sinch.domains.conversation.models.v1.sinch_events.smart_conversations.analysis_result module

class sinch.domains.conversation.models.v1.sinch_events.smart_conversations.analysis_result.AnalysisResult(
*,
ml_sentiment_result: Annotated[list[MachineLearningSentimentResult], Len(min_length=0, max_length=None)] | None = None,
ml_nlu_result: Annotated[list[MachineLearningNLUResult], Len(min_length=0, max_length=None)] | None = None,
ml_image_recognition_result: Annotated[list[MachineLearningImageRecognitionResult], Len(min_length=0, max_length=None)] | None = None,
ml_pii_result: Annotated[list[MachineLearningPIIResult], Len(min_length=0, max_length=None)] | None = None,
ml_offensive_analysis_result: Annotated[list[OffensiveAnalysis], Len(min_length=0, max_length=None)] | None = None,
**extra_data: Any,
)[source]

Bases: SinchEvent

The analysis provided by the Smart Conversations machine learning engine(s). The contents of the object are determined by the functionalities that are enabled for your solution.

Parameters:
ml_sentiment_result: Annotated[list[MachineLearningSentimentResult], Len(min_length=0, max_length=None)] | None
ml_nlu_result: Annotated[list[MachineLearningNLUResult], Len(min_length=0, max_length=None)] | None
ml_image_recognition_result: Annotated[list[MachineLearningImageRecognitionResult], Len(min_length=0, max_length=None)] | None
ml_pii_result: Annotated[list[MachineLearningPIIResult], Len(min_length=0, max_length=None)] | None
ml_offensive_analysis_result: Annotated[list[OffensiveAnalysis], Len(min_length=0, max_length=None)] | None
model_config = {'extra': 'allow', 'populate_by_name': True}

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

model_post_init(_SnakifyExtrasOnInit__context: Any) → None

Marks applied non-None defaults as set.

Parameters:

_SnakifyExtrasOnInit__context (Any)

Return type:

None