Update app.py

app.py

@@ -289,12 +289,83 @@ def split_message(message, max_length=500):
 #             response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
         
 #         return generate_response_xml(from_user_name, to_user_name, response_content)
+# @app.route('/api/wx', methods=['GET', 'POST'])
+# def wechatai():
+#     if request.method == 'GET':
+#         return verify_wechat(request)
+#     else:
+#         # 处理POST请求
+#         xml_str = request.data
+#         if not xml_str:
+#             return ""
+        
+#         user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(xml_str)
+        
+#         if from_user_name not in user_sessions:
+#             user_sessions[from_user_name] = {'model': 'gpt-4o-mini', 'messages': [], 'pending_response': []}
+
+#         session = user_sessions[from_user_name]
+        
+#         if user_message_content.lower() == '/models':
+#             response_content = f"可用的模型列表：\n{list_available_models()}\n\n使用 /model 模型名称 来切换模型"
+#             return generate_response_xml(from_user_name, to_user_name, response_content)
+#         elif user_message_content.lower().startswith('/model'):
+#             model = user_message_content.split(' ')[1]
+#             if model in AVAILABLE_MODELS:
+#                 session['model'] = model
+#                 response_content = f'模型已切换为 {AVAILABLE_MODELS[model]}'
+#             else:
+#                 response_content = f'无效的模型名称。可用的模型有：\n{list_available_models()}'
+#             return generate_response_xml(from_user_name, to_user_name, response_content)
+#         elif user_message_content.lower() == '继续':
+#             if session['pending_response']:
+#                 response_content = session['pending_response'].pop(0)
+#                 if session['pending_response']:
+#                     response_content += '\n\n回复"继续"获取下一部分。'
+#                 else:
+#                     response_content += '\n\n回复结束。'
+#             else:
+#                 response_content = "没有待发送的消息。"
+#             return generate_response_xml(from_user_name, to_user_name, response_content)
+        
+#         session['messages'].append({"role": "user", "content": user_message_content})
+        
+#         # 调用OpenAI API
+#         ai_response = get_openai_response(session['messages'], model=session['model'], functions=FUNCTIONS, function_call="auto")
+        
+#         if ai_response.function_call:
+#             function_name = ai_response.function_call.name
+#             function_args = json.loads(ai_response.function_call.arguments)
+#             function_result = process_function_call(function_name, function_args)
+            
+#             session['messages'].append(ai_response.model_dump())
+#             session['messages'].append({
+#                 "role": "function",
+#                 "name": function_name,
+#                 "content": json.dumps(function_result, ensure_ascii=False)
+#             })
+            
+#             # 再次调用OpenAI API，将函数执行结果作为上下文
+#             final_response = get_openai_response(session['messages'], model=session['model'])
+#             response_content = final_response.content
+#         else:
+#             response_content = ai_response.content
+        
+#         session['messages'].append({"role": "assistant", "content": response_content})
+        
+#         # 处理长消息
+#         response_parts = split_message(response_content)
+#         if len(response_parts) > 1:
+#             session['pending_response'] = response_parts[1:]
+#             response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
+        
+#         return generate_response_xml(from_user_name, to_user_name, response_content)
+
 @app.route('/api/wx', methods=['GET', 'POST'])
 def wechatai():
     if request.method == 'GET':
         return verify_wechat(request)
     else:
-        # 处理POST请求
         xml_str = request.data
         if not xml_str:
             return ""
@@ -302,10 +373,11 @@ def wechatai():
         user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(xml_str)
         
         if from_user_name not in user_sessions:
-            user_sessions[from_user_name] = {'model': 'gpt-4o-mini', 'messages': [], 'pending_response': []}
+            user_sessions[from_user_name] = {'model': 'gpt-3.5-turbo', 'messages': [], 'pending_response': []}
 
         session = user_sessions[from_user_name]
         
+        # 处理特殊命令
         if user_message_content.lower() == '/models':
             response_content = f"可用的模型列表：\n{list_available_models()}\n\n使用 /model 模型名称 来切换模型"
             return generate_response_xml(from_user_name, to_user_name, response_content)
@@ -327,29 +399,48 @@ def wechatai():
             else:
                 response_content = "没有待发送的消息。"
             return generate_response_xml(from_user_name, to_user_name, response_content)
-        
+
         session['messages'].append({"role": "user", "content": user_message_content})
+        messages = session['messages']
+        
+        # 次级模型1: 处理搜索相关函数
+        sub_model_1_response = get_openai_response(messages, model=session['model'], functions=FUNCTIONS_GROUP_1, function_call="auto")
+        
+        # 次级模型2: 处理邮件发送相关函数
+        sub_model_2_response = get_openai_response(messages, model=session['model'], functions=FUNCTIONS_GROUP_2, function_call="auto")
         
-        # 调用OpenAI API
-        ai_response = get_openai_response(session['messages'], model=session['model'], functions=FUNCTIONS, function_call="auto")
+        function_call_1 = sub_model_1_response.function_call if sub_model_1_response and sub_model_1_response.function_call else None
+        function_call_2 = sub_model_2_response.function_call if sub_model_2_response and sub_model_2_response.function_call else None
         
-        if ai_response.function_call:
-            function_name = ai_response.function_call.name
-            function_args = json.loads(ai_response.function_call.arguments)
+        final_function_call = None
+        
+        if function_call_1 and function_call_2:
+            # 裁决模型: 决定使用哪个函数调用
+            arbitration_messages = messages + [
+                {"role": "system", "content": "两个次级模型都建议使用函数。请决定使用哪个函数更合适。"},
+                {"role": "assistant", "content": f"次级模型1建议使用函数：{function_call_1.name}"},
+                {"role": "assistant", "content": f"次级模型2建议使用函数：{function_call_2.name}"}
+            ]
+            arbitration_response = get_openai_response(arbitration_messages, model=session['model'])
+            if arbitration_response and ("模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content):
+                final_function_call = function_call_1
+            else:
+                final_function_call = function_call_2
+        elif function_call_1:
+            final_function_call = function_call_1
+        elif function_call_2:
+            final_function_call = function_call_2
+        
+        if final_function_call:
+            function_name = final_function_call.name
+            function_args = json.loads(final_function_call.arguments)
             function_result = process_function_call(function_name, function_args)
             
-            session['messages'].append(ai_response.model_dump())
-            session['messages'].append({
-                "role": "function",
-                "name": function_name,
-                "content": json.dumps(function_result, ensure_ascii=False)
-            })
-            
-            # 再次调用OpenAI API，将函数执行结果作为上下文
-            final_response = get_openai_response(session['messages'], model=session['model'])
-            response_content = final_response.content
-        else:
-            response_content = ai_response.content
+            messages.append({"role": "function", "name": function_name, "content": json.dumps(function_result, ensure_ascii=False)})
+        
+        # 主模型: 生成最终回复
+        final_response = get_openai_response(messages, model=session['model'])
+        response_content = final_response.content if final_response else "Error occurred"
         
         session['messages'].append({"role": "assistant", "content": response_content})