Essential GraphRAG
Knowledge Graph-Enhanced RAG
Book Details
| Authors | Tomaž Bratanič, Oskar Hane |
| Publisher | Manning |
| Published | 2025 |
| Edition | 1st |
| Paperback | 176 pages |
| Language | English |
| ISBN-13 | 9781633436268, 9781633434394 |
| ISBN-10 | 1633436268, 1633434397 |
| License | Compliments of Neo4j |
Book Description
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.
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.
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.
This book is published as open-access, which means it is freely available to read, download, and share without restrictions.
If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.
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