app = FastAPI()


@app.post("/sync/validated", response_model=ItemOut)
def sync_validated(item: ItemIn, dep: Annotated[int, Depends(dep_b)]):
    return ItemOut(name=item.name, value=item.value, dep=dep)


@app.get("/sync/dict-no-response-model")
def sync_dict_no_response_model():
    return {"name": "foo", "value": 123}


@app.get("/sync/dict-with-response-model", response_model=ItemOut)
def sync_dict_with_response_model(

@app.get("/items/valid", response_model=Item)
def get_valid():
    return {"name": "valid", "date": datetime(2021, 7, 26), "price": 1.0}


@app.get("/items/object", response_model=Item)
def get_object():
    return Item(
        name="object", date=datetime(2021, 7, 26), price=1.0, owner_ids=[1, 2, 3]
    )


@app.get("/items/coerce", response_model=Item)
def get_coerce():

    owner_ids: list[int] | None = 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"},

@app.get("/response_model-no_annotation-return_invalid_dict", response_model=User)
def response_model_no_annotation_return_invalid_dict():
    return {"name": "John"}


@app.get("/response_model-no_annotation-return_invalid_model", response_model=User)
def response_model_no_annotation_return_invalid_model():
    return Item(name="Foo", price=42.0)


@app.get(
    "/response_model-no_annotation-return_dict_with_extra_data", response_model=User
)

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",
    )

In those cases, you can use the *path operation decorator* parameter `response_model` instead of the return type.

You can use the `response_model` parameter in any of the *path operations*:

* `@app.get()`
* `@app.post()`
* `@app.put()`
* `@app.delete()`
* etc.

{* ../../docs_src/response_model/tutorial001_py310.py hl[17,22,24:27] *}

/// note

///

### Prioridade `response_model` { #response-model-priority }

Se você declarar tanto um tipo de retorno quanto um `response_model`, o `response_model` terá prioridade e será usado pelo FastAPI.

Wenn Sie jedoch eine Menge Datenklassen herumliegen haben, ist dies ein guter Trick, um sie für eine Web-API mithilfe von FastAPI zu verwenden. 🤓

///

## Datenklassen in `response_model` { #dataclasses-in-response-model }

Sie können `dataclasses` auch im Parameter `response_model` verwenden:

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

Die Datenklasse wird automatisch in eine Pydantic-Datenklasse konvertiert.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

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

You can also use `dataclasses` in the `response_model` parameter:

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

The dataclass will be automatically converted to a Pydantic dataclass.