The byte value of the file to attach, encoded as Base-64 string. The maximum size of all files that is attached is 10MB. You can attach a maximum of 5 files.
" + "documentation":"The raw bytes of the file to attach. The maximum size of all files that is attached is 10MB. You can attach a maximum of 5 files.
" }, "mediaType":{ "shape":"MimeType", @@ -2628,7 +2630,6 @@ "RetrievalFilterList":{ "type":"list", "member":{"shape":"RetrievalFilter"}, - "max":5, "min":2 }, "RetrievalResultConfluenceLocation":{ diff --git a/botocore/data/bedrock-runtime/2023-09-30/service-2.json b/botocore/data/bedrock-runtime/2023-09-30/service-2.json index 0989dc48c8..283f7d6eb4 100644 --- a/botocore/data/bedrock-runtime/2023-09-30/service-2.json +++ b/botocore/data/bedrock-runtime/2023-09-30/service-2.json @@ -368,7 +368,7 @@ "members":{ "modelId":{ "shape":"ConversationalModelId", - "documentation":"The identifier for the model that you want to call.
The modelId to provide depends on the type of model that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
The identifier for the model that you want to call.
The modelId to provide depends on the type of model or throughput that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
The Converse API doesn't support imported models.
", "location":"uri", "locationName":"modelId" }, @@ -542,7 +542,7 @@ "members":{ "modelId":{ "shape":"ConversationalModelId", - "documentation":"The ID for the model.
The modelId to provide depends on the type of model that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
The ID for the model.
The modelId to provide depends on the type of model or throughput that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
The Converse API doesn't support imported models.
", "location":"uri", "locationName":"modelId" }, @@ -1448,7 +1448,7 @@ "type":"string", "max":2048, "min":1, - "pattern":"(arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|([0-9]{12}:provisioned-model/[a-z0-9]{12})|([0-9]{12}:inference-profile/[a-zA-Z0-9-:.]+)))|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|(([0-9a-zA-Z][_-]?)+)|([a-zA-Z0-9-:.]+)" + "pattern":"(arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|([0-9]{12}:imported-model/[a-z0-9]{12})|([0-9]{12}:provisioned-model/[a-z0-9]{12})|([0-9]{12}:inference-profile/[a-zA-Z0-9-:.]+)))|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|(([0-9a-zA-Z][_-]?)+)|([a-zA-Z0-9-:.]+)" }, "InvokeModelRequest":{ "type":"structure", @@ -1475,7 +1475,7 @@ }, "modelId":{ "shape":"InvokeModelIdentifier", - "documentation":"The unique identifier of the model to invoke to run inference.
The modelId to provide depends on the type of model that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
The unique identifier of the model to invoke to run inference.
The modelId to provide depends on the type of model that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
If you use an imported model, specify the ARN of the imported model. You can get the model ARN from a successful call to CreateModelImportJob or from the Imported models page in the Amazon Bedrock console.
The unique identifier of the model to invoke to run inference.
The modelId to provide depends on the type of model that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
The unique identifier of the model to invoke to run inference.
The modelId to provide depends on the type of model that you use:
If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.
If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.
If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.
If you use an imported model, specify the ARN of the imported model. You can get the model ARN from a successful call to CreateModelImportJob or from the Imported models page in the Amazon Bedrock console.
The model specified in the request is not ready to serve inference requests.
", + "documentation":"The model specified in the request is not ready to serve inference requests. The AWS SDK will automatically retry the operation up to 5 times. For information about configuring automatic retries, see Retry behavior in the AWS SDKs and Tools reference guide.
", "error":{ "httpStatusCode":429, "senderFault":true }, - "exception":true + "exception":true, + "retryable":{"throttling":false} }, "ModelOutputs":{ "type":"list", diff --git a/botocore/data/personalize/2018-05-22/service-2.json b/botocore/data/personalize/2018-05-22/service-2.json index 6e54714268..b0a368f1c5 100644 --- a/botocore/data/personalize/2018-05-22/service-2.json +++ b/botocore/data/personalize/2018-05-22/service-2.json @@ -11,7 +11,8 @@ "signatureVersion":"v4", "signingName":"personalize", "targetPrefix":"AmazonPersonalize", - "uid":"personalize-2018-05-22" + "uid":"personalize-2018-05-22", + "auth":["aws.auth#sigv4"] }, "operations":{ "CreateBatchInferenceJob":{ @@ -263,7 +264,7 @@ {"shape":"ResourceInUseException"}, {"shape":"TooManyTagsException"} ], - "documentation":"After you create a solution, you can’t change its configuration. By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. You can't stop automatic training for a solution. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training costs, see Amazon Personalize pricing.
Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution.
By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training.
To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.
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:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion.
Related APIs
" + "documentation":"By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing.
Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution.
By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training.
To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.
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:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion.
Related APIs
" }, "CreateSolutionVersion":{ "name":"CreateSolutionVersion", @@ -1113,6 +1114,23 @@ ], "documentation":"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 operation.
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 Updating a solution.
A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of a solution update, call the DescribeSolution API operation and find the status in the latestSolutionUpdate.
Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.
Automatic solution version creation starts one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
" + "documentation":"Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.
Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
" }, "recipeArn":{ "shape":"Arn", @@ -2260,7 +2278,7 @@ }, "solutionConfig":{ "shape":"SolutionConfig", - "documentation":"The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.
Amazon Personalize doesn't support configuring the hpoObjective at this time.
The configuration properties for the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.
Amazon Personalize doesn't support configuring the hpoObjective at this time.
The status of the recommender update.
A recommender can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
DELETE PENDING > DELETE IN_PROGRESS
The status of the recommender update. A recommender update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
" }, "failureReason":{ "shape":"FailureReason", @@ -4839,9 +4857,13 @@ "latestSolutionVersion":{ "shape":"SolutionVersionSummary", "documentation":"Describes the latest version of the solution, including the status and the ARN.
" + }, + "latestSolutionUpdate":{ + "shape":"SolutionUpdateSummary", + "documentation":"Provides a summary of the latest updates to the solution.
" } }, - "documentation":"After you create a solution, you can’t change its configuration. By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. You can't stop automatic training for a solution. To avoid unnecessary costs, make sure to delete the solution when you are finished. For information about training costs, see Amazon Personalize pricing.
An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.
After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.
" + "documentation":"By default, all new solutions use automatic training. With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing.
An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.
After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.
" }, "SolutionConfig":{ "type":"structure", @@ -4911,6 +4933,43 @@ }, "documentation":"Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.
" }, + "SolutionUpdateConfig":{ + "type":"structure", + "members":{ + "autoTrainingConfig":{"shape":"AutoTrainingConfig"} + }, + "documentation":"The configuration details of the solution update.
" + }, + "SolutionUpdateSummary":{ + "type":"structure", + "members":{ + "solutionUpdateConfig":{ + "shape":"SolutionUpdateConfig", + "documentation":"The configuration details of the solution.
" + }, + "status":{ + "shape":"Status", + "documentation":"The status of the solution update. A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
" + }, + "performAutoTraining":{ + "shape":"PerformAutoTraining", + "documentation":"Whether the solution automatically creates solution versions.
" + }, + "creationDateTime":{ + "shape":"Date", + "documentation":"The date and time (in Unix format) that the solution update was created.
" + }, + "lastUpdatedDateTime":{ + "shape":"Date", + "documentation":"The date and time (in Unix time) that the solution update was last updated.
" + }, + "failureReason":{ + "shape":"FailureReason", + "documentation":"If a solution update fails, the reason behind the failure.
" + } + }, + "documentation":"Provides a summary of the properties of a solution update. For a complete listing, call the DescribeSolution API.
" + }, "SolutionVersion":{ "type":"structure", "members":{ @@ -5358,6 +5417,33 @@ "documentation":"The same recommender Amazon Resource Name (ARN) as given in the request.
" } } + }, + "UpdateSolutionRequest":{ + "type":"structure", + "required":["solutionArn"], + "members":{ + "solutionArn":{ + "shape":"Arn", + "documentation":"The Amazon Resource Name (ARN) of the solution to update.
" + }, + "performAutoTraining":{ + "shape":"PerformAutoTraining", + "documentation":"Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration.
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 Configuring automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
" + }, + "solutionUpdateConfig":{ + "shape":"SolutionUpdateConfig", + "documentation":"The new configuration details of the solution.
" + } + } + }, + "UpdateSolutionResponse":{ + "type":"structure", + "members":{ + "solutionArn":{ + "shape":"Arn", + "documentation":"The same solution Amazon Resource Name (ARN) as given in the request.
" + } + } } }, "documentation":"Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.
" diff --git a/botocore/data/quicksight/2018-04-01/service-2.json b/botocore/data/quicksight/2018-04-01/service-2.json index 23bd0af6f8..65faa8277b 100644 --- a/botocore/data/quicksight/2018-04-01/service-2.json +++ b/botocore/data/quicksight/2018-04-01/service-2.json @@ -6335,7 +6335,7 @@ "documentation":"A unique ID to identify a calculated column. During a dataset update, if the column ID of a calculated column matches that of an existing calculated column, Amazon QuickSight preserves the existing calculated column.
" }, "Expression":{ - "shape":"Expression", + "shape":"DataSetCalculatedFieldExpression", "documentation":"An expression that defines the calculated column.
" } }, @@ -6891,7 +6891,8 @@ "ColumnDescriptiveText":{ "type":"string", "max":500, - "min":0 + "min":0, + "sensitive":true }, "ColumnGroup":{ "type":"structure", @@ -10433,6 +10434,12 @@ "member":{"shape":"Arn"}, "max":100 }, + "DataSetCalculatedFieldExpression":{ + "type":"string", + "max":250000, + "min":1, + "sensitive":true + }, "DataSetConfiguration":{ "type":"structure", "members":{ diff --git a/botocore/data/stepfunctions/2016-11-23/service-2.json b/botocore/data/stepfunctions/2016-11-23/service-2.json index 9462c7dfec..b7e565c574 100644 --- a/botocore/data/stepfunctions/2016-11-23/service-2.json +++ b/botocore/data/stepfunctions/2016-11-23/service-2.json @@ -3964,10 +3964,23 @@ "type":{ "shape":"StateMachineType", "documentation":"The target type of state machine for this definition. The default is STANDARD.
Minimum level of diagnostics to return. ERROR returns only ERROR diagnostics, whereas WARNING returns both WARNING and ERROR diagnostics. The default is ERROR.
The maximum number of diagnostics that are returned per call. The default and maximum value is 100. Setting the value to 0 will also use the default of 100.
If the number of diagnostics returned in the response exceeds maxResults, the value of the truncated field in the response will be set to true.
If the result is OK, this field will be empty. When there are errors, this field will contain an array of Diagnostic objects to help you troubleshoot.
The result value will be true if the number of diagnostics found in the workflow definition exceeds maxResults. When all diagnostics results are returned, the value will be false.
Attaches an IAM policy to the specified resource. Use this to share a rule group across accounts.
You must be the owner of the rule group to perform this operation.
This action is subject to the following restrictions:
You can attach only one policy with each PutPermissionPolicy request.
The ARN in the request must be a valid WAF RuleGroup ARN and the rule group must exist in the same Region.
The user making the request must be the owner of the rule group.
Use this to share a rule group with other accounts.
This action attaches an IAM policy to the specified resource. You must be the owner of the rule group to perform this operation.
This action is subject to the following restrictions:
You can attach only one policy with each PutPermissionPolicy request.
The ARN in the request must be a valid WAF RuleGroup ARN and the rule group must exist in the same Region.
The user making the request must be the owner of the rule group.
If a rule group has been shared with your account, you can access it through the call GetRuleGroup, and you can reference it in CreateWebACL and UpdateWebACL. Rule groups that are shared with you don't appear in your WAF console rule groups listing.