> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fiddler.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# VectorFeature

> Represents custom features derived from a single vector column using clustering analysis.

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 theme={null}
vector_feature = VectorFeature(
    name="embedding_clusters",
    column="user_embedding",
    n_clusters=10
)
```

Creating a feature from model hidden states:

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

## type

## n\_clusters

## centroids

## column

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

### Returns

`int`

## model\_config

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