Upbound Official Providers and some other packages include Pydantic models for their resources. These models enable in-line documentation, linting, autocompletion, and other features from the VSCode Python extension when working with Crossplane resources in embedded Python functions.

The up dependency add command unpacks dependencies that contain Python models into the .up/python directory in your project. You can skip adding the .up directory to source control, as up commands (like up project build or up project run) auto-populate it, or you can manually populate it with up dependency update-cache.

Import models into a function

Each provider’s models are available in their own package, named after the package’s resource names. You can import models to your main.py function file with the following syntax:

from .model.io.upbound.aws.s3.bucket import v1beta1 as bucketv1beta1

Using model types

Once you import the model, you can convert import resources to model types and construct output resources using model types.

Input resources like the observed composite resource are passed to functions as generic structures. You can convert them to model types to take advantage of type checking, linting, and autocompletion:

from crossplane.function.proto.v1 import run_function_pb2 as fnv1
from .model.com.example.platform.xmytype import v1alpha1

def compose(req: fnv1.RunFunctionRequest, rsp: fnv1.RunFunctionResponse):
    observed_xr = v1alpha1.XMyType(**req.observed.composite.resource)

Output resources from a function are also generic structures, but they can be generated using typed models:

from crossplane.function import resource
from crossplane.function.proto.v1 import run_function_pb2 as fnv1
from .model.io.upbound.aws.s3.bucket import v1beta1

def compose(req: fnv1.RunFunctionRequest, rsp: fnv1.RunFunctionResponse):
    bucket = v1beta1.Bucket(
        apiVersion="s3.aws.upbound.io/v1beta1",
        kind="Bucket",
        metadata=metav1.ObjectMeta(
            name="composed-bucket",
        ),
        spec=bucketv1beta1.Spec(
            forProvider=bucketv1beta1.ForProvider(
                region="us-west-1",
            ),
        ),
    )
    resource.update(rsp.desired.resources[bucket.metadata.name], bucket)

Supported packages

All Upbound Official Providers include Python models.

When you build your project with up project build, the generated artifact contains the generated models for your XRDs. You can build a project and then import that project as a dependency for the resources you define. You can also use your own project’s models in your functions as described above.