# VectorFeature

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.

## source\_column

Optional original column if this feature is derived from an embedding

### Examples

Creating a feature from a general embedding column:

```python
vector_feature = VectorFeature(
    name="embedding_clusters",
    column="user_embedding",
    n_clusters=10
)
```

Creating a feature from model hidden states:

```python
hidden_feature = VectorFeature(
    name="hidden_state_clusters",
    column="model_hidden_layer",
    n_clusters=15,
    source_column="input_features"
)
```

## type *: Literal\['FROM\_VECTOR']*

## n\_clusters *: int | None*

## centroids *: List | None*

## column *: str*

## *classmethod* validate\_n\_clusters(value)

**Return type:** int


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