## Mix `Path`, `Query` and body parameters { #mix-path-query-and-body-parameters }

First, of course, you can mix `Path`, `Query` and request body parameter declarations freely and **FastAPI** will know what to do.

And you can also declare body parameters as optional, by setting the default to `None`:

{* ../../docs_src/body_multiple_params/tutorial001_an_py310.py hl[18:20] *}

/// note

```JSON
{
    "name": "Foo",
    "price": 45.2
}
```

## Declare it as a parameter { #declare-it-as-a-parameter }

To add it to your *path operation*, declare it the same way you declared path and query parameters:

{* ../../docs_src/body/tutorial001_py310.py hl[16] *}

...and declare its type as the model you created, `Item`.

## Results { #results }

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 { #header-parameters-with-a-pydantic-model }

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 { #query-parameters-with-a-pydantic-model }

```Python
q: Annotated[str | None, Query(min_length=3)] = None
```

So, when you need to declare a value as required while using `Query`, you can simply not declare a default value:

{* ../../docs_src/query_params_str_validations/tutorial006_an_py39.py hl[9] *}

### Required, can be `None` { #required-can-be-none }

    val releasedVersions: Property<ReleasedVersionsDetails>

}

interface ModuleTargetRuntimes {

    /**
     * Declare that this Gradle module runs as part of worker process.
     */
    val usedInWorkers: Property<Boolean>

    /**
     * Declare that this Gradle module runs as part of the wrapper or as part of a client process.
     * Client processes include the Tooling API client or the CLI client.

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

{* ../../docs_src/path_params/tutorial005_py39.py hl[16] *}

# Declare Request Example Data { #declare-request-example-data }

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models }

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

{* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *}

With `Form` you can declare the same configurations as with `Body` (and `Query`, `Path`, `Cookie`), including validation, examples, an alias (e.g. `user-name` instead of `username`), etc.

/// info

`Form` is a class that inherits directly from `Body`.

///

/// tip

# Cookie Parameter Models { #cookie-parameter-models }

If you have a group of **cookies** that are related, you can create a **Pydantic model** to declare them. 🍪

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`. 🤓

///

/// tip