Sexual Content Metric

pydantic model ibm_watsonx_gov.metrics.sexual_content.sexual_content_metric.SexualContentMetric

Bases: GenAIMetric

Defines the Sexual Content metric class.

The Sexual Content metric measures the risk of content explicitly related to sexual activities, anatomy, or desires. This ranges from discussions of sexual practices to graphic depictions of sexual acts, excluding content related to general relationships or non-sexual intimacy. It is computed using the granite guardian model.

Examples

  1. Create Sexual Content metric with default parameters and compute using metrics evaluator.
    metric = SexualContentMetric()
result = MetricsEvaluator().evaluate(data={"input_text": "...", metrics=[metric])
  2. Create Sexual Content metric with a custom threshold.
    threshold  = MetricThreshold(type="lower_limit", value=0.5)
metric = SexualContentMetric(threshold=threshold)

Show JSON schema
{
   "title": "SexualContentMetric",
   "description": "Defines the Sexual Content metric class.\n\nThe Sexual Content metric measures the risk of content explicitly related to sexual activities, anatomy, or desires. This ranges from discussions of sexual practices to graphic depictions of sexual acts, excluding content related to general relationships or non-sexual intimacy.\nIt is computed using the granite guardian model.\n\nExamples:\n    1. Create Sexual Content metric with default parameters and compute using metrics evaluator.\n        .. code-block:: python\n\n            metric = SexualContentMetric()\n            result = MetricsEvaluator().evaluate(data={\"input_text\": \"...\", metrics=[metric])\n\n    2. Create Sexual Content metric with a custom threshold.\n        .. code-block:: python\n\n            threshold  = MetricThreshold(type=\"lower_limit\", value=0.5)\n            metric = SexualContentMetric(threshold=threshold)",
   "type": "object",
   "properties": {
      "name": {
         "const": "sexual_content",
         "default": "sexual_content",
         "description": "The sexual content metric name.",
         "title": "Name",
         "type": "string"
      },
      "display_name": {
         "const": "Sexual Content",
         "default": "Sexual Content",
         "description": "The sexual content metric display name.",
         "title": "Display Name",
         "type": "string"
      },
      "type_": {
         "default": "ootb",
         "description": "The type of the metric. Indicates whether the metric is ootb or custom.",
         "examples": [
            "ootb",
            "custom"
         ],
         "title": "Metric type",
         "type": "string"
      },
      "value_type": {
         "default": "numeric",
         "description": "The type of the metric value. Indicates whether the metric value is numeric or categorical.",
         "examples": [
            "numeric",
            "categorical"
         ],
         "title": "Metric value type",
         "type": "string"
      },
      "thresholds": {
         "default": [
            {
               "type": "upper_limit",
               "value": 0.5
            }
         ],
         "description": "The metric thresholds.",
         "items": {
            "$ref": "#/$defs/MetricThreshold"
         },
         "title": "Thresholds",
         "type": "array"
      },
      "tasks": {
         "default": [
            "question_answering",
            "classification",
            "summarization",
            "generation",
            "extraction",
            "retrieval_augmented_generation"
         ],
         "description": "The list of supported tasks.",
         "items": {
            "$ref": "#/$defs/TaskType"
         },
         "title": "Tasks",
         "type": "array"
      },
      "group": {
         "$ref": "#/$defs/MetricGroup",
         "default": "content_safety",
         "description": "The metric group.",
         "title": "Group"
      },
      "is_reference_free": {
         "default": true,
         "description": "Decides whether this metric needs a reference for computation",
         "title": "Is Reference Free",
         "type": "boolean"
      },
      "method": {
         "const": "granite_guardian",
         "default": "granite_guardian",
         "description": "The method used to compute harm metric.",
         "title": "Method",
         "type": "string"
      },
      "metric_dependencies": {
         "default": [],
         "description": "Metrics that needs to be evaluated first",
         "items": {
            "$ref": "#/$defs/GenAIMetric"
         },
         "title": "Metric Dependencies",
         "type": "array"
      },
      "applies_to": {
         "anyOf": [
            {
               "type": "string"
            },
            {
               "type": "null"
            }
         ],
         "default": "message",
         "description": "The tag to indicate for which the metric is applied to. Used for agentic application metric computation.",
         "examples": [
            "message",
            "conversation",
            "sub_agent"
         ],
         "title": "Applies to"
      },
      "mapping": {
         "anyOf": [
            {
               "$ref": "#/$defs/Mapping"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "The data mapping details for the metric which are used to read the values needed to compute the metric.",
         "examples": {
            "items": [
               {
                  "attribute_name": "traceloop.entity.input",
                  "column_name": null,
                  "json_path": "$.inputs.input_text",
                  "lookup_child_spans": false,
                  "name": "input_text",
                  "span_name": "LangGraph.workflow",
                  "type": "input"
               },
               {
                  "attribute_name": "traceloop.entity.output",
                  "column_name": null,
                  "json_path": "$.outputs.generated_text",
                  "lookup_child_spans": false,
                  "name": "generated_text",
                  "span_name": "LangGraph.workflow",
                  "type": "output"
               }
            ],
            "source": "trace"
         },
         "title": "Mapping"
      }
   },
   "$defs": {
      "GenAIMetric": {
         "description": "Defines the Generative AI metric interface",
         "properties": {
            "name": {
               "description": "The name of the metric.",
               "examples": [
                  "answer_relevance",
                  "context_relevance"
               ],
               "title": "Metric Name",
               "type": "string"
            },
            "display_name": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The display name of the metric.",
               "examples": [
                  "Answer Relevance",
                  "Context Relevance"
               ],
               "title": "Metric display name"
            },
            "type_": {
               "default": "ootb",
               "description": "The type of the metric. Indicates whether the metric is ootb or custom.",
               "examples": [
                  "ootb",
                  "custom"
               ],
               "title": "Metric type",
               "type": "string"
            },
            "value_type": {
               "default": "numeric",
               "description": "The type of the metric value. Indicates whether the metric value is numeric or categorical.",
               "examples": [
                  "numeric",
                  "categorical"
               ],
               "title": "Metric value type",
               "type": "string"
            },
            "thresholds": {
               "default": [],
               "description": "The list of thresholds",
               "items": {
                  "$ref": "#/$defs/MetricThreshold"
               },
               "title": "Thresholds",
               "type": "array"
            },
            "tasks": {
               "default": [],
               "description": "The task types this metric is associated with.",
               "items": {
                  "$ref": "#/$defs/TaskType"
               },
               "title": "Tasks",
               "type": "array"
            },
            "group": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/MetricGroup"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The metric group this metric belongs to."
            },
            "is_reference_free": {
               "default": true,
               "description": "Decides whether this metric needs a reference for computation",
               "title": "Is Reference Free",
               "type": "boolean"
            },
            "method": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The method used to compute the metric.",
               "title": "Method"
            },
            "metric_dependencies": {
               "default": [],
               "description": "Metrics that needs to be evaluated first",
               "items": {
                  "$ref": "#/$defs/GenAIMetric"
               },
               "title": "Metric Dependencies",
               "type": "array"
            },
            "applies_to": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": "message",
               "description": "The tag to indicate for which the metric is applied to. Used for agentic application metric computation.",
               "examples": [
                  "message",
                  "conversation",
                  "sub_agent"
               ],
               "title": "Applies to"
            },
            "mapping": {
               "anyOf": [
                  {
                     "$ref": "#/$defs/Mapping"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The data mapping details for the metric which are used to read the values needed to compute the metric.",
               "examples": {
                  "items": [
                     {
                        "attribute_name": "traceloop.entity.input",
                        "column_name": null,
                        "json_path": "$.inputs.input_text",
                        "lookup_child_spans": false,
                        "name": "input_text",
                        "span_name": "LangGraph.workflow",
                        "type": "input"
                     },
                     {
                        "attribute_name": "traceloop.entity.output",
                        "column_name": null,
                        "json_path": "$.outputs.generated_text",
                        "lookup_child_spans": false,
                        "name": "generated_text",
                        "span_name": "LangGraph.workflow",
                        "type": "output"
                     }
                  ],
                  "source": "trace"
               },
               "title": "Mapping"
            }
         },
         "required": [
            "name"
         ],
         "title": "GenAIMetric",
         "type": "object"
      },
      "Mapping": {
         "description": "Defines the field mapping details to be used for computing a metric.",
         "properties": {
            "source": {
               "default": "trace",
               "description": "The source type of the data. Use trace if the data should be read from span in trace. Use tabular if the data is passed as a dataframe.",
               "enum": [
                  "trace",
                  "tabular"
               ],
               "examples": [
                  "trace",
                  "tabular"
               ],
               "title": "Source",
               "type": "string"
            },
            "items": {
               "description": "The list of mapping items for the field. They are used to read the data from trace or tabular data for computing the metric.",
               "items": {
                  "$ref": "#/$defs/MappingItem"
               },
               "title": "Mapping Items",
               "type": "array"
            }
         },
         "required": [
            "items"
         ],
         "title": "Mapping",
         "type": "object"
      },
      "MappingItem": {
         "description": "The mapping details to be used for reading the values from the data.",
         "properties": {
            "name": {
               "description": "The name of the item.",
               "examples": [
                  "input_text",
                  "generated_text",
                  "context",
                  "ground_truth"
               ],
               "title": "Name",
               "type": "string"
            },
            "type": {
               "description": "The type of the item.",
               "enum": [
                  "input",
                  "output",
                  "reference",
                  "context",
                  "tool_call"
               ],
               "examples": [
                  "input"
               ],
               "title": "Type",
               "type": "string"
            },
            "column_name": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The column name in the tabular data to be used for reading the field value. Applicable for tabular source.",
               "title": "Column Name"
            },
            "span_name": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The span name in the trace data to be used for reading the field value. Applicable for trace source.",
               "title": "Span Name"
            },
            "attribute_name": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The attribute name in the trace to be used for reading the field value. Applicable for trace source.",
               "title": "Attribute Name"
            },
            "json_path": {
               "anyOf": [
                  {
                     "type": "string"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": null,
               "description": "The json path to be used for reading the field value from the attribute value. Applicable for trace source. If not provided, the span attribute value is read as the field value.",
               "title": "Json Path"
            },
            "lookup_child_spans": {
               "anyOf": [
                  {
                     "type": "boolean"
                  },
                  {
                     "type": "null"
                  }
               ],
               "default": false,
               "description": "The flag to indicate if all the child spans should be searched for the attribute value. Applicable for trace source.",
               "title": "Look up child spans"
            }
         },
         "required": [
            "name",
            "type"
         ],
         "title": "MappingItem",
         "type": "object"
      },
      "MetricGroup": {
         "enum": [
            "retrieval_quality",
            "answer_quality",
            "content_safety",
            "performance",
            "usage",
            "message_completion",
            "tool_call_quality",
            "readability",
            "custom"
         ],
         "title": "MetricGroup",
         "type": "string"
      },
      "MetricThreshold": {
         "description": "The class that defines the threshold for a metric.",
         "properties": {
            "type": {
               "description": "Threshold type. One of 'lower_limit', 'upper_limit'",
               "enum": [
                  "lower_limit",
                  "upper_limit"
               ],
               "title": "Type",
               "type": "string"
            },
            "value": {
               "default": 0,
               "description": "The value of metric threshold",
               "title": "Threshold value",
               "type": "number"
            }
         },
         "required": [
            "type"
         ],
         "title": "MetricThreshold",
         "type": "object"
      },
      "TaskType": {
         "description": "Supported task types for generative AI models",
         "enum": [
            "question_answering",
            "classification",
            "summarization",
            "generation",
            "extraction",
            "retrieval_augmented_generation"
         ],
         "title": "TaskType",
         "type": "string"
      }
   }
}

Fields:
Validators:

field display_name: Annotated[Literal['Sexual Content'], FieldInfo(annotation=NoneType, required=False, default='Sexual Content', title='Display Name', description='The sexual content metric display name.', frozen=True)] = 'Sexual Content'

The sexual content metric display name.

Validated by:
field group: ', frozen=True)] = MetricGroup.CONTENT_SAFETY

The metric group.

Validated by:
field method: Annotated[Literal['granite_guardian'], FieldInfo(annotation=NoneType, required=False, default='granite_guardian', title='Method', description='The method used to compute harm metric.')] = 'granite_guardian'

The method used to compute harm metric.

Validated by:
field name: Annotated[Literal['sexual_content'], FieldInfo(annotation=NoneType, required=False, default='sexual_content', title='Name', description='The sexual content metric name.', frozen=True)] = 'sexual_content'

The sexual content metric name.

Validated by:
field tasks: Annotated[list[TaskType], FieldInfo(annotation=NoneType, required=False, default=['question_answering', 'classification', 'summarization', 'generation', 'extraction', 'retrieval_augmented_generation'], title='Tasks', description='The list of supported tasks.', frozen=True)] = ['question_answering', 'classification', 'summarization', 'generation', 'extraction', 'retrieval_augmented_generation']

The list of supported tasks.

Validated by:
field thresholds: Annotated[list[MetricThreshold], FieldInfo(annotation=NoneType, required=False, default=[MetricThreshold(type='upper_limit', value=0.5)], title='Thresholds', description='The metric thresholds.')] = [MetricThreshold(type='upper_limit', value=0.5)]

The metric thresholds.

Validated by:
evaluate(data: DataFrame | dict, configuration: GenAIConfiguration, **kwargs)
async evaluate_async(data: DataFrame, configuration: GenAIConfiguration, **kwargs) → list[AggregateMetricResult]
model_post_init(context: Any, /) → None

We need to both initialize private attributes and call the user-defined model_post_init method.