@app.get("/items/invalid", response_model=Item)
def get_invalid():
    return {"name": "invalid", "price": "foo"}


@app.get("/items/innerinvalid", response_model=Item)
def get_innerinvalid():
    return {"name": "double invalid", "price": "foo", "owner_ids": ["foo", "bar"]}


@app.get("/items/invalidlist", response_model=list[Item])
def get_invalidlist():
    return [

    "baz": {"name": "Baz", "description": None, "price": 50.2, "tax": 10.5, "tags": []},
}


@app.get("/items/{item_id}", response_model=Item)
async def read_item(item_id: str):
    return items[item_id]


@app.patch("/items/{item_id}", response_model=Item)
async def update_item(item_id: str, item: Item):
    stored_item_data = items[item_id]
    stored_item_model = Item(**stored_item_data)

    owner_ids: Optional[list[int]] = None


@app.get("/items/valid", response_model=Item)
def get_valid():
    return {"name": "valid", "price": 1.0}


@app.get("/items/coerce", response_model=Item)
def get_coerce():
    return {"name": "coerce", "price": "1.0"}


@app.get("/items/validlist", response_model=list[Item])
def get_validlist():
    return [
        {"name": "foo"},

Mas se você tem um monte de dataclasses por aí, este é um truque legal para usá-las para alimentar uma API web usando FastAPI. 🤓

///

## Dataclasses em `response_model` { #dataclasses-in-response-model }

Você também pode usar `dataclasses` no parâmetro `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

A dataclass será automaticamente convertida para uma dataclass Pydantic.

Pero si tienes un montón de dataclasses por ahí, este es un buen truco para usarlos para potenciar una API web usando FastAPI. 🤓

///

## Dataclasses en `response_model` { #dataclasses-in-response-model }

También puedes usar `dataclasses` en el parámetro `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

El dataclass será automáticamente convertido a un dataclass de Pydantic.

Но если у вас уже есть набор dataclasses, это полезный приём — задействовать их для веб-API на FastAPI. 🤓

///

## Dataclasses в `response_model` { #dataclasses-in-response-model }

Вы также можете использовать `dataclasses` в параметре `response_model`:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

Этот dataclass будет автоматически преобразован в Pydantic dataclass.

class PetDB(BaseModel):
    name: str
    owner: UserDB


class PetOut(BaseModel):
    name: str
    owner: User


@app.post("/users/", response_model=User)
async def create_user(user: UserCreate):
    return user


@app.get("/pets/{pet_id}", response_model=PetOut)
async def read_pet(pet_id: int):
    user = UserDB(
        email="******@****.***",
        hashed_password="secrethashed",
    )

数据类的和运作方式与 Pydantic 模型相同。实际上，它的底层使用的也是 Pydantic。

/// info | 说明

注意，数据类不支持 Pydantic 模型的所有功能。

因此，开发时仍需要使用 Pydantic 模型。

但如果数据类很多，这一技巧能给 FastAPI 开发 Web API 增添不少助力。🤓

///

## `response_model` 使用数据类

在 `response_model` 参数中使用 `dataclasses`：

{* ../../docs_src/dataclasses_/tutorial002.py hl[1,7:13,19] *}

本例把数据类自动转换为 Pydantic 数据类。

API 文档中也会显示相关概图：

<img src="/img/tutorial/dataclasses/image01.png">

    message: str


class User(BaseModel):
    username: str
    email: str


@app.post("/items/", response_model=ResponseMessage, tags=["items"])
async def create_item(item: Item):
    return {"message": "Item received"}


@app.get("/items/", response_model=list[Item], tags=["items"])
async def get_items():
    return [
        {"name": "Plumbus", "price": 3},

@app.on_event("startup")
def on_startup():
    create_db_and_tables()


@app.post("/heroes/", response_model=HeroPublic)
def create_hero(hero: HeroCreate, session: SessionDep):
    db_hero = Hero.model_validate(hero)
    session.add(db_hero)
    session.commit()
    session.refresh(db_hero)
    return db_hero


@app.get("/heroes/", response_model=list[HeroPublic])
def read_heroes(
    session: SessionDep,
    offset: int = 0,