pylyzer VS fastapi

There's pylyzer[0], but it's in the early stages.

[0] https://github.com/mtshiba/pylyzer

I’ve just found out pylyzer https://github.com/mtshiba/pylyzer

Not sure how good it is.

That explains why I didn't see textDocument/semanticTokens/full or anything like it in pyright's code. Do you happen to know of any Python LSP that has semantic tokens? pylsp, pyright, and jedi-language-server all don't have it. The only one I could find that might support it is https://github.com/mtshiba/pylyzer but I haven't tried that LSP yet

You might want to check Pylyzer then (https://github.com/mtshiba/pylyzer).

I'm not involved at all. It is still very very early in development. But as it is in the same vein, I thought I'd mentioned it here.

I don't need a lot. Something like "go to definition" and "show docstring" would go a long way, "signature help" would be nice... I'd take a nice completion, but that would just be a cherry on top. Any low-effort ways to achieve this? I've found https://github.com/mtshiba/pylyzer which looked really nice (and I could just download the right binary), but it has another programming language as a dependency, which is kind of intimidating...

Perhaps pylyzer can deliver. https://github.com/mtshiba/pylyzer

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

It has been an interesting exercise developing this wrapper component. The fact that it seamlessly integrates with the FastAPI framework is just a bonus for me; I didn't plan for it since I hadn't learned FastAPI at the time. I hope you find this post useful. Thank you for reading, and stay safe as always.

FastAPI is a modern, high-performance web framework for building APIs with Python, based on standard Python type hints.

In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

That’s when I found FastAPI.

FastAPI & Uvicorn

Discussion at FastAPI GitHub: https://github.com/tiangolo/fastapi/issues/2696

An API application framework (such as FastAPI)

It’s been my experience that async Python frameworks tend to turn IO bound problems into CPU bound problems with a high enough request rate, because due to their nature they act as unbounded queues.

This ends up made worse if you’re using sync routes.

If you’re constrained on a resource such as a database connection pool, your framework will continue to pull http requests off the wire that a sane client will cancel and retry due to timeouts because it takes too long to get a connection out of the pool. Since there isn’t a straightforward way to cancel the execution of a route handler in every Python http framework I’ve seen exhibit this problem, the problem quickly snowballs.

This is an issue with fastapi, too- https://github.com/tiangolo/fastapi/issues/5759

Fast API.