The LLMChain for generating the vector store queries.
field search_kwargs: dict [Optional]#
Keyword arguments to pass in to the vector store search.
field search_type: str = 'similarity'#
The search type to perform on the vector store.
field structured_query_translator: langchain.chains.query_constructor.ir.Visitor [Required]#
Translator for turning internal query language into vectorstore search params.
field vectorstore: langchain.vectorstores.base.VectorStore [Required]#
The underlying vector store from which documents will be retrieved.
field verbose: bool = False#
async aget_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
classmethod from_llm(llm: langchain.base_language.BaseLanguageModel, vectorstore: langchain.vectorstores.base.VectorStore, document_contents: str, metadata_field_info: List[langchain.chains.query_constructor.schema.AttributeInfo], structured_query_translator: Optional[langchain.chains.query_constructor.ir.Visitor] = None, chain_kwargs: Optional[Dict] = None, enable_limit: bool = False, **kwargs: Any) → langchain.retrievers.self_query.base.SelfQueryRetriever[source]#
get_relevant_documents(query: str) → List[langchain.schema.Document][source]#
Get documents relevant for a query.
Parameters
query – string to find relevant documents for
Returns
List of relevant documents
pydantic model langchain.retrievers.TFIDFRetriever[source]#
field docs: List[langchain.schema.Document] [Required]#
field k: int = 4#
field tfidf_array: Any = None#
field vectorizer: Any = None#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html