Building a GraphRAG App: Concepts, Code, and Demo
This video walks through the concepts and implementation of a GraphRAG application, explaining what graph databases are and how they enhance language model retrieval. It also contrasts GraphRAG with vector RAG to highlight their respective strengths and applications. The walkthrough includes a detailed code review of a full-stack GraphRAG app, providing insight into its backend, graph database integration, LLM usage, and user interface.
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