You can combine `dataclasses` with other type annotations in many different combinations to form complex data structures.

Check the in-code annotation tips above to see more specific details.

## Learn More

You can also combine `dataclasses` with other Pydantic models, inherit from them, include them in your own models, etc.

```

The following arguments are supported:

* `minimum_size` - Do not GZip responses that are smaller than this minimum size in bytes. Defaults to `500`.

## Other middlewares

There are many other ASGI middlewares.

For example:

* <a href="https://docs.sentry.io/platforms/python/guides/fastapi/" class="external-link" target="_blank">Sentry</a>

    # to be read from files and other types, but I'm not even sure it's a good idea
    # to support it as a first class "feature"
    assert is_bytes_sequence_annotation(Union[List[str], List[bytes]])


def test_is_uploadfile_sequence_annotation():
    # For coverage
    # TODO: in theory this would allow declaring types that could be lists of UploadFile

```Python hl_lines="10  12-13"
{!../../../docs_src/middleware/tutorial001.py!}
```

## Other middlewares

You can later read more about other middlewares in the [Advanced User Guide: Advanced Middleware](../advanced/middleware.md){.internal-link target=_blank}.

    We are not restricted to having only one dependency that can return that type of data.

## Other models

You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`.

FastAPI uses a standard for building Python web frameworks and servers called <abbr title="Asynchronous Server Gateway Interface">ASGI</abbr>. FastAPI is an ASGI web framework.

The main thing you need to run a **FastAPI** application (or any other ASGI application) in a remote server machine is an ASGI server program like **Uvicorn**, this is the one that comes by default in the `fastapi` command.

There are several alternatives, including:

    assert response.json() == {"detail": "Not authenticated"}


def test_security_http_digest_incorrect_scheme_credentials():
    response = client.get(
        "/users/me", headers={"Authorization": "Other invalidauthorization"}
    )
    assert response.status_code == 403, response.text
    assert response.json() == {"detail": "Invalid authentication credentials"}


def test_openapi_schema():

class OtherItem(BaseModel):
    price: int


@app.post("/items/")
def save_union_body(item: Union[OtherItem, Item]):
    return {"item": item}


client = TestClient(app)


def test_post_other_item():
    response = client.post("/items/", json={"price": 100})
    assert response.status_code == 200, response.text
    assert response.json() == {"item": {"price": 100}}


def test_post_item():

## Benchmarks and speed

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

```txt
fastapi>=0.45.0,<0.46.0
```

that would mean that you would use the versions `0.45.0` or above, but less than `0.46.0`, for example, a version `0.45.2` would still be accepted.

If you use any other tool to manage your installations, like Poetry, Pipenv, or others, they all have a way that you can use to define specific versions for your packages.

## Available versions