The latest release of LangChain4j (version 0.31) added the capability of grounding large language models with results from web searches. There’s an integration with Google Custom Search Engine, and also Tavily. The fact of grounding an LLM’s response with the results from a search engine allows the LLM to find relevant information about the query from web searches, which will likely include up-to-date information that the model won’t have seen during its training, past its cut-off date when the training ended. Read more...

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 contribution to LangChain4j. Read more...

My go-to framework when developing Java apps or microservices is Micronaut. For the apps that should have a web frontend, I rarely use Micronaut Views and its templating support. Instead, I prefer to just serve static assets from my resource folder, and have some JavaScript framework (usually Vue.js) to populate my HTML content (often using Shoelace for its nice Web Components). However, the static asset documentation is a bit light on explanations. Read more...

A while ago, my friend Sylvain Wallez shared a little bookmarlet on Twitter/X that transforms a dark mode site into light mode. I know the trend is towards dark mode, but for a lot of people with certain vision issues, for example with astigmatism like me, certain dark modes can very painful. This site about vision (and you’ll find other similar references) mentions that: People who have myopia or astigmatism also may experience halation (from the word “halo”). Read more...

In Java, builders are a pretty classical pattern for creating complex objects with lots of attributes. A nice aspect of builders is that they help reduce the number of constructors you need to create, in particular when not all attributes are required to be set (or if they have default values). However, I’ve always found builders a bit verbose with their newBuilder() / build() method combos, especially when you work with deeply nested object graphs, leading to lines of code of builders of builders of… Read more...

In this article, we’ll figure out how to create slugs. Not the slobbery kind of little gastropods that crawls on the ground. Instead, we’ll see how to create the short hyphened text you can see in the URL of your web browser, and that is often a URL-friendly variation of the title of the article. Interestingly, one of the most popular posts on my blog is an almost 20 year old article that explains how to remove accents from a string. 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...