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src/api/admin/adaptive.py
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377
src/api/admin/adaptive.py
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"""
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自适应并发管理 API 端点
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设计原则：
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- 自适应模式由 max_concurrent 字段决定：
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- max_concurrent = NULL：启用自适应模式，系统自动学习并调整并发限制
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- max_concurrent = 数字：固定限制模式，使用用户指定的并发限制
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- learned_max_concurrent：自适应模式下学习到的并发限制值
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- adaptive_mode 是计算字段，基于 max_concurrent 是否为 NULL
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"""
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from dataclasses import dataclass
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from typing import List, Optional
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from fastapi import APIRouter, Depends, HTTPException, Query, Request
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from pydantic import BaseModel, Field, ValidationError
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from sqlalchemy.orm import Session
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from src.api.base.admin_adapter import AdminApiAdapter
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from src.api.base.pipeline import ApiRequestPipeline
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from src.core.exceptions import InvalidRequestException, translate_pydantic_error
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from src.database import get_db
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from src.models.database import ProviderAPIKey
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from src.services.rate_limit.adaptive_concurrency import get_adaptive_manager
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router = APIRouter(prefix="/api/admin/adaptive", tags=["Adaptive Concurrency"])
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pipeline = ApiRequestPipeline()
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# ==================== Pydantic Models ====================
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class EnableAdaptiveRequest(BaseModel):
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"""启用自适应模式请求"""
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enabled: bool = Field(..., description="是否启用自适应模式（true=自适应，false=固定限制）")
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fixed_limit: Optional[int] = Field(
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None, ge=1, le=100, description="固定并发限制（仅当 enabled=false 时生效）"
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)
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class AdaptiveStatsResponse(BaseModel):
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"""自适应统计响应"""
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adaptive_mode: bool = Field(..., description="是否为自适应模式（max_concurrent=NULL）")
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max_concurrent: Optional[int] = Field(None, description="用户配置的固定限制（NULL=自适应）")
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effective_limit: Optional[int] = Field(
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None, description="当前有效限制（自适应使用学习值，固定使用配置值）"
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)
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learned_limit: Optional[int] = Field(None, description="学习到的并发限制")
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concurrent_429_count: int
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rpm_429_count: int
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last_429_at: Optional[str]
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last_429_type: Optional[str]
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adjustment_count: int
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recent_adjustments: List[dict]
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class KeyListItem(BaseModel):
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"""Key 列表项"""
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id: str
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name: Optional[str]
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endpoint_id: str
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is_adaptive: bool = Field(..., description="是否为自适应模式（max_concurrent=NULL）")
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max_concurrent: Optional[int] = Field(None, description="固定并发限制（NULL=自适应）")
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effective_limit: Optional[int] = Field(None, description="当前有效限制")
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learned_max_concurrent: Optional[int] = Field(None, description="学习到的并发限制")
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concurrent_429_count: int
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rpm_429_count: int
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# ==================== API Endpoints ====================
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@router.get(
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"/keys",
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response_model=List[KeyListItem],
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summary="获取所有启用自适应模式的Key",
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)
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async def list_adaptive_keys(
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request: Request,
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endpoint_id: Optional[str] = Query(None, description="按 Endpoint 过滤"),
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db: Session = Depends(get_db),
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):
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"""
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获取所有启用自适应模式的Key列表
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可选参数：
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- endpoint_id: 按 Endpoint 过滤
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"""
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adapter = ListAdaptiveKeysAdapter(endpoint_id=endpoint_id)
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return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)
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@router.patch(
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"/keys/{key_id}/mode",
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summary="Toggle key's concurrency control mode",
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)
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async def toggle_adaptive_mode(
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key_id: str,
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request: Request,
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db: Session = Depends(get_db),
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):
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"""
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Toggle the concurrency control mode for a specific key
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Parameters:
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- enabled: true=adaptive mode (max_concurrent=NULL), false=fixed limit mode
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- fixed_limit: fixed limit value (required when enabled=false)
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"""
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adapter = ToggleAdaptiveModeAdapter(key_id=key_id)
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return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)
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@router.get(
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"/keys/{key_id}/stats",
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response_model=AdaptiveStatsResponse,
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summary="获取Key的自适应统计",
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)
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async def get_adaptive_stats(
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key_id: str,
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request: Request,
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db: Session = Depends(get_db),
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):
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"""
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获取指定Key的自适应并发统计信息
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包括：
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- 当前配置
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- 学习到的限制
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- 429错误统计
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- 调整历史
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"""
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adapter = GetAdaptiveStatsAdapter(key_id=key_id)
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return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)
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@router.delete(
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"/keys/{key_id}/learning",
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summary="Reset key's learning state",
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)
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async def reset_adaptive_learning(
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key_id: str,
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request: Request,
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db: Session = Depends(get_db),
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):
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"""
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Reset the adaptive learning state for a specific key
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Clears:
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- Learned concurrency limit (learned_max_concurrent)
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- 429 error counts
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- Adjustment history
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Does not change:
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- max_concurrent config (determines adaptive mode)
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"""
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adapter = ResetAdaptiveLearningAdapter(key_id=key_id)
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return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)
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@router.patch(
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"/keys/{key_id}/limit",
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summary="Set key to fixed concurrency limit mode",
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)
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async def set_concurrent_limit(
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key_id: str,
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request: Request,
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limit: int = Query(..., ge=1, le=100, description="Concurrency limit value"),
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db: Session = Depends(get_db),
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):
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"""
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Set key to fixed concurrency limit mode
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Note:
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- After setting this value, key switches to fixed limit mode and won't auto-adjust
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- To restore adaptive mode, use PATCH /keys/{key_id}/mode
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"""
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adapter = SetConcurrentLimitAdapter(key_id=key_id, limit=limit)
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return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)
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@router.get(
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"/summary",
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summary="获取自适应并发的全局统计",
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)
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async def get_adaptive_summary(
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request: Request,
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db: Session = Depends(get_db),
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):
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"""
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获取自适应并发的全局统计摘要
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包括：
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- 启用自适应模式的Key数量
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- 总429错误数
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- 并发限制调整次数
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"""
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adapter = AdaptiveSummaryAdapter()
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return await pipeline.run(adapter=adapter, http_request=request, db=db, mode=adapter.mode)
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# ==================== Pipeline 适配器 ====================
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@dataclass
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class ListAdaptiveKeysAdapter(AdminApiAdapter):
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endpoint_id: Optional[str] = None
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async def handle(self, context): # type: ignore[override]
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# 自适应模式：max_concurrent = NULL
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query = context.db.query(ProviderAPIKey).filter(ProviderAPIKey.max_concurrent.is_(None))
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if self.endpoint_id:
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query = query.filter(ProviderAPIKey.endpoint_id == self.endpoint_id)
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keys = query.all()
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return [
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KeyListItem(
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id=key.id,
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name=key.name,
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endpoint_id=key.endpoint_id,
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is_adaptive=key.max_concurrent is None,
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max_concurrent=key.max_concurrent,
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effective_limit=(
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key.learned_max_concurrent if key.max_concurrent is None else key.max_concurrent
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),
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learned_max_concurrent=key.learned_max_concurrent,
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concurrent_429_count=key.concurrent_429_count or 0,
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rpm_429_count=key.rpm_429_count or 0,
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)
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for key in keys
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]
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@dataclass
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class ToggleAdaptiveModeAdapter(AdminApiAdapter):
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key_id: str
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async def handle(self, context): # type: ignore[override]
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key = context.db.query(ProviderAPIKey).filter(ProviderAPIKey.id == self.key_id).first()
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if not key:
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raise HTTPException(status_code=404, detail="Key not found")
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payload = context.ensure_json_body()
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try:
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body = EnableAdaptiveRequest.model_validate(payload)
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except ValidationError as e:
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errors = e.errors()
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if errors:
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raise InvalidRequestException(translate_pydantic_error(errors[0]))
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raise InvalidRequestException("请求数据验证失败")
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if body.enabled:
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# 启用自适应模式：将 max_concurrent 设为 NULL
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key.max_concurrent = None
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message = "已切换为自适应模式，系统将自动学习并调整并发限制"
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else:
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# 禁用自适应模式：设置固定限制
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if body.fixed_limit is None:
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raise HTTPException(
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status_code=400, detail="禁用自适应模式时必须提供 fixed_limit 参数"
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)
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key.max_concurrent = body.fixed_limit
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message = f"已切换为固定限制模式，并发限制设为 {body.fixed_limit}"
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context.db.commit()
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context.db.refresh(key)
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is_adaptive = key.max_concurrent is None
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return {
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"message": message,
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"key_id": key.id,
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"is_adaptive": is_adaptive,
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"max_concurrent": key.max_concurrent,
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"effective_limit": key.learned_max_concurrent if is_adaptive else key.max_concurrent,
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}
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@dataclass
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class GetAdaptiveStatsAdapter(AdminApiAdapter):
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key_id: str
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async def handle(self, context): # type: ignore[override]
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key = context.db.query(ProviderAPIKey).filter(ProviderAPIKey.id == self.key_id).first()
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if not key:
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raise HTTPException(status_code=404, detail="Key not found")
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adaptive_manager = get_adaptive_manager()
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stats = adaptive_manager.get_adjustment_stats(key)
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# 转换字段名以匹配响应模型
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return AdaptiveStatsResponse(
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adaptive_mode=stats["adaptive_mode"],
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max_concurrent=stats["max_concurrent"],
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effective_limit=stats["effective_limit"],
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learned_limit=stats["learned_limit"],
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concurrent_429_count=stats["concurrent_429_count"],
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rpm_429_count=stats["rpm_429_count"],
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last_429_at=stats["last_429_at"],
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last_429_type=stats["last_429_type"],
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adjustment_count=stats["adjustment_count"],
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recent_adjustments=stats["recent_adjustments"],
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)
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@dataclass
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class ResetAdaptiveLearningAdapter(AdminApiAdapter):
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key_id: str
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async def handle(self, context): # type: ignore[override]
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key = context.db.query(ProviderAPIKey).filter(ProviderAPIKey.id == self.key_id).first()
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if not key:
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raise HTTPException(status_code=404, detail="Key not found")
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adaptive_manager = get_adaptive_manager()
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adaptive_manager.reset_learning(context.db, key)
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return {"message": "学习状态已重置", "key_id": key.id}
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@dataclass
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class SetConcurrentLimitAdapter(AdminApiAdapter):
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key_id: str
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limit: int
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async def handle(self, context): # type: ignore[override]
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key = context.db.query(ProviderAPIKey).filter(ProviderAPIKey.id == self.key_id).first()
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if not key:
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raise HTTPException(status_code=404, detail="Key not found")
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was_adaptive = key.max_concurrent is None
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key.max_concurrent = self.limit
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context.db.commit()
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context.db.refresh(key)
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return {
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"message": f"已设置为固定限制模式，并发限制为 {self.limit}",
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"key_id": key.id,
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"is_adaptive": False,
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"max_concurrent": key.max_concurrent,
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"previous_mode": "adaptive" if was_adaptive else "fixed",
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}
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class AdaptiveSummaryAdapter(AdminApiAdapter):
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async def handle(self, context): # type: ignore[override]
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# 自适应模式：max_concurrent = NULL
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adaptive_keys = (
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context.db.query(ProviderAPIKey).filter(ProviderAPIKey.max_concurrent.is_(None)).all()
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)
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total_keys = len(adaptive_keys)
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total_concurrent_429 = sum(key.concurrent_429_count or 0 for key in adaptive_keys)
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total_rpm_429 = sum(key.rpm_429_count or 0 for key in adaptive_keys)
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total_adjustments = sum(len(key.adjustment_history or []) for key in adaptive_keys)
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recent_adjustments = []
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for key in adaptive_keys:
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if key.adjustment_history:
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for adj in key.adjustment_history[-3:]:
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recent_adjustments.append(
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{
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"key_id": key.id,
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"key_name": key.name,
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**adj,
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}
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)
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recent_adjustments.sort(key=lambda item: item.get("timestamp", ""), reverse=True)
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return {
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"total_adaptive_keys": total_keys,
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"total_concurrent_429_errors": total_concurrent_429,
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"total_rpm_429_errors": total_rpm_429,
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"total_adjustments": total_adjustments,
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"recent_adjustments": recent_adjustments[:10],
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}
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