{"id":30113847,"url":"https://github.com/dmitry-brazhenko/rag-tutorial","last_synced_at":"2025-08-10T07:31:03.988Z","repository":{"id":241696007,"uuid":"807425831","full_name":"dmitry-brazhenko/rag-tutorial","owner":"dmitry-brazhenko","description":"A comprehensive tutorial on Retrieval-Augmented Generation (RAG), combining retrieval-based and generative models for enhanced text generation. 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The tutorial demonstrates how to:\n\n- Develop a copilot agent that works seamlessly with data.\n- Execute actions upon request.\n- Utilize advanced embedding models and vector databases.\n- Implement various strategies to improve search relevance.\n\n## Contents\n\n- **Installation Instructions**: Step-by-step guide to install necessary libraries.\n- **OpenAI Token Setup**: Instructions to set up your OpenAI token for authentication.\n- **Data Download and Preparation**: Methods to download and prepare data for processing.\n- **Embeddings and Similarity Measures**: How to use SentenceTransformer models to create embeddings and measure similarity.\n- **Vector Database Integration**: Steps to integrate and use LanceDB for efficient data storage and retrieval.\n- **LangChain Agents**: Examples of using LangChain to build agents that can handle factual queries and nutritional facts extraction.\n- **Re-ranking and Improving Search Relevance**: Techniques to enhance the relevance of search results using various embedding models and re-ranking methods.\n- **Example Queries and Responses**: Demonstrations of how to interact with the copilot agent using example queries and responses.\n\nThis tutorial provides a hands-on approach to building and refining a copilot agent, making use of state-of-the-art NLP models and tools.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmitry-brazhenko%2Frag-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmitry-brazhenko%2Frag-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmitry-brazhenko%2Frag-tutorial/lists"}