{!../../../docs_src/openapi_callbacks/tutorial001.py!}
```

There are 2 main differences from a normal *path operation*:

* It doesn't need to have any actual code, because your app will never call this code. It's only used to document the *external API*. So, the function could just have `pass`.

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

Or you might have any other way to communicate with the WebSocket endpoint.

---

But for this example, we'll use a very simple HTML document with some JavaScript, all inside a long string.

This, of course, is not optimal and you wouldn't use it for production.

In production you would have one of the options above.

* **Güvenli şifreleme** .
* **JWT token** kimlik doğrulama.
* **SQLAlchemy** models (Flask dan bağımsızdır. Celery worker'ları ile kullanılabilir).
* Kullanıcılar için temel başlangıç ​​modeli (gerektiği gibi değiştirin ve kaldırın).
* **Alembic** migration.
* **CORS** (Cross Origin Resource Sharing).
* **Celery** worker'ları ile backend içerisinden seçilen işleri çalıştırabilirsiniz.

```

### Cache the OpenAPI schema

You can use the property `.openapi_schema` as a "cache", to store your generated schema.

That way, your application won't have to generate the schema every time a user opens your API docs.

It will be generated only once, and then the same cached schema will be used for the next requests.

```Python hl_lines="13-14  25-26"
{!../../../docs_src/extending_openapi/tutorial001.py!}

* **セキュアなパスワード** ハッシュ化 (デフォルトで)。
* **JWTトークン** 認証。
* **SQLAlchemy** モデル (Flask用の拡張と独立しているので、Celeryワーカーと直接的に併用できます)。
* 基本的なユーザーモデル (任意の修正や削除が可能)。
* **Alembic** マイグレーション。
* **CORS** (Cross Origin Resource Sharing (オリジン間リソース共有))。
* **Celery** ワーカー。バックエンドの残りの部分からモデルとコードを選択的にインポートし、使用可能。

如果您需要执行繁重的后台计算，并且不一定需要由同一进程运行（例如，您不需要共享内存、变量等），那么使用其他更大的工具（如 <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>）可能更好。

它们往往需要更复杂的配置，即消息/作业队列管理器，如RabbitMQ或Redis，但它们允许您在多个进程中运行后台任务，甚至是在多个服务器中。

要查看示例，查阅 [Project Generators](../project-generation.md){.internal-link target=_blank}，它们都包括已经配置的Celery。

但是，如果您需要从同一个**FastAPI**应用程序访问变量和对象，或者您需要执行小型后台任务（如发送电子邮件通知），您只需使用 `BackgroundTasks` 即可。

## 回顾

# Test required and embedded body parameters with no bodies sent
def test_post_body_example():
    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


def test_openapi_schema():
    response = client.get("/openapi.json")

@needs_py310
def test_post_body_example(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


@needs_py310
def test_openapi_schema(client: TestClient):

@needs_py310
@needs_pydanticv1
def test_post_body_example(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


@needs_py310
@needs_pydanticv1
def test_openapi_schema(client: TestClient):