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