create_ai_guardrail_version ¶

def create_ai_guardrail_version(
    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,
    modified_time: Optional[datetime.datetime] = None,
    client_token: Optional["capo_qconnect.types.client_token.ClientToken"] = None,
) -> "capo_qconnect.types.create_ai_guardrail_version_response.CreateAIGuardrailVersionResponse":
    """<p>Creates an Amazon Q in Connect AI Guardrail version.</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>
        modified_time: <p>The time the AI Guardrail was last modified.</p>
        client_token: <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the request. If not provided, the Amazon Web Services SDK populates this field. For more information about idempotency, see <a href="http://aws.amazon.com/builders-library/making-retries-safe-with-idempotent-APIs/">Making retries safe with idempotent APIs</a>..</p>

    Raises:
        capo_qconnect.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_qconnect.errors.conflict_exception.ConflictException: <p>The request could not be processed because of conflict in the current state of the resource. For example, if you're using a <code>Create</code> API (such as <code>CreateAssistant</code>) that accepts name, a conflicting resource (usually with the same name) is being created or mutated.</p>
        capo_qconnect.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource does not exist.</p>
        capo_qconnect.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>You've exceeded your service quota. To perform the requested action, remove some of the relevant resources, or use service quotas to request a service quota increase.</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.create_ai_guardrail_version_request.CreateAIGuardrailVersionRequest]",
    ) -> OperationResponse[
        "capo_qconnect.types.create_ai_guardrail_version_response.CreateAIGuardrailVersionResponse"
    ]:
        import capo_qconnect._operations.wisdom_service.create_ai_guardrail_version

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

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

        return capo_qconnect.types.create_ai_guardrail_version_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateAIGuardrailVersion", redact_output=_redact_output),
    )
    input_: capo_qconnect.types.create_ai_guardrail_version_request.CreateAIGuardrailVersionRequest = {
        "assistant_id": assistant_id,
        "ai_guardrail_id": ai_guardrail_id,
    }
    if modified_time is not None:
        input_["modified_time"] = modified_time
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token

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