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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

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Product type Paperback
Published in Jun 2025
Publisher Packt
ISBN-13 9781836206231
Length 312 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Ravindranatha Anthapu Ravindranatha Anthapu
Author Profile Icon Ravindranatha Anthapu
Ravindranatha Anthapu
Siddhant Agarwal Siddhant Agarwal
Author Profile Icon Siddhant Agarwal
Siddhant Agarwal
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Toc

Table of Contents (20) Chapters Close

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

Closing remarks

We have looked at various aspects of emerging LLM frameworks and how we can build better solutions using these frameworks. While this technology is exciting and opening new doors for how we look at and solve problems, it is still in its nascent stages. It requires a lot of processing power and may not have the service-level agreement (SLA) response times we might be looking for. While we might get excited about solving all the problems using this approach, it’s important to evaluate whether trying to solve a given problem really needs to leverage these technologies or whether we can solve them more reasonably and effectively. For example, many people are excited about LLM capabilities and trying to generate data models (be it graphs, SQL, or other) by describing a problem. This can be a double-edged sword. If we are not familiar with the data we are working with, we may not be able to validate the model generated. If we are very familiar with the data, we might...

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