Knowledge Graphs for Llms (6 results)

Taschenbuch. Condition: Neu. Neuware - Building a Knowledge Graph is only the beginning.Modern AI applications increasingly need to retrieve information across connected entities, reason over multiple relationships, coordinate tools and data sources, and operate reliably at production scale. Advanced Knowledge Graphs for LLMs explores the techniques and architectures required to take graph-powered LLM applications beyond basic retrieval and into sophisticated intelligent systems.This volume builds on the foundations of Knowledge Graph construction and LLM integration to explore advanced retrieval, reasoning, agentic workflows, optimization, evaluation, security, and production deployment.You will learn how to: - Design advanced graph-based retrieval architectures- Build Graph RAG systems for complex information needs- Implement multi-hop retrieval and relationship-aware reasoning- Combine graph search, vector search, and semantic retrieval- Improve context selection and reduce irrelevant information- Build LLM-powered agents that interact with Knowledge Graphs- Design agentic workflows for research, question answering, and decision support- Optimize graph queries, retrieval pipelines, and LLM context windows- Evaluate Knowledge Graph quality and LLM application performance- Develop testing, benchmarking, human-in-the-loop, and A/B evaluation strategies- Address security, access control, privacy, and data governance- Design scalable architectures for large Knowledge Graph and LLM workloads- Monitor, maintain, and continuously improve graph-powered AI systems- Transition experimental Knowledge Graph applications into production environmentsThe book focuses on the engineering challenges that emerge when Knowledge Graphs and LLMs move from prototypes to real-world systems.Through practical architectures, implementation patterns, evaluation strategies, and production considerations, you will learn how to build AI applications that can retrieve connected knowledge, reason across relationships, use structured context, and operate reliably at scale.If Foundations of Knowledge Graphs for LLMs teaches you how to build the foundation, this volume teaches you how to extend, optimize, evaluate, and productionize it.Go beyond basic RAG. Build AI systems that can connect knowledge, retrieve context, and reason across relationships. …

Taschenbuch. Condition: Neu. Neuware - Large language models are powerful but they do not inherently possess a reliable, structured representation of the world. They can struggle with factual consistency, complex relationships, long-context retrieval, and explaining where their answers come from.Foundations of Knowledge Graphs for LLMs provides a practical introduction to designing and building Knowledge Graph systems that give LLM applications structured, connected, and meaningful context.Rather than treating Knowledge Graphs as an isolated database technology, this book explores how graphs can become an essential component of modern AI architectures.You will learn how to: - Understand the core concepts behind Knowledge Graphs and knowledge representation- Design graph schemas, entities, relationships, properties, and ontologies- Work with structured and unstructured data as sources for graph construction- Extract entities and relationships from documents using LLMs and modern NLP techniques- Build Knowledge Graph construction pipelines- Integrate Knowledge Graphs with Large Language Models- Connect graph retrieval with Retrieval-Augmented Generation architectures- Improve contextual grounding and reduce unsupported LLM responses- Implement graph-based retrieval and context assembly strategies- Design systems capable of following relationships across connected information- Understand graph databases, embeddings, vector search, and hybrid retrieval- Build practical Python-based Knowledge Graph and LLM workflows- Design architectures that can evolve from prototypes into scalable AI systemsThe book combines foundational concepts with practical implementation patterns, helping readers understand not only what Knowledge Graphs are, but also how and why they fit into modern LLM applications.Whether you are new to Knowledge Graphs or already working with LLMs and RAG systems, this volume provides the foundation needed to design context-aware AI applications built around structured knowledge.Build the foundation. Structure the knowledge. Give your LLMs better context.…

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Condition: New. Print on Demand.

Paperback. Condition: new. Paperback. Building a Knowledge Graph is only the beginning.Modern AI applications increasingly need to retrieve information across connected entities, reason over multiple relationships, coordinate tools and data sources, and operate reliably at production scale. Advanced Knowledge Graphs for LLMs explores the techniques and architectures required to take graph-powered LLM applications beyond basic retrieval and into sophisticated intelligent systems.This volume builds on the foundations of Knowledge Graph construction and LLM integration to explore advanced retrieval, reasoning, agentic workflows, optimization, evaluation, security, and production deployment.You will learn how to: Design advanced graph-based retrieval architecturesBuild Graph RAG systems for complex information needsImplement multi-hop retrieval and relationship-aware reasoningCombine graph search, vector search, and semantic retrievalImprove context selection and reduce irrelevant informationBuild LLM-powered agents that interact with Knowledge GraphsDesign agentic workflows for research, question answering, and decision supportOptimize graph queries, retrieval pipelines, and LLM context windowsEvaluate Knowledge Graph quality and LLM application performanceDevelop testing, benchmarking, human-in-the-loop, and A/B evaluation strategiesAddress security, access control, privacy, and data governanceDesign scalable architectures for large Knowledge Graph and LLM workloadsMonitor, maintain, and continuously improve graph-powered AI systemsTransition experimental Knowledge Graph applications into production environmentsThe book focuses on the engineering challenges that emerge when Knowledge Graphs and LLMs move from prototypes to real-world systems.Through practical architectures, implementation patterns, evaluation strategies, and production considerations, you will learn how to build AI applications that can retrieve connected knowledge, reason across relationships, use structured context, and operate reliably at scale.If Foundations of Knowledge Graphs for LLMs teaches you how to build the foundation, this volume teaches you how to extend, optimize, evaluate, and productionize it.Go beyond basic RAG. Build AI systems that can connect knowledge, retrieve context, and reason across relationships. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. Large language models are powerful but they do not inherently possess a reliable, structured representation of the world. They can struggle with factual consistency, complex relationships, long-context retrieval, and explaining where their answers come from.Foundations of Knowledge Graphs for LLMs provides a practical introduction to designing and building Knowledge Graph systems that give LLM applications structured, connected, and meaningful context.Rather than treating Knowledge Graphs as an isolated database technology, this book explores how graphs can become an essential component of modern AI architectures.You will learn how to: Understand the core concepts behind Knowledge Graphs and knowledge representationDesign graph schemas, entities, relationships, properties, and ontologiesWork with structured and unstructured data as sources for graph constructionExtract entities and relationships from documents using LLMs and modern NLP techniquesBuild Knowledge Graph construction pipelinesIntegrate Knowledge Graphs with Large Language ModelsConnect graph retrieval with Retrieval-Augmented Generation architecturesImprove contextual grounding and reduce unsupported LLM responsesImplement graph-based retrieval and context assembly strategiesDesign systems capable of following relationships across connected informationUnderstand graph databases, embeddings, vector search, and hybrid retrievalBuild practical Python-based Knowledge Graph and LLM workflowsDesign architectures that can evolve from prototypes into scalable AI systemsThe book combines foundational concepts with practical implementation patterns, helping readers understand not only what Knowledge Graphs are, but also how and why they fit into modern LLM applications.Whether you are new to Knowledge Graphs or already working with LLMs and RAG systems, this volume provides the foundation needed to design context-aware AI applications built around structured knowledge.Build the foundation. Structure the knowledge. Give your LLMs better context. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…