Photo credit I’ve recently been working on expanding my horizons in different Java frameworks, and Quarkus was on my list to try. Quarkus is a Kubernetes-native Java framework designed to reduce an application’s footprint, ideal for addressing challenges in deploying, running, and maintaining applications in cloud-based environments. It has become a major player in list of frameworks, so naturally, I wanted to try it out! I started by following the first few steps in this blog post, as it outlines how to get a project up and running and the dependency needed for Neo4j.

Photo credit As of the Neo4j Java Driver 5.28.5, the driver now includes a basic object mapping feature. This allows you to map Java objects to Neo4j nodes and relationships without needing to do so manually or deal with raw results. Note: The driver’s new object mapping is not a full-fledged Object Graph Mapping (OGM) solution. For a more comprehensive option, check out the Neo4j OGM library. Code example I put together a brief example of how to use the new object mapping feature in a Maven project.

Photo credit In the last post, we discussed the basics of Retrieval Augmented Generation (RAG) and how it enhances the capabilities of Large Language Models (LLMs) by integrating them with external knowledge sources. We also introduced the concept of vector embeddings and their role in semantic search. In this post, we’ll dive deeper into the different layers of RAG, including vector RAG, graph RAG, and agents. We’ll explore how these layers can be combined to create more powerful and effective AI systems.