PMFlow/backend/app/dispatcher.py

"""Neural Sync - Role Dispatcher。"""

from __future__ import annotations

import logging
import re
from typing import Any, Optional

from pydantic import BaseModel, Field

from .dispatcher_rules import ROLE_RULES, TECH_KEYWORDS, Role, RoutingRule
from .utils.timezone import now_cn_aware

logger = logging.getLogger(__name__)


class RoleRouter:
    """角色路由引擎。"""

    ROLE_RULES: list[RoutingRule] = ROLE_RULES
    TECH_KEYWORDS = TECH_KEYWORDS

    def __init__(self):
        self._compiled_patterns: dict[Role, list[re.Pattern]] = {}
        for rule in self.ROLE_RULES:
            patterns = [re.compile(pattern, re.IGNORECASE) for pattern in rule.patterns]
            self._compiled_patterns[rule.role] = patterns

    def route(
        self,
        signal_content: str,
        analysis: Optional[dict[str, Any]] = None,
        source: Optional[str] = None,
    ) -> tuple[Role, float, dict[str, Any]]:
        analysis = analysis or {}
        details = {
            "matched_rules": [],
            "scores": {},
            "reasons": [],
        }

        if self._should_escalate_to_boss(analysis):
            details["reasons"].append("触发老板决策阈值")
            details["matched_rules"].append("threshold_check")
            return Role.BOSS, 1.0, details

        scores: dict[Role, float] = {rule.role: 0.0 for rule in self.ROLE_RULES}

        content_lower = signal_content.lower()
        for rule in self.ROLE_RULES:
            keyword_score = sum(1 for kw in rule.keywords if kw.lower() in content_lower)
            if keyword_score > 0:
                scores[rule.role] += keyword_score * 0.3
                details["scores"][f"{rule.role}_keywords"] = keyword_score

        for rule in self.ROLE_RULES:
            if rule.role in self._compiled_patterns:
                pattern_score = sum(
                    1
                    for pattern in self._compiled_patterns[rule.role]
                    if pattern.search(signal_content)
                )
                if pattern_score > 0:
                    scores[rule.role] += pattern_score * 0.5
                    details["scores"][f"{rule.role}_patterns"] = pattern_score

        for rule in self.ROLE_RULES:
            trigger_score = sum(1 for trigger in rule.triggers if trigger.lower() in content_lower)
            if trigger_score > 0:
                scores[rule.role] += trigger_score * 0.7
                details["scores"][f"{rule.role}_triggers"] = trigger_score

        for rule in self.ROLE_RULES:
            if scores[rule.role] > 0:
                scores[rule.role] *= 1 + rule.priority * 0.01

        max_role = Role.AI_ENGINEER
        max_score = 0.0

        for role, score in scores.items():
            if score > max_score:
                max_score = score
                max_role = role
            elif score == max_score and score > 0:
                role_priority = 0
                max_role_priority = 0
                for rule in self.ROLE_RULES:
                    if rule.role == role:
                        role_priority = rule.priority
                    if rule.role == max_role:
                        max_role_priority = rule.priority
                if role_priority > max_role_priority:
                    max_role = role

        confidence = min(max_score / 3.0, 1.0) if max_score > 0 else 0.5

        details["matched_rules"] = [
            rule.name
            for rule in self.ROLE_RULES
            if scores[rule.role] > 0
        ]
        details["final_scores"] = {role.value: score for role, score in scores.items()}
        details["reasons"].append("基于关键词和模式匹配")

        return max_role, confidence, details

    def _should_escalate_to_boss(self, analysis: dict[str, Any]) -> bool:
        dev_days = analysis.get("resource_cost", {}).get("dev_days", 0)
        if dev_days >= 5:
            return True

        risk_level = analysis.get("risk_level", "")
        if risk_level in ["CRITICAL", "HIGH"]:
            return True

        is_architecture = analysis.get("is_architecture_change", False)
        if is_architecture:
            return True

        return False

    def get_role_info(self, role: Role) -> dict[str, Any]:
        for rule in self.ROLE_RULES:
            if rule.role == role:
                return {
                    "role": role.value,
                    "name": rule.name,
                    "description": rule.description,
                    "keywords": rule.keywords,
                    "priority": rule.priority,
                }
        return {}

    def get_all_roles(self) -> list[dict[str, Any]]:
        return [
            {
                "role": rule.role.value,
                "name": rule.name,
                "description": rule.description,
                "priority": rule.priority,
            }
            for rule in sorted(self.ROLE_RULES, key=lambda item: -item.priority)
        ]


class RoutingResult(BaseModel):
    """路由结果模型。"""

    role: str
    confidence: float
    details: dict[str, Any]
    signal_id: Optional[str] = None
    timestamp: float = Field(default_factory=lambda: now_cn_aware().timestamp())


_global_router: Optional[RoleRouter] = None


def get_router() -> RoleRouter:
    """获取全局路由器实例。"""
    global _global_router
    if _global_router is None:
        _global_router = RoleRouter()
    return _global_router


def route_signal(
    signal_content: str,
    analysis: Optional[dict[str, Any]] = None,
    signal_id: Optional[str] = None,
) -> RoutingResult:
    """路由信号到对应角色。"""
    router = get_router()
    role, confidence, details = router.route(signal_content, analysis)
    return RoutingResult(
        role=role.value,
        confidence=confidence,
        details=details,
        signal_id=signal_id,
    )