RAG Model with Llama and SHAP Interpretability

Prerequisites

• Python 3.8+
• pip (Python Package Manager)
• Virtual Environment (recommended)

Setup Guide

1. Clone the Repository

git clone https://github.com/gober9/Rag-Creation-Python-Llama.git
cd Rag-Creation-Python-Llama

2. Create a Virtual Environment

# On Windows
python -m venv venv
venv\Scripts\activate

# On macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Download Llama Model

• Visit Hugging Face: https://huggingface.co/models
• Search for and download a Llama model
• Place the model in a models/ directory

5. Run the RAG Model

python rag_model.py

Troubleshooting

• Ensure all dependencies are installed
• Check Python version compatibility
• Verify model file paths

Project Structure

• rag_model.py: Main RAG model implementation
• requirements.txt: Project dependencies
• models/: Directory for storing Llama models
• data/: Directory for input/output data

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request