Parameters

The parameters of a ProcessingStep are specified by the type annotations given. These are required so the functions signature is well-defined and can be passed to the functions manifest. For larger inputs, or inputs that should depend on other computations, use a DataSlots instead.

Standard parameter types, including primitives bool, str, int, float, and list, can be passed directly. For more complex parameters, such as argument lists and StrEnums, it is recommended to use a dataclass or Pydantic classes to create a well-described type.

primitives.py

from typing import Any
from pydantic import conlist, NonNegativeInt

def many_parameters(
            self,
            a: dict,
            b: int,
            c: conlist(int, max_length=3) = (1, 2, 3),
            d: bool = True
) -> int:

my_dataclass.py

import dataclasses
from dataclasses import dataclass

@dataclass
class SubDataclass:
    x: int
    y: float

@dataclass
class MyDataclass:
    i: int
    f: float
    sub: SubDataclass

def use_dataclass(self, my_dc: MyDataclass) -> int:
    ...

my_pydantic_model.py

from pydantic import BaseModel
from enum import StrEnum

class Lang(StrEnum):
    ENGLISH = 'EN'
    GERMAN = 'DE'

class SubBaseModel(BaseModel):
    text: str
    number: float

class MyBaseModel(BaseModel):
    lang: Lang
    sub: SubBaseModel

def use_basemodel(self, my_bm: MyBaseModel) -> int:
    ...