Название: Essential GraphRAG: Knowledge Graph-Enhanced RAG (MEAP v4)
Автор: Tomaž Bratanič, Oskar Hane
Издательство: Manning Publications
Год: 2025
Страниц: 208
Язык: английский
Формат: pdf, epub
Размер: 10.1 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.

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.






ОТСУТСТВУЕТ ССЫЛКА/ НЕ РАБОЧАЯ ССЫЛКА ЕСТЬ РЕШЕНИЕ, ПИШЕМ СЮДА!