❯ Guillaume Laforge

Google-Cloud

Get Started with Gemini in Java

Google announced today the availability of Gemini, its latest and more powerful Large Language Model. Gemini is multimodal, which means it’s able to consume not only text, but also images or videos. I had the pleasure of working on the Java samples and help with the Java SDK, with wonderful engineer colleagues, and I’d like to share some examples of what you can do with Gemini, using Java! First of all, you’ll need to have an account on Google Cloud and created a project. Read more...

Discovering LangChain4J, the Generative AI orchestration library for Java developers

As I started my journey with Generative AI and Large Language Models, I’ve been overwhelmed with the omnipresence of Python. Tons of resources are available with Python front and center. However, I’m a Java developer (with a penchant for Apache Groovy, of course). So what is there for me to create cool new Generative AI projects? When I built my first experiment with the PaLM API, using the integration within the Google Cloud’s Vertex AI offering, I called the available REST API, from my Micronaut application. Read more...

Custom Environment Variables in Workflows

In addition to the built-in environment variables available by default in Google Cloud Workflows (like the project ID, the location, the workflow ID, etc.) it’s now possible to define your own custom environment variables! Why is it useful and important? It’s particularly handy when you want to read information that is dependent on the deployment of your workflow, like, for example, information about the environment you’re running in. Is my workflow running in development, staging, or production environment? Read more...

Creating kids stories with Generative AI

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...

Getting started with the PaLM API in the Java ecosystem

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...

New blog location

I started blogging 20 years ago, in April 2003. My first blog engine was a PHP CMS, called Nucleus. I was hosting it on my ISP, at free.fr. Then in 2011, I wrote my own blog engine, called Bloogaey, which was written in Groovy, using my little Gaelyk web framework, and running on App Engine. As it became a bit painful to properly format my blog posts, and evolve my blog engine, I decided I should move to something that is more static, with a static site generator that eats Markdown files: I chose the Hugo static site generator that I used in some previous projects. Read more...

Google Cloud Workflows API automation, patterns, and best practices

Workflows at a glance, benefits, key features, use cases UI interface in Google Cloud console Deep dive into the Workflows syntax Workflows connectors Demos Patterns and best practices

Calculating your potential reach on Mastodon with Google Cloud Workflows orchestrating the Mastodon APIs

With the turmoil around Twitter, like many, I’ve decided to look into Mastodon. My friend Geert is running his own Mastodon server, and welcomed me on his instance at: uwyn.net/@glaforge. With Twitter, you can access your analytics to know how your tweets are doing, how many views you’re getting. Working in developer relations, it’s always interesting to get some insights into those numbers to figure out if what you’re sharing is interesting for your community. Read more...

Workflows patterns and best practices — Part 3

This is a three-part series of posts, in which we summarize Workflows and service orchestration patterns. In this third and final post, we talk about managing workflow life cycles and the benefits of using Firestore with Workflows. Use subworkflows and Terraform to manage workflow definitions If you’re not careful, the workflow definitions you create with YAML or JSON can get out of hand pretty quickly. While it is possible to use subworkflows to define snippets of a workflow that can be reused from multiple workflows, Workflows does not support importing these subworkflows. Read more...

Workflows patterns and best practices — Part 2

This is part 2 of a three-part series of posts, in which we summarize Workflows and service orchestration patterns. You can apply these patterns to better take advantage of Workflows and service orchestration on Google Cloud. In the first post, we introduced some general tips and tricks, as well as patterns for event-driven orchestrations, parallel steps, and connectors. This second post covers more advanced patterns. Let’s dive in! Design for resiliency with retries and the saga pattern It’s easy to put together a workflow that chains a series of services, especially if you assume that those services will never fail. Read more...