Construct Neo4jVector wrapper from raw documents and pre- generated embeddings.
Return Neo4jVector initialized from documents and embeddings.
Neo4j credentials are required in the form of url, username,
and password and optional database parameters.
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 = {}
) -> Neo4jVectorExample:
from langchain_neo4j import Neo4jVector
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
vectorstore = Neo4jVector.from_embeddings(
text_embedding_pairs, embeddings)