Microsoft Reactor Redmond

Our third stream in the Python + AI series is all about vision models!

Vision models are LLMs that can accept both text and images, like GPT 4o and 4o-mini. You can use those models for image captioning, data extraction, question-answering, classification, and more!

We'll use Python to send images to vision models, build a basic chat-on-images app, and build a multimodal search engine.

Únete a nosotros en una transmisión práctica en vivo donde aprenderás a liberar todo el potencial de VS Code y GitHub Copilot y a mejorar tu experiencia de programación. Tanto si eres principiante como desarrollador experimentado, esta sesión te presentará los flujos de trabajo de desarrollo asistidos por IA, lo que te ayudará a escribir mejor código, más rápido.

In our fifth stream of the Python + AI series, we'll discover how to get LLMs to output structured responses that adhere to a schema. In Python, all we need to do is define a @dataclass or a Pydantic BaseModel, and we get validated output that meets our needs perfectly.

We'll focus on the structured outputs mode available in OpenAI models, but you can use similar techniques with other model providers. Our examples will demonstrate the many ways you can use structured responses, like entity extraction, classification, and agentic workflows.