RAG Project

This project uses a Retrieval-Augmented Generation (RAG) model to answer questions based on a custom corpus created from PDF and DOCX files.

Setup

1. Clone the repository.
2. Install dependencies: pip install -r requirements.txt.
3. Run the extraction: sh scripts/run_extraction.sh.
4. Generate answers using the RAG model.

Project Structure

• data/: Contains input files.
• corpus/: Stores processed corpus data.
• src/: Source code.
• models/: Trained models.
• notebooks/: Jupyter notebooks for experimentation.
• tests/: Unit tests.
• scripts/: Helper scripts.
• requirements.txt: Dependencies list.