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

    So this will help us avoiding confusion while using both.

### Create initial Pydantic *models* / schemas

Create an `ItemBase` and `UserBase` Pydantic *models* (or let's say "schemas") to have common attributes while creating or reading data.

    return (
        _annotation_is_complex(annotation)
        or _annotation_is_complex(origin)
        or hasattr(origin, "__pydantic_core_schema__")
        or hasattr(origin, "__get_pydantic_core_schema__")
    )


def field_annotation_is_scalar(annotation: Any) -> bool:
    # handle Ellipsis here to make tuple[int, ...] work nicely
    return annotation is Ellipsis or not field_annotation_is_complex(annotation)

# Settings and Environment Variables

In many cases your application could need some external settings or configurations, for example secret keys, database credentials, credentials for email services, etc.

Most of these settings are variable (can change), like database URLs. And many could be sensitive, like secrets.

For this reason it's common to provide them in environment variables that are read by the application.

## Environment Variables

!!! tip

# If running behind a proxy like Nginx or Traefik add --proxy-headers
# CMD ["fastapi", "run", "app/main.py", "--port", "80", "--proxy-headers"]
```

</details>

## What is a Container

Containers (mainly Linux containers) are a very **lightweight** way to package applications including all their dependencies and necessary files while keeping them isolated from other containers (other applications or components) in the same system.

        for key, value in obj.items():
            if (
                (
                    not sqlalchemy_safe
                    or (not isinstance(key, str))
                    or (not key.startswith("_sa"))
                )
                and (value is not None or not exclude_none)
                and key in allowed_keys
            ):
                encoded_key = jsonable_encoder(
                    key,

        {
            "detail": [
                {
                    "type": "greater_than_equal",
                    "loc": ["path", "item_id"],
                    "msg": "Input should be greater than or equal to 3",
                    "input": "2",
                    "ctx": {"ge": 3.0},
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {

А также вы можете добавить валидацию числовых данных:

* `gt`: больше (`g`reater `t`han)
* `ge`: больше или равно (`g`reater than or `e`qual)
* `lt`: меньше (`l`ess `t`han)
* `le`: меньше или равно (`l`ess than or `e`qual)

!!! info "Информация"
    `Query`, `Path` и другие классы, которые мы разберём позже, являются наследниками общего класса `Param`.

You do that with standard modern Python types.

You don't have to learn a new syntax, the methods or classes of a specific library, etc.

Just standard **Python**.

For example, for an `int`:

```Python
item_id: int
```

or for a more complex `Item` model:

```Python
item: Item
```

...and with that single declaration you get:

    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py39
def test_login_incorrect_username(client: TestClient):
    response = client.post("/token", data={"username": "foo", "password": "secret"})
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py39
def test_no_token(client: TestClient):

    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py310
def test_login_incorrect_username(client: TestClient):
    response = client.post("/token", data={"username": "foo", "password": "secret"})
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "Incorrect username or password"}


@needs_py310
def test_no_token(client: TestClient):