AWS AmazonS3 documentation change
Summary
Added documentation for new 'vector buckets' type supporting vector data storage/querying and integration with ML services
Security assessment
Change introduces a new bucket type for technical functionality (vector storage/ML integration) without any security-specific context or vulnerability mitigation
Diff
diff --git a/AmazonS3/latest/userguide/Welcome.md b/AmazonS3/latest/userguide/Welcome.md index 17ba6fd63..1a0a649ee 100644 --- a//AmazonS3/latest/userguide/Welcome.md +++ b//AmazonS3/latest/userguide/Welcome.md @@ -167 +167 @@ Buckets and the objects in them are private and can be accessed only if you expl -Amazon S3 supports three types of buckets—general purpose buckets, directory buckets, and table buckets. Each type of bucket provides a unique set of features for different use cases. +Amazon S3 supports four types of buckets—general purpose buckets, directory buckets, table buckets, and vector buckets. Each type of bucket provides a unique set of features for different use cases. @@ -193,0 +194,4 @@ All table buckets and tables are private and can't be made public. These resourc +**Vector buckets** – S3 vector buckets are a type of Amazon S3 bucket that are purpose-built to store and query vectors. Vector buckets use dedicated API operations to write and query vector data efficiently. With S3 vector buckets, you can store vector embeddings for machine learning models, perform similarity searches, and integrate with services like Amazon Bedrock and Amazon OpenSearch. + +S3 vector buckets organize data using vector indexes, which are resources within a bucket that store and organize vector data for efficient similarity search. Each vector index can be configured with specific dimensions, distance metrics (like cosine similarity), and metadata configurations to optimize for your specific use case. For more information, see [Working with S3 Vectors and vector buckets](./s3-vectors.html). +