025a493f06
Wave 8 P3.2 模型版本追蹤 + ADR-100 SLO 自我治理 + 配套： P3.2 — Model Version Tracking: - model_version_probe.py (268 行) — 探測 Ollama / OpenRouter 等 provider 的 model version - model_version_tracker.py (101 行) — 對齊 PG provider_version_history 表 - migrations/p3_2_provider_version_history.sql + rollback — 25 行 schema - db/models.py +32 行 — ProviderVersionHistory ORM ADR-100 — AI 自主化 SLO: - docs/adr/ADR-100-ai-autonomous-slo.md (167 行) — 飛輪 SLO 設計與閾值 - ops/monitoring/slo-rules.yml (254 行) — Prometheus SLO recording rules + alerts - ops/monitoring/tests/test_slo_rules.yaml (242 行) — promtool unit tests 整合修改: - main.py +72 行 — Lifespan 啟動 model_version_probe + KB rot cleaner schedule - gitea_webhook.py +45 行 — webhook 接收 model 版本變化通知 - ci_auto_repair.py / evidence_snapshot.py / pre_decision_investigator.py — 配合接線 新測試: - test_kb_rot_cleaner_schedule.py (120 行) — 9 tests pass - test_slo_rules.yaml — promtool 驗收 Tests: 9 passed (test_kb_rot_cleaner_schedule) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-Authored-By: Multiple Engineers (P3.2 + ADR-100) <noreply@anthropic.com>
243 lines
8.8 KiB
YAML
243 lines
8.8 KiB
YAML
# ops/monitoring/tests/test_slo_rules.yaml
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# promtool unit tests for AI Autonomous SLO rules
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# 2026-04-27 P3.4 by Claude — AI SLO
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#
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# 執行方式:
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# promtool test rules ops/monitoring/tests/test_slo_rules.yaml
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#
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# 覆蓋範圍:
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# - sli:autonomy_rate:5m recording rule 數值正確性
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# - sli:decision_accuracy:5m recording rule
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# - sli:km_growth_rate:24h recording rule
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# - SLO_AutonomyRate_FastBurn alert 觸發與不觸發
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# - SLO_DecisionAccuracy_FastBurn alert
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# - SLO_KMGrowthRate_Critical alert
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rule_files:
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- ../slo-rules.yml
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evaluation_interval: 1m
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tests:
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# ============================================================
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# Recording Rule Tests
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# ============================================================
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# ---- SLI 1: 自主化率 = 80% (auto=8, human=2 per tick) ----
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- interval: 1m
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name: "sli:autonomy_rate:5m 應為 0.8（auto_executed=8, total=10）"
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input_series:
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+8x30"
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- series: 'automation_operation_log_total{outcome="human_required"}'
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values: "0+2x30"
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promql_expr_test:
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- expr: sli:autonomy_rate:5m
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eval_time: 15m
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exp_samples:
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- value: 0.8
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# ---- SLI 1: 自主化率 = 100%（無 human_required）----
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- interval: 1m
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name: "sli:autonomy_rate:5m 應為 1.0（無人工）"
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input_series:
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+10x30"
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promql_expr_test:
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- expr: sli:autonomy_rate:5m
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eval_time: 15m
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exp_samples:
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- value: 1.0
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# ---- SLI 2: 決策準確率 = 90% (success=9, auto_executed=10) ----
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- interval: 1m
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name: "sli:decision_accuracy:5m 應為 0.9"
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input_series:
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- series: 'post_execution_verification_total{outcome="success"}'
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values: "0+9x30"
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+10x30"
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promql_expr_test:
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- expr: sli:decision_accuracy:5m
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eval_time: 15m
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exp_samples:
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- value: 0.9
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# ---- SLI 4: KM 增長率（24h increase）----
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- interval: 1m
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name: "sli:km_growth_rate:24h 應約為 1440（每分鐘 +1 × 24h）"
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input_series:
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- series: "knowledge_entries_total"
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values: "0+1x1500"
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promql_expr_test:
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- expr: sli:km_growth_rate:24h
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eval_time: 25h
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exp_samples:
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# increase over 24h = 1440 samples × 1/min
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- value: 1440
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# ============================================================
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# Alert Tests — SLO 1: 自主化率
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# ============================================================
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# ---- 負測: 自主化率 = 80% → FastBurn 不觸發 ----
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- interval: 1m
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name: "SLO_AutonomyRate_FastBurn 不觸發（自主化率 = 80%，達標）"
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input_series:
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+8x30"
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- series: 'automation_operation_log_total{outcome="human_required"}'
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values: "0+2x30"
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alert_rule_test:
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- eval_time: 10m
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alertname: SLO_AutonomyRate_FastBurn
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exp_alerts: []
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# ---- 正測: 自主化率 = 40%（error_rate=0.6 > 0.20×14.4=2.88 → 不對）
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# 注意：0.20 * 14.4 = 2.88，但 error_rate 最大為 1.0，所以正確觸發條件：
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# error_rate > 2.88 不可能，實際上 fast burn alert 只在 burn rate 非常高時觸發。
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# 重新計算：SLO=0.80, budget=0.20; 1h burn 2% = 消耗了 budget × 2/100 = 0.004
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# 在 1h 內消耗了這麼多，error_rate 需 > 0.20 × 14.4 = 2.88（PromQL burn rate 係數）
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# 由於 error_rate ∈ [0,1]，2.88 > 1 → fast burn 永遠不觸發（正確行為）
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# 改用 medium burn 測試（threshold = 0.20 × 6 = 1.2 > 1 → 也不觸發）
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# 實際上只有 slow burn 可觸發（threshold = 0.20 × 1.1 = 0.22 < 1）
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# ---- 正測: 自主化率 = 50%（error_rate=0.5 > 0.22）→ SlowBurn 觸發 ----
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- interval: 1m
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name: "SLO_AutonomyRate_SlowBurn 觸發（自主化率 = 50%，error_rate 0.5 > 0.22）"
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input_series:
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+5x120"
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- series: 'automation_operation_log_total{outcome="human_required"}'
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values: "0+5x120"
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alert_rule_test:
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- eval_time: 70m
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alertname: SLO_AutonomyRate_SlowBurn
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exp_alerts:
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- exp_labels:
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alertname: SLO_AutonomyRate_SlowBurn
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severity: info
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slo_name: autonomy_rate
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burn_window: 3d
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team: ai
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auto_repair: "false"
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# ---- 負測: 自主化率 = 85% → SlowBurn 不觸發 ----
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- interval: 1m
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name: "SLO_AutonomyRate_SlowBurn 不觸發（自主化率 = 85%）"
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input_series:
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+85x120"
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- series: 'automation_operation_log_total{outcome="human_required"}'
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values: "0+15x120"
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alert_rule_test:
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- eval_time: 70m
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alertname: SLO_AutonomyRate_SlowBurn
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exp_alerts: []
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# ============================================================
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# Alert Tests — SLO 2: 決策準確率
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# ============================================================
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# ---- 正測: 決策準確率 = 75%（error_rate=0.25 > 0.10×1.1=0.11）→ SlowBurn 觸發 ----
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- interval: 1m
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name: "SLO_DecisionAccuracy_SlowBurn 觸發（決策準確率 75%）"
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input_series:
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- series: 'post_execution_verification_total{outcome="success"}'
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values: "0+75x120"
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+100x120"
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alert_rule_test:
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- eval_time: 70m
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alertname: SLO_DecisionAccuracy_SlowBurn
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exp_alerts:
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- exp_labels:
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alertname: SLO_DecisionAccuracy_SlowBurn
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severity: info
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slo_name: decision_accuracy
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burn_window: 3d
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team: ai
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auto_repair: "false"
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# ---- 負測: 決策準確率 = 92% → SlowBurn 不觸發 ----
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- interval: 1m
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name: "SLO_DecisionAccuracy_SlowBurn 不觸發（決策準確率 92%）"
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input_series:
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- series: 'post_execution_verification_total{outcome="success"}'
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values: "0+92x120"
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- series: 'automation_operation_log_total{outcome="auto_executed"}'
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values: "0+100x120"
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alert_rule_test:
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- eval_time: 70m
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alertname: SLO_DecisionAccuracy_SlowBurn
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exp_alerts: []
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# ============================================================
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# Alert Tests — SLO 4: KM 增長率
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# ============================================================
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# ---- 正測: KM 增長率 = 0 → Critical 觸發 ----
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- interval: 1m
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name: "SLO_KMGrowthRate_Critical 觸發（KM 停止增長）"
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input_series:
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# counter 停止，increase[24h] = 0
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- series: "knowledge_entries_total"
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values: "100x1600"
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alert_rule_test:
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- eval_time: 25h
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alertname: SLO_KMGrowthRate_Critical
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exp_alerts:
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- exp_labels:
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alertname: SLO_KMGrowthRate_Critical
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severity: critical
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slo_name: km_growth_rate
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team: ai
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auto_repair: "false"
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# ---- 正測: KM 增長率 = 3/day → Critical 觸發（< 5）----
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- interval: 30m
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name: "SLO_KMGrowthRate_Critical 觸發（KM 增長 = 3/day）"
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input_series:
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# 每 30min +0.0625 次 ≈ 3/day
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- series: "knowledge_entries_total"
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values: "0+0.0625x50"
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alert_rule_test:
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- eval_time: 25h
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alertname: SLO_KMGrowthRate_Critical
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exp_alerts:
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- exp_labels:
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alertname: SLO_KMGrowthRate_Critical
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severity: critical
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slo_name: km_growth_rate
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team: ai
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auto_repair: "false"
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# ---- 負測: KM 增長率 = 30/day → Critical 不觸發 ----
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- interval: 1m
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name: "SLO_KMGrowthRate_Critical 不觸發（KM 增長 = 30/day）"
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input_series:
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# 每分鐘 +0.0208 次 = 30/day
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- series: "knowledge_entries_total"
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values: "0+0.0208x1600"
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alert_rule_test:
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- eval_time: 25h
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alertname: SLO_KMGrowthRate_Critical
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exp_alerts: []
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# ---- 正測: KM 增長率 = 15/day → Low 觸發（< 20）但 Critical 不觸發 ----
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- interval: 1m
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name: "SLO_KMGrowthRate_Low 觸發，Critical 不觸發（KM 增長 15/day）"
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input_series:
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# 每分鐘 +0.0104 次 ≈ 15/day
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- series: "knowledge_entries_total"
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values: "0+0.0104x1600"
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alert_rule_test:
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- eval_time: 25h
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alertname: SLO_KMGrowthRate_Low
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exp_alerts:
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- exp_labels:
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alertname: SLO_KMGrowthRate_Low
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severity: warning
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slo_name: km_growth_rate
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team: ai
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auto_repair: "false"
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