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 serves as the starting point for developers exploring alternatives to LangChain, covering frameworks like Haystack, Spring AI, and LangChain4j.
As LLMs reshape how businesses interact with customers, this book helps you to 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 also learn how to model and construct Neo4j knowledge graphs with Cypher to enhance 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. Supported by 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.

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Spis treści

Building Neo4j-Powered Applications with LLMs. Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI eBook -- spis treści

• 1. Introducing LLMs, RAGs, and Neo4j Knowledge Graphs
• 2. Demystifying RAG
• 3. Building a Foundational Understanding of Knowledge Graph for Intelligent Applications
• 4. Building Your Neo4j Graph with Movies Dataset
• 5. Implementing Powerful Search Functionalities with Neo4j and Haystack
• 6. Exploring Advanced Knowledge Graph Capabilities
• 7. Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems
• 8. Constructing a Recommendation Graph with H&M Personalization Dataset
• 9. Integrating LangChain4j and SpringAI with Neo4j
• 10. Creating an Intelligent Recommendation System
• 11. Choosing the Right Cloud Platform for GenAI Applications
• 12. Deploying your Application on Cloud
• 13. Epilogue