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Usage

The following example shows how the step can be used in a recipe.

Examples

Bring the number of categories down to 10 categories

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
column[category|list[category]]
required
original column.
column
required
cleaned column.

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

string
required
Associated integration.
array[string]
Categories desired in the result column. If passed instructions are ignored.
string
Each item in array.
string
Further instructions to generate the desired set of categories. You can ask for things like ‘generate around 5 categories’
[number, integer]
Approximate number of categories to generate. Can be a float from 0 to 1 (percentage of unique categories) or an integer greater than 1.
number
number.Values must be in the following range:
object
Model Configuration. Configuration for OpenAI’s model.
string
default:"gpt-4.1-mini"
OpenAI model to choose.Values must be one of the following:gpt-4.1 gpt-4.1-mini gpt-4.1-nano gpt-5-mini gpt-5-nano o4-mini
number
default:"0.7"
Temperature. Higher means more creativity, but also makes the model more likely to hallucinate. Lower temperature yields more deterministic results. Ignored for reasoning models (gpt-5-mini, gpt-5-nano, o4-mini).Values must be in the following range: