❯ Guillaume Laforge

Large-Language-Model

Text classification with Gemini and LangChain4j

Generative AI has potential applications far beyond chatbots and Retrieval Augmented Generation. For example, a nice use case is: text classification. I had the chance of meeting some customers and prospects who had the need for triaging incoming requests, or for labeling existing data. In the first case, a government entity was tasked with routing citizen requests to access undisclosed information to the right governmental service that could grant or reject that access. Read more...

Latest Gemini features support in LangChain4j 0.32.0

LangChain4j 0.32.0 was released yesterday, including my pull request with the support for lots of new Gemini features: JSON output mode, to force Gemini to reply using JSON, without any markup, JSON schema, to control and constrain the JSON output to comply with a schema, Response grounding with Google Search web results and with private data in Vertex AI datastores, Easier debugging, thanks to new builder methods to log requests and responses, Function calling mode (none, automatic, or a subset of functions), Safety settings to catch harmful prompts and responses. Read more...

Generative AI in practice: Concrete LLM use cases in Java, with the PaLM API

Large Language Models, available through easy to use APIs, bring powerful machine learning tools in the hands of developers. Although Python is usually seen as the lingua franca of everything ML, with LLM APIs and LLM orchestration frameworks, complex tasks become easier to implement for enterprise developers. Abstract Large language models (LLMs) are a powerful new technology that can be used for a variety of tasks, including generating text, translating languages, and writing different kinds of creative content. Read more...