awoooi/apps/api/src/services/complexity_scorer.py

"""
Complexity Scorer - Phase 13.3 #86
===================================
複雜度評分，用於智能路由模型選擇

目標: < 10ms 延遲 (純規則引擎)
策略: 基於特徵提取的加權評分

Phase 13.3 (2026-03-26): 初始實作
"""

from dataclasses import dataclass, field

import structlog

logger = structlog.get_logger(__name__)


@dataclass
class ComplexityScore:
    """複雜度評分結果"""

    score: int  # 1-5 (1=簡單, 5=極複雜)
    features: dict[str, int] = field(default_factory=dict)
    recommended_model: str = "qwen2.5:7b-instruct"
    reasoning: str = ""


# 模型映射 (依複雜度)
MODEL_BY_COMPLEXITY = {
    1: "llama3.2:3b",  # 簡單任務，快速回應
    2: "qwen2.5:7b-instruct",  # 中等任務
    3: "qwen2.5:7b-instruct",  # 複雜任務
    4: "gemini",  # 需要雲端能力
    5: "claude",  # 極複雜，需要最強模型
}


class ComplexityScorer:
    """
    複雜度評分器

    基於規則的複雜度評估，無 LLM 依賴，確保 < 10ms

    評分維度:
    1. 服務數量 (affected_services)
    2. 指標數量 (metrics)
    3. 是否需要程式碼分析 (requires_code_analysis)
    4. 是否跨系統 (cross_system)
    5. 是否有歷史關聯 (has_history)
    6. 嚴重程度 (severity)
    """

    # 權重配置
    WEIGHTS = {
        "service_count": 0.5,  # 每增加一個服務 +0.5
        "metric_count": 0.3,  # 每增加一個指標 +0.3
        "code_analysis": 1.5,  # 需要代碼分析 +1.5
        "cross_system": 1.0,  # 跨系統 +1.0
        "has_history": -0.5,  # 有歷史案例 -0.5 (降低複雜度)
        "critical_severity": 1.0,  # CRITICAL 告警 +1.0
    }

    def score(self, context: dict) -> ComplexityScore:
        """
        計算複雜度分數

        Args:
            context: 上下文資訊，包含:
                - affected_services: list[str]
                - metrics: list[str]
                - requires_code_analysis: bool
                - cross_system: bool
                - has_history: bool
                - severity: str

        Returns:
            ComplexityScore: 評分結果
        """
        raw_score = 1.0  # 基準分
        features: dict[str, int] = {}
        reasons: list[str] = []

        # 特徵 1: 服務數量
        services = context.get("affected_services", [])
        service_count = len(services)
        if service_count > 1:
            delta = (service_count - 1) * self.WEIGHTS["service_count"]
            raw_score += delta
            features["service_count"] = service_count
            reasons.append(f"涉及 {service_count} 個服務")

        # 特徵 2: 指標數量
        metrics = context.get("metrics", [])
        metric_count = len(metrics)
        if metric_count > 2:
            delta = (metric_count - 2) * self.WEIGHTS["metric_count"]
            raw_score += delta
            features["metric_count"] = metric_count
            reasons.append(f"涉及 {metric_count} 個指標")

        # 特徵 3: 是否需要程式碼分析
        if context.get("requires_code_analysis", False):
            raw_score += self.WEIGHTS["code_analysis"]
            features["code_analysis"] = 1
            reasons.append("需要程式碼分析")

        # 特徵 4: 是否跨系統
        if context.get("cross_system", False):
            raw_score += self.WEIGHTS["cross_system"]
            features["cross_system"] = 1
            reasons.append("跨系統問題")

        # 特徵 5: 是否有歷史關聯
        if context.get("has_history", False):
            raw_score += self.WEIGHTS["has_history"]  # 負數，降低複雜度
            features["has_history"] = 1
            reasons.append("有歷史案例參考")

        # 特徵 6: 嚴重程度
        severity = context.get("severity", "").upper()
        if severity == "CRITICAL":
            raw_score += self.WEIGHTS["critical_severity"]
            features["severity"] = 4
            reasons.append("CRITICAL 嚴重程度")
        elif severity == "HIGH":
            raw_score += 0.5
            features["severity"] = 3

        # 正規化到 1-5
        final_score = max(1, min(5, round(raw_score)))

        # 選擇推薦模型
        recommended_model = MODEL_BY_COMPLEXITY.get(
            final_score, "qwen2.5:7b-instruct"
        )

        result = ComplexityScore(
            score=final_score,
            features=features,
            recommended_model=recommended_model,
            reasoning="; ".join(reasons) if reasons else "基本複雜度",
        )

        logger.debug(
            "complexity_scored",
            score=final_score,
            features=features,
            model=recommended_model,
        )

        return result


# 單例
_scorer: ComplexityScorer | None = None


def get_complexity_scorer() -> ComplexityScorer:
    """取得 ComplexityScorer 單例"""
    global _scorer
    if _scorer is None:
        _scorer = ComplexityScorer()
    return _scorer