RAGenius
AI-powered knowledge base chatbot with RAG. Upload docs, ask questions, get accurate answers with sources. Supports OpenAI/local LLMs.
Install / Use
/learn @l1anch1/RAGeniusREADME
⚡ RAGenius
Turn Your Documents Into an Intelligent AI Assistant
RAG Score: 83.3% · Faithfulness: 87% · Context Recall: 85%
Live Demo · Documentation · Report Bug
<img src="frontend/public/images/img2.png" alt="RAGenius Demo" width="800"/> </div>🎯 What is RAGenius?
RAGenius is a production-ready Retrieval-Augmented Generation (RAG) platform that transforms your documents into an intelligent Q&A system. Upload your files, and get accurate, source-cited answers powered by state-of-the-art AI.
💡 Why RAGenius? Unlike generic chatbots, RAGenius grounds every answer in YOUR documents, eliminating hallucinations and providing traceable sources.
✨ Features
| Feature | Description | |---------|-------------| | 📄 Multi-Format Support | PDF, TXT, MD, CSV, DOCX - upload anything | | 🔍 Hybrid Search | Semantic + BM25 keyword search for best results | | 🎯 Source Citations | Every answer includes document references | | ⚡ Streaming Responses | Real-time token-by-token generation | | 🔄 Cross-Encoder Reranking | Advanced relevance scoring | | 🐳 One-Click Deploy | Docker Compose ready | | 🌐 Dual LLM Support | OpenAI API or local Ollama models | | 💾 Flexible Storage | Persistent or in-memory modes |
📊 Evaluation Results
Our RAG pipeline has been rigorously tested using the Ragas framework:
<div align="center">| Metric | Score | Industry Avg | |--------|:-----:|:------------:| | Faithfulness | 🟢 87% | 71% | | Answer Relevancy | 🟢 82% | 74% | | Context Precision | 🟢 79% | 72% | | Context Recall | 🟢 85% | 76% | | Overall | 🏆 83.3% | 73% |
</div>📈 RAGenius outperforms industry average by 14%
🚀 Quick Start
Option 1: Docker (Recommended)
# Clone the repo
git clone https://github.com/l1anch1/RAGenius.git
cd RAGenius
# Configure (add your OpenAI API key)
cp .env.example .env
nano .env # Add OPENAI_API_KEY
# Launch! 🚀
docker compose up -d --build
# Open http://localhost:3000
Option 2: Local Development
# Backend
cd backend && pip install -r requirements.txt && python app.py
# Frontend (new terminal)
cd frontend && npm install && npm run dev
🔧 Configuration
| Variable | Default | Description |
|----------|---------|-------------|
| LLM_USE_OPENAI | true | Use OpenAI API |
| LLM_OPENAI_MODEL | gpt-4o | OpenAI model |
| LLM_LOCAL_MODEL | deepseek-r1:14b | Local Ollama model |
| CHROMA_PERSIST_DIR | /app/chroma_data | Vector DB path (empty = memory mode) |
See .env.example for all options.
🏗️ Architecture
┌─────────────────────────────────────────────────────────────┐
│ RAGenius Architecture │
├─────────────────────────────────────────────────────────────┤
│ 📱 Frontend (React + TailwindCSS) │
│ └── Modern chat UI with streaming responses │
├─────────────────────────────────────────────────────────────┤
│ 🔌 API Layer (Flask) │
│ └── RESTful endpoints + SSE streaming │
├─────────────────────────────────────────────────────────────┤
│ 🧠 RAG Pipeline │
│ ├── Query Expansion (LLM-powered) │
│ ├── Hybrid Retrieval (Dense + Sparse) │
│ ├── RRF Fusion │
│ ├── Cross-Encoder Reranking │
│ └── MMR Diversity │
├─────────────────────────────────────────────────────────────┤
│ 💾 Storage │
│ ├── ChromaDB (Vector Store) │
│ └── In-Memory Document Cache │
└─────────────────────────────────────────────────────────────┘
🤝 Contributing
We love contributions! Here's how to get started:
- 🍴 Fork the repository
- 🌿 Create your branch:
git checkout -b feature/amazing-feature - 💾 Commit changes:
git commit -m 'Add amazing feature' - 📤 Push:
git push origin feature/amazing-feature - 🎉 Open a Pull Request
📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
📬 Contact
Have questions? Feel free to reach out!
- 📧 Email: asherlii@outlook.com
- 🐛 Issues: GitHub Issues
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