Unlocking Data with Generative AI and RAG (Paperback)
Keith Bourne
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Add to basketSold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since 12 October 2005
Condition: New
Quantity: 1 available
Add to basketPaperback. 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 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 This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller Inventory # 9781806381654
Design intelligent AI agents with retrieval-augmented generation, memory components, and graph-based context integration
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
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). 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 required
If 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.
Keith Bourne is an agent engineer at Magnifi by TIFIN, founder of Memriq AI, and producer of The Memriq AI Inference Brief. With over a decade of experience building production ML and AI systems across start-ups and Fortune 50 enterprises, Keith holds an MBA from Babson College and a master's in applied data science from the University of Michigan. He has built sophisticated generative AI platforms using advanced RAG techniques, agentic architectures, and model fine-tuning.
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