GraphRAG Hands-On: Knowledge Graphs for RAG

About this course

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.

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