:param k: Number of Documents to return. Defaults to 4.
:param fetch_k: Number of Documents to fetch to pass to MMR algorithm.
:param lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns
List of Documents selected by maximal marginal relevance.
persist() → None[source]#
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[langchain.schema.Document][source]#
Return docs most similar to query.
similarity_search_with_score(query: str, *, k: int = 4, **kwargs: Any) → List[Tuple[langchain.schema.Document, float]][source]#
class langchain.vectorstores.SingleStoreDB(embedding: langchain.embeddings.base.Embeddings, *, table_name: str = 'embeddings', content_field: str = 'content', metadata_field: str = 'metadata', vector_field: str = 'vector', pool_size: int = 5, max_overflow: int = 10, timeout: float = 30, **kwargs: Any)[source]#
This class serves as a Pythonic interface to the SingleStore DB database.
The prerequisite for using this class is the installation of the singlestoredb
Python package.
The SingleStoreDB vectorstore can be created by providing an embedding function and
the relevant parameters for the database connection, connection pool, and
optionally, the names of the table and the fields to use.
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, embeddings: Optional[List[List[float]]] = None, **kwargs: Any) → List[str][source]#
Add more texts to the vectorstore.
Parameters
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/vectorstores.html