PdfQuery

A powerful application that combines document processing, semantic search, and question answering capabilities. This application uses FAISS for efficient similarity search and Hugging Face models for text embeddings and generation.

Features

• PDF document processing and text extraction
• Semantic search using FAISS indexing
• RESTful API endpoints for querying
• Web interface for easy interaction
• Docker support for containerized deployment
• Efficient document chunking and embedding

Tech Stack

Backend

• Python 3.x
• Flask (Web Framework)
• FAISS (Vector Similarity Search)
• LangChain (Document Processing)
• Hugging Face Transformers
• Sentence Transformers

Frontend

• Node.js
• Express.js
• Material Design Components
• Axios for API calls

Prerequisites

• Python 3.x
• Node.js and npm
• Docker (optional)

Usage

1. Start the Python backend server:

python api.py

2. Start the Node.js server:

node server.js

3. Access the web interface at http://localhost:3000

API Endpoints

Query Endpoint

• URL: /query
• Method: POST
• Body:

{
    "query": "Your question here"
}

Project Structure

rag-application/
├── api.py                 # Flask API server
├── create_database.py     # Database creation and indexing
├── query_data.py         # Query processing logic
├── pdf_util.py           # PDF processing utilities
├── server.js             # Node.js server
├── public/               # Frontend static files
├── faiss_index/          # FAISS index storage
├── Data/                 # Document storage
└── requirements.txt      # Python dependencies

Docker Support

Build and run using Docker:

docker build -t rag-application .
docker run -p 8000:8000 rag-application

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 ISC License.

Acknowledgments

• Hugging Face for their transformer models
• FAISS for efficient similarity search
• LangChain for document processing capabilities