From 7fe062a545874152bad62ae9127797b14657408f Mon Sep 17 00:00:00 2001 From: Shorthills AI <141953346+ShorthillsAI@users.noreply.github.com> Date: Thu, 14 Dec 2023 23:47:38 +0530 Subject: [PATCH] Fixed Docs Spelling Mistakes (#9517) --- CHANGELOG.md | 2 +- docs/changes/deprecated_terms.md | 2 +- docs/community/integrations/graph_stores.md | 2 +- docs/community/integrations/guidance.md | 4 ++-- docs/community/integrations/vector_stores.md | 2 +- docs/getting_started/concepts.md | 12 ++++++------ docs/getting_started/discover_llamaindex.md | 4 ++-- docs/module_guides/models/llms.md | 2 +- .../advanced_retrieval/query_transformations.md | 2 +- .../basic_strategies/basic_strategies.md | 2 +- docs/optimizing/building_rag_from_scratch.md | 2 +- docs/use_cases/agents.md | 2 +- examples/gatsby/gatsby_license.txt | 14 +++++++------- 13 files changed, 26 insertions(+), 26 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 224fee307b..fd9b09fd15 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -109,7 +109,7 @@ ### Breaking Changes / Deprecations - Added `LocalAI` demo and began deprecation cycle (#9151) -- Deprecate `QueryResponseDataset` and `DatasetGenerator` of `evaluaton` module (#9165) +- Deprecate `QueryResponseDataset` and `DatasetGenerator` of `evaluation` module (#9165) ### Bug Fixes / Nits diff --git a/docs/changes/deprecated_terms.md b/docs/changes/deprecated_terms.md index def9fc8ee0..843da0e792 100644 --- a/docs/changes/deprecated_terms.md +++ b/docs/changes/deprecated_terms.md @@ -24,7 +24,7 @@ This has been renamed to `VectorStoreIndex`, but it is only a cosmetic change. P ## LLMPredictor -The `LLMPredictor` object is no longer intended to be used by users. Instead, you can setup an LLM directly and pass it into the `ServiceContext`. THe `LLM` class itself has similar attributes and methods as the `LLMPredictor`. +The `LLMPredictor` object is no longer intended to be used by users. Instead, you can setup an LLM directly and pass it into the `ServiceContext`. The `LLM` class itself has similar attributes and methods as the `LLMPredictor`. - [LLMs in LlamaIndex](/module_guides/models/llms.md) - [Setting LLMs in the ServiceContext](/module_guides/supporting_modules/service_context.md) diff --git a/docs/community/integrations/graph_stores.md b/docs/community/integrations/graph_stores.md index 9587d18ae8..f9b49dace8 100644 --- a/docs/community/integrations/graph_stores.md +++ b/docs/community/integrations/graph_stores.md @@ -2,7 +2,7 @@ ## `Neo4jGraphStore` -`Neo4j` is supported as a graph store integration. You can persist, visualze, and query graphs using LlamaIndex and Neo4j. Furthermore, existing Neo4j graphs are directly supported using `text2cypher` and the `KnowledgeGraphQueryEngine`. +`Neo4j` is supported as a graph store integration. You can persist, visualize, and query graphs using LlamaIndex and Neo4j. Furthermore, existing Neo4j graphs are directly supported using `text2cypher` and the `KnowledgeGraphQueryEngine`. If you've never used Neo4j before, you can download the desktop client [here](https://neo4j.com/download/). diff --git a/docs/community/integrations/guidance.md b/docs/community/integrations/guidance.md index 130173d7ad..2d10cc5a84 100644 --- a/docs/community/integrations/guidance.md +++ b/docs/community/integrations/guidance.md @@ -33,7 +33,7 @@ and supplying a suitable prompt template. > Note: guidance uses handlebars-style templates, which uses double braces for variable substitution, and single braces for literal braces. This is the opposite convention of Python format strings. -> Note: We provide an utility function `from llama_index.prompts.guidance_utils import convert_to_handlebars` that can convert from the Python format string style template to guidance handlebars-style template. +> Note: We provide a utility function `from llama_index.prompts.guidance_utils import convert_to_handlebars` that can convert from the Python format string style template to guidance handlebars-style template. ```python program = GuidancePydanticProgram( @@ -70,7 +70,7 @@ You can play with [this notebook](/examples/output_parsing/guidance_pydantic_pro ### Using guidance to improve the robustness of our sub-question query engine. LlamaIndex provides a toolkit of advanced query engines for tackling different use-cases. -Several relies on structured output in intermediate steps. +Several rely on structured output in intermediate steps. We can use guidance to improve the robustness of these query engines, by making sure the intermediate response has the expected structure (so that they can be parsed correctly to a structured object). diff --git a/docs/community/integrations/vector_stores.md b/docs/community/integrations/vector_stores.md index 38fc627cef..05c24f0911 100644 --- a/docs/community/integrations/vector_stores.md +++ b/docs/community/integrations/vector_stores.md @@ -45,7 +45,7 @@ Once constructed, the index can be used for querying. **Default Vector Store Index Construction/Querying** -By default, `VectorStoreIndex` uses a in-memory `SimpleVectorStore` +By default, `VectorStoreIndex` uses an in-memory `SimpleVectorStore` that's initialized as part of the default storage context. ```python diff --git a/docs/getting_started/concepts.md b/docs/getting_started/concepts.md index 3dc1097a54..60bf005387 100644 --- a/docs/getting_started/concepts.md +++ b/docs/getting_started/concepts.md @@ -38,7 +38,7 @@ There are also some terms you'll encounter that refer to steps within each of th ### Loading stage -[**Nodes and Documents**](/module_guides/loading/documents_and_nodes/root.md): A `Document` is a container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. Nodes have metadata that relate them to the document they are in and to other nodes. +[**Nodes and Documents**](/module_guides/loading/documents_and_nodes/root.md): A `Document` is a container around any data source - for instance, a PDF, an API output, or retrieve data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. Nodes have metadata that relate them to the document they are in and to other nodes. [**Connectors**](/module_guides/loading/connector/root.md): A data connector (often called a `Reader`) ingests data from different data sources and data formats into `Document`s and `Nodes`. @@ -48,7 +48,7 @@ A data connector (often called a `Reader`) ingests data from different data sour [**Indexes**](/module_guides/indexing/indexing.md): Once you've ingested your data, LlamaIndex will help you index the data into a structure that's easy to retrieve. This usually involves generating `vector embeddings` which are stored in a specialized database called a `vector store`. Indexes can also store a variety of metadata about your data. -[**Embeddings**](/module_guides/models/embeddings.md) LLMs generate numerical representations of data called `embeddings`. When filtering your data for relevance, LlamaIndex will convert queries into embeddings, and your vector store will find data which is numerically similar to the embedding of your query. +[**Embeddings**](/module_guides/models/embeddings.md) LLMs generate numerical representations of data called `embeddings`. When filtering your data for relevance, LlamaIndex will convert queries into embeddings, and your vector store will find data that is numerically similar to the embedding of your query. ### Querying Stage @@ -56,7 +56,7 @@ Once you've ingested your data, LlamaIndex will help you index the data into a s A retriever defines how to efficiently retrieve relevant context from an index when given a query. Your retrieval strategy is key to the relevancy of the data retrieved and the efficiency with which it's done. [**Routers**](/module_guides/querying/router/root.md): -A router determines which retriever will be used to retrieve relevant context from the knowledge base. More specifically, the `RouterRetriever` class, are responsible for selecting one or multiple candidate retrievers to execute a query. They use a selector to choose the best option based on each candidate's metadata and the query. +A router determines which retriever will be used to retrieve relevant context from the knowledge base. More specifically, the `RouterRetriever` class, is responsible for selecting one or multiple candidate retrievers to execute a query. They use a selector to choose the best option based on each candidate's metadata and the query. [**Node Postprocessors**](/module_guides/querying/node_postprocessors/root.md): A node postprocessor takes in a set of retrieved nodes and applies transformations, filtering, or re-ranking logic to them. @@ -69,13 +69,13 @@ A response synthesizer generates a response from an LLM, using a user query and There are endless use cases for data-backed LLM applications but they can be roughly grouped into three categories: [**Query Engines**](/module_guides/deploying/query_engine/root.md): -A query engine is an end-to-end pipeline that allow you to ask question over your data. It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM. +A query engine is an end-to-end pipeline that allows you to ask questions over your data. It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM. [**Chat Engines**](/module_guides/deploying/chat_engines/root.md): -A chat engine is an end-to-end pipeline for having a conversation with your data (multiple back-and-forth instead of a single question & answer). +A chat engine is an end-to-end pipeline for having a conversation with your data (multiple back-and-forth instead of a single question-and-answer). [**Agents**](/module_guides/deploying/agents/root.md): -An agent is an automated decision maker powered by an LLM that interacts with the world via a set of [tools](/module_guides/deploying/agents/tools/llamahub_tools_guide.md). Agents can take an arbitrary number of steps to complete a given task, dynamically deciding on the best course of action rather than following pre-determined steps. This gives it additional flexibility to tackle more complex tasks. +An agent is an automated decision-maker powered by an LLM that interacts with the world via a set of [tools](/module_guides/deploying/agents/tools/llamahub_tools_guide.md). Agents can take an arbitrary number of steps to complete a given task, dynamically deciding on the best course of action rather than following pre-determined steps. This gives it additional flexibility to tackle more complex tasks. ```{admonition} Next Steps * Tell me how to [customize things](/getting_started/customization.rst) diff --git a/docs/getting_started/discover_llamaindex.md b/docs/getting_started/discover_llamaindex.md index ee16aa02ed..8b75c8833a 100644 --- a/docs/getting_started/discover_llamaindex.md +++ b/docs/getting_started/discover_llamaindex.md @@ -25,11 +25,11 @@ This video covers the `SubQuestionQueryEngine` and how it can be applied to fina ## Discord Document Management -This video covers managing documents from a source that is consantly updating (i.e Discord) and how you can avoid document duplication and save embedding tokens. +This video covers managing documents from a source that is constantly updating (i.e Discord) and how you can avoid document duplication and save embedding tokens. [Youtube](https://www.youtube.com/watch?v=j6dJcODLd_c) -[Notebook + Supplementary Material](https://github.com/jerryjliu/llama_index/tree/main/docs/examples/discover_llamaindex/document_management/) +[Notebook and Supplementary Material](https://github.com/jerryjliu/llama_index/tree/main/docs/examples/discover_llamaindex/document_management/) [Reference Docs](/module_guides/indexing/document_management.md) diff --git a/docs/module_guides/models/llms.md b/docs/module_guides/models/llms.md index 40645d63e9..cce3d549dc 100644 --- a/docs/module_guides/models/llms.md +++ b/docs/module_guides/models/llms.md @@ -69,7 +69,7 @@ The tables below attempt to validate the **initial** experience with various Lla Generally, paid APIs such as OpenAI or Anthropic are viewed as more reliable. However, local open-source models have been gaining popularity due to their customizability and approach to transparency. -**Contributing:** Anyone is welcome to contribute new LLMs to the documentation. Simply copy an existing notebook, setup and test your LLM, and open a PR with your resutls. +**Contributing:** Anyone is welcome to contribute new LLMs to the documentation. Simply copy an existing notebook, setup and test your LLM, and open a PR with your results. If you have ways to improve the setup for existing notebooks, contributions to change this are welcome! diff --git a/docs/optimizing/advanced_retrieval/query_transformations.md b/docs/optimizing/advanced_retrieval/query_transformations.md index d10af98c38..6ab0750d6a 100644 --- a/docs/optimizing/advanced_retrieval/query_transformations.md +++ b/docs/optimizing/advanced_retrieval/query_transformations.md @@ -101,7 +101,7 @@ Check out our [example notebook](https://github.com/jerryjliu/llama_index/blob/m Multi-step query transformations are a generalization on top of existing single-step query transformation approaches. Given an initial, complex query, the query is transformed and executed against an index. The response is retrieved from the query. -Given the response (along with prior responses) and the query, followup questions may be asked against the index as well. This technique allows a query to be run against a single knowledge source until that query has satisfied all questions. +Given the response (along with prior responses) and the query, follow-up questions may be asked against the index as well. This technique allows a query to be run against a single knowledge source until that query has satisfied all questions. An example image is shown below. diff --git a/docs/optimizing/basic_strategies/basic_strategies.md b/docs/optimizing/basic_strategies/basic_strategies.md index 3db6d39314..ee83d7c38a 100644 --- a/docs/optimizing/basic_strategies/basic_strategies.md +++ b/docs/optimizing/basic_strategies/basic_strategies.md @@ -49,7 +49,7 @@ We have a list of [all supported embedding model integrations](/module_guides/mo Depending on the type of data you are indexing, or the results from your retrieval, you may want to customize the chunk size or chunk overlap. -When documents are ingested into an index, the are split into chunks with a certain amount of overlap. The default chunk size is 1024, while the default chunk overlap is 20. +When documents are ingested into an index, they are split into chunks with a certain amount of overlap. The default chunk size is 1024, while the default chunk overlap is 20. Changing either of these parameters will change the embeddings that are calculated. A smaller chunk size means the embeddings are more precise, while a larger chunk size means that the embeddings may be more general, but can miss fine-grained details. diff --git a/docs/optimizing/building_rag_from_scratch.md b/docs/optimizing/building_rag_from_scratch.md index 639d0b6aaa..dfb9a7ca26 100644 --- a/docs/optimizing/building_rag_from_scratch.md +++ b/docs/optimizing/building_rag_from_scratch.md @@ -25,7 +25,7 @@ maxdepth: 1 ## Building Vector Retrieval from Scratch -This tutorial shows you how to build a retriever to query an vector store. +This tutorial shows you how to build a retriever to query a vector store. ```{toctree} --- diff --git a/docs/use_cases/agents.md b/docs/use_cases/agents.md index 9d2ffb86d0..5b7664afe4 100644 --- a/docs/use_cases/agents.md +++ b/docs/use_cases/agents.md @@ -21,7 +21,7 @@ In general, LlamaIndex components offer more explicit, constrained behavior for capable of general reasoning. There are tradeoffs for using both - less-capable LLMs typically do better with more constraints. Take a look at [our blog post on this](https://medium.com/llamaindex-blog/dumber-llm-agents-need-more-constraints-and-better-tools-17a524c59e12) for -a more information + a detailed analysis. +more information + a detailed analysis. ## Learn more diff --git a/examples/gatsby/gatsby_license.txt b/examples/gatsby/gatsby_license.txt index c5ded33b9f..f0a6f5055b 100644 --- a/examples/gatsby/gatsby_license.txt +++ b/examples/gatsby/gatsby_license.txt @@ -38,13 +38,13 @@ Section 1. General Terms of Use and Redistributing Project Gutenberg-tm electronic works 1.A. 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