sample async ¶

async def sample(
    self,
    job_arn: "capo_sagemakerjobruntime.types.job_arn.JobArn",
    trajectory_id: "capo_sagemakerjobruntime.types.trajectory_id.TrajectoryId",
    body: "capo_sagemakerjobruntime.types.inference_request_body.InferenceRequestBody",
    *,
    config_overrides: Optional[AsyncSagemakerJobRuntimeClientConfig] = None,
) -> "capo_sagemakerjobruntime.types.sample_response.SampleResponse":
    """Sends an inference request to the model during a job execution. The request and response bodies are forwarded to and from the model without modification. Each turn (prompt and response) is captured for later use.

    Args:
        job_arn: The job ARN that identifies which model session to route the inference request to.
        trajectory_id: The trajectory ID for grouping turns into a single rollout. Each turn (prompt and response) is captured for later use.
        body: The raw inference request body in OpenAI-compatible JSON format.

    Raises:
        capo_sagemakerjobruntime.errors.access_denied_exception.AccessDeniedException: You do not have permission to perform this operation.
        capo_sagemakerjobruntime.errors.internal_service_error.InternalServiceError: An internal service error occurred. Retry the request.
        capo_sagemakerjobruntime.errors.resource_not_found_exception.ResourceNotFoundException: The specified resource was not found.
        capo_sagemakerjobruntime.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: You have exceeded a service quota.
        capo_sagemakerjobruntime.errors.throttling_exception.ThrottlingException: The request was throttled. Retry the request after a brief wait.
        capo_sagemakerjobruntime.errors.validation_exception.ValidationException: The request is not valid. Check the request syntax and parameters
        capo_sagemakerjobruntime.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        Invoke Sample
        Sends an inference request to the model and receives the response.

        >>> await client.sample(job_arn='arn:aws:sagemaker:us-east-1:123456789012:job/AgentRFT/my-training-job', trajectory_id='trajectory-001', body='eyJtb2RlbCI6Im15LW1vZGVsIiwibWVzc2FnZXMiOlt7InJvbGUiOiJ1c2VyIiwiY29udGVudCI6IkhlbGxvIn1dfQ==')
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemakerjobruntime.types.sample_request.SampleRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemakerjobruntime.types.sample_response.SampleResponse"
    ]:
        import capo_sagemakerjobruntime._operations.agentic_rft_runtime_service.sample

        (
            output,
            http_response,
        ) = await capo_sagemakerjobruntime._operations.agentic_rft_runtime_service.sample.async_sample(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_sagemakerjobruntime.types.sample_request.SampleRequest",
    ) -> dict[str, Any]:
        import capo_sagemakerjobruntime.types.sample_request

        return capo_sagemakerjobruntime.types.sample_request.redact_sensitive(
            input_
        )

    def _redact_output(
        output: "capo_sagemakerjobruntime.types.sample_response.SampleResponse",
    ) -> dict[str, Any]:
        import capo_sagemakerjobruntime.types.sample_response

        return capo_sagemakerjobruntime.types.sample_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "Sample", redact_input=_redact_input, redact_output=_redact_output
        ),
    )
    input_: capo_sagemakerjobruntime.types.sample_request.SampleRequest = {
        "job_arn": job_arn,
        "trajectory_id": trajectory_id,
        "body": body,
    }

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