"Mounting" means adding a complete "independent" application in a specific path, that then takes care of handling all the sub-paths.

This is different from using an `APIRouter` as a mounted application is completely independent. The OpenAPI and docs from your main application won't include anything from the mounted application, etc.

You can read more about this in the [Advanced User Guide](../advanced/index.md){.internal-link target=_blank}.

## Details

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"

```Python hl_lines="7"
{!../../../docs_src/path_params/tutorial002.py!}
```

In this case, `item_id` is declared to be an `int`.

!!! check
    This will give you editor support inside of your function, with error checks, completion, etc.

## Data <abbr title="also known as: serialization, parsing, marshalling">conversion</abbr>

    * 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**:

That way, you don't have to "install" your local version to be able to test every change.

!!! note "Technical Details"
    This only happens when you install using this included `requirements.txt` instead of running `pip install fastapi` directly.

!!! 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.

    {!> ../../../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. 🤓

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