neo4j-graphrag-python and VeritasGraph
Neo4j's official GraphRAG library provides the core abstraction layer and Neo4j integration for building retrieval-augmented generation systems on graph databases, while VeritasGraph is a specialized enterprise distribution that builds on top of such graph RAG concepts to add on-premise deployment, verification, and attribution tracking capabilities.
About neo4j-graphrag-python
neo4j/neo4j-graphrag-python
Neo4j GraphRAG for Python
Supports automated knowledge graph construction from unstructured text and PDFs via LLM-powered entity/relation extraction, alongside multiple retrieval strategies (vector search, graph traversal, hybrid, and Text2Cypher). Integrates with major LLM providers (OpenAI, Anthropic, Google, Cohere, Ollama, MistralAI) and optional external vector stores (Weaviate, Pinecone, Qdrant), with experimental NLP components using spaCy for semantic resolution.
About VeritasGraph
bibinprathap/VeritasGraph
VeritasGraph: Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution
Combines hierarchical tree-based navigation with semantic knowledge graph reasoning to enable multi-hop retrieval and 100% verifiable attribution. Supports multiple deployment modes—lite with cloud APIs (OpenAI/Anthropic), local with Ollama (~8GB RAM), or full production with Neo4j—while including built-in vision RAG for extracting structured data from charts and tables. Integrates with optional UI (Gradio), Microsoft GraphRAG, and ingestion tools (YouTube/web articles).
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