| # 📚 Document Q&A System | |
| A powerful document question-answering system built with LlamaIndex and Gradio. Upload your documents and ask questions about them using state-of-the-art AI models. | |
| ## Features | |
| 🔍 **Smart Document Processing**: Automatically processes various document formats (PDF, TXT, DOCX, MD, CSV, JSON) | |
| 🤖 **Multiple AI Models**: Choose from GPT-4o, Claude 3.5 Sonnet, Llama 3.1, Mistral, and more | |
| 📊 **Performance Monitoring**: Track response times and query statistics | |
| 🎯 **Source Attribution**: See which document sections were used to generate answers | |
| ⚙️ **Customizable Settings**: Adjust temperature, token limits, and retrieval parameters | |
| 🔒 **Secure API Key Management**: Use environment variables or direct input | |
| ## How to Use | |
| ### 1. Upload Documents | |
| - Go to the "Upload Documents" tab | |
| - Select your files (PDF, TXT, DOCX, MD, CSV, JSON) | |
| - Click "Process Documents" to create the searchable index | |
| ### 2. Configure Settings | |
| - Add your OpenRouter API key (or set as HF Space secret) | |
| - Choose your preferred AI model | |
| - Adjust parameters like temperature and max tokens | |
| ### 3. Ask Questions | |
| - Enter your question in the "Ask Questions" tab | |
| - Click "Ask Question" to get AI-powered answers | |
| - View sources and performance metrics | |
| ## API Key Setup | |
| You can provide your OpenRouter API key in two ways: | |
| 1. **Direct Input**: Enter it in the "API Key" field in the interface | |
| 2. **Environment Variable**: Set `OPENROUTER_API_KEY` as a Hugging Face Space secret | |
| Get your API key from [OpenRouter](https://openrouter.ai/) | |
| ## Best Practices for Questions | |
| - 🎯 **Be specific**: "What does the author say about climate change?" vs "Tell me about climate" | |
| - 📚 **Ask about concepts**: "What is the main methodology discussed?" | |
| - 🔍 **Use comparative questions**: "How do different studies approach this topic?" | |
| - 📊 **Request analysis**: "What are the key findings presented?" | |
| - 🏛️ **Ask about methodology**: "What research methods are used?" | |
| ## Available Models | |
| - **GPT-4o**: Best overall performance, most accurate | |
| - **GPT-4o Mini**: Faster, cost-effective option | |
| - **Claude 3.5 Sonnet**: Excellent reasoning and analysis | |
| - **Claude 3 Haiku**: Fast and efficient | |
| - **Llama 3.1 70B/8B**: Open source, strong performance | |
| - **Mistral Large**: Strong multilingual capabilities | |
| - **Gemini Pro**: Google's advanced model | |
| ## Technical Details | |
| Built with: | |
| - **LlamaIndex**: Document indexing and retrieval | |
| - **Gradio**: Web interface | |
| - **OpenRouter**: Multi-model API access | |
| - **HuggingFace Embeddings**: Text vectorization | |
| - **BGE-small-en-v1.5**: Efficient embedding model | |
| ## Performance | |
| - Vector-based semantic search for accurate retrieval | |
| - Cached indexing for fast subsequent queries | |
| - Configurable chunk sizes and overlap for optimal results | |
| - Real-time performance monitoring | |
| ## Development | |
| To run locally: | |
| ```bash | |
| git clone <your-repo> | |
| cd document-qa-system | |
| pip install -r requirements.txt | |
| python app.py | |
| ``` | |
| ## License | |
| This project is open source and available under the MIT License. | |
| ## Support | |
| For issues or questions, please check the Help tab in the application or create an issue in the repository. |