From 94efede93c074daad4134007da0074ca9a0aa009 Mon Sep 17 00:00:00 2001 From: Aashish Saini <141953346+ShorthillsAI@users.noreply.github.com> Date: Mon, 4 Sep 2023 04:39:14 +0530 Subject: [PATCH] Fixed Typos and grammatical issues in document files (#9789) Fixed typos and grammatical issues in document files. @baskaryan , @eyurtsev --------- Co-authored-by: Aashish Saini <141953346+AashishSainiShorthillsAI@users.noreply.github.com> Co-authored-by: AryamanJaiswalShorthillsAI <142397527+AryamanJaiswalShorthillsAI@users.noreply.github.com> Co-authored-by: Adarsh Shrivastav <142413097+AdarshKumarShorthillsAI@users.noreply.github.com> Co-authored-by: Vishal <141389263+VishalYadavShorthillsAI@users.noreply.github.com> Co-authored-by: ChetnaGuptaShorthillsAI <142381084+ChetnaGuptaShorthillsAI@users.noreply.github.com> Co-authored-by: PankajKumarShorthillsAI <142473460+PankajKumarShorthillsAI@users.noreply.github.com> Co-authored-by: AbhishekYadavShorthillsAI <142393903+AbhishekYadavShorthillsAI@users.noreply.github.com> --- .../docs/modules/data_connection/index.mdx | 10 +++++----- .../data_connection/retrievers/self_query/index.mdx | 2 +- .../retrievers/time_weighted_vectorstore.mdx | 2 +- docs/docs_skeleton/docs/modules/memory/index.mdx | 2 +- .../modules/chains/additional/analyze_document.mdx | 2 +- 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/docs_skeleton/docs/modules/data_connection/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/index.mdx index b7b1bf87c9..31a9e2d2bb 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/index.mdx @@ -18,9 +18,9 @@ This encompasses several key modules. **[Document loaders](/docs/modules/data_connection/document_loaders/)** Load documents from many different sources. -LangChain provides over a 100 different document loaders as well as integrations with other major providers in the space, +LangChain provides over 100 different document loaders as well as integrations with other major providers in the space, like AirByte and Unstructured. -We provide integrations to load all types of documents (html, PDF, code) from all types of locations (private s3 buckets, public websites). +We provide integrations to load all types of documents (HTML, PDF, code) from all types of locations (private s3 buckets, public websites). **[Document transformers](/docs/modules/data_connection/document_transformers/)** @@ -32,7 +32,7 @@ LangChain provides several different algorithms for doing this, as well as logic **[Text embedding models](/docs/modules/data_connection/text_embedding/)** Another key part of retrieval has become creating embeddings for documents. -Embeddings capture the semantic meaning of text, allowing you to quickly and +Embeddings capture the semantic meaning of the text, allowing you to quickly and efficiently find other pieces of text that are similar. LangChain provides integrations with over 25 different embedding providers and methods, from open-source to proprietary API, @@ -43,7 +43,7 @@ LangChain exposes a standard interface, allowing you to easily swap between mode With the rise of embeddings, there has emerged a need for databases to support efficient storage and searching of these embeddings. LangChain provides integrations with over 50 different vectorstores, from open-source local ones to cloud-hosted proprietary ones, -allowing you choose the one best suited for your needs. +allowing you to choose the one best suited for your needs. LangChain exposes a standard interface, allowing you to easily swap between vector stores. **[Retrievers](/docs/modules/data_connection/retrievers/)** @@ -55,7 +55,7 @@ However, we have also added a collection of algorithms on top of this to increas These include: - [Parent Document Retriever](/docs/modules/data_connection/retrievers/parent_document_retriever): This allows you to create multiple embeddings per parent document, allowing you to look up smaller chunks but return larger context. -- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain reference to something that isn't just semantic, but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query +- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain a reference to something that isn't just semantic but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query - [Ensemble Retriever](/docs/modules/data_connection/retrievers/ensemble): Sometimes you may want to retrieve documents from multiple different sources, or using multiple different algorithms. The ensemble retriever allows you to easily do this. - And more! diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx index 22209cc3b7..ee9c2b4eca 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx @@ -1,6 +1,6 @@ # Self-querying -A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters. +A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to its underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documents but to also extract filters from the user query on the metadata of stored documents and to execute those filters. ![](https://drive.google.com/uc?id=1OQUN-0MJcDUxmPXofgS7MqReEs720pqS) diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx index 0b62952247..5160879ae7 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx @@ -8,7 +8,7 @@ The algorithm for scoring them is: semantic_similarity + (1.0 - decay_rate) ^ hours_passed ``` -Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh." +Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh". import Example from "@snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx" diff --git a/docs/docs_skeleton/docs/modules/memory/index.mdx b/docs/docs_skeleton/docs/modules/memory/index.mdx index d8f9d25590..598d36a5e1 100644 --- a/docs/docs_skeleton/docs/modules/memory/index.mdx +++ b/docs/docs_skeleton/docs/modules/memory/index.mdx @@ -32,7 +32,7 @@ Even if these are not all used directly, they need to be stored in some form. One of the key parts of the LangChain memory module is a series of integrations for storing these chat messages, from in-memory lists to persistent databases. -- [Chat message storage](/docs/modules/memory/chat_messages/): How to work with Chat Messages, and the various integrations offered +- [Chat message storage](/docs/modules/memory/chat_messages/): How to work with Chat Messages, and the various integrations offered. ### Querying: Data structures and algorithms on top of chat messages Keeping a list of chat messages is fairly straight-forward. diff --git a/docs/snippets/modules/chains/additional/analyze_document.mdx b/docs/snippets/modules/chains/additional/analyze_document.mdx index 989c3c0aec..b9d6bdeaa3 100644 --- a/docs/snippets/modules/chains/additional/analyze_document.mdx +++ b/docs/snippets/modules/chains/additional/analyze_document.mdx @@ -4,7 +4,7 @@ with open("../../state_of_the_union.txt") as f: ``` ## Summarize -Let's take a look at it in action below, using it summarize a long document. +Let's take a look at it in action below, using it to summarize a long document. ```python