Graph RAG Tutorial | Build Knowledge Graph Powered Retrieval Systems LangChain, ChromaDB & RAGAS

Welcome to GraphRAG: Build Knowledge Graph Powered Retrieval Systems. In this comprehensive graph rag tutorial, we move beyond standard vector search to build a knowledge graph powered retrieval system that understands complex relationships and multi-hop queries. Course Resources - https://github.com/sjsoumil/RAG-vs-GraphRAG Traditional RAG often fails when answers are scattered across multiple document chunks or require understanding the connection between entities. This video provides a step-by-step graphrag implementation guide, taking you from vanilla RAG limitations to a fully evaluated graph rag project. What you will learn in this course: ✅ Graph RAG Explained: Why standard RAG breaks on multi-hop and relationship-heavy queries. ✅ Architecture Deep Dive: Understanding entities, relations, graph stores (NetworkX), and vector indexes (Chroma DB). ✅ Graph RAG from Scratch: Hands-on coding using graph rag langchain and Python. ✅ The Podcast Project: Build a system to query 20+ hours of transcripts from Sam Altman, Elon Musk, and Jensen Huang. ✅ Query Modes: Learn the difference between Local, Global, and Hybrid retrieval. ✅ Evaluation: How to use RAGAS and structural metrics (entity coverage, graph utilization) to prove performance. Whether you are looking for a graph rag tutorial python or a deep dive into knowledge graph rag tutorial concepts, this course is your complete guide to building production-ready AI systems. 🚀 Resources: 📂 Get the Code & Notebooks: [Link] 🔔 Subscribe for more AI Engineering Masterclasses: [Link] Timestamps 0:00 - Course Introduction: Graph RAG Foundations 3:07 - What is RAG & Where Does it Break? (Factual vs. Multi-hop) 7:04 - Graph RAG Explained: How to Fix the Gaps 12:24 - Components of a Graph RAG System (Indexing vs. Query Time) 13:59 - Data Transformation: Structured vs. Unstructured 14:28 - Entity & Relation Extraction using LLMs 15:54 - Building the Knowledge Graph with NetworkX 19:01 - Module 2: The Podcast Transcript P