The era of simple vector search is ending. Modern AI systems demand retrieval that is structured, explainable, multi-hop, and capable of reasoning across relationships-not just matching embeddings. This book shows you how to build the next generation of Retrieval-Augmented Generation (RAG): systems enhanced with knowledge graphs, hybrid indexing, graph traversal, and agentic AI workflows that plan, retrieve, reason, and explain.
Knowledge-Graph Enhanced RAG is the definitive, hands-on guide for developers, engineers, data scientists, and AI practitioners who want to build retrieval systems that outperform standard RAG in accuracy, reasoning ability, reliability, and transparency. You will learn how to construct high-quality knowledge graphs from real data, integrate them into vector-based retrieval pipelines, design multi-hop reasoning workflows, and deploy advanced agentic systems that use graph structures to guide decisions.
Through practical explanations, step-by-step implementations, real code examples, and industry-grade mini-projects, this book teaches you not just how Graph-RAG works-but how to build it yourself. You will see how to extract entities, relationships, and schemas from documents; design graph databases with Neo4j, Memgraph, and many more; create hybrid retrieval pipelines using LangChain and LlamaIndex; apply graph-guided planning for complex queries; and deploy end-to-end solutions for healthcare, law, finance, cybersecurity, enterprise automation, and scientific research.
Whether you are building AI copilots, domain-specific expert systems, enterprise knowledge assistants, reasoning-driven chatbots, or large-scale information architectures, this book gives you the frameworks, tooling, and mental models required to build systems that think in structure, not just text.
What You Will Learn
- How to design high-quality knowledge graphs that unlock multi-hop reasoning, context precision, and transparent retrieval
- How to build complete Graph-RAG pipelines that combine vector search, graph traversal, and LLM synthesis
- How to extract entities, relations, and canonicalized concepts from real documents using LLMs and rule-based tools
- How to structure ontologies, taxonomies, and schemas for scalable domain modeling
- How to use Neo4j, Memgraph, ArangoDB, and AWS Neptune for production-ready graph storage
- How to write queries with Cypher, SPARQL, Gremlin, and emerging GQL standards
- How to implement hybrid retrieval architectures and two-layer indexing for high-accuracy answers
- How to build intelligent agents that plan retrieval steps, call tools, and traverse graphs autonomously
- How to evaluate Graph-RAG systems using faithfulness, multi-hop consistency, and context-coverage metrics
- How real companies use Graph-RAG across healthcare, legal, finance, cybersecurity, and research domains
Who This Book Is For
- AI developers and engineers building advanced RAG applications
- Enterprise teams building internal knowledge systems or AI copilots
- Data scientists and ML researchers exploring graph-structured reasoning
- Students and professionals entering the agentic AI and RAG ecosystem
Why This Book Matters
Traditional RAG is useful but shallow. It retrieves isolated text chunks-often inconsistent, redundant, or lacking semantic structure-leaving LLMs to guess the connections. Graph-RAG fixes this by adding knowledge graphs, relationships, hierarchies, (entity, relation) triples, and reasoning paths that guide retrieval with precision and interpretability.
This book shows you how to make that leap: from simple embedding search to structured, reasoning-driven retrieval systems powered by graph intelligence and agentic planning.
A Complete, Hands-On Guide to the Future of Retrieval-Augmented AI
