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

Analytics Vidhya · Beginner ·🔍 RAG & Vector Search ·2mo ago

Skills: RAG Basics90%

Key Takeaways

Builds a knowledge graph powered retrieval system using LangChain, ChromaDB, and RAGAS

Original Description

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

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

More on: RAG Basics

View skill →

High Performance (Realtime) RAG Chains: From Basic to Advanced

High Performance (Realtime) RAG Chains: From Basic to Advanced

Coding the Ultimate RAG Engine from Zero

Coding the Ultimate RAG Engine from Zero

Building Agentic RAG From Scratch in Pure Python

Building Agentic RAG From Scratch in Pure Python

Build an LLM and RAG-based Chat Application using AlloyDB and LangChain

I Built a RAG App to Decode Airline Bureaucracy (So You Don't Have To)

I Built a RAG App to Decode Airline Bureaucracy (So You Don't Have To)

Akamai Developers

RAG Demo for Beginners: Full Hands-On Tutorial in Tamil | Build Your Own RAG AI | Karthik's Show

RAG Demo for Beginners: Full Hands-On Tutorial in Tamil | Build Your Own RAG AI | Karthik's Show

Related Reads

A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers

Learn to build a production-ready RAG pipeline for PDFs, enabling efficient document parsing, question answering, and retrieval

Towards Data Science

How to Cut RAG Token Costs 90% by Caching the Prefix

Cut RAG token costs by 90% using prefix caching, reducing the financial burden of large payloads

How to Cut RAG Token Costs 90% by Caching the Prefix

Cut RAG token costs by 90% by caching the prefix, reducing the payload size and saving on input prices

Medium · Programming

Why Your Chunking Strategy Matters More Than Your Model

Optimizing your chunking strategy can be more crucial to your app's performance than the model you choose, learn why and how to improve it

Chapters (8)

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

The Black Box of RAG-1 || 30 days 30 concepts || Day-3