TSG Suggester: Tree-Structured Knowledge-Graph Retrieval for Troubleshooting Guide Recommendation in Cloud Incident Management

Abstract

On call engineers in large scale cloud services work under intense time pressure, yet locating the correct Troubleshooting Guide (TSG) for an incident remains a largely manual, keyword driven process, and prior empirical work finds that guide search consumes a substantial fraction of total mitigation time. We present TSG Suggester, a retrieval system that recommends relevant TSGs directly from an incident description. We evaluate five retrieval strategies: Text Only RAG, Image Augmented RAG, RAPTOR, Tree Structured Retrieval, and our proposed Tree + Knowledge Graph (Tree+KG), on 314 real world incidents spanning 112 unique TSGs drawn from 18 service teams on a production incident management system. Tree+KG converts each guide into a tree that preserves its native section hierarchy, attaches LLM generated problem abstractions to internal nodes to bridge the solution oriented language of guides and the problem oriented language of incidents, extracts a per guide entity knowledge graph, and fuses embedding similarity with entity level matching at query time. Tree+KG attains 54.78% Top 1 and 82.48% Top 5 accuracy, leading every baseline at every cutoff, with an 8.58 point Top 1 gain over text only RAG. Two findings are of independent interest. First, structural alignment dominates: methods that preserve or rebuild document structure outperform flat chunking where precise discrimination matters. Second, and contrary to our initial hypothesis, multimodal enrichment actively hurts. Captioning guide screenshots and injecting the captions costs 22.64 Top 5 points relative to the text only baseline because generic captions dilute embeddings rather than sharpen them. We report error analyses for both results and provide concrete deployment guidance.