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

Preface

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

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

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

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

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

Technical requirements

This chapter requires familiarity with the Python programming language (version 3.6 or higher is recommended) and basic concepts of deep learning.

We will be leveraging popular AI toolkits such as Hugging Face’s Transformers library (https://huggingface.co/docs/transformers/en/index) to build and experiment with RAG. While not mandatory, having a basic understanding of Git version control can be helpful.

Git allows you to easily clone the code repository for this chapter and track any changes you make. Do not worry about finding or typing the code yourself! We have created a dedicated public repository on GitHub, https://github.com/PacktPublishing/Building-Neo4j-Powered-Applications-with-LLMs/tree/main/ch2, allowing you to easily clone it and follow along with the chapter’s hands-on exercises.

This repository contains all the necessary scripts, files, and configurations required to implement the RAG model and integrate Neo4j with...