Building a GraphRAG App: Concepts, Code, and Demo
This video walks through the concepts and implementation of GraphRAG, a method that enhances language model retrieval by incorporating graph databases. Viewers will learn how to build a full-stack GraphRAG application, including setting up the backend, integrating a graph database, and connecting it with an LLM for a more accurate and explainable AI experience.
Description
If you’ve heard about RAG but aren’t sure how “GraphRAG” fits in, this video gives you a clear, accessible walkthrough—no heavy technical background needed.
In this video, you’ll learn:
🔹 What a Graph Database Is — and why companies use them to model real-world relationships
🔹 How GraphRAG Works — and how it boosts LLM retrieval quality with structure, context, and reasoning
🔹 GraphRAG vs. Vector RAG — when each excels, and how they complement each other in real applications
Once the concepts are clear, we dive into a real (but easy to understand) full-stack GraphRAG application, including a full code walkthrough showing exactly how the backend, graph database, LLM, and UI work together behind the scenes.
If you’re exploring ways to make your AI apps more accurate, contextual, and explainable, this is a great place to start.
📌 Full source code is available on GitHub — ideal for experimenting, learning, or adapting GraphRAG into your own projects.
https://github.com/robkerr/robkerrai-demo-code/tree/main/dgx-spark-graphrag-movies
Chapters:
0:00 Introduction
0:33 What is GraphRag
1:00 Common RAG Approaches
1:17 What's a Graph Database?
2:00 How does GraphRAG Work?
2:52 Combining GraphRAG + RAG
3:18 Introducing the Example App
5:30 Dataset
6:45 App Demo
8:52 Deployment
10:28 Code Walkthrough
15:03 Demo #2
17:34 Summary