THE AI-NATIVE KNOWLEDGE GraphRAG : Designing Knowledge Retrieval on Graphs - From Core Concepts to Twelve Real-World Use Cases
Language: English
Published by Independently Published Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 32.74
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Seller Inventory # 9798194044313
- Title
- THE AI-NATIVE KNOWLEDGE GraphRAG : Designing Knowledge Retrieval on Graphs - From Core Concepts to Twelve Real-World Use Cases
- Author
- Vu Tri Cong Mobiluck-Code247. Ai
- Publisher
- Independently Published Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798194044313
- Item weight
- 491 grams
- Dimensions
- 229x152x19 mm
Your search box can find documents. It cannot answer questions.
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
What makes this book different:
- 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.
"Synopsis" may belong to another edition of this title.
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