Python Fire VS fastapi

The cli tool [fire](https://github.com/google/python-fire/blob/master/docs/guide...) has a nifty feature where it can generate a cli for any file for you.

So random and math are somewhat usable that way

    $ python -m fire random uniform 0 1

With python-fire you can use either function or class to create your subcommands. But I find working with classes more intuitive and manageable. Our first command is going to be a sub-command that shows us the UTC time.

Have you checked out fire? Personally, I think it's a really elegant solution to turning a callable object into command line. Plus, the chaining function calls feature lets you build some pretty complex command line patterns likes you never seen with other frameworks. Definitely worth giving it a try!

I started with click but found python fire to be so much easier to use.

I highly recommend checking out fire for adding a CLI quickly to little utility scripts that aren't going to be published to the world but just for you.

python fire autogenerates CLI wrappers for python modules, which really synergizes with method-chaining APIs like pandas.

Fire can basically do the first step (object -> CLI):

https://github.com/google/python-fire

Gooey can do (CLI -> GUI):

https://github.com/chriskiehl/Gooey

also, while we're on the subject, fire may not be the same kind of workhorse as argparse or click, but for really simple stuff it's pretty awesome

I'm relatively new to Go (coming from Python) so I haven't been using Cobra (or Go, for that matter) for long but it's clearly very polished -- only friction I was experiencing with it is there's a lot of boilerplate to creating commands and subcommands, that IMO (idea as proven by python-fire) can be naturally (better) expressed as types / fields / methods that are already built into the language.

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.

We will create our API using FastAPI, a modern high-performance web framework for building fast APIs with Python. It is designed to be easy to use, efficient, and highly scalable. Some key features of FastAPI include: