Return type
Optional[bool]
delete_collection() → None[source]¶
drop_tables() → None[source]¶
classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Return VectorStore initialized from documents and embeddings.
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGEmbedding[source]¶
Return VectorStore initialized from texts and embeddings.
get_collection(session: Session) → Optional[CollectionStore][source]¶
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
query – Text to look up documents similar to.