> ## 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.

# Enrichment

> Represents custom features derived from enrichment operations.

Represents custom features derived from enrichment operations.

Enrichment features apply external processing or analysis to existing columns
to create derived insights. This can include operations like sentiment analysis,
toxicity detection, entity extraction, SQL validation, JSON validation, or any
custom transformation that adds value to the original data.

The feature type is automatically set to CustomFeatureType.ENRICHMENT and enables
domain-specific analysis through configurable enrichment operations.

## Examples

```python theme={null}
# Creating a sentiment analysis enrichment:

sentiment_enrichment = Enrichment(
    name="review_sentiment",
    columns=["review_text"],
    enrichment="sentiment_analysis",
    config={
        "model": "vader",
        "return_scores": True
    }
)

# Creating a SQL validation enrichment:

sql_enrichment = Enrichment(
    name="sql_validation",
    columns=["query_string"],
    enrichment="sql_validation",
    config={
        "dialect": "postgresql",
        "strict": True
    }
)

# Creating a JSON validation enrichment:

json_enrichment = Enrichment(
    name="json_validation",
    columns=["json_string"],
    enrichment="json_validation",
    config={
        "strict": True,
        "validation_schema": {
            "$schema": "https://json-schema.org/draft/2020-12/schema",
            "type": "object",
            "properties": {
                "prop_1": {"type": "number"}
            },
            "required": ["prop_1"],
            "additionalProperties": False
        }
    }
)

# Creating a toxicity detection enrichment:

toxicity_enrichment = Enrichment(
    name="content_toxicity",
    columns=["user_comment"],
    enrichment="toxicity_detection",
    config={
        "threshold": 0.7,
        "categories": ["toxic", "severe_toxic", "obscene"]
    }
)
```

## type

## columns

## enrichment

## config

## model\_config

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