Agentic Graphrag: Integrating Knowledge Graphs, Reasoning, and Agency for Enterprise AI (Paperback)
暫譯: Agentic GraphRAG：整合知識圖譜、推理與自主代理，打造企業 AI
Alcaraz, Anthony, Julien, Sam
- 出版商: O'Reilly
- 出版日期: 2026-09-29
- 定價: $2,880
- 售價: 9.0 折 $2,592
- 語言: 英文
- 頁數: 386
- 裝訂: Quality Paper - also called trade paper
- ISBN: 9798341623170
- ISBN-13: 9798341623170
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相關分類:
Large language model
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商品描述
What if your AI systems could retrieve information, reason over complex knowledge, plan actions, and continuously learn--all while maintaining enterprise-grade security and compliance? Agentic Graph RAG guides technical leaders, engineers, and architects through the next evolution of generative AI. Combining retrieval-augmented generation (RAG) with graph-based reasoning and agentic capabilities, this guide provides a practical blueprint for building scalable, auditable, and intelligent AI systems.
Written by Anthony Alcaraz and Sam Julien, this book demystifies knowledge graphs, graph memory, neural-symbolic reasoning, and agent orchestration through real-world case studies, hands-on design patterns, and production-ready architectures. Readers will learn how to construct graph-native retrieval systems, integrate advanced reasoning into agent workflows, and address enterprise challenges around governance, scalability, and transparency.
- Design graph-augmented architectures that surpass traditional RAG
- Implement agents with dynamic memory, planning, and decision-making capabilities
- Integrate knowledge graphs with large language models for robust, explainable AI
- Deploy scalable, governable multiagent systems ready for production environments
商品描述(中文翻譯)
如果您的 AI 系統能夠擷取資訊、推理複雜知識、規劃行動，並持續學習，同時維持企業級的安全性與法規遵循，會是什麼樣子？《Agentic Graph RAG》將引導技術領導者、工程師與架構師，探索生成式 AI 的下一個演進階段。本書結合檢索增強生成（retrieval-augmented generation，RAG）、圖形化推理與代理式（agentic）能力，提供建構可擴充、可稽核且智慧化 AI 系統的實用藍圖。
本書由 Anthony Alcaraz 與 Sam Julien 撰寫，透過真實世界案例研究、實作導向的設計模式，以及可直接用於生產環境的架構，深入解析知識圖譜（knowledge graphs）、圖形記憶體（graph memory）、神經符號推理（neural-symbolic reasoning）與代理協同（agent orchestration）。讀者將學習如何建構以圖譜為原生核心的檢索系統、將進階推理整合至代理工作流程中，並因應企業在治理、可擴充性與透明度方面所面臨的挑戰。
• 設計超越傳統 RAG 的圖譜擴充架構
• 實作具備動態記憶、規劃與決策能力的代理
• 將知識圖譜與大型語言模型（large language models）整合，打造穩健且可解釋的 AI
• 部署可擴充、可治理，並已準備好用於生產環境的多代理系統
