!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

```Python hl_lines="16"

!!! warning "警告"

    如果您觉得现在还用不到以下技术细节，可以先跳过下面的内容。


`RequestValidationError` 是 Pydantic 的 <a href="https://docs.pydantic.dev/latest/concepts/models/#error-handling" class="external-link" target="_blank">`ValidationError`</a> 的子类。

**FastAPI** 调用的就是 `RequestValidationError` 类，因此，如果在 `response_model` 中使用 Pydantic 模型，且数据有错误时，在日志中就会看到这个错误。

但客户端或用户看不到这个错误。反之，客户端接收到的是 HTTP 状态码为 `500` 的「内部服务器错误」。

from docs_src.events.tutorial003 import (
    app,
    fake_answer_to_everything_ml_model,
    ml_models,
)


def test_events():
    assert not ml_models, "ml_models should be empty"
    with TestClient(app) as client:
        assert ml_models["answer_to_everything"] == fake_answer_to_everything_ml_model
        response = client.get("/predict", params={"x": 2})
        assert response.status_code == 200, response.text

## Create the Pydantic models

Now let's check the file `sql_app/schemas.py`.

!!! tip
    To avoid confusion between the SQLAlchemy *models* and the Pydantic *models*, we will have the file `models.py` with the SQLAlchemy models, and the file `schemas.py` with the Pydantic models.

    These Pydantic models define more or less a "schema" (a valid data shape).

    - tutorial/body-multiple-params.md
    - tutorial/body-fields.md
    - tutorial/body-nested-models.md
    - tutorial/schema-extra-example.md
    - tutorial/extra-data-types.md
    - tutorial/cookie-params.md
    - tutorial/header-params.md
    - tutorial/response-model.md
    - tutorial/extra-models.md
    - tutorial/response-status-code.md
    - tutorial/request-forms.md
    - tutorial/request-files.md

        Doc(
            """
            Pydantic's `include` parameter, passed to Pydantic models to set the
            fields to include.
            """
        ),
    ] = None,
    exclude: Annotated[
        Optional[IncEx],
        Doc(
            """
            Pydantic's `exclude` parameter, passed to Pydantic models to set the
            fields to exclude.
            """
        ),
    ] = None,

    def _model_rebuild(model: Type[BaseModel]) -> None:
        model.model_rebuild()

    def _model_dump(
        model: BaseModel, mode: Literal["json", "python"] = "json", **kwargs: Any
    ) -> Any:
        return model.model_dump(mode=mode, **kwargs)

    def _get_model_config(model: BaseModel) -> Any:
        return model.model_config

    def get_schema_from_model_field(

from fastapi.testclient import TestClient

from docs_src.extra_models.tutorial004 import app

client = TestClient(app)


def test_get_items():
    response = client.get("/items/")
    assert response.status_code == 200, response.text
    assert response.json() == [
        {"name": "Foo", "description": "There comes my hero"},
        {"name": "Red", "description": "It's my aeroplane"},
    ]


def test_openapi_schema():

!!! warning
    👫 📡 ℹ 👈 👆 💪 🚶 🚥 ⚫️ 🚫 ⚠ 👆 🔜.

`RequestValidationError` 🎧-🎓 Pydantic <a href="https://docs.pydantic.dev/latest/concepts/models/#error-handling" class="external-link" target="_blank">`ValidationError`</a>.

**FastAPI** ⚙️ ⚫️ 👈, 🚥 👆 ⚙️ Pydantic 🏷 `response_model`, &amp; 👆 💽 ✔️ ❌, 👆 🔜 👀 ❌ 👆 🕹.

✋️ 👩‍💻/👩‍💻 🔜 🚫 👀 ⚫️. ↩️, 👩‍💻 🔜 📨 "🔗 💽 ❌" ⏮️ 🇺🇸🔍 👔 📟 `500`.

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

## Create Pydantic models

As **Couchbase** "documents" are actually just "JSON objects", we can model them with Pydantic.

### `User` model

First, let's create a `User` model:

```Python hl_lines="24-28"
{!../../../docs_src/nosql_databases/tutorial001.py!}
```

We will use this model in our *path operation function*, so, we don't include in it the `hashed_password`.