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THE AI-NATIVE KNOWLEDGE - GraphRAG: Designing Knowledge Retrieval on Graphs - From Core Concepts to Twelve Real-World Use Cases
Paperback
Publisher: Independently Published
Published: Aug 21 2026
Pages: 366
Weight: 1.08
Height: 0.76 Width: 6.00 Depth: 9.00
Language: English
Somewhere in your organization there is a question no single passage contains: Which form does this process need, and who signs it? - How many products share this component? - What changed since yesterday? Classic RAG fails on all of them - and it fails quietly, with answers that sound confident and are wrong.
GraphRAG is the engineering discipline that fixes this - and this book teaches it end to end: not as theory, but as a complete, measurable method you can defend in front of your team.
What you will be able to do after reading:- Decide with evidence, not fashion - classify your real questions, walk a 35-point decision matrix, and know exactly when you need a graph (and when you don't)
- Build the graph from any source - LLM extraction, rules, database import, and images; entity resolution that never merges the wrong people
- Master the five retrieval patterns - local, global, DRIFT, path traversal, and Text2Cypher - and route every question to the right one
- Ship answers people can trust - citations that open, reasoning paths that are recorded (never invented), and refusals that are honest
- Evaluate and operate for real - golden sets, three-tier diagnosis, incremental updates, cost budgets, and access control that never leaks
- Go agentic when it pays - multi-step research loops with tools, budgets, and guards
- Three complete projects built chapter by chapter on realistic synthetic data - an internal knowledge assistant, a support chatbot, and a research assistant - with real token bills, real failures, and real fixes
- Nine industry deep-dives: code assistants, legal contracts, BI, CRM, e-commerce, medicine, fraud detection, news intelligence, and technical manuals
- Runnable companion datasets with 45 expert-keyed test questions and planted traps - so you can reproduce every number in the book
- Self-check quizzes in every chapter, with answer keys
Written for engineers, architects, and technical leaders building AI systems over private data. Python examples throughout; works with NetworkX for learning and Neo4j for production.
Measure first. Decide with criteria. Cite everything. Refuse honestly. That is GraphRAG as this book teaches it - 31 chapters, one method, and a system you can keep honest for years.
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