Neo4jVector

Query Neo4j database with retries and exponential backoff.

Check if the connected Neo4j database version supports vector indexing.

Queries the Neo4j database to retrieve its version and compares it against a target version (5.11.0) that is known to support vector indexing. Raises a ValueError if the connected Neo4j version is not supported.

Check if the vector index exists in the Neo4j database and returns its embedding dimension.

This method queries the Neo4j database for existing indexes and attempts to retrieve the dimension of the vector index with the specified name. If the index exists, its dimension is returned. If the index doesn't exist, None is returned.

Check if the fulltext index exists in the Neo4j database

This method queries the Neo4j database for existing fts indexes with the specified name.

This method constructs a Cypher query and executes it to create a new vector index in Neo4j.

This method constructs a Cypher query and executes it to create a new full text index in Neo4j.

Add embeddings to the VectorStore.

Run more texts through the embeddings and add to the VectorStore.

Run similarity search with Neo4jVector.

Return docs most similar to query.

Perform a similarity search in the Neo4j database using a given vector and return the top k similar documents with their scores.

This method uses a Cypher query to find the top k documents that are most similar to a given embedding. The similarity is measured using a vector index in the Neo4j database. The results are returned as a list of tuples, each containing a Document object and its similarity score.

Return docs most similar to embedding vector.

Return Neo4jVector initialized from texts and embeddings. Neo4j credentials are required in the form of url, username, and password and optional database parameters.

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.

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.

Get instance of an existing Neo4j relationship 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.

Return Neo4jVector initialized from documents and embeddings. Neo4j credentials are required in the form of url, username, and password and optional database parameters.

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.

Return docs selected using the maximal marginal relevance.

Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.