EmbedFlow: Upgrading Legacy Embeddings Without Full Upfront Re-Embedding

Upgrading an embedding model usually means rebuilding the document index. This is a natural consequence of independently trained models living in different vector spaces: embeddings from the new model generally cannot be searched directly against vectors produced by the old one. But this does not necessarily make the old index useless. We ask whether a legacy retriever can still return enough useful candidates for a new model to recover its retrieval quality without first re-embedding the full corpus. We call this property candidate compatibility. We study candidate compatibility across 63 source-target migrations. At K = 50, 42 of 63 settings are within 0.01 nDCG@10 of native target retrieval. The same pattern appears in a frozen BRIGHT evaluation and remains stable in a controlled Natural Questions study from 100K to one million documents. At 1M documents, Qwen3-4B → 8B is effectively lossless with only 50 source candidates. Qwen3-0.6B → 8B and MiniLM → 8B require deeper candidate pools, reaching the same stringent near-target criterion at K = 200 and K = 500, respectively. A practical challenge is deciding whether a migration is compatible without first building the target index. To address this, we introduce a frozen residual candidate-tail diagnostic that uses neither relevance labels nor native target retrieval. Across 28 frozen holdout cells, it makes 17 SAFE predictions with zero observed false-safes, and it recovers the same migration ordering in a subsequent blind million-document evaluation. Finally, we study what happens when the legacy index itself is approximate. We show that deployed migration error separates into two terms: an intrinsic source-target compatibility gap and an additional penalty from approximate nearest-neighbor search. Our million-document ANN experiments match this decomposition. Together, these results suggest that embedding migration need not be treated as an all-or-nothing re-indexing operation. For compatible model transitions, a legacy index can remain useful while the new model is introduced progressively.