Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.

翻译：密集检索服务日益成为语义搜索、推荐和检索增强生成的基础，然而客户端通常仅收到一个前$k$项列表，且无法获得关于其生成过程的可审计证据。本文提出V3DB，一个可验证的版本化向量搜索服务，支持对潜在不可信服务提供商执行的近似最近邻检索进行按需正确性审计。V3DB对每个语料库快照生成承诺，并将标准化的IVF-PQ搜索流程固化为具有固定结构的五步查询语义。给定公开的快照承诺和查询嵌入，服务端返回前$k$个有效载荷；当受到挑战时，可生成简洁的零知识证明，证实输出结果完全是在承诺快照上执行已发布语义的产物——且无需泄露嵌入语料库或私有索引内容。为实现高效证明，V3DB通过结合多重集相等性/包含性检查与轻量级边界条件，避免了电路内高成本排序和随机访问操作。基于Plonky2的原型实现相比纯电路基线方案，证明速度最高提升$22$倍，峰值内存消耗降低$40\%$，验证时间达到毫秒级。项目代码库位于 https://github.com/TabibitoQZP/zk-IVF-PQ。