More advanced (but equally easy) techniques for declaring deeply nested JSON models (thanks to Pydantic).Security and authentication, including support for OAuth2 with JWT tokens and HTTP Basic auth.A very powerful and easy to use Dependency Injection system.How to set validation constraints as maximum_length or regex.Declaration of parameters from other different places as: headers, cookies, form fields and files.Spoiler alert: the tutorial - user guide includes: ![]() and see how your editor will auto-complete the attributes and know their types:įor a more complete example including more features, see the Tutorial - User Guide. We just scratched the surface, but you already get the idea of how it all works. Provide 2 interactive documentation web interfaces directly.Automatic client code generation systems, for many languages.Document everything with OpenAPI, that can be used by:.Convert from and to JSON automatically.All this would also work for deeply nested JSON objects.Check that it has an optional attribute is_offer, that should be a bool, if present.Check that it has a required attribute price that has to be a float.Check that it has a required attribute name that should be a str.get ( "/" ) def read_root (): return, Read the body as JSON: ![]() Some of them are getting integrated into the core Windows product and some Office products."įrom typing import Union from fastapi import FastAPI from pydantic import BaseModel app = FastAPI () class Item ( BaseModel ): name : str price : float is_offer : Union = None. I'm actually planning to use it for all of my team's ML services at Microsoft. * estimation based on tests on an internal development team, building production applications.
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