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.