Building Neo4j-Powered Applications with LLMs
Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI
RACreated by Siddhant Agarwal, Ravindranatha Anthapu
Explore how to build intelligent search and recommendation systems powered by large language models and Neo4j knowledge graphs. Learn to integrate tools like Haystack, LangChain4j, and Spring AI to create applications that deliver accurate and relevant results. Gain practical skills for deploying GenAI solutions on Google Cloud.
Packt | Jun 2025 | 312 min
What You Will Learn
You will start by addressing LLM limitations using Neo4j, then move on to building intelligent search with Haystack and vectors. Next, you'll integrate recommendation engines with Spring AI and LangChain4j. Finally, you'll learn how to deploy your applications on Google Cloud, applying each concept through hands-on examples.
Key Features
- Build intelligent search and recommendation apps using Neo4j and LLM integrations
- Apply best practices for modeling, optimizing, and reasoning with knowledge graphs
- Deploy GenAI applications to Google Cloud for scalable, real-world use
Target Audience
Ideal for database developers and data scientists with experience in Python and Java who want to use Neo4j and vector search for intelligent applications. If you are comfortable with Cypher and database fundamentals, you will gain the skills needed to design, build, and deploy advanced GenAI-powered search and recommendation systems.
