Parameters

Graphbook supports a variety of parameter types that can be used to define the inputs and outputs of your steps.

Available Parameters

Below is a list of the available values that can be passed as a parameter type in Graphbook:

• string

• number

• boolean

• function

• list[string]

• list[number]

• list[boolean]

• list[function]

• dict

• resource*

* Any type that is not (string..dict) will default to resource. A resource does not correspond to a widget type but is used to indicate that the parameter accepts resource nodes.

Examples

Below shows an example for string and number parameters. Multiple parameters can be used at the same time:

function

custom_nodes/my_steps.py

from graphbook import step, param

@step("MyStep")
@param("message", type="string", default="Hello, World!")
@param("offset", type="number", default=0)
def my_step(ctx, data: dict):
    data["message"] = ctx.message
    data["offset"] = ctx.offset

class

custom_nodes/my_steps.py

from graphbook.steps import Step

class MyStep(Step):
    RequiresInput = True
    Parameters = {
        "message": {
            "type": "string",
            "default": "Hello, World!"
        },
        "offset": {
            "type": "number",
            "default": 0
        }
    }
    Outputs = ["out"]
    Category = ""
    def __init__(self, message, offset):
        super().__init__()
        self.message = message
        self.offset = offset

    def on_data(self, data: dict) -> str:
        data["message"] = self.message
        data["offset"] = self.offset

Below shows an example for a list of strings:

function

custom_nodes/my_steps.py

from graphbook import step, param

@step("MyStep")
@param("cars", type="list[string]", default=["car", "truck"])
def my_step(ctx, data: dict):
    # Access from ctx.cars
    ...

class

custom_nodes/my_steps.py

from graphbook.steps import Step

class MyStep(Step):
    RequiresInput = True
    Parameters = {
        "cars": {
            "type": "list[string]",
            "default": ["car", "truck"]
        },
    }
    Outputs = ["out"]
    Category = ""
    def __init__(self, cars):
        super().__init__()
        self.cars = cars

Below shows an example for a dictionary:

function

custom_nodes/my_steps.py

from graphbook import step, param

@step("MyStep")
@param("car", type="dict", default={
    "make": "Toyota",
    "model": "Camry",
    "price": 25000,
    "in_stock": True
})
def my_step(ctx, data: dict):
    # Access from ctx.car
    ...

class

custom_nodes/my_steps.py

from graphbook.steps import Step

class MyStep(Step):
    RequiresInput = True
    Parameters = {
        "car": {
            "type": "dict",
            "default": {
                "make": "Toyota",
                "model": "Camry",
                "price": 25000,
                "in_stock": True
            }
        },
    }
    Outputs = ["out"]
    Category = ""
    def __init__(self, car):
        super().__init__()
        self.car = car

    def on_data(self, data: dict):
        ...