Document Q&A Assistant with RAG

A powerful Streamlit application that allows you to upload documents and ask questions about them using Retrieval-Augmented Generation (RAG) with local LLM processing.

🚀 Features

• Document Upload Support: PDF, DOCX, and TXT files
• Real-time Progress Tracking: Visual progress indicators during document processing
• Local Processing: All data processing happens locally using Ollama
• Smart Chunking: Intelligent text splitting with sentence boundary detection
• Vector Search: ChromaDB for efficient similarity search
• Caching: Optimized performance with smart caching mechanisms
• Performance Metrics: Track response times and query statistics
• Interactive Chat Interface: User-friendly chat experience with streaming responses

📋 Prerequisites

Before running this application, make sure you have:

1. Python 3.8+ installed
2. Ollama installed and running locally
3. Gemma3 model downloaded in Ollama

Installing Ollama

1. Download and install Ollama from https://ollama.ai
2. Pull the Gemma3 model:

   ollama pull gemma3

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/hari7261/Document-Q-A-LLM.git
   cd Document-Q-A-LLM

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

🚀 Usage

1. Start the application:

   streamlit run app.py

2. Open your browser and navigate to http://localhost:8501

3. Upload documents:

   • Use the sidebar to upload PDF, DOCX, or TXT files
   • Click "Process Documents" to index your files
   • Watch the real-time progress tracking

4. Ask questions:

   • Type your questions in the chat input
   • Get AI-powered answers based on your documents
   • View source citations for transparency

📁 Project Structure

Document-Q-A-LLM/
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
├── .gitignore            # Git ignore rules
├── LICENSE               # License file
├── setup.py              # Package setup file
├── chroma_db/            # ChromaDB storage (created automatically)
│   └── chroma.sqlite3    # Vector database
├── .github/              # GitHub templates and workflows
│   └── ISSUE_TEMPLATE/   # Issue templates
└── docs/                 # Additional documentation
    ├── installation.md
    ├── usage.md
    └── api.md

⚙️ Configuration

Model Settings

• Temperature: Control randomness in responses (0.0 - 1.0)
• Model: Currently supports Gemma3 (more models can be added)

Performance Optimization

• Caching: Responses are cached for 1 minute, search results for 5 minutes
• Chunking: Documents are split into 1000-character chunks with sentence boundary detection
• Batch Processing: Documents are processed in optimized batches

🔧 Advanced Configuration

You can customize the application by modifying these parameters in app.py:

# Chunk size for document splitting
chunk_size = 1000

# Number of relevant chunks to retrieve
k = 5

# Cache TTL settings
cache_ttl_responses = 60    # 1 minute
cache_ttl_search = 300      # 5 minutes

📊 Performance Metrics

The application tracks:

• Average response time
• Last response time
• Total queries processed

Access these metrics through the expandable "Performance Metrics" section in the sidebar.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🐛 Known Issues

• Large PDF files may take longer to process
• Ensure Ollama is running before starting the application
• ChromaDB requires write permissions in the project directory

🆘 Troubleshooting

Common Issues

1. "Model not found" error:

   ollama pull gemma3

2. ChromaDB permission errors:

   • Ensure the application has write permissions in the project directory

3. Slow processing:

   • Check if Ollama is running locally
   • Verify system resources (RAM, CPU)

📞 Support

If you encounter any issues or have questions:

• Open an issue on GitHub
• Check the documentation
• Review the troubleshooting section

🙏 Acknowledgments

• Streamlit for the amazing web framework
• Ollama for local LLM capabilities
• ChromaDB for vector database functionality
• SentenceTransformers for embeddings

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Made with ❤️ by Hariom Kumar for the open-source community

Repository: https://github.com/hari7261/Document-Q-A-LLM