blossom VS fastapi

When looking at the API, the way we get all of the information we need, we have access to a fairly large amount of information. The Developer Platform, while it has its uses, is not designed with large and busy bots in mind. It's much better suited for entry-level 'learning to code' style bots that we see frequently pop up on the site. Issues from data usage limits, data storage limits (500kb! Our database in Blossom is over 1.4GB), connecting to services outside reddit (which we have gotten special permissions for, I'll give them that), and more plague the development experience at the time of this writing.

u/goldieczr here's the repo for our primary site, data storage, and API. Python / Django. https://github.com/grafeasGroup/blossom

Really, there was only one choice, and Blossom was brought to life. As the leader of the trio, it just made sense.

In software development, tooling refers to programs or scripts that only serve to maintain other programs or scripts. In our case, we write tooling for the QA and Engagment teams so that they can directly interact with the keeper of data, Blossom, or various spreadsheets that are used to keep track of different things (like the delightfully awesome Treasure Hunts in Discord).

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.