app = FastAPI(
    servers=[
        {"url": "/", "description": "Default, relative server"},
        {
            "url": "http://staging.localhost.tiangolo.com:8000",
            "description": "Staging but actually localhost still",
        },
        {"url": "https://prod.example.com"},
    ]
)


@app.get("/foo")
def foo():
    return {"message": "Hello World"}


client = TestClient(app)

                                    "converted": {
                                        "summary": "An example with converted data",
                                        "description": "FastAPI can convert price `strings` to actual `numbers` automatically",
                                        "value": {"name": "Bar", "price": "35.4"},
                                    },
                                    "invalid": {

And of course, it supports the same:

* 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.

    "groups" : [ {
      "testProject" : "largeJavaMultiProject",
      "comment" : "We only test the multi-project here since for the monolithic project we would have no cache hits. This would mean we actually would test incremental compilation.",
      "coverage" : {
        "per_commit" : [ "linux" ]
      }
    } ]
  }, {

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## Opinions

"_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"

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## Opinions { #opinions }

"_[...] I'm using **FastAPI** a ton these days. [...] I'm actually planning to use it for all of my team's **ML services at Microsoft**. Some of them are getting integrated into the core **Windows** product and some **Office** products._"

# END TF TEST SUITE OPTIONS

# START CROSS-COMPILE CONFIGS
# Set execution platform to Linux x86
# Note: Lot of the "host_" flags such as "host_cpu" and "host_crosstool_top"
# flags seem to be actually used to specify the execution platform details. It
# seems it is this way because these flags are old and predate the distinction
# between host and execution platform.
common:cross_compile_base --config=clang_local

This has **several benefits**, one of the main ones is that now the parameters of your functions with `Annotated` would **not be affected** at all.