# TextEmbedding

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

```python
# 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
)
```

## type *: Literal\['FROM\_TEXT\_EMBEDDING']*

## source\_column *: str*

## n\_tags *: int | None*

## tf\_idf *: Dict\[str, List] | None*

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

**Return type:** int


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