Based on the research, I can confidently say that Prefect will not have issues differentiating between a Pydantic object and a string in your flow parameter annotation

source_fetch: SourceFetchSpec | str

. This is because: 1. Prefect uses Pydantic internally for parameter validation, which has robust support for Union types 2. When you pass either a string or a Pydantic object through a deployment, Pydantic will: - Accept the string if it's a valid string - Accept and validate the Pydantic object if one is provided - Raise a validation error if the provided value doesn't match either type Here's a practical example to illustrate:

python
from prefect import flow
from pydantic import BaseModel

class SourceFetchSpec(BaseModel):
    url: str
    branch: str = "main"

@flow
def my_flow(source_fetch: SourceFetchSpec | str):
    if isinstance(source_fetch, str):
        print(f"Got string: {source_fetch}")
    else:
        print(f"Got SourceFetchSpec: {source_fetch.url}, {source_fetch.branch}")

# These will both work:
my_flow("<https://github.com/org/repo>")
my_flow(SourceFetchSpec(url="<https://github.com/org/repo>"))

When creating a deployment, you can specify either type in your parameters, and Prefect will handle the validation appropriately:

python
flow.from_source(...).deploy(
    name="str-param",
    parameters={"source_fetch": "<https://github.com/org/repo>"}
)

# Or with a Pydantic model (will be serialized/deserialized automatically)
flow.from_source(...).deploy(
    name="pydantic-param",
    parameters={"source_fetch": {"url": "<https://github.com/org/repo>", "branch": "main"}}
)

The only thing to keep in mind is that when passing a Pydantic model through a deployment, it should be provided as a dictionary that matches the model's structure, as shown in the second deployment example above. Prefect will automatically handle the conversion between the dictionary and your Pydantic model.