aws-sdk-personalize 1.102.0

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::update_solution::_update_solution_input::UpdateSolutionInputBuilder;

pub use crate::operation::update_solution::_update_solution_output::UpdateSolutionOutputBuilder;

impl crate::operation::update_solution::builders::UpdateSolutionInputBuilder {
    /// Sends a request with this input using the given client.
    pub async fn send_with(
        self,
        client: &crate::Client,
    ) -> ::std::result::Result<
        crate::operation::update_solution::UpdateSolutionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::update_solution::UpdateSolutionError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.update_solution();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `UpdateSolution`.
///
/// <p>Updates an Amazon Personalize solution to use a different automatic training configuration. When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/updating-solution.html">Updating a solution</a>.</p>
/// <p>A solution update can be in one of the following states:</p>
/// <p>CREATE PENDING &gt; CREATE IN_PROGRESS &gt; ACTIVE -or- CREATE FAILED</p>
/// <p>To get the status of a solution update, call the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html">DescribeSolution</a> API operation and find the status in the <code>latestSolutionUpdate</code>.</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct UpdateSolutionFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::update_solution::builders::UpdateSolutionInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::update_solution::UpdateSolutionOutput,
        crate::operation::update_solution::UpdateSolutionError,
    > for UpdateSolutionFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::update_solution::UpdateSolutionOutput,
            crate::operation::update_solution::UpdateSolutionError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl UpdateSolutionFluentBuilder {
    /// Creates a new `UpdateSolutionFluentBuilder`.
    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 UpdateSolution as a reference.
    pub fn as_input(&self) -> &crate::operation::update_solution::builders::UpdateSolutionInputBuilder {
        &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::update_solution::UpdateSolutionOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::update_solution::UpdateSolutionError,
            ::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::update_solution::UpdateSolution::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::update_solution::UpdateSolution::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::update_solution::UpdateSolutionOutput,
        crate::operation::update_solution::UpdateSolutionError,
        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>The Amazon Resource Name (ARN) of the solution to update.</p>
    pub fn solution_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
        self.inner = self.inner.solution_arn(input.into());
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the solution to update.</p>
    pub fn set_solution_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
        self.inner = self.inner.set_solution_arn(input);
        self
    }
    /// <p>The Amazon Resource Name (ARN) of the solution to update.</p>
    pub fn get_solution_arn(&self) -> &::std::option::Option<::std::string::String> {
        self.inner.get_solution_arn()
    }
    /// <p>Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a <code>schedulingExpression</code> in the <code>AutoTrainingConfig</code> as part of solution configuration.</p>
    /// <p>If you turn on automatic training, the first automatic training starts within one hour after the solution update completes. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information about automatic training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
    /// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
    pub fn perform_auto_training(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_auto_training(input);
        self
    }
    /// <p>Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a <code>schedulingExpression</code> in the <code>AutoTrainingConfig</code> as part of solution configuration.</p>
    /// <p>If you turn on automatic training, the first automatic training starts within one hour after the solution update completes. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information about automatic training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
    /// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
    pub fn set_perform_auto_training(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_auto_training(input);
        self
    }
    /// <p>Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a <code>schedulingExpression</code> in the <code>AutoTrainingConfig</code> as part of solution configuration.</p>
    /// <p>If you turn on automatic training, the first automatic training starts within one hour after the solution update completes. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information about automatic training, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html">Configuring automatic training</a>.</p>
    /// <p>After training starts, you can get the solution version's Amazon Resource Name (ARN) with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> API operation. To get its status, use the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>.</p>
    pub fn get_perform_auto_training(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_auto_training()
    }
    /// <p>Whether to perform incremental training updates on your model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.</p>
    pub fn perform_incremental_update(mut self, input: bool) -> Self {
        self.inner = self.inner.perform_incremental_update(input);
        self
    }
    /// <p>Whether to perform incremental training updates on your model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.</p>
    pub fn set_perform_incremental_update(mut self, input: ::std::option::Option<bool>) -> Self {
        self.inner = self.inner.set_perform_incremental_update(input);
        self
    }
    /// <p>Whether to perform incremental training updates on your model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.</p>
    pub fn get_perform_incremental_update(&self) -> &::std::option::Option<bool> {
        self.inner.get_perform_incremental_update()
    }
    /// <p>The new configuration details of the solution.</p>
    pub fn solution_update_config(mut self, input: crate::types::SolutionUpdateConfig) -> Self {
        self.inner = self.inner.solution_update_config(input);
        self
    }
    /// <p>The new configuration details of the solution.</p>
    pub fn set_solution_update_config(mut self, input: ::std::option::Option<crate::types::SolutionUpdateConfig>) -> Self {
        self.inner = self.inner.set_solution_update_config(input);
        self
    }
    /// <p>The new configuration details of the solution.</p>
    pub fn get_solution_update_config(&self) -> &::std::option::Option<crate::types::SolutionUpdateConfig> {
        self.inner.get_solution_update_config()
    }
}