infer_topics

Usage

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

Examples

Example 1
Signature

Generate topics for a given text column

infer_topics(ds.texts, {
    "integration": "open-ai-1",
    "n_topics": 10,
    "n_subtopics": 5,
    "inference_model": "openai/gpt-4.1",
    "assignment_model": "openai/gpt-4.1-mini",
}) ->(ds.topic, ds.subtopic)

General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.

infer_topics(texts: category|text, {
    "param": value,
    ...
}) -> (topic: category, subtopic: category)

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").

Inputs

column[category|text]

required

Column containing the texts to infer topics from.

Outputs

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:

2 ≤ n_topics ≤ 20

integer

default:"5"

Number of Subtopics. Maximum approximate number of subtopics to infer per topic.Values must be in the following range:

2 ≤ n_subtopics ≤ 10

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:

1 ≤ n_samples ≤ 10000

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-4.1-mini openai/gpt-4.1-nano openai/gpt-5 openai/gpt-5-mini openai/gpt-5-nano openai/gpt-5.1 openai/gpt-5.2

string

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-4.1-mini openai/gpt-4.1-nano openai/gpt-5 openai/gpt-5-mini openai/gpt-5-nano openai/gpt-5.1 openai/gpt-5.2

object

Inference Parameters. Additional parameters passed to the responses API for the inference call.

object

Assignment Parameters. Additional parameters passed to the responses API for the assignment call.

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Usage

Inputs & Outputs

Configuration

​Usage

​Inputs & Outputs

​Configuration

Usage

Inputs & Outputs

Configuration