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.
- 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.