{"product_id":"essential-graphrag-knowledge-graph-enhanced-rag-9781633436268","title":"Essential Graphrag: Knowledge Graph-Enhanced Rag","description":"\u003cb\u003eUpgrade your RAG applications with the power of knowledge graphs.\u003c\/b\u003e \u003cp\u003e\u003c\/p\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. \u003ci\u003eEssential GraphRAG\u003c\/i\u003e shows you how to use knowledge graphs to model your RAG data and deliver better performance, accuracy, traceability, and completeness. \u003cp\u003e\u003c\/p\u003eInside \u003ci\u003eEssential GraphRAG\u003c\/i\u003e you'll learn: \u003cp\u003e\u003c\/p\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 \u003cp\u003e\u003c\/p\u003e \u003ci\u003eEssential GraphRAG\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eEssential GraphRAG\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\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 \u003cp\u003e\u003c\/p\u003eAbout the reader \u003cp\u003e\u003c\/p\u003e For readers with intermediate Python skills and some experience with a graph database like Neo4j. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e The author of Manning's Graph Algorithms for Data Science and a contributor to LangChain and LlamaIndex, \u003cb\u003eTomaz Bratanic\u003c\/b\u003e has extensive experience with graphs, machine learning, and generative AI. \u003cb\u003eOskar Hane\u003c\/b\u003e leads the Generative AI engineering team at Neo4j. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 Improving LLM accuracy\u003cbr\u003e 2 Vector similarity search and hybrid search\u003cbr\u003e 3 Advanced vector retrieval strategies\u003cbr\u003e 4 Generating Cypher queries from natural language questions\u003cbr\u003e 5 Agentic RAG\u003cbr\u003e 6 Constructing knowledge graphs with LLMs\u003cbr\u003e 7 Microsoft's GraphRAG implementation\u003cbr\u003e 8 RAG application evaluation\u003cbr\u003e A The Neo4j environment \u003cp\u003e\u003c\/p\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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Tomaz Bratanic,Oskar Hane\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Manning Publications\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/02\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 176\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.83lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.13h x 7.24w x 0.39d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781633436268","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":45133367345289,"sku":"9781633436268","price":49.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0636\/9240\/6921\/files\/img_3eb9e634-fb31-4246-bcb0-8965cfacfc12.jpg?v=1767677243","url":"https:\/\/sonsanddaughtersbooks.com\/products\/essential-graphrag-knowledge-graph-enhanced-rag-9781633436268","provider":"Sons and Daughters Books","version":"1.0","type":"link"}