Lately, for my Generative AI powered Java apps, I’ve used the Gemini multimodal large language model from Google. But there’s also Gemma, its little sister model. Gemma is a family of lightweight, state-of-the-art open models built from the same research and technology used to create the Gemini models. Gemma is available in two sizes: 2B and 7B. Its weights are freely available, and its small size means you can run it on your own, even on your laptop. Read more...

No need to be a Python developer to do Generative AI! If you’re a Java developer, you can take advantage of LangChain4j to implement some advanced LLM integrations in your Java applications. And if you’re interested in using Gemini, one of the best models available, I invite you to have a look at the following “codelab” that I worked on: Codelab — Gemini for Java Developers using LangChain4j In this workshop, you’ll find various examples covering the following use cases, in crescendo approach: Read more...

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...

This week LangChain4j, the LLM orchestration framework for Java developers, released version 0.26.1, which contains my first significant contribution to the open source project: support for the Imagen image generation model. Imagen is a text-to-image diffusion model that was announced last year. And it recently upgraded to Imagen v2, with even higher quality graphics generation. As I was curious to integrate it in some of my generative AI projects, I thought that would be a great first Read more...

A promising feature of the Gemini large language model released recently by Google DeepMind, is the support for function calls. It’s a way to supplement the model, by letting it know an external functions or APIs can be called. So you’re not limited by the knowledge cut-off of the model: instead, in the flow of the conversation with the model, you can pass a list of functions the model will know are available to get the information it needs, to complete the generation of its answer. Read more...

Hot on the heels of the release of Gemini, I’d like to share a couple of resources I created to get your hands on large language models, using LangChain4J, and the PaLM 2 model. Later on, I’ll also share with you articles and codelabs that take advantage of Gemini, of course. The PaLM 2 model supports 2 modes: text generation, and chat. In the 2 codelabs, you’ll need to have created an account on Google Cloud, and created a project. Read more...

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...

Large Language Models, available through easy to use APIs, bring powerful machine learning tools in the hands of developers. Although Python is usually seen as the lingua franca of everything ML, with LLM APIs and LLM orchestration frameworks, complex tasks become easier to implement for enterprise developers. Abstract Large language models (LLMs) are a powerful new technology that can be used for a variety of tasks, including generating text, translating languages, and writing different kinds of creative content. Read more...

Some friends shared this article from Uwe Friedrichsen, tilted back to the future, that talks about this feeling of “déjà-vu”, this impression that in IT we keep on reinventing the wheel. With references to mainframes, Uwe compared CICS to Lambda function scheduling, JCL to step functions, mainframe software development environments to the trendy platform engineering. There are two things I like about this article. First of all, it rings a bell with me, as we’ve seen the pendulum swing as we keep reinventing some patterns or rediscovering certain best practices, sometimes favoring an approach one day, and coming back to another approach the next day. Read more...

The State of AI report is pretty interesting to read (even if long!). Among the major sections: research, industry, but also politics, safety, and some predictions. You’ll find an executive summary in one slide, on slide #8. On #22, emergent capabilities of LLMs is covered and mentions Stanford’s research that talks about the importance of more linear and continuous measures as otherwise capabilities sound like they emerge out of the blue. Read more...