Advanced Graph RAG and Reasoning Systems : Multi-Hop Retrieval, Agentic Workflows, and Relationship-Aware Intelligence for LLM Applications
Language: English
Published by Independently Published Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 40.50
Quantity: 2 available
Add to basketItem description from seller
Neuware - Building a knowledge graph is only the beginning.The real challenge is making your system think.Most AI applications today rely on basic retrieval pulling fragments of information and hoping the model can make sense of them. But when problems become complex, when context spans multiple relationships, and when accuracy truly matters, these systems begin to fail.They retrieve but they do not reason.Advanced Graph RAG and Reasoning Systems is where AI systems evolve from simple retrieval pipelines into intelligent, relationship-aware architectures capable of multi-step reasoning.This book takes you beyond traditional RAG and introduces a more powerful paradigm one where knowledge is not just retrieved, but connected, traversed, and interpreted across complex structures.Inside this book, you will learn how to: - Design advanced Graph RAG architectures for complex information needs- Implement multi-hop retrieval across deeply connected knowledge graphs- Build reasoning pipelines that follow relationships instead of isolated data points- Combine graph search, vector search, and semantic retrieval into unified systems- Develop LLM-powered agents that interact intelligently with structured knowledge- Orchestrate agentic workflows for research, analysis, and decision-making- Optimize context construction for accuracy, relevance, and efficiency- Evaluate reasoning quality and continuously improve system performanceMore importantly, you will learn how to move from 'query and response' systems to true reasoning systems systems that can navigate relationships, adapt to complexity, and deliver higher-quality, context-aware outputs.This is not theory.This is a deep, engineering-focused guide to building the intelligence layer of modern AI systems where retrieval becomes reasoning, and systems begin to behave like problem solvers rather than search tools.Book 1 gave you the foundation, this book gives you the capability to build AI systems that think in connections, not just text.Go beyond retrieval.Build systems that reason.…
Seller Inventory # 9798173074706
- Title
- Advanced Graph RAG and Reasoning Systems : Multi-Hop Retrieval, Agentic Workflows, and Relationship-Aware Intelligence for LLM Applications
- Author
- Cyran Devereux
- Publisher
- Independently Published Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798173074706
- Item weight
- 291 grams
- Dimensions
- 254x178x9 mm
- Series
- Book 2 of 3: Mastering Knowledge Graph AI Systems
Building a knowledge graph is only the beginning.
The real challenge is making your system think.
Most AI applications today rely on basic retrieval pulling fragments of information and hoping the model can make sense of them. But when problems become complex, when context spans multiple relationships, and when accuracy truly matters, these systems begin to fail.
They retrieve but they do not reason.
Advanced Graph RAG and Reasoning Systems is where AI systems evolve from simple retrieval pipelines into intelligent, relationship-aware architectures capable of multi-step reasoning.
This book takes you beyond traditional RAG and introduces a more powerful paradigm one where knowledge is not just retrieved, but connected, traversed, and interpreted across complex structures.
Inside this book, you will learn how to:
-
Design advanced Graph RAG architectures for complex information needs
-
Implement multi-hop retrieval across deeply connected knowledge graphs
-
Build reasoning pipelines that follow relationships instead of isolated data points
-
Combine graph search, vector search, and semantic retrieval into unified systems
-
Develop LLM-powered agents that interact intelligently with structured knowledge
-
Orchestrate agentic workflows for research, analysis, and decision-making
-
Optimize context construction for accuracy, relevance, and efficiency
-
Evaluate reasoning quality and continuously improve system performance
More importantly, you will learn how to move from “query and response” systems to true reasoning systems systems that can navigate relationships, adapt to complexity, and deliver higher-quality, context-aware outputs.
This is not theory.
This is a deep, engineering-focused guide to building the intelligence layer of modern AI systems where retrieval becomes reasoning, and systems begin to behave like problem solvers rather than search tools.
Book 1 gave you the foundation, this book gives you the capability to build AI systems that think in connections, not just text.
Go beyond retrieval.
Build systems that reason.
"Synopsis" may belong to another edition of this title.
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