589 lines
20 KiB
Python
589 lines
20 KiB
Python
import asyncio
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import logging
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def test_run_with_timeout_supports_sync_function():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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result = asyncio.run(ElephantAlphaAutonomousEngine._run_with_timeout(lambda value: value + 1, 41))
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assert result == 42
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def test_execute_step_rejects_unknown_action():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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engine = ElephantAlphaAutonomousEngine()
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try:
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asyncio.run(engine._execute_step({"agent": "mystery", "action": "do_anything"}))
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except ValueError as exc:
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assert "Unrecognized step" in str(exc)
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else:
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raise AssertionError("unknown action should fail")
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def test_execute_step_routes_code_fix_to_autoheal(monkeypatch):
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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calls = []
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engine = ElephantAlphaAutonomousEngine()
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monkeypatch.setattr(
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engine,
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"_run_auto_heal",
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lambda error_type, context: calls.append((error_type, context)) or {"ok": True},
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)
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asyncio.run(engine._execute_step({
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"agent": "elephant_alpha",
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"action": "code_fix",
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"parameters": {"target_file": "services/example.py", "error_message": "Traceback"},
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}))
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assert calls == [("python_exception", {"target_file": "services/example.py", "error_message": "Traceback"})]
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def test_execute_step_routes_price_adjustment_to_human_review(monkeypatch):
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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calls = []
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engine = ElephantAlphaAutonomousEngine()
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monkeypatch.setattr(
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engine,
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"_record_price_adjustment_review",
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lambda step: calls.append(step) or {"status": "pending_review", "sku": "SKU-9"},
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)
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result = asyncio.run(engine._execute_step({
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"agent": "nemotron",
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"action": "execute_price_adjustment",
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"parameters": {"sku": "SKU-9", "recommended_price": 1280},
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}))
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assert result == {"status": "pending_review", "sku": "SKU-9"}
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assert calls[0]["parameters"]["recommended_price"] == 1280
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def test_execute_step_skips_legacy_openclaw_resource_strategy():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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engine = ElephantAlphaAutonomousEngine()
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result = asyncio.run(engine._execute_step({
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"agent": "openclaw",
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"action": "generate_resource_optimization_strategy",
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}))
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assert result is None
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def test_autoheal_derives_python_exception_from_traceback():
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from services.auto_heal_service import AutoHealService
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svc = AutoHealService()
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assert svc._derive_error_type({"traceback_str": "Traceback (most recent call last):\nNameError"}) == "python_exception"
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def test_execute_autonomous_decision_logs_short_circuit_telemetry_failure(monkeypatch, caplog):
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from services.elephant_alpha_autonomous_engine import (
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AutonomousTrigger,
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ElephantAlphaAutonomousEngine,
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)
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import services.ai_call_logger as ai_call_logger
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engine = ElephantAlphaAutonomousEngine()
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async def _no_hermes_threats(top_n=5):
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return None
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def _broken_log_ai_call(*args, **kwargs):
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raise RuntimeError("ai telemetry unavailable")
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monkeypatch.setattr(engine, "_fetch_hermes_threats_summary", _no_hermes_threats)
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monkeypatch.setattr(ai_call_logger, "log_ai_call", _broken_log_ai_call)
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caplog.set_level(logging.WARNING, logger="services.elephant_alpha_autonomous_engine")
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trigger = AutonomousTrigger(
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trigger_type="price_drop_alert",
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conditions={},
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threshold=0.8,
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enabled=True,
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)
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asyncio.run(engine._execute_autonomous_decision(trigger))
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assert "EA short-circuit telemetry failed" in caplog.text
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def test_competitor_db_evidence_actions_are_concrete():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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actions = ElephantAlphaAutonomousEngine._format_competitor_evidence_actions(
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[
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{
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"sku": "SKU-1",
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"name": "測試商品",
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"momo_price": 1200,
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"competitor_price": 990,
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"price_gap_pct": 17.5,
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"competitor_product_id": "D123456",
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"match_score": 0.84,
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"tags": [
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"identity_v2",
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"match_type_exact",
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"price_basis_total_price",
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"alert_tier_price_alert_exact",
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],
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}
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],
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trigger_type="price_drop_alert",
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)
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assert actions == [
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"[SKU-1] 測試商品｜MOMO $1,200 vs PChome $990 (+17.5%)｜每件價差 NT$ 210｜證據：高信心同款 / 總價可比 / 可直接價格告警 / score 0.84｜建議人工確認 PChome identity_v2 後評估跟價或促銷｜PChome D123456"
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]
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def test_execute_autonomous_decision_uses_db_evidence_without_hermes_prefetch(monkeypatch):
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import services.elephant_alpha_autonomous_engine as engine_module
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from services.elephant_alpha_autonomous_engine import (
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AutonomousTrigger,
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ElephantAlphaAutonomousEngine,
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)
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from services.elephant_alpha_orchestrator import StrategicDecision
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engine = ElephantAlphaAutonomousEngine()
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contexts = []
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notified = []
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async def _capture_context(context):
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contexts.append(context)
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return StrategicDecision(
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priority="high",
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agents_required=["elephant_alpha"],
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reasoning="已有 DB 價差實證，允許進入決策流程。",
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expected_outcome="生成可稽核行動",
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confidence=0.95,
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execution_plan=[],
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resource_requirements={},
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)
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async def _fetch_should_not_run(top_n=5):
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raise AssertionError("DB evidence should avoid Hermes LLM prefetch")
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async def _capture_notify(decision, trigger):
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notified.append(trigger.trigger_type)
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monkeypatch.setattr(engine_module.elephant_orchestrator, "analyze_and_coordinate", _capture_context)
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monkeypatch.setattr(engine, "_fetch_hermes_threats_summary", _fetch_should_not_run)
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monkeypatch.setattr(engine, "_notify_telegram_executed", _capture_notify)
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monkeypatch.setattr(engine, "_store_escalation", lambda trigger_type: None)
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trigger = AutonomousTrigger(
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trigger_type="price_drop_alert",
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conditions={"_db_evidence_actions": ["[SKU-1] DB 實證價差"]},
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threshold=0.8,
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enabled=True,
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)
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asyncio.run(engine._execute_autonomous_decision(trigger))
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assert contexts[0]["conditions"]["_prefetched_hermes_threats"] == ["[SKU-1] DB 實證價差"]
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assert notified == ["price_drop_alert"]
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def test_escalate_resource_optimization_without_evidence_is_suppressed(monkeypatch):
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import services.elephant_alpha_autonomous_engine as engine_module
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from services.elephant_alpha_autonomous_engine import (
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AutonomousTrigger,
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ElephantAlphaAutonomousEngine,
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)
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from services.elephant_alpha_orchestrator import StrategicDecision
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engine = ElephantAlphaAutonomousEngine()
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suppressed = []
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cooldown = []
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def _raise_if_db_opened():
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raise AssertionError("no-concrete resource escalation should not write human_review")
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monkeypatch.setattr(engine_module, "get_session", _raise_if_db_opened)
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monkeypatch.setattr(engine, "_store_escalation", lambda trigger_type: cooldown.append(trigger_type))
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monkeypatch.setattr(
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engine,
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"_record_suppressed_escalation",
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lambda decision, trigger, reason: suppressed.append((trigger.trigger_type, reason)),
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)
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decision = StrategicDecision(
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priority="medium",
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agents_required=["openclaw"],
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reasoning="資源調配建議信心不足",
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expected_outcome="待人工確認",
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confidence=0.60,
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execution_plan=[],
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resource_requirements={},
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)
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trigger = AutonomousTrigger(
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trigger_type="resource_optimization",
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conditions={"_resource_metrics": {"action_queue_size": 14, "system_load_pct": 52.0}},
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threshold=0.6,
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enabled=True,
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)
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asyncio.run(engine._escalate_to_human(decision, trigger))
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assert cooldown == ["resource_optimization"]
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assert suppressed == [("resource_optimization", "no_concrete_evidence")]
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def test_escalate_price_alert_without_evidence_is_suppressed(monkeypatch):
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import services.elephant_alpha_autonomous_engine as engine_module
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from services.elephant_alpha_autonomous_engine import (
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AutonomousTrigger,
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ElephantAlphaAutonomousEngine,
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)
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from services.elephant_alpha_orchestrator import StrategicDecision
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engine = ElephantAlphaAutonomousEngine()
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suppressed = []
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cooldown = []
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async def _no_concrete_actions(top_n=5):
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return None
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def _raise_if_db_opened():
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raise AssertionError("no-concrete price escalation should not write human_review")
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monkeypatch.setattr(engine, "_fetch_hermes_threats_summary", _no_concrete_actions)
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monkeypatch.setattr(engine_module, "get_session", _raise_if_db_opened)
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monkeypatch.setattr(engine, "_store_escalation", lambda trigger_type: cooldown.append(trigger_type))
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monkeypatch.setattr(
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engine,
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"_record_suppressed_escalation",
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lambda decision, trigger, reason: suppressed.append((trigger.trigger_type, reason)),
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)
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decision = StrategicDecision(
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priority="medium",
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agents_required=["openclaw"],
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reasoning="價格調整建議信心不足",
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expected_outcome="待人工確認",
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confidence=0.62,
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execution_plan=[],
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resource_requirements={},
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)
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trigger = AutonomousTrigger(
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trigger_type="price_drop_alert",
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conditions={},
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threshold=0.8,
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enabled=True,
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)
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asyncio.run(engine._escalate_to_human(decision, trigger))
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assert cooldown == ["price_drop_alert"]
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assert suppressed == [("price_drop_alert", "no_concrete_evidence")]
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def test_resource_pressure_classifier_does_not_equate_backlog_with_cpu_load():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
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"action_queue_size": 34,
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"high_priority_count": 0,
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"human_review_count": 0,
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"stale_count": 0,
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"system_load_pct": 19.2,
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"queue_threshold": 10,
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"load_threshold_pct": 80,
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"high_priority_threshold": 5,
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"stale_threshold": 5,
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})
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assert metrics["pressure_level"] == "backlog_only"
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assert metrics["should_alert"] is False
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assert metrics["load_pressure"] is False
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assert "system_load=19.2%/80%" in metrics["evidence"]
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def test_resource_pressure_classifier_alerts_on_actionable_review_backlog():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
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"action_queue_size": 34,
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"high_priority_count": 8,
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"human_review_count": 6,
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"stale_count": 0,
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"system_load_pct": 22.0,
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"queue_threshold": 10,
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"load_threshold_pct": 80,
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"high_priority_threshold": 5,
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"stale_threshold": 5,
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})
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assert metrics["pressure_level"] == "warning"
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assert metrics["should_alert"] is True
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assert metrics["high_priority_pressure"] is True
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assert metrics["load_pressure"] is False
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def test_resource_pressure_message_is_measurement_based_not_llm_theatre():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
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"action_queue_size": 34,
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"high_priority_count": 8,
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"human_review_count": 6,
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"stale_count": 5,
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"system_load_pct": 22.0,
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"queue_threshold": 10,
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"load_threshold_pct": 80,
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"high_priority_threshold": 5,
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"stale_threshold": 5,
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"stale_hours": 24,
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"top_action_items": [
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{
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"id": 42,
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"status": "pending",
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"priority": 1,
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"age_hours": 31.5,
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"type": "code_review_fix",
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"description": "修正高優先錯誤",
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}
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],
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})
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msg = ElephantAlphaAutonomousEngine._build_resource_pressure_telegram_message(
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metrics,
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insight_id=123,
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previous_limit=10,
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new_limit=8,
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)
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assert "量測指標" in msg
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assert "系統處置紀錄" in msg
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assert "待處理焦點" in msg
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assert "#42 P1 pending code_review_fix" in msg
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assert "主機 CPU 未達高負載門檻" in msg
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assert "未啟動 Hermes/NemoTron 價格分析" in msg
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assert "預期效益" not in msg
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assert "48小時" not in msg
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assert "48 小時效益預測" in msg
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def test_resource_pressure_decision_envelope_is_measurement_based():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
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"action_queue_size": 34,
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"high_priority_count": 8,
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"human_review_count": 6,
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"stale_count": 5,
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"system_load_pct": 22.0,
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"queue_threshold": 10,
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"load_threshold_pct": 80,
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"high_priority_threshold": 5,
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"stale_threshold": 5,
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"stale_hours": 24,
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})
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envelope = ElephantAlphaAutonomousEngine._build_resource_pressure_decision_envelope(
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metrics,
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insight_id=1942,
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previous_limit=10,
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new_limit=8,
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)
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msg = ElephantAlphaAutonomousEngine._build_resource_pressure_telegram_message(
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metrics,
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insight_id=1942,
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previous_limit=10,
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new_limit=8,
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decision_envelope=envelope,
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)
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assert envelope["decision_id"] == "ea_resource_pressure_1942"
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assert envelope["source_agent"] == "elephant_alpha"
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assert envelope["decision_type"] == "resource_optimization"
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assert envelope["severity"] == "P2"
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assert envelope["guardrails"]["can_auto_execute"] is False
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assert envelope["guardrails"]["llm_used"] is False
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assert envelope["trace"]["provider"] == "action_plans_cpu_probe"
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assert "🧭 <b>決策信封</b>" in msg
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assert "資料品質：<code>complete</code> 自動執行：<b>不允許</b>" in msg
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assert "不採用 LLM 生成的 48 小時效益預測" in msg
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assert "Gemini" not in msg
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def test_resource_optimization_bypasses_llm_orchestrator(monkeypatch):
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import services.elephant_alpha_autonomous_engine as engine_module
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from services.elephant_alpha_autonomous_engine import (
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AutonomousTrigger,
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ElephantAlphaAutonomousEngine,
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)
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engine = ElephantAlphaAutonomousEngine()
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sent = []
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stored = []
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async def _boom(*args, **kwargs):
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raise AssertionError("resource_optimization must not call LLM orchestrator")
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async def _capture_send(*args, **kwargs):
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sent.append(args)
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monkeypatch.setattr(engine_module.elephant_orchestrator, "analyze_and_coordinate", _boom)
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monkeypatch.setattr(engine, "_run_action_plan_hygiene", lambda: {"updated_count": 0})
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monkeypatch.setattr(engine, "_record_resource_pressure_insight", lambda *args, **kwargs: 77)
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monkeypatch.setattr(engine, "_send_resource_pressure_telegram", _capture_send)
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monkeypatch.setattr(engine, "_store_escalation", lambda trigger_type: stored.append(trigger_type))
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trigger = AutonomousTrigger(
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trigger_type="resource_optimization",
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conditions={
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"_resource_metrics": {
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"action_queue_size": 34,
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"high_priority_count": 9,
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"human_review_count": 6,
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"stale_count": 0,
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"system_load_pct": 20.0,
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"queue_threshold": 10,
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"load_threshold_pct": 80,
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"high_priority_threshold": 5,
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"stale_threshold": 5,
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}
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},
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threshold=0.6,
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enabled=True,
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)
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asyncio.run(engine._execute_autonomous_decision(trigger))
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assert stored == ["resource_optimization"]
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assert len(sent) == 1
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assert engine.max_autonomous_decisions_per_hour == 8
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def test_resource_pressure_message_reports_hygiene_result():
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from services.elephant_alpha_autonomous_engine import ElephantAlphaAutonomousEngine
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metrics = ElephantAlphaAutonomousEngine._classify_resource_pressure({
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"action_queue_size": 9,
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"high_priority_count": 0,
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"human_review_count": 0,
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"stale_count": 0,
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"system_load_pct": 20.0,
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"queue_threshold": 10,
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"load_threshold_pct": 80,
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"high_priority_threshold": 5,
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"stale_threshold": 5,
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})
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metrics["pre_hygiene"] = {
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"action_queue_size": 100,
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"high_priority_count": 47,
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"stale_count": 99,
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}
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metrics["hygiene_result"] = {
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"updated_count": 91,
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"by_source": {"code_review_fix": 66, "openclaw_recommendation": 25},
|
||
}
|
||
|
||
msg = ElephantAlphaAutonomousEngine._build_resource_pressure_telegram_message(
|
||
metrics,
|
||
insight_id=124,
|
||
previous_limit=10,
|
||
new_limit=10,
|
||
)
|
||
|
||
assert "P4 resolved" in msg
|
||
assert "清理前 Action queue：100" in msg
|
||
assert "已自動關閉過期 action_plans 91 筆" in msg
|
||
assert "只改 status/metadata，不刪除資料" in msg
|
||
|
||
|
||
def test_resource_optimization_cannot_use_legacy_autonomous_execution_template(monkeypatch):
|
||
from services.elephant_alpha_autonomous_engine import (
|
||
AutonomousTrigger,
|
||
ElephantAlphaAutonomousEngine,
|
||
)
|
||
from services.elephant_alpha_orchestrator import StrategicDecision
|
||
|
||
engine = ElephantAlphaAutonomousEngine()
|
||
sent = []
|
||
|
||
async def _capture_send(message):
|
||
sent.append(message)
|
||
|
||
monkeypatch.setattr("services.telegram_templates._send_telegram_raw", _capture_send)
|
||
|
||
decision = StrategicDecision(
|
||
priority="medium",
|
||
agents_required=["elephant_alpha"],
|
||
reasoning="resource pressure test",
|
||
expected_outcome="old style outcome must not be sent",
|
||
confidence=0.8,
|
||
execution_plan=[{"agent": "Hermes", "action": "generate_resource_optimization_strategy"}],
|
||
resource_requirements={},
|
||
)
|
||
trigger = AutonomousTrigger(
|
||
trigger_type="resource_optimization",
|
||
conditions={},
|
||
threshold=0.6,
|
||
enabled=True,
|
||
)
|
||
|
||
asyncio.run(engine._notify_telegram_executed(decision, trigger))
|
||
|
||
assert sent == []
|
||
|
||
|
||
def test_human_escalation_decision_envelope_blocks_auto_execution():
|
||
from services.elephant_alpha_autonomous_engine import (
|
||
AutonomousTrigger,
|
||
ElephantAlphaAutonomousEngine,
|
||
)
|
||
from services.elephant_alpha_orchestrator import StrategicDecision
|
||
|
||
decision = StrategicDecision(
|
||
priority="medium",
|
||
agents_required=["elephant_alpha"],
|
||
reasoning="需要人工判讀。",
|
||
expected_outcome="不自動執行。",
|
||
confidence=0.62,
|
||
execution_plan=[],
|
||
resource_requirements={},
|
||
)
|
||
trigger = AutonomousTrigger(
|
||
trigger_type="code_exception",
|
||
conditions={"scan_containers": ["momo-pro-system"]},
|
||
threshold=1.0,
|
||
enabled=True,
|
||
)
|
||
|
||
envelope = ElephantAlphaAutonomousEngine._build_human_escalation_decision_envelope(
|
||
decision,
|
||
trigger,
|
||
insight_id=77,
|
||
concrete_actions=[],
|
||
)
|
||
|
||
assert envelope["decision_id"] == "ea_review_77"
|
||
assert envelope["decision_type"] == "ea_escalation"
|
||
assert envelope["guardrails"]["can_auto_execute"] is False
|
||
assert envelope["guardrails"]["data_quality"] == "partial"
|
||
assert envelope["recommended_action"]["requires_hitl"] is True
|
||
assert envelope["trace"]["provider"] == "elephant_alpha"
|
||
|
||
|
||
def test_elephant_alpha_openclaw_registry_is_ollama_first():
|
||
from services.elephant_alpha_orchestrator import ElephantAlphaOrchestrator
|
||
|
||
orchestrator = ElephantAlphaOrchestrator()
|
||
openclaw = orchestrator.agents["openclaw"]
|
||
|
||
assert not openclaw.model.startswith("gemini")
|
||
assert openclaw.model == "qwen2.5-coder:7b"
|
||
assert "Gemini is not a primary agent model" in orchestrator.system_prompt
|
||
assert "Ollama cascade" in orchestrator.system_prompt
|