AIServiceConfiguration

class openapi_client.models.ai_service_configuration.AIServiceConfiguration(**data)

Bases: BaseModel

service_name: StrictStr

status: StrictStr

assigned_models: Optional[List[StrictStr]]

default_model: Optional[StrictStr]

assigned_guard_rails: Optional[List[AllowedGuardRail]]

classmethod status_validate_enum(value)

Validates the enum

model_config: ClassVar[ConfigDict] = {'populate_by_name': True, 'protected_namespaces': (), 'validate_assignment': True}

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

to_str()

Returns the string representation of the model using alias

Return type:

str

to_json()

Returns the JSON representation of the model using alias

Return type:

str

classmethod from_json(json_str)

Create an instance of AIServiceConfiguration from a JSON string

Return type:

Optional[Self]

to_dict()

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

• None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Return type:

Dict[str, Any]

classmethod from_dict(obj)

Create an instance of AIServiceConfiguration from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'assigned_guard_rails': FieldInfo(annotation=Union[List[AllowedGuardRail], NoneType], required=False, alias='AssignedGuardRails', alias_priority=2, description='List of guard rails assigned to this component/service.'), 'assigned_models': FieldInfo(annotation=Union[List[Annotated[str, Strict(strict=True)]], NoneType], required=False, alias='AssignedModels', alias_priority=2, description='List of assigned/allowed model IDs for this component/service.'), 'default_model': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='DefaultModel', alias_priority=2, description="Default model ID or 'N/A' when not applicable."), 'service_name': FieldInfo(annotation=str, required=True, alias='ServiceName', alias_priority=2, description='Component/service identifier (e.g., nl2sql, knowledgebases, agents, chats, datasets, jobs, datalabs, data-pipelines).', metadata=[Strict(strict=True)]), 'status': FieldInfo(annotation=str, required=True, alias='Status', alias_priority=2, description='Enablement status based on associated service toggle.', metadata=[Strict(strict=True)])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_post_init(__context)

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

• self (BaseModel) – The BaseModel instance.

• __context (Any) – The context.

Return type:

None