Photo credit The NODES 2024 conference is approaching fast, and we have a lot of exciting activities, including talks, workshops, and more. Here’s what you need to know to get ready for NODES 2024. What is NODES? NODES is a free, virtual conference run by Neo4j for developers, data scientists, and other technical roles who want to learn more about graph databases and how they can be used to solve complex problems.

Photo credit Every year, Neo4j hosts the virtual event NODES 2024 - a free, technical event spanning global timezones and a variety of graph-related topics. This is one of my favorite events of the year, but it doesn’t happen without countless hours of planning and work to make it valuable to the graph community. This year, my colleague and I were put in charge of organizing the schedule, determining how sessions are lined up between the start and end time of the event.

Photo credit Migrating an application always feels a bit daunting, and that was the way I felt when I looked at the Spring AI 1.0 milestone release. However, the upgrade didn’t make as many major shifts as anticipated. This guide will help you upgrade your Spring AI application from version 0.8 to 1.0 and explain why each change is needed. Changes include some naming shifts, configuration options, and more.

Photo credit For an upcoming event, I was asked to give a demo of how to run Neo4j on Kubernetes. I had very little experience with Kubernetes, so I decided to document my journey for later reference. What is Kubernetes? Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. It was originally developed by Google and is now maintained by the Cloud Native Computing Foundation.

Photo credit There are so many options when it comes to languages, frameworks, and tools for building generative AI (GenAI) applications. When you are just getting started, these decisions and figuring out how to integrate everything can be overwhelming. My team has been working on some pre-packaged solutions to make this process easier by providing starter kit projects with a few key technologies. One of those is the topic of today’s post - building a GenAI application with Spring AI in Java.

Photo credit There is no shortage of technical events such as conferences, meetups, trainings, hackathons, and so on. These events are a great way to learn new things, connect with people, and share knowledge with others. One of the most valuable and exciting ways to share knowledge is by giving a technical presentation. Today, we will look at how to submit a technical presentation for an event and get some personal recommendations from me, as well.

Photo credit Generative AI (GenAI) is currently a hot topic in the tech world. It’s a subset of artificial intelligence that focuses on creating new content, such as text, images, or music. One popular type of GenAI component is the Large Language Model (LLM), which can generate human-like text based on a prompt. Retrieval-Augmented Generation (RAG) is a technique that enhances the accuracy and reliability of generative AI models by grounding them in external knowledge sources.

Photo credit It’s been awhile since I wrote a Cypher Sleuthing post, but I’m back with a new one! This time, I’m going to show you how to find the data types of properties in Neo4j. I frequently want to find out the data type of a property so that I can map it in an application (in Java). I can never remember off the top of my head, and when I Google the question, the results are often outdated (here’s the answer post, but the syntax is old).

Photo credit What is RAG? This term is used a lot in today’s technical landscape, but what does it actually mean? Here are a few definitions from various sources: "Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response." - Amazon Web Services "Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources.