fastapi-azure-auth VS pydantic

I'm under the impression that you work for a company that sells services related to FastAPI? https://github.com/Intility/fastapi-azure-auth

I maintain an open source library in my spare time for free, that you are welcome to ignore if you find better alternatives.

The documentation has a full tutorial in “Tiangolo-style”, which means it guided through setting up a project from scratch, and how to configure Azure appregs from scratch.

I think, in general, most FastAPI and Pydantic related libraries are heavily typed, use poetry, GitHub pipelines, black, isort, flake8 etc. so if you want to look at the ecosystem around a package I’ll recommend a few here, that has a smaller scope than the huge libraries Pydantic/FastAPI are. All packages listed below has all these things.

FastAPI-Azure-Auth [0] is a library to do authentication and authorization through Azure AD using tokens.

ASGI—Correlation-ID[1] is a package that utilizes contextvars to store information through the asyncio stack, in order to attach correlation/request ID to every log message from a request. Available for Django in [2].

Pydantic-factories [3] is an awesome library to mock data for your pydantic models.

[0] https://github.com/Intility/fastapi-azure-auth

If you want to use an external auth provider, I have written a library called FastAPI-Azure-Auth for authentication and authorization using Azure AD (which is free for something like 10.000 users). The tutorial should get you up and running quickly. Please note that this library is only intended to use for APIs (such as I sing a SPA frontend), so if you use jinja templates or render HTML from FastAPI this might not be the solution for you.

Hi! I’m the author of FastAPI-Azure-Auth, a package to handle Azure AD authentication and authorization for your FastAPI APIs. It’s a heavily tested package, supports trio, and the documentation has a full tutorial on how to set up both Azure and FastAPI from scratch.

I agree with you. Most my packages are around ~100 stars, and I'm met with a lot of respect and appreciatio.n[1][2]

My library for Correlation-IDs in Django[3] got implemented by AWX, which also was a nice experience![4] I maintain a lot of small packages, and maybe it is the Django/FastAPI community, but "you'll get a load of entitled users" is straight up not true in my experience.

[1] https://github.com/Intility/fastapi-azure-auth/issues/24

The documentation[1] contains a full tutorial on how to configure Azure AD and FastAPI for both single- and multi-tenant applications. It includes examples on how to lock down your APIs to certain scopes, tenants, roles etc.

[1] https://intility.github.io/fastapi-azure-auth/)

First, note the method prefix_allowed_tokens_fn. This method applies a Pydantic model to constrain/guide how the LLM generates tokens. Next, see how that constrain can be applied to txtai's LLM pipeline.

utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses

I like some of the changes from v1 to v2. But then you have something like this [0] removed from the library without proper documentation or replacement, resulting in ugly workarounds in the link that wont' work properly.

[0]: https://github.com/pydantic/pydantic/discussions/6337

The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.

Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.

Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages

Not sure if it is exactly related, but there is an open issue on Github for that exact message.

Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…

I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.

The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.

pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.

On pypy, v2 beta was really really really slow.