fix(vector_stores): normalize scores to similarity (higher = better) across all backends (#5391)

mem0/vector_stores/milvus.py

@@ -11,7 +11,14 @@ try:
 except ImportError:
     raise ImportError("The 'pymilvus' library is required. Please install it using 'pip install pymilvus'.")
 
-from pymilvus import CollectionSchema, DataType, FieldSchema, Function, FunctionType, MilvusClient
+from pymilvus import (
+    CollectionSchema,
+    DataType,
+    FieldSchema,
+    Function,
+    FunctionType,
+    MilvusClient,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -167,11 +174,14 @@ class MilvusDB(VectorStoreBase):
         memory = []
 
         for value in data:
-            uid, score, metadata = (
-                value.get("id"),
-                value.get("distance"),
-                value.get("entity", {}).get("metadata"),
-            )
+            uid = value.get("id")
+            raw_distance = value.get("distance")
+            metadata = value.get("entity", {}).get("metadata")
+
+            if raw_distance is not None and self.metric_type in (MetricType.L2, "L2"):
+                score = 1.0 / (1.0 + raw_distance)
+            else:
+                score = raw_distance
 
             memory_obj = OutputData(id=uid, score=score, payload=metadata)
             memory.append(memory_obj)