Isbn: 9781806381654 - unlocking data with generative ai and rag: learn ai agent fundamentals with rag-powered memory, graph-based rag, and intelligent recall (18 risultati)

Paperback. Condizione: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

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Paperback or Softback. Condizione: New. Unlocking Data with Generative AI and RAG - Second Edition: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent reca. Book.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: New. Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integrationFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild next-gen AI systems using agent memory, semantic caches, and LangMemImplement graph-based retrieval pipelines with ontologies and vector searchCreate intelligent, self-improving AI agents with agentic memory architecturesBook DescriptionDeveloping AI agents that remember, adapt, and reason over complex knowledge isn't a distant vision anymore; it's happening now with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide leads you to the forefront of agentic system design, showing you how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.You'll master the building blocks of agentic memory, including semantic caches, procedural learning with LangMem, and the emerging CoALA framework for cognitive agents. You'll also learn how to integrate GraphRAG with tools such as Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops to create systems that continuously learn and refine their behavior. With hands-on code and production-ready patterns, you'll be ready to build advanced AI systems that not only generate answers but also learn, recall, and evolve.Written by a seasoned AI educator and engineer, this book blends conceptual clarity with practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.*Email sign-up and proof of purchase requiredWhat you will learnArchitect graph-powered RAG agents with ontology-driven knowledge basesBuild semantic caches to improve response speed and reduce hallucinationsCode memory pipelines for working, episodic, semantic, and procedural recallImplement agentic learning using LangMem and prompt optimization strategiesIntegrate retrieval, generation, and consolidation for self-improving agentsDesign caching and memory schemas for scalable, adaptive AI systemsUse Neo4j, LangChain, and vector databases in production-ready RAG pipelinesWho this book is forIf you're an AI engineer, data scientist, or developer building agent-based AI systems, this book will guide you with its deep coverage of retrieval-augmented generation, memory components, and intelligent prompting. With a basic understanding of Python and LLMs, you'll be able to make the most of what this book offers. …

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: New. Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integrationFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild next-gen AI systems using agent memory, semantic caches, and LangMemImplement graph-based retrieval pipelines with ontologies and vector searchCreate intelligent, self-improving AI agents with agentic memory architecturesBook DescriptionDeveloping AI agents that remember, adapt, and reason over complex knowledge isn't a distant vision anymore; it's happening now with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide leads you to the forefront of agentic system design, showing you how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.You'll master the building blocks of agentic memory, including semantic caches, procedural learning with LangMem, and the emerging CoALA framework for cognitive agents. You'll also learn how to integrate GraphRAG with tools such as Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops to create systems that continuously learn and refine their behavior. With hands-on code and production-ready patterns, you'll be ready to build advanced AI systems that not only generate answers but also learn, recall, and evolve.Written by a seasoned AI educator and engineer, this book blends conceptual clarity with practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.*Email sign-up and proof of purchase requiredWhat you will learnArchitect graph-powered RAG agents with ontology-driven knowledge basesBuild semantic caches to improve response speed and reduce hallucinationsCode memory pipelines for working, episodic, semantic, and procedural recallImplement agentic learning using LangMem and prompt optimization strategiesIntegrate retrieval, generation, and consolidation for self-improving agentsDesign caching and memory schemas for scalable, adaptive AI systemsUse Neo4j, LangChain, and vector databases in production-ready RAG pipelinesWho this book is forIf you're an AI engineer, data scientist, or developer building agent-based AI systems, this book will guide you with its deep coverage of retrieval-augmented generation, memory components, and intelligent prompting. With a basic understanding of Python and LLMs, you'll be able to make the most of what this book offers. …

Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integrationFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesBuild next-gen AI systems using agent memory, semantic caches, and LangMemImplement graph-based retrieval pipelines with ontologies and vector searchCreate intelligent, self-improving AI agents with agentic memory architecturesBook DescriptionDeveloping AI agents that remember, adapt, and reason over complex knowledge isnt a distant vision anymore; its happening now with Retrieval-Augmented Generation (RAG). This second edition of the bestselling guide leads you to the forefront of agentic system design, showing you how to build intelligent, explainable, and context-aware applications powered by RAG pipelines.Youll master the building blocks of agentic memory, including semantic caches, procedural learning with LangMem, and the emerging CoALA framework for cognitive agents. Youll also learn how to integrate GraphRAG with tools such as Neo4j to create deeply contextualized AI responses grounded in ontology-driven data.This book walks you through real implementations of working, episodic, semantic, and procedural memory using vector stores, prompting strategies, and feedback loops to create systems that continuously learn and refine their behavior. With hands-on code and production-ready patterns, youll be ready to build advanced AI systems that not only generate answers but also learn, recall, and evolve.Written by a seasoned AI educator and engineer, this book blends conceptual clarity with practical insight, offering both foundational knowledge and cutting-edge tools for modern AI development.*Email sign-up and proof of purchase requiredWhat you will learnArchitect graph-powered RAG agents with ontology-driven knowledge basesBuild semantic caches to improve response speed and reduce hallucinationsCode memory pipelines for working, episodic, semantic, and procedural recallImplement agentic learning using LangMem and prompt optimization strategiesIntegrate retrieval, generation, and consolidation for self-improving agentsDesign caching and memory schemas for scalable, adaptive AI systemsUse Neo4j, LangChain, and vector databases in production-ready RAG pipelinesWho this book is forIf youre an AI engineer, data scientist, or developer building agent-based AI systems, this book will guide you with its deep coverage of retrieval-augmented generation, memory components, and intelligent prompting. With a basic understanding of Python and LLMs, youll be able to make the most of what this book offers. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This hands-on guide explores how to design AI agents powered by Retrieval-Augmented Generation (RAG), with cutting-edge coverage of memory systems, LangMem, and GraphRAG.

Taschenbuch. Condizione: Neu. Unlocking Data with Generative AI and RAG - Second Edition | Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall | Keith Bourne | Taschenbuch | Englisch | 2025 | Packt Publishing | EAN 9781806381654 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…