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Building Neo4j-Powered Applications with LLMs: Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI
A comprehensive guide to building cutting-edge generative AI applications using Neo4j's knowledge graphs and vector search capabilities
Key FeaturesDesign vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4jApply best practices for graph exploration, modeling, reasoning, and performance optimizationBuild and consume Neo4j knowledge graphs and deploy your GenAI apps to Google CloudPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEmbark 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 learnDesign, populate, and integrate a Neo4j knowledge graph with RAGModel data for knowledge graphsIntegrate AI-powered search to enhance knowledge explorationMaintain and monitor your AI search application with HaystackUse LangChain4j and Spring AI for recommendations and personalizationSeamlessly deploy your applications to Google Cloud PlatformWho this book is forThis 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 ContentsIntroducing LLMs, RAGs, and Neo4j Knowledge GraphsDemystifying RAGBuilding a Foundational Understanding of Knowledge Graph for Intelligent ApplicationsBuilding Your Neo4j Graph with Movies DatasetImplementing Powerful Search Functionalities with Neo4j and HaystackExploring Advanced Knowledge Graph CapabilitiesIntroducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation SystemsConstructing a Recommendation Graph with H&M Personalization DatasetIntegrating LangChain4j and SpringAI with Neo4jCreating an Intelligent Recommendation SystemChoosing the Right
Key FeaturesDesign vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4jApply best practices for graph exploration, modeling, reasoning, and performance optimizationBuild and consume Neo4j knowledge graphs and deploy your GenAI apps to Google CloudPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionEmbark 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 learnDesign, populate, and integrate a Neo4j knowledge graph with RAGModel data for knowledge graphsIntegrate AI-powered search to enhance knowledge explorationMaintain and monitor your AI search application with HaystackUse LangChain4j and Spring AI for recommendations and personalizationSeamlessly deploy your applications to Google Cloud PlatformWho this book is forThis 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 ContentsIntroducing LLMs, RAGs, and Neo4j Knowledge GraphsDemystifying RAGBuilding a Foundational Understanding of Knowledge Graph for Intelligent ApplicationsBuilding Your Neo4j Graph with Movies DatasetImplementing Powerful Search Functionalities with Neo4j and HaystackExploring Advanced Knowledge Graph CapabilitiesIntroducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation SystemsConstructing a Recommendation Graph with H&M Personalization DatasetIntegrating LangChain4j and SpringAI with Neo4jCreating an Intelligent Recommendation SystemChoosing the Right
450 pages, Kindle Edition
Published June 20, 2025
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