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Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall
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Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall
Developing 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).
Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall
Item #: 238044007

Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall

90% of respondents would recommend this to a friend

Item #: 238044007

MYR 332

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Shop Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall online at a best price in Malaysia. 1806381656
  • 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.Table of ContentsWhat is Retrieval-Augmented Generation?Code Lab: An Entire RAG PipelinePractical Applications of RAGComponents of a RAG SystemManaging Security in RAG ApplicationsInterfacing with RAG and GradioThe Key Role Vectors and Vector Stores Play in RAGSimilarity Searching with VectorsEvaluating RAG Quantitatively and with VisualizationsKey RAG Components in LangChainUsing LangChain to Get More from RAGCombining RAG with the Power of AI Agents and LangGraphOntology-Based Knowledge Engineering for GraphsGraph-Based RAGSemantic CachesAgentic Memory: Extending RAG with Stateful IntelligenceRAG-Based Agentic Memory in CodeProcedural Memory for RAG with LangMemAdvanced RAG with Complete Memory Integration
Publisher Packt Publishing
Publication date 30 Dec. 2025
Edition 2nd
Language English
Print length 606 pages
ISBN-10 1806381656
ISBN-13 978-1806381654
Item weight 1.02 kg
Dimensions 19.05 x 3.48 x 23.5 cm

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Unlocking Data with Generative AI and RAG: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent recall

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Information Systems Editorial Review

Unlocking Data With Generative AI And RAG: Learn AI Agent Fundamentals With RAG-powered Memory, Graph-based RAG, And Intelligent Recall offers a comprehensive guide for those eager to understand the intersection of artificial intelligence and data management. This 2nd edition by Packt Publishing contains 606 pages of in-depth content, exploring concepts such as RAG-powered memory and intelligent recall, making it an indispensable resource for practitioners. Readers will appreciate the clearly structured chapters designed to facilitate learning. The publication date of December 30, 2025, positions it as a timely addition to the rapidly evolving field of AI.

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  • Comprehensive coverage of AI and data handling techniques
  • Well-structured chapters enhance learning experience
  • In-depth exploration of RAG-powered memory
  • Insights into intelligent recall mechanisms
  • Written by a reputable publisher, Packt Publishing

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  • The format may require some adjustment for newcomers to AI

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  • Unlock the potential of AI with cutting-edge techniques.
  • Learn to implement graph-based retrieval pipelines for deeper context.
  • Build intelligent, self-improving AI agents seamlessly.
  • Enhance your applications with advanced memory architectures.
  • Receive exclusive access to a DRM-free PDF and next-gen Reader.
  • Perfect for AI engineers, data scientists, and developers.

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