feat:客户agent以及记忆模块优化测试

rag/retrieve.py

@@ -90,9 +90,12 @@ def _format_candidates(candidates) -> list[dict]:
             distance = hit.get("distance")
             raw_score = hit.get("score")
             if distance is not None:
-                score = 1.0 - distance
+                # All project Milvus collections use COSINE. For COSINE,
+                # Milvus returns a similarity score: larger means more relevant.
+                # Do not invert it as if it were an L2 distance.
+                score = float(distance)
             else:
-                score = raw_score
+                score = float(raw_score) if raw_score is not None else None
             if score is None or score < threshold:
                 continue
             sources.append(
@@ -160,4 +163,4 @@ async def retrieve_with_status(
     except Exception:
         logger.exception("Milvus retrieval failed")
         return {"status": "milvus_unavailable", "sources": []}
-    return {"status": "ok", "sources": _format_candidates(candidates)}
+    return {"status": "ok", "sources": _format_candidates(candidates)}