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docs/en/docs/benchmarks.md
* So, by using FastAPI you are saving development time, bugs, lines of code, and you would probably get the same performance (or better) you would...
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docs/en/docs/deployment/versions.md
You shouldn't pin the version of `starlette`. Different versions of **FastAPI** will use a specific newer version of Starlette. So, you can just let **FastAPI** use the correct Starlette version. ## About Pydantic Pydantic includes the tests for **FastAPI** with its own tests, so new versions of Pydantic (above `1.0.0`) are always compatible with FastAPI.
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docs/en/docs/tutorial/path-params.md
```Python hl_lines="6-7" {!../../../docs_src/path_params/tutorial001.py!} ``` The value of the path parameter `item_id` will be passed to your function as the argument `item_id`. So, if you run this example and go to <a href="http://127.0.0.1:8000/items/foo" class="external-link" target="_blank">http://127.0.0.1:8000/items/foo</a>, you will see a response of: ```JSON {"item_id":"foo"} ```
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docs/en/docs/advanced/dataclasses.md
This is still supported thanks to **Pydantic**, as it has <a href="https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">internal support for `dataclasses`</a>. So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses. And of course, it supports the same: * data validation
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docs/en/docs/advanced/testing-database.md
In this example we'll create a temporary database only for the tests. ## File structure We create a new file at `sql_app/tests/test_sql_app.py`. So the new file structure looks like: ``` hl_lines="9-11" . └── sql_app ├── __init__.py ├── crud.py ├── database.py ├── main.py ├── models.py ├── schemas.py └── tests
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docs/de/docs/project-generation.md
* Frontend-Tests, welche zur Erstellungszeit ausgeführt werden (können auch deaktiviert werden). * So modular wie möglich gestaltet, sodass es sofort einsatzbereit ist. Sie können es aber mit Vue CLI neu generieren oder es so wie Sie möchten erstellen und wiederverwenden, was Sie möchten. * **PGAdmin** für die PostgreSQL-Datenbank, können Sie problemlos ändern, sodass PHPMyAdmin und MySQL verwendet wird.
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docs/de/docs/tutorial/dependencies/sub-dependencies.md
# Unterabhängigkeiten Sie können Abhängigkeiten erstellen, die **Unterabhängigkeiten** haben. Diese können so **tief** verschachtelt sein, wie nötig. **FastAPI** kümmert sich darum, sie aufzulösen. ## Erste Abhängigkeit, „Dependable“ Sie könnten eine erste Abhängigkeit („Dependable“) wie folgt erstellen: === "Python 3.10+" ```Python hl_lines="8-9" {!> ../../../docs_src/dependencies/tutorial005_an_py310.py!} ```
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docs/en/docs/tutorial/static-files.md
You can read more about this in the [Advanced User Guide](../advanced/index.md){.internal-link target=_blank}. ## Details The first `"/static"` refers to the sub-path this "sub-application" will be "mounted" on. So, any path that starts with `"/static"` will be handled by it. The `directory="static"` refers to the name of the directory that contains your static files.
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docs/de/docs/benchmarks.md
* Wenn Sie Starlette vergleichen, vergleichen Sie es mit Webframeworks (oder Mikroframeworks) wie Sanic, Flask, Django, usw. * **FastAPI**: * So wie Starlette Uvicorn verwendet und nicht schneller als dieses sein kann, verwendet **FastAPI** Starlette, sodass es nicht schneller als dieses sein kann.
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docs/en/docs/advanced/middleware.md
A middleware doesn't have to be made for FastAPI or Starlette to work, as long as it follows the ASGI spec. In general, ASGI middlewares are classes that expect to receive an ASGI app as the first argument. So, in the documentation for third-party ASGI middlewares they will probably tell you to do something like: ```Python from unicorn import UnicornMiddleware app = SomeASGIApp()
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