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Zustand: New. Über den AutorToma Bratani is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, .
Sprache: Englisch
Verlag: Manning Publications Sep 2025, 2025
ISBN 10: 1633436268 ISBN 13: 9781633436268
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - 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, deliver agentic RAG, and generate Cypher statements to retrieve data from a knowledge graph. About the technology 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. About the book 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. What's inside Embeddings, vector similarity search, and hybrid search Turning natural language into Cypher database queries Microsoft's GraphRAG pipeline Agentic RAG About the reader For readers with intermediate Python skills and some experience with a graph database like Neo4j. About the author The author of Manning's Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, Toma Bratanic has extensive experience with graphs, machine learning, and generative AI. Oskar Hane leads the Generative AI engineering team at Neo4j. Table of Contents 1 Improving LLM accuracy 2 Vector similarity search and hybrid search 3 Advanced vector retrieval strategies 4 Generating Cypher queries from natural language questions 5 Agentic RAG 6 Constructing knowledge graphs with LLMs 7 Microsoft's GraphRAG implementation 8 RAG application evaluation A The Neo4j environment Get a free Elektronisches Buch (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
Sprache: Englisch
Verlag: Manning Publications Feb 2024, 2024
ISBN 10: 1617299464 ISBN 13: 9781617299469
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Practical methods for analyzing your data with graphs, revealing hidden connections and new insights.Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn:
Taschenbuch. Zustand: Neu. Graph Algorithms for Data Science | Tomaz Bratanic | Taschenbuch | Kartoniert / Broschiert | Englisch | 2024 | Manning Publications | EAN 9781617299469 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.