pydantic-factories VS datamodel-code-generator

Last but not least, we moved pydantic-factories from my GitHub namespace (Goldziher) into the starlite-api GitHub organization, so its now an official package of the org.

I'm the author and sole maintainer of a library called pydantic-factories.

You can see the docs here.

Here is the link: https://github.com/Goldziher/pydantic-factories

I'm glad to say pydantic-factories is now at v1.0.0 and includes support for pydantic v1.9.0+.

So - some of you perhaps read my previous post regarding pydantic-factories, but for those who didn't- its a python package that allows you to generate mock data for your pydantic models and dataclasses. It also works for vanilla python dataclasses, so its actually not only for pydantic.

looking at your badges - i don't care your "docs" pipeline is passing, what I am not seeing is test coverage. I suggest you checkout integrating sonar-cloud with your github repository. You can see how this looks here: https://github.com/Goldziher/pydantic-factories

Here is a link to the repository - https://github.com/Goldziher/pydantic-factories, I would of course love getting more stars. But I would especially love getting more users and contributors - really do feel free to add PRs, write issues and suggestions etc.

Like generating pydantic models or dataclasses for an OpenAPI schema? I haven't needed to go in that direction myself, but this[0] looks promising!

Apologies if I've misunderstood your comment

https://koxudaxi.github.io/datamodel-code-generator/

I'm sorry, but you have completely misunderstood the purpose of Open API.

It is not a specification to define your business logic classes and objects -- either client or server side. Its goal is to define the interface of an API, and to provide a single source of truth that requests and responses can be validated against. It contains everything you need to know to make requests to an API; code generation is nice to have (and I use it myself, but mainly on the server side, for routing and validation), but not something required or expected from OpenAPI

For what it's worth, my personal preferred workflow to build an API is as follows:

1. Build the OpenAPI spec first. A smaller spec could easily be done by hand, but I prefer using a design tool like Stoplight [0]; it has the best Web-based OpenAPI (and JSON Schema) editor I have encountered, and integrates with git nearly flawlessly.

2. Use an automated tool to generate the API code implementation. Again, a static generation tool such as datamodel-code-generator [1] (which generates Pydantic models) would suffice, but for Python I prefer the dynamic request routing and validation provided by pyapi-server [2].

3. Finally, I use automated testing tools such as schemathesis [3] to test the implementation against the specification.

[0] https://stoplight.io/

[1] https://koxudaxi.github.io/datamodel-code-generator/

[2] https://pyapi-server.readthedocs.io

[3] https://schemathesis.readthedocs.io

The site also provide a link to the github repo of the underlying program.

Not sure who owns/hosts the site, but its based on this github repo.

That's what datamodel-code-generator propose.

had generally good experience creating typed wrappers for api's with json-schema-to-pydantic[0] converter

[0] https://github.com/koxudaxi/datamodel-code-generator

To save you some work, if you have already an OpenAPI specification at hand, you can use datamodel-code-generator to generate your Pydantic models from the spec.

And there are code generators for it! https://github.com/koxudaxi/datamodel-code-generator/