Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalRetrievalMany LLM applications require user-specific data that is not part of the model's training set.
The primary way of accomplishing this is through Retrieval Augmented Generation (RAG).
In this process, external data is retrieved and then passed to the LLM when doing the generation step.LangChain provides all the building blocks for RAG applications - from simple to complex.
This section of the documentation covers everything related to the retrieval step - e.g. the fetching of the data.
Although this sounds simple, it can be subtly complex.
This encompasses several key modules.Document loadersLoad documents from many different sources.
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).Document transformersA key part of retrieval is fetching only the relevant parts of documents.
This involves several transformation steps in order to best prepare the documents for retrieval.
One of the primary ones here is splitting (or chunking) a large document into smaller chunks.
LangChain provides several different algorithms for doing this, as well as logic optimized for specific document types (code, markdown, etc).Text embedding modelsAnother key part of retrieval has become creating embeddings for documents.
Embeddings capture the semantic meaning of the text, allowing you to quickly and
Many LLM applications require user-specific data that is not part of the model's training set.
Many LLM applications require user-specific data that is not part of the model's training set. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalRetrievalMany LLM applications require user-specific data that is not part of the model's training set.
The primary way of accomplishing this is through Retrieval Augmented Generation (RAG).
In this process, external data is retrieved and then passed to the LLM when doing the generation step.LangChain provides all the building blocks for RAG applications - from simple to complex.
This section of the documentation covers everything related to the retrieval step - e.g. the fetching of the data.
Although this sounds simple, it can be subtly complex.
This encompasses several key modules.Document loadersLoad documents from many different sources.
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).Document transformersA key part of retrieval is fetching only the relevant parts of documents.
This involves several transformation steps in order to best prepare the documents for retrieval.
One of the primary ones here is splitting (or chunking) a large document into smaller chunks.
LangChain provides several different algorithms for doing this, as well as logic optimized for specific document types (code, markdown, etc).Text embedding modelsAnother key part of retrieval has become creating embeddings for documents.
Embeddings capture the semantic meaning of the text, allowing you to quickly and