112 lines
4.0 KiB
Python
112 lines
4.0 KiB
Python
# ============================================================
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# Test 2: LLM Quality Baseline — needs GEMINI_API_KEY
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# 测试 2：LLM 质量基准 —— 需要 GEMINI_API_KEY
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#
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# Verifies LLM auto-tagging returns reasonable results:
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# - domain is a non-empty list of strings
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# - valence ∈ [0, 1]
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# - arousal ∈ [0, 1]
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# - tags is a list
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# - suggested_name is a string
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# - domain matches content semantics (loose check)
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# ============================================================
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import os
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import pytest
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# Skip all tests if no API key
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pytestmark = pytest.mark.skipif(
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not os.environ.get("OMBRE_API_KEY"),
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reason="OMBRE_API_KEY not set — skipping LLM quality tests"
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)
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@pytest.fixture
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def dehydrator(test_config):
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from dehydrator import Dehydrator
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return Dehydrator(test_config)
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# Test cases: (content, expected_domains_superset, valence_range)
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LLM_CASES = [
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(
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"今天学了 Python 的 asyncio，终于搞懂了 event loop，心情不错",
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{"学习", "编程", "技术", "数字", "Python"},
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(0.5, 1.0), # positive
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),
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(
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"被导师骂了一顿，论文写得太差了，很沮丧",
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{"学习", "学业", "心理", "工作"},
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(0.0, 0.4), # negative
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),
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(
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"和朋友去爬了一座山，山顶的风景超美，累但值得",
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{"生活", "旅行", "社交", "运动", "健康"},
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(0.6, 1.0), # positive
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),
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(
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"在阳台上看日落，什么都没想，很平静",
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{"生活", "心理", "自省"},
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(0.4, 0.8), # calm positive
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),
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(
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"I built a FastAPI app with Docker and deployed it on Render",
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{"编程", "技术", "学习", "数字", "工作"},
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(0.5, 1.0), # positive
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),
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]
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class TestLLMQuality:
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"""Verify LLM auto-tagging produces reasonable outputs."""
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@pytest.mark.asyncio
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@pytest.mark.parametrize("content,expected_domains,valence_range", LLM_CASES)
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async def test_analyze_structure(self, dehydrator, content, expected_domains, valence_range):
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"""Check that analyze() returns valid structure and reasonable values."""
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result = await dehydrator.analyze(content)
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# Structure checks
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assert isinstance(result, dict)
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assert "domain" in result
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assert "valence" in result
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assert "arousal" in result
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assert "tags" in result
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# Domain is non-empty list of strings
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assert isinstance(result["domain"], list)
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assert len(result["domain"]) >= 1
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assert all(isinstance(d, str) for d in result["domain"])
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# Valence and arousal in range
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assert 0.0 <= result["valence"] <= 1.0, f"valence {result['valence']} out of range"
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assert 0.0 <= result["arousal"] <= 1.0, f"arousal {result['arousal']} out of range"
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# Valence roughly matches expected range (with tolerance)
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lo, hi = valence_range
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assert lo - 0.15 <= result["valence"] <= hi + 0.15, \
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f"valence {result['valence']} not in expected range ({lo}, {hi}) for: {content[:30]}..."
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# Tags is a list
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assert isinstance(result["tags"], list)
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@pytest.mark.asyncio
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async def test_analyze_domain_semantic_match(self, dehydrator):
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"""Check that domain has at least some semantic relevance."""
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result = await dehydrator.analyze("我家的橘猫小橘今天又偷吃了桌上的鱼")
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domains = set(result["domain"])
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# Should contain something life/pet related
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life_related = {"生活", "宠物", "家庭", "日常", "动物"}
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assert domains & life_related, f"Expected life-related domain, got {domains}"
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@pytest.mark.asyncio
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async def test_analyze_empty_content(self, dehydrator):
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"""Empty content should raise or return defaults gracefully."""
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try:
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result = await dehydrator.analyze("。")
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# If it doesn't raise, should still return valid structure
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assert isinstance(result, dict)
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assert 0.0 <= result["valence"] <= 1.0
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except Exception:
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pass # Raising is also acceptable
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