AWS S3 Vectors | Cost-optimized AI-ready storage | query vectors at scale, reduce costs up to 90%
Amazon S3 Vectors, a purpose-built durable vector storage solution that can reduce the total cost of uploading, storing, and querying vectors by up to 90 percent. Amazon S3 Vectors is the first cloud object store with native support to store large vector datasets and provide subsecond query performance that makes it affordable for businesses to store AI-ready data at massive scale.
Vector search is an emerging technique used in generative AI applications to find similar data points to given data by comparing their vector representations using distance or similarity metrics. Vectors are numerical representation of unstructured data created from embedding models. You use embedding models to generate vector embeddings of your data and store them in S3 Vectors to perform semantic searches.
Getting Started With S3 Vectors
https://aws.amazon.com/s3/features/ve...
AWS Embed CLI
https://github.com/awslabs/s3vectors-...
Commands
Aws Configure
pip install s3vectors-embed-cli
PUT
s3vectors-embed put --vector-bucket-name {{bucketname}} --index-name
{Indexname} --model-id amazon.titan-embed-text-v2:0 --text-value "Hello, world!"
QUERY
s3vectors-embed query --vector-bucket-name {{bucketname}} --index-name {Indexname}--model-id amazon.titan-embed-text-v2:0 --query-input "h" --k 10
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