optimize_prompt ¶

@contextmanager
def optimize_prompt(
    self,
    input: "capo_bedrock_agent_runtime.types.input_prompt.InputPrompt",
    target_model_id: str,
    *,
    config_overrides: Optional[BedrockAgentRuntimeClientConfig] = None,
) -> "Generator[capo_bedrock_agent_runtime.types.optimize_prompt_response.OptimizePromptResponse]":
    """<p>Optimizes a prompt for the task that you specify. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-management-optimize.html">Optimize a prompt</a> in the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-service.html">Amazon Bedrock User Guide</a>.</p>

    Args:
        input: <p>Contains the prompt to optimize.</p>
        target_model_id: <p>The unique identifier of the model that you want to optimize the prompt for.</p>

    Raises:
        capo_bedrock_agent_runtime.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions. Check your permissions and retry your request.</p>
        capo_bedrock_agent_runtime.errors.bad_gateway_exception.BadGatewayException: <p>There was an issue with a dependency due to a server issue. Retry your request.</p>
        capo_bedrock_agent_runtime.errors.dependency_failed_exception.DependencyFailedException: <p>There was an issue with a dependency. Check the resource configurations and retry the request.</p>
        capo_bedrock_agent_runtime.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock_agent_runtime.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock_agent_runtime.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock_agent_runtime.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock_agent_runtime.types.optimize_prompt_request.OptimizePromptRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent_runtime.types.optimize_prompt_response.OptimizePromptResponse"
    ]:
        import capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.optimize_prompt

        output, http_response = (
            capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.optimize_prompt.optimize_prompt(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_bedrock_agent_runtime.types.optimize_prompt_request.OptimizePromptRequest",
    ) -> dict[str, Any]:
        import capo_bedrock_agent_runtime.types.optimize_prompt_request

        return capo_bedrock_agent_runtime.types.optimize_prompt_request.redact_sensitive(
            input_
        )

    def _redact_output(
        output: "capo_bedrock_agent_runtime.types.optimize_prompt_response.OptimizePromptResponse",
    ) -> dict[str, Any]:
        import capo_bedrock_agent_runtime.types.optimize_prompt_response

        return capo_bedrock_agent_runtime.types.optimize_prompt_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "OptimizePrompt",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_bedrock_agent_runtime.types.optimize_prompt_request.OptimizePromptRequest = {
        "input": input,
        "target_model_id": target_model_id,
    }

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    try:
        yield response.output
    finally:
        response.response.close()