Struct Client

pub struct Client { /* private fields */ }

Client for Amazon Personalize Runtime

Client for invoking operations on Amazon Personalize Runtime. Each operation on Amazon Personalize Runtime is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_personalizeruntime::Client::new(&config);

Occasionally, SDKs may have additional service-specific values that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Builder struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_personalizeruntime::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the GetActionRecommendations operation has a Client::get_action_recommendations, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.get_action_recommendations()
    .campaign_arn("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Implementations

impl Client

pub fn get_action_recommendations( &self, ) -> GetActionRecommendationsFluentBuilder

Constructs a fluent builder for the GetActionRecommendations operation.

• The fluent builder is configurable:
  • campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the campaign to use for getting action recommendations. This campaign must deploy a solution version trained with a PERSONALIZED_ACTIONS recipe.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: false
    

    The user ID of the user to provide action recommendations for.

    
    
  • num_results(i32) / set_num_results(Option<i32>):
    required: false
    

    The number of results to return. The default is 5. The maximum is 100.

    
    
  • filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
    required: false
    

    The ARN of the filter to apply to the returned recommendations. For more information, see Filtering Recommendations.

    When using this parameter, be sure the filter resource is ACTIVE.

    
    
  • filter_values(impl Into<String>, impl Into<String>) / set_filter_values(Option<HashMap::<String, String>>):
    required: false
    

    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.

    For filter expressions that use an INCLUDE element to include actions, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an EXCLUDE element to exclude actions, you can omit the filter-values. In this case, Amazon Personalize doesn’t use that portion of the expression to filter recommendations.

    For more information, see Filtering recommendations and user segments.

    
    
• On success, responds with GetActionRecommendationsOutput with field(s):
  • action_list(Option<Vec::<PredictedAction>>):

    A list of action recommendations sorted in descending order by prediction score. There can be a maximum of 100 actions in the list. For information about action scores, see How action recommendation scoring works.

  • recommendation_id(Option<String>):

    The ID of the recommendation.

• On failure, responds with SdkError<GetActionRecommendationsError>

impl Client

pub fn get_personalized_ranking(&self) -> GetPersonalizedRankingFluentBuilder

Constructs a fluent builder for the GetPersonalizedRanking operation.

• The fluent builder is configurable:
  • campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
    required: true
    

    The Amazon Resource Name (ARN) of the campaign to use for generating the personalized ranking.

    
    
  • input_list(impl Into<String>) / set_input_list(Option<Vec::<String>>):
    required: true
    

    A list of items (by itemId) to rank. If an item was not included in the training dataset, the item is appended to the end of the reranked list. If you are including metadata in recommendations, the maximum is 50. Otherwise, the maximum is 500.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: true
    

    The user for which you want the campaign to provide a personalized ranking.

    
    
  • context(impl Into<String>, impl Into<String>) / set_context(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
  • filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of a filter you created to include items or exclude items from recommendations for a given user. For more information, see Filtering Recommendations.

    
    
  • filter_values(impl Into<String>, impl Into<String>) / set_filter_values(Option<HashMap::<String, String>>):
    required: false
    

    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.

    For filter expressions that use an INCLUDE element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an EXCLUDE element to exclude items, you can omit the filter-values.In this case, Amazon Personalize doesn’t use that portion of the expression to filter recommendations.

    For more information, see Filtering Recommendations.

    
    
  • metadata_columns(impl Into<String>, Vec::<String>) / set_metadata_columns(Option<HashMap::<String, Vec::<String>>>):
    required: false
    

    If you enabled metadata in recommendations when you created or updated the campaign, specify metadata columns from your Items dataset to include in the personalized ranking. The map key is ITEMS and the value is a list of column names from your Items dataset. The maximum number of columns you can provide is 10.

    For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign.

    
    
• On success, responds with GetPersonalizedRankingOutput with field(s):
  • personalized_ranking(Option<Vec::<PredictedItem>>):

    A list of items in order of most likely interest to the user. The maximum is 500.

  • recommendation_id(Option<String>):

    The ID of the recommendation.

• On failure, responds with SdkError<GetPersonalizedRankingError>

impl Client

pub fn get_recommendations(&self) -> GetRecommendationsFluentBuilder

Constructs a fluent builder for the GetRecommendations operation.

• The fluent builder is configurable:
  • campaign_arn(impl Into<String>) / set_campaign_arn(Option<String>):
    required: false
    

    The Amazon Resource Name (ARN) of the campaign to use for getting recommendations.

    
    
  • item_id(impl Into<String>) / set_item_id(Option<String>):
    required: false
    

    The item ID to provide recommendations for.

    Required for RELATED_ITEMS recipe type.

    
    
  • user_id(impl Into<String>) / set_user_id(Option<String>):
    required: false
    

    The user ID to provide recommendations for.

    Required for USER_PERSONALIZATION recipe type.

    
    
  • num_results(i32) / set_num_results(Option<i32>):
    required: false
    

    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.

    
    
  • context(impl Into<String>, impl Into<String>) / set_context(Option<HashMap::<String, String>>):
    required: false
    

    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.

    
    
  • filter_arn(impl Into<String>) / set_filter_arn(Option<String>):
    required: false
    

    The ARN of the filter to apply to the returned recommendations. For more information, see Filtering Recommendations.

    When using this parameter, be sure the filter resource is ACTIVE.

    
    
  • filter_values(impl Into<String>, impl Into<String>) / set_filter_values(Option<HashMap::<String, String>>):
    required: false
    

    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.

    For filter expressions that use an INCLUDE element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an EXCLUDE element to exclude items, you can omit the filter-values.In this case, Amazon Personalize doesn’t use that portion of the expression to filter recommendations.

    For more information, see Filtering recommendations and user segments.

    
    
  • recommender_arn(impl Into<String>) / set_recommender_arn(Option<String>):
    required: false
    

    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.

    
    
  • promotions(Promotion) / set_promotions(Option<Vec::<Promotion>>):
    required: false
    

    The promotions to apply to the recommendation request. A promotion defines additional business rules that apply to a configurable subset of recommended items.

    
    
  • metadata_columns(impl Into<String>, Vec::<String>) / set_metadata_columns(Option<HashMap::<String, Vec::<String>>>):
    required: false
    

    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 ITEMS and the value is a list of column names from your Items dataset. The maximum number of columns you can provide is 10.

    For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.

    
    
• On success, responds with GetRecommendationsOutput with field(s):
  • item_list(Option<Vec::<PredictedItem>>):

    A list of recommendations sorted in descending order by prediction score. There can be a maximum of 500 items in the list.

  • recommendation_id(Option<String>):

    The ID of the recommendation.

• On failure, responds with SdkError<GetRecommendationsError>

impl Client

pub fn from_conf(conf: Config) -> Self

Creates a new client from the service Config.

Panics

This method will panic in the following cases:

• Retries or timeouts are enabled without a sleep_impl configured.
• Identity caching is enabled without a sleep_impl and time_source configured.
• No behavior_version is provided.

The panic message for each of these will have instructions on how to resolve them.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn new(sdk_config: &SdkConfig) -> Self

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.
• This method will panic if no BehaviorVersion is provided. If you experience this panic, set behavior_version on the Config or enable the behavior-version-latest Cargo feature.

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Client

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.