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28.0 MB
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July 15, 2025, 10:29 a.m.
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(Last updated: July 15, 2025, 10:30 a.m.)
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| ['Bratanic T. Essential GraphRAG. Knowledge Graph-Enhanced RAG 2025.pdf'] | 0 bytes |
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8.6 MB
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2023-07-04
| Uploaded by indexFroggy | Size 8.6 MB | Health [ 29 /25 ] | Added 2023-07-04 |
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35.7 MB
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| Uploaded by indexFroggy | Size 35.7 MB | Health [ 14 /2 ] | Added 2024-01-19 |
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28.0 MB
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2025-07-15
| Uploaded by andryold1 | Size 28.0 MB | Health [ 45 /43 ] | Added 2025-07-15 |
NOTE
SOURCE: Bratanic T. Essential GraphRAG. Knowledge Graph-Enhanced RAG 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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. About the author Tomaz Bratanic has extensive experience with graphs, machine learning, and generative AI. He has written an in-depth book about using graph algorithms in practical examples. Nowadays, he focuses on generative AI and LLMs by contributing to popular frameworks like LangChain and LlamaIndex and writing blog posts about LLM-based applications. Oskar Hane is a Senior Staff Software Engineer at Neo4j. He has over 20 years of experience as a Software Engineer and 10 years of experience working with Neo4j and knowledge graphs. He is currently leading the Generative AI engineering team within Neo4j, with the focus to provide the best possible experience for other developers to build GenAI applications with Neo4j. Get 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
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