{ "item_title" : "Graph RAG Foundations", "item_author" : [" Roman Hayes "], "item_description" : "Graph RAG Foundations: Knowledge Graph Engineering and Advanced Retrieval for Large Language ModelsIn the era of large language models, generating fluent responses is no longer enough-real intelligence requires structured knowledge, deep context, and reliable reasoning.Traditional Retrieval-Augmented Generation (RAG) systems rely heavily on vector search, but they often struggle with complex reasoning, multi-document synthesis, and maintaining factual consistency. Graph RAG changes this paradigm entirely.Graph RAG Foundations is a practical, engineering-focused guide to building next-generation AI retrieval systems powered by knowledge graphs and advanced reasoning architectures. It takes you beyond basic embeddings and into the world of structured intelligence-where relationships matter as much as content.Written for AI engineers, machine learning practitioners, and system architects, this book provides a complete roadmap for designing, building, and deploying production-grade Graph RAG systems.Inside, you will learn how to: Build knowledge graphs from unstructured text using modern LLM-based extraction techniquesDesign robust ontologies and graph schemas for real-world AI applicationsImplement entity extraction, relationship modeling, and multi-document fusion pipelinesApply community detection techniques such as Leiden clustering to organize large-scale knowledgeEngineer advanced retrieval strategies including local, global, and multi-hop searchCombine vector search, keyword search, and graph traversal into hybrid retrieval systemsEnable complex reasoning workflows with structured, graph-aware context assemblyEvaluate and optimize Graph RAG systems for accuracy, latency, and cost efficiencyDeploy scalable, production-ready Graph RAG architectures in enterprise environmentsUnlike theory-heavy texts, this book focuses on implementation, architecture, and real-world engineering decisions. It includes practical design patterns, system blueprints, and insights drawn from production AI systems.Whether you are building enterprise search engines, intelligent assistants, research tools, or domain-specific AI systems, Graph RAG Foundations equips you with the tools to move beyond flat retrieval and into structured, relationship-aware intelligence.The future of AI retrieval is not just semantic-it is graph-connected, context-rich, and reasoning-driven.This book shows you how to build it.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/9/79/818/402/9798184023977_b.jpg", "price_data" : { "retail_price" : "16.00", "online_price" : "16.00", "our_price" : "16.00", "club_price" : "16.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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