Название: Essential GraphRAG: Knowledge Graph-Enhanced RAG (Final Release) Автор: Tomaž Bratanič, Oskar Hane Издательство: Manning Publications Год: 2025 Страниц: 178 Язык: английский Формат: pdf (true) Размер: 28.0 MB
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.
Essential GraphRAG was written to guide readers in enhancing retrieval-augmented generation (RAG) systems by integrating knowledge graphs with large language models (LLMs). The book aims to address the limitations of LLMs, such as outdated knowledge, hallucinations, and a lack of domain-specific data, by combining structured and unstructured data through practical methodologies and hands-on examples.
The primary goal of Essential GraphRAG is to demonstrate how knowledge graphs can improve the accuracy, performance, and traceability of RAG systems in generative AI applications. The book explores grounding LLMs with both structured and unstructured data, offering a comprehensive guide to building a GraphRAG system from scratch. It combines years of expertise in graphs, machine learning, and application development to present stable architectural patterns in a rapidly evolving field. Readers will learn to implement GraphRAG without relying on existing frameworks, extract structured knowledge from text, and develop applications that blend vector-based and graph-based retrieval methods, including Microsoft’s GraphRAG approach. The book encourages active participation through its liveBook discussion forum to refine content and deepen collective understanding.
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 book
Essential GraphRAG teaches you to implement accurate, performant, and traceable RAG by structuring the context data as a knowledge graph. Filled with practical techniques, this book teaches you how to build RAG on both unstructured and structured data. You’ll go hands-on to build a vector similarity search retrieval tool and an Agentic RAG application, extract information from text to create a Knowledge Graph, evaluate performance and accuracy, and more.
About the reader For readers with intermediate Python skills and some experience with a graph database like Neo4j.
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Essential GraphRAG: Knowledge Graph-Enhanced RAG (Final Release) 
