Aether/src/api/admin/monitoring/cache.py

"""
缓存监控端点

提供缓存亲和性统计、管理和监控功能
"""

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple

from fastapi import APIRouter, Depends, HTTPException, Query, Request
from fastapi.responses import PlainTextResponse
from sqlalchemy.orm import Session

from src.api.base.admin_adapter import AdminApiAdapter
from src.api.base.context import ApiRequestContext
from src.api.base.pagination import PaginationMeta, build_pagination_payload, paginate_sequence
from src.api.base.pipeline import ApiRequestPipeline
from src.clients.redis_client import get_redis_client_sync
from src.core.crypto import crypto_service
from src.core.logger import logger
from src.database import get_db
from src.models.database import ApiKey, User
from src.services.cache.affinity_manager import get_affinity_manager
from src.services.cache.aware_scheduler import get_cache_aware_scheduler

router = APIRouter(prefix="/api/admin/monitoring/cache", tags=["Admin - Monitoring: Cache"])
pipeline = ApiRequestPipeline()


def mask_api_key(api_key: Optional[str], prefix_len: int = 8, suffix_len: int = 4) -> Optional[str]:
    """
    脱敏 API Key，显示前缀 + 星号 + 后缀
    例如: sk-jhiId-xxxxxxxxxxxAABB -> sk-jhiId-********AABB

    Args:
        api_key: 原始 API Key
        prefix_len: 显示的前缀长度，默认 8
        suffix_len: 显示的后缀长度，默认 4
    """
    if not api_key:
        return None
    total_visible = prefix_len + suffix_len
    if len(api_key) <= total_visible:
        # Key 太短，直接返回部分内容 + 星号
        return api_key[:prefix_len] + "********"
    return f"{api_key[:prefix_len]}********{api_key[-suffix_len:]}"


def decrypt_and_mask(encrypted_key: Optional[str], prefix_len: int = 8) -> Optional[str]:
    """
    解密 API Key 后脱敏显示

    Args:
        encrypted_key: 加密后的 API Key
        prefix_len: 显示的前缀长度
    """
    if not encrypted_key:
        return None
    try:
        decrypted = crypto_service.decrypt(encrypted_key)
        return mask_api_key(decrypted, prefix_len)
    except Exception:
        # 解密失败时返回 None
        return None


def resolve_user_identifier(db: Session, identifier: str) -> Optional[str]:
    """
    将用户标识符（username/email/user_id/api_key_id）解析为 user_id

    支持的输入格式：
    1. User UUID (36位，带横杠)
    2. Username (用户名)
    3. Email (邮箱)
    4. API Key ID (36位UUID)

    返回：
    - user_id (UUID字符串) 或 None
    """
    identifier = identifier.strip()

    # 1. 先尝试作为 User UUID 查询
    user = db.query(User).filter(User.id == identifier).first()
    if user:
        logger.debug(f"通过User ID解析: {identifier[:8]}... -> {user.username}")
        return user.id

    # 2. 尝试作为 Username 查询
    user = db.query(User).filter(User.username == identifier).first()
    if user:
        logger.debug(f"通过Username解析: {identifier} -> {user.id[:8]}...")  # type: ignore[index]
        return user.id

    # 3. 尝试作为 Email 查询
    user = db.query(User).filter(User.email == identifier).first()
    if user:
        logger.debug(f"通过Email解析: {identifier} -> {user.id[:8]}...")  # type: ignore[index]
        return user.id

    # 4. 尝试作为 API Key ID 查询
    api_key = db.query(ApiKey).filter(ApiKey.id == identifier).first()
    if api_key:
        logger.debug(f"通过API Key ID解析: {identifier[:8]}... -> User ID: {api_key.user_id[:8]}...")  # type: ignore[index]
        return api_key.user_id

    # 无法识别
    logger.debug(f"无法识别的用户标识符: {identifier}")
    return None


@router.get("/stats")
async def get_cache_stats(
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    获取缓存亲和性统计信息

    返回:
    - 缓存命中率
    - 缓存用户数
    - Provider切换次数
    - Key切换次数
    - 缓存预留配置
    """
    adapter = AdminCacheStatsAdapter()
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.get("/affinity/{user_identifier}")
async def get_user_affinity(
    user_identifier: str,
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    查询指定用户的所有缓存亲和性

    参数:
    - user_identifier: 用户标识符，支持以下格式：
      * 用户名 (username)，如: yuanhonghu
      * 邮箱 (email)，如: user@example.com
      * 用户UUID (user_id)，如: 550e8400-e29b-41d4-a716-446655440000
      * API Key ID，如: 660e8400-e29b-41d4-a716-446655440000

    返回:
    - 用户信息
    - 所有端点的缓存亲和性列表（每个端点一条记录）
    """
    adapter = AdminGetUserAffinityAdapter(user_identifier=user_identifier)
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.get("/affinities")
async def list_affinities(
    request: Request,
    keyword: Optional[str] = None,
    limit: int = Query(100, ge=1, le=1000, description="返回数量限制"),
    offset: int = Query(0, ge=0, description="偏移量"),
    db: Session = Depends(get_db),
) -> Any:
    """
    获取所有缓存亲和性列表，可选按关键词过滤

    参数:
    - keyword: 可选，支持用户名/邮箱/User ID/API Key ID 或模糊匹配
    """
    adapter = AdminListAffinitiesAdapter(keyword=keyword, limit=limit, offset=offset)
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.delete("/users/{user_identifier}")
async def clear_user_cache(
    user_identifier: str,
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    Clear cache affinity for a specific user

    Parameters:
    - user_identifier: User identifier (username, email, user_id, or API Key ID)
    """
    adapter = AdminClearUserCacheAdapter(user_identifier=user_identifier)
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.delete("")
async def clear_all_cache(
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    Clear all cache affinities

    Warning: This affects all users, use with caution
    """
    adapter = AdminClearAllCacheAdapter()
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.delete("/providers/{provider_id}")
async def clear_provider_cache(
    provider_id: str,
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    Clear cache affinities for a specific provider

    Parameters:
    - provider_id: Provider ID
    """
    adapter = AdminClearProviderCacheAdapter(provider_id=provider_id)
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.get("/config")
async def get_cache_config(
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    获取缓存相关配置

    返回:
    - 缓存TTL
    - 缓存预留比例
    """
    adapter = AdminCacheConfigAdapter()
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.get("/metrics", response_class=PlainTextResponse)
async def get_cache_metrics(
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    以 Prometheus 文本格式暴露缓存调度指标，方便接入 Grafana。
    """
    adapter = AdminCacheMetricsAdapter()
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


# -------- 缓存监控适配器 --------


class AdminCacheStatsAdapter(AdminApiAdapter):
    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        try:
            redis_client = get_redis_client_sync()
            scheduler = await get_cache_aware_scheduler(redis_client)
            stats = await scheduler.get_stats()
            logger.info("缓存统计信息查询成功")
            context.add_audit_metadata(
                action="cache_stats",
                scheduler=stats.get("scheduler"),
                total_affinities=stats.get("total_affinities"),
                cache_hit_rate=stats.get("cache_hit_rate"),
                provider_switches=stats.get("provider_switches"),
            )
            return {"status": "ok", "data": stats}
        except Exception as exc:
            logger.exception(f"获取缓存统计信息失败: {exc}")
            raise HTTPException(status_code=500, detail=f"获取缓存统计失败: {exc}")


class AdminCacheMetricsAdapter(AdminApiAdapter):
    async def handle(self, context: ApiRequestContext) -> PlainTextResponse:
        try:
            redis_client = get_redis_client_sync()
            scheduler = await get_cache_aware_scheduler(redis_client)
            stats = await scheduler.get_stats()
            payload = self._format_prometheus(stats)
            context.add_audit_metadata(
                action="cache_metrics_export",
                scheduler=stats.get("scheduler"),
                metrics_lines=payload.count("\n"),
            )
            return PlainTextResponse(payload)
        except Exception as exc:
            logger.exception(f"导出缓存指标失败: {exc}")
            raise HTTPException(status_code=500, detail=f"导出缓存指标失败: {exc}")

    def _format_prometheus(self, stats: Dict[str, Any]) -> str:
        """
        将 scheduler/affinity 指标转换为 Prometheus 文本格式。
        """
        scheduler_metrics = stats.get("scheduler_metrics", {})
        affinity_stats = stats.get("affinity_stats", {})

        metric_map: List[Tuple[str, str, float]] = [
            (
                "cache_scheduler_total_batches",
                "Total batches pulled from provider list",
                float(scheduler_metrics.get("total_batches", 0)),
            ),
            (
                "cache_scheduler_last_batch_size",
                "Size of the latest candidate batch",
                float(scheduler_metrics.get("last_batch_size", 0)),
            ),
            (
                "cache_scheduler_total_candidates",
                "Total candidates enumerated by scheduler",
                float(scheduler_metrics.get("total_candidates", 0)),
            ),
            (
                "cache_scheduler_last_candidate_count",
                "Number of candidates in the most recent batch",
                float(scheduler_metrics.get("last_candidate_count", 0)),
            ),
            (
                "cache_scheduler_cache_hits",
                "Cache hits counted during scheduling",
                float(scheduler_metrics.get("cache_hits", 0)),
            ),
            (
                "cache_scheduler_cache_misses",
                "Cache misses counted during scheduling",
                float(scheduler_metrics.get("cache_misses", 0)),
            ),
            (
                "cache_scheduler_cache_hit_rate",
                "Cache hit rate during scheduling",
                float(scheduler_metrics.get("cache_hit_rate", 0.0)),
            ),
            (
                "cache_scheduler_concurrency_denied",
                "Times candidate rejected due to concurrency limits",
                float(scheduler_metrics.get("concurrency_denied", 0)),
            ),
            (
                "cache_scheduler_avg_candidates_per_batch",
                "Average candidates per batch",
                float(scheduler_metrics.get("avg_candidates_per_batch", 0.0)),
            ),
        ]

        affinity_map: List[Tuple[str, str, float]] = [
            (
                "cache_affinity_total",
                "Total cache affinities stored",
                float(affinity_stats.get("total_affinities", 0)),
            ),
            (
                "cache_affinity_hits",
                "Affinity cache hits",
                float(affinity_stats.get("cache_hits", 0)),
            ),
            (
                "cache_affinity_misses",
                "Affinity cache misses",
                float(affinity_stats.get("cache_misses", 0)),
            ),
            (
                "cache_affinity_hit_rate",
                "Affinity cache hit rate",
                float(affinity_stats.get("cache_hit_rate", 0.0)),
            ),
            (
                "cache_affinity_invalidations",
                "Affinity invalidations",
                float(affinity_stats.get("cache_invalidations", 0)),
            ),
            (
                "cache_affinity_provider_switches",
                "Affinity provider switches",
                float(affinity_stats.get("provider_switches", 0)),
            ),
            (
                "cache_affinity_key_switches",
                "Affinity key switches",
                float(affinity_stats.get("key_switches", 0)),
            ),
        ]

        lines = []
        for name, help_text, value in metric_map + affinity_map:
            lines.append(f"# HELP {name} {help_text}")
            lines.append(f"# TYPE {name} gauge")
            lines.append(f"{name} {value}")

        scheduler_name = stats.get("scheduler", "cache_aware")
        lines.append(f'cache_scheduler_info{{scheduler="{scheduler_name}"}} 1')

        return "\n".join(lines) + "\n"


@dataclass
class AdminGetUserAffinityAdapter(AdminApiAdapter):
    user_identifier: str

    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        db = context.db
        try:
            user_id = resolve_user_identifier(db, self.user_identifier)
            if not user_id:
                raise HTTPException(
                    status_code=404,
                    detail=f"无法识别的用户标识符: {self.user_identifier}。支持用户名、邮箱、User ID或API Key ID",
                )

            user = db.query(User).filter(User.id == user_id).first()
            redis_client = get_redis_client_sync()
            affinity_mgr = await get_affinity_manager(redis_client)

            # 获取该用户的所有缓存亲和性
            all_affinities = await affinity_mgr.list_affinities()
            user_affinities = [aff for aff in all_affinities if aff.get("user_id") == user_id]

            if not user_affinities:
                response = {
                    "status": "not_found",
                    "message": f"用户 {user.username} ({user.email}) 没有缓存亲和性",
                    "user_info": {
                        "user_id": user_id,
                        "username": user.username,
                        "email": user.email,
                    },
                    "affinities": [],
                }
                context.add_audit_metadata(
                    action="cache_user_affinity",
                    user_identifier=self.user_identifier,
                    resolved_user_id=user_id,
                    affinity_count=0,
                    status="not_found",
                )
                return response

            response = {
                "status": "ok",
                "user_info": {
                    "user_id": user_id,
                    "username": user.username,
                    "email": user.email,
                },
                "affinities": [
                    {
                        "provider_id": aff["provider_id"],
                        "endpoint_id": aff["endpoint_id"],
                        "key_id": aff["key_id"],
                        "api_format": aff.get("api_format"),
                        "model_name": aff.get("model_name"),
                        "created_at": aff["created_at"],
                        "expire_at": aff["expire_at"],
                        "request_count": aff["request_count"],
                    }
                    for aff in user_affinities
                ],
                "total_endpoints": len(user_affinities),
            }
            context.add_audit_metadata(
                action="cache_user_affinity",
                user_identifier=self.user_identifier,
                resolved_user_id=user_id,
                affinity_count=len(user_affinities),
                status="ok",
            )
            return response
        except HTTPException:
            raise
        except Exception as exc:
            logger.exception(f"查询用户缓存亲和性失败: {exc}")
            raise HTTPException(status_code=500, detail=f"查询失败: {exc}")


@dataclass
class AdminListAffinitiesAdapter(AdminApiAdapter):
    keyword: Optional[str]
    limit: int
    offset: int

    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        db = context.db
        redis_client = get_redis_client_sync()
        if not redis_client:
            raise HTTPException(status_code=503, detail="Redis未初始化，无法获取缓存亲和性")

        affinity_mgr = await get_affinity_manager(redis_client)
        matched_user_id = None
        matched_api_key_id = None
        raw_affinities: List[Dict[str, Any]] = []

        if self.keyword:
            # 首先检查是否是 API Key ID（affinity_key）
            api_key = db.query(ApiKey).filter(ApiKey.id == self.keyword).first()
            if api_key:
                # 直接通过 affinity_key 过滤
                matched_api_key_id = str(api_key.id)
                matched_user_id = str(api_key.user_id)
                all_affinities = await affinity_mgr.list_affinities()
                raw_affinities = [
                    aff for aff in all_affinities if aff.get("affinity_key") == matched_api_key_id
                ]
            else:
                # 尝试解析为用户标识
                user_id = resolve_user_identifier(db, self.keyword)
                if user_id:
                    matched_user_id = user_id
                    # 获取该用户所有的 API Key ID
                    user_api_keys = db.query(ApiKey).filter(ApiKey.user_id == user_id).all()
                    user_api_key_ids = {str(k.id) for k in user_api_keys}
                    # 过滤出该用户所有 API Key 的亲和性
                    all_affinities = await affinity_mgr.list_affinities()
                    raw_affinities = [
                        aff for aff in all_affinities if aff.get("affinity_key") in user_api_key_ids
                    ]
                else:
                    # 关键词不是有效标识，返回所有亲和性（后续会进行模糊匹配）
                    raw_affinities = await affinity_mgr.list_affinities()
        else:
            raw_affinities = await affinity_mgr.list_affinities()

        # 收集所有 affinity_key (API Key ID)
        affinity_keys = {
            item.get("affinity_key") for item in raw_affinities if item.get("affinity_key")
        }

        # 批量查询用户 API Key 信息
        user_api_key_map: Dict[str, ApiKey] = {}
        if affinity_keys:
            user_api_keys = db.query(ApiKey).filter(ApiKey.id.in_(list(affinity_keys))).all()
            user_api_key_map = {str(k.id): k for k in user_api_keys}

        # 收集所有 user_id
        user_ids = {str(k.user_id) for k in user_api_key_map.values()}
        user_map: Dict[str, User] = {}
        if user_ids:
            users = db.query(User).filter(User.id.in_(list(user_ids))).all()
            user_map = {str(user.id): user for user in users}

        # 收集所有provider_id、endpoint_id、key_id
        provider_ids = {
            item.get("provider_id") for item in raw_affinities if item.get("provider_id")
        }
        endpoint_ids = {
            item.get("endpoint_id") for item in raw_affinities if item.get("endpoint_id")
        }
        key_ids = {item.get("key_id") for item in raw_affinities if item.get("key_id")}

        # 批量查询Provider、Endpoint、Key信息
        from src.models.database import Provider, ProviderAPIKey, ProviderEndpoint

        provider_map = {}
        if provider_ids:
            providers = db.query(Provider).filter(Provider.id.in_(list(provider_ids))).all()
            provider_map = {p.id: p for p in providers}

        endpoint_map = {}
        if endpoint_ids:
            endpoints = (
                db.query(ProviderEndpoint).filter(ProviderEndpoint.id.in_(list(endpoint_ids))).all()
            )
            endpoint_map = {e.id: e for e in endpoints}

        key_map = {}
        if key_ids:
            keys = db.query(ProviderAPIKey).filter(ProviderAPIKey.id.in_(list(key_ids))).all()
            key_map = {k.id: k for k in keys}

        # 收集所有 model_name（实际存储的是 global_model_id）并批量查询 GlobalModel
        from src.models.database import GlobalModel

        global_model_ids = {
            item.get("model_name") for item in raw_affinities if item.get("model_name")
        }
        global_model_map: Dict[str, GlobalModel] = {}
        if global_model_ids:
            # model_name 可能是 UUID 格式的 global_model_id，也可能是原始模型名称
            global_models = db.query(GlobalModel).filter(
                GlobalModel.id.in_(list(global_model_ids))
            ).all()
            global_model_map = {str(gm.id): gm for gm in global_models}

        keyword_lower = self.keyword.lower() if self.keyword else None
        items = []
        for affinity in raw_affinities:
            affinity_key = affinity.get("affinity_key")
            if not affinity_key:
                continue

            # 通过 affinity_key（API Key ID）找到用户 API Key 和用户
            user_api_key = user_api_key_map.get(affinity_key)
            user = user_map.get(str(user_api_key.user_id)) if user_api_key else None
            user_id = str(user_api_key.user_id) if user_api_key else None

            provider_id = affinity.get("provider_id")
            endpoint_id = affinity.get("endpoint_id")
            key_id = affinity.get("key_id")

            provider = provider_map.get(provider_id)
            endpoint = endpoint_map.get(endpoint_id)
            key = key_map.get(key_id)

            # 用户 API Key 脱敏显示（解密 key_encrypted 后脱敏）
            user_api_key_masked = None
            if user_api_key and user_api_key.key_encrypted:
                user_api_key_masked = decrypt_and_mask(user_api_key.key_encrypted)

            # Provider Key 脱敏显示（解密 api_key 后脱敏）
            provider_key_masked = None
            if key and key.api_key:
                provider_key_masked = decrypt_and_mask(key.api_key)

            item = {
                "affinity_key": affinity_key,
                "user_api_key_name": user_api_key.name if user_api_key else None,
                "user_api_key_prefix": user_api_key_masked,
                "is_standalone": user_api_key.is_standalone if user_api_key else False,
                "user_id": user_id,
                "username": user.username if user else None,
                "email": user.email if user else None,
                "provider_id": provider_id,
                "provider_name": provider.display_name if provider else None,
                "endpoint_id": endpoint_id,
                "endpoint_api_format": (
                    endpoint.api_format if endpoint and endpoint.api_format else None
                ),
                "endpoint_url": endpoint.base_url if endpoint else None,
                "key_id": key_id,
                "key_name": key.name if key else None,
                "key_prefix": provider_key_masked,
                "rate_multiplier": key.rate_multiplier if key else 1.0,
                "model_name": (
                    global_model_map.get(affinity.get("model_name")).name
                    if affinity.get("model_name") and global_model_map.get(affinity.get("model_name"))
                    else affinity.get("model_name")  # 如果找不到 GlobalModel，显示原始值
                ),
                "model_display_name": (
                    global_model_map.get(affinity.get("model_name")).display_name
                    if affinity.get("model_name") and global_model_map.get(affinity.get("model_name"))
                    else None
                ),
                "api_format": affinity.get("api_format"),
                "created_at": affinity.get("created_at"),
                "expire_at": affinity.get("expire_at"),
                "request_count": affinity.get("request_count", 0),
            }

            if keyword_lower and not matched_user_id and not matched_api_key_id:
                searchable = [
                    item["affinity_key"],
                    item["user_api_key_name"] or "",
                    item["user_id"] or "",
                    item["username"] or "",
                    item["email"] or "",
                    item["provider_id"] or "",
                    item["key_id"] or "",
                ]
                if not any(keyword_lower in str(value).lower() for value in searchable if value):
                    continue

            items.append(item)

        items.sort(key=lambda x: x.get("expire_at") or 0, reverse=True)
        paged_items, meta = paginate_sequence(items, self.limit, self.offset)
        payload = build_pagination_payload(
            paged_items,
            meta,
            matched_user_id=matched_user_id,
        )
        response = {
            "status": "ok",
            "data": payload,
        }
        result_count = meta.count if hasattr(meta, "count") else len(paged_items)
        context.add_audit_metadata(
            action="cache_affinity_list",
            keyword=self.keyword,
            matched_user_id=matched_user_id,
            matched_api_key_id=matched_api_key_id,
            limit=self.limit,
            offset=self.offset,
            result_count=result_count,
        )
        return response


@dataclass
class AdminClearUserCacheAdapter(AdminApiAdapter):
    user_identifier: str

    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        db = context.db
        try:
            redis_client = get_redis_client_sync()
            affinity_mgr = await get_affinity_manager(redis_client)

            # 首先检查是否直接是 API Key ID (affinity_key)
            api_key = db.query(ApiKey).filter(ApiKey.id == self.user_identifier).first()
            if api_key:
                # 直接按 affinity_key 清除
                affinity_key = str(api_key.id)
                user = db.query(User).filter(User.id == api_key.user_id).first()

                all_affinities = await affinity_mgr.list_affinities()
                target_affinities = [
                    aff for aff in all_affinities if aff.get("affinity_key") == affinity_key
                ]

                count = 0
                for aff in target_affinities:
                    api_format = aff.get("api_format")
                    model_name = aff.get("model_name")
                    endpoint_id = aff.get("endpoint_id")
                    if api_format and model_name:
                        await affinity_mgr.invalidate_affinity(
                            affinity_key, api_format, model_name, endpoint_id=endpoint_id
                        )
                        count += 1

                logger.info(f"已清除API Key缓存亲和性: api_key_name={api_key.name}, affinity_key={affinity_key[:8]}..., 清除数量={count}")

                response = {
                    "status": "ok",
                    "message": f"已清除 API Key {api_key.name} 的缓存亲和性",
                    "user_info": {
                        "user_id": str(api_key.user_id),
                        "username": user.username if user else None,
                        "email": user.email if user else None,
                        "api_key_id": affinity_key,
                        "api_key_name": api_key.name,
                    },
                }
                context.add_audit_metadata(
                    action="cache_clear_api_key",
                    user_identifier=self.user_identifier,
                    resolved_api_key_id=affinity_key,
                    cleared_count=count,
                )
                return response

            # 如果不是 API Key ID，尝试解析为用户标识
            user_id = resolve_user_identifier(db, self.user_identifier)
            if not user_id:
                raise HTTPException(
                    status_code=404,
                    detail=f"无法识别的标识符: {self.user_identifier}。支持用户名、邮箱、User ID或API Key ID",
                )

            user = db.query(User).filter(User.id == user_id).first()

            # 获取该用户所有的 API Key
            user_api_keys = db.query(ApiKey).filter(ApiKey.user_id == user_id).all()
            user_api_key_ids = {str(k.id) for k in user_api_keys}

            # 获取该用户所有 API Key 的缓存亲和性并逐个失效
            all_affinities = await affinity_mgr.list_affinities()
            user_affinities = [
                aff for aff in all_affinities if aff.get("affinity_key") in user_api_key_ids
            ]

            count = 0
            for aff in user_affinities:
                affinity_key = aff.get("affinity_key")
                api_format = aff.get("api_format")
                model_name = aff.get("model_name")
                endpoint_id = aff.get("endpoint_id")
                if affinity_key and api_format and model_name:
                    await affinity_mgr.invalidate_affinity(
                        affinity_key, api_format, model_name, endpoint_id=endpoint_id
                    )
                    count += 1

            logger.info(f"已清除用户缓存亲和性: username={user.username}, user_id={user_id[:8]}..., 清除数量={count}")

            response = {
                "status": "ok",
                "message": f"已清除用户 {user.username} 的所有缓存亲和性",
                "user_info": {"user_id": user_id, "username": user.username, "email": user.email},
            }
            context.add_audit_metadata(
                action="cache_clear_user",
                user_identifier=self.user_identifier,
                resolved_user_id=user_id,
                cleared_count=count,
            )
            return response
        except HTTPException:
            raise
        except Exception as exc:
            logger.exception(f"清除用户缓存亲和性失败: {exc}")
            raise HTTPException(status_code=500, detail=f"清除失败: {exc}")


class AdminClearAllCacheAdapter(AdminApiAdapter):
    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        try:
            redis_client = get_redis_client_sync()
            affinity_mgr = await get_affinity_manager(redis_client)
            count = await affinity_mgr.clear_all()
            logger.warning(f"已清除所有缓存亲和性（管理员操作）: {count} 个")
            context.add_audit_metadata(
                action="cache_clear_all",
                cleared_count=count,
            )
            return {"status": "ok", "message": "已清除所有缓存亲和性", "count": count}
        except Exception as exc:
            logger.exception(f"清除所有缓存亲和性失败: {exc}")
            raise HTTPException(status_code=500, detail=f"清除失败: {exc}")


@dataclass
class AdminClearProviderCacheAdapter(AdminApiAdapter):
    provider_id: str

    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        try:
            redis_client = get_redis_client_sync()
            affinity_mgr = await get_affinity_manager(redis_client)
            count = await affinity_mgr.invalidate_all_for_provider(self.provider_id)
            logger.info(f"已清除Provider缓存亲和性: provider_id={self.provider_id[:8]}..., count={count}")
            context.add_audit_metadata(
                action="cache_clear_provider",
                provider_id=self.provider_id,
                cleared_count=count,
            )
            return {
                "status": "ok",
                "message": "已清除Provider的缓存亲和性",
                "provider_id": self.provider_id,
                "count": count,
            }
        except Exception as exc:
            logger.exception(f"清除Provider缓存亲和性失败: {exc}")
            raise HTTPException(status_code=500, detail=f"清除失败: {exc}")


class AdminCacheConfigAdapter(AdminApiAdapter):
    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        from src.services.cache.affinity_manager import CacheAffinityManager
        from src.services.cache.aware_scheduler import CacheAwareScheduler
        from src.services.rate_limit.adaptive_reservation import get_adaptive_reservation_manager

        # 获取动态预留管理器的配置
        reservation_manager = get_adaptive_reservation_manager()
        reservation_stats = reservation_manager.get_stats()

        response = {
            "status": "ok",
            "data": {
                "cache_ttl_seconds": CacheAffinityManager.DEFAULT_CACHE_TTL,
                "cache_reservation_ratio": CacheAwareScheduler.CACHE_RESERVATION_RATIO,
                "dynamic_reservation": {
                    "enabled": True,
                    "config": reservation_stats["config"],
                    "description": {
                        "probe_phase_requests": "探测阶段请求数阈值",
                        "probe_reservation": "探测阶段预留比例",
                        "stable_min_reservation": "稳定阶段最小预留比例",
                        "stable_max_reservation": "稳定阶段最大预留比例",
                        "low_load_threshold": "低负载阈值（低于此值使用最小预留）",
                        "high_load_threshold": "高负载阈值（高于此值根据置信度使用较高预留）",
                    },
                },
                "description": {
                    "cache_ttl": "缓存亲和性有效期（秒）",
                    "cache_reservation_ratio": "静态预留比例（已被动态预留替代）",
                    "dynamic_reservation": "动态预留机制配置",
                },
            },
        }
        context.add_audit_metadata(
            action="cache_config",
            cache_ttl_seconds=CacheAffinityManager.DEFAULT_CACHE_TTL,
            cache_reservation_ratio=CacheAwareScheduler.CACHE_RESERVATION_RATIO,
            dynamic_reservation_enabled=True,
        )
        return response


# ==================== 模型映射缓存管理 ====================


@router.get("/model-mapping/stats")
async def get_model_mapping_cache_stats(
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    获取模型映射缓存统计信息

    返回:
    - 缓存键数量
    - 缓存 TTL 配置
    - 各类型缓存数量
    """
    adapter = AdminModelMappingCacheStatsAdapter()
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.delete("/model-mapping")
async def clear_all_model_mapping_cache(
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    清除所有模型映射缓存

    警告: 这会影响所有模型解析，请谨慎使用
    """
    adapter = AdminClearAllModelMappingCacheAdapter()
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.delete("/model-mapping/{model_name}")
async def clear_model_mapping_cache_by_name(
    model_name: str,
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    清除指定模型名称的映射缓存

    参数:
    - model_name: 模型名称（可以是 GlobalModel.name 或映射名称）
    """
    adapter = AdminClearModelMappingCacheByNameAdapter(model_name=model_name)
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


@router.delete("/model-mapping/provider/{provider_id}/{global_model_id}")
async def clear_provider_model_mapping_cache(
    provider_id: str,
    global_model_id: str,
    request: Request,
    db: Session = Depends(get_db),
) -> Any:
    """
    清除指定 Provider 和 GlobalModel 的模型映射缓存

    参数:
    - provider_id: Provider ID
    - global_model_id: GlobalModel ID
    """
    adapter = AdminClearProviderModelMappingCacheAdapter(
        provider_id=provider_id, global_model_id=global_model_id
    )
    return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)


class AdminModelMappingCacheStatsAdapter(AdminApiAdapter):
    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        import json

        from src.clients.redis_client import get_redis_client
        from src.config.constants import CacheTTL
        from src.models.database import GlobalModel, Model, Provider

        db = context.db

        try:
            redis = await get_redis_client(require_redis=False)
            if not redis:
                return {
                    "status": "ok",
                    "data": {
                        "available": False,
                        "message": "Redis 未启用，模型映射缓存不可用",
                    },
                }

            # 统计各类型缓存键数量
            model_id_keys = []
            global_model_id_keys = []
            global_model_name_keys = []
            global_model_resolve_keys = []
            provider_global_keys = []

            # 扫描所有模型相关的缓存键
            async for key in redis.scan_iter(match="model:*", count=100):
                key_str = key.decode() if isinstance(key, bytes) else key
                if key_str.startswith("model:id:"):
                    model_id_keys.append(key_str)
                elif key_str.startswith("model:provider_global:"):
                    # 过滤掉 hits 统计键，只保留实际的缓存键
                    if not key_str.startswith("model:provider_global:hits:"):
                        provider_global_keys.append(key_str)

            async for key in redis.scan_iter(match="global_model:*", count=100):
                key_str = key.decode() if isinstance(key, bytes) else key
                if key_str.startswith("global_model:id:"):
                    global_model_id_keys.append(key_str)
                elif key_str.startswith("global_model:name:"):
                    global_model_name_keys.append(key_str)
                elif key_str.startswith("global_model:resolve:"):
                    global_model_resolve_keys.append(key_str)

            total_keys = (
                len(model_id_keys)
                + len(global_model_id_keys)
                + len(global_model_name_keys)
                + len(global_model_resolve_keys)
                + len(provider_global_keys)
            )

            # 解析缓存内容，构建映射列表
            mappings = []
            unmapped_entries = []

            for key in global_model_resolve_keys[:100]:  # 最多处理 100 个
                mapping_name = key.replace("global_model:resolve:", "")
                try:
                    cached_value = await redis.get(key)
                    ttl = await redis.ttl(key)

                    if cached_value:
                        cached_str = (
                            cached_value.decode()
                            if isinstance(cached_value, bytes)
                            else cached_value
                        )

                        if cached_str == "NOT_FOUND":
                            unmapped_entries.append({
                                "mapping_name": mapping_name,
                                "status": "not_found",
                                "ttl": ttl if ttl > 0 else None,
                            })
                        else:
                            try:
                                cached_data = json.loads(cached_str)
                                global_model_id = cached_data.get("id")
                                global_model_name = cached_data.get("name")
                                global_model_display_name = cached_data.get("display_name")

                                # 跳过 mapping_name == global_model_name 的情况（直接匹配，不是映射）
                                if mapping_name == global_model_name:
                                    continue

                                # 查询哪些 Provider 配置了这个映射名称
                                provider_names = []
                                if global_model_id:
                                    models = (
                                        db.query(Model, Provider)
                                        .join(Provider, Model.provider_id == Provider.id)
                                        .filter(
                                            Model.global_model_id == global_model_id,
                                            Model.is_active,
                                            Provider.is_active,
                                        )
                                        .all()
                                    )
                                    # 只显示配置了该映射名称的 Provider
                                    for model, provider in models:
                                        # 检查是否是主模型名称
                                        if model.provider_model_name == mapping_name:
                                            provider_names.append(
                                                provider.display_name or provider.name
                                            )
                                            continue
                                        # 检查是否在别名列表中
                                        if model.provider_model_aliases:
                                            alias_names = [
                                                a.get("name")
                                                for a in model.provider_model_aliases
                                                if isinstance(a, dict)
                                            ]
                                            if mapping_name in alias_names:
                                                provider_names.append(
                                                    provider.display_name or provider.name
                                                )
                                    provider_names = sorted(list(set(provider_names)))

                                mappings.append({
                                    "mapping_name": mapping_name,
                                    "global_model_name": global_model_name,
                                    "global_model_display_name": global_model_display_name,
                                    "providers": provider_names,
                                    "ttl": ttl if ttl > 0 else None,
                                })

                            except json.JSONDecodeError:
                                unmapped_entries.append({
                                    "mapping_name": mapping_name,
                                    "status": "invalid",
                                    "ttl": ttl if ttl > 0 else None,
                                })
                except Exception as e:
                    logger.warning(f"解析缓存键 {key} 失败: {e}")
                    unmapped_entries.append({
                        "mapping_name": mapping_name,
                        "status": "error",
                        "ttl": None,
                    })

            # 按 mapping_name 排序
            mappings.sort(key=lambda x: x["mapping_name"])

            # 3. 解析 provider_global 缓存（Provider 级别的模型解析缓存）
            provider_model_mappings = []
            # 预加载 Provider 和 GlobalModel 数据
            provider_map = {str(p.id): p for p in db.query(Provider).filter(Provider.is_active.is_(True)).all()}
            global_model_map = {str(gm.id): gm for gm in db.query(GlobalModel).filter(GlobalModel.is_active.is_(True)).all()}

            for key in provider_global_keys[:100]:  # 最多处理 100 个
                # key 格式: model:provider_global:{provider_id}:{global_model_id}
                try:
                    parts = key.replace("model:provider_global:", "").split(":")
                    if len(parts) != 2:
                        continue
                    provider_id, global_model_id = parts

                    cached_value = await redis.get(key)
                    ttl = await redis.ttl(key)

                    # 获取命中次数
                    hit_count_key = f"model:provider_global:hits:{provider_id}:{global_model_id}"
                    hit_count_raw = await redis.get(hit_count_key)
                    hit_count = int(hit_count_raw) if hit_count_raw else 0

                    if cached_value:
                        cached_str = (
                            cached_value.decode()
                            if isinstance(cached_value, bytes)
                            else cached_value
                        )
                        try:
                            cached_data = json.loads(cached_str)
                            provider_model_name = cached_data.get("provider_model_name")
                            provider_model_aliases = cached_data.get("provider_model_aliases", [])

                            # 获取 Provider 和 GlobalModel 信息
                            provider = provider_map.get(provider_id)
                            global_model = global_model_map.get(global_model_id)

                            if provider and global_model:
                                # 提取别名名称
                                alias_names = []
                                if provider_model_aliases:
                                    for alias_entry in provider_model_aliases:
                                        if isinstance(alias_entry, dict) and alias_entry.get("name"):
                                            alias_names.append(alias_entry["name"])

                                # provider_model_name 为空时跳过
                                if not provider_model_name:
                                    continue

                                # 只显示有实际映射的条目：
                                # 1. 全局模型名 != Provider 模型名（模型名称映射）
                                # 2. 或者有别名配置
                                has_name_mapping = global_model.name != provider_model_name
                                has_aliases = len(alias_names) > 0

                                if has_name_mapping or has_aliases:
                                    # 构建用于展示的别名列表
                                    # 如果只有名称映射没有别名，则用 global_model_name 作为"请求名称"
                                    display_aliases = alias_names if alias_names else [global_model.name]

                                    provider_model_mappings.append({
                                        "provider_id": provider_id,
                                        "provider_name": provider.display_name or provider.name,
                                        "global_model_id": global_model_id,
                                        "global_model_name": global_model.name,
                                        "global_model_display_name": global_model.display_name,
                                        "provider_model_name": provider_model_name,
                                        "aliases": display_aliases,
                                        "ttl": ttl if ttl > 0 else None,
                                        "hit_count": hit_count,
                                    })
                        except json.JSONDecodeError:
                            pass
                except Exception as e:
                    logger.warning(f"解析 provider_global 缓存键 {key} 失败: {e}")

            # 按 provider_name + global_model_name 排序
            provider_model_mappings.sort(key=lambda x: (x["provider_name"], x["global_model_name"]))

            response_data = {
                "available": True,
                "ttl_seconds": CacheTTL.MODEL,
                "total_keys": total_keys,
                "breakdown": {
                    "model_by_id": len(model_id_keys),
                    "model_by_provider_global": len(provider_global_keys),
                    "global_model_by_id": len(global_model_id_keys),
                    "global_model_by_name": len(global_model_name_keys),
                    "global_model_resolve": len(global_model_resolve_keys),
                },
                "mappings": mappings,
                "provider_model_mappings": provider_model_mappings if provider_model_mappings else None,
                "unmapped": unmapped_entries if unmapped_entries else None,
            }

            context.add_audit_metadata(
                action="model_mapping_cache_stats",
                total_keys=total_keys,
            )
            return {"status": "ok", "data": response_data}

        except Exception as exc:
            logger.exception(f"获取模型映射缓存统计失败: {exc}")
            raise HTTPException(status_code=500, detail=f"获取统计失败: {exc}")


class AdminClearAllModelMappingCacheAdapter(AdminApiAdapter):
    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        from src.clients.redis_client import get_redis_client

        try:
            redis = await get_redis_client(require_redis=False)
            if not redis:
                raise HTTPException(status_code=503, detail="Redis 未启用")

            deleted_count = 0

            # 删除所有模型相关的缓存键
            keys_to_delete = []
            async for key in redis.scan_iter(match="model:*", count=100):
                keys_to_delete.append(key)
            async for key in redis.scan_iter(match="global_model:*", count=100):
                keys_to_delete.append(key)

            if keys_to_delete:
                deleted_count = await redis.delete(*keys_to_delete)

            logger.warning(f"已清除所有模型映射缓存（管理员操作）: {deleted_count} 个键")
            context.add_audit_metadata(
                action="model_mapping_cache_clear_all",
                deleted_count=deleted_count,
            )
            return {
                "status": "ok",
                "message": f"已清除所有模型映射缓存",
                "deleted_count": deleted_count,
            }

        except HTTPException:
            raise
        except Exception as exc:
            logger.exception(f"清除模型映射缓存失败: {exc}")
            raise HTTPException(status_code=500, detail=f"清除失败: {exc}")


@dataclass
class AdminClearModelMappingCacheByNameAdapter(AdminApiAdapter):
    model_name: str

    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        from src.clients.redis_client import get_redis_client

        try:
            redis = await get_redis_client(require_redis=False)
            if not redis:
                raise HTTPException(status_code=503, detail="Redis 未启用")

            deleted_keys = []

            # 清除 resolve 缓存
            resolve_key = f"global_model:resolve:{self.model_name}"
            if await redis.exists(resolve_key):
                await redis.delete(resolve_key)
                deleted_keys.append(resolve_key)

            # 清除 name 缓存
            name_key = f"global_model:name:{self.model_name}"
            if await redis.exists(name_key):
                await redis.delete(name_key)
                deleted_keys.append(name_key)

            logger.info(f"已清除模型映射缓存: model_name={self.model_name}, 删除键={deleted_keys}")
            context.add_audit_metadata(
                action="model_mapping_cache_clear_by_name",
                model_name=self.model_name,
                deleted_keys=deleted_keys,
            )
            return {
                "status": "ok",
                "message": f"已清除模型 {self.model_name} 的映射缓存",
                "model_name": self.model_name,
                "deleted_keys": deleted_keys,
            }

        except HTTPException:
            raise
        except Exception as exc:
            logger.exception(f"清除模型映射缓存失败: {exc}")
            raise HTTPException(status_code=500, detail=f"清除失败: {exc}")


@dataclass
class AdminClearProviderModelMappingCacheAdapter(AdminApiAdapter):
    provider_id: str
    global_model_id: str

    async def handle(self, context: ApiRequestContext) -> Dict[str, Any]:  # type: ignore[override]
        from src.clients.redis_client import get_redis_client

        try:
            redis = await get_redis_client(require_redis=False)
            if not redis:
                raise HTTPException(status_code=503, detail="Redis 未启用")

            deleted_keys = []

            # 清除 provider_global 缓存
            provider_global_key = f"model:provider_global:{self.provider_id}:{self.global_model_id}"
            if await redis.exists(provider_global_key):
                await redis.delete(provider_global_key)
                deleted_keys.append(provider_global_key)

            # 清除对应的 hit_count 缓存
            hit_count_key = f"model:provider_global:hits:{self.provider_id}:{self.global_model_id}"
            if await redis.exists(hit_count_key):
                await redis.delete(hit_count_key)
                deleted_keys.append(hit_count_key)

            logger.info(
                f"已清除 Provider 模型映射缓存: provider_id={self.provider_id[:8]}..., "
                f"global_model_id={self.global_model_id[:8]}..., 删除键={deleted_keys}"
            )
            context.add_audit_metadata(
                action="provider_model_mapping_cache_clear",
                provider_id=self.provider_id,
                global_model_id=self.global_model_id,
                deleted_keys=deleted_keys,
            )
            return {
                "status": "ok",
                "message": "已清除 Provider 模型映射缓存",
                "provider_id": self.provider_id,
                "global_model_id": self.global_model_id,
                "deleted_keys": deleted_keys,
            }

        except HTTPException:
            raise
        except Exception as exc:
            logger.exception(f"清除 Provider 模型映射缓存失败: {exc}")
            raise HTTPException(status_code=500, detail=f"清除失败: {exc}")