"msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["path", "item_id"],
                    "msg": "value is not a valid integer",

                    "msg": "Input should be a valid integer, unable to parse string as an integer",
                    "input": "foo",
                }
            ]
        }
    ) | IsDict(
        # TODO: remove when deprecating Pydantic v1
        {
            "detail": [
                {
                    "loc": ["body", "__key__"],
                    "msg": "value is not a valid integer",

                "path",
                "item_id"
            ],
            "msg": "value is not a valid integer",
            "type": "type_error.integer"
        }
    ]
}
```

you will get a text version, with:

```
1 validation error
path -> item_id
  value is not a valid integer (type=type_error.integer)
```

#### `RequestValidationError` vs `ValidationError`

!!! warning

    ```

=== "Python 3.8+"

    ```Python hl_lines="4  10"
    {!> ../../../docs_src/body_nested_models/tutorial005.py!}
    ```

The string will be checked to be a valid URL, and documented in JSON Schema / OpenAPI as such.

## Attributes with lists of submodels

You can also use Pydantic models as subtypes of `list`, `set`, etc.:

=== "Python 3.10+"

                "path",
                "item_id"
            ],
            "msg": "value is not a valid integer",
            "type": "type_error.integer"
        }
    ]
}
```

вы получите текстовую версию:

```
1 validation error
path -> item_id
  value is not a valid integer (type=type_error.integer)
```

#### `RequestValidationError` или `ValidationError`

    )
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "X-Key header invalid"}


@needs_py39
def test_get_valid_headers(client: TestClient):
    response = client.get(
        "/items/",
        headers={
            "X-Token": "fake-super-secret-token",
            "X-Key": "fake-super-secret-key",
        },
    )

    # insert_assert(response.json())
    assert response.json() == {
        "detail": [
            {
                "type": "string_type",
                "loc": ["tags", 3],
                "msg": "Input should be a valid string",
                "input": {"sneaky": "object"},
            }
        ]
    }


@needs_pydanticv2
def test_openapi_schema(client: TestClient):
    response = client.get("/openapi.json")

FastAPI will now:

* **Validate** the data making sure that the max length is 50 characters
* Show a **clear error** for the client when the data is not valid
* **Document** the parameter in the OpenAPI schema *path operation* (so it will show up in the **automatic docs UI**)

## Alternative (old) `Query` as the default value

    )
    assert response.status_code == 400, response.text
    assert response.json() == {"detail": "X-Key header invalid"}


def test_get_valid_headers_items():
    response = client.get(
        "/items/",
        headers={
            "X-Token": "fake-super-secret-token",
            "X-Key": "fake-super-secret-key",
        },
    )

@pytest.fixture(name="client")
def get_client():
    from docs_src.body_multiple_params.tutorial003_an_py310 import app

    client = TestClient(app)
    return client


@needs_py310
def test_post_body_valid(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "importance": 2,
            "item": {"name": "Foo", "price": 50.5},
            "user": {"username": "Dave"},
        },