将事件名称转义成中文及优化关键字检索

backend/app/api/routers/app.py

@@ -32,6 +32,24 @@ v1_router = v = APIRouter()
 gEvent_handler = None
 
 
+CH_Event_map={
+    'CHUNKING':'文本切片',
+    'NODE_PARSING':'节点解析',
+    'EMBEDDING':'生成向量',
+    'LLM':'知识问答',
+    'QUERY':'查询',
+    'RETRIEVE':'检索',
+    'SYNTHESIZE':'答案合成',
+    'TREE':'总结',
+    'SUB_QUESTION':'问题分解',
+    'TEMPLATING':'生成提示词模板',
+    'FUNCTION_CALL':'函数调用',
+    'RERANKING':'节点重排',
+    'EXCEPTION':'执行异常',
+    'AGENT_STEP':'单步执行'
+}
+
+
 class ChatCallbackEvent(BaseModel):
     event_type: ChatEventType
     payload: Optional[Dict[str, Any]] = None
@@ -99,7 +117,7 @@ class ChatCallbackEvent(BaseModel):
                 "id": self.payload.get('nodeid'),
                 "node_id": self.payload.get('nodeid'),
                 "node_type": "http-request",
-                "title": f"正在执行事件：{self.payload.get('title')}",
+                "title": CH_Event_map[self.payload.get('title')],
                 "index": self.payload.get('index'),
                 "predecessor_node_id": self.payload.get('predecessor_node_id'),
                 "inputs": '',
@@ -117,7 +135,7 @@ class ChatCallbackEvent(BaseModel):
                 "id": self.payload.get('nodeid'),
                 "node_id": self.payload.get('nodeid'),
                 "node_type": "http-request",
-                "title": f"事件执行结束：{self.payload.get('title')}",
+                "title": CH_Event_map[self.payload.get('title')],
                 "index": self.payload.get('index'),
                 "predecessor_node_id": self.payload.get('predecessor_node_id'),
                 "inputs": '',

backend/app/engine/retriever/HybridRetriever.py

@@ -43,6 +43,16 @@ class HybridRetriever(BaseRetriever):
         for node in bmNodes:
             bmDic[node.node_id] = node
 
+        vecScores = [node_with_score.score for node_with_score in vecNodes]
+        bmSores = [node_with_score.score for node_with_score in bmNodes]
+
+        vec_min_score = min(vecScores)
+        vec_max_score = max(vecScores)
+
+        bm_min_score = min(bmSores)
+        bm_max_score = max(bmSores)
+
+
         result_tups = []
         for i in range(len(vecNodes)):
             node = vecNodes[i]
@@ -52,7 +62,11 @@ class HybridRetriever(BaseRetriever):
                 bmDic.pop(node.node_id)
             else:
                 bmScore = 0.0
-            full_similarity = (self._alpha * node.score) + (
+
+            bmScore =  self.normal_score(bmScore,bm_min_score,bm_max_score)
+            vecScore = self.normal_score(node.score,vec_min_score,vec_max_score)
+
+            full_similarity = (self._alpha * vecScore) + (
                     (1 - self._alpha) * bmScore
             )
             result_tups.append((full_similarity, node))
@@ -64,4 +78,10 @@ class HybridRetriever(BaseRetriever):
         result_tups = sorted(result_tups, key=lambda x: x[0], reverse=True)
         for full_score, node in result_tups:
             node.score = full_score
-        return [n for _, n in result_tups][:self._out_top_k]
+        return [n for _, n in result_tups][:self._out_top_k]
+    
+    def normal_score(self,score,min,max):
+        if min == max:
+            return 1.0 if score > 0 else 0.0
+        else:
+            return (score - min) / (max - min)