feat: 添加 Cross-Encoder Reranker + 集成到 Searcher

tests/test_reranker.py

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+"""重排序器测试."""
+import pytest
+
+from src.core.reranker import Reranker
+
+
+class FakeCrossEncoder:
+    """模拟 Cross-Encoder 模型."""
+
+    def predict(self, pairs, **kwargs):
+        # 包含"重要"的 pair 分数高
+        scores = []
+        for pair in pairs:
+            score = 5.0 if "重要" in pair[1] else 1.0
+            scores.append(score)
+        return scores
+
+
+def test_reranker_returns_same_count():
+    """重排序不改变结果数量."""
+    reranker = Reranker(model_name="test-model")
+    reranker._model = FakeCrossEncoder()
+    candidates = [
+        {"content": "普通文档", "score": 0.8},
+        {"content": "重要文档", "score": 0.6},
+        {"content": "另一个普通", "score": 0.7},
+    ]
+    result = reranker.rerank("查询", candidates, top_k=3)
+    assert len(result) == 3
+
+
+def test_reranker_promotes_relevant():
+    """重排序将更相关的内容提前."""
+    reranker = Reranker(model_name="test-model")
+    reranker._model = FakeCrossEncoder()
+    candidates = [
+        {"content": "普通 A", "score": 0.9},
+        {"content": "重要内容在这里", "score": 0.5},
+        {"content": "普通 B", "score": 0.7},
+    ]
+    result = reranker.rerank("查询", candidates, top_k=3)
+    assert "重要" in result[0]["content"]
+
+
+def test_reranker_truncates_to_top_k():
+    """rerank 截断到指定的 top_k."""
+    reranker = Reranker(model_name="test-model")
+    reranker._model = FakeCrossEncoder()
+    candidates = [
+        {"content": f"文档{i}", "score": 0.9 - i * 0.1}
+        for i in range(20)
+    ]
+    result = reranker.rerank("查询", candidates, top_k=5)
+    assert len(result) == 5
+
+
+def test_reranker_empty_input():
+    """空输入返回空列表."""
+    reranker = Reranker(model_name="test-model")
+    result = reranker.rerank("查询", [], top_k=5)
+    assert result == []
+
+
+def test_reranker_preserves_metadata():
+    """重排序保留文档元数据."""
+    reranker = Reranker(model_name="test-model")
+    reranker._model = FakeCrossEncoder()
+    candidates = [
+        {
+            "content": "带元数据的文档",
+            "score": 0.5,
+            "source_file": "meta.md",
+            "section_title": "第一章",
+        }
+    ]
+    result = reranker.rerank("查询", candidates, top_k=1)
+    assert result[0]["source_file"] == "meta.md"
+    assert result[0]["section_title"] == "第一章"
+
+
+def test_reranker_score_replaced_with_rerank():
+    """重排序后 score 更新为 rerank_score."""
+    reranker = Reranker(model_name="test-model")
+    reranker._model = FakeCrossEncoder()
+    candidates = [{"content": "测试", "score": 0.5}]
+    result = reranker.rerank("查询", candidates, top_k=1)
+    assert "rerank_score" in result[0]
+    assert result[0]["score"] == result[0]["rerank_score"]