# RAG Chatbot Application

## Project Overview
A modular Retrieval Augmented Generation (RAG) chatbot application built with FastAPI, supporting multiple LLM providers and embedding models.

## Project Structure
- `config/`: Configuration management
- `src/`: Main application source code
- `tests/`: Unit and integration tests
- `data/`: Document storage and ingestion

## Prerequisites
- Python 3.9+
- pip
- (Optional) Virtual environment

## Installation

1. Clone the repository
```bash
git clone https://your-repo-url.git
cd rag-chatbot
```

2. Create a virtual environment
```bash
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
```

3. Install dependencies
```bash
pip install -r requirements.txt
```

4. Set up environment variables
```bash
cp .env.example .env
# Edit .env with your credentials
```

## Configuration

### Environment Variables
- `OPENAI_API_KEY`: OpenAI API key
- `OLLAMA_BASE_URL`: Ollama server URL
- `EMBEDDING_MODEL`: Hugging Face embedding model
- `CHROMA_PATH`: Vector store persistence path
- `DEBUG`: Enable debug mode

## Running the Application

### Development Server
```bash
uvicorn src.main:app --reload
```

### Production Deployment
```bash
gunicorn -w 4 -k uvicorn.workers.UvicornWorker src.main:app
```

## Testing
```bash
pytest tests/
```

## Features
- Multiple LLM Provider Support
- Retrieval Augmented Generation
- Document Ingestion
- Flexible Configuration
- FastAPI Backend

## Contributing
1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request