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

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