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. 🤓

## Dataclasses in `response_model`

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.

In that case, it would mean the JSON attributes, and data types they have, etc.

#### OpenAPI and JSON Schema

If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.

!!! tip

### Disable Response Model

Continuing from the example above, you might not want to have the default data validation, documentation, filtering, etc. that is performed by FastAPI.

But you might want to still keep the return type annotation in the function to get the support from tools like editors and type checkers (e.g. mypy).

    url: https://github.com/tiangolo/fastapi/discussions/categories/questions
  - name: Feature Request
    about: To suggest an idea or ask about a feature, please start with a question saying what you would like to achieve. There might be a way to do it already.
    url: https://github.com/tiangolo/fastapi/discussions/categories/questions
  - name: Show and tell
    about: Show what you built with FastAPI or to be used with FastAPI.

    # For coverage
    assert UploadFile.__get_pydantic_core_schema__(str, lambda x: None) == {}


@needs_pydanticv1
def test_union_scalar_list():
    # For coverage
    # TODO: there might not be a current valid code path that uses this, it would
    # potentially enable query parameters defined as both a scalar and a list
    # but that would require more refactors, also not sure it's really useful

    ```

Notice that we declare the type of `current_user` as the Pydantic model `User`.

This will help us inside of the function with all the completion and type checks.

!!! tip
    You might remember that request bodies are also declared with Pydantic models.

    Here **FastAPI** won't get confused because you are using `Depends`.

!!! check

Depending on your use case, you might prefer to use a different library, but if you asked me, I would probably suggest you try **Strawberry**.

Here's a small preview of how you could integrate Strawberry with FastAPI:

```Python hl_lines="3  22  25-26"

## Separating tests

In a real application, you probably would have your tests in a different file.

And your **FastAPI** application might also be composed of several files/modules, etc.

### **FastAPI** app file

Let's say you have a file structure as described in [Bigger Applications](bigger-applications.md){.internal-link target=_blank}:

```
.

# Development - Contributing

First, you might want to see the basic ways to [help FastAPI and get help](help-fastapi.md){.internal-link target=_blank}.

## Developing

If you already cloned the <a href="https://github.com/tiangolo/fastapi" class="external-link" target="_blank">fastapi repository</a> and you want to deep dive in the code, here are some guidelines to set up your environment.

### Virtual environment with `venv`