PDF-RAG-Application

PDF-RAG-Application 📄🤖

Welcome to PDF-RAG-Application! This project provides a simple and efficient framework for Retrieval-Augmented Generation (RAG) using PDF documents. Harness the power of Large Language Models with your own PDF knowledge base—search, query, and analyze PDF content interactively.

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🚀 Introduction

PDF-RAG-Application enables users to upload and query PDF files using state-of-the-art embedding models (via SentenceTransformers) and fast similarity search with FAISS. The application features an intuitive Gradio interface for seamless user interaction.

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✨ Features

• PDF Upload & Parsing: Easily upload and process PDF documents.
• Semantic Search: Powerful vector search using FAISS and sentence-transformer embeddings.
• Interactive Interface: User-friendly Gradio UI for uploading PDFs and querying contents.
• Asynchronous Processing: Efficient handling of large documents.
• Secure & Lightweight: Minimal dependencies, runs locally.

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⚙️ Installation

1. Clone the Repository

   git clone https://github.com/your-username/PDF-RAG-Application.git
   cd PDF-RAG-Application

2. Create a Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

   pip install -r requirements.txt

   Note: Main dependencies are gradio, sentence-transformers, faiss-cpu, PyMuPDF, and numpy.

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▶️ Usage

1. Start the Application

   python app.py

2. Interact via Web Browser

   • Open the Gradio link displayed in your terminal.
   • Upload your PDF files.
   • Ask questions or search for content within your PDFs.

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🤝 Contributing

Contributions are welcome! To get started:

1. Fork the repository.
2. Create your feature branch:

   git checkout -b feature/YourFeature

3. Commit your changes:

   git commit -m "Add YourFeature"

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a Pull Request.

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📄 License

This project is licensed under the MIT License.

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Enjoy using PDF-RAG-Application! If you find this project useful, please ⭐ star the repo and share your feedback.

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For questions or suggestions, feel free to open an issue or contact the maintainer.

License

This project is licensed under the MIT License.

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🔗 GitHub Repo: https://github.com/selvaganesh19/PDF-RAG-Application