Chat with your Data - RAG

answer from your own files

This track teaches you how to make an AI answer questions using your own files instead of only its training data. It is for developers, data engineers, or anyone who wants a private chat assistant that can cite documents, papers, and databases. After completing the track you will be able to: create a system that answers from your own documents with citations (Chapter 1); set up an open-source, privacy‑focused notebook environment as an alternative to commercial notebook AI (Chapter 2); deploy Onyx to provide cited answers, agent actions, and deep research over your team’s docs, apps, and people (Chapter 3); and run AnythingLLM as a private, all‑in‑one chat interface that supports agents, connectors, and local or self‑hosted keys for your document collections (Chapter 4). Start with Chapter 1 to learn the basic retrieval‑augmented generation workflow, then move to Chapter 2 to explore the notebook environment. Continue with Chapter 3 to see how Onyx extends the core ideas into a multi‑agent research tool, and finish with Chapter 4 for a full private chat deployment. If you need only a quick proof of concept, you can skip Chapter 2 and go straight from Chapter 1 to Chapter 4, but following the full order gives the most coherent learning path.