Building Neo4j-Powered Applications with LLMs

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