return name

    async def get_model_c() -> ModelC:
        return ModelC(username="test-user", password="test-password")

    @app.get("/model/{name}", response_model=ModelA)
    async def get_model_a(name: str, model_c=Depends(get_model_c)):
        return {"name": name, "description": "model-a-desc", "foo": model_c}

    client = TestClient(app)
    return client


@needs_pydanticv2

            "field (e.g. Union[Response, dict, None]) you can disable generating the "
            "response model from the type annotation with the path operation decorator "
            "parameter response_model=None. Read more: "
            "https://fastapi.tiangolo.com/tutorial/response-model/"
        ) from None


def create_cloned_field(
    field: ModelField,
    *,

    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`

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

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

The dataclass will be automatically converted to a Pydantic dataclass.

## Réponse supplémentaire avec `model`

Vous pouvez ajouter à votre décorateur de *paramètre de chemin* un paramètre `responses`.

Il prend comme valeur un `dict` dont les clés sont des codes HTTP pour chaque réponse, comme `200`, et la valeur de ces clés sont d'autres `dict` avec des informations pour chacun d'eux.

Chacun de ces `dict` de réponse peut avoir une clé `model`, contenant un modèle Pydantic, tout comme `response_model`.

            "content": {"application/x-yaml": {"schema": Item.model_json_schema()}},
            "required": True,
        },
    },
)
async def create_item(request: Request):
    raw_body = await request.body()
    try:
        data = yaml.safe_load(raw_body)
    except yaml.YAMLError:
        raise HTTPException(status_code=422, detail="Invalid YAML")
    try:
        item = Item.model_validate(data)
    except ValidationError as e:

Тим самим способом ця база даних не прийматиме об'єкт типу Pydantic model (об'єкт з атрибутами), а лише `dict`.

Ви можете використовувати `jsonable_encoder` для цього.

Вона приймає об'єкт, такий як Pydantic model, і повертає його версію, сумісну з JSON:

=== "Python 3.10+"

    ```Python hl_lines="4  21"

```Python hl_lines="34"
{!../../../docs_src/body_updates/tutorial002.py!}
```

### 使用 Pydantic 的 `update` 参数

接下来，用 `.copy()` 为已有模型创建调用 `update` 参数的副本，该参数为包含更新数据的 `dict`。

例如，`stored_item_model.copy(update=update_data)`：

```Python hl_lines="35"
{!../../../docs_src/body_updates/tutorial002.py!}
```

### 更新部分数据小结

简而言之，更新部分数据应：

* 使用 `PATCH` 而不是 `PUT` （可选，也可以用 `PUT`）；
* 提取存储的数据；

```

!!! info
    In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`.

    The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2.

!!! note

!!! info
    Note that the `TestClient` receives data that can be converted to JSON, not Pydantic models.

    If you have a Pydantic model in your test and you want to send its data to the application during testing, you can use the `jsonable_encoder` described in [JSON Compatible Encoder](encoder.md){.internal-link target=_blank}.

## Run it

⚫️ 🏆 🚫 🎦 🆙 🧾, ✋️ 🎏 🧰 (✅ 🐉) 🔜 💪 ⚙️ 🎂.

```Python hl_lines="19-29"
{!../../../docs_src/path_operation_advanced_configuration/tutorial004.py!}
```

## 🌖 📨

👆 🎲 ✔️ 👀 ❔ 📣 `response_model` & `status_code` *➡ 🛠️*.

👈 🔬 🗃 🔃 👑 📨 *➡ 🛠️*.

👆 💪 📣 🌖 📨 ⏮️ 👫 🏷, 👔 📟, ♒️.

📤 🎂 📃 📥 🧾 🔃 ⚫️, 👆 💪 ✍ ⚫️ [🌖 📨 🗄](additional-responses.md){.internal-link target=_blank}.

## 🗄 ➕