Bep Neo4j

This companion crate implements a Bep vector store based on Neo4j Graph database. It uses the neo4rs crate to interact with Neo4j. Note that the neo4rs crate is a work in progress and does not yet support all Neo4j features. Further documentation on Neo4j & vector search integration can be found on the neo4rs docs.

Prerequisites

The GenAI plugin is enabled by default in Neo4j Aura.

The plugin needs to be installed on self-managed instances. This is done by moving the neo4j-genai.jar file from /products to /plugins in the Neo4j home directory, or, if you are using Docker, by starting the Docker container with the extra parameter --env NEO4J_PLUGINS='["genai"]'. For more information, see Operations Manual → Configure plugins.

Usage

Add the companion crate to your Cargo.toml, along with the bep-core crate:

[dependencies]
bep-neo4j = "0.1"

You can also run cargo add bep-neo4j bep-core to add the most recent versions of the dependencies to your project.

See the examples folder for usage examples.

• examples/vector_search_simple.rs shows how to create an index on simple data.
• examples/vector_search_movies_consume.rs shows how to query an existing index.
• examples/vector_search_movies_create.rs shows how to create embeddings & index on a large DB and query it in one go.

Notes

• The bep-neo4j::vector_index module offers utility functions to create and query a Neo4j vector index. You can also create indexes using the Neo4j browser or directly call cypther queries with the Neo4rs crate. See the Neo4j documentation for more information. Example examples/vector_search_simple.rs shows how to create an index on existing data.

CREATE VECTOR INDEX moviePlots
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
    `vector.dimensions`: 1536,
    `vector.similarity_function`: 'cosine'
}}

Roadmap

• Add support for creating the vector index through BEP.
• Add support for adding embeddings to an existing database
• Add support for uploading documents to an existing database