https://github.com/dmitry-brazhenko/rag-tutorial

A comprehensive tutorial on Retrieval-Augmented Generation (RAG), combining retrieval-based and generative models for enhanced text generation. Includes setup instructions, basic and advanced examples, datasets, and evaluation methods.
https://github.com/dmitry-brazhenko/rag-tutorial

Last synced: 11 months ago
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A comprehensive tutorial on Retrieval-Augmented Generation (RAG), combining retrieval-based and generative models for enhanced text generation. Includes setup instructions, basic and advanced examples, datasets, and evaluation methods.

• Host: GitHub
• URL: https://github.com/dmitry-brazhenko/rag-tutorial
• Owner: dmitry-brazhenko
• License: mit
• Created: 2024-05-29T04:50:05.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-05-29T06:14:47.000Z (about 2 years ago)
• Last Synced: 2024-05-29T18:30:20.927Z (about 2 years ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 211 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# RAG Tutorial: How to Build a Copilot from Scratch

This repository contains a comprehensive tutorial for building a copilot agent from scratch. The tutorial demonstrates how to:

- Develop a copilot agent that works seamlessly with data.

- Execute actions upon request.

- Utilize advanced embedding models and vector databases.

- Implement various strategies to improve search relevance.

## Contents

- **Installation Instructions**: Step-by-step guide to install necessary libraries.

- **OpenAI Token Setup**: Instructions to set up your OpenAI token for authentication.

- **Data Download and Preparation**: Methods to download and prepare data for processing.

- **Embeddings and Similarity Measures**: How to use SentenceTransformer models to create embeddings and measure similarity.

- **Vector Database Integration**: Steps to integrate and use LanceDB for efficient data storage and retrieval.

- **LangChain Agents**: Examples of using LangChain to build agents that can handle factual queries and nutritional facts extraction.

- **Re-ranking and Improving Search Relevance**: Techniques to enhance the relevance of search results using various embedding models and re-ranking methods.

- **Example Queries and Responses**: Demonstrations of how to interact with the copilot agent using example queries and responses.

This tutorial provides a hands-on approach to building and refining a copilot agent, making use of state-of-the-art NLP models and tools.