GuardRailChatRequest

class openapi_client.models.guard_rail_chat_request.GuardRailChatRequest(**data)

Bases: BaseModel

Request body for guard rail chat endpoint.

model_id: Optional[StrictStr]

model_parameters: Optional[GuardRailChatRequestModelParameters]

input_text: StrictStr

invoke_model: StrictBool

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 GuardRailChatRequest 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 GuardRailChatRequest from a dict

Return type:

Optional[Self]

model_fields: ClassVar[dict[str, FieldInfo]] = {'input_text': FieldInfo(annotation=str, required=True, alias='InputText', alias_priority=2, description='The input text to be processed by the model.', metadata=[Strict(strict=True)]), 'invoke_model': FieldInfo(annotation=bool, required=True, alias='InvokeModel', alias_priority=2, description='Whether to invoke the model (true) or apply guard rail validation only (false).', metadata=[Strict(strict=True)]), 'model_id': FieldInfo(annotation=Union[Annotated[str, Strict(strict=True)], NoneType], required=False, alias='ModelId', alias_priority=2, description='The ID of the AI model to use for response generation.'), 'model_parameters': FieldInfo(annotation=Union[GuardRailChatRequestModelParameters, NoneType], required=False, alias='ModelParameters', alias_priority=2)}

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