diff --git a/docs/community/integrations/vector_stores.md b/docs/community/integrations/vector_stores.md
index d7406108820d69cf61a2cfd721b35e855190143d..c843ce8186d80f4f8f8bd339f4f07e5128419ab3 100644
--- a/docs/community/integrations/vector_stores.md
+++ b/docs/community/integrations/vector_stores.md
@@ -14,7 +14,7 @@ as the storage backend for `VectorStoreIndex`.
- Apache Cassandra® and Astra DB through CQL (`CassandraVectorStore`). [Installation](https://cassandra.apache.org/doc/stable/cassandra/getting_started/installing.html) [Quickstart](https://docs.datastax.com/en/astra-serverless/docs/vector-search/overview.html)
- Astra DB (`AstraDBVectorStore`). [Quickstart](https://docs.datastax.com/en/astra/home/astra.html).
-- Azure Cognitive Search (`CognitiveSearchVectorStore`). [Quickstart](https://learn.microsoft.com/en-us/azure/search/search-get-started-vector)
+- Azure AI Search (`AzureAISearchVectorStore`). [Quickstart](https://learn.microsoft.com/en-us/azure/search/search-get-started-vector)
- Chroma (`ChromaVectorStore`) [Installation](https://docs.trychroma.com/getting-started)
- DashVector (`DashVectorStore`). [Installation](https://help.aliyun.com/document_detail/2510230.html).
- DeepLake (`DeepLakeVectorStore`) [Installation](https://docs.deeplake.ai/en/latest/Installation.html)
@@ -126,28 +126,26 @@ astra_db_store = AstraDBVectorStore(
**Azure Cognitive Search**
```python
-from azure.search.documents import SearchClient
-from llama_index.vector_stores import ChromaVectorStore
from azure.core.credentials import AzureKeyCredential
+from llama_index.vector_stores import AzureAISearchVectorStore
-service_endpoint = f"https://{search_service_name}.search.windows.net"
-index_name = "quickstart"
-cognitive_search_credential = AzureKeyCredential("<API key>")
+search_service_api_key = "YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY"
+search_service_endpoint = "YOUR-AZURE-SEARCH-SERVICE-ENDPOINT"
+search_service_api_version = "2023-11-01"
+credential = AzureKeyCredential(search_service_api_key)
-search_client = SearchClient(
- endpoint=service_endpoint,
- index_name=index_name,
- credential=cognitive_search_credential,
+# Index name to use
+index_name = "llamaindex-vector-demo"
+
+client = SearchIndexClient(
+ endpoint=search_service_endpoint,
+ credential=credential,
)
-# construct vector store
-vector_store = CognitiveSearchVectorStore(
- search_client,
- id_field_key="id",
- chunk_field_key="content",
- embedding_field_key="embedding",
- metadata_field_key="li_jsonMetadata",
- doc_id_field_key="li_doc_id",
+vector_store = AzureAISearchVectorStore(
+ search_or_index_client=client,
+ index_name=index_name,
+ embedding_dimensionality=1536,
)
```
@@ -756,10 +754,10 @@ maxdepth: 1
---
../../examples/vector_stores/AstraDBIndexDemo.ipynb
../../examples/vector_stores/AsyncIndexCreationDemo.ipynb
+../../examples/vector_stores/AzureAISearchIndexDemo.ipynb
../../examples/vector_stores/AzureCosmosDBMongoDBvCoreDemo.ipynb
../../examples/vector_stores/CassandraIndexDemo.ipynb
../../examples/vector_stores/ChromaIndexDemo.ipynb
-../../examples/vector_stores/CognitiveSearchIndexDemo.ipynb
../../examples/vector_stores/DashvectorIndexDemo.ipynb
../../examples/vector_stores/DashvectorIndexDemo-Hybrid.ipynb
../../examples/vector_stores/DeepLakeIndexDemo.ipynb
diff --git a/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb b/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e8f44d20891867f18d822bebd57cdaedd6d03b46
--- /dev/null
+++ b/docs/examples/vector_stores/AzureAISearchIndexDemo.ipynb
@@ -0,0 +1,731 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/CognitiveSearchIndexDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Azure AI Search"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic Example\n",
+ "\n",
+ "In this notebook, we take a Paul Graham essay, split it into chunks, embed it using an Azure OpenAI embedding model, load it into an Azure AI Search index, and then query it."
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install llama-index\n",
+ "!pip install wget"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "from azure.core.credentials import AzureKeyCredential\n",
+ "from azure.search.documents import SearchClient\n",
+ "from azure.search.documents.indexes import SearchIndexClient\n",
+ "from IPython.display import Markdown, display\n",
+ "from llama_index import (\n",
+ " ServiceContext,\n",
+ " SimpleDirectoryReader,\n",
+ " StorageContext,\n",
+ " VectorStoreIndex,\n",
+ ")\n",
+ "from llama_index.embeddings import AzureOpenAIEmbedding\n",
+ "from llama_index.llms import AzureOpenAI\n",
+ "from llama_index.vector_stores import AzureAISearchVectorStore\n",
+ "from llama_index.vector_stores.azureaisearch import (\n",
+ " IndexManagement,\n",
+ " MetadataIndexFieldType,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup Azure OpenAI"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "aoai_api_key = \"YOUR_AZURE_OPENAI_API_KEY\"\n",
+ "aoai_endpoint = \"YOUR_AZURE_OPENAI_ENDPOINT\"\n",
+ "aoai_api_version = \"2023-05-15\"\n",
+ "\n",
+ "llm = AzureOpenAI(\n",
+ " model=\"YOUR_AZURE_OPENAI_COMPLETION_MODEL_NAME\",\n",
+ " deployment_name=\"YOUR_AZURE_OPENAI_COMPLETION_DEPLOYMENT_NAME\",\n",
+ " api_key=aoai_api_key,\n",
+ " azure_endpoint=aoai_endpoint,\n",
+ " api_version=aoai_api_version,\n",
+ ")\n",
+ "\n",
+ "# You need to deploy your own embedding model as well as your own chat completion model\n",
+ "embed_model = AzureOpenAIEmbedding(\n",
+ " model=\"YOUR_AZURE_OPENAI_EMBEDDING_MODEL_NAME\",\n",
+ " deployment_name=\"YOUR_AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME\",\n",
+ " api_key=aoai_api_key,\n",
+ " azure_endpoint=aoai_endpoint,\n",
+ " api_version=aoai_api_version,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup Azure AI Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "search_service_api_key = \"YOUR-AZURE-SEARCH-SERVICE-ADMIN-KEY\"\n",
+ "search_service_endpoint = \"YOUR-AZURE-SEARCH-SERVICE-ENDPOINT\"\n",
+ "search_service_api_version = \"2023-11-01\"\n",
+ "credential = AzureKeyCredential(search_service_api_key)\n",
+ "\n",
+ "\n",
+ "# Index name to use\n",
+ "index_name = \"llamaindex-vector-demo\"\n",
+ "\n",
+ "# Use index client to demonstrate creating an index\n",
+ "index_client = SearchIndexClient(\n",
+ " endpoint=search_service_endpoint,\n",
+ " credential=credential,\n",
+ ")\n",
+ "\n",
+ "# Use search client to demonstration using existing index\n",
+ "search_client = SearchClient(\n",
+ " endpoint=search_service_endpoint,\n",
+ " index_name=index_name,\n",
+ " credential=credential,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Create Index (if it does not exist)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Demonstrates creating a vector index named \"llamaindex-vector-demo\" if one doesn't exist. The index has the following fields:\n",
+ "| Field Name | OData Type | \n",
+ "|------------|---------------------------| \n",
+ "| id | `Edm.String` | \n",
+ "| chunk | `Edm.String` | \n",
+ "| embedding | `Collection(Edm.Single)` | \n",
+ "| metadata | `Edm.String` | \n",
+ "| doc_id | `Edm.String` | \n",
+ "| author | `Edm.String` | \n",
+ "| theme | `Edm.String` | \n",
+ "| director | `Edm.String` | "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metadata_fields = {\n",
+ " \"author\": \"author\",\n",
+ " \"theme\": (\"topic\", MetadataIndexFieldType.STRING),\n",
+ " \"director\": \"director\",\n",
+ "}\n",
+ "\n",
+ "vector_store = AzureAISearchVectorStore(\n",
+ " search_or_index_client=index_client,\n",
+ " filterable_metadata_field_keys=metadata_fields,\n",
+ " index_name=index_name,\n",
+ " index_management=IndexManagement.CREATE_IF_NOT_EXISTS,\n",
+ " id_field_key=\"id\",\n",
+ " chunk_field_key=\"chunk\",\n",
+ " embedding_field_key=\"embedding\",\n",
+ " embedding_dimensionality=1536,\n",
+ " metadata_string_field_key=\"metadata\",\n",
+ " doc_id_field_key=\"doc_id\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!mkdir -p 'data/paul_graham/'\n",
+ "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Loading documents\n",
+ "Load the documents stored in the `data/paul_graham/` using the SimpleDirectoryReader"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load documents\n",
+ "documents = SimpleDirectoryReader(\"../data/paul_graham/\").load_data()\n",
+ "\n",
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm=llm, embed_model=embed_model\n",
+ ")\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " documents, storage_context=storage_context, service_context=service_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The author engaged in writing and programming activities during their formative years. They initially wrote short stories and later transitioned to programming on the IBM 1401 using an early version of Fortran. Subsequently, with the advent of microcomputers, the author began programming on a TRS-80, writing simple games, a rocket flight prediction program, and a word processor.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Query Data\n",
+ "query_engine = index.as_query_engine(similarity_top_k=3)\n",
+ "response = query_engine.query(\"What did the author do growing up?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The author learned that the study of philosophy in college did not live up to their expectations, as they found the courses to be boring and lacking in ultimate truths. This led them to switch their focus to AI, which was influenced by a novel featuring an intelligent computer and a PBS documentary showcasing advanced technology.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\n",
+ " \"What did the author learn?\",\n",
+ ")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use Existing Index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index_name = \"llamaindex-vector-demo\"\n",
+ "\n",
+ "metadata_fields = {\n",
+ " \"author\": \"author\",\n",
+ " \"theme\": (\"topic\", MetadataIndexFieldType.STRING),\n",
+ " \"director\": \"director\",\n",
+ "}\n",
+ "vector_store = AzureAISearchVectorStore(\n",
+ " search_or_index_client=search_client,\n",
+ " filterable_metadata_field_keys=metadata_fields,\n",
+ " index_management=IndexManagement.VALIDATE_INDEX,\n",
+ " id_field_key=\"id\",\n",
+ " chunk_field_key=\"chunk\",\n",
+ " embedding_field_key=\"embedding\",\n",
+ " embedding_dimensionality=1536,\n",
+ " metadata_string_field_key=\"metadata\",\n",
+ " doc_id_field_key=\"doc_id\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
+ "service_context = ServiceContext.from_defaults(\n",
+ " llm=llm, embed_model=embed_model\n",
+ ")\n",
+ "index = VectorStoreIndex.from_documents(\n",
+ " [], storage_context=storage_context, service_context=service_context\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The author faced a challenging moment when he couldn't figure out what to do with the early computer he had access to in 9th grade. This was due to the limited options for input and the lack of knowledge in math to do anything interesting with the available resources.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "query_engine = index.as_query_engine()\n",
+ "response = query_engine.query(\"What was a hard moment for the author?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>Paul Graham</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"Who is the author?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The author worked at Interleaf, where they learned several lessons, including the importance of product-focused leadership in technology companies, the drawbacks of code being edited by too many people, the limitations of conventional office hours for optimal hacking, and the risks associated with bureaucratic customers. Additionally, the author discovered the concept that the low end tends to dominate the high end, and that being the \"entry level\" option can be advantageous.\n",
+ "\n",
+ "Streamed output at 99.40073103089465 tokens/s\n"
+ ]
+ }
+ ],
+ "source": [
+ "import time\n",
+ "\n",
+ "query_engine = index.as_query_engine(streaming=True)\n",
+ "response = query_engine.query(\"What happened at interleaf?\")\n",
+ "\n",
+ "start_time = time.time()\n",
+ "\n",
+ "token_count = 0\n",
+ "for token in response.response_gen:\n",
+ " print(token, end=\"\")\n",
+ " token_count += 1\n",
+ "\n",
+ "time_elapsed = time.time() - start_time\n",
+ "tokens_per_second = token_count / time_elapsed\n",
+ "\n",
+ "print(f\"\\n\\nStreamed output at {tokens_per_second} tokens/s\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Adding a document to existing index"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>Blue</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What colour is the sky?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from llama_index import Document\n",
+ "\n",
+ "index.insert_nodes([Document(text=\"The sky is indigo today\")])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "<b>The sky is indigo today.</b>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Markdown object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response = query_engine.query(\"What colour is the sky?\")\n",
+ "display(Markdown(f\"<b>{response}</b>\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Filtering"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "index.insert_nodes(nodes)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=TextNode(id_='049f00de-13be-4af3-ab56-8c16352fe799', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='ad2a08d4364262546db9711b915348d43e0ccc41bd8c3c41775e133624e1fa1b', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.8120511)]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
+ "\n",
+ "\n",
+ "filters = MetadataFilters(\n",
+ " filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")]\n",
+ ")\n",
+ "\n",
+ "retriever = index.as_retriever(filters=filters)\n",
+ "retriever.retrieve(\"What is inception about?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Query Mode\n",
+ "Four query modes are supported: DEFAULT (vector search), SPARSE, HYBRID, and SEMANTIC_HYBRID."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Perform a Vector Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Score: 0.8748552\n",
+ "File Name: Unknown\n",
+ "Id: bae0df75-ff37-4725-b659-b9fd8bf2ef3c\n",
+ "\n",
+ "Extracted Content:\n",
+ "Inception\n",
+ "\n",
+ "======================================== End of Result ========================================\n",
+ "\n",
+ "Score: 0.8155207\n",
+ "File Name: paul_graham_essay.txt\n",
+ "Id: ae5aee85-a083-4141-bf75-bbb872f53760\n",
+ "\n",
+ "Extracted Content:\n",
+ "It'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",
+ "\n",
+ "Over 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",
+ "\n",
+ "One 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",
+ "\n",
+ "Jessica 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",
+ "\n",
+ "When 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",
+ "\n",
+ "One 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",
+ "\n",
+ "So 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",
+ "\n",
+ "Meanwhile 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",
+ "\n",
+ "As 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",
+ "\n",
+ "Once 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",
+ "\n",
+ "There 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.\n",
+ "\n",
+ "======================================== End of Result ========================================\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "from llama_index.vector_stores.types import VectorStoreQueryMode\n",
+ "\n",
+ "default_retriever = index.as_retriever(\n",
+ " vector_store_query_mode=VectorStoreQueryMode.DEFAULT\n",
+ ")\n",
+ "response = default_retriever.retrieve(\"What is inception about?\")\n",
+ "\n",
+ "# Loop through each NodeWithScore in the response\n",
+ "for node_with_score in response:\n",
+ " node = node_with_score.node # The TextNode object\n",
+ " score = node_with_score.score # The similarity score\n",
+ " chunk_id = node.id_ # The chunk ID\n",
+ "\n",
+ " # Extract the relevant metadata from the node\n",
+ " file_name = node.metadata.get(\"file_name\", \"Unknown\")\n",
+ " file_path = node.metadata.get(\"file_path\", \"Unknown\")\n",
+ "\n",
+ " # Extract the text content from the node\n",
+ " text_content = node.text if node.text else \"No content available\"\n",
+ "\n",
+ " # Print the results in a user-friendly format\n",
+ " print(f\"Score: {score}\")\n",
+ " print(f\"File Name: {file_name}\")\n",
+ " print(f\"Id: {chunk_id}\")\n",
+ " print(\"\\nExtracted Content:\")\n",
+ " print(text_content)\n",
+ " print(\"\\n\" + \"=\" * 40 + \" End of Result \" + \"=\" * 40 + \"\\n\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Perform a Hybrid Search"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=TextNode(id_='bae0df75-ff37-4725-b659-b9fd8bf2ef3c', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9792a1fd7d2e1a08f1b1d70a597357bb6b68d69ed5685117eaa37ac9e9a3565e', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03181818127632141),\n",
+ " NodeWithScore(node=TextNode(id_='ae5aee85-a083-4141-bf75-bbb872f53760', embedding=None, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='627552ee-116a-4132-a7d3-7e7232f75866', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='0a59e1ce8e50a67680a5669164f79e524087270ce183a3971fcd18ac4cad1fa0'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='24a1d375-31e3-492c-ac02-5091e3572e3f', node_type=<ObjectType.TEXT: '1'>, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='51c474a12ac8e9748258b2c7bbe77bb7c8bf35b775ed44f016057a0aa8b0bd76'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='196569e0-2b10-4ba3-8263-a69fb78dd98c', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='192082e7ba84b8c5e2a64bd1d422c6c503189fc3ba325bb3e6e8bdb43db03fbb')}, hash='a3ea638857f1daadf7af967322480f97e1235dac3ee7d72b8024670785df8810', text='It\\'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.', start_char_idx=45670, end_char_idx=50105, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03009207174181938)]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from llama_index.vector_stores.types import VectorStoreQueryMode\n",
+ "\n",
+ "hybrid_retriever = index.as_retriever(\n",
+ " vector_store_query_mode=VectorStoreQueryMode.HYBRID\n",
+ ")\n",
+ "hybrid_retriever.retrieve(\"What is inception about?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Perform a Hybrid Search with Semantic Reranking\n",
+ "This mode incorporates semantic reranking to hybrid search results to improve search relevance. \n",
+ "\n",
+ "Please see this link for further details: https://learn.microsoft.com/azure/search/semantic-search-overview"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[NodeWithScore(node=TextNode(id_='bae0df75-ff37-4725-b659-b9fd8bf2ef3c', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9792a1fd7d2e1a08f1b1d70a597357bb6b68d69ed5685117eaa37ac9e9a3565e', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3949906826019287),\n",
+ " NodeWithScore(node=TextNode(id_='fc9782a2-c255-4265-a618-3a864abe598d', embedding=None, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='627552ee-116a-4132-a7d3-7e7232f75866', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='0a59e1ce8e50a67680a5669164f79e524087270ce183a3971fcd18ac4cad1fa0'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='94d87013-ea3d-4a9c-982a-dde5ff219983', node_type=<ObjectType.TEXT: '1'>, metadata={'file_path': '..\\\\data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75395, 'creation_date': '2023-12-12', 'last_modified_date': '2023-12-12', 'last_accessed_date': '2024-02-02'}, hash='f28897170c6b61162069af9ee83dc11e13fa0f6bf6efaa7b3911e6ad9093da84'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='dc3852e5-4c1e-484e-9e65-f17084d3f7b4', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='deaee6d5c992dbf757876957aa9112a42d30a636c6c83d81fcfac4aaf2d24dee')}, hash='a3b31e5ec2b5d4a9b3648de310c8a5962c17afdb800ea0e16faa47956607866d', text='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 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.', start_char_idx=14179, end_char_idx=18443, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=1.0986518859863281)]"
+ ]
+ },
+ "execution_count": null,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "hybrid_retriever = index.as_retriever(\n",
+ " vector_store_query_mode=VectorStoreQueryMode.SEMANTIC_HYBRID\n",
+ ")\n",
+ "hybrid_retriever.retrieve(\"What is inception about?\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "llamaindextest01",
+ "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"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/vector_stores/CognitiveSearchIndexDemo.ipynb b/docs/examples/vector_stores/CognitiveSearchIndexDemo.ipynb
deleted file mode 100644
index cc68450b2a50a224bcab2fa2dc618b94eab28538..0000000000000000000000000000000000000000
--- a/docs/examples/vector_stores/CognitiveSearchIndexDemo.ipynb
+++ /dev/null
@@ -1,659 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/vector_stores/CognitiveSearchIndexDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Azure Cognitive Search"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Basic Example\n",
- "\n",
- "In this basic example, we take a Paul Graham essay, split it into chunks, embed it using an OpenAI embedding model, load it into an Azure Cognitive Search index, and then query it."
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install llama-index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import logging\n",
- "import sys\n",
- "from IPython.display import Markdown, display\n",
- "\n",
- "# logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
- "# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
- "\n",
- "# logger = logging.getLogger(__name__)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#!{sys.executable} -m pip install llama-index\n",
- "#!{sys.executable} -m pip install azure-search-documents==11.4.0b8\n",
- "#!{sys.executable} -m pip install azure-identity"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# set up OpenAI\n",
- "import os\n",
- "import getpass\n",
- "\n",
- "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
- "import openai\n",
- "\n",
- "openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# set up Azure Cognitive Search\n",
- "from azure.search.documents.indexes import SearchIndexClient\n",
- "from azure.search.documents import SearchClient\n",
- "from azure.core.credentials import AzureKeyCredential\n",
- "\n",
- "search_service_name = getpass.getpass(\"Azure Cognitive Search Service Name\")\n",
- "\n",
- "key = getpass.getpass(\"Azure Cognitive Search Key\")\n",
- "\n",
- "cognitive_search_credential = AzureKeyCredential(key)\n",
- "\n",
- "service_endpoint = f\"https://{search_service_name}.search.windows.net\"\n",
- "\n",
- "# Index name to use\n",
- "index_name = \"quickstart\"\n",
- "\n",
- "# Use index client to demonstrate creating an index\n",
- "index_client = SearchIndexClient(\n",
- " endpoint=service_endpoint,\n",
- " credential=cognitive_search_credential,\n",
- ")\n",
- "\n",
- "# Use search client to demonstration using existing index\n",
- "search_client = SearchClient(\n",
- " endpoint=service_endpoint,\n",
- " index_name=index_name,\n",
- " credential=cognitive_search_credential,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Create Index (if it does not exist)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Demonstrates creating a vector index named quickstart01 if one doesn't exist. The index has the following fields:\n",
- "- id (Edm.String)\n",
- "- content (Edm.String)\n",
- "- embedding (Edm.SingleCollection)\n",
- "- li_jsonMetadata (Edm.String)\n",
- "- li_doc_id (Edm.String)\n",
- "- author (Edm.String)\n",
- "- theme (Edm.String)\n",
- "- director (Edm.String)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from azure.search.documents import SearchClient\n",
- "from llama_index.vector_stores import CognitiveSearchVectorStore\n",
- "from llama_index.vector_stores.cogsearch import (\n",
- " IndexManagement,\n",
- " MetadataIndexFieldType,\n",
- " CognitiveSearchVectorStore,\n",
- ")\n",
- "\n",
- "# Example of a complex mapping, metadata field 'theme' is mapped to a differently name index field 'topic' with its type explicitly set\n",
- "metadata_fields = {\n",
- " \"author\": \"author\",\n",
- " \"theme\": (\"topic\", MetadataIndexFieldType.STRING),\n",
- " \"director\": \"director\",\n",
- "}\n",
- "\n",
- "# A simplified metadata specification is available if all metadata and index fields are similarly named\n",
- "# metadata_fields = {\"author\", \"theme\", \"director\"}\n",
- "\n",
- "\n",
- "vector_store = CognitiveSearchVectorStore(\n",
- " search_or_index_client=index_client,\n",
- " index_name=index_name,\n",
- " filterable_metadata_field_keys=metadata_fields,\n",
- " index_management=IndexManagement.CREATE_IF_NOT_EXISTS,\n",
- " id_field_key=\"id\",\n",
- " chunk_field_key=\"content\",\n",
- " embedding_field_key=\"embedding\",\n",
- " metadata_string_field_key=\"li_jsonMetadata\",\n",
- " doc_id_field_key=\"li_doc_id\",\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Download Data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!mkdir -p 'data/paul_graham/'\n",
- "!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# define embedding function\n",
- "from llama_index.embeddings import OpenAIEmbedding\n",
- "from llama_index import (\n",
- " SimpleDirectoryReader,\n",
- " StorageContext,\n",
- " ServiceContext,\n",
- " VectorStoreIndex,\n",
- ")\n",
- "\n",
- "embed_model = OpenAIEmbedding()\n",
- "\n",
- "# load documents\n",
- "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()\n",
- "\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
- "index = VectorStoreIndex.from_documents(\n",
- " documents, storage_context=storage_context, service_context=service_context\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author wrote short stories and programmed on an IBM 1401 computer during their time in school. They later got their own microcomputer, a TRS-80, and started programming games and a word processor.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Query Data\n",
- "query_engine = index.as_query_engine(similarity_top_k=3)\n",
- "response = query_engine.query(\"What did the author do growing up?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author learned several things during their time at Interleaf. They learned that it's better for technology companies to be run by product people than sales people, that code edited by too many people leads to bugs, that cheap office space is not worth it if it's depressing, that planned meetings are inferior to corridor conversations, that big bureaucratic customers can be a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking. However, the most important thing the author learned is that the low end eats the high end, meaning that it's better to be the \"entry level\" option because if you're not, someone else will be and will surpass you.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "response = query_engine.query(\n",
- " \"What did the author learn?\",\n",
- ")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Use Existing Index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index.vector_stores import CognitiveSearchVectorStore\n",
- "from llama_index.vector_stores.cogsearch import (\n",
- " IndexManagement,\n",
- " MetadataIndexFieldType,\n",
- " CognitiveSearchVectorStore,\n",
- ")\n",
- "\n",
- "\n",
- "index_name = \"quickstart\"\n",
- "\n",
- "metadata_fields = {\n",
- " \"author\": \"author\",\n",
- " \"theme\": (\"topic\", MetadataIndexFieldType.STRING),\n",
- " \"director\": \"director\",\n",
- "}\n",
- "vector_store = CognitiveSearchVectorStore(\n",
- " search_or_index_client=search_client,\n",
- " filterable_metadata_field_keys=metadata_fields,\n",
- " index_management=IndexManagement.NO_VALIDATION,\n",
- " id_field_key=\"id\",\n",
- " chunk_field_key=\"content\",\n",
- " embedding_field_key=\"embedding\",\n",
- " metadata_string_field_key=\"li_jsonMetadata\",\n",
- " doc_id_field_key=\"li_doc_id\",\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# define embedding function\n",
- "from llama_index.embeddings import OpenAIEmbedding\n",
- "from llama_index import (\n",
- " SimpleDirectoryReader,\n",
- " StorageContext,\n",
- " ServiceContext,\n",
- " VectorStoreIndex,\n",
- ")\n",
- "\n",
- "embed_model = OpenAIEmbedding()\n",
- "\n",
- "storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
- "service_context = ServiceContext.from_defaults(embed_model=embed_model)\n",
- "index = VectorStoreIndex.from_documents(\n",
- " [], storage_context=storage_context, service_context=service_context\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author experienced a difficult moment when their mother had a stroke and was put in a nursing home. The stroke destroyed her balance, and the author and their sister were determined to help her get out of the nursing home and back to her house.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "query_engine = index.as_query_engine()\n",
- "response = query_engine.query(\"What was a hard moment for the author?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The author of the given context is Paul Graham.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "response = query_engine.query(\"Who is the author?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "At Interleaf, there was a group called Release Engineering that seemed to be as big as the group that actually wrote the software. The software at Interleaf had to be updated on the server, and there was a lot of emphasis on high production values to make the online store builders look legitimate.\n",
- "\n",
- "Streamed output at 20.953424485215063 tokens/s\n"
- ]
- }
- ],
- "source": [
- "import time\n",
- "\n",
- "query_engine = index.as_query_engine(streaming=True)\n",
- "response = query_engine.query(\"What happened at interleaf?\")\n",
- "\n",
- "start_time = time.time()\n",
- "\n",
- "token_count = 0\n",
- "for token in response.response_gen:\n",
- " print(token, end=\"\")\n",
- " token_count += 1\n",
- "\n",
- "time_elapsed = time.time() - start_time\n",
- "tokens_per_second = token_count / time_elapsed\n",
- "\n",
- "print(f\"\\n\\nStreamed output at {tokens_per_second} tokens/s\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Adding a document to existing index"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The color of the sky can vary depending on various factors such as time of day, weather conditions, and location. It can range from shades of blue during the day to hues of orange, pink, and purple during sunrise or sunset.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "response = query_engine.query(\"What colour is the sky?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from llama_index import Document\n",
- "\n",
- "index.insert_nodes([Document(text=\"The sky is indigo today\")])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<b>The colour of the sky is indigo.</b>"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "response = query_engine.query(\"What colour is the sky?\")\n",
- "display(Markdown(f\"<b>{response}</b>\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Filtering"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "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,
- "metadata": {},
- "outputs": [],
- "source": [
- "index.insert_nodes(nodes)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=TextNode(id_='5a97da0c-8f04-4c63-b90b-8c474d8c273d', embedding=None, metadata={'director': 'Francis Ford Coppola', 'theme': 'Mafia'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='81cf4b9e847ba42e83fc401e31af8e17d629f0d5cf9c0c320ec7ac69dd0257e1', text='The Godfather', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.81316805)]"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters\n",
- "\n",
- "\n",
- "filters = MetadataFilters(\n",
- " filters=[ExactMatchFilter(key=\"theme\", value=\"Mafia\")]\n",
- ")\n",
- "\n",
- "retriever = index.as_retriever(filters=filters)\n",
- "retriever.retrieve(\"What is inception about?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Query Mode\n",
- "Four query modes are supported: DEFAULT (vector search), SPARSE, HYBRID, and SEMANTIC_HYBRID."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Search using Hybrid Search"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=TextNode(id_='9ab81017-1b1d-4cb2-b70a-fca430dd7661', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9021b97511b42619080a39ace8463c7b963b282938a279e03346e956425740b9', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03159204125404358),\n",
- " NodeWithScore(node=TextNode(id_='5d1ba583-504e-4d90-8552-bdb19a41ebac', embedding=None, metadata={'file_path': 'data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75393, 'creation_date': '2023-12-30', 'last_modified_date': '2023-12-30', 'last_accessed_date': '2023-12-30'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='15d668ff-69f4-43da-8626-06ac255aa259', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'file_path': 'data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75393, 'creation_date': '2023-12-30', 'last_modified_date': '2023-12-30', 'last_accessed_date': '2023-12-30'}, hash='9f9d580e21ef8e986f2bf21e429149121227f8b46669d97e92bfb142fa353038'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='82516030-dadf-4941-a183-529715f21a01', node_type=<ObjectType.TEXT: '1'>, metadata={'file_path': 'data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75393, 'creation_date': '2023-12-30', 'last_modified_date': '2023-12-30', 'last_accessed_date': '2023-12-30'}, hash='51c474a12ac8e9748258b2c7bbe77bb7c8bf35b775ed44f016057a0aa8b0bd76'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='fca92bb2-9398-4627-b4b8-1764fd80e533', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='192082e7ba84b8c5e2a64bd1d422c6c503189fc3ba325bb3e6e8bdb43db03fbb')}, hash='81c35a00b2b9444d0c46c3414400a345ab624a2ec7a43cc8ff133e7fcdc437f8', text='It\\'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.', start_char_idx=45670, end_char_idx=50105, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=0.03028232976794243)]"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from llama_index.vector_stores.types import VectorStoreQueryMode\n",
- "\n",
- "hybrid_retriever = index.as_retriever(\n",
- " vector_store_query_mode=VectorStoreQueryMode.HYBRID\n",
- ")\n",
- "hybrid_retriever.retrieve(\"What is inception about?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Hybrid Search with Semantic Reranking\n",
- "This mode incorporates semantic reranking to hybrid search results to improve search relevance. \n",
- "\n",
- "Please see this link for further details. https://learn.microsoft.com/en-us/azure/search/semantic-search-overview"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[NodeWithScore(node=TextNode(id_='9ab81017-1b1d-4cb2-b70a-fca430dd7661', embedding=None, metadata={'director': 'Christopher Nolan'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='9021b97511b42619080a39ace8463c7b963b282938a279e03346e956425740b9', text='Inception', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=2.3949906826019287),\n",
- " NodeWithScore(node=TextNode(id_='7e6b8b71-c8f3-4c11-a9ad-cf4261b9196f', embedding=None, metadata={'file_path': 'data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75393, 'creation_date': '2023-12-30', 'last_modified_date': '2023-12-30', 'last_accessed_date': '2023-12-30'}, excluded_embed_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], excluded_llm_metadata_keys=['file_name', 'file_type', 'file_size', 'creation_date', 'last_modified_date', 'last_accessed_date'], relationships={<NodeRelationship.SOURCE: '1'>: RelatedNodeInfo(node_id='15d668ff-69f4-43da-8626-06ac255aa259', node_type=<ObjectType.DOCUMENT: '4'>, metadata={'file_path': 'data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75393, 'creation_date': '2023-12-30', 'last_modified_date': '2023-12-30', 'last_accessed_date': '2023-12-30'}, hash='9f9d580e21ef8e986f2bf21e429149121227f8b46669d97e92bfb142fa353038'), <NodeRelationship.PREVIOUS: '2'>: RelatedNodeInfo(node_id='3188ac75-6fca-43fc-8be4-725a975c4d90', node_type=<ObjectType.TEXT: '1'>, metadata={'file_path': 'data\\\\paul_graham\\\\paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75393, 'creation_date': '2023-12-30', 'last_modified_date': '2023-12-30', 'last_accessed_date': '2023-12-30'}, hash='db7cc1a67fa3afd1e5f24c8c61583781ce6a00c444da8f25a5374468c17b7de0'), <NodeRelationship.NEXT: '3'>: RelatedNodeInfo(node_id='c8403059-48a7-4946-9ca7-db5a7b7cf988', node_type=<ObjectType.TEXT: '1'>, metadata={}, hash='aec09c4a114c909775f4053eba58f3160ddfd61329b030bd8111aef7da76bc5a')}, hash='81c35a00b2b9444d0c46c3414400a345ab624a2ec7a43cc8ff133e7fcdc437f8', text='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 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.', start_char_idx=14282, end_char_idx=18597, text_template='{metadata_str}\\n\\n{content}', metadata_template='{key}: {value}', metadata_seperator='\\n'), score=1.0243566036224365)]"
- ]
- },
- "execution_count": null,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "hybrid_retriever = index.as_retriever(\n",
- " vector_store_query_mode=VectorStoreQueryMode.SEMANTIC_HYBRID\n",
- ")\n",
- "hybrid_retriever.retrieve(\"What is inception about?\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "llamaindextest01",
- "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"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/module_guides/storing/customization.md b/docs/module_guides/storing/customization.md
index f0f3af8cdfc9933b0ac7d65e8cfdfb3e402f96b2..67f7f4837ecda9c3722433f4861ca91ba65a7205 100644
--- a/docs/module_guides/storing/customization.md
+++ b/docs/module_guides/storing/customization.md
@@ -84,7 +84,7 @@ Most of our vector store integrations store the entire index (vectors + text) in
The vector stores that support this practice are:
-- CognitiveSearchVectorStore
+- AzureAISearchVectorStore
- ChatGPTRetrievalPluginClient
- CassandraVectorStore
- ChromaVectorStore
diff --git a/docs/module_guides/storing/vector_stores.md b/docs/module_guides/storing/vector_stores.md
index 156d1d84a82b237ca2d349a1ce6b14bea60f8972..e09e63318e9db273c738c3983c4353a4858a6689 100644
--- a/docs/module_guides/storing/vector_stores.md
+++ b/docs/module_guides/storing/vector_stores.md
@@ -61,10 +61,10 @@ maxdepth: 1
---
/examples/vector_stores/AstraDBIndexDemo.ipynb
/examples/vector_stores/AsyncIndexCreationDemo.ipynb
+/examples/vector_stores/AzureAISearchIndexDemo.ipynb
/examples/vector_stores/AzureCosmosDBMongoDBvCoreDemo.ipynb
/examples/vector_stores/CassandraIndexDemo.ipynb
/examples/vector_stores/ChromaIndexDemo.ipynb
-/examples/vector_stores/CognitiveSearchIndexDemo.ipynb
/examples/vector_stores/DashvectorIndexDemo.ipynb
/examples/vector_stores/DashvectorIndexDemo-Hybrid.ipynb
/examples/vector_stores/DeepLakeIndexDemo.ipynb
diff --git a/llama_index/vector_stores/__init__.py b/llama_index/vector_stores/__init__.py
index a720026257f7a7eb205629335f3467869b77e843..d090f229f310995bda395f24e3293a8303e7ab3f 100644
--- a/llama_index/vector_stores/__init__.py
+++ b/llama_index/vector_stores/__init__.py
@@ -3,12 +3,12 @@
from llama_index.vector_stores.astra import AstraDBVectorStore
from llama_index.vector_stores.awadb import AwaDBVectorStore
+from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore
from llama_index.vector_stores.azurecosmosmongo import AzureCosmosDBMongoDBVectorSearch
from llama_index.vector_stores.bagel import BagelVectorStore
from llama_index.vector_stores.cassandra import CassandraVectorStore
from llama_index.vector_stores.chatgpt_plugin import ChatGPTRetrievalPluginClient
from llama_index.vector_stores.chroma import ChromaVectorStore
-from llama_index.vector_stores.cogsearch import CognitiveSearchVectorStore
from llama_index.vector_stores.dashvector import DashVectorStore
from llama_index.vector_stores.deeplake import DeepLakeVectorStore
from llama_index.vector_stores.docarray import (
@@ -88,6 +88,7 @@ __all__ = [
"AwaDBVectorStore",
"BagelVectorStore",
"Neo4jVectorStore",
+ "AzureAISearchVectorStore",
"CognitiveSearchVectorStore",
"EpsillaVectorStore",
"SingleStoreVectorStore",
diff --git a/llama_index/vector_stores/cogsearch.py b/llama_index/vector_stores/azureaisearch.py
similarity index 87%
rename from llama_index/vector_stores/cogsearch.py
rename to llama_index/vector_stores/azureaisearch.py
index fe4cf203fedc4fac17f94b5064b52a77589a63c0..a63854186c27adddcb01c8a64c073cda0f18748c 100644
--- a/llama_index/vector_stores/cogsearch.py
+++ b/llama_index/vector_stores/azureaisearch.py
@@ -1,4 +1,4 @@
-"""Azure Cognitive Search vector store."""
+"""Azure AI Search vector store."""
import enum
import json
import logging
@@ -26,7 +26,7 @@ logger = logging.getLogger(__name__)
class MetadataIndexFieldType(int, enum.Enum):
"""
Enumeration representing the supported types for metadata fields in an
- Azure Cognitive Search Index, corresponds with types supported in a flat
+ Azure AI Search Index, corresponds with types supported in a flat
metadata dictionary.
"""
@@ -45,7 +45,7 @@ class IndexManagement(int, enum.Enum):
CREATE_IF_NOT_EXISTS = auto()
-class CognitiveSearchVectorStore(VectorStore):
+class AzureAISearchVectorStore(VectorStore):
stores_text: bool = True
flat_metadata: bool = True
@@ -81,7 +81,9 @@ class CognitiveSearchVectorStore(VectorStore):
def _create_index_if_not_exists(self, index_name: str) -> None:
if index_name not in self._index_client.list_index_names():
- logger.info(f"Index {index_name} does not exist, creating index")
+ logger.info(
+ f"Index {index_name} does not exist in Azure AI Search, creating index"
+ )
self._create_index(index_name)
def _create_metadata_index_fields(self) -> List[Any]:
@@ -116,21 +118,26 @@ class CognitiveSearchVectorStore(VectorStore):
metadata filtering keys.
"""
from azure.search.documents.indexes.models import (
+ ExhaustiveKnnAlgorithmConfiguration,
+ ExhaustiveKnnParameters,
+ HnswAlgorithmConfiguration,
HnswParameters,
- HnswVectorSearchAlgorithmConfiguration,
- PrioritizedFields,
SearchableField,
SearchField,
SearchFieldDataType,
SearchIndex,
SemanticConfiguration,
SemanticField,
- SemanticSettings,
+ SemanticPrioritizedFields,
+ SemanticSearch,
SimpleField,
VectorSearch,
+ VectorSearchAlgorithmKind,
+ VectorSearchAlgorithmMetric,
+ VectorSearchProfile,
)
- logger.info(f"Configuring {index_name} fields")
+ logger.info(f"Configuring {index_name} fields for Azure AI Search")
fields = [
SimpleField(name=self._field_mapping["id"], type="Edm.String", key=True),
SearchableField(
@@ -141,68 +148,70 @@ class CognitiveSearchVectorStore(VectorStore):
SearchField(
name=self._field_mapping["embedding"],
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
- hidden=False,
searchable=True,
- filterable=False,
- sortable=False,
- facetable=False,
vector_search_dimensions=self.embedding_dimensionality,
- vector_search_configuration="default",
+ vector_search_profile_name="default",
),
SimpleField(name=self._field_mapping["metadata"], type="Edm.String"),
SimpleField(
name=self._field_mapping["doc_id"], type="Edm.String", filterable=True
),
]
-
logger.info(f"Configuring {index_name} metadata fields")
- # Add on the metadata fields
-
metadata_index_fields = self._create_metadata_index_fields()
-
fields.extend(metadata_index_fields)
-
logger.info(f"Configuring {index_name} vector search")
-
- hnsw_param = HnswParameters(
- m=4,
- ef_construction=500,
- ef_search=1000,
- metric="cosine",
- )
-
+ # Configure the vector search algorithms and profiles
vector_search = VectorSearch(
- algorithm_configurations=[
- HnswVectorSearchAlgorithmConfiguration(
- name="default",
- kind="hnsw",
- parameters=hnsw_param,
- )
- ]
+ algorithms=[
+ HnswAlgorithmConfiguration(
+ name="myHnsw",
+ kind=VectorSearchAlgorithmKind.HNSW,
+ # For more information on HNSw parameters, visit https://learn.microsoft.com//azure/search/vector-search-ranking#creating-the-hnsw-graph
+ parameters=HnswParameters(
+ m=4,
+ ef_construction=400,
+ ef_search=500,
+ metric=VectorSearchAlgorithmMetric.COSINE,
+ ),
+ ),
+ ExhaustiveKnnAlgorithmConfiguration(
+ name="myExhaustiveKnn",
+ kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN,
+ parameters=ExhaustiveKnnParameters(
+ metric=VectorSearchAlgorithmMetric.COSINE,
+ ),
+ ),
+ ],
+ profiles=[
+ VectorSearchProfile(
+ name="myHnswProfile",
+ algorithm_configuration_name="myHnsw",
+ ),
+ # Add more profiles if needed
+ VectorSearchProfile(
+ name="myExhaustiveKnnProfile",
+ algorithm_configuration_name="myExhaustiveKnn",
+ ),
+ # Add more profiles if needed
+ ],
)
-
logger.info(f"Configuring {index_name} semantic search")
- semantic_settings = SemanticSettings(
- configurations=[
- SemanticConfiguration(
- name="default",
- prioritized_fields=PrioritizedFields(
- title_field=None,
- prioritized_content_fields=[
- SemanticField(field_name=self._field_mapping["chunk"])
- ],
- ),
- )
- ]
+ semantic_config = SemanticConfiguration(
+ name="mySemanticConfig",
+ prioritized_fields=SemanticPrioritizedFields(
+ content_fields=[SemanticField(field_name=self._field_mapping["chunk"])],
+ ),
)
+ semantic_search = SemanticSearch(configurations=[semantic_config])
+
index = SearchIndex(
name=index_name,
fields=fields,
- semantic_settings=semantic_settings,
vector_search=vector_search,
+ semantic_search=semantic_search,
)
-
logger.debug(f"Creating {index_name} search index")
self._index_client.create_index(index)
@@ -238,7 +247,7 @@ class CognitiveSearchVectorStore(VectorStore):
) -> None:
# ruff: noqa: E501
"""
- Embeddings and documents are stored in an Azure Cognitive Search index,
+ Embeddings and documents are stored in an Azure AI Search index,
a merge or upload approach is used when adding embeddings.
When adding multiple embeddings the index is updated by this vector store
in batches of 10 documents, very large nodes may result in failure due to
@@ -259,7 +268,7 @@ class CognitiveSearchVectorStore(VectorStore):
index_mapping:
Optional function with definition
(enriched_doc: Dict[str, str], metadata: Dict[str, Any]): Dict[str,str]
- used to map document fields to the Cognitive search index fields
+ used to map document fields to the AI search index fields
(return value of function).
If none is specified a default mapping is provided which uses
the field keys. The keys in the enriched_doc are
@@ -283,7 +292,7 @@ class CognitiveSearchVectorStore(VectorStore):
"""
import_err_msg = (
"`azure-search-documents` package not found, please run "
- "`pip install azure-search-documents==11.4.0b8`"
+ "`pip install azure-search-documents==11.4.0`"
)
try:
@@ -439,7 +448,7 @@ class CognitiveSearchVectorStore(VectorStore):
return ids
def _create_index_document(self, node: BaseNode) -> Dict[str, Any]:
- """Create Cognitive Search index document from embedding result."""
+ """Create AI Search index document from embedding result."""
doc: Dict[str, Any] = {}
doc["id"] = node.node_id
doc["chunk"] = node.get_content(metadata_mode=MetadataMode.NONE) or ""
@@ -458,7 +467,7 @@ class CognitiveSearchVectorStore(VectorStore):
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
- Delete documents from the Cognitive Search Index
+ Delete documents from the AI Search Index
with doc_id_field_key field equal to ref_doc_id.
"""
# Locate documents to delete
@@ -571,7 +580,7 @@ class AzureQueryResultSearchBase:
) -> VectorStoreQueryResult:
results = self._search_client.search(
search_text=search_query,
- vectors=vectors,
+ vector_queries=vectors,
top=self._query.similarity_top_k,
select=self._select_fields,
filter=self._odata_filter,
@@ -632,14 +641,14 @@ class AzureQueryResultSearchDefault(AzureQueryResultSearchBase):
if not self._query.query_embedding:
raise ValueError("Query missing embedding")
- vector = VectorizedQuery(
- value=self._query.query_embedding,
- k=self._query.similarity_top_k,
+ vectorized_query = VectorizedQuery(
+ vector=self._query.query_embedding,
+ k_nearest_neighbors=self._query.similarity_top_k,
fields=self._field_mapping["embedding"],
)
- vectors = [vector]
+ vector_queries = [vectorized_query]
logger.info("Vector search with supplied embedding")
- return vectors
+ return vector_queries
class AzureQueryResultSearchSparse(AzureQueryResultSearchBase):
@@ -671,27 +680,27 @@ class AzureQueryResultSearchSemanticHybrid(AzureQueryResultSearchHybrid):
if not self._query.query_embedding:
raise ValueError("Query missing embedding")
# k is set to 50 to align with the number of accept document in azure semantic reranking model.
- # https://learn.microsoft.com/en-us/azure/search/semantic-search-overview
- vector = VectorizedQuery(
- value=self._query.query_embedding,
- k=50,
+ # https://learn.microsoft.com/azure/search/semantic-search-overview
+ vectorized_query = VectorizedQuery(
+ vector=self._query.query_embedding,
+ k_nearest_neighbors=50,
fields=self._field_mapping["embedding"],
)
- vectors = [vector]
+ vector_queries = [vectorized_query]
logger.info("Vector search with supplied embedding")
- return vectors
+ return vector_queries
def _create_query_result(
- self, search_query: str, vectors: Optional[List[Any]]
+ self, search_query: str, vector_queries: Optional[List[Any]]
) -> VectorStoreQueryResult:
results = self._search_client.search(
search_text=search_query,
- vectors=vectors,
+ vector_queries=vector_queries,
top=self._query.similarity_top_k,
select=self._select_fields,
filter=self._odata_filter,
query_type="semantic",
- semantic_configuration_name="default",
+ semantic_configuration_name="mySemanticConfig",
)
id_result = []
@@ -735,3 +744,6 @@ class AzureQueryResultSearchSemanticHybrid(AzureQueryResultSearchHybrid):
return VectorStoreQueryResult(
nodes=node_result, similarities=score_result, ids=id_result
)
+
+
+CognitiveSearchVectorStore = AzureAISearchVectorStore
diff --git a/poetry.lock b/poetry.lock
index a7fc471c9ad9a91f466612395575ef3046283ccc..fa7653384def0988bdcda91c33e1fc5072405198 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "accelerate"
@@ -44,87 +44,87 @@ files = [
[[package]]
name = "aiohttp"
-version = "3.9.2"
+version = "3.9.3"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
files = [
- {file = "aiohttp-3.9.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:772fbe371788e61c58d6d3d904268e48a594ba866804d08c995ad71b144f94cb"},
- {file = "aiohttp-3.9.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:edd4f1af2253f227ae311ab3d403d0c506c9b4410c7fc8d9573dec6d9740369f"},
- {file = "aiohttp-3.9.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cfee9287778399fdef6f8a11c9e425e1cb13cc9920fd3a3df8f122500978292b"},
- {file = "aiohttp-3.9.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3cc158466f6a980a6095ee55174d1de5730ad7dec251be655d9a6a9dd7ea1ff9"},
- {file = "aiohttp-3.9.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:54ec82f45d57c9a65a1ead3953b51c704f9587440e6682f689da97f3e8defa35"},
- {file = "aiohttp-3.9.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:abeb813a18eb387f0d835ef51f88568540ad0325807a77a6e501fed4610f864e"},
- {file = "aiohttp-3.9.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc91d07280d7d169f3a0f9179d8babd0ee05c79d4d891447629ff0d7d8089ec2"},
- {file = "aiohttp-3.9.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b65e861f4bebfb660f7f0f40fa3eb9f2ab9af10647d05dac824390e7af8f75b7"},
- {file = "aiohttp-3.9.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:04fd8ffd2be73d42bcf55fd78cde7958eeee6d4d8f73c3846b7cba491ecdb570"},
- {file = "aiohttp-3.9.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:3d8d962b439a859b3ded9a1e111a4615357b01620a546bc601f25b0211f2da81"},
- {file = "aiohttp-3.9.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:8ceb658afd12b27552597cf9a65d9807d58aef45adbb58616cdd5ad4c258c39e"},
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name = "dataclasses-json"
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description = "Easily serialize dataclasses to and from JSON."
optional = false
python-versions = ">=3.7,<4.0"
files = [
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@@ -1066,43 +1066,41 @@ typing-inspect = ">=0.4.0,<1"
[[package]]
name = "datasets"
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description = "HuggingFace community-driven open-source library of datasets"
optional = true
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numpy = ">=1.17"
packaging = "*"
pandas = "*"
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requests = ">=2.19.0"
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tqdm = ">=4.62.1"
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vision = ["Pillow (>=6.2.1)"]
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[[package]]
name = "dill"
-version = "0.3.7"
+version = "0.3.8"
description = "serialize all of Python"
optional = false
-python-versions = ">=3.7"
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files = [
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graph = ["objgraph (>=1.7.2)"]
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name = "dirtyjson"
@@ -1497,18 +1496,17 @@ files = [
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description = "File-system specification"
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python-versions = ">=3.8"
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abfs = ["adlfs"]
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gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
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libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
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name = "google-ai-generativelanguage"
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description = "Google Ai Generativelanguage API client library"
optional = false
python-versions = ">=3.7"
files = [
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name = "google-api-core"
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description = "Google API client core library"
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python-versions = ">=3.7"
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grpcio-status = [
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[[package]]
name = "google-auth"
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description = "Google Authentication Library"
optional = false
python-versions = ">=3.7"
files = [
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@@ -1655,13 +1653,13 @@ requests = "*"
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name = "gradientai"
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+version = "1.7.0"
description = "Gradient AI API"
optional = true
python-versions = ">=3.8.1,<4.0.0"
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@@ -1743,84 +1741,84 @@ test = ["objgraph", "psutil"]
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description = "Status proto mapping for gRPC"
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python-versions = ">=3.6"
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protobuf = ">=4.21.6"
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@@ -1910,13 +1908,13 @@ socks = ["socksio (==1.*)"]
[[package]]
name = "huggingface-hub"
-version = "0.20.2"
+version = "0.20.3"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = true
python-versions = ">=3.8.0"
files = [
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[[package]]
name = "jupyter-server-terminals"
-version = "0.5.1"
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description = "A Jupyter Server Extension Providing Terminals."
optional = false
python-versions = ">=3.8"
files = [
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[[package]]
name = "jupyterlab"
-version = "4.0.11"
+version = "4.1.0"
description = "JupyterLab computational environment"
optional = false
python-versions = ">=3.8"
files = [
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importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
ipykernel = "*"
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traitlets = "*"
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[[package]]
@@ -2618,13 +2617,13 @@ files = [
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name = "langchain"
-version = "0.1.1"
+version = "0.1.5"
description = "Building applications with LLMs through composability"
optional = true
python-versions = ">=3.8.1,<4.0"
files = [
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dataclasses-json = ">=0.5.7,<0.7"
jsonpatch = ">=1.33,<2.0"
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-langchain-core = ">=0.1.9,<0.2"
-langsmith = ">=0.0.77,<0.1.0"
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numpy = ">=1,<2"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
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docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
embeddings = ["sentence-transformers (>=2,<3)"]
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[[package]]
name = "langchain-community"
-version = "0.0.13"
+version = "0.0.17"
description = "Community contributed LangChain integrations."
optional = true
python-versions = ">=3.8.1,<4.0"
files = [
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dataclasses-json = ">=0.5.7,<0.7"
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-langsmith = ">=0.0.63,<0.1.0"
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numpy = ">=1,<2"
PyYAML = ">=5.3"
requests = ">=2,<3"
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flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
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integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
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optuna = ["optuna"]
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tokenizers = ["tokenizers (>=0.14,<0.19)"]
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[[package]]
name = "tree-sitter-languages"
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optional = false
python-versions = "*"
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]
[package.dependencies]
@@ -7121,28 +7146,26 @@ tree-sitter = "*"
[[package]]
name = "triton"
-version = "2.1.0"
+version = "2.2.0"
description = "A language and compiler for custom Deep Learning operations"
optional = true
python-versions = "*"
files = [
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[package.dependencies]
filelock = "*"
[package.extras]
-build = ["cmake (>=3.18)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
-tutorials = ["matplotlib", "pandas", "tabulate"]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
+tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
[[package]]
name = "typer"
@@ -7178,35 +7201,35 @@ files = [
[[package]]
name = "types-docutils"
-version = "0.20.0.20240106"
+version = "0.20.0.20240201"
description = "Typing stubs for docutils"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-docutils-0.20.0.20240106.tar.gz", hash = "sha256:03992ec976fbe080db588e1e56a83c5e4aba5c733022b25bb26cb84397b96049"},
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+ {file = "types-docutils-0.20.0.20240201.tar.gz", hash = "sha256:ba4bfd4ff6dd19640ba7ab5d93900393a65897880f3650997964a943f4e79a6b"},
+ {file = "types_docutils-0.20.0.20240201-py3-none-any.whl", hash = "sha256:79d3bcef235f7c81a63f4f3dcf1d0b138985079bb32d02f5a7d266e1f9f361ba"},
]
[[package]]
name = "types-protobuf"
-version = "4.24.0.20240106"
+version = "4.24.0.20240129"
description = "Typing stubs for protobuf"
optional = false
python-versions = ">=3.8"
files = [
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+ {file = "types-protobuf-4.24.0.20240129.tar.gz", hash = "sha256:8a83dd3b9b76a33e08d8636c5daa212ace1396418ed91837635fcd564a624891"},
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]
[[package]]
name = "types-pyopenssl"
-version = "23.3.0.20240106"
+version = "24.0.0.20240130"
description = "Typing stubs for pyOpenSSL"
optional = false
python-versions = ">=3.8"
files = [
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+ {file = "types-pyOpenSSL-24.0.0.20240130.tar.gz", hash = "sha256:c812e5c1c35249f75ef5935708b2a997d62abf9745be222e5f94b9595472ab25"},
+ {file = "types_pyOpenSSL-24.0.0.20240130-py3-none-any.whl", hash = "sha256:24a255458b5b8a7fca8139cf56f2a8ad5a4f1a5f711b73a5bb9cb50dc688fab5"},
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[package.dependencies]
diff --git a/tests/vector_stores/test_cogsearch.py b/tests/vector_stores/test_azureaisearch.py
similarity index 68%
rename from tests/vector_stores/test_cogsearch.py
rename to tests/vector_stores/test_azureaisearch.py
index 862aea4ed9d0236f964c51a44e3fca3fed120e81..2fb8144b5120f556580de6f89166b7d81ca13e7b 100644
--- a/tests/vector_stores/test_cogsearch.py
+++ b/tests/vector_stores/test_azureaisearch.py
@@ -3,16 +3,18 @@ from unittest.mock import MagicMock
import pytest
from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode
-from llama_index.vector_stores import CognitiveSearchVectorStore
-from llama_index.vector_stores.cogsearch import IndexManagement
+from llama_index.vector_stores.azureaisearch import (
+ AzureAISearchVectorStore,
+ IndexManagement,
+)
try:
from azure.search.documents import SearchClient
from azure.search.documents.indexes import SearchIndexClient
- cogsearch_installed = True
+ azureaisearch_installed = True
except ImportError:
- cogsearch_installed = False
+ azureaisearch_installed = False
search_client = None # type: ignore
@@ -20,16 +22,18 @@ def create_mock_vector_store(
search_client: Any,
index_name: Optional[str] = None,
index_management: IndexManagement = IndexManagement.NO_VALIDATION,
-) -> CognitiveSearchVectorStore:
- return CognitiveSearchVectorStore(
+) -> AzureAISearchVectorStore:
+ return AzureAISearchVectorStore(
search_or_index_client=search_client,
id_field_key="id",
chunk_field_key="content",
embedding_field_key="embedding",
- metadata_string_field_key="li_jsonMetadata",
- doc_id_field_key="li_doc_id",
+ metadata_string_field_key="metadata",
+ doc_id_field_key="doc_id",
+ filterable_metadata_field_keys=[], # Added to match the updated constructor
index_name=index_name,
index_management=index_management,
+ embedding_dimensionality=2, # Assuming a dimensionality of 2 for simplicity
)
@@ -51,9 +55,9 @@ def create_sample_documents(n: int) -> List[TextNode]:
@pytest.mark.skipif(
- not cogsearch_installed, reason="azure-search-documents package not installed"
+ not azureaisearch_installed, reason="azure-search-documents package not installed"
)
-def test_cogsearch_add_two_batches() -> None:
+def test_azureaisearch_add_two_batches() -> None:
search_client = MagicMock(spec=SearchClient)
vector_store = create_mock_vector_store(search_client)
@@ -69,9 +73,9 @@ def test_cogsearch_add_two_batches() -> None:
@pytest.mark.skipif(
- not cogsearch_installed, reason="azure-search-documents package not installed"
+ not azureaisearch_installed, reason="azure-search-documents package not installed"
)
-def test_cogsearch_add_one_batch() -> None:
+def test_azureaisearch_add_one_batch() -> None:
search_client = MagicMock(spec=SearchClient)
vector_store = create_mock_vector_store(search_client)
@@ -87,7 +91,7 @@ def test_cogsearch_add_one_batch() -> None:
@pytest.mark.skipif(
- not cogsearch_installed, reason="azure-search-documents package not installed"
+ not azureaisearch_installed, reason="azure-search-documents package not installed"
)
def test_invalid_index_management_for_searchclient() -> None:
search_client = MagicMock(spec=SearchClient)
@@ -114,13 +118,23 @@ def test_invalid_index_management_for_searchclient() -> None:
@pytest.mark.skipif(
- not cogsearch_installed, reason="azure-search-documents package not installed"
+ not azureaisearch_installed, reason="azure-search-documents package not installed"
)
def test_invalid_index_management_for_searchindexclient() -> None:
search_client = MagicMock(spec=SearchIndexClient)
# Index name must be supplied
- with pytest.raises(ValueError):
+ with pytest.raises(
+ ValueError,
+ match="index_name must be supplied if search_or_index_client is of type azure.search.documents.SearchIndexClient",
+ ):
create_mock_vector_store(
search_client, index_management=IndexManagement.VALIDATE_INDEX
)
+
+ # No error when index name is supplied with SearchIndexClient
+ create_mock_vector_store(
+ search_client,
+ index_name="test01",
+ index_management=IndexManagement.CREATE_IF_NOT_EXISTS,
+ )