AI-POWERED RAG SYSTEM LAB Overview: Developed and deployed an AI-powered Retrieval-Augmented Generation (RAG) system using OpenAI’s API, open-source OpenWebUI, and agentic tools written in python. Used SearXNG for anonymous internet queries as well as a vectorized database of my homelab notes for context-aware generation. Containerized the application stack using Docker on my homelab server for simplified deployment and maintenance. Overview of RAG process, combining external documents and user input into an LLM prompt to get tailored output. Credit Watching AI answer questions using your own notes as context feels like having a second brain—not literally, of course; that’s a different project.   For this project, I decided to build a Retrieval-Augmented Generation (RAG) system for my homelab that can not only search my personal knowledgebase, but the internet as a whole as well. Using OpenAI’s API plus OpenWebUI, it combines real-time SearXNG searches with a vectorized database of my own notes to generate context-aware responses.   The gravy on top: everything runs in Docker containers on my homelab Unraid server for easy deployment and maintenance. Reference: https://www.youtube.com/watch?v=T-D1OfcDW1M