"""
        ),
    ] = _Unset,
    # TODO: update when deprecating Pydantic v1, import these types
    # validation_alias: str | AliasPath | AliasChoices | None
    validation_alias: Annotated[
        Union[str, None],
        Doc(
            """
            'Whitelist' validation step. The parameter field will be the single one
            allowed by the alias or set of aliases defined.
            """
        ),

        alias: Optional[str] = None,
        alias_priority: Union[int, None] = _Unset,
        # TODO: update when deprecating Pydantic v1, import these types
        # validation_alias: str | AliasPath | AliasChoices | None
        validation_alias: Union[str, None] = None,
        serialization_alias: Union[str, None] = None,
        title: Optional[str] = None,
        description: Optional[str] = None,

                        "400": {"description": "Client error level 0"},
                        "401": {"description": "Client error level 1"},
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would still have...

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`, and not some random string. This is especially useful for incoming data.

Without a data validation system, you would have to do all the checks by hand, in code.

    return await http_exception_handler(request, exc)


@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request, exc):
    print(f"OMG! The client sent invalid data!: {exc}")
    return await request_validation_exception_handler(request, exc)


@app.get("/items/{item_id}")
async def read_item(item_id: int):
    if item_id == 3:
        raise HTTPException(status_code=418, detail="Nope! I don't like 3.")

## Rajouter plus de validation

Vous pouvez aussi rajouter un second paramètre `min_length` :

```Python hl_lines="9"
{!../../../docs_src/query_params_str_validations/tutorial003.py!}
```

## Ajouter des validations par expressions régulières

    As these keys may not necessarily be part of the OpenAPI specification, some OpenAPI tools, for example [the OpenAPI validator](https://validator.swagger.io/), may not work with your generated schema.

## Recap

You can use Pydantic's `Field` to declare extra validations and metadata for model attributes.

Nhưng mặc định, tất cả **đều hoạt động**.

### Validation

* Validation cho đa số (hoặc tất cả?) **các kiểu dữ liệu** Python, bao gồm:
    * JSON objects (`dict`).
    * Mảng JSON (`list`) định nghĩa kiểu dữ liệu từng phần tử.
    * Xâu (`str`), định nghĩa độ dài lớn nhất, nhỏ nhất.
    * Số (`int`, `float`) với các giá trị lớn nhất, nhỏ nhất, etc.

* Validation cho nhiều kiểu dữ liệu bên ngoài như:
    * URL.

                            "content": {"application/json": {"schema": {}}},
                        },
                        "422": {
                            "description": "Validation Error",
                            "content": {
                                "application/json": {
                                    "schema": {