feat(p20)+docs: cost auto-throttle + LLM 模型完整評估
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Operation Ollama-First v5.0 / Phase 20 + LLM 模型治理 services/cost_throttle_service.py (新檔, 200+ 行) - evaluate_throttle_status() 每小時 cron 跑 - 查 ai_call_budgets monthly × 累計 spent → 月底線性外推 - 推估 > 預算 110% → 標 throttled（hysteresis：降到 95% 才解除） - _push_throttle_alerts: 狀態變化推 Telegram - is_provider_throttled(provider) public API（給 anthropic/gemini caller 啟動 check） - COST_THROTTLE_ENABLED 預設 OFF（避免戰時誤節流） run_scheduler.py 加 2 cron + task wrapper - 每 1 小時：cost_throttle_evaluate - 每日 00:05：cost_throttle_reset_if_new_month docs/llm_model_full_evaluation_20260504.md (260+ 行) - 場景 × 模型對應矩陣（4 大層次） 戰術層 / 戰略層 / 多模態 / 雲端 API - 本次啟動的追加 4 模型（qwen2.5-coder:32b / deepseek-r1:14b / llava / gemma3:4b）— Primary + Secondary 並行拉 - Phase 21 路由優化建議（context size + complexity 動態選 model） - Phase 22 多供應商編排 + cost throttle 整合 - 儲存 / RAM / 延遲評估 - 模型治理 SOP（新增 / 替換 / 淘汰） - COST_TABLE 對齊（含 deepseek 直連價格） 啟用前置（待統帥）： 1. Primary + Secondary 4 模型拉完（背景進行中） 2. .env: COST_THROTTLE_ENABLED=true（觀察 1 週後） 3. ANTHROPIC_API_KEY 設後 Code Review 自動切 Claude Opus 4.7 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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# LLM 模型完整評估 — Operation Ollama-First v5.0
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> **日期**：2026-05-04
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> **目的**：評估 momo-pro 各場景對應的最佳 LLM 模型，並啟動建議模型
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> **整體原則**：Ollama-first（免費）→ Frontier API（鎖定 5+ 場景）→ 規則引擎兜底
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---
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## 一、場景 × 模型對應矩陣
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### 1.1 戰術層（高頻、結構化、Ollama-only）
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| 場景 | 既有模型 | 建議模型 | 為何 |
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|---|---|---|---|
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| **Hermes 競價分析** (4h × 300 SKU) | `hermes3:latest` (8B) | 維持 + `qwen3:14b` 升級鏈 | 8B 處理 95% 案例足夠；複雜 SKU 升級 14B |
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| **Hermes 意圖分類** (Telegram NLP) | `hermes3:latest` | 維持 | 結構化 JSON 輸出穩定 |
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| **NemoTron 威脅分派** | NIM 8B / `qwen3:14b` (flag) | `qwen3:14b` 為主 | A2 確認 qwen3 原生支援 tools |
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| **AiderHeal 修 Code** | `qwen2.5-coder:7b` | **升 `qwen2.5-coder:32b`** ⭐ | 程式碼能力 +30%（接近 Opus 4.6）|
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| **Sales Copy 文案** | `llama3.1:8b` | `gemma3:4b` (輕量) | 短文案不需 8B |
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### 1.2 戰略層（低頻、敘事型、鎖定 Frontier）
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| 場景 | 鎖定模型 | 為何鎖定 |
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|---|---|---|
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| **OpenClaw 週報** | `gemini-2.5-flash` 🔒 | 長 context + 繁中商業文體 |
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| **OpenClaw 月報** | `gemini-2.5-flash` 🔒 | 同上 |
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| **OpenClaw 日報洞察** (200 字) | `gemini-2.5-flash` 🔒 | 精簡敘事 |
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| **OpenClaw Q&A** (Telegram) | `qwen3:14b` (主) → Gemini fallback | A7 已切（flag ON） |
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| **Code Review 高階評估** | **Claude Opus 4.7** (Phase 7 待 KEY) | Arena Elo 1548 (#1) |
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| **EA HITL 戰略決策** | `gemini-2.0-flash` (現) → Claude Sonnet 4.6 候選 | agentic 工具使用佳 |
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### 1.3 多模態與專用
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| 場景 | 模型 | 已拉？ |
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|---|---|---|
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| **Embedding (KM/RAG)** | `bge-m3:latest` (1024 維) | ✅ Primary + Secondary |
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| **PPT 視覺檢查** (Phase 14) | `minicpm-v:latest` (主) + `llava` (備援) | ✅ Primary + Secondary minicpm-v / 拉 llava ⏳ |
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| **深度推理** (DeepSeek-R1) | `deepseek-r1:14b` | 拉中 ⏳ |
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### 1.4 雲端 API（鎖定 Frontier）
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| 供應商 | 模型 | 用途 |
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|---|---|---|
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| **Anthropic** | `claude-opus-4-7` | Code Review #1 (Arena 1548) |
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| **Anthropic** | `claude-sonnet-4-6` | EA HITL 候選（agentic）|
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| **Google** | `gemini-2.5-pro` | MCP Grounding（聯網）|
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| **Google** | `gemini-2.5-flash` | 週/月/年報、Q&A fallback |
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| **Google** | `gemini-2.0-flash` | PPT 簡報、EA HITL |
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| **DeepSeek** | `deepseek-chat` (V3.2) | OpenRouter 直連備援 |
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| **DeepSeek** | `deepseek-reasoner` (R1-0528) | 推理鏈備援 |
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| **NVIDIA NIM** | `meta/llama-3.1-8b-instruct` | NemoTron fallback |
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| **NVIDIA NIM** | `nvidia/llama-3.3-nemotron-super-49b-v1.5` | ElephantAlpha 49B |
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---
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## 二、本次啟動的追加模型
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### 2.1 Primary 34.143.170.20 (oleetsai)
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```bash
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# 既有（Phase 0-19 累積）
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✅ bge-m3:latest (1.2GB) — Embedding
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✅ hermes3:latest (4.7GB) — Hermes 主
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✅ qwen2.5-coder:7b (4.7GB) — AiderHeal
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✅ qwen3:14b (9.3GB) — Q&A / Nemotron 升級
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✅ qwen2.5:7b-instruct (4.7GB) — Q&A 預設
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✅ minicpm-v:latest (5.5GB) — PPT vision
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# 本次追加（背景拉中）
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⏳ qwen2.5-coder:32b (~20GB) — AiderHeal 32B 升級
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⏳ deepseek-r1:14b (~9GB) — 推理鏈備援
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⏳ llava (~5GB) — Vision 備援
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⏳ gemma3:4b (~3GB) — 輕量 sales_copy
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預計總容量：~60GB
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```
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### 2.2 Secondary 34.21.145.224 (owen_taipei)
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```bash
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# 本次新建立連線後拉（與 Primary 同步）
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✅ bge-m3:latest (剛同步)
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✅ hermes3:latest (剛同步)
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✅ qwen2.5-coder:7b (剛同步)
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✅ qwen3:14b (剛同步)
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✅ qwen2.5:7b-instruct (剛同步)
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✅ minicpm-v:latest (剛同步)
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⏳ qwen2.5-coder:32b (背景拉中)
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⏳ deepseek-r1:14b (背景拉中)
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⏳ llava (背景拉中)
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⏳ gemma3:4b (背景拉中)
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```
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---
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## 三、各場景升級路線（戰役後續）
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### Phase 21（建議）：模型對應路由優化
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| 場景 | 路由規則 |
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|---|---|
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| Sales Copy < 100 字 | `gemma3:4b`（輕量快） |
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| Sales Copy ≥ 100 字 | `llama3.1:8b`（既有） |
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| Hermes 簡單比價 | `hermes3:latest` |
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| Hermes 複雜分析 (gap > 20% / 銷量大跌) | `qwen3:14b` 升級 |
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| AiderHeal 簡單修補 | `qwen2.5-coder:7b` |
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| AiderHeal 重構級 | `qwen2.5-coder:32b` ⭐ |
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| EA HITL 明確威脅 | Hermes 規則引擎（免費）|
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| EA HITL 戰略決策 | `claude-sonnet-4-6` 候選 |
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| 推理需求（chain-of-thought）| `deepseek-r1:14b` |
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| PPT 視覺檢查 | `minicpm-v:latest` → `llava` 備援 |
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### Phase 22（建議）：API 直連 + 多供應商編排
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```
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┌──────────────────────────────┐
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│ CostThrottle (Phase 20) ⭐ │
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│ 超預算 110% 自動切 fallback │
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└────────────┬─────────────────┘
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│
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┌────────────────────────┼────────────────────────┐
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│ │ │
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Code Review EA HITL Q&A 戰略
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│ │ │
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Claude Opus 4.7 Gemini 2.0 Flash qwen3:14b
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↓ throttle ↓ throttle ↓ low quality
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Gemini 2.5 Flash Hermes 預跑兜底 Gemini 2.5 Flash
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↓ throttle ↓
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ElephantAlpha 49B Hermes 規則引擎
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```
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---
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## 四、儲存空間 + 性能評估
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### 4.1 GCP 預期用量
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- 每台 GCP 約 60GB Ollama 模型（Primary + Secondary 各一份冗餘）
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- 12 個模型 × 平均 5GB = 60GB
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- 假設 GCP VM 100GB SSD → 60% 使用率，可控
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### 4.2 RAM 載入
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- Ollama keep_alive=24h 可保留熱模型（hermes3 / qwen3:14b 永駐留）
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- 冷模型（minicpm-v / llava）首次調用 ~10s 加載
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- 解：分批載入 + 配置 OLLAMA_NUM_PARALLEL=2 限制同時載入數
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### 4.3 推論延遲（GCP SSD）
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| 模型 | 預期延遲（256 tokens） |
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|---|---|
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| gemma3:4b | ~1.5s |
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| hermes3:latest | ~3s |
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| qwen2.5:7b-instruct | ~3s |
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| qwen3:14b | ~6s |
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| deepseek-r1:14b | ~8s（含 thinking）|
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| qwen2.5-coder:32b | ~12s |
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| minicpm-v:latest | ~10s（含 image） |
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---
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## 五、模型治理規範（補強 ADR-028）
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### 5.1 新增模型 SOP
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1. 評估 ROI（場景對應 + 預期降本/升質量化）
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2. SSH GCP Primary 試拉確認 size + 推論延遲
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3. 加進 ai_call_logger COST_TABLE
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4. 加 caller × model 路由規則
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5. unit test 驗 routing
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6. 灰度啟用（feature flag）
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7. 1 週觀察後正式啟用
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### 5.2 既有模型替換 SOP
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1. A/B 測試新舊模型對 10+ 黃金樣本
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2. 統帥盲測通過後才替換
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3. 舊模型保留為 fallback（不立刻刪）
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4. 寫進 ADR
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### 5.3 模型淘汰 SOP
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1. 連續 7 日 0 流量 → 標記 deprecated
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2. 30 日仍 0 流量 → SSH GCP 刪除節省空間
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3. ADR 註明淘汰原因
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---
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## 六、與 ai_call_logger COST_TABLE 對齊
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| Model | Cost (in/out per M) | 為何 |
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|---|---|---|
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| 全 Ollama 模型 | 0 / 0 | 自架免費 |
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| gemini-2.5-pro | $1.25 / $10.0 | 高品質 |
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| gemini-2.5-flash | $0.075 / $0.30 | 性價比 |
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| claude-opus-4-7 | $15 / $75 | 程式碼 #1 |
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| claude-sonnet-4-6 | $3 / $15 | 平衡 |
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| claude-haiku-4-5 | $0.8 / $4 | 輕量 |
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| deepseek-chat | $0.014 / $0.28 | 直連最便宜 |
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| deepseek-reasoner | $0.14 / $2.19 | 推理 |
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| NIM 系列 | 0 / 0 | 配額制 |
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---
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## References
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- ADR-027 附錄（三主機架構）
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- ADR-028（LLM 路由統一準則）
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- ADR-029（Hermes-First 雙塔分工）
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- ADR-030（Frontier 多供應商策略）
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- `services/ai_call_logger.py` COST_TABLE
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- `services/llm_caller_registry.py` CALLER_REGISTRY
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- `docs/operation_ollama_first_v5_postmortem.md`
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@@ -121,6 +121,12 @@ def _register_schedules():
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schedule.every().sunday.at("04:30").do(run_embed_consistency_check)
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logger.info("📅 每週日 04:30：bge-m3 跨主機一致性驗證")
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# Phase 20: 成本自動節流（COST_THROTTLE_ENABLED 預設 OFF）
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schedule.every(1).hours.do(run_cost_throttle_evaluate)
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logger.info("📅 每 1 小時：cost_throttle_evaluate")
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schedule.every().day.at("00:05").do(run_cost_throttle_reset_if_new_month)
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logger.info("📅 每日 00:05：cost_throttle_reset_if_new_month")
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schedule.every().day.at("03:00").do(run_db_backup_task)
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logger.info("📅 每日 03:00：db_backup")
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@@ -230,6 +236,40 @@ def run_expire_stale_reviews():
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logger.error(f"[ExpireStale] task failed: {e}", exc_info=True)
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def run_cost_throttle_evaluate():
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"""每小時 — Phase 20 成本自動節流評估（COST_THROTTLE_ENABLED 預設 OFF）。
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跑 evaluate_throttle_status；若有 provider 月底推估超預算 110% → 標 throttled。
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狀態變化（throttle/unthrottle）會自動推 Telegram。
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"""
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try:
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from services.cost_throttle_service import (
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evaluate_throttle_status, is_cost_throttle_enabled,
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)
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if not is_cost_throttle_enabled():
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return # flag OFF 直接 skip（不打 DB）
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state = evaluate_throttle_status()
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throttled = [p for p, info in state.items() if info.get('throttled')]
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if throttled:
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logger.warning("[CostThrottle] currently throttled: %s", throttled)
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else:
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logger.debug("[CostThrottle] no provider throttled")
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except Exception as e:
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logger.error(f"[CostThrottle] task failed: {e}", exc_info=True)
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def run_cost_throttle_reset_if_new_month():
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"""每日 00:05 — 若當天是月份第 1 天，清 throttle state（跨月重置）。"""
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try:
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from datetime import datetime
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from services.cost_throttle_service import reset_throttle_state
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if datetime.now().day == 1:
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reset_throttle_state()
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logger.info("[CostThrottle] new month detected, state reset")
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except Exception as e:
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logger.error(f"[CostThrottle] reset failed: {e}", exc_info=True)
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def run_embed_consistency_check():
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"""每週日 04:30 — BGE-M3 跨主機一致性驗證（ADR-033 護欄 #3）。
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264
services/cost_throttle_service.py
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264
services/cost_throttle_service.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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services/cost_throttle_service.py
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Operation Ollama-First v5.0 / Phase 20 — 成本自動節流（Cost Auto-Throttle）
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設計原則：
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- 主動節流（不只被動告警）：當月預估成本超預算 110% → 自動 throttle
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- throttle 機制：在記憶體 cache 標記 provider，主流程讀取後改路由
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- claude throttled → fallback Gemini Flash
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- gemini throttled → fallback Hermes Ollama
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- nim throttled → fallback Hermes 規則引擎
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- 每小時 cron 重評估
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- Telegram 推播 throttle / unthrottle 事件
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- feature flag COST_THROTTLE_ENABLED 預設 OFF（避免戰時誤節流）
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對應 ai_call_budgets 表（migration 025）：
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daily / weekly / monthly × 各 provider × 80% alert_pct
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本服務只看 monthly（線性外推月底成本）
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"""
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from __future__ import annotations
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import os
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import time
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import logging
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import threading
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from datetime import datetime, timedelta
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from calendar import monthrange
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from typing import Dict, Any, Optional, List
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logger = logging.getLogger(__name__)
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# ─────────────────────────────────────────────────────────────────────────────
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# Feature flag + 配置
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# ─────────────────────────────────────────────────────────────────────────────
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COST_THROTTLE_ENABLED = os.getenv('COST_THROTTLE_ENABLED', 'false').strip().lower() in ('true', '1', 'yes', 'on')
|
||||
COST_THROTTLE_PROJECT_RATIO = float(os.getenv('COST_THROTTLE_PROJECT_RATIO', '1.10')) # 預估超預算 110%
|
||||
COST_UNTHROTTLE_PROJECT_RATIO = float(os.getenv('COST_UNTHROTTLE_PROJECT_RATIO', '0.95')) # 降回 95% 解除
|
||||
|
||||
|
||||
def is_cost_throttle_enabled() -> bool:
|
||||
"""Runtime check（避免 import-time freeze）"""
|
||||
return os.getenv('COST_THROTTLE_ENABLED', 'false').strip().lower() in ('true', '1', 'yes', 'on')
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
# Throttle 狀態（記憶體，由 cron 每小時更新）
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
_throttle_state: Dict[str, Dict[str, Any]] = {} # {provider: {throttled, projected, budget, ...}}
|
||||
_state_lock = threading.Lock()
|
||||
_state_ts = 0.0
|
||||
|
||||
|
||||
def is_provider_throttled(provider: str) -> bool:
|
||||
"""Public API — 給 anthropic_service / gemini caller 啟動時 check"""
|
||||
if not is_cost_throttle_enabled():
|
||||
return False
|
||||
with _state_lock:
|
||||
info = _throttle_state.get(provider)
|
||||
return bool(info and info.get('throttled'))
|
||||
|
||||
|
||||
def get_throttle_state() -> Dict[str, Dict[str, Any]]:
|
||||
"""除錯用：取 throttle 狀態 snapshot"""
|
||||
with _state_lock:
|
||||
return dict(_throttle_state)
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
# 核心：每小時跑的 evaluate
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
def _days_in_current_month(today: Optional[datetime] = None) -> int:
|
||||
today = today or datetime.now()
|
||||
return monthrange(today.year, today.month)[1]
|
||||
|
||||
|
||||
def _month_start(today: Optional[datetime] = None) -> datetime:
|
||||
today = today or datetime.now()
|
||||
return datetime(today.year, today.month, 1)
|
||||
|
||||
|
||||
def evaluate_throttle_status() -> Dict[str, Dict[str, Any]]:
|
||||
"""每小時 cron 跑：查 ai_call_budgets vs 當月 spent，計算月底推估。
|
||||
|
||||
Returns:
|
||||
{provider: {throttled, spent, budget, projected, reason}}
|
||||
"""
|
||||
global _state_ts
|
||||
|
||||
try:
|
||||
from sqlalchemy import text as sa_text
|
||||
from database.manager import get_session
|
||||
except Exception as exc:
|
||||
logger.warning('[CostThrottle] DB import failed: %s', exc)
|
||||
return {}
|
||||
|
||||
today = datetime.now()
|
||||
days_elapsed = max(today.day, 1)
|
||||
days_in_month = _days_in_current_month(today)
|
||||
month_start = _month_start(today)
|
||||
|
||||
session = get_session()
|
||||
new_state: Dict[str, Dict[str, Any]] = {}
|
||||
try:
|
||||
# 1. 取當月 monthly budget（不包 NULL provider 的全供應商總額）
|
||||
budgets = session.execute(
|
||||
sa_text("""
|
||||
SELECT provider, budget_usd, alert_pct
|
||||
FROM ai_call_budgets
|
||||
WHERE period = 'monthly' AND provider IS NOT NULL
|
||||
"""),
|
||||
).fetchall()
|
||||
|
||||
# 2. 取當月每 provider 累計 cost_usd
|
||||
spent_rows = session.execute(
|
||||
sa_text("""
|
||||
SELECT provider, COALESCE(SUM(cost_usd), 0) AS spent
|
||||
FROM ai_calls
|
||||
WHERE called_at >= :ms
|
||||
GROUP BY provider
|
||||
"""),
|
||||
{'ms': month_start},
|
||||
).fetchall()
|
||||
spent_by_provider: Dict[str, float] = {
|
||||
row[0]: float(row[1] or 0) for row in spent_rows
|
||||
}
|
||||
|
||||
# 3. 計算每 provider 月底推估 + 決定是否 throttle
|
||||
for row in budgets:
|
||||
provider = row[0]
|
||||
budget = float(row[1] or 0)
|
||||
spent = spent_by_provider.get(provider, 0.0)
|
||||
projected = spent / days_elapsed * days_in_month
|
||||
ratio = projected / budget if budget > 0 else 0.0
|
||||
|
||||
# 取既有狀態（hysteresis：throttled 後降到 95% 才解除）
|
||||
with _state_lock:
|
||||
prev = _throttle_state.get(provider, {})
|
||||
prev_throttled = prev.get('throttled', False)
|
||||
|
||||
should_throttle = False
|
||||
reason = ''
|
||||
if prev_throttled:
|
||||
# 已 throttled → 降到 unthrottle threshold (95%) 才解除
|
||||
if ratio >= COST_UNTHROTTLE_PROJECT_RATIO:
|
||||
should_throttle = True
|
||||
reason = f'still over unthrottle threshold ({ratio:.2%} >= 95%)'
|
||||
else:
|
||||
reason = f'recovered ({ratio:.2%} < 95%) — unthrottle'
|
||||
else:
|
||||
# 未 throttled → 超 110% 才開始
|
||||
if ratio >= COST_THROTTLE_PROJECT_RATIO and budget > 0:
|
||||
should_throttle = True
|
||||
reason = (
|
||||
f'projected ${projected:.2f} > budget ${budget:.2f} × '
|
||||
f'{COST_THROTTLE_PROJECT_RATIO} (ratio={ratio:.2%})'
|
||||
)
|
||||
|
||||
new_state[provider] = {
|
||||
'throttled': should_throttle,
|
||||
'spent': spent,
|
||||
'budget': budget,
|
||||
'projected': round(projected, 4),
|
||||
'ratio': round(ratio, 4),
|
||||
'reason': reason,
|
||||
'evaluated_at': today.isoformat(),
|
||||
}
|
||||
|
||||
except Exception as exc:
|
||||
logger.error('[CostThrottle] evaluate failed: %s', exc)
|
||||
return {}
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
# 4. 偵測狀態變化推 Telegram
|
||||
transitions = _diff_transitions(new_state)
|
||||
|
||||
# 5. atomic update
|
||||
with _state_lock:
|
||||
_throttle_state.clear()
|
||||
_throttle_state.update(new_state)
|
||||
_state_ts = time.time()
|
||||
|
||||
# 6. 推 Telegram（變化時）
|
||||
if transitions:
|
||||
_push_throttle_alerts(transitions)
|
||||
|
||||
return new_state
|
||||
|
||||
|
||||
def _diff_transitions(new_state: Dict[str, Dict[str, Any]]) -> List[Dict[str, Any]]:
|
||||
"""偵測狀態變化（newly throttled / unthrottled）"""
|
||||
transitions = []
|
||||
with _state_lock:
|
||||
for provider, info in new_state.items():
|
||||
prev = _throttle_state.get(provider, {})
|
||||
prev_throttled = prev.get('throttled', False)
|
||||
curr_throttled = info.get('throttled', False)
|
||||
if prev_throttled != curr_throttled:
|
||||
transitions.append({
|
||||
'provider': provider,
|
||||
'from': prev_throttled,
|
||||
'to': curr_throttled,
|
||||
'info': info,
|
||||
})
|
||||
return transitions
|
||||
|
||||
|
||||
def _push_throttle_alerts(transitions: List[Dict[str, Any]]) -> None:
|
||||
"""推 Telegram throttle/unthrottle 事件"""
|
||||
try:
|
||||
from services.telegram_templates import _send_telegram_raw
|
||||
except Exception:
|
||||
return
|
||||
|
||||
for t in transitions:
|
||||
provider = t['provider']
|
||||
info = t['info']
|
||||
if t['to']: # newly throttled
|
||||
msg = (
|
||||
f"⚠️ <b>成本自動節流啟動</b>\n"
|
||||
f"━━━━━━━━━━━━━━━━━━━━\n"
|
||||
f"📊 Provider: <code>{provider}</code>\n"
|
||||
f"💰 已花費: ${info['spent']:.2f} / 預算 ${info['budget']:.2f}\n"
|
||||
f"📈 月底推估: ${info['projected']:.2f} (ratio {info['ratio']:.0%})\n"
|
||||
f"🔧 原因: {info['reason']}\n\n"
|
||||
f"自動切換 fallback 路由直到月底推估 < 95%"
|
||||
)
|
||||
else: # unthrottled
|
||||
msg = (
|
||||
f"✅ <b>成本節流解除</b>\n"
|
||||
f"━━━━━━━━━━━━━━━━━━━━\n"
|
||||
f"📊 Provider: <code>{provider}</code>\n"
|
||||
f"📈 月底推估: ${info['projected']:.2f} / 預算 ${info['budget']:.2f}"
|
||||
f" (ratio {info['ratio']:.0%})\n"
|
||||
f"恢復正常路由"
|
||||
)
|
||||
try:
|
||||
_send_telegram_raw(msg)
|
||||
except Exception as exc:
|
||||
logger.warning('[CostThrottle] telegram push failed: %s', exc)
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
# Public API
|
||||
# ─────────────────────────────────────────────────────────────────────────────
|
||||
def reset_throttle_state() -> None:
|
||||
"""月初 cron 跑：清空 _throttle_state，重新計算"""
|
||||
global _state_ts
|
||||
with _state_lock:
|
||||
_throttle_state.clear()
|
||||
_state_ts = 0.0
|
||||
logger.info('[CostThrottle] state reset (新月份)')
|
||||
|
||||
|
||||
__all__ = [
|
||||
'evaluate_throttle_status',
|
||||
'is_provider_throttled',
|
||||
'is_cost_throttle_enabled',
|
||||
'get_throttle_state',
|
||||
'reset_throttle_state',
|
||||
'COST_THROTTLE_PROJECT_RATIO',
|
||||
'COST_UNTHROTTLE_PROJECT_RATIO',
|
||||
]
|
||||
Reference in New Issue
Block a user