July 2025
3h 42m
English
Overview
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:
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
About the Reader
For readers with intermediate Python skills and some experience with a graph database like Neo4j.
About the Authors
The author of Manning’s Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, Tomaž Bratanič has extensive experience with graphs, machine learning, and generative AI. Oskar Hane leads the Generative AI engineering team at Neo4j.
Quotes
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
The authors pull back the curtain, revealing the code behind contemporary AI applications. A solid foundation for building the future.
- From the Foreword by Paco Nathan
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 Authors
The author of Manning’s Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, Tomaž Bratanič has extensive experience with graphs, machine learning, and generative AI. Oskar Hane leads the Generative AI engineering team at Neo4j.
Quotes
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
The authors pull back the curtain, revealing the code behind contemporary AI applications. A solid foundation for building the future.
- From the Foreword by Paco Nathan
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