打造企业级智能助手，帮工程师快速查文档、修故障。
ENCO: Life-Cycle Management of Enterprise-Grade Copilots
- 用自然语言转搜索查询，精准找分散在各处的技术资料。
- 上线后支持数万次交互，数百名工程师每月活跃使用。
- 自动生成结构化排障指南，解决老旧文档难查找问题。
软件工程师常面临访问分散的文档与监控数据的难题，包括故障排查指南（TSGs）、事故报告、代码仓库及多个利益相关方开发的内部工具。值班时因遗留系统信息模糊且时间紧迫，排错尤为困难。为提升值班工程师效率并优化日常流程，我们推出了DECO——一个专为企业级智能助手设计的开发、部署与管理框架。本文详述DECO的设计与实现，重点介绍其创新的NL2SearchQuery功能和轻量级代理框架。这些能力支持高效的检索增强生成（RAG）算法，不仅能从多源数据中提取相关信息，还能根据用户提问自动选择最适配的技能。从而应对复杂技术问题，并实现内部资源的无缝自动化访问。此外，DECO还具备将非结构化事故日志转化为易读结构化指南的能力，有效填补文档空白。自2023年9月上线以来，ENCO已广泛采用，累计支持数万次交互，覆盖数十个组织，每月活跃用户达数百人。
原文摘要 · Abstract (English)
Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including TroubleShooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes even more daunting due to the obscurity of legacy sources and the pressures of strict time constraints. To enhance the efficiency of on-call engineers (OCEs) and streamline their daily workflows, we introduced DECO-a comprehensive framework for developing, deploying, and managing enterprise-grade copilots tailored to improve productivity in engineering routines. This paper details the design and implementation of the DECO framework, emphasizing its innovative NL2SearchQuery functionality and a lightweight agentic framework. These features support efficient and customized retrieval-augmented-generation (RAG) algorithms that not only extract relevant information from diverse sources but also select the most pertinent skills in response to user queries. This enables the addressing of complex technical questions and provides seamless, automated access to internal resources. Additionally, DECO incorporates a robust mechanism for converting unstructured incident logs into user-friendly, structured guides, effectively bridging the documentation gap. Since its launch in September 2023, ENCO has demonstrated its effectiveness through widespread adoption, enabling tens of thousands of interactions and engaging hundreds of monthly active users (MAU) across dozens of organizations within the company.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。