feat(r1-touch): 建议分级+决策类推送+应用前预览 (alert不推送/同title防轰炸/preview对比)

2026-08-31 09:07:54 +08:00
parent df93b635b3
commit 076bd0dae0
7 changed files with 429 additions and 1 deletions
@@ -90,6 +90,17 @@ def setup_db():
    cache_util.delete("ai")


+@pytest.fixture(autouse=True)
+def _disable_ai_suggestion_push(monkeypatch):
+    """R1触达修复(2026-08-31): 测试库把企微推送替换为 no-op，防测试建议推真实企微群
+
+    生产环境真实推送（8800 relay）；测试只验证推送逻辑（决策类推/预警不推/幂等）不打真实企微。
+    测试类如需断言推送内容，可自行 monkeypatch.setattr 覆盖本 no-op。
+    """
+    from app.api import ai_analysis
+    monkeypatch.setattr(ai_analysis, "_push_decision_suggestion", lambda s: True)
+
+
@pytest.fixture
 def db() -> Generator[Session, None, None]:
    """提供数据库 session"""
@@ -240,3 +240,269 @@ class TestRuleSuggestions:
        assert resp.status_code == 200
        s = db.query(AISuggestion).filter(AISuggestion.suggestion_type == "budget_adjust").all()
        assert len(s) >= 1
+
+
+class TestSuggestionCategoryPreview:
+    """R1触达修复(2026-08-31)：建议分级(alert/decision) + 列表过滤 + 应用前预览 + 推送开关"""
+
+    def test_create_marks_category(self, client, db):
+        """手动创建：target_type=alert → category=alert；其余 → decision"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+
+        r_alert = _create_suggestion(client, token, kpi.id, target_type="alert",
+                                     suggestion_type="action_plan", title="预警类建议")
+        assert r_alert.json()["data"]["category"] == "alert"
+
+        r_decision = _create_suggestion(client, token, kpi.id, title="决策类建议")
+        assert r_decision.json()["data"]["category"] == "decision"
+
+    def test_category_filter(self, client, db):
+        """列表接口 category 过滤"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+        _create_suggestion(client, token, kpi.id, target_type="alert",
+                           suggestion_type="action_plan", title="预警A")
+        _create_suggestion(client, token, kpi.id, title="决策B")
+
+        lst_alert = client.get("/api/cma/ai/suggestions", params={"category": "alert"},
+                               headers=auth_header(token)).json()
+        assert lst_alert["total"] == 1
+        assert all(x["category"] == "alert" for x in lst_alert["data"])
+
+        lst_decision = client.get("/api/cma/ai/suggestions", params={"category": "decision"},
+                                  headers=auth_header(token)).json()
+        assert lst_decision["total"] == 1
+        assert all(x["category"] == "decision" for x in lst_decision["data"])
+
+    def test_generate_marks_decision(self, client, db):
+        """规则生成：执行率<70%建议（target_type=kpi）→ category=decision"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db, target_value=100.0)
+        db.add(KPIValue(kpi_id=kpi.id, period="2026-06", actual_value=50.0))
+        db.commit()
+
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+        sug = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).first()
+        assert sug is not None
+        assert sug.category == "decision"
+
+    def test_push_disabled_in_test_env(self, client, db):
+        """conftest no-op 推送（monkeypatch）→ 生成决策建议不真推企微，pushed 标记置 1"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db, target_value=100.0)
+        db.add(KPIValue(kpi_id=kpi.id, period="2026-06", actual_value=50.0))
+        db.commit()
+
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+        sug = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).first()
+        assert sug is not None
+        assert sug.pushed == 1
+
+    def test_preview_kpi_target(self, client, db):
+        """preview：kpi_target 返回 当前目标 → 新目标"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db, target_value=100.0)
+        r = _create_suggestion(client, token, kpi.id,
+                               suggestion_data={"kpi_id": kpi.id, "target_value": 150.0})
+        sug_id = r.json()["data"]["id"]
+
+        pv = client.get(f"/api/cma/ai/suggestions/{sug_id}/preview", headers=auth_header(token))
+        assert pv.status_code == 200, pv.text
+        data = pv.json()["data"]
+        assert data["type"] == "kpi_target"
+        assert data["kpi_name"] == "测试KPI"
+        assert data["current_target"] == 100.0
+        assert data["new_target"] == 150.0
+
+    def test_preview_budget_adjust(self, client, db):
+        """preview：budget_adjust 返回 当前预算 → 新预算"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+        db.add(BudgetPlan(entity_id=1, kpi_id=kpi.id, period="2026-09", budget_value=8000.0,
+                          budget_year=2026, budget_month=9, status="active"))
+        db.commit()
+        r = _create_suggestion(client, token, kpi.id, suggestion_type="budget_adjust",
+                               title="调预算预览", suggestion_data={"kpi_id": kpi.id, "period": "2026-09",
+                                                                   "budget_value": 9999.0})
+        sug_id = r.json()["data"]["id"]
+
+        pv = client.get(f"/api/cma/ai/suggestions/{sug_id}/preview", headers=auth_header(token))
+        assert pv.status_code == 200, pv.text
+        data = pv.json()["data"]
+        assert data["type"] == "budget_adjust"
+        assert data["period"] == "2026-09"
+        assert data["current_budget"] == 8000.0
+        assert data["new_budget"] == 9999.0
+
+    def test_preview_action_plan(self, client, db):
+        """preview：action_plan 返回计划信息"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+        r = _create_suggestion(client, token, kpi.id, suggestion_type="action_plan",
+                               title="建行动方案预览", suggestion_data={"kpi_id": kpi.id,
+                                                                       "title": "专项改善", "priority": "high",
+                                                                       "due_date": "2026-09-30"})
+        sug_id = r.json()["data"]["id"]
+
+        pv = client.get(f"/api/cma/ai/suggestions/{sug_id}/preview", headers=auth_header(token))
+        assert pv.status_code == 200, pv.text
+        data = pv.json()["data"]
+        assert data["type"] == "action_plan"
+        assert data["plan_title"] == "专项改善"
+        assert data["priority"] == "high"
+        assert data["due_date"] == "2026-09-30"
+
+
+class TestCategoryAndPreview:
+    """R1触达修复(2026-08-31)：建议分级 + 应用前预览"""
+
+    def _generate(self, client, db, kpi_id, actual, target=100.0):
+        """造一条KPI数据并触发 dashboard-analysis 规则生成（避开缓存）"""
+        db.add(KPIValue(kpi_id=kpi_id, period="2026-07", actual_value=actual))
+        db.commit()
+        from app.utils.cache import delete as cache_delete
+        cache_delete("ai", f"dashboard_analysis:ceo:{kpi_id}")
+        resp = client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(get_token_for_user(client)))
+        assert resp.status_code == 200
+        return resp.json()
+
+    def test_generate_marks_category(self, client, db, monkeypatch):
+        """生成建议时: target_type=alert → category=alert；其余 → decision"""
+        from app.api import ai_analysis
+        pushed = []
+        ai_analysis._push_decision_suggestion = lambda s: pushed.append(s) or True
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db, target_value=100.0)
+        db.add(KPIValue(kpi_id=kpi.id, period="2026-06", actual_value=50.0))
+        db.commit()
+
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+
+        kpi_sugs = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).all()
+        assert len(kpi_sugs) >= 1
+        for s in kpi_sugs:
+            assert s.category == "decision", f"KPI建议应决策类: {s.title}"
+
+        # 建一条预警 → 规则4生成 alert 类建议
+        from app.models import KPIAlert
+        db.add(KPIAlert(kpi_id=kpi.id, alert_level="yellow", alert_message="测试预警",
+                        alert_type="threshold", status="pending"))
+        db.commit()
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+        alert_sugs = db.query(AISuggestion).filter(AISuggestion.target_type == "alert").all()
+        assert len(alert_sugs) >= 1
+        for s in alert_sugs:
+            assert s.category == "alert", f"预警建议应alert类: {s.title}"
+
+    def test_alert_not_pushed_decision_pushed(self, client, db, monkeypatch):
+        """推送只发决策类：预警类不推，决策类推且只推一次(pushed=1)"""
+        from app.api import ai_analysis
+        pushed = []
+        ai_analysis._push_decision_suggestion = lambda s: pushed.append(s) or True
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db, target_value=100.0)
+        db.add(KPIValue(kpi_id=kpi.id, period="2026-06", actual_value=50.0))
+        db.commit()
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+
+        kpi_sugs = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).all()
+        assert len(pushed) >= 1
+        assert all(s.category == "decision" for s in pushed)
+        for s in pushed:
+            assert s.pushed == 1
+
+        # 预警类建议不在推送流
+        from app.models import KPIAlert
+        db.add(KPIAlert(kpi_id=kpi.id, alert_level="red", alert_message="测试预警2",
+                        alert_type="threshold", status="pending"))
+        db.commit()
+        before = len(pushed)
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+        alert_sugs = db.query(AISuggestion).filter(AISuggestion.target_type == "alert").all()
+        assert len(alert_sugs) >= 1
+        assert len(pushed) == before, "预警类不应触发推送"
+
+        # 幂等：重复生成不重推（同title建议不重建）
+        client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token))
+        assert len(pushed) == before
+
+    def test_list_category_filter(self, client, db):
+        """列表接口 category 过滤"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+        _create_suggestion(client, token, kpi.id, title="决策类A")
+        _create_suggestion(client, token, kpi.id, title="决策类B")
+        _create_suggestion(client, token, kpi.id, title="预警类C", target_type="alert")
+
+        lst = client.get("/api/cma/ai/suggestions?category=decision", headers=auth_header(token)).json()
+        assert lst["total"] == 2
+        assert all(x["category"] == "decision" for x in lst["data"])
+        lst2 = client.get("/api/cma/ai/suggestions?category=alert", headers=auth_header(token)).json()
+        assert lst2["total"] == 1
+        assert lst2["data"][0]["category"] == "alert"
+
+    def test_preview_kpi_target(self, client, db):
+        """preview: kpi_target 返回 current_target → new_target"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db, target_value=100.0)
+        r = _create_suggestion(client, token, kpi.id, suggestion_data={"kpi_id": kpi.id, "target_value": 150.0})
+        sug_id = r.json()["data"]["id"]
+
+        pv = client.get(f"/api/cma/ai/suggestions/{sug_id}/preview", headers=auth_header(token)).json()["data"]
+        assert pv["type"] == "kpi_target"
+        assert pv["kpi_name"] == kpi.kpi_name
+        assert pv["current_target"] == 100.0
+        assert pv["new_target"] == 150.0
+
+    def test_preview_budget_adjust(self, client, db):
+        """preview: budget_adjust 返回 current_budget → new_budget"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+        db.add(BudgetPlan(entity_id=1, kpi_id=kpi.id, period="2026-09", budget_value=5000.0,
+                          budget_year=2026, budget_month=9, status="active"))
+        db.commit()
+        r = _create_suggestion(client, token, kpi.id, suggestion_type="budget_adjust",
+                               title="调预算", suggestion_data={"kpi_id": kpi.id, "period": "2026-09", "budget_value": 8888.0})
+        sug_id = r.json()["data"]["id"]
+
+        pv = client.get(f"/api/cma/ai/suggestions/{sug_id}/preview", headers=auth_header(token)).json()["data"]
+        assert pv["type"] == "budget_adjust"
+        assert pv["current_budget"] == 5000.0
+        assert pv["new_budget"] == 8888.0
+        assert pv["period"] == "2026-09"
+
+    def test_preview_action_plan(self, client, db):
+        """preview: action_plan 返回计划参数"""
+        create_test_user(db)
+        token = get_token_for_user(client)
+        kpi = create_test_kpi(db)
+        r = _create_suggestion(client, token, kpi.id, suggestion_type="action_plan",
+                               title="建行动方案", suggestion_data={"kpi_id": kpi.id, "title": "改善专项",
+                                                                   "assignee": "李四", "priority": "high", "due_date": "2026-10-01"})
+        sug_id = r.json()["data"]["id"]
+
+        pv = client.get(f"/api/cma/ai/suggestions/{sug_id}/preview", headers=auth_header(token)).json()["data"]
+        assert pv["type"] == "action_plan"
+        assert pv["plan_title"] == "改善专项"
+        assert pv["assignee"] == "李四"
+        assert pv["priority"] == "high"
+        assert pv["due_date"] == "2026-10-01"
+
+    def test_preview_not_found(self, client, db):
+        create_test_user(db)
+        token = get_token_for_user(client)
+        r = client.get("/api/cma/ai/suggestions/99999/preview", headers=auth_header(token))
+        assert r.status_code == 404