* Validálja hogy az `item_id` `int` típusú a `GET` és `PUT` kérésekben.
    * Ha nem akkor látni fogunk egy tiszta hibát ezzel kapcsolatban.
* ellenőrzi hogyha van egy opcionális query paraméter `q` névvel (azaz `http://127.0.0.1:8000/items/foo?q=somequery`) `GET` kérések esetén.
    * Mivel a `q` paraméter `= None`-al van deklarálva, ezért opcionális.
    * `None` nélkül ez a mező kötelező lenne (mint például a body `PUT` kérések esetén).

Sebastián Ramírez <******@****.***> 1714435394 -0700

        if: steps.cache.outputs.cache-hit != 'true'
        run: pip install -r requirements-tests.txt
      - name: Install Pydantic v2
        run: pip install "pydantic>=2.0.2,<3.0.0"
      - name: Lint
        run: bash scripts/lint.sh

  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version:
          - "3.12"
          - "3.11"
          - "3.10"
          - "3.9"
          - "3.8"

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

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

* You can then modify further the `response` before returning it.

```Python hl_lines="8-9  11  14"
{!../../../docs_src/middleware/tutorial001.py!}
```

!!! tip
    Keep in mind that custom proprietary headers can be added <a href="https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers" class="external-link" target="_blank">using the 'X-' prefix</a>.

## Server Machine and Server Program

There's a small detail about names to keep in mind. 💡

The word "**server**" is commonly used to refer to both the remote/cloud computer (the physical or virtual machine) and also the program that is running on that machine (e.g. Uvicorn).

Just keep in mind that when you read "server" in general, it could refer to one of those two things.

* data validation
* 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.

{!../../../docs_src/behind_a_proxy/tutorial002.py!}
```

Passing the `root_path` to `FastAPI` would be the equivalent of passing the `--root-path` command line option to Uvicorn or Hypercorn.

### About `root_path`

Keep in mind that the server (Uvicorn) won't use that `root_path` for anything else than passing it to the app.

    try:
        return ModelField(**kwargs)  # type: ignore[arg-type]
    except (RuntimeError, PydanticSchemaGenerationError):
        raise fastapi.exceptions.FastAPIError(
            "Invalid args for response field! Hint: "
            f"check that {type_} is a valid Pydantic field type. "
            "If you are using a return type annotation that is not a valid Pydantic "

Just use any kind of model, any kind of class, any kind of database that you need for your application. **FastAPI** has you covered with the dependency injection system.

## Code size

This example might seem verbose. Keep in mind that we are mixing security, data models, utility functions and *path operations* in the same file.

But here's the key point.

The security and dependency injection stuff is written once.