RAG Model for Research Paper Summarizing

A Retrieval-Augmented Generation (RAG) system that helps users understand and analyze research papers. Upload a PDF of a research paper and ask questions about its content.

Features

• 📄 PDF Research Paper Processing
• 🔍 Semantic Search with Embeddings
• 💡 Intelligent Question Answering
• 🤖 Gemini Pro Integration
• 🎯 Context-Aware Responses
• 🌐 Web Interface

Installation

1. Clone the repository:

git clone https://github.com/yourusername/simple-rag-model.git
cd simple-rag-model

2. Create and activate a virtual environment:

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

3. Install dependencies:

pip install -r requirements.txt

Dependencies

Create a requirements.txt file with:

flask==2.0.1
flask-cors==3.0.10
google-generativeai==0.3.0
numpy==1.24.3
pandas==2.0.2
PyMuPDF==1.22.5
python-dotenv==1.0.0
sentence-transformers==2.2.2
torch==2.0.1
tqdm==4.65.0

Environment Setup

1. Create a .env file in the project root:

GEMINI_API_KEY=your_gemini_api_key_here

2. Get your Gemini API key from Google AI Studio

Usage

1. Start the server:

python src/application.py

2. Open your browser and go to:

http://localhost:5000

3. Upload a research paper PDF and start asking questions!

Project Structure

simple-rag-model/
├── src/
│   ├── templates/
│   │   └── index.html
│   ├── application.py
│   ├── data_processing.py
│   ├── embeddings.py
│   ├── query.py
│   ├── response_generator.py
│   └── retriever.py
├── uploads/
├── requirements.txt
└── README.md

How It Works

1. PDF Processing:

   • Extracts text from PDF
   • Splits into meaningful chunks
   • Maintains document structure

2. Embedding Generation:

   • Uses SentenceTransformers
   • Creates semantic embeddings
   • Enables similarity search

3. Query Processing:

   • Processes user questions
   • Finds relevant contexts
   • Ranks by similarity

4. Response Generation:

   • Uses Gemini Pro API
   • Context-aware responses
   • Natural language answers

API Endpoints

• POST /upload: Upload PDF file
• POST /query: Process questions about the PDF

Contributing

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

License

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

Acknowledgments

• Google Gemini API for text generation
• Sentence Transformers for embeddings
• Flask for web framework
• PyMuPDF for PDF processing

Contact

Your Name - @ant_vedaya GitHub: @ved1beta

Project Link: https://github.com/ved1beta/simple-rag-model

To create a demo video:
1. Record your screen while:
   - Starting the application
   - Uploading a research paper
   - Asking various questions
   - Showing the responses
2. Convert to GIF using a tool like [ScreenToGif](https://www.screentogif.com/)
3. Save as `demo.gif` in your project root
4. Reference it in the README as shown above

Would you like me to help with creating the demo video or make any adjustments to the README?