Hot on the heels of the announcement of Gemini 2.0, I played with the new experimental model both from within Google AI Studio, and with LangChain4j.

Google released Gemini 2.0 Flash, with new modalities, including interleaving images, audio, text, video, both in input and output. Even a live bidirectional speech-to-speech mode, which is really exciting!

When experimenting with AI Studio, what attracted my attention was AI Studio’s new starter apps section. There are 3 examples (including links to Github projects showing how they were implemented):

By Guillaume Laforge & Valentin Deleplace

The Programming Idioms community website created by Valentin lets developers share typical implementations in various programming languages for usual tasks like printing the famous “Hello World!” message, counting the characters in a string, sorting collections, or formatting dates, to name a few. And many more: there are currently 350 idioms, covering 32 programming languages. It’s a nice way to discover how various languages implement such common tasks!

As we are making our apps smarter with the help of Large Language Models, we must keep in mind that we are often dealing with potentially sensitive information coming from our users. In particular, in the context of chatbots, our application users have the ability to input any text in the conversation.

Personally Identifiable Information (PII) should be dealt with the highest level of attention, because we care about our users, we don’t want to leak their personal details, and we must comply with all sorts of laws or regulations. In a word, we are responsible AI developers.

Data extraction from unstructured text is a very important task where LLMs shine, as they understand human languages well. Rumor has it that 80% of the worldwide knowledge and data comes in the form of unstructured text (vs 20% for data stored in databases, spreadsheets, JSON/XML, etc.) Let’s see how we can get access to that trove of information thanks to LLMs.

In this article, we’ll have a look at different techniques to make LLMs generate JSON output and extract data from text. This applies to most LLMs and frameworks, but for illustration purposes, we’ll use Gemini and LangChain4j in Java.

It’s always interesting to know how the sausage is made, don’t you think? That’s why, a while ago, I looked at embedding model tokenization, and I implemented a little visualization to see the tokens in a colorful manner. Yet, I was still curious to see how Gemini would tokenize text…

Both LangChain4j Gemini modules (from Vertex AI and from Google AI Labs) can count the tokens included in a piece of text. However, both do so by calling a REST API endpoint method called countTokens. This is not ideal, as it requires a network hop to get the token counts, thus adding undesired extra latency. Wouldn’t it be nicer if we could count tokens locally instead?

Every year, in October, takes place the Inktober challenge: every day of the month, you have to do a drawing representing the word of the day. The list of prompts this year is the following:

I participated to some of the daily challenges the past few years, but I never did all of them. But this year, for the fun, I thought I could ask Google’s Imagen 3 image model to draw for me! (Or at least to draw something I could try to reproduce.)

When integrating an LLM into your applicaton to extend it and make it smarter, it’s important to be aware of the pitfalls and best practices you need to follow to avoid some common problems and integrate them successfully. This article will guide you through some key best practices that I’ve come across.

Understanding the Challenges of Implementing LLMs in Real-World Applications

One of the first challenges is that LLMs are constantly being improved. This means that the model you start using could change under the hood, and suddenly your application doesn’t work as it did before. Your prompts might need adjustments to work with the newer version, or worse, they might even lead to unintended results!

Prelude

As I’m focusing a lot on Generative AI, I’m curious about how things work under the hood, to better understand what I’m using in my gen-ai powered projects. A topic I’d like to focus on more is: vector embeddings, to explain more clearly what they are, how they are calculated, and what you can do with them.

A colleague of mine, André, was showing me a cool experiment he’s been working on, to help people prepare an interview, with the help of an AI, to shape the structure of the resulting final article to write.

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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.

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.