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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

Testing and verifying the deployment on Google Cloud

Once your deployment is complete, Google Cloud Run will return a public service URL, typically in the following format:

https://movies-chatbot-[UNIQUE_ID].${GCP_REGION}.run.app

Open this URL in your browser. You should see your Gradio-powered chatbot interface live on the web—identical in functionality to your local version. You can now interact with the chatbot, submit queries, and receive movie recommendations just as before, but this time, it is running fully in the cloud.

If something does not work as expected, keep the following checklist ready for troubleshooting:

  • Dependency check: Make sure your Dockerfile correctly installs all dependencies using pip install -r requirements.txt. Missing dependencies can result in build or runtime errors on Cloud Run.
  • Cloud Shell versus local environment: If you are not using Google Cloud Shell, ensure your local environment is authenticated with Google...
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