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