mkdocstrings VS fastapi

Mkdocs has the mkdocstrings plugin, offering limited automated API documentation capabilities. It is however nowhere near as capable as Sphinx' autodoc, missing granularity in its configuration, limited intersphinx-like cross-referencing support, and essential features like documentation of inherited members, or the ability to manually describe objects if needed.

mkdocstrings works well, although it is not as powerful as the API documentation in Sphinx.

Author of Materia for MkDocs here. MkDocstrings [1] implements automatic generation of reference documentation from sources. It's language-agnostic, actively maintained and currently supports Python [2] and Crystal [3]. It also integrates nicely with Material for MkDocs.

[1]: https://mkdocstrings.github.io/

Some time has passed since I first introduced mkdocstrings here on reddit. If you don't know what mkdocstrings is: it's the equivalent of the autodoc Sphinx extension, but for MkDocs, a Markdown static site generator. It works differently though, and supports multiple languages by design (not only Python). Someone actually wrote a very good handler for the Crystal language, and another user on GitHub recently expressed their interest for writing one for Go.

Write proper docstrings as you go along (every time you write a new class/method/function you can document what it’s doing as you’ll know why and what from the pattern you chose). Using a tool like mkdocstrings makes maintaining documentation for larger projects automatic.

I learned of https://github.com/pawamoy/mkdocstrings recently. Is it similar ?

A user of mkdocstrings wrote a Crystal handler for their own use-case. They asked on the Gitter channel if we could allow to load external handlers, so they don't have to fork the project and install the fork, but rather just install their lightweight package containing just the handler.

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:

FastAPI is a modern, fast, web framework for building APIs with Python 3.7+ based on standard Python type hints. It is designed to be easy to use, fast to run, and secure. In this blog post, we’ll explore the key features of FastAPI and walk through the process of creating a simple API using this powerful framework.