Overview

In a world where AI systems increasingly rely on factual accuracy, contextual awareness, and explainability, Graph RAG for AI Applications introduces the definitive framework for integrating structured knowledge graphs with retrieval-augmented generation (RAG). This book provides a complete roadmap for building intelligent retrieval systems that can reason, learn, and evolve - bridging the gap between semantic search, graph intelligence, and large language models. Written by a seasoned AI systems engineer and author recognized for authoritative works on LangChain, LangGraph, and agentic AI frameworks, this book delivers depth, clarity, and practical wisdom. Every chapter reflects hands-on expertise drawn from real-world enterprise deployments and production-grade AI architectures, ensuring that what you learn is both authentic and field-tested. About the Technology: At the heart of this book is Graph RAG (Graph-based Retrieval-Augmented Generation) - a next-generation architecture that enhances LLMs with structured knowledge graphs. Unlike traditional vector-based RAG, which retrieves text fragments based on similarity, Graph RAG connects entities and relationships, enabling AI to reason contextually, explain its decisions, and reduce hallucinations. You'll explore technologies like Neo4j, LangGraph, FAISS, MCP, SPARQL, and Graph Neural Networks, learning how they come together to create a unified knowledge reasoning pipeline. From ingestion and graph construction to hybrid retrieval and orchestration, this book covers it all in practical, implementation-driven detail. What's Inside: A complete breakdown of how RAG evolved and how Graph RAG redefines intelligent retrieval. Hands-on tutorials on constructing, storing, and querying knowledge graphs. Working code examples integrating Neo4j, LangChain, and FAISS for hybrid retrieval. Step-by-step instructions for deploying scalable Graph RAG pipelines using Docker, FastAPI, and CI/CD workflows. Techniques for semantic enrichment, dynamic subgraph selection, and reasoning with Graph Neural Networks. Evaluation methods for factual grounding, latency management, and observability with LangSmith and Weights & Biases. A forward-looking exploration of self-updating knowledge systems and autonomous graph agents. Every concept is presented with crystal-clear explanations, real-world case studies, and verified code implementations - ensuring that you not only understand the theory but can build systems that work in production. Who This Book Is For: This book is written for AI engineers, data scientists, knowledge graph developers, and machine learning practitioners who want to go beyond simple vector search and build intelligent, context-aware AI systems. It's equally valuable for researchers, architects, and enterprise teams exploring explainable AI, knowledge integration, or next-generation retrieval workflows. Whether you're scaling enterprise AI or designing your first knowledge-aware assistant, this guide provides everything you need. Step into the future of intelligent retrieval. Learn how to make your AI systems think contextually, reason intelligently, and explain transparently. Start building Graph-Augmented AI applications that redefine what's possible with knowledge, structure, and language. Get your copy of Graph RAG for AI Applications today - and lead the new era of knowledge-integrated, reasoning-aware AI systems.

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