feat: 实现日历提醒完整功能（操作执行、通知服务重构、归档）

- 新增 ReminderActionExecutor 处理取消/稍后提醒操作
- 新增 ReminderOutboxStore 本地存储待处理操作
- 重构 LocalNotificationService 支持聚合提醒和交互操作
- 新增 event_color_resolver 工具类统一颜色解析
- 新增 CalendarService.archiveEvent 归档方法
- 增强 ModelTracking 支持缓存命中、推理token和成本追踪
- 添加 qwen3.5-35b-a3b 模型配置
- 更新 AndroidManifest 全屏intent权限
- 补充相关单元测试和文档

backend/tests/unit/services/test_litellm_service.py

@@ -44,10 +44,75 @@ def test_build_usage_metadata_calculates_cost_from_usage_summary() -> None:
         },
     )
 
-    assert metadata == {
-        "model": "dashscope/qwen3.5-flash",
-        "inputTokens": 2000,
-        "outputTokens": 100,
-        "cost": pytest.approx(0.00051),
-        "latencyMs": 321,
-    }
+    assert metadata["model"] == "dashscope/qwen3.5-flash"
+    assert metadata["inputTokens"] == 2000
+    assert metadata["outputTokens"] == 100
+    assert metadata["totalTokens"] == 2100
+    assert metadata["cachedPromptTokens"] == 500
+    assert metadata["promptCacheHitTokens"] == 500
+    assert metadata["promptCacheMissTokens"] == 1500
+    assert metadata["reasoningTokens"] == 0
+    assert metadata["cost"] == pytest.approx(0.00051)
+    assert metadata["costSource"] == "catalog_fallback"
+    assert metadata["usageComplete"] is True
+    assert metadata["latencyMs"] == 321
+
+
+def test_build_usage_metadata_prefers_provider_direct_cost() -> None:
+    service = LiteLLMService()
+
+    metadata = service.build_usage_metadata(
+        model="deepseek-chat",
+        usage_summary={
+            "input_tokens": 1000,
+            "output_tokens": 100,
+            "latency_ms": 100,
+            "cached_prompt_tokens": 0,
+            "direct_cost": 0.1234,
+            "direct_cost_observed": 1,
+            "direct_cost_complete": 1,
+        },
+    )
+
+    assert metadata["cost"] == pytest.approx(0.1234)
+    assert metadata["costSource"] == "provider"
+    assert metadata["usageComplete"] is True
+
+
+def test_build_usage_metadata_falls_back_when_provider_cost_incomplete() -> None:
+    service = LiteLLMService()
+
+    metadata = service.build_usage_metadata(
+        model="deepseek-chat",
+        usage_summary={
+            "input_tokens": 1000,
+            "output_tokens": 100,
+            "latency_ms": 100,
+            "cached_prompt_tokens": 0,
+            "direct_cost": 0.1234,
+            "direct_cost_observed": 1,
+            "direct_cost_complete": 0,
+        },
+    )
+
+    assert metadata["cost"] == pytest.approx(0.0023)
+    assert metadata["costSource"] == "catalog_fallback_incomplete_provider_cost"
+
+
+def test_build_usage_metadata_marks_incomplete_usage_fallback() -> None:
+    service = LiteLLMService()
+
+    metadata = service.build_usage_metadata(
+        model="deepseek-chat",
+        usage_summary={
+            "input_tokens": 0,
+            "output_tokens": 0,
+            "latency_ms": 0,
+            "cached_prompt_tokens": 0,
+            "model_call_records": 1,
+            "usage_records": 0,
+        },
+    )
+
+    assert metadata["costSource"] == "incomplete_usage_fallback"
+    assert metadata["usageComplete"] is False