Initial commit: QMDSearch 分层信息检索服务

- FastAPI + Qdrant + Redis + Ollama 技术栈
- L1→L2→L3→chunk 四层分层检索（dense + sparse RRF 融合）
- 文档三级总结与 2.5 级回退
- query 解析路由与分类
- /admin 管理页面

tests/test_eval_metrics.py

@@ -0,0 +1,94 @@
+"""评测指标纯函数单元测试（不依赖 Qdrant / Ollama）"""
+
+import pytest
+
+from scripts.eval.metrics import (
+    aggregate,
+    entity_recall,
+    precision_at_k,
+    pruning_loss,
+    recall_at_k,
+    routing_f1,
+)
+
+
+class TestEntityRecall:
+    def test_all_entities_kept(self) -> None:
+        source = "使用 Qdrant 存储 1536 维向量，模型 bge-m3，见《部署手册》第三条。"
+        summary = "基于 Qdrant 与 bge-m3 的 1536 维向量检索，详见《部署手册》第三条。"
+        assert entity_recall(source, summary) == pytest.approx(1.0)
+
+    def test_partial_entities_kept(self) -> None:
+        source = "支持 text-embedding-3-small 和 bge-m3 两个模型。"
+        summary = "支持 bge-m3 模型。"
+        score = entity_recall(source, summary)
+        assert 0.0 < score < 1.0
+
+    def test_no_entities_returns_full_score(self) -> None:
+        assert entity_recall("这是一段没有任何实体的中文普通句子。", "很短") == 1.0
+
+    def test_empty_summary_drops_all(self) -> None:
+        source = "版本 v1.2.0 修复了 3 个问题。"
+        assert entity_recall(source, "") == pytest.approx(0.0)
+
+
+class TestRoutingF1:
+    def test_perfect_routing(self) -> None:
+        golden = [{"d1"}, {"d2"}]
+        routed = [{"d1"}, {"d2"}]
+        result = routing_f1(golden, routed)
+        assert result == {"precision": 1.0, "recall": 1.0, "f1": 1.0}
+
+    def test_micro_average(self) -> None:
+        # query1: tp=1 fp=1；query2: tp=0 fn=1 → micro p=1/2 r=1/2 f1=1/2
+        golden = [{"d1"}, {"d2"}]
+        routed = [{"d1", "d3"}, set()]
+        result = routing_f1(golden, routed)
+        assert result["precision"] == pytest.approx(0.5)
+        assert result["recall"] == pytest.approx(0.5)
+        assert result["f1"] == pytest.approx(0.5)
+
+    def test_empty_routed(self) -> None:
+        result = routing_f1([{"d1"}], [set()])
+        assert result["precision"] == 0.0
+        assert result["recall"] == 0.0
+
+
+class TestPruningLoss:
+    def test_mixed_cases(self) -> None:
+        assert pruning_loss([False, True, False, False]) == pytest.approx(0.25)
+
+    def test_empty(self) -> None:
+        assert pruning_loss([]) == 0.0
+
+
+class TestPrecisionRecallAtK:
+    def test_precision_at_k(self) -> None:
+        hits = ["d1", "d2", "d3", "d4", "d5"]
+        assert precision_at_k(hits, {"d1", "d3"}, 5) == pytest.approx(0.4)
+
+    def test_precision_at_k_empty_hits(self) -> None:
+        assert precision_at_k([], {"d1"}, 5) == 0.0
+
+    def test_recall_at_k_dedup(self) -> None:
+        hits = ["d1", "d1", "d2"]
+        assert recall_at_k(hits, {"d1", "d2", "d3"}, 3) == pytest.approx(2 / 3)
+
+    def test_recall_at_k_empty_golden(self) -> None:
+        assert recall_at_k(["d1"], set(), 5) == 0.0
+
+
+class TestAggregate:
+    def test_mean_per_key(self) -> None:
+        records = [{"a": 1.0, "b": 0.5}, {"a": 0.0, "b": 1.0}]
+        result = aggregate(records)
+        assert result["a"] == pytest.approx(0.5)
+        assert result["b"] == pytest.approx(0.75)
+
+    def test_partial_keys(self) -> None:
+        result = aggregate([{"a": 1.0}, {"a": 0.0, "b": 1.0}])
+        assert result["a"] == pytest.approx(0.5)
+        assert result["b"] == pytest.approx(1.0)
+
+    def test_empty(self) -> None:
+        assert aggregate([]) == {}