tzt
b2fa8c3c81
算法： - RouterCache：语义条目写入时预计算向量范数、语义查找单遍完成（消除命中后二次 O(N) 查找）、 相似度=1.0 提前终止；微基准（3000 条目×200 查询）：3986ms -> 1685ms，2.37x - TaskGraph.topo_order：O(V²logV) 重排序/成员扫描 -> 邻接表+deque 的 O(V+E) Kahn， 输出顺序契约不变（初始就绪层按插入序、循环依赖按插入序兜底、未知依赖忽略） - RuleClassifier：同分决胜按领域名字典序（与规则表排列无关），次高分 O(n) 扫描 工程卫生： - .mimosa/（扫描器工作目录）加入 .gitignore 并移出索引 - test_review 抽样测试改用内联确定性 LCG，消除 2 个低危（不安全随机数） 测试：新增 11 项（topo 契约 6 + 缓存回归 3 + 分类器 2） pytest 230 passed（基线 219 全绿 + 11）
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
from router_system.cache import RouterCache
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def test_semantic_lookup_after_many_entries():
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"""多条目下语义命中正确（范数预计算 + 单遍扫描的回归）。"""
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c = RouterCache(similarity_threshold=0.5)
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for i in range(50):
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c.put(f"完全不相关的查询主题编号{i}关于烹饪的意见", {"response": f"r{i}"})
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c.put("用 Python 实现快速排序函数", {"response": "code-answer"})
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level, got = c.get("用 Python 实现快速排序的函数写法") # 相似但不完全相同
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assert level in ("semantic", "exact")
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assert got["response"] == "code-answer"
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def test_promotion_clears_semantic_state():
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"""提升为精确缓存后，语义列表与范数索引无残留。"""
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c = RouterCache(promote_frequency=2)
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c.put("查询甲", {"response": "a"})
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first = c.get("查询甲") # 相似度=1.0 计 exact，hits 达阈值即提升
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assert first is not None and first[0] == "exact"
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second = c.get("查询甲")
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assert second is not None and second[0] == "exact"
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assert c.stats()["exact_size"] == 1
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assert c.stats()["semantic_size"] == 0
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assert len(c._sem_norms) == 0
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def test_semantic_eviction_clears_norms():
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"""语义缓存满员淘汰最旧条目时，向量与范数索引同步清理。"""
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c = RouterCache(max_semantic=2)
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c.put("查询一", {"response": "1"})
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c.put("查询二", {"response": "2"})
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c.put("查询三", {"response": "3"}) # 淘汰查询一
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assert len(c._semantic) == 2
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assert len(c._sem_vecs) == 2
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assert len(c._sem_norms) == 2
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assert c.get("查询一") is None
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def test_exact_hit():
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c = RouterCache()
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result = {"response": "hello", "domain": "general"}
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assert c.get("query") is None
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c.put("query", result)
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level, got = c.get("query")
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assert level == "exact"
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assert got["response"] == "hello"
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def test_semantic_hit():
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c = RouterCache(semantic_enabled=True, similarity_threshold=0.5)
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c.put("用python写一个快速排序", {"response": "code", "domain": "code"})
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# 相似改写查询命中 L2 语义缓存
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hit = c.get("用python写一个快速排序算法")
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assert hit is not None
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assert hit[0] == "semantic"
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def test_promote_to_exact():
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c = RouterCache(promote_frequency=3)
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result = {"response": "x", "domain": "general"}
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c.put("query", result)
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# 语义命中 3 次后提升为精确缓存
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for _ in range(3):
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hit = c.get("query")
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assert hit is not None
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assert c.stats()["exact_size"] == 1
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def test_stats():
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c = RouterCache()
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c.put("q", {"response": "r"})
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c.get("q")
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c.get("q")
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c.get("miss")
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s = c.stats()
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assert s["exact_hits"] == 2
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assert s["misses"] == 1
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