60 lines
2.0 KiB
Python
60 lines
2.0 KiB
Python
def test_enqueue_insight_embedding_builds_queue_payload(monkeypatch):
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import services.openclaw_learning_service as learning
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calls = []
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monkeypatch.setattr(
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learning,
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"_enqueue_embedding",
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lambda table, target_id, text: calls.append((table, target_id, text)) or True,
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)
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assert learning.enqueue_insight_embedding(42, "agent_action", "hello", "2026-04-29") is True
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assert calls == [("ai_insights", 42, "agent_action (2026-04-29): hello")]
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def test_enqueue_insight_embedding_rejects_missing_content(monkeypatch):
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import services.openclaw_learning_service as learning
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monkeypatch.setattr(
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learning,
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"_enqueue_embedding",
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lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("should not enqueue")),
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)
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assert learning.enqueue_insight_embedding(42, "agent_action", "") is False
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assert learning.enqueue_insight_embedding(None, "agent_action", "hello") is False
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def test_enqueue_missing_insight_embeddings_queues_rows(monkeypatch):
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from types import SimpleNamespace
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import services.openclaw_learning_service as learning
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rows = [
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SimpleNamespace(id=1, insight_type="mcp_cache", period=None, content="市場資料"),
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SimpleNamespace(id=2, insight_type="human_review", period="2026-04-29", content="人工審核"),
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]
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class Result:
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def fetchall(self):
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return rows
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class Session:
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def execute(self, *args, **kwargs):
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return Result()
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def close(self):
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pass
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calls = []
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monkeypatch.setattr(learning, "get_session", lambda: Session())
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monkeypatch.setattr(
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learning,
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"enqueue_insight_embedding",
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lambda insight_id, insight_type, content, period=None: calls.append((insight_id, insight_type, content, period)) or True,
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)
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result = learning.enqueue_missing_insight_embeddings(limit=10)
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assert result == {"scanned": 2, "enqueued": 2, "status": "ok"}
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assert calls[0] == (1, "mcp_cache", "市場資料", None)
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