tzt
9fdf91c2fb
架构： - Router：低置信度直连/专家异常两条兜底路径的收尾逻辑（回退→finalize→record）提取为 _fallback_result 公共方法，消除三处重复收尾块 算法： - RouterCache：语义条目写入时预计算向量范数（原每次两两比较重算）、 语义查找单遍完成（原命中后二次 O(N) 查找）、相似度=1.0 提前终止扫描； 微基准（3000 条目×200 查询）：3986ms -> 1685ms，2.37x - RuleClassifier：同分决胜改为按领域名字典序（与规则表排列顺序无关的确定性）、 次高分由全排序改 O(n) 扫描 测试：新增 5 项（缓存范数一致性/提升后无残留/淘汰同步清理、决胜确定性、区分度惩罚） pytest 25 passed（原 20 全绿 + 新增 5） 基线检查点：66b6fd8（操作前已提交，20 passed）
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
from router_system.cache import RouterCache
|
||
|
||
|
||
def test_semantic_lookup_after_many_entries():
|
||
"""多条目下语义命中正确（范数预计算 + 单遍扫描的回归）。"""
|
||
c = RouterCache(similarity_threshold=0.5)
|
||
for i in range(50):
|
||
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}"})
|
||
c.put("用 Python 实现快速排序函数", {"response": "code-answer"})
|
||
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
|
||
assert level in ("semantic", "exact")
|
||
assert got["response"] == "code-answer"
|
||
|
||
|
||
def test_promotion_clears_semantic_state():
|
||
"""提升为精确缓存后，语义列表与范数索引无残留。"""
|
||
c = RouterCache(promote_frequency=2)
|
||
c.put("查询甲", {"response": "a"})
|
||
first = c.get("查询甲") # 相似度=1.0 计 exact，hits 达阈值即提升
|
||
assert first is not None and first[0] == "exact"
|
||
second = c.get("查询甲")
|
||
assert second is not None and second[0] == "exact"
|
||
assert c.stats()["exact_size"] == 1
|
||
assert c.stats()["semantic_size"] == 0
|
||
assert len(c._sem_norms) == 0
|
||
|
||
|
||
def test_semantic_eviction_clears_norms():
|
||
"""语义缓存满员淘汰最旧条目时，向量与范数索引同步清理。"""
|
||
c = RouterCache(max_semantic=2)
|
||
c.put("查询一", {"response": "1"})
|
||
c.put("查询二", {"response": "2"})
|
||
c.put("查询三", {"response": "3"}) # 淘汰查询一
|
||
assert len(c._semantic) == 2
|
||
assert len(c._sem_vecs) == 2
|
||
assert len(c._sem_norms) == 2
|
||
assert c.get("查询一") is None
|
||
|
||
|
||
def test_exact_hit():
|
||
c = RouterCache()
|
||
result = {"response": "hello", "domain": "general"}
|
||
assert c.get("query") is None
|
||
c.put("query", result)
|
||
level, got = c.get("query")
|
||
assert level == "exact"
|
||
assert got["response"] == "hello"
|
||
|
||
|
||
def test_semantic_hit():
|
||
c = RouterCache(semantic_enabled=True, similarity_threshold=0.5)
|
||
c.put("?python?????", {"response": "code", "domain": "code"})
|
||
# ?????????? L2
|
||
hit = c.get("?python????????")
|
||
assert hit is not None
|
||
assert hit[0] == "semantic"
|
||
|
||
|
||
def test_promote_to_exact():
|
||
c = RouterCache(promote_frequency=3)
|
||
result = {"response": "x", "domain": "general"}
|
||
c.put("query", result)
|
||
# ?????? 3 ? ? ???????
|
||
for _ in range(3):
|
||
hit = c.get("query")
|
||
assert hit is not None
|
||
assert c.stats()["exact_size"] == 1
|
||
|
||
|
||
def test_stats():
|
||
c = RouterCache()
|
||
c.put("q", {"response": "r"})
|
||
c.get("q")
|
||
c.get("q")
|
||
c.get("miss")
|
||
s = c.stats()
|
||
assert s["exact_hits"] == 2
|
||
assert s["misses"] == 1
|