51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
def test_flag_for_human_review_writes_pending_memory(monkeypatch):
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import services.agent_actions as actions
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import services.openclaw_learning_service as learning
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calls = []
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monkeypatch.setattr(actions, "_audit", lambda *args, **kwargs: 999)
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monkeypatch.setattr(
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learning,
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"store_insight",
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lambda **kwargs: calls.append(kwargs) or 123,
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)
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result = actions.flag_for_human_review("SKU-1", "銷量斷崖，請人工確認")
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assert result["status"] == "pending_review"
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assert result["insight_id"] == 123
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assert calls[0]["insight_type"] == "human_review"
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assert calls[0]["status"] == "pending"
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assert calls[0]["product_sku"] == "SKU-1"
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def test_route_to_km_writes_archived_memory(monkeypatch):
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import services.agent_actions as actions
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import services.openclaw_learning_service as learning
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calls = []
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monkeypatch.setattr(actions, "_audit", lambda *args, **kwargs: 999)
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monkeypatch.setattr(learning, "store_insight", lambda **kwargs: calls.append(kwargs) or 456)
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result = actions.route_to_km("SKU-2", "pricing", "競品價差擴大")
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assert result == {"status": "archived", "sku": "SKU-2", "domain": "pricing", "insight_id": 456}
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assert calls[0]["insight_type"] == "km_entry"
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assert calls[0]["metadata"]["domain"] == "pricing"
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def test_mark_for_relearn_writes_pending_marker(monkeypatch):
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import services.agent_actions as actions
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import services.openclaw_learning_service as learning
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calls = []
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monkeypatch.setattr(actions, "_audit", lambda *args, **kwargs: 999)
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monkeypatch.setattr(learning, "store_insight", lambda **kwargs: calls.append(kwargs) or 789)
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result = actions.mark_for_relearn("SKU-3", "NemoTron 信心不足")
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assert result["status"] == "marked"
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assert result["insight_id"] == 789
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assert calls[0]["insight_type"] == "relearn_marker"
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assert calls[0]["status"] == "pending"
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