0 is dissimilar, 1 is most similar.
Parameters
query – input text
k – Number of Documents to return. Defaults to 4.
**kwargs – kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
connect() → Connection[source]¶
classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
create_collection() → None[source]¶
create_tables_if_not_exists() → None[source]¶
create_vector_extension() → None[source]¶
delete(ids: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Delete vectors by ids or uuids.
Parameters
ids – List of ids to delete.
delete_collection() → None[source]¶
drop_tables() → None[source]¶
classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGVector[source]¶
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.