diff --git a/src/blackbox/chroma_query.py b/src/blackbox/chroma_query.py
index 4fafb29..72d0262 100755
--- a/src/blackbox/chroma_query.py
+++ b/src/blackbox/chroma_query.py
@@ -21,7 +21,7 @@ class ChromaQuery(Blackbox):
         # config = read_yaml(args[0])
         # load chromadb and embedding model
         self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda")
-        # self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/model/Weight/BAAI/bge-small-en-v1.5", device = "cuda")
+        self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-large-en-v1.5", device = "cuda")
         self.client_1 = chromadb.HttpClient(host='172.16.5.8', port=7000)
         # self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000)
 
@@ -73,20 +73,40 @@ class ChromaQuery(Blackbox):
 
         if re.search(r"/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
             embedding_model = self.embedding_model_1
+        elif re.search(r"/home/administrator/Workspace/Models/BAAI/bge-large-en-v1.5", chroma_embedding_model):
+            embedding_model = self.embedding_model_2
         else:
             embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=chroma_embedding_model, device = "cuda")
 
         # load collection
         collection = client.get_collection(chroma_collection_id, embedding_function=embedding_model)
 
+        print(usr_question)
         # query it
         results = collection.query(
             query_texts=[usr_question],
-            n_results=3,    
+            n_results=chroma_n_results,    
         )
 
         # response = str(results["documents"] + results["metadatas"])
-        response = str(results["documents"])
+        # response = str(results["documents"])
+
+        final_result = ''
+
+        if results is not None:
+            results_distances = results["distances"][0]
+
+            #distance越高越不准确
+            top_distance = 0.8
+
+            
+            for i in range(len(results_distances)):
+                if results_distances[i] < top_distance:
+                    final_result += results["documents"][0][i]
+
+            print("\n final_result: ", final_result)
+
+        return final_result
 
         return response