It is used by Pydantic and FastAPI to explicitly declare that a value is required.

///

This will let **FastAPI** know that this parameter is required.

### Required, can be `None`

You can declare that a parameter can accept `None`, but that it's still required. This would force clients to send a value, even if the value is `None`.

To do that, you can declare that `None` is a valid type but still use `...` as the default:

## Multiple path and query parameters

You can declare multiple path parameters and query parameters at the same time, **FastAPI** knows which is which.

And you don't have to declare them in any specific order.

They will be detected by name:

//// tab | Python 3.10+

```Python hl_lines="6  8"

Isso permitiria que você **reutilizasse o modelo** em **diversos lugares**, e também declarasse validações e metadados de todos os parâmetros de uma única vez. 😎

/// note | Nota

Isso é suportado desde o FastAPI versão `0.115.0`. 🤓

///

## Parâmetros de Consulta com um Modelo Pydantic

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Query Parameters with a Pydantic Model

/// tip

Prefer to use the `Annotated` version if possible.

///

```Python hl_lines="3"
{!> ../../docs_src/header_params/tutorial001.py!}
```

////

## Declare `Header` parameters

Then declare the header parameters using the same structure as with `Path`, `Query` and `Cookie`.

You can define the default value as well as all the extra validation or annotation parameters:

//// tab | Python 3.10+

/// tip

If you have strict type checks in your editor, mypy, etc, you can declare the function return type as `Any`.

That way you tell the editor that you are intentionally returning anything. But FastAPI will still do the data documentation, validation, filtering, etc. with the `response_model`.

///

### `response_model` Priority

###  ------------------------------- ###

declare -a residual_args
declare -a java_args
declare -a app_commands
declare -r real_script_path="$(realpath "$0")"
declare -r activator_home="$(realpath "$(dirname "$real_script_path")")"
declare -r app_version="1.3.2"

declare -r app_launcher="${activator_home}/activator-launch-${app_version}.jar"
declare -r script_name=activator
java_cmd=$(get_java_cmd)

```JSON
{"item_id":"foo"}
```

## Path parameters with types

You can declare the type of a path parameter in the function, using standard Python type annotations:

```Python hl_lines="7"
{!../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

/// check

This will give you editor support inside of your function, with error checks, completion, etc.

This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎

/// note

This is supported since FastAPI version `0.115.0`. 🤓

///

## Header Parameters with a Pydantic Model

# Advanced Dependencies

## Parameterized dependencies

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.