Essential GraphRAG (MEAP v4) by Tomaž Bratanič (.ePUB)+

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File Size: 10 MB

Essential GraphRAG: Knowledge Graph-Enhanced RAG (MEAP v4) by Tomaž Bratanič, Oskar Hane
Requirements: .ePUB, .PDF reader, 10 MB
Overview: 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. 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. For readers with intermediate Python skills and some experience with a graph database like Neo4j.
Genre: Non-Fiction > Tech & Devices

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