iKnow: Intent-Guided Chatbot for Cloud Operations with RAG
Takeaway
Deployed RAG chatbot at Huawei Cloud (“CloudA”) for 6 months, serving thousands of engineers
Studied 2000 real queries across 3 ops teams, found 5 intent types:
Symptom analysis (40.6%, most common): “this error happened, what does it mean?”
Multi-facet summary: “give me an overview of X”
Terminology explanation: “what is X?”
Fact verification: “is X true?”
Operation guidance: “how do I do X?”
6 root causes for chatbot failures: incomplete query (32%), lacking knowledge (27%), out-of-scope (10%), invalid query (9%), retrieval issues, generation issues
iKnow pipeline: intent detection (prototypical network) → intent-specific query rewriting → retrieval → missing knowledge detection → LLM generation
Key insight: different intents need different query rewriting strategies. Symptom queries need context enrichment, terminology queries need expansion
Results: accuracy from 65.8% to 81.3%, end-to-end latency 22.5s
Tech stack: LangChain + FAISS + BGE-M3 embedding + bce-reranker + Qwen2.5-32B
For LAPP “chat with logs” feature: intent detection is important — users ask very different types of questions about logs, and the system should handle each differently