Construct PGVector wrapper from raw documents and pre- generated embeddings.
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.
afrom_embeddings(
cls,
text_embeddings: List[Tuple[str, List[float]]],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
ids: Optional[List[str]] = None,
pre_delete_collection: bool = False,
**kwargs: Any = {}
) -> PGVectorExample:
.. code-block:: python
from langchain_community.vectorstores import PGVector from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() text_embeddings = embeddings.embed_documents(texts) text_embedding_pairs = list(zip(texts, text_embeddings)) faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings)