As I was working on tweaking the Vertex AI text embedding model in LangChain4j, I wanted to better understand how the textembedding-gecko model tokenizes the text, in particular when we implement the Retrieval Augmented Generation approach. The various PaLM-based models offer a computeTokens endpoint, which returns a list of tokens (encoded in Base 64) and their respective IDs. Note: At the time of this writing, there’s no equivalent endpoint for Gemini models. Read more...

Last week, I wrote about how to get started with the PaLM API in the Java ecosystem, and particularly, how to overcome the lack of Java client libraries (at least for now) for the PaLM API, and how to properly authenticate. However, what I didn’t explain was what I was building! Let’s fix that today, by telling you a story, a kid story! Yes, I was using the trendy Generative AI approach to generate bedtime stories for kids. Read more...

Large Language Models (LLMs for short) are taking the world by storm, and things like ChatGPT have become very popular and used by millions of users daily. Google came up with its own chatbot called Bard, which is powered by its ground-breaking PaLM 2 model and API. You can also find and use the PaLM API from withing Google Cloud as well (as part of Vertex AI Generative AI products) and thus create your own applications based on that API. Read more...

In this article, let’s revisit the topic of deploying Java apps on Cloud Run. In particular, I’ll deploy a Micronaut app, written with Java 17, and built with Gradle. With a custom Dockerfile On Cloud Run, you deploy containerised applications, so you have to decide the way you want to build a container for your application. In a previous article, I showed an example of using your own Dockerfile, which would look as follows with an OpenJDK 17, and enabling preview features of the language: Read more...

It’s all Richard Seroter’s fault, I ended up dabbling with Golang! We were chatting about a use case using Google Cloud Workflows and a Cloud Run service implemented in Go. So it was the occasion to play a bit with Go. Well, I still don’t like error handling… But let’s rewind the story a bit! Workflows is a fully-managed service/API orchestrator on Google Cloud. You can create some advanced business workflows using YAML syntax. Read more...

Instead of spending too much time on infrastructure, take advantage of readily available serverless solutions. Focus on your Micronaut code, and deploy it rapidly as a function, an application, or within a container, on Google Cloud Platform, with Cloud Functions, App Engine, or Cloud Run. In this presentation, you’ll discover the options you have to deploy your Micronaut applications and services on Google Cloud. With Micronaut Launch, it’s easy to get started with a template project, and with a few tweaks, you can then push your code to production. Read more...

Last week, I had the pleasure of presenting Micronaut in action on Google Cloud Platform, via a webinar organized by OCI. Particularly, I focused on the serverless compute options available: Cloud Functions, App Engine, and Cloud Run. Here are the slides I presented. However, the real meat is in the demos which are not displayed on this deck! So let’s have a closer look at them, until the video is published online. Read more...

With my partner in crime, Mete Atamel, we ran two editions of our “Pic-a-Daily” serverless workshop. It’s an online, hands-on, workshop, where developers get their hands on the the serverless products provided by Google Cloud Platform: Cloud Functions — to develop and run functions, small units of logic glue, to react to events of your cloud projects and services App Engine — to deploy web apps, for web frontends, or API backends Cloud Run — to deploy and scale containerised services The theme of the workshop is to build a simple photosharing application (hence the play on words, with a picture a day) with those serverless products, but along the way, developers also get to use other services like: Read more...

Hot on the heels of the announcement of the general availability of JDK 14, I couldn’t resist taking it for a spin. Without messing up my environment — I’ll confess I’m running 11 on my machine, but I’m still not even using everything that came past Java 8! — I decided to test this new edition within the comfy setting of a Docker container. Minimal OpenJDK 14 image running JShell Super easy to get started (assuming you have Docker installed on your machine), create a Dockerfile with the following content: Read more...

Requirements: an existing Google Cloud Platform account with a project you have enabled the Cloud Run service and already deployed a container image your local environment’s gcloud is already configured to point at your GCP project By default, when you deploy a Cloud Run service, it is secured by default, unless you use the –allow-unauthenticated flag when using the gcloud command-line (or the appropriate checkbox on the Google Cloud Console). Read more...