返回值更完善

app/router.py

@@ -94,13 +94,28 @@ def search_error(query: ErrorQuery):
     for idx, score in zip(indices[0], sim[0]):
         db_id = index_to_id[idx]
         db_entry = error_memory[db_id]
+
+        # 分层匹配得分
         layer_score = compute_layered_similarity_sco(user_vectors, db_entry["layer_vectors"])
+
+        # 逐层匹配，提取每一层的关键字
+        matched_keywords = []
+        for user_layer, db_layer in zip(user_layers, db_entry["layers"]):
+            # 这里简单取两个文本的公共单词作为关键字（你可以换成更复杂的匹配方法）
+            user_tokens = set(user_layer.split())
+            db_tokens = set(db_layer.split())
+            common_tokens = user_tokens & db_tokens
+            matched_keywords.append(list(common_tokens))
+
         results.append({
             "db_id": db_id,
-            "similarity": round(layer_score, 4),
+            "aggregate_similarity": round(float(score), 4),    # 聚合索引匹配得分
-            "matched_layers": db_entry["layers"]
+            "layer_similarity": round(layer_score, 4),         # 分层匹配得分
+            "matched_layers": db_entry["layers"],              # 匹配到的 traceback 每一层
+            "matched_keywords": matched_keywords               # 每一层匹配到的关键字列表
         })
 
-    # 还需要根据相似度排序
+    # 先按分层得分排，再按聚合得分排
-    results.sort(key=lambda x: x["similarity"], reverse=True)
+    results.sort(key=lambda x: (x["layer_similarity"], x["aggregate_similarity"]), reverse=True)
+
     return results