Building Neo4j-Powered Applications with LLMs
活動訊息
分享今年你最有感的書，送50元電子禮券，再抽5000點金幣！
普發一萬放大術：滿千登記抽萬元好禮
內容簡介
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilities
Key Features:
- Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j
- Apply best practices for graph exploration, modeling, reasoning, and performance optimization
- Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description:
Embark on an expert-led journey into building LLM-powered applications using Retrieval-Augmented Generation (RAG) and Neo4j knowledge graphs. Written by Ravindranatha Anthapu, Principal Consultant at Neo4j, and Siddhant Agrawal, a Google Developer Expert in GenAI, this comprehensive guide is your starting point for exploring alternatives to LangChain, covering frameworks such as Haystack, Spring AI, and LangChain4j.
As LLMs (large language models) reshape how businesses interact with customers, this book helps you develop intelligent applications using RAG architecture and knowledge graphs, with a strong focus on overcoming one of AI's most persistent challenges-mitigating hallucinations. You'll learn how to model and construct Neo4j knowledge graphs with Cypher to enhance the accuracy and relevance of LLM responses.
Through real-world use cases like vector-powered search and personalized recommendations, the authors help you build hands-on experience with Neo4j GenAI integrations across Haystack and Spring AI. With access to a companion GitHub repository, you'll work through code-heavy examples to confidently build and deploy GenAI apps on Google Cloud.
By the end of this book, you'll have the skills to ground LLMs with RAG and Neo4j, optimize graph performance, and strategically select the right cloud platform for your GenAI applications.
What You Will Learn:
- Design, populate, and integrate a Neo4j knowledge graph with RAG
- Model data for knowledge graphs
- Integrate AI-powered search to enhance knowledge exploration
- Maintain and monitor your AI search application with Haystack
- Use LangChain4j and Spring AI for recommendations and personalization
- Seamlessly deploy your applications to Google Cloud Platform
Who this book is for:
This LLM book is for database developers and data scientists who want to leverage knowledge graphs with Neo4j and its vector search capabilities to build intelligent search and recommendation systems. Working knowledge of Python and Java is essential to follow along. Familiarity with Neo4j, the Cypher query language, and fundamental concepts of databases will come in handy.
Table of Contents
- Introducing LLMs, RAGs, and Neo4j Knowledge Graphs
- Demystifying RAG
- Building a Foundational Understanding of Knowledge Graph for Intelligent Applications
- Building Your Neo4j Graph with Movies Dataset
- Implementing Powerful Search Functionalities with Neo4j and Haystack
- Exploring Advanced Knowledge Graph Capabilities
- Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems
- Constructing a Recommendation Graph with H&M Personalization Dataset
- Integrating LangChain4j and SpringAI with Neo4j
- Creating an Intelligent Recommendation System
- Choosing the Right Cloud Platform for GenAI Applications
- Deploying your Application on Cloud
- Epilogue
配送方式
-
台灣
- 國內宅配：本島、離島
-
到店取貨：
不限金額免運費
-
海外
- 國際快遞：全球
-
港澳店取：
訂購/退換貨須知
加入金石堂 LINE 官方帳號『完成綁定』，隨時掌握出貨動態：
商品運送說明：
- 本公司所提供的產品配送區域範圍目前僅限台灣本島。注意！收件地址請勿為郵政信箱。
- 商品將由廠商透過貨運或是郵局寄送。消費者訂購之商品若無法送達，經電話或 E-mail無法聯繫逾三天者，本公司將取消該筆訂單，並且全額退款。
- 當廠商出貨後，您會收到E-mail出貨通知，您也可透過【訂單查詢】確認出貨情況。
- 產品顏色可能會因網頁呈現與拍攝關係產生色差，圖片僅供參考，商品依實際供貨樣式為準。
- 如果是大型商品（如：傢俱、床墊、家電、運動器材等）及需安裝商品，請依商品頁面說明為主。訂單完成收款確認後，出貨廠商將會和您聯繫確認相關配送等細節。
- 偏遠地區、樓層費及其它加價費用，皆由廠商於約定配送時一併告知，廠商將保留出貨與否的權利。
提醒您！！
金石堂及銀行均不會請您操作ATM! 如接獲電話要求您前往ATM提款機，請不要聽從指示，以免受騙上當！
退換貨須知：
**提醒您，鑑賞期不等於試用期，退回商品須為全新狀態**
-
依據「消費者保護法」第19條及行政院消費者保護處公告之「通訊交易解除權合理例外情事適用準則」，以下商品購買後，除商品本身有瑕疵外，將不提供7天的猶豫期：
- 易於腐敗、保存期限較短或解約時即將逾期。（如：生鮮食品）
- 依消費者要求所為之客製化給付。（客製化商品）
- 報紙、期刊或雜誌。（含MOOK、外文雜誌）
- 經消費者拆封之影音商品或電腦軟體。
- 非以有形媒介提供之數位內容或一經提供即為完成之線上服務，經消費者事先同意始提供。（如：電子書、電子雜誌、下載版軟體、虛擬商品…等）
- 已拆封之個人衛生用品。（如：內衣褲、刮鬍刀、除毛刀…等）
- 若非上列種類商品，均享有到貨7天的猶豫期（含例假日）。
- 辦理退換貨時，商品（組合商品恕無法接受單獨退貨）必須是您收到商品時的原始狀態（包含商品本體、配件、贈品、保證書、所有附隨資料文件及原廠內外包裝…等），請勿直接使用原廠包裝寄送，或於原廠包裝上黏貼紙張或書寫文字。
- 退回商品若無法回復原狀，將請您負擔回復原狀所需費用，嚴重時將影響您的退貨權益。
商品評價