VeritasGraph
An enterprise-grade Graph RAG framework combining hierarchical tree navigation with knowledge graph reasoning for verifiable, on-premise AI.
What is VeritasGraph?
VeritasGraph is an enterprise-grade Graph RAG framework that combines hierarchical tree navigation with knowledge graph reasoning to solve context blindness in traditional retrieval-augmented generation. It enables multi-hop reasoning, verifiable source attribution, and secure on-premise deployment, transforming unstructured documents into structured, queryable knowledge.
AI engineers, data scientists, and enterprise teams building secure, transparent, and attributable AI systems that require complex reasoning over private document collections.
Developers choose VeritasGraph for its unique hybrid approach that preserves document structure while enabling semantic graph traversal, offering 100% verifiable attribution and full data sovereignty without relying on opaque cloud APIs.
Overview
VeritasGraph — open-source Knowledge Graph & GraphRAG framework on GitHub. Build multi-hop reasoning, ontology-aware retrieval, and verifiable attribution over your own data. Nodes, edges, RDF, linked-data — runs locally or in the cloud.
Use Cases
Best For
- Building enterprise AI systems that require verifiable source attribution for compliance
- Answering complex multi-hop questions across interconnected documents
- Deploying secure, on-premise RAG pipelines for sensitive data
- Navigating large documents hierarchically while maintaining semantic search
- Visualizing knowledge graphs and reasoning paths for explainable AI
- Ingesting diverse content types (PDFs, YouTube, web) into a unified knowledge base
Not Ideal For
- Projects needing rapid prototyping with minimal infrastructure setup
- Environments with limited computational resources or budget constraints
- Applications where basic similarity search suffices without complex reasoning
- Teams requiring immediate cloud-hosted solutions without on-premise management
Pros & Cons
Pros
Combines tree-based hierarchical navigation with graph-based semantic search, enabling both structured document traversal and cross-document reasoning as shown in the feature comparison table.
Every generated answer is traced back to specific source documents with 100% provenance, addressing transparency and compliance needs emphasized throughout the README.
Answers complex questions by traversing relationships in the knowledge graph, going beyond simple similarity search to connect disparate information for deeper insights.
Fully deployable within your own infrastructure with local LLM options via Ollama, ensuring data privacy and control as highlighted in the project philosophy.
Includes a built-in 2D graph explorer powered by PyVis to visualize entities, relationships, and reasoning paths in real-time, enhancing explainability and debugging.
Cons
Requires multiple dependencies like Ollama, Neo4j, and Docker for full deployment, with a detailed guide showing steps that can be time-consuming and error-prone for newcomers.
Recommended hardware includes 16+ CPU cores, 64GB+ RAM, and GPUs with 24GB+ VRAM for optimal performance, making it resource-intensive and costly for small teams.
Involves understanding graph databases, LLM configuration, and ingestion modes, which may be daunting for developers unfamiliar with GraphRAG concepts.
The graph construction and multi-hop traversal can add latency compared to simpler vector search methods, especially for large document sets or real-time queries.
Frequently Asked Questions
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