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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::create_model_invocation_job::_create_model_invocation_job_input::CreateModelInvocationJobInputBuilder;
pub use crate::operation::create_model_invocation_job::_create_model_invocation_job_output::CreateModelInvocationJobOutputBuilder;
impl crate::operation::create_model_invocation_job::builders::CreateModelInvocationJobInputBuilder {
/// Sends a request with this input using the given client.
pub async fn send_with(
self,
client: &crate::Client,
) -> ::std::result::Result<
crate::operation::create_model_invocation_job::CreateModelInvocationJobOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_model_invocation_job::CreateModelInvocationJobError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.create_model_invocation_job();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `CreateModelInvocationJob`.
///
/// <p>Creates a batch inference job to invoke a model on multiple prompts. Format your data according to <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference-data">Format your inference data</a> and upload it to an Amazon S3 bucket. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-inference.html">Process multiple prompts with batch inference</a>.</p>
/// <p>The response returns a <code>jobArn</code> that you can use to stop or get details about the job.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct CreateModelInvocationJobFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::create_model_invocation_job::builders::CreateModelInvocationJobInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl
crate::client::customize::internal::CustomizableSend<
crate::operation::create_model_invocation_job::CreateModelInvocationJobOutput,
crate::operation::create_model_invocation_job::CreateModelInvocationJobError,
> for CreateModelInvocationJobFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<
crate::operation::create_model_invocation_job::CreateModelInvocationJobOutput,
crate::operation::create_model_invocation_job::CreateModelInvocationJobError,
>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl CreateModelInvocationJobFluentBuilder {
/// Creates a new `CreateModelInvocationJobFluentBuilder`.
pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
Self {
handle,
inner: ::std::default::Default::default(),
config_override: ::std::option::Option::None,
}
}
/// Access the CreateModelInvocationJob as a reference.
pub fn as_input(&self) -> &crate::operation::create_model_invocation_job::builders::CreateModelInvocationJobInputBuilder {
&self.inner
}
/// Sends the request and returns the response.
///
/// If an error occurs, an `SdkError` will be returned with additional details that
/// can be matched against.
///
/// By default, any retryable failures will be retried twice. Retry behavior
/// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
/// set when configuring the client.
pub async fn send(
self,
) -> ::std::result::Result<
crate::operation::create_model_invocation_job::CreateModelInvocationJobOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::create_model_invocation_job::CreateModelInvocationJobError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let input = self
.inner
.build()
.map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
let runtime_plugins = crate::operation::create_model_invocation_job::CreateModelInvocationJob::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::create_model_invocation_job::CreateModelInvocationJob::orchestrate(&runtime_plugins, input).await
}
/// Consumes this builder, creating a customizable operation that can be modified before being sent.
pub fn customize(
self,
) -> crate::client::customize::CustomizableOperation<
crate::operation::create_model_invocation_job::CreateModelInvocationJobOutput,
crate::operation::create_model_invocation_job::CreateModelInvocationJobError,
Self,
> {
crate::client::customize::CustomizableOperation::new(self)
}
pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
self.set_config_override(::std::option::Option::Some(config_override.into()));
self
}
pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
self.config_override = config_override;
self
}
/// <p>A name to give the batch inference job.</p>
pub fn job_name(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.job_name(input.into());
self
}
/// <p>A name to give the batch inference job.</p>
pub fn set_job_name(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_job_name(input);
self
}
/// <p>A name to give the batch inference job.</p>
pub fn get_job_name(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_job_name()
}
/// <p>The Amazon Resource Name (ARN) of the service role with permissions to carry out and manage batch inference. You can use the console to create a default service role or follow the steps at <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-iam-sr.html">Create a service role for batch inference</a>.</p>
pub fn role_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.role_arn(input.into());
self
}
/// <p>The Amazon Resource Name (ARN) of the service role with permissions to carry out and manage batch inference. You can use the console to create a default service role or follow the steps at <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-iam-sr.html">Create a service role for batch inference</a>.</p>
pub fn set_role_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_role_arn(input);
self
}
/// <p>The Amazon Resource Name (ARN) of the service role with permissions to carry out and manage batch inference. You can use the console to create a default service role or follow the steps at <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-iam-sr.html">Create a service role for batch inference</a>.</p>
pub fn get_role_arn(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_role_arn()
}
/// <p>A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Run_Instance_Idempotency.html">Ensuring idempotency</a>.</p>
pub fn client_request_token(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.client_request_token(input.into());
self
}
/// <p>A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Run_Instance_Idempotency.html">Ensuring idempotency</a>.</p>
pub fn set_client_request_token(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_client_request_token(input);
self
}
/// <p>A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Run_Instance_Idempotency.html">Ensuring idempotency</a>.</p>
pub fn get_client_request_token(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_client_request_token()
}
/// <p>The unique identifier of the foundation model to use for the batch inference job.</p>
pub fn model_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.model_id(input.into());
self
}
/// <p>The unique identifier of the foundation model to use for the batch inference job.</p>
pub fn set_model_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_model_id(input);
self
}
/// <p>The unique identifier of the foundation model to use for the batch inference job.</p>
pub fn get_model_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_model_id()
}
/// <p>Details about the location of the input to the batch inference job.</p>
pub fn input_data_config(mut self, input: crate::types::ModelInvocationJobInputDataConfig) -> Self {
self.inner = self.inner.input_data_config(input);
self
}
/// <p>Details about the location of the input to the batch inference job.</p>
pub fn set_input_data_config(mut self, input: ::std::option::Option<crate::types::ModelInvocationJobInputDataConfig>) -> Self {
self.inner = self.inner.set_input_data_config(input);
self
}
/// <p>Details about the location of the input to the batch inference job.</p>
pub fn get_input_data_config(&self) -> &::std::option::Option<crate::types::ModelInvocationJobInputDataConfig> {
self.inner.get_input_data_config()
}
/// <p>Details about the location of the output of the batch inference job.</p>
pub fn output_data_config(mut self, input: crate::types::ModelInvocationJobOutputDataConfig) -> Self {
self.inner = self.inner.output_data_config(input);
self
}
/// <p>Details about the location of the output of the batch inference job.</p>
pub fn set_output_data_config(mut self, input: ::std::option::Option<crate::types::ModelInvocationJobOutputDataConfig>) -> Self {
self.inner = self.inner.set_output_data_config(input);
self
}
/// <p>Details about the location of the output of the batch inference job.</p>
pub fn get_output_data_config(&self) -> &::std::option::Option<crate::types::ModelInvocationJobOutputDataConfig> {
self.inner.get_output_data_config()
}
/// <p>The configuration of the Virtual Private Cloud (VPC) for the data in the batch inference job. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-vpc">Protect batch inference jobs using a VPC</a>.</p>
pub fn vpc_config(mut self, input: crate::types::VpcConfig) -> Self {
self.inner = self.inner.vpc_config(input);
self
}
/// <p>The configuration of the Virtual Private Cloud (VPC) for the data in the batch inference job. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-vpc">Protect batch inference jobs using a VPC</a>.</p>
pub fn set_vpc_config(mut self, input: ::std::option::Option<crate::types::VpcConfig>) -> Self {
self.inner = self.inner.set_vpc_config(input);
self
}
/// <p>The configuration of the Virtual Private Cloud (VPC) for the data in the batch inference job. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/batch-vpc">Protect batch inference jobs using a VPC</a>.</p>
pub fn get_vpc_config(&self) -> &::std::option::Option<crate::types::VpcConfig> {
self.inner.get_vpc_config()
}
/// <p>The number of hours after which to force the batch inference job to time out.</p>
pub fn timeout_duration_in_hours(mut self, input: i32) -> Self {
self.inner = self.inner.timeout_duration_in_hours(input);
self
}
/// <p>The number of hours after which to force the batch inference job to time out.</p>
pub fn set_timeout_duration_in_hours(mut self, input: ::std::option::Option<i32>) -> Self {
self.inner = self.inner.set_timeout_duration_in_hours(input);
self
}
/// <p>The number of hours after which to force the batch inference job to time out.</p>
pub fn get_timeout_duration_in_hours(&self) -> &::std::option::Option<i32> {
self.inner.get_timeout_duration_in_hours()
}
///
/// Appends an item to `tags`.
///
/// To override the contents of this collection use [`set_tags`](Self::set_tags).
///
/// <p>Any tags to associate with the batch inference job. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/tagging.html">Tagging Amazon Bedrock resources</a>.</p>
pub fn tags(mut self, input: crate::types::Tag) -> Self {
self.inner = self.inner.tags(input);
self
}
/// <p>Any tags to associate with the batch inference job. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/tagging.html">Tagging Amazon Bedrock resources</a>.</p>
pub fn set_tags(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Tag>>) -> Self {
self.inner = self.inner.set_tags(input);
self
}
/// <p>Any tags to associate with the batch inference job. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/tagging.html">Tagging Amazon Bedrock resources</a>.</p>
pub fn get_tags(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Tag>> {
self.inner.get_tags()
}
/// <p>The invocation endpoint for ModelInvocationJob</p>
pub fn model_invocation_type(mut self, input: crate::types::ModelInvocationType) -> Self {
self.inner = self.inner.model_invocation_type(input);
self
}
/// <p>The invocation endpoint for ModelInvocationJob</p>
pub fn set_model_invocation_type(mut self, input: ::std::option::Option<crate::types::ModelInvocationType>) -> Self {
self.inner = self.inner.set_model_invocation_type(input);
self
}
/// <p>The invocation endpoint for ModelInvocationJob</p>
pub fn get_model_invocation_type(&self) -> &::std::option::Option<crate::types::ModelInvocationType> {
self.inner.get_model_invocation_type()
}
}