added 35 semantic similarity

index_retriever.py

@@ -21,8 +21,8 @@ def create_query_engine(vector_index):
         
         vector_retriever = VectorIndexRetriever(
             index=vector_index, 
-            similarity_top_k=35,  # Increased
-            similarity_cutoff=0.65  # Slightly lower for recall
+            similarity_top_k=30,  # Increased
+            similarity_cutoff=0.7  # Slightly lower for recall
         )
         
         hybrid_retriever = QueryFusionRetriever(
@@ -74,7 +74,7 @@ def rerank_nodes(query, nodes, reranker, top_k=20, min_score_threshold=0.5, dive
             log_message("Нет узлов после фильтрации, снижаю порог")
             scored_nodes = list(zip(nodes, scores))
             scored_nodes.sort(key=lambda x: x[1], reverse=True)
-            min_score_threshold = scored_nodes[0][1] * 0.5  # 50% of top score
+            min_score_threshold = scored_nodes[0][1] * 0.6  # 60% of top score
             scored_nodes = [(node, score) for node, score in scored_nodes 
                           if score >= min_score_threshold]