Unlocking Data with Generative AI and RAG - Second Edition: Learn AI agent fundamentals with RAG-powered memory, graph-based RAG, and intelligent reca
暫譯: 利用生成式 AI 和 RAG 解鎖數據 - 第二版：學習 RAG 驅動的記憶、基於圖形的 RAG 及智能回憶的 AI 代理基礎知識
Bourne, Keith
- 出版商: Packt Publishing
- 出版日期: 2025-12-30
- 售價: $1,840
- 貴賓價: 9.5 折 $1,748
- 語言: 英文
- 頁數: 606
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1806381656
- ISBN-13: 9781806381654
-
相關分類:
LangChain
海外代購書籍(需單獨結帳)
相關主題
商品描述
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 Description:
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
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 for:
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.
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
商品描述(中文翻譯)
設計智能 AI 代理，結合檢索增強生成、記憶組件和基於圖形的上下文整合
隨書附贈：無 DRM 的 PDF 版本 + Packt 下一代閱讀器的訪問權限*
主要特點：
- 使用代理記憶、語義快取和 LangMem 構建下一代 AI 系統
- 實現基於圖形的檢索管道，結合本體論和向量搜索
- 創建智能、自我改進的 AI 代理，具備代理記憶架構
書籍描述：
開發能夠記住、適應和推理複雜知識的 AI 代理不再是遙不可及的願景；這一切正在通過檢索增強生成（Retrieval-Augmented Generation, RAG）實現。本書的第二版是暢銷指南，將引領您走向代理系統設計的前沿，展示如何構建由 RAG 管道驅動的智能、可解釋和上下文感知的應用程序。
您將掌握代理記憶的基本構建塊，包括語義快取、使用 LangMem 的程序學習，以及新興的 CoALA 框架，用於認知代理。您還將學習如何將 GraphRAG 與 Neo4j 等工具整合，以創建基於本體驅動數據的深度上下文化 AI 回應。
本書將引導您通過實際實現工作記憶、情節記憶、語義記憶和程序記憶，使用向量存儲、提示策略和反饋循環來創建不斷學習和改進行為的系統。通過實用的代碼和生產就緒的模式，您將準備好構建不僅能生成答案，還能學習、回憶和進化的高級 AI 系統。
本書由一位經驗豐富的 AI 教育者和工程師撰寫，將概念清晰性與實用見解相結合，提供現代 AI 開發所需的基礎知識和尖端工具。
*需要電子郵件註冊和購買證明
您將學到的內容：
- 設計基於圖形的 RAG 代理，使用本體驅動的知識庫
- 構建語義快取以提高響應速度並減少幻覺
- 編碼記憶管道以實現工作記憶、情節記憶、語義記憶和程序回憶
- 使用 LangMem 和提示優化策略實現代理學習
- 整合檢索、生成和整合以實現自我改進的代理
- 設計快取和記憶架構以支持可擴展的自適應 AI 系統
- 在生產就緒的 RAG 管道中使用 Neo4j、LangChain 和向量數據庫
本書適合對象：
如果您是 AI 工程師、數據科學家或開發者，正在構建基於代理的 AI 系統，本書將通過深入探討檢索增強生成、記憶組件和智能提示來指導您。具備基本的 Python 和 LLM 知識，您將能夠充分利用本書所提供的內容。
目錄：
- 什麼是檢索增強生成？
- 代碼實驗室：整個 RAG 管道
- RAG 的實際應用
- RAG 系統的組件
- 管理 RAG 應用中的安全性
- 與 RAG 和 Gradio 的接口
- 向量和向量存儲在 RAG 中的關鍵角色
- 使用向量進行相似性搜索
- 定量評估 RAG 及其可視化
- LangChain 中的關鍵 RAG 組件
- 使用 LangChain 從 RAG 中獲取更多
- 將 RAG 與 AI 代理和 LangGraph 的力量結合
- 基於本體的圖形知識工程
- 基於圖形的 RAG
- 語義快取
- 代理記憶：用有狀態智能擴展 RAG
- RAG 基於的代理記憶代碼
- 使用 LangMem 的 RAG 程序記憶
- 完整記憶整合的高級 RAG
