GraphRAG: The Definitive Guide

Chapter 3. Better Answers with GraphRAG

A Note for Early Release Readers

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This will be the 3rd chapter of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

In this chapter we continue to follow OMG consulting as it transforms its basic vector-search based assistant that helps them to manage their employee skills and assignments to a knowledge-graph-augmented retrieval system using GraphRAG. Through their story, we’ll uncover the practical steps, technical architecture, and mindset shift needed to move from text-chunks to connections, and from isolated facts to contextual insights.

Note

Note that we’re not using any framework or library for the RAG and GraphRAG implementation in this chapter, as those evolve quickly and we want to focus on the concepts and techniques and show what’s happening under the hood.

Introduction: From Isolated Documents to Connected Knowledge

OMG Consulting, a small consulting firm, wanted to better match consultants to projects based on their skills, past experiences, and client industries to improve staffing efficiency. To support this, the team initially implemented a basic ...