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Full Description
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 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 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 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.
Contents
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
