### Eine `Enum`-Klasse erstellen { #create-an-enum-class }

Importieren Sie `Enum` und erstellen Sie eine Unterklasse, die von `str` und `Enum` erbt.

Indem Sie von `str` erben, weiß die API-Dokumentation, dass die Werte vom Typ `str` sein müssen, und wird in der Lage sein, korrekt zu rendern.

Erstellen Sie dann Klassen-Attribute mit festgelegten Werten, welches die erlaubten Werte sein werden:

app = FastAPI()

items = {"foo": {"name": "Fighters", "size": 6}, "bar": {"name": "Tenders", "size": 3}}


@app.put("/items/{item_id}")
async def upsert_item(
    item_id: str,
    name: Annotated[Union[str, None], Body()] = None,
    size: Annotated[Union[int, None], Body()] = None,
):
    if item_id in items:
        item = items[item_id]
        item["name"] = name
        item["size"] = size

def get_username():
    try:
        yield "Rick"
    except InternalError:
        print("Oops, we didn't raise again, Britney 😱")


@app.get("/items/{item_id}")
def get_item(item_id: str, username: Annotated[str, Depends(get_username)]):
    if item_id == "portal-gun":
        raise InternalError(
            f"The portal gun is too dangerous to be owned by {username}"
        )
    if item_id != "plumbus":

        write.setParam(0x1234, 100L, 50, new byte[10], 0, 10);

        String str = write.toString();
        assertNotNull(str);
        assertTrue(str.contains("SmbComWrite"), "Should contain class name");
        assertTrue(str.contains("fid="), "Should contain fid");
        assertTrue(str.contains("count="), "Should contain count");
        assertTrue(str.contains("offset="), "Should contain offset");
    }

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None
    tags: set[str] = set()


@app.post("/items/", response_model=Item, summary="Create an item")
async def create_item(item: Item):
    """
    Create an item with all the information:

    - **name**: each item must have a name

    return "alice", required_scopes.scopes


def get_data():
    return [1, 2, 3]


def get_data_override():
    return [3, 4, 5]


@app.get("/user")
def read_user(
    user_data: tuple[str, list[str]] = Security(get_user, scopes=["foo", "bar"]),
    data: list[int] = Depends(get_data),
):
    return {"user": user_data[0], "scopes": user_data[1], "data": data}


client = TestClient(app)

    return mod_path


@pytest.fixture(name="main_mod")
def get_main_mod(mod_path: str) -> ModuleType:
    main_mod = importlib.import_module(f"{mod_path}.main")
    return main_mod


@pytest.fixture(name="test_main_mod")
def get_test_main_mod(mod_path: str) -> ModuleType:
    test_main_mod = importlib.import_module(f"{mod_path}.test_main")
    return test_main_mod

같은 방식으로 이 데이터베이스는 Pydantic 모델(속성이 있는 객체)을 받지 않고, `dict` 만을 받습니다.

이를 위해 `jsonable_encoder` 를 사용할 수 있습니다.

Pydantic 모델과 같은 객체를 받고 JSON 호환 가능한 버전으로 반환합니다:

{* ../../docs_src/encoder/tutorial001.py hl[5,22] *}

이 예시는 Pydantic 모델을 `dict`로, `datetime` 형식을 `str`로 변환합니다.

    "isbn-9781439512982": "Isaac Asimov: The Complete Stories, Vol. 2",
}


def check_valid_id(id: str):
    if not id.startswith(("isbn-", "imdb-")):
        raise ValueError('Invalid ID format, it must start with "isbn-" or "imdb-"')
    return id


@app.get("/items/")
async def read_items(
    id: Annotated[str | None, AfterValidator(check_valid_id)] = None,
):
    if id:
        item = data.get(id)
    else:

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: set[str] = set()


@app.post("/items/", response_model=Item, summary="Create an item")
async def create_item(item: Item):
    """
    Create an item with all the information: