Essential Graphrag : Knowledge Graph-Enhanced Rag

Tomaz Bratanic

ISBN 10: 1633436268 / ISBN 13: 9781633436268

Published by Manning Publications Sep 2025, 2025

About this Item

Neuware - Upgrade your RAG applications with the power of knowledge graphs.Retrieval Augmented Generation (RAG) is a great way to harness the power of generative AI for information not contained in a LLM s training data and to avoid depending on LLM for factual information. However, RAG only works when you can quickly identify and supply the most relevant context to your LLM. Essential GraphRAG shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. Inside Essential GraphRAG you ll learn: The benefits of using Knowledge Graphs in a RAG system How to implement a GraphRAG system from scratch The process of building a fully working production RAG system Constructing knowledge graphs using LLMs Evaluating performance of a RAG pipeline Essential GraphRAG is a practical guide to empowering LLMs with RAG. You ll learn to deliver vector similarity-based approaches to find relevant information, as well as work with semantic layers, deliver agentic RAG, and generate Cypher statements to retrieve data from a knowledge graph. About the technology A Retrieval Augmented Generation (RAG) system automatically selects and supplies domain-specific context to an LLM, radically improving its ability to generate accurate, hallucination-free responses. The GraphRAG pattern employs a knowledge graph to structure the RAG s input, taking advantage of existing relationships in the data to generate rich, relevant prompts. About the book Essential GraphRAG shows you how to build and deploy a production-quality GraphRAG system. You ll learn to extract structured knowledge from text and how to combine vector-based and graph-based retrieval methods. The book is rich in practical examples, from building a vector similarity search retrieval tool and an Agentic RAG application, to evaluating performance and accuracy, and more. What's inside Embeddings, vector similarity search, and hybrid search Turning natural language into Cypher database queries Microsoft s GraphRAG pipeline Agentic RAG About the reader For readers with intermediate Python skills and some experience with a graph database like Neo4j. About the author The author of Manning s Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, Toma Bratanic has extensive experience with graphs, machine learning, and generative AI. Oskar Hane leads the Generative AI engineering team at Neo4j. Table of Contents 1 Improving LLM accuracy 2 Vector similarity search and hybrid search 3 Advanced vector retrieval strategies 4 Generating Cypher queries from natural language questions 5 Agentic RAG 6 Constructing knowledge graphs with LLMs 7 Microsoft s GraphRAG implementation 8 RAG application evaluation A The Neo4j environment Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Seller Inventory # 9781633436268

Bibliographic Details

• Title: Essential Graphrag : Knowledge Graph-Enhanced Rag
• Publisher: Manning Publications Sep 2025
• Publication Date: 2025
• Language: English
• ISBN 10: 1633436268
• ISBN 13: 9781633436268
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 230W x 183H x 13D millimeters 230 millimeters width by 183 millimeters height by 13 millimeters depth
• Weight: 322 grams

About this title

Synopsis

Your LLM keeps hallucinating, and clients are beginning to lose trust. Generative AI can amaze users one moment and confuse them the next when answers are based on guesswork rather than verified facts. What if you could design systems that deliver accurate, traceable, and relevant information every time? By combining knowledge graphs with retrieval-augmented generation, you can build solutions that power GenAI models with structured, reliable data and keep stakeholders confident in every interaction.

• Knowledge graph basics: Model context data for instant, precise retrieval.

• Vector similarity search toolkit: Surface only the most relevant passages, cut noise.

• Agentic RAG workflow: Orchestrate multi-step reasoning that scales to production.

• Cypher and Python templates: Drop-in code accelerates prototypes to deployable services.

• Evaluation framework: Measure accuracy, latency, and traceability with confidence.

• Hybrid structured plus unstructured guidance: Integrate PDFs, databases, and APIs into one coherent knowledge base.

Essential GraphRAG by graph experts Tomaž Bratanič and Oskar Hane arrives to show data teams exactly how to hard-wire reliability into GenAI projects.

Through concise explanations and fully worked examples, the authors guide you from raw text to a Neo4j-backed knowledge graph powering Retrieval Augmented Generation. Each chapter pairs theory with runnable notebooks, so you see instant results.

Finish the book able to architect, build, and benchmark a production-ready RAG pipeline that your stakeholders can audit and trust. The techniques transfer to any domain and future model.

For data scientists and Python developers with basic Neo4j skills who want bulletproof GenAI, this is your next step.

About the Author

Tomaž Bratanič and Oskar Hane are seasoned graph technologists known for transforming complex GenAI theory into workable code. With decades of Neo4j engineering, open-source leadership, and global workshops, they bring practical clarity to every chapter. They distill their production RAG expertise into reproducible Python projects that help readers build trustworthy language applications.