{"product_id":"libro-essential-graphrag-knowledge-graph-enhanced-rag","title":"Libro: Essential GraphRAG: Knowledge Graph-Enhanced RAG","description":"\u003ctable style=\"border-collapse:collapse;margin-bottom:16px;width:100%\"\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFormato\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eTapa Blanda\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNúmero de páginas\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e176\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\u003cp\u003eUpgrade your RAG applications with the power of knowledge graphs.\u003cbr\u003e\u003cbr\u003eRetrieval 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.\u003cbr\u003e\u003cbr\u003eInside Essential GraphRAG you’ll learn:\u003cbr\u003e\u003cbr\u003e• The benefits of using Knowledge Graphs in a RAG system\u003cbr\u003e• How to implement a GraphRAG system from scratch\u003cbr\u003e• The process of building a fully working production RAG system\u003cbr\u003e• Constructing knowledge graphs using LLMs\u003cbr\u003e• Evaluating performance of a RAG pipeline\u003cbr\u003e\u003cbr\u003eEssential 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.\u003cbr\u003e\u003cbr\u003eAbout the technology\u003cbr\u003e\u003cbr\u003eA 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.\u003cbr\u003e\u003cbr\u003eAbout the book\u003cbr\u003e\u003cbr\u003eEssential 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.\u003cbr\u003e\u003cbr\u003eWhat's inside\u003cbr\u003e\u003cbr\u003e• Embeddings, vector similarity search, and hybrid search\u003cbr\u003e• Turning natural language into Cypher database queries\u003cbr\u003e• Microsoft’s GraphRAG pipeline\u003cbr\u003e• Agentic RAG\u003cbr\u003e\u003cbr\u003eAbout the reader\u003cbr\u003e\u003cbr\u003eFor readers with intermediate Python skills and some experience with a graph database like Neo4j.\u003cbr\u003e\u003cbr\u003eAbout the author\u003cbr\u003e\u003cbr\u003eThe author of Manning’s Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, Tomaž Bratanic has extensive experience with graphs, machine learning, and generative AI. Oskar Hane leads the Generative AI engineering team at Neo4j.\u003cbr\u003e\u003cbr\u003eTable of Contents\u003cbr\u003e\u003cbr\u003e1 Improving LLM accuracy\u003cbr\u003e2 Vector similarity search and hybrid search\u003cbr\u003e3 Advanced vector retrieval strategies\u003cbr\u003e4 Generating Cypher queries from natural language questions\u003cbr\u003e5 Agentic RAG\u003cbr\u003e6 Constructing knowledge graphs with LLMs\u003cbr\u003e7 Microsoft’s GraphRAG implementation\u003cbr\u003e8 RAG application evaluation\u003cbr\u003eA The Neo4j environment\u003cbr\u003e\u003cbr\u003eGet a free eBook (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.\u003c\/p\u003e","brand":"Manning","offers":[{"title":"Default Title","offer_id":59764379189329,"sku":"1633436268","price":307.0,"currency_code":"PEN","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0669\/6455\/3809\/files\/716w_uPEzjL.jpg?v=1783441315","url":"https:\/\/provetodo.cl\/es-pe\/products\/libro-essential-graphrag-knowledge-graph-enhanced-rag","provider":"Provetodo ","version":"1.0","type":"link"}