![]() ![]() Gather accurate recognition data for people, objects and content.ĭetailed media insights: MIME type, size, resolution, format & more. Reduce image size by up to 80% for better UX and SEO.Īutomatically convert images to the most efficient next-gen formats.Ĭrop, resize and adjust colors. Create custom workflows and process in batch via REST API. Use integrated apps to validate, manage and analyze files. ![]() Ship faster with a complete file platform in your toolbelt. This project is licensed under the terms of the MIT license.2 platform Handle all files smart & RESTfully You can install all of these with pip install "fastapi". orjson - Required if you want to use ORJSONResponse.uvicorn - for the server that loads and serves your application.ujson - Required if you want to use UJSONResponse.pyyaml - Required for Starlette's SchemaGenerator support (you probably don't need it with FastAPI).itsdangerous - Required for SessionMiddleware support.python-multipart - Required if you want to support form "parsing", with request.form().jinja2 - Required if you want to use the default template configuration.httpx - Required if you want to use the TestClient.pydantic-extra-types - for extra types to be used with Pydantic.pydantic-settings - for settings management.email_validator - for email validation.To understand more about it, see the section Benchmarks. Independent TechEmpower benchmarks show FastAPI applications running under Uvicorn as one of the fastest Python frameworks available, only below Starlette and Uvicorn themselves (used internally by FastAPI). extremely easy tests based on HTTPX and pytest.Many extra features (thanks to Starlette) as:.GraphQL integration with Strawberry and other libraries.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. Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.With automatic interactive documentation. ![]() Multiple features from each parameter declaration.
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