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title: BeRU Chat - RAG Assistant
emoji: 🤖
colorFrom: indigo
colorTo: yellow
sdk: streamlit
app_file: app.py
pinned: false
short_description: 100% Offline RAG System with Mistral 7B and VLM2Vec
---
# 🤖 BeRU Chat - RAG Assistant
A powerful **100% offline Retrieval-Augmented Generation (RAG) system** combining Mistral 7B LLM with VLM2Vec embeddings for intelligent document search and conversation.
## ✨ Features
- 🔒 **100% Offline Operation** - No internet required after startup
- 🧠 **Advanced RAG Architecture**
- Hybrid retrieval (Vector + BM25 keyword search)
- Ensemble retriever combining multiple strategies
- Re-ranking with FlashRank for relevance
- Multi-turn conversation with history awareness
- ⚡ **Optimized Performance**
- 4-bit quantization with BitsAndBytes
- Flash Attention 2 support
- FAISS vector indexing
- 📚 **Source Citations** - Every answer cites original sources
## 🎯 Models Used
| Component | Model | Details |
|-----------|-------|---------|
| **LLM** | Mistral-7B-Instruct-v0.3 | 7B parameters |
| **Embedding** | VLM2Vec-Qwen2VL-2B | 2B parameters |
| **Vector Store** | FAISS | Meta's similarity search |
## 🚀 Getting Started
1. **Wait for Models** - First load takes 5-8 minutes (models download from HF Hub)
2. **Upload Documents** - Add PDFs or text files for RAG
3. **Ask Questions** - Chat with context-aware answers
4. **Get Sources** - Each answer includes citations
## 💻 System Requirements
- **GPU**: A10G (24GB VRAM) recommended
- **RAM**: 16GB minimum
- **Cold Start**: ~5-8 minutes (first time)
- **Runtime**: Streamlit app on port 7860
## 📖 Documentation
For more information, visit the [GitHub repository](https://github.com/AnwinJosy/BeRU)
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