You can declare a `response_model`, using the default status code `200` (or a custom one if you need), and then declare additional information for that same response in `responses`, directly in the OpenAPI schema.

**FastAPI** will keep the additional information from `responses`, and combine it with the JSON Schema from your model.

## 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"

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+

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

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)

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.

/// tip

Prefer to use the `Annotated` version if possible.

///

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

////

## Declare `Cookie` parameters

Then declare the cookie parameters using the same structure as with `Path` and `Query`.

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

//// tab | Python 3.10+

type MessageThreshold struct {
	diag.Level
}

// String is a function declared in the pflag.Value interface
func (m *MessageThreshold) String() string {
	return m.Level.String()
}

// Type is a function declared in the pflag.Value interface
func (m *MessageThreshold) Type() string {
	return "Level"
}

// Set is a function declared in the pflag.Value interface
func (m *MessageThreshold) Set(s string) error {

* La documentación alternativa también reflejará el nuevo parámetro de query y el body:

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Resumen

En resumen, declaras los tipos de parámetros, body, etc. **una vez** como parámetros de la función.

Lo haces con tipos modernos estándar de Python.

No tienes que aprender una sintaxis nueva, los métodos o clases de una library específica, etc.