The docs UI would also need the OpenAPI schema to declare that this API `server` is located at `/api/v1` (behind the proxy). For example:

```JSON hl_lines="4-8"
{
    "openapi": "3.1.0",
    // More stuff here
    "servers": [
        {
            "url": "/api/v1"
        }
    ],
    "paths": {
            // More stuff here
    }
}
```

#### 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"

    * But it provides you the tools to build simple web applications, with routing based on paths, etc.
    * If you are comparing Starlette, compare it against Sanic, Flask, Django, etc. Web frameworks (or microframeworks).
* **FastAPI**:

!!! check "Inspired **FastAPI** to"
    Define extra validations for data types using the "default" value of model attributes. This improves editor support, and it was not available in Pydantic before.

            This doesn't affect `Path` parameters as the value is always required.
            The parameter is available only for compatibility.
            """
        ),
    ] = _Unset,
    alias: Annotated[
        Optional[str],
        Doc(
            """
            An alternative name for the parameter field.

            This will be used to extract the data and for the generated OpenAPI.

    {!> ../../../docs_src/response_model/tutorial003_01.py!}
    ```

With this, we get tooling support, from editors and mypy as this code is correct in terms of types, but we also get the data filtering from FastAPI.

How does this work? Let's check that out. 🤓

### Type Annotations and Tooling

First let's see how editors, mypy and other tools would see this.

* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

    But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

        "optional-dependencies", {}
    )
    # Get custom optional dependencies name to always include in this (non-slim) package
    include_optional_dependencies: List[str] = config.get(
        "include-optional-dependencies", []
    )
    # Override main [project] configs with custom configs for this package
    for key, value in project_config.items():
        metadata[key] = value

If you generate a client for a FastAPI app using tags, it will normally also separate the client code based on the tags.

This way you will be able to have things ordered and grouped correctly for the client code:

<img src="/img/tutorial/generate-clients/image06.png">

In this case you have:

* `ItemsService`
* `UsersService`

### Client Method Names

│   ├── main.py
│   └── test_main.py
```

Because this file is in the same package, you can use relative imports to import the object `app` from the `main` module (`main.py`):

```Python hl_lines="3"
{!../../../docs_src/app_testing/test_main.py!}
```

...and have the code for the tests just like before.

## Testing: extended example

Now let's extend this example and add more details to see how to test different parts.