Graph RAG Powered Systems Building Semantic Retrieval Pipelines with Neo4j

Graph RAG powered Systems is the definitive engineering guide to building intelligent retrieval architectures that combine knowledge graphs, vector embeddings, and large language models into unified, high-accuracy RAG workflows.
This volume provides a deep, technical foundation for designing and implementing semantic retrieval systems using Neo4j, RDF, SPARQL, Cypher, and modern vector databases.
Readers will learn how to construct expressive graph schemas, model relationships, unify structured and unstructured data, and design multi-hop retrieval chains that outperform traditional dense retrieval techniques. The book covers end-to-end pipeline design from ETL ingestion, ontology creation, entity canonicalization, graph cleansing, and semantic indexing to hybrid vector-graph querying, context ranking, and RAG orchestration.
Included are practical engineering patterns for building domain-specific retrieval systems in finance, science, legal research, and enterprise knowledge hubs. Readers will implement workflows that ensure precision, traceability, explainability, and repeatability, creating retrieval systems suitable for mission-critical applications.