Neo4j GraphRAG Python
Official Neo4j package for building graph retrieval augmented generation (GraphRAG) applications in Python. Enables developers to create knowledge graphs and implement advanced retrieval methods including graph traversals, text-to-Cypher, and vector searches.
About this tool
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.
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Information
Websitegithub.com
PublishedMar 15, 2026
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