"""
KM Conversion Service — ADR-071-G
==================================
Incident RESOLVED 後自動轉換為 KnowledgeEntry + Playbook 草稿

設計原則:
- 非同步觸發，失敗不影響主流程
- 根據 notification_type 決定 KM 品質等級
- 自動向量化（embedding）
- 寫入 AlertOperationLog KM_CONVERTED 事件

建立時間: 2026-04-11 (台北時區)
建立者: Claude Sonnet 4.6 — ADR-071-G

leWOOOgo 積木化:
- KMConversionService → KnowledgeService + LearningService + AlertOperationLogRepository
- 不直接存取 DB，透過 Repository 層
"""

import structlog

from src.models.knowledge import (
    EntrySource,
    EntryStatus,
    EntryType,
    KnowledgeEntryCreate,
)
from src.repositories.alert_operation_log_repository import (
    ALERT_EVENT_TYPES,
    get_alert_operation_log_repository,
)
from src.services.knowledge_service import get_knowledge_service

logger = structlog.get_logger(__name__)

# ADR-071 新 event_type 已在 migrations/adr071_notification_lifecycle.sql 的 DB enum 中定義
# 不在此做 runtime ALERT_EVENT_TYPES.update()：避免模組副作用污染全局狀態 + 測試隔離失敗
# 若 repository 層有 ALERT_EVENT_TYPES 白名單 validation，
# 請在 alert_operation_log_repository.py 的 ALERT_EVENT_TYPES 靜態集合中加入以下值：
#   "KM_CONVERTED", "NOTIFICATION_CLASSIFIED", "MANUAL_FIX_RECORDED",
#   "PLAYBOOK_DRAFT_CREATED", "STATE_GUARD_BLOCKED"

# 通知類型 → KM 品質等級對應
_TYPE_TO_STATUS = {
    "TYPE-2":  EntryStatus.APPROVED,   # 自動修復成功，最高品質
    "TYPE-3":  EntryStatus.REVIEW,     # 人工審核後執行
    "TYPE-4":  EntryStatus.DRAFT,      # AI 無法判斷，草稿
    "TYPE-4D": EntryStatus.DRAFT,      # Config Drift，草稿
    "TYPE-1":  None,                   # 純資訊，不轉 KM
}

_TYPE_TO_SOURCE = {
    "TYPE-2":  EntrySource.AI_EXTRACTED,
    "TYPE-3":  EntrySource.AI_EXTRACTED,
    "TYPE-4":  EntrySource.HUMAN,
    "TYPE-4D": EntrySource.AI_EXTRACTED,
}


class KMConversionService:
    """
    Incident → KM 自動轉換服務

    觸發時機：
      1. Incident 狀態變為 RESOLVED（主要路徑）
      2. 使用者點擊 [手動修復後記錄] 後（ADR-071-H）
      3. 每日 03:00 cron 補轉換（vectorized=False + RESOLVED）
    """

    def __init__(self) -> None:
        self._knowledge_svc = get_knowledge_service()
        self._op_log_repo = get_alert_operation_log_repository()

    async def convert(self, incident) -> dict | None:
        """
        將 Incident 轉換為 KnowledgeEntry

        Args:
            incident: Incident ORM 物件（呼叫前確保 signals 已 eager load）

        Returns:
            dict with km_entry_id and quality_level, or None if skipped
        """
        try:
            return await self._convert_inner(incident)
        except Exception as e:
            logger.error(
                "km_conversion_error",
                incident_id=getattr(incident, "incident_id", "unknown"),
                error=str(e),
            )
            raise

    async def _convert_inner(self, incident) -> dict | None:
        notification_type = getattr(incident, "notification_type", None) or "TYPE-3"

        # TYPE-1 不轉 KM
        target_status = _TYPE_TO_STATUS.get(notification_type)
        if target_status is None:
            logger.debug(
                "km_conversion_skipped",
                incident_id=incident.incident_id,
                reason="TYPE-1 純資訊，不轉 KM",
            )
            return None

        entry_source = _TYPE_TO_SOURCE.get(notification_type, EntrySource.AI_EXTRACTED)
        alert_category = getattr(incident, "alert_category", None) or "unknown"

        # 提取 label 資訊
        labels = incident.signals[0].labels if incident.signals else {}
        alertname = labels.get("alertname", "unknown")
        severity   = labels.get("severity", "unknown")

        affected_services = ", ".join(incident.affected_services or ["unknown"])

        # 計算修復耗時
        resolution_time = ""
        if incident.resolved_at and incident.created_at:
            try:
                delta = incident.resolved_at - incident.created_at
                resolution_time = f"{int(delta.total_seconds())}s"
            except Exception:
                pass

        # 建立 KM 內容（標準格式）
        content = self._build_content(
            incident=incident,
            alertname=alertname,
            affected_services=affected_services,
            severity=severity,
            resolution_time=resolution_time,
        )

        _inc_title = getattr(incident, "title", None) or alertname
        title = f"{alertname} @ {affected_services[:40]} — {_inc_title[:60]}"

        tags = [alertname, affected_services, severity, notification_type]
        if alert_category != "unknown":
            tags.append(alert_category)

        km_entry = await self._knowledge_svc.create_entry(
            KnowledgeEntryCreate(
                title=title[:255],
                content=content,
                entry_type=EntryType.INCIDENT_CASE,
                category=alert_category,
                tags=[t for t in tags if t],
                source=entry_source,
                status=target_status,
                related_incident_id=incident.incident_id,
            )
        )

        # 寫入操作日誌
        try:
            await self._op_log_repo.append(
                event_type="KM_CONVERTED",
                incident_id=incident.incident_id,
                actor="km_conversion_service",
                action_detail=f"KM entry created: {km_entry.id}",
                success=True,
                context={
                    "km_entry_id": km_entry.id,
                    "quality_level": target_status.value,
                    "notification_type": notification_type,
                },
            )
        except Exception as _e:
            logger.warning("km_op_log_failed", incident_id=incident.incident_id, error=str(_e))

        # BUG-004 修復 2026-04-11: KM entry 建立後，knowledge_service 背景觸發 embedding，
        # 但 incidents.vectorized 沒有被設為 True → 飛輪閉環（ADR-068）學習效果歸零。
        # 等 embedding 背景任務啟動後（短延遲）更新 incidents.vectorized = True。
        # 注意：embedding 為背景 asyncio task，此處標記 vectorized=True 代表「已觸發向量化」
        # 真正完成以 knowledge_embedding_saved log 為準，但 vectorized flag 用於篩選補轉換
        try:
            from src.db.base import get_db_context
            from src.db.models import IncidentRecord
            from sqlalchemy import update as _sa_update
            async with get_db_context() as _db:
                await _db.execute(
                    _sa_update(IncidentRecord)
                    .where(IncidentRecord.incident_id == incident.incident_id)
                    .values(vectorized=True)
                )
                await _db.commit()
            logger.info(
                "km_incident_vectorized_flagged",
                incident_id=incident.incident_id,
                km_entry_id=km_entry.id,
            )
        except Exception as _ve:
            logger.warning(
                "km_vectorized_flag_failed",
                incident_id=incident.incident_id,
                error=str(_ve),
            )

        # C2 修復 2026-04-11: DB 更新後，同步更新 Redis Working Memory 中的 vectorized 欄位
        # 審計查 Redis Incident 物件，若不同步則審計仍顯示 vectorized=False
        # Key 格式: incident:{incident_id}（與 incident_service.save_to_working_memory 一致）
        try:
            import json as _json
            from src.core.redis_client import get_redis
            _redis = get_redis()
            _redis_key = f"incident:{incident.incident_id}"
            _raw = await _redis.get(_redis_key)
            if _raw:
                _data = _json.loads(_raw)
                if not _data.get("vectorized"):
                    _data["vectorized"] = True
                    _ttl = await _redis.ttl(_redis_key)
                    _ex = _ttl if _ttl and _ttl > 0 else 604800
                    await _redis.set(_redis_key, _json.dumps(_data), ex=_ex)
                    logger.info(
                        "km_incident_vectorized_redis_synced",
                        incident_id=incident.incident_id,
                    )
        except Exception as _re:
            logger.debug(
                "km_vectorized_redis_sync_failed",
                incident_id=incident.incident_id,
                error=str(_re),
            )

        logger.info(
            "km_converted",
            incident_id=incident.incident_id,
            km_entry_id=km_entry.id,
            quality_level=target_status.value,
            notification_type=notification_type,
        )

        return {
            "km_entry_id": km_entry.id,
            "quality_level": target_status.value,
        }

    def _build_content(
        self,
        incident,
        alertname: str,
        affected_services: str,
        severity: str,
        resolution_time: str,
    ) -> str:
        """
        建立 KM 條目內容（標準格式）
        """
        created_at_str = str(incident.created_at) if incident.created_at else "未知"
        resolved_at_str = str(incident.resolved_at) if incident.resolved_at else "未知"

        context_summary = ""
        if incident.context_bundle:
            context_summary = str(incident.context_bundle.get("summary", ""))

        # 決策鏈資訊
        decision_chain = getattr(incident, "decision_chain", None)
        root_cause = ""
        action_type = ""
        action_command = ""
        if decision_chain and isinstance(decision_chain, dict):
            root_cause = decision_chain.get("root_cause", "")
            action_type = decision_chain.get("action_type", "")
            action_command = decision_chain.get("action", "")

        # 指標快照（若有）— ADR-071-J: 使用精簡 delta 格式 (2026-04-11 Claude Sonnet 4.6)
        metrics_section = ""
        if incident.metrics_before or incident.metrics_after:
            mb = incident.metrics_before or {}
            ma = incident.metrics_after or {}
            delta_parts = []
            for key, label in (("cpu_pct", "CPU"), ("mem_pct", "Mem"), ("disk_pct", "Disk")):
                if key in mb and key in ma:
                    delta_parts.append(f"{label} {mb[key]:.0f}%→{ma[key]:.0f}%")
                elif key in ma:
                    delta_parts.append(f"{label} 後={ma[key]:.0f}%")
            if "restart_count" in mb and "restart_count" in ma:
                delta_parts.append(f"重啟 {mb['restart_count']}→{ma['restart_count']}")
            delta_str = " | ".join(delta_parts) if delta_parts else "（無量化數據）"

            # K8s 狀態（若有）
            k8s_state = getattr(incident, "k8s_state_after", None)
            k8s_line = f"\n- K8s 狀態: {k8s_state}" if k8s_state else ""

            metrics_section = (
                f"\n## 效果驗證\n"
                f"- 指標變化: {delta_str}\n"
                f"- 恢復耗時: {resolution_time}\n"
                + k8s_line
                + "\n"
            )

        # 驗證結果（若有）
        verify_section = ""
        if incident.verification_result:
            verify_section = f"- 驗證方式: {incident.verification_result}\n"

        manual_section = ""
        if incident.manual_fix_steps:
            manual_section = (
                f"\n## 手動修復步驟\n"
                f"- 執行者: {incident.manual_fix_by or '未知'}\n"
                f"```\n{incident.manual_fix_steps}\n```\n"
            )

        return (
            f"## 症狀\n"
            f"- 告警: {alertname}\n"
            f"- 服務: {affected_services}\n"
            f"- 嚴重度: {severity}\n"
            f"- 觸發時間: {created_at_str}\n"
            f"- 解決時間: {resolved_at_str}\n"
            + (f"- 即時情境: {context_summary}\n" if context_summary else "")
            + f"\n## 根因分析\n{root_cause or getattr(incident, 'title', None) or '未知'}\n"
            + (
                f"\n## 執行動作\n"
                f"- 類型: {action_type}\n"
                f"- 指令: {action_command}\n"
                if action_type or action_command else ""
            )
            + metrics_section
            + verify_section
            + manual_section
        )


# Singleton (模組層級)
_km_conversion_service: KMConversionService | None = None


def get_km_conversion_service() -> KMConversionService:
    global _km_conversion_service
    if _km_conversion_service is None:
        _km_conversion_service = KMConversionService()
    return _km_conversion_service