Essential Graphrag: Knowledge Graph-Enhanced Rag
暫譯: 基本圖形：知識圖譜增強的RAG
Bratanic, Tomaz, Hane, Oscar
- 出版商: Manning
- 出版日期: 2025-09-02
- 售價: $1,810
- 貴賓價: 9.5 折 $1,720
- 語言: 英文
- 頁數: 176
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633436268
- ISBN-13: 9781633436268
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相關分類:
Natural Language Processing、資料庫
海外代購書籍(需單獨結帳)
相關主題
商品描述
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, Tomaz 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 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.
- 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, Tomaz 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 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.
商品描述(中文翻譯)
利用知識圖譜提升您的 RAG 應用程式。
檢索增強生成 (Retrieval Augmented Generation, RAG) 是一種利用生成式 AI 的強大能力來獲取不在大型語言模型 (LLM) 訓練數據中的資訊，並避免依賴 LLM 獲取事實資訊的好方法。然而，RAG 只有在您能快速識別並提供最相關的上下文給 LLM 時才能運作。Essential GraphRAG 將教您如何使用知識圖譜來建模您的 RAG 數據，並提供更好的性能、準確性、可追溯性和完整性。 在 Essential GraphRAG 中，您將學到： - 在 RAG 系統中使用知識圖譜的好處- 如何從零開始實現 GraphRAG 系統
- 建立一個完全運作的生產 RAG 系統的過程
- 使用 LLM 構建知識圖譜
- 評估 RAG 管道的性能 Essential GraphRAG 是一本實用指南，旨在賦能 LLM 與 RAG。您將學會如何提供基於向量相似度的方法來尋找相關資訊，以及如何處理語義層、提供代理 RAG，並生成 Cypher 語句以從知識圖譜中檢索數據。 關於技術 檢索增強生成 (RAG) 系統自動選擇並提供特定領域的上下文給 LLM，徹底改善其生成準確且無幻覺回應的能力。GraphRAG 模式利用知識圖譜來結構化 RAG 的輸入，利用數據中現有的關係來生成豐富且相關的提示。 關於本書 Essential GraphRAG 將教您如何構建和部署生產級的 GraphRAG 系統。您將學會如何從文本中提取結構化知識，以及如何結合基於向量和基於圖形的檢索方法。本書充滿實用範例，從構建向量相似度搜索檢索工具和代理 RAG 應用程式，到評估性能和準確性等。 內容概覽 - 嵌入、向量相似度搜索和混合搜索
- 將自然語言轉換為 Cypher 數據庫查詢
- 微軟的 GraphRAG 管道
- 代理 RAG 讀者對象 適合具備中級 Python 技能及對 Neo4j 等圖形數據庫有一定經驗的讀者。 關於作者 本書作者為 Manning 的《Graph Algorithms for Data Science》，並且是 LangChain 和 LlamaIndex 的貢獻者，Tomaz Bratanic 在圖形、機器學習和生成式 AI 方面擁有豐富的經驗。Oskar Hane 領導 Neo4j 的生成式 AI 工程團隊。 目錄 1 提升 LLM 準確性
2 向量相似度搜索和混合搜索
3 進階向量檢索策略
4 從自然語言問題生成 Cypher 查詢
5 代理 RAG
6 使用 LLM 構建知識圖譜
7 微軟的 GraphRAG 實現
8 RAG 應用評估
A Neo4j 環境 購買印刷版書籍時，您將獲得 Manning 提供的免費電子書 (PDF 或 ePub)，以及訪問在線 liveBook 格式 (及其 AI 助手，能用任何語言回答您的問題)。
作者簡介
Tomaz Bratanic 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, business-oriented analytics, and recommendations. 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.
作者簡介(中文翻譯)
Tomaz Bratanic 是一位網路科學家，專注於圖形與機器學習的交集。他將這些圖形技術應用於多個領域的專案，包括詐騙檢測、生物醫學、商業分析和推薦系統。
Oskar Hane 是 Neo4j 的高級員工軟體工程師。他擁有超過 20 年的軟體工程師經驗，以及 10 年與 Neo4j 和知識圖譜相關的工作經驗。目前，他正在領導 Neo4j 的生成式 AI 工程團隊，專注於為其他開發人員提供最佳的使用體驗，以便使用 Neo4j 建立生成式 AI 應用程式。
