用AI助手自动处理大数据平台运维，减少20.8%工单量。
SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms
- 通过意图识别和专家路径路由，精准响应不同运维需求。
- 结合深度搜索与分层知识库，实现多跳检索且不超载上下文。
- 自动分析失败原因并提炼标准流程，持续优化知识库。
大型数据平台在现代企业中广泛应用，生产环境中的智能助手对快速获取可操作指导、减轻运维负担至关重要。现有基于大模型与检索增强生成（LLM+RAG）的助手在实际部署中面临三大挑战：通用咨询与领域特定故障排查场景覆盖不足；因缺乏多跳检索和扁平化知识组织导致知识获取效率低；以及升级工单非结构化，难以转化为助手改进和可复用的标准操作流程（SOP）。本文提出已上线的智能助手SiriusHelper，作为统一在线助手，能自动识别用户意图并路由至合适处理路径，包括针对特定场景（如SQL执行诊断）的专用专家流程。为支持复杂故障排查，SiriusHelper结合深度搜索机制与基于优先级的分层知识库，实现无上下文过载的多跳检索，提升回答可靠性与延迟表现。为降低专家负担，该系统引入自动化工单理解与SOP提炼：诊断助手失败原因（如知识缺失或路由错误），并提取领域SOP以持续丰富知识库。在腾讯大数据平台的实验与线上部署显示，SiriusHelper优于代表性方案，工单量减少20.8%。
原文摘要 · Abstract (English)
Big data platforms are widely used in modern enterprises, and an in-production intelligent assistant is increasingly important to help users quickly find actionable guidance and reduce operational burden. While recent LLM+RAG assistants provide a natural interface, they face practical challenges in real deployments: limited scenario coverage across both general consultation and domain-specific troubleshooting workflows, inefficient knowledge access due to inadequate multi-hop retrieval and flat knowledge organization, and high maintenance cost because escalated tickets are unstructured and hard to convert into assistant improvements and reusable SOPs. In this paper, we present SiriusHelper, a deployed intelligent assistant for big data platforms. SiriusHelper serves as a unified online assistant that automatically identifies user intent and routes queries to the right handling path, including dedicated expert workflows for specialized scenarios (e.g., SQL execution diagnosis). To support complex troubleshooting, SiriusHelper combines a DeepSearch-driven mechanism with a priority-based hierarchical knowledge base to enable multi-hop retrieval without context overload, thus improving answer reliability and latency. To reduce expert overhead, SiriusHelper further introduces automated ticket understanding and SOP distillation: it diagnoses the assistant failure reason (e.g., missing knowledge or wrong routing) and extracts domain-specific SOPs to continuously enrich the knowledge base. Experiments and online deployment on Tencent Big Data platform show that SiriusHelper outperforms representative alternatives and reduces online ticket volume by 20.8\%.
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