- Access keys consist of an access key ID (like AKIAIOSFODNN7EXAMPLE) and a
secret access key (like wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY). You use access keys to sign
programmatic requests that you make to AWS whether you're using the AWS SDK,
REST, or Query APIs. The AWS SDKs use your access keys (which you store as
part of the application) to sign requests for you so that you don't have to
handle the signing process. If you're unable to use the AWS SDK, you can
sign requests manually.
Access keys are also used with command line interfaces (CLIs). When you use a CLI, the commands that you issue are signed by your access keys, which you can either pass with the command or store as configuration settings on your computer.
You can also create and use temporary access keys, known as temporary security credentials. In addition to the access key ID and secret access key, temporary security credentials include a security token that you must submit to AWS when you use temporary security credentials. The advantage of temporary security credentials is that they have a limited life (after they expire, they're no longer valid), so you can use them in less secure environments or distribute them to grant users temporary access to resources in your AWS account. For example, you can use temporary security credentials to grant entities from other AWS accounts access to resources in your AWS account (cross-account access) or grant users who don't have AWS security credentials access to resources in your AWS account (federation).
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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