RAG 101: Building Your Own Personal Retrieval Augmented Generation Model

Prerequisites:

Python Skills: Basic understanding of Python programming.
Jupyter Notebook/Google Colab Experience: Familiarity with Jupyter Notebook or Google Colab environments.
OpenAI API Key: Access to your own OpenAI API key for model interactions.

In this tutorial, we will explore the following key areas of RAG:

Text Splitting and Chunking: Learn how to prepare and process text data for efficient retrieval.
Vector Embeddings: Understand the role of vector embeddings in representing text data for semantic search.
Semantic Search: Discover how to implement semantic search to find the most relevant text chunks for query answering.
LLM (Large Language Model) Answer Generation and Evaluation: Explore how to generate answers using a large language model and evaluate their relevance and accuracy.
Gradio WebUI: Get hands-on experience building a simple web interface with Gradio to interact with your RAG model.

Getting Started

Launch the Colab Notebook:
- Access the Colab Notebook by clicking on the following link: RAG 101 Colab Notebook.
- This notebook contains all the code and detailed instructions needed to build your RAG model.
Set Up Your Environment:
- Once in Colab, ensure that your runtime is set to use a GPU (e.g., Tesla T4) for optimal performance. This is crucial for handling the computational demands of model training and inference.

Presentation Slides: For a comprehensive overview of the concepts and processes involved in this project, refer to the accompanying slides: RAG 101 Slides.