面向电信SNOC环境中高效云知识库搜索的多智能体检索增强生成

中文摘要 AI 辅助

电信服务与网络运营中心（SNOC）依赖大量云文档（包括标准操作程序SOP、厂商技术手册、事件报告和配置指南）来维持网络持续运行。在关键事件发生时，工程师必须快速检索准确信息，但传统的基于关键词和单阶段的检索方法往往难以提供精确结果。本文提出了适用于云知识库的Athena框架，这是一个完全离线的多智能体检索增强生成（RAG）框架，专为沃达丰 Idea 的SNOC环境中的企业云文档搜索设计。该系统整合了使用E5 Large V2嵌入的密集检索、BM25稀疏检索以及基于LangGraph编排框架的知识图谱扩展。检索到的候选内容通过Weighted CombSUM进行融合，随后使用交叉编码器重排序和最大边际相关性（MMR）以获取多样化且相关的证据集。为提升答案可靠性，该框架会对每个分块进行大语言模型（LLM）评估，并进行明确的归因验证，在生成响应前独立评估每个MMR选中的分块。不支持或较弱的证据会被丢弃，若没有分块满足验证标准，系统会自动将多个分块一起评估作为 fallback。在包含312000个索引分块的4200个SNOC云文档语料库上进行的实验表明，所提方法的10步平均倒数排名（MRR@10）为0.910，精确匹配（EM）得分为78.4%，比单阶段密集检索高出14.6个百分点。整个管道在完全离线环境中运行，满足企业数据主权要求，同时为云文档搜索提供准确且有依据的响应。

英文摘要

Telecom Service and Network Operations Centers (SNOCs) rely on large collections of cloud documents, including Standard Operating Procedures (SOPs), vendor technical manuals, incident reports, and configuration guides, to maintain uninterrupted network operations. During critical incidents, engineers must quickly retrieve accurate information, yet traditional keyword based and single stage retrieval approaches often struggle to provide precise results. This paper presents Athena for Cloud Knowledge Base, a fully offline, multi agent Retrieval Augmented Generation (RAG) framework designed for enterprise cloud document search in Vodafone Idea's SNOC environment. The system integrates dense retrieval using E5 Large V2 embeddings, BM25 sparse retrieval, and Knowledge Graph expansion within a LangGraph based orchestration framework. Retrieved candidates are fused using Weighted CombSUM, followed by cross encoder reranking and Maximal Marginal Relevance (MMR) to obtain a diverse and relevant evidence set. To improve answer reliability, the framework performs per chunk LLM evaluation with explicit attribution verification, assessing each MMR selected chunk independently before generating a response. Unsupported or weak evidence is discarded, and if no chunk satisfies the verification criteria, the system automatically evaluates multiple chunks together as a fallback. Experiments on a corpus of 4200 SNOC cloud documents containing 312000 indexed chunks show that the proposed approach achieves an MRR at 10 of 0.910 and an Exact Match (EM) score of 78.4 percent, outperforming single stage dense retrieval by 14.6 percentage points. The entire pipeline operates in a fully offline environment, satisfying enterprise data sovereignty requirements while delivering accurate and grounded responses for cloud document search.

发表机构

• Vodafone Idea（沃达丰创意公司）

机构由 AI 辅助整理，请以论文原文为准。