* `title`: The OpenAPI title, shown in the docs.
* `version`: The version of your API, e.g. `2.5.0`.
* `openapi_version`: The version of the OpenAPI specification used. By default, the latest: `3.1.0`.
* `summary`: A short summary of the API.
* `description`: The description of your API, this can include markdown and will be shown in the docs.

{* ../../docs_src/metadata/tutorial004.py hl[3:16,18] *}

Notice that you can use Markdown inside of the descriptions, for example "login" will be shown in bold (**login**) and "fancy" will be shown in italics (_fancy_).

/// tip

You don't have to add metadata for all the tags that you use.

///

### Use your tags { #use-your-tags }

## Documentation Response description - OpenAPI { #documentation-response-description-openapi }

To define the description of the response, shown in the docs UI, read the docs for [Tutorial - Path Operation Configurations - Response description](../tutorial/path-operation-configuration.md#response-description){.internal-link target=_blank}.

{* ../../docs_src/additional_responses/tutorial003.py hl[20:31] *}

It will all be combined and included in your OpenAPI, and shown in the API docs:

<img src="/img/tutorial/additional-responses/image01.png">

## Combine predefined responses and custom ones { #combine-predefined-responses-and-custom-ones }

<img src="/img/tutorial/generate-clients/image01.png">

You can see those schemas because they were declared with the models in the app.

That information is available in the app's **OpenAPI schema**, and then shown in the API docs.

That same information from the models that is included in OpenAPI is what can be used to **generate the client code**.

### Hey API { #hey-api }

## Automatic docs { #automatic-docs }

The JSON Schemas of your models will be part of your OpenAPI generated schema, and will be shown in the interactive API docs:

<img src="/img/tutorial/body/image01.png">

And will also be used in the API docs inside each *path operation* that needs them:

<img src="/img/tutorial/body/image02.png">

All these dependencies, while declaring their requirements, also add parameters, validations, etc. to your *path operations*.

## Custom Validation { #custom-validation }

There could be cases where you need to do some **custom validation** that can't be done with the parameters shown above.

In those cases, you can use a **custom validator function** that is applied after the normal validation (e.g. after validating that the value is a `str`).

Each specific example `dict` in the `examples` can contain:

* `summary`: Short description for the example.
* `description`: A long description that can contain Markdown text.
* `value`: This is the actual example shown, e.g. a `dict`.
* `externalValue`: alternative to `value`, a URL pointing to the example. Although this might not be supported by as many tools as `value`.

You can use it like this:

    *   Add `tf.keras.layers.AbstractRNNCell` as the preferred implementation
        for RNN cells in TF v2. User can use it to implement RNN cells with
        custom behavior.
    *   Keras training and validation curves are shown on the same plot when
        using the TensorBoard callback.
    *   Switched Keras `fit/evaluate/predict` execution to use only a single
        unified path by default unless eager execution has been explicitly