fix(vector-stores/baidu): convert L2 distance to similarity score in search() (#6435)

mem0/vector_stores/baidu.py

@@ -224,8 +224,15 @@ class BaiduDB(VectorStoreBase):
         output = []
         for row in res.rows:
             row_data = row.get("row", {})
+            # Mochow returns the raw L2 distance (lower = closer). Convert it to a
+            # similarity score (higher = better) to satisfy the VectorStoreBase
+            # contract, mirroring the milvus provider. Non-L2 metrics already
+            # return a higher-is-better score.
+            raw_score = row.get("score", 0.0)
+            if self.metric_type in (MetricType.L2, "L2"):
+                raw_score = 1.0 / (1.0 + raw_score)
             output_data = OutputData(
-                id=row_data.get("id"), score=row.get("score", 0.0), payload=row_data.get("metadata", {})
+                id=row_data.get("id"), score=raw_score, payload=row_data.get("metadata", {})
             )
             output.append(output_data)
 

tests/vector_stores/test_baidu.py

@@ -137,6 +137,27 @@ def test_search(mochow_instance, mock_mochow_client):
     assert results[1].payload == {"name": "vector2"}
 
 
+def test_search_converts_l2_distance_to_similarity(mochow_instance):
+    # On the L2 metric, Mochow returns raw distances (lower = closer). search()
+    # must convert them to similarity scores (higher = better) to satisfy the
+    # VectorStoreBase contract, mirroring the milvus provider.
+    mochow_instance.metric_type = "L2"
+
+    mock_search_results = Mock()
+    mock_search_results.rows = [
+        {"row": {"id": "id1", "metadata": {"name": "vector1"}}, "score": 0.5},
+        {"row": {"id": "id2", "metadata": {"name": "vector2"}}, "score": 2.0},
+    ]
+    mochow_instance._table.vector_search.return_value = mock_search_results
+
+    results = mochow_instance.search(query="test", vectors=[0.1, 0.2, 0.3], top_k=2)
+
+    # 1.0 / (1.0 + distance): the closer memory must score higher than the far one.
+    assert results[0].score == pytest.approx(1.0 / 1.5)
+    assert results[1].score == pytest.approx(1.0 / 3.0)
+    assert results[0].score > results[1].score
+
+
 def test_search_with_filters(mochow_instance, mock_mochow_client):
     mochow_instance._table.vector_search.return_value = Mock(rows=[])