#### API "schema"

In this case, <a href="https://github.com/OAI/OpenAPI-Specification" class="external-link" target="_blank">OpenAPI</a> is a specification that dictates how to define a schema of your API.

This schema definition includes your API paths, the possible parameters they take, etc.

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

But it was created before there existed Python type hints. So, to define every <abbr title="the definition of how data should be formed">schema</abbr> you need to use specific utils and classes provided by Marshmallow.

!!! check "Inspired **FastAPI** to"
    Use code to define "schemas" that provide data types and validation, automatically.

    ```

=== "Python 3.8+"

    ```Python hl_lines="9-11  14-15  18  19  23"
    {!> ../../../docs_src/generate_clients/tutorial001.py!}
    ```

Notice that the *path operations* define the models they use for request payload and response payload, using the models `Item` and `ResponseMessage`.

### API Docs

If you can use the **latest versions of Python**, use the examples for the latest version, those will have the **best and simplest syntax**, for example, "**Python 3.10+**".

#### List

For example, let's define a variable to be a `list` of `str`.

=== "Python 3.9+"

    Declare the variable, with the same colon (`:`) syntax.

    As the type, put `list`.

=== "Python 3.8+"

    ```Python hl_lines="31  37"
    {!> ../../../docs_src/response_model/tutorial006.py!}
    ```

## Recap

Use the *path operation decorator's* parameter `response_model` to define response models and especially to ensure private data is filtered out.

            if it should exclude from the output the fields that had the same default
            value, even when they were explicitly set.
            """
        ),
    ] = False,
    exclude_none: Annotated[
        bool,
        Doc(
            """
            Pydantic's `exclude_none` parameter, passed to Pydantic models to define

* API usage monitoring systems
* response data injection systems
* etc.

## Simple and Powerful

Although the hierarchical dependency injection system is very simple to define and use, it's still very powerful.

You can define dependencies that in turn can define dependencies themselves.

Create a variable `ALGORITHM` with the algorithm used to sign the JWT token and set it to `"HS256"`.

Create a variable for the expiration of the token.

Define a Pydantic Model that will be used in the token endpoint for the response.

Create a utility function to generate a new access token.

=== "Python 3.10+"

    ```Python hl_lines="6  12-14  28-30  78-86"

* **Cloud services** that handle this for you
    * The cloud service will probably **handle replication for you**. It would possibly let you define **a process to run**, or a **container image** to use, in any case, it would most probably be **a single Uvicorn process**, and the cloud service would be in charge of replicating it.

!!! tip

```Python hl_lines="2  7"
{!../../../docs_src/custom_response/tutorial001b.py!}
```

!!! info
    The parameter `response_class` will also be used to define the "media type" of the response.

    In this case, the HTTP header `Content-Type` will be set to `application/json`.

    And it will be documented as such in OpenAPI.

!!! tip