/** Array of fields that can be highlighted in search results */
    protected String[] highlightedFields;

    /** Array of fields that can be searched against */
    protected String[] searchFields;

    /** Set of fields that can be searched against for O(1) lookup */
    protected Set<String> searchFieldSet;

    /** Array of fields that can be used for faceted search */
    protected String[] facetFields;

{* ../../docs_src/query_params_str_validations/tutorial006_an_py310.py hl[9] *}

### Required, can be `None` { #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`.

    # o filterVariablesMap: (NotRequired - Default map:{})
    #  You can specify the filter variables for DDL.
    #
    #; filterVariablesMap = map:{abc=AAA}
    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    # o additionalUserMap: (NotRequired - Default map:{})
    #  You can set additional users.
    #  Elements of this map are as below:

There are several parameters that you can pass to your *path operation decorator* to configure it.

/// warning

Notice that these parameters are passed directly to the *path operation decorator*, not to your *path operation function*.

///

## Response Status Code { #response-status-code }

You can define the (HTTP) `status_code` to be used in the response of your *path operation*.

You can pass directly the `int` code, like `404`.

# Response Model - Return Type { #response-model-return-type }

You can declare the type used for the response by annotating the *path operation function* **return type**.

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

{* ../../docs_src/response_model/tutorial001_01_py310.py hl[16,21] *}

///

This is of course not the frontend for the final users, but it's a great automatic tool to document interactively all your API.

It can be used by the frontend team (that can also be yourself).

It can be used by third party applications and systems.

And it can also be used by yourself, to debug, check and test the same application.

## The `password` flow { #the-password-flow }

You can probably skip this part.

///

There's an alternative way to define this logic to be executed during *startup* and during *shutdown*.

You can define event handlers (functions) that need to be executed before the application starts up, or when the application is shutting down.

These functions can be declared with `async def` or normal `def`.

# Path Parameters and Numeric Validations { #path-parameters-and-numeric-validations }

In the same way that you can declare more validations and metadata for query parameters with `Query`, you can declare the same type of validations and metadata for path parameters with `Path`.

## Import `Path` { #import-path }

First, import `Path` from `fastapi`, and import `Annotated`:

{* ../../docs_src/path_params_numeric_validations/tutorial001_an_py310.py hl[1,3] *}

There is a `#contributing` channel on the community Slack which you can use
to ask any questions.

## Finding issues to work on

If you are looking for good first issues, take a look at the list of [good first issues](https://github.com/gradle/gradle/labels/good%20first%20issue) that should be actionable and ready for a contribution.

## Base64 vs Files { #base64-vs-files }

Consider first if you can use [Request Files](../tutorial/request-files.md) for uploading binary data and [Custom Response - FileResponse](./custom-response.md#fileresponse--fileresponse-) for sending binary data, instead of encoding it in JSON.

JSON can only contain UTF-8 encoded strings, so it can't contain raw bytes.