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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.

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Examples

# Creating a text embedding feature for review analysis:

text_feature = TextEmbedding(
    name="review_sentiment_clusters",
    column="review_embedding",
    source_column="review_text",
    n_clusters=8,
    n_tags=20
)

# Creating a feature for document classification:

doc_feature = TextEmbedding(
    name="document_topic_clusters",
    column="doc_embedding",
    source_column="document_content",
    n_clusters=12,
    n_tags=15
)

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type

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source_column

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n_tags

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tf_idf

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classmethod validate_n_tags()

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Returns

int

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model_config

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