EmbedFlow: Upgrading Embedding Models Without Re-Embedding the Corpus

Upgrading text embedding models typically demands re-encoding an entire corpus, incurring substantial compute costs and operational downtime. Open-source utility EmbedFlow tackles this friction by learning transformations between legacy and target vector spaces. Instead of re-embedding millions of documents from scratch, the framework aligns existing document representations to work alongside updated query encoders, offering a pragmatic pathway for continuous maintenance in production vector retrieval pipelines.