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Building Neo4j-Powered Applications with LLMs

You're reading from Building Neo4j-Powered Applications with LLMs Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI

Product type Paperback

Published in Jun 2025

Publisher Packt

ISBN-13 9781836206231

Length 312 pages

Edition 1st Edition

Languages

Cypher

Tools

Cypher Query

Concepts

GPT/LLMs

Authors (2):

Ravindranatha Anthapu

Siddhant Agarwal

View More author details

Table of Contents (20) Chapters

Preface

1. Part: 1 Introducing RAG and Knowledge Graphs for LLM Grounding

Stay tuned

2. Introducing LLMs, RAGs, and Neo4j Knowledge Graphs FREE CHAPTER

3. Demystifying RAG

4. Building a Foundational Understanding of Knowledge Graph for Intelligent Applications

5. Part 2: Integrating Haystack with Neo4j: A Practical Guide to Building AI-Powered Search

Stay tuned

6. Building Your Neo4j Graph with Movies Dataset

7. Implementing Powerful Search Functionalities with Neo4j and Haystack

8. Exploring Advanced Knowledge Graph Capabilities with Neo4j

9. Part 3: Building an Intelligent Recommendation System with Neo4j, Spring AI, and LangChain4j

Stay tuned

10. Introducing the Neo4j Spring AI and LangChain4j Frameworks for Building Recommendation Systems

11. Constructing a Recommendation Graph with H&M Personalization Dataset

12. Integrating LangChain4j and Spring AI with Neo4j

13. Creating an Intelligent Recommendation System

14. Part 4: Deploying Your GenAI Application in the Cloud

Stay tuned

15. Choosing the Right Cloud Platform for GenAI Applications

16. Deploying Your Application on the Google Cloud

17. Epilogue

18. Other Books You May Enjoy

19. Index

Summary

In this chapter, we looked at the capabilities of Neo4j that help us build intelligent applications, why the personalization that these applications can provide is useful, and how they are different from the existing rule-based applications. We looked at what Spring AI and LangChain4j are and their capabilities to build intelligent applications.

In the next chapter, Chapter 8, we will build a graph data model to support intelligent and personalized recommendations with the H&M dataset and see how this data can be loaded into a graph data model with the aim of providing recommendations. Chapter 9 of this book will enable you to integrate this intelligent recommendation system with the Spring AI and LangChain4j frameworks.

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Authors (2)

Ravindranatha Anthapu

Ravindranatha Anthapu

Ravindranatha Anthapu has more than 25 years of experience in working with W3C standards and building cutting-edge technologies, including integrating speech into mobile applications in the 2000s. He is a technology enthusiast who has worked on many projects, from operating system device drivers to writing compilers for C language and modern web technologies, transitioning seamlessly and bringing experience from each of these domains and technologies to deliver successful solutions today. As a principal consultant at Neo4j today, Ravindranatha works with large enterprise customers to make sure they are able to leverage graph technologies effectively across various domains.

See other products by Ravindranatha Anthapu

Siddhant Agarwal

Siddhant Agarwal

Siddhant Agarwal is a seasoned DevRel professional with over a decade of experience cultivating innovation and scaling developer ecosystems globally. Currently leading Developer Relations across APAC at Neo4j and recognized as a Google Developer Expert in Gen-AI, Sid transforms local developer initiatives into global success stories with his signature "Local to Global" approach. Previously at Google managing flagship developer programs, he has shared his technical expertise at diverse forums worldwide, fueling inspiration and innovation.

See other products by Siddhant Agarwal

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