RAG Bot with GPT-OSS Integration

A Retrieval-Augmented Generation (RAG) bot that integrates with GPT-OSS via Hugging Face and provides access via Slack and WhatsApp. Optimized for Raspberry Pi deployment.

Features

• 🤖 RAG System: Upload documents and query them using natural language
• 💬 Slack Integration: Access the bot directly from Slack
• 📱 WhatsApp Integration: Query via WhatsApp using Twilio
• 🔍 Vector Search: Powered by ChromaDB for efficient document retrieval
• 🧠 AI Responses: Uses GPT-OSS via Hugging Face for intelligent answers
• 🍓 Raspberry Pi Ready: Optimized for low-resource environments

Quick Start

1. Setup

chmod +x setup.sh
./setup.sh

2. Configure

Edit .env file with your credentials:

cp .env.example .env
nano .env

Required configuration:

• HUGGINGFACE_API_TOKEN: Your Hugging Face API token

Optional (for integrations):

• Slack: SLACK_BOT_TOKEN, SLACK_SIGNING_SECRET
• WhatsApp: TWILIO_ACCOUNT_SID, TWILIO_AUTH_TOKEN, TWILIO_PHONE_NUMBER

3. Start

./start.sh

API Endpoints

• GET / - API information
• GET /health - Health check
• POST /upload - Upload documents (PDF or text)
• POST /query - Query the RAG system
• GET /stats - Get knowledge base statistics
• POST /slack/events - Slack webhook
• POST /whatsapp/webhook - WhatsApp webhook

Usage Examples

Upload a Document

curl -X POST "http://localhost:8000/upload" \
  -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@document.pdf"

Query the System

curl -X POST "http://localhost:8000/query" \
  -H "Content-Type: application/json" \
  -d '{"question": "What is machine learning?"}'

Slack Setup

1. Create a Slack app at https://api.slack.com/apps
2. Add bot token scopes: app_mentions:read, chat:write, files:read
3. Enable events: app_mention, file_shared
4. Set event request URL: https://your-domain.com/slack/events
5. Install app to workspace

WhatsApp Setup

1. Create Twilio account at https://www.twilio.com
2. Set up WhatsApp sandbox or get approved number
3. Configure webhook URL: https://your-domain.com/whatsapp/webhook

Raspberry Pi Deployment

System Requirements

• Raspberry Pi 4 (4GB+ RAM recommended)
• Python 3.8+
• 16GB+ SD card

Performance Tips

• Use SSD instead of SD card for better I/O
• Increase swap space if needed
• Monitor temperature and use cooling

Auto-start Service

Create systemd service:

sudo nano /etc/systemd/system/ragbot.service

[Unit]
Description=RAG Bot Service
After=network.target

[Service]
Type=simple
User=pi
WorkingDirectory=/home/pi/ragbot
ExecStart=/home/pi/ragbot/venv/bin/python main.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

Enable and start:

sudo systemctl enable ragbot.service
sudo systemctl start ragbot.service

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Slack/WhatsApp│    │   FastAPI App   │    │   FlexaAI API   │
│                 │───▶│                 │───▶│                 │
│   User Input    │    │   RAG System    │    │   GPT-OSS-120B  │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                              │
                              ▼
                       ┌─────────────────┐
                       │   ChromaDB      │
                       │   Vector Store  │
                       └─────────────────┘

Troubleshooting

Common Issues

1. Memory Issues on Pi: Reduce CHUNK_SIZE in config.py
2. Slow Responses: Check network connection to Hugging Face
3. ChromaDB Errors: Ensure write permissions to chroma_db directory
4. Import Errors: Activate virtual environment before running
5. GPT-OSS API Errors: Verify your Hugging Face token has access to the model

Logs

Check application logs:

tail -f logs/ragbot.log

Health Check

curl http://localhost:8000/health

Contributing

1. Fork the repository
2. Create feature branch
3. Make changes
4. Test on Raspberry Pi
5. Submit pull request

License

MIT License - see LICENSE file for details.