PDF RAG Assistant

An intelligent document assistant that helps researchers interact with academic papers and other PDF documents using RAG (Retrieval Augmented Generation) technology. This system allows users to query their document collection and receive contextually relevant responses based on the content of their papers.

Features

• PDF document processing and storage
• Semantic search across academic papers
• Contextual question-answering using RAG
• Support for academic paper analysis and exploration

Installation

1. Clone the repository

git clone https://github.com/pdf-rag-assistant

2. Create and activate a virtual environment

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

3. Install dependencies

pip install -r requirements.txt

Usage

1. Place your PDF documents in the academic_papers directory
2. Run the main script:

python main.py

3. Interact with your documents through natural language queries

Project Structure

├── main.py              # Main application entry point
├── academic_papers/     # Directory for PDF documents
├── academic_db/         # Vector store database
└── venv/                # Python virtual environment

Contributing

Contributions are welcome! Please feel free to submit a pull request.

License

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