Query Auto-Completion (QAC) suggests query completions as users type, helping them articulate intent and reach results more efficiently. Existing approaches face fundamental challenges: traditional retrieve-and-rank pipelines have limited long-tail coverage and require extensive feature engineering, while recent generative methods suffer from hallucination and safety risks. We present a unified framework that reformulates QAC as end-to-end list generation through Retrieval-Augmented Generation (RAG) and multi-objective Direct Preference Optimization (DPO). Our approach combines three key innovations: (1) reformulating QAC as end-to-end list generation with multi-objective optimization; (2) defining and deploying a suite of rule-based, model-based, and LLM-as-judge verifiers for QAC, and using them in a comprehensive methodology that combines RAG, multi-objective DPO, and iterative critique-revision for high-quality synthetic data; (3) a hybrid serving architecture enabling efficient production deployment under strict latency constraints. Evaluation on a large-scale commercial search platform demonstrates substantial improvements: offline metrics show gains across all dimensions, human evaluation yields +0.40 to +0.69 preference scores, and a controlled online experiment achieves 5.44\% reduction in keystrokes and 3.46\% increase in suggestion adoption, validating that unified generation with RAG and multi-objective alignment provides an effective solution for production QAC. This work represents a paradigm shift to end-to-end generation powered by large language models, RAG, and multi-objective alignment, establishing a production-validated framework that can benefit the broader search and recommendation industry.

翻译：查询自动补全（QAC）在用户输入时提供查询补全建议，帮助用户更清晰地表达意图并高效获取结果。现有方法面临根本性挑战：传统的检索-排序流水线长尾覆盖能力有限且需要大量特征工程，而近期生成式方法存在幻觉与安全风险。本文提出一个统一框架，通过检索增强生成（RAG）与多目标直接偏好优化（DPO）将QAC重新定义为端到端列表生成任务。我们的方法融合三项关键创新：（1）通过多目标优化将QAC重构为端到端列表生成任务；（2）为QAC定义并部署一套基于规则、基于模型及以LLM作为评判器的验证器，结合RAG、多目标DPO与迭代批判-修订流程构建高质量合成数据；（3）支持在严格延迟约束下高效生产部署的混合服务架构。在大型商业搜索平台上的评估显示显著改进：离线指标在所有维度均获得提升，人工评估获得+0.40至+0.69的偏好分数提升，受控在线实验实现击键次数减少5.44%、建议采纳率提升3.46%，验证了基于RAG与多目标对齐的统一生成方案为生产环境QAC提供了有效解决方案。本工作标志着向以大语言模型、RAG及多目标对齐驱动的端到端生成范式转变，建立了经过生产验证的框架，可为更广泛的搜索与推荐行业提供借鉴。