Description A technical manual on enhancing Retrieval-Augmented Generation (RAG) by incorporating knowledge graphs to improve the accuracy and traceability of Large Language Model responses. Key Topics: Knowledge graph construction, vector similarity search, hybrid retrieval, agentic RAG, and Cypher query generation. About the Technology: GraphRAG uses the relationship-rich structure of knowledge graphs to provide more relevant context to an LLM, mitigating common issues like factual hallucinations. About the Book: The book provides instructions for building a production GraphRAG system, including methods for extracting structured knowledge from text and evaluating pipeline performance. About the Reader: Requires intermediate Python skills and basic experience with graph databases like Neo4j. About the Author: Tomaž Bratanic and Oscar Hane are specialists in graph technologies and generative AI engineering.