# OpenAPI `models`

OpenAPI Pydantic models used to generate and validate the generated OpenAPI.

from docs_src.path_operation_advanced_configuration.tutorial006_py39 import app

client = TestClient(app)


def test_post():
    response = client.post("/items/", content=b"this is actually not validated")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "size": 30,
        "content": {
            "name": "Maaaagic",
            "price": 42,

func (tag Tag) String() string {
	return tag.Key + "=" + tag.Value
}

// IsEmpty returns whether this tag is empty or not.
func (tag Tag) IsEmpty() bool {
	return tag.Key == ""
}

// Validate checks this tag.
func (tag Tag) Validate() error {
	if len(tag.Key) == 0 || utf8.RuneCountInString(tag.Key) > 128 {
		return errInvalidTagKey
	}

	if utf8.RuneCountInString(tag.Value) > 256 {
		return errInvalidTagValue
	}

            throw new CIFSException("Failed to encrypt message", e);
        }
    }

    /**
     * Validate encryption parameters to prevent security issues
     *
     * @return true if parameters are valid
     */
    private boolean validateEncryptionParameters() {
        // Validate cipher ID
        if (cipherId != CIPHER_AES_128_CCM && cipherId != CIPHER_AES_128_GCM && cipherId != CIPHER_AES_256_CCM

## Pour résumer

Il est possible d'ajouter des validateurs et métadonnées pour vos paramètres.

Validateurs et métadonnées génériques:

* `alias`
* `title`
* `description`
* `deprecated`

Validateurs spécifiques aux chaînes de caractères :

* `min_length`
* `max_length`
* `regex`

Parmi ces exemples, vous avez pu voir comment déclarer des validateurs pour les chaînes de caractères.

from .v2 import RequiredParam as RequiredParam
from .v2 import Undefined as Undefined
from .v2 import UndefinedType as UndefinedType
from .v2 import Url as Url
from .v2 import Validator as Validator
from .v2 import _regenerate_error_with_loc as _regenerate_error_with_loc
from .v2 import copy_field_info as copy_field_info
from .v2 import create_body_model as create_body_model

    * Porque las estructuras de datos de pydantic son solo instances de clases que defines; autocompletado, linting, mypy y tu intuición deberían funcionar correctamente con tus datos validados.
* Valida **estructuras complejas**:
    * Uso de modelos jerárquicos de Pydantic, `List` y `Dict` de `typing` de Python, etc.

     *
     * @param form the edit form
     * @return HTML response for the edit page
     */
    @Execute
    @Secured({ ROLE })
    public HtmlResponse edit(final EditForm form) {
        validate(form, messages -> {}, this::asListHtml);
        final PermissionHelper permissionHelper = ComponentUtil.getPermissionHelper();
        final String id = form.id;
        fileConfigService.getFileConfig(id).ifPresent(entity -> {

import org.lastaflute.web.validation.ActionValidator;

/**
 * Fess-specific action validator that extends the LastaFlute ActionValidator.
 * This validator provides validation functionality for Fess web actions with custom
 * message handling and runtime group validation.
 *
 * @param <MESSAGES> the type of user messages used by this validator
 */

        Maven maven = getContainer().lookup(Maven.class);
        MavenExecutionRequest request =
                createMavenExecutionRequest(getProject("simple")).setGoals(asList("validate"));

        MavenExecutionResult result = maven.execute(request);
        assertNotNull(result);

        Class<?> wsrClass = wsrClassCatcher.wsrClassRef.get();
        assertNotNull(wsrClass, "wsr cannot be null");