
Essential GraphRAG
Knowledge Graph-Enhanced Rag
Bratanic Tomaz(Author)
Manning Publications (Publisher)
Published on 28. August 2025
Book
Paperback/Softback
176 pages
978-1-63343-626-8 (ISBN)
Description
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 Tomaz Bratanic 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.
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 Tomaz Bratanic 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.
Reviews / Votes
Gives you the confidence and clarity to build your own GraphRAG solutions.Darren Edge, Microsoft GraphRAG
Distills the chaos of RAG into clear, practical strategies. A must-read for anyone serious about building intelligent, production-ready LLM applications.
Yilun Zhang, Mozilla
Gives you both the understanding and the code to get started on your GraphRAG journey.
Michael Hunger, Neo4j
More details
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 230 mm
Width: 183 mm
Thickness: 13 mm
Weight
322 gr
ISBN-13
978-1-63343-626-8 (9781633436268)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

E-Book
08/2025
Simon + Schuster LLC
€49.44
Available for download
Person
Tomaz Bratanic 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.
Content
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
APPENDIX
APPENDIX A: THE NEO4J ENVIRONMENT
APPENDIX B: REFERENCES
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
APPENDIX
APPENDIX A: THE NEO4J ENVIRONMENT
APPENDIX B: REFERENCES