@Marvin I'm using pydantic to define the flow parameters, and I'm trying to validate the parameters of some schedule using

flow.validate_parameters

and getting an error stating that the BaseModel parameter itself must be provided, even though all arguments in the model are optional.

To assist you with this issue, I need to consider the version of Prefect you are using, as the behavior and specific APIs may differ between Prefect 2.x and 3.x versions. I will investigate the Prefect documentation and GitHub issues for both versions to understand how parameter validation works with Pydantic models and potential fixes to your problem. Please hold on for a moment while I gather the necessary information. I found some relevant insights regarding your issue with parameter validation using Pydantic models in Prefect flows: 1. Prefect Versions Overview: - Prefect 2.x and 3.x handle Pydantic models similarly by coercing types and carrying out validations on flow parameters. - In general, Prefect's flow parameters expect keyword arguments, meaning you need to specify parameter names when invoking flow runs. 2. Parameter Validation with Pydantic: - Both Prefect 2.x and 3.x support using Pydantic models for parameter validation. When using a Pydantic model, each field can have type enforcement, and the validation would throw errors if a type doesn't match. Therefore, if any default or provided value does not comply with the model definition (even as optional), it may lead to validation errors. - To properly handle validations, ensure that even optional fields in your Pydantic model have suitable defaults or are set to

None

. 3. GitHub Issues: - There are issues related to validation errors with Pydantic models. A notable problem indicates that a

ValidationError

might not clearly state which field failed during validation (issue link). This could be why you are seeing errors related to validation not expressing itself clearly. 4. Your Specific Error: - If you are receiving an error about a "BaseModel parameter" not being provided, ensure that your model's instantiation in the flow explicitly accommodates optional fields either with default values or handles

None

appropriately. 5. Resolution Steps: - Verify your Pydantic model: Make sure each optional field in your model is set to

None

by default or try passing the parameters specifically to avoid unintended defaults. - Check your Prefect version and Pydantic versions for compatibility. There might be ongoing fixes in more recent versions (compatibility issue). You can find more information in the documentation links for Prefect 2.x and 3.x: - Prefect 2.x Documentation - Prefect 3.x Documentation

@Marvin I am using 2.x. Related question, in my model all fields have default value, when submitting a flow run how do I structure the parameters so that the default value for all fields in the Pydantic model are used/

When using Prefect 2.x and you have a Pydantic model where all fields have default values, you can invoke the flow without providing any parameters. By default, Prefect will utilize the defaults specified within your Pydantic model. Here's how you can approach this: 1. Define Your Pydantic Model: Ensure your Pydantic model has all fields with default values or default factories:

python
   from pydantic import BaseModel

   class MyModel(BaseModel):
       field1: int = 10
       field2: str = "default_string"
       field3: bool = True

2. Create Your Flow: When defining your flow, use this Pydantic model as a parameter for which Prefect will automatically handle defaults:

python
   from prefect import flow

   @flow
   def my_flow(model: MyModel = MyModel()):
       print(f"Model fields: {model}")

   if __name__ == "__main__":
       my_flow()

3. Submit Flow Runs: When you execute the flow, you don't need to pass any arguments if you want to use the default values:

python
   my_flow()  # This will use the default values: 10, "default_string", True for field1, field2, field3 respectively.

4. Adjusting in Deployment: If you are using Prefect deployments, ensure the deployment doesn't override these defaults unless explicitly needed. The default behavior should respect the defaults set in your Pydantic model unless overridden in the deployment configuration. By structuring your flow and model this way, Prefect will inherently pick up and utilize the default values specified in your Pydantic model without requiring additional input when running the flow. This seamless approach avoids any need for parameter specification unless deviations from defaults are necessary.

Thank you!