docs: update README/INTERNALS for import feature, harden .gitignore

tests/test_llm_quality.py

@@ -0,0 +1,111 @@
+# ============================================================
+# Test 2: LLM Quality Baseline — needs GEMINI_API_KEY
+# 测试 2：LLM 质量基准 —— 需要 GEMINI_API_KEY
+#
+# Verifies LLM auto-tagging returns reasonable results:
+#   - domain is a non-empty list of strings
+#   - valence ∈ [0, 1]
+#   - arousal ∈ [0, 1]
+#   - tags is a list
+#   - suggested_name is a string
+#   - domain matches content semantics (loose check)
+# ============================================================
+
+import os
+import pytest
+
+# Skip all tests if no API key
+pytestmark = pytest.mark.skipif(
+    not os.environ.get("OMBRE_API_KEY"),
+    reason="OMBRE_API_KEY not set — skipping LLM quality tests"
+)
+
+
+@pytest.fixture
+def dehydrator(test_config):
+    from dehydrator import Dehydrator
+    return Dehydrator(test_config)
+
+
+# Test cases: (content, expected_domains_superset, valence_range)
+LLM_CASES = [
+    (
+        "今天学了 Python 的 asyncio，终于搞懂了 event loop，心情不错",
+        {"学习", "编程", "技术", "数字", "Python"},
+        (0.5, 1.0),  # positive
+    ),
+    (
+        "被导师骂了一顿，论文写得太差了，很沮丧",
+        {"学习", "学业", "心理", "工作"},
+        (0.0, 0.4),  # negative
+    ),
+    (
+        "和朋友去爬了一座山，山顶的风景超美，累但值得",
+        {"生活", "旅行", "社交", "运动", "健康"},
+        (0.6, 1.0),  # positive
+    ),
+    (
+        "在阳台上看日落，什么都没想，很平静",
+        {"生活", "心理", "自省"},
+        (0.4, 0.8),  # calm positive
+    ),
+    (
+        "I built a FastAPI app with Docker and deployed it on Render",
+        {"编程", "技术", "学习", "数字", "工作"},
+        (0.5, 1.0),  # positive
+    ),
+]
+
+
+class TestLLMQuality:
+    """Verify LLM auto-tagging produces reasonable outputs."""
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize("content,expected_domains,valence_range", LLM_CASES)
+    async def test_analyze_structure(self, dehydrator, content, expected_domains, valence_range):
+        """Check that analyze() returns valid structure and reasonable values."""
+        result = await dehydrator.analyze(content)
+
+        # Structure checks
+        assert isinstance(result, dict)
+        assert "domain" in result
+        assert "valence" in result
+        assert "arousal" in result
+        assert "tags" in result
+
+        # Domain is non-empty list of strings
+        assert isinstance(result["domain"], list)
+        assert len(result["domain"]) >= 1
+        assert all(isinstance(d, str) for d in result["domain"])
+
+        # Valence and arousal in range
+        assert 0.0 <= result["valence"] <= 1.0, f"valence {result['valence']} out of range"
+        assert 0.0 <= result["arousal"] <= 1.0, f"arousal {result['arousal']} out of range"
+
+        # Valence roughly matches expected range (with tolerance)
+        lo, hi = valence_range
+        assert lo - 0.15 <= result["valence"] <= hi + 0.15, \
+            f"valence {result['valence']} not in expected range ({lo}, {hi}) for: {content[:30]}..."
+
+        # Tags is a list
+        assert isinstance(result["tags"], list)
+
+    @pytest.mark.asyncio
+    async def test_analyze_domain_semantic_match(self, dehydrator):
+        """Check that domain has at least some semantic relevance."""
+        result = await dehydrator.analyze("我家的橘猫小橘今天又偷吃了桌上的鱼")
+        domains = set(result["domain"])
+        # Should contain something life/pet related
+        life_related = {"生活", "宠物", "家庭", "日常", "动物"}
+        assert domains & life_related, f"Expected life-related domain, got {domains}"
+
+    @pytest.mark.asyncio
+    async def test_analyze_empty_content(self, dehydrator):
+        """Empty content should raise or return defaults gracefully."""
+        try:
+            result = await dehydrator.analyze("。")
+            # If it doesn't raise, should still return valid structure
+            assert isinstance(result, dict)
+            assert 0.0 <= result["valence"] <= 1.0
+        except Exception:
+            pass  # Raising is also acceptable