field vectorstore: VectorStore [Required]#
Vector Database to connect to.
pydantic model langchain.chains.VectorDBQAWithSourcesChain[source]#
Question-answering with sources over a vector database.
Validators
raise_deprecation » all fields
set_verbose » verbose
validate_naming » all fields
field k: int = 4#
Number of results to return from store
field max_tokens_limit: int = 3375#
Restrict the docs to return from store based on tokens,
enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true
field reduce_k_below_max_tokens: bool = False#
Reduce the number of results to return from store based on tokens limit
field search_kwargs: Dict[str, Any] [Optional]#
Extra search args.
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
Vector Database to connect to.
langchain.chains.create_extraction_chain(schema: dict, llm: langchain.base_language.BaseLanguageModel) → langchain.chains.base.Chain[source]#
langchain.chains.create_extraction_chain_pydantic(pydantic_schema: Any, llm: langchain.base_language.BaseLanguageModel) → langchain.chains.base.Chain[source]#
langchain.chains.create_tagging_chain(schema: dict, llm: langchain.base_language.BaseLanguageModel) → langchain.chains.base.Chain[source]#
langchain.chains.create_tagging_chain_pydantic(pydantic_schema: Any, llm: langchain.base_language.BaseLanguageModel) → langchain.chains.base.Chain[source]#
langchain.chains.load_chain(path: Union[str, pathlib.Path], **kwargs: Any) → langchain.chains.base.Chain[source]#
Unified method for loading a chain from LangChainHub or local fs.
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rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/chains.html