Compare vector-search storage and quality.
Select an embedding specification for requirements.
Choose two appropriate considerations.
Measure dimension, retrieval quality, storage, and latency, and use the same model and preprocessing for queries and documents.
Detailed explanation
A larger dimension is not automatically the best trade-off.
A larger dimension is not automatically the best trade-off.
Vectors must share a comparable space.
Vectors must share a comparable space.
Data, model, queries, and evaluation also matter.
Data, model, queries, and evaluation also matter.
Distances may no longer be meaningful.
Distances may no longer be meaningful.
Try it yourself
An example you can run in a temporary verification environment.
Amazon Bedrock Knowledge Bases公式の埋め込み仕様とAIF-C01 Domain 2.1を確認する。Expected result
ベクトル次元と検索品質・コストの関係を説明できる。Key points
- Dimensions
- Vector space
- Re-embedding
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.