Master RAG (Retrieval Augmented Generation) Locally: Step-by-Step Guide with LMStudio & AnythingLLM

In this video, I walk you through the basics of setting up a Retrieval Augmented Generation (RAG) system using LMStudio and AnythingLLM. Whether you're a beginner or just curious about leveraging AI for powerful question-answering systems, this guide is for you! We’ll cover: By the end of this tutorial, you’ll have a fully functional RAG system running on your machine, ready to handle complex queries with ease. 🔧 My System Specs: CPU: AMD Ryzen 7 7800X3D GPU: AMD Radeon RX 7900 XTX Tools Used: LMStudio, AnythingLLM, and a custom Vector DB setup. 📂 What You’ll Learn: How to install and configure LMStudio for local model management. Setting up AnythingLLM to create a seamless interface for your RAG system. Loading and embedding the MTG card dataset into a Vector DB. Demo: Asking questions like “Show me some creates with power/toughness 4/4” or “Show me all blue cards with mana cost 3.” and getting accurate, context-aware answers. ✨ Why RAG? Retrieval Augmented Generation combines the power of large language models with external knowledge bases, making it perfect for tasks like document retrieval, Q&A systems, and more. Things I used: LMStudio AnythingLLM Mistral Nemo 2407 - Instruct MTG Card Dataset https://mtgjson.com/downloads/all-files/ 💬 Let me know in the comments: What dataset would you would like to know more about with RAG? 📌 Hashtags: #RAG #RetrievalAugmentedGeneration #LMStudio #AnythingLLM #AI #MachineLearning #MTG #MagicTheGathering #AMD7800X3D #AMD7900XTX #VectorDatabase #AITutorial #LocalAI #TextEmbedding #AIDemo #TechTutorial #AIForBeginners Don’t forget to like, subscribe for more content like this!