prisma-examples VS fastapi

The usefulness of vector search is already evident by the rate of generative AI adoption. Almost every mainstream documentation site now has an Ask AI button, including Supabase, Netlify, Prisma, and many more.

Prisma

We'll use a simple blogging app as an example through out this article. Our work will be based on the NestJS + Prisma starter project here. You can also create a new project from it with a one liner:

Prisma's docs[0] are one of the best I've read

[0] https://www.prisma.io/docs

Quiero algo de este estilo: https://github.com/prisma/prisma-examples/tree/latest/typescript/graphql-nexus

Prisma is a package that invites new users. It is well-documented and well-suited for your first or 50th Node project. Their documentation contains comprehensive information on the features available. You can also access several guides, including introductory "how-tos," steps for deploying applications that use Prisma, and instructions on migrating from another ORM. The guides are thorough, up-to-date, and easy to follow. In addition, Prisma's team has made sure your experience using their ORM is as painless as possible.

And that's how we can use a Node.js library like Prisma to work with NoSQL and SQL databases. Prisma's documentation page gives further details about this process.

We will follow the documentation to get started with Prisma. You can read and also take a reference to the documentation.

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.