RAG Learning

This repository contains examples and Jupyter notebooks for learning Retrieval-Augmented Generation (RAG) using LlamaIndex.

Getting Started

1. Clone the repository:

   git clone https://github.com/donniv86/rag-learning.git
   cd rag-learning

2. Set up a virtual environment and install dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Set your OpenAI API key in a .env file in the root directory:

   OPENAI_API_KEY=your_api_key_here
   

Jupyter Notebooks for Hands-On Learning

The repository includes the following Jupyter notebooks for interactive learning:

• indexing_basics.ipynb: Learn basic indexing techniques with LlamaIndex.
• multi_modal_examples.ipynb: Explore indexing and querying text, PDF, and image files.
• embedding_techniques.ipynb: Understand different embedding models and their characteristics.

How to Use the Notebooks

1. Start JupyterLab or Jupyter Notebook:

   jupyter lab
   # or
   jupyter notebook

2. Open the desired notebook and run each cell interactively to see the results and experiment with the code.

Additional Resources

• LlamaIndex Documentation
• OpenAI API Documentation

Contributing

Feel free to submit issues, fork the repository, and create pull requests for any improvements.

License

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