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Represents custom features derived from a single vector column using clustering analysis. VectorFeature processes high-dimensional vector data (like embeddings or feature vectors) by applying k-means clustering to create discrete clusters that can be monitored for distribution changes over time. This is particularly useful for monitoring embedding drift in high-dimensional spaces. The feature type is automatically set to CustomFeatureType.FROM_VECTOR and creates meaningful groupings from vector data for drift detection and anomaly identification.

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source_column

Optional original column if this feature is derived from an embedding

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Examples

Creating a feature from a general embedding column: 
vector_feature = VectorFeature(
    name="embedding_clusters",
    column="user_embedding",
    n_clusters=10
)
Creating a feature from model hidden states: 
hidden_feature = VectorFeature(
    name="hidden_state_clusters",
    column="model_hidden_layer",
    n_clusters=15,
    source_column="input_features"
)

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type

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n_clusters

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centroids

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column

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classmethod validate_n_clusters()

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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].