rst2nitrile VS fastapi

Last week I ported my static physical book building tooling from rst-based [0] to markdown (pandoc filter) based.

I've used my rst tooling to publish many books (like Effective Pandas) and am wanting to drop rst in an effort to simplify my life. My pandoc toolchain is not in github yet, but preliminary exploration validates that I can publish my next physical book with it (with things like front matter, indices, etc).

In the process I messed around with MyST and mistletoe. I dropped MyST because it was evident I would need to mess around with Sphinx again. Been there done that. Too much abstraction.

Mistletoe would have worked too (I need to create custom fences/markup for a few features) but I wanted to see if I could do it with Pandoc.

The Pandoc distinction between Blocks and Inlines is annoying as is the requirement to handle everything at once. With Pandoc, you only get notified at the start of an element, not the end which probably complicates it a bit more than Mistletoe would have.

(I still need to port my slide generation tooling and will probably use mistletoe for that. For epub generation I think I will stick with Pandoc.)

0 - https://github.com/mattharrison/rst2nitrile

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: