Dive into building applications that combine the power of Large Language Models (LLMs) with Neo4j knowledge graphs, Haystack, and Spring AI to deliver intelligent, data-driven recommendations and search outcomes. This book provides actionable insights and techniques to create scalable, robust solutions by leveraging the best-in-class frameworks and a real-world project-oriented approach. What this Book will help me do Understand how to use Neo4j to build knowledge graphs integrated with LLMs for enhanced data insights. Develop skills in creating intelligent search functionalities by combining Haystack and vector-based graph techniques. Learn to design and implement recommendation systems using LangChain4j and Spring AI frameworks. Acquire the ability to optimize graph data architectures for LLM-driven applications. Gain proficiency in deploying and managing applications on platforms like Google Cloud for scalability. Author(s) Ravindranatha Anthapu, a Principal Consultant at Neo4j, and Siddhant Agarwal, a Google Developer Expert in Generative AI, bring together their vast experience to offer practical implementations and cutting-edge techniques in this book. Their combined expertise in Neo4j, graph technology, and real-world AI applications makes them authoritative voices in the field. Who is it for? Designed for database developers and data scientists, this book caters to professionals aiming to leverage the transformational capabilities of knowledge graphs alongside LLMs. Readers should have a working knowledge of Python and Java as well as familiarity with Neo4j and the Cypher query language. If you're looking to enhance search or recommendation functionalities through state-of-the-art AI integrations, this book is for you.