ewoooc/tests/test_ai_insight_embedding_bridge.py

def test_enqueue_insight_embedding_builds_queue_payload(monkeypatch):
    import services.openclaw_learning_service as learning

    calls = []
    monkeypatch.setattr(
        learning,
        "_enqueue_embedding",
        lambda table, target_id, text: calls.append((table, target_id, text)) or True,
    )

    assert learning.enqueue_insight_embedding(42, "agent_action", "hello", "2026-04-29") is True
    assert calls == [("ai_insights", 42, "agent_action (2026-04-29): hello")]


def test_enqueue_insight_embedding_rejects_missing_content(monkeypatch):
    import services.openclaw_learning_service as learning

    monkeypatch.setattr(
        learning,
        "_enqueue_embedding",
        lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("should not enqueue")),
    )

    assert learning.enqueue_insight_embedding(42, "agent_action", "") is False
    assert learning.enqueue_insight_embedding(None, "agent_action", "hello") is False


def test_enqueue_missing_insight_embeddings_queues_rows(monkeypatch):
    from types import SimpleNamespace
    import services.openclaw_learning_service as learning

    rows = [
        SimpleNamespace(id=1, insight_type="mcp_cache", period=None, content="市場資料"),
        SimpleNamespace(id=2, insight_type="human_review", period="2026-04-29", content="人工審核"),
    ]

    class Result:
        def fetchall(self):
            return rows

    class Session:
        def execute(self, *args, **kwargs):
            return Result()

        def close(self):
            pass

    calls = []
    monkeypatch.setattr(learning, "get_session", lambda: Session())
    monkeypatch.setattr(
        learning,
        "enqueue_insight_embedding",
        lambda insight_id, insight_type, content, period=None: calls.append((insight_id, insight_type, content, period)) or True,
    )

    result = learning.enqueue_missing_insight_embeddings(limit=10)

    assert result == {"scanned": 2, "enqueued": 2, "status": "ok"}
    assert calls[0] == (1, "mcp_cache", "市場資料", None)


def test_process_one_embedding_writes_signature(monkeypatch):
    import services.openclaw_learning_service as learning
    from services.rag_service import get_embedding_signature

    executed = []

    class Session:
        def execute(self, stmt, params=None):
            executed.append((str(stmt), params or {}))

        def commit(self):
            pass

        def rollback(self):
            pass

        def close(self):
            pass

    monkeypatch.setattr(learning, "get_session", lambda: Session())
    monkeypatch.setattr(
        learning.ollama_service,
        "generate_embedding",
        lambda text, model="bge-m3:latest", **_kwargs: [0.1] * 1024,
    )

    ok = learning._process_one_embedding(
        row_id=7,
        target_table="learning_episodes",
        target_id=42,
        text_content="測試內容",
        model="bge-m3:latest",
    )

    assert ok is True
    target_updates = [item for item in executed if "UPDATE learning_episodes" in item[0]]
    assert target_updates
    assert "embedding_signature" in target_updates[0][0]
    assert target_updates[0][1]["sig"] == get_embedding_signature(model="bge-m3:latest", dim=1024)


def test_process_one_embedding_defers_without_attempt_when_gcp_circuit_open(monkeypatch):
    import services.openclaw_learning_service as learning

    executed = []

    class Session:
        def execute(self, stmt, params=None):
            executed.append((str(stmt), params or {}))

        def commit(self):
            pass

        def rollback(self):
            pass

        def close(self):
            pass

    monkeypatch.setattr(learning, "get_session", lambda: Session())
    monkeypatch.setattr(
        learning.ollama_service,
        "generate_embedding",
        lambda text, model="bge-m3:latest", **_kwargs: [],
    )
    monkeypatch.setattr(learning, "is_embedding_gcp_circuit_open", lambda: True)
    monkeypatch.setattr(learning, "embedding_gcp_circuit_remaining_seconds", lambda: 42.0)

    ok = learning._process_one_embedding(
        row_id=7,
        target_table="learning_episodes",
        target_id=42,
        text_content="測試內容",
        model="bge-m3:latest",
    )

    assert ok is False
    assert not any("attempts = attempts + 1" in stmt for stmt, _ in executed)
    defer_updates = [item for item in executed if "SET status='pending'" in item[0]]
    assert defer_updates
    assert "不扣 attempts" in defer_updates[0][1]["err"]


def test_claim_pending_embeddings_pauses_when_gcp_circuit_open(monkeypatch):
    import services.openclaw_learning_service as learning

    monkeypatch.setattr(learning, "is_embedding_gcp_circuit_open", lambda: True)
    monkeypatch.setattr(learning, "embedding_gcp_circuit_remaining_seconds", lambda: 30.0)
    monkeypatch.setattr(
        learning,
        "get_session",
        lambda: (_ for _ in ()).throw(AssertionError("熔斷中不應 claim DB rows")),
    )

    assert learning._claim_pending_embeddings(limit=3, max_attempts=5) == []


def test_claim_pending_embeddings_uses_skip_locked(monkeypatch):
    import services.openclaw_learning_service as learning

    executed = []
    claimed_rows = [(7, "ai_insights", 42, "測試內容", "bge-m3:latest")]

    class Result:
        def fetchall(self):
            return claimed_rows

    class Session:
        def execute(self, stmt, params=None):
            executed.append((str(stmt), params or {}))
            if "RETURNING q.id" in str(stmt):
                return Result()
            return Result()

        def commit(self):
            pass

        def rollback(self):
            pass

        def close(self):
            pass

    monkeypatch.setattr(learning, "get_session", lambda: Session())

    rows = learning._claim_pending_embeddings(limit=3, max_attempts=5)

    assert rows == claimed_rows
    claim_sql = [stmt for stmt, _ in executed if "RETURNING q.id" in stmt][0]
    assert "FOR UPDATE SKIP LOCKED" in claim_sql
    assert "UPDATE embedding_retry_queue q" in claim_sql
    assert executed[-1][1]["lim"] == 3
    assert executed[-1][1]["max"] == 5