qzl
00f37d7e19
- 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
119 lines
3.4 KiB
Python
119 lines
3.4 KiB
Python
from __future__ import annotations
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import pytest
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from services.litellm.service import LiteLLMService
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def test_calculate_cost_uses_first_qwen_tier() -> None:
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service = LiteLLMService()
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cost = service.calculate_cost(
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model="dashscope/qwen3.5-flash",
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prompt_tokens=100_000,
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completion_tokens=1_000,
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cached_prompt_tokens=10_000,
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)
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assert cost == pytest.approx(0.0202)
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def test_calculate_cost_uses_second_qwen_tier() -> None:
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service = LiteLLMService()
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cost = service.calculate_cost(
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model="dashscope/qwen3.5-flash",
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prompt_tokens=200_000,
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completion_tokens=5_000,
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cached_prompt_tokens=20_000,
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)
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assert cost == pytest.approx(0.1856)
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def test_build_usage_metadata_calculates_cost_from_usage_summary() -> None:
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service = LiteLLMService()
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metadata = service.build_usage_metadata(
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model="dashscope/qwen3.5-flash",
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usage_summary={
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"input_tokens": 2000,
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"output_tokens": 100,
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"latency_ms": 321,
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"cached_prompt_tokens": 500,
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},
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)
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assert metadata["model"] == "dashscope/qwen3.5-flash"
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assert metadata["inputTokens"] == 2000
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assert metadata["outputTokens"] == 100
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assert metadata["totalTokens"] == 2100
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assert metadata["cachedPromptTokens"] == 500
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assert metadata["promptCacheHitTokens"] == 500
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assert metadata["promptCacheMissTokens"] == 1500
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assert metadata["reasoningTokens"] == 0
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assert metadata["cost"] == pytest.approx(0.00051)
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assert metadata["costSource"] == "catalog_fallback"
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assert metadata["usageComplete"] is True
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assert metadata["latencyMs"] == 321
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def test_build_usage_metadata_prefers_provider_direct_cost() -> None:
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service = LiteLLMService()
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metadata = service.build_usage_metadata(
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model="deepseek-chat",
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usage_summary={
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"input_tokens": 1000,
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"output_tokens": 100,
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"latency_ms": 100,
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"cached_prompt_tokens": 0,
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"direct_cost": 0.1234,
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"direct_cost_observed": 1,
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"direct_cost_complete": 1,
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},
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)
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assert metadata["cost"] == pytest.approx(0.1234)
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assert metadata["costSource"] == "provider"
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assert metadata["usageComplete"] is True
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def test_build_usage_metadata_falls_back_when_provider_cost_incomplete() -> None:
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service = LiteLLMService()
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metadata = service.build_usage_metadata(
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model="deepseek-chat",
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usage_summary={
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"input_tokens": 1000,
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"output_tokens": 100,
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"latency_ms": 100,
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"cached_prompt_tokens": 0,
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"direct_cost": 0.1234,
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"direct_cost_observed": 1,
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"direct_cost_complete": 0,
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},
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)
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assert metadata["cost"] == pytest.approx(0.0023)
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assert metadata["costSource"] == "catalog_fallback_incomplete_provider_cost"
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def test_build_usage_metadata_marks_incomplete_usage_fallback() -> None:
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service = LiteLLMService()
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metadata = service.build_usage_metadata(
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model="deepseek-chat",
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usage_summary={
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"input_tokens": 0,
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"output_tokens": 0,
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"latency_ms": 0,
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"cached_prompt_tokens": 0,
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"model_call_records": 1,
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"usage_records": 0,
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},
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)
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assert metadata["costSource"] == "incomplete_usage_fallback"
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assert metadata["usageComplete"] is False
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