flask-restx VS pydantic

When it comes to Flask, in particular, there’re plenty of choices. And in fairness, not all frameworks are created equal. You’ve got flasgger, restx (successor of flask-restplus), flask-RESTful, and flask-smorest, to mention a few. How do you choose among those???

Flask-RESTX is an extension that adds support for quickly building REST APIs - GitHub - python-restx/flask-restx

Dependendo da linguagem, existem algumas ferramentas que auxiliam na geração automática das docs do Swagger. Por exemplo, no Python+flask existe o flask-restx

However, after following [the example](https://github.com/python-restx/flask-restx/tree/88497ced96674916403fa7829de693eaa3485a08/examples/zoo_app), [more complex tutorials](https://github.com/TheMorpheus407/Tutorials/tree/master/Flask/RestplusAPI) or other [basic tutorials](https://blog.ordix.de/rest), I still only receive 404s when calling get on /api or the endpoints I tried to define.

I have a flask app which is a REST API endpoint built using flask-restx. The APIs are organized neatly using namespaces. I now need to add a new set of API to access mysql data and flask-rest-jsonapi seems to be good fit for it. However I would like to add these new APIs implemented inside a flask-restx namespace.

But if you are using a Flask based app, I recently tried Flask-RESTX library which includes some great features:

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.