让向量数据库升级时无需重算全部嵌入，实现几乎零停机。
Drift-Adapter: A Practical Approach to Near Zero-Downtime Embedding Model Upgrades in Vector Databases
- 加个轻量适配层，把新模型输出映射到旧嵌入空间。
- 召回率保留95%-99%，查询延迟增加不足10微秒。
- 适合需要频繁更新模型的生产系统快速部署。
在生产环境升级向量数据库的嵌入模型时，传统方法需重新编码整个语料库并重建近似最近邻（ANN）索引，造成严重中断和高昂计算成本。本文提出Drift-Adapter，一种轻量级可学习的变换层，用于衔接不同版本模型之间的嵌入空间。通过将新查询映射至旧嵌入空间，可继续使用原有ANN索引，有效推迟全量重计算。我们在MTEB文本语料和一个100万项的CLIP图像模型升级中系统评估了三种适配器参数化方式：正交Procrustes、低秩仿射和紧凑残差MLP，均基于少量成对新旧嵌入进行训练。实验表明，Drift-Adapter恢复了95%-99%的完整重编码召回率（Recall@10, MRR），查询延迟增加不足10微秒。相比全量重索引或双索引服务等策略，其重计算成本降低超100倍，支持近乎零中断的模型升级。我们分析了模型漂移、训练数据规模、千亿级系统扩展性及设计选择（如对角缩放）的影响，验证了Drift-Adapter作为敏捷模型部署的实际可行性。
原文摘要 · Abstract (English)
Upgrading embedding models in production vector databases typically requires 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 and 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 microseconds of query latency. Compared to operational strategies like full re-indexing or dual-index serving, Drift-Adapter 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.
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