mirror of
https://github.com/fawney19/Aether.git
synced 2026-09-02 09:20:22 +08:00
refactor: 全局适配 ApiFamily/EndpointKind 结构化标识体系
将新的 (ApiFamily, EndpointKind) / `family:kind` 签名体系应用到整个代码库： - API Handlers: 所有 adapter/handler 使用新的签名格式 - Services: provider, model, usage, cache, auth 等服务层适配 - Database: ProviderEndpoint 新增 api_family/endpoint_kind 字段 - Frontend: Provider 管理、Usage 表格等组件适配 - Tests: 更新所有相关测试用例
This commit is contained in:
fawney19
@@ -2,9 +2,9 @@
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from __future__ import annotations
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from typing import Any
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from dataclasses import dataclass
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from datetime import datetime, timedelta, timezone
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from typing import Any
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from fastapi import APIRouter, Depends, HTTPException, Query, Request
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from sqlalchemy import and_, func
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@@ -12,15 +12,23 @@ from sqlalchemy.orm import Session
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from src.api.base.adapter import ApiAdapter, ApiMode
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from src.api.base.admin_adapter import AdminApiAdapter
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from src.api.base.context import ApiRequestContext
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from src.api.base.pipeline import ApiRequestPipeline
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from src.config.constants import CacheTTL
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from src.core.enums import UserRole
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from src.database import get_db
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from src.models.database import ApiKey, Provider, RequestCandidate, StatsDaily, StatsDailyModel, StatsDailyProvider, Usage
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from src.models.database import (
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ApiKey,
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Provider,
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RequestCandidate,
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StatsDaily,
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StatsDailyModel,
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StatsDailyProvider,
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Usage,
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)
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from src.models.database import User as DBUser
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from src.services.system.stats_aggregator import StatsAggregatorService
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from src.utils.cache_decorator import cache_result
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from src.api.base.context import ApiRequestContext
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router = APIRouter(prefix="/api/dashboard", tags=["Dashboard"])
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pipeline = ApiRequestPipeline()
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@@ -181,10 +189,13 @@ class DashboardStatsAdapter(DashboardAdapter):
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class AdminDashboardStatsAdapter(AdminApiAdapter):
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@cache_result(key_prefix="dashboard:admin:stats", ttl=CacheTTL.DASHBOARD_STATS, user_specific=False)
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@cache_result(
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key_prefix="dashboard:admin:stats", ttl=CacheTTL.DASHBOARD_STATS, user_specific=False
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)
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async def handle(self, context: ApiRequestContext) -> Any: # type: ignore[override]
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"""管理员仪表盘统计 - 使用预聚合数据优化性能"""
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from zoneinfo import ZoneInfo
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from src.services.system.stats_aggregator import APP_TIMEZONE
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db = context.db
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@@ -218,7 +229,9 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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active_users = combined_stats.get("active_users") or (
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db.query(func.count(DBUser.id)).filter(DBUser.is_active.is_(True)).scalar()
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)
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total_api_keys = combined_stats.get("total_api_keys") or db.query(func.count(ApiKey.id)).scalar()
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total_api_keys = (
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combined_stats.get("total_api_keys") or db.query(func.count(ApiKey.id)).scalar()
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)
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active_api_keys = combined_stats.get("active_api_keys") or (
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db.query(func.count(ApiKey.id)).filter(ApiKey.is_active.is_(True)).scalar()
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)
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@@ -250,12 +263,14 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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requests_yesterday = (
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db.query(func.count(Usage.id))
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.filter(Usage.created_at >= yesterday, Usage.created_at < today)
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.scalar() or 0
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.scalar()
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or 0
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)
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cost_yesterday = (
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db.query(func.sum(Usage.total_cost_usd))
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.filter(Usage.created_at >= yesterday, Usage.created_at < today)
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.scalar() or 0
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.scalar()
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or 0
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)
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yesterday_token_stats = (
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db.query(
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@@ -267,10 +282,20 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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.filter(Usage.created_at >= yesterday, Usage.created_at < today)
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.first()
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)
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input_tokens_yesterday = int(yesterday_token_stats.input_tokens or 0) if yesterday_token_stats else 0
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output_tokens_yesterday = int(yesterday_token_stats.output_tokens or 0) if yesterday_token_stats else 0
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cache_creation_yesterday = int(yesterday_token_stats.cache_creation_tokens or 0) if yesterday_token_stats else 0
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cache_read_yesterday = int(yesterday_token_stats.cache_read_tokens or 0) if yesterday_token_stats else 0
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input_tokens_yesterday = (
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int(yesterday_token_stats.input_tokens or 0) if yesterday_token_stats else 0
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)
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output_tokens_yesterday = (
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int(yesterday_token_stats.output_tokens or 0) if yesterday_token_stats else 0
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)
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cache_creation_yesterday = (
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int(yesterday_token_stats.cache_creation_tokens or 0)
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if yesterday_token_stats
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else 0
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)
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cache_read_yesterday = (
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int(yesterday_token_stats.cache_read_tokens or 0) if yesterday_token_stats else 0
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)
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# ==================== 本月统计（从预聚合表聚合）====================
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monthly_stats = (
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@@ -279,8 +304,12 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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func.sum(StatsDaily.error_requests).label("error_requests"),
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func.sum(StatsDaily.total_cost).label("total_cost"),
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func.sum(StatsDaily.actual_total_cost).label("actual_total_cost"),
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func.sum(StatsDaily.input_tokens + StatsDaily.output_tokens +
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StatsDaily.cache_creation_tokens + StatsDaily.cache_read_tokens).label("total_tokens"),
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func.sum(
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StatsDaily.input_tokens
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+ StatsDaily.output_tokens
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+ StatsDaily.cache_creation_tokens
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+ StatsDaily.cache_read_tokens
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).label("total_tokens"),
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func.sum(StatsDaily.cache_creation_tokens).label("cache_creation_tokens"),
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func.sum(StatsDaily.cache_read_tokens).label("cache_read_tokens"),
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func.sum(StatsDaily.cache_creation_cost).label("cache_creation_cost"),
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@@ -298,7 +327,9 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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total_cost = float(monthly_stats.total_cost or 0) + cost_today
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total_actual_cost = float(monthly_stats.actual_total_cost or 0) + actual_cost_today
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total_tokens = int(monthly_stats.total_tokens or 0) + tokens_today
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cache_creation_tokens = int(monthly_stats.cache_creation_tokens or 0) + cache_creation_today
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cache_creation_tokens = (
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int(monthly_stats.cache_creation_tokens or 0) + cache_creation_today
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)
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cache_read_tokens = int(monthly_stats.cache_read_tokens or 0) + cache_read_today
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cache_creation_cost = float(monthly_stats.cache_creation_cost or 0)
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cache_read_cost = float(monthly_stats.cache_read_cost or 0)
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@@ -309,21 +340,31 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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db.query(func.count(Usage.id)).filter(Usage.created_at >= month_start).scalar() or 0
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)
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total_cost = (
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db.query(func.sum(Usage.total_cost_usd)).filter(Usage.created_at >= month_start).scalar() or 0
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db.query(func.sum(Usage.total_cost_usd))
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.filter(Usage.created_at >= month_start)
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.scalar()
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or 0
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)
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total_actual_cost = (
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db.query(func.sum(Usage.actual_total_cost_usd))
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.filter(Usage.created_at >= month_start).scalar() or 0
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.filter(Usage.created_at >= month_start)
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.scalar()
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or 0
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)
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error_requests = (
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db.query(func.count(Usage.id))
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.filter(
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Usage.created_at >= month_start,
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(Usage.status_code >= 400) | (Usage.error_message.isnot(None)),
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).scalar() or 0
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)
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.scalar()
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or 0
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)
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total_tokens = (
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db.query(func.sum(Usage.total_tokens)).filter(Usage.created_at >= month_start).scalar() or 0
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db.query(func.sum(Usage.total_tokens))
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.filter(Usage.created_at >= month_start)
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.scalar()
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or 0
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)
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cache_stats = (
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db.query(
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@@ -335,7 +376,9 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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.filter(Usage.created_at >= month_start)
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.first()
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)
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cache_creation_tokens = int(cache_stats.cache_creation_tokens or 0) if cache_stats else 0
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cache_creation_tokens = (
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int(cache_stats.cache_creation_tokens or 0) if cache_stats else 0
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)
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cache_read_tokens = int(cache_stats.cache_read_tokens or 0) if cache_stats else 0
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cache_creation_cost = float(cache_stats.cache_creation_cost or 0) if cache_stats else 0
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cache_read_cost = float(cache_stats.cache_read_cost or 0) if cache_stats else 0
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@@ -343,7 +386,8 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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# Fallback 统计
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fallback_subquery = (
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db.query(
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RequestCandidate.request_id, func.count(RequestCandidate.id).label("executed_count")
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RequestCandidate.request_id,
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func.count(RequestCandidate.id).label("executed_count"),
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)
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.filter(
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RequestCandidate.created_at >= month_start,
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@@ -356,7 +400,8 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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db.query(func.count())
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.select_from(fallback_subquery)
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.filter(fallback_subquery.c.executed_count > 1)
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.scalar() or 0
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.scalar()
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or 0
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)
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# ==================== 系统健康指标 ====================
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@@ -370,7 +415,8 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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Usage.status_code == 200,
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Usage.response_time_ms.isnot(None),
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)
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.scalar() or 0
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.scalar()
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or 0
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)
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avg_response_time_seconds = float(avg_response_time) / 1000.0
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@@ -427,18 +473,53 @@ class AdminDashboardStatsAdapter(AdminApiAdapter):
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"subValue": f"输入 {format_tokens(all_time_input_tokens)} / 输出 {format_tokens(all_time_output_tokens)}",
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"change": (
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f"+{format_tokens(input_tokens_today + output_tokens_today + cache_creation_today + cache_read_today)}"
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if (input_tokens_today + output_tokens_today + cache_creation_today + cache_read_today)
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> (input_tokens_yesterday + output_tokens_yesterday + cache_creation_yesterday + cache_read_yesterday)
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else format_tokens(input_tokens_today + output_tokens_today + cache_creation_today + cache_read_today)
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if (
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input_tokens_today
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+ output_tokens_today
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+ cache_creation_today
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+ cache_read_today
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)
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> (
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input_tokens_yesterday
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+ output_tokens_yesterday
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+ cache_creation_yesterday
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+ cache_read_yesterday
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)
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else format_tokens(
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input_tokens_today
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+ output_tokens_today
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+ cache_creation_today
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+ cache_read_today
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)
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),
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"changeType": (
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"increase"
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if (input_tokens_today + output_tokens_today + cache_creation_today + cache_read_today)
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> (input_tokens_yesterday + output_tokens_yesterday + cache_creation_yesterday + cache_read_yesterday)
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if (
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input_tokens_today
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+ output_tokens_today
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+ cache_creation_today
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+ cache_read_today
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)
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> (
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input_tokens_yesterday
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+ output_tokens_yesterday
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+ cache_creation_yesterday
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+ cache_read_yesterday
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)
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else (
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"decrease"
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if (input_tokens_today + output_tokens_today + cache_creation_today + cache_read_today)
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< (input_tokens_yesterday + output_tokens_yesterday + cache_creation_yesterday + cache_read_yesterday)
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if (
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input_tokens_today
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+ output_tokens_today
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+ cache_creation_today
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+ cache_read_today
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)
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< (
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input_tokens_yesterday
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+ output_tokens_yesterday
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+ cache_creation_yesterday
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+ cache_read_yesterday
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)
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else "neutral"
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)
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),
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@@ -515,6 +596,7 @@ class UserDashboardStatsAdapter(DashboardAdapter):
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@cache_result(key_prefix="dashboard:user:stats", ttl=30, user_specific=True)
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async def handle(self, context: ApiRequestContext) -> Any: # type: ignore[override]
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from zoneinfo import ZoneInfo
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from src.services.system.stats_aggregator import APP_TIMEZONE
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db = context.db
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@@ -790,9 +872,12 @@ class DashboardProviderStatusAdapter(DashboardAdapter):
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class DashboardDailyStatsAdapter(DashboardAdapter):
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days: int
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@cache_result(key_prefix="dashboard:daily:stats", ttl=CacheTTL.DASHBOARD_DAILY, user_specific=True)
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@cache_result(
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key_prefix="dashboard:daily:stats", ttl=CacheTTL.DASHBOARD_DAILY, user_specific=True
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)
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async def handle(self, context: ApiRequestContext) -> Any: # type: ignore[override]
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from zoneinfo import ZoneInfo
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from src.services.system.stats_aggregator import APP_TIMEZONE
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db = context.db
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@@ -807,7 +892,7 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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today = today_local.astimezone(timezone.utc)
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end_date_local = now_local.replace(hour=23, minute=59, second=59, microsecond=999999)
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end_date = end_date_local.astimezone(timezone.utc)
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start_date_local = (today_local - timedelta(days=self.days - 1))
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start_date_local = today_local - timedelta(days=self.days - 1)
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start_date = start_date_local.astimezone(timezone.utc)
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# ==================== 使用预聚合数据优化 ====================
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@@ -819,6 +904,7 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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.order_by(StatsDaily.date.asc())
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.all()
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)
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# stats_daily.date 存储的是业务日期对应的 UTC 开始时间
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# 需要转回业务时区再取日期，才能与日期序列匹配
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def _to_business_date_str(value: datetime) -> str:
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@@ -831,11 +917,16 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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stats_map = {
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_to_business_date_str(stat.date): {
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"requests": stat.total_requests,
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"tokens": stat.input_tokens + stat.output_tokens + stat.cache_creation_tokens + stat.cache_read_tokens,
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"tokens": stat.input_tokens
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+ stat.output_tokens
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+ stat.cache_creation_tokens
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+ stat.cache_read_tokens,
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"cost": stat.total_cost,
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"avg_response_time": stat.avg_response_time_ms / 1000.0 if stat.avg_response_time_ms else 0,
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"unique_models": getattr(stat, 'unique_models', 0) or 0,
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"unique_providers": getattr(stat, 'unique_providers', 0) or 0,
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"avg_response_time": (
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stat.avg_response_time_ms / 1000.0 if stat.avg_response_time_ms else 0
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),
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"unique_models": getattr(stat, "unique_models", 0) or 0,
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"unique_providers": getattr(stat, "unique_providers", 0) or 0,
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"fallback_count": stat.fallback_count or 0,
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}
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for stat in daily_stats
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@@ -849,18 +940,21 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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today_avg_rt = (
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db.query(func.avg(Usage.response_time_ms))
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.filter(Usage.created_at >= today, Usage.response_time_ms.isnot(None))
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.scalar() or 0
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.scalar()
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or 0
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)
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# 今日 unique_models 和 unique_providers
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today_unique_models = (
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db.query(func.count(func.distinct(Usage.model)))
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.filter(Usage.created_at >= today)
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.scalar() or 0
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.scalar()
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or 0
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)
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today_unique_providers = (
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db.query(func.count(func.distinct(Usage.provider_name)))
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.filter(Usage.created_at >= today)
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.scalar() or 0
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.scalar()
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or 0
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)
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# 今日 fallback_count
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today_fallback_count = (
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@@ -875,12 +969,17 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
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.having(func.count(RequestCandidate.id) > 1)
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.subquery()
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)
|
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.scalar() or 0
|
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.scalar()
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or 0
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)
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stats_map[today_str] = {
|
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"requests": today_stats["total_requests"],
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"tokens": (today_stats["input_tokens"] + today_stats["output_tokens"] +
|
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today_stats["cache_creation_tokens"] + today_stats["cache_read_tokens"]),
|
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"tokens": (
|
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today_stats["input_tokens"]
|
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+ today_stats["output_tokens"]
|
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+ today_stats["cache_creation_tokens"]
|
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+ today_stats["cache_read_tokens"]
|
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),
|
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"cost": today_stats["total_cost"],
|
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"avg_response_time": float(today_avg_rt) / 1000.0 if today_avg_rt else 0,
|
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"unique_models": today_unique_models,
|
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@@ -912,9 +1011,11 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
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+ computed["cache_read_tokens"]
|
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),
|
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"cost": computed["total_cost"],
|
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"avg_response_time": computed["avg_response_time_ms"] / 1000.0
|
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if computed["avg_response_time_ms"]
|
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else 0,
|
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"avg_response_time": (
|
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computed["avg_response_time_ms"] / 1000.0
|
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if computed["avg_response_time_ms"]
|
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else 0
|
||||
),
|
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"unique_models": computed["unique_models"],
|
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"unique_providers": computed["unique_providers"],
|
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"fallback_count": computed["fallback_count"],
|
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@@ -947,7 +1048,9 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
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"requests": stat.requests or 0,
|
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"tokens": int(stat.tokens or 0),
|
||||
"cost": float(stat.cost or 0),
|
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"avg_response_time": float(stat.avg_response_time or 0) / 1000.0 if stat.avg_response_time else 0,
|
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"avg_response_time": (
|
||||
float(stat.avg_response_time or 0) / 1000.0 if stat.avg_response_time else 0
|
||||
),
|
||||
}
|
||||
for stat in user_daily_stats
|
||||
}
|
||||
@@ -1007,16 +1110,25 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
||||
model = stat.model
|
||||
if model not in model_agg:
|
||||
model_agg[model] = {
|
||||
"requests": 0, "tokens": 0, "cost": 0.0,
|
||||
"total_response_time": 0.0, "response_count": 0
|
||||
"requests": 0,
|
||||
"tokens": 0,
|
||||
"cost": 0.0,
|
||||
"total_response_time": 0.0,
|
||||
"response_count": 0,
|
||||
}
|
||||
model_agg[model]["requests"] += stat.total_requests
|
||||
tokens = (stat.input_tokens + stat.output_tokens +
|
||||
stat.cache_creation_tokens + stat.cache_read_tokens)
|
||||
tokens = (
|
||||
stat.input_tokens
|
||||
+ stat.output_tokens
|
||||
+ stat.cache_creation_tokens
|
||||
+ stat.cache_read_tokens
|
||||
)
|
||||
model_agg[model]["tokens"] += tokens
|
||||
model_agg[model]["cost"] += stat.total_cost
|
||||
if stat.avg_response_time_ms is not None:
|
||||
model_agg[model]["total_response_time"] += stat.avg_response_time_ms * stat.total_requests
|
||||
model_agg[model]["total_response_time"] += (
|
||||
stat.avg_response_time_ms * stat.total_requests
|
||||
)
|
||||
model_agg[model]["response_count"] += stat.total_requests
|
||||
|
||||
# 按日期分组
|
||||
@@ -1026,12 +1138,14 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
||||
date_utc = stat.date.astimezone(timezone.utc)
|
||||
date_str = date_utc.astimezone(app_tz).date().isoformat()
|
||||
|
||||
daily_breakdown.setdefault(date_str, []).append({
|
||||
"model": model,
|
||||
"requests": stat.total_requests,
|
||||
"tokens": tokens,
|
||||
"cost": stat.total_cost,
|
||||
})
|
||||
daily_breakdown.setdefault(date_str, []).append(
|
||||
{
|
||||
"model": model,
|
||||
"requests": stat.total_requests,
|
||||
"tokens": tokens,
|
||||
"cost": stat.total_cost,
|
||||
}
|
||||
)
|
||||
|
||||
# 今日实时模型统计
|
||||
today_model_stats = (
|
||||
@@ -1052,38 +1166,50 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
||||
model = stat.model
|
||||
if model not in model_agg:
|
||||
model_agg[model] = {
|
||||
"requests": 0, "tokens": 0, "cost": 0.0,
|
||||
"total_response_time": 0.0, "response_count": 0
|
||||
"requests": 0,
|
||||
"tokens": 0,
|
||||
"cost": 0.0,
|
||||
"total_response_time": 0.0,
|
||||
"response_count": 0,
|
||||
}
|
||||
model_agg[model]["requests"] += stat.requests or 0
|
||||
model_agg[model]["tokens"] += int(stat.tokens or 0)
|
||||
model_agg[model]["cost"] += float(stat.cost or 0)
|
||||
if stat.avg_response_time is not None:
|
||||
model_agg[model]["total_response_time"] += float(stat.avg_response_time) * (stat.requests or 0)
|
||||
model_agg[model]["total_response_time"] += float(stat.avg_response_time) * (
|
||||
stat.requests or 0
|
||||
)
|
||||
model_agg[model]["response_count"] += stat.requests or 0
|
||||
|
||||
# 今日 breakdown
|
||||
daily_breakdown.setdefault(today_str, []).append({
|
||||
"model": model,
|
||||
"requests": stat.requests or 0,
|
||||
"tokens": int(stat.tokens or 0),
|
||||
"cost": float(stat.cost or 0),
|
||||
})
|
||||
daily_breakdown.setdefault(today_str, []).append(
|
||||
{
|
||||
"model": model,
|
||||
"requests": stat.requests or 0,
|
||||
"tokens": int(stat.tokens or 0),
|
||||
"cost": float(stat.cost or 0),
|
||||
}
|
||||
)
|
||||
|
||||
# 构建 model_summary
|
||||
model_summary = []
|
||||
for model, agg in model_agg.items():
|
||||
avg_rt = (agg["total_response_time"] / agg["response_count"] / 1000.0
|
||||
if agg["response_count"] > 0 else 0)
|
||||
model_summary.append({
|
||||
"model": model,
|
||||
"requests": agg["requests"],
|
||||
"tokens": agg["tokens"],
|
||||
"cost": agg["cost"],
|
||||
"avg_response_time": avg_rt,
|
||||
"cost_per_request": agg["cost"] / max(agg["requests"], 1),
|
||||
"tokens_per_request": agg["tokens"] / max(agg["requests"], 1),
|
||||
})
|
||||
avg_rt = (
|
||||
agg["total_response_time"] / agg["response_count"] / 1000.0
|
||||
if agg["response_count"] > 0
|
||||
else 0
|
||||
)
|
||||
model_summary.append(
|
||||
{
|
||||
"model": model,
|
||||
"requests": agg["requests"],
|
||||
"tokens": agg["tokens"],
|
||||
"cost": agg["cost"],
|
||||
"avg_response_time": avg_rt,
|
||||
"cost_per_request": agg["cost"] / max(agg["requests"], 1),
|
||||
"tokens_per_request": agg["tokens"] / max(agg["requests"], 1),
|
||||
}
|
||||
)
|
||||
model_summary.sort(key=lambda x: x["cost"], reverse=True)
|
||||
|
||||
# 填充 model_breakdown
|
||||
@@ -1096,7 +1222,7 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
||||
and_(
|
||||
Usage.user_id == user.id,
|
||||
Usage.created_at >= start_date,
|
||||
Usage.created_at <= end_date
|
||||
Usage.created_at <= end_date,
|
||||
)
|
||||
)
|
||||
|
||||
@@ -1166,7 +1292,9 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
||||
# 历史数据从 stats_daily_provider 获取
|
||||
historical_provider_stats = (
|
||||
db.query(StatsDailyProvider)
|
||||
.filter(and_(StatsDailyProvider.date >= start_date, StatsDailyProvider.date < today))
|
||||
.filter(
|
||||
and_(StatsDailyProvider.date >= start_date, StatsDailyProvider.date < today)
|
||||
)
|
||||
.all()
|
||||
)
|
||||
|
||||
@@ -1177,8 +1305,12 @@ class DashboardDailyStatsAdapter(DashboardAdapter):
|
||||
if provider not in provider_agg:
|
||||
provider_agg[provider] = {"requests": 0, "tokens": 0, "cost": 0.0}
|
||||
provider_agg[provider]["requests"] += stat.total_requests
|
||||
tokens = (stat.input_tokens + stat.output_tokens +
|
||||
stat.cache_creation_tokens + stat.cache_read_tokens)
|
||||
tokens = (
|
||||
stat.input_tokens
|
||||
+ stat.output_tokens
|
||||
+ stat.cache_creation_tokens
|
||||
+ stat.cache_read_tokens
|
||||
)
|
||||
provider_agg[provider]["tokens"] += tokens
|
||||
provider_agg[provider]["cost"] += stat.total_cost
|
||||
|
||||
|
||||
Reference in New Issue
Block a user