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. Read more...

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. Read more...

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! Read more...

While LangChain4j 0.34 introduced my new Google AI Gemini module, a new 0.35.0 version is already here today, with some more cool stuff for Gemini and Google Cloud! Let’s have a look at what’s in store! Gemini 1.5 Pro 002 and Gemini 1.5 Flash 002 This week, Google announced the release of the new versions of the Google 1.5 models: google-1.5-pro-002 google-1.5-flash-002 Of course, both models are supported by LangChain4j! The Google AI Gemini module also supports the gemini-1. Read more...

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. Read more...

A new version of LangChain4j, the super powerful LLM toolbox for Java developers, was released today. In 0.34.0, a new Gemini model has been added. This time, this is not the Gemini flavor from Google Cloud Vertex AI, but the Google AI variant. It was a frequently requested feature by LangChain4j users, so I took a stab at developing a new chat model for it, during my summer vacation break. Read more...

As I work a lot with Large Language Models (LLMs), I often have to deal with flaky test cases, because LLMs are not always consistent and deterministic in their responses. Thus, sometimes, a test passes maybe a few times in a row, but then, once in a while, it fails. Maybe some prompt tweaks will make the test pass more consistently, lowering the temperature too, or using techniques like few-shot prompting will help the model better understand what it has to do. Read more...

In this article, we’ll explore another great task where Large Language Models shine: entity and data extraction. LLMs are really useful beyond just mere chatbots (even smart ones using Retrieval Augmented Generation). Let me tell you a little story of a handy application we could build, for wannabe Instagram influencers! Great Instagram hashtags, thanks to LLMs When posting Instagram pictures, I often struggle with finding the right hashtags to engage with the community. Read more...

Generative AI use cases are usually about running large language models somewhere in the cloud. However, with the advent of smaller models and open models, you can run them locally on your machine, with projects like llama.cpp or Ollama. And what about in the browser? With MediaPipe and TensorFlow.js, you can train and run small neural networks for tons of fun and useful tasks (like recognising hand movements through the webcam of your computer), and it’s also possible to run Gemma 2B and even 7B models. Read more...

In a rencent article, we talked about text classification using Gemini and LangChain4j. A typical example of text classification is the case of sentiment analysis. In my LangChain4j-powered Gemini workshop, I used this use case to illustrate the classification problem: ChatLanguageModel model = VertexAiGeminiChatModel.builder() .project(System.getenv("PROJECT_ID")) .location(System.getenv("LOCATION")) .modelName("gemini-1.5-flash-001") .maxOutputTokens(10) .maxRetries(3) .build(); PromptTemplate promptTemplate = PromptTemplate.from(""" Analyze the sentiment of the text below. Respond only with one word to describe the sentiment. INPUT: This is fantastic news! Read more...