%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Amazon Personalize is a machine learning service that makes it easy %% to add individualized recommendations to customers. -module(aws_personalize). -export([create_batch_inference_job/2, create_batch_inference_job/3, create_batch_segment_job/2, create_batch_segment_job/3, create_campaign/2, create_campaign/3, create_dataset/2, create_dataset/3, create_dataset_export_job/2, create_dataset_export_job/3, create_dataset_group/2, create_dataset_group/3, create_dataset_import_job/2, create_dataset_import_job/3, create_event_tracker/2, create_event_tracker/3, create_filter/2, create_filter/3, create_metric_attribution/2, create_metric_attribution/3, create_recommender/2, create_recommender/3, create_schema/2, create_schema/3, create_solution/2, create_solution/3, create_solution_version/2, create_solution_version/3, delete_campaign/2, delete_campaign/3, delete_dataset/2, delete_dataset/3, delete_dataset_group/2, delete_dataset_group/3, delete_event_tracker/2, delete_event_tracker/3, delete_filter/2, delete_filter/3, delete_metric_attribution/2, delete_metric_attribution/3, delete_recommender/2, delete_recommender/3, delete_schema/2, delete_schema/3, delete_solution/2, delete_solution/3, describe_algorithm/2, describe_algorithm/3, describe_batch_inference_job/2, describe_batch_inference_job/3, describe_batch_segment_job/2, describe_batch_segment_job/3, describe_campaign/2, describe_campaign/3, describe_dataset/2, describe_dataset/3, describe_dataset_export_job/2, describe_dataset_export_job/3, describe_dataset_group/2, describe_dataset_group/3, describe_dataset_import_job/2, describe_dataset_import_job/3, describe_event_tracker/2, describe_event_tracker/3, describe_feature_transformation/2, describe_feature_transformation/3, describe_filter/2, describe_filter/3, describe_metric_attribution/2, describe_metric_attribution/3, describe_recipe/2, describe_recipe/3, describe_recommender/2, describe_recommender/3, describe_schema/2, describe_schema/3, describe_solution/2, describe_solution/3, describe_solution_version/2, describe_solution_version/3, get_solution_metrics/2, get_solution_metrics/3, list_batch_inference_jobs/2, list_batch_inference_jobs/3, list_batch_segment_jobs/2, list_batch_segment_jobs/3, list_campaigns/2, list_campaigns/3, list_dataset_export_jobs/2, list_dataset_export_jobs/3, list_dataset_groups/2, list_dataset_groups/3, list_dataset_import_jobs/2, list_dataset_import_jobs/3, list_datasets/2, list_datasets/3, list_event_trackers/2, list_event_trackers/3, list_filters/2, list_filters/3, list_metric_attribution_metrics/2, list_metric_attribution_metrics/3, list_metric_attributions/2, list_metric_attributions/3, list_recipes/2, list_recipes/3, list_recommenders/2, list_recommenders/3, list_schemas/2, list_schemas/3, list_solution_versions/2, list_solution_versions/3, list_solutions/2, list_solutions/3, list_tags_for_resource/2, list_tags_for_resource/3, start_recommender/2, start_recommender/3, stop_recommender/2, stop_recommender/3, stop_solution_version_creation/2, stop_solution_version_creation/3, tag_resource/2, tag_resource/3, untag_resource/2, untag_resource/3, update_campaign/2, update_campaign/3, update_dataset/2, update_dataset/3, update_metric_attribution/2, update_metric_attribution/3, update_recommender/2, update_recommender/3]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc Generates batch recommendations based on a list of items or users %% stored in Amazon S3 and exports the recommendations to an Amazon S3 %% bucket. %% %% To generate batch recommendations, specify the ARN of a solution version %% and an Amazon S3 URI for the input and output data. For user %% personalization, popular items, and personalized ranking solutions, the %% batch inference job generates a list of recommended items for each user ID %% in the input file. For related items solutions, the job generates a list %% of recommended items for each item ID in the input file. %% %% For more information, see Creating a batch inference job : %% https://docs.aws.amazon.com/personalize/latest/dg/getting-batch-recommendations.html. %% %% If you use the Similar-Items recipe, Amazon Personalize can add %% descriptive themes to batch recommendations. To generate themes, set the %% job's mode to `THEME_GENERATION' and specify the name of the field %% that contains item names in the input data. %% %% For more information about generating themes, see Batch recommendations %% with themes from Content Generator : %% https://docs.aws.amazon.com/personalize/latest/dg/themed-batch-recommendations.html. %% %% You can't get batch recommendations with the Trending-Now or %% Next-Best-Action recipes. create_batch_inference_job(Client, Input) when is_map(Client), is_map(Input) -> create_batch_inference_job(Client, Input, []). create_batch_inference_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateBatchInferenceJob">>, Input, Options). %% @doc Creates a batch segment job. %% %% The operation can handle up to 50 million records and the input file must %% be in JSON format. For more information, see Getting batch recommendations %% and user segments: %% https://docs.aws.amazon.com/personalize/latest/dg/recommendations-batch.html. create_batch_segment_job(Client, Input) when is_map(Client), is_map(Input) -> create_batch_segment_job(Client, Input, []). create_batch_segment_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateBatchSegmentJob">>, Input, Options). %% @doc Creates a campaign that deploys a solution version. %% %% When a client calls the GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% and GetPersonalizedRanking: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetPersonalizedRanking.html %% APIs, a campaign is specified in the request. %% %% Minimum Provisioned TPS and Auto-Scaling %% %% A high `minProvisionedTPS' will increase your cost. We recommend %% starting with 1 for `minProvisionedTPS' (the default). Track your %% usage using Amazon CloudWatch metrics, and increase the %% `minProvisionedTPS' as necessary. %% %% When you create an Amazon Personalize campaign, you can specify the %% minimum provisioned transactions per second (`minProvisionedTPS') for %% the campaign. This is the baseline transaction throughput for the campaign %% provisioned by Amazon Personalize. It sets the minimum billing charge for %% the campaign while it is active. A transaction is a single %% `GetRecommendations' or `GetPersonalizedRanking' request. The %% default `minProvisionedTPS' is 1. %% %% If your TPS increases beyond the `minProvisionedTPS', Amazon %% Personalize auto-scales the provisioned capacity up and down, but never %% below `minProvisionedTPS'. There's a short time delay while the %% capacity is increased that might cause loss of transactions. When your %% traffic reduces, capacity returns to the `minProvisionedTPS'. %% %% You are charged for the the minimum provisioned TPS or, if your requests %% exceed the `minProvisionedTPS', the actual TPS. The actual TPS is the %% total number of recommendation requests you make. We recommend starting %% with a low `minProvisionedTPS', track your usage using Amazon %% CloudWatch metrics, and then increase the `minProvisionedTPS' as %% necessary. %% %% For more information about campaign costs, see Amazon Personalize pricing: %% https://aws.amazon.com/personalize/pricing/. %% %% Status %% %% A campaign can be in one of the following states: %% %% To get the campaign status, call DescribeCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeCampaign.html. %% %% Wait until the `status' of the campaign is `ACTIVE' before asking %% the campaign for recommendations. %% %% == Related APIs == %% %% create_campaign(Client, Input) when is_map(Client), is_map(Input) -> create_campaign(Client, Input, []). create_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateCampaign">>, Input, Options). %% @doc Creates an empty dataset and adds it to the specified dataset group. %% %% Use CreateDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetImportJob.html %% to import your training data to a dataset. %% %% There are 5 types of datasets: %% %% Each dataset type has an associated schema with required field %% types. Only the `Item interactions' dataset is required in order to %% train a model (also referred to as creating a solution). %% %% A dataset can be in one of the following states: %% %% To get the status of the dataset, call DescribeDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDataset.html. %% %% == Related APIs == %% %% create_dataset(Client, Input) when is_map(Client), is_map(Input) -> create_dataset(Client, Input, []). create_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDataset">>, Input, Options). %% @doc Creates a job that exports data from your dataset to an Amazon S3 %% bucket. %% %% To allow Amazon Personalize to export the training data, you must specify %% an service-linked IAM role that gives Amazon Personalize `PutObject' %% permissions for your Amazon S3 bucket. For information, see Exporting a %% dataset: %% https://docs.aws.amazon.com/personalize/latest/dg/export-data.html in the %% Amazon Personalize developer guide. %% %% Status %% %% A dataset export job can be in one of the following states: %% %% To get the status of the export job, call %% DescribeDatasetExportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetExportJob.html, %% and specify the Amazon Resource Name (ARN) of the dataset export job. The %% dataset export is complete when the status shows as ACTIVE. If the status %% shows as CREATE FAILED, the response includes a `failureReason' key, %% which describes why the job failed. create_dataset_export_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_export_job(Client, Input, []). create_dataset_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetExportJob">>, Input, Options). %% @doc Creates an empty dataset group. %% %% A dataset group is a container for Amazon Personalize resources. A dataset %% group can contain at most three datasets, one for each type of dataset: %% %% A dataset group can be a Domain dataset group, where you %% specify a domain and use pre-configured resources like recommenders, or a %% Custom dataset group, where you use custom resources, such as a solution %% with a solution version, that you deploy with a campaign. If you start %% with a Domain dataset group, you can still add custom resources such as %% solutions and solution versions trained with recipes for custom use cases %% and deployed with campaigns. %% %% A dataset group can be in one of the following states: %% %% To get the status of the dataset group, call %% DescribeDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetGroup.html. %% If the status shows as CREATE FAILED, the response includes a %% `failureReason' key, which describes why the creation failed. %% %% You must wait until the `status' of the dataset group is `ACTIVE' %% before adding a dataset to the group. %% %% You can specify an Key Management Service (KMS) key to encrypt the %% datasets in the group. If you specify a KMS key, you must also include an %% Identity and Access Management (IAM) role that has permission to access %% the key. %% %% == APIs that require a dataset group ARN in the request == %% %% == Related APIs == %% %% create_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_group(Client, Input, []). create_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetGroup">>, Input, Options). %% @doc Creates a job that imports training data from your data source (an %% Amazon S3 bucket) to an Amazon Personalize dataset. %% %% To allow Amazon Personalize to import the training data, you must specify %% an IAM service role that has permission to read from the data source, as %% Amazon Personalize makes a copy of your data and processes it internally. %% For information on granting access to your Amazon S3 bucket, see Giving %% Amazon Personalize Access to Amazon S3 Resources: %% https://docs.aws.amazon.com/personalize/latest/dg/granting-personalize-s3-access.html. %% %% If you already created a recommender or deployed a custom solution version %% with a campaign, how new bulk records influence recommendations depends on %% the domain use case or recipe that you use. For more information, see How %% new data influences real-time recommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/how-new-data-influences-recommendations.html. %% %% By default, a dataset import job replaces any existing data in the dataset %% that you imported in bulk. To add new records without replacing existing %% data, specify INCREMENTAL for the import mode in the %% CreateDatasetImportJob operation. %% %% Status %% %% A dataset import job can be in one of the following states: %% %% To get the status of the import job, call %% DescribeDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetImportJob.html, %% providing the Amazon Resource Name (ARN) of the dataset import job. The %% dataset import is complete when the status shows as ACTIVE. If the status %% shows as CREATE FAILED, the response includes a `failureReason' key, %% which describes why the job failed. %% %% Importing takes time. You must wait until the status shows as ACTIVE %% before training a model using the dataset. %% %% == Related APIs == %% %% create_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_import_job(Client, Input, []). create_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetImportJob">>, Input, Options). %% @doc Creates an event tracker that you use when adding event data to a %% specified dataset group using the PutEvents: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UBS_PutEvents.html %% API. %% %% Only one event tracker can be associated with a dataset group. You will %% get an error if you call `CreateEventTracker' using the same dataset %% group as an existing event tracker. %% %% When you create an event tracker, the response includes a tracking ID, %% which you pass as a parameter when you use the PutEvents: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UBS_PutEvents.html %% operation. Amazon Personalize then appends the event data to the Item %% interactions dataset of the dataset group you specify in your event %% tracker. %% %% The event tracker can be in one of the following states: %% %% To get the status of the event tracker, call %% DescribeEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeEventTracker.html. %% %% The event tracker must be in the ACTIVE state before using the tracking %% ID. %% %% == Related APIs == %% %% create_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> create_event_tracker(Client, Input, []). create_event_tracker(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateEventTracker">>, Input, Options). %% @doc Creates a recommendation filter. %% %% For more information, see Filtering recommendations and user segments: %% https://docs.aws.amazon.com/personalize/latest/dg/filter.html. create_filter(Client, Input) when is_map(Client), is_map(Input) -> create_filter(Client, Input, []). create_filter(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateFilter">>, Input, Options). %% @doc Creates a metric attribution. %% %% A metric attribution creates reports on the data that you import into %% Amazon Personalize. Depending on how you imported the data, you can view %% reports in Amazon CloudWatch or Amazon S3. For more information, see %% Measuring impact of recommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/measuring-recommendation-impact.html. create_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> create_metric_attribution(Client, Input, []). create_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateMetricAttribution">>, Input, Options). %% @doc Creates a recommender with the recipe (a Domain dataset group use %% case) you specify. %% %% You create recommenders for a Domain dataset group and specify the %% recommender's Amazon Resource Name (ARN) when you make a %% GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% request. %% %% Minimum recommendation requests per second %% %% A high `minRecommendationRequestsPerSecond' will increase your bill. %% We recommend starting with 1 for `minRecommendationRequestsPerSecond' %% (the default). Track your usage using Amazon CloudWatch metrics, and %% increase the `minRecommendationRequestsPerSecond' as necessary. %% %% When you create a recommender, you can configure the recommender's %% minimum recommendation requests per second. The minimum recommendation %% requests per second (`minRecommendationRequestsPerSecond') specifies %% the baseline recommendation request throughput provisioned by Amazon %% Personalize. The default minRecommendationRequestsPerSecond is `1'. A %% recommendation request is a single `GetRecommendations' operation. %% Request throughput is measured in requests per second and Amazon %% Personalize uses your requests per second to derive your requests per hour %% and the price of your recommender usage. %% %% If your requests per second increases beyond %% `minRecommendationRequestsPerSecond', Amazon Personalize auto-scales %% the provisioned capacity up and down, but never below %% `minRecommendationRequestsPerSecond'. There's a short time delay %% while the capacity is increased that might cause loss of requests. %% %% Your bill is the greater of either the minimum requests per hour (based on %% minRecommendationRequestsPerSecond) or the actual number of requests. The %% actual request throughput used is calculated as the average %% requests/second within a one-hour window. We recommend starting with the %% default `minRecommendationRequestsPerSecond', track your usage using %% Amazon CloudWatch metrics, and then increase the %% `minRecommendationRequestsPerSecond' as necessary. %% %% Status %% %% A recommender can be in one of the following states: %% %% To get the recommender status, call DescribeRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeRecommender.html. %% %% Wait until the `status' of the recommender is `ACTIVE' before %% asking the recommender for recommendations. %% %% == Related APIs == %% %% create_recommender(Client, Input) when is_map(Client), is_map(Input) -> create_recommender(Client, Input, []). create_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateRecommender">>, Input, Options). %% @doc Creates an Amazon Personalize schema from the specified schema %% string. %% %% The schema you create must be in Avro JSON format. %% %% Amazon Personalize recognizes three schema variants. Each schema is %% associated with a dataset type and has a set of required field and %% keywords. If you are creating a schema for a dataset in a Domain dataset %% group, you provide the domain of the Domain dataset group. You specify a %% schema when you call CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. %% %% == Related APIs == %% %% create_schema(Client, Input) when is_map(Client), is_map(Input) -> create_schema(Client, Input, []). create_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSchema">>, Input, Options). %% @doc Creates the configuration for training a model. %% %% A trained model is known as a solution version. After the configuration is %% created, you train the model (create a solution version) by calling the %% CreateSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html %% operation. Every time you call `CreateSolutionVersion', a new version %% of the solution is created. %% %% After creating a solution version, you check its accuracy by calling %% GetSolutionMetrics: %% https://docs.aws.amazon.com/personalize/latest/dg/API_GetSolutionMetrics.html. %% When you are satisfied with the version, you deploy it using %% CreateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. %% The campaign provides recommendations to a client through the %% GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% API. %% %% To train a model, Amazon Personalize requires training data and a recipe. %% The training data comes from the dataset group that you provide in the %% request. A recipe specifies the training algorithm and a feature %% transformation. You can specify one of the predefined recipes provided by %% Amazon Personalize. %% %% Amazon Personalize doesn't support configuring the `hpoObjective' %% for solution hyperparameter optimization at this time. %% %% Status %% %% A solution can be in one of the following states: %% %% To get the status of the solution, call DescribeSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html. %% Wait until the status shows as ACTIVE before calling %% `CreateSolutionVersion'. %% %% == Related APIs == %% %% create_solution(Client, Input) when is_map(Client), is_map(Input) -> create_solution(Client, Input, []). create_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSolution">>, Input, Options). %% @doc Trains or retrains an active solution in a Custom dataset group. %% %% A solution is created using the CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html %% operation and must be in the ACTIVE state before calling %% `CreateSolutionVersion'. A new version of the solution is created %% every time you call this operation. %% %% Status %% %% A solution version can be in one of the following states: %% %% To get the status of the version, call %% DescribeSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html. %% Wait until the status shows as ACTIVE before calling `CreateCampaign'. %% %% If the status shows as CREATE FAILED, the response includes a %% `failureReason' key, which describes why the job failed. %% %% == Related APIs == %% %% create_solution_version(Client, Input) when is_map(Client), is_map(Input) -> create_solution_version(Client, Input, []). create_solution_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSolutionVersion">>, Input, Options). %% @doc Removes a campaign by deleting the solution deployment. %% %% The solution that the campaign is based on is not deleted and can be %% redeployed when needed. A deleted campaign can no longer be specified in a %% GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% request. For information on creating campaigns, see CreateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. delete_campaign(Client, Input) when is_map(Client), is_map(Input) -> delete_campaign(Client, Input, []). delete_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteCampaign">>, Input, Options). %% @doc Deletes a dataset. %% %% You can't delete a dataset if an associated `DatasetImportJob' or %% `SolutionVersion' is in the CREATE PENDING or IN PROGRESS state. For %% more information on datasets, see CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. delete_dataset(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset(Client, Input, []). delete_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDataset">>, Input, Options). %% @doc Deletes a dataset group. %% %% Before you delete a dataset group, you must delete the following: %% %% delete_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset_group(Client, Input, []). delete_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDatasetGroup">>, Input, Options). %% @doc Deletes the event tracker. %% %% Does not delete the dataset from the dataset group. For more information %% on event trackers, see CreateEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html. delete_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> delete_event_tracker(Client, Input, []). delete_event_tracker(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteEventTracker">>, Input, Options). %% @doc Deletes a filter. delete_filter(Client, Input) when is_map(Client), is_map(Input) -> delete_filter(Client, Input, []). delete_filter(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteFilter">>, Input, Options). %% @doc Deletes a metric attribution. delete_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> delete_metric_attribution(Client, Input, []). delete_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteMetricAttribution">>, Input, Options). %% @doc Deactivates and removes a recommender. %% %% A deleted recommender can no longer be specified in a GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% request. delete_recommender(Client, Input) when is_map(Client), is_map(Input) -> delete_recommender(Client, Input, []). delete_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteRecommender">>, Input, Options). %% @doc Deletes a schema. %% %% Before deleting a schema, you must delete all datasets referencing the %% schema. For more information on schemas, see CreateSchema: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSchema.html. delete_schema(Client, Input) when is_map(Client), is_map(Input) -> delete_schema(Client, Input, []). delete_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteSchema">>, Input, Options). %% @doc Deletes all versions of a solution and the `Solution' object %% itself. %% %% Before deleting a solution, you must delete all campaigns based on the %% solution. To determine what campaigns are using the solution, call %% ListCampaigns: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListCampaigns.html %% and supply the Amazon Resource Name (ARN) of the solution. You can't %% delete a solution if an associated `SolutionVersion' is in the CREATE %% PENDING or IN PROGRESS state. For more information on solutions, see %% CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html. delete_solution(Client, Input) when is_map(Client), is_map(Input) -> delete_solution(Client, Input, []). delete_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteSolution">>, Input, Options). %% @doc Describes the given algorithm. describe_algorithm(Client, Input) when is_map(Client), is_map(Input) -> describe_algorithm(Client, Input, []). describe_algorithm(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAlgorithm">>, Input, Options). %% @doc Gets the properties of a batch inference job including name, Amazon %% Resource Name (ARN), status, input and output configurations, and the ARN %% of the solution version used to generate the recommendations. describe_batch_inference_job(Client, Input) when is_map(Client), is_map(Input) -> describe_batch_inference_job(Client, Input, []). describe_batch_inference_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeBatchInferenceJob">>, Input, Options). %% @doc Gets the properties of a batch segment job including name, Amazon %% Resource Name (ARN), status, input and output configurations, and the ARN %% of the solution version used to generate segments. describe_batch_segment_job(Client, Input) when is_map(Client), is_map(Input) -> describe_batch_segment_job(Client, Input, []). describe_batch_segment_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeBatchSegmentJob">>, Input, Options). %% @doc Describes the given campaign, including its status. %% %% A campaign can be in one of the following states: %% %% When the `status' is `CREATE FAILED', the response %% includes the `failureReason' key, which describes why. %% %% For more information on campaigns, see CreateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. describe_campaign(Client, Input) when is_map(Client), is_map(Input) -> describe_campaign(Client, Input, []). describe_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeCampaign">>, Input, Options). %% @doc Describes the given dataset. %% %% For more information on datasets, see CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. describe_dataset(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset(Client, Input, []). describe_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDataset">>, Input, Options). %% @doc Describes the dataset export job created by CreateDatasetExportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetExportJob.html, %% including the export job status. describe_dataset_export_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_export_job(Client, Input, []). describe_dataset_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetExportJob">>, Input, Options). %% @doc Describes the given dataset group. %% %% For more information on dataset groups, see CreateDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetGroup.html. describe_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_group(Client, Input, []). describe_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetGroup">>, Input, Options). %% @doc Describes the dataset import job created by CreateDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetImportJob.html, %% including the import job status. describe_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_import_job(Client, Input, []). describe_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetImportJob">>, Input, Options). %% @doc Describes an event tracker. %% %% The response includes the `trackingId' and `status' of the event %% tracker. For more information on event trackers, see CreateEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html. describe_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> describe_event_tracker(Client, Input, []). describe_event_tracker(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeEventTracker">>, Input, Options). %% @doc Describes the given feature transformation. describe_feature_transformation(Client, Input) when is_map(Client), is_map(Input) -> describe_feature_transformation(Client, Input, []). describe_feature_transformation(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeFeatureTransformation">>, Input, Options). %% @doc Describes a filter's properties. describe_filter(Client, Input) when is_map(Client), is_map(Input) -> describe_filter(Client, Input, []). describe_filter(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeFilter">>, Input, Options). %% @doc Describes a metric attribution. describe_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> describe_metric_attribution(Client, Input, []). describe_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeMetricAttribution">>, Input, Options). %% @doc Describes a recipe. %% %% A recipe contains three items: %% %% Amazon Personalize provides a set of predefined recipes. You %% specify a recipe when you create a solution with the CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html %% API. `CreateSolution' trains a model by using the algorithm in the %% specified recipe and a training dataset. The solution, when deployed as a %% campaign, can provide recommendations using the GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% API. describe_recipe(Client, Input) when is_map(Client), is_map(Input) -> describe_recipe(Client, Input, []). describe_recipe(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeRecipe">>, Input, Options). %% @doc Describes the given recommender, including its status. %% %% A recommender can be in one of the following states: %% %% When the `status' is `CREATE FAILED', the response %% includes the `failureReason' key, which describes why. %% %% The `modelMetrics' key is null when the recommender is being created %% or deleted. %% %% For more information on recommenders, see CreateRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateRecommender.html. describe_recommender(Client, Input) when is_map(Client), is_map(Input) -> describe_recommender(Client, Input, []). describe_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeRecommender">>, Input, Options). %% @doc Describes a schema. %% %% For more information on schemas, see CreateSchema: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSchema.html. describe_schema(Client, Input) when is_map(Client), is_map(Input) -> describe_schema(Client, Input, []). describe_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSchema">>, Input, Options). %% @doc Describes a solution. %% %% For more information on solutions, see CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html. describe_solution(Client, Input) when is_map(Client), is_map(Input) -> describe_solution(Client, Input, []). describe_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSolution">>, Input, Options). %% @doc Describes a specific version of a solution. %% %% For more information on solutions, see CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html describe_solution_version(Client, Input) when is_map(Client), is_map(Input) -> describe_solution_version(Client, Input, []). describe_solution_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSolutionVersion">>, Input, Options). %% @doc Gets the metrics for the specified solution version. get_solution_metrics(Client, Input) when is_map(Client), is_map(Input) -> get_solution_metrics(Client, Input, []). get_solution_metrics(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetSolutionMetrics">>, Input, Options). %% @doc Gets a list of the batch inference jobs that have been performed off %% of a solution version. list_batch_inference_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_batch_inference_jobs(Client, Input, []). list_batch_inference_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListBatchInferenceJobs">>, Input, Options). %% @doc Gets a list of the batch segment jobs that have been performed off of %% a solution version that you specify. list_batch_segment_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_batch_segment_jobs(Client, Input, []). list_batch_segment_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListBatchSegmentJobs">>, Input, Options). %% @doc Returns a list of campaigns that use the given solution. %% %% When a solution is not specified, all the campaigns associated with the %% account are listed. The response provides the properties for each %% campaign, including the Amazon Resource Name (ARN). For more information %% on campaigns, see CreateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. list_campaigns(Client, Input) when is_map(Client), is_map(Input) -> list_campaigns(Client, Input, []). list_campaigns(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListCampaigns">>, Input, Options). %% @doc Returns a list of dataset export jobs that use the given dataset. %% %% When a dataset is not specified, all the dataset export jobs associated %% with the account are listed. The response provides the properties for each %% dataset export job, including the Amazon Resource Name (ARN). For more %% information on dataset export jobs, see CreateDatasetExportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetExportJob.html. %% For more information on datasets, see CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. list_dataset_export_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_export_jobs(Client, Input, []). list_dataset_export_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetExportJobs">>, Input, Options). %% @doc Returns a list of dataset groups. %% %% The response provides the properties for each dataset group, including the %% Amazon Resource Name (ARN). For more information on dataset groups, see %% CreateDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetGroup.html. list_dataset_groups(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_groups(Client, Input, []). list_dataset_groups(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetGroups">>, Input, Options). %% @doc Returns a list of dataset import jobs that use the given dataset. %% %% When a dataset is not specified, all the dataset import jobs associated %% with the account are listed. The response provides the properties for each %% dataset import job, including the Amazon Resource Name (ARN). For more %% information on dataset import jobs, see CreateDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetImportJob.html. %% For more information on datasets, see CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. list_dataset_import_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_import_jobs(Client, Input, []). list_dataset_import_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetImportJobs">>, Input, Options). %% @doc Returns the list of datasets contained in the given dataset group. %% %% The response provides the properties for each dataset, including the %% Amazon Resource Name (ARN). For more information on datasets, see %% CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. list_datasets(Client, Input) when is_map(Client), is_map(Input) -> list_datasets(Client, Input, []). list_datasets(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasets">>, Input, Options). %% @doc Returns the list of event trackers associated with the account. %% %% The response provides the properties for each event tracker, including the %% Amazon Resource Name (ARN) and tracking ID. For more information on event %% trackers, see CreateEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html. list_event_trackers(Client, Input) when is_map(Client), is_map(Input) -> list_event_trackers(Client, Input, []). list_event_trackers(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListEventTrackers">>, Input, Options). %% @doc Lists all filters that belong to a given dataset group. list_filters(Client, Input) when is_map(Client), is_map(Input) -> list_filters(Client, Input, []). list_filters(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListFilters">>, Input, Options). %% @doc Lists the metrics for the metric attribution. list_metric_attribution_metrics(Client, Input) when is_map(Client), is_map(Input) -> list_metric_attribution_metrics(Client, Input, []). list_metric_attribution_metrics(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMetricAttributionMetrics">>, Input, Options). %% @doc Lists metric attributions. list_metric_attributions(Client, Input) when is_map(Client), is_map(Input) -> list_metric_attributions(Client, Input, []). list_metric_attributions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMetricAttributions">>, Input, Options). %% @doc Returns a list of available recipes. %% %% The response provides the properties for each recipe, including the %% recipe's Amazon Resource Name (ARN). list_recipes(Client, Input) when is_map(Client), is_map(Input) -> list_recipes(Client, Input, []). list_recipes(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListRecipes">>, Input, Options). %% @doc Returns a list of recommenders in a given Domain dataset group. %% %% When a Domain dataset group is not specified, all the recommenders %% associated with the account are listed. The response provides the %% properties for each recommender, including the Amazon Resource Name (ARN). %% For more information on recommenders, see CreateRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateRecommender.html. list_recommenders(Client, Input) when is_map(Client), is_map(Input) -> list_recommenders(Client, Input, []). list_recommenders(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListRecommenders">>, Input, Options). %% @doc Returns the list of schemas associated with the account. %% %% The response provides the properties for each schema, including the Amazon %% Resource Name (ARN). For more information on schemas, see CreateSchema: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSchema.html. list_schemas(Client, Input) when is_map(Client), is_map(Input) -> list_schemas(Client, Input, []). list_schemas(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSchemas">>, Input, Options). %% @doc Returns a list of solution versions for the given solution. %% %% When a solution is not specified, all the solution versions associated %% with the account are listed. The response provides the properties for each %% solution version, including the Amazon Resource Name (ARN). list_solution_versions(Client, Input) when is_map(Client), is_map(Input) -> list_solution_versions(Client, Input, []). list_solution_versions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSolutionVersions">>, Input, Options). %% @doc Returns a list of solutions that use the given dataset group. %% %% When a dataset group is not specified, all the solutions associated with %% the account are listed. The response provides the properties for each %% solution, including the Amazon Resource Name (ARN). For more information %% on solutions, see CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html. list_solutions(Client, Input) when is_map(Client), is_map(Input) -> list_solutions(Client, Input, []). list_solutions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSolutions">>, Input, Options). %% @doc Get a list of tags: %% https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html %% attached to a resource. list_tags_for_resource(Client, Input) when is_map(Client), is_map(Input) -> list_tags_for_resource(Client, Input, []). list_tags_for_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTagsForResource">>, Input, Options). %% @doc Starts a recommender that is INACTIVE. %% %% Starting a recommender does not create any new models, but resumes billing %% and automatic retraining for the recommender. start_recommender(Client, Input) when is_map(Client), is_map(Input) -> start_recommender(Client, Input, []). start_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StartRecommender">>, Input, Options). %% @doc Stops a recommender that is ACTIVE. %% %% Stopping a recommender halts billing and automatic retraining for the %% recommender. stop_recommender(Client, Input) when is_map(Client), is_map(Input) -> stop_recommender(Client, Input, []). stop_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopRecommender">>, Input, Options). %% @doc Stops creating a solution version that is in a state of %% CREATE_PENDING or CREATE IN_PROGRESS. %% %% Depending on the current state of the solution version, the solution %% version state changes as follows: %% %% You are billed for all of the training completed up until you %% stop the solution version creation. You cannot resume creating a solution %% version once it has been stopped. stop_solution_version_creation(Client, Input) when is_map(Client), is_map(Input) -> stop_solution_version_creation(Client, Input, []). stop_solution_version_creation(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopSolutionVersionCreation">>, Input, Options). %% @doc Add a list of tags to a resource. tag_resource(Client, Input) when is_map(Client), is_map(Input) -> tag_resource(Client, Input, []). tag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"TagResource">>, Input, Options). %% @doc Remove tags: %% https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html %% that are attached to a resource. untag_resource(Client, Input) when is_map(Client), is_map(Input) -> untag_resource(Client, Input, []). untag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UntagResource">>, Input, Options). %% @doc Updates a campaign to deploy a retrained solution version with an %% existing campaign, change your campaign's `minProvisionedTPS', or %% modify your campaign's configuration, such as the exploration %% configuration. %% %% To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. %% Check the campaign status using the DescribeCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeCampaign.html %% operation. %% %% You can still get recommendations from a campaign while an update is in %% progress. The campaign will use the previous solution version and campaign %% configuration to generate recommendations until the latest campaign update %% status is `Active'. %% %% For more information about updating a campaign, including code samples, %% see Updating a campaign: %% https://docs.aws.amazon.com/personalize/latest/dg/update-campaigns.html. %% For more information about campaigns, see Creating a campaign: %% https://docs.aws.amazon.com/personalize/latest/dg/campaigns.html. update_campaign(Client, Input) when is_map(Client), is_map(Input) -> update_campaign(Client, Input, []). update_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateCampaign">>, Input, Options). %% @doc Update a dataset to replace its schema with a new or existing one. %% %% For more information, see Replacing a dataset's schema: %% https://docs.aws.amazon.com/personalize/latest/dg/updating-dataset-schema.html. update_dataset(Client, Input) when is_map(Client), is_map(Input) -> update_dataset(Client, Input, []). update_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateDataset">>, Input, Options). %% @doc Updates a metric attribution. update_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> update_metric_attribution(Client, Input, []). update_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateMetricAttribution">>, Input, Options). %% @doc Updates the recommender to modify the recommender configuration. %% %% If you update the recommender to modify the columns used in training, %% Amazon Personalize automatically starts a full retraining of the models %% backing your recommender. While the update completes, you can still get %% recommendations from the recommender. The recommender uses the previous %% configuration until the update completes. To track the status of this %% update, use the `latestRecommenderUpdate' returned in the %% DescribeRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeRecommender.html %% operation. update_recommender(Client, Input) when is_map(Client), is_map(Input) -> update_recommender(Client, Input, []). update_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateRecommender">>, Input, Options). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), binary(), map(), list()) -> {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map() | undefined, Error :: map(). request(Client, Action, Input, Options) -> RequestFun = fun() -> do_request(Client, Action, Input, Options) end, aws_request:request(RequestFun, Options). do_request(Client, Action, Input0, Options) -> Client1 = Client#{service => <<"personalize">>}, Host = build_host(<<"personalize">>, Client1), URL = build_url(Host, Client1), Headers = [ {<<"Host">>, Host}, {<<"Content-Type">>, <<"application/x-amz-json-1.1">>}, {<<"X-Amz-Target">>, <<"AmazonPersonalize.", Action/binary>>} ], Input = Input0, Payload = jsx:encode(Input), SignedHeaders = aws_request:sign_request(Client1, <<"POST">>, URL, Headers, Payload), Response = hackney:request(post, URL, SignedHeaders, Payload, Options), handle_response(Response). handle_response({ok, 200, ResponseHeaders, Client}) -> case hackney:body(Client) of {ok, <<>>} -> {ok, undefined, {200, ResponseHeaders, Client}}; {ok, Body} -> Result = jsx:decode(Body), {ok, Result, {200, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, ResponseHeaders, Client}) -> {ok, Body} = hackney:body(Client), Error = jsx:decode(Body), {error, Error, {StatusCode, ResponseHeaders, Client}}; handle_response({error, Reason}) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Client) -> Proto = aws_client:proto(Client), Port = aws_client:port(Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, <<"/">>], <<"">>).