@inproceedings{vejendla-2025-drift,
    title = "Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases",
    author = "Vejendla, Harshil",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.805/",
    doi = "10.18653/v1/2025.emnlp-main.805",
    pages = "15949--15960",
    ISBN = "979-8-89176-332-6",
    abstract = "Upgrading embedding models in production vector databases typically necessitates re-encoding the entire corpus and rebuilding the Approximate Nearest Neighbor (ANN) index, leading to significant operational disruption and computational cost. This paper presents Drift-Adapter, a lightweight, learnable transformation layer designed to bridge embedding spaces between model versions. By mapping new queries into the legacy embedding space, Drift-Adapter enables the continued use of the existing ANN index, effectively deferring full re-computation. We systematically evaluate three adapter parameterizations: Orthogonal Procrustes, Low-Rank Affine, and a compact Residual MLP, trained on a small sample of paired old/new embeddings. Experiments on MTEB text corpora and a CLIP image model upgrade (1M items) show that Drift-Adapter recovers 95{--}99{\%} of the retrieval recall (Recall@10, MRR) of a full re-embedding, adding less than $10,\mu\text{s}$ query latency. Compared to operational strategies like full re-indexing or dual-index serving, Drift-Adapter dramatically reduces recompute costs (by over 100 times) and facilitates upgrades with near-zero operational interruption. We analyze robustness to varied model drift, training data size, scalability to billion-item systems, and the impact of design choices like diagonal scaling, demonstrating Drift-Adapter{'}s viability as a pragmatic solution for agile model deployment."
}