field index: Any = None#
field sparse_encoder: Any = None#
field top_k: int = 4#
add_texts(texts: List[str], ids: Optional[List[str]] = None, metadatas: Optional[List[dict]] = None) → None[source]#
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
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.PubMedRetriever[source]#
It is effectively a wrapper for PubMedAPIWrapper.
It wraps load() to get_relevant_documents().
It uses all PubMedAPIWrapper arguments without any change.
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
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.RemoteLangChainRetriever[source]#
field headers: Optional[dict] = None#
field input_key: str = 'message'#
field metadata_key: str = 'metadata'#
field page_content_key: str = 'page_content'#
field response_key: str = 'response'#
field url: str [Required]#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html