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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The happy users of Gemini Advanced, the powerful AI web assistant powered by the Gemini model, can execute some Python code, thanks to a built-in Python interpreter. So, for math, logic, calculation questions, the assistant can let Gemini invent a Python script, and execute it, to let users get a more accurate answer to their queries.

But wearing my Apache Groovy hat on, I wondered if I could get Gemini to invoke some Groovy scripts as well, for advanced math questions!

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.

As a follow-up to my talk on generative AI for Java developers, I’ve developed a new presentation that focuses more on the Gemini large multimodal model by Google.

In this talk, we cover the multimodality capabilities of the model, as it’s able to ingest code, PDF, audio, video, and is able to reason about them. Another specificity of Gemini is its huge context window of up to 1 million tokens! This opens interesting perspectives, especially in multimodal scenarios.

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. So I was curious to give it a run with LangChain4j.

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:

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.

So I decided to create a small application that lets users:

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.

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. The codelabs will guide you through the steps to setup the environment, and show you how to use the Google Cloud built-in shell and code editor, to develop in the cloud.