这将允许你在**多个地方**去**复用模型**，并且一次性为所有参数声明验证和元数据。😎

/// note

FastAPI 从 `0.115.0` 版本开始支持这个特性。🤓

///

## 使用 Pydantic 模型的查询参数

在一个 **Pydantic 模型**中声明你需要的**查询参数**，然后将参数声明为 `Query`：

{* ../../docs_src/query_param_models/tutorial001_an_py310.py hl[9:13,17] *}

**FastAPI** 将会从请求的**查询参数**中**提取**出**每个字段**的数据，并将其提供给你定义的 Pydantic 模型。

## 查看文档

你可以在 `/docs` 页面的 UI 中查看查询参数：

<div class="screenshot">

@pytest.fixture(
    name="client",
    params=[
        pytest.param("tutorial001_py39"),
        pytest.param("tutorial001_py310", marks=needs_py310),
        pytest.param("tutorial001_an_py39"),
        pytest.param("tutorial001_an_py310", marks=needs_py310),
    ],
)
def get_client(request: pytest.FixtureRequest):
    mod = importlib.import_module(f"docs_src.query_param_models.{request.param}")

Y también puedes declarar parámetros del cuerpo como opcionales, estableciendo el valor por defecto a `None`:

{* ../../docs_src/body_multiple_params/tutorial001_an_py310.py hl[18:20] *}

/// note | Nota

Ten en cuenta que, en este caso, el `item` que se tomaría del cuerpo es opcional. Ya que tiene un valor por defecto de `None`.

///

## Import `Path` { #import-path }

First, import `Path` from `fastapi`, and import `Annotated`:

{* ../../docs_src/path_params_numeric_validations/tutorial001_an_py310.py hl[1,3] *}

/// info

FastAPI added support for `Annotated` (and started recommending it) in version 0.95.0.

If you have an older version, you would get errors when trying to use `Annotated`.

E você também pode declarar parâmetros de corpo como opcionais, definindo o valor padrão com `None`:

{* ../../docs_src/body_multiple_params/tutorial001_an_py310.py hl[18:20] *}

/// note | Nota

Repare que, neste caso, o `item` que seria capturado a partir do corpo é opcional. Visto que ele possui `None` como valor padrão.

///

{* ../../docs_src/pydantic_v1_in_v2/tutorial001_an_py310.py hl[1,4] *}

### FastAPI-Unterstützung für Pydantic v1 in v2 { #fastapi-support-for-pydantic-v1-in-v2 }

Deklarieren Sie die erforderlichen **Header-Parameter** in einem **Pydantic-Modell** und dann den Parameter als `Header`:

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

Declare the **header parameters** that you need in a **Pydantic model**, and then declare the parameter as `Header`:

{* ../../docs_src/header_param_models/tutorial001_an_py310.py hl[9:14,18] *}

**FastAPI** will **extract** the data for **each field** from the **headers** in the request and give you the Pydantic model you defined.

## Check the Docs { #check-the-docs }

from ...utils import needs_py310


@pytest.fixture(
    name="mod",
    params=[
        "tutorial001_py39",
        pytest.param("tutorial001_py310", marks=needs_py310),
        "tutorial001_an_py39",
        pytest.param("tutorial001_an_py310", marks=needs_py310),
    ],
)
def get_mod(request: pytest.FixtureRequest):
    mod = importlib.import_module(f"docs_src.cookie_params.{request.param}")

    return mod

@pytest.fixture(
    name="client",
    params=[
        pytest.param("tutorial001_py39"),
        pytest.param("tutorial001_py310", marks=needs_py310),
        pytest.param("tutorial001_an_py39"),
        pytest.param("tutorial001_an_py310", marks=needs_py310),
    ],
)
def get_client(request: pytest.FixtureRequest):
    mod = importlib.import_module(f"docs_src.header_params.{request.param}")

    client = TestClient(mod.app)