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

class sinch.domains.conversation.models.v1.sinch_events.smart_conversations.machine_learning_nlu_result.MachineLearningNLUResult(
*,
intent: Annotated[str, Strict(strict=True)] | None = None,
score: Annotated[float, Strict(strict=True)] | Annotated[int, Strict(strict=True)] | None = None,
message: Annotated[str, Strict(strict=True)] | None = None,
results: Annotated[list[IntentResult], Len(min_length=0, max_length=None)] | None = None,
**extra_data: Any,
)[source]

Bases: IntentResult

Parameters:

  • intent (Annotated[str, Strict(strict=True)] | None)

  • score (Annotated[float, Strict(strict=True)] | Annotated[int, Strict(strict=True)] | None)

  • message (Annotated[str, Strict(strict=True)] | None)

  • results (Annotated[list[IntentResult], Len(min_length=0, max_length=None)] | None)

  • extra_data (Any)

message: Annotated[str, Strict(strict=True)] | None
results: Annotated[list[IntentResult], 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