Return docs most similar to query.
create_new_index()
This method constructs a Cypher query and executes it to create a new vector index in Neo4j.
create_new_keyword_index([text_node_properties])
This method constructs a Cypher query and executes it to create a new full text index in Neo4j.
delete([ids])
Delete by vector ID or other criteria.
from_documents(documents, embedding[, ...])
Return Neo4jVector initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct Neo4jVector wrapper from raw documents and pre- generated embeddings.
from_existing_graph(embedding, node_label, ...)
Initialize and return a Neo4jVector instance from an existing graph.
from_existing_index(embedding, index_name[, ...])
Get instance of an existing Neo4j vector index.
from_texts(texts, embedding[, metadatas, ...])
Return Neo4jVector initialized from texts and embeddings.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
query(query, *[, params])
This method sends a Cypher query to the connected Neo4j database and returns the results as a list of dictionaries.
retrieve_existing_fts_index([...])
Check if the fulltext index exists in the Neo4j database
retrieve_existing_index()
Check if the vector index exists in the Neo4j database and returns its embedding dimension.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k])
Run similarity search with Neo4jVector.
similarity_search_by_vector(embedding[, k])