field response_key: str = 'response'#
field url: str [Required]#
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.SVMRetriever[source]#
field embeddings: langchain.embeddings.base.Embeddings [Required]#
field index: Any = None#
field k: int = 4#
field relevancy_threshold: Optional[float] = None#
field texts: List[str] [Required]#
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_texts(texts: List[str], embeddings: langchain.embeddings.base.Embeddings, **kwargs: Any) → langchain.retrievers.svm.SVMRetriever[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.SelfQueryRetriever[source]#
Retriever that wraps around a vector store and uses an LLM to generate
the vector store queries.
field llm_chain: langchain.chains.llm.LLMChain [Required]#
The LLMChain for generating the vector store queries.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html