Hi ! As anyone tried to validate the parameters of a flow using a Pydandic model or dataclasses ?

Hey @Didier Marin, we actually have plans for this later in the year, but I do know some community members have done this so let’s see if they chime in.

hey, we don’t use pydandic (tho thats a good idea), but we do have some task code for handling things like date params!

@task(name="set-date-params", nout=2)
def set_date_params(start_date: typing.Optional[str], end_date: typing.Optional[str]) -> typing.Tuple[date, date]:
    """
    If start date and end date are explicitly set on the Flow, we simply construct date objects and return them.

    If start_date is not set, we default it to 7 days from the local context date of the flow.

    If end_date is not set, we default it to the local context date of the flow.
    """
    context_run_datetime = context.get("scheduled_start_time")
    aware_utc_run_datetime = context_run_datetime.replace(tzinfo=utc)
    aware_et_run_datetime = aware_utc_run_datetime.astimezone(et)

    if not start_date:
        seven_days_ago = aware_et_run_datetime - timedelta(days=7)
        start_date = seven_days_ago.date().isoformat()

    if not end_date:
        end_date = aware_et_run_datetime.date().isoformat()

    <http://logger.info|logger.info>(f"start_date={start_date}, end_date={end_date}")
    start_date = cast(str, start_date)
    end_date = cast(str, end_date)
    return date.fromisoformat(start_date), date.fromisoformat(end_date)

Hey Zach, thank you for the reply ! I will try this out