get_ai_guardrail ¶

def get_ai_guardrail(
    self,
    assistant_id: "capo_qconnect.types.uuid_or_arn.UuidOrArn",
    ai_guardrail_id: "capo_qconnect.types.uuid_or_arn_or_either_with_qualifier.UuidOrArnOrEitherWithQualifier",
    *,
    config_overrides: Optional[QConnectClientConfig] = None,
) -> "capo_qconnect.types.get_ai_guardrail_response.GetAIGuardrailResponse":
    """<p>Gets the Amazon Q in Connect AI Guardrail.</p>

    Args:
        assistant_id: <p>The identifier of the Amazon Q in Connect assistant. Can be either the ID or the ARN. URLs cannot contain the ARN.</p>
        ai_guardrail_id: <p>The identifier of the Amazon Q in Connect AI Guardrail.</p>

    Raises:
        capo_qconnect.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_qconnect.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource does not exist.</p>
        capo_qconnect.errors.throttling_exception.ThrottlingException: <p>The throttling limit has been exceeded.</p>
        capo_qconnect.errors.unauthorized_exception.UnauthorizedException: <p>You do not have permission to perform this action.</p>
        capo_qconnect.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by a service.</p>
        capo_qconnect.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_qconnect.types.get_ai_guardrail_request.GetAIGuardrailRequest]",
    ) -> OperationResponse[
        "capo_qconnect.types.get_ai_guardrail_response.GetAIGuardrailResponse"
    ]:
        import capo_qconnect._operations.wisdom_service.get_ai_guardrail

        output, http_response = (
            capo_qconnect._operations.wisdom_service.get_ai_guardrail.get_ai_guardrail(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_output(
        output: "capo_qconnect.types.get_ai_guardrail_response.GetAIGuardrailResponse",
    ) -> dict[str, Any]:
        import capo_qconnect.types.get_ai_guardrail_response

        return capo_qconnect.types.get_ai_guardrail_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("GetAIGuardrail", redact_output=_redact_output),
    )
    input_: capo_qconnect.types.get_ai_guardrail_request.GetAIGuardrailRequest = {
        "assistant_id": assistant_id,
        "ai_guardrail_id": ai_guardrail_id,
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output