📄 Document Search Engine App

A powerful Streamlit-powered RAG (Retrieval-Augmented Generation) application that allows you to upload documents and ask natural language questions. It intelligently retrieves relevant information and generates accurate, context-aware answers using embeddings and LLMs.

Built as part of the RAG-Mastery learning journey — covering everything from basic chunking & retrieval to advanced RAG concepts.

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✨ Features

• Semantic Document Search — Upload PDFs, TXT, or other documents and get intelligent answers
• End-to-End RAG Pipeline — Document loading, chunking, embedding generation, vector storage & retrieval
• LLM Integration — Powered by OpenAI (easily extendable to other models)
• Clean & Interactive UI — Built with Streamlit for a smooth user experience
• Modular Codebase — Well-organized src/ structure for easy learning and extension
• Great for Experimentation — Perfect foundation to try advanced RAG techniques, agents, LangGraph, etc.

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🚀 Live Demo

Deployed Application: Document Search Engine App

Demo Video:

https://github.com/mani24singh/Document-Search-Engine-App/blob/main/assets/output_demo.mp4

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🛠️ Tech Stack

• Language: Python 3.11+
• UI Framework: Streamlit
• RAG Framework: LangChain
• Embeddings: OpenAI Embeddings
• LLM: OpenAI GPT models
• Package Manager: uv (recommended)

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📥 How to Clone and Run Locally

1. Clone the Repository

git clone https://github.com/mani24singh/Document-Search-Engine-App.git
cd Document-Search-Engine-App

2. Create Virtual Environment (Recommended)

# Install uv (fast Python package manager)
pip install uv

# Create and activate virtual environment
uv venv --python 3.11

# Activate environment
# Windows:
.venv\Scripts\activate
# macOS / Linux:
source .venv/bin/activate

3. Install Dependencies

uv pip install -r requirements.txt

4. Add Your OpenAI API Key

Create a .env file in the root directory and add your key:

OPENAI_API_KEY=sk-your-openai-api-key-here

Note: .env is already added to .gitignore for security.

5. Run the Application

streamlit run streamlit_app.py

The app will automatically open in your browser.

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📁 Project Structure

Document-Search-Engine-App/
├── assets/                  # Demo videos and images
├── data/                    # Sample documents
├── src/                     # Core RAG logic and modules
├── .env.example             # Example environment variables
├── .gitignore
├── LICENSE
├── main.py                  # Alternative entry point
├── requirements.txt
├── README.md
└── streamlit_app.py         # Main Streamlit application

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🤝 Contributing

Contributions, issues, and feature requests are welcome!

Feel free to fork the repository and submit a pull request.

1. Fork the project
2. Create your 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

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📜 License

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

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⭐ Show Your Support

If you liked this project or found it helpful for learning RAG, please give it a ⭐ on GitHub!
It really helps the project reach more people.

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Made with ❤️ for learning and building better RAG systems.