Industrial e-commerce search serves hundreds of millions of items through a multi-branch retrieval stage fused by hand-tuned merging without joint optimization. Generative retrieval (GR) raises the prospect of collapsing this stage into a single model, yet unification is gated by more than retrieval quality: the inverted-index branch converts below the platform average yet persists because it is almost the only branch where operations can inject a new term within hours without any model update; a one-model substitute must preserve this real-time editability. Existing GR methods structurally lack it: closed-codebook methods fix each slot to a quantized embedding at training, while open-vocabulary methods leave new-term routing to model generalization. We present OneRetrieval, a one-model GR framework built on Keyword-Aligned Encoding (KAE), which ties each identifier position to an interpretable attribute word, pairing competitive recall quality with the editability of the inverted index -- to our knowledge the first editable generative retrieval method. An information-theoretic merging organizes 18 attribute categories into six codebook groups with non-uniform capacity; reserved slots in each codebook can be bound to new words after deployment without retraining; and a four-stage fine-tuning pipeline secures quality and editability jointly. On five million real-traffic requests, OneRetrieval matches the deep recall of the strongest generative baseline, with an intervention hit rate over an order of magnitude above closed-codebook encodings. Online, replacing the inverted-index branch significantly lifts order volume; extending to nearly the entire stage holds conversion while improving CTR. The system is deployed at Kuaishou, serving hundreds of millions of PVs daily.

翻译：工业级电商搜索通过手工调参融合的多分支检索阶段服务于数亿商品，该过程缺乏联合优化。生成式检索有望将这一阶段压缩为单一模型，但统一化受限于检索质量之外的因素：倒排索引分支的平均表现虽低于平台水平，却因其几乎是唯一无需模型更新即可在数小时内注入新术语的操作分支而得以保留；单一模型替代品必须保留这种实时可编辑性。现有生成式检索方法在结构上缺乏此能力：封闭码本方法在训练时将每个槽位固定于量化嵌入，而开放词汇方法则将新术语路由依赖于模型泛化。我们提出OneRetrieval，一个基于关键词对齐编码构建的单一模型生成式检索框架——将每个标识符位置与可解释的属性词绑定，在保持有竞争力的召回质量的同时实现倒排索引的可编辑性，据我们所知这是首个可编辑的生成式检索方法。信息论融合机制将18个属性类别组织成六个非均匀容量的码本组；每个码本中预留的槽位可在部署后绑定新词而无需重新训练；四阶段微调流水线同时确保质量与可编辑性。在五百万真实流量请求上，OneRetrieval达到最强生成式基线的深度召回水平，干预命中率比封闭码本编码高出一个数量级以上。在线实验中，替换倒排索引分支显著提升了订单量；将其扩展至几乎整个检索阶段则在提升点击率的同时保持转化率。该系统已部署于快手，每日服务数亿次页面浏览量。