Skip to main content
Represents custom features derived from text embeddings with TF-IDF analysis. TextEmbedding extends VectorFeature to handle text-based embeddings with additional text-specific analysis capabilities. It combines vector clustering with TF-IDF analysis to provide both semantic clustering and keyword extraction for text data. The feature type is automatically set to CustomFeatureType.FROM_TEXT_EMBEDDING and uses clustering combined with TF-IDF summarization for drift computation.

Examples

type

source_column

n_tags

tf_idf

classmethod validate_n_tags()

Returns

int

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].