Wenn Sie die `async`-Methoden verwenden, führt **FastAPI** die Datei-Methoden in einem <abbr title="Mehrere unabhängige Kindprozesse">Threadpool</abbr> aus und erwartet sie.

///

/// note | Technische Details zu Starlette

**FastAPI**s `UploadFile` erbt direkt von **Starlette**s `UploadFile`, fügt aber ein paar notwendige Teile hinzu, um es kompatibel mit **Pydantic** und anderen Teilen von FastAPI zu machen.

///

Você pode iniciar vários trabalhadores com a opção de linha de comando `--workers`:

//// tab | `fastapi`

Se você usar o comando `fastapi`:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run --workers 4 <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

Они полезны главным образом, если у вас было приложение FastAPI версии ниже 0.121.0 и вы столкнулись с проблемами зависимостей с `yield`.

///

Зависимости с `yield` со временем изменялись, чтобы учитывать разные случаи применения и исправлять проблемы. Ниже — краткое резюме изменений.

### Зависимости с `yield` и `scope` { #dependencies-with-yield-and-scope }

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: str | None = Query(
        default=None, min_length=3, max_length=50, pattern="^fixedquery$"
    ),
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from functools import lru_cache

from fastapi import Depends, FastAPI

from .config import Settings

app = FastAPI()


@lru_cache
def get_settings():
    return Settings()


@app.get("/info")
async def info(settings: Settings = Depends(get_settings)):
    return {
        "app_name": settings.app_name,
        "admin_email": settings.admin_email,
        "items_per_user": settings.items_per_user,

from typing import Annotated

from fastapi import Cookie, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Cookies(BaseModel):
    session_id: str
    fatebook_tracker: str | None = None
    googall_tracker: str | None = None


@app.get("/items/")
async def read_items(cookies: Annotated[Cookies, Cookie()]):

from typing import Annotated, Union

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: Annotated[Union[str, None], Query(min_length=3)]):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

## Crear un `websocket` { #create-a-websocket }

En tu aplicación de **FastAPI**, crea un `websocket`:

{* ../../docs_src/websockets/tutorial001_py39.py hl[1,46:47] *}

/// note | Detalles Técnicos

También podrías usar `from starlette.websockets import WebSocket`.

from typing import Annotated, Union

from fastapi import Cookie, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Cookies(BaseModel):
    session_id: str
    fatebook_tracker: Union[str, None] = None
    googall_tracker: Union[str, None] = None


@app.get("/items/")
async def read_items(cookies: Annotated[Cookies, Cookie()]):

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str


items = [
    {"name": "Foo", "description": "There comes my hero"},
    {"name": "Red", "description": "It's my aeroplane"},
]


@app.get("/items/", response_model=list[Item])
async def read_items():