741 lines
22 KiB
Python
741 lines
22 KiB
Python
"""
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Intent Classifier - Phase 13.3 #85
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===================================
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K8s 操作意圖分類器，用於智能路由模型選擇
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目標: < 100ms 延遲 (規則引擎 < 10ms)
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策略: 方案 A (規則引擎) → 方案 B (LLM 備援)
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版本: v2.0
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建立: 2026-03-26 (台北時區)
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建立者: Claude Code
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最後修改: 2026-03-26 (台北時區)
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修改者: Claude Code
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變更紀錄:
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| 版本 | 日期 | 執行者 | 變更內容 |
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|------|------|--------|----------|
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| v1.0 | 2026-03-26 | Claude Code | 初始實作 (舊版 IntentType) |
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| v2.0 | 2026-03-26 | Claude Code | Phase 13.3 #85 升級 (四大核心+輔助意圖) |
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"""
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from __future__ import annotations
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import json
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import re
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import time
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from dataclasses import dataclass, field
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from enum import Enum
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from typing import Protocol, runtime_checkable
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import httpx
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import structlog
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from src.core.config import settings
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from src.services.model_registry import get_model_registry
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logger = structlog.get_logger(__name__)
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# LLM 分類 Prompt 模板 (Phase 13.4)
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_LLM_CLASSIFY_PROMPT = """你是 K8s 操作意圖分類專家。根據以下輸入，判斷用戶的操作意圖。
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可選意圖類型:
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- restart: 重啟 Pod/Deployment/StatefulSet
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- scale: 擴縮容、HPA 調整
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- config: ConfigMap/Secret/ENV 變更
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- diagnose: 日誌查詢、健康檢查、RCA
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- delete: 刪除資源（高風險）
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- rollback: 回滾版本
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- unknown: 無法判斷
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輸入: {text}
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請以 JSON 格式回答，只輸出 JSON:
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{{"intent": "<類型>", "confidence": <0.0-1.0>, "reasoning": "<判斷依據>"}}"""
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# =============================================================================
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# 意圖類型定義 (Phase 13.3 #85)
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# =============================================================================
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class IntentType(Enum):
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"""
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K8s 操作意圖類型
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四大核心意圖:
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- RESTART: 重啟 Pod/Deployment/StatefulSet
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- SCALE: 擴縮容、HPA 調整
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- CONFIG: ConfigMap/Secret/ENV 變更
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- DIAGNOSE: 日誌查詢、健康檢查、RCA
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輔助意圖:
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- DELETE: 刪除資源（高風險）
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- ROLLBACK: 回滾版本
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- UNKNOWN: 無法判斷
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舊版兼容 (已棄用，映射到新意圖):
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- ALERT_TRIAGE → DIAGNOSE
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- DEPLOYMENT → CONFIG
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- QUERY → DIAGNOSE
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- MAINTENANCE → RESTART
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- CODE_REVIEW → DIAGNOSE
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"""
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# 四大核心意圖
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RESTART = "restart" # 重啟 Pod/Deployment/StatefulSet
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SCALE = "scale" # 擴縮容、HPA 調整
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CONFIG = "config" # ConfigMap/Secret/ENV 變更
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DIAGNOSE = "diagnose" # 日誌查詢、健康檢查、RCA
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# 輔助意圖
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DELETE = "delete" # 刪除資源（高風險）
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ROLLBACK = "rollback" # 回滾版本
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UNKNOWN = "unknown" # 無法判斷
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# 舊版兼容 (棄用，保留向後兼容)
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ALERT_TRIAGE = "alert_triage" # → DIAGNOSE
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DEPLOYMENT = "deployment" # → CONFIG
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QUERY = "query" # → DIAGNOSE
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MAINTENANCE = "maintenance" # → RESTART
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CODE_REVIEW = "code_review" # → DIAGNOSE
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# 舊版意圖到新版的映射
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LEGACY_INTENT_MAP: dict[IntentType, IntentType] = {
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IntentType.ALERT_TRIAGE: IntentType.DIAGNOSE,
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IntentType.DEPLOYMENT: IntentType.CONFIG,
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IntentType.QUERY: IntentType.DIAGNOSE,
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IntentType.MAINTENANCE: IntentType.RESTART,
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IntentType.CODE_REVIEW: IntentType.DIAGNOSE,
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}
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def normalize_intent(intent: IntentType) -> IntentType:
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"""
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正規化意圖 (將舊版意圖映射到新版)
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Args:
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intent: 原始意圖
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Returns:
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正規化後的意圖
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"""
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return LEGACY_INTENT_MAP.get(intent, intent)
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# =============================================================================
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# 風險等級定義
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# =============================================================================
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class RiskLevel(Enum):
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"""意圖風險等級"""
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LOW = "low" # 只讀操作 (DIAGNOSE)
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MEDIUM = "medium" # 可逆操作 (RESTART, SCALE, ROLLBACK)
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HIGH = "high" # 配置變更 (CONFIG)
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CRITICAL = "critical" # 不可逆操作 (DELETE)
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# 意圖對應風險等級
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INTENT_RISK_MAP: dict[IntentType, RiskLevel] = {
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IntentType.DIAGNOSE: RiskLevel.LOW,
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IntentType.RESTART: RiskLevel.MEDIUM,
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IntentType.SCALE: RiskLevel.MEDIUM,
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IntentType.ROLLBACK: RiskLevel.MEDIUM,
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IntentType.CONFIG: RiskLevel.HIGH,
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IntentType.DELETE: RiskLevel.CRITICAL,
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IntentType.UNKNOWN: RiskLevel.MEDIUM,
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# 舊版兼容
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IntentType.ALERT_TRIAGE: RiskLevel.LOW,
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IntentType.DEPLOYMENT: RiskLevel.HIGH,
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IntentType.QUERY: RiskLevel.LOW,
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IntentType.MAINTENANCE: RiskLevel.MEDIUM,
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IntentType.CODE_REVIEW: RiskLevel.LOW,
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}
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# =============================================================================
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# 關鍵字規則引擎 (方案 A, < 10ms)
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# =============================================================================
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# 核心意圖關鍵字映射
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INTENT_KEYWORDS: dict[IntentType, list[str]] = {
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# 四大核心意圖
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IntentType.RESTART: [
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# 英文
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"restart",
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"reboot",
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"recreate",
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"kill",
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"delete pod",
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"rollout restart",
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# 中文
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"重啟",
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"重新啟動",
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"重建",
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"刪除 pod",
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"殺掉",
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],
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IntentType.SCALE: [
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# 英文
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"scale",
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"replica",
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"hpa",
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"autoscale",
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"scale up",
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"scale down",
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"horizontal pod autoscaler",
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# 中文
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"擴容",
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"縮容",
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"擴縮",
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"副本",
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"水平擴展",
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],
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IntentType.CONFIG: [
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# 英文
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"configmap",
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"secret",
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"env",
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"environment",
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"config",
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"setting",
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"configuration",
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"kubectl apply",
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"helm upgrade",
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# 中文
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"配置",
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"設定",
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"環境變數",
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"部署",
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"更新配置",
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],
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IntentType.DIAGNOSE: [
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# 英文
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"log",
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"logs",
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"describe",
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"get",
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"status",
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"health",
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"check",
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"debug",
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"trace",
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"diagnose",
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"rca",
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"root cause",
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"investigate",
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"why",
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"what happened",
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# 中文
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"日誌",
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"查看",
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"檢查",
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"狀態",
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"健康",
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"診斷",
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"原因",
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"為什麼",
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"什麼問題",
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"分析",
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],
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# 輔助意圖
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IntentType.DELETE: [
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# 英文
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"delete",
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"remove",
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"destroy",
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"kubectl delete",
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"helm uninstall",
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"drop",
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# 中文
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"刪除",
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"移除",
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"銷毀",
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"清除",
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],
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IntentType.ROLLBACK: [
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# 英文
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"rollback",
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"rollout undo",
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"revert",
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"previous version",
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"last version",
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# 中文
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"回滾",
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"回復",
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"還原",
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"上一版",
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"前一版",
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],
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}
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# 告警關鍵字 (強化 DIAGNOSE 分類)
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ALERT_KEYWORDS: list[str] = [
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"alert",
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"alerting",
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"firing",
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"告警",
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"警報",
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"異常",
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"error",
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"critical",
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"warning",
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"high cpu",
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"high memory",
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"oom",
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"crash",
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"down",
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"timeout",
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"failed",
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"unhealthy",
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]
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# 資源類型關鍵字 (用於上下文判斷)
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RESOURCE_KEYWORDS: dict[str, list[str]] = {
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"pod": ["pod", "pods", "po"],
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"deployment": ["deployment", "deployments", "deploy"],
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"statefulset": ["statefulset", "statefulsets", "sts"],
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"daemonset": ["daemonset", "daemonsets", "ds"],
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"service": ["service", "services", "svc"],
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"configmap": ["configmap", "configmaps", "cm"],
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"secret": ["secret", "secrets"],
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"ingress": ["ingress", "ingresses", "ing"],
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"namespace": ["namespace", "namespaces", "ns"],
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}
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# =============================================================================
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# 分類結果
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# =============================================================================
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@dataclass
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class IntentResult:
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"""意圖分類結果"""
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intent: IntentType # 分類意圖
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confidence: float # 信心度 (0.0-1.0)
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method: str # 分類方法 (keyword/llm)
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risk_level: RiskLevel = field(default=RiskLevel.MEDIUM)
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matched_keywords: list[str] = field(default_factory=list)
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detected_resources: list[str] = field(default_factory=list)
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reasoning: str = ""
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def __post_init__(self):
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"""初始化後設定風險等級"""
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self.risk_level = INTENT_RISK_MAP.get(self.intent, RiskLevel.MEDIUM)
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# =============================================================================
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# Protocol 介面 (支援 DI)
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# =============================================================================
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@runtime_checkable
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class IIntentClassifier(Protocol):
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"""Intent Classifier Interface for DI"""
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async def classify(self, text: str) -> IntentResult:
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"""分類意圖 (非同步)"""
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...
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def classify_sync(self, text: str) -> IntentResult:
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"""分類意圖 (同步)"""
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...
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# =============================================================================
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# 實作
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# =============================================================================
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class IntentClassifier:
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"""
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K8s 操作意圖分類器
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使用兩階段分類策略:
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1. 方案 A: 規則引擎 (關鍵字匹配, < 10ms)
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2. 方案 B: 輕量 LLM (qwen2.5:1b, < 100ms) - 備援
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Usage:
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classifier = get_intent_classifier()
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result = await classifier.classify("重啟 api-server pod")
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# IntentResult(intent=RESTART, confidence=0.95, method='keyword')
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"""
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# LLM 備援模型 (從 ModelRegistry 取得)
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_llm_model: str | None = None
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def __init__(self):
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self._keyword_cache: dict[str, IntentResult] = {}
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self._cache_max_size = 1000 # 最大快取條目
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@property
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def llm_model(self) -> str:
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"""取得 LLM 備援模型 (延遲載入)"""
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if self._llm_model is None:
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try:
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registry = get_model_registry()
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self._llm_model = registry.get_model("ollama", "intent")
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except Exception:
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self._llm_model = "qwen2.5:1b" # fallback
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return self._llm_model
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async def classify(self, text: str) -> IntentResult:
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"""
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分類意圖 (非同步)
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Args:
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text: 用戶輸入或告警內容
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Returns:
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IntentResult: 分類結果
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"""
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text_lower = text.lower().strip()
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# 階段 1: 規則引擎快速匹配 (< 10ms)
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result = self._keyword_classify(text_lower)
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if result.confidence >= 0.7: # 信心度閾值
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logger.debug(
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"intent_classified_by_keyword",
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intent=result.intent.value,
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confidence=result.confidence,
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matched_keywords=result.matched_keywords,
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text_preview=text[:50],
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)
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return result
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# 階段 2: LLM 分類 (< 100ms)
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llm_result = await self._llm_classify(text_lower)
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if llm_result.confidence > result.confidence:
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logger.debug(
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"intent_classified_by_llm",
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intent=llm_result.intent.value,
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confidence=llm_result.confidence,
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text_preview=text[:50],
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)
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return llm_result
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# 使用規則引擎結果
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logger.debug(
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"intent_classified_fallback",
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intent=result.intent.value,
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confidence=result.confidence,
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text_preview=text[:50],
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)
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return result
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def classify_sync(self, text: str) -> IntentResult:
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"""
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同步版本 (僅關鍵字匹配)
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Args:
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text: 用戶輸入或告警內容
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Returns:
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IntentResult: 分類結果
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"""
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return self._keyword_classify(text.lower().strip())
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def _keyword_classify(self, text: str) -> IntentResult:
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"""
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規則引擎分類 (方案 A)
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目標延遲: < 10ms
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Args:
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text: 已轉小寫的輸入文字
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Returns:
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IntentResult: 分類結果
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"""
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# 檢查快取
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cache_key = text[:100]
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if cache_key in self._keyword_cache:
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return self._keyword_cache[cache_key]
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# 計算每個意圖的匹配分數
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scores: dict[IntentType, tuple[int, list[str]]] = {}
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for intent, keywords in INTENT_KEYWORDS.items():
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score = 0
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matched: list[str] = []
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for keyword in keywords:
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if keyword in text:
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score += 1
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matched.append(keyword)
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# 完整詞匹配加分
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if re.search(rf"\b{re.escape(keyword)}\b", text):
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score += 1
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if score > 0:
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scores[intent] = (score, matched)
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# 檢測告警內容 (強化 DIAGNOSE)
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is_alert = any(kw in text for kw in ALERT_KEYWORDS)
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if is_alert and IntentType.DIAGNOSE not in scores:
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scores[IntentType.DIAGNOSE] = (1, ["(alert_detected)"])
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# 檢測資源類型
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detected_resources: list[str] = []
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for resource_type, keywords in RESOURCE_KEYWORDS.items():
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if any(kw in text for kw in keywords):
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detected_resources.append(resource_type)
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# 選擇最高分意圖
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if not scores:
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result = IntentResult(
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intent=IntentType.UNKNOWN,
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confidence=0.0,
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method="keyword",
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matched_keywords=[],
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detected_resources=detected_resources,
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reasoning="無匹配關鍵字",
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)
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else:
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best_intent = max(scores, key=lambda k: scores[k][0])
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best_score, matched_keywords = scores[best_intent]
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# 🔴 2026-03-29 修正: 關鍵字匹配不是 AI 分析，信心度設 0
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# 根據 feedback_confidence_truthfulness.md 鐵律
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confidence = 0.0
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result = IntentResult(
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intent=best_intent,
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confidence=confidence,
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method="keyword",
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matched_keywords=matched_keywords,
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detected_resources=detected_resources,
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reasoning=f"匹配關鍵字: {', '.join(matched_keywords)}",
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)
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# 快取結果 (LRU 簡易實作)
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if len(self._keyword_cache) >= self._cache_max_size:
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# 移除最舊的一半
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keys = list(self._keyword_cache.keys())
|
|
for k in keys[: len(keys) // 2]:
|
|
del self._keyword_cache[k]
|
|
|
|
self._keyword_cache[cache_key] = result
|
|
return result
|
|
|
|
async def _llm_classify(self, text: str) -> IntentResult:
|
|
"""
|
|
LLM 分類 (方案 B) - Phase 13.4
|
|
|
|
目標延遲: < 100ms (使用 qwen2.5:1b 或配置的 intent 模型)
|
|
|
|
Args:
|
|
text: 已轉小寫的輸入文字
|
|
|
|
Returns:
|
|
IntentResult: 分類結果
|
|
|
|
2026-03-30 Claude Code: 實作 Ollama 整合
|
|
"""
|
|
start_time = time.time()
|
|
|
|
try:
|
|
# 建構 Prompt
|
|
prompt = _LLM_CLASSIFY_PROMPT.format(text=text)
|
|
|
|
# 取得模型配置
|
|
model_name = self.llm_model # qwen2.5:1b 或配置值
|
|
|
|
# 呼叫 Ollama
|
|
async with httpx.AsyncClient() as client:
|
|
response = await client.post(
|
|
f"{settings.OLLAMA_URL}/api/generate",
|
|
json={
|
|
"model": model_name,
|
|
"prompt": prompt,
|
|
"stream": False,
|
|
"format": "json",
|
|
"options": {
|
|
"num_predict": 128, # 意圖分類只需短回應
|
|
"temperature": 0.0, # 確定性輸出
|
|
"top_p": 0.9,
|
|
},
|
|
},
|
|
timeout=httpx.Timeout(5.0, connect=2.0), # 嚴格超時
|
|
)
|
|
|
|
response.raise_for_status()
|
|
data = response.json()
|
|
result_text = data.get("response", "")
|
|
|
|
# 解析 JSON 回應
|
|
elapsed_ms = (time.time() - start_time) * 1000
|
|
|
|
try:
|
|
parsed = json.loads(result_text)
|
|
intent_str = parsed.get("intent", "unknown").lower()
|
|
confidence = float(parsed.get("confidence", 0.5))
|
|
reasoning = parsed.get("reasoning", "")
|
|
|
|
# 映射到 IntentType
|
|
intent = self._parse_intent_type(intent_str)
|
|
|
|
logger.info(
|
|
"intent_llm_classified",
|
|
intent=intent.value,
|
|
confidence=confidence,
|
|
elapsed_ms=round(elapsed_ms, 1),
|
|
model=model_name,
|
|
)
|
|
|
|
return IntentResult(
|
|
intent=intent,
|
|
confidence=confidence,
|
|
method="llm",
|
|
matched_keywords=[],
|
|
detected_resources=[],
|
|
reasoning=reasoning,
|
|
)
|
|
|
|
except (json.JSONDecodeError, KeyError, ValueError) as e:
|
|
logger.warning(
|
|
"intent_llm_parse_failed",
|
|
error=str(e),
|
|
response_preview=result_text[:100],
|
|
)
|
|
return self._llm_fallback_result("JSON 解析失敗")
|
|
|
|
except httpx.TimeoutException:
|
|
elapsed_ms = (time.time() - start_time) * 1000
|
|
logger.warning(
|
|
"intent_llm_timeout",
|
|
elapsed_ms=round(elapsed_ms, 1),
|
|
)
|
|
return self._llm_fallback_result("LLM 超時")
|
|
|
|
except Exception as e:
|
|
logger.warning(
|
|
"intent_llm_error",
|
|
error=str(e),
|
|
error_type=type(e).__name__,
|
|
)
|
|
return self._llm_fallback_result(f"LLM 錯誤: {type(e).__name__}")
|
|
|
|
def _parse_intent_type(self, intent_str: str) -> IntentType:
|
|
"""解析意圖字串為 IntentType"""
|
|
intent_map = {
|
|
"restart": IntentType.RESTART,
|
|
"scale": IntentType.SCALE,
|
|
"config": IntentType.CONFIG,
|
|
"diagnose": IntentType.DIAGNOSE,
|
|
"delete": IntentType.DELETE,
|
|
"rollback": IntentType.ROLLBACK,
|
|
"unknown": IntentType.UNKNOWN,
|
|
}
|
|
return intent_map.get(intent_str.lower(), IntentType.UNKNOWN)
|
|
|
|
def _llm_fallback_result(self, reason: str) -> IntentResult:
|
|
"""LLM 失敗時的 fallback 結果"""
|
|
return IntentResult(
|
|
intent=IntentType.UNKNOWN,
|
|
confidence=0.0,
|
|
method="llm",
|
|
matched_keywords=[],
|
|
detected_resources=[],
|
|
reasoning=reason,
|
|
)
|
|
|
|
def get_supported_intents(self) -> list[dict]:
|
|
"""
|
|
取得支援的意圖清單
|
|
|
|
Returns:
|
|
意圖清單 (含描述和風險等級)
|
|
"""
|
|
intents = [
|
|
{
|
|
"intent": IntentType.RESTART.value,
|
|
"description": "重啟 Pod/Deployment/StatefulSet",
|
|
"risk_level": RiskLevel.MEDIUM.value,
|
|
"keywords_sample": INTENT_KEYWORDS[IntentType.RESTART][:5],
|
|
},
|
|
{
|
|
"intent": IntentType.SCALE.value,
|
|
"description": "擴縮容、HPA 調整",
|
|
"risk_level": RiskLevel.MEDIUM.value,
|
|
"keywords_sample": INTENT_KEYWORDS[IntentType.SCALE][:5],
|
|
},
|
|
{
|
|
"intent": IntentType.CONFIG.value,
|
|
"description": "ConfigMap/Secret/ENV 變更",
|
|
"risk_level": RiskLevel.HIGH.value,
|
|
"keywords_sample": INTENT_KEYWORDS[IntentType.CONFIG][:5],
|
|
},
|
|
{
|
|
"intent": IntentType.DIAGNOSE.value,
|
|
"description": "日誌查詢、健康檢查、RCA",
|
|
"risk_level": RiskLevel.LOW.value,
|
|
"keywords_sample": INTENT_KEYWORDS[IntentType.DIAGNOSE][:5],
|
|
},
|
|
{
|
|
"intent": IntentType.DELETE.value,
|
|
"description": "刪除資源（高風險）",
|
|
"risk_level": RiskLevel.CRITICAL.value,
|
|
"keywords_sample": INTENT_KEYWORDS[IntentType.DELETE][:5],
|
|
},
|
|
{
|
|
"intent": IntentType.ROLLBACK.value,
|
|
"description": "回滾版本",
|
|
"risk_level": RiskLevel.MEDIUM.value,
|
|
"keywords_sample": INTENT_KEYWORDS[IntentType.ROLLBACK][:5],
|
|
},
|
|
{
|
|
"intent": IntentType.UNKNOWN.value,
|
|
"description": "無法判斷意圖",
|
|
"risk_level": RiskLevel.MEDIUM.value,
|
|
"keywords_sample": [],
|
|
},
|
|
]
|
|
return intents
|
|
|
|
|
|
# =============================================================================
|
|
# Singleton
|
|
# =============================================================================
|
|
|
|
_classifier: IntentClassifier | None = None
|
|
|
|
|
|
def get_intent_classifier() -> IntentClassifier:
|
|
"""取得 IntentClassifier 單例"""
|
|
global _classifier
|
|
if _classifier is None:
|
|
_classifier = IntentClassifier()
|
|
return _classifier
|
|
|
|
|
|
def reset_intent_classifier() -> None:
|
|
"""重置單例 (用於測試)"""
|
|
global _classifier
|
|
_classifier = None
|
|
|
|
|
|
# =============================================================================
|
|
# Convenience Functions
|
|
# =============================================================================
|
|
|
|
|
|
async def classify_intent(text: str) -> IntentResult:
|
|
"""便捷函數: 分類意圖 (非同步)"""
|
|
return await get_intent_classifier().classify(text)
|
|
|
|
|
|
def classify_intent_sync(text: str) -> IntentResult:
|
|
"""便捷函數: 分類意圖 (同步)"""
|
|
return get_intent_classifier().classify_sync(text)
|
|
|
|
|
|
def get_intent_risk(intent: IntentType) -> RiskLevel:
|
|
"""便捷函數: 取得意圖風險等級"""
|
|
return INTENT_RISK_MAP.get(intent, RiskLevel.MEDIUM)
|