修改答案合成的策略

backend/app/engine/__init__.py

@@ -11,6 +11,7 @@ from app.engine.prompt import ReActChatFormatter_messages, tree_summary_query_en
 #from app.engine.loaders.db import makeDescriptionByEngine
 from app.engine.tools import ToolFactory
 from app.api.routers.request.base import ProjectInfo
+from llama_index.core.response_synthesizers import ResponseMode
 
 def getPrjFalg(params:dict=None)->str:
     prjFlag = ''
@@ -42,11 +43,11 @@ def get_chat_engine(filters=None, params:dict=None):
                                                             description=summary_query_tool_messages,
                                                             )
 
-        query_engine = create_query_engine(index,top_k,use_reranker,filters,response_mode = "COMPACT")
+        query_engine = create_query_engine(index,top_k,use_reranker,filters,response_mode = ResponseMode.TREE_SUMMARIZE)
         query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine, name="zj_query_tool",
                                                           description=query_engine_tool_messages)
 
-        query_engine = create_query_engine(index,top_k,use_reranker,filters,response_mode = "TREE_SUMMARIZE")
+        query_engine = create_query_engine(index,top_k,use_reranker,filters,response_mode = ResponseMode.TREE_SUMMARIZE)
         query_engine_tool_1 = QueryEngineTool.from_defaults(query_engine=query_engine, name="zj_query_tool_1",
                                                           description=tree_summary_query_engine_tool_messages)
 

backend/app/engine/engine.py

@@ -103,7 +103,7 @@ def create_query_engine(index, top_k=3, use_reranker=False, filters=None, respon
         node_postprocessors=postprocess,
         use_async=True,
         streaming=False,
-        ResponseMode = response_mode
+        response_mode = response_mode
     )
 
     return query_engine

backend/app/engine/retriever/HybridRetriever.py

@@ -48,11 +48,11 @@ class HybridRetriever(BaseRetriever):
         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_min_score = min(vecScores) if len(vecScores) > 0 else 0
-        vec_max_score = max(vecScores)
+        vec_max_score = max(vecScores) if len(vecScores) > 0 else 0
 
-        bm_min_score = min(bmSores)
+        bm_min_score = min(bmSores) if len(bmSores) > 0 else 0
-        bm_max_score = max(bmSores)
+        bm_max_score = max(bmSores) if len(bmSores) > 0 else 0
 
 
         result_tups = []