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Full Description
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*
Key Features
Build next-gen AI systems using agent memory, semantic caches, and LangMem
Implement graph-based retrieval pipelines with ontologies and vector search
Create intelligent, self-improving AI agents with agentic memory architectures
Book 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.
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What you will learn
Architect graph-powered RAG agents with ontology-driven knowledge bases
Build semantic caches to improve response speed and reduce hallucinations
Code memory pipelines for working, episodic, semantic, and procedural recall
Implement agentic learning using LangMem and prompt optimization strategies
Integrate retrieval, generation, and consolidation for self-improving agents
Design caching and memory schemas for scalable, adaptive AI systems
Use Neo4j, LangChain, and vector databases in production-ready RAG pipelines
Who 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.
Contents
Table of Contents
What is Retrieval-Augmented Generation?
Code Lab: An Entire RAG Pipeline
Practical Applications of RAG
Components of a RAG System
Managing Security in RAG Applications
Interfacing with RAG and Gradio
The Key Role Vectors and Vector Stores Play in RAG
Similarity Searching with Vectors
Evaluating RAG Quantitatively and with Visualizations
Key RAG Components in LangChain
Using LangChain to Get More from RAG
Combining RAG with the Power of AI Agents and LangGraph
Ontology-Based Knowledge Engineering for Graphs
Graph-Based RAG
Semantic Caches
Agentic Memory: Extending RAG with Stateful Intelligence
RAG-Based Agentic Memory in Code
Procedural Memory for RAG with LangMem
Advanced RAG with Complete Memory Integration
