zl-q
85 lines
2.3 KiB
Python
85 lines
2.3 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_run_completion_extracts_usage_and_cost() -> None:
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service = LiteLLMService()
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captured: dict[str, object] = {}
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def _fake_completion(**kwargs: object) -> dict[str, object]:
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captured.update(kwargs)
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return {
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"model": "dashscope/qwen3.5-flash",
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"usage": {
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"prompt_tokens": 2000,
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"completion_tokens": 100,
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"total_tokens": 2100,
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"prompt_tokens_details": {"cached_tokens": 500},
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},
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"choices": [{"message": {"content": "ok"}}],
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}
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result = service.run_completion_with_cost(
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model="dashscope/qwen3.5-flash",
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messages=[{"role": "user", "content": "hello"}],
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response_format={"type": "json_object"},
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completion_fn=_fake_completion,
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)
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assert result.usage.prompt_tokens == 2000
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assert result.usage.completion_tokens == 100
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assert result.usage.total_tokens == 2100
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assert result.usage.cost == pytest.approx(0.00051)
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assert captured["response_format"] == {"type": "json_object"}
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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 == {
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"model": "dashscope/qwen3.5-flash",
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"inputTokens": 2000,
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"outputTokens": 100,
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"cost": pytest.approx(0.00051),
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"latencyMs": 321,
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}
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