Seu modelo de resposta pode ter valores padrão, como:

{* ../../docs_src/response_model/tutorial004_py310.py hl[9,11:12] *}

* `description: Union[str, None] = None` (ou `str | None = None` no Python 3.10) tem um padrão de `None`.
* `tax: float = 10.5` tem um padrão de `10.5`.
* `tags: List[str] = []` tem um padrão de uma lista vazia: `[]`.

mas você pode querer omiti-los do resultado se eles não foram realmente armazenados.

然后使用 Pydantic 模型验证这些数据（捕获 `ValidationError` 异常），如果读取 JWT 令牌或用 Pydantic 验证数据时出错，就抛出我们之前创建的 `HTTPException`。

为此，我们给 Pydantic 模型 `TokenData` 添加了一个新属性 `scopes`。

通过用 Pydantic 验证数据，我们可以确保确实得到了例如一个由作用域组成的 `list[str]`，以及一个 `str` 类型的 `username`。

而不是，例如得到一个 `dict` 或其它什么，这可能会在后续某个时刻破坏应用，形成安全风险。

我们还验证是否存在该用户名的用户，如果没有，就抛出前面创建的同一个异常。

{* ../../docs_src/security/tutorial005_an_py310.py hl[47,117:129] *}

接著用 Pydantic 模型驗證這些資料（捕捉 `ValidationError` 例外），若在讀取 JWT token 或用 Pydantic 驗證資料時出錯，就丟出先前建立的 `HTTPException`。

為此，我們更新了 Pydantic 模型 `TokenData`，加入新屬性 `scopes`。

透過 Pydantic 驗證資料，我們可以確保，例如，scopes 正好是 `list` 的 `str`，而 `username` 是 `str`。

否則若是 `dict` 或其他型別，可能在後續某處使應用壞掉，造成安全風險。

我們也會確認該 `username` 對應的使用者是否存在，否則同樣丟出之前建立的例外。

{* ../../docs_src/security/tutorial005_an_py310.py hl[47,117:129] *}

## 驗證 `scopes` { #verify-the-scopes }

from typing import Annotated

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    hidden_query: Annotated[str | None, Query(include_in_schema=False)] = None,
):
    if hidden_query:
        return {"hidden_query": hidden_query}
    else:

Ancak bir query parametresini zorunlu yapmak istediğinizde, herhangi bir varsayılan değer tanımlamamanız yeterlidir:

{* ../../docs_src/query_params/tutorial005_py310.py hl[6:7] *}

Burada query parametresi `needy`, `str` tipinde zorunlu bir query parametresidir.

Tarayıcınızda şöyle bir URL açarsanız:

```
http://127.0.0.1:8000/items/foo-item
```

...zorunlu `needy` parametresini eklemeden, şuna benzer bir hata görürsünüz:

from typing import Annotated

from fastapi import FastAPI, Form
from fastapi.testclient import TestClient
from inline_snapshot import snapshot

app = FastAPI()


@app.post("/form/")
def post_form(username: Annotated[str, Form()]):
    return username


client = TestClient(app)


def test_single_form_field():
    response = client.post("/form/", data={"username": "Rick"})
    assert response.status_code == 200, response.text

## Classes as dependencies { #classes-as-dependencies_1 }

You might notice that to create an instance of a Python class, you use that same syntax.

For example:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

In this case, `fluffy` is an instance of the class `Cat`.

And to create `fluffy`, you are "calling" `Cat`.

Você deve ter percebido que para criar um instância de uma classe em Python, a mesma sintaxe é utilizada.

Por exemplo:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

Nesse caso,  `fluffy` é uma instância da classe `Cat`.

E para criar `fluffy`, você está "chamando" `Cat`.

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(q: str | None = Query(default=None, alias="item-query")):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})

from fastapi import FastAPI, Path, Query

app = FastAPI()


@app.get("/items/{item_id}")
async def read_items(
    *,
    item_id: int = Path(title="The ID of the item to get", ge=0, le=1000),
    q: str,
    size: float = Query(gt=0, lt=10.5),
):
    results = {"item_id": item_id}
    if q:
        results.update({"q": q})
    if size:
        results.update({"size": size})