Essential Graphrag

Knowledge Graph-Enhanced Rag

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Beschreibung

<b>Upgrade your RAG applications with the power of knowledge graphs.</b><br><br>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. <i>Essential GraphRAG</i> shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness.<br><br>Inside <i>Essential GraphRAG</i> you’ll learn:<ul><li>The benefits of using Knowledge Graphs in a RAG system</li><li>How to implement a GraphRAG system from scratch</li><li>The process of building a fully working production RAG system</li><li>Constructing knowledge graphs using LLMs</li><li>Evaluating performance of a RAG pipeline</li></ul><i>Essential GraphRAG</i> 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.<br><br><b>About the technology</b><br><br>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.<br><br><b>About the book</b><br><br><i>Essential GraphRAG</i> 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 pe

Artikeldetails

EAN

9781633436268

Sprache

Englisch

Einband / Art

Taschenbuch

Maße

230 x 183 x 13 mm

Erscheinungsjahr

2025

Verlag / Hersteller

Manning Publications