❯ Guillaume Laforge

google-cloud

Grounding Gemini with Web Search results in LangChain4j

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...

Gemini, Google's Large Language Model, for Java Developers

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. Read more...

Calling Gemma with Ollama, TestContainers, and LangChain4j

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...

Gemini codelab for Java developers using 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: Read more...

Visualize PaLM-based LLM tokens

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...

Image generation with Imagen and LangChain4j

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...

Gemini Function Calling

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...

Visualize and Inspect Workflows Executions

When using a service like Google Cloud Workflows, in particular as your workflows get bigger, it can be difficult to understand what’s going on under the hood. With multiple branches, step jumps, iterations, and also parallel branches and iterations, if your workflow fails during an execution, until now, you had to check the execution status, or go deep through the logs to find more details about the failed step. I have good news for you! Read more...

Hands on Codelabs to dabble with Large Language Models in Java

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...

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...