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For GPT-6 models, configure reasoning_effort in the corresponding API parameters object. Luna defaults to none; Sol defaults to medium. Explicit reasoning levels are preserved, except minimal becomes low for either model and Sol uses low when none is requested. Temperature and sampling settings are ignored when reasoning is enabled.

Usage

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

Examples

Generate topics for a given text column

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|text]
required
Column containing the texts to infer topics from.
column[category]
required
Inferred topic for each text.
column[category]
required
Inferred subtopic for each text.

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.
integer
default:"10"
Number of Topics. Maximum approximate number of topics to infer.Values must be in the following range:
integer
default:"5"
Number of Subtopics. Maximum approximate number of subtopics to infer per topic.Values must be in the following range:
integer
default:"500"
Number of Samples. Maximum number of text samples to use for topic extraction. More texts consume more tokens and increase cost.Values must be in the following range:
boolean
default:"false"
Multi-topic Assignment. Whether to allow assigning multiple topics to each text. If enabled, the output columns will contain lists of topics/subtopics instead of single values.
string
Inference AI Model. AI model used to infer topic hierarchy. This model will receive all sample texts, so potentially a large context, and needs to be reasonably capable to generate a well-structured topic hierarchy (no repeated or similar topics etc.).Values must be one of the following:openai/gpt-4.1 openai/gpt-6-luna openai/gpt-5 openai/gpt-6.1-sol openai/gpt-5.1 openai/gpt-5.2
string
default:"openai/gpt-6-luna"
Assignment AI Model. AI model used to assign topic and subtopics to each text (row). This model will receive individual texts along with the inferred topic hierarchy, so it can be a smaller model focused on classification.Values must be one of the following:openai/gpt-4.1 openai/gpt-6-luna openai/gpt-5 openai/gpt-6.1-sol openai/gpt-5.1 openai/gpt-5.2
object
Inference Parameters. Additional parameters for the inference call, such as {"reasoning_effort": "high"}.
object
Assignment Parameters. Additional parameters for the assignment call, such as {"reasoning_effort": "none"} for Luna.