wh/fr3lIce/r9S/dw7GjaMl38/9we56idj5d//ekteMVWETxt2smjYec7s723Itk/frvLUX+8JS0j5yqvPl7ff/NSGpk9L/bkMHNRHkpo3sUYbfswQdRtz+zWl3nf3mtn2FtBgrTanYq0+p6Wl2X2pqan2Wf9x+vl2VHBDA7XhmquCS2DYMSyQcpxWqHXeUg3RavDVqUarr8PdNFzbI6n4d2wkzhHuNTMfAlUlcLzfg6rKmfMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIHCsCAQM1cbERMsDf/iNnH7mqfLC/6bbcOnqVRvsdWugUiu8/urGUTJ85NkBQ5wa+nxq8j3y1BMvyrp1m+Sdt2ba8RoQvfqai+T2O35aIxzr1q0jz/zjfnnkz/+R99/7ygZHNWCrrVXr5nLnXdfJpZefYV+7/ykad59MNOM+eP9rSZu12B7WgPGAgX3kTxPGmCByK/cQu33LmKvN8xHj+q6sWrlBdmzfVWaoVgfqWv77/MMy4U//tu/HjI++tfNp9Vuttnvf72+SuPrhCXQ1bdpIprwyUSY9+j+Z8dEsW21YTxYdHSVapffOcT+Xs88ZaM/PP+UXcEKuTrA2LW2WncR51hdOn/LPXjyCQG2xBVulBQabarWbXp/hO6CVJDWgNjut6F7mO3B0Q/tv3rRNNpkHraRApIOukZ6/5NXwCoGqEeAeVDXOnAUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSKBaJycwuPFL8MvFVQcMBWZT148KCtWBsXV1w1L/CooiNalVYrum7blmWrt7Zr19JWvC1rXHUc359fYANjubn7RKvVarXZUJpWjE1P32qq2op069ZO6tQJmFf2TacuGRnbpWHDBPOI9+0PZSM7e7cNt2mwt63xTEws3/hQzuH0UYvNmzLlwIGD5trahy2468zvPMfHRzmbJZ4zM3fa18nJTUvsPxZezJo1y1asdYdpnevKzNzhbAZ81vEjRw4vdZwwbSkSdgQQeGLS86JhWg236bM2Dc2+4Qrb6r4rr7rAdzztaOhWw7d3jb1eD9MCCExMy/JVsXW6aGVbdwulj7s/2wgcSwLcg46ld5NrQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg5guEHKqt+ZfCCmu7wPEYqnXes8cff8yGa53X+lzRUC2BWrci22UJaIDWCdO6+2pw1qlYq4HbVPOglV9gRXaBaGjWaSO6NZQR3Ut+WcP/s3cfYHaV573oP/UuVBDqBQFCAkl0gUTHiGawacbGuKXYTm5ujm3sJHaSQ+L4nPjcc4yJ4xzHJTZuYAPGmF5N771I9CKQQIiOJDqC+71rWFt79uwZjUaaopnf9zxbe+3V129VPc/813vOQ6vSOQ+vLEdJ9capDNRBoJsJuAZ1sx1qcwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBcXEKrt4juoJ61eTw7Vlvu5rFy7YMGC9Dd/87dl7xa/jzrqyBx4XFCM09ppWpyhgQQ+ECgr0grUbtghUYZmZ40ekI7Mgdr4rm2tGad2Gr8JdHcB16DuvodtHwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6HgBodqON7fEZgSEapuB0ZsAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYYIHeGzwHMyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQxQWEarv4DrJ6BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGy4gVLvhhuZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQxQWEarv4DrJ6BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGy4gVLvhhuZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQxQWEarv4DrJ6BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGy4gVLvhhuZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQxQWEarv4Duopq9erV0/ZUttJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdIaAUG1nqFsmAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAhwoI1XYot4U1J9Dbkdgcjf4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDARhAQZdwIiGax4QJCtRtuaA4ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA8wJCtc3bGNKBAr179+rApVkUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0NMEhGp72h7votvbp08XXTGrRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHQLAaHabrEbN+2N6J2Pwl4K1W7aO9HaEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBLi4gVNvFd1BPWL2+fSVqe8J+to0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAzBYRqO1PfsguBvn1BECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTaV0Cotn19zX0dAv36pdRLodp1KBlMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIbKiAUO2GCpp+gwT69ZOo3SBAExMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKtEhCqbRWTkdpDoH//XqrUtgeseRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJNBIRqm5Do0RECvfOR169fRyzJMggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECKQnVOgo6RWDAgF6dslwLJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHqmgFBtz9zvnbrVEaiNSrUaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCjBEQbO0racgqB/v17pb59YRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOlZAqLZjvXv00iJQ269fjyaw8QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAnCagZ2knwPW2xAwaoUNvT9rntJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECXUlAqLYr7Y1uuC69cy3kCNTGt0aAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6CwBodrOku8By+3fv1fq168HbKhNJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLq8gFBtl99Fm94KRpC2X79eqVevTW/drTEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQPQX6Dh7cK737bsqf99N773XPjbRV7S/Qu3dKffv2yp8kTNv+3JZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIrKdA36gmWlYWff/9lNasSTlc2xCwLUO20V8jEAJl9dkI0TZ8eqU+fdb2p0SAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6IoCua7o2haByag0miOSa3vqIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCJC+R6oxoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB7i0gVNu996+tI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyAJCtQ4DAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBbi8gVNvtd7ENJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEKp1DBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHR7AaHabr+LbSABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIBQrWOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg2wsI1Xb7XWwDCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEhGodAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAt1eQKi22+9iG0iAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICBU6xggQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDo9gJCtd1+F9tAAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABoVrHAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQLcXEKrt9rvYBhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAjVOgYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6vYBQbbffxTaQAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAqNYxQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0O0FhGq7/S62gQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkK1jgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFuLyBU2+13sQ0kQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQqnUMECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdHsBodpuv4ttIAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgFCtY4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDbCwjVdvtdbAMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSEah0DBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3V5AqLbb72IbSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIFTrGCBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOj2AkK13X4X20ACBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAGhWscAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAtxcQqu32u9gGEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICNU6BggQIECgWwm8+ebb3Wp7bAyBWoE33nirttcm99t5usntsh63wi+99Gp64omn04oVL6Y1a9b0uO1vjw123reHas+bZ3e4B/a8vWaLCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJdS6Bv11qdzl2b737ntGIFbr5pUfF90433Fd/zF8yprNhXvvrJJv0qA3UQaKPA+++/nx5//Ol03z2PpnvvfTT169c3zZm7dZqbP1OmjmvjXE3WnQWWL38+vfzyymITx24xOo3efMRG3dzXXnsjPfnkM8U8hw4dnKZMGb9R5x8zq17GwIED0vTpk9a5jDhXHnjg8cp4s2ZNT7169ar8vu7aO9Ltty9OQ4cNTscee1AaOXJ4ZZiO7itQfSzV28p+/fqlIUMGpdGjN0sDBvSvN0qX6nf//Y8V6xPHdhzjZYvj/8wzL03Llj6bJk0el4477uBGx385Xlf/dp52zB4qj6O2LC3Ol6lTJ7Rl0k1+mkX3PZKuvvq29NZba1/QsN32W6VDD917k9+2ztwA533n6UdAPMLh8enTu3caO3Z0GjtudNpss2Gdt1JtWHJ3uQe2YdNNQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsJEFenyoNoK0EaItA7T1fKuH3XTjN4pRImgbAdvqwG29afUjsC6Byy+9JZ345X9Lr7yyqu6oEyZsnn7wo6+nXXadWXe4nj1TIEI/9977cLHx++yz60YP1b744ivp4ouuK+a/ZQ67tkeotnoZY8aMalWods2a9yrrFSu3zTZTixB6dL/77pp0xx2L03vvvZdWvro63b/4sbTnXjvFIK2bC1QfSy1tap8+vdO0aRPTAR/aIw0fPqSlUTt1WHnu9enTp1GodmkO00agNlp8x+/2ODfbc+Odp+2pu3becR0sj6O1fVvfFcdVTwzVxksZrsmB2toW1w2t7QLO+7bbbciUjz22NF1y8fXpzTfrVzcfNmxIOvyI/dKECWM2ZDEdNm13uAd2GJYFESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCjQY0O1EaY95eTftIjT0sAI2pYB26989fh04tdOaGl0wwg0EXjvvfdTHIf//m9npKi81Fx75pkX0seO/nr61r/+RTrhU4c0Ge3uux5Onzr+pKL/vN23Sz/7RUN3kxE3Qo8v/Nm/phtvuLeY06m/Ointttt2G2GuZkFg4wj07dunCIE9/viy1DtXYosw8MZs5517VQ4xLi9meeRRB6aJE7fYmLM3rw4QiFB2hIyef/7l9LFc5XXEiE2rSt/48WPS4MED0+uvv1l8x++u1J5d/kI6++zLilWaOHFsOvKoDzVZvfY+T5ssUA8C6yFw910PVsaOIO2s7RoqRW+55ca9n1QW0g06nPddbyfG/ytuvPHu/OKoe1pcuVWrXktnnnFx8aKJuXNntDhuWwe29tkpXopSru+8eXPTbvNmN1lkV78HNllhPQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBLqsQI8L1W5omLbenoxwblS7Vbm2no5+zQn8+7/9Nn3vlN9WBn/6M4emww7fK82cNTU9/9wr6bpr70pXXXl7uv66e9I777ybvv43/5E233xEOviQPSrTREdU/3o1V+WMtnr1G8V3e/2z+rU3Ksta8+577bUY8yXQZoGjjj6wqOA5ctRmaciQQW2eT70J3377nVzt7e1i0Pu5CqTWNQU222xYWnjQ/MrKRbjo5ZdX5uvqy+nRR59Kb7zxZlq5cnU647cXp+M/eViuWDu0Mm5X7+jXr2/6888fm55++rki1B2/u1J77/33KudInC/NtfY8T5tbZk/rHy8WOObYg+pu9i0335OWLVtRDNtll+3StDqB0UED+9edtjv3jGepV19dVWxiv379cij8gBTVorWWBZz3Lft0xtCbb763ElCN5e+ww7ZpxrbT8v8jRqbX8rP8k08+k57ILyB56qnlKV42cfllNxYvath66ykbfXVb++wU/58pn7HefffduuvR1e+BdVdaTwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgS4p0LXSGO1MtK5A7fwFc9Ie82en+Qvm1l2TU04+vegfVWprW1m5VtXaWhm/6wnEH5j/+Ed/qAz69v/+q/SpTx9a+R3h2VnbTUtf+Iuj0j+f9OP005+cVwz74Q/ObhKqrUykgwCBQmDS5HEkerBA//79iorF1QRRcTLarrttn3531mUpqvOtXv16evihJblf02p41dN2te4IFU2bNqGrrdZ6r4/zdL3J1nuC5o6T++59uDKv0fl5o7nxKiP1kI4I25dt/PjNBWpLjI347bzfiJjNzGrNmjXpjtsXV4YuXDg/zc2h2rJFtfMxY0amXXfdPr+859Z05533F4Nuu3VRao9QbbncjfXdXe6BG8vDfAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNom0GNCtS0FalsbhJ2/4NuFchmqPe6YbzRRj6q10U782glNhulBoBRY8sTytGrla8XPYcOHpI9/YmE5qMn3f/vyJ9KvfnFxei9XxnzwgSX5+/3Uu3evXI1zRa4y9Uy6/fYHKtPEsXndNXcVvwfkKnPzdt++Mqy644033kqPPrI0PfbosvTCi6+msWNHpdmzt0pbTq8f1IpquVGZs5x3zOvaa+5M73xQiXDGtlPT2HGjikU8uWR5eurJZ4vumN+kyWOL7tp/7rj9wfR6rpYVbdd526VBgwbUjlL8fuutt9NDDz6ZFi96PL2XK05OmrRF2mHHbdKIEcPqjq9nU4EIWERly6jC926uehzH3Lhxm6ehQwc3HXkdfSKI+OyzLxTH7/DNhuZqmWPTwC5U0XBFXrc38zETLbZxwID61RajItqLL7ycnnv+pZQPq1yttMFk4MCmx2FUcotqp1HZrWxLljyT1nxQrXb06BHNWkZV1OXLXygqo0blyFjOxIlbpKiC2Fyr3obw7du3T1GtOtbj+by+06dPTltsMSq9kNc/Ks5F2yzvixEjhjc3yzzdy+n118txh/XY82dUrmC8YMGO6dJLbyis4rzYdbdm2Roq3GbzOO6HDRuSYl+PHDk89erVq+5EUWVvxYoXimFRKTmqAkarPm/i/Js0Kc6bpsdaMfI6/imPw1iHKVPGtzh2nKtRpffNN9/KIapRxbW+pWOvemYRMHzppVfTKy+vSn369s7bPzRNztfzepU7V+Zry8uvrEzPZM+yLV36bOWciWlim8tWfYy3dJ7G+K/k+UaV4VWrXyvO57j2T5iwRbP7IKYpjfr27Vucb9EvzuHnnnupuH7FfozlxrmltSzwyiurKtVby2tdWD7zzPO52u2zxTlRL4gXzxkvvvhKcfytydfbocMGF/ti0KCBzS5wY++3CM8/n/f5K7n67JDBg9KIfO6OHTu6yfJjPeMcXfpUw7NLjBDX23J94vfUqfWfj9rzGh/LLdeh+liO+1fYv7b6jTRl6vji2hTjVrdYrzgH4xkqrkNxvDd33aqerquc9+W16/XX30xD87U0wt/l9bR6fau761n1xPM+rtmx36PFM9D2s7epZmrUvcf8uemeex4qro/xTBFezR0na58lV+X/E7xf2SfNPWe19tkprstxvD6Zn6vKFs9YE/KzUrTqe2n8LvdzvXtgOaz6fNmQYyCem59d/nx+fnqz2N64fpTb+/TTK1Kci9HiXtycWzwXxLNb+A7o3794Doxti2dCjQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPMEekSotrlAbWvDtLW7JyraRlu6/IIUwdoyZFuOJ1hbSvhuTiDCJmWLaFYEZptro0YNT49RAyMUAABAAElEQVQ9eU6Tweefd3361/9xapP+n/zEfy/6TZw4Jt18e+Ph8cffp//6knTy/zkth6VWNpk2ju2TvvnnRcC2euCffOabOZTV8Af6Zf/vf+/MFJ9oJ5/y5XTcJw4sus/+3VWprOr89b//bPqrv/5Y0b/2n6//7X8UIeHof80NP8pBwYZKkuV4YfSPf/+f6eyzrkxr1jT2iSpbn/nch9Pfff3TqW+u2qg1L3B7rlZ22633FYGA6rHij/kjCHVgrmDWXKC5evw4Rv94xc3p3qoKhzE8KobttNOstNfeOzcbKKieT3t3X33N7Tlw3hCM+uQJH07jx49ptMh3cqj4yj/ekhYvfrQIj1QPjG3ZcceZxbZUhx3O+f0VleBEOf4tt9yb4hPt4EP2zOdM49BKnGtX/vHm9MADjzeZNgIZO+y4bdprr/pm1dvw+c8fmx597Kl03bV3VOYT00eoNgJTsS3RIvB17McOKrrr/fOHc/5YBHtj2DHHLOyxodrY/ghkli1CtfVaBDmvuPzmSninepywX3jQgiKkVt0/uiM4E5Vwo82cOT0dethexT6K0FJ1a+5Yqx6nue5y/hFU/fJXPl13tCVPPJ2uvua2HBx/pdHwCP1EqPuwD+9TN4gXI0eQ9pp8Hj3+2NJG08aPCETGsbvnnjs1GvZQrvh77bW3N+oXP8p1jTD557+w9l5QfYzXO09j2pb2QQT64/yZNWt6jNqklcuNAPSf/tnR+Rq4qDhfy6BZTBB+O+40M+23Xwup6iZz7nk97r//sfyce3ex4YccslcamZ9J4npSPsdsM2Nqo+qW8axw8013p7vuerDJs02Yz8jjH/Ch3euGyjfWfotw7MUXX1dUpK7dY+NyBdr99989XwfW3hviPrnovkdqR60cvzHgKyd+plEIriOu8bHcWpOrr74t3Z1tI+BYtgj6xXV9UH42iiDxuX+4Mof7XywHF99x3Tvk0Lz/8jlRr3WV8764duVtjKBzbYt9dsCH9qgbjI5xa6166nn/zrvvNqKLUGlzLa7pzd1HqqeJc+TW/Cz5Rg6XVrc+fXqn3ebNSbvvPrfJSwpa++x0/XV3pCfyPau6PfPMc5X9ud32W6VDD927Mrjcz/XugeWwDb32R8j38stvyi8geqrRs2I8G+63/27Fc+9FF12X4oUS0b705fz/kZqXNERA/cILrs0vIGj6nBEv6Viw545Nnh0rG6mDAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg3QW6fYmUjR2ord0jZ5797RSfMmhbDo9gbSxbI1BPYMa2U4pqszFsZa5Ye/qvL6032kbtF39U/+lPnpT+4Rv/2ShQG38QX7YIiH/qEyelxx9v/Mft5fCO+l7x7EvpiMNOTGf+9opKoDaChOUfrEfFqB/+4Oz0pf/23UZ/7N5R67epLOf22xala3I4JbxqW4RkH354SYrQQwRN19UuveSGJoHamCamjaDFH3KIp6Xgxrrm3xHDoxLh6addmBYteqSyrnFMlQHa2JbbstnFOSixIS0cLjj/6nRfDmmVVcyq5xfBvltvuS9dfdWt1b3rdkcltGuuvr3ufGZuu2Vl3SNgG9VI67WoChrhjmhR8W3qtPoVF+tN2y37NSoy2zRsFKGhs393ed1AbXhEVb04jmoD5vWsLrvsxqIKYO2w8lhrj/MmgsLnnntlk0BtrEMcm8uWrUi//tUFdYM+UVnwFz8/t1Ggtrr6XgSNbr7pnnTVlQ1h7trt2li/o6ry785qfh9EkCnO07iGratFsD0Cv9WB2pgmQol35KDY9dfdua5ZGP6BQOz/C86/phKorYWJc+fnp56T7rjj/kaB2vIYCvPYH62577R1v91xx+J01lmXNgrUVldnfjZXDo/gXVRCbWvrqGt87frdd9/DxTFbHaiNcSJAe8EFDfvl7LMvbxKojXEi2HfeuVc12i/RP1pXOe/j2lRcu+oEamM9ozpyXJuj+va6WluPn3XNd1MYHhWly3Murnv31bwMZX23Ia6T8SxZG6iN+cRLb+Ke8Ktfnpfefvud9Z11u47f1mPgtdfeyPf4i9IjDz9ZeVYsVzSeneNlJnfd+UDZq+53PJOFSXWgtvo6FOH3eK6+5+4H606vJwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQPsLdOsSjxEQLKvGVlNGhdmN2SJQO3/BtzfmLM2rmwtEZdCPHrlvDpZcXWzpSf/4o3T77Q+kL/7FUWnuDo0rXjZH8enPHpY+etS+6Y7bHkj/z1/8f8VoU6eNL0Le8aNvVVg2fp9+2qU5PHRPdBZVKv/6S8elfffbOQfsxudw3x3pv358brrl5kW5Otir6aR/+FH69W/+pRg3/rnupp/kIMb76YSP/2N69NFlRf/v/cdX0x7zG6o2jxgxtDLuxug484zL00MPPlnMavc9ZqcvfeUTad7u2xXVcq/84+3pW9/8aXr+uZfTeX+4Nh338QOL7dgYy+1O84gwQVSbjNa/f780Z+6MtM02U3N1ysF5Hy7NVfkezlU1X07Llz9fhHGOPPKASgij1mHZ0hU5PPtOUdlv++23TlNyVdTnc7DwkUeeLAJSMX5Utbwzhwx22WW72sm7zO/Fix5NL7zwcrE+kyaNzcfvDrlq59gisPrEE8uK0EiEKR588Im0/eyt07RpDdWT/+zPjymCFRHEimp+0Q47bO80afK4onvgwAHFd/nPFbm62WMfVPkcMWJYmj1nm8I+ArYR0ogwZoQGw2vI0MFpXq7y1lyLUGYEf+fMmZUmTtqiqAw8fHjD+RZVCadtObGwj6DHo48+Vbfq2cN5P5UtqqeWYZuyX0/7rg65VFetDYcIu/7+nCtyldRVBcuMbael7bbbKsXxEkGuML7l5nuL4yFC0dNyQLncH7WOcUxFoCmqJcfxNHXq+OKceyRXvnsgV/+M9sTjy4oA4q67bl87eZt+x/EZVUTLMPfMmVumGTl8PW7c6LRkydPp/sWPFaHaOP7OP+/q9IUvfqxyPMS2X56PtziWokUl61jvKVPGpwikx7F7Uw5PRaAvjt2ZuUpsWQk6qtfOnLVleiYHeiPcFy2O/eM+fkjRXQbXix/r+CeCWb8/+4r06qsN+yDso1LhlltOKqpXPpzXI6qKxnpedOF1aXCutFiei7WzLoNLMY+5O2xbVLiMF1lElemyonV0x7pvvvnI2sn9rhG44Ya7imNrVj4npkwZVxz71fv2yitvTXENjTZmzKi0886zimtUn1zd8ZE4d7J1BKIjHBmhtHm717/2tXW/xXF/w/V3F+dnrNc+++5SHMebbTYsxcsF7r77oeKlCnE/i2BcVEmOtu++u6UFC3Ysqn+XL1UYNXqzdOyxa6t/V29nR13ji5X74J8w+eMVt6Rt8zUp7udhujifzw888FgRbIxg7M9++vvi/Izqy/HyhDjWH8zPAvE8EC3uf3ENiHtS2brKeR9VtauvXVGFOq6/UZ32ySeXF9vxeL5eRrD77N9dlo7/5IeLl0SU21H93dbjp3oem3J3VEKPa3+536/ML0GI+17cZ8aO23y9Ni2qkEd15GiD8zNHnLPxbBQVbpfmYy5eqhIvmoh7T7ygIKpQl621z06H5uepOHfj3lpWdZ+Tj9H5+ZyMFtuzvm1DjoE4v6NSerTi+Tmvy1b5fhj/d3v8sWX5Ge6h4kUNLb1IJrYlqnZHi3vP3HzORpX7OCfjZRDXXXtHcZ2K5/Rt83PZwIH9i3H9Q4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0HEC6/9Xqh23bhu8pOOO+UaTeURVWY1AVxD4n9/+yxzeW5LDVUuK1YmAaHwmThyT9tpnx7TPPjvlP07fNQ3NIch6bejQQSk+z0xY+wfyE3J3fOq1l3JYdrfdtsshjMfTf/7462mvvXeojHbIofNzYGlCOnD/vyr63XXnQ8Ufe5fhuwhkRRuf160M1UbQobllFSNvwD8XX3RTZeof/PBv0xZjRxW/o1rtUUfvVwRD/8c3f5aDUFMrf7RemUBHDqOtTpdcfH1F4sCF81MEVMoWQadZOUT281PPLcKdEYi97dZFzQacIoA0alQOGH3s4Eq4IQJz28yYmvrnfVJW2rrpxrvTjjvOTNXVj8tlrus7gljXX7/uipFl4G9d86s3PELAZTv8iP0qgZwIrYbPgBw+joBDhOvKUGKMPzQHX6MNGzakEqqN7vjUtgixlBVMIwhy9DEL08iRwyujRagiwpVnnHFJ0S+CFZMmjSuCQ5WRqjpiHsfkYNfYsQ3nYNWgonO7vN6x/6JF2HD27LVhqaJn9H9o7XZHOLEntwhu3XD9XRWCCFtWt9j/UckyWhwTh314n8rgcTmMFJ8hgwelCClFGO2qHCL8aA6k12sRqI19/7HjDsrnTb9ilBEjhhcB64H5vLnrroZKd3He7LRTnDd96s1mvfqdmytGlxWLI/gY4e+yzZkzI0UoPirRRgAqgrIRVotgcLQIEsf2xTk2cuRm6SMf3b8SuI3zf/c95qYVz71YhGtj/AhGlqHaCB7FZ9jwtedEc+dITNtSu/qq2yqVNuNcPPZjB1Vs4lycmkP9w/J3BDwj4BuVLb/wxeMq16bqecd5HIHbY/J5WFY6j22Jc/BXvzw/h5xfKkaPqqVCtdVy9bujKuXBh+xZ9zoTYej+A/oVjnHsH3Pswso1NuYWobLYXxFmjRYvdGiutXW/PfXkM8ULIGK+s3MgfJddtq8sIsKEBx08uqhgG+ux+ZiRxTkc19gItMWn+po+vItc48sNCJMI1Ma9q2xxbEewb/HiR4tece7HNau838cxPX36pOLcLMOKEYCsbl3lvI/zOI6baHGtOujgBZXVjO2Jz1lnXlpU1S2fceIYq9faevzUm9em2u9DB+5RhKjj5SnR4mUh8Rmer9HxYpS4jpbHRnPbGAHQMmQe97B4nql+Ftk2B3en5Gvpb39zcXFPuTtXXY0XMZTjtPbZKQK60TbG/aPclrYeA3EviJdnlO2ggxbk0OuW5c/i+rb1NlNytffzGz0nVkbIHRGmjUq10eIZYGF+Bi/bZpsNzf8Xm13cp59c8kxxHVq9+jWh2hLINwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgAwW6baj2u985rQljBGqjqqxGoCsIxB+PX3DxKem73zk9/ey/zsvVt94qVuvpp59PZ/zm8uITIaWFB81Lf/uNz+Q/fm+omtnWdf/rL308xScqzvbu3avJbLadOTVttdWkXGFzWVFJackTy9OW0xvCVk1GbuceK1asDX1ExdoyVFsu9sCF81J8tPoCDz74eKNqk2XApnrsCDEcuHCPdN65VxW9F9//aLOh2hjhkEP3qhtai4p4UZEzqg9GICaq5W2Zq6eub4vKmVHZqz1bhAjLFhX7hgwZVP4svqdvNTnFZ0Pa/R+Em2Iee+29c6NAbTnfCELtlIPNUakx2v25ammE1Ou1qChYBlTqDY/qaXGdiEBbBMpiH0T4vGwRGiwrrkXAKkK93b3FNsc5ULacOUuv5sDo83mfR4ilDG5F4GdGDqmVLQKDUdUxWpjGMV+vxb67/fZFxXXyyWzeUms4bxoCtdXj7bPvrimqLkY11th3sV4beuxFGLispBzBqQgD1baothkh+zJgtzpXvyzbmBwy/PDh+xY/I6hXvlShHB7fM2ZMq4Rqo/Lnxm4RdnzooScqs40KhvXCxhHwjZB8BAQjwBSWETis1+blAFMZqC2Hx7btmIPMUZk32rPPvlg3KFqO77tBYGx+wUa94H4MLZ5XPgiPtXT8lKHaCK+11Nqy31Z/UCU35vtCDtDXttjvEdLekNbR1/jqdd25TiX4uEeUodo4zqNCaW3bMgdry3P+5Q8qcJbjdIXzPgL6UQk8WgSb9z9gt3L1Gn3H9fTUn51ThKHj2hsv46i9j5cTtOX4KaftDt/xHHDCpw5PN+aXNsSzRrwEIloEZaPSd3zi2rpVfuZp7lklrsVxTY62x/y5dZ9F4llyt3mz06WX3FAEvOMZsKVnlmJmHfRPW46BqCJbtrinVAdqy/7xYoY999o5RbX6ei2qKZcvgFm9+o3iHlVbiXbvvXdJ8dEIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQ6T6B35y26fZd8ysm/abSAr3z1eIHaRiJ+dAWBCKF8/e8/m+6899fpO9/9UlGZdtCgAZVVi7DVhRfckD6031+lH//wnEr/DemoDdS+m//QPgK0v/7VxWlJDnaV7aWXGgIO5e+O/N55520ri/vyl05J3//emem5qqBtZaCOugJP5eqTZdtq6+ZDolGlrAzORSXj6tBpOX18RyW/siJldf/ojhDP5FxptWzPPP1c2dnlvquDq1F97ZZb7i1CORtrRSNI9tRTDdXJYp5bbTWl2VlHkKVsEYZtrk2ZPL65QUX/8I+gY7QIhZZVa4se+Z+oXlu27XLl0p7SLrzg2nztbPhcdOG1RVXThx9aUgnURqXl448/LFftG1ohiZBfGbidMHGL/PKB5h+RJkzYopgugkqrqoKplZnljtg3LZ43U6rOmxwq29C25MmnK7OYMGFskyBpOXByDnUfnsOz8YlAXr1WXhfKYVEBMyrvRRCrbBEc2thteQ4Gx30vWgSDmwuBx/pFULBsLZ1D45sJrI/MVYPL9nKu3KutW2Bd16NyDrXHT5wnEYC+NV9zy7au46ct+238+M3L2edKys+lP5zzx1yN+Zki7FcZsAEdnXGNL1c3TMeOXbt9Zf+4lpUtXhRQax/DBg9uqAQa3fHM11yrnbajzvvq83dyvi6Wlb1r1zMCt+U1oWFfrH3WqR23LcdP7Tw29d8Rmo3g5l/85cfTwQfvWVwz43mubBGYjRDpz0/9Q35RxOKyd+U7XvZQtrhvNNfK+2EML1/s0Ny4Hdm/LcfA8qp78bRpzb8gZkrV/bt2m+KcLKvvvvPOO7nC8iVF8L0MNteO7zcBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDnCKz9y9rOWX67LPW4Y77RZL4nfu2EJv3as0dUyu3oZbbn9ph3+woMHTooffz4hcUn/uj65psWpQvPv74I1L6SKyxGCOJb3/xpDhOMTEcevd8Grcy99zySLsjzvn/xE7ni4FNFhb6oXtuV2jf+8XPp+uvvSatXvV6Eaf/3//plik9Uzt11t+2K6r37H7Brqq381JW2oTPXpTocu/nokc2uSgQu4o//ywpxMV2EcmrbqNEjans1+j26anhUjmtLGzV6s3TMMQvXOem7764pKtWtc8Q6I+y9z645YLW8CO3Fel5/3Z3FZ+TI4SlClBF03XLLSc2GEevMslGv119fW50sgiubbbY2sNloxPwjqsaWrXp/lf3K7wED+5edzX5HWHbRooawY4RoZ1WFZ8uqaxGWmjmraQXDZmfajQdMnDg2HfGR/ZpUOKyu2rrkiafTv53yy2YVqq+ZL+ZAeoS9altUs6sNqVWPszHOm+r5rcrXy7JF9cm2tgi1RoXCZctWpBeefzlfH1blKocNQde2zrO101WfC9U+9abffPO116VVVVWoq8eNa9zAgQOqe1W6+/RdG5qOgJ62boHqKtjNjR2WUTn4iXwOxfETIbt1BWhr59XW/TZmzKi0ww7bVqqyPvbY0hSfCGlG4DYCm/ESgjg329I66xof6xoh/z591h6z9de/V/3erejbmed99fnb0jNLbEZcF57+4OUd1deL6k1s6/FTPY/u1B0v8IkXKMQnXr6xbNmzKV4yEc8LEZyOqqrXXH1bcU+cNWt6ZdOrXxhx+mkXtuL4y6HaF5tWiK7MsAM72noMVD/Djq66x9Suenl/r3fviPv+AR+aV7zYI6aLFwpccvH16bJLb0xjthiVJuVnkK23mZImTRpbO1u/CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOlCgW4Zqa/3mL5hT26vdf0el3AhGnnn2t9t9WRbQvQQijLf3PjsWn7/LVWz/7mvfTxdfdGOxkd/6l5+1OVQblWf/9LP/ku64/cG6YDO2nZJWvvpaEbKtO0IH9pw+fWK67Irvp5P+8Ufp6qvuSBGkjPbE488Un7POuKIILP7L//xiOvqY/TtwzTaNRUVIomxDhzUNyZbD4jvCgGWo9s03364eVOkeOmRQpbtex5AcCi/b22/Xn0c5vLnvPr37NKoa2tx45bHQ3PCW+kd49jOf/Wi68spbcnXmp4sgSYwf2x+fxYseLYLaBxywe6NgakvzrB72VpVfvXBy9bhDsmmEpCLMEkH6CLqsOzBVPYe13ZMmjy32YwRglix5ughARojsxRxuiQrE0aLKXL3g59q5dK+uo48+sNEGXXbZjZVKzP37920SqI2Rq8+b+B37pDWtOoxbPf6QOgH1RsOrzqu32njeVM/vrarzfliu8tqWdu+9D+dr7q3FMVk7fQSzIgweFUDbqzXahjpB5erlDh26dhurp6seR3fHCjybKw2fe+6VlXOteulxvZs4aYu0tKqad/XwjdV94ML5KcJwt926qFJFOkLhTz21vPjccP1dxQsUFh60oO51oKX16KxrfEvrtDGGdaXzvjXPLOU2V++Psp/vlgXiOWPq1AnFZ69cxfbyfG985JGGivYRrK0O1bblnlj9coeW16RrDq2uJltWm623phHajZcM1BqV486cOT1XiB5UhJUjVBstnvdWPPtC8bnjjsVFQPyQQ/dK48Y1rUBdzsc3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA+wl0u1DtTTfel+JT3b7y1U9W/+yw7liPyeMPL4K1nRHs7bANtaB2Exg5clj63n98NV0z+84UFdKeW/FSeurJZ9OUqePWa5kRDosKzg892PCH88Nz4OqjR+2bq77OyuGSSWl6rgAbIaxPfOwfOixUu65w5OQpY9OpvzypqJIYwdrbb3sg3ZE/ixc/Xmz7q6+uTl/6f08uQpCHfXjP9fLo7iMPGjywOF5iO6PScUsBzxhetsF5unqtDN3WGxb9qocPHFR/Hs1N29H9o3rsUUd9KFdOfKsIoD6TK95FSLAMPUSw+KKLrsvVavumbWZMXa/VGzR4bUXMlStXFwGKCJLVayvz8RsBi2gRVmxroDamj6poEYS59db7igD6448/nbbddlquRN1wvsc4222/VXz1iBbVKrecPqnRth5z7ML0y1+cl6KqXFTQjIDdlCnjG41THaCJ4+Tgg/dqNLy5H1FluV57JQe1W2rVwwc1U021pelrh1Wvfxmmrh2npd8PPfhEEbAqx4kqelG5eYuxo3JF6+HFiwyiQuQZv724HGWjf8e1q2zV16ayX/X3q1XXruptrx5Hd8cJxH3grLMuLSqBx1LjJQZxDR03bkzuHlb8jiDad0/+RXEetuea7bTTrBSf5cufL0K8cY2PystvvdXw0oeoXrvq7MvTCZ86vHi5QWvXpbOu8a1dv7aM19XO++rzut72VF8XqvdHvXH1a1lg0KAB6dDD9k5LfhAv43g3RZXWV19dla/1w4oJ47oa//eIduihe7fqxRx9+vYpxt9U/4nn4LICclTdHTGiwaJ2e8KruUBtOW48Y3z6Mx/Jz+Er8/PmM/lZ8/n0dK4SvHLla8Uo8eKTM8+4NH3ucx9Nw/Mzh0aAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCxAt0uVFvLF2HWzg60RpjxK189Pp34tRNqV8/vHirw/e+dkR5/7Oli6z9+/EFpj/mzm5WIP3rfautJ6b57Hy3GefTRZesdqn3ggScqgdoIq/7hvP9TBKVqF7quPxCvHb/e7wED+lV6R/C1Xlu9+o1cKfSZyqAIujXXIoxz1NH7FZ8YZ9Gix9JJf/+jdNtt9xeT/Me/n5W6W6g2gp3vvddQnXfAgAF1A5cRCi3b0KpKsdEvKpK++MIrxeAIBUQ4rl6LUECEP8s2rJnKmhFiaamSagQDyla7LmX/rvYd51UEUcuqbBGqvfKPN6cIDUa75ZZ71z9UmwMofXOgJALj4RVuo0bVD1xWm7W1qmi16aztGkK10e/hh5cUodr4jhbVr2esZ0C4mLAb/RMVVsPo/sWPFVt17TW3p099+ohGWzi0qjJqVPqdPGX9Xl7QaGb5R8N5syafv/VDRtXHQEvB99r5Nvd7+PC1oZwXXni5udGa7f/AAw0vLIgR9t5nlzRv3pwm467rZQhNJljPHtXVlKt96s2mevjGOIfqLUO/1gs8/vjSSqB2662npMOP2LfJsb9mzZp2D9RWr/H48WNSfKLF/W7RfY8Ulcrjd1zzo2L59K0mx89WtQgZdtY1vlUr2IaRNunzvuqa3YZN77aT3HLzvemllxuq1M+ZvU2alCvVN9fi+SCeU1aseLEYJV7IUIZqhw0bXFS8jwEx3obeE5tbh67UP+6j5UtWns/30eauD+U4rVn3eCnFjjvGZ2YxeoT6o0JwhJijivZddz2Q9t1vt9bMyjgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIbUaB+Cb2NuICOntUpJ5/eaJEthRUbjdjOP045+Tfpu985rZ2XYvabisCTudrs7866svj85//9XYurveLZl3IQpCEIFiOOH795s+NHaKReu+fuRyq9Dz9ir7qB2ghp3ntPQ3C3MnILHe+8/U7dodttv2Wl/113PlTpru646YZ7i+Bhdb/q7lW5itPddz2cA3BPVPcuumfP3ir90798vtL/ySXLK93dpeO6a29P//mDM4rP3Xc/UHeznn/+pUr/CRO3qHRHx/SqKp333/9YsyGmxYvX7u9x+biqrhJZPcOoqPrII0uqe1W6V616LVdPXrsPpkyZUBnW1TqiSuGzy19I1XblOm6xxai03/7zyp9FILLyo07Hmg+qzNYOqq6QWu1bO96iKvvq/VU7Xmt/R2g0tiHaE48vy9v4cvGJ3xFwi5BoT2977rlTJeQXAaLqMFnYjBs3OpUVTyOwWVboq+cW00e14ZZavCzg4YeW1B0lKuE9+eTaFwtMmdq4am7didbRs/o4iqqcZbW92skiWHzqqecUn9tuXVQZ/OyzLxTdUfl4p50awj+VgR90LMtV9lrbIli+vm3cuM3T4MGDiskicLRkydN1ZxFBpIerKjFPn976YGTdGeq5wQLPLm8I5cWM5u6wbeVcq55xHJft2eKci0qbUY269viLUOBOO89qVMX6lTzu+rbOusav73q2dvyucN5XX7uiomec+/Va3LvLMGNUd586tes+b9Rb/47qF8d1XOfjc+tta6/x9ZYf94kyUBvDq18uMS1XKi/b0qXNX/vjhTxRFbq5/4OU84jv5p6dqscpxmvD/aN2Hm35vVVVyP6uOx9o9li87db7Wpx9HMNhVu8+HMvYZdftK9NXV1+u9NRBgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ7gLdLlRbKzZ/wdzaXp32O4K1UbVWI3DwIfMrCFdfdUf68Q/PyZVJm1Zrffrp59NffvF/VUKRUWV2xrZTKtNGx7jxoyu/H37oqfRunWDt9OkTK+NESLdeO/n/nLbOP4iP0FnZFi9eW9Ww7Bffc+ZuXfl5802L0llnXFH5HR333vNI+tqJ32vUr/pHTLPdth9PRxx2Yjr+uH/IVbIaqm1Vj/PCB1VYo9/ue6z9w/TqcTbl7uqgyOJFj6aoXFvdIgxY/hH+kCGDKlXFynG23XbLNGBA/+JnVF6tDs6V47z88soU1TrLNnfutmVn3e8rLr85V7V9rcmwSy6+PkVYNVpU+JowoaEqYJMRO7nHshxu+I/vn55OO+2CdNaZl6U3Xn+zyRpVhyjrVfcdmqu2la0M9pS/y++5c2eUnYV7Wfm20jN3xP4rw5ZRxXT77deeM9XjrW/3rO22KiaJYMull1xfmXy7D/pXevTQjjg+y0pxQXD99Xfm4N2aikbv3r3zvmgwjHDe739/RaXyZmWk3BGB5dNPuzD95Ce/Sz/58VktviDgj3+8pW74Ns6b8ryO6qwTJjQOxlcvr7XdozcfUZlPhJwuOP+afF9pHGx9440301VX3ZqiGmF8Jk1eW8V65AdVlWPbX3ut6fkR14y4HrXUqivuRjC5dvktTRvDItA7Z842ldEuveSG7PRW5XfZcfVVtxXhyfi92WZDc7huw0PJ5bx9t01g5KjhlQnrBcniunRzrqDZnu2sMy9N//WTs9Pvz768qDZeu6w4tt+sqvI+ceLa47923OZ+d+Y1vrl12pD+XeG8j5dClM8Ocb5fUnX/KrctrtUXXXhd5Zoyo+o5pxzHd4NAvEijbFGN+fbbF1f+H1H2j+94pov7RNniWrp5vo+UbebMLSsv5IhqqrUvoijH++MVNxf3xO//+2kpgqi1rTXPTjFN9f3juaoXx9TOrz1/bzNjauXlGhGMveD8q4t7ZbnMuKdddeUtKarNNtduuvHu9MP/PCOdecYl+Zi9tu5o63rerDuRngQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhtVoO9GnVs3mtnk8Ye3y9bcdON9KeZ95tnfTvMXzGmXZZhp1xdYeNC8tO9+O6drrr6zCNN+65s/Tb//3VVpjwWz01ZbT06v5PDSA/cvKYaXQca+ucraP570p7n6W+Ms/KRJW6RpW45PS55YXvyB/HHH/H3a/0O7FGGpo47er8DYdbdZufrfwKLy4jm/vzptNmJoWrhwXtp6xuSiOu35512Xzj3nmmLetdXdqjX33mfHSkj2O//7tFwx7OU0cuSwdNzHF6ax4xqqZMYf5EfwNwK+0U788r/lgMtVaebMaenBB5ekW2+5vxg31uGJx9dWaiyXM2/37VNs07Jlz6WXXlqZ/vSz30rHHvehNH/+7Fz1aUW6/rp70m9Pv6wcPR364T0r3d2lI6pWRsAv/ni/IcB3QYqg7LDhQ9LyZ55vFGyoF5iMfX3kUR/KlZAvK0KDER5cunR52nqbqWnY0MHp0UeX5s9TlRD1TjvNahRkq3UcnoMWUT34tF9fkLbZZkqaOm1i3vcvpkdypcgIzpWtutJr2a+rfE+cNLYI/a5cuTpFsPCcc/6Ytp+9dZo8aVx6Nfd7KlcNve++tRWdI1hR2yLsXIYKb7zhrqKC2aCBA4r5lGGQadlmn312TdfmasNFMDOHu6Iq2dbZ7d131xRmUUUxWgQIDzts7zR69NoQS+0y1+f3rFnTi6B0LLesPBeh66nTVPMrHXffY27ezw8XYdmoNHv3XQ82qhi39z67pBfyMR1BpBW5cutvf3NRce7F8ZN3aIqQ9C233FcJdu2446zUt2+fcvaNvsM+KoBHkDvOvWl5Pzz/3Eu56vNT6YUXXq6Mu9/+uxXHQqXHBnR89Mj9i3DTq3nbnn56Rfr1r87P5+zUXIV38/RUDpY//PCSSkh11OjNcuXztSH4WL8In0f7wx/+mKvVzirCqm+99U56Jm93BCLj3GmpRXB5xIjhOfS/sgjbR6hoy1ztMK4hcXy2pu25106FT4SWIpz5y1+cV2zDlvnlEC/mFyqEX2xbtEGDBqSjj1lYXC9bM2/jtJ9AHD8RJot2zdW35eeNN9K0fM0cMLB/Pn6eT3flqutRKbw9W7xYoKyoeUs+XuMlI1PyOsR9b2mushz3rKioGS0ChGPHrn1RSGvXqzOv8a1dx/UZr6uc9x/56AHpN6dfmMPyq4vr769+mc/7fB+Oa9TSpxquXRHsjxb9Fi5c+3Ka9dnenjBuPHPEcRqVvuN5IM7HB+5/LD9bj0tx3Y9geVT9jarA5UtR4plz330b34viHnbkUQcUIfX4v0G8DGLpU8vT5MkxnxHFiw3iHlqec3E9jueq2taaZ6eYJsYr2xOPL0sXXnBtGjNmZJqY/0/QlgB8Oa/1+Y4X0sSxdd55VxWTRXXtqOwe97Ch2SOeyeMFAWEcw0q/6mXM2m56uvGDa2HYXHDBNWmbHHTeIl9vojL0k9l98eK1L6iIe7RGgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ8QLdLlQbodXq1lWDq1Gx9itfPT6d+LUTqldXdw8S+Pmv/in967dOzZUO/1BsdVR+ba7666hcAe7HP/37XJV1dl2hz/3JEemfT/pxMey22+5P8YmgbRmq7ZcDuV/7u0+lb/3zT4s/sD/1p+en+FS3BXvOTVFN6rJLbq7u3ah7v/13qQR44w/Jf/KjhnWPaoflsmKCn/38v6cjP/I36YXnXymmjyBsfKINHToonfqLk9I3/vb/VkK1ES4sW+/evYpt/cTH/qEICd95x4MpPvXan3/+o+moo/atN2iT7jcwBzWP/dhB6bxzryoCcBEkufnmBr/qDdthx5lpn313re5V6Y5Kqx8+fJ90/nlXF/s8whPxqW0ztp2W9j9gXm3vRr8jzDBq9vB0Qw6S3nPPQ8Wn0Qj5x7x5c4rAbW3/rvI7jrGPfHT/XKX20iIEEcGqMlxVu4677LJd3QBghFTKwGAEZO/I1d+i1QYGd5s3uwgD3nnn/UV4M6q71avwtv/+83IAfVoxj43xTwRgpkwZn57MAeGyzcxBxurzq+zfU78j9BPHagTNo0VQdPvZ26SBOfgXLYJFH/lIw3ESx0cEaOJTr0UQZuddZtUbVPQbM2ZU8XKD66+7M7+84KHiUzvyrrvNTjNmTKvt3ebfgwcPKkKmvzn9ouLa0dz6x7oddfSHGi0nKibHcRrB1fhccflNjYbHcbRvvt5cnQNaLbWddp6ZK/ndWowSIeT4xHnT2lBtLOfwI/bL5+ol6Zn8EoFVq15LcS7Fp7rFfe2oow9Moz6osFs9THfHC0TQMY7lCG7H80Ec9/GpbnvM36EIsterPlw9Xlu7Z+dA37Icnr1/8WNF8P222xbl56FFTWYX1+w4xtraOusa39b1bWm6rnLex/3rmGMPSr/JLzKISvJRDb5eRfg43+PaFee/1rxAGF17zR3pjjsanlOa84w5DBo0sHg+iufG2hbPFId9eJ9K5fN4+Uj1C0jK8WMehxy6d+rfv1/Zq/Ld2meneDlJvLglXpwQ7cEHH8+flF/6MqPDQrWx3AhzH374vunSS2+ovHwmXsIRn2jhdPAhe6VTf3ZO8bv2n7jfHXTwgnTZpTcWgx568IkUn9oWx/CHDtyjeIasHeY3AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA+ws0LnnZ/suzBAIEPhCI6oYnffPP0w9+9HcpKsCOztWjqlsMnzlrWvpYrtJ64SWnNBuojWn+7PMfST/44d+lyVPW/kH80qdWFNVFy3l+/gtHph/91zfShAmbl72K76h8+yd/dkT6aQ7C9smBspZaVKW94OJT8h/Ozy8q35bjLrrvsbKz+J46bXw67TffSgsP3j1XBx1SGTZnzlbp1F/+U9p2ZstVmebM3Tqde+HJxbbXBieiilQMj235p3/5fIoKvt2xRSWwT57w4bTl9ElF4KHcxthfEyaMycfMLunA/Mf4LbUI/X30yAMaVaMsx4/w3fwccIpKqa0JXUYY6sBcvStCidUtAi4RuIj16eotqhKGaYSIais+x/kWwyNQGRV3I1xZ22LbT/jU4UXoo/q4fG7Fi7WjFkHlfffbrW5YYvPNR6ZDc/hkp52bD2Q2mWEre2y3/VaNxqxXybjRCD3wx845NB0BrmgR7rvllnsbKcS+PfqYA9POO2+X4npT3eJciWM+AnkR0u7Tp36V2nKa3XefmxYetKDRORzDRo4cng7N516EVDd2i/U77uMHF8dp7bkdYcIIKB3/yUPTsGFrr82xDhFo+uQnP5ynm9qk+m6sbwT9W1P1ONwikBSVQMv26qur6lb0K4fXfsf5GIHZnfM50lxIK9anutJu7Tz87liBONaO+Mh+aY89dqiE1Ms1iPMowmMLFuxY9mqX71iHQ3LYLSpNxnlQ2+KYj2vipz/9kaJ6c+3w9fkdL6PojGv8+qxja8btSud9XGeOOy5fu3KosfbaFdeE2XO2Ka5DEeDUWhaIZ5iogh73qqgAO2hwY7MYHs8i8Tz0qfxcUy9QWy4hwvLxLDlx4hZlr8p3nNvxwoTP/cmRuSr5xEr/6o71eXY6Ij+Dzdt9TqN75nPPNX3Gqp5/e3RvO3PL9IUvfqwwnDlzemEYz2zxzHzcxw9p8ixcuw5xn417VISSa1uYxT4pn0drh/tNgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQMQK93s+tYxbVMUuJCrDV1WrPPPvbqS3VaiePP7xdV7it69WuK9VFZ75ixUvFmo0dO6qLruHGW63nn3s5PfjAkjQih1cjeFovTLSupa1c+Vp6dvmLRXg2Ks/Way/kKoT3L34ijcwVcLfZZnKTAEy9aWr7vffe++nJJctztbA+aeKkLZoEIMrxY7wlS57JAZfhuVrhsLJ3q7/feOOt9PSy59LyZ15IY8eNTtO3mtgk8NXqmW3CI65cuTq9/tqbacwWI9cZ5Ku3ma+99kZ6NVfZeuedd3LQeWgReKsXHK03bW2/mE+sTzmf2uGbwu933nm3CJ2vWv1aGjpkcHEurI9H3DpfeWVVEUSPoGJzLcYLq5WvvpZ65SrMYVYdNG9uurb2X7ZsRTrjtxcXk0dg5rOf+2hbZ2W6LLBmzXv5vFlVVEuNIFcE9SLc1VxbuvTZdOYZlxSDozrfMccurIwaVe5eLc6bIfn8W/9rYWVG69ERFUPjOI3vMfl4qA1WNTer9957L8V94vVcMTJe+FAbwG1uutr+sdzVq15Pw/LLFdpyP4v5RVXohn3wenGvirCuUF2tdNf7HcddVFmP/RX3/vW5vm6srVm9+vXi3I1jKK6HtS+F2BjL6ehr/MZY5+bm0ZXO+/La9cYbbxaB/7hmVr/Morlt0L95gXgOfOGFl/N1dEA+H0a06Vny9dffKJ5n3s7PknE/jFD2+rTWPjvFPKNKeZy78cy0rhdYrM86bIxxYzv+/Xu/LtYvAuBf/sqnm73Gxcs74v9mcSxHcDyeAzUCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHOF+j2odqvfPX4dOLXTuhw6eZCuRHwjUCt1nqBnhSqbb2KMQkQINBU4Pzzr04PP7SkGLDPPrum3ebNbjqSPu0m0FKott0WasYECBAgQIDARhOI8HG0COPXay+++Er6+al/KAZFsPiLf3FcvdH0I0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6MICfbvwurVp1faYP7tRpdo2zaSdJuqsgG87bY7ZEiBAgEAXEnjwwScqgdoBA/qn2XO26UJrZ1UIECBAgAABAl1XIKrjXnThtWnZshVFhfajjj4wjRnTOFgb1XOvuOLmykZMmTq+0q2DAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg0xHodqHarkovUNtV94z1IkCAwKYr8Nprb6T/+snvUv/+/dLrr79Z2ZDdd5+bBg0aUPmtgwABAgQIECBAoHmBYcOGpN69excjRMD2tF+fn2bMmJY2z8HagQMHpFdeWZnuX/xYimevaPGcFc9bGgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwKYn0Ov93Da91W55jSePP7zRCEuXX9Dod0f8qF6HM8/+dpq/YE5HLLZbLmPFipeK7Ro7dlS33D4bRYAAgbYK3Hffw+myS29sNPnOO89K++63WyUY0migH+0qsHTps+nMMy4pljFt2sR0zLEL23V5Zk6AAAECBAhsPIG3334nnX/+1WnJE0+3ONMJE8akw4/Yr6ho2+KIBhIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHRJgW5ZqTYCrDfdeF8F/LvfOS2d+LUTKr87qiPWIwK1GgECBAgQaA+BIUMGp5kzp6c1a95NozcfmaZMHp8mTxnXHosyz1YIDBzYP02dOqEYc+zY0a2YwigECBAgQIBAVxHo379fOvroA4tQ7b33Ppyee/6l9Nrqhsq0o0dvVjxrjRu3edpxx229vKSr7DTrQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBog0C3rFQbIdpTTv5NI46OrlbbWUHeRhvdTX6oVNtNdqTNIECAAAECBAgQIECAwCYm8P7776devXptYmttdQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJoT6Jah2tjYyeMPb7TNX/nq8Z1SrbbRSvjRJgGh2jaxmYgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoEuhd1d2tOiNEW92icm1Uj9UIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR6nkC3rVQbu7K2Wm30O/Psb6f5C+ZEp7aJCKhUu4nsKKtJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6sEC3rVQb5rXVaqPfccd8I740AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHiTQrUO1J37thGaDtTfdeN9G283f/c5pRVXcqIxb/Yn+GgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOcL9Ho/t85fjfZdgwi3nnLyb5osZP6COenMs7/dpP/69Ghu3lElN0K92oYLrFjxUjGTsWNHbfjMzIEAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDokQI9IlQbe7a58GsMK4O1EbJtbWtpfgK1rVVs3XhCta1zMhYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQvECPCdUGQUtB2Bhehmq/8tVPxs8m7aYb700337Qo3XTjfU2GlT0EakuJjfctVLvxLM2JAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0VIEeFaqNnRyB2OOO+Ua77G+B2nZhTUK17eNqrgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCcJ9O5JGxvbGtVoly6/IEUAdmO1mFfM88SvnbCxZmk+BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECG1Ggx1WqrbUrq9ZGBdv1aRHO3WP+bEHa9UFr47gq1bYRzmQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBARaDHh2pLiTJUe8rJp5e9UtkvArTRIkQbTUXagqHD/hGq7TBqCyJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAt1WQKi22+7a7rNhQrXdZ1/aEgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0FkCvTtrwZZLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKMEhGo7StpyCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOk1AqLbT6C2YAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgowSEajtK2nIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQ6TUCottPoLZgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCjBIRqO0racggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDpNQKi20+gtmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKMEhGo7StpyCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEOk2gb6ct2YIJdDOByeMP72Zb1D03Z+nyC7rnhtkqAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhRQKXaFnkMJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6A4CQrXdYS/aBgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRYFhGpb5DGQAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgOwj0ej+37rAhtqH7CqxY8VL33ThbRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHSIgEq1HcJsIQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAp0p0LczF27ZBNZHYMSIYeszunEJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhUBlWorFDoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIo3g6pAAAQABJREFUECBAgAABAgQIECBAgAABAgQIECBAgAABAgS6q4BQbXfds7aLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgIiBUW6HQQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0F0FhGq76561XQQIECBAgAABAgQIECBAgMD/z96dAEhS1fcDfz3HLseuLCuIXIqIkSggaP4qeGLMocGgEvFMosYrEjUgavBA8UKNgEdMjBpUDKAo3jGJmhhBQEXlFBVREVBYgd2BXdhrjn/9qvb1NTO7M7Mz090zn0p66+jq6lef7verGpzvPAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6gJCtXUKCwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgtVQKh2oX6yzosAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAuIFRbp7BAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwUAWEahfqJ+u8CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6gID9aUOLvz2t7ely370s3TVldelW26+Pf3+g/ZLBx9yQDr0sN9LO+20Qwdb5q0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQWgkDHR6r98D+fn454+AvTy158avrQBz+bzv/c/6a3v/XM9My/eH164pHHpR//+JcLwdk5zJrA2JYj5Xk+cF5vn3u+Emh3yet8+IRA/j60z30/fD98P/SP9rqQ19UH9UF9UB9yPWifqw/qg/qgPrTXhbyuPqgP6oP6kOtB+1x9UB/UB/WhvS7kdfVBfVAf1IdcD9rn6oP6oD6oD+11Ia+rD+qD+qA+5HrQPlcf1Af1QX1orwt5XX1QH9QH9SHXg/a5+jDV+jBW0I3FP+WU51tW/X5qmwuf/M2o5tmjfZ73at+e1z2f/abb/6r9+WW/ied8JnbR//I3g08I5O9D+zwrtW/P656vBLJH+7zVJ+6vqlusvF/r8+M/B89P5Ds0tDrFo69I0LY/agVyu25WnPP5K497b/rC5/8vLVu2Y3rB3/x5OuQhBxTLO6WLL7oyXXjBZenyy65NO+ywJJ35iTelxzzusDlvjzfoToFVq1aXDdt113t0vIGtvaW169RqtY63b2sNaG3t1vb03EwEuvvTn8kZeQ0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACByQVGR0fLJ/sipWAiQGBeBabb/6a7/7yejDcjQIAAgRaBXqnZ42OpjXRVN8Ts1qy5vXRduXJli2+sdCxU++vrb06PPvzFabfdVqTPffFd6f7332dc4077x7PT+04/Nz3+yIelT51zyrjnbVgcAjlUu8suy8sTju7VHBCd3/WxNNH7jRYN6uuLZ4pO1dH2Tf7+2axb21fidbHfttpXffqT+2/r9Z6vBHw/O1nffH99/3z/8rUyKpLvg++D70O+O9Ef1AP1QD1QD7KAeqAeqAe5N7g/UA/UA/VAPcgC6oF6oB7k3uD+QD1QD9QD9SALqAfqgXqQe4P7A/VAPVAP1IMsoB6oB+pB7g3uDxZqPRgZGSk/5IH+/g7+frfv10L9fuUK4vwmvp5Ot/9Nd3/+lYDv38TfP98P348Q0D/mrn9EzQ7fvuIeK0/d6D1SBOpyeHZ8+2odvz8cuqMa6HOiUO1Ahp3v+df/+3vlWz7l6MdMGKiNJ//ulc9In/vs/6af/uT6tPbOu9Lye+w8YTPXrFmbbvj1LWlwcCDd5773Lke+nWjHkZHRtGb1nal/oD/tumsV0Gzf7/bb70hjxQe68p671EOS+XUDg/1pxYrqdUNDa9OVV1yXHvjA+6Y97j0+rfzb396Wbrn5tnTf/fZM9yyONZUpn8ey5Tum/fbbK/X3T+0v9vzmN7emVbfcnvbe+14TtmUq790L+8QfMIqRYsvO1jTPVTi2z/3ztZb3H4s/rlS8b3TzCNXO/ft3+vy9/2Tfv8hU+/x9Pyb7fsxPfeLPv7gmdez66Pvn++f7p/816sD83p833jf3Q+/f6I/uT30/cr/Ic/1D/8jXK/VBfch1Ic/VB/VBfajqgvqoPua6mOfqo/qoPqqPUQ9cH1wf8nUhz10fXB9cH1wfXB9cH90fuD/I9wV57v7A/YH7A/cH7g/cHyzu+4P4/ekiTFH+Im3jPsH10fXR9XG+ro9FvmHK/a/6D57N+y/u+uX65fNvXLf8fFddt1y/u+f6Xf7dkuKLWSsf3fv5FKO9lm0sr/sRg9ySZ+ia+lI0abKpYyPVvv51H0qfOus/00te9rT0pjf/zWTt2+r2n197Q3rLyR9NF15weXkzHjsPFMHapzzl0enNb33xuDDrddfdlI58zMvS/fbfK11w0UcmPPZhhzwv3XbrUPrBZWfVA6r5dYc99IHpk//+lvSSv3lHuvT716QI27739FelZz77j+rH+kYRFn7XqZ9M1/7shvq2ffa5V3rhi/88vfglT61va16I8zjlzR9L3/6/H9U3L1kymP76BX+WTjjxuROGhIc3D6d3vv0T6fzP/W9aXQSF83ToYb+XTn3Pcemgg+6fN/X8PI9Uu2LFsq45l+jc1VQtxPpUQ9D5lfM9j/yvae4EphaBn7v3d2QCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC8ykwPDySRkdH08DAQH1Aq/l8f+9FYLEKjBYDyQ0PDxf9rq/of41RDLfmob9uTcdzBAgQ6B6BmdT4TrU+spVlKL1sQPHHG4qpsV6udvSfoaE15ft31Ui1Dz64Cn1+/vxvpRe95Oi05567TQvp5mIU2Oc8803plmKE1p122iE97vEPTevW3Z0uufiq9IXP/1+6/vqb03nnn5p22GHJtI67tZ03bdycXvbiU8tA7YMedL+0z757pN3vtWv9Jd+58Ir0kuL5CLzutddu6VGPfki66qpflCPtvrUIzV5x+c/TBz90YpXA3vKqVbesTs991skpzidGwX3M4w5N69auT9/77tXpo//6xfTNb3w//dc3PlCeY36j+MIde8zr06WXXlOO3vukJx9RBoD/55uXpssvu7YI/b4zff1//mnCMG4+Ri/Oa7UtkfX8pw/meT42Fp07QrSR9K+GqK3CtbE9ikBn2xftyu2baF60uthctX+i57f1es9vxbdW/GWFbfjz24rfln7le6l/6if6iTqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAPqgDrQO3UgAn0Rqh0rAn61/vhdat9f39/e+f728vd1bHRz2d/6ilFqp5pj0F/VJ/VJferlureYvr9jxb1VTFG3Y6Ta7v7coq1Vnq5qa/SzRr6uVuStOtv+4u0nmTo2Uu2mTZvTUU86Pv3kmuvTsuU7pb97xTPSU5/++LT33rtP0tTG5nVr705PO/q1ZVj1xS99anrdSX+Vli6twrN33LEu/f0rTi/DqH/8p49MH/23N9T/6k0ecXamI9VGC/bff+909mfelmL02eYpRps9+imvSWvvvCt9oAjOPq04lzzFOT796NcUod/16dzz3pEe/ZiHlE/dffeGdMzRr0tXX/2LYkTa56RXHf/selvjuWcf+8b0ox/+NL3oxUeXI+/m40Vw+NhjTioDvTHi7rJlO+an0jve9vH04X8+P73wRX+eTnnbS+rbe3mhMVLt8o6eRmN02mjGWJVPjaUtT0z1L7x06iSiDBU/r5rmQKD4WaT8TwBzcGiHJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCVAvF71Bs3birbFr/PX4UpurKpGkVgwQjMtN/N9HULBs6JECBAoAcEeq1WxyjoMeV7wGqU2ggCV1OnR60dGlpdNqSrRqpdsmQwnfPpt6fXnviB9I2vfz+9652fLB8H/v5+6bGPOyz96ZMPT3/wB79fR82YMf/Kly8sA7UPOfQB6fVvfEHLcPW77LIsvfeMV6UnHnlc+vp/fTdddeV1KfabremfP/K6cYHaOPYH339eGag9rggHNwdq47nff9B+6c2nvDh99CNfTN+58PJ6qPbLX7ygDNTG/se/+jmxa32K0Xc/+e9vTo942AvSmf/25TJ0u/weO5fPX3jB5eX86KMf2xKojY2v/Ye/TH/6pMPToYf9XrnPQvonOljkV6NDdWJeBPzLAG28/+horQhA5/VasT5WBqI70a5peRR/AGBa+3fQu5faGeU+grVd//n7PH3/fU/1U3VAHVAH1AF1QB1QB9QBdUAdUAfUAXVAHVAH1AF1QB1QB9QBdUAdUAfUAXVAHVAH1AF1QB1QB9QBdUAdUAfUAXVAHZilOhCjZPb396eRkZEUoYqlSwf1L/1L/5ql/jXZ79MPDw+X/WxwsL/IMMQohlP7PXb9dWpOU/W0H0/5EfmkuagDGzcOl1HAuL/q7+/r+vuqaGxcX2LKub8qb9dYnwunqfa/smGT/DMwyfZ52bzb7ivSmZ88uRyN9dPnfqMMwf70J9eXgdmPfPgL6aCD7p/ecPIL6yHU3KgrLv95ufiylx/TEqjNz9/znrukZz/nT4qg62fS5Zf9bNZCtXHcBz3ofvltWua5Tc9/4VEt2/PKs57zxykezVN+zTOe+cTmzfXlFSuWl+HYiy+6Mv385zemhz7swPK5vfepRvP9chEujmM+8MD71l8zODiQHvYH1X71jQtkIdL28aWPKc+LAWPj/+vreXuez+7z1fvHsfPxx4owbfxfjKzd+fY1tWuLU/P5R4mKNsaU29/8fPN2z5dMBVT5/3Wv7JLn+flaMU5tyObteZ6fz+vtc89PzbfdLa/z4zdRv/P9mF79yl7tc/1L/9K/xl/Xcz/RP/QP/UP/yPWgfa4+qA/qg/rQXhfyuvqgPqgP6kOuB+1z9UF9UB/Uh/a6kNfVB/VBfVAfcj1on6sP6oP6oD6014W8rj6oD+qD+pDrQftcfVAf1Af1ob0u5HX1QX2YzfoQoY/h4c1p06bRctCigf6B+IqN+71a3z+/31cKFF8O34+Z94/hzcNFX9tcUvb3Lyn6WWg2jret+j7Q1l/7i/46nddv6/ie9/3Wvxv9MV/38lz/0D+21T82F380IWp8hFJzje/2708MUhmfbOSqUjl4ZQxkWV5ayu1jY1XgtlPf/9ySiea14iJaXUUnenaet0VTrrziuvS1/7gofekL306/+c2tZar6wx89qRx9NTfnT5/4yvTjH/8yfeuCf0kHPGDfvLllHqPAHve370nPOPYP0+nvP7587rrrbkpHPuZl6X7775UuuOgjLfvnlcMOeV667dah9IPLzkp73HvllF535513pYMOfFbaddfl6Yofn5MPtc35k//4Vemqq36Rjn7a49LOxci0E03f//416boiUHv6+/4+5fDt+vUb05888RXpV7/8bfmSRzzyoHTkEx6WjnjUIWUINw+ZPNHxenHbqlXVUMvh2+mpubfkrhPzeMToy90+jURbu72RPda+KO/98WcTTAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQWocDmIui3ceOm8syXLl2SYqAoEwECsyswW/1sto4zu2fnaAQIEFjcAr1amyMEHDnGnGXM8/g0uyFqtWZNlUlcubLKiDZ/y7rqbjXgHnLoA8rHa177vPSP7/n39M//9Ln0ipf/Y7rqJ59OO+ywpGx7hGNj2vc+e5Tzif65z33vXW6OEV7neorQa4Qq83tO9f2uvbZqWwSItzVFwDhPO+64NH3la6end7/zk+lLRXj4e9+9unzE83vvvXs67pXHpr/8qyfl3RfMPIdYi25VnFNzNHR+1ouPeMtUjEo6VsXmq0BtrFfB2mqH+WlPbs10ParzqJ9McZjubm+3t69Wi+HUec70+9jtn6/2qQ+duN7oT1nA98/3z/U19wbXI/VAPVAP1IMsoB6oB+pB7g3uD9QD9UA9UA+ygHqgHqgHuTe4P1AP1AP1QD3IAuqBeqAe5N7g/kA9UA/UA/UgC6gH6oF6kHuD+wP1YC7rwcDAQDEq2WgZrF2/fkMxcu1gWrp0sB6w8P3z/ZvL799C/37Fr6pv3LgxReAqpqVLl6aBgf6m32Gf3vdLf52e10L/fjk/3wf1ubM/L0Q+Lf4wSVXja+X9U6PGd//3M0aqjfBsrRbzyFeN1u//YpTa2N6YOnE+jXdvX+qqUG1z4waKv05z0huen/7zaxeXI7L+8Ac/SY969EPKXfa//17pJ9dcn2666Xfp/vffp/ll9eUbb1xVLk/2fH3HtoWR4ZG2Ldtevd/+e5c7Xf+rm7e9c9Me+91vz/Szn/46feZz70z77HOvpmfGL+6yYlnLxl12WZbe+e7j0ilvf2n60Q9/mr7/vWvSf3zlO+UIvq9/3YfS6tvvSK86/lktr+n9lWIc6C1DQlddqupMEcauMo1b1ouQ6Fw8X6XlizBtHLx8z+jcOVwbmzrbvnwzE0NmT3b+xa1z+dxkz3fSdyrt78b2DfTNz/evV33mq3/ymdv6x5dvN9Zf9cX1Zz7u/9Q/9U/9K/4jx1Z+vuDDx/dj8p+/9Q/9Q//QP/z3t/iv0VvuJ+fpv9/W38/9y6T/fdj1yfXJ9cn1yfXJ9al+vXR9npf/fbnu7f7E/UmUH/fHhUFxP6b+qD/lDYn//cHPZ34+8/OZn8/8fOb+yP2h/368UO+PY4Ta+H3qDRs2lMGQGLlsyZLBMvw3WIRuiyf9fOTnQz8fTvHn4wgpDQ+PFo/NafOmkeJV1c/VMVBd9LVqUKiZ/3zV0l83DaeW/lqONN3UX/33rULf/Yv7F/cvC/X+pRP9O34Henh4OEWGcWNxvxT9K26TYhDOwXoN7o3/flKWxy3tr3J3Vb0sQ7ZdcP2o2jfxvx0J1UZ6+p8+cF75H4pf+KKnpBUrlk/cumLrfvvtVYZqf/e7NfV9Dj3sgWWoNgKpk4Vm47mYDj3s9+qvu8fyncrlO++4q76teeGGX9+S1qxZ27xpSsu77rq8HKU2Xn/zzbelPffcbUqvi7ZFO4eG1qYjHnXIlF7TvlN0lkc88qDy8YpXHZv+9V8+n97+1jPTh//5/PTKv39m+T9ItL+md9e3jAZbFIq4CYyCUYUpqu3VelFZ5vD5nKCv5mPFX1Sqymd1U9r59pXt2Mb5x3+UHe2Q31TaN5ef32y/f22s+A+8hWU85uP7N9vtn+/+o/1zW5/48u2l+qn+xN2Y64frp/sH90/VzzXqd+d+vnP/5P5J/9P/Gvcj7s/cn7o/b/QH1wfXB9eHRn9wfXB9cH1o9AfXB9cH14dGf3B9cH1wfWj0B9cH1wfXh0Z/cH1wfXB9aPQH1wfXB9eHRn9wfXB9WJzXhyVLBlJ//07liGsx6lo8NmyI35Opfuc2/7Gd/HsjrdvzflX+tupPza/zPL/4PizO70fkRiJsVY0CODv1peqvOxb9dHO9zxaZ+Prvxeuf6s/4Ouz74fqV7/f1j9nsH/GHDuLR3x8jvfbi7xdHvi6lvr7R8jrdV4xZGXm7vr7m61Vnfj6Kd51sqhXYxW3F/E9PefIJ6fLLrk1vPPmF6aV/+/QJG3DnnXelww5+XvmXan50xafSbruvKPc75+z/Tq878YPpYX9wYDr/i+8pvzTNB4hg7B894bi06pbV6StfO70lWHvYIc9Lt906lL514YfTAQe0jnL70Y98Mb31zR8rD/WDy85Ke9x7Zbl83XU3pSMf87J0v/33Shdc9JHmt6ovv/yl705f+fKF6W+POya9/o0vqG/PC+cWbf74mV9Jf/jE/5ded9Jfl5s/ddZ/phhV9qinPDr9y0f+Ie/aMv/EmV9Nv/fA+6SHP/xBKUbvjemSi68qH89/4VFp5cp7tOy/tjB70AOfWf5Fn2uuPa+8cWrZoQdXVq1aXbZ6111bz3W+T6W5q+TlmEdHj3ncpPbKtLkz3b5XeKbczsG4IzIRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINAiMDIyWuQAilE2N48Uv28dj47EFlraZIVArwj09dWKMFJ/MWJhPAbHZWZm+zz019kWdTwCBAhMLjDfNX7ylmz/M+vXbyz/eEqcU/wxhpjyvH25fHKe/1mz5vbyHVeurDKizW/fkZFqowFPfdrjy1DtB99/Xtr9Xrumpx9zZHO7yhFcj/vb95TDyB/20AfWA7WxU4RQP/aRL6Uf/uCn6T3vOiud+NrnbRneOKV1a+9Orznh/WWg9ol/9PB08CEHtBz3kGL9f//nB+n4V56ePnrmG9K9733PYsjkkfTpc7+e3vvufy+Dq8PFSLrTnf7ulccWx700/cuHzk8PfvD+6einPa5+iAjlvuXkj6a7795QhIj/pr79z49+TDrzY19OX/3Kd9I+b/t4EcZ9fssX5+P/9pV08hv/Ne2224r07Yv+Nd1jS6j2g+//TLrwgsvTr3712/TBD51YP14EO//9U/9Vrj/y8IN6KuRZP4mtLOQg61Z2mdOn8vtXedQI0lZ/YSG252DtnDZgFg9eK9o+UoyQZ5q5QH/xJwWLb8HMD+CVBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBaoQIQrqpHXFugJOi0C8yiQswxz9Zb661zJOi4BAgS2LTDXNX7bLZj5HtWItDEybTWSdTWac7U886POzys7NlLtpk2b0xtf/+EUI7jGtM++e6QHFiOy7rDDkvSTa65P11//2zKoGKHYj3/y5PqosZnlN7+5NR191KvL8OyyZTumxx/5sLRu3fp00XeuKEe2fcihD0if/fy7xgVLr7ryunTsMSeV+8bIr/sXo8/+5qbfpbvu2lAGVE95y8fKkWynO1JttOt/vnlp+pvnvy3FX+nYe+/d06Mfe2gZDr7i8p+XI5lG0DZCsM2J6+bz2HXX5emRhx9cjj4br7n66l+Uf1HkfR84IT316Y/Pp54u/f416TnPfGPasGFT2muv3cr32bRpOH2nCNredttQ2nnnHdJH/+0N6TGPO6z+ml5eyCPVrlixvOOnkQtVI1jbCNTutNMOHW/fdBoQo9WKhE5HrLFv/O0Eo9Q2PCwRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILB4BGIA0shJNkaqjRFrq/Nvzk92SmRoaHX51hONVNuxUG3G+PQ5X08fP/Mr6dprb0x5hNiA3H//vdOjHnNoesObXjAuGJtf+7Of/roYAfYj6TsXXpE3pYGB/vTkox6VTnnbS8oRXutPNC1cfNGV6d2nnpWuvKIKuz70YQemZz7rj9Izn/1H6bBDnjfjUG28RYw6+4/v+lT65S9/U3/HCNj+5V8/OR33imfUtzUvXPuzG9JbizDvBd++rAzfxnNhEIHid5z68hQB4fYpgrWnvv0T6dJLr6k/FX/JJ/Z99z/+XTrgAfvWt/f6Qg7V7rLLsvJUcmo9n9d8rkeoNt4vEvSxXIVsx4og9VgZZo42zWd7tuf9Ihi8acv5xHKeeqX9nWzvYDFKbTjFxCv6QmXBw/dBf9Af1AP1MAuoB+qBepB7g/sD9UA9UA/UgyygHqgH6kHuDe4P1AP1QD1QD7KAeqAeqAe5N7g/UA/UA/VAPcgC6oF6oB7k3uD+QD1QD9QD9SALqAfqgXqQe4P7A/VAPVAP1IMsoB6oB+pB7g3uD9SDzteDdes21AO1kYUsvpX1vFWsd7q/Dg2tKTtMV4Zqc1fevHm4CNbekCKh/KAH7V8PKObntzaP0Vlv+PUtaXDJYNrvvvdOy++x89Z2rz8Xo9OOjoxMef/6C7exEEHLGP325ltuL9qzZ9r9Xrtu4xXV02vWrE033nBLqvX1pQcUodgYtXdb0223DqWbivfaccel6f4H7FOGirf1ml57PodqYyTfcmjVourlcOt8rpdvXoQHi1ht8f5FqraYxkaLkWqLzztGJ16+fMeOti8uBtP1GCnaP1yeTxUWnu7rF+P+A0VR7yuoZ+K9GL0CqhP91ecz/Xrg+1l1bN9X1wP1Q/1QD9VD9y/u39wPuB9wP+B+wP2A+wH3A+4H3A+4H3A/4H7A/YD7AfcD7gfcD7gfcD/gfsD9gPsB9wPuB9wPuB9wP+B+wP2A+wH3A+4H3A+4H3A/4H7A/YD7AfcD7gfcD2z9fmDt2rtTf3+Rs6r1lXnIMnVXXEDLcQzLC2ln81dr1txeFLKUujpUW7bQPwQmEMih2hUrilDtpFMMlbll6NAJ99n+53M6PuZRFGMaLYasjeWYL1u204TvXG3c/vefq/MbLto/Up7P3PrNVfvn07eo82lgS1Fv/bC79/OdT59Wk+Y1Pgvh+9/8ibYu+3x9vq4frX2ieU3/0D/0j+Ye0bqsf+gf+kdrn2he0z/0D/2juUe0Lusf+of+0donmtf0D/1D/2juEa3L+of+oX+09onmNf1D/9A/mntE67L+oX/oH619onlN/9A/9I/mHtG6rH/oH/pHa59oXtM/9A/9o7lHtC7rH/qH/tHaJ5rX9A/9Q/9o7hGty/qH/qF/tPaJ5jX9Q//QP5p7ROvy7PaPdevWN41UG0MYRgi3CNhu+QhqeaHeiNl9//ph6wutxx8aWl0+I1RbB7LQSwKNUO09OtzssSJAG4HaaEYsV4/RGK22CNUuX761UG2Hm76Nt49g7XB5XtvYcRE/PVAU9IkDtYsYxakTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILDoBGKk2r6+vnqwtgrRFiPVFhmsKk+7tYDz3HMNDU0+Uu3A3L+9dyAwOwJ5dNjZOdpMjpKDtNVrG6Ha0SJUWz03k6N2w2v6y0aMpc2CtRN+HINFDe8vRkLu/HdwwubZSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXkTiDxdSqPl6LSNN83htM4GahvtmXhJqHZiF1u7UKAIrpejxEZSPUaLne95Hv483jc6faTnI2TZ19c8n/92zZbDYHEe/aMpbS5G4R3pgO9sncdsHqdWOCwtXGKazeN24vur/Z2pG9y56+/qpzqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOLLQ6EN/pyNVVuatq3r7eye992bBJ/qkVocAc/51kF5sJdFZg1arVZQNWrFg2SUOi023tazw7z0dPyd0l5vkxUiRQR0dH0y677NzR9k3y5sXm6Z//cHF+jVFrp//61rb05utjdNqBqNwz8FsI5984h978/LQ/C/j85uP6kLXHz/nzn/v7k/Hfu7zF98/3z/cv94bxc/1D/9A/xveLvEX/0D/0j9wbxs/1D/1D/xjfL/IW/UP/0D9ybxg/1z/0D/1jfL/IW/QP/UP/yL1h/Fz/0D/0j/H9Im/RP/QP/SP3hvFz/UP/0D/G94u8Rf/QP/SP3BvGz/UP/UP/GN8v8hb9Q//QP3JvGD/XP/QP/WN8v8hb9A/9Y3H1jzvuuKsM1fb395Wj1UYMKwaxjCnmWxa3dJD57x9DQ2vK9165cuWWNjRmRqptWFjqcoFarRiqtgzP5k7UPI/GN69HEWpen43n4xhFC8biuDE0dYxQO9LU2Tvdvubz3b7zHyyGaB0o/IaLsPBwedbxz2THzzssjOcHUl8aKD7KonQXJ5Ydt3b+sZ/nJ/9+8PH90D/0j+Z6mutqnvt++H74fjTuN3K/yHP9Q//QP/SPXA/a5+qD+qA+qA/tdSGvqw/qg/qgPuR60D5XH9QH9UF9aK8LeV19UB/UB/Uh14P2ufqgPqgP6kN7Xcjr6oP6oD6oD7ketM/VB/VBfVAf2utCXlcf1Af1QX3I9aB9rj6oD+qD+tBeF/K6+qA+qA+9Ux+Kb2uZ94t5fG4xRcB2rFyvNjV/nvF883ru93k+28/H8SaehGondrG1CwVGR6ODxCOm+Z9XYzqPlqHasbE8j1FqG6PWdqJdc+kxUNSp/qJYxci1I8WjOOvq7RbYvK84n/6iUsfItGUJL861OtOFeb4L7XvqfHxP57IO+n75fvl+hYDvge+B74F+oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAPqgDqgDqgD6oA6oA6oA+qAOqAOqAONOjAWWbNiQMcI1Ea+LsK02WdsLMK1W1Y79vvo+f3Hz2tF43Nrxz9rC4EuEFi1anXZihUr7lHOo0PFtzb6VXx583r5ZNP6bD8fXSUe8X5lwLdYHikDtcVorsOjadddl5dNyO2Z7fef6/ObyvHDO4K1I6OVfRmyLU40Po88dfv595UfTK0IC1ffnYHY0EPt73Zf7av6w0Ls/9HHfb4+36j3vt/VZSP3h165/uX2+vx8fnHbk78Pvr+VQPbQP/QP/UN9yPVAfVQfQyB/H1wfXB9cHxr9QX1UH9XHRn9wfXB9cH1o9AfXB9cH14dGf3B9cH1wfWj0B9cH1wfXh0Z/cH1wfXB9aPQH1wfXB9eHRn9wfXB9cH1o9AfXB9cH14dGf3B9cH1wfWj0B9cH1wfXh0Z/6Obrw+rVa9NAfxGe7etL/f3R0mKww/jFq2LqKwNc1eCHnapva9bcXrZl5cqV5bz5H6HaZg3LXSnQCNVWodVONTKHaiNQ1FgeTSMjRci0SJmuXNnZ9nXKxfsSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwOIRiFBtfxGqjUBtrRYj01ah2sjV5uVOagwNVQN9ThSqHehkw7w3gekIbAmqT+cls7xvlZRvPujYWGyrRq/NSfrm5y0TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGFJNDI+uUwbZ7nUG33nq1Qbfd+NlrWJjA6GoM9d3Ia2zJCbYxU2zxa7ViKtsXotSYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILCQBSJPV6vlvF3O1eV5rcjajR/gsls8hGq75ZPQjm0KdHok2NbMbO7gjWZ3un2NllgiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLzKZCDtNWotfP5ztN5r77p7GxfAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAr0oIFTbi5+aNhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECExLQKh2Wlx2JkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6EUBodpe/NS0mQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYFoCQrXT4rIzAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBALwoI1fbip6bNBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC0xIQqp0Wl50JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR6UUCothc/NW0mQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYloBQ7bS47EyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCLAkK1vfipaTMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMC0BIRqp8VlZwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgV4UEKrtxU9NmwkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKYlIFQ7LS47EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9KKAUG0vfmraTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMC0BodppcdmZAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgFwWEanvxU9NmAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBaQkI1U6Ly84ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQK9KCBU24ufmjYTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMS0Codlpcdl7sArVaLcVjomlkZGSizbYRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEFITBZjm5r2btuOnGh2m76NLSlJwVyZx8eHu3J9ms0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYikDk6HKmbir7d9s+QrXd9oloT88ItI9YOzxspNqe+fA0lAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSmLdCeo2vP2U37gPP8AqHaeQb3dgtLoLnDDw8PL6yTczYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBJoDlH15yva9qlqxeFarv649G4bhWIzj42NtbSvM2bh9PoaOu2lh2sECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBHhWI/Fzk6JqnyNn1UrhWqLb507NMYAYC0eGL/y9Ctilt2LBpBkfwEgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0N0CkZ+LHF3k6XopSNusKlTbrGGZwDQEqjBt0fuLKS9v2LBxGkewKwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ6A2ByM/lLF20uHm5N84gJaHaXvmktLPjAtHBq6mI0jdNeXM8Pzo6mu6+e0PTsxYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBvC0RuLvJzOWeXc3WNs6pyd/n5xvbuWhKq7a7PQ2t6QCA6de7YeTmvx9DVd921oRjCujV42wOnpYkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGCcQOTlqtxc9VR7ri6vj3thF24Qqu3CD0WTekMgOnoOz1advhquOqWxdOedd/fGSWglAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYikCVlxsrB6uMEWojTxdT5Ovy8lZe3lVPCdV21cehMb0mUIVpY+TalPr6+rYUhVpav35Dkbxf32uno70ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAuEDm5yMvlLF2Vo6uCtb0WqI2TEqqtf7QWCGxbIHfy5jR9vCoXhNhehOtTf39fOVrthg2bt31QexAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgS4TiHxcjFIbebnIzeVcXc7ZRXOrbF3V8ObtXXYq9eYI1dYpLBCYvkAjTNsYrTaPWNvXV0tr1txRpPA3Tf/AXkGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDokELm4yMdFTi5ydJGbax+lthdCtO18QrXtItYJTFlgrCwGefccsI0iUeTry/R9bBsaujPdddeGvJs5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoWoHIw0UuLvJxMUpt5OVyuLY5SFstF0PY9tAkVNtDH5amdodA7vTN81iuHtVw1ZG4bwRr+4ohrtcVqfy1xRDXvVUgukNcKwgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgrgUi/xY5uMjD1Wp9TYHavpb8XM7TRXuac3Zz3b7ZOP7AbBzEMQgsXoHGaLVRMKow7WjJMTZWK0K0kcRPaWSkL23YsDHdcsvmtHz5jmnZsh0XL5kzJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGuEli3bn1au3Z90abRLYHaWrFcDUYZo9RWj2qc10aoNgahjP16ZxKq7Z3PSku7SCA6fYRoq3nVsOZtxdNlkSj+TaOjo1uCtfGa0TKlf9dd69OOOy5NO++845a0fhednKYQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwIIXGBkZTZF1W79+YzGw5MiW0WhjoMkqUBuDUEaYthGibYxMGzixvXlernT5P0K1Xf4BaV73CjRCtNHGRpq+KgQxWm2k7qt5BHAHBmpFYRktQrZjZdB23bq7i+T+3WnJksHyMTg4UMwHypBtLiZxZBMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIENgegci4Rb5t06bhtHnzcDHfXD4iFxt5tlqtCtD290curnmE2mp7BGxjqvaN58vVcr1a6o1/hWp743PSyi4XqApAVQWiuFQFogrU1moRoq2CtFFQ+vqq4lMVlrGiAFXFJ04xXts8L1f8Q4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEtkMgDwTZmBfDShaj0FY5t9Q0WGStzMflEWpjPlmgdjua07GXCtV2jN4bLwSBKCA5CFtEYuup+uZg7ZacbPFcXxmujf1i1NrYJ8K28Xx1jAjU5uNtienX1xeClnMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfkSqAK01UCQ8Z45UFsFaavRZnN4Nm/L64156wi1kY+LfVuPV672xD9CtT3xMWlkNwvkYG3zPNobQdnYVhWa0WI9lqsRa2M5ikeMWhuj3DYCtrFeFZkqaNtcqLpZQdsIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoPsEcsYtWlZl3nJgNgaMzBm4yLnFiLTVerWczyXvk18f26vcXN6jd+ZCtb3zWWlpFwtEAcgh2jzPzY31KCYxr4pMfzFCbYRsI0xbJfKjmOSAbfPr8rI5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYiUBzALbKuFVB2wjSVo/alkBtI2Qb75PDtNXrq0Bu3j6TdnTDa4Rqu+FT0IYFIdAcrK1OqArM5u2xLS/HaLRVqDaCttVw1zl0mzGiGFXP5S3mBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg6gJVILaxfzF+ZH2U2XiuejQCtLFn3t5YLpfin/pry5Ue/Eeotgc/NE3uXoEoFjkIG6HYokRM2NjGftX+Za622DO/Nr+ofT1vNydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAtsSiCxb85TXY3Njudqneb2x3Hh13tbY0ntLQrW995lpcZcLRGHIYdgoLLEc25q351No3la9pio++fWThXLz680JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMC2BCLLlqe8nPNtzet5uUjG1UO38brG9nyU3pwL1fbm56bVXS6QC0QO1EZz83I8l5fzvPl0ml/bvN0yAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCYrkB7Zi2vx3H6+vrKwzW25TBt6+i1033Pbt1fqLZbPxntWhACUUgiOBtTo6iUa/Vt7c+3r5c7+ocAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGyHQA7Q5kM0Z94aA9mOH9E2778Q5kK1C+FTdA5dLZALSw7LRmMbBSY3ffJC0/y6vLc5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYmkDOtk28T4xIO/EzW3/dxK/pla1Ctb3ySWlnzws0F5LmoGzz9p4/SSdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0hMFlotmpca6J2seTchGq74qupEYtNoL3ANIds2y3a921/3joBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJiOwGLNrQnVTudbYl8CcySwWAvQHHE6LAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQGCfQN26LDQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQWmIBQ7QL7QJ0OAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAeIGB8ZtsueTiq9IlF19ZQnz3kquL5avqKIcfcXB9+fhXP6dcbt5Wf9ICAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA1wjUxoqpa1rT4Yac/t6z0xmnnTvtVuRQ7Xnnnzrt13rBtgVWrVpd7rRixfJt79yje+iGPfrBaTYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR6UKBWq/Vgq6fW5KGhKpO4cuXKcS8Qqi1IZhqmHadZbDj+1c9OJ5z43Imesm2GAgsxVDudEO109p0hsZcRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwAITmE5wdjr7djuTUO0kn9Bshmnb3yJGrc0j2LY/Z316AgslVDtZOLbaPnmqf7LXTU/R3gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwmAS2HpQdS5M9P9n2XrETqp3gk5pKoDZGnT38iEMmeHW16YzTzkmXXHzVpM8btXZSmmk90euh2olCsWNjDYL259vXG3taIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECMxNoD8s2r9cmGDey+fmZvWNnXiVU2+a+tUBtDtJOdZTZCNVuLVwrWNuGP4PVXg7VNgdkq+WqsuTtMe/v7yse/amvr5b6ispTK+YmAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwmwJjo2NptMi0jRbzkZGR4jFaH622EaBtHcG2sX02WzK3xxKqbfI99piTJhxddjbCr5OFdWfj2E2nsOgWezFUm0Oz8WG1h2ljva+vLw0M9JePiRL8i+5DdsIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMK8CRdQtDQ+PlI/R0ZEPj68AAEAASURBVCpg2wjR9m64dmuh2oF5Fe7wm0XoNUaWbZ9mK/R6wonPLQ99xmnntrxFXs/PtzxpZcEJtAZq4/RqZbA2tkdBWbJkIA0OLqqut+A+YydEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBXheIASMHB/vLx+bNw2nz5pFiFNtGuDZCt3lQyZyP6/lzLk6kOK2FP0WYNkapbZ/OO//UdPgRB7dv3q71yd5rtsK729W4HnxxL41U29ydomfl9ZjHyLRLlw724CegyQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwGAQ2btxcjlybR6yNeQ7Wxvnn7d1ssbWRamtF2G9RhGr33fOocZ/RjTd/ddy22doQwdozTjtn3Mi4c/mes9X2bjtOr4Rqc1eq5o3RaWN96dIlZVq/22y1hwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQINAvEiLUbN24qA7RVqLYY0jaN1QO13R6s3Vqotq/5RBfq8unvPXvcqcUItXM5xei3jzz8oHFvMVFbxu1kQ88JbC1Qu+OOSwVqe+4T1WACBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgsToHBwf4UubjIzeVHMUZtuRwiOU/XizqLYqTa9lFqI/A616Ha/GWIEO0Zp52bV8u50WpbOLa50u0j1eYCUM0bI9TGiS1dOpgGBvq3eY52IECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3SQwPBwj1m4um9RLI9Yu6pFqJxoZ9vhXP2dK36uJXjulFzbtdMKJz21aqxZn47jjDmpDRwRyoLZ689ZA7eDggEBtRz4Vb0qAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC2ysQA05GTi6myNLlgSnzcVvzdXlrd8/7urt529+6715ydctBjn/1s1OMVDuVKUaYPfaYk6ay61b3ifdsntrb1Pyc5d4UKOpBU1FIRZi2Ly1ZUhWL3jwjrSZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBxS4QObnIy8WUg7WRp+vVqVacRA83f27Z993zqPobnHf+qVMO49Zf1LTQfKzYfOPNX2161uLWBFatWl0+vWLF8q3tNu/P5a7THKjN25Yt23He2+MNCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAXAisW7e+PGytVkuNR/VOsd5N09BQlUlcuXLluGYt+JFqx53xDDfEiLWnv/fsGb46jQvkbs+xZtwIL5w1gRyezfM4cF5esmRw1t7HgQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKcFcm4u5+iiPXk5zzvdxqm8v1DtVJS27HPGaefOOFj7yMMPmsY72bV3BGr1Iatzm2M4axMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFgoAs25uQjRVkHa7hqhdirWQrVTUWraJ4K1MWrt9k7fveTq7T2E13dIIKfmm+e5COS0fYea5m0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCcCER+LmfpmvN18WZ5fU7eeBYPuiCH1Nx3z6NmkWj8oS65+KoU73He+aemw484ePwOE2w5/IhDUgRyTQtJoBqlNp9RdPrBwQXZpfIpmhMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAIhWI/NyGDRtTrVaNUBuZurzcKyRGqt2OTypGrD39vWdP6Qjt4dsI5pp6XyA6fX709/cVBaD3z8kZECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBdoHIz0WOLmfqYt5rk1Btr31i2ttRgdzJo6/n5WhQLA8MGKW2ox+ONydAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBORWIHF17ti5na5u3z2kjtuPgUoDbgXfe+aem9hFoJztc+8i0U33dZMezvTsEopNXj5T6+mTUu+NT0QoCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmAuByNE1D1pZi+Fre2hakKHaG2/+6qx8BPvuedSEx4lAbARqTYtbIMK00eFzsLa/v7c6/+L+9Jw9AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECExXIHJ0kalLqVbP1/VSsNbQmtP8xI9/9bNnFKg947RzpvlOdu9WgRyijfbl5ZgbqbZbPzHtIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHZEKhGqh2rZ+vimDlnNxvHn+tjCNVOQzgCtSec+NxpvGLyXY9/9XMmf9IzXSkQHbuaWkekrW/uylZrFAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQmF2B8bm6KnfXyOHN7vvN1tEGZutAC/04551/ajr8iINnfJqXXHzVjF/rhd0p0Jye7/aO3p2CWkWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECvSaQ83R5Xqu1DmTZzeez4EO1++55VIv/dEebjSBtBGq3Zzr9vWe3vDyOuT0B3ZaDWem4QBWu7XgzNIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECMy5QIxSWwVqeydMm1H68sJCnUeItnk647Rzm1e3uhyv3d5AbYxQO5333GqDPNk1AtHheyk93zVwGkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECC0YgcnZ5xNpeOKkFH6o94cTnjvsc2keOHbfDlg0TvXayfSfbfsZp54x7anuDuuMOaEPHBHqps3cMyRsTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwIIT6MV83YIP1ca3bKLRaqcarN2eb2mMUhuP5qm9Lc3PWe4tgV7s8L0lrLUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0M0CvZazqxUNHutm0Nlq2757HjXuUDfe/NVx22ZrQ4Rpjz3mpHGHm8v3HPdmC2TDqlWryzNZsWJ5R88od5XoMaOjo+WQ1DEfGakeu+22S0fb580JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBcC9x22x2pv7+vfPT19aVarZaqefXOsd7JaWioyiSuXLlyXDMWxUi1cdYTjRAbQdv2kWTHCc1gQ4yCO1Gg9rzzT53B0bykmwQiWJvDtd3ULm0hQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKdEuiV7N2iCdWecOJzJwzWRvg1QrCzNcWxzjjt3HGHi1Dv4UccPG67DQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnMvsGhCtUE5WbA2QrCzEazdWqA23ttEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQGYGBzrxt5941h1vbR5ON9e9ecnXZsONf/Zwpjyqbw7jtx8tnGCPU5vfM28wJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTmV2DRhWqDN4dc24Owl1x8Val/ycUn1UO1EbCdaLrk4itT++vb9xOobRexToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDojEBtrJg689adf9cI0R57zElz0hCB2tljXbVqdXmwFSuWz95BZ3Ck6CrVI22Zj6XR0dE0MlI9dtttlxkc1UsIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDvCNx22x2pv7+vfPT19aVarbblkerLnTyboaEqk7hy5cpxzegbt2URbTj8iIPTjTd/NUUAdramOFYcM4+GO1vHdRwCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGZCyzqkWrb2U5/79npu5dcnWIE2+lMEc595OEHCdJOB20a+xqpdhpYdiVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnMo0Msj1Q7MoUvPHTqPLptDtWecdk79HPK2CNDGFCHamPJryhX/ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdKWAUO0EH0sOzh5+xKkTPGsTAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBArwn09VqDtZcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAdAWEaqcrZn8CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGeExCq7bmPTIMJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSmKyBUO10x+xMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPScwEDPtViDCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0GGBzZuH04b1G9OmYj4yMpLGxjrcIG9PgAABAgQIECBAgEDPCNRqKfX396clgwNphx2XpsFibpofAdLz4+xdCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYIAJr77wr3V0Eak0ECBAgQIAAAQIECBCYiUD8UZ7h4ZHyET9b7FQEa5ffY+eZHMprpikgVDtNMLsTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILB4BYaG1qaNGzeXADvvvENaunRJGhgYSDHSlIkAAQIECBAgQIAAAQJTEahCtcPFzxab0l13bSj/aM/I6GhasWL5VF5un+0QEKrdDjwvJUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg8QjECLURqO3v70u77LIsDQ76dezF8+k7UwIECBAgQIAAAQKzJxB/lCd+nohH/KGeO+5YV/6sET9zGLF29pwnOlLfRBttI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGGwObNw+XoUbFFoLbhYokAAQIECBAgQIAAge0TiGBt/IwR093rN6b42cM0dwJCtXNn68gECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCwQgQ3FL7fHtPPOOxihdoF8pk6DAAECBAgQIECAQLcIRLA2ftaIKf/s0S1tW2jtEKpdaJ+o8yFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYNYFNm0ZLWrp0iWzfmwHJECAAAECBAgQIECAQP5ZI//sQWRuBIRq58bVUQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWEACIyMj5dkMDAwsoLNyKgQIECBAgAABAgQIdItA/lkj/+zRLe1aaO0Qql1on6jzIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg1gXGxqpD1mqzfmgHJECAAAECBAgQIECAQMo/a+SfPZDMjYBQ7dy4OioBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAXCQjVdtGHoSkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJzIyBUOzeujkqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINBFAkK1XfRhaAoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMDcCAjVzo2roxIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECHSRgFBtF30YmkKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDA3AkK1c+PqqAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0kIFTbRR+GphAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECMyNgFDt3Lg6KgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQBcJCNV20YehKQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnMjMDDRYdfeeVc65+z/HvdU/0B/2uNeK9Oee+2WDj30AWlgcMKXj3tdpzds2rQ5Pe/ZJ6dbf7cmfewTb0z3v/8+nW7ShO/fK+2csPE2EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAw6wK33TZUHjPmt992R7l8z912qb/PgQfuV1+2QIAAAQIECBAgQIAAAQJbF5gwFbtmzdr09reeudVX7rbbivSMZ/5hetXxz04777zDVvedjyd/9tNfp7Vr704H/v5+admyHVveMp675OKrym0X/N9lHQ3V9ko7WwCtECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwrwIRoo3fPc6h2uY3b94W+8TvdkfQVsC2WckyAQIECBAgQIAAAQIExgtMGKrNu/X396VT3vaSvJo2bR5Oq25Zna6+6hfp4ouuTP/yofPTN79xaTrzE29K+91vz/p+nVj4h9f+U/rBpT9J53/x3enhj3hwSxMOPuSA9LqT/jrdeuua9PS/OLLlufle6ZV2zreL9yNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIJUh2snCtJP5RMg2B20jYBsPEwECBAgQIECAAAECBAiMF9hqqLbW15f++gVHjX9VseW6625Kz37GG9LPr70hvfD5b0v/+fX3paVLl0y4bzds/LtXPqMbmrHNNvRKO7d5InYgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBaAj/96fXl6LQTvSgHZWNE2ttvu6Meom3eN8K4P0u/Tg888L5GrW2GsUyAAAECBAgQIECAAIEtAlsN1W5N6YAD9klnnX1KevrRrymDte8/49Pptf/wV5O+5O67N6RfX39LGh4ZSQ94wL5phx2mFsAdGlqbfvmL35R/LWnvfe6VYvTc5umOO9alzZuG0+9+t6bcfOutxV9ZKh4x7bZ74y8s3X77HWlsdCytvOcuqa+vVj4/MjKa1qy+Mw0M9qcVK5aX20aLfeKHyd2L1za/vnxykn9WF8e44de3lMfZ7757pmXLdxq35/a0s/1gYRnvt2HDprTPvvfa6l+SyufdfC433rAqbR4eTvvvv3f7oSdcD6df/uKmtGHj5nSf++yRdtll2YT72dj9AmvvvCtdftm1M27o/fbfq/jO7THj18/nC2+6cVX61S9/2/KW8R+I7rXHypZt01n50Q9/mu5at77lJY9+7KGpVqtqSssT87wSteuiCy9vedeoRYc99IEt26wQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC3SkwUaA2grTx+48x5VBte+snel38PnRMBx64Xzn3DwECBAgQIECAAAECBAhUArWxYmrHiMDmox75oiIkOpB+dcMX259uWf/Gf3+vHKl293vtmn5w2Vn1wGread3au9Mpb/lY+vznvpU2bdpcbo5Q6xP+8P+lt779pWnfIqQ50fTZz3wznX7auSmCcXnaa6/d0hve9ML05099bN6Ujj3mpHTJxVfV15sXrrv+8/XRcw875Hll2DbauMe9q1BdjLZ75GNeVobOzv/iu9NrTvhA+uY3vp8iABvTfffbM73x5BemP33S4c2HrS//59cuTu87/dx0zY9/Vd+2ZMlgetKfHZHe/s6X1YO68eT2tDMfPFt+4fz/Sxs3bsqb0yMPPyid/JYXpYMPOaC+LS/k8w6LMz/2lXTWJ/4j3XTT78qn71kEjJ/3V09KJ772eXn3lnmEk1974gfSdy68Iq1fv7F8Lj67J//Zo9Kp7zmu5fxaXjjLK6tWrS6PmIPPs3z4KR8uukr1SFvmY2l0dDRF6DgeuxV/9avbpwiFHn3UiTNu5hve9IL0spcfM+PXz+cLP/TBz6Z3vfOTLW/5vg+ekI75iye0bJvOyp888RUt/T1eGzUyamWnpwjZP+B+T29pRtSEr/33+1q2WSFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRmJpB/p3WP7RjgY7J3nigYO93RZic6xqMe/ZBJw7iTtWWutsfvI8eAS/EYGR5Nu6xYVv4+8rJlOxUDnMzVu/bGcWPQpRhMKaZ9e2QAnN6Q1UoCBAgQIECAQO8JzOXPHbOpcdttd5QDqMYgqn19feWghTFwYdzbV/PO3uQPDVWZxJUrxw/QuN1JsCOf8LA0WATKbi1Gir30+z9Oj3jkQXXb4eGR9NIXn5ou+PZlKcKmj33cYWnXlfdIF33nijK8+p1iVMUvffW09KAH36/+mliIIFwE4uK4hx9xcBkW/d53r05XXnFdetUrT0/7H7B3Ouig+5evecIf/kG6z33vnT5z7jfK9RhJ8+GPeHC53F98GFOZIhB5wt+/L33z699L/+/hD0733vOexblck677+Y3p5S99d/r4WSenxz3+oS2H+thHv5ROOfmj5bYIFD+uOLc1a9aW5/qlL3w7xWiwn/7sO9KOOy4t99nedg5vHk4vedE704UXXF5+2SJIu/vuu6ZvfeuH6buXXJ2e+6w3lZZx/hNNZ33ya+k97/5UeshDDkiPP/Jh6brrbixfFyMMx5f2hBOf0/Ky/8/encBbUdf9A/+xCAgiiwguICjuiqjlrmmp/V1a3MtsLzMzc80ne3oqbTF3zTLLskUt18y10kxN3FdcUXEBEQFRQUABWf7zHZjTOeeee7nce9nuef9ez2lmfrO/53DrzDOf+Uaw+oD9TkoTJ7yVIsy83wG7pVlZaO/ftz+cbrpxRB4qvejikyvWMUGAAIFyge9996IULwMoWvfuXdNPTz+qmDQkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEMoHqMGxRnbaxyrSNoRVVaYsqtbFcPLe9rIO1M2a8lz+bXRQ+qj7+rl27pA9us8lyE/6tPr6lMf3WW++kKJ4TTah2aYjbBwECBAgQIECAQD0LtDpUGxUaN9pocHrqqRezoOa4ilDt907+VR4yjRDnb3733VLANMB/8H+/ySqn3pBOPP78dOMt5+RB0eiPNxD9LgusRhr5r9efkbbcasPoztvt/3ooffFzp6QTjz0/3XLr+XlV3KJq5ovZvh9+6Nl01jnHlEK1xXqLGj4x8oU0bdqMdNeIX6f+C98cFUHb//n2BXlY9/xzrqgI1Ubg9LQf/yHf7NnnHpsO/tTu+fFGR/zY22O3o/IfNSOyAOye/2+7fLnWHudJJ16QB2ojtByWfbNwcrT3s7BtBJDPPvPy9LnDfpAFa89KUYG2up35s0vTFVf9uOL6XHXFv9IJx52XLjj/ynTU0QeVqvrGutdc/e88UBth4D9e9sPoylsEpQ8+4Dvp5pvuSf/4+32NVvEtljckQKB+BW6+aUReIbwQiGrTQrWFhiEBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFgg8GZW4am8RYXaxQ3UFutHsDbWjTBt0SJk22/n3sXkUh1Gla1HHn42f+Y5ng+PqrS9evfInh3vlN6dMTNNnjwlzZo1O917zxNZoab10vrrD1yqx2dnBAgQIECAAAECBAjUn0CrQ7VBtunm6+ah2knZj56ijXnl9XT5Zf/If9j86jffqQjUxjI/PPXwrPLsC3kQNqqf7vnRbfNVIxg7M6uIuln2o6g8UBszd99jmzw0uvEmQ/JAbb5CG/3HOecdVwrUxiaj7PAPfvjVdG0WLn366ZfSvHnzS/uMyrh33n1RGvXsK6XQbHEYvXqtkj7+yV3Sxb/+W3r88RcazC+WW5zhKy+/nq6+6vbUc9Ue6bwLji8FamMbUc33mOM+naKS74i7R6arr/xXKgK85fs4+phPVQRqY94hn94j/emPN6eR2XE+8/TLaautNyqtEoHgaId9dq9SX4x07twpxfWMN0YNHepHawXOCjoR39n1hq7drKMfsMZqzVrOQgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAosWiCq1ESwtWmsCtcU2IlQbn2K7MYxPS4O6xXYXdzh92rv5M87z589PPXt2T9tuu1laJRuWtyjINPLx51OEb5/Oijz16NEtrblmv/JFjBMgQIAAAQIECBAgQKBNBdokVFtURo0fNUWLoGa0vffdKXuj0MpFd2kYbxradbet81Dt88+NKYVq1x64er5MBFn/fsu9eSXUWLZoW39g42K0zYZdu3ZJw7fcoMH2IsQ6aJ0B6eWXxqeXXnqt4s1H0R+fWq1Pn5559wvPj601e7H74oditP0P2C0NHNi/wfrh841vHpyHah9/bMGy1Qttt/1m1V35dISX41o99dRLFaHateI6PJTS7y+5MYV5v9V7l9ZfQ7CyZNEeRrbdbtN0yR+/3x5OxTkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFYogfIqtRF6jUqzbdF22nl4uv5vd5U2tSyq1b6YPX8dgdqoSvuhXbfOi/uUDmjhyMord03bbrdZuvOOR9K0LIT74uhxQrXVSKYJECBAgAABAgQIEGhTgTYJ1T77zCv5Qa2+ep/SwT2+MAh61ZW3pTfL3p5UWiAbGTt2Qj4ZP36Ktt56a+fh0ev+emf62ld+mmJ67313TNvvsHnaYcdhKQKwbd3WWrtfXvG11nZ7dF8QCH43q8xa3eJH3qOPPJeefGJ0ej4L0I57dVIaPfrV9OrYifmic+bOrV6lRdNFUHbjjQc3un4xrwgzVy84ePCa1V35dI8etc/vqCyk+8+/35cHdbf9wBfTR//fdmmnXbZMu2SfIevW3lbNHehs1wL33vNE/v0vP8mvfPUTqXNWQfn99+ekm28ckVWkHp3eeuudtMXw9bN/w1ukjTcZnMqD8uXrVo+/886MvCJ03MgZlX3i3+HAQVmgPft8ZI8PtviNaXPmzM2+24/n1bJfHP1aWr1/nzxYv/feO+THXn0crZmOytvxb+mFF15N4197I3XLbv7Ev9d4k9zwLTdM3bo1/29a/Pv+9+0PpbFjJqQ+fVdNw4dvkP27HN5ih6bOK/6+xd+057KXHjz/3Ng0Ojv++Pu71tqrp8FZte49suri3bt3a7CJOLZ4IUK0yW/89815MT1lyrT061/9NUbzFi8K6D+gbz4eFbn/+Y/7Fs5ZMPjwRz6YNtxonfzteH/OKp8/lx1Hx44d03EnHJr/d0PFwtlEW1pPmPBm6bsXf99X6tw5rZ291CCqOu+x5zZN/ndRU/8upmXf6WuyCujxnZ49+/387+n+B3y4wUsaYrn478Hnn381zZj+blp/g0H5TcNttt20+rQbTLf02jXYkA4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQWOICRTXZ2NFq/Xq16f4iWHvPiJH5Nsv306Y7aWJj70ydns8dkD0r2Llzp0aXjGcD4zm5ceMm5c/rNbpgNmPevHnpzTenZs+UzswXiyJKq2afprZfvr25c+flzzPG+rPffz97jrNrigJTTT3P+Va2v1gvquxGCDh7xDJNzZ6JfCN7Rr5rl5XSOtlzlbXa229PS9OzZwDjudVevVbJP506day1aKN98SxtPIcb++2bPTu6UvaMrkaAAAECBAgQIECAQOsE2uR/VT/11Iv5UcQPnqJFCCvaxAlvpT9f/s+iu+bwtSxoVt7O/fnxaZNN101/+uMteYXYX15wdYpP/BD59Kf3TCed/Pn8h0H5Okt7PEJjp/7gt/mPt2Lf8WMsAle7ffgD+duSiv7WDiOMF62xyrgxL0KB8WMufkxGxeD44dSatsmmQ9LN/zgvnfaTP6Q773w03XzTPfkntrlFFuT7vx98JQ86t2Yf1l3xBW795/3pdxffUHEin/vCPmnCxLfSAZ84Kb3++uTSvGuv+Xc+HtWWr7r2tCa/zxEK/OPvb04/+dEleVCytJGykS7ZTYj9D9wtC1h+Jq2dBT2b2yZlx/b1r/0sPfTgMw1WWXPNfumsc4/J3oa2VYN5i9sRN0DOPfsv2XnclKYuvClUvY2w+Onp30gRHm2qvTHp7fTNb5yZIqxZ3eIGyS8u/HbaJntLW1u1CJQe882za+6v2Efv3j3TF760bzr6mEMqAqYRQP3xqZcUizUYls+LN8sVodoI75bPixXj3Lp375r23P3oND17+1zRDvnUHhWh2ra0fvfdmfnf9suzEG9jLW6eff6L+6ajjj6o4tyL5Rv7d/FcZnPwASenCMyWt/ienPy/X0xf+/r+efdfr70jffd/fplmLLzhV77sgQd9JJ36kyPyG4Dl/cV4a65dsQ1DAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFg6AqNGvVKxo7aqUlux0bKJCNZGNdyl1jp0yHcVRSgW1dZZZ40Un6bayy+Pz4tazJo1u2KxCJrGs+dDhqyVFX6pmFWaiFDsy1nl3CigNGtW5fFEsZg11lgtbbX1RjVDq48+9lxWIOO9/BnqPn16pvvve7K0jT59Vm0Qqg3nKNoUgdjyFvuJZ0ejKMuiwrWvZOf6zDMv50VuyrcRxxkFOiKIrBEgQIAAAQIECBAg0DKBVodqI6AW1Qjjf9jvuNMWpaNYd7210l1ZGPPY4w9NBx+ye6m/1kh19dnY1pFHHZh/nnn65Sz89nS649+PZBUaH06/vfj6rGLlK+nPV/642dUua+2zNX13/+fx9PXDT8vedDQ/ffwTu6R9P75zGjZsaF7BMYK1/8iqUt55xyOt2UXFukPXH5hbRmC2sRY/vqJK41pr9Wt1oLbYR1SI/P2fvp//oHvg/qfTgw88nSLs9cTIF9KnDjo5XXH1T/PqwcXyhgRCIELdh3/pJxWB2nKZ+B4f+qnvpeuuPyMPg5fPi/GoZnrU189I/7nrsepZFdNxg+XKv9yWHrj/qXTdDWc26ybP+7PnpE8f8r30QhZurNUiBPyFz/4wnZcF+z+5/661FmlWX4Roo9J2rRBs+QbC4vOH/TD/G3n2ecfW/Js27tWJ6aD9v5OqXz5QbCfePva5w36Qrv7rz4quVg3/dduD6fhjzk3xdrSmWlyn88+9Iq/m+uvffneRN3ea2lZj8+JNbid9+xcVgdrqZdvSOl4QcWQWuI6quU21eMPduWf/OT2b3ayKc+/YsZE7cGUbiVBwbLs6UBuLRCj4R6f8Lg0esmb+Br0INDfWIpz+/pw56Ze/OqnBIsvTtWtwcDoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEmBJRF2rd7m0g7V9urVI0WV19hvPO8X1Vpb2saOnZA/w1ysH8VB4rntt96amgdP4/nmeK59u+03LxapGD780DMpClcULarbrpod34zpM7NnJt/Jn3udff/72bPRWzT6TOSs7FnteKY6Qrmx755RuTYrHlLeIkgbz7bGs4HRolBSHOuMGe/lz2S/mj0XGhbb7zCs0We+J2bH+UQWyu2QPZ8YhUA6deqUrTMt32+cQxSW2WbbzZr1/GL5sRknQIAAAQIECBAgQGCBQKtCtfHGnpO/c2G+pfgf9v1W/++bi7bcasO8P4JX6wxu+q1BTV2MTTdbN8XnC1/6WHr8sefTJ/Y9IY24e2R66skX07At1m9q1SU2L0KzEaj96uGfTD849fAG+3k7C7m1ZSssnxs1ptHNFvO23GqjRpdp6Yz40bjnR7fNP9/+n8+mbx55ZopKvb+56Dqh2paituP1jj36nFRUr27sNMe88no66sgz8oq11ctccP5VNQO1UZk2AvcR2i1vEYA8/Ms/yYO15f21xi/85TXZW8bG15pV6oubGN856Rd55dcIqbekHX/seTUDtRG+jLeMxd/O8nb1Vbfnbw079LD/V96dj5904gWNBmqLhWN7Jxx3XjHZ4mG8IOEbR5zewDg2OHTowNJNrfId/PMf96efn3dFXjG4vL8txqNa7KOPjGpyU21qfcIFNQO1PbO/ge9mN7Oqr1v8d8FZZ1yaTvrO55s8xph5XBZUXtR37+wzL0vTszfZLard8Lf/pM9+bu+Kv7/L27Vb1DmYT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIpvTl5aolhtX69SuNtORLB2gi1Los2dOig9OrYiXnA9D//eSxFJd51110rD6QuzvHE8Y98/Pl8lfU3GJTWX39Q6tp1pXw6nu174YWxeQXbCJxGaHXQoAEVm3/pxddKgdrYfxQe6tbtv2HYWO/BB55KUXRj3LiJafDgNSvWLyaee25Mtl6XtPMuW6a+fXtlz4QWcxYMo0BSVLGNZ1EjSBvPgJcHiSdNeisP3EbwdtSzr+SVcSu3sGDqwSw0GwWuNttsaCk4G+f5xBMvpLFjJuTnMiErJLPW2qvXWl0fAQIECBAgQIAAAQKLEGhVqPb/vvurdGsWqIqg20nf+VzFroZvuSBUGxVmozJfhJKqW4Sx5s6dm3bJflgU86OC5J13PJq2/sDG6QMf3Lhilfhhsemm66ann36pQRXFLKqWLzstqwa4pFtUs4y2xZYb1NxVUxU2W3KcWy60vO7aO9NRRx+c1lhjtYr9zs/KOV504V/zviKAW7HAYk7ED7o7/v1wenH0a9n+DqqonhnBxoMO/kgeqo23MmkrvsDIkaPTEV89bZEnsu12m6WvHP6JRS4XVZojiP2xrIJzfB/jRsOf/nhLXtG6fOWHHno2e2PW7OymRpdSd1RdvexPfy9Nx8igdQakn/7sG/kbueLmw3/uejwd/Y0zsjd2zSwt93C2rXi7WP8BfUt9tUYi1Bjf4ahCu/feO+Rv/brl5nvz73P58hFsPOv0S9M55x9X3t2s8agkGn8Xy1uEc79/yuFpp52zt5d17Jjuz95S9r2TL0zjxy/4WxLL/uRHv8+C69tVvJzgtlsfTFEZu7rFiwb2P2C3tMXwDfKXDFx79e2LDGxWb6PW9N/+dleDQG38Lf7d77+XH1fckIm3p33lSz+uqB57zdX/LoVq4wULt97+i3zzH939mw12U8yLGUPWrX3TqVipCNTGW9bizXHrDV0rTZr0durdp2e+SFtax9+8J7O3upW33T78gXTCtw/LneMFEVddcVt+ncqXueWme5sVqo3/Xohg8oHZ38/478wr/nJrg2v27DOv5JseOLB/+vJXP5Hf8Lvur3em+H5Xt/h3tsOOw0rdbXHtShszQoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtUqA8xLs0TrBHj27pgx/cJD2SFdh4//056ZnsGfAIk/bv3yd/5jOGPXqs3OShRED1wQeezgsiRaGnzTZbr2L5eCYvwrqzZ8/Jnst7LSvcNDrFc5tR3bVo8dzhkCFr5sU1hm2xQYMwbDybveaaq2fPdb6Rprw9rdFQbexrlw9tlbp371ZsumIYzz1G8ZgI/MZzt1Gltrz17983bbDhOnkA+LXXJqXNhw1NK63U8HH+tbOw7LBhlcWnYt/Dh2+YB7Gj6m081yhUW65rnAABAgQIECBAgEDzBRr+r/BFrBs/aOJ/8P/x9zenG2+4O1/6+BM/k4dgy1cdOnTt9In9PpSiot6XvvCjdOmfT6n4YfDIw6OyYNyZKQKc115/etpmm03z1aPy3xk/uzQPW0X4qvzHRPwgej4L3cYPrO2236x8d2mzYeulhx56Jt1+24Np9z22qZjX1hM77rRFuu2fD6RLfntDHmwr3/5vL74+3XTjiPKuivGWHGe8aSgCdBGuiiqgv/7tyaW3FsUPxQjURsBqYPZWpYMP2b1ify2ZiJDuCcedn4eh+/TtmQ777F6lzcT1v/LKf+XTH9n9g6V+IyuuQIRRb7n5nkWewEpdmvfnYpVVVk5/vvLHaXhZ6Pxzn98nfWTXIyvC8HOy79JTT75UEZ6Pt2cdlH2Ho9pzvO0rPj8745vpg9tsUjq+qJq83/67pahiWt7ihsve++xY3lVz/Dv/+4V0+Nf2K83bL/u39b8n/yr96Q83l/pi5B9ZMPb07Bhr3ayoWLBq4g+X3FTRE294u/Ka0yoCpHEOG244KO2713Fp6tTp+fIx/M2vr0vf/d6XSuvftPBvbKkjG4kbQn+9/sz872D0x9+jAw/+cNprj2/lf0/Ll13c8aggXN2OPOrAUtA3bsjE/k798RHp6uzvQISnV+21SuqVDeOtaTEd13+TTYfkm4nq5VFBtWjx1rViXtG3qOEuu26VLvnD/+Vvdqteti2t35k6I30mqxQc37kId8ff1gsuPDF/U1zst2/fVdPXv3Fg+svlt6aXsptuRXvxxXH597rPwqBv0V89jHD4NX/7WYrvQ7RvfPOgtPeexzSo6hzVjK+78czSyxM+/8V907eOOiv/+1++zXhrXnlri2tXvj3jBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINA+BKICblGpdklVw21KakAWWP3I7tukZ595OUWQNAp8RMGW+ESLZw8HrbNGXsE2nlOsbhOz5eL55QWh0toFkWKd9dcfmIdqI1wbRZrimcWixTOA8Wmq9Vy1e0rjU3o7C6s21lZbrXejgdqZM2elN954O191883Xr3gGvnx7G244uHRs8+bNL59VGo9Qba0WzxhGEPnllyNUu+D501rL6SNAgAABAgQIECBAoGmBJlNyEXrbYZsvl7YQb86JsFGELqN169Yl/fT0o2oGOTtkJSXP+/nx+dtw7hkxMm2x6aFZeG6TNHThD5YRd4/Mt/O5z+9dCtTGNj+TBTiv+Mtt6ZWXX08f2PLzacesEl/8j/97RjyRB5liu8efeFhFdctYL8J0EfS9NKtyeffdj2c/fHql6244M8WPh7Zue2UVLn/586vT4489nx9jVNrtvFKn9NADz+THGIG5qDBZq7X0OM8+79i8QmNYbrn5YXnVxtUzlztufzgP5cUPv8v+ckop/FZr383tiyDzcccfmk794W/TySf9MnO9Kd/fmFcm5FUq3313Zh4QPOiQPZq7ScvVkcCBB+9eEaiNU49w5WezYO0F519ZIZH/GyqrSB2VbZtTbbnvag1vbIx+YVzFtmtNbLX1Rumrh3+ywazvff/LeXXZ4gZNLBAVth/IKsruvMvwBss31hF/t+Kcytsn9/9QRaC2mDc4e+PZh3bbKt14/YKXE0R/Uam0WOZf2UsCqtvpZx5dCtQW86ICavxd/OmPf190tWgYFWGrWwRXN9lkSIrjLVqE99siwF9sr7Fh/F37w5++n1cXrl6mra2jenF8FtXiu1ceqo3lXxw9riL4XWsb4VUEaov5Bxz04Qah2qjIW12NPILf8VKF8lZe5Tj6l7drV36sxgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg0QJLu4rsoo+o7ZaIZ87jGc5hW6yfV4OdOOGtPIAaYdko6vH0Uy/mz+Jt/YGN0uqr96nY8aSFQdV4prBjx4ah22LhqB4b+4mCT9Wh2mKZWsM4hqj6Ov61yfnsWTPfr7VY3tdUAY43yoqQ1HrOtdhoPNte/ZxgMa8Y9smeg2+srbywSu706e82toh+AgQIECBAgAABAgQWIdBkqDbWHTduUmkTXbqslL0JaECKt99EsHT/Az+cmvpxEBUef3vJ/6ZTsnDm37JAUARC4xMttvE/3/1Cg0qvEQy6+q8/Sz/50SXplpvuSf/MqkVGix8Q628wKJ2crfPRvbbP+8r/I6onxr6+992L8kBuBK5eHP1q2mDDdcoXa5PxgQP7pxtvOTsdecTpaeTjL6Rrr/l3vt2e2ZuSTj/r6PxNRo2Falt6nGF58e++m37w/YvT9dfdVXKMHW+z7abpB6ccniJY11bt8CP2Sz17dk8X/PyqPOhXhP16ZVUpI+D109OOTHG+GoFqgbihUattkFVmrW5RFXRRLSqdPvfcmDTu1UnZG8reyIPdD9z/VIPV5s+f16CvumPb7TZLEcyvbhEkj5s1f7/l3opZ416dWDG9qImnn36pwSL33/tUOv/cKxr0R0f1W8JGv/BqabkI9caNovLWuXOntH1Vle5i/vY7bF6MtngYLz6obvE3e5cdv5Y+mM3bKru2W2Q3tCL4XB6yrV6nrabjnOK/d2q1trSutf14ecSrYyem0Vlg9rXsvwfHZFWU77rz0TTq2VcaLF68aKLBjLKOzYetXza1YDS+c9Vtlw9tWd2Val3b6n0ub9euwUnoIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIEGAuVVZBvMbKOO8rBudXGINtpFszcTz0Guk1WljU88Bzdp0tv5M3rxvHpUeo1iKDvtvEX2fPp/i6/MfG92vv145vS2Wx9ocl9RpTba9KxSba0Wgdvx2bOoEaKNZzSj0FCEapvbaj2DWqw7MyteFS2e+Y6Ab2taly6NP+LfceFzsNXPEbZmf9YlQIAAAQIECBAgUG8CNf8X9zqD10ivvn5Tm1iskgUzzzz7W+nUHx+RxmahpHjzz/obDEzxtqDG2lpr9Uu//NVJafoZ76axWahp9uz300YbD04RfGuqRdg2PvH2ovi90H9A39Lijz1xWWm8GFk/q5q7qPP8+23nF4tXDAcOGpAFa8/Jq1k+/fTLWZXAVdO6662d4sdetKa2u7jHWew4QqznZBVrw3LMmNdT/EgcNKh/xXkWyxbDWuddzIvh90/5av4p7yvGP/2Zj6ZDPr1n9uNxUnp9wptpzTVWS3HeWvsS2Gyz9dKXv/qJRZ5U/F1oTlu9X++aiy3q32/5ShGY/N3FN6R//+uh1Jzgbfm6TY1vkAXzG2vxd6m6TZ686NBv+ToTXn+zfDIfj3OpFQBtsGDWEaHhuEETN1PemDylwSJxE6lzdrOlVou/Z61tEej8f9nf0OJlBsX24sbLQw89k3+KvvWyv3d77bNDOuLIA/IXCRT9bTmsdU2K7beldbHNOM/bs+9cVOd96MFn8mtRzGvtsFvX2uHg6u127tzw+jb1Zr1i/eXt2hXHZUiAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECjQtEyPW5NCZfYHKN5wYbX7P5c8q3u6xDteVHHQHVAdmz3vEZsu5a6eHsOcX3smDqk0+MTh/adevSovEcedHiGcvmtAjPlrd58+anxx97Lis2NTEL85bPSVnxj86p18IquBOz56Vb2opA7+I8L9vSfVmPAAECBAgQIECAAIHWCTRM77Rue42uHT8QIhi7OC0CuZtutu7irJIvO2CN/4ZpF3vlxVwhgq61qgg2ZzMtPc5VVlk5RRByabSoEBxBWmHapaG9bPax1tr9svD0Hstm5zX2esbPLk0XnH9ljTn/7YrvZdzgWNzWVGXt8reaFdtt7s2XYvlp0yoryxb9zR1GqDNeCrDuemvVDHT26dv4ywh6rNI9f7vZ4rwxrdZx/SJ7ocFpP/lD+v3vbszfAldrmeh76aXX0oW/uCZd8edb0y8u/HbaZdetGlu0xf1NvdGtLa3jAOOm2xc++8M04u6RTR5vS797TW60jWYuT9eujU7JZggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgjVp6fAABAAElEQVQQIECAAAECBAgQINCuBapDrhGAre5rDcCoUa+0ZvWltm7fvqumoVlxkaeefDGrIjs9zZkzt1ToqEuXBUUtVs2e2d5mm02bdUzVBUweefjZNH78G/m6q63WK629dv+sGm7P1CN7Jjsqy0YbPfrV7BnOlodquywsvjF9+nv585dNPQOZ79B/ECBAgAABAgQIECCwzASWWqh2mZ2hHRMgsMIIXH7ZP2oGalfv3ydtu+2maYMN10nDhg1N72QVr4/71jmLfV4RBG2svVxjXt+sAvXitNVqVOkdvuUGabcPf6DZm+md3aSJFjdtqtvLL42v7ipNvzZuUmptoDY21q1bl3TKj76WvpJVL46KtVG5deTIF9L0zLxWe+utd9JRR56Rbr/zwhTXaWm1trSOYz7+2PNqBmqjAvBWW2+Uf/dieOMNd6c//eHmpXWai7WfFeXaLdZJWZgAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtXCBCtEU12XtGjEyf3G/XNjvj50YtqIIbG1zc4kitPYhZs2anaQufPeydVYLt3LlTk5ssnpuMAiXT3pmR+mRB22hF5dcI2kbBpsVtsd7rr0/OV4tnAjfbfGjNTcydO69mf3M7i+OcN29eimBtzxYca3P3ZTkCBAgQIECAAAECBFonIFTbOj9rEyDQhgJXXfGviq11794tnXXuMenjn9ilov/mm+6pmG7uxAsvvNroorXm9asRbG10A9mMNQY0rJK99z47pqOOPrip1WrOK24Olc+MAGt84o1s1a3W8VcvszjT6wxeIx1+xH75J25Qvfzy+PTwg8+mK6+4LT34wNMVm3r77Wnp1lsfSId9dq+K/iU50ZbWUZH4piwsW96GrLtmuujikxtUBb/pxhHliy2X48v7tVsu0RwUAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIElpFAhF0nj5hS2nsEa3faeXhpuqUj1VVqN954SEs31aL15rw/N8W5RNs8K6gydOjAJrczadLbpfldswIhRes/oE8aM+b1FM/6zZo5O5XPK5YphlGcpKg8W/RNmTItrxwb00PWXavobjB8c/LUBn2L09E/K0wS1WnjmctJk95qNFQbxxjnEy0q5hZh3MXZl2UJECBAgAABAgQIEGidQMfWrW5tAgQItI1A3CR46skXKza2z747NgjUxgJPPjG6YrnmTvz9lvvS+PEL3jZWvs7Ix19IDz/0bHlXPj58yw0b9DXV8YFtNmnwJrXqAGr5+lF5Nvb7wvNj06SJb6WZ2c2eonXpslLaeJMhxWRp+Nvf/K00XozEDZjfXXx9MdmiYWzjzTenpjGvvJ6eeurF9MD9T6VHHh6Vbytu8qy33trpkE/vka792+npoIM/0mAfL74wrkFfdcfc7O1rbdXa0vqJkaNT9Rvmjv7WpxoEauPYW/rda6vzrrWdpXHtau1XHwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0XiAq1canaFG1tqhcW/Qt7jACtcuySm0cb49VVk5RoTZaPC8Zz4k21mZklV3j+cVoUeE1irIUbcCA1VLXrgtCtg899EyD5/2K5SZk1Wj/fss96T93PZpmzHiv6K4It0bV2lrt7azgSTxD2ZoWz32uuWa/fBPPPPNymjples3NPfnk6PT0Uy+l558bm2IdjQABAgQIECBAgACBpS+gUu3SN7dHAsuVwJw589L07GZEc1qXLp2X2A/42bPnpHlVoctaAdi4UXTD9f9pzuE2WGbaOzPScd86J/32kv9NPVftkc9/I3uz2bFHn52qb5RsuNE6ad31Gn8jWYONZx1RQXbX3bZOt//rodLsf9/+cPptFnj96uGfLPXFSFR3PeTAk9OECW+W+odvuUG66e/nlqb32nuHNOrZV0rTMXLhL65J222/eb6fmI4w6G8uui67CfRYTLaq7b3nMen17KZS0Tp16phuuPnstMXwDYqufNi9x8oV0zERb4Krbr17rZImv/Hft+eF/0svvZYHdKuXXdzptrSeNeu/YebiOMaPf6MYLQ0fe/S59Phjz5Wml6eRtr52y9O5ORYCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAi0d4Fa1WqjryXVZasDtRHYbcl22sJ8yJA10+OPT8tDrrff/lDafLOhafXVe+fVZqOgRIRp33jj7RQh1HiOs2PHDmnTzdar2HU8y7jDDsPSiBGP58HXKHYSz3j26bNqvny2mTTu1YlZQZHRWZXYlPV1TD3KnnOMQG6v7HnGqVOnp5GPP5+GDVs/9e7TM68qG/uMqrLxfOD8+a0vHLLV1huladPezT4z0oMPPp023XTd7PnKvnn13BkzZqbXxk1Mr46dmJ/foHUGpDg3jQABAgQIECBAgACBpS8gVLv0ze2RwHIlcMe/H06bbHBws47p2OMPTSd8+7BmLbu4C/Xo0S1tudWGFRVj773niXTB+VemvffZMX9jWVR1Pf20P5VuKCzuPmL52ObwzQ9LO++yZX6T5uHsrWXz5mV3Uara57+wT1VP8ya/+a1D0p13PFLxJrRTf/DbfL9xU2ftgavnbxf7wyU3NXir2THHHVqxk0M+tUe66MJrKyrYRoj2s4d+P60zeI0UIdx7RzzRYDsVG2nmRFSj/fgnd8kDusUqsa8jDv9Z+lRWoXbHnbbI35p2371Ppsv+dEuxSD6MdXfZdauKvpjYdrvN0ujRlRVsD/jESWnrD26cv0XuFxd+u8E6i9PRVtbbbLtp6rxS5zSn7C14v7v4huxa9U8f+tBW6Z13pqcRd49Mp/3kDzW/K4tzzEti2SVx7ZbEcdomAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNQWiODrTjsPT/eMGFlaoKg029xAbBQtiXWqq9zGdHzKq+GWdrKERwZnodoIuT4+8vk0a+bs9Mgjz+Z7XCl7Zi8KscRzikWLqq3x3OFqq/UqukrDXr1XSdtsu1l64P4n8xBsBGEjkBoVbWe8OzPNW7idmI5nK6vbxpsMyZ5PfSYviPKf/zyWh1xXXrlrHoCNcG8EbyMAXP3MY/V2FjXduXOntP0Om+eFUt7Njuvhh5/Nw7uxr5gu2hprrJY22WTdYtKQAAECBAgQIECAAIGlLCBUu5TB7Y4AgcYF4oZQBGfL2xk/uzTFp7wNHDQgf6tYeV9zxg8+ZPd09VW3p/ez8GSEiRtrcUPlc1/Yt7HZTfZ/cJtN0tHHfCqdd85fSsvFDZfb/vlA/il1Vo3EPvf86LYVvfEWsgjann7aHyv6Y2LsmAn5p5ix3wG7pZtuHFERDC3mNXf4laya7o3X311RrTbe3nb2mZfnn8a289nP7502q3ozXCwbQdw/X/7PitXefHNq7hBB0NN+9o1SxeCKhZo50VbWcRNt6+ztcPH2uqJNmTItHX/Mf6sGF/0t/e4V6y+pYVtfuyV1nLZLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABArUFIvQa1WmLMG0sFePxif5otQK2jYVp8xUW/keEdeMZzWURrI1nIVft1SOvRjt1yvQ0a9bs/DnO4vhWWWXl1Lt3zxTB1/IKs8X8Yti/f5+03fbDssImY/JiJBHIjaqw0SKkOyh7tnSTrDJsBFurW4RYoxjLo4+MyteJ50jjEy22O3zLDdP48W9Ur9ai6Xgmcaedt0jPPvNKmjBhclYBd34pUBvnN3BQ/7TRRkOysG2LNm8lAgQIECBAgAABAgTaQECotg0QbYIAgbYROOLr+6dHH30u3X3XY41usG/fVVNUON3v4yc2ukxjM/b52E6pe3ZD4o+/v6mxRfJw6CV/+L/szWgtv1tx/ImfSd26dUlnnn5pxVvUGtvppz/z0fSDUw6vOfsb3zwof6va7393Y8350Tl06MD009OOTLfcdE+jyzRnxlpr9UtXXPOTdND+30lvTHp7kavEOX4/O+7PZaHaWu2je22f9tl3p3TLzQ2PK24SjRw5OrtJNbzWqs3uayvr004/Kn3hc6c0GdbeZptN06GHfTQdf+x5zT6+pbVgW1+7pXXc9kOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/xWI0GwEX8sr1sbcImhbDItwbHVV2mJLMX+1fr1K6xXb6Ldz72KRpTrs1WuVtMMOw/J9RsXaqe9MzyrNdkrRXysE29jBRQA2PrGNqPw6Z87ctErP7ikqwS6qRXD3I7tvk4dpp7w9LXXInhNdddUeKSrkRlt//UH5p9Z29tijsmhKrWXK+3r27JFX3Z09e056d8Z76f05c/JzLfZVvmwxPnBg/xSfRbWh6w9M8dEIECBAgAABAgQIEGi5QMeWr2pNAgQItK1Az+zmxJ8u+2E6/Gv7pdWzmx7V7eOf2CXd/I9zm3XToHrdYvrHP/16OuOso9OANfoWXfkwwrrHHn9ouuZvp6f+AyrnVSzYjImownrU0QenG24+O0V13Fo3a+LmzPY7bJ5+/6fvpzPP/laKN63VahHuPfXHR6QLL/qfBtVge/Tolj516J7p5n+e16qKr+X7XW+9tdOI+y7O9zl4yJrls/LxOLch666Zh2Vv+vu5jQZqY+E474su/k769v98Nr9m5UHlGH917IQG21/cjray3nCjddJNfz8nrxZcfS26du2SVR8+JF36l1Pzt9kt7jEureXb8totrWO2HwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoFIhA7Cf327VUnbZy7oKpCNPWCtTGulGRNj4R0C0q3MZasXx1WLfWtpd0X9esoEf//n3Taqv1WqxAbflxxTb6ZM99xrOmtZ7RLF+2ejyq2sZ6YdVUyLV6vZZMd+nSOfXu0zOtvnqfJb6vlhyfdQgQIECAAAECBAjUq0CHrFrg/Ho9eee9YghMnPhWfqARQlyWLf6pLPikhcP5ad68eXkl0rlz52U/rnsty8Nrl/uelF37UaPGpD7ZDYV1s7BnddixtSc9+Y0p6aWXXstvzgwc1L/FN2cWdRwzszeijRs3Kb0+fnJ8efKbVC0N7kYF2TFjJuQ3WSJEWR5UXdRxtGT+O+/MSK+NeyO9/vrk/DrEDbbu3bu1ZFNp1qzZ6eWXxqcIwg4cNCBFKLitW1tYx7/zl18en8ZmzvHWtwgXx020Fa215bVb0c7d8RIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGBJCBTPtA5oZfGOxTm2UaNeqag4W2vdojJtBGmrW/X6RfC2ejnTBAgQIECAAAECBAgsHwLL4ndHS8588uSpqVOnjvmnY8eOeV4oMkPZ/5XGW7LdtlpnypQFmcS+fRsWXxSqbStl21liAsUfAqHaJUZswwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAosQKJ5pXZqh2uKQisq0xTDCsdGKYbFcraFgbS0VfQQIECBAgAABAgSWT4Fl+btjcURW5FDtild6b3GujGUJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCwggsU4dliuDinU1SwfW7UmHy1CObeM2Jk2mnn4YuzGcsSIECAAAECBAgQIECgXQh0bBdn4SQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoKZABGs32nhwzXk6CRAgQIAAAQIECBAgUE8CKtXW09V2rgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1KVAUbH2zclTVamty2+AkyZAgAABAgQIECBAIASEan0PCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUAcCRbC2Dk7VKRIgQIAAAQIECBAgQKCmQMeavToJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItCMBodp2dDGdCgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQG0BodraLnoJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTakYBQbTu6mE6FAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgtoBQbW0XvQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAu1IQKi2HV1Mp0KAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBbQKi2toteAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBdiQgVNuOLqZTIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqC0gVFvbRS8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA7EhCqbUcX06kQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUFhCqre2ilwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiWBDh0WjM6fX+oyQoAAAQIECBAgQIAAgTYTKH5rFL892mzDNlQhIFRbwWGCAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECDQU6deqUd86ZM6fhTD0ECBAgQIAAAQIECBBopUDxW6P47dHKzVm9EQGh2kZgdBMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAQ6LJS53x01qzZRZchAQIECBAgQIAAAQIE2kyg+K1R/PZosw3bUIWAUG0FhwkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0FOi2cte8c8aMmen991WrbSikhwABAgQIECBAgACBlgrEb4z4rRGt+O3R0m1Zr2kBodqmfcwlQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAWimrVNt9YbB26tTpgrW+EwQIECBAgAABAgQItIlABGrjN0a0+M0Rvz20JSdAd8nZ2jIBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAu1IoOeqPdLcefPSrFnvp7feeif16NEtde3aJXXu3Dl16NCOTtSpECBAgAABAgQIECCwRAXmz09pzpw52W+L2aUKtV27rpTiN4e2ZAWEapesr60TIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQINCOBHr37pmmvTMjvfverPzh9xkzZrajs3MqBAgQIECAAAECBAgsC4GoUCtQu3TkhWqXjrO9ECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQTgTiYfdu2UPvM7Ng7ez356S5c+emqDSlESBAgAABAgQIECBAoDkCHTqk1KlTp9Rlpc75b4uVsqG2dARILx1neyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoB0JxEPvHnxvRxfUqRAgQIAAAQIECBAgUBcCHeviLJ0kAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAXQsI1db15XfyBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6EBCqrY/r7CwJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnUtIFRb15ffyRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6kNAqLY+rrOzJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUtYBQbV1ffidPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgPAaHa+rjOzpIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUNcCQrV1ffmdPAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgPgSEauvjOjtLAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBdCwjV1vXld/IECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfoQEKqtj+vsLAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECdS0gVFvXl9/JEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgTqQ0Cotj6us7MkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNS1gFBtXV9+J0+AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqA8Bodr6uM7OkgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQ1wJCtXV9+Z08AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA+BIRq6+M6O0sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQF0LCNXW9eV38gQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB+hAQqq2P6+wsCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ1LSBUW9eX38kTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpDQKi2Pq6zsyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LWAUG1dX34nT4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoDwGh2vq4zs6SAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFDXAkK1dX35nTwBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoD4EhGrr4zo7SwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAXQsI1db15XfyBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIH6EBCqrY/r7CwJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAnUtIFRb15ffyRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6kNAqLY+rrOzJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUtYBQbV1ffidPgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKgPAaHa+rjOzpIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUNcCnevx7M856/LSae+w4xZphx2HlaaNECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItD+BugzVxmU89+y/5FezGB53wqHp+BMPa7MrXAR3hXbbjNSGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQItFugwP2stXnsFXjFCr0WgtjiNqFh71bWnFZMtHlZvO7apGm6LOdPEiW/lK/fu3bPlG2mDNeOfyoJPWjicn+bNm5fmzl3w6devVxvsxSYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMDyKzB58tTUqVPH/NOxY8fUoUOHhZ9UGl+WRz9lyoJMYt++fRscRscGPXXSEVVpozptebvv3idTUWG2vL+547HuoDU/1iCse8iBJ6fYtkaAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQILBsBDovm90uH3uNYG208oq1xXgxrzlHWl2ZtjnrWIYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGDpCbT7SrWLqjxbq2Lt4vLff99TTa6yw47DUnw0AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBZSPQ7kO1EXg95MCTm9TdYcctKuYX1WorOpuYuO/eJ5uYm9JxJ3ymyflmEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQILFmBdh+qjcBrfJoK1ramiuyiArWq1C7ZL7CtEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSaI9C5OQutyMtEqLU8WHvVtac1OJ3qYOzihGzvu/eJBtsr76i1v/L5xtuPwMSJb7Wfk3EmBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECghkCnTp1q9K4YXe2+Uu32O2xeuhKNVaw99+w/l5ZZnJFzzro8nXv2Xxpd5bgTDm10nhkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJLT6DdV6qtpoxg7aA1P5aiGm0EbmuFYmOZCMwef+Jh1auXphsL1BbbjQWbWr+0ISPtRmDAgL7t5lycCAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqCUwefLUWt0rRF+7D9UWwdbq8GwEZ+PTWCuWL9YvX65WoDbCtFdde1r5YsYJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSWE4GOy8lxLNHDiGBshF4Xt0WwNgK05S2CuEXgtugXqC0kDAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECy6dApx9mbfk8tLY9qoM/tUe2wfmpQ4cOadyrk5q98fvveypfb4cdt8jXGTRoQD69oD/lYV0VapvN2aIFZ8x4L1+vW7euLVp/Sa40f/78VHy6d++2JHdl2wQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAYJkLvPvurNSxY4f8E5nN/35SaXxZHuTMmQsyiSuvvHKDw+jcoKcdd0TF2mhRbXbB8IksFLsgLHvIgSfnfbX+o6hMW6xfDCNYK1BbS0wfAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGD5EuiQVdmcv3wd0rI5mgjaNhWsjaM67oRDUxGoXTZHWZ97nTjxrfzEe/fuuUwBioq08S+mGJ83b16aO3fBp1+/Xsv0+OycAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgsaYHJk6emTp065p+OHTuWqtNmRWtL40v6GJra/pQpCzKJffv2bbBYxwY9ddqxw47DFll1NirWnnPW5XUq5LQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiuugFBt2bUTrC3DMEqAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaEcCQrVVF1OwtgrEJAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHQgI1da4iIK1NVB0ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWYAGh2oUX7757n0znnHV56VIK1pYojBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEVniBziv8GbTBCUSg9pADTy5t6fgTD8vHi2Bt+bzSQgtHzj37L/lYsU71fNMECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLLXqDuK9VWB2ojJKti7bL/YjoCAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBbCtR1qLY6UFvACtYWEoYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfYhULeh2sYCtcVljWBtLFO0HXYclq669rRisuawOoxbcyGdBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECS12gLkO1iwrUxlU47oRDUwRpy5tgbbmGcQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAiiNQd6Ha5gZqjz/xsJpXUbC2JotOAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMByLVBXodrWBmqLKylYW0gYEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRWDIG6CdW2VaC2uKyCtYWEIQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBg+Reoi1BtWwdqi8sqWFtIGBIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIElm+BugjVnnv2n5u8CsedcGg6/sTDmlymsZmCtY3J6CdAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQILD8C7T5UG1Vq49NYa02gtthmc4O1TR1HsS1DAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBtheog1DtE42qtUWgttj4ooK1sa9YRiNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEFj6Au0+VHv8iYfVVG3LQG2xg8aCtUtiX8U+DQkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBYt0O5DtUFw1bWnlarERsB1SYZcq4O1S3Jfi768liBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEAiBDvOzVi8U9937ZClcu6TPOfZ1371PpMYq5S7p/ben7U+c+FZ+Or1791ympxX/VBZ80sLh/DRv3rw0d+6CT79+vZbp8dk5AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBY0gKTJ09NnTp1zD8dO3ZMHTp0WPjJQqsLx5f0MTS1/SlTFmQS+/bt22CxugrVNjh7HSuEgFDtCnGZHCQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1IHAihyq7VgH18cpEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LmAUG2dfwGcPgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHgSEauvhKjtHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECdCwjV1vkXwOkTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpBQKi2Hq6ycyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LmAUG2dfwGcPgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHgSEauvhKjtHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECdCwjV1vkXwOkTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpBQKi2Hq6ycyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LmAUG2dfwGcPgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHgSEauvhKjtHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECdCwjV1vkXwOkTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpBQKi2Hq6ycyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LmAUG2dfwGcPgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHgSEauvhKjtHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECdCwjV1vkXwOkTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpBQKi2Hq6ycyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LmAUG2dfwGcPgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgHgSEauvhKjtHAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECdCwjV1vkXwOkTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBOpBQKi2Hq6ycyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1LmAUG2dfwGcPgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECzReYPHlKio9GgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAiueQOcV75AdMQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBJaewKhRr6Q3J09tEKbt1693Wq1fr/xANt54yNI7IHsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRYJCNW2iM1KBJZfgffem5XGjhmfxo2bkCZMnJz6rdYnDRy0Rho8eK20yirdl98Dd2RLTGDKlHey78Tr6dXsOzF16rS01pr98+/EOuusmbp0WWmJ7deGCRAgQIAAAQIECBAgQIAAAQIECBAgQIDAii4QFWnvGTGy0dOorlorWNsolRkECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBYLgSWy1DtZZf+Pc2Y/l4DoF69VklbDN8gbbjROqlz504N5rem40en/C7d8Lf/pB/+6Gtp34/tVNrUpX+8Jb377sz0uS/sk7p371bqX9TIxAlvpb9dd2fqP6Bv2v+A3Ra1uPkE2kTguedeTpddekP+na3eYIQnDzlkr7TV1ptWz0ozZ85KEyZMTiuv3DUNGNCvwfyWdsyfn9KYMa/lqw8ZsnZLN2O9VgjccccD6Zab70rz5mUXY2F7JD2dj/Xt2yt98UsHpLXX7l/MKg3ffvudPIDbp/eqqVfvnqV+IwQIECBAgAABAgQIECBAgAABAgTag8CE199Io0a9nOIe2RbDN2oPp+QcGhH4z10P5fdHP7TrNqljxw6NLKV7UQI33nBHeuyxZ9N+++2+Qv2bae31jzBthGajRUXajTYeXBrPR7L/KEK1z40ak4rPTjsPz5cvljEkQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeVHYLkM1V5w3pVp/PjJjSp169Ylff6L+6bvff/LqUOH1j8AEWGzv/z51jTtnRnp+uvuqgjVnnP2n9PkN6ak/fbfbbFCta++OjH9+NRLsgDjRkK1jV5JM9pS4L77Hk/XXnNrtsn5abvttkhDh66T+mQPhI0dOz49//wrafQLY9Nll92YJr85Je25544Vux479vX064uuTOtvMDgdeeSnK+a1ZmLevHnpgp9flv87Pevsk1qzKeu2QODyy29Mjz7yTOratUvaZZcP5NVpu3Xrmn0XxuTfibjuF/z80vTlrxyYNtxwSMUe4vt0+7/uS3vttUva86OV35eKBU0QIECAAAECBAgQIECAAAECBAgQWAEF4t7YjTfekd8XWx5DtUvqRYgr4KVq9SHfdNOdae7ceWnnXbbOQrXL5f9brNXnuKQ3MD97g+b994/MX9D56GPPVIRqm/NdXZYvcWzN9R816pVSoDbCtI1VoI2wbXyiRag2WoRxP7nfrvm4/yBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACB5UtguX564PAj9kuDB69REnv33VnppZdey4Ovv7noujRt2rvpjLOOLs1v6Ui8mfyc845Nt936QPraEfu3dDPWI7BMBf51230pHmz54pf2T8OGbVg6lvXWG5h2223bPER58W+uSnfe8WD68Ie3a/Nqz6UdGlkuBN7MwtMRqF1lle7pqG8elvr371s6rg2y8PTe+3wo/fMfI9Ktt96T7rrzoQah2tLCRggQIECAAAECBAgQIECAAAECBAgQWOoCS+pFiEv9ROywXQjEC24/feg+6ZmnR6dds4q/5a0539UV8SWOEagtArK1qs6WV68tPCJ0G5+ium0MY12NAAEC/5+9+4Cvurr/P/6BsCEQQhJGElaYMgIIyEYBB2oFrThQq61inbVa62rrbGtbW3/uVXfdExQVFJS9ZO+wQoBAdgKBkDB/53OS7+Xem3uzSG4I93V+/3jv/e7v83tD/4+T8z4fBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBE4tgVM6VHvhRUOl/4DuxcRuv2OCjD3/Lvnw/elyy22XSceO0cW2Ke+CC8YOFv2hIVATBXbtSpWcnH3SunWkR6DW/V60EmmPnp1le2KyCafvtCFKnZ0/L++grdKs2+7PPWDC6gfsbvXq1bUVTt2Poe81uLt3b645X65o1dPIyHAJCantvZk9ztGjR+1y3cc5bkhIiKvqs1aJPnAgz1QHqC2NGzcsdgxdsH9/nj2nhkP9VabOy8uX9PQsGyBt3ryZOd7JV7D2eTE1aOHatZvt1fbp290jUOt+C6PHDJKlS9fKnj3ptsKAPs9Dhw5LQcEh1/PK3X/iO9GoUQPzrEPsIbT6wOHDR+yz1GXmEUtycqrdNy4u1v009r0eNzMjR46a6sUtW7aQunXL9j8/Bw4cFA0I63esRYsw+50rdnCzQK9Fr8n9e6v75u7bLy1bRZrvjude+p1M2ZMh9erXlXBT0dnfd8tzrxOfnO9laGhj18KsrL2m4sVR+zvhWljCG3XOytwrhw4fttfgfiz33QJxb2qVZZzrm+9ARERzfofcHwDvEUAAAQQQQAABBBBAAAEEEEAAAQQQQMD+7cF9Qs/TncQJ1GqFWqcKrfs9a2BWm6/Are6TMS/HVrnV8K2v/d2PxXsEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcAKlC3VFNhrKvVs7Tu0lssnjJK335wqixau9QjV5u47YEJdhyW0aSOfgcD8/EMmOJhngln1pEloI9e5StvPtaGPN1lZ+2RHUopERjWX6OhIH1v4X5SdnWv3bRLaUNq3b+MznKh7O9fd0ITaGjduYA+4c0eqbNmyUwYP6W3vx/9ZWHO6C2RkZNlbdAKP/u73hhs8KzFv354sL734gWvzlJQMefSRF+znUaMGyUUXj3St02DfggUr5KcfF7sCl7pSw7CDB/cxofQRHsFF5zjOAZzPbdu1kbvuus4u1uv+5z9el4jI5vLggzc7m3q8/vupN+35Hnn0dmnatInHup9/XiPfT58vGmZ0WlhYU7n4F2dLXxMmDeaWkZ5tb79OUQjWl0WdOnXkT3++xWOVVjKePn2ea9n8ecvNrPrL7edJN08ws+x3tO8//WS6rFy5QXRZdvY+W/VWg9Pq/5eHb3Xtr0HXr6b8KMuWrZMjRwpD1hpg7d69o1x62bk2TOra2O3NmtWbbBXd3bvTXEvr1AmRXr27ymVmPw34ujf9Lnz+2fei39sz+/eQDz+YakO+GvbVsKpWTzhn1FnmGo7IJ59MMxUVtsrBg/n2EBqqvfKqC6VTp7buhyzxvfO9/Oe//iBz5yyT+fOXWwfdSQPgg4eY34kLhvs8hobgvzfG60xVB6dp6Ldjx1i5ZNxoiYlp6Sy2r1V5b6mpGTLFPJ+EjYmuc6rz0KH95PwLhvn831HXhrxBAAEEEEAAAQQQQAABBBBAAAEEglygrBPCuTPpRIPp6ZlmkrijZhK5ZtKwoWc/V0UmQnQ/vr4P1IR4zkR6es6WrVrYyRP1fWlNJ4jUSRsbNWpoDfz1a/uabE77gtNSMyXO9OV5T9yn15ORkW2vQyf2K+9Eev6uuyL3qc8x21zrfjOpZLNmobbf1HviP+d8vu7zZCcM9P4O6Ll08kKd4LJ586bOqV2v2m+6Z3e6hJl1/ib/c47ZsGF90b7lsnxX1a4skzi6LqTojfMdCQsrtPNe7+tzZT5/rVKrUGjCHAAAQABJREFUTcOxWnm2vE1DtLqvBnP1J2JYWHkPwfYIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAlUoUCNDteqhFRW1paUWhgntB/Of++97Qb6eMleef/FeGX/Z2c5i1+unn8yUh+5/USZec7788993upaXtp9rQ7c3u3alyZ8ffFl+nLnUVtLUVRqs/Zc5rv6hv6S2edMOeeyR12X2rMKwmm6rFRav//VFcs+915hQlmfVTue6b7vjchN0GiR/+P0zJlC7y55i/qLXpW27ViWdjnWnuUB0dGEIb9euFNm0abutQluWW9bBMQPP6i2bErbbSre6j37WFtvW8zv13ntfydo1hdVPdUBSXFxb2WnOt3PHHpkxY6EdGDP+0jF2X/2PHkerdi5bus4uc46r1UYro33zzWzzu7fIVk7VAVSxMa1kq6nAu2vnHhuojIoKNyF3z3BiZZy3phwjOjrKXqqGWUeM6C/NSvk3ybmvaBPo1Ge1ZPFqZ5HrOxFmBn95t40bttlAqQ6AO+OMONHgv9OOmaq0774zRRISEs0gqxDp2rWDNDIh7C2bk2T9+q2y2bz+zgSs27QpvFZnvzlzlsqUyTPtR/2O6n4HTEVl/Z6uWL7eVlS95dar7L+Zzj7Oq2735hufSy1Trbh//57m38kdNuw6deosM5FCY1mzOkE0TK6hXh2stnHjNhvK1n0eeHBSseC2c1x/rwvmr5DvvpsjsbGtbeA4LS1Ttm7dKT98v8AO2jv//GEeu+rvy/PPv2cHnNWvX88GeXXg5Lp1m+1+r736sdz5u2t9Vrut7HvbZ6r4vvrqJ7LXDmBsYP7d6CD5BQWyzVz/7Nk/y7r1W+QPf/i1T2ePm+IDAggggAACCCCAAAIIIIAAAgggEGQC5Z0QTnl0QrpPzWRv2iem4T9tGvrsbSaRu3zC+a5J5MozEaI9iI//VPWEeL4m0tOAa4cOMTLhigv8TqSnk8x9a/p1dXJHp2nf2LBhhRO8eYdg3Seb69mrs3z04bei/W/aHvrTb00gt7CvWfuhtT9x8eI1dlI9Xa99b2efPVDOPW+ofqxQq+h9ar+19q/t35/nOq9OLHmB6Svs2+8M1zLnjft9VtaEge7fgYMHC6y7Mzml9p2PHz/GhD472L7Rjz76VpJMn6kzKaJOPqiTEOpkhO7NOeY11/5C+pn7KMt3tayTODrn8fUd0UkMx44dLmcNivcZlK6K55+ZcWIiT+fayvuqYVwN1GqlWhoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwKklUCNDtTqT+48zf7aS8X06V4vo/v0H5YbrHrN/ENcw7JChvc2M8vVlzuzlcuMNT8jdf5jo97pSU7Lkmqselj17Mmz4dvjIPqZ67kFZvGit/PfVyTLjhyUy7YfnXINo3A+kQd6bfvM3W21Xz6lVa+vVr+u+Ce+DUCAyMtxWi124cKW8ZkJyWjlWB5h4V7z0ptHBM1deOdYGcV995WPp1Lmd/ey93fJl622gVsPst91+tUdYdYcJCT77zLsyb94yE/gebn8PdH89rs5Ur6FaHQylnyur6aCzuSZ4qTP733HnNdK2bWvXoTeYsObrr38mH5uBQHffc4PPQTaujU/jN/0H9JJ5pnqqVhf417/ekNGjB9kBW76qELgz9OjRSfRHw6wzTVhaq62ee94Q90083s+du0wuumikjDLH926ff/6DDdRqddvrbxjvEc6c/OUM0X0//ug7uev3vzIVEszDNE2rJXxjArDadODWAHMfTgUHrSz71L/elKSk3XbwoV6nd1u8aJX9/utARG1aqXb6tLnyww8LzKC7b+x9/fG+G13VFrRKgv7O6AAwrcg79sIR3ocs8fN3382VW265SjrGxbq2W7Jkjf3+zfhhoXXXqg3aNGT8obkG/b3Q389f/vJcGwrXdTrw6913p9jfswULVsq4caN0sUerzHvT36HX//uZDdRq8FefsTNoUde98vJH1vm7b+fIuPGjPa6DDwgggAACCCCAAAIIIIAAAggggEAwC1RkQjjt83rh+fdFJzkLC2tq++mOmAnfNmzYKqtWbZTj5v+uv368ZS3PRIilPYeqmBBP+7jeeXuy7VMOCalt+5Qbmwn3dPI6nfDxmf97R35z4y+lfftoj8vTdW+/9aXtI3Mm0tuzJ02Sk9Ns393B/AK51G3SRveds7P3yltvfmEndtTApwZmdRI/p33y8TRZunSt7UeMNX3FLaNa2GuZPn2ea0JUZ9uyvlb0PvWc30+fb0/TsmWE6btuZfoyd0hGera8//5U2x/YO76rz8uo7En19CTal6rB1tatI+0z0ckO09KybF/kDb++1Aa9tXrtmWf2MBWUsyQxMdlOVPjuO5Ntv63TZ+jrgsvyXS3PJI4aOH/n7S9t/6kGpuNMn2uqqUq800yk+emn0+2khBro9W5V8fydIKxWnC2t6eSNhwoKg/K6rfvf7HR/PZb+lOVYpZ2L9QgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAClSNQ40K1mZl75b57nzcVC3fIsOHx5qdP5UiU4yga6r39ln/aQO05o/rLS6/e76osq3881yqy/3nqfZ9HzMvLt2FcDdTec+9Euevuq11hMl139RV/luXLNspT//ifPPL4pGLH+GryHFup9sVX7rMDR4ptwIKgFbj0sjG2SuisnxbLggUr7I9WJ+3apb0JSXaWbt07mIFGFfuV73fmGa4BH97VXzXQquHdXbtSzU+KdDbB3KpuGoDU3zWtxuoeqNXzdjfVUrX6qQ4SKmnAT1VfY3UfXweV/fa3V9pBUTq7v1b21R910cqvWtmhffsYV2C1otfbs2dnn4FaHSi4yIS8o8wAtl9dP84jUKvnGmcqIezcmWLDrDp40AnI6mCp+x+YZCYdSHctc65Nq1b06dPNVnnQiq/OPs56fW1sKuH+8vLzXIs0kDvcVOr98cfFNriqlSB0sJfTdADe0KH97HWkmUFj5W1jxgz2CNTq/gMH9rK/f3qNu82gwLbt2tjD6uC0O393nR0sqfehn50WEhJi9uttQ7W6n69Wmfe2YsUGM2AxVfR3+7zzPat16EQRN02aIE88/pINPp9/wTBXdXhf18UyBBBAAAEEEEAAAQQQQAABBBBAIFgEKjohnAY+NVDbvXuc6Xe53MWlwc0XX/hAVq9KkDVrNkmvXl1Mf1rZJkJ0HaSEN5U9IZ6e6vPPvreBVQ2v3nLLla5+oyNHjphqsT/afrGpX8+ykyE6l7Z7d5oN1B4/fsz2Ffbu3c3VL6mmL734ocwzE/BpZdSRIwc4u7letS9L+zOvu+6SYn3cWhVWfbUv/M47rxVnUkGdbE/7yb/4/HvXccrzpiL3qZPtaaBW+/Fuu32itGoV4Tqlho7feP1zG6wNbdrYVvV1rSx6U5mT6jnH/uH7BTJx4sW2H1CXab/6m298bp+hTqyn1WpvNCFo7Z/UlpGRbYLR79q+Ww25djF/X/DXyvJd1T5c/dE+2ZImcdTwrAZqTf1mmXTzBNGJGp2mIeAXnn9Pli9fL/GmX1X7pJ1WVc/fOX5ZgrBLFq9zNjd90X0lnIlwXR68QQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBE5VgROJnlPwCjU8e8lFf3D9DBs8Sfr0vEYWLlgjf7z/Wnn9rb9I3boVCwmezO0mbEwylXKXSkRkmDz97O9dgVo9pl7Pk/+6Q9p3OFE50/1cGopdu3arXHrZ2baarVOdUbdp1KiBvPPeI/b1zTe+ktx9B9x3te+bNw+VV/77IIHaYjIs0EEvWjH0wYd+K+eeO8R8P5vbKpQ6kOett76Qvz7xih1EpIOJKtI0GOiEA9331+M1btzILkpNyXBfVWXvnYFRWsVgzepNthqp+8namWvVYGCwNx2opFUhNGSslVGbNGlkw6qzZi2xlTH+7+m3bcXXk3HyN6hKKxhr6927i89/rzTsqgO2tHl/b3TwnK/ArG7byAxI05aS6vu71rFjbLEwtQ5i07Cutl5uA67sAvOfMDPgTpuvf3PtihL+08Gcz1eLbhNlF+8ywVX3phXN+5lgr3ug1lmv/xugLTUA9+YEd7USsK+m16KB9ePmF1wHtNEQQAABBBBAAAEEEEAAAQQQQAABBMT2MemEcNrnphOraR+X05wJ4fSz0/firNu8Kcm+HTQ43llkX7WPSCek02NqoLay28lMiKeVZnUSRZ0Qz2kauFy0aJXtZ/z1DZe6ArW6Xid0/MUl59iw6MGD+ZKTs8/ZTX4yE0EWFByS4cP7S3z8iUCtbqD9dhMnXmT79KZP811ZVvv3tJKvr0kjdTI9nVzxGhMcdfqN9bj6bIYO7St9+3bXj+VqFb3Pad/Ntee54oqxHoFaXagh0XPPGyIaPp45Y5HP6/E3qZ4TePU3YaAezN+EgRpG1on1nKZ/wxo2/Ezno51w0Dm+LoyIaC5OJd10E2YNVNPA7cGDBfLLX57rEajV82t416lQ+8P38z0uqWqef449R1kCtR4X4+NDi4hmdqlT+dbHJixCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEKgGgcAnUstxk5s37fC5dd6Bg6YC4lozEKC7rVbrc6MqXLhm9RZ79EvGjXBV73Q/XePGDczgkRHy/LMfuy+271et3GxfJ1w5ptg6XaDhrj59u8iC+atN2G2nucduHtsNGdrbDBwpnDHcYwUfECgS0EDiBWOH2x+d5X/d2s12BnetCqqz66enZ8u4caMq5KUDfrZs3iFaWUBnh8/O3mtDmgfM76Q2rawQiBYZGW4HAi1ftl7eNrPX6+deJrwZFxcrnTq19Tm4KhDXdaqeQ0PG+nN8wgWya+ceWW2CyCtWrLeVSl979WMzcG98hQftNSwKgnrfuzNwUEPd+/fnea+2n/X7qS0trXhoU8PaSUnJtgKyhm6zzHctzYQ7s7L22n2OHfX9XdOBZyW1ur7C1kWjHzVAWt7mb2BVPVMBV9uhgsM+D6m/P1pxOcVU5NV7ysjMNu8Lg8JaDd1Xq8x722m+B9r0+axaudHX6SQ3t3BiB30++v2hIYAAAggggAACCCCAAAIIIIAAAgiIraaqfbC+mr8J4cKaNxVJFFuNVftZdDI8pzVrVjjhm/O5Ml9PdkI87b/SvjlnAjynz6+HmbhOK8N6N53o8I47r/FebELGKXbZ6DGDi63TBXGmT1dDvImJu+wEb+4VXp31viap0341DfC2bhNpj+Hr4APP6m36Qjf4WuV3WUXuUysR792bKy1bRtiqur4OPmrUWaKhUKdvznubkiYM1D66ikwYGBfX1vs0djI9XahhWveKsM6GziSEek+BamoSElLb9Pv38HnKwn7/EPv90G5c7dKtqufv9PmWFoQdN36kz2t1X5iZUdif7b6M9wgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC1S9wSodqP/joCenbr6tLqeDQYVOVcotM+3ahvP/eNFmyeL289+HjMmhwT9c2gXizZk1hqLZL1+KDEZzzd+niu3rhqpWb7CYff/SDTP2qcNZyZx/nNS0t277dumVXsVBts2ZNnM14RaBUAZ3lf8TIAfZn+fL18ukn02TO7J+lZ49OfgcZ+TuoVgmYM2ep5OcXuDbRQVJtTEXO8PCjZiBQ4cAo18oqfnP11RdLm9ZRtvpuenqWqR69yP40aFDfVokYe+EIqtV6PQMdaBRrqo/qjwavtXLCjz8uMv+efi1P/PWuSq387VSS1YFXWr2ipJadfaJqhW6n1YenTJlpQtsnluugOa1IoIO8Nm7cVtLhTul1WvX1ww+/8ahWos8lLKyp9OnTrdwD/Cp6sykphUHmFebfhdKa+3MobVvWI4AAAggggAACCCCAAAIIIIAAAsEgUN4J4UaPGiRr12wyE2kmyROPvyw9enaSzp3biYZetTJoVbXKnhBvx47CidqiTZ9wWZtWH83IyLLVbZs0aeR3Nw3SaqhWw5XeodpGDRv43E8r6Wpr1SrS5/rCdRF+1/lbUZH7dPZp3dr/+bTSrj5vnXBPA6He4ezKnFTPubeSjqkhVv3xbrXEdFia5nvqP++tT/6z/s1B+/j192rylzP8HlCr7Or3SSf7VLuqev56ARqs1VCt/jghW78XVsIKJ5jbrVv7ErZiFQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAoAVO6VCtDvhoEnpikIXGSc8+50z7Ex0TJf/6x7vyyF9elekzng+o27atyfZ8MeYa/DW9Pl9t06addvGUL2f7Wu2xLDk53eMzHxA4GYF+/c6QrVt22IDj5i1J5QrVTp8+T743M+jroJ+zzxkoXbt2sAObQkOb2BnhdaBLVYVq/VW/rV27lpxjZvbXH62cm7htl2wwYcuNG7ba8O8eUwH0t7dcZa/vZNxO1311sNRFF4+U1WsSJMNUL04yVSc6mYF8ldUizeCwBFN+47zzhkr/ASVPfKCDoZy2adN2eeedyWYA1XEbMo2P7ybRMS2luanmocHaNWbwYU0N1WrF3hdfeF+0srMGm4cM7mOrQoSb8LsG1LWqRnmrZjhu5X2NiGxuq+TeetvV1rak/Rv5qUZc0j6sQwABBBBAAAEEEEAAAQQQQAABBE5XgYpMCKeVVH9/9/XyzdRZkpCQKKtXJdgfNYqNbSW/uGSUxMX5nqizKhwrOiFeqqmWqk0ncyxr0wqrGpbUPrCSmnNMnZSurC3d9Gtq075Df037sLVf0V8/s6/9KnSfRdcdHl7yfaqDhmrTzPbeoVpf1xIMy/SZ63dEW2kTNOo2OWYyRrWrquev53BawsYkiRhW8jN1tvV+3bhxu/ciPiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIInCICJ5JMp8gFlfUybr5lvDz7fx+ZAN122Z+b5xG+LekYR48cLWl1mdbFdYox1T5XmBBa4azsvnZKSvJdtbN9h9aif4T/+LO/S0mhXD1mszCq0vqyZVlxgUULV8peUxG0R4/O5nvVsvgGRUucqgf79h3wu42vFatWJtjF1/3qEunZs3OxTQ7kHSy2rCwLtKqstnwzu7yvlpmZYwOIvta5L9NqufozdFg/0YoAzz37rq36kJycYjxauW8aFO+PHj0qM2csMpUEjsvw4f2lpFCkfic0VFve70RpkG3btjGbLJc8891wBsSVto+u14odGqgdMaK/jBs/utgueSaQWlPb1q077PdZv5N33XWdCXwXVnxw7kfDtoFqbU2oN8UEz/X5dOrkv+p6oK6H8yCAAAIIIIAAAggggAACCCCAAAI1QeBkJoTT6qs33nS5rbS5bdtOO0ngsmXr7GSFL7/0odx661XlmgjxZLwqOiFeZGS4+ftGoqneWRhmLcs16D7ask1l1pKaVm7VFlW0fUnbOuucbTNNNVF/LSdnX7kCtXqcCt1nVNF9miqqJTXHIbJo+5K2DZZ1zt8tNPz8wIOTSr3tZs0K/3ZWVc9fL6Brt3aSMa+wUm1Fq9Xq3wKdY9k3/AcBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBA4JQRqLGh2vr160mHjm1sqHbnzjTpfkZ7i9o0tLF99RcQW7li00nj9+oVZ4+RkLDD77E2b/K9rk/fLjZUm5OTK0OG9va7PysQKI9Ahgmf/vTjYjsA6yYzMMtfW726MBzbpUt7n5sU5PsOt+7dm2u316oJ3u3YseOyZbPv77uTGdSQZEHBIdHfW/fWtGkTCTW/s7m5B8zs/JkSFdXCfbWsW7vZ47PzITU1w1QrTZR27dpI+/bRzmL7qmFBDdgmJ6eVKZDrsfNp8iEkJEQ2mIq9GjCuX6+erS7s69YOmjDzls1Jtppv5y6eVWqduGd+ge/vhK/juS+LbVv4XdmwYZuMNd8rJ0Dtvs3aNZvl2PFjot9HZ73+26gtNra1+6au9wkJ213va9qbvUX3ppV3vQO1ei+BvDf9XV6yeLWsXLFRevfu6pNy3rzltiJ1hw4xopWNaQgggAACCCCAAAIIIIAAAggggECwC1TGhHANG9Y3kyN2sj8XjB0u7733lWj121mzlgQsVFvRCfHaFvXZ7d6dVuavgk74Zyf2M0FcnVSuceOGPvdNSUm3y7V/t6xN+9m0paRk+N1F+5LL2ypyn8517ynhWnQyRK2uqgblmYiwvNdf07Z3PHSSTe0n9vcd8b6vqnr+ep6IiDAbrNVgbEWq1bpXqe3Wrb3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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/github_3.png\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f69ea94e",
+ "metadata": {},
+ "source": [
+ "Sync your data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b176a01e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/airbyte_9.png\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "768c7b3c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<IPython.core.display.Image object>"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(filename=\"img/airbyte_8.png\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "58e0d239",
+ "metadata": {},
+ "source": [
+ "### Snowflake-SQLAlchemy version fix"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "b937785b",
+ "metadata": {},
+ "source": [
+ "Hack to make snowflake-sqlalchemy work despite incompatible sqlalchemy versions\n",
+ "\n",
+ "Taken from https://github.com/snowflakedb/snowflake-sqlalchemy/issues/380#issuecomment-1470762025"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "6559dbbe",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/hongyishi/Documents/GitHub/gpt_index/.venv/lib/python3.9/site-packages/snowflake/connector/options.py:108: UserWarning: You have an incompatible version of 'pyarrow' installed (12.0.1), please install a version that adheres to: 'pyarrow<10.1.0,>=10.0.1; extra == \"pandas\"'\n",
+ " warn_incompatible_dep(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Hack to make snowflake-sqlalchemy work until they patch it\n",
+ "\n",
+ "\n",
+ "def snowflake_sqlalchemy_20_monkey_patches():\n",
+ " import sqlalchemy.util.compat\n",
+ "\n",
+ " # make strings always return unicode strings\n",
+ " sqlalchemy.util.compat.string_types = (str,)\n",
+ " sqlalchemy.types.String.RETURNS_UNICODE = True\n",
+ "\n",
+ " import snowflake.sqlalchemy.snowdialect\n",
+ "\n",
+ " snowflake.sqlalchemy.snowdialect.SnowflakeDialect.returns_unicode_strings = True\n",
+ "\n",
+ " # make has_table() support the `info_cache` kwarg\n",
+ " import snowflake.sqlalchemy.snowdialect\n",
+ "\n",
+ " def has_table(self, connection, table_name, schema=None, info_cache=None):\n",
+ " \"\"\"\n",
+ " Checks if the table exists\n",
+ " \"\"\"\n",
+ " return self._has_object(connection, \"TABLE\", table_name, schema)\n",
+ "\n",
+ " snowflake.sqlalchemy.snowdialect.SnowflakeDialect.has_table = has_table\n",
+ "\n",
+ "\n",
+ "# usage: call this function before creating an engine:\n",
+ "try:\n",
+ " snowflake_sqlalchemy_20_monkey_patches()\n",
+ "except Exception as e:\n",
+ " raise ValueError(\"Please run `pip install snowflake-sqlalchemy`\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "461438c8-302d-45c5-8e69-16ad604686d1",
+ "metadata": {},
+ "source": [
+ "### Define database\n",
+ "\n",
+ "We pass the Snowflake uri to the SQL db constructor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "b4154b29-7e23-4c26-a507-370a66186ae7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "snowflake_uri = \"snowflake://<user_login_name>:<password>@<account_identifier>/<database_name>/<schema_name>?warehouse=<warehouse_name>&role=<role_name>\""
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7ac38b7c",
+ "metadata": {},
+ "source": [
+ "First we try connecting with sqlalchemy to check the db works."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "f06e0ba4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(False, 'test case', '[]', datetime.datetime(2022, 7, 18, 16, 59, 13, tzinfo=<UTC>), 'test to', None, None, 'question', '{\\n \"channel\": \"web\",\\n \"source\": {\\n \"from\": {},\\n \"rel\": null,\\n \"to\": {}\\n }\\n}', True, datetime.datetime(2022, 7, 18, 18, 1, 37, tzinfo=<UTC>), None, '[]', None, 134, None, 1658167297, 'test case', None, '[]', False, '{\\n \"score\": \"offered\"\\n}', 360786799676, 'low', '[]', 'https://d3v-airbyte.zendesk.com/api/v2/tickets/134.json', '[]', 360000358316, 360000084116, '[]', None, '[]', 360033549136, True, None, False, 'new', 360786799676, 'abd39a87-b1f9-4390-bf8b-cf3c288b1f74', datetime.datetime(2023, 6, 9, 0, 25, 23, 501000, tzinfo=pytz.FixedOffset(-420)), datetime.datetime(2023, 6, 9, 0, 38, 20, 440000, tzinfo=<UTC>), '6577ef036668746df889983970579a55', '02522a2b2726fb0a03bb19f2d8d9524d')\n",
+ "RMKeyView(['from_messaging_channel', 'subject', 'email_cc_ids', 'created_at', 'description', 'custom_status_id', 'external_id', 'type', 'via', 'allow_attachments', 'updated_at', 'problem_id', 'follower_ids', 'due_at', 'id', 'assignee_id', 'generated_timestamp', 'raw_subject', 'forum_topic_id', 'custom_fields', 'allow_channelback', 'satisfaction_rating', 'submitter_id', 'priority', 'collaborator_ids', 'url', 'tags', 'brand_id', 'ticket_form_id', 'sharing_agreement_ids', 'group_id', 'followup_ids', 'organization_id', 'is_public', 'recipient', 'has_incidents', 'status', 'requester_id', '_airbyte_ab_id', '_airbyte_emitted_at', '_airbyte_normalized_at', '_airbyte_zendesk_tickets_hashid', '_airbyte_unique_key'])\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sqlalchemy import select, create_engine, MetaData, Table\n",
+ "\n",
+ "# view current table\n",
+ "engine = create_engine(snowflake_uri)\n",
+ "metadata = MetaData(bind=None)\n",
+ "table = Table(\"ZENDESK_TICKETS\", metadata, autoload=True, autoload_with=engine)\n",
+ "stmt = select(table.columns)\n",
+ "\n",
+ "\n",
+ "with engine.connect() as connection:\n",
+ " results = connection.execute(stmt).fetchone()\n",
+ " print(results)\n",
+ " print(results.keys())"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1c09089a-6bcd-48db-8120-a84c8da3f82e",
"metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.6"
- }
+ "tags": []
+ },
+ "source": [
+ "### Define SQL DB\n",
+ "\n",
+ "Once we have defined the SQLDatabase, we can wrap it in a query engine to query it.\n",
+ "If we know what tables we want to use we can use `NLSQLTableQueryEngine`.\n",
+ "This will generate a SQL query on the specified tables."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3869e15e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import SQLDatabase\n",
+ "\n",
+ "# You can specify table filters during engine creation.\n",
+ "# sql_database = SQLDatabase(engine, include_tables=[\"github_issues\",\"github_comments\", \"github_users\"])\n",
+ "\n",
+ "sql_database = SQLDatabase(engine)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "051a171f-8c97-40ed-ae17-4e3fa3785487",
+ "metadata": {},
+ "source": [
+ "### Synthesize Query"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "abff69de-80c4-4fe6-afa1-b3d7208a5c4c",
+ "metadata": {},
+ "source": [
+ "We then show a natural language query, which is translated to a SQL query under the hood with our text-to-SQL prompt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d71045c0-7a96-4e86-b38c-c378b7759aa4",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The top 10 issues with the most comments, based on a join on url, are: 'Proof of concept parallel source stream reading implementation for MySQL', 'Remove noisy logging for `LegacyStateManager`', 'Track stream status in source', 'Source Google Analytics v4: - add pk and lookback window', 'Connector Health: Fixed SAT for marketo, close, chargebee, facebook marketing, paystack, hubspot, pipedrive and marketo', '📝 Update outdated docs urls in metadata files', 'Fix emitted intermediate state for initial incremental non-CDC syncs', 'source-postgres : Add logic to handle xmin wraparound', ':bug: Source HubSpot: fix cast string as boolean using string comparison', and 'Fix db-lib JdbcUtils.java to accept JDBC parameters with = sign.'.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from llama_index.indices.struct_store.sql_query import NLSQLTableQueryEngine\n",
+ "from IPython.display import Markdown, display\n",
+ "\n",
+ "query_engine = NLSQLTableQueryEngine(\n",
+ " sql_database=sql_database,\n",
+ " tables=[\"github_issues\", \"github_comments\", \"github_users\"],\n",
+ ")\n",
+ "query_str = (\n",
+ " \"Which issues have the most comments? Give the top 10 and use a join on url.\"\n",
+ ")\n",
+ "response = query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "431e684e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>[('Proof of concept parallel source stream reading implementation for MySQL', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 104), ('Remove noisy logging for `LegacyStateManager`', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 39), ('Track stream status in source', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 35), ('Source Google Analytics v4: - add pk and lookback window', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 29), ('Connector Health: Fixed SAT for marketo, close, chargebee, facebook marketing, paystack, hubspot, pipedrive and marketo', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 28), ('📝 Update outdated docs urls in metadata files', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 26), ('Fix emitted intermediate state for initial incremental non-CDC syncs', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 25), ('source-postgres : Add logic to handle xmin wraparound', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 24), (':bug: Source HubSpot: fix cast string as boolean using string comparison', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 24), ('Fix db-lib JdbcUtils.java to accept JDBC parameters with = sign.', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 22)]</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# You can also get only the SQL query result.\n",
+ "\n",
+ "query_engine = NLSQLTableQueryEngine(\n",
+ " sql_database=sql_database,\n",
+ " synthesize_response=False,\n",
+ " tables=[\"github_issues\", \"github_comments\", \"github_users\"],\n",
+ ")\n",
+ "response = query_engine.query(query_str)\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c79eeef5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>SELECT gi.title, gi.url, gc.issue_url, COUNT(gc.id) AS comment_count \n",
+ "FROM github_issues gi \n",
+ "JOIN github_comments gc ON gi.url = gc.issue_url \n",
+ "GROUP BY gi.title, gi.url, gc.issue_url \n",
+ "ORDER BY comment_count DESC \n",
+ "LIMIT 10;</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# You can also get the original SQL query\n",
+ "sql_query = response.metadata[\"sql_query\"]\n",
+ "display(Markdown(f\"<b>{sql_query}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7607cd6a",
+ "metadata": {},
+ "source": [
+ "We can also use LLM prediction to figure out what tables to use."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8c418f13",
+ "metadata": {},
+ "source": [
+ "We first need to create an ObjectIndex of SQLTableSchema. In this case we only pass in the table names.\n",
+ "The query engine will fetch the relevant table schema at query time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "cf1f4b04",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>[('Proof of concept parallel source stream reading implementation for MySQL', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 'https://api.github.com/repos/airbytehq/airbyte/issues/26580', 104), ('Remove noisy logging for `LegacyStateManager`', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 'https://api.github.com/repos/airbytehq/airbyte/issues/27335', 39), ('Track stream status in source', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 'https://api.github.com/repos/airbytehq/airbyte/issues/24971', 35), ('Source Google Analytics v4: - add pk and lookback window', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 'https://api.github.com/repos/airbytehq/airbyte/issues/26283', 29), ('Connector Health: Fixed SAT for marketo, close, chargebee, facebook marketing, paystack, hubspot, pipedrive and marketo', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 'https://api.github.com/repos/airbytehq/airbyte/issues/24802', 28), ('📝 Update outdated docs urls in metadata files', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 'https://api.github.com/repos/airbytehq/airbyte/issues/27420', 26), ('Fix emitted intermediate state for initial incremental non-CDC syncs', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 'https://api.github.com/repos/airbytehq/airbyte/issues/24820', 25), ('source-postgres : Add logic to handle xmin wraparound', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 'https://api.github.com/repos/airbytehq/airbyte/issues/27384', 24), (':bug: Source HubSpot: fix cast string as boolean using string comparison', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 'https://api.github.com/repos/airbytehq/airbyte/issues/26082', 24), ('Fix db-lib JdbcUtils.java to accept JDBC parameters with = sign.', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 'https://api.github.com/repos/airbytehq/airbyte/issues/25386', 22)]</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from llama_index.indices.struct_store.sql_query import SQLTableRetrieverQueryEngine\n",
+ "from llama_index.objects import SQLTableNodeMapping, ObjectIndex, SQLTableSchema\n",
+ "from llama_index import VectorStoreIndex\n",
+ "\n",
+ "table_node_mapping = SQLTableNodeMapping(sql_database)\n",
+ "all_table_names = sql_database.get_table_names()\n",
+ "table_schema_objs = []\n",
+ "for table_name in all_table_names:\n",
+ " table_schema_objs.append(SQLTableSchema(table_name=table_name))\n",
+ "\n",
+ "obj_index = ObjectIndex.from_objects(\n",
+ " table_schema_objs,\n",
+ " table_node_mapping,\n",
+ " VectorStoreIndex,\n",
+ ")\n",
+ "table_retriever_query_engine = SQLTableRetrieverQueryEngine(\n",
+ " sql_database, obj_index.as_retriever(similarity_top_k=1)\n",
+ ")\n",
+ "response = query_engine.query(query_str)\n",
+ "\n",
+ "display(Markdown(f\"<b>{response}</b>\"))\n",
+ "sql_query = response.extra_info[\"sql_query\"]\n",
+ "display(Markdown(f\"<b>{sql_query}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/docs/how_to/index/index_progress_bars.ipynb b/docs/how_to/index/index_progress_bars.ipynb
index edbd74af6b..7184b8ad1c 100644
--- a/docs/how_to/index/index_progress_bars.ipynb
+++ b/docs/how_to/index/index_progress_bars.ipynb
@@ -60,7 +60,7 @@
"outputs": [],
"source": [
"# Set environment variable\n",
- "os.environ['OPENAI_API_KEY'] = '<OPEN AI KEY>'\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"<OPEN AI KEY>\"\n",
"openai.api_key = os.getenv(\"OPENAI_API_KEY\")"
]
},
@@ -457,7 +457,7 @@
"TreeIndex.from_documents(documents, service_context=service_context, use_async=True)\n",
"\n",
"print(\"\\nTreeIndex with show_progress=False, use_async=False\\n\")\n",
- "TreeIndex.from_documents(documents, service_context=service_context)\n"
+ "TreeIndex.from_documents(documents, service_context=service_context)"
]
}
],
diff --git a/docs/how_to/index/vector_store_guide.ipynb b/docs/how_to/index/vector_store_guide.ipynb
index 2c6f371315..8a08e73841 100644
--- a/docs/how_to/index/vector_store_guide.ipynb
+++ b/docs/how_to/index/vector_store_guide.ipynb
@@ -36,7 +36,7 @@
"from llama_index import VectorStoreIndex, SimpleDirectoryReader\n",
"\n",
"# Load documents and build index\n",
- "documents = SimpleDirectoryReader('../../examples/data/paul_graham').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../../examples/data/paul_graham\").load_data()\n",
"index = VectorStoreIndex.from_documents(documents)"
]
},
@@ -67,11 +67,11 @@
"\n",
"# construct vector store and customize storage context\n",
"storage_context = StorageContext.from_defaults(\n",
- " vector_store = PineconeVectorStore(pinecone.Index(\"quickstart\"))\n",
+ " vector_store=PineconeVectorStore(pinecone.Index(\"quickstart\"))\n",
")\n",
"\n",
"# Load documents and build index\n",
- "documents = SimpleDirectoryReader('../../examples/data/paul_graham').load_data()\n",
+ "documents = SimpleDirectoryReader(\"../../examples/data/paul_graham\").load_data()\n",
"index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
]
},
@@ -156,9 +156,11 @@
"query_engine = index.as_query_engine(\n",
" similarity_top_k=3,\n",
" vector_store_query_mode=\"default\",\n",
- " filters=MetadataFilters(filters=[\n",
- " ExactMatchFilter(key='name', value='paul graham'),\n",
- " ]),\n",
+ " filters=MetadataFilters(\n",
+ " filters=[\n",
+ " ExactMatchFilter(key=\"name\", value=\"paul graham\"),\n",
+ " ]\n",
+ " ),\n",
" alpha=None,\n",
" doc_ids=None,\n",
")\n",
@@ -196,17 +198,14 @@
" index=index,\n",
" similarity_top_k=3,\n",
" vector_store_query_mode=\"default\",\n",
- " filters=[\n",
- " ExactMatchFilter(key='name', value='paul graham')\n",
- " ],\n",
+ " filters=[ExactMatchFilter(key=\"name\", value=\"paul graham\")],\n",
" alpha=None,\n",
" doc_ids=None,\n",
")\n",
"\n",
"# build query engine\n",
"query_engine = RetrieverQueryEngine(\n",
- " retriever=retriever,\n",
- " response_synthesizer=get_response_synthesizer()\n",
+ " retriever=retriever, response_synthesizer=get_response_synthesizer()\n",
")\n",
"\n",
"# query\n",
@@ -233,7 +232,7 @@
" similarity_top_k=3,\n",
" # only works for pinecone\n",
" vector_store_kwargs={\n",
- " \"filter\": {'name': 'paul graham'},\n",
+ " \"filter\": {\"name\": \"paul graham\"},\n",
" },\n",
")\n",
"response = query_engine.query(\"what did the author do growing up?\")"
@@ -263,14 +262,19 @@
"\n",
"\n",
"vector_store_info = VectorStoreInfo(\n",
- " content_info='brief biography of celebrities',\n",
+ " content_info=\"brief biography of celebrities\",\n",
" metadata_info=[\n",
" MetadataInfo(\n",
- " name='category', \n",
- " type='str', \n",
- " description='Category of the celebrity, one of [Sports, Entertainment, Business, Music]'),\n",
- " MetadataInfo(name='country', type='str', description='Country of the celebrity, one of [United States, Barbados, Portugal]'),\n",
- " ]\n",
+ " name=\"category\",\n",
+ " type=\"str\",\n",
+ " description=\"Category of the celebrity, one of [Sports, Entertainment, Business, Music]\",\n",
+ " ),\n",
+ " MetadataInfo(\n",
+ " name=\"country\",\n",
+ " type=\"str\",\n",
+ " description=\"Country of the celebrity, one of [United States, Barbados, Portugal]\",\n",
+ " ),\n",
+ " ],\n",
")\n",
"\n",
"# build retriever\n",
@@ -278,8 +282,7 @@
"\n",
"# build query engine\n",
"query_engine = RetrieverQueryEngine(\n",
- " retriever=retriever,\n",
- " response_synthesizer=get_response_synthesizer()\n",
+ " retriever=retriever, response_synthesizer=get_response_synthesizer()\n",
")\n",
"\n",
"# query\n",
diff --git a/examples/async/AsyncComposableIndicesSEC.ipynb b/examples/async/AsyncComposableIndicesSEC.ipynb
index 21551454af..96b03f40ec 100644
--- a/examples/async/AsyncComposableIndicesSEC.ipynb
+++ b/examples/async/AsyncComposableIndicesSEC.ipynb
@@ -1,1679 +1,1681 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "YC4R6nkCp91d",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "YC4R6nkCp91d",
- "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# download files\n",
- "!mkdir data\n",
- "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
- "!unzip data/UBER.zip -d data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c1df01cb-ad50-44b8-9f36-5a5788b498df",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
- "metadata": {
- "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
- },
- "outputs": [],
- "source": [
- "from llama_index import download_loader, VectorStoreIndex\n",
- "from pathlib import Path"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
- "metadata": {
- "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
- "tags": []
- },
- "source": [
- "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
- "\n",
- "Leverage the capabilities of Unstructured.io HTML parsing.\n",
- "Downloaded through LlamaHub."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "years = [2022, 2021, 2020, 2019]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
- "metadata": {
- "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
- },
- "outputs": [],
- "source": [
- "UnstructuredReader = download_loader(\"UnstructuredReader\", refresh_cache=True, use_gpt_index_import=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "29084a11-f3da-428d-ae17-27b85a365e84",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "29084a11-f3da-428d-ae17-27b85a365e84",
- "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
- },
- "outputs": [],
- "source": [
- "loader = UnstructuredReader()\n",
- "doc_set = {}\n",
- "all_docs = []\n",
- "for year in years:\n",
- " year_docs = loader.load_data(file=Path(f'./data/UBER/UBER_{year}.html'), split_documents=False)\n",
- " # insert year metadata into each year\n",
- " for d in year_docs:\n",
- " d.metadata = {\"year\": year}\n",
- " doc_set[year] = year_docs\n",
- " all_docs.extend(year_docs)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
- "metadata": {
- "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
- },
- "source": [
- "### Setup a Vector Index for each SEC filing\n",
- "\n",
- "We setup a separate vector index for each SEC filing from 2019-2022.\n",
- "\n",
- "We also optionally initialize a \"global\" index by dumping all files into the vector store."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
- "metadata": {
- "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
- "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.service_context import ServiceContext\n",
- "\n",
- "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
- "# initialize simple vector indices + global vector index\n",
- "# NOTE: don't run this cell if the indices are already loaded! \n",
- "index_set = {}\n",
- "for year in years:\n",
- " cur_index = VectorStoreIndex.from_documents(doc_set[year], service_context=service_context)\n",
- " index_set[year] = cur_index\n",
- " "
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
- "metadata": {
- "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
- },
- "source": [
- "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
- "\n",
- "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a List index\n",
- "on top of the 4 vector indices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
- "metadata": {
- "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
- },
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, LLMPredictor\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
- "metadata": {
- "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
- },
- "outputs": [],
- "source": [
- "# set summary text for each doc\n",
- "index_summaries = {}\n",
- "for year in years:\n",
- " index_summaries[year] = f\"UBER 10-k Filing for {year} fiscal year\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
- "metadata": {
- "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
- },
- "outputs": [],
- "source": [
- "# set number of output tokens\n",
- "llm = OpenAI(temperature=0, max_tokens=512)\n",
- "service_context = ServiceContext.from_defaults(llm=llm)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
- "metadata": {
- "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
- "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
- },
- "outputs": [],
- "source": [
- "# define a list index over the vector indices\n",
- "# allows us to synthesize information across each index\n",
- "graph = ComposableGraph.from_indices(\n",
- " ListIndex,\n",
- " children_indices=[index_set[y] for y in years],\n",
- " index_summaries=index_summaries,\n",
- " service_context=service_context\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "30803f70",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_engine = graph.as_query_engine(\n",
- " response_mode='tree_summarize'\n",
- ")"
- ]
- },
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- "cell_type": "code",
- "execution_count": null,
- "id": "2f23ba33-416f-433a-9e83-5a37ed060344",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import asyncio\n",
- "import time\n",
- "\n",
- "cross_query_str = (\n",
- " \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
- ")\n",
- "\n",
- "start_time = time.perf_counter()\n",
- "task = query_engine.aquery(cross_query_str)\n",
- "response = asyncio.run(task)\n",
- "elapsed_time = time.perf_counter() - start_time"
- ]
- },
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- "cell_type": "code",
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- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "print(str(response))\n",
- "print(str(elapsed_time))"
- ]
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+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "YC4R6nkCp91d",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "id": "YC4R6nkCp91d",
+ "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# download files\n",
+ "!mkdir data\n",
+ "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
+ "!unzip data/UBER.zip -d data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c1df01cb-ad50-44b8-9f36-5a5788b498df",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
+ "metadata": {
+ "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import download_loader, VectorStoreIndex\n",
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
+ "metadata": {
+ "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
+ "tags": []
+ },
+ "source": [
+ "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
+ "\n",
+ "Leverage the capabilities of Unstructured.io HTML parsing.\n",
+ "Downloaded through LlamaHub."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "years = [2022, 2021, 2020, 2019]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
+ "metadata": {
+ "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
+ },
+ "outputs": [],
+ "source": [
+ "UnstructuredReader = download_loader(\n",
+ " \"UnstructuredReader\", refresh_cache=True, use_gpt_index_import=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29084a11-f3da-428d-ae17-27b85a365e84",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "29084a11-f3da-428d-ae17-27b85a365e84",
+ "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
+ },
+ "outputs": [],
+ "source": [
+ "loader = UnstructuredReader()\n",
+ "doc_set = {}\n",
+ "all_docs = []\n",
+ "for year in years:\n",
+ " year_docs = loader.load_data(\n",
+ " file=Path(f\"./data/UBER/UBER_{year}.html\"), split_documents=False\n",
+ " )\n",
+ " # insert year metadata into each year\n",
+ " for d in year_docs:\n",
+ " d.metadata = {\"year\": year}\n",
+ " doc_set[year] = year_docs\n",
+ " all_docs.extend(year_docs)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
+ "metadata": {
+ "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
+ },
+ "source": [
+ "### Setup a Vector Index for each SEC filing\n",
+ "\n",
+ "We setup a separate vector index for each SEC filing from 2019-2022.\n",
+ "\n",
+ "We also optionally initialize a \"global\" index by dumping all files into the vector store."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
+ "metadata": {
+ "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
+ "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.service_context import ServiceContext\n",
+ "\n",
+ "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
+ "# initialize simple vector indices + global vector index\n",
+ "# NOTE: don't run this cell if the indices are already loaded!\n",
+ "index_set = {}\n",
+ "for year in years:\n",
+ " cur_index = VectorStoreIndex.from_documents(\n",
+ " doc_set[year], service_context=service_context\n",
+ " )\n",
+ " index_set[year] = cur_index"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
+ "metadata": {
+ "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
+ },
+ "source": [
+ "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
+ "\n",
+ "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a List index\n",
+ "on top of the 4 vector indices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
+ "metadata": {
+ "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, LLMPredictor\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
+ "metadata": {
+ "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
+ },
+ "outputs": [],
+ "source": [
+ "# set summary text for each doc\n",
+ "index_summaries = {}\n",
+ "for year in years:\n",
+ " index_summaries[year] = f\"UBER 10-k Filing for {year} fiscal year\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
+ "metadata": {
+ "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
+ },
+ "outputs": [],
+ "source": [
+ "# set number of output tokens\n",
+ "llm = OpenAI(temperature=0, max_tokens=512)\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
+ "metadata": {
+ "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
+ "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
+ },
+ "outputs": [],
+ "source": [
+ "# define a list index over the vector indices\n",
+ "# allows us to synthesize information across each index\n",
+ "graph = ComposableGraph.from_indices(\n",
+ " ListIndex,\n",
+ " children_indices=[index_set[y] for y in years],\n",
+ " index_summaries=index_summaries,\n",
+ " service_context=service_context,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30803f70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = graph.as_query_engine(response_mode=\"tree_summarize\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f23ba33-416f-433a-9e83-5a37ed060344",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import asyncio\n",
+ "import time\n",
+ "\n",
+ "cross_query_str = \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
+ "\n",
+ "start_time = time.perf_counter()\n",
+ "task = query_engine.aquery(cross_query_str)\n",
+ "response = asyncio.run(task)\n",
+ "elapsed_time = time.perf_counter() - start_time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "716c9452-ef9f-4105-a4ad-5d72ad42f94e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "print(str(response))\n",
+ "print(str(elapsed_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38842da1",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
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diff --git a/examples/async/AsyncGPTTreeIndexDemo.ipynb b/examples/async/AsyncGPTTreeIndexDemo.ipynb
index 195fd4acf6..493e8669c8 100644
--- a/examples/async/AsyncGPTTreeIndexDemo.ipynb
+++ b/examples/async/AsyncGPTTreeIndexDemo.ipynb
@@ -1,136 +1,137 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "96b2b1e4",
- "metadata": {},
- "source": [
- "# Async TreeIndex Demo"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9331cfeb",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
- "# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8a1d2821",
- "metadata": {},
- "outputs": [],
- "source": [
- "import time\n",
- "from llama_index import TreeIndex, SimpleDirectoryReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6948df36",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2d9115d1",
- "metadata": {},
- "source": [
- "#### By default, TreeIndex makes blocking LLM calls"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d9ef0fef",
- "metadata": {},
- "outputs": [],
- "source": [
- "start_time = time.perf_counter()\n",
- "index = TreeIndex.from_documents(documents)\n",
- "elapsed_time = time.perf_counter() - start_time"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "9392d573",
- "metadata": {},
- "source": [
- "It takes ~47s to finish building TreeIndex from 5 text chunks."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "474f82d1",
- "metadata": {},
- "source": [
- "#### Pass in `use_async=True` to enable asynchronous LLM calls"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "78a02987",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "start_time = time.perf_counter()\n",
- "index = TreeIndex.from_documents(documents, use_async=True)\n",
- "elapsed_time = time.perf_counter() - start_time"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "23469128",
- "metadata": {},
- "source": [
- "It takes ~12s to finish building the TreeIndex from 5 text chunks."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "59c1c27e",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
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+ "id": "96b2b1e4",
+ "metadata": {},
+ "source": [
+ "# Async TreeIndex Demo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9331cfeb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: This is ONLY necessary in jupyter notebook.\n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
+ "# This results in nested event-loops when we start an event-loop to make async queries.\n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8a1d2821",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "from llama_index import TreeIndex, SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6948df36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d9115d1",
+ "metadata": {},
+ "source": [
+ "#### By default, TreeIndex makes blocking LLM calls"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d9ef0fef",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "start_time = time.perf_counter()\n",
+ "index = TreeIndex.from_documents(documents)\n",
+ "elapsed_time = time.perf_counter() - start_time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9392d573",
+ "metadata": {},
+ "source": [
+ "It takes ~47s to finish building TreeIndex from 5 text chunks."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "474f82d1",
+ "metadata": {},
+ "source": [
+ "#### Pass in `use_async=True` to enable asynchronous LLM calls"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "78a02987",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "start_time = time.perf_counter()\n",
+ "index = TreeIndex.from_documents(documents, use_async=True)\n",
+ "elapsed_time = time.perf_counter() - start_time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "23469128",
+ "metadata": {},
+ "source": [
+ "It takes ~12s to finish building the TreeIndex from 5 text chunks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59c1c27e",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
\ No newline at end of file
diff --git a/examples/async/AsyncLLMPredictorDemo.ipynb b/examples/async/AsyncLLMPredictorDemo.ipynb
index 57b96e0ce5..ca3eeacfeb 100644
--- a/examples/async/AsyncLLMPredictorDemo.ipynb
+++ b/examples/async/AsyncLLMPredictorDemo.ipynb
@@ -29,9 +29,9 @@
"outputs": [],
"source": [
"context_strs = [\n",
- " 'Paul Graham',\n",
- " 'Steve Jobs',\n",
- " 'Barack Obama',\n",
+ " \"Paul Graham\",\n",
+ " \"Steve Jobs\",\n",
+ " \"Barack Obama\",\n",
"]\n",
"\n",
"prompt = DEFAULT_SUMMARY_PROMPT"
diff --git a/examples/async/AsyncQueryDemo.ipynb b/examples/async/AsyncQueryDemo.ipynb
index 005d817380..347b40f669 100644
--- a/examples/async/AsyncQueryDemo.ipynb
+++ b/examples/async/AsyncQueryDemo.ipynb
@@ -18,10 +18,11 @@
"outputs": [],
"source": [
"# NOTE: This is ONLY necessary in jupyter notebook.\n",
- "# Details: Jupyter runs an event-loop behind the scenes. \n",
+ "# Details: Jupyter runs an event-loop behind the scenes.\n",
"# This results in nested event-loops when we start an event-loop to make async queries.\n",
- "# This is normally not allowed, we use nest_asyncio to allow it for convenience. \n",
+ "# This is normally not allowed, we use nest_asyncio to allow it for convenience.\n",
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -60,7 +61,7 @@
"outputs": [],
"source": [
"# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
]
},
{
@@ -101,7 +102,7 @@
],
"source": [
"start_time = time.perf_counter()\n",
- "query_engine = index.as_query_engine(response_mode='tree_summarize')\n",
+ "query_engine = index.as_query_engine(response_mode=\"tree_summarize\")\n",
"query_engine.query(query_str)\n",
"elapsed_time = time.perf_counter() - start_time\n",
"\n",
@@ -144,6 +145,7 @@
],
"source": [
"import asyncio\n",
+ "\n",
"start_time = time.perf_counter()\n",
"task = query_engine.aquery(query_str)\n",
"asyncio.run(task)\n",
@@ -192,7 +194,7 @@
"source": [
"start_time = time.perf_counter()\n",
"query_engine = index.as_query_engine(\n",
- " response_mode='tree_summarize', \n",
+ " response_mode=\"tree_summarize\",\n",
" use_async=True,\n",
")\n",
"query_engine.query(query_str)\n",
@@ -234,11 +236,11 @@
],
"source": [
"# a list of different queries (yeah I cheated in this part)\n",
- "query_list = [query_str]*3\n",
+ "query_list = [query_str] * 3\n",
"\n",
"start_time = time.perf_counter()\n",
"query_engine = index.as_query_engine(\n",
- " response_mode='tree_summarize', \n",
+ " response_mode=\"tree_summarize\",\n",
" use_async=True,\n",
")\n",
"for q in query_list:\n",
@@ -273,7 +275,7 @@
"source": [
"start_time = time.perf_counter()\n",
"query_engine = index.as_query_engine(\n",
- " response_mode='tree_summarize',\n",
+ " response_mode=\"tree_summarize\",\n",
")\n",
"\n",
"# run each query in parallel\n",
@@ -282,6 +284,7 @@
" r = await asyncio.gather(*tasks)\n",
" return r\n",
"\n",
+ "\n",
"_ = asyncio.run(async_query(query_engine, query_list))\n",
"elapsed_time = time.perf_counter() - start_time\n",
"\n",
diff --git a/examples/chatbot/Chatbot_SEC.ipynb b/examples/chatbot/Chatbot_SEC.ipynb
index 3e518044ea..02dc7f536f 100644
--- a/examples/chatbot/Chatbot_SEC.ipynb
+++ b/examples/chatbot/Chatbot_SEC.ipynb
@@ -1,1868 +1,1871 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "YC4R6nkCp91d",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "YC4R6nkCp91d",
- "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
- "tags": []
- },
- "outputs": [],
- "source": [
- "# download files\n",
- "!mkdir data\n",
- "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
- "!unzip data/UBER.zip -d data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "CuHeyb224pI2",
- "metadata": {
- "id": "CuHeyb224pI2"
- },
- "outputs": [],
- "source": [
- "# set text wrapping\n",
- "from IPython.display import HTML, display\n",
- "\n",
- "def set_css():\n",
- " display(HTML('''\n",
- " <style>\n",
- " pre {\n",
- " white-space: pre-wrap;\n",
- " }\n",
- " </style>\n",
- " '''))\n",
- "get_ipython().events.register('pre_run_cell', set_css)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
- "metadata": {
- "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
- },
- "outputs": [],
- "source": [
- "from llama_index import download_loader, VectorStoreIndex, ServiceContext\n",
- "from pathlib import Path"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
- "metadata": {
- "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
- "tags": []
- },
- "source": [
- "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
- "\n",
- "Leverage the capabilities of Unstructured.io HTML parsing.\n",
- "Downloaded through LlamaHub."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "years = [2022, 2021, 2020, 2019]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
- "metadata": {
- "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
- },
- "outputs": [],
- "source": [
- "UnstructuredReader = download_loader(\"UnstructuredReader\", refresh_cache=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "29084a11-f3da-428d-ae17-27b85a365e84",
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "29084a11-f3da-428d-ae17-27b85a365e84",
- "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
- },
- "outputs": [],
- "source": [
- "loader = UnstructuredReader()\n",
- "doc_set = {}\n",
- "all_docs = []\n",
- "for year in years:\n",
- " year_docs = loader.load_data(file=Path(f'./data/UBER/UBER_{year}.html'), split_documents=False)\n",
- " # insert year metadata into each year\n",
- " for d in year_docs:\n",
- " d.metadata = {\"year\": year}\n",
- " doc_set[year] = year_docs\n",
- " all_docs.extend(year_docs)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
- "metadata": {
- "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
- },
- "source": [
- "### Setup a Vector Index for each SEC filing\n",
- "\n",
- "We setup a separate vector index for each SEC filing from 2019-2022.\n",
- "\n",
- "We also optionally initialize a \"global\" index by dumping all files into the vector store."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
- "metadata": {
- "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
- "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
- },
- "outputs": [],
- "source": [
- "# initialize simple vector indices + global vector index\n",
- "# NOTE: don't run this cell if the indices are already loaded! \n",
- "index_set = {}\n",
- "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
- "for year in years:\n",
- " cur_index = VectorStoreIndex.from_documents(doc_set[year], service_context=service_context)\n",
- " index_set[year] = cur_index\n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34",
- "metadata": {
- "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34"
- },
- "outputs": [],
- "source": [
- "# Load indices from disk\n",
- "index_set = {}\n",
- "for year in years:\n",
- " index_set[year] = cur_index"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
- "metadata": {
- "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
- },
- "source": [
- "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
- "\n",
- "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a List index\n",
- "on top of the 4 vector indices."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
- "metadata": {
- "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
- },
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, LLMPredictor\n",
- "from langchain import OpenAI\n",
- "from llama_index.indices.composability import ComposableGraph"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
- "metadata": {
- "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
- },
- "outputs": [],
- "source": [
- "# set summary text for each doc\n",
- "index_summaries = [f\"UBER 10-k Filing for {year} fiscal year\" for year in years]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
- "metadata": {
- "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
- },
- "outputs": [],
- "source": [
- "# set number of output tokens\n",
- "llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, max_tokens=512))\n",
- "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
- "metadata": {
- "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
- "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
- },
- "outputs": [],
- "source": [
- "# define a list index over the vector indices\n",
- "# allows us to synthesize information across each index\n",
- "graph = ComposableGraph.from_indices(\n",
- " ListIndex, \n",
- " [index_set[y] for y in years], \n",
- " index_summaries=index_summaries,\n",
- " service_context=service_context\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7e2edef1-005f-4733-b64c-5987efa4c88d",
- "metadata": {
- "id": "e46dd052-ab34-4974-b696-16ff217970e0"
- },
- "source": [
- "## Setting up the Chatbot Agent\n",
- "\n",
- "We use Langchain to define the outer chatbot abstraction. We use LlamaIndex as a core Tool within this abstraction."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6c2de0f3-9ec3-4f80-bd36-a509f8bfd4b8",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
- "from langchain.agents import initialize_agent\n",
- "\n",
- "from llama_index.langchain_helpers.agents import LlamaToolkit, create_llama_chat_agent, IndexToolConfig"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c9a6faa1-dc06-4d9c-98b4-06e7ee19c559",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# define a decompose transform\n",
- "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
- "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
- "decompose_transform = DecomposeQueryTransform(\n",
- " llm_predictor, verbose=True\n",
- ")\n",
- "\n",
- "# define custom query engines\n",
- "custom_query_engines = {}\n",
- "for index in index_set.values():\n",
- " query_engine = index.as_query_engine()\n",
- " query_engine = TransformQueryEngine(\n",
- " query_engine,\n",
- " query_transform=decompose_transform,\n",
- " transform_extra_info={'index_summary': index.index_struct.summary},\n",
- " )\n",
- " custom_query_engines[index.index_id] = query_engine\n",
- "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
- " response_mode='tree_summarize',\n",
- " verbose=True,\n",
- ")\n",
- "\n",
- "# construct query engine\n",
- "graph_query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e763ceba-1c54-4abb-9589-c0628d18c5e5",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# index configs\n",
- "index_configs = []\n",
- "for y in range(2019, 2023):\n",
- " query_engine = index_set[y].as_query_engine(\n",
- " similarity_top_k=3,\n",
- " )\n",
- " tool_config = IndexToolConfig(\n",
- " query_engine=query_engine, \n",
- " name=f\"Vector Index {y}\",\n",
- " description=f\"useful for when you want to answer queries about the {y} SEC 10-K for Uber\",\n",
- " tool_kwargs={\"return_direct\": True, \"return_sources\": True},\n",
- " )\n",
- " index_configs.append(tool_config)\n",
- " \n",
- "# graph config\n",
- "graph_config = IndexToolConfig(\n",
- " query_engine=graph_query_engine,\n",
- " name=f\"Graph Index\",\n",
- " description=\"useful for when you want to answer queries that require analyzing multiple SEC 10-K documents for Uber.\",\n",
- " tool_kwargs={\"return_direct\": True, \"return_sources\": True},\n",
- " return_sources=True\n",
- ")\n",
- "\n",
- "toolkit = LlamaToolkit(\n",
- " index_configs=index_configs,\n",
- " graph_configs=[graph_config]\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c28cd7ee-15ef-4240-aff3-ecc0ba335df2",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
- "llm=OpenAI(temperature=0)\n",
- "agent_chain = create_llama_chat_agent(\n",
- " toolkit,\n",
- " llm,\n",
- " memory=memory,\n",
- " verbose=True\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "92bd0eaa-73ad-4735-8234-a376fd1b3a6a",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_chain.run(input=\"hi, i am bob\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "19ca8476-bfeb-4c88-a0ff-15424e2d91c7",
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_chain.run(input=\"What were some of the biggest risk factors in 2020 for Uber?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "05b5cd50-cf28-4ad1-b1c2-067f626bad93",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "cross_query_str = (\n",
- " \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
- ")\n",
- "\n",
- "response = agent_chain.run(input=cross_query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "54305c87",
- "metadata": {},
- "outputs": [],
- "source": [
- "# parse the response w/ sources\n",
- "import json\n",
- "response_json = json.loads(response)\n",
- "print(response)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "a066a1c8-2108-447a-ab9a-b82b4f35696e",
- "metadata": {},
- "source": [
- "### Setup Chatbot Loop Within Notebook\n",
- "\n",
- "We'll keep a running loop so that you can converse with the agent. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9dcd609-3921-493e-a4c1-3312dac277d2",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# reinitialize agent\n",
- "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
- "llm=OpenAI(temperature=0)\n",
- "agent_chain = create_llama_chat_agent(\n",
- " toolkit,\n",
- " llm,\n",
- " memory=memory,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "edf6b5a9-bac5-4599-93f8-21c2fc44fa4b",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "while True:\n",
- " text_input = input(\"User: \")\n",
- " response = agent_chain.run(input=text_input)\n",
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+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "YC4R6nkCp91d",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "id": "YC4R6nkCp91d",
+ "outputId": "1792fab3-0a0c-48c1-c3ce-f07091a06d3e",
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# download files\n",
+ "!mkdir data\n",
+ "!wget \"https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1\" -O data/UBER.zip\n",
+ "!unzip data/UBER.zip -d data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "CuHeyb224pI2",
+ "metadata": {
+ "id": "CuHeyb224pI2"
+ },
+ "outputs": [],
+ "source": [
+ "# set text wrapping\n",
+ "from IPython.display import HTML, display\n",
+ "\n",
+ "\n",
+ "def set_css():\n",
+ " display(\n",
+ " HTML(\n",
+ " \"\"\"\n",
+ " <style>\n",
+ " pre {\n",
+ " white-space: pre-wrap;\n",
+ " }\n",
+ " </style>\n",
+ " \"\"\"\n",
+ " )\n",
+ " )\n",
+ "\n",
+ "\n",
+ "get_ipython().events.register(\"pre_run_cell\", set_css)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61",
+ "metadata": {
+ "id": "3a5cb155-cfe0-4c89-a2b4-c61d5b7cbe61"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import download_loader, VectorStoreIndex, ServiceContext\n",
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
+ "metadata": {
+ "id": "354ddbdf-0045-474b-b6f2-1b4c22ad360a",
+ "tags": []
+ },
+ "source": [
+ "### Ingest Unstructured Data Through the Unstructured.io Reader\n",
+ "\n",
+ "Leverage the capabilities of Unstructured.io HTML parsing.\n",
+ "Downloaded through LlamaHub."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61fed4f1-8cca-4d98-b916-a3184279e256",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "years = [2022, 2021, 2020, 2019]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3",
+ "metadata": {
+ "id": "0371970e-c11c-4534-aa22-7cfdfe411bb3"
+ },
+ "outputs": [],
+ "source": [
+ "UnstructuredReader = download_loader(\"UnstructuredReader\", refresh_cache=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29084a11-f3da-428d-ae17-27b85a365e84",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "29084a11-f3da-428d-ae17-27b85a365e84",
+ "outputId": "819e4c48-0bb0-4e22-a84c-3021fbedbc4a"
+ },
+ "outputs": [],
+ "source": [
+ "loader = UnstructuredReader()\n",
+ "doc_set = {}\n",
+ "all_docs = []\n",
+ "for year in years:\n",
+ " year_docs = loader.load_data(\n",
+ " file=Path(f\"./data/UBER/UBER_{year}.html\"), split_documents=False\n",
+ " )\n",
+ " # insert year metadata into each year\n",
+ " for d in year_docs:\n",
+ " d.metadata = {\"year\": year}\n",
+ " doc_set[year] = year_docs\n",
+ " all_docs.extend(year_docs)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d",
+ "metadata": {
+ "id": "08b367eb-f9cc-449a-99c5-dfcfbef20a9d"
+ },
+ "source": [
+ "### Setup a Vector Index for each SEC filing\n",
+ "\n",
+ "We setup a separate vector index for each SEC filing from 2019-2022.\n",
+ "\n",
+ "We also optionally initialize a \"global\" index by dumping all files into the vector store."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
+ "metadata": {
+ "id": "8fbe18a6-4a08-441d-adae-a6195f2cdcb1",
+ "outputId": "bdc1d207-efb2-4f82-bf6c-668cfb7ca98f"
+ },
+ "outputs": [],
+ "source": [
+ "# initialize simple vector indices + global vector index\n",
+ "# NOTE: don't run this cell if the indices are already loaded!\n",
+ "index_set = {}\n",
+ "service_context = ServiceContext.from_defaults(chunk_size=512)\n",
+ "for year in years:\n",
+ " cur_index = VectorStoreIndex.from_documents(\n",
+ " doc_set[year], service_context=service_context\n",
+ " )\n",
+ " index_set[year] = cur_index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34",
+ "metadata": {
+ "id": "d3a5e993-99ae-445a-bcae-e2d415d84e34"
+ },
+ "outputs": [],
+ "source": [
+ "# Load indices from disk\n",
+ "index_set = {}\n",
+ "for year in years:\n",
+ " index_set[year] = cur_index"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "007a99d7-4f63-44bf-957b-c9f5669bce28",
+ "metadata": {
+ "id": "007a99d7-4f63-44bf-957b-c9f5669bce28"
+ },
+ "source": [
+ "### Composing a Graph to synthesize answers across 10-K filings (2019-2022)\n",
+ "\n",
+ "We want our queries to aggregate/synthesize information across *all* 10-K filings. To do this, we define a List index\n",
+ "on top of the 4 vector indices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6",
+ "metadata": {
+ "id": "f3fb341a-19a3-4e4a-9369-7ddc46b8c2a6"
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, LLMPredictor\n",
+ "from langchain import OpenAI\n",
+ "from llama_index.indices.composability import ComposableGraph"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "966388cb-2fe4-4427-ad2e-cea6a0375233",
+ "metadata": {
+ "id": "966388cb-2fe4-4427-ad2e-cea6a0375233"
+ },
+ "outputs": [],
+ "source": [
+ "# set summary text for each doc\n",
+ "index_summaries = [f\"UBER 10-k Filing for {year} fiscal year\" for year in years]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22",
+ "metadata": {
+ "id": "f9e453b1-6782-45b5-8258-dfc386e6cf22"
+ },
+ "outputs": [],
+ "source": [
+ "# set number of output tokens\n",
+ "llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, max_tokens=512))\n",
+ "service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
+ "metadata": {
+ "id": "3b758fc2-bb21-46da-91b5-53e399eecfb0",
+ "outputId": "e7c99f46-8757-48a2-c84b-cb95a0a3eab9"
+ },
+ "outputs": [],
+ "source": [
+ "# define a list index over the vector indices\n",
+ "# allows us to synthesize information across each index\n",
+ "graph = ComposableGraph.from_indices(\n",
+ " ListIndex,\n",
+ " [index_set[y] for y in years],\n",
+ " index_summaries=index_summaries,\n",
+ " service_context=service_context,\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7e2edef1-005f-4733-b64c-5987efa4c88d",
+ "metadata": {
+ "id": "e46dd052-ab34-4974-b696-16ff217970e0"
+ },
+ "source": [
+ "## Setting up the Chatbot Agent\n",
+ "\n",
+ "We use Langchain to define the outer chatbot abstraction. We use LlamaIndex as a core Tool within this abstraction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6c2de0f3-9ec3-4f80-bd36-a509f8bfd4b8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
+ "from langchain.agents import initialize_agent\n",
+ "\n",
+ "from llama_index.langchain_helpers.agents import (\n",
+ " LlamaToolkit,\n",
+ " create_llama_chat_agent,\n",
+ " IndexToolConfig,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c9a6faa1-dc06-4d9c-98b4-06e7ee19c559",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# define a decompose transform\n",
+ "from llama_index.indices.query.query_transform.base import DecomposeQueryTransform\n",
+ "from llama_index.query_engine.transform_query_engine import TransformQueryEngine\n",
+ "\n",
+ "decompose_transform = DecomposeQueryTransform(llm_predictor, verbose=True)\n",
+ "\n",
+ "# define custom query engines\n",
+ "custom_query_engines = {}\n",
+ "for index in index_set.values():\n",
+ " query_engine = index.as_query_engine()\n",
+ " query_engine = TransformQueryEngine(\n",
+ " query_engine,\n",
+ " query_transform=decompose_transform,\n",
+ " transform_extra_info={\"index_summary\": index.index_struct.summary},\n",
+ " )\n",
+ " custom_query_engines[index.index_id] = query_engine\n",
+ "custom_query_engines[graph.root_id] = graph.root_index.as_query_engine(\n",
+ " response_mode=\"tree_summarize\",\n",
+ " verbose=True,\n",
+ ")\n",
+ "\n",
+ "# construct query engine\n",
+ "graph_query_engine = graph.as_query_engine(custom_query_engines=custom_query_engines)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e763ceba-1c54-4abb-9589-c0628d18c5e5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# index configs\n",
+ "index_configs = []\n",
+ "for y in range(2019, 2023):\n",
+ " query_engine = index_set[y].as_query_engine(\n",
+ " similarity_top_k=3,\n",
+ " )\n",
+ " tool_config = IndexToolConfig(\n",
+ " query_engine=query_engine,\n",
+ " name=f\"Vector Index {y}\",\n",
+ " description=f\"useful for when you want to answer queries about the {y} SEC 10-K for Uber\",\n",
+ " tool_kwargs={\"return_direct\": True, \"return_sources\": True},\n",
+ " )\n",
+ " index_configs.append(tool_config)\n",
+ "\n",
+ "# graph config\n",
+ "graph_config = IndexToolConfig(\n",
+ " query_engine=graph_query_engine,\n",
+ " name=f\"Graph Index\",\n",
+ " description=\"useful for when you want to answer queries that require analyzing multiple SEC 10-K documents for Uber.\",\n",
+ " tool_kwargs={\"return_direct\": True, \"return_sources\": True},\n",
+ " return_sources=True,\n",
+ ")\n",
+ "\n",
+ "toolkit = LlamaToolkit(index_configs=index_configs, graph_configs=[graph_config])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c28cd7ee-15ef-4240-aff3-ecc0ba335df2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+ "llm = OpenAI(temperature=0)\n",
+ "agent_chain = create_llama_chat_agent(toolkit, llm, memory=memory, verbose=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "92bd0eaa-73ad-4735-8234-a376fd1b3a6a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agent_chain.run(input=\"hi, i am bob\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19ca8476-bfeb-4c88-a0ff-15424e2d91c7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "agent_chain.run(input=\"What were some of the biggest risk factors in 2020 for Uber?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "05b5cd50-cf28-4ad1-b1c2-067f626bad93",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "cross_query_str = \"Compare/contrast the risk factors described in the Uber 10-K across years. Give answer in bullet points.\"\n",
+ "\n",
+ "response = agent_chain.run(input=cross_query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54305c87",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# parse the response w/ sources\n",
+ "import json\n",
+ "\n",
+ "response_json = json.loads(response)\n",
+ "print(response)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a066a1c8-2108-447a-ab9a-b82b4f35696e",
+ "metadata": {},
+ "source": [
+ "### Setup Chatbot Loop Within Notebook\n",
+ "\n",
+ "We'll keep a running loop so that you can converse with the agent. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9dcd609-3921-493e-a4c1-3312dac277d2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# reinitialize agent\n",
+ "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+ "llm = OpenAI(temperature=0)\n",
+ "agent_chain = create_llama_chat_agent(\n",
+ " toolkit,\n",
+ " llm,\n",
+ " memory=memory,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "edf6b5a9-bac5-4599-93f8-21c2fc44fa4b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "while True:\n",
+ " text_input = input(\"User: \")\n",
+ " response = agent_chain.run(input=text_input)\n",
+ " print(f\"Agent: {response}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "50fa7bb1-817c-4ac1-af39-adbcb14139e2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
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+ "colab": {
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+ },
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diff --git a/examples/chatgpt_plugin/ChatGPTRetrievalPluginIndexDemo.ipynb b/examples/chatgpt_plugin/ChatGPTRetrievalPluginIndexDemo.ipynb
index dd610b321a..41e473f2e4 100644
--- a/examples/chatgpt_plugin/ChatGPTRetrievalPluginIndexDemo.ipynb
+++ b/examples/chatgpt_plugin/ChatGPTRetrievalPluginIndexDemo.ipynb
@@ -1,237 +1,237 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# ChatGPT Retrieval Plugin Index Demo"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "d48af8e1",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents, build index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "0a2bcc07",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/jerryliu/Programming/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
- " from .autonotebook import tqdm as notebook_tqdm\n"
- ]
- }
- ],
- "source": [
- "from llama_index.indices.vector_store import ChatGPTRetrievalPluginIndex\n",
- "from llama_index import SimpleDirectoryReader\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# load documents\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2d04ea94-37bd-4368-926e-b34ba9a4a827",
- "metadata": {},
- "source": [
- "Get bearer token.\n",
- "\n",
- "Try following [this tutorial](https://www.ibm.com/docs/da/order-management?topic=SSGTJF/configuration/t_GeneratingJWTToken.htm) to generate a JWT token."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "891ee367-8df6-4d28-9917-8c4c0455d726",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "bearer_token = os.getenv(\"BEARER_TOKEN\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "ba1558b3",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:06<00:00, 6.93s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n",
- "> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "# initialize without metadata filter\n",
- "index = ChatGPTRetrievalPluginIndex.from_documents(\n",
- " documents, \n",
- " endpoint_url=\"http://localhost:8000\",\n",
- " bearer_token=bearer_token,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "eef29a5f-73e7-49ad-88c4-dd20a60ec91a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "35369eda",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INDEX STRUCT TYPE: chatgpt_retrieval_plugin\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 895 tokens\n",
- "> [query] Total LLM token usage: 895 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(similarity_top_k=3)\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author grew up writing short stories, programming on an IBM 1401, living in England with his family, and later, when he returned to New York, he resumed his old life with newfound wealth, exploring new neighborhoods and experimenting with painting.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "86c2a2d3-78e1-4468-9f93-2becae70e920",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# ChatGPT Retrieval Plugin Index Demo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d48af8e1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents, build index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0a2bcc07",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jerryliu/Programming/llama_index/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.indices.vector_store import ChatGPTRetrievalPluginIndex\n",
+ "from llama_index import SimpleDirectoryReader\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d04ea94-37bd-4368-926e-b34ba9a4a827",
+ "metadata": {},
+ "source": [
+ "Get bearer token.\n",
+ "\n",
+ "Try following [this tutorial](https://www.ibm.com/docs/da/order-management?topic=SSGTJF/configuration/t_GeneratingJWTToken.htm) to generate a JWT token."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "891ee367-8df6-4d28-9917-8c4c0455d726",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "bearer_token = os.getenv(\"BEARER_TOKEN\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "ba1558b3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:06<00:00, 6.93s/it]"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_nodes] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n",
+ "> [build_index_from_nodes] Total embedding token usage: 17617 tokens\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# initialize without metadata filter\n",
+ "index = ChatGPTRetrievalPluginIndex.from_documents(\n",
+ " documents,\n",
+ " endpoint_url=\"http://localhost:8000\",\n",
+ " bearer_token=bearer_token,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "eef29a5f-73e7-49ad-88c4-dd20a60ec91a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "35369eda",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INDEX STRUCT TYPE: chatgpt_retrieval_plugin\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 895 tokens\n",
+ "> [query] Total LLM token usage: 895 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(similarity_top_k=3)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author grew up writing short stories, programming on an IBM 1401, living in England with his family, and later, when he returned to New York, he resumed his old life with newfound wealth, exploring new neighborhoods and experimenting with painting.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86c2a2d3-78e1-4468-9f93-2becae70e920",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
\ No newline at end of file
diff --git a/examples/chatgpt_plugin/ChatGPTRetrievalPluginReaderDemo.ipynb b/examples/chatgpt_plugin/ChatGPTRetrievalPluginReaderDemo.ipynb
index 0c62885053..5a937c7ad0 100644
--- a/examples/chatgpt_plugin/ChatGPTRetrievalPluginReaderDemo.ipynb
+++ b/examples/chatgpt_plugin/ChatGPTRetrievalPluginReaderDemo.ipynb
@@ -1,249 +1,248 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
- "metadata": {},
- "source": [
- "# ChatGPT Retrieval Plugin Reader Demo\n",
- "\n",
- "Use our reader plugin to load data from ChatGPT"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "d48af8e1",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
- "metadata": {},
- "source": [
- "#### Load documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a2bcc07",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.readers import ChatGPTRetrievalPluginReader\n",
- "import os"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "899522d1-3fd8-428d-be13-ca2c064c3a85",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "bearer_token = os.getenv(\"BEARER_TOKEN\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# load documents\n",
- "reader = ChatGPTRetrievalPluginReader(\n",
- " endpoint_url=\"http://localhost:8000\",\n",
- " bearer_token=bearer_token\n",
- ")\n",
- "\n",
- "documents = reader.load_data(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "ddd435b1-aba4-4847-bcf5-e0c61df50a9b",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "10"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "len(documents)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "da9a6785-0bd9-44de-a0a2-4dd513b0730a",
- "metadata": {},
- "source": [
- "#### Build Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "1270551f-0e4b-4429-a2bf-71146958f4c4",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import ListIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "aa7443ef-77b1-4d71-b164-362e72a29064",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n",
- "> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "index = ListIndex(documents)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "04304299-fc3e-40a0-8600-f50c3292767e",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "eef29a5f-73e7-49ad-88c4-dd20a60ec91a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "35369eda",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 2020 tokens\n",
- "> [query] Total LLM token usage: 2020 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(response_mode=\"compact\")\n",
- "response = query_engine.query(\n",
- " \"Summarize the retrieved content and describe what the author did growing up\",\n",
- ") "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author grew up writing short stories and programming on an IBM 1401. After high school, they moved to England and worked on a program called Bel. They then started taking art classes at Harvard and RISD, and eventually dropped out to pursue painting. They moved to New York and started writing essays, which they published online. They also worked on spam filters and hosted dinner parties. In 2003, they had a big party at their house.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "86c2a2d3-78e1-4468-9f93-2becae70e920",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "307804a3-c02b-4a57-ac0d-172c30ddc851",
+ "metadata": {},
+ "source": [
+ "# ChatGPT Retrieval Plugin Reader Demo\n",
+ "\n",
+ "Use our reader plugin to load data from ChatGPT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d48af8e1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8ee4473a-094f-4d0a-a825-e1213db07240",
+ "metadata": {},
+ "source": [
+ "#### Load documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a2bcc07",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.readers import ChatGPTRetrievalPluginReader\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "899522d1-3fd8-428d-be13-ca2c064c3a85",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "bearer_token = os.getenv(\"BEARER_TOKEN\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "68cbd239-880e-41a3-98d8-dbb3fab55431",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# load documents\n",
+ "reader = ChatGPTRetrievalPluginReader(\n",
+ " endpoint_url=\"http://localhost:8000\", bearer_token=bearer_token\n",
+ ")\n",
+ "\n",
+ "documents = reader.load_data(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "ddd435b1-aba4-4847-bcf5-e0c61df50a9b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "da9a6785-0bd9-44de-a0a2-4dd513b0730a",
+ "metadata": {},
+ "source": [
+ "#### Build Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "1270551f-0e4b-4429-a2bf-71146958f4c4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "aa7443ef-77b1-4d71-b164-362e72a29064",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n",
+ "> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = ListIndex(documents)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04304299-fc3e-40a0-8600-f50c3292767e",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "eef29a5f-73e7-49ad-88c4-dd20a60ec91a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "35369eda",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 2020 tokens\n",
+ "> [query] Total LLM token usage: 2020 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(response_mode=\"compact\")\n",
+ "response = query_engine.query(\n",
+ " \"Summarize the retrieved content and describe what the author did growing up\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "bedbb693-725f-478f-be26-fa7180ea38b2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author grew up writing short stories and programming on an IBM 1401. After high school, they moved to England and worked on a program called Bel. They then started taking art classes at Harvard and RISD, and eventually dropped out to pursue painting. They moved to New York and started writing essays, which they published online. They also worked on spam filters and hosted dinner parties. In 2003, they had a big party at their house.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86c2a2d3-78e1-4468-9f93-2becae70e920",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
\ No newline at end of file
diff --git a/examples/chatgpt_plugin/ChatGPT_Retrieval_Plugin_Upload.ipynb b/examples/chatgpt_plugin/ChatGPT_Retrieval_Plugin_Upload.ipynb
index b2da4bb54d..8cdcca9df2 100644
--- a/examples/chatgpt_plugin/ChatGPT_Retrieval_Plugin_Upload.ipynb
+++ b/examples/chatgpt_plugin/ChatGPT_Retrieval_Plugin_Upload.ipynb
@@ -122,6 +122,7 @@
"source": [
"# Convert LlamaIndex Documents to JSON format\n",
"\n",
+ "\n",
"def dump_docs_to_json(documents: List[Document], out_path: str) -> Dict:\n",
" \"\"\"Convert LlamaIndex Documents to JSON format and save it.\"\"\"\n",
" result_json = []\n",
@@ -138,8 +139,8 @@
" # \"author\": \"Paul Graham\",\n",
" }\n",
" result_json.append(cur_dict)\n",
- " \n",
- " json.dump(result_json, open(out_path, 'w'))"
+ "\n",
+ " json.dump(result_json, open(out_path, \"w\"))"
]
},
{
diff --git a/examples/docstore/DocstoreDemo.ipynb b/examples/docstore/DocstoreDemo.ipynb
index a78f6e2a36..cf1df96745 100644
--- a/examples/docstore/DocstoreDemo.ipynb
+++ b/examples/docstore/DocstoreDemo.ipynb
@@ -1,285 +1,288 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
- "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
- "from llama_index.composability import ComposableGraph\n",
- "from llama_index.llms import OpenAI"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
- "metadata": {},
- "source": [
- "#### Load Documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
- "documents = reader.load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
- "metadata": {},
- "source": [
- "#### Parse into Nodes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.node_parser import SimpleNodeParser\n",
- "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
- "metadata": {},
- "source": [
- "#### Add to Docstore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.storage.docstore import SimpleDocumentStore\n",
- "docstore = SimpleDocumentStore()\n",
- "docstore.add_documents(nodes)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
- "metadata": {},
- "source": [
- "#### Define Multiple Indexes\n",
- "\n",
- "Each index uses the same underlying Node."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.storage.storage_context import StorageContext\n",
- "\n",
- "\n",
- "storage_context = StorageContext.from_defaults(docstore=docstore)\n",
- "list_index = ListIndex(nodes, storage_context=storage_context)\n",
- "vector_index = VectorStoreIndex(nodes, storage_context=storage_context) \n",
- "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "6"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NOTE: the docstore sitll has the same nodes\n",
- "len(storage_context.docstore.docs)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d3bf6aaf-3375-4212-8323-777969a918f7",
- "metadata": {},
- "source": [
- "#### Test out some Queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:llama_index.llm_predictor.base:Unknown max input size for gpt-3.5-turbo, using defaults.\n",
- "Unknown max input size for gpt-3.5-turbo, using defaults.\n"
- ]
- }
- ],
- "source": [
- "llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context_chatgpt = ServiceContext.from_defaults(llm=llm, chunk_size=1024)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "544c0565-72a0-434b-98e5-83138ebdaa2b",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_engine = list_index.as_query_engine()\n",
- "response = query_engine.query(\"What is a summary of this document?\") "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "036077b7-108e-4026-9628-44c694343460",
- "metadata": {},
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\") "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ecd7719c-f663-4edb-a239-d2a8f0a5c091",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = keyword_table_index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do after his time at YC?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "37524641-2632-4a76-8ae6-00f1285256d9",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "After his time at YC, the author decided to take a break and focus on painting. He spent most of 2014 painting and then, in November, he ran out of steam and stopped. He then moved to Florence, Italy to attend the Accademia di Belle Arti di Firenze, where he studied painting and drawing. He also started painting still lives in his bedroom at night. In March 2015, he started working on Lisp again and wrote a new Lisp, called Bel, in itself in Arc. He wrote essays through 2020, but also started to think about other things he could work on. He wrote an essay for himself to answer the question of how he should choose what to do next and then wrote a more detailed version for others to read. He also created the Y Combinator logo, which was an inside joke referencing the Viaweb logo, a white V on a red circle, so he made the YC logo a white Y on an orange square. He also created a fund for YC for a couple of years, but after Heroku got bought, he had enough money to go back to being self-funded. He also disliked the term \"deal flow\" because it implies that the number of new startups at any given time\n"
- ]
- }
- ],
- "source": [
- "print(response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ff58018c-3117-4d50-abff-16a1873eda9c",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
+ "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
+ "from llama_index.composability import ComposableGraph\n",
+ "from llama_index.llms import OpenAI"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
+ "metadata": {},
+ "source": [
+ "#### Load Documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
+ "documents = reader.load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
+ "metadata": {},
+ "source": [
+ "#### Parse into Nodes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.node_parser import SimpleNodeParser\n",
+ "\n",
+ "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
+ "metadata": {},
+ "source": [
+ "#### Add to Docstore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.docstore import SimpleDocumentStore\n",
+ "\n",
+ "docstore = SimpleDocumentStore()\n",
+ "docstore.add_documents(nodes)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
+ "metadata": {},
+ "source": [
+ "#### Define Multiple Indexes\n",
+ "\n",
+ "Each index uses the same underlying Node."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.storage_context import StorageContext\n",
+ "\n",
+ "\n",
+ "storage_context = StorageContext.from_defaults(docstore=docstore)\n",
+ "list_index = ListIndex(nodes, storage_context=storage_context)\n",
+ "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)\n",
+ "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NOTE: the docstore sitll has the same nodes\n",
+ "len(storage_context.docstore.docs)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d3bf6aaf-3375-4212-8323-777969a918f7",
+ "metadata": {},
+ "source": [
+ "#### Test out some Queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:llama_index.llm_predictor.base:Unknown max input size for gpt-3.5-turbo, using defaults.\n",
+ "Unknown max input size for gpt-3.5-turbo, using defaults.\n"
+ ]
+ }
+ ],
+ "source": [
+ "llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context_chatgpt = ServiceContext.from_defaults(llm=llm, chunk_size=1024)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "544c0565-72a0-434b-98e5-83138ebdaa2b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = list_index.as_query_engine()\n",
+ "response = query_engine.query(\"What is a summary of this document?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "036077b7-108e-4026-9628-44c694343460",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ecd7719c-f663-4edb-a239-d2a8f0a5c091",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = keyword_table_index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do after his time at YC?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "37524641-2632-4a76-8ae6-00f1285256d9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "After his time at YC, the author decided to take a break and focus on painting. He spent most of 2014 painting and then, in November, he ran out of steam and stopped. He then moved to Florence, Italy to attend the Accademia di Belle Arti di Firenze, where he studied painting and drawing. He also started painting still lives in his bedroom at night. In March 2015, he started working on Lisp again and wrote a new Lisp, called Bel, in itself in Arc. He wrote essays through 2020, but also started to think about other things he could work on. He wrote an essay for himself to answer the question of how he should choose what to do next and then wrote a more detailed version for others to read. He also created the Y Combinator logo, which was an inside joke referencing the Viaweb logo, a white V on a red circle, so he made the YC logo a white Y on an orange square. He also created a fund for YC for a couple of years, but after Heroku got bought, he had enough money to go back to being self-funded. He also disliked the term \"deal flow\" because it implies that the number of new startups at any given time\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff58018c-3117-4d50-abff-16a1873eda9c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/docstore/DynamoDBDocstoreDemo.ipynb b/examples/docstore/DynamoDBDocstoreDemo.ipynb
index d28a650eba..776f097c5b 100644
--- a/examples/docstore/DynamoDBDocstoreDemo.ipynb
+++ b/examples/docstore/DynamoDBDocstoreDemo.ipynb
@@ -1,393 +1,404 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import os\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor, StorageContext\n",
- "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
- "from llama_index.composability import ComposableGraph\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.response.notebook_utils import display_response"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
- "metadata": {},
- "source": [
- "#### Load Documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
- "documents = reader.load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
- "metadata": {},
- "source": [
- "#### Parse into Nodes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.node_parser import SimpleNodeParser\n",
- "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
- "metadata": {},
- "source": [
- "#### Add to Docstore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f9998976",
- "metadata": {},
- "outputs": [],
- "source": [
- "TABLE_NAME = os.environ[\"DYNAMODB_TABLE_NAME\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "54b9bd36",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.storage.docstore.dynamodb_docstore import DynamoDBDocumentStore\n",
- "from llama_index.storage.index_store.dynamodb_index_store import DynamoDBIndexStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "storage_context = StorageContext.from_defaults(\n",
- " docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),\n",
- " index_store=DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME)\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e88378b2",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "storage_context.docstore.add_documents(nodes)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
- "metadata": {},
- "source": [
- "#### Define Multiple Indexes\n",
- "\n",
- "Each index uses the same underlying Node."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "list_index = ListIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "144bc7eb",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4ccbe86c",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# NOTE: the docstore still has the same nodes\n",
- "len(storage_context.docstore.docs)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1059ec3c",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "#### Test out saving and loading"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d0f258d6",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# NOTE: docstore and index_store is persisted in DynamoDB by default\n",
- "# NOTE: here only need to persist simple vector store to dick\n",
- "storage_context.persist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9155c1a9",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# note down index IDs\n",
- "list_id = list_index.index_id\n",
- "vector_id = vector_index.index_id\n",
- "keyword_id = keyword_table_index.index_id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "555de7fa",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "from llama_index.indices.loading import load_index_from_storage\n",
- "\n",
- "# re-create storage context\n",
- "storage_context = StorageContext.from_defaults(\n",
- " docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),\n",
- " index_store=DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME),\n",
- ")\n",
- "\n",
- "list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
- "vector_index = load_index_from_storage(storage_context=storage_context, index_id=vector_id)\n",
- "keyword_table_index = load_index_from_storage(storage_context=storage_context, index_id=keyword_id)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "c5bc40a7",
- "metadata": {
- "collapsed": false
- },
- "source": [
- "#### Test out some Queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8db82de3",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "244bc6ae",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "query_engine = list_index.as_query_engine()\n",
- "list_response = query_engine.query(\"What is a summary of this document?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6cbe77ef",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "display_response(list_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "02b800ab",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "vector_response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "70b63767",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "display_response(vector_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b93478b6",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "query_engine = keyword_table_index.as_query_engine()\n",
- "keyword_response = query_engine.query(\"What did the author do after his time at YC?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8044da9c",
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "display_response(keyword_response)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llama_index",
- "language": "python",
- "name": "llama_index"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
+ " StorageContext,\n",
+ ")\n",
+ "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
+ "from llama_index.composability import ComposableGraph\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.response.notebook_utils import display_response"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
+ "metadata": {},
+ "source": [
+ "#### Load Documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
+ "documents = reader.load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
+ "metadata": {},
+ "source": [
+ "#### Parse into Nodes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.node_parser import SimpleNodeParser\n",
+ "\n",
+ "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
+ "metadata": {},
+ "source": [
+ "#### Add to Docstore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9998976",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "TABLE_NAME = os.environ[\"DYNAMODB_TABLE_NAME\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54b9bd36",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.docstore.dynamodb_docstore import DynamoDBDocumentStore\n",
+ "from llama_index.storage.index_store.dynamodb_index_store import DynamoDBIndexStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "storage_context = StorageContext.from_defaults(\n",
+ " docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),\n",
+ " index_store=DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e88378b2",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "storage_context.docstore.add_documents(nodes)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
+ "metadata": {},
+ "source": [
+ "#### Define Multiple Indexes\n",
+ "\n",
+ "Each index uses the same underlying Node."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "list_index = ListIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "144bc7eb",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4ccbe86c",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: the docstore still has the same nodes\n",
+ "len(storage_context.docstore.docs)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1059ec3c",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "#### Test out saving and loading"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0f258d6",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: docstore and index_store is persisted in DynamoDB by default\n",
+ "# NOTE: here only need to persist simple vector store to dick\n",
+ "storage_context.persist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9155c1a9",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "# note down index IDs\n",
+ "list_id = list_index.index_id\n",
+ "vector_id = vector_index.index_id\n",
+ "keyword_id = keyword_table_index.index_id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "555de7fa",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.loading import load_index_from_storage\n",
+ "\n",
+ "# re-create storage context\n",
+ "storage_context = StorageContext.from_defaults(\n",
+ " docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),\n",
+ " index_store=DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME),\n",
+ ")\n",
+ "\n",
+ "list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
+ "vector_index = load_index_from_storage(\n",
+ " storage_context=storage_context, index_id=vector_id\n",
+ ")\n",
+ "keyword_table_index = load_index_from_storage(\n",
+ " storage_context=storage_context, index_id=keyword_id\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "c5bc40a7",
+ "metadata": {
+ "collapsed": false
+ },
+ "source": [
+ "#### Test out some Queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8db82de3",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context_chatgpt = ServiceContext.from_defaults(llm=chatgpt, chunk_size=1024)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "244bc6ae",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = list_index.as_query_engine()\n",
+ "list_response = query_engine.query(\"What is a summary of this document?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6cbe77ef",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "display_response(list_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "02b800ab",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "vector_response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "70b63767",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "display_response(vector_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b93478b6",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = keyword_table_index.as_query_engine()\n",
+ "keyword_response = query_engine.query(\"What did the author do after his time at YC?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8044da9c",
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "display_response(keyword_response)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llama_index",
+ "language": "python",
+ "name": "llama_index"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/docstore/MongoDocstoreDemo.ipynb b/examples/docstore/MongoDocstoreDemo.ipynb
index 0c5736b2f3..debb9b1b22 100644
--- a/examples/docstore/MongoDocstoreDemo.ipynb
+++ b/examples/docstore/MongoDocstoreDemo.ipynb
@@ -1,404 +1,409 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "import os\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index import SimpleDirectoryReader, ServiceContext, StorageContext\n",
- "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
- "from llama_index.composability import ComposableGraph\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.response.notebook_utils import display_response"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
- "metadata": {},
- "source": [
- "#### Load Documents"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
- "documents = reader.load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
- "metadata": {},
- "source": [
- "#### Parse into Nodes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.node_parser import SimpleNodeParser\n",
- "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
- "metadata": {},
- "source": [
- "#### Add to Docstore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1514211c",
- "metadata": {},
- "outputs": [],
- "source": [
- "MONGO_URI = os.environ['MONGO_URI']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "from llama_index.storage.docstore import MongoDocumentStore\n",
- "from llama_index.storage.index_store.mongo_index_store import MongoIndexStore"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "60e781d1",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "storage_context = StorageContext.from_defaults(\n",
- " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
- " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e0b18789",
- "metadata": {},
- "outputs": [],
- "source": [
- "storage_context.docstore.add_documents(nodes)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
- "metadata": {},
- "source": [
- "#### Define Multiple Indexes\n",
- "\n",
- "Each index uses the same underlying Node."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "list_index = ListIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9440f405-fa75-4788-bc7c-11d021a0a17b",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "364ef89f-4ba2-4b1a-b5e5-619e0e8420ef",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "# NOTE: the docstore still has the same nodes\n",
- "len(storage_context.docstore.docs)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "365a025b",
- "metadata": {},
- "source": [
- "#### Test out saving and loading"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1b359a08",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: docstore and index_store is persisted in MongoDB by default\n",
- "# NOTE: here only need to persist simple vector store to disk\n",
- "storage_context.persist()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "84b3d2f4",
- "metadata": {},
- "outputs": [],
- "source": [
- "# note down index IDs\n",
- "list_id = list_index.index_id\n",
- "vector_id = vector_index.index_id\n",
- "keyword_id = keyword_table_index.index_id"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1593ca1d",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.indices.loading import load_index_from_storage\n",
- "\n",
- "# re-create storage context\n",
- "storage_context = StorageContext.from_defaults(\n",
- " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
- " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
- ")\n",
- "\n",
- "# load indices\n",
- "list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
- "vector_index = load_index_from_storage(storage_context=storage_context, vector_id=vector_id)\n",
- "keyword_table_index = load_index_from_storage(storage_context=storage_context, keyword_id=keyword_id)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d3bf6aaf-3375-4212-8323-777969a918f7",
- "metadata": {},
- "source": [
- "#### Test out some Queries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "chat_gpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context_chatgpt = ServiceContext.from_defaults(llm=chat_gpt, chunk_size=1024)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "544c0565-72a0-434b-98e5-83138ebdaa2b",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "query_engine = list_index.as_query_engine()\n",
- "list_response = query_engine.query(\"What is a summary of this document?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "39d250be",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "display_response(list_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "036077b7-108e-4026-9628-44c694343460",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": [
- "query_engine = vector_index.as_query_engine()\n",
- "vector_response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "42229e09",
- "metadata": {},
- "outputs": [],
- "source": [
- "display_response(vector_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ecd7719c-f663-4edb-a239-d2a8f0a5c091",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "query_engine = keyword_table_index.as_query_engine()\n",
- "keyword_response = query_engine.query(\"What did the author do after his time at YC?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "37524641-2632-4a76-8ae6-00f1285256d9",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "display_response(keyword_response)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ff58018c-3117-4d50-abff-16a1873eda9c",
- "metadata": {
- "scrolled": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a54d1c43-4b7f-4917-939f-a964f6f3dafc",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fa67fa07-1395-4aab-a356-72bdb302f6b2",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import os\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1d12d766-3ca8-4012-9da2-248be80bb6ab",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index import SimpleDirectoryReader, ServiceContext, StorageContext\n",
+ "from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
+ "from llama_index.composability import ComposableGraph\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.response.notebook_utils import display_response"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f6dd9d5f-a601-4097-894e-fe98a0c35a5b",
+ "metadata": {},
+ "source": [
+ "#### Load Documents"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7cdaf9d-cfbd-4ced-8d4e-6eef8508224d",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
+ "documents = reader.load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bae82b55-5c9f-432a-9e06-1fccb6f9fc7f",
+ "metadata": {},
+ "source": [
+ "#### Parse into Nodes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f97e558a-c29f-44ec-ab33-1f481da1a6ef",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.node_parser import SimpleNodeParser\n",
+ "\n",
+ "nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "aff4c8e1-b2ba-4ea6-a8df-978c2788fedc",
+ "metadata": {},
+ "source": [
+ "#### Add to Docstore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1514211c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MONGO_URI = os.environ[\"MONGO_URI\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ba8b0da-67a8-4653-8cdb-09e39583a2d8",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "from llama_index.storage.docstore import MongoDocumentStore\n",
+ "from llama_index.storage.index_store.mongo_index_store import MongoIndexStore"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60e781d1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "storage_context = StorageContext.from_defaults(\n",
+ " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
+ " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0b18789",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "storage_context.docstore.add_documents(nodes)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "528149c1-5bde-4eba-b75a-e8fa1da17d7c",
+ "metadata": {},
+ "source": [
+ "#### Define Multiple Indexes\n",
+ "\n",
+ "Each index uses the same underlying Node."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "316fb6ac-2031-4d17-9999-ffdb827f46d1",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "list_index = ListIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9440f405-fa75-4788-bc7c-11d021a0a17b",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "vector_index = VectorStoreIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "364ef89f-4ba2-4b1a-b5e5-619e0e8420ef",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "keyword_table_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c6b2141-fc77-4dec-891b-d4dad0633b35",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# NOTE: the docstore still has the same nodes\n",
+ "len(storage_context.docstore.docs)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "365a025b",
+ "metadata": {},
+ "source": [
+ "#### Test out saving and loading"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b359a08",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: docstore and index_store is persisted in MongoDB by default\n",
+ "# NOTE: here only need to persist simple vector store to disk\n",
+ "storage_context.persist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84b3d2f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# note down index IDs\n",
+ "list_id = list_index.index_id\n",
+ "vector_id = vector_index.index_id\n",
+ "keyword_id = keyword_table_index.index_id"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1593ca1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.indices.loading import load_index_from_storage\n",
+ "\n",
+ "# re-create storage context\n",
+ "storage_context = StorageContext.from_defaults(\n",
+ " docstore=MongoDocumentStore.from_uri(uri=MONGO_URI),\n",
+ " index_store=MongoIndexStore.from_uri(uri=MONGO_URI),\n",
+ ")\n",
+ "\n",
+ "# load indices\n",
+ "list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
+ "vector_index = load_index_from_storage(\n",
+ " storage_context=storage_context, vector_id=vector_id\n",
+ ")\n",
+ "keyword_table_index = load_index_from_storage(\n",
+ " storage_context=storage_context, keyword_id=keyword_id\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d3bf6aaf-3375-4212-8323-777969a918f7",
+ "metadata": {},
+ "source": [
+ "#### Test out some Queries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9bba68f3-2743-437e-93b6-ce9ba92e40c3",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "chat_gpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
+ "service_context_chatgpt = ServiceContext.from_defaults(llm=chat_gpt, chunk_size=1024)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "544c0565-72a0-434b-98e5-83138ebdaa2b",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = list_index.as_query_engine()\n",
+ "list_response = query_engine.query(\"What is a summary of this document?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "39d250be",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "display_response(list_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "036077b7-108e-4026-9628-44c694343460",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = vector_index.as_query_engine()\n",
+ "vector_response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "42229e09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display_response(vector_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ecd7719c-f663-4edb-a239-d2a8f0a5c091",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "query_engine = keyword_table_index.as_query_engine()\n",
+ "keyword_response = query_engine.query(\"What did the author do after his time at YC?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "37524641-2632-4a76-8ae6-00f1285256d9",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "display_response(keyword_response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff58018c-3117-4d50-abff-16a1873eda9c",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb b/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
index 14095e5851..a75c424099 100644
--- a/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
+++ b/examples/docstore/RedisDocstoreIndexStoreDemo.ipynb
@@ -20,6 +20,7 @@
"outputs": [],
"source": [
"import nest_asyncio\n",
+ "\n",
"nest_asyncio.apply()"
]
},
@@ -70,7 +71,12 @@
}
],
"source": [
- "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor, StorageContext\n",
+ "from llama_index import (\n",
+ " SimpleDirectoryReader,\n",
+ " ServiceContext,\n",
+ " LLMPredictor,\n",
+ " StorageContext,\n",
+ ")\n",
"from llama_index import VectorStoreIndex, ListIndex, SimpleKeywordTableIndex\n",
"from llama_index.composability import ComposableGraph\n",
"from llama_index.llms import OpenAI\n",
@@ -96,7 +102,7 @@
},
"outputs": [],
"source": [
- "reader = SimpleDirectoryReader('../paul_graham_essay/data')\n",
+ "reader = SimpleDirectoryReader(\"../paul_graham_essay/data\")\n",
"documents = reader.load_data()"
]
},
@@ -120,6 +126,7 @@
"outputs": [],
"source": [
"from llama_index.node_parser import SimpleNodeParser\n",
+ "\n",
"nodes = SimpleNodeParser().get_nodes_from_documents(documents)"
]
},
@@ -139,8 +146,8 @@
"metadata": {},
"outputs": [],
"source": [
- "REDIS_HOST = os.getenv('REDIS_HOST', '127.0.0.1')\n",
- "REDIS_PORT = os.getenv('REDIS_PORT', 6379)"
+ "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"127.0.0.1\")\n",
+ "REDIS_PORT = os.getenv(\"REDIS_PORT\", 6379)"
]
},
{
@@ -165,8 +172,12 @@
"outputs": [],
"source": [
"storage_context = StorageContext.from_defaults(\n",
- " docstore=RedisDocumentStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace='llama_index'),\n",
- " index_store=RedisIndexStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace='llama_index'),\n",
+ " docstore=RedisDocumentStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
+ " index_store=RedisIndexStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
")"
]
},
@@ -386,14 +397,22 @@
"\n",
"# re-create storage context\n",
"storage_context = StorageContext.from_defaults(\n",
- " docstore=RedisDocumentStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"),\n",
- " index_store=RedisIndexStore.from_host_and_port(host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"),\n",
+ " docstore=RedisDocumentStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
+ " index_store=RedisIndexStore.from_host_and_port(\n",
+ " host=REDIS_HOST, port=REDIS_PORT, namespace=\"llama_index\"\n",
+ " ),\n",
")\n",
"\n",
"# load indices\n",
"list_index = load_index_from_storage(storage_context=storage_context, index_id=list_id)\n",
- "vector_index = load_index_from_storage(storage_context=storage_context, index_id=vector_id)\n",
- "keyword_table_index = load_index_from_storage(storage_context=storage_context, index_id=keyword_id)"
+ "vector_index = load_index_from_storage(\n",
+ " storage_context=storage_context, index_id=vector_id\n",
+ ")\n",
+ "keyword_table_index = load_index_from_storage(\n",
+ " storage_context=storage_context, index_id=keyword_id\n",
+ ")"
]
},
{
diff --git a/examples/experimental/Evaporate.ipynb b/examples/experimental/Evaporate.ipynb
index 669c2ee496..72c94e8da0 100644
--- a/examples/experimental/Evaporate.ipynb
+++ b/examples/experimental/Evaporate.ipynb
@@ -20,11 +20,7 @@
},
"outputs": [],
"source": [
- "from llama_index import (\n",
- " SimpleDirectoryReader,\n",
- " ServiceContext,\n",
- " LLMPredictor\n",
- ")\n",
+ "from llama_index import SimpleDirectoryReader, ServiceContext, LLMPredictor\n",
"from llama_index.experimental.evaporate import EvaporateExtractor\n",
"from llama_index.llms import OpenAI\n",
"import requests"
@@ -63,27 +59,28 @@
"from pathlib import Path\n",
"\n",
"import requests\n",
+ "\n",
"for title in wiki_titles:\n",
" response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
" params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': title,\n",
- " 'prop': 'extracts',\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": title,\n",
+ " \"prop\": \"extracts\",\n",
" # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
+ " \"explaintext\": True,\n",
+ " },\n",
" ).json()\n",
- " page = next(iter(response['query']['pages'].values()))\n",
- " wiki_text = page['extract']\n",
+ " page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ " wiki_text = page[\"extract\"]\n",
"\n",
- " data_path = Path('data')\n",
+ " data_path = Path(\"data\")\n",
" if not data_path.exists():\n",
" Path.mkdir(data_path)\n",
"\n",
- " with open(data_path / f\"{title}.txt\", 'w') as fp:\n",
- " fp.write(wiki_text)\n"
+ " with open(data_path / f\"{title}.txt\", \"w\") as fp:\n",
+ " fp.write(wiki_text)"
]
},
{
@@ -98,7 +95,9 @@
"# Load all wiki documents\n",
"city_docs = {}\n",
"for wiki_title in wiki_titles:\n",
- " city_docs[wiki_title] = SimpleDirectoryReader(input_files=[f\"data/{wiki_title}.txt\"]).load_data()"
+ " city_docs[wiki_title] = SimpleDirectoryReader(\n",
+ " input_files=[f\"data/{wiki_title}.txt\"]\n",
+ " ).load_data()"
]
},
{
@@ -111,9 +110,7 @@
"outputs": [],
"source": [
"llm = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")\n",
- "service_context = ServiceContext.from_defaults(\n",
- " llm=llm, chunk_size=512\n",
- ")"
+ "service_context = ServiceContext.from_defaults(llm=llm, chunk_size=512)"
]
},
{
@@ -251,7 +248,9 @@
"outputs": [],
"source": [
"# Try with Toronto and Seattle (should extract \"population\")\n",
- "existing_fields = extractor.identify_fields(city_pop_nodes, topic=\"city\", fields_top_k=1)"
+ "existing_fields = extractor.identify_fields(\n",
+ " city_pop_nodes, topic=\"city\", fields_top_k=1\n",
+ ")"
]
},
{
diff --git a/examples/experimental/NotionToolSpec.ipynb b/examples/experimental/NotionToolSpec.ipynb
index fcdaf285f0..d3d3b0ea66 100644
--- a/examples/experimental/NotionToolSpec.ipynb
+++ b/examples/experimental/NotionToolSpec.ipynb
@@ -48,7 +48,9 @@
},
"outputs": [],
"source": [
- "lc_tools = [t.to_langchain_structured_tool(return_direct=False, verbose=True) for t in tools]"
+ "lc_tools = [\n",
+ " t.to_langchain_structured_tool(return_direct=False, verbose=True) for t in tools\n",
+ "]"
]
},
{
@@ -86,10 +88,7 @@
"outputs": [],
"source": [
"agent = initialize_agent(\n",
- " lc_tools,\n",
- " llm=llm,\n",
- " agent=\"structured-chat-zero-shot-react-description\",\n",
- " verbose=True\n",
+ " lc_tools, llm=llm, agent=\"structured-chat-zero-shot-react-description\", verbose=True\n",
")"
]
},
diff --git a/examples/gatsby/TestGatsby.ipynb b/examples/gatsby/TestGatsby.ipynb
index 769da62f30..acd75a96a5 100644
--- a/examples/gatsby/TestGatsby.ipynb
+++ b/examples/gatsby/TestGatsby.ipynb
@@ -1,165 +1,166 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ffeb4eee",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import TreeIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Building index from nodes: 9 chunks\n",
- "0/95\n",
- "10/95\n",
- "20/95\n",
- "30/95\n",
- "40/95\n",
- "50/95\n",
- "60/95\n",
- "70/95\n",
- "80/95\n",
- "90/95\n",
- "> [build_index_from_documents] Total token usage: 34226 tokens\n"
- ]
- }
- ],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "new_index = TreeIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the narrator do after getting back to Chicago?\n",
- ">[Level 0] Selected node: [8]/[8]\n",
- ">[Level 1] Selected node: [8]/[8]\n",
- "> [query] Total token usage: 6058 tokens\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "\n",
- "query_engine = new_index.as_query_engine()\n",
- "response = query_engine.query(\"What did the narrator do after getting back to Chicago?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "91581e60-6051-40ae-bba6-8fa08ffbb728",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The narrator returned to his home in Chicago and began calling people to inform them of Gatsby's funeral. He was worried that the funeral would draw a sightseeing crowd and wanted to keep it private. He was relieved when Klipspringer called and promised to tell anyone who might be interested about the funeral. He then asked Klipspringer to commit to attending the funeral, but Klipspringer hesitated.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ca10a9c1-9dff-476d-b218-3208a1b8e7f6",
- "metadata": {},
- "outputs": [],
- "source": [
- "# GPT is confused by the text evidence\n",
- "query_engine = new_index.as_query_engine()\n",
- "response = query_engine.query(\"What did Gatsby do before he met Daisy?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ffeb4eee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import TreeIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Building index from nodes: 9 chunks\n",
+ "0/95\n",
+ "10/95\n",
+ "20/95\n",
+ "30/95\n",
+ "40/95\n",
+ "50/95\n",
+ "60/95\n",
+ "70/95\n",
+ "80/95\n",
+ "90/95\n",
+ "> [build_index_from_documents] Total token usage: 34226 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "new_index = TreeIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the narrator do after getting back to Chicago?\n",
+ ">[Level 0] Selected node: [8]/[8]\n",
+ ">[Level 1] Selected node: [8]/[8]\n",
+ "> [query] Total token usage: 6058 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "\n",
+ "query_engine = new_index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the narrator do after getting back to Chicago?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "91581e60-6051-40ae-bba6-8fa08ffbb728",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The narrator returned to his home in Chicago and began calling people to inform them of Gatsby's funeral. He was worried that the funeral would draw a sightseeing crowd and wanted to keep it private. He was relieved when Klipspringer called and promised to tell anyone who might be interested about the funeral. He then asked Klipspringer to commit to attending the funeral, but Klipspringer hesitated.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ca10a9c1-9dff-476d-b218-3208a1b8e7f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# GPT is confused by the text evidence\n",
+ "query_engine = new_index.as_query_engine()\n",
+ "response = query_engine.query(\"What did Gatsby do before he met Daisy?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/langchain_demo/LangchainDemo.ipynb b/examples/langchain_demo/LangchainDemo.ipynb
index c00c043632..b54e16f546 100644
--- a/examples/langchain_demo/LangchainDemo.ipynb
+++ b/examples/langchain_demo/LangchainDemo.ipynb
@@ -1,353 +1,357 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "3bf01a75-a01b-472e-bc0c-9fe97658eb46",
- "metadata": {},
- "source": [
- "## LlamaIndex <> Langchain Integrations\n",
- "\n",
- "This demo notebook shows how you can provide integrations between LlamaIndex and Langchain. It provides the following examples:\n",
- "- Using LlamaIndex as a callable tool with a Langchain agent\n",
- "- Using LlamaIndex as a memory module; this allows you to insert arbitrary amounts of conversation history with a Langchain chatbot!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c1568569",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "bb9177a4-9cb7-4211-b463-121d850b5917",
- "metadata": {},
- "source": [
- "#### Using LlamaIndex as a Callable Tool"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "12af1b0e-983f-4fc1-b5b4-2edeb2e8f07e",
- "metadata": {},
- "outputs": [],
- "source": [
- "from langchain.agents import Tool\n",
- "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
- "from langchain.chat_models import ChatOpenAI\n",
- "from langchain.agents import initialize_agent\n",
- "\n",
- "from llama_index import VectorStoreIndex, SimpleDirectoryReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "1226132b-2c5f-4073-bfdb-0e36c681c12f",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = VectorStoreIndex.from_documents(documents=documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8c9b3567-c95c-473d-afc0-516b5f35e197",
- "metadata": {},
- "outputs": [],
- "source": [
- "tools = [\n",
- " Tool(\n",
- " name = \"LlamaIndex\",\n",
- " func=lambda q: str(index.as_query_engine().query(q)),\n",
- " description=\"useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.\",\n",
- " return_direct=True\n",
- " ),\n",
- "]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "374e9f69-1f75-4a62-afdc-22f748d4bddd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
- "llm = ChatOpenAI(temperature=0)\n",
- "agent_executor = initialize_agent(tools, llm, agent=\"conversational-react-description\", memory=memory)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "579fbc9f-9f13-416c-bde4-7e56fb899727",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m\n",
- "Thought: Do I need to use a tool? No\n",
- "AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'Hi Bob, nice to meet you! How can I help you today?'"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "agent_executor.run(input=\"hi, i am bob\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "a9841c8e-f90b-4e40-a2f9-ad1e98bb9eef",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m\n",
- "Thought: Do I need to use a tool? Yes\n",
- "Action: GPT Index\n",
- "Action Input: What did the author do growing up?\u001b[0m> [query] Total LLM token usage: 3841 tokens\n",
- "> [query] Total embedding token usage: 8 tokens\n",
- "\n",
- "Observation: \u001b[36;1m\u001b[1;3m\n",
- "\n",
- "The author grew up writing short stories, programming on an IBM 1401, and building a computer kit with a friend. He also wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He studied philosophy in college, but switched to AI and taught himself Lisp. He wrote a book about Lisp hacking and reverse-engineered SHRDLU. He also took art classes at Harvard and applied to art schools, but was disappointed by the lack of teaching and learning in the painting department at the Accademia. He also had experience with 19th century studio painting conventions, such as having a little stove fed with kindling and a nude model sitting as close to it as possible.\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'\\n\\nThe author grew up writing short stories, programming on an IBM 1401, and building a computer kit with a friend. He also wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He studied philosophy in college, but switched to AI and taught himself Lisp. He wrote a book about Lisp hacking and reverse-engineered SHRDLU. He also took art classes at Harvard and applied to art schools, but was disappointed by the lack of teaching and learning in the painting department at the Accademia. He also had experience with 19th century studio painting conventions, such as having a little stove fed with kindling and a nude model sitting as close to it as possible.'"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "agent_executor.run(input=\"What did the author do growing up?\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7c02eb88-5a4a-4694-9b77-cd46adc691f5",
- "metadata": {},
- "source": [
- "#### Using GPT Index as a memory module"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "e06a04c1-c5fa-482c-b4d7-9b3fa0f904af",
- "metadata": {},
- "outputs": [],
- "source": [
- "# try using GPT List Index!\n",
- "from langchain import OpenAI\n",
- "from langchain.llms import OpenAIChat\n",
- "from langchain.agents import initialize_agent\n",
- "\n",
- "from llama_index import ListIndex\n",
- "from llama_index.langchain_helpers.memory_wrapper import GPTIndexChatMemory"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "00e6694b-25fc-4fbc-8223-9a5605dc641f",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "index = ListIndex([])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "25c0b10c-bca4-49f1-9353-646a182050cf",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# NOTE: you can also use a conversational chain\n",
- "\n",
- "memory = GPTIndexChatMemory(\n",
- " index=index, \n",
- " memory_key=\"chat_history\", \n",
- " query_kwargs={\"response_mode\": \"compact\"},\n",
- " # return_source returns source nodes instead of querying index\n",
- " return_source=True,\n",
- " # return_messages returns context in message format\n",
- " return_messages=True\n",
- ")\n",
- "llm = OpenAIChat(temperature=0)\n",
- "# llm=OpenAI(temperature=0)\n",
- "agent_executor = initialize_agent([], llm, agent=\"conversational-react-description\", memory=memory)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "76193275-c47c-426c-b7e4-c54d31fda92d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 0 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n",
- "\n",
- "\n",
- "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m\n",
- "Thought: Do I need to use a tool? No\n",
- "AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n",
- "> Adding chunk: Human: hi, i am bob\n",
- "AI: Hi Bob, nice to meet yo...\n",
- "> [insert] Total LLM token usage: 0 tokens\n",
- "> [insert] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'Hi Bob, nice to meet you! How can I help you today?'"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "agent_executor.run(input=\"hi, i am bob\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "d426239a-a38b-4ae9-838b-b6fab43970e0",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [query] Total LLM token usage: 64 tokens\n",
- "> [query] Total embedding token usage: 0 tokens\n",
- "\n",
- "\n",
- "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
- "\u001b[32;1m\u001b[1;3m\n",
- "Thought: Do I need to use a tool? No\n",
- "AI: Your name is Bob.\u001b[0m\n",
- "\n",
- "\u001b[1m> Finished chain.\u001b[0m\n",
- "> Adding chunk: Human: what's my name?\n",
- "AI: Your name is Bob....\n",
- "> [insert] Total LLM token usage: 0 tokens\n",
- "> [insert] Total embedding token usage: 0 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'Your name is Bob.'"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# NOTE: the query now calls the ListIndex memory module. \n",
- "agent_executor.run(input=\"what's my name?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ddb06074-917e-4c54-acc6-d74ffae23766",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "3bf01a75-a01b-472e-bc0c-9fe97658eb46",
+ "metadata": {},
+ "source": [
+ "## LlamaIndex <> Langchain Integrations\n",
+ "\n",
+ "This demo notebook shows how you can provide integrations between LlamaIndex and Langchain. It provides the following examples:\n",
+ "- Using LlamaIndex as a callable tool with a Langchain agent\n",
+ "- Using LlamaIndex as a memory module; this allows you to insert arbitrary amounts of conversation history with a Langchain chatbot!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c1568569",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "bb9177a4-9cb7-4211-b463-121d850b5917",
+ "metadata": {},
+ "source": [
+ "#### Using LlamaIndex as a Callable Tool"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "12af1b0e-983f-4fc1-b5b4-2edeb2e8f07e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain.agents import Tool\n",
+ "from langchain.chains.conversation.memory import ConversationBufferMemory\n",
+ "from langchain.chat_models import ChatOpenAI\n",
+ "from langchain.agents import initialize_agent\n",
+ "\n",
+ "from llama_index import VectorStoreIndex, SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "1226132b-2c5f-4073-bfdb-0e36c681c12f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = VectorStoreIndex.from_documents(documents=documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8c9b3567-c95c-473d-afc0-516b5f35e197",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tools = [\n",
+ " Tool(\n",
+ " name=\"LlamaIndex\",\n",
+ " func=lambda q: str(index.as_query_engine().query(q)),\n",
+ " description=\"useful for when you want to answer questions about the author. The input to this tool should be a complete english sentence.\",\n",
+ " return_direct=True,\n",
+ " ),\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "374e9f69-1f75-4a62-afdc-22f748d4bddd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
+ "llm = ChatOpenAI(temperature=0)\n",
+ "agent_executor = initialize_agent(\n",
+ " tools, llm, agent=\"conversational-react-description\", memory=memory\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "579fbc9f-9f13-416c-bde4-7e56fb899727",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m\n",
+ "Thought: Do I need to use a tool? No\n",
+ "AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
+ "\n",
+ "\u001b[1m> Finished chain.\u001b[0m\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/plain": [
+ "'Hi Bob, nice to meet you! How can I help you today?'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agent_executor.run(input=\"hi, i am bob\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a9841c8e-f90b-4e40-a2f9-ad1e98bb9eef",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m\n",
+ "Thought: Do I need to use a tool? Yes\n",
+ "Action: GPT Index\n",
+ "Action Input: What did the author do growing up?\u001b[0m> [query] Total LLM token usage: 3841 tokens\n",
+ "> [query] Total embedding token usage: 8 tokens\n",
+ "\n",
+ "Observation: \u001b[36;1m\u001b[1;3m\n",
+ "\n",
+ "The author grew up writing short stories, programming on an IBM 1401, and building a computer kit with a friend. He also wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He studied philosophy in college, but switched to AI and taught himself Lisp. He wrote a book about Lisp hacking and reverse-engineered SHRDLU. He also took art classes at Harvard and applied to art schools, but was disappointed by the lack of teaching and learning in the painting department at the Accademia. He also had experience with 19th century studio painting conventions, such as having a little stove fed with kindling and a nude model sitting as close to it as possible.\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m\u001b[0m\n",
+ "\n",
+ "\u001b[1m> Finished chain.\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'\\n\\nThe author grew up writing short stories, programming on an IBM 1401, and building a computer kit with a friend. He also wrote simple games, a program to predict how high his model rockets would fly, and a word processor. He studied philosophy in college, but switched to AI and taught himself Lisp. He wrote a book about Lisp hacking and reverse-engineered SHRDLU. He also took art classes at Harvard and applied to art schools, but was disappointed by the lack of teaching and learning in the painting department at the Accademia. He also had experience with 19th century studio painting conventions, such as having a little stove fed with kindling and a nude model sitting as close to it as possible.'"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agent_executor.run(input=\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7c02eb88-5a4a-4694-9b77-cd46adc691f5",
+ "metadata": {},
+ "source": [
+ "#### Using GPT Index as a memory module"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e06a04c1-c5fa-482c-b4d7-9b3fa0f904af",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# try using GPT List Index!\n",
+ "from langchain import OpenAI\n",
+ "from langchain.llms import OpenAIChat\n",
+ "from langchain.agents import initialize_agent\n",
+ "\n",
+ "from llama_index import ListIndex\n",
+ "from llama_index.langchain_helpers.memory_wrapper import GPTIndexChatMemory"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "00e6694b-25fc-4fbc-8223-9a5605dc641f",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = ListIndex([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "25c0b10c-bca4-49f1-9353-646a182050cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# NOTE: you can also use a conversational chain\n",
+ "\n",
+ "memory = GPTIndexChatMemory(\n",
+ " index=index,\n",
+ " memory_key=\"chat_history\",\n",
+ " query_kwargs={\"response_mode\": \"compact\"},\n",
+ " # return_source returns source nodes instead of querying index\n",
+ " return_source=True,\n",
+ " # return_messages returns context in message format\n",
+ " return_messages=True,\n",
+ ")\n",
+ "llm = OpenAIChat(temperature=0)\n",
+ "# llm=OpenAI(temperature=0)\n",
+ "agent_executor = initialize_agent(\n",
+ " [], llm, agent=\"conversational-react-description\", memory=memory\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "76193275-c47c-426c-b7e4-c54d31fda92d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 0 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n",
+ "\n",
+ "\n",
+ "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m\n",
+ "Thought: Do I need to use a tool? No\n",
+ "AI: Hi Bob, nice to meet you! How can I help you today?\u001b[0m\n",
+ "\n",
+ "\u001b[1m> Finished chain.\u001b[0m\n",
+ "> Adding chunk: Human: hi, i am bob\n",
+ "AI: Hi Bob, nice to meet yo...\n",
+ "> [insert] Total LLM token usage: 0 tokens\n",
+ "> [insert] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Hi Bob, nice to meet you! How can I help you today?'"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agent_executor.run(input=\"hi, i am bob\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "d426239a-a38b-4ae9-838b-b6fab43970e0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [query] Total LLM token usage: 64 tokens\n",
+ "> [query] Total embedding token usage: 0 tokens\n",
+ "\n",
+ "\n",
+ "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+ "\u001b[32;1m\u001b[1;3m\n",
+ "Thought: Do I need to use a tool? No\n",
+ "AI: Your name is Bob.\u001b[0m\n",
+ "\n",
+ "\u001b[1m> Finished chain.\u001b[0m\n",
+ "> Adding chunk: Human: what's my name?\n",
+ "AI: Your name is Bob....\n",
+ "> [insert] Total LLM token usage: 0 tokens\n",
+ "> [insert] Total embedding token usage: 0 tokens\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Your name is Bob.'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# NOTE: the query now calls the ListIndex memory module.\n",
+ "agent_executor.run(input=\"what's my name?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ddb06074-917e-4c54-acc6-d74ffae23766",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/multimodal/Multimodal.ipynb b/examples/multimodal/Multimodal.ipynb
index 285d9e680e..83af104099 100644
--- a/examples/multimodal/Multimodal.ipynb
+++ b/examples/multimodal/Multimodal.ipynb
@@ -9,11 +9,11 @@
"source": [
"from llama_index import SimpleDirectoryReader, VectorStoreIndex\n",
"from llama_index.readers.file.base import (\n",
- " DEFAULT_FILE_READER_CLS, \n",
+ " DEFAULT_FILE_READER_CLS,\n",
" ImageReader,\n",
")\n",
"from llama_index.response.notebook_utils import (\n",
- " display_response, \n",
+ " display_response,\n",
" display_image,\n",
")\n",
"from llama_index.indices.query.query_transform.base import (\n",
@@ -29,7 +29,7 @@
"outputs": [],
"source": [
"# NOTE: we add filename as metadata for all documents\n",
- "filename_fn = lambda filename: {'file_name': filename}"
+ "filename_fn = lambda filename: {\"file_name\": filename}"
]
},
{
@@ -65,7 +65,7 @@
],
"source": [
"receipt_reader = SimpleDirectoryReader(\n",
- " input_dir='data/receipts', \n",
+ " input_dir=\"data/receipts\",\n",
" file_metadata=filename_fn,\n",
")\n",
"receipt_documents = receipt_reader.load_data()"
@@ -109,9 +109,11 @@
"\n",
"\n",
"query_engine = receipts_index.as_query_engine()\n",
- "query_engine = TransformQueryEngine(query_engine, query_transform=ImageOutputQueryTransform(width=400))\n",
+ "query_engine = TransformQueryEngine(\n",
+ " query_engine, query_transform=ImageOutputQueryTransform(width=400)\n",
+ ")\n",
"receipts_response = query_engine.query(\n",
- " 'When was the last time I went to McDonald\\'s and how much did I spend?',\n",
+ " \"When was the last time I went to McDonald's and how much did I spend?\",\n",
")"
]
},
@@ -184,7 +186,7 @@
],
"source": [
"llama_reader = SimpleDirectoryReader(\n",
- " input_dir='./data/llama',\n",
+ " input_dir=\"./data/llama\",\n",
" file_metadata=filename_fn,\n",
")\n",
"llama_documents = llama_reader.load_data()"
@@ -210,12 +212,12 @@
"from llama_index.query_engine import TransformQueryEngine\n",
"\n",
"\n",
- "query_engine = llama_index.as_query_engine(\n",
- " similarity_top_k=2\n",
+ "query_engine = llama_index.as_query_engine(similarity_top_k=2)\n",
+ "query_engine = TransformQueryEngine(\n",
+ " query_engine, query_transform=ImageOutputQueryTransform(width=400)\n",
")\n",
- "query_engine = TransformQueryEngine(query_engine, query_transform=ImageOutputQueryTransform(width=400))\n",
"llama_response = query_engine.query(\n",
- " 'Show an image to illustrate how tree index works and explain briefly.', \n",
+ " \"Show an image to illustrate how tree index works and explain briefly.\",\n",
")"
]
},
@@ -268,7 +270,7 @@
"outputs": [],
"source": [
"llama_response = query_engine.query(\n",
- " 'Show an image to illustrate how vector store index works and explain briefly.', \n",
+ " \"Show an image to illustrate how vector store index works and explain briefly.\",\n",
")"
]
},
diff --git a/examples/paul_graham_essay/DavinciComparison.ipynb b/examples/paul_graham_essay/DavinciComparison.ipynb
index 1b1c1a3881..d090c354be 100644
--- a/examples/paul_graham_essay/DavinciComparison.ipynb
+++ b/examples/paul_graham_essay/DavinciComparison.ipynb
@@ -9,7 +9,8 @@
"source": [
"# My OpenAI Key\n",
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
]
},
{
@@ -68,8 +69,10 @@
"llm = OpenAI(temperature=0, model=\"text-davinci-002\")\n",
"service_context = ServiceContext.from_defaults(llm=llm)\n",
"\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = KeywordTableIndex.from_documents(documents=documents, service_context=service_context)"
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = KeywordTableIndex.from_documents(\n",
+ " documents=documents, service_context=service_context\n",
+ ")"
]
},
{
@@ -152,8 +155,10 @@
"llm = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
"service_context = ServiceContext.from_defaults(llm=llm)\n",
"\n",
- "documents = SimpleDirectoryReader('../paul_graham_essay/data').load_data()\n",
- "index = KeywordTableIndex.from_documents(documents=documents, service_context=service_context)"
+ "documents = SimpleDirectoryReader(\"../paul_graham_essay/data\").load_data()\n",
+ "index = KeywordTableIndex.from_documents(\n",
+ " documents=documents, service_context=service_context\n",
+ ")"
]
},
{
diff --git a/examples/paul_graham_essay/GPT4Comparison.ipynb b/examples/paul_graham_essay/GPT4Comparison.ipynb
index 2a78d560b5..475db06e5a 100644
--- a/examples/paul_graham_essay/GPT4Comparison.ipynb
+++ b/examples/paul_graham_essay/GPT4Comparison.ipynb
@@ -1,657 +1,654 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 62,
- "id": "4921c412",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext\n",
- "from llama_index.response.notebook_utils import display_response\n",
- "from llama_index.llms import OpenAI\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "261d923e",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f23b5169",
- "metadata": {},
- "source": [
- "# davinci-003"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "id": "0c635cdb",
- "metadata": {},
- "outputs": [],
- "source": [
- "llm = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
- "service_context = ServiceContext.from_defaults(llm=llm)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "id": "b8ad1a2a",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "davinci_index = ListIndex.from_documents(documents, service_context=service_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "id": "c9925597",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Document is split into 6 nodes.'"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "f'Document is split into {len(davinci_index._index_struct.nodes)} nodes.'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 68,
- "id": "fa1d7242",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.common.tree.base:> Building index from nodes: 5 chunks\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 19882 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = davinci_index.as_query_engine(\n",
- "\n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What happened on one night in October 2003?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 69,
- "id": "d758bdb7",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**`Final Response:`** It is not possible to answer this question with the given context information."
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 1/6`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** What I Worked On\n",
- "\n",
- "February 2021\n",
- "\n",
- "Before college the two main things I worked on, outside of schoo...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 2/6`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** whereby the students wouldn't require the faculty to teach anything, and in return the faculty wo...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 3/6`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husba...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 4/6`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hacke...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 5/6`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]\n",
- "\n",
- "As w...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 6/6`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** and some people dislike being told such things.\n",
- "\n",
- "[11] People put plenty of stuff on the internet ...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_response(response)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "3f843a73",
- "metadata": {},
- "source": [
- "# gpt-4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 63,
- "id": "0849d860",
- "metadata": {},
- "outputs": [],
- "source": [
- "llm = OpenAI(temperature=0, model=\"gpt-4\")\n",
- "service_context = ServiceContext.from_defaults(llm=llm)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 64,
- "id": "bb9eff4a",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "gpt4_index = ListIndex.from_documents(documents, service_context=service_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "id": "cb56a205",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Document is split into 3 nodes.'"
- ]
- },
- "execution_count": 65,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "f'Document is split into {len(gpt4_index._index_struct.nodes)} nodes.'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 70,
- "id": "44dda700",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:llama_index.indices.common.tree.base:> Building index from nodes: 2 chunks\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 18006 tokens\n",
- "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "query_engine = gpt4_index.as_query_engine(\n",
- " response_mode=\"tree_summarize\"\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What happened on one night in October 2003?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 71,
- "id": "42bd0984",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "**`Final Response:`** On one night in October 2003, there was a big party at Paul Graham's house, organized by his friend Maria Daniels. At this party, Paul met Jessica Livingston, who would later become his partner in starting Y Combinator. Additionally, Paul Graham had a conversation with his friend Robert Morris about starting a new kind of venture firm that would fund startups in batches, which eventually led to the creation of Y Combinator."
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 1/3`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** 0.740238551627948<br>**Text:** What I Worked On\n",
- "\n",
- "February 2021\n",
- "\n",
- "Before college the two main things I worked on, outside of schoo...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 2/3`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** really good. He recommended Trevor Blackwell, which surprised me at first, because at that point ...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "---"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**`Source Node 3/3`**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd t...<br>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display_response(response)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "fd981e5e",
- "metadata": {},
- "source": [
- "# gpt-4-32k"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "9d9f20a9",
- "metadata": {},
- "source": [
- "NOTE: not available yet"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "71137f57",
- "metadata": {},
- "outputs": [],
- "source": [
- "llm = OpenAI(temperature=0, model_name=\"gpt-4-32k\")\n",
- "service_context = ServiceContext.from_defaults(llm=llm)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bb619782",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import VectorStoreIndex\n",
- "\n",
- "\n",
- "gpt4_32k_index = VectorStoreIndex.from_documents(documents, service_context=service_context)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7d417f6a",
- "metadata": {},
- "outputs": [],
- "source": [
- "len(gpt4_32k_index._index_struct.nodes_dict)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "4921c412",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, SimpleDirectoryReader, LLMPredictor, ServiceContext\n",
+ "from llama_index.response.notebook_utils import display_response\n",
+ "from llama_index.llms import OpenAI\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "261d923e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f23b5169",
+ "metadata": {},
+ "source": [
+ "# davinci-003"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "0c635cdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = OpenAI(temperature=0, model=\"text-davinci-003\")\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "b8ad1a2a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "davinci_index = ListIndex.from_documents(documents, service_context=service_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "c9925597",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Document is split into 6 nodes.'"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f\"Document is split into {len(davinci_index._index_struct.nodes)} nodes.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "fa1d7242",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.common.tree.base:> Building index from nodes: 5 chunks\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 19882 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = davinci_index.as_query_engine(response_mode=\"tree_summarize\")\n",
+ "response = query_engine.query(\n",
+ " \"What happened on one night in October 2003?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "d758bdb7",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**`Final Response:`** It is not possible to answer this question with the given context information."
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 1/6`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** What I Worked On\n",
+ "\n",
+ "February 2021\n",
+ "\n",
+ "Before college the two main things I worked on, outside of schoo...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 2/6`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** whereby the students wouldn't require the faculty to teach anything, and in return the faculty wo...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 3/6`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husba...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 4/6`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hacke...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 5/6`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** chance it had to do with HN, and a 40% chance it had do with everything else combined. [17]\n",
+ "\n",
+ "As w...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 6/6`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** and some people dislike being told such things.\n",
+ "\n",
+ "[11] People put plenty of stuff on the internet ...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_response(response)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "3f843a73",
+ "metadata": {},
+ "source": [
+ "# gpt-4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "0849d860",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = OpenAI(temperature=0, model=\"gpt-4\")\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "id": "bb9eff4a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total LLM token usage: 0 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [build_index_from_documents] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "gpt4_index = ListIndex.from_documents(documents, service_context=service_context)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "cb56a205",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Document is split into 3 nodes.'"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f\"Document is split into {len(gpt4_index._index_struct.nodes)} nodes.\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "44dda700",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:llama_index.indices.common.tree.base:> Building index from nodes: 2 chunks\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total LLM token usage: 18006 tokens\n",
+ "INFO:llama_index.token_counter.token_counter:> [query] Total embedding token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = gpt4_index.as_query_engine(response_mode=\"tree_summarize\")\n",
+ "response = query_engine.query(\n",
+ " \"What happened on one night in October 2003?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "42bd0984",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "**`Final Response:`** On one night in October 2003, there was a big party at Paul Graham's house, organized by his friend Maria Daniels. At this party, Paul met Jessica Livingston, who would later become his partner in starting Y Combinator. Additionally, Paul Graham had a conversation with his friend Robert Morris about starting a new kind of venture firm that would fund startups in batches, which eventually led to the creation of Y Combinator."
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 1/3`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** 0.740238551627948<br>**Text:** What I Worked On\n",
+ "\n",
+ "February 2021\n",
+ "\n",
+ "Before college the two main things I worked on, outside of schoo...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 2/3`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** really good. He recommended Trevor Blackwell, which surprised me at first, because at that point ...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "---"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**`Source Node 3/3`**"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "**Document ID:** 2ea119c7-fc3d-4090-a47a-8dd2a0d37416<br>**Similarity:** None<br>**Text:** make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd t...<br>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display_response(response)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "fd981e5e",
+ "metadata": {},
+ "source": [
+ "# gpt-4-32k"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "9d9f20a9",
+ "metadata": {},
+ "source": [
+ "NOTE: not available yet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71137f57",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "llm = OpenAI(temperature=0, model_name=\"gpt-4-32k\")\n",
+ "service_context = ServiceContext.from_defaults(llm=llm)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb619782",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import VectorStoreIndex\n",
+ "\n",
+ "\n",
+ "gpt4_32k_index = VectorStoreIndex.from_documents(\n",
+ " documents, service_context=service_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d417f6a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(gpt4_32k_index._index_struct.nodes_dict)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/paul_graham_essay/InsertDemo.ipynb b/examples/paul_graham_essay/InsertDemo.ipynb
index 0065b4bcb7..8d96e28b58 100644
--- a/examples/paul_graham_essay/InsertDemo.ipynb
+++ b/examples/paul_graham_essay/InsertDemo.ipynb
@@ -20,7 +20,8 @@
"source": [
"# My OpenAI Key\n",
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
]
},
{
@@ -62,7 +63,7 @@
"metadata": {},
"outputs": [],
"source": [
- "document = SimpleDirectoryReader('data').load_data()[0]\n",
+ "document = SimpleDirectoryReader(\"data\").load_data()[0]\n",
"text_splitter = TokenTextSplitter(separator=\" \", chunk_size=2048, chunk_overlap=20)\n",
"text_chunks = text_splitter.split_text(document.text)\n",
"doc_chunks = [Document(text=t) for t in text_chunks]"
@@ -198,7 +199,7 @@
"outputs": [],
"source": [
"# NOTE: we truncate to the first 30 nodes to save on cost\n",
- "document = SimpleDirectoryReader('data').load_data()[0]\n",
+ "document = SimpleDirectoryReader(\"data\").load_data()[0]\n",
"text_splitter = TokenTextSplitter(separator=\" \", chunk_size=256, chunk_overlap=20)\n",
"text_chunks = text_splitter.split_text(document.get_text())\n",
"doc_chunks = [Document(text=t) for t in text_chunks]\n",
diff --git a/examples/paul_graham_essay/KeywordTableComparison.ipynb b/examples/paul_graham_essay/KeywordTableComparison.ipynb
index 4f69edbcd9..092a0c4f84 100644
--- a/examples/paul_graham_essay/KeywordTableComparison.ipynb
+++ b/examples/paul_graham_essay/KeywordTableComparison.ipynb
@@ -1,441 +1,435 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "a6457769-dfaf-4241-ab32-dcf901dde902",
- "metadata": {
- "tags": []
- },
- "source": [
- "## GPT Keyword Table Index Comparisons\n",
- "\n",
- "Comparing SimpleKeywordTableIndex, RAKEKeywordTableIndex, KeywordTableIndex.\n",
- "\n",
- "- SimpleKeywordTableIndex - uses simple regex to extract keywords.\n",
- "- RAKEKeywordTableIndex - uses RAKE to extract keywords.\n",
- "- KeywordTableIndex - uses GPT to extract keywords."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "075080e5-c255-4a5c-9330-9da11532e1c8",
- "metadata": {
- "tags": []
- },
- "source": [
- "#### SimpleKeywordTableIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "b367b7ef-6a7d-4aee-b174-dba6ec4d2e21",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package stopwords to /home/jerry/nltk_data...\n",
- "[nltk_data] Package stopwords is already up-to-date!\n"
- ]
- }
- ],
- "source": [
- "from llama_index import SimpleKeywordTableIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1f8248fa-e0bd-494a-ad68-8192ccc87696",
- "metadata": {},
- "outputs": [],
- "source": [
- "# build keyword index\n",
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = SimpleKeywordTableIndex(documents)\n",
- "query_engine = index.as_query_engine()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53833655-0296-4bcb-b501-259b043d68b3",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = query_engine.query(\"What did the author do after his time at YC?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "62bcca18-b644-4393-ad29-6c5f0424fb22",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "The author went on to write essays and work on other projects, including a new version of the Arc programming language and Hacker News. He also started painting, but stopped after a few months. In 2015, he started working on a new Lisp programming language, which he finished in 2019. The author then moved to England in 2016 with his family and continued writing essays. In 2019, he finished Bel and wrote a bunch of essays on various topics.\n",
- "\n",
- "The author also worked on building online stores in 1995 after finishing ANSI Common Lisp. He ran the software on servers and let users control it by clicking on links, which was a new concept at the time. In 1996, he co-founded Viaweb with Robert Morris, which was later acquired by Yahoo in 1998. After leaving Yahoo, the author moved back to New York and started painting again. In 2000, he had the idea for a web application that would let people edit code on a server and host the resulting applications, which later became known as \"Reddit\".</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d24f9a20-48a6-4131-91b9-b01448c6ecb5",
- "metadata": {},
- "source": [
- "#### RAKEKeywordTableIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "c4d3f293-e608-4b90-86aa-9bce666dbcd5",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package stopwords to /home/jerry/nltk_data...\n",
- "[nltk_data] Package stopwords is already up-to-date!\n"
- ]
- }
- ],
- "source": [
- "from llama_index import RAKEKeywordTableIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "66b1da3b-8231-4da9-8026-4f95481c79df",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [],
- "source": [
- "# build keyword index\n",
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = RAKEKeywordTableIndex(documents)\n",
- "query_engine = index.as_query_engine()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "f13e5543-c6cb-4651-986c-ecde0f4bf789",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the author do after his time at YC?\n",
- "Extracted keywords: []\n"
- ]
- }
- ],
- "source": [
- "response = query_engine.query(\"What did the author do after his time at YC?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "5ae01ac3-55fa-43a3-9b24-f733072d5f8d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>Empty response</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "59cee6cf-92df-40d8-8dad-a40b792de96f",
- "metadata": {},
- "source": [
- "#### KeywordTableIndex"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "78d59ef6-70b0-47bb-818d-7237a3b7de75",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import KeywordTableIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5a3f1c67-6d73-4f37-afcf-9e637002fcff",
- "metadata": {},
- "outputs": [],
- "source": [
- "# build keyword index\n",
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = KeywordTableIndex.from_documents(documents)\n",
- "query_engine = index.as_query_engine()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "69d4f686-6825-49cf-a113-d2fdd484de77",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "a483514d-4ab5-489d-8b99-7250df491ce3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "After a few years, the author decided to step away from Y Combinator to focus on other projects, such as painting and writing essays. In 2013, he handed over control of Y Combinator to Sam Altman. The author's mother passed away in 2014, and after taking some time to grieve, he returned to writing essays and working on Lisp. He continued working on Lisp until 2019, when he finally completed the project.\n",
- "\n",
- "In 2015, the author decided to move to England with his family. They originally intended to only stay for a year, but ended up liking it so much that they remained there. The author wrote Bel while living in England. In 2019, he finally finished the project. After completing Bel, the author wrote a number of essays on various topics. He continued writing essays through 2020, but also started thinking about other things he could work on.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "112e21ee-587c-4d8b-871e-cb99b94e3778",
- "metadata": {},
- "source": [
- "## GPT Keyword Table Query Comparisons\n",
- "Compare retriever_mode={\"default\", \"simple\", \"rake\"}"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3029961a-ec22-42a1-90d6-f5892eb81e34",
- "metadata": {},
- "outputs": [],
- "source": [
- "# build table with default KeywordTableIndex\n",
- "from llama_index import KeywordTableIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display\n",
- "\n",
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = KeywordTableIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d75b31da-4788-4295-8642-07ac5c4f11a5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "Extracted keywords: ['y combinator', 'combinator']\n",
- "> Querying with idx: 235042210695008001: of excluding them, because there were so many s...\n",
- "> Querying with idx: 7029274505691774319: it was like living in another country, and sinc...\n",
- "> Querying with idx: 1773317813360405038: browser, and then host the resulting applicatio...\n",
- "> Querying with idx: 3866067077574405334: person, and from those we picked 8 to fund. The...\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "The author went on to write a book about his experiences at Y Combinator, and then moved to England. He started writing essays again and also began working on a new Lisp programming language. He also wrote an essay about how he chooses what to work on.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# default\n",
- "query_engine = index.as_query_engine(\n",
- " retriever_mode=\"default\"\n",
- ")\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "07b713f4-adfc-46f7-a795-5b333e33d49d",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "Extracted keywords: ['combinator']\n",
- "> Querying with idx: 235042210695008001: of excluding them, because there were so many s...\n",
- "> Querying with idx: 7029274505691774319: it was like living in another country, and sinc...\n",
- "> Querying with idx: 1773317813360405038: browser, and then host the resulting applicatio...\n",
- "> Querying with idx: 3866067077574405334: person, and from those we picked 8 to fund. The...\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "The author went on to write a book about his experiences at Y Combinator, and then moved to England. He started writing essays again and also began working on a new Lisp programming language. He also wrote an essay about how he chooses what to work on.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# simple\n",
- "query_engine = index.as_query_engine(\n",
- " retriever_mode=\"simple\"\n",
- ")\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "d2e19ad9-3190-45e5-a28d-235c28296d70",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "Extracted keywords: ['combinator']\n",
- "> Querying with idx: 235042210695008001: of excluding them, because there were so many s...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package punkt to /home/jerry/nltk_data...\n",
- "[nltk_data] Package punkt is already up-to-date!\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Querying with idx: 7029274505691774319: it was like living in another country, and sinc...\n",
- "> Querying with idx: 1773317813360405038: browser, and then host the resulting applicatio...\n",
- "> Querying with idx: 3866067077574405334: person, and from those we picked 8 to fund. The...\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "The author went on to write a book about his experiences at Y Combinator, and then moved to England. He started writing essays again and also began working on a new Lisp programming language. He also wrote an essay about how he chooses what to work on.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# rake\n",
- "query_engine = index.as_query_engine(\n",
- " retriever_mode=\"rake\"\n",
- ")\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "myvenv",
- "language": "python",
- "name": "myvenv"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.0"
- }
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "a6457769-dfaf-4241-ab32-dcf901dde902",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## GPT Keyword Table Index Comparisons\n",
+ "\n",
+ "Comparing SimpleKeywordTableIndex, RAKEKeywordTableIndex, KeywordTableIndex.\n",
+ "\n",
+ "- SimpleKeywordTableIndex - uses simple regex to extract keywords.\n",
+ "- RAKEKeywordTableIndex - uses RAKE to extract keywords.\n",
+ "- KeywordTableIndex - uses GPT to extract keywords."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "075080e5-c255-4a5c-9330-9da11532e1c8",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "#### SimpleKeywordTableIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b367b7ef-6a7d-4aee-b174-dba6ec4d2e21",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package stopwords to /home/jerry/nltk_data...\n",
+ "[nltk_data] Package stopwords is already up-to-date!\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import SimpleKeywordTableIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1f8248fa-e0bd-494a-ad68-8192ccc87696",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build keyword index\n",
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = SimpleKeywordTableIndex(documents)\n",
+ "query_engine = index.as_query_engine()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53833655-0296-4bcb-b501-259b043d68b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\"What did the author do after his time at YC?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "62bcca18-b644-4393-ad29-6c5f0424fb22",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "The author went on to write essays and work on other projects, including a new version of the Arc programming language and Hacker News. He also started painting, but stopped after a few months. In 2015, he started working on a new Lisp programming language, which he finished in 2019. The author then moved to England in 2016 with his family and continued writing essays. In 2019, he finished Bel and wrote a bunch of essays on various topics.\n",
+ "\n",
+ "The author also worked on building online stores in 1995 after finishing ANSI Common Lisp. He ran the software on servers and let users control it by clicking on links, which was a new concept at the time. In 1996, he co-founded Viaweb with Robert Morris, which was later acquired by Yahoo in 1998. After leaving Yahoo, the author moved back to New York and started painting again. In 2000, he had the idea for a web application that would let people edit code on a server and host the resulting applications, which later became known as \"Reddit\".</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d24f9a20-48a6-4131-91b9-b01448c6ecb5",
+ "metadata": {},
+ "source": [
+ "#### RAKEKeywordTableIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c4d3f293-e608-4b90-86aa-9bce666dbcd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package stopwords to /home/jerry/nltk_data...\n",
+ "[nltk_data] Package stopwords is already up-to-date!\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index import RAKEKeywordTableIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "66b1da3b-8231-4da9-8026-4f95481c79df",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# build keyword index\n",
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = RAKEKeywordTableIndex(documents)\n",
+ "query_engine = index.as_query_engine()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "f13e5543-c6cb-4651-986c-ecde0f4bf789",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the author do after his time at YC?\n",
+ "Extracted keywords: []\n"
+ ]
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What did the author do after his time at YC?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "5ae01ac3-55fa-43a3-9b24-f733072d5f8d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>Empty response</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59cee6cf-92df-40d8-8dad-a40b792de96f",
+ "metadata": {},
+ "source": [
+ "#### KeywordTableIndex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "78d59ef6-70b0-47bb-818d-7237a3b7de75",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import KeywordTableIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a3f1c67-6d73-4f37-afcf-9e637002fcff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build keyword index\n",
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = KeywordTableIndex.from_documents(documents)\n",
+ "query_engine = index.as_query_engine()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "69d4f686-6825-49cf-a113-d2fdd484de77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a483514d-4ab5-489d-8b99-7250df491ce3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "After a few years, the author decided to step away from Y Combinator to focus on other projects, such as painting and writing essays. In 2013, he handed over control of Y Combinator to Sam Altman. The author's mother passed away in 2014, and after taking some time to grieve, he returned to writing essays and working on Lisp. He continued working on Lisp until 2019, when he finally completed the project.\n",
+ "\n",
+ "In 2015, the author decided to move to England with his family. They originally intended to only stay for a year, but ended up liking it so much that they remained there. The author wrote Bel while living in England. In 2019, he finally finished the project. After completing Bel, the author wrote a number of essays on various topics. He continued writing essays through 2020, but also started thinking about other things he could work on.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "112e21ee-587c-4d8b-871e-cb99b94e3778",
+ "metadata": {},
+ "source": [
+ "## GPT Keyword Table Query Comparisons\n",
+ "Compare retriever_mode={\"default\", \"simple\", \"rake\"}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3029961a-ec22-42a1-90d6-f5892eb81e34",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# build table with default KeywordTableIndex\n",
+ "from llama_index import KeywordTableIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display\n",
+ "\n",
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = KeywordTableIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d75b31da-4788-4295-8642-07ac5c4f11a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "Extracted keywords: ['y combinator', 'combinator']\n",
+ "> Querying with idx: 235042210695008001: of excluding them, because there were so many s...\n",
+ "> Querying with idx: 7029274505691774319: it was like living in another country, and sinc...\n",
+ "> Querying with idx: 1773317813360405038: browser, and then host the resulting applicatio...\n",
+ "> Querying with idx: 3866067077574405334: person, and from those we picked 8 to fund. The...\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "The author went on to write a book about his experiences at Y Combinator, and then moved to England. He started writing essays again and also began working on a new Lisp programming language. He also wrote an essay about how he chooses what to work on.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# default\n",
+ "query_engine = index.as_query_engine(retriever_mode=\"default\")\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "07b713f4-adfc-46f7-a795-5b333e33d49d",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "Extracted keywords: ['combinator']\n",
+ "> Querying with idx: 235042210695008001: of excluding them, because there were so many s...\n",
+ "> Querying with idx: 7029274505691774319: it was like living in another country, and sinc...\n",
+ "> Querying with idx: 1773317813360405038: browser, and then host the resulting applicatio...\n",
+ "> Querying with idx: 3866067077574405334: person, and from those we picked 8 to fund. The...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "The author went on to write a book about his experiences at Y Combinator, and then moved to England. He started writing essays again and also began working on a new Lisp programming language. He also wrote an essay about how he chooses what to work on.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# simple\n",
+ "query_engine = index.as_query_engine(retriever_mode=\"simple\")\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "d2e19ad9-3190-45e5-a28d-235c28296d70",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "Extracted keywords: ['combinator']\n",
+ "> Querying with idx: 235042210695008001: of excluding them, because there were so many s...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "[nltk_data] Downloading package punkt to /home/jerry/nltk_data...\n",
+ "[nltk_data] Package punkt is already up-to-date!\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Querying with idx: 7029274505691774319: it was like living in another country, and sinc...\n",
+ "> Querying with idx: 1773317813360405038: browser, and then host the resulting applicatio...\n",
+ "> Querying with idx: 3866067077574405334: person, and from those we picked 8 to fund. The...\n"
+ ]
+ },
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "The author went on to write a book about his experiences at Y Combinator, and then moved to England. He started writing essays again and also began working on a new Lisp programming language. He also wrote an essay about how he chooses what to work on.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# rake\n",
+ "query_engine = index.as_query_engine(retriever_mode=\"rake\")\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "myvenv",
+ "language": "python",
+ "name": "myvenv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/paul_graham_essay/SentenceSplittingDemo.ipynb b/examples/paul_graham_essay/SentenceSplittingDemo.ipynb
index 33b24cf929..7cd3bd1758 100644
--- a/examples/paul_graham_essay/SentenceSplittingDemo.ipynb
+++ b/examples/paul_graham_essay/SentenceSplittingDemo.ipynb
@@ -1,151 +1,162 @@
{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "2d202140",
- "metadata": {},
- "source": [
- "# Example of using sentence splitter chunking\n",
- "Compare the diff of splitting_1.txt and splitting_2.txt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0a23c1a8-71ea-4b6d-ae42-5c1cf4014dff",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.langchain_helpers.text_splitter import TokenTextSplitter\n",
- "from llama_index import SimpleDirectoryReader, Document\n",
- "from llama_index.utils import globals_helper\n",
- "from langchain.text_splitter import NLTKTextSplitter, SpacyTextSplitter, RecursiveCharacterTextSplitter\n",
- "\n",
- "document = SimpleDirectoryReader('data').load_data()[0]\n",
- "text_splitter_default = TokenTextSplitter() # use default settings\n",
- "text_chunks = text_splitter_default.split_text(document.text)\n",
- "doc_chunks = [Document(text=t) for t in text_chunks]\n",
- "tokenizer = globals_helper.tokenizer\n",
- "with open('splitting_1.txt', 'w') as f:\n",
- " for idx, doc in enumerate(doc_chunks):\n",
- " f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
- "\n",
- "from llama_index.langchain_helpers.text_splitter import SentenceSplitter\n",
- "\n",
- "sentence_splitter = SentenceSplitter()\n",
- "text_chunks = sentence_splitter.split_text(document.text)\n",
- "doc_chunks = [Document(text=t) for t in text_chunks]\n",
- "with open('splitting_2.txt', 'w') as f:\n",
- " for idx, doc in enumerate(doc_chunks):\n",
- " f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
- "\n",
- "nltk_splitter = NLTKTextSplitter()\n",
- "text_chunks = nltk_splitter.split_text(document.text)\n",
- "doc_chunks = [Document(text=t) for t in text_chunks]\n",
- "tokenizer = globals_helper.tokenizer\n",
- "with open('splitting_3.txt', 'w') as f:\n",
- " for idx, doc in enumerate(doc_chunks):\n",
- " f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
- "\n",
- "# spacy_splitter = SpacyTextSplitter()\n",
- "# text_chunks = spacy_splitter.split_text(document.text)\n",
- "# tokenizer = globals_helper.tokenizer\n",
- "# with open('splitting_4.txt', 'w') as f:\n",
- "# for idx, doc in enumerate(doc_chunks):\n",
- "# f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
- "\n",
- "# from langchain.text_splitter import TokenTextSplitter\n",
- "# token_text_splitter = TokenTextSplitter()\n",
- "# text_chunks = token_text_splitter.split_text(document.text)\n",
- "# doc_chunks = [Document(text=t) for t in text_chunks]\n",
- "# tokenizer = globals_helper.tokenizer\n",
- "# with open('splitting_5.txt', 'w') as f:\n",
- "# for idx, doc in enumerate(doc_chunks):\n",
- "# f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
- "\n",
- "# recursive_splitter = RecursiveCharacterTextSplitter()\n",
- "# text_chunks = recursive_splitter.split_text(document.text)\n",
- "# doc_chunks = [Document(text=t) for t in text_chunks]\n",
- "# tokenizer = globals_helper.tokenizer\n",
- "# with open('splitting_6.txt', 'w') as f:\n",
- "# for idx, doc in enumerate(doc_chunks):\n",
- "# f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "7e62ef7d",
- "metadata": {},
- "source": [
- "## Testing with Chinese"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "44711ded",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.langchain_helpers.text_splitter import SentenceSplitter\n",
- "from llama_index.schema import Document\n",
- "from llama_index.indices.service_context import ServiceContext\n",
- "from llama_index.node_parser.simple import SimpleNodeParser\n",
- "from llama_index.indices.vector_store import VectorStoreIndex\n",
- "import wikipedia"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8e1262b9",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "sentence_splitter = SentenceSplitter()\n",
- "wikipedia.set_lang('zh')\n",
- "page = wikipedia.page('美国', auto_suggest=True).content\n",
- "sentence_splitter.split_text(page)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "78dc563c",
- "metadata": {},
- "outputs": [],
- "source": [
- "\n",
- "node_parser = SimpleNodeParser(text_splitter=sentence_splitter)\n",
- "service_context = ServiceContext.from_defaults(node_parser=node_parser)\n",
- "documents = []\n",
- "documents.append(Document(text=page))\n",
- "index = VectorStoreIndex.from_documents(documents, service_context=service_context)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "2d202140",
+ "metadata": {},
+ "source": [
+ "# Example of using sentence splitter chunking\n",
+ "Compare the diff of splitting_1.txt and splitting_2.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0a23c1a8-71ea-4b6d-ae42-5c1cf4014dff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.langchain_helpers.text_splitter import TokenTextSplitter\n",
+ "from llama_index import SimpleDirectoryReader, Document\n",
+ "from llama_index.utils import globals_helper\n",
+ "from langchain.text_splitter import (\n",
+ " NLTKTextSplitter,\n",
+ " SpacyTextSplitter,\n",
+ " RecursiveCharacterTextSplitter,\n",
+ ")\n",
+ "\n",
+ "document = SimpleDirectoryReader(\"data\").load_data()[0]\n",
+ "text_splitter_default = TokenTextSplitter() # use default settings\n",
+ "text_chunks = text_splitter_default.split_text(document.text)\n",
+ "doc_chunks = [Document(text=t) for t in text_chunks]\n",
+ "tokenizer = globals_helper.tokenizer\n",
+ "with open(\"splitting_1.txt\", \"w\") as f:\n",
+ " for idx, doc in enumerate(doc_chunks):\n",
+ " f.write(\n",
+ " \"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text)))\n",
+ " + doc.text\n",
+ " )\n",
+ "\n",
+ "from llama_index.langchain_helpers.text_splitter import SentenceSplitter\n",
+ "\n",
+ "sentence_splitter = SentenceSplitter()\n",
+ "text_chunks = sentence_splitter.split_text(document.text)\n",
+ "doc_chunks = [Document(text=t) for t in text_chunks]\n",
+ "with open(\"splitting_2.txt\", \"w\") as f:\n",
+ " for idx, doc in enumerate(doc_chunks):\n",
+ " f.write(\n",
+ " \"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text)))\n",
+ " + doc.text\n",
+ " )\n",
+ "\n",
+ "nltk_splitter = NLTKTextSplitter()\n",
+ "text_chunks = nltk_splitter.split_text(document.text)\n",
+ "doc_chunks = [Document(text=t) for t in text_chunks]\n",
+ "tokenizer = globals_helper.tokenizer\n",
+ "with open(\"splitting_3.txt\", \"w\") as f:\n",
+ " for idx, doc in enumerate(doc_chunks):\n",
+ " f.write(\n",
+ " \"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text)))\n",
+ " + doc.text\n",
+ " )\n",
+ "\n",
+ "# spacy_splitter = SpacyTextSplitter()\n",
+ "# text_chunks = spacy_splitter.split_text(document.text)\n",
+ "# tokenizer = globals_helper.tokenizer\n",
+ "# with open('splitting_4.txt', 'w') as f:\n",
+ "# for idx, doc in enumerate(doc_chunks):\n",
+ "# f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
+ "\n",
+ "# from langchain.text_splitter import TokenTextSplitter\n",
+ "# token_text_splitter = TokenTextSplitter()\n",
+ "# text_chunks = token_text_splitter.split_text(document.text)\n",
+ "# doc_chunks = [Document(text=t) for t in text_chunks]\n",
+ "# tokenizer = globals_helper.tokenizer\n",
+ "# with open('splitting_5.txt', 'w') as f:\n",
+ "# for idx, doc in enumerate(doc_chunks):\n",
+ "# f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)\n",
+ "\n",
+ "# recursive_splitter = RecursiveCharacterTextSplitter()\n",
+ "# text_chunks = recursive_splitter.split_text(document.text)\n",
+ "# doc_chunks = [Document(text=t) for t in text_chunks]\n",
+ "# tokenizer = globals_helper.tokenizer\n",
+ "# with open('splitting_6.txt', 'w') as f:\n",
+ "# for idx, doc in enumerate(doc_chunks):\n",
+ "# f.write(\"\\n-------\\n\\n{}. Size: {} tokens\\n\".format(idx, len(tokenizer(doc.text))) + doc.text)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7e62ef7d",
+ "metadata": {},
+ "source": [
+ "## Testing with Chinese"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "44711ded",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index.langchain_helpers.text_splitter import SentenceSplitter\n",
+ "from llama_index.schema import Document\n",
+ "from llama_index.indices.service_context import ServiceContext\n",
+ "from llama_index.node_parser.simple import SimpleNodeParser\n",
+ "from llama_index.indices.vector_store import VectorStoreIndex\n",
+ "import wikipedia"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1262b9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sentence_splitter = SentenceSplitter()\n",
+ "wikipedia.set_lang(\"zh\")\n",
+ "page = wikipedia.page(\"美国\", auto_suggest=True).content\n",
+ "sentence_splitter.split_text(page)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "78dc563c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "node_parser = SimpleNodeParser(text_splitter=sentence_splitter)\n",
+ "service_context = ServiceContext.from_defaults(node_parser=node_parser)\n",
+ "documents = []\n",
+ "documents.append(Document(text=page))\n",
+ "index = VectorStoreIndex.from_documents(documents, service_context=service_context)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/paul_graham_essay/TestEssay.ipynb b/examples/paul_graham_essay/TestEssay.ipynb
index f9e00449a9..4a1a20116c 100644
--- a/examples/paul_graham_essay/TestEssay.ipynb
+++ b/examples/paul_graham_essay/TestEssay.ipynb
@@ -1,604 +1,601 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "6a712b56",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "be3f7baa-1c0a-430b-981b-83ddca9e71f2",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Using GPT Tree Index"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "0881f151-279e-4910-95c7-f49d3d6a4c69",
- "metadata": {},
- "source": [
- "#### [Demo] Default leaf traversal "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import TreeIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "1c297fd3-3424-41d8-9d0d-25fe6310ab62",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "370fd08f-56ff-4c24-b0c4-c93116a6d482",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "new_index = TreeIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bd14686d-1c53-4637-9340-3745f2121ae2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = new_index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "b4c87d14-d2d8-4d80-89f6-1e5972973528",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author wrote short stories and tried to program on an IBM 1401.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "a5ab5943-7c84-4c2b-ac99-ec4b5fc67e64",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author went on to start his own company.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "85c62ec3-c3cf-467e-ab0f-88ffb9f990be",
- "metadata": {},
- "source": [
- "#### [Demo] Leaf traversal with child_branch_factor=2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "46714db4-9592-4c55-9ca7-916758f2ce68",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# try using branching factor 2\n",
- "query_engine = new_index.as_query_engine(\n",
- " child_branch_factor=2\n",
- ")\n",
- "response = query_engine.query(\"What did the author do growing up?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "1ea7f891-b7e1-497a-a965-14201b220404",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author grew up writing simple programs on a TRS-80 computer, as well as trying to program on an IBM 1401. In the early 1990s, the author was a student at the Rhode Island School of Design (RISD) and then the Accademia di Belle Arti in Florence, Italy. They eventually dropped out of RISD and moved to New York City, where they got a job at Interleaf, a software company. While working there, they learned about a new markup language called HTML, which would later become a big part of their life. He also wrote a book on Lisp programming.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "3c572726-bb95-49c3-a762-d966de59ee5f",
- "metadata": {},
- "source": [
- "#### [Demo] Build Tree Index during Query-Time"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "255fb052-1ff6-4f27-881f-28d4790e9520",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "85371256-292c-473e-9485-7de5c1997a59",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> [build_index_from_documents] Total token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "index_light = TreeIndex.from_documents(documents, build_tree=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "77b0acb3-5593-4f00-8eef-315a031fedc2",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "> Building index from nodes: 5 chunks\n",
- "0/57\n",
- "10/57\n",
- "20/57\n",
- "30/57\n",
- "40/57\n",
- "50/57\n",
- "> [query] Total token usage: 18200 tokens\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'\\nThe author went back to painting.'"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query_engine = index_light.as_query_engine(\n",
- " retriever_mode=\"all_leaf\",\n",
- " response_mode='tree_summarize',\n",
- ")\n",
- "query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f9773497-9aa6-4a16-884a-cd882e63d012",
- "metadata": {},
- "source": [
- "#### [Demo] Build Tree Index with a custom Summary Prompt, directly retrieve answer from root node"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "8ab6d3ad-95e1-477a-a0dc-2ce4763ff2c4",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import Prompt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "5a91a445-6ab2-457c-850e-79c5386129db",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Building index from nodes: 5 chunks\n",
- "0/57\n",
- "10/57\n",
- "20/57\n",
- "30/57\n",
- "40/57\n",
- "50/57\n",
- "> [build_index_from_documents] Total token usage: 18031 tokens\n"
- ]
- }
- ],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "\n",
- "query_str = \"What did the author do growing up?\"\n",
- "SUMMARY_PROMPT_TMPL = (\n",
- " \"Context information is below. \\n\"\n",
- " \"---------------------\\n\"\n",
- " \"{context_str}\"\n",
- " \"\\n---------------------\\n\"\n",
- " \"Given the context information and not prior knowledge, \"\n",
- " f\"answer the question: {query_str}\\n\"\n",
- ")\n",
- "SUMMARY_PROMPT = Prompt(SUMMARY_PROMPT_TMPL)\n",
- "index_with_query = TreeIndex.from_documents(documents, summary_template=SUMMARY_PROMPT)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9223ffa8-d49d-4de3-821a-701b2a0352d4",
- "metadata": {},
- "outputs": [],
- "source": [
- "# directly retrieve response from root nodes instead of traversing tree\n",
- "query_engine = index_with_query.as_query_engine(\n",
- " retriever_mode=\"root\"\n",
- ")\n",
- "response = index_with_query.query(query_str)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "fdca6970-2f3f-4741-ae98-555db8d3d9a0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "The author was homeschooled and then attended a prestigious art school. The author grew up writing essays and thinking about other things he could work on.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "a6457769-dfaf-4241-ab32-dcf901dde902",
- "metadata": {},
- "source": [
- "## Using GPT Keyword Table Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "78d59ef6-70b0-47bb-818d-7237a3b7de75",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import KeywordTableIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "5a3f1c67-6d73-4f37-afcf-9e637002fcff",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Processing chunk 0 of 6: \t\t\n",
- "\n",
- "What I Worked On\n",
- "\n",
- "February 2021\n",
- "\n",
- "Before col...\n",
- "> Keywords: ['painting', 'computers', 'programming', 'lisp', 'ai', 'college', 'graduate school', 'graduate', 'school', 'writing']\n",
- "> Processing chunk 1 of 6: of excluding them, because there were so many s...\n",
- "> Keywords: ['school', 'students', 'painting', 'florence', 'risd', 'accademia', 'still lives', 'still', 'lives', 'color', 'new york', 'new', 'york', 'yorkville', 'idelle weber', 'idelle', 'weber', 'harvard', 'world wide web', 'world', 'wide', 'web', 'y combinator', 'combinator', 'software', 'lisp']\n",
- "> Processing chunk 2 of 6: an alarming prospect, because neither of us kne...\n",
- "> Keywords: ['windows', 'unix', 'lisp', 'web app', 'web', 'app', 'browser', 'store builder', 'store', 'builder', 'ecommerce', 'startup', 'painting']\n",
- "> Processing chunk 3 of 6: browser, and then host the resulting applicatio...\n",
- "> Keywords: ['y combinator', 'combinator', 'investment', 'summer founders program', 'summer', 'founders', 'program', 'microsoft', 'goldman sachs', 'goldman', 'sachs']\n",
- "> Processing chunk 4 of 6: person, and from those we picked 8 to fund. The...\n",
- "> Keywords: ['y combinator', 'combinator', 'yc', 'lisp', 'bel', 'essays', 'writing', 'software', 'programming', 'arc']\n",
- "> Processing chunk 5 of 6: it was like living in another country, and sinc...\n",
- "> Keywords: ['software', 'technology', 'y combinator', 'combinator', 'essays', 'online publishing', 'online', 'publishing', 'venture capital', 'venture', 'capital', 'startups', 'space aliens', 'space', 'aliens', 'lisp']\n"
- ]
- }
- ],
- "source": [
- "# build keyword index\n",
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = KeywordTableIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "69d4f686-6825-49cf-a113-d2fdd484de77",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: What did the author do after his time at Y Combinator?\n",
- "Extracted keywords: ['y combinator', 'combinator']\n",
- "> Querying with idx: 7143669651211954504: of excluding them, because there were so many s...\n",
- "> Querying with idx: 4978118451876167434: browser, and then host the resulting applicatio...\n",
- "> Querying with idx: 7378313280237489139: person, and from those we picked 8 to fund. The...\n",
- "> Querying with idx: 2670584622494666310: it was like living in another country, and sinc...\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "a483514d-4ab5-489d-8b99-7250df491ce3",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "After a few years, the author decided to step away from Y Combinator to focus on other projects, such as painting and writing essays. In 2013, he handed over control of Y Combinator to Sam Altman. The author's mother passed away in 2014, and after taking some time to grieve, he returned to writing essays and working on Lisp. He continued working on Lisp until 2019, when he finally completed the project.\n",
- "\n",
- "In 2015, the author decided to move to England with his family. They originally intended to only stay for a year, but ended up liking it so much that they remained there. The author wrote Bel while living in England. In 2019, he finally finished the project. After completing Bel, the author wrote a number of essays on various topics. He continued writing essays through 2020, but also started thinking about other things he could work on.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "aae1bea9-b534-430a-a52b-1f4414957ac9",
- "metadata": {},
- "source": [
- "## Using GPT List Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1aa8c8c1-7fce-4737-9141-d14fd37a779c",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown, display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "191caa65-a77f-4d8c-b095-4aed61300ea5",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Adding chunk: \t\t\n",
- "\n",
- "What I Worked On\n",
- "\n",
- "February 2021\n",
- "\n",
- "Before col...\n",
- "> Adding chunk: only up to age 25 and already there are such co...\n",
- "> Adding chunk: clear that it was even possible. To find out, w...\n",
- "> Adding chunk: a name for the kind of company Viaweb was, an \"...\n",
- "> Adding chunk: get their initial set of customers almost entir...\n",
- "> Adding chunk: had smart people and built impressive technolog...\n",
- "> [build_index_from_documents] Total token usage: 0 tokens\n"
- ]
- }
- ],
- "source": [
- "# build list index\n",
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1b3d4bd8-7540-4c6f-8616-ab2d8c6ae2b2",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "5101b979-175f-490e-9b32-27689fe4b789",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>\n",
- "\n",
- "After his time at Y Combinator, the author moved back to Providence to continue at RISD. However, he found that art school was not what he expected it to be and dropped out. He then moved to New York City and started writing a book on Lisp. When that didn't work out, he started a company to put art galleries online. However, that also failed. He then had the idea to start a company to build online stores, which became a success.\n",
- "\n",
- "The author then left his position at Yahoo to pursue painting full-time. However, he found it difficult to get back into the painting mindset and eventually returned to New York City. It was there that he had the idea to create a web application that would allow users to create and host their own web applications.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "65cfce56-853e-431b-888e-946771c3b07e",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.16"
- }
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6a712b56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "be3f7baa-1c0a-430b-981b-83ddca9e71f2",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Using GPT Tree Index"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "0881f151-279e-4910-95c7-f49d3d6a4c69",
+ "metadata": {},
+ "source": [
+ "#### [Demo] Default leaf traversal "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import TreeIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "1c297fd3-3424-41d8-9d0d-25fe6310ab62",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "370fd08f-56ff-4c24-b0c4-c93116a6d482",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "new_index = TreeIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd14686d-1c53-4637-9340-3745f2121ae2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = new_index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "b4c87d14-d2d8-4d80-89f6-1e5972973528",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The author wrote short stories and tried to program on an IBM 1401.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "a5ab5943-7c84-4c2b-ac99-ec4b5fc67e64",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The author went on to start his own company.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "85c62ec3-c3cf-467e-ab0f-88ffb9f990be",
+ "metadata": {},
+ "source": [
+ "#### [Demo] Leaf traversal with child_branch_factor=2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "46714db4-9592-4c55-9ca7-916758f2ce68",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# try using branching factor 2\n",
+ "query_engine = new_index.as_query_engine(child_branch_factor=2)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "1ea7f891-b7e1-497a-a965-14201b220404",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The author grew up writing simple programs on a TRS-80 computer, as well as trying to program on an IBM 1401. In the early 1990s, the author was a student at the Rhode Island School of Design (RISD) and then the Accademia di Belle Arti in Florence, Italy. They eventually dropped out of RISD and moved to New York City, where they got a job at Interleaf, a software company. While working there, they learned about a new markup language called HTML, which would later become a big part of their life. He also wrote a book on Lisp programming.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "3c572726-bb95-49c3-a762-d966de59ee5f",
+ "metadata": {},
+ "source": [
+ "#### [Demo] Build Tree Index during Query-Time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "255fb052-1ff6-4f27-881f-28d4790e9520",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "85371256-292c-473e-9485-7de5c1997a59",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> [build_index_from_documents] Total token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "index_light = TreeIndex.from_documents(documents, build_tree=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "77b0acb3-5593-4f00-8eef-315a031fedc2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "> Building index from nodes: 5 chunks\n",
+ "0/57\n",
+ "10/57\n",
+ "20/57\n",
+ "30/57\n",
+ "40/57\n",
+ "50/57\n",
+ "> [query] Total token usage: 18200 tokens\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/plain": [
+ "'\\nThe author went back to painting.'"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "query_engine = index_light.as_query_engine(\n",
+ " retriever_mode=\"all_leaf\",\n",
+ " response_mode=\"tree_summarize\",\n",
+ ")\n",
+ "query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f9773497-9aa6-4a16-884a-cd882e63d012",
+ "metadata": {},
+ "source": [
+ "#### [Demo] Build Tree Index with a custom Summary Prompt, directly retrieve answer from root node"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "8ab6d3ad-95e1-477a-a0dc-2ce4763ff2c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import Prompt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "5a91a445-6ab2-457c-850e-79c5386129db",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Building index from nodes: 5 chunks\n",
+ "0/57\n",
+ "10/57\n",
+ "20/57\n",
+ "30/57\n",
+ "40/57\n",
+ "50/57\n",
+ "> [build_index_from_documents] Total token usage: 18031 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "\n",
+ "query_str = \"What did the author do growing up?\"\n",
+ "SUMMARY_PROMPT_TMPL = (\n",
+ " \"Context information is below. \\n\"\n",
+ " \"---------------------\\n\"\n",
+ " \"{context_str}\"\n",
+ " \"\\n---------------------\\n\"\n",
+ " \"Given the context information and not prior knowledge, \"\n",
+ " f\"answer the question: {query_str}\\n\"\n",
+ ")\n",
+ "SUMMARY_PROMPT = Prompt(SUMMARY_PROMPT_TMPL)\n",
+ "index_with_query = TreeIndex.from_documents(documents, summary_template=SUMMARY_PROMPT)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9223ffa8-d49d-4de3-821a-701b2a0352d4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# directly retrieve response from root nodes instead of traversing tree\n",
+ "query_engine = index_with_query.as_query_engine(retriever_mode=\"root\")\n",
+ "response = index_with_query.query(query_str)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "fdca6970-2f3f-4741-ae98-555db8d3d9a0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "The author was homeschooled and then attended a prestigious art school. The author grew up writing essays and thinking about other things he could work on.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a6457769-dfaf-4241-ab32-dcf901dde902",
+ "metadata": {},
+ "source": [
+ "## Using GPT Keyword Table Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "78d59ef6-70b0-47bb-818d-7237a3b7de75",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import KeywordTableIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5a3f1c67-6d73-4f37-afcf-9e637002fcff",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Processing chunk 0 of 6: \t\t\n",
+ "\n",
+ "What I Worked On\n",
+ "\n",
+ "February 2021\n",
+ "\n",
+ "Before col...\n",
+ "> Keywords: ['painting', 'computers', 'programming', 'lisp', 'ai', 'college', 'graduate school', 'graduate', 'school', 'writing']\n",
+ "> Processing chunk 1 of 6: of excluding them, because there were so many s...\n",
+ "> Keywords: ['school', 'students', 'painting', 'florence', 'risd', 'accademia', 'still lives', 'still', 'lives', 'color', 'new york', 'new', 'york', 'yorkville', 'idelle weber', 'idelle', 'weber', 'harvard', 'world wide web', 'world', 'wide', 'web', 'y combinator', 'combinator', 'software', 'lisp']\n",
+ "> Processing chunk 2 of 6: an alarming prospect, because neither of us kne...\n",
+ "> Keywords: ['windows', 'unix', 'lisp', 'web app', 'web', 'app', 'browser', 'store builder', 'store', 'builder', 'ecommerce', 'startup', 'painting']\n",
+ "> Processing chunk 3 of 6: browser, and then host the resulting applicatio...\n",
+ "> Keywords: ['y combinator', 'combinator', 'investment', 'summer founders program', 'summer', 'founders', 'program', 'microsoft', 'goldman sachs', 'goldman', 'sachs']\n",
+ "> Processing chunk 4 of 6: person, and from those we picked 8 to fund. The...\n",
+ "> Keywords: ['y combinator', 'combinator', 'yc', 'lisp', 'bel', 'essays', 'writing', 'software', 'programming', 'arc']\n",
+ "> Processing chunk 5 of 6: it was like living in another country, and sinc...\n",
+ "> Keywords: ['software', 'technology', 'y combinator', 'combinator', 'essays', 'online publishing', 'online', 'publishing', 'venture capital', 'venture', 'capital', 'startups', 'space aliens', 'space', 'aliens', 'lisp']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# build keyword index\n",
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = KeywordTableIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "69d4f686-6825-49cf-a113-d2fdd484de77",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: What did the author do after his time at Y Combinator?\n",
+ "Extracted keywords: ['y combinator', 'combinator']\n",
+ "> Querying with idx: 7143669651211954504: of excluding them, because there were so many s...\n",
+ "> Querying with idx: 4978118451876167434: browser, and then host the resulting applicatio...\n",
+ "> Querying with idx: 7378313280237489139: person, and from those we picked 8 to fund. The...\n",
+ "> Querying with idx: 2670584622494666310: it was like living in another country, and sinc...\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a483514d-4ab5-489d-8b99-7250df491ce3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "After a few years, the author decided to step away from Y Combinator to focus on other projects, such as painting and writing essays. In 2013, he handed over control of Y Combinator to Sam Altman. The author's mother passed away in 2014, and after taking some time to grieve, he returned to writing essays and working on Lisp. He continued working on Lisp until 2019, when he finally completed the project.\n",
+ "\n",
+ "In 2015, the author decided to move to England with his family. They originally intended to only stay for a year, but ended up liking it so much that they remained there. The author wrote Bel while living in England. In 2019, he finally finished the project. After completing Bel, the author wrote a number of essays on various topics. He continued writing essays through 2020, but also started thinking about other things he could work on.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "aae1bea9-b534-430a-a52b-1f4414957ac9",
+ "metadata": {},
+ "source": [
+ "## Using GPT List Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1aa8c8c1-7fce-4737-9141-d14fd37a779c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown, display"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "191caa65-a77f-4d8c-b095-4aed61300ea5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Adding chunk: \t\t\n",
+ "\n",
+ "What I Worked On\n",
+ "\n",
+ "February 2021\n",
+ "\n",
+ "Before col...\n",
+ "> Adding chunk: only up to age 25 and already there are such co...\n",
+ "> Adding chunk: clear that it was even possible. To find out, w...\n",
+ "> Adding chunk: a name for the kind of company Viaweb was, an \"...\n",
+ "> Adding chunk: get their initial set of customers almost entir...\n",
+ "> Adding chunk: had smart people and built impressive technolog...\n",
+ "> [build_index_from_documents] Total token usage: 0 tokens\n"
+ ]
+ }
+ ],
+ "source": [
+ "# build list index\n",
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b3d4bd8-7540-4c6f-8616-ab2d8c6ae2b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What did the author do after his time at Y Combinator?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "5101b979-175f-490e-9b32-27689fe4b789",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>\n",
+ "\n",
+ "After his time at Y Combinator, the author moved back to Providence to continue at RISD. However, he found that art school was not what he expected it to be and dropped out. He then moved to New York City and started writing a book on Lisp. When that didn't work out, he started a company to put art galleries online. However, that also failed. He then had the idea to start a company to build online stores, which became a success.\n",
+ "\n",
+ "The author then left his position at Yahoo to pursue painting full-time. However, he found it difficult to get back into the painting mindset and eventually returned to New York City. It was there that he had the idea to create a web application that would allow users to create and host their own web applications.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "65cfce56-853e-431b-888e-946771c3b07e",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/test_wiki/TestNYC-Benchmark-GPT4.ipynb b/examples/test_wiki/TestNYC-Benchmark-GPT4.ipynb
index 7e695e4453..01dd4e954e 100644
--- a/examples/test_wiki/TestNYC-Benchmark-GPT4.ipynb
+++ b/examples/test_wiki/TestNYC-Benchmark-GPT4.ipynb
@@ -1,1681 +1,1724 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "9080b39e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging, sys\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "# Uncomment if you want to temporarily disable logger\n",
- "logging.disable(sys.maxsize)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "7de92ce3",
- "metadata": {},
- "outputs": [],
- "source": [
- "# NOTE: only necessary for querying with `use_async=True` in notebook\n",
- "import nest_asyncio\n",
- "nest_asyncio.apply()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import TreeIndex, SimpleDirectoryReader, LLMPredictor, VectorStoreIndex, ListIndex, Prompt, ServiceContext\n",
- "from llama_index.indices.base import BaseIndex\n",
- "from llama_index.llms.base import LLM\n",
- "from llama_index.llms import OpenAI\n",
- "from llama_index.response.schema import Response\n",
- "import pandas as pd\n",
- "from typing import Tuple"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "707662e5",
- "metadata": {},
- "source": [
- "# Setup data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "b4b4387b-413e-4016-ba1e-88b3d9410a38",
- "metadata": {},
- "outputs": [],
- "source": [
- "# fetch \"New York City\" page from Wikipedia\n",
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- ").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
- "\n",
- "data_path = Path('data')\n",
- "if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- "with open('data/nyc_text.txt', 'w') as fp:\n",
- " fp.write(nyc_text)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "523fbebe-6e79-4d7b-b400-188b711a0e8f",
- "metadata": {
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "DEBUG:llama_index.readers.file.base:> [SimpleDirectoryReader] Total files added: 1\n",
- "> [SimpleDirectoryReader] Total files added: 1\n"
- ]
- }
- ],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "f4a269bd",
- "metadata": {},
- "source": [
- "# Setup benchmark"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "62f01ddf",
- "metadata": {},
- "outputs": [],
- "source": [
- "from dataclasses import dataclass\n",
- "from typing import List"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "id": "4ff13cd4",
- "metadata": {},
- "outputs": [],
- "source": [
- "@dataclass\n",
- "class TestCase:\n",
- " query: str \n",
- " must_contain: List[str]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "9c653b72",
- "metadata": {},
- "outputs": [],
- "source": [
- "@dataclass\n",
- "class TestOutcome:\n",
- " test: TestCase\n",
- " response: Response\n",
- " \n",
- " @property\n",
- " def is_correct_response(self) -> bool:\n",
- " is_correct = True\n",
- " for answer in self.test.must_contain:\n",
- " if answer not in self.response.response:\n",
- " is_correct = False\n",
- " return is_correct\n",
- " \n",
- " @property\n",
- " def is_correct_source(self) -> bool:\n",
- " is_correct = True\n",
- " for answer in self.test.must_contain:\n",
- " if all(answer not in node.source_text for node in self.response.source_nodes):\n",
- " is_correct = False\n",
- " return is_correct"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "b9cd18ae",
- "metadata": {},
- "outputs": [],
- "source": [
- "class Benchmark:\n",
- " def __init__(self, tests: List[TestCase]) -> None:\n",
- " self._tests = tests\n",
- " \n",
- " def test(self, index: BaseIndex, llm: LLM, **kwargs) -> List[TestOutcome]:\n",
- " outcomes: List[TestOutcome] = []\n",
- " service_context = ServiceContext.from_defaults(llm=llm)\n",
- " for test in self._tests:\n",
- " query_engine = index.as_query_engine(\n",
- " service_context=service_context,\n",
- " **kwargs\n",
- " )\n",
- " response = query_engine.query(\n",
- " test.query,\n",
- " )\n",
- " outcome = TestOutcome(test=test, response=response)\n",
- " outcomes.append(outcome)\n",
- " return outcomes"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "id": "8edad985",
- "metadata": {},
- "outputs": [],
- "source": [
- "def analyze_outcome(outcomes: List[TestOutcome]) -> None:\n",
- " rows = []\n",
- " for outcome in outcomes:\n",
- " row = [outcome.test.query, outcome.is_correct_response, outcome.is_correct_source]\n",
- " rows.append(row)\n",
- " df = pd.DataFrame(rows, columns=['Test Query', 'Correct Response', 'Correct Source'])\n",
- " return df"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "4bc38077",
- "metadata": {},
- "outputs": [],
- "source": [
- "test_battle = TestCase(\n",
- " query=\"What battles took place in New York City in the American Revolution?\",\n",
- " must_contain=[\"Battle of Long Island\"]\n",
- ")\n",
- "\n",
- "test_mayor = TestCase(\n",
- " query='Who was elected as the mayor after the Great Depression?',\n",
- " must_contain=[\"Fiorello La Guardia\"]\n",
- ")\n",
- "\n",
- "test_tourists = TestCase(\n",
- " query='How many tourists visited New York City in 2019?',\n",
- " must_contain=['66.6 million']\n",
- ")\n",
- "test_airport = TestCase(\n",
- " query='What are the airports in New York City?',\n",
- " must_contain=['LaGuardia Airport']\n",
- ")\n",
- "test_visit = TestCase(\n",
- " query='When was the first documented visit into New York Harbor?',\n",
- " must_contain=['1524']\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "f159dadb",
- "metadata": {},
- "outputs": [],
- "source": [
- "bm = Benchmark([\n",
- " test_battle,\n",
- " test_mayor,\n",
- " test_tourists,\n",
- " test_airport,\n",
- " test_visit,\n",
- "])"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "65ddbd56",
- "metadata": {},
- "source": [
- "# LLM based evaluation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 592,
- "id": "ed175de5",
- "metadata": {},
- "outputs": [],
- "source": [
- "EVAL_PROMPT_TMPL = (\n",
- " \"Given the question below. \\n\"\n",
- " \"---------------------\\n\"\n",
- " \"{query_str}\"\n",
- " \"\\n---------------------\\n\"\n",
- " \"Decide if the following retreived context is relevant. \\n\"\n",
- " \"\\n---------------------\\n\"\n",
- " \"{context_str}\"\n",
- " \"\\n---------------------\\n\"\n",
- " \"Then decide if the answer is correct. \\n\"\n",
- " \"\\n---------------------\\n\"\n",
- " \"{answer_str}\"\n",
- " \"\\n---------------------\\n\"\n",
- " \"Answer in the following format:\\n\"\n",
- " \"'Context is relevant: <True>\\nAnswer is correct: <True>' \"\n",
- " \"and explain why.\"\n",
- ")\n",
- "\n",
- "DEFAULT_EVAL_PROMPT = Prompt(EVAL_PROMPT_TMPL)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 593,
- "id": "93c498b6",
- "metadata": {},
- "outputs": [],
- "source": [
- "import re\n",
- "def extract_eval_result(result_str: str):\n",
- " boolean_pattern = r\"(True|False)\"\n",
- " matches = re.findall(boolean_pattern, result_str)\n",
- " return [match == \"True\" for match in matches] "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 594,
- "id": "4c8109c3",
- "metadata": {},
- "outputs": [],
- "source": [
- "def analyze_outcome_llm_single(outcome: TestOutcome, llm_predictor: LLMPredictor) -> Tuple[bool, bool]:\n",
- " try:\n",
- " source_text = outcome.response.source_nodes[0].source_text\n",
- " except:\n",
- " source_text = \"Failed to retrieve any context\"\n",
- " result_str, _ = llm_predictor.predict(\n",
- " DEFAULT_EVAL_PROMPT,\n",
- " query_str=outcome.test.query,\n",
- " context_str=source_text,\n",
- " answer_str=outcome.response.response\n",
- " )\n",
- " is_context_relevant, is_answer_correct = extract_eval_result(result_str)\n",
- " return is_answer_correct, is_context_relevant, result_str\n",
- "\n",
- "def analyze_outcome_llm(outcomes: List[TestOutcome], llm_predictor: LLMPredictor) -> None:\n",
- " rows = []\n",
- " for outcome in outcomes:\n",
- " is_correct_response, is_correct_source, result_str = analyze_outcome_llm_single(outcome, llm_predictor)\n",
- " row = [outcome.test.query, is_correct_response, is_correct_source, result_str]\n",
- " rows.append(row)\n",
- " df = pd.DataFrame(rows, columns=['Test Query', 'Correct Response (LLM)', 'Correct Source (LLM)', 'Eval (LLM)'])\n",
- " return df"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "5a9f43a6",
- "metadata": {},
- "source": [
- "# Build Indices"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 643,
- "id": "790bad05",
- "metadata": {},
- "outputs": [],
- "source": [
- "vector_index = VectorStoreIndex.from_documents(\n",
- " documents, \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 473,
- "id": "64c970e0",
- "metadata": {},
- "outputs": [],
- "source": [
- "list_index = ListIndex.from_documents(\n",
- " documents, \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 468,
- "id": "bacc4f1c",
- "metadata": {},
- "outputs": [],
- "source": [
- "tree_index = TreeIndex.from_documents(documents)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "5b2e7fdd",
- "metadata": {},
- "source": [
- "# Create LLMPredictors"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "4766ac56-ac8d-4f33-b994-6901964241ea",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# gpt-4\n",
- "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 169,
- "id": "c8692cf6",
- "metadata": {},
- "outputs": [],
- "source": [
- "# gpt-3 (text-davinci-003)\n",
- "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "id": "fb74ec62",
- "metadata": {},
- "outputs": [],
- "source": [
- "# chatgpt (gpt-3.5-turbo)\n",
- "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "1354f668",
- "metadata": {},
- "source": [
- "# Benchmarking "
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "01124a3f",
- "metadata": {},
- "source": [
- "### Tree Index + GPT4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 583,
- "id": "6f418554",
- "metadata": {},
- "outputs": [],
- "source": [
- "outcomes_tree_gpt4 = bm.test(tree_index, gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 584,
- "id": "de98ceba",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Test Query</th>\n",
- " <th>Correct Response</th>\n",
- " <th>Correct Source</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>What battles took place in New York City in th...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>Who was elected as the mayor after the Great D...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>How many tourists visited New York City in 2019?</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>What are the airports in New York City?</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>When was the first documented visit into New Y...</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Test Query Correct Response \\\n",
- "0 What battles took place in New York City in th... True \n",
- "1 Who was elected as the mayor after the Great D... False \n",
- "2 How many tourists visited New York City in 2019? False \n",
- "3 What are the airports in New York City? False \n",
- "4 When was the first documented visit into New Y... False \n",
- "\n",
- " Correct Source \n",
- "0 True \n",
- "1 False \n",
- "2 False \n",
- "3 False \n",
- "4 False "
- ]
- },
- "execution_count": 584,
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- },
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- "### Tree Index + GPT3"
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- "execution_count": 549,
- "id": "ba871d2a",
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- "outputs": [],
- "source": [
- "outcomes_tree_gpt3 = bm.test(tree_index, gpt3)"
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- {
- "cell_type": "code",
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- " Test Query Correct Response \\\n",
- "0 What battles took place in New York City in th... True \n",
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- "\n",
- " Correct Source \n",
- "0 False \n",
- "1 False \n",
- "2 False \n",
- "3 False \n",
- "4 False "
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- "execution_count": 550,
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- "analyze_outcome(outcomes_tree_gpt3)"
- ]
- },
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- "metadata": {},
- "source": [
- "### List Index + GPT4"
- ]
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- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "bc0f05d1",
- "metadata": {
- "scrolled": true
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- "outputs": [],
- "source": [
- "outcomes_list_gpt4 = bm.test(list_index, gpt4, response_mode=\"tree_summarize\", use_async=True)"
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- },
- {
- "cell_type": "code",
- "execution_count": 19,
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- " Test Query Correct Response \\\n",
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- "\n",
- " Correct Source \n",
- "0 True \n",
- "1 True \n",
- "2 True \n",
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- "4 True "
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- "execution_count": 501,
- "id": "66cfa3fa",
- "metadata": {},
- "outputs": [],
- "source": [
- "outcomes_list_gpt3 = bm.test(list_index, gpt3, response_mode=\"tree_summarize\", use_async=True)"
- ]
- },
- {
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- " Test Query Correct Response \\\n",
- "0 What battles took place in New York City in th... True \n",
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- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... True \n",
- "\n",
- " Correct Source \n",
- "0 True \n",
- "1 True \n",
- "2 True \n",
- "3 True \n",
- "4 True "
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- },
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- "source": [
- "### List Index + ChatGPT"
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- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "id": "f146c74e",
- "metadata": {},
- "outputs": [],
- "source": [
- "outcomes_list_chatgpt = bm.test(list_index, chatgpt, response_mode=\"tree_summarize\", use_async=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
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- " Test Query Correct Response \\\n",
- "0 What battles took place in New York City in th... False \n",
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- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... True \n",
- "\n",
- " Correct Source \n",
- "0 True \n",
- "1 True \n",
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- "4 True "
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- "execution_count": 487,
- "id": "5349d1e7",
- "metadata": {},
- "outputs": [],
- "source": [
- "outcomes_vector_gpt4 = bm.test(vector_index, gpt4)"
- ]
- },
- {
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- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... True \n",
- "\n",
- " Correct Source \n",
- "0 True \n",
- "1 True \n",
- "2 False \n",
- "3 True \n",
- "4 True "
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- "execution_count": 644,
- "id": "e35ebdf9",
- "metadata": {},
- "outputs": [],
- "source": [
- "outcomes_vector_gpt3 = bm.test(vector_index, gpt3)"
- ]
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- " Correct Source \n",
- "0 True \n",
- "1 False \n",
- "2 False \n",
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- "4 False "
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- "# LLM based Evaluation"
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- },
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- "execution_count": 646,
- "id": "59ff561c",
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- " <td>False</td>\n",
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- "text/plain": [
- " Test Query Correct Response \\\n",
- "0 What battles took place in New York City in th... True \n",
- "1 Who was elected as the mayor after the Great D... True \n",
- "2 How many tourists visited New York City in 2019? False \n",
- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... True \n",
- "\n",
- " Correct Source \n",
- "0 True \n",
- "1 False \n",
- "2 False \n",
- "3 False \n",
- "4 False "
- ]
- },
- "execution_count": 646,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "analyze_outcome(outcomes_vector_gpt3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 647,
- "id": "e4ffaca6",
- "metadata": {},
- "outputs": [],
- "source": [
- "eval_gpt4 = analyze_outcome_llm(outcomes_vector_gpt3, gpt4)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 657,
- "id": "85c4e415",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Test Query</th>\n",
- " <th>Correct Response (LLM)</th>\n",
- " <th>Correct Source (LLM)</th>\n",
- " <th>Eval (LLM)</th>\n",
- " </tr>\n",
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- " <th>1</th>\n",
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- " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>How many tourists visited New York City in 2019?</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>What are the airports in New York City?</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>When was the first documented visit into New Y...</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Test Query Correct Response (LLM) \\\n",
- "0 What battles took place in New York City in th... True \n",
- "1 Who was elected as the mayor after the Great D... True \n",
- "2 How many tourists visited New York City in 2019? True \n",
- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... True \n",
- "\n",
- " Correct Source (LLM) Eval (LLM) \n",
- "0 True Context is relevant: True\\nAnswer is correct: ... \n",
- "1 False Context is relevant: False\\nAnswer is correct:... \n",
- "2 False Context is relevant: False\\nAnswer is correct:... \n",
- "3 False Context is relevant: False\\nAnswer is correct:... \n",
- "4 False Context is relevant: False\\nAnswer is correct:... "
- ]
- },
- "execution_count": 657,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eval_gpt4"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 651,
- "id": "3efb66d6",
- "metadata": {},
- "outputs": [],
- "source": [
- "eval_chatgpt = analyze_outcome_llm(outcomes_vector_gpt3, chatgpt)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 652,
- "id": "4c452767",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
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- " .dataframe tbody tr th {\n",
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- " }\n",
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- " .dataframe thead th {\n",
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- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Test Query</th>\n",
- " <th>Correct Response (LLM)</th>\n",
- " <th>Correct Source (LLM)</th>\n",
- " <th>Eval (LLM)</th>\n",
- " </tr>\n",
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- " </tr>\n",
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- " <th>1</th>\n",
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- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>How many tourists visited New York City in 2019?</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>\\n\\nContext is relevant: False\\nAnswer is corr...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>What are the airports in New York City?</td>\n",
- " <td>True</td>\n",
- " <td>False</td>\n",
- " <td>\\n\\nContext is relevant: False\\nAnswer is corr...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>When was the first documented visit into New Y...</td>\n",
- " <td>False</td>\n",
- " <td>True</td>\n",
- " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Test Query Correct Response (LLM) \\\n",
- "0 What battles took place in New York City in th... True \n",
- "1 Who was elected as the mayor after the Great D... True \n",
- "2 How many tourists visited New York City in 2019? False \n",
- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... False \n",
- "\n",
- " Correct Source (LLM) Eval (LLM) \n",
- "0 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
- "1 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
- "2 False \\n\\nContext is relevant: False\\nAnswer is corr... \n",
- "3 False \\n\\nContext is relevant: False\\nAnswer is corr... \n",
- "4 True \\n\\nContext is relevant: True\\nAnswer is corre... "
- ]
- },
- "execution_count": 652,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eval_chatgpt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 649,
- "id": "61e8dad2",
- "metadata": {},
- "outputs": [],
- "source": [
- "eval_gpt3 = analyze_outcome_llm(outcomes_vector_gpt3, gpt3)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 650,
- "id": "170400c3",
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>Test Query</th>\n",
- " <th>Correct Response (LLM)</th>\n",
- " <th>Correct Source (LLM)</th>\n",
- " <th>Eval (LLM)</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>What battles took place in New York City in th...</td>\n",
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- " <tr>\n",
- " <th>1</th>\n",
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- " <tr>\n",
- " <th>2</th>\n",
- " <td>How many tourists visited New York City in 2019?</td>\n",
- " <td>False</td>\n",
- " <td>False</td>\n",
- " <td>\\n\\nContext is relevant: False\\nAnswer is corr...</td>\n",
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- " <tr>\n",
- " <th>3</th>\n",
- " <td>What are the airports in New York City?</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>When was the first documented visit into New Y...</td>\n",
- " <td>True</td>\n",
- " <td>True</td>\n",
- " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "text/plain": [
- " Test Query Correct Response (LLM) \\\n",
- "0 What battles took place in New York City in th... True \n",
- "1 Who was elected as the mayor after the Great D... True \n",
- "2 How many tourists visited New York City in 2019? False \n",
- "3 What are the airports in New York City? True \n",
- "4 When was the first documented visit into New Y... True \n",
- "\n",
- " Correct Source (LLM) Eval (LLM) \n",
- "0 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
- "1 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
- "2 False \\n\\nContext is relevant: False\\nAnswer is corr... \n",
- "3 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
- "4 True \\n\\nContext is relevant: True\\nAnswer is corre... "
- ]
- },
- "execution_count": 650,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "eval_gpt3"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "9080b39e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging, sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
+ "\n",
+ "# Uncomment if you want to temporarily disable logger\n",
+ "logging.disable(sys.maxsize)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7de92ce3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# NOTE: only necessary for querying with `use_async=True` in notebook\n",
+ "import nest_asyncio\n",
+ "\n",
+ "nest_asyncio.apply()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import (\n",
+ " TreeIndex,\n",
+ " SimpleDirectoryReader,\n",
+ " LLMPredictor,\n",
+ " VectorStoreIndex,\n",
+ " ListIndex,\n",
+ " Prompt,\n",
+ " ServiceContext,\n",
+ ")\n",
+ "from llama_index.indices.base import BaseIndex\n",
+ "from llama_index.llms.base import LLM\n",
+ "from llama_index.llms import OpenAI\n",
+ "from llama_index.response.schema import Response\n",
+ "import pandas as pd\n",
+ "from typing import Tuple"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "707662e5",
+ "metadata": {},
+ "source": [
+ "# Setup data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "b4b4387b-413e-4016-ba1e-88b3d9410a38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fetch \"New York City\" page from Wikipedia\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ ").json()\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
+ "\n",
+ "data_path = Path(\"data\")\n",
+ "if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ "with open(\"data/nyc_text.txt\", \"w\") as fp:\n",
+ " fp.write(nyc_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "523fbebe-6e79-4d7b-b400-188b711a0e8f",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:llama_index.readers.file.base:> [SimpleDirectoryReader] Total files added: 1\n",
+ "> [SimpleDirectoryReader] Total files added: 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f4a269bd",
+ "metadata": {},
+ "source": [
+ "# Setup benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "62f01ddf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from dataclasses import dataclass\n",
+ "from typing import List"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "4ff13cd4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@dataclass\n",
+ "class TestCase:\n",
+ " query: str\n",
+ " must_contain: List[str]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "9c653b72",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "@dataclass\n",
+ "class TestOutcome:\n",
+ " test: TestCase\n",
+ " response: Response\n",
+ "\n",
+ " @property\n",
+ " def is_correct_response(self) -> bool:\n",
+ " is_correct = True\n",
+ " for answer in self.test.must_contain:\n",
+ " if answer not in self.response.response:\n",
+ " is_correct = False\n",
+ " return is_correct\n",
+ "\n",
+ " @property\n",
+ " def is_correct_source(self) -> bool:\n",
+ " is_correct = True\n",
+ " for answer in self.test.must_contain:\n",
+ " if all(\n",
+ " answer not in node.source_text for node in self.response.source_nodes\n",
+ " ):\n",
+ " is_correct = False\n",
+ " return is_correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "b9cd18ae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class Benchmark:\n",
+ " def __init__(self, tests: List[TestCase]) -> None:\n",
+ " self._tests = tests\n",
+ "\n",
+ " def test(self, index: BaseIndex, llm: LLM, **kwargs) -> List[TestOutcome]:\n",
+ " outcomes: List[TestOutcome] = []\n",
+ " service_context = ServiceContext.from_defaults(llm=llm)\n",
+ " for test in self._tests:\n",
+ " query_engine = index.as_query_engine(\n",
+ " service_context=service_context, **kwargs\n",
+ " )\n",
+ " response = query_engine.query(\n",
+ " test.query,\n",
+ " )\n",
+ " outcome = TestOutcome(test=test, response=response)\n",
+ " outcomes.append(outcome)\n",
+ " return outcomes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "8edad985",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyze_outcome(outcomes: List[TestOutcome]) -> None:\n",
+ " rows = []\n",
+ " for outcome in outcomes:\n",
+ " row = [\n",
+ " outcome.test.query,\n",
+ " outcome.is_correct_response,\n",
+ " outcome.is_correct_source,\n",
+ " ]\n",
+ " rows.append(row)\n",
+ " df = pd.DataFrame(\n",
+ " rows, columns=[\"Test Query\", \"Correct Response\", \"Correct Source\"]\n",
+ " )\n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "4bc38077",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_battle = TestCase(\n",
+ " query=\"What battles took place in New York City in the American Revolution?\",\n",
+ " must_contain=[\"Battle of Long Island\"],\n",
+ ")\n",
+ "\n",
+ "test_mayor = TestCase(\n",
+ " query=\"Who was elected as the mayor after the Great Depression?\",\n",
+ " must_contain=[\"Fiorello La Guardia\"],\n",
+ ")\n",
+ "\n",
+ "test_tourists = TestCase(\n",
+ " query=\"How many tourists visited New York City in 2019?\",\n",
+ " must_contain=[\"66.6 million\"],\n",
+ ")\n",
+ "test_airport = TestCase(\n",
+ " query=\"What are the airports in New York City?\", must_contain=[\"LaGuardia Airport\"]\n",
+ ")\n",
+ "test_visit = TestCase(\n",
+ " query=\"When was the first documented visit into New York Harbor?\",\n",
+ " must_contain=[\"1524\"],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "f159dadb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bm = Benchmark(\n",
+ " [\n",
+ " test_battle,\n",
+ " test_mayor,\n",
+ " test_tourists,\n",
+ " test_airport,\n",
+ " test_visit,\n",
+ " ]\n",
+ ")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "65ddbd56",
+ "metadata": {},
+ "source": [
+ "# LLM based evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 592,
+ "id": "ed175de5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "EVAL_PROMPT_TMPL = (\n",
+ " \"Given the question below. \\n\"\n",
+ " \"---------------------\\n\"\n",
+ " \"{query_str}\"\n",
+ " \"\\n---------------------\\n\"\n",
+ " \"Decide if the following retreived context is relevant. \\n\"\n",
+ " \"\\n---------------------\\n\"\n",
+ " \"{context_str}\"\n",
+ " \"\\n---------------------\\n\"\n",
+ " \"Then decide if the answer is correct. \\n\"\n",
+ " \"\\n---------------------\\n\"\n",
+ " \"{answer_str}\"\n",
+ " \"\\n---------------------\\n\"\n",
+ " \"Answer in the following format:\\n\"\n",
+ " \"'Context is relevant: <True>\\nAnswer is correct: <True>' \"\n",
+ " \"and explain why.\"\n",
+ ")\n",
+ "\n",
+ "DEFAULT_EVAL_PROMPT = Prompt(EVAL_PROMPT_TMPL)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 593,
+ "id": "93c498b6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "\n",
+ "def extract_eval_result(result_str: str):\n",
+ " boolean_pattern = r\"(True|False)\"\n",
+ " matches = re.findall(boolean_pattern, result_str)\n",
+ " return [match == \"True\" for match in matches]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 594,
+ "id": "4c8109c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def analyze_outcome_llm_single(\n",
+ " outcome: TestOutcome, llm_predictor: LLMPredictor\n",
+ ") -> Tuple[bool, bool]:\n",
+ " try:\n",
+ " source_text = outcome.response.source_nodes[0].source_text\n",
+ " except:\n",
+ " source_text = \"Failed to retrieve any context\"\n",
+ " result_str, _ = llm_predictor.predict(\n",
+ " DEFAULT_EVAL_PROMPT,\n",
+ " query_str=outcome.test.query,\n",
+ " context_str=source_text,\n",
+ " answer_str=outcome.response.response,\n",
+ " )\n",
+ " is_context_relevant, is_answer_correct = extract_eval_result(result_str)\n",
+ " return is_answer_correct, is_context_relevant, result_str\n",
+ "\n",
+ "\n",
+ "def analyze_outcome_llm(\n",
+ " outcomes: List[TestOutcome], llm_predictor: LLMPredictor\n",
+ ") -> None:\n",
+ " rows = []\n",
+ " for outcome in outcomes:\n",
+ " is_correct_response, is_correct_source, result_str = analyze_outcome_llm_single(\n",
+ " outcome, llm_predictor\n",
+ " )\n",
+ " row = [outcome.test.query, is_correct_response, is_correct_source, result_str]\n",
+ " rows.append(row)\n",
+ " df = pd.DataFrame(\n",
+ " rows,\n",
+ " columns=[\n",
+ " \"Test Query\",\n",
+ " \"Correct Response (LLM)\",\n",
+ " \"Correct Source (LLM)\",\n",
+ " \"Eval (LLM)\",\n",
+ " ],\n",
+ " )\n",
+ " return df"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "5a9f43a6",
+ "metadata": {},
+ "source": [
+ "# Build Indices"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 643,
+ "id": "790bad05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vector_index = VectorStoreIndex.from_documents(\n",
+ " documents,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 473,
+ "id": "64c970e0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list_index = ListIndex.from_documents(\n",
+ " documents,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 468,
+ "id": "bacc4f1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tree_index = TreeIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "5b2e7fdd",
+ "metadata": {},
+ "source": [
+ "# Create LLMPredictors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "4766ac56-ac8d-4f33-b994-6901964241ea",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# gpt-4\n",
+ "gpt4 = OpenAI(temperature=0, model=\"gpt-4\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 169,
+ "id": "c8692cf6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# gpt-3 (text-davinci-003)\n",
+ "gpt3 = OpenAI(temperature=0, model=\"text-davinci-003\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "fb74ec62",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# chatgpt (gpt-3.5-turbo)\n",
+ "chatgpt = OpenAI(temperature=0, model=\"gpt-3.5-turbo\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "1354f668",
+ "metadata": {},
+ "source": [
+ "# Benchmarking "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "01124a3f",
+ "metadata": {},
+ "source": [
+ "### Tree Index + GPT4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 583,
+ "id": "6f418554",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outcomes_tree_gpt4 = bm.test(tree_index, gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 584,
+ "id": "de98ceba",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... False \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? False \n",
+ "4 When was the first documented visit into New Y... False \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 False \n",
+ "2 False \n",
+ "3 False \n",
+ "4 False "
+ ]
+ },
+ "execution_count": 584,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_tree_gpt4)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "f5ef33a0",
+ "metadata": {},
+ "source": [
+ "### Tree Index + GPT3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 549,
+ "id": "ba871d2a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outcomes_tree_gpt3 = bm.test(tree_index, gpt3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 550,
+ "id": "7d4c6930",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... False \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 False \n",
+ "1 False \n",
+ "2 False \n",
+ "3 False \n",
+ "4 False "
+ ]
+ },
+ "execution_count": 550,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_tree_gpt3)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "30a9ba34",
+ "metadata": {},
+ "source": [
+ "### List Index + GPT4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "bc0f05d1",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "outcomes_list_gpt4 = bm.test(\n",
+ " list_index, gpt4, response_mode=\"tree_summarize\", use_async=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "2d2e879d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... False \n",
+ "1 Who was elected as the mayor after the Great D... False \n",
+ "2 How many tourists visited New York City in 2019? True \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 True \n",
+ "2 True \n",
+ "3 True \n",
+ "4 True "
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_list_gpt4)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8cba793c",
+ "metadata": {},
+ "source": [
+ "### List Index + GPT3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 501,
+ "id": "66cfa3fa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outcomes_list_gpt3 = bm.test(\n",
+ " list_index, gpt3, response_mode=\"tree_summarize\", use_async=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 502,
+ "id": "06bc98d8",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor during the Great ...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor during the Great ... True \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 True \n",
+ "2 True \n",
+ "3 True \n",
+ "4 True "
+ ]
+ },
+ "execution_count": 502,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_list_gpt3)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "c4d0b3eb",
+ "metadata": {},
+ "source": [
+ "### List Index + ChatGPT"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "f146c74e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outcomes_list_chatgpt = bm.test(\n",
+ " list_index, chatgpt, response_mode=\"tree_summarize\", use_async=True\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "8eb9d392",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... False \n",
+ "1 Who was elected as the mayor after the Great D... False \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 True \n",
+ "2 True \n",
+ "3 True \n",
+ "4 True "
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_list_chatgpt)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "38fc1438",
+ "metadata": {},
+ "source": [
+ "### Vector Store Index + GPT4 "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 487,
+ "id": "5349d1e7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outcomes_vector_gpt4 = bm.test(vector_index, gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 488,
+ "id": "7fc53e19",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor during the Great ...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor during the Great ... True \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 True \n",
+ "2 False \n",
+ "3 True \n",
+ "4 True "
+ ]
+ },
+ "execution_count": 488,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_vector_gpt4)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "70eb711f",
+ "metadata": {},
+ "source": [
+ "### Vector Store Index + GPT3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 644,
+ "id": "e35ebdf9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "outcomes_vector_gpt3 = bm.test(vector_index, gpt3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 645,
+ "id": "95c49697",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... True \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 False \n",
+ "2 False \n",
+ "3 False \n",
+ "4 False "
+ ]
+ },
+ "execution_count": 645,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_vector_gpt3)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "a36ba2ee",
+ "metadata": {},
+ "source": [
+ "# LLM based Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 646,
+ "id": "59ff561c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response</th>\n",
+ " <th>Correct Source</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... True \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source \n",
+ "0 True \n",
+ "1 False \n",
+ "2 False \n",
+ "3 False \n",
+ "4 False "
+ ]
+ },
+ "execution_count": 646,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "analyze_outcome(outcomes_vector_gpt3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 647,
+ "id": "e4ffaca6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "eval_gpt4 = analyze_outcome_llm(outcomes_vector_gpt3, gpt4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 657,
+ "id": "85c4e415",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response (LLM)</th>\n",
+ " <th>Correct Source (LLM)</th>\n",
+ " <th>Eval (LLM)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>Context is relevant: True\\nAnswer is correct: ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>Context is relevant: False\\nAnswer is correct:...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response (LLM) \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... True \n",
+ "2 How many tourists visited New York City in 2019? True \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source (LLM) Eval (LLM) \n",
+ "0 True Context is relevant: True\\nAnswer is correct: ... \n",
+ "1 False Context is relevant: False\\nAnswer is correct:... \n",
+ "2 False Context is relevant: False\\nAnswer is correct:... \n",
+ "3 False Context is relevant: False\\nAnswer is correct:... \n",
+ "4 False Context is relevant: False\\nAnswer is correct:... "
+ ]
+ },
+ "execution_count": 657,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_gpt4"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 651,
+ "id": "3efb66d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "eval_chatgpt = analyze_outcome_llm(outcomes_vector_gpt3, chatgpt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 652,
+ "id": "4c452767",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response (LLM)</th>\n",
+ " <th>Correct Source (LLM)</th>\n",
+ " <th>Eval (LLM)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>\\n\\nContext is relevant: False\\nAnswer is corr...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>False</td>\n",
+ " <td>\\n\\nContext is relevant: False\\nAnswer is corr...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>False</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response (LLM) \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... True \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... False \n",
+ "\n",
+ " Correct Source (LLM) Eval (LLM) \n",
+ "0 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
+ "1 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
+ "2 False \\n\\nContext is relevant: False\\nAnswer is corr... \n",
+ "3 False \\n\\nContext is relevant: False\\nAnswer is corr... \n",
+ "4 True \\n\\nContext is relevant: True\\nAnswer is corre... "
+ ]
+ },
+ "execution_count": 652,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_chatgpt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 649,
+ "id": "61e8dad2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "eval_gpt3 = analyze_outcome_llm(outcomes_vector_gpt3, gpt3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 650,
+ "id": "170400c3",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Test Query</th>\n",
+ " <th>Correct Response (LLM)</th>\n",
+ " <th>Correct Source (LLM)</th>\n",
+ " <th>Eval (LLM)</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>What battles took place in New York City in th...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Who was elected as the mayor after the Great D...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>How many tourists visited New York City in 2019?</td>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " <td>\\n\\nContext is relevant: False\\nAnswer is corr...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>What are the airports in New York City?</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>When was the first documented visit into New Y...</td>\n",
+ " <td>True</td>\n",
+ " <td>True</td>\n",
+ " <td>\\n\\nContext is relevant: True\\nAnswer is corre...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Test Query Correct Response (LLM) \\\n",
+ "0 What battles took place in New York City in th... True \n",
+ "1 Who was elected as the mayor after the Great D... True \n",
+ "2 How many tourists visited New York City in 2019? False \n",
+ "3 What are the airports in New York City? True \n",
+ "4 When was the first documented visit into New Y... True \n",
+ "\n",
+ " Correct Source (LLM) Eval (LLM) \n",
+ "0 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
+ "1 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
+ "2 False \\n\\nContext is relevant: False\\nAnswer is corr... \n",
+ "3 True \\n\\nContext is relevant: True\\nAnswer is corre... \n",
+ "4 True \\n\\nContext is relevant: True\\nAnswer is corre... "
+ ]
+ },
+ "execution_count": 650,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eval_gpt3"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/test_wiki/TestNYC-Tree-GPT4.ipynb b/examples/test_wiki/TestNYC-Tree-GPT4.ipynb
index 354bede546..bb9ec50b7a 100644
--- a/examples/test_wiki/TestNYC-Tree-GPT4.ipynb
+++ b/examples/test_wiki/TestNYC-Tree-GPT4.ipynb
@@ -10,6 +10,7 @@
"outputs": [],
"source": [
"import logging, sys\n",
+ "\n",
"# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
"# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
"\n",
@@ -30,25 +31,26 @@
"from pathlib import Path\n",
"\n",
"import requests\n",
+ "\n",
"response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
" params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
" # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
+ " \"explaintext\": True,\n",
+ " },\n",
").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
"\n",
- "data_path = Path('data')\n",
+ "data_path = Path(\"data\")\n",
"if not data_path.exists():\n",
" Path.mkdir(data_path)\n",
"\n",
- "with open('data/nyc_text.txt', 'w') as fp:\n",
+ "with open(\"data/nyc_text.txt\", \"w\") as fp:\n",
" fp.write(nyc_text)"
]
},
@@ -61,7 +63,8 @@
"source": [
"# My OpenAI Key\n",
"import os\n",
- "os.environ['OPENAI_API_KEY'] = \"\""
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"\""
]
},
{
@@ -103,7 +106,7 @@
},
"outputs": [],
"source": [
- "documents = SimpleDirectoryReader('data').load_data()"
+ "documents = SimpleDirectoryReader(\"data\").load_data()"
]
},
{
@@ -150,10 +153,7 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt4,\n",
- " verbose=True\n",
- ")\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt4, verbose=True)\n",
"response_gpt4 = query_engine.query(\n",
" \"What battles took place in New York City in the American Revolution?\",\n",
")"
@@ -230,10 +230,7 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt3,\n",
- " verbose=True\n",
- ")\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt3, verbose=True)\n",
"response_gpt3 = query_engine.query(\n",
" \"What battles took place in New York City in the American Revolution?\",\n",
")"
@@ -318,10 +315,7 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt4,\n",
- " verbose=True\n",
- ")\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt4, verbose=True)\n",
"\n",
"response_gpt4 = query_engine.query(\n",
" \"What are the airports in New York City?\",\n",
@@ -406,10 +400,7 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt3,\n",
- " verbose=True\n",
- ")\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt3, verbose=True)\n",
"\n",
"response_gpt3 = query_engine.query(\n",
" \"What are the airports in New York City?\",\n",
@@ -466,10 +457,7 @@
"metadata": {},
"outputs": [],
"source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt4,\n",
- " verbose=True\n",
- ")\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt4, verbose=True)\n",
"\n",
"response_gpt4 = query_engine.query(\n",
" \"What battles took place in New York City in the American Revolution?\",\n",
@@ -612,10 +600,7 @@
}
],
"source": [
- "query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt3,\n",
- " verbose=True\n",
- ")\n",
+ "query_engine = index.as_query_engine(service_context=service_context_gpt3, verbose=True)\n",
"\n",
"response_gpt3 = query_engine.query(\n",
" \"Who is the current mayor of New York City?\",\n",
@@ -929,9 +914,7 @@
],
"source": [
"query_engine = index.as_query_engine(\n",
- " service_context=service_context_gpt3,\n",
- " llama_logger=logger, \n",
- " verbose=True\n",
+ " service_context=service_context_gpt3, llama_logger=logger, verbose=True\n",
")\n",
"\n",
"response_gpt3 = query_engine.query(\n",
diff --git a/examples/test_wiki/TestNYC.ipynb b/examples/test_wiki/TestNYC.ipynb
index f5d70a0da8..9153d70fce 100644
--- a/examples/test_wiki/TestNYC.ipynb
+++ b/examples/test_wiki/TestNYC.ipynb
@@ -1,159 +1,167 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "9080b39e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b4b4387b-413e-4016-ba1e-88b3d9410a38",
- "metadata": {},
- "outputs": [],
- "source": [
- "# fetch \"New York City\" page from Wikipedia\n",
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- ").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
- "\n",
- "data_path = Path('data')\n",
- "if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- "with open('data/nyc_text.txt', 'w') as fp:\n",
- " fp.write(nyc_text)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import TreeIndex, SimpleDirectoryReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = TreeIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "query_engine.query(\"What is the name of the professional women's basketball team in New York City?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
- "metadata": {},
- "outputs": [],
- "source": [
- "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "\n",
- "query_engine.query(\"What battles took place in New York City in the American Revolution?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955",
- "metadata": {},
- "outputs": [],
- "source": [
- "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "\n",
- "query_engine.query(\"What are the airports in New York City?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53265fd4-da98-4cf9-abfb-3f76105fd2ff",
- "metadata": {},
- "outputs": [],
- "source": [
- "# Try using embedding query\n",
- "query_engine.query(\"What are the airports in New York City?\", retriever_mode=\"embedding\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9080b39e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4b4387b-413e-4016-ba1e-88b3d9410a38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fetch \"New York City\" page from Wikipedia\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ ").json()\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
+ "\n",
+ "data_path = Path(\"data\")\n",
+ "if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ "with open(\"data/nyc_text.txt\", \"w\") as fp:\n",
+ " fp.write(nyc_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import TreeIndex, SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = TreeIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "query_engine.query(\n",
+ " \"What is the name of the professional women's basketball team in New York City?\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "\n",
+ "query_engine.query(\n",
+ " \"What battles took place in New York City in the American Revolution?\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "\n",
+ "query_engine.query(\"What are the airports in New York City?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53265fd4-da98-4cf9-abfb-3f76105fd2ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try using embedding query\n",
+ "query_engine.query(\n",
+ " \"What are the airports in New York City?\", retriever_mode=\"embedding\"\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/test_wiki/TestNYC_Embeddings.ipynb b/examples/test_wiki/TestNYC_Embeddings.ipynb
index c20c7432d8..1998d3b6dc 100644
--- a/examples/test_wiki/TestNYC_Embeddings.ipynb
+++ b/examples/test_wiki/TestNYC_Embeddings.ipynb
@@ -1,398 +1,408 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "7a9f093e-e027-405b-ae3d-17dda9e30cd0",
- "metadata": {},
- "source": [
- "# NYC Wikipedia Embeddings Demo"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "cadae9f2",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "3e594a62-110e-40b3-ad1e-c99f49a4e537",
- "metadata": {},
- "source": [
- "Demonstrate embedding capabilities in TreeIndex and ListIndex"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b145f093-afb0-46b8-a81f-466af8478439",
- "metadata": {},
- "source": [
- "### Setup + Data Prep"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "d038dcc1",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b4b4387b-413e-4016-ba1e-88b3d9410a38",
- "metadata": {},
- "outputs": [],
- "source": [
- "# fetch \"New York City\" page from Wikipedia\n",
- "from pathlib import Path\n",
- "\n",
- "import requests\n",
- "response = requests.get(\n",
- " 'https://en.wikipedia.org/w/api.php',\n",
- " params={\n",
- " 'action': 'query',\n",
- " 'format': 'json',\n",
- " 'titles': 'New York City',\n",
- " 'prop': 'extracts',\n",
- " # 'exintro': True,\n",
- " 'explaintext': True,\n",
- " }\n",
- ").json()\n",
- "page = next(iter(response['query']['pages'].values()))\n",
- "nyc_text = page['extract']\n",
- "\n",
- "data_path = Path('data')\n",
- "if not data_path.exists():\n",
- " Path.mkdir(data_path)\n",
- "\n",
- "with open('data/nyc_text.txt', 'w') as fp:\n",
- " fp.write(nyc_text)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "def4eca7-ba03-48e2-b18f-fd669b91a5fc",
- "metadata": {},
- "source": [
- "### TreeIndex - Embedding-based Query"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
- ]
- }
- ],
- "source": [
- "from llama_index import TreeIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = TreeIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(\n",
- " retriever_mode=\"embedding\"\n",
- ")\n",
- "response = query_engine.query(\"What is the name of the professional women's basketball team in New York City?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e1000018-18de-410d-b6d9-c66bf37ccf1d",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
- "metadata": {},
- "outputs": [],
- "source": [
- "response = query_engine.query(\n",
- " \"What battles took place in New York City in the American Revolution?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "5588289b-9fdc-4b86-bab9-808c97be05e1",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "53265fd4-da98-4cf9-abfb-3f76105fd2ff",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "response = query_engine.query(\"What are the airports in New York City?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "bc08060f-b031-4dc5-a980-427dd2407b5d",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "63009734-deda-4159-9f2b-0af19720e913",
- "metadata": {},
- "source": [
- "### ListIndex - Embedding-based Query"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "fd8920ae-8115-457c-b092-21e50cc3bcc0",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, SimpleDirectoryReader\n",
- "from IPython.display import Markdown"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "27c8bbee-daf5-494d-ba66-b60142592a96",
- "metadata": {},
- "outputs": [],
- "source": [
- "documents = SimpleDirectoryReader('data').load_data()\n",
- "index = ListIndex.from_documents(documents)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2cbf24c2-060e-4216-9188-a6746af1830d",
- "metadata": {
- "tags": []
- },
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(retriever_mode=\"embedding\")\n",
- "response = query_engine.query(\"What is the name of the professional women's basketball team in New York City?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "14e1b19f-fbf7-49fd-a96f-cbb37bafd498",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "48b86c8d-9149-4395-9d52-6070597c814d",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "response = query_engine.query(\"What battles took place in New York City in the American Revolution?\", retriever_mode=\"embedding\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "57fbd90c-a8d3-4738-8531-e8f48a953167",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "7ab01446-9b07-4222-a577-eeb4617ce4fc",
- "metadata": {},
- "outputs": [],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "response = query_engine.query(\"What are the airports in New York City?\", retriever_mode=\"embedding\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "091afaea-a61e-4a7c-b2f1-7df387380b8b",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aca03087-d6cc-4d87-8ec6-185fa03d9fea",
- "metadata": {},
- "source": [
- "## Try out other embeddings! \n",
- "(courtesy of langchain)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "27c24411-7049-45c7-862c-0857c03db580",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, SimpleDirectoryReader, ServiceContext\n",
- "from IPython.display import Markdown"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b9ff1944-a06a-4b05-adae-a2ef25e74e8b",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load in HF embedding model from langchain\n",
- "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
- "from llama_index import LangchainEmbedding\n",
- "embed_model = LangchainEmbedding(HuggingFaceEmbeddings())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1494cabb-0123-408a-9d81-8e02db9b3acd",
- "metadata": {},
- "outputs": [],
- "source": [
- "# configure\n",
- "service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
- "\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine(\n",
- " retriever_mode=\"embedding\", \n",
- " service_context=service_context, \n",
- ")\n",
- "response = query_engine.query(\n",
- " \"What is the name of the professional women's basketball team in New York City?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4d96a2e7-4eb1-474e-b855-eca3efed1bad",
- "metadata": {},
- "outputs": [],
- "source": [
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "80510d3a-8bf8-47f2-b1d4-3d1bd0d5a1bb",
- "metadata": {},
- "outputs": [],
- "source": []
- }
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7a9f093e-e027-405b-ae3d-17dda9e30cd0",
+ "metadata": {},
+ "source": [
+ "# NYC Wikipedia Embeddings Demo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cadae9f2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e594a62-110e-40b3-ad1e-c99f49a4e537",
+ "metadata": {},
+ "source": [
+ "Demonstrate embedding capabilities in TreeIndex and ListIndex"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b145f093-afb0-46b8-a81f-466af8478439",
+ "metadata": {},
+ "source": [
+ "### Setup + Data Prep"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d038dcc1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b4b4387b-413e-4016-ba1e-88b3d9410a38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# fetch \"New York City\" page from Wikipedia\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import requests\n",
+ "\n",
+ "response = requests.get(\n",
+ " \"https://en.wikipedia.org/w/api.php\",\n",
+ " params={\n",
+ " \"action\": \"query\",\n",
+ " \"format\": \"json\",\n",
+ " \"titles\": \"New York City\",\n",
+ " \"prop\": \"extracts\",\n",
+ " # 'exintro': True,\n",
+ " \"explaintext\": True,\n",
+ " },\n",
+ ").json()\n",
+ "page = next(iter(response[\"query\"][\"pages\"].values()))\n",
+ "nyc_text = page[\"extract\"]\n",
+ "\n",
+ "data_path = Path(\"data\")\n",
+ "if not data_path.exists():\n",
+ " Path.mkdir(data_path)\n",
+ "\n",
+ "with open(\"data/nyc_text.txt\", \"w\") as fp:\n",
+ " fp.write(nyc_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1a9eb90-335c-4214-8bb6-fd1edbe3ccbd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "def4eca7-ba03-48e2-b18f-fd669b91a5fc",
+ "metadata": {},
+ "source": [
+ "### TreeIndex - Embedding-based Query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b2364-4806-4656-81e7-3f6e4b910b5b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+ ]
+ }
],
+ "source": [
+ "from llama_index import TreeIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1298bbb4-c99e-431e-93ef-eb32c0a2fc2a",
"metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.9"
- }
+ "tags": []
},
- "nbformat": 4,
- "nbformat_minor": 5
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = TreeIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68c9ebfe-b1b6-4f4e-9278-174346de8c90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(retriever_mode=\"embedding\")\n",
+ "response = query_engine.query(\n",
+ " \"What is the name of the professional women's basketball team in New York City?\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1000018-18de-410d-b6d9-c66bf37ccf1d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4fc3f18a-0ef9-453c-acf8-7aedd784cdcf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What battles took place in New York City in the American Revolution?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5588289b-9fdc-4b86-bab9-808c97be05e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "53265fd4-da98-4cf9-abfb-3f76105fd2ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "response = query_engine.query(\"What are the airports in New York City?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc08060f-b031-4dc5-a980-427dd2407b5d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "63009734-deda-4159-9f2b-0af19720e913",
+ "metadata": {},
+ "source": [
+ "### ListIndex - Embedding-based Query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd8920ae-8115-457c-b092-21e50cc3bcc0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, SimpleDirectoryReader\n",
+ "from IPython.display import Markdown"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27c8bbee-daf5-494d-ba66-b60142592a96",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "documents = SimpleDirectoryReader(\"data\").load_data()\n",
+ "index = ListIndex.from_documents(documents)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2cbf24c2-060e-4216-9188-a6746af1830d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(retriever_mode=\"embedding\")\n",
+ "response = query_engine.query(\n",
+ " \"What is the name of the professional women's basketball team in New York City?\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "14e1b19f-fbf7-49fd-a96f-cbb37bafd498",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48b86c8d-9149-4395-9d52-6070597c814d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "response = query_engine.query(\n",
+ " \"What battles took place in New York City in the American Revolution?\",\n",
+ " retriever_mode=\"embedding\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "57fbd90c-a8d3-4738-8531-e8f48a953167",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ab01446-9b07-4222-a577-eeb4617ce4fc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "response = query_engine.query(\n",
+ " \"What are the airports in New York City?\", retriever_mode=\"embedding\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "091afaea-a61e-4a7c-b2f1-7df387380b8b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aca03087-d6cc-4d87-8ec6-185fa03d9fea",
+ "metadata": {},
+ "source": [
+ "## Try out other embeddings! \n",
+ "(courtesy of langchain)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27c24411-7049-45c7-862c-0857c03db580",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, SimpleDirectoryReader, ServiceContext\n",
+ "from IPython.display import Markdown"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b9ff1944-a06a-4b05-adae-a2ef25e74e8b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load in HF embedding model from langchain\n",
+ "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
+ "from llama_index import LangchainEmbedding\n",
+ "\n",
+ "embed_model = LangchainEmbedding(HuggingFaceEmbeddings())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1494cabb-0123-408a-9d81-8e02db9b3acd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# configure\n",
+ "service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
+ "\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine(\n",
+ " retriever_mode=\"embedding\",\n",
+ " service_context=service_context,\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"What is the name of the professional women's basketball team in New York City?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d96a2e7-4eb1-474e-b855-eca3efed1bad",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "80510d3a-8bf8-47f2-b1d4-3d1bd0d5a1bb",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/test_wiki/TestWikiReader.ipynb b/examples/test_wiki/TestWikiReader.ipynb
index 5dabeae63d..28e71f72bf 100644
--- a/examples/test_wiki/TestWikiReader.ipynb
+++ b/examples/test_wiki/TestWikiReader.ipynb
@@ -1,277 +1,276 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "52295407",
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "\n",
- "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c5d167a5-81f8-4d2c-b42f-0a190577132f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# My OpenAI Key\n",
- "import os\n",
- "os.environ['OPENAI_API_KEY'] = \"INSERT OPENAI KEY\""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "575750cc-479f-4b1f-b93f-4b00ed756d52",
- "metadata": {},
- "source": [
- "## Wikipedia Reader + Keyword Table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "id": "5f60348e-731d-4a95-bae2-426e184a914e",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import KeywordTableIndex, WikipediaReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "952c4659-7fbb-447e-8caf-06916412cc37",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "page: Covid-19\n"
- ]
- }
- ],
- "source": [
- "wiki_docs = WikipediaReader().load_data(pages=['Covid-19'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "3be202db-a4c7-41d2-ba7d-446d1f934830",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = KeywordTableIndex.from_documents(wiki_docs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "id": "28d7163e-f26f-4ad8-89d5-9cb7662c4d9c",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: Which country included tocilizumab in treatment for covid-19?\n",
- "Extracted keywords: ['tocilizumab', 'treatment', 'covid-19', 'covid', '19']\n",
- "> Querying with idx: 1105763466456338724: of age or older weighing at least 40 kilograms ...\n",
- "> Querying with idx: 2820318727532393752: Coronavirus disease 2019 (COVID-19) is a contag...\n",
- "> Querying with idx: 897499143815831368: if the mask includes an exhalation valve, a wea...\n",
- "> Querying with idx: 8628144746434065339: pulmonary fibrosis, cystic fibrosis. Evidence s...\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "'\\n\\nChina'"
- ]
- },
- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
- "# set Logging to DEBUG for more detailed outputs\n",
- "query_engine = index.as_query_engine()\n",
- "query_engine.query(\"Which country included tocilizumab in treatment for covid-19?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "addb0c4d-f1ae-40c1-8b69-5a989609672f",
- "metadata": {},
- "source": [
- "## Wikipedia Reader + List"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a0fc24e1-eca5-4267-a962-f7fe0fc5c7df",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex, WikipediaReader"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "872a651a-ca4a-43e2-8b29-e4f667f9d3c5",
- "metadata": {},
- "outputs": [],
- "source": [
- "wiki_docs = WikipediaReader().load_data(pages=['Covid-19'])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "37e85af0-b1c3-4c18-b239-6e32a7acf8d6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Adding chunk: Coronavirus disease 2019 (COVID-19) is a contag...\n",
- "> Adding chunk: people with COVID‑19 and acute respiratory dist...\n",
- "> Adding chunk: encourage or mandate the use of face masks or c...\n",
- "> Adding chunk: have elevated liver enzymes, reflecting liver i...\n",
- "> Adding chunk: insofar as their drug use may have caused lung ...\n",
- "> Adding chunk: treatment of mild-to-moderate COVID‑19 in adult...\n"
- ]
- }
- ],
- "source": [
- "index = ListIndex.from_documents(wiki_docs)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "ec0119ef-786e-40ea-89af-f1ca0ad26de6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: Which country included tocilizumab in treatment for covid-19?\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# with keyword lookup\n",
- "from llama_index.indices.postprocessor import KeywordNodePostprocessor\n",
- "\n",
- "\n",
- "query_engine = index.as_query_engine(\n",
- " node_postprocessors=[\n",
- " KeywordNodePostprocessor(required_keywords=['tocilizumab'])\n",
- " ]\n",
- ")\n",
- "response = query_engine.query(\n",
- " \"Which country included tocilizumab in treatment for covid-19?\", \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "id": "b4087a84-0939-444f-93f2-a1a7aa32db3f",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'China'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(response.strip())"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "fb155bc7-cb50-47b6-b92b-895852c2d8f4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: Which country included tocilizumab in treatment for covid-19?\n"
- ]
- }
- ],
- "source": [
- "# set Logging to DEBUG for more detailed outputs\n",
- "# without keyword lookup\n",
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\n",
- " \"Which country included tocilizumab in treatment for covid-19?\"\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "id": "5b45c07a-4e76-4a45-86b6-6b2df1ef4f7b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'There is no definite answer to this question as different countries have different treatment methods for covid-19. However, according to the context information, it is known that the virus SARS-CoV-2 can cause severe damage to various organs in the human body by inducing systemic inflammation. Therefore, it is possible that tocilizumab, which is a drug that inhibits the virus, may be included in treatment for covid-19 in some countries in order to prevent or reduce the severity of a cytokine storm. Additionally, passive antibodies may be used to treat people with active COVID-19 in order to help them recover.'"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(response.strip())"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.1"
- }
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "52295407",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "\n",
+ "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
+ "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c5d167a5-81f8-4d2c-b42f-0a190577132f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# My OpenAI Key\n",
+ "import os\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = \"INSERT OPENAI KEY\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "575750cc-479f-4b1f-b93f-4b00ed756d52",
+ "metadata": {},
+ "source": [
+ "## Wikipedia Reader + Keyword Table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "5f60348e-731d-4a95-bae2-426e184a914e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import KeywordTableIndex, WikipediaReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "952c4659-7fbb-447e-8caf-06916412cc37",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "page: Covid-19\n"
+ ]
+ }
+ ],
+ "source": [
+ "wiki_docs = WikipediaReader().load_data(pages=[\"Covid-19\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3be202db-a4c7-41d2-ba7d-446d1f934830",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = KeywordTableIndex.from_documents(wiki_docs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "28d7163e-f26f-4ad8-89d5-9cb7662c4d9c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: Which country included tocilizumab in treatment for covid-19?\n",
+ "Extracted keywords: ['tocilizumab', 'treatment', 'covid-19', 'covid', '19']\n",
+ "> Querying with idx: 1105763466456338724: of age or older weighing at least 40 kilograms ...\n",
+ "> Querying with idx: 2820318727532393752: Coronavirus disease 2019 (COVID-19) is a contag...\n",
+ "> Querying with idx: 897499143815831368: if the mask includes an exhalation valve, a wea...\n",
+ "> Querying with idx: 8628144746434065339: pulmonary fibrosis, cystic fibrosis. Evidence s...\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 5
+ {
+ "data": {
+ "text/plain": [
+ "'\\n\\nChina'"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# GPT doesn't find the corresponding evidence in the leaf node, but still gives the correct answer\n",
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "query_engine = index.as_query_engine()\n",
+ "query_engine.query(\"Which country included tocilizumab in treatment for covid-19?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "addb0c4d-f1ae-40c1-8b69-5a989609672f",
+ "metadata": {},
+ "source": [
+ "## Wikipedia Reader + List"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a0fc24e1-eca5-4267-a962-f7fe0fc5c7df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex, WikipediaReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "872a651a-ca4a-43e2-8b29-e4f667f9d3c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "wiki_docs = WikipediaReader().load_data(pages=[\"Covid-19\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "37e85af0-b1c3-4c18-b239-6e32a7acf8d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Adding chunk: Coronavirus disease 2019 (COVID-19) is a contag...\n",
+ "> Adding chunk: people with COVID‑19 and acute respiratory dist...\n",
+ "> Adding chunk: encourage or mandate the use of face masks or c...\n",
+ "> Adding chunk: have elevated liver enzymes, reflecting liver i...\n",
+ "> Adding chunk: insofar as their drug use may have caused lung ...\n",
+ "> Adding chunk: treatment of mild-to-moderate COVID‑19 in adult...\n"
+ ]
+ }
+ ],
+ "source": [
+ "index = ListIndex.from_documents(wiki_docs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "ec0119ef-786e-40ea-89af-f1ca0ad26de6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: Which country included tocilizumab in treatment for covid-19?\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# with keyword lookup\n",
+ "from llama_index.indices.postprocessor import KeywordNodePostprocessor\n",
+ "\n",
+ "\n",
+ "query_engine = index.as_query_engine(\n",
+ " node_postprocessors=[KeywordNodePostprocessor(required_keywords=[\"tocilizumab\"])]\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " \"Which country included tocilizumab in treatment for covid-19?\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "b4087a84-0939-444f-93f2-a1a7aa32db3f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'China'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(response.strip())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "fb155bc7-cb50-47b6-b92b-895852c2d8f4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: Which country included tocilizumab in treatment for covid-19?\n"
+ ]
+ }
+ ],
+ "source": [
+ "# set Logging to DEBUG for more detailed outputs\n",
+ "# without keyword lookup\n",
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\n",
+ " \"Which country included tocilizumab in treatment for covid-19?\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "5b45c07a-4e76-4a45-86b6-6b2df1ef4f7b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'There is no definite answer to this question as different countries have different treatment methods for covid-19. However, according to the context information, it is known that the virus SARS-CoV-2 can cause severe damage to various organs in the human body by inducing systemic inflammation. Therefore, it is possible that tocilizumab, which is a drug that inhibits the virus, may be included in treatment for covid-19 in some countries in order to prevent or reduce the severity of a cytokine storm. Additionally, passive antibodies may be used to treat people with active COVID-19 in order to help them recover.'"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(response.strip())"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/examples/vellum/Vellum Integration Demo.ipynb b/examples/vellum/Vellum Integration Demo.ipynb
index 24a482b99b..43197b821a 100644
--- a/examples/vellum/Vellum Integration Demo.ipynb
+++ b/examples/vellum/Vellum Integration Demo.ipynb
@@ -93,9 +93,7 @@
"outputs": [],
"source": [
"completion, formatted_prompt = predictor.predict(\n",
- " prompt,\n",
- " context_str=\"The earth is flat\",\n",
- " query_str=\"Is the earth round or flat?\"\n",
+ " prompt, context_str=\"The earth is flat\", query_str=\"Is the earth round or flat?\"\n",
")\n",
"\n",
"print(completion)"
diff --git a/experimental/classifier/TitanicModel.ipynb b/experimental/classifier/TitanicModel.ipynb
index ae3df5924c..d78774c03e 100644
--- a/experimental/classifier/TitanicModel.ipynb
+++ b/experimental/classifier/TitanicModel.ipynb
@@ -1,561 +1,558 @@
{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "f445c1d1-acb9-431e-a7ff-50c41f064359",
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n",
- "[nltk_data] Downloading package stopwords to\n",
- "[nltk_data] /Users/jerryliu/nltk_data...\n",
- "[nltk_data] Package stopwords is already up-to-date!\n"
- ]
- }
- ],
- "source": [
- "from utils import (\n",
- " get_train_str,\n",
- " get_train_and_eval_data,\n",
- " get_eval_preds,\n",
- " train_prompt\n",
- ")\n",
- "\n",
- "import warnings\n",
- "warnings.filterwarnings('ignore')\n",
- "warnings.simplefilter('ignore')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "cf3cbd90-d5e1-4c30-a3bc-8b39fbd85d70",
- "metadata": {},
- "outputs": [],
- "source": [
- "# load up the titanic data\n",
- "train_df, train_labels, eval_df, eval_labels = get_train_and_eval_data('data/train.csv')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "fa2634f9-cb33-4f1e-81f9-3a3b285e2580",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Few-shot Prompting with GPT-3 for Titanic Dataset\n",
- "In this section, we can show how we can prompt GPT-3 on its own (without using GPT Index) to attain ~80% accuracy on Titanic! \n",
- "\n",
- "We can do this by simply providing a few example inputs. Or we can simply provide no example inputs at all (zero-shot). Both achieve the same results."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "d0698fd2-1361-49ae-8c17-8124e9b932a4",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The following structured data is provided in \"Feature Name\":\"Feature Value\" format.\n",
- "Each datapoint describes a passenger on the Titanic.\n",
- "The task is to decide whether the passenger survived.\n",
- "Some example datapoints are given below: \n",
- "-------------------\n",
- "{train_str}\n",
- "-------------------\n",
- "Given this, predict whether the following passenger survived. Return answer as a number between 0 or 1. \n",
- "{eval_str}\n",
- "Survived: \n"
- ]
- }
- ],
- "source": [
- "# first demonstrate the prompt template\n",
- "print(train_prompt.template)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "4b39e2e7-be07-42f8-a27a-3419e84cfb2c",
- "metadata": {
- "scrolled": true,
- "tags": []
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Example datapoints in `train_str`: \n",
- "This is the Data:\n",
- "Age:28.0\n",
- "Embarked:S\n",
- "Fare:7.8958\n",
- "Parch:0\n",
- "Pclass:3\n",
- "Sex:male\n",
- "SibSp:0\n",
- "This is the correct answer:\n",
- "Survived: 0\n",
- "\n",
- "This is the Data:\n",
- "Age:17.0\n",
- "Embarked:S\n",
- "Fare:7.925\n",
- "Parch:2\n",
- "Pclass:3\n",
- "Sex:female\n",
- "SibSp:4\n",
- "This is the correct answer:\n",
- "Survived: 1\n",
- "\n",
- "This is the Data:\n",
- "Age:30.0\n",
- "Embarked:S\n",
- "Fare:16.1\n",
- "Parch:0\n",
- "Pclass:3\n",
- "Sex:male\n",
- "SibSp:1\n",
- "This is the correct answer:\n",
- "Survived: 0\n",
- "\n",
- "This is the Data:\n",
- "Age:22.0\n",
- "Embarked:S\n",
- "Fare:7.25\n",
- "Parch:0\n",
- "Pclass:3\n",
- "Sex:male\n",
- "SibSp:0\n",
- "This is the correct answer:\n",
- "Survived: 0\n",
- "\n",
- "This is the Data:\n",
- "Age:45.0\n",
- "Embarked:S\n",
- "Fare:13.5\n",
- "Parch:0\n",
- "Pclass:2\n",
- "Sex:female\n",
- "SibSp:0\n",
- "This is the correct answer:\n",
- "Survived: 1\n",
- "\n",
- "This is the Data:\n",
- "Age:25.0\n",
- "Embarked:S\n",
- "Fare:0.0\n",
- "Parch:0\n",
- "Pclass:3\n",
- "Sex:male\n",
- "SibSp:0\n",
- "This is the correct answer:\n",
- "Survived: 1\n",
- "\n",
- "This is the Data:\n",
- "Age:18.0\n",
- "Embarked:S\n",
- "Fare:20.2125\n",
- "Parch:1\n",
- "Pclass:3\n",
- "Sex:male\n",
- "SibSp:1\n",
- "This is the correct answer:\n",
- "Survived: 0\n",
- "\n",
- "This is the Data:\n",
- "Age:33.0\n",
- "Embarked:S\n",
- "Fare:9.5\n",
- "Parch:0\n",
- "Pclass:3\n",
- "Sex:male\n",
- "SibSp:0\n",
- "This is the correct answer:\n",
- "Survived: 0\n",
- "\n",
- "This is the Data:\n",
- "Age:24.0\n",
- "Embarked:S\n",
- "Fare:65.0\n",
- "Parch:2\n",
- "Pclass:2\n",
- "Sex:female\n",
- "SibSp:1\n",
- "This is the correct answer:\n",
- "Survived: 1\n",
- "\n",
- "This is the Data:\n",
- "Age:26.0\n",
- "Embarked:S\n",
- "Fare:7.925\n",
- "Parch:0\n",
- "Pclass:3\n",
- "Sex:female\n",
- "SibSp:0\n",
- "This is the correct answer:\n",
- "Survived: 1\n"
- ]
- }
- ],
- "source": [
- "# Get \"training\" prompt string \n",
- "train_n = 10\n",
- "eval_n = 40\n",
- "train_str = get_train_str(train_df, train_labels, train_n=train_n)\n",
- "print(f\"Example datapoints in `train_str`: \\n{train_str}\")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "819a06f7-3171-4edb-b90c-0a3eae308a04",
- "metadata": {},
- "source": [
- "#### Do evaluation with the training prompt string"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4a7f2202-518c-41a3-80ab-1e98bbcca903",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.metrics import accuracy_score\n",
- "import numpy as np\n",
- "\n",
- "eval_preds = get_eval_preds(train_prompt, train_str, eval_df, n=eval_n)\n",
- "eval_label_chunk = eval_labels[:eval_n]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "id": "64323a4d-6eea-4e40-9eac-b2deed60192b",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ACCURACY: 0.8\n"
- ]
- }
- ],
- "source": [
- "acc = accuracy_score(eval_label_chunk, np.array(eval_preds).round())\n",
- "print(f'ACCURACY: {acc}')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "11790d28-8f34-42dd-b11f-6aad21fd5f46",
- "metadata": {},
- "source": [
- "#### Do evaluation with no training prompt string! "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "aaf993e5-c363-4f18-a28f-09761e49cb6d",
- "metadata": {},
- "outputs": [],
- "source": [
- "from sklearn.metrics import accuracy_score\n",
- "import numpy as np\n",
- "\n",
- "eval_preds_null = get_eval_preds(train_prompt, \"\", eval_df, n=eval_n)\n",
- "eval_label_chunk = eval_labels[:eval_n]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "c3b8bcd5-5972-4ce5-9aa1-57460cdde199",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ACCURACY: 0.8\n"
- ]
- }
- ],
- "source": [
- "acc_null = accuracy_score(eval_label_chunk, np.array(eval_preds_null).round())\n",
- "print(f'ACCURACY: {acc_null}')"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "8f0a5e4b-e627-4b47-a807-939813596594",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Extending with List Index"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "42a1ca28-96e9-4cd2-bd48-0673917ad057",
- "metadata": {},
- "source": [
- "#### Build Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "id": "6c59b030-855d-4e27-89c3-74c972d1bf19",
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import ListIndex\n",
- "from llama_index.schema import Document"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "id": "8f9556de-e323-4318-bb71-cff75bf8c3c1",
- "metadata": {},
- "outputs": [],
- "source": [
- "index = ListIndex([])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e27720fc-af36-40fd-8c55-41485248aa9f",
- "metadata": {},
- "outputs": [],
- "source": [
- "# insertion into index \n",
- "batch_size = 40\n",
- "num_train_chunks = 5\n",
- "\n",
- "for i in range(num_train_chunks):\n",
- " print(f\"Inserting chunk: {i}/{num_train_chunks}\")\n",
- " start_idx = i*batch_size\n",
- " end_idx = (i+1)*batch_size\n",
- " train_batch = train_df.iloc[start_idx:end_idx+batch_size]\n",
- " labels_batch = train_labels.iloc[start_idx:end_idx+batch_size]\n",
- " all_train_str = get_train_str(train_batch, labels_batch, train_n=batch_size)\n",
- " index.insert(Document(text=all_train_str))"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "e78db088-6649-44db-b52a-766316713b96",
- "metadata": {},
- "source": [
- "#### Query Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "9cb90564-1de2-412f-8318-d5280855004e",
- "metadata": {},
- "outputs": [],
- "source": [
- "from utils import query_str, qa_data_prompt, refine_prompt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "id": "77c1ae36-e0af-47bc-a656-4971af699755",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'Which is the relationship between these features and predicting survival?'"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "query_str"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "id": "c403710f-d4b3-4287-94f5-e275ea19b476",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "> Starting query: Which is the relationship between these features and predicting survival?\n"
- ]
- }
- ],
- "source": [
- "query_engine = index.as_query_engine(\n",
- " text_qa_template=qa_data_prompt, \n",
- " refine_template=refine_prompt, \n",
- ")\n",
- "response = query_engine.query(\n",
- " query_str, \n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "d2545ab1-980a-4fbd-8add-7ef957801644",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "There is no definitive answer to this question, as the relationship between the features and predicting survival will vary depending on the data. However, some possible relationships include: age (younger passengers are more likely to survive), sex (females are more likely to survive), fare (passengers who paid more for their ticket are more likely to survive), and pclass (passengers in first or second class are more likely to survive).\n"
- ]
- }
- ],
- "source": [
- "print(response)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "id": "d0d7d260-2283-49f6-ac40-35c7071cc54d",
- "metadata": {},
- "source": [
- "#### Get Predictions and Evaluate"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "e7b98057-957c-48ef-be85-59ff9813d201",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The following structured data is provided in \"Feature Name\":\"Feature Value\" format.\n",
- "Each datapoint describes a passenger on the Titanic.\n",
- "The task is to decide whether the passenger survived.\n",
- "We discovered the following relationship between features and survival:\n",
- "-------------------\n",
- "{train_str}\n",
- "-------------------\n",
- "Given this, predict whether the following passenger survived. \n",
- "Return answer as a number between 0 or 1. \n",
- "{eval_str}\n",
- "Survived: \n",
- "\n",
- "\n",
- "`train_str`: \n",
- "\n",
- "There is no definitive answer to this question, as the relationship between the features and predicting survival will vary depending on the data. However, some possible relationships include: age (younger passengers are more likely to survive), sex (females are more likely to survive), fare (passengers who paid more for their ticket are more likely to survive), and pclass (passengers in first or second class are more likely to survive).\n"
- ]
- }
- ],
- "source": [
- "# get eval preds\n",
- "from utils import train_prompt_with_context\n",
- "\n",
- "train_str = response\n",
- "print(train_prompt_with_context.template)\n",
- "print(f'\\n\\n`train_str`: {train_str}')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "659c6a3f-1c5d-4314-87dc-908e76d50e4a",
- "metadata": {},
- "outputs": [],
- "source": [
- "# do evaluation\n",
- "from sklearn.metrics import accuracy_score\n",
- "import numpy as np\n",
- "eval_n = 40\n",
- "eval_preds = get_eval_preds(train_prompt_with_context, train_str, eval_df, n=eval_n)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "id": "7424e7d3-2576-42bc-b626-cf8088265004",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "ACCURACY: 0.85\n"
- ]
- }
- ],
- "source": [
- "eval_label_chunk = eval_labels[:eval_n]\n",
- "acc = accuracy_score(eval_label_chunk, np.array(eval_preds).round())\n",
- "print(f'ACCURACY: {acc}')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e010b497-eeed-4142-a8ac-f5545e85fcc2",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "gpt_retrieve_venv",
- "language": "python",
- "name": "gpt_retrieve_venv"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "f445c1d1-acb9-431e-a7ff-50c41f064359",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n",
+ "[nltk_data] Downloading package stopwords to\n",
+ "[nltk_data] /Users/jerryliu/nltk_data...\n",
+ "[nltk_data] Package stopwords is already up-to-date!\n"
+ ]
+ }
+ ],
+ "source": [
+ "from utils import get_train_str, get_train_and_eval_data, get_eval_preds, train_prompt\n",
+ "\n",
+ "import warnings\n",
+ "\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "cf3cbd90-d5e1-4c30-a3bc-8b39fbd85d70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# load up the titanic data\n",
+ "train_df, train_labels, eval_df, eval_labels = get_train_and_eval_data(\"data/train.csv\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "fa2634f9-cb33-4f1e-81f9-3a3b285e2580",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Few-shot Prompting with GPT-3 for Titanic Dataset\n",
+ "In this section, we can show how we can prompt GPT-3 on its own (without using GPT Index) to attain ~80% accuracy on Titanic! \n",
+ "\n",
+ "We can do this by simply providing a few example inputs. Or we can simply provide no example inputs at all (zero-shot). Both achieve the same results."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "d0698fd2-1361-49ae-8c17-8124e9b932a4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following structured data is provided in \"Feature Name\":\"Feature Value\" format.\n",
+ "Each datapoint describes a passenger on the Titanic.\n",
+ "The task is to decide whether the passenger survived.\n",
+ "Some example datapoints are given below: \n",
+ "-------------------\n",
+ "{train_str}\n",
+ "-------------------\n",
+ "Given this, predict whether the following passenger survived. Return answer as a number between 0 or 1. \n",
+ "{eval_str}\n",
+ "Survived: \n"
+ ]
+ }
+ ],
+ "source": [
+ "# first demonstrate the prompt template\n",
+ "print(train_prompt.template)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4b39e2e7-be07-42f8-a27a-3419e84cfb2c",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Example datapoints in `train_str`: \n",
+ "This is the Data:\n",
+ "Age:28.0\n",
+ "Embarked:S\n",
+ "Fare:7.8958\n",
+ "Parch:0\n",
+ "Pclass:3\n",
+ "Sex:male\n",
+ "SibSp:0\n",
+ "This is the correct answer:\n",
+ "Survived: 0\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:17.0\n",
+ "Embarked:S\n",
+ "Fare:7.925\n",
+ "Parch:2\n",
+ "Pclass:3\n",
+ "Sex:female\n",
+ "SibSp:4\n",
+ "This is the correct answer:\n",
+ "Survived: 1\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:30.0\n",
+ "Embarked:S\n",
+ "Fare:16.1\n",
+ "Parch:0\n",
+ "Pclass:3\n",
+ "Sex:male\n",
+ "SibSp:1\n",
+ "This is the correct answer:\n",
+ "Survived: 0\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:22.0\n",
+ "Embarked:S\n",
+ "Fare:7.25\n",
+ "Parch:0\n",
+ "Pclass:3\n",
+ "Sex:male\n",
+ "SibSp:0\n",
+ "This is the correct answer:\n",
+ "Survived: 0\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:45.0\n",
+ "Embarked:S\n",
+ "Fare:13.5\n",
+ "Parch:0\n",
+ "Pclass:2\n",
+ "Sex:female\n",
+ "SibSp:0\n",
+ "This is the correct answer:\n",
+ "Survived: 1\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:25.0\n",
+ "Embarked:S\n",
+ "Fare:0.0\n",
+ "Parch:0\n",
+ "Pclass:3\n",
+ "Sex:male\n",
+ "SibSp:0\n",
+ "This is the correct answer:\n",
+ "Survived: 1\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:18.0\n",
+ "Embarked:S\n",
+ "Fare:20.2125\n",
+ "Parch:1\n",
+ "Pclass:3\n",
+ "Sex:male\n",
+ "SibSp:1\n",
+ "This is the correct answer:\n",
+ "Survived: 0\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:33.0\n",
+ "Embarked:S\n",
+ "Fare:9.5\n",
+ "Parch:0\n",
+ "Pclass:3\n",
+ "Sex:male\n",
+ "SibSp:0\n",
+ "This is the correct answer:\n",
+ "Survived: 0\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:24.0\n",
+ "Embarked:S\n",
+ "Fare:65.0\n",
+ "Parch:2\n",
+ "Pclass:2\n",
+ "Sex:female\n",
+ "SibSp:1\n",
+ "This is the correct answer:\n",
+ "Survived: 1\n",
+ "\n",
+ "This is the Data:\n",
+ "Age:26.0\n",
+ "Embarked:S\n",
+ "Fare:7.925\n",
+ "Parch:0\n",
+ "Pclass:3\n",
+ "Sex:female\n",
+ "SibSp:0\n",
+ "This is the correct answer:\n",
+ "Survived: 1\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Get \"training\" prompt string\n",
+ "train_n = 10\n",
+ "eval_n = 40\n",
+ "train_str = get_train_str(train_df, train_labels, train_n=train_n)\n",
+ "print(f\"Example datapoints in `train_str`: \\n{train_str}\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "819a06f7-3171-4edb-b90c-0a3eae308a04",
+ "metadata": {},
+ "source": [
+ "#### Do evaluation with the training prompt string"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4a7f2202-518c-41a3-80ab-1e98bbcca903",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import accuracy_score\n",
+ "import numpy as np\n",
+ "\n",
+ "eval_preds = get_eval_preds(train_prompt, train_str, eval_df, n=eval_n)\n",
+ "eval_label_chunk = eval_labels[:eval_n]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "64323a4d-6eea-4e40-9eac-b2deed60192b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ACCURACY: 0.8\n"
+ ]
+ }
+ ],
+ "source": [
+ "acc = accuracy_score(eval_label_chunk, np.array(eval_preds).round())\n",
+ "print(f\"ACCURACY: {acc}\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "11790d28-8f34-42dd-b11f-6aad21fd5f46",
+ "metadata": {},
+ "source": [
+ "#### Do evaluation with no training prompt string! "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aaf993e5-c363-4f18-a28f-09761e49cb6d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import accuracy_score\n",
+ "import numpy as np\n",
+ "\n",
+ "eval_preds_null = get_eval_preds(train_prompt, \"\", eval_df, n=eval_n)\n",
+ "eval_label_chunk = eval_labels[:eval_n]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "c3b8bcd5-5972-4ce5-9aa1-57460cdde199",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ACCURACY: 0.8\n"
+ ]
+ }
+ ],
+ "source": [
+ "acc_null = accuracy_score(eval_label_chunk, np.array(eval_preds_null).round())\n",
+ "print(f\"ACCURACY: {acc_null}\")"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "8f0a5e4b-e627-4b47-a807-939813596594",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Extending with List Index"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "42a1ca28-96e9-4cd2-bd48-0673917ad057",
+ "metadata": {},
+ "source": [
+ "#### Build Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "6c59b030-855d-4e27-89c3-74c972d1bf19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import ListIndex\n",
+ "from llama_index.schema import Document"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "8f9556de-e323-4318-bb71-cff75bf8c3c1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index = ListIndex([])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e27720fc-af36-40fd-8c55-41485248aa9f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# insertion into index\n",
+ "batch_size = 40\n",
+ "num_train_chunks = 5\n",
+ "\n",
+ "for i in range(num_train_chunks):\n",
+ " print(f\"Inserting chunk: {i}/{num_train_chunks}\")\n",
+ " start_idx = i * batch_size\n",
+ " end_idx = (i + 1) * batch_size\n",
+ " train_batch = train_df.iloc[start_idx : end_idx + batch_size]\n",
+ " labels_batch = train_labels.iloc[start_idx : end_idx + batch_size]\n",
+ " all_train_str = get_train_str(train_batch, labels_batch, train_n=batch_size)\n",
+ " index.insert(Document(text=all_train_str))"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e78db088-6649-44db-b52a-766316713b96",
+ "metadata": {},
+ "source": [
+ "#### Query Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "9cb90564-1de2-412f-8318-d5280855004e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from utils import query_str, qa_data_prompt, refine_prompt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "77c1ae36-e0af-47bc-a656-4971af699755",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'Which is the relationship between these features and predicting survival?'"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "query_str"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "c403710f-d4b3-4287-94f5-e275ea19b476",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "> Starting query: Which is the relationship between these features and predicting survival?\n"
+ ]
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine(\n",
+ " text_qa_template=qa_data_prompt,\n",
+ " refine_template=refine_prompt,\n",
+ ")\n",
+ "response = query_engine.query(\n",
+ " query_str,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "d2545ab1-980a-4fbd-8add-7ef957801644",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "There is no definitive answer to this question, as the relationship between the features and predicting survival will vary depending on the data. However, some possible relationships include: age (younger passengers are more likely to survive), sex (females are more likely to survive), fare (passengers who paid more for their ticket are more likely to survive), and pclass (passengers in first or second class are more likely to survive).\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(response)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "d0d7d260-2283-49f6-ac40-35c7071cc54d",
+ "metadata": {},
+ "source": [
+ "#### Get Predictions and Evaluate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "e7b98057-957c-48ef-be85-59ff9813d201",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The following structured data is provided in \"Feature Name\":\"Feature Value\" format.\n",
+ "Each datapoint describes a passenger on the Titanic.\n",
+ "The task is to decide whether the passenger survived.\n",
+ "We discovered the following relationship between features and survival:\n",
+ "-------------------\n",
+ "{train_str}\n",
+ "-------------------\n",
+ "Given this, predict whether the following passenger survived. \n",
+ "Return answer as a number between 0 or 1. \n",
+ "{eval_str}\n",
+ "Survived: \n",
+ "\n",
+ "\n",
+ "`train_str`: \n",
+ "\n",
+ "There is no definitive answer to this question, as the relationship between the features and predicting survival will vary depending on the data. However, some possible relationships include: age (younger passengers are more likely to survive), sex (females are more likely to survive), fare (passengers who paid more for their ticket are more likely to survive), and pclass (passengers in first or second class are more likely to survive).\n"
+ ]
+ }
+ ],
+ "source": [
+ "# get eval preds\n",
+ "from utils import train_prompt_with_context\n",
+ "\n",
+ "train_str = response\n",
+ "print(train_prompt_with_context.template)\n",
+ "print(f\"\\n\\n`train_str`: {train_str}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "659c6a3f-1c5d-4314-87dc-908e76d50e4a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# do evaluation\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "import numpy as np\n",
+ "\n",
+ "eval_n = 40\n",
+ "eval_preds = get_eval_preds(train_prompt_with_context, train_str, eval_df, n=eval_n)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "7424e7d3-2576-42bc-b626-cf8088265004",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ACCURACY: 0.85\n"
+ ]
+ }
+ ],
+ "source": [
+ "eval_label_chunk = eval_labels[:eval_n]\n",
+ "acc = accuracy_score(eval_label_chunk, np.array(eval_preds).round())\n",
+ "print(f\"ACCURACY: {acc}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e010b497-eeed-4142-a8ac-f5545e85fcc2",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "gpt_retrieve_venv",
+ "language": "python",
+ "name": "gpt_retrieve_venv"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
}
diff --git a/requirements.txt b/requirements.txt
index f0a1603b54..4dc8d42588 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,7 +14,7 @@ types-requests==2.28.11.8
types-setuptools==67.1.0.0
# linting
-black==22.12.0
+black[jupyter]==22.12.0
mypy==0.991
pre-commit==3.2.0
pylint==2.15.10
--
GitLab