Ragviz

Graph RAG Visualization Platform

[](https://codecov.io/gh/yourusername/ragviz) [](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://nodejs.org/)

A developer-oriented Graph Retrieval-Augmented Generation (Graph RAG) visualization and parameter tuning platform. This tool combines knowledge graphs with vector retrieval to provide contextual support for local large language models (like Ollama or llama.cpp), enabling interactive document uploads, knowledge graph construction, and real-time parameter adjustments while visualizing their impact on answers and subgraph structures.


*Upload documents, explore interactive knowledge graphs, and chat with your data using local LLMs*

Features

⚙️ Real-time Parameter Tuning

Adjust retrieval and generation parameters instantly. See how Top K, similarity threshold, and temperature affect your results in real-time.


Core Capabilities

• 📊 Interactive Knowledge Graph Visualization - Build and explore knowledge graphs with multiple layout algorithms and smooth animations
• 🔍 Vector-Enhanced Retrieval - Combine graph structure with semantic search using Neo4j vector indexing
• 🤖 Local LLM Integration - Works with Ollama, llama.cpp and other local language models
• 📄 Multi-format Document Support - Upload PDF, TXT and other document formats
• 🎯 Hallucination Reduction - Leverage structured knowledge graphs to improve answer accuracy
• 🔧 Developer-Friendly - Built for research and data science teams with debugging visualization

Architecture

• Frontend: Next.js/React with interactive