Vector Embedding Refresh

Vector Embedding Refresh is a ml index workflow that updates vector representations after source data changes for numeric representation and similarity search. It uses batch jobs, backfills, and index validation so teams can keep retrieval results current while keeping evidence, reliability, and public-safe operational boundaries clear.

“The machine learning team used Vector Embedding Refresh when the vector store returned close matches, so the team could keep retrieval results current before the model moved into evaluation.”