field tfidf_array: Any = None#
field vectorizer: Any = None#
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_documents(documents: Iterable[langchain.schema.Document], *, tfidf_params: Optional[Dict[str, Any]] = None, **kwargs: Any) → langchain.retrievers.tfidf.TFIDFRetriever[source]#
classmethod from_texts(texts: Iterable[str], metadatas: Optional[Iterable[dict]] = None, tfidf_params: Optional[Dict[str, Any]] = None, **kwargs: Any) → langchain.retrievers.tfidf.TFIDFRetriever[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.TimeWeightedVectorStoreRetriever[source]#
Retriever combining embedding similarity with recency.
field decay_rate: float = 0.01#
The exponential decay factor used as (1.0-decay_rate)**(hrs_passed).
field default_salience: Optional[float] = None#
The salience to assign memories not retrieved from the vector store.
None assigns no salience to documents not fetched from the vector store.
field k: int = 4#
The maximum number of documents to retrieve in a given call.
field memory_stream: List[langchain.schema.Document] [Optional]#
The memory_stream of documents to search through.
field other_score_keys: List[str] = []#
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/retrievers.html