Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
fetch_k – Number of Documents to fetch to pass to MMR algorithm.
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.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
classmethod service_url_from_db_params(host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
similarity_search(query: str, k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with TimescaleVector with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
Returns
List of Documents most similar to the query.
similarity_search_by_vector(embedding: Optional[List[float]], k: int = 4, filter: Optional[Union[dict, list]] = None, predicates: Optional[Predicates] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding – Embedding to look up documents similar to.