In this final stage of the project, I built a full-stack RAG (Retrieval-Augmented Generation) system that combines content retrieval, semantic search, and natural language generation. The implementation is based on the excellent tutorial Build a Full Stack RAG System with React, Langchain & Node.js by notJust․dev. The tutorial was essential in guiding the architecture and agent design of my chatbot, which is focused on delivering accurate course information.
While the original tutorial is centred around retrieving answers from YouTube transcripts, my implementation adapts the same principles to a different use case: scraping and processing publicly available content from the Creative Computing Institute’’’s website. For step-by-step details, I also referred to the companion Notion guide, which was incredibly helpful throughout the build.
Full tutorial code is available in the git repository: https://github.com/filippopresti/AIChatWithYoutube
The YouTube chatbot, deployed online using Genezio, is available at this link: https://rose-sparkling-kangaroo.app.genez.io/
Give the chatbot a YouTube link (make sure the video transcript is available) and it will (try to) answer any question about it.