diff --git a/src/blackbox/chroma_query.py b/src/blackbox/chroma_query.py
index 4f22d68..ae851e4 100755
--- a/src/blackbox/chroma_query.py
+++ b/src/blackbox/chroma_query.py
@@ -2,6 +2,7 @@ from typing import Any, Coroutine
 
 from fastapi import Request, Response, status
 from fastapi.responses import JSONResponse
+import numpy as np
 from .blackbox import Blackbox
 
 import chromadb
@@ -9,6 +10,7 @@ from chromadb.utils import embedding_functions
 import logging
 from ..log.logging_time import logging_time
 import re
+from sentence_transformers import CrossEncoder 
 
 logger = logging.getLogger
 DEFAULT_COLLECTION_ID = "123"
@@ -24,6 +26,7 @@ class ChromaQuery(Blackbox):
         self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/gpu/Workspace/Models/BAAI/bge-small-en-v1.5", device = "cuda:1")
         self.client_1 = chromadb.HttpClient(host='10.6.81.119', port=7000)
         # self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000)
+        self.reranker_model_1 = CrossEncoder("/home/gpu/Workspace/Models/bge-reranker-v2-m3", max_length=512, device = "cuda")
 
     def __call__(self, *args, **kwargs):
         return self.processing(*args, **kwargs)
@@ -46,6 +49,8 @@ class ChromaQuery(Blackbox):
         chroma_port = settings.get("chroma_port")
         chroma_collection_id = settings.get("chroma_collection_id")
         chroma_n_results = settings.get("chroma_n_results")
+        chroma_reranker_model = settings.get("chroma_reranker_model")
+        chroma_reranker_num = settings.get("chroma_reranker_num")
 
         if usr_question is None:
             return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
@@ -63,20 +68,26 @@ class ChromaQuery(Blackbox):
             chroma_collection_id = "g2e"
 
         if chroma_n_results is None or chroma_n_results == "":
-            chroma_n_results = 3
+            chroma_n_results = 10
 
         # load client and embedding model from init
         if re.search(r"10.6.81.119", chroma_host) and re.search(r"7000", chroma_port):
             client = self.client_1
         else:
-            client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
+            try:
+                client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
+            except:
+                return JSONResponse(content={"error": "chroma client not found"}, status_code=status.HTTP_400_BAD_REQUEST)
 
         if re.search(r"/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
             embedding_model = self.embedding_model_1
         elif re.search(r"/home/gpu/Workspace/Models/BAAI/bge-small-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:1")
+            try:
+                embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=chroma_embedding_model, device = "cuda:1")
+            except:
+                return JSONResponse(content={"error": "embedding model not found"}, status_code=status.HTTP_400_BAD_REQUEST)
 
         # load collection
         collection = client.get_collection(chroma_collection_id, embedding_function=embedding_model)
@@ -93,18 +104,53 @@ class ChromaQuery(Blackbox):
 
         final_result = ''
 
-        if results is not None:
-            results_distances = results["distances"][0]
+        # if results is not None:
+        #     results_distances = results["distances"][0]
 
-            #distance越高越不准确
-            top_distance = 0.8
+        #     #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]
+        #     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)
+        #     print("\n final_result: ", final_result)
+
+        final_result =  str(results["documents"])
+
+        if chroma_reranker_model:
+            if re.search(r"/home/gpu/Workspace/Models/BAAI/bge-reranker-v2-m3", chroma_embedding_model):
+                reranker_model = self.chroma_reranker_model_1
+            else:
+                try:
+                    reranker_model = CrossEncoder(chroma_reranker_model, max_length=512, device = "cuda")
+                except:
+                    return JSONResponse(content={"error": "reranker model not found"}, status_code=status.HTTP_400_BAD_REQUEST)
+
+            if chroma_reranker_num:
+                if chroma_reranker_num > chroma_n_results:
+                    chroma_reranker_num = chroma_n_results
+            else:
+                chroma_reranker_num = 5
+
+            #对每一对（查询、文档）进行评分
+            pairs = [[usr_question, doc] for doc in results["documents"][0]] 
+            print('\n',pairs)
+            scores = reranker_model.predict(pairs) 
+
+            #重新排列文件顺序：
+            print("New Ordering:") 
+            i = 0 
+            final_result = ''
+            for o in np.argsort(scores)[::-1]:
+                if i == chroma_reranker_num or scores[o] < 0.5:
+                    break
+                i += 1
+                print(o+1)
+                print("Scores:", scores[o])
+                print(results["documents"][0][o],'\n')
+                final_result += results["documents"][0][o] + '\n'
 
         return final_result