ISBN: 9798194044313 - the ai-native knowledge · graphrag: designing knowledge retrieval on graphs — from core concepts to twelve real-world use cases (6 Ergebnisse)

Taschenbuch. Zustand: Neu. Neuware - 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.…

Zustand: New. Print on Demand.

Paperback. Zustand: new. Paperback. 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 peopleMaster the five retrieval patterns - local, global, DRIFT, path traversal, and Text2Cypher - and route every question to the right oneShip answers people can trust - citations that open, reasoning paths that are recorded (never invented), and refusals that are honestEvaluate and operate for real - golden sets, three-tier diagnosis, incremental updates, cost budgets, and access control that never leaksGo 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 fixesNine industry deep-dives: code assistants, legal contracts, BI, CRM, e-commerce, medicine, fraud detection, news intelligence, and technical manualsRunnable companion datasets with 45 expert-keyed test questions and planted traps - so you can reproduce every number in the bookSelf-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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Zustand: new. Paperback. 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 peopleMaster the five retrieval patterns - local, global, DRIFT, path traversal, and Text2Cypher - and route every question to the right oneShip answers people can trust - citations that open, reasoning paths that are recorded (never invented), and refusals that are honestEvaluate and operate for real - golden sets, three-tier diagnosis, incremental updates, cost budgets, and access control that never leaksGo 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 fixesNine industry deep-dives: code assistants, legal contracts, BI, CRM, e-commerce, medicine, fraud detection, news intelligence, and technical manualsRunnable companion datasets with 45 expert-keyed test questions and planted traps - so you can reproduce every number in the bookSelf-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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…