Essential GraphRAG

Bratanic Tomaz

ISBN 10: 1633436268 / ISBN 13: 9781633436268

Published by Manning Publications, US, 2025

About this Item

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 systemHow to implement a GraphRAG system from scratchThe process of building a fully working production RAG systemConstructing knowledge graphs using LLMsEvaluating 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, and generate Cypher statements to retrieve data from a knowledge graph.

Seller Inventory # LU-9781633436268

Bibliographic Details

• Title: Essential GraphRAG
• Publisher: Manning Publications, US
• Publication Date: 2025
• Language: English
• ISBN 10: 1633436268
• ISBN 13: 9781633436268
• Binding: Paperback
• Edition: 1st.
• Condition: New
• Dimensions: 18.73W x 0.76H x 23.5D centimeters 18.73 centimeters width by 0.76 centimeters height by 23.5 centimeters depth
• Weight: 338 grams

About this title

Synopsis

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, and generate Cypher statements to retrieve data from a knowledge graph.

About the Author

Tomaz Bratanic has extensive experience with graphs, machine learning, and generative AI. He has written an in-depth book about using graph algorithms in practical examples. Nowadays, he focuses on generative AI and LLMs by contributing to popular frameworks like LangChain and LlamaIndex and writing blog posts about LLM-based applications.

Oskar Hane is a Senior Staff Software Engineer at Neo4j. He has over 20 years of experience as a Software Engineer and 10 years of experience working with Neo4j and knowledge graphs. He is currently leading the Generative AI engineering team within Neo4j, with the focus to provide the best possible experience for other developers to build GenAI applications with Neo4j.