create_ai_agent async ¶

async def create_ai_agent(
    self,
    assistant_id: "capo_qconnect.types.uuid_or_arn.UuidOrArn",
    name: "capo_qconnect.types.name.Name",
    type: "capo_qconnect.types.ai_agent_type.AIAgentType",
    configuration: "capo_qconnect.types.ai_agent_configuration.AIAgentConfiguration",
    visibility_status: "capo_qconnect.types.visibility_status.VisibilityStatus",
    *,
    config_overrides: Optional[AsyncQConnectClientConfig] = None,
    client_token: Optional["capo_qconnect.types.client_token.ClientToken"] = None,
    tags: Optional["capo_qconnect.types.tags.Tags"] = None,
    description: Optional["capo_qconnect.types.description.Description"] = None,
) -> "capo_qconnect.types.create_ai_agent_response.CreateAIAgentResponse":
    """<p>Creates an Amazon Q in Connect AI Agent.</p>

    Args:
        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>
        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>
        name: <p>The name of the AI Agent.</p>
        type: <p>The type of the AI Agent.</p>
        configuration: <p>The configuration of the AI Agent.</p>
        visibility_status: <p>The visibility status of the AI Agent.</p>
        tags: <p>The tags used to organize, track, or control access for this resource.</p>
        description: <p>The description of the AI Agent.</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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_qconnect.types.create_ai_agent_request.CreateAIAgentRequest]",
    ) -> AsyncOperationResponse[
        "capo_qconnect.types.create_ai_agent_response.CreateAIAgentResponse"
    ]:
        import capo_qconnect._operations.wisdom_service.create_ai_agent

        (
            output,
            http_response,
        ) = await capo_qconnect._operations.wisdom_service.create_ai_agent.async_create_ai_agent(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_qconnect.types.create_ai_agent_request.CreateAIAgentRequest",
    ) -> dict[str, Any]:
        import capo_qconnect.types.create_ai_agent_request

        return capo_qconnect.types.create_ai_agent_request.redact_sensitive(input_)

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

        return capo_qconnect.types.create_ai_agent_response.redact_sensitive(output)

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "CreateAIAgent",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_qconnect.types.create_ai_agent_request.CreateAIAgentRequest = {
        "assistant_id": assistant_id,
        "name": name,
        "type": type,
        "configuration": configuration,
        "visibility_status": visibility_status,
    }
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    if tags is not None:
        input_["tags"] = tags
    if description is not None:
        input_["description"] = description

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output