利用残差可达性实现近似最近邻搜索中基于图的索引的跨模型迁移
Exploiting Residual Reachability for Cross-Model Migration of Graph-Based Indexes in Approximate Nearest Neighbor Search
- Tongji University（同济大学）
- Zhejiang University（浙江大学）
- Ant Group（蚂蚁集团）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对嵌入模型变化导致图索引重建成本高的问题，提出利用旧索引残差可达性的漂移引导迁移方法，通过两种路径加速新索引构建，最高提速17.43倍且保持召回率。
AI中文摘要：
近似最近邻搜索（ANNS）支撑着搜索、推荐和检索增强生成中的大规模向量检索。基于图的索引在ANNS中展现了最先进的搜索性能。它们将每个语料库向量连接到一小部分邻近或对导航有用的顶点，并通过遍历生成的图来回答查询。由于这些边是使用构建时的距离选择的，因此图索引与嵌入模型紧密相关。使用新模型对语料库重新编码可能会改变向量的距离和邻域。为新嵌入向量重建图会产生大量构建成本并延迟部署。当嵌入模型改变时，我们在旧图索引中观察到一种我们称之为残差可达性的现象。具体来说，尽管向量源自不同的模型，但它们描述相同的底层对象，并且通常保留部分相似性结构。这些共享关系反映在旧图索引的连通性中，使得许多精确的新模型邻居在旧图索引中只需几跳即可到达。受此观察启发，我们开发了一种索引迁移方法，利用旧图索引中的残差可达性来更快地为新嵌入向量构建新图索引。我们的方法，即漂移引导迁移（DGM），提供了两种迁移路径。DGM-Local在继承的图索引上执行并行浅层扩展，并在精确评估之前使用打包的位置符号代码筛选第二跳候选。DGM-Search使用跳数受限的波束遍历来探索浅层扩展之外的范围。在八次文本和图像迁移中，我们的DGM方法在构建新图索引时比最快的度匹配重建方法实现了高达17.43倍的加速，同时保持了有竞争力的召回率。
英文摘要：
Approximate nearest neighbor search (ANNS) underpins large-scale vector retrieval in search, recommendation, and retrieval-augmented generation. Graph-based indexes have demonstrated state-of-the-art search performance for ANNS. They connect each corpus vector to a small set of nearby or navigationally useful vertices and answer queries by traversing the resulting graph. Because these edges are selected using construction-time distances, the graph index is tied to the embedding model. Re-encoding a corpus with a new model may change distances and neighborhoods of the vectors. Reconstructing the graph for the new embedding vectors incurs substantial construction cost and delays deployment. When the embedding model changes, we observe a phenomenon in the old graph index that we call residual reachability. Specifically, although derived from different models, the vectors describe the same underlying objects and often retain part of their similarity structure. These shared relations are reflected in the connectivity of the old graph index, leaving many exact new-model neighbors reachable within a few hops in the old graph index. Motivated by this observation, we develop an index-migration approach that utilize the residual reachability in the old graph index to faster construct the new graph index for the new embedding vectors. Our method, Drift-Guided Migration (DGM), provides two migration paths. DGM-Local performs parallel shallow expansion over the inherited graph index and screens second-hop candidates with packed position sign codes before exact evaluation. DGM-Search uses hop-bounded beam traversal to explore beyond shallow expansion. Across eight text and image migrations, our DGM methods can achieve up to 17.43 times speedup on constructing the new graph index than the fastest degree-matched reconstruction method while keeping competitive recalls.
