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GraphRAG Hands– On Knowledge Graphs for RAG
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With GraphRAG Hands– On Knowledge Graphs for RAG, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 1h 47m | Size: 826.93 MB
Why vector RAG fails, what a knowledge graph fixes, how to index and query with Microsoft GraphRAG · Hands-On track
What you'll learn
Recognise the three question types that make vector RAG fail and explain why
Build and walk a knowledge graph by hand, then extract entities and relations with an LLM
Configure and run Microsoft GraphRAG indexing and read its outputs
Use local and global search and answer multi-hop questions
Estimate the token cost of GraphRAG and decide when it is worth it
Requirements
A computer with Python 3.10+ (a free Google Colab account is enough for most sessions)
Comfort reading Python code; you do not need to be an expert
An OpenAI API key or a local Ollama installation
Basic familiarity with LLM APIs and embeddings
Description
This course contains the use of artificial intelligence.
The voice-over in this course is synthesized with a text-to-speech model from scripts written and reviewed by the instructor, and the on-screen material (notebooks, code, slides) is the instructor's own work.
Vector RAG works until the question needs more than one chunk. Multi-hop questions, global summary questions and questions about relations all fail the same way: the right chunk is never retrieved, because the answer lives in the connections between documents. GraphRAG fixes that with a knowledge graph — and this course shows exactly how, on a small fictional corpus with planted facts so you can verify every answer by hand.
Session 1 builds a naive RAG pipeline from scratch and breaks it on purpose on three question types. Session 2 covers knowledge-graph fundamentals: nodes, edges, building a graph by hand with NetworkX, walking it, and LLM-based entity and relation extraction with a prompt you can read. Session 3 is Microsoft GraphRAG hands-on: installation, the settings file line by line, the indexing pipeline, and the entities, relationships and communities it produces. Session 4 covers querying: local versus global search and what each is for, multi-hop reasoning, community visualisation, token cost, and — importantly — when not to use GraphRAG.
Every session is a notebook shown on screen while the narration explains each cell; the notebooks work with OpenAI or with a local Ollama model. Cost is measured, not assumed.
The follow-up course, *GraphRAG in Production*, takes this to real public documents with evaluation, provenance and agents.
Who this course is for
Developers who have built a RAG chatbot and hit its limits
Data and knowledge engineers evaluating knowledge-graph retrieval
Teams considering Microsoft GraphRAG and needing a realistic cost picture
Anyone who wants to understand retrieval beyond similarity search
Homepage
https://www.udemy.com/course/graphrag-hands-on-knowledge-graphs-for-rag/
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