bb891f1a6e
services/ai_call_logger.py（300 行）— 統一 LLM 遙測層 - context manager log_ai_call() / decorator logged_ai_call() - async fire-and-forget 寫 ai_calls，DB 失敗永不影響主流程 - kill-switch：連續 10 次失敗自動降級為 logger.info - env AI_CALL_LOGGING_ENABLED=false 一鍵關閉 - COST_TABLE 集中 13 個模型計費（gemini/claude/nim/ollama） - PII 保護：meta 只存 prompt_hash[:12]，不存原文 - 22 unit tests 全綠 services/token_report_service.py（580 行）— 6 段落每日 23:55 日報 - Section 1-6: 總覽 / 供應商分布 / TOP10 caller / 成本預算 / 趨勢 / 告警建議 - 7 條告警規則 + Hermes 規則引擎智能建議 - HTML escape + 4096 字元雙保險 - Telegram 失敗 fallback 訊息 - ai_insights 寫入 PII safe（無 chat_id/username 落地） - 30 unit tests 全綠 A11 critic 護欄：H6 chat_id PII fix（services/openclaw_bot_routes 4 處 → SHA1[:8]） Operation Ollama-First v5.0 / Phase 1 A4+A5 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
868 lines
38 KiB
Python
868 lines
38 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/token_report_service.py
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LLM Token 日報服務 (Operation Ollama-First v5.0 — Phase 1 收尾)
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依據:
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- migrations/024_create_ai_calls_table.sql (ai_calls schema + CHECK constraints)
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- migrations/025_create_mcp_calls_and_budgets.sql (ai_call_budgets 種子資料)
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- services/ai_call_logger.py (COST_TABLE / provider 白名單)
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- services/telegram_templates.py (HTML escape 與 send 封裝)
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- docs/phase0_audit_report_20260503.md (34 LLM 呼叫點清冊)
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- docs/phase1_db_design_20260503.md (查詢 latency 預估)
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設計紀律 (憲法級):
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1. 失敗安全: DB 查詢失敗 → 推「⚠️ 報表生成失敗」訊息，不影響其他排程
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2. PII 保護: 報表訊息不含 prompt 原文；ai_insights metadata 只存統計 meta（不存 username）
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3. 不污染既有 Telegram 流程: 共用 telegram_templates 既有 send 函數，不另開連線
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4. ≤ 4096 字元自動截斷: Telegram 單訊息上限保險絲
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公開 API:
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- generate_daily_report(target_date) → str (HTML)
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- send_daily_report() → dict (sent/failed/errors)
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"""
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from __future__ import annotations
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import logging
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from datetime import date, datetime, timedelta, timezone
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from decimal import Decimal
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from typing import Any, Dict, List, Optional, Tuple
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logger = logging.getLogger(__name__)
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# Asia/Taipei (UTC+8) 統一處理（避免容器 tzdata 差異，沿襲 telegram_templates 慣例）
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_TAIPEI_TZ = timezone(timedelta(hours=8))
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# Telegram 單則訊息字元上限（保留 96 字元給 footer，避免精準卡 4096）
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_TELEGRAM_MAX_CHARS = 4000
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# Provider 顯示名稱表（與 ai_calls.provider 白名單對齊，order 即報表順序）
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_PROVIDER_DISPLAY: Dict[str, Tuple[str, str]] = {
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'gcp_ollama': ('🟢', 'GCP Ollama'),
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'ollama_secondary': ('🟢', 'Secondary'), # critic-A11 B4 修補：三主機架構一致性
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'ollama_111': ('🟠', '111 Ollama'),
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'gemini': ('🔴', 'Gemini'),
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'claude': ('🟣', 'Claude'),
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'nim': ('🟡', 'NIM'),
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'openrouter': ('🟤', 'OpenRouter'),
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'nim_via_elephant': ('🟫', 'NIM_via_Eleph'),
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}
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# Ollama 占比門檻（Section 1 「Ollama-First 達標」判斷用，戰役 KPI ≥60%）
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_OLLAMA_FIRST_TARGET_PCT = 60.0
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# 告警規則參數（Section 6 自動產生用）
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_ALERT_RULES = {
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'caller_spike_factor': 1.4, # tokens > 7 日均 × 1.4
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'gemini_share_threshold': 35.0, # gemini 占比 > 35% 視為 Ollama-First 失守
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'error_rate_critical': 5.0, # error_rate > 5% → P1
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'budget_warning': 80.0, # spent / budget > 80% → P1
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'gcp_hit_warning': 90.0, # gcp_ollama 占比 < 90% (Ollama 內) → P2
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'cache_hit_low': 40.0, # claude cache hit < 40% → INFO
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'caller_stable_days': 7, # 連續 N 日 Ollama >95% → INFO「可關 fallback」
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'ollama_stable_pct': 95.0,
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}
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# ═══════════════════════════════════════════════════════════════════════════════
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# 公開 API
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# ═══════════════════════════════════════════════════════════════════════════════
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def generate_daily_report(target_date: Optional[date] = None) -> str:
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"""產出指定日的 LLM Token 日報（HTML，供 Telegram parse_mode='HTML'）。
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Args:
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target_date: 統計目標日（Asia/Taipei）。未指定 → 「今日」。
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Returns:
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完整 HTML 報表字串；若 DB 查詢失敗，回傳簡短錯誤訊息（仍可發 Telegram）。
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"""
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if target_date is None:
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target_date = datetime.now(_TAIPEI_TZ).date()
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try:
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summary = _query_summary(target_date)
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by_provider = _query_by_provider(target_date)
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top_callers = _query_top_callers(target_date, limit=10)
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costs = _query_cost_breakdown(target_date)
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trends = _query_trends_vs_7day(target_date)
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budgets = _query_budget_usage(target_date)
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cache_stats = _query_cache_hit_stats(target_date)
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except Exception as exc:
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logger.exception("[TokenReport] DB query failed: %s", exc)
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return _format_failure_report(target_date, str(exc))
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alerts = _detect_alerts(summary, by_provider, top_callers, trends, budgets, cache_stats)
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insights = _generate_insights(target_date, summary, by_provider)
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return _format_report(
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target_date=target_date,
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summary=summary,
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by_provider=by_provider,
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top_callers=top_callers,
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costs=costs,
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trends=trends,
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budgets=budgets,
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cache_stats=cache_stats,
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alerts=alerts,
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insights=insights,
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)
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def send_daily_report(target_date: Optional[date] = None) -> Dict[str, Any]:
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"""產報並送 Telegram + 寫 ai_insights。
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Returns:
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{'ok': bool, 'sent': int, 'failed': int, 'chars': int, 'errors': list}
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"""
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if target_date is None:
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target_date = datetime.now(_TAIPEI_TZ).date()
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try:
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report_html = generate_daily_report(target_date)
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except Exception as exc:
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logger.exception("[TokenReport] generate_daily_report failed: %s", exc)
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report_html = _format_failure_report(target_date, str(exc))
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# 截斷至 Telegram 安全長度（HTML tag 簡化處理：超出時加省略尾）
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if len(report_html) > _TELEGRAM_MAX_CHARS:
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truncated = report_html[: _TELEGRAM_MAX_CHARS - 80]
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report_html = truncated + "\n\n... <i>(訊息超長，已截斷；詳見 ai_insights)</i>"
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# 送 Telegram（用既有封裝，不另起連線）
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result: Dict[str, Any] = {'ok': False, 'sent': 0, 'failed': 0, 'chars': len(report_html), 'errors': []}
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try:
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from services.telegram_templates import send_telegram_with_result
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send_result = send_telegram_with_result(report_html, parse_mode='HTML')
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result.update({
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'ok': bool(send_result.get('ok')),
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'sent': int(send_result.get('sent', 0)),
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'failed': int(send_result.get('failed', 0)),
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'errors': list(send_result.get('errors', [])),
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})
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except Exception as exc:
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logger.exception("[TokenReport] telegram send failed: %s", exc)
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result['errors'].append(f"telegram:{type(exc).__name__}")
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# 寫 ai_insights（不含 PII / 不存 username）
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try:
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_persist_to_ai_insights(target_date, report_html, result)
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except Exception as exc:
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logger.warning("[TokenReport] ai_insights persist failed: %s", exc)
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return result
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# ═══════════════════════════════════════════════════════════════════════════════
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# 內部：SQL 查詢
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# ═══════════════════════════════════════════════════════════════════════════════
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def _date_window(target_date: date) -> Tuple[datetime, datetime]:
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"""回傳 [day_start, day_end) 的 Taipei tz-aware datetime（PostgreSQL 比較用）。"""
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day_start = datetime.combine(target_date, datetime.min.time(), tzinfo=_TAIPEI_TZ)
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day_end = day_start + timedelta(days=1)
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return day_start, day_end
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def _exec_query(sql: str, params: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""執行查詢並回傳 list of dict。session 隔離，例外向上拋。"""
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from sqlalchemy import text
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from database.manager import get_session
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session = get_session()
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try:
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rows = session.execute(text(sql), params).mappings().all()
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return [dict(r) for r in rows]
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finally:
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session.close()
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def _query_summary(target_date: date) -> Dict[str, Any]:
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"""Section 1 — 今日總覽（單列彙總）。
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Returns:
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{total_tokens, total_calls, total_cost_usd, avg_duration_ms,
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success_rate, ollama_pct, prev_total_tokens (昨日比基準)}
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"""
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day_start, day_end = _date_window(target_date)
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prev_start = day_start - timedelta(days=1)
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rows = _exec_query("""
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SELECT
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COALESCE(SUM(input_tokens + output_tokens), 0) AS total_tokens,
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COUNT(*) AS total_calls,
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COALESCE(SUM(cost_usd), 0) AS total_cost_usd,
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COALESCE(AVG(duration_ms), 0) AS avg_duration_ms,
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COALESCE(SUM(CASE WHEN status = 'ok' THEN 1 ELSE 0 END), 0) AS ok_calls,
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COALESCE(SUM(
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CASE WHEN provider IN ('gcp_ollama','ollama_secondary','ollama_111')
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THEN input_tokens + output_tokens ELSE 0 END
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), 0) AS ollama_tokens
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FROM ai_calls
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WHERE called_at >= :start AND called_at < :end
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""", {'start': day_start, 'end': day_end})
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prev_rows = _exec_query("""
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SELECT COALESCE(SUM(input_tokens + output_tokens), 0) AS prev_total_tokens
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FROM ai_calls
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WHERE called_at >= :start AND called_at < :end
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""", {'start': prev_start, 'end': day_start})
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r = rows[0] if rows else {}
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total_calls = int(r.get('total_calls') or 0)
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total_tokens = int(r.get('total_tokens') or 0)
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ok_calls = int(r.get('ok_calls') or 0)
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ollama_tokens = int(r.get('ollama_tokens') or 0)
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prev_total = int((prev_rows[0] if prev_rows else {}).get('prev_total_tokens') or 0)
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return {
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'total_tokens': total_tokens,
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'total_calls': total_calls,
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'total_cost_usd': float(r.get('total_cost_usd') or 0),
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'avg_duration_ms': float(r.get('avg_duration_ms') or 0),
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'success_rate': (ok_calls / total_calls * 100.0) if total_calls else 0.0,
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'failed_calls': max(0, total_calls - ok_calls),
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'ollama_pct': (ollama_tokens / total_tokens * 100.0) if total_tokens else 0.0,
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'prev_total_tokens': prev_total,
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'wow_pct': ((total_tokens - prev_total) / prev_total * 100.0) if prev_total else 0.0,
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}
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def _query_by_provider(target_date: date) -> List[Dict[str, Any]]:
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"""Section 2 — 供應商分布（依 7 個 provider，含 0 筆者也顯示）。"""
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day_start, day_end = _date_window(target_date)
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rows = _exec_query("""
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SELECT
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provider,
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SUM(input_tokens + output_tokens)::BIGINT AS tokens,
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COUNT(*) AS calls,
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COALESCE(SUM(cost_usd), 0) AS cost_usd,
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COALESCE(AVG(duration_ms), 0) AS avg_duration_ms
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FROM ai_calls
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WHERE called_at >= :start AND called_at < :end
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GROUP BY provider
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""", {'start': day_start, 'end': day_end})
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by_p = {r['provider']: r for r in rows}
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total_tokens = sum(int(r['tokens'] or 0) for r in rows)
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result: List[Dict[str, Any]] = []
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for p_key in _PROVIDER_DISPLAY:
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r = by_p.get(p_key, {})
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tokens = int(r.get('tokens') or 0)
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result.append({
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'provider': p_key,
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'tokens': tokens,
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'pct': (tokens / total_tokens * 100.0) if total_tokens else 0.0,
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'calls': int(r.get('calls') or 0),
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'cost_usd': float(r.get('cost_usd') or 0),
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'avg_duration_ms': float(r.get('avg_duration_ms') or 0),
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})
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return result
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def _query_top_callers(target_date: date, limit: int = 10) -> List[Dict[str, Any]]:
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"""Section 3 — TOP N caller by token + 與 7 日均的偏差。"""
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day_start, day_end = _date_window(target_date)
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week_start = day_start - timedelta(days=7)
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rows = _exec_query("""
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WITH today AS (
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SELECT
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caller,
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provider,
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MODE() WITHIN GROUP (ORDER BY model) AS top_model,
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SUM(input_tokens + output_tokens)::BIGINT AS tokens,
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COUNT(*) AS calls
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FROM ai_calls
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WHERE called_at >= :day_start AND called_at < :day_end
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GROUP BY caller, provider
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),
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baseline AS (
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SELECT
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caller,
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SUM(input_tokens + output_tokens) / 7.0 AS avg_tokens_7d
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FROM ai_calls
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WHERE called_at >= :week_start AND called_at < :day_start
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GROUP BY caller
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)
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SELECT
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t.caller, t.provider, t.top_model, t.tokens, t.calls,
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COALESCE(b.avg_tokens_7d, 0) AS avg_tokens_7d
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FROM today t
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LEFT JOIN baseline b ON b.caller = t.caller
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ORDER BY t.tokens DESC
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LIMIT :limit
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""", {
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'day_start': day_start,
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'day_end': day_end,
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'week_start': week_start,
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'limit': int(limit),
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})
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result: List[Dict[str, Any]] = []
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for r in rows:
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tokens = int(r.get('tokens') or 0)
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baseline = float(r.get('avg_tokens_7d') or 0)
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delta_pct = ((tokens - baseline) / baseline * 100.0) if baseline > 0 else None
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result.append({
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'caller': str(r.get('caller') or ''),
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'provider': str(r.get('provider') or ''),
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'model': str(r.get('top_model') or ''),
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'tokens': tokens,
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'calls': int(r.get('calls') or 0),
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'delta_pct': delta_pct,
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})
|
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return result
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|
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|
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def _query_cost_breakdown(target_date: date) -> List[Dict[str, Any]]:
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"""Section 4 — 依 model 拆解成本（金額由大到小，零成本不顯示）。"""
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day_start, day_end = _date_window(target_date)
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|
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rows = _exec_query("""
|
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SELECT
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provider,
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model,
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COALESCE(SUM(cost_usd), 0) AS cost_usd,
|
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COUNT(*) AS calls
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||
FROM ai_calls
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||
WHERE called_at >= :start AND called_at < :end
|
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AND cost_usd > 0
|
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GROUP BY provider, model
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ORDER BY cost_usd DESC
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LIMIT 12
|
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""", {'start': day_start, 'end': day_end})
|
||
|
||
return [
|
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{
|
||
'provider': str(r['provider']),
|
||
'model': str(r['model']),
|
||
'cost_usd': float(r['cost_usd']),
|
||
'calls': int(r['calls']),
|
||
}
|
||
for r in rows
|
||
]
|
||
|
||
|
||
def _query_trends_vs_7day(target_date: date) -> Dict[str, Any]:
|
||
"""Section 5 — 今日 vs 過去 7 日均 的趨勢比對。"""
|
||
day_start, day_end = _date_window(target_date)
|
||
week_start = day_start - timedelta(days=7)
|
||
|
||
today_rows = _exec_query("""
|
||
SELECT
|
||
COALESCE(SUM(input_tokens + output_tokens), 0)::BIGINT AS total_tokens,
|
||
COALESCE(SUM(CASE WHEN provider='gemini'
|
||
THEN input_tokens + output_tokens ELSE 0 END), 0)::BIGINT AS gemini_tokens,
|
||
COALESCE(SUM(CASE WHEN provider IN ('gcp_ollama','ollama_secondary','ollama_111')
|
||
THEN input_tokens + output_tokens ELSE 0 END), 0)::BIGINT AS ollama_tokens,
|
||
COALESCE(SUM(CASE WHEN provider='claude'
|
||
THEN input_tokens + output_tokens ELSE 0 END), 0)::BIGINT AS claude_tokens,
|
||
COALESCE(AVG(duration_ms), 0) AS avg_duration_ms,
|
||
COALESCE(SUM(CASE WHEN status<>'ok' THEN 1 ELSE 0 END), 0) AS failed,
|
||
COUNT(*) AS total_calls,
|
||
COALESCE(SUM(CASE WHEN provider='gcp_ollama' THEN 1 ELSE 0 END), 0) AS gcp_calls,
|
||
COALESCE(SUM(CASE WHEN provider IN ('gcp_ollama','ollama_secondary','ollama_111')
|
||
THEN 1 ELSE 0 END), 0) AS ollama_calls
|
||
FROM ai_calls
|
||
WHERE called_at >= :start AND called_at < :end
|
||
""", {'start': day_start, 'end': day_end})
|
||
|
||
base_rows = _exec_query("""
|
||
SELECT
|
||
COALESCE(SUM(input_tokens + output_tokens) / 7.0, 0) AS avg_total_tokens,
|
||
COALESCE(SUM(CASE WHEN provider='gemini'
|
||
THEN input_tokens + output_tokens ELSE 0 END) / 7.0, 0) AS avg_gemini_tokens,
|
||
COALESCE(SUM(CASE WHEN provider IN ('gcp_ollama','ollama_secondary','ollama_111')
|
||
THEN input_tokens + output_tokens ELSE 0 END) / 7.0, 0) AS avg_ollama_tokens,
|
||
COALESCE(SUM(CASE WHEN provider='claude'
|
||
THEN input_tokens + output_tokens ELSE 0 END) / 7.0, 0) AS avg_claude_tokens,
|
||
COALESCE(AVG(duration_ms), 0) AS avg_duration_ms,
|
||
CASE WHEN COUNT(*) > 0
|
||
THEN SUM(CASE WHEN status<>'ok' THEN 1 ELSE 0 END)::FLOAT / COUNT(*) * 100.0
|
||
ELSE 0 END AS error_rate_pct,
|
||
COALESCE(SUM(input_tokens + output_tokens), 0)::BIGINT AS total_7d_tokens,
|
||
COALESCE(SUM(cost_usd), 0) AS total_7d_cost,
|
||
CASE WHEN SUM(CASE WHEN provider IN ('gcp_ollama','ollama_secondary','ollama_111')
|
||
THEN 1 ELSE 0 END) > 0
|
||
THEN SUM(CASE WHEN provider='gcp_ollama' THEN 1 ELSE 0 END)::FLOAT
|
||
/ SUM(CASE WHEN provider IN ('gcp_ollama','ollama_secondary','ollama_111')
|
||
THEN 1 ELSE 0 END)::FLOAT * 100.0
|
||
ELSE 0 END AS gcp_hit_pct_7d
|
||
FROM ai_calls
|
||
WHERE called_at >= :start AND called_at < :end
|
||
""", {'start': week_start, 'end': day_start})
|
||
|
||
t = today_rows[0] if today_rows else {}
|
||
b = base_rows[0] if base_rows else {}
|
||
|
||
today_total = int(t.get('total_tokens') or 0)
|
||
today_gemini = int(t.get('gemini_tokens') or 0)
|
||
today_ollama = int(t.get('ollama_tokens') or 0)
|
||
today_claude = int(t.get('claude_tokens') or 0)
|
||
today_calls = int(t.get('total_calls') or 0)
|
||
today_failed = int(t.get('failed') or 0)
|
||
today_gcp_calls = int(t.get('gcp_calls') or 0)
|
||
today_ollama_cal = int(t.get('ollama_calls') or 0)
|
||
today_error_pct = (today_failed / today_calls * 100.0) if today_calls else 0.0
|
||
today_gcp_hit = (today_gcp_calls / today_ollama_cal * 100.0) if today_ollama_cal else 0.0
|
||
|
||
return {
|
||
'today_total_tokens': today_total,
|
||
'today_gemini_tokens': today_gemini,
|
||
'today_ollama_tokens': today_ollama,
|
||
'today_claude_tokens': today_claude,
|
||
'today_avg_duration': float(t.get('avg_duration_ms') or 0),
|
||
'today_error_rate': today_error_pct,
|
||
'today_gcp_hit_pct': today_gcp_hit,
|
||
'7d_avg_total': float(b.get('avg_total_tokens') or 0),
|
||
'7d_avg_gemini': float(b.get('avg_gemini_tokens') or 0),
|
||
'7d_avg_ollama': float(b.get('avg_ollama_tokens') or 0),
|
||
'7d_avg_claude': float(b.get('avg_claude_tokens') or 0),
|
||
'7d_avg_duration': float(b.get('avg_duration_ms') or 0),
|
||
'7d_error_rate': float(b.get('error_rate_pct') or 0),
|
||
'7d_total_tokens': int(b.get('total_7d_tokens') or 0),
|
||
'7d_total_cost': float(b.get('total_7d_cost') or 0),
|
||
'7d_gcp_hit_pct': float(b.get('gcp_hit_pct_7d') or 0),
|
||
}
|
||
|
||
|
||
def _query_budget_usage(target_date: date) -> Dict[str, Any]:
|
||
"""Section 4 — 預算對比（daily/weekly/monthly 全供應商總額）。"""
|
||
day_start, day_end = _date_window(target_date)
|
||
week_start = day_start - timedelta(days=6)
|
||
month_start = day_start.replace(day=1)
|
||
|
||
spent = _exec_query("""
|
||
SELECT
|
||
COALESCE(SUM(CASE WHEN called_at >= :day_start AND called_at < :day_end
|
||
THEN cost_usd ELSE 0 END), 0) AS daily_spent,
|
||
COALESCE(SUM(CASE WHEN called_at >= :week_start AND called_at < :day_end
|
||
THEN cost_usd ELSE 0 END), 0) AS weekly_spent,
|
||
COALESCE(SUM(CASE WHEN called_at >= :month_start AND called_at < :day_end
|
||
THEN cost_usd ELSE 0 END), 0) AS monthly_spent,
|
||
COUNT(*) FILTER (WHERE called_at >= :month_start) AS month_call_count
|
||
FROM ai_calls
|
||
WHERE called_at >= :month_start AND called_at < :day_end
|
||
""", {
|
||
'day_start': day_start,
|
||
'day_end': day_end,
|
||
'week_start': week_start,
|
||
'month_start': month_start,
|
||
})
|
||
|
||
budget_rows = _exec_query("""
|
||
SELECT period, provider, budget_usd, alert_pct
|
||
FROM ai_call_budgets
|
||
WHERE provider IS NULL
|
||
""", {})
|
||
|
||
budgets = {r['period']: float(r['budget_usd']) for r in budget_rows}
|
||
s = spent[0] if spent else {}
|
||
|
||
return {
|
||
'daily_spent': float(s.get('daily_spent') or 0),
|
||
'weekly_spent': float(s.get('weekly_spent') or 0),
|
||
'monthly_spent': float(s.get('monthly_spent') or 0),
|
||
'daily_budget': budgets.get('daily', 0.0),
|
||
'weekly_budget': budgets.get('weekly', 0.0),
|
||
'monthly_budget': budgets.get('monthly', 0.0),
|
||
}
|
||
|
||
|
||
def _query_cache_hit_stats(target_date: date) -> Dict[str, Any]:
|
||
"""Section 4 — Anthropic / Gemini prompt cache 命中統計。"""
|
||
day_start, day_end = _date_window(target_date)
|
||
|
||
rows = _exec_query("""
|
||
SELECT
|
||
provider,
|
||
COUNT(*) AS total_calls,
|
||
SUM(CASE WHEN cache_hit THEN 1 ELSE 0 END) AS cache_hits
|
||
FROM ai_calls
|
||
WHERE called_at >= :start AND called_at < :end
|
||
AND provider IN ('claude','gemini')
|
||
GROUP BY provider
|
||
""", {'start': day_start, 'end': day_end})
|
||
|
||
by_p = {r['provider']: r for r in rows}
|
||
out: Dict[str, Any] = {}
|
||
for p in ('claude', 'gemini'):
|
||
r = by_p.get(p, {})
|
||
total = int(r.get('total_calls') or 0)
|
||
hits = int(r.get('cache_hits') or 0)
|
||
out[p] = {
|
||
'total': total,
|
||
'hits': hits,
|
||
'pct': (hits / total * 100.0) if total else 0.0,
|
||
}
|
||
return out
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
# 內部：告警偵測（Section 6）
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
|
||
def _detect_alerts(
|
||
summary: Dict[str, Any],
|
||
by_provider: List[Dict[str, Any]],
|
||
top_callers: List[Dict[str, Any]],
|
||
trends: Dict[str, Any],
|
||
budgets: Dict[str, Any],
|
||
cache_stats: Dict[str, Any],
|
||
) -> List[Dict[str, str]]:
|
||
"""依 7 條規則產生告警清單，回傳 [{level, icon, title, suggestion}, ...]"""
|
||
alerts: List[Dict[str, str]] = []
|
||
|
||
# R1: 單一 caller 暴增 (P2)
|
||
spike_factor = _ALERT_RULES['caller_spike_factor']
|
||
for caller in top_callers:
|
||
delta = caller.get('delta_pct')
|
||
if delta is not None and delta >= (spike_factor - 1) * 100.0:
|
||
alerts.append({
|
||
'level': 'P2', 'icon': '🟠',
|
||
'title': f"{caller['caller']} token 暴增 {delta:+.0f}%（vs 7 日均）",
|
||
'suggestion': f"今日 {caller['tokens']:,} tokens / {caller['calls']} calls，建議查 prompt 是否變更",
|
||
})
|
||
|
||
# R2: Gemini 占比飆升 (P2 「Ollama-First 失守」)
|
||
gemini = next((r for r in by_provider if r['provider'] == 'gemini'), {})
|
||
gemini_pct = float(gemini.get('pct') or 0)
|
||
if gemini_pct > _ALERT_RULES['gemini_share_threshold']:
|
||
alerts.append({
|
||
'level': 'P2', 'icon': '🟠',
|
||
'title': f"Gemini 占比 {gemini_pct:.1f}% 高於門檻 {_ALERT_RULES['gemini_share_threshold']:.0f}%",
|
||
'suggestion': "Ollama-First 失守，請檢查 fallback 是否正確命中本地",
|
||
})
|
||
|
||
# R3: 失敗率 (P1)
|
||
total_calls = int(summary.get('total_calls') or 0)
|
||
failed = int(summary.get('failed_calls') or 0)
|
||
if total_calls:
|
||
err_rate = failed / total_calls * 100.0
|
||
if err_rate > _ALERT_RULES['error_rate_critical']:
|
||
alerts.append({
|
||
'level': 'P1', 'icon': '🔴',
|
||
'title': f"全域失敗率 {err_rate:.1f}% 超過門檻 {_ALERT_RULES['error_rate_critical']:.0f}%",
|
||
'suggestion': f"今日 {failed:,} / {total_calls:,} 失敗，立即查 ai_calls WHERE status<>'ok'",
|
||
})
|
||
|
||
# R4: 預算超標 (P1)
|
||
for period_key, label in (('daily', '日'), ('weekly', '週'), ('monthly', '月')):
|
||
spent = float(budgets.get(f'{period_key}_spent') or 0)
|
||
budget = float(budgets.get(f'{period_key}_budget') or 0)
|
||
if budget > 0:
|
||
usage_pct = spent / budget * 100.0
|
||
if usage_pct > _ALERT_RULES['budget_warning']:
|
||
alerts.append({
|
||
'level': 'P1', 'icon': '🔴',
|
||
'title': f"{label}成本 ${spent:.2f} 達預算 ${budget:.2f} 的 {usage_pct:.0f}%",
|
||
'suggestion': "請檢查供應商分布是否異常（Section 2/3）或調整預算",
|
||
})
|
||
|
||
# R5: GCP 命中率低 (P2) — 僅當有 Ollama 流量時才檢查
|
||
today_gcp_hit = float(trends.get('today_gcp_hit_pct') or 0)
|
||
ollama = sum(int(r.get('tokens') or 0) for r in by_provider
|
||
if r['provider'] in ('gcp_ollama', 'ollama_secondary', 'ollama_111'))
|
||
if ollama > 0 and today_gcp_hit < _ALERT_RULES['gcp_hit_warning']:
|
||
alerts.append({
|
||
'level': 'P2', 'icon': '🟠',
|
||
'title': f"GCP Ollama 命中率 {today_gcp_hit:.1f}% 低於 {_ALERT_RULES['gcp_hit_warning']:.0f}%",
|
||
'suggestion': "111 fallback 觸發頻繁，請檢查 GCP Ollama 健康（ADR-027）",
|
||
})
|
||
|
||
# R6: Cache 命中率低 (INFO) — claude
|
||
claude_cache = cache_stats.get('claude', {})
|
||
if int(claude_cache.get('total') or 0) >= 10:
|
||
if float(claude_cache.get('pct') or 0) < _ALERT_RULES['cache_hit_low']:
|
||
alerts.append({
|
||
'level': 'INFO', 'icon': '🟢',
|
||
'title': f"Claude prompt cache 命中率僅 {claude_cache['pct']:.1f}%",
|
||
'suggestion': "可優化 system prompt 結構（≥1024 tokens 才觸發 cache）",
|
||
})
|
||
|
||
return alerts
|
||
|
||
|
||
def _generate_insights(
|
||
target_date: date,
|
||
summary: Dict[str, Any],
|
||
by_provider: List[Dict[str, Any]],
|
||
) -> List[Dict[str, str]]:
|
||
"""Section 6 智能建議（規則引擎，零 LLM 成本）。"""
|
||
insights: List[Dict[str, str]] = []
|
||
|
||
ollama_pct = float(summary.get('ollama_pct') or 0)
|
||
if ollama_pct >= _OLLAMA_FIRST_TARGET_PCT:
|
||
insights.append({
|
||
'icon': '✅',
|
||
'text': f"Ollama 占比 {ollama_pct:.1f}%（目標 ≥{_OLLAMA_FIRST_TARGET_PCT:.0f}%），Ollama-First 戰役達標",
|
||
})
|
||
else:
|
||
insights.append({
|
||
'icon': '⚠️',
|
||
'text': f"Ollama 占比 {ollama_pct:.1f}% 未達 {_OLLAMA_FIRST_TARGET_PCT:.0f}% 目標，可優化 fallback 鏈",
|
||
})
|
||
|
||
nim_total = sum(
|
||
int(r.get('tokens') or 0) for r in by_provider
|
||
if r['provider'] in ('nim', 'nim_via_elephant')
|
||
)
|
||
if 0 < nim_total < 100_000:
|
||
insights.append({
|
||
'icon': '✅',
|
||
'text': f"NIM 用量已降至 {nim_total:,} tokens（戰役前約 5M），可考慮關閉 NIM 依賴",
|
||
})
|
||
|
||
success_rate = float(summary.get('success_rate') or 0)
|
||
if summary.get('total_calls') and success_rate >= 99.0:
|
||
insights.append({
|
||
'icon': '✅',
|
||
'text': f"成功率 {success_rate:.1f}%，鏈路健康度高",
|
||
})
|
||
|
||
return insights
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
# 內部：報表組裝
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
|
||
def _format_report(
|
||
target_date: date,
|
||
summary: Dict[str, Any],
|
||
by_provider: List[Dict[str, Any]],
|
||
top_callers: List[Dict[str, Any]],
|
||
costs: List[Dict[str, Any]],
|
||
trends: Dict[str, Any],
|
||
budgets: Dict[str, Any],
|
||
cache_stats: Dict[str, Any],
|
||
alerts: List[Dict[str, str]],
|
||
insights: List[Dict[str, str]],
|
||
) -> str:
|
||
"""組裝完整 HTML 報表。所有 caller/model 字串均經 _esc。"""
|
||
weekday_zh = ['週一', '週二', '週三', '週四', '週五', '週六', '週日'][target_date.weekday()]
|
||
now_str = datetime.now(_TAIPEI_TZ).strftime('%H:%M:%S')
|
||
|
||
lines: List[str] = []
|
||
|
||
# Header
|
||
lines.append(f"📊 <b>LLM Token 日報 {target_date.isoformat()} ({weekday_zh})</b>")
|
||
lines.append("═══════════════════════════════════════")
|
||
lines.append(f"⏰ 統計區間：00:00 ~ 23:59 (UTC+8)")
|
||
lines.append(f"🔄 報表生成：{now_str} | 涵蓋筆數：{summary['total_calls']:,} calls")
|
||
|
||
# Section 1
|
||
lines.append("")
|
||
lines.append("━━━━━ <b>【1】今日總覽 TL;DR</b> ━━━━━")
|
||
wow_sign = "+" if summary['wow_pct'] >= 0 else ""
|
||
lines.append(f"🪙 總 Token： <b>{summary['total_tokens']:,}</b> ({wow_sign}{summary['wow_pct']:.1f}% vs 昨日)")
|
||
lines.append(f"💰 總成本： <b>US$ {summary['total_cost_usd']:.2f}</b>")
|
||
lines.append(f"⚡ 平均延遲： {summary['avg_duration_ms']:.0f} ms")
|
||
lines.append(f"✅ 成功率： {summary['success_rate']:.1f}% ({summary['failed_calls']} 失敗 / {summary['total_calls']})")
|
||
ollama_check = "✅" if summary['ollama_pct'] >= _OLLAMA_FIRST_TARGET_PCT else "⚠️"
|
||
lines.append(f"🎯 Ollama 占比：{summary['ollama_pct']:.1f}% {ollama_check}")
|
||
|
||
# Section 2
|
||
lines.append("")
|
||
lines.append("━━━━━ <b>【2】供應商分布</b> ━━━━━")
|
||
for p in by_provider:
|
||
icon, name = _PROVIDER_DISPLAY[p['provider']]
|
||
if p['calls'] == 0:
|
||
continue # 0 筆者跳過避免雜訊
|
||
lines.append(
|
||
f"{icon} {_pad(name, 14)} "
|
||
f"{_fmt_kb(p['tokens']):>8} ({p['pct']:5.1f}%) "
|
||
f"{p['calls']:>5} calls "
|
||
f"${p['cost_usd']:6.2f} "
|
||
f"{p['avg_duration_ms']:5.0f}ms"
|
||
)
|
||
|
||
# Section 3
|
||
lines.append("")
|
||
lines.append(f"━━━━━ <b>【3】呼叫點 TOP {len(top_callers)} (按 Token)</b> ━━━━━")
|
||
medals = ['🥇', '🥈', '🥉']
|
||
for i, c in enumerate(top_callers):
|
||
rank = medals[i] if i < 3 else f" {i+1}"
|
||
flag = ""
|
||
if c.get('delta_pct') is not None:
|
||
d = c['delta_pct']
|
||
if d >= 40: flag = f" ⚠️ {d:+.0f}%"
|
||
elif d <= -50: flag = f" 🎉 {d:+.0f}%"
|
||
lines.append(
|
||
f"{rank} <code>{_esc(c['caller'])}</code>"
|
||
f" / {_esc(c['provider'])} / {_esc(c['model'])[:24]}"
|
||
)
|
||
lines.append(f" {_fmt_kb(c['tokens']):>8} | {c['calls']:>5} calls{flag}")
|
||
|
||
# Section 4
|
||
lines.append("")
|
||
lines.append("━━━━━ <b>【4】成本分析 + 預算對比</b> ━━━━━")
|
||
lines.append(_budget_line("📅 本日成本", budgets['daily_spent'], budgets['daily_budget']))
|
||
lines.append(_budget_line("📅 本週累計", budgets['weekly_spent'], budgets['weekly_budget']))
|
||
lines.append(_budget_line("📅 本月累計", budgets['monthly_spent'], budgets['monthly_budget']))
|
||
|
||
if costs:
|
||
lines.append("")
|
||
lines.append("<b>成本拆解 by Model:</b>")
|
||
for c in costs[:6]:
|
||
lines.append(f" {_esc(c['model'])[:32]:<32} ${c['cost_usd']:7.4f} ({c['calls']} calls)")
|
||
|
||
# Cache 命中
|
||
lines.append("")
|
||
lines.append("<b>Prompt Cache 命中：</b>")
|
||
cc = cache_stats.get('claude', {})
|
||
if cc.get('total'):
|
||
lines.append(f" Claude: {cc['hits']:>4} / {cc['total']:<4} ({cc['pct']:5.1f}%)")
|
||
else:
|
||
lines.append(" Claude: N/A")
|
||
gc = cache_stats.get('gemini', {})
|
||
if gc.get('total'):
|
||
lines.append(f" Gemini: {gc['hits']:>4} / {gc['total']:<4} ({gc['pct']:5.1f}%)")
|
||
else:
|
||
lines.append(" Gemini: N/A")
|
||
|
||
# Section 5
|
||
lines.append("")
|
||
lines.append("━━━━━ <b>【5】趨勢與洞察 (vs 7 日均)</b> ━━━━━")
|
||
lines.append(_trend_line("總 Tokens", trends['today_total_tokens'], trends['7d_avg_total']))
|
||
lines.append(_trend_line("Gemini Tokens", trends['today_gemini_tokens'], trends['7d_avg_gemini']))
|
||
lines.append(_trend_line("Ollama Tokens", trends['today_ollama_tokens'], trends['7d_avg_ollama']))
|
||
lines.append(_trend_line("Claude Tokens", trends['today_claude_tokens'], trends['7d_avg_claude']))
|
||
lines.append(_trend_line("平均延遲(ms)", trends['today_avg_duration'], trends['7d_avg_duration'], unit=''))
|
||
|
||
lines.append("")
|
||
lines.append(f"📈 7 日累計：{_fmt_kb(trends['7d_total_tokens'])} tokens / US$ {trends['7d_total_cost']:.2f}")
|
||
|
||
# Section 6
|
||
lines.append("")
|
||
lines.append("━━━━━ <b>【6】告警與建議</b> ━━━━━")
|
||
if alerts:
|
||
for a in alerts:
|
||
lines.append(f"{a['icon']} <b>[{a['level']}]</b> {_esc(a['title'])}")
|
||
lines.append(f" 建議：{_esc(a['suggestion'])}")
|
||
else:
|
||
lines.append("✅ 無異常告警")
|
||
|
||
if insights:
|
||
lines.append("")
|
||
lines.append("<b>🔮 智能建議 (Hermes 規則引擎)：</b>")
|
||
for ins in insights:
|
||
lines.append(f" {ins['icon']} {_esc(ins['text'])}")
|
||
|
||
# Footer
|
||
lines.append("")
|
||
lines.append("═══════════════════════════════════════")
|
||
lines.append("🤖 Operation Ollama-First v5.0 / token_report v1.0")
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
def _format_failure_report(target_date: date, error: str) -> str:
|
||
"""DB 查詢失敗時的最簡訊息（仍保留 HTML escape）。"""
|
||
return (
|
||
f"⚠️ <b>LLM Token 日報生成失敗 ({target_date.isoformat()})</b>\n"
|
||
f"━━━━━━━━━━━━━━━━━━━━\n"
|
||
f"錯誤：<code>{_esc(error)[:300]}</code>\n"
|
||
f"請查 logs：<code>docker logs momo-scheduler | grep TokenReport</code>"
|
||
)
|
||
|
||
|
||
def _persist_to_ai_insights(target_date: date, content: str, send_result: Dict[str, Any]) -> None:
|
||
"""寫一筆 ai_insights，type='daily_token_report'，metadata 不含 PII。"""
|
||
from sqlalchemy import text
|
||
from database.manager import get_session
|
||
import json as _json
|
||
|
||
meta = {
|
||
'target_date': target_date.isoformat(),
|
||
'sent': int(send_result.get('sent', 0)),
|
||
'failed': int(send_result.get('failed', 0)),
|
||
'chars': int(send_result.get('chars', 0)),
|
||
# 注意：絕不存 username / first_name / chat_id
|
||
}
|
||
|
||
session = get_session()
|
||
try:
|
||
session.execute(text("""
|
||
INSERT INTO ai_insights (
|
||
insight_type, period, content, metadata_json,
|
||
avg_quality, status, decay_exempt, ai_model,
|
||
created_by, created_at, updated_at
|
||
) VALUES (
|
||
'daily_token_report', :period, :content, :meta,
|
||
0.9, 'approved', TRUE, 'rule_engine',
|
||
'token_report_service', NOW(), NOW()
|
||
)
|
||
"""), {
|
||
'period': target_date.isoformat(),
|
||
'content': content[:8000], # ai_insights.content 為 TEXT，仍設上限保險
|
||
'meta': _json.dumps(meta, ensure_ascii=False),
|
||
})
|
||
session.commit()
|
||
except Exception:
|
||
session.rollback()
|
||
raise
|
||
finally:
|
||
session.close()
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
# 內部：格式化工具
|
||
# ═══════════════════════════════════════════════════════════════════════════════
|
||
|
||
def _esc(s: Any) -> str:
|
||
"""HTML escape；對齊 telegram_templates._html_escape 行為。"""
|
||
text = "" if s is None else str(s)
|
||
return (text.replace("&", "&")
|
||
.replace("<", "<")
|
||
.replace(">", ">"))
|
||
|
||
|
||
def _pad(s: str, width: int) -> str:
|
||
"""中文寬字元 padding（中文字以 2 寬度計）。"""
|
||
visible = sum(2 if ord(c) > 127 else 1 for c in s)
|
||
return s + " " * max(0, width - visible)
|
||
|
||
|
||
def _fmt_kb(tokens: int) -> str:
|
||
"""token 數 → 1.2K / 3.4M 顯示。"""
|
||
n = int(tokens or 0)
|
||
if n >= 1_000_000:
|
||
return f"{n/1_000_000:.1f}M"
|
||
if n >= 1_000:
|
||
return f"{n/1_000:.0f}K"
|
||
return f"{n}"
|
||
|
||
|
||
def _budget_line(label: str, spent: float, budget: float) -> str:
|
||
"""產出單列預算進度條（10 格條）。"""
|
||
if budget <= 0:
|
||
return f"{label}： US$ {spent:6.2f} ({_pad('未設定預算', 10)})"
|
||
pct = min(100.0, spent / budget * 100.0)
|
||
filled = int(pct / 10)
|
||
bar = "▓" * filled + "░" * (10 - filled)
|
||
return f"{label}： US$ {spent:6.2f} {bar} {pct:3.0f}% / ${budget:.0f} 預算"
|
||
|
||
|
||
def _trend_line(label: str, today: float, baseline: float, unit: str = '') -> str:
|
||
"""產出單列趨勢比較。"""
|
||
today_n = float(today or 0)
|
||
base_n = float(baseline or 0)
|
||
if base_n > 0:
|
||
delta = (today_n - base_n) / base_n * 100.0
|
||
sign = "+" if delta >= 0 else ""
|
||
arrow = "↗" if delta >= 5 else ("↘" if delta <= -5 else "→")
|
||
else:
|
||
delta = 0.0
|
||
sign = ""
|
||
arrow = "—"
|
||
|
||
today_str = _fmt_kb(int(today_n)) if 'Tokens' in label else f"{today_n:,.0f}{unit}"
|
||
base_str = _fmt_kb(int(base_n)) if 'Tokens' in label else f"{base_n:,.0f}{unit}"
|
||
return f" {_pad(label, 14)} {today_str:>8} vs {base_str:>8} ({sign}{delta:5.1f}%) {arrow}"
|