Haystack Neo4j Integration

An integration of Neo4j graph database with Haystack v2.0 by deepset. In Neo4j Vector search index is being used for storing document embeddings and dense retrievals.

The library allows using Neo4j as a DocumentStore, and implements the required Protocol methods. You can start working with the implementation by importing it from neo4_haystack package.

Installation

# pip install neo4j-haystack
from neo4j_haystack import Neo4jDocumentStore

document_store = Neo4jDocumentStore(
    url="bolt://localhost:7687",
    username="neo4j",
    password="passw0rd",
    database="neo4j",
    embedding_dim=384,
    embedding_field="embedding",
    index="document-embeddings", # The name of the Vector Index in Neo4j
    node_label="Document", # Providing a label to Neo4j nodes which store Documents
)

Functionality Includes

Neo4jEmbeddingRetriever - is a typical retriever component which can be used to query vector store index and find related Documents. The component uses Neo4jDocumentStore to query embeddings.
Neo4jDynamicDocumentRetriever is also a retriever component in a sense that it can be used to query Documents in Neo4j. However it is decoupled from Neo4jDocumentStore and allows to run arbitrary Cypher query to extract documents. Practically it is possible to query Neo4j same way Neo4jDocumentStore does, including vector search.