vectorstore = Neo4jVector.from_embeddings(
    text_embedding_pairs, embeddings)
classmethod from_existing_graph(embedding: Embeddings, node_label: str, embedding_node_property: str, text_node_properties: List[str], *, keyword_index_name: Optional[str] = 'keyword', index_name: str = 'vector', search_type: SearchType = SearchType.VECTOR, retrieval_query: str = '', **kwargs: Any) → Neo4jVector[source]¶
Initialize and return a Neo4jVector instance from an existing graph.
This method initializes a Neo4jVector instance using the provided
parameters and the existing graph. It validates the existence of
the indices and creates new ones if they don’t exist.
Returns:
Neo4jVector: An instance of Neo4jVector initialized with the provided parameters
and existing graph.
Example:
>>> neo4j_vector = Neo4jVector.from_existing_graph(
…     embedding=my_embedding,
…     node_label=”Document”,
…     embedding_node_property=”embedding”,
…     text_node_properties=[“title”, “content”]
… )
Note:
Neo4j credentials are required in the form of url, username, and password,
and optional database parameters passed as additional keyword arguments.
classmethod from_existing_index(embedding: Embeddings, index_name: str, search_type: SearchType = SearchType.VECTOR, keyword_index_name: Optional[str] = None, **kwargs: Any) → Neo4jVector[source]¶
Get instance of an existing Neo4j vector index. This method will
return the instance of the store without inserting any new
embeddings.
Neo4j credentials are required in the form of url, username,
and password and optional database parameters along with
the index_name definition.