4:30 odp. - 5:30 odp. (UTC)

Join us for the first session in our Python + AI series! In this session, we'll talk about Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We'll use Python to interact with LLMs using popular packages like the OpenAI SDK and Langchain. We'll experiment with prompt engineering and few-shot examples to improve our outputs. We'll show how to build a full stack app powered by LLMs, and explain the importance of concurrency and streaming for user-facing AI apps. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here

4:30 odp. - 5:30 odp. (UTC)

In our second session of the Python + AI series, we'll dive into a different kind of model: the vector embedding model. A vector embedding is a way to encode a text or image as an array of floating point numbers. Vector embeddings make it possible to perform similarity search on many kinds of content. In this session, we'll explore different vector embedding models, like the OpenAI text-embedding-3 series, with both visualizations and Python code. We'll compare distance metrics, use quantization to reduce vector size, and try out multimodal embedding models. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here

4:30 odp. - 5:30 odp. (UTC)

In our third Python + AI session, we'll explore one of the most popular techniques used with LLMs: Retrieval Augmented Generation. RAG is an approach that sends context to the LLM so that it can provide well-grounded answers for a particular domain. The RAG approach can be used with many kinds of data sources like CSVs, webpages, documents, databases. In this session, we'll walk through RAG flows in Python, starting with a simple flow and culminating in a full-stack RAG application based on Azure AI Search. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here

4:30 odp. - 5:30 odp. (UTC)

Our fourth 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 GPT 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 app with image upload, and even use vision models inside a RAG application. Follow-along live, thanks to GitHub Models and GitHub Codespaces. If you'd like to follow along with the live examples, make sure you've got a GitHub account. You can also join a weekly office hours to ask any questions that don't get answered in the chat, in our AI Discord. Check out more resources here