Welcome to the AI Tutor RAG (Retrieval-Augmented Generation) System repository! This repository contains a collection of Jupyter notebooks designed to support the RAG course, focusing on techniques for enhancing AI models with retrieval-based methods.
You can find all the course notebooks in the notebooks directory. These notebooks cover various aspects of building and fine-tuning RAG models, providing both theoretical background and practical, hands-on examples.
You have two options for running the code in these notebooks:
- Run Locally: You can clone the repository and run the notebooks on your local machine. To do this, ensure you have a Python installation with the necessary dependencies.
- Run on Google Colab: Each notebook includes a link at the top to open it directly in Google Colab, making it easy to run without local setup.
- Audience: Designed for students and professionals interested in AI and natural language processing.
- Topics Covered: The notebooks cover foundational and advanced concepts in retrieval-augmented generation, including:
- Data retrieval techniques
- Model integration with retrieval systems
- Practical applications of RAG in real-world scenarios
Clone the repository and explore the notebooks at your own pace. Whether running them locally or in Colab, these notebooks will guide you step-by-step, enhancing your learning experience.