Neo4j GraphRAG Python

Overview

The official Neo4j GraphRAG package for Python enables developers to build graph retrieval augmented generation (GraphRAG) applications using the power of Neo4j and Python. It provides a robust, feature-rich solution with long-term support directly from Neo4j.

Features

• Knowledge Graph Creation: Build and manage knowledge graphs from unstructured data
• Advanced Retrieval Methods: Graph traversals, text-to-Cypher query generation, vector searches, and full-text searches
• Hybrid Search: Combine vector similarity with graph relationships
• LLM Integration: Works with popular LLM providers (OpenAI, Anthropic, etc.)
• Production Ready: Official support and maintenance from Neo4j
• Explainable Results: Graph-based retrieval provides transparent reasoning chains

Key Capabilities

• Entity extraction and relationship mapping
• Multi-hop question answering through graph traversals
• Cypher query generation from natural language
• Integration with vector embeddings for semantic search
• Structured and unstructured data combination

Use Cases

• Question answering over complex, interconnected data
• Multi-hop reasoning tasks
• Knowledge base construction
• Explainable AI applications
• Enterprise knowledge management

Pricing

Free and open-source. Neo4j database licensing applies separately.