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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::get_recommendations::_get_recommendations_output::GetRecommendationsOutputBuilder;
pub use crate::operation::get_recommendations::_get_recommendations_input::GetRecommendationsInputBuilder;
impl GetRecommendationsInputBuilder {
/// Sends a request with this input using the given client.
pub async fn send_with(
self,
client: &crate::Client,
) -> ::std::result::Result<
crate::operation::get_recommendations::GetRecommendationsOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::get_recommendations::GetRecommendationsError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.get_recommendations();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `GetRecommendations`.
///
/// <p>Returns a list of recommended items. For campaigns, the campaign's Amazon Resource Name (ARN) is required and the required user and item input depends on the recipe type used to create the solution backing the campaign as follows:</p>
/// <ul>
/// <li> <p>USER_PERSONALIZATION - <code>userId</code> required, <code>itemId</code> not used</p> </li>
/// <li> <p>RELATED_ITEMS - <code>itemId</code> required, <code>userId</code> not used</p> </li>
/// </ul> <note>
/// <p>Campaigns that are backed by a solution created using a recipe of type PERSONALIZED_RANKING use the API.</p>
/// </note>
/// <p> For recommenders, the recommender's ARN is required and the required item and user input depends on the use case (domain-based recipe) backing the recommender. For information on use case requirements see <a href="https://docs.aws.amazon.com/personalize/latest/dg/domain-use-cases.html">Choosing recommender use cases</a>. </p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct GetRecommendationsFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::get_recommendations::builders::GetRecommendationsInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl
crate::client::customize::internal::CustomizableSend<
crate::operation::get_recommendations::GetRecommendationsOutput,
crate::operation::get_recommendations::GetRecommendationsError,
> for GetRecommendationsFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<
crate::operation::get_recommendations::GetRecommendationsOutput,
crate::operation::get_recommendations::GetRecommendationsError,
>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl GetRecommendationsFluentBuilder {
/// Creates a new `GetRecommendations`.
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 GetRecommendations as a reference.
pub fn as_input(&self) -> &crate::operation::get_recommendations::builders::GetRecommendationsInputBuilder {
&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::get_recommendations::GetRecommendationsOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::get_recommendations::GetRecommendationsError,
::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::get_recommendations::GetRecommendations::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::get_recommendations::GetRecommendations::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::get_recommendations::GetRecommendationsOutput,
crate::operation::get_recommendations::GetRecommendationsError,
Self,
> {
crate::client::customize::CustomizableOperation::new(self)
}
pub(crate) fn config_override(mut self, config_override: impl Into<crate::config::Builder>) -> Self {
self.set_config_override(Some(config_override.into()));
self
}
pub(crate) fn set_config_override(&mut self, config_override: Option<crate::config::Builder>) -> &mut Self {
self.config_override = config_override;
self
}
/// <p>The Amazon Resource Name (ARN) of the campaign to use for getting recommendations.</p>
pub fn campaign_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.campaign_arn(input.into());
self
}
/// <p>The Amazon Resource Name (ARN) of the campaign to use for getting recommendations.</p>
pub fn set_campaign_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_campaign_arn(input);
self
}
/// <p>The Amazon Resource Name (ARN) of the campaign to use for getting recommendations.</p>
pub fn get_campaign_arn(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_campaign_arn()
}
/// <p>The item ID to provide recommendations for.</p>
/// <p>Required for <code>RELATED_ITEMS</code> recipe type.</p>
pub fn item_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.item_id(input.into());
self
}
/// <p>The item ID to provide recommendations for.</p>
/// <p>Required for <code>RELATED_ITEMS</code> recipe type.</p>
pub fn set_item_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_item_id(input);
self
}
/// <p>The item ID to provide recommendations for.</p>
/// <p>Required for <code>RELATED_ITEMS</code> recipe type.</p>
pub fn get_item_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_item_id()
}
/// <p>The user ID to provide recommendations for.</p>
/// <p>Required for <code>USER_PERSONALIZATION</code> recipe type.</p>
pub fn user_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.user_id(input.into());
self
}
/// <p>The user ID to provide recommendations for.</p>
/// <p>Required for <code>USER_PERSONALIZATION</code> recipe type.</p>
pub fn set_user_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_user_id(input);
self
}
/// <p>The user ID to provide recommendations for.</p>
/// <p>Required for <code>USER_PERSONALIZATION</code> recipe type.</p>
pub fn get_user_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_user_id()
}
/// <p>The number of results to return. The default is 25. If you are including metadata in recommendations, the maximum is 50. Otherwise, the maximum is 500.</p>
pub fn num_results(mut self, input: i32) -> Self {
self.inner = self.inner.num_results(input);
self
}
/// <p>The number of results to return. The default is 25. If you are including metadata in recommendations, the maximum is 50. Otherwise, the maximum is 500.</p>
pub fn set_num_results(mut self, input: ::std::option::Option<i32>) -> Self {
self.inner = self.inner.set_num_results(input);
self
}
/// <p>The number of results to return. The default is 25. If you are including metadata in recommendations, the maximum is 50. Otherwise, the maximum is 500.</p>
pub fn get_num_results(&self) -> &::std::option::Option<i32> {
self.inner.get_num_results()
}
/// Adds a key-value pair to `context`.
///
/// To override the contents of this collection use [`set_context`](Self::set_context).
///
/// <p>The contextual metadata to use when getting recommendations. Contextual metadata includes any interaction information that might be relevant when getting a user's recommendations, such as the user's current location or device type.</p>
pub fn context(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.context(k.into(), v.into());
self
}
/// <p>The contextual metadata to use when getting recommendations. Contextual metadata includes any interaction information that might be relevant when getting a user's recommendations, such as the user's current location or device type.</p>
pub fn set_context(mut self, input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>) -> Self {
self.inner = self.inner.set_context(input);
self
}
/// <p>The contextual metadata to use when getting recommendations. Contextual metadata includes any interaction information that might be relevant when getting a user's recommendations, such as the user's current location or device type.</p>
pub fn get_context(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
self.inner.get_context()
}
/// <p>The ARN of the filter to apply to the returned recommendations. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering Recommendations</a>.</p>
/// <p>When using this parameter, be sure the filter resource is <code>ACTIVE</code>.</p>
pub fn filter_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.filter_arn(input.into());
self
}
/// <p>The ARN of the filter to apply to the returned recommendations. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering Recommendations</a>.</p>
/// <p>When using this parameter, be sure the filter resource is <code>ACTIVE</code>.</p>
pub fn set_filter_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_filter_arn(input);
self
}
/// <p>The ARN of the filter to apply to the returned recommendations. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering Recommendations</a>.</p>
/// <p>When using this parameter, be sure the filter resource is <code>ACTIVE</code>.</p>
pub fn get_filter_arn(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_filter_arn()
}
/// Adds a key-value pair to `filterValues`.
///
/// To override the contents of this collection use [`set_filter_values`](Self::set_filter_values).
///
/// <p>The values to use when filtering recommendations. For each placeholder parameter in your filter expression, provide the parameter name (in matching case) as a key and the filter value(s) as the corresponding value. Separate multiple values for one parameter with a comma. </p>
/// <p>For filter expressions that use an <code>INCLUDE</code> element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an <code>EXCLUDE</code> element to exclude items, you can omit the <code>filter-values</code>.In this case, Amazon Personalize doesn't use that portion of the expression to filter recommendations.</p>
/// <p>For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering recommendations and user segments</a>.</p>
pub fn filter_values(mut self, k: impl ::std::convert::Into<::std::string::String>, v: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.filter_values(k.into(), v.into());
self
}
/// <p>The values to use when filtering recommendations. For each placeholder parameter in your filter expression, provide the parameter name (in matching case) as a key and the filter value(s) as the corresponding value. Separate multiple values for one parameter with a comma. </p>
/// <p>For filter expressions that use an <code>INCLUDE</code> element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an <code>EXCLUDE</code> element to exclude items, you can omit the <code>filter-values</code>.In this case, Amazon Personalize doesn't use that portion of the expression to filter recommendations.</p>
/// <p>For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering recommendations and user segments</a>.</p>
pub fn set_filter_values(
mut self,
input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
) -> Self {
self.inner = self.inner.set_filter_values(input);
self
}
/// <p>The values to use when filtering recommendations. For each placeholder parameter in your filter expression, provide the parameter name (in matching case) as a key and the filter value(s) as the corresponding value. Separate multiple values for one parameter with a comma. </p>
/// <p>For filter expressions that use an <code>INCLUDE</code> element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an <code>EXCLUDE</code> element to exclude items, you can omit the <code>filter-values</code>.In this case, Amazon Personalize doesn't use that portion of the expression to filter recommendations.</p>
/// <p>For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering recommendations and user segments</a>.</p>
pub fn get_filter_values(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
self.inner.get_filter_values()
}
/// <p>The Amazon Resource Name (ARN) of the recommender to use to get recommendations. Provide a recommender ARN if you created a Domain dataset group with a recommender for a domain use case.</p>
pub fn recommender_arn(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.recommender_arn(input.into());
self
}
/// <p>The Amazon Resource Name (ARN) of the recommender to use to get recommendations. Provide a recommender ARN if you created a Domain dataset group with a recommender for a domain use case.</p>
pub fn set_recommender_arn(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_recommender_arn(input);
self
}
/// <p>The Amazon Resource Name (ARN) of the recommender to use to get recommendations. Provide a recommender ARN if you created a Domain dataset group with a recommender for a domain use case.</p>
pub fn get_recommender_arn(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_recommender_arn()
}
/// Appends an item to `promotions`.
///
/// To override the contents of this collection use [`set_promotions`](Self::set_promotions).
///
/// <p>The promotions to apply to the recommendation request. A promotion defines additional business rules that apply to a configurable subset of recommended items.</p>
pub fn promotions(mut self, input: crate::types::Promotion) -> Self {
self.inner = self.inner.promotions(input);
self
}
/// <p>The promotions to apply to the recommendation request. A promotion defines additional business rules that apply to a configurable subset of recommended items.</p>
pub fn set_promotions(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Promotion>>) -> Self {
self.inner = self.inner.set_promotions(input);
self
}
/// <p>The promotions to apply to the recommendation request. A promotion defines additional business rules that apply to a configurable subset of recommended items.</p>
pub fn get_promotions(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Promotion>> {
self.inner.get_promotions()
}
/// Adds a key-value pair to `metadataColumns`.
///
/// To override the contents of this collection use [`set_metadata_columns`](Self::set_metadata_columns).
///
/// <p>If you enabled metadata in recommendations when you created or updated the campaign or recommender, specify the metadata columns from your Items dataset to include in item recommendations. The map key is <code>ITEMS</code> and the value is a list of column names from your Items dataset. The maximum number of columns you can provide is 10.</p>
/// <p> For information about enabling metadata for a campaign, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/create-campaign-return-metadata.html">Enabling metadata in recommendations for a campaign</a>. For information about enabling metadata for a recommender, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/create-recommender-return-metadata.html">Enabling metadata in recommendations for a recommender</a>. </p>
pub fn metadata_columns(mut self, k: impl ::std::convert::Into<::std::string::String>, v: ::std::vec::Vec<::std::string::String>) -> Self {
self.inner = self.inner.metadata_columns(k.into(), v);
self
}
/// <p>If you enabled metadata in recommendations when you created or updated the campaign or recommender, specify the metadata columns from your Items dataset to include in item recommendations. The map key is <code>ITEMS</code> and the value is a list of column names from your Items dataset. The maximum number of columns you can provide is 10.</p>
/// <p> For information about enabling metadata for a campaign, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/create-campaign-return-metadata.html">Enabling metadata in recommendations for a campaign</a>. For information about enabling metadata for a recommender, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/create-recommender-return-metadata.html">Enabling metadata in recommendations for a recommender</a>. </p>
pub fn set_metadata_columns(
mut self,
input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::vec::Vec<::std::string::String>>>,
) -> Self {
self.inner = self.inner.set_metadata_columns(input);
self
}
/// <p>If you enabled metadata in recommendations when you created or updated the campaign or recommender, specify the metadata columns from your Items dataset to include in item recommendations. The map key is <code>ITEMS</code> and the value is a list of column names from your Items dataset. The maximum number of columns you can provide is 10.</p>
/// <p> For information about enabling metadata for a campaign, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/create-campaign-return-metadata.html">Enabling metadata in recommendations for a campaign</a>. For information about enabling metadata for a recommender, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/create-recommender-return-metadata.html">Enabling metadata in recommendations for a recommender</a>. </p>
pub fn get_metadata_columns(
&self,
) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::vec::Vec<::std::string::String>>> {
self.inner.get_metadata_columns()
}
}