It’s called embeddings in other models as well:
https://huggingface.co/blog/getting-started-with-embeddings
https://ollama.com/blog/embedding-models

Also some feedback, a bit more technical, since I was trying to see how it works, more of a suggestion I suppose

It looks like you’re looping through the documents and asking it for known tags, right? ({str(db.current_library.tags)}.)

I don’t know if I would do this through a chat completion and a chat response, there are special functions for keyword-like searching, like embeddings. It’s a lot faster, and also probably way cheaper, since you’re paying barely anything for embeddings compared to chat tokens

So the common way to do something like this in AI would be to use Vectors and embeddings: https://platform.openai.com/docs/guides/embeddings

So - you’d ask for an embedding (A vector) for all your tags first. Then you ask for embeddings of your document.

Then you can do a Nearest Neighbor Search for the tags, and see how closely they match

Recruitment is now basically Dead Internet theory…

Since others already suggested mostly on-topic suggests, here’s an alternative suggestion:

Instead of looking specifically for a mentor - look for an open source project that you can help with. Ideally one with a discord or something to it’s easy to be in contact the the lead dev. A lot people don’t mind mentoring juniors, but in my experience it doesn’t happens that explicitly - “be my mentor” - and it might sound like you’re asking them a lot.

If you invert it into “Hey I wanna help you with your open-source project, but I don’t really know what to do, what your expectations are, how to implement a specific feature” - then you’re offering to do work them, instead of asking for something. And implicitly you’ll get mentorship in return.

And “real” projects probably also look better on your github / portfolio than only some dummy projects for learning purposes