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Merge branch 'release-1.35.17' into develop
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* release-1.35.17:
  Bumping version to 1.35.17
  Update endpoints model
  Update to latest models
  • Loading branch information
aws-sdk-python-automation committed Sep 11, 2024
2 parents 1fd6e69 + 8b08a32 commit 2707445
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32 changes: 32 additions & 0 deletions .changes/1.35.17.json
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[
{
"category": "``bedrock-agent``",
"description": "Amazon Bedrock Knowledge Bases now supports using inference profiles to increase throughput and improve resilience.",
"type": "api-change"
},
{
"category": "``bedrock-agent-runtime``",
"description": "Amazon Bedrock Knowledge Bases now supports using inference profiles to increase throughput and improve resilience.",
"type": "api-change"
},
{
"category": "``ecr``",
"description": "Added KMS_DSSE to EncryptionType",
"type": "api-change"
},
{
"category": "``guardduty``",
"description": "Add support for new statistic types in GetFindingsStatistics.",
"type": "api-change"
},
{
"category": "``lexv2-models``",
"description": "Support new Polly voice engines in VoiceSettings: long-form and generative",
"type": "api-change"
},
{
"category": "``medialive``",
"description": "Adds AV1 Codec support, SRT ouputs, and MediaLive Anywhere support.",
"type": "api-change"
}
]
11 changes: 11 additions & 0 deletions CHANGELOG.rst
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CHANGELOG
=========

1.35.17
=======

* api-change:``bedrock-agent``: Amazon Bedrock Knowledge Bases now supports using inference profiles to increase throughput and improve resilience.
* api-change:``bedrock-agent-runtime``: Amazon Bedrock Knowledge Bases now supports using inference profiles to increase throughput and improve resilience.
* api-change:``ecr``: Added KMS_DSSE to EncryptionType
* api-change:``guardduty``: Add support for new statistic types in GetFindingsStatistics.
* api-change:``lexv2-models``: Support new Polly voice engines in VoiceSettings: long-form and generative
* api-change:``medialive``: Adds AV1 Codec support, SRT ouputs, and MediaLive Anywhere support.


1.35.16
=======

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2 changes: 1 addition & 1 deletion botocore/__init__.py
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import os
import re

__version__ = '1.35.16'
__version__ = '1.35.17'


class NullHandler(logging.Handler):
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12 changes: 6 additions & 6 deletions botocore/data/bedrock-agent-runtime/2023-07-26/service-2.json
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{"shape":"AccessDeniedException"},
{"shape":"ServiceQuotaExceededException"}
],
"documentation":"<p>Queries a knowledge base and generates responses based on the retrieved results. The response only cites sources that are relevant to the query.</p>"
"documentation":"<p>Queries a knowledge base and generates responses based on the retrieved results and using the specified foundation model or <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html\">inference profile</a>. The response only cites sources that are relevant to the query.</p>"
}
},
"shapes":{
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},
"BedrockModelArn":{
"type":"string",
"max":1011,
"min":20,
"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}))$"
"max":2048,
"min":1,
"pattern":"^(arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}:(([0-9]{12}:custom-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}/[a-z0-9]{12})|(:foundation-model/[a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))))|(arn:aws(|-us-gov|-cn|-iso|-iso-b):bedrock:(|[0-9a-z-]{1,20}):(|[0-9]{12}):inference-profile/[a-zA-Z0-9-:.]+)|([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.:]?[a-z0-9-]{1,63}))|(([0-9a-zA-Z][_-]?)+)$"
},
"Boolean":{
"type":"boolean",
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},
"knowledgeBaseId":{
"shape":"KnowledgeBaseId",
"documentation":"<p>The unique identifier of the knowledge base that is queried and the foundation model used for generation.</p>"
"documentation":"<p>The unique identifier of the knowledge base that is queried.</p>"
},
"modelArn":{
"shape":"BedrockModelArn",
"documentation":"<p>The ARN of the foundation model used to generate a response.</p>"
"documentation":"<p>The ARN of the foundation model or <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html\">inference profile</a> used to generate a response.</p>"
},
"orchestrationConfiguration":{
"shape":"OrchestrationConfiguration",
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8 changes: 4 additions & 4 deletions botocore/data/bedrock-agent/2023-06-05/service-2.json
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"members":{
"modelArn":{
"shape":"BedrockModelArn",
"documentation":"<p>The model's ARN.</p>"
"documentation":"<p>The ARN of the foundation model or <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html\">inference profile</a>.</p>"
},
"parsingPrompt":{
"shape":"ParsingPrompt",
"documentation":"<p>Instructions for interpreting the contents of a document.</p>"
}
},
"documentation":"<p>Settings for a foundation model used to parse documents for a data source.</p>"
"documentation":"<p>Settings for a foundation model or <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html\">inference profile</a> used to parse documents for a data source.</p>"
},
"BedrockModelArn":{
"type":"string",
"max":2048,
"min":1,
"pattern":"^arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}::foundation-model/([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.]?[a-z0-9-]{1,63})([:][a-z0-9-]{1,63}){0,2})$"
"pattern":"^arn:aws(-[^:]+)?:bedrock:[a-z0-9-]{1,20}::foundation-model/([a-z0-9-]{1,63}[.]{1}[a-z0-9-]{1,63}([.]?[a-z0-9-]{1,63})([:][a-z0-9-]{1,63}){0,2})|(arn:aws(|-us-gov|-cn|-iso|-iso-b):bedrock:(|[0-9a-z-]{1,20}):(|[0-9]{12}):inference-profile/[a-zA-Z0-9-:.]+)$"
},
"Boolean":{
"type":"boolean",
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"documentation":"<p>The parsing strategy for the data source.</p>"
}
},
"documentation":"<p>Settings for parsing document contents. By default, the service converts the contents of each document into text before splitting it into chunks. To improve processing of PDF files with tables and images, you can configure the data source to convert the pages of text into images and use a model to describe the contents of each page.</p> <p>To use a model to parse PDF documents, set the parsing strategy to <code>BEDROCK_FOUNDATION_MODEL</code> and specify the model to use by ARN. You can also override the default parsing prompt with instructions for how to interpret images and tables in your documents. The following models are supported.</p> <ul> <li> <p>Anthropic Claude 3 Sonnet - <code>anthropic.claude-3-sonnet-20240229-v1:0</code> </p> </li> <li> <p>Anthropic Claude 3 Haiku - <code>anthropic.claude-3-haiku-20240307-v1:0</code> </p> </li> </ul> <p>You can get the ARN of a model with the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html\">ListFoundationModels</a> action. Standard model usage charges apply for the foundation model parsing strategy.</p>"
"documentation":"<p>Settings for parsing document contents. By default, the service converts the contents of each document into text before splitting it into chunks. To improve processing of PDF files with tables and images, you can configure the data source to convert the pages of text into images and use a model to describe the contents of each page.</p> <p>To use a model to parse PDF documents, set the parsing strategy to <code>BEDROCK_FOUNDATION_MODEL</code> and specify the model or <a href=\"https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html\">inference profile</a> to use by ARN. You can also override the default parsing prompt with instructions for how to interpret images and tables in your documents. The following models are supported.</p> <ul> <li> <p>Anthropic Claude 3 Sonnet - <code>anthropic.claude-3-sonnet-20240229-v1:0</code> </p> </li> <li> <p>Anthropic Claude 3 Haiku - <code>anthropic.claude-3-haiku-20240307-v1:0</code> </p> </li> </ul> <p>You can get the ARN of a model with the <a href=\"https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListFoundationModels.html\">ListFoundationModels</a> action. Standard model usage charges apply for the foundation model parsing strategy.</p>"
},
"ParsingPrompt":{
"type":"structure",
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7 changes: 4 additions & 3 deletions botocore/data/ecr/2015-09-21/service-2.json
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"protocol":"json",
"protocols":["json"],
"serviceAbbreviation":"Amazon ECR",
"serviceFullName":"Amazon EC2 Container Registry",
"serviceFullName":"Amazon Elastic Container Registry",
"serviceId":"ECR",
"signatureVersion":"v4",
"signingName":"ecr",
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"members":{
"encryptionType":{
"shape":"EncryptionType",
"documentation":"<p>The encryption type to use.</p> <p>If you use the <code>KMS</code> encryption type, the contents of the repository will be encrypted using server-side encryption with Key Management Service key stored in KMS. When you use KMS to encrypt your data, you can either use the default Amazon Web Services managed KMS key for Amazon ECR, or specify your own KMS key, which you already created. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingKMSEncryption.html\">Protecting data using server-side encryption with an KMS key stored in Key Management Service (SSE-KMS)</a> in the <i>Amazon Simple Storage Service Console Developer Guide</i>.</p> <p>If you use the <code>AES256</code> encryption type, Amazon ECR uses server-side encryption with Amazon S3-managed encryption keys which encrypts the images in the repository using an AES256 encryption algorithm. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingServerSideEncryption.html\">Protecting data using server-side encryption with Amazon S3-managed encryption keys (SSE-S3)</a> in the <i>Amazon Simple Storage Service Console Developer Guide</i>.</p>"
"documentation":"<p>The encryption type to use.</p> <p>If you use the <code>KMS</code> encryption type, the contents of the repository will be encrypted using server-side encryption with Key Management Service key stored in KMS. When you use KMS to encrypt your data, you can either use the default Amazon Web Services managed KMS key for Amazon ECR, or specify your own KMS key, which you already created.</p> <p>If you use the <code>KMS_DSSE</code> encryption type, the contents of the repository will be encrypted with two layers of encryption using server-side encryption with the KMS Management Service key stored in KMS. Similar to the KMS encryption type, you can either use the default Amazon Web Services managed KMS key for Amazon ECR, or specify your own KMS key, which you've already created. </p> <p>If you use the <code>AES256</code> encryption type, Amazon ECR uses server-side encryption with Amazon S3-managed encryption keys which encrypts the images in the repository using an AES256 encryption algorithm. For more information, see <a href=\"https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingServerSideEncryption.html\">Protecting data using server-side encryption with Amazon S3-managed encryption keys (SSE-S3)</a> in the <i>Amazon Simple Storage Service Console Developer Guide</i>.</p>"
},
"kmsKey":{
"shape":"KmsKey",
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"type":"string",
"enum":[
"AES256",
"KMS"
"KMS",
"KMS_DSSE"
]
},
"EnhancedImageScanFinding":{
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11 changes: 11 additions & 0 deletions botocore/data/endpoints.json
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"ap-southeast-2" : { },
"ap-southeast-3" : { },
"ap-southeast-4" : { },
"ap-southeast-5" : { },
"ca-central-1" : { },
"ca-west-1" : { },
"eu-central-1" : { },
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"us-iso-west-1" : { }
}
},
"oam" : {
"endpoints" : {
"us-iso-east-1" : { }
}
},
"outposts" : {
"endpoints" : {
"us-iso-east-1" : { }
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"us-isob-east-1" : { }
}
},
"oam" : {
"endpoints" : {
"us-isob-east-1" : { }
}
},
"outposts" : {
"endpoints" : {
"us-isob-east-1" : { }
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