reformat

pqai_agent/dialogue_manager.py

@@ -345,14 +345,14 @@ class DialogueManager:
 
     def _send_human_intervention_alert(self, reason: Optional[str] = None) -> None:
         time_str = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-        staff_info = f"{self.staff_profile.get("agent_name", "未知")}[{self.staff_id}]"
-        user_info = f"{self.user_profile.get("nickname", "未知")}[{self.user_id}]"
+        staff_info = f"{self.staff_profile.get('agent_name', '未知')}[{self.staff_id}]"
+        user_info = f"{self.user_profile.get('nickname', '未知')}[{self.user_id}]"
         alert_message = f"""
         人工介入告警
         员工: {staff_info}
         用户: {user_info}
         时间: {time_str}
-        原因：{reason if reason else "未知"}
+        原因：{reason if reason else '未知'}
         最近对话:
         """
 

pqai_agent_server/api_server.py

@@ -1,301 +1,153 @@
 #! /usr/bin/env python
 # -*- coding: utf-8 -*-
 # vim:fenc=utf-8
-
 import logging
 import werkzeug.exceptions
 from flask import Flask, request, jsonify
-from datetime import datetime
 from argparse import ArgumentParser
 
-from openai import OpenAI
-from pqai_agent.message import MessageType
 from pqai_agent import configs
-import json
+
 from pqai_agent import logging_service, chat_service, prompt_templates
-from pqai_agent.dialogue_manager import DialogueManager
 from pqai_agent.history_dialogue_service import HistoryDialogueService
-from pqai_agent.response_type_detector import ResponseTypeDetector
 from pqai_agent.user_manager import MySQLUserManager, MySQLUserRelationManager
-from pqai_agent.user_profile_extractor import UserProfileExtractor
-
-app = Flask('agent_api_server')
+from pqai_agent_server.utils import wrap_response
+from pqai_agent_server.utils import (
+    run_extractor_prompt,
+    run_chat_prompt,
+    run_response_type_prompt,
+)
+
+app = Flask("agent_api_server")
 logger = logging_service.logger
 
-def compose_openai_chat_messages_no_time(dialogue_history, multimodal=False):
-    messages = []
-    for entry in dialogue_history:
-        role = entry['role']
-        msg_type = entry.get('type', MessageType.TEXT)
-        fmt_time = DialogueManager.format_timestamp(entry['timestamp'])
-        if msg_type in (MessageType.IMAGE_GW, MessageType.IMAGE_QW, MessageType.GIF):
-            if multimodal:
-                messages.append({
-                    "role": role,
-                    "content": [
-                        {"type": "image_url", "image_url": {"url": entry["content"]}}
-                    ]
-                })
-            else:
-                logger.warning("Image in non-multimodal mode")
-                messages.append({
-                    "role": role,
-                    "content": "[图片]"
-                })
-        else:
-            messages.append({
-                "role": role,
-                "content": f'{entry["content"]}'
-            })
-    return messages
 
-def wrap_response(code, msg=None, data=None):
-    resp = {
-        'code': code,
-        'msg': msg
-    }
-    if code == 200 and not msg:
-        resp['msg'] = 'success'
-    if data:
-        resp['data'] = data
-    return jsonify(resp)
-
-@app.route('/api/listStaffs', methods=['GET'])
+@app.route("/api/listStaffs", methods=["GET"])
 def list_staffs():
     staff_data = app.user_relation_manager.list_staffs()
     return wrap_response(200, data=staff_data)
 
-@app.route('/api/getStaffProfile', methods=['GET'])
+
+@app.route("/api/getStaffProfile", methods=["GET"])
 def get_staff_profile():
-    staff_id = request.args['staff_id']
+    staff_id = request.args["staff_id"]
     profile = app.user_manager.get_staff_profile(staff_id)
     if not profile:
-        return wrap_response(404, msg='staff not found')
+        return wrap_response(404, msg="staff not found")
     else:
         return wrap_response(200, data=profile)
 
-@app.route('/api/getUserProfile', methods=['GET'])
+
+@app.route("/api/getUserProfile", methods=["GET"])
 def get_user_profile():
-    user_id = request.args['user_id']
+    user_id = request.args["user_id"]
     profile = app.user_manager.get_user_profile(user_id)
     if not profile:
-        resp = {
-            'code': 404,
-            'msg': 'user not found'
-        }
+        resp = {"code": 404, "msg": "user not found"}
     else:
-        resp = {
-            'code': 200,
-            'msg': 'success',
-            'data': profile
-        }
+        resp = {"code": 200, "msg": "success", "data": profile}
     return jsonify(resp)
 
-@app.route('/api/listUsers', methods=['GET'])
+
+@app.route("/api/listUsers", methods=["GET"])
 def list_users():
-    user_name = request.args.get('user_name', None)
-    user_union_id = request.args.get('user_union_id', None)
+    user_name = request.args.get("user_name", None)
+    user_union_id = request.args.get("user_union_id", None)
     if not user_name and not user_union_id:
-        resp = {
-            'code': 400,
-            'msg': 'user_name or user_union_id is required'
-        }
+        resp = {"code": 400, "msg": "user_name or user_union_id is required"}
         return jsonify(resp)
     data = app.user_manager.list_users(user_name=user_name, user_union_id=user_union_id)
-    return jsonify({'code': 200, 'data': data})
+    return jsonify({"code": 200, "data": data})
+
 
-@app.route('/api/getDialogueHistory', methods=['GET'])
+@app.route("/api/getDialogueHistory", methods=["GET"])
 def get_dialogue_history():
-    staff_id = request.args['staff_id']
-    user_id = request.args['user_id']
-    recent_minutes = int(request.args.get('recent_minutes', 1440))
-    dialogue_history = app.history_dialogue_service.get_dialogue_history(staff_id, user_id, recent_minutes)
-    return jsonify({'code': 200, 'data': dialogue_history})
+    staff_id = request.args["staff_id"]
+    user_id = request.args["user_id"]
+    recent_minutes = int(request.args.get("recent_minutes", 1440))
+    dialogue_history = app.history_dialogue_service.get_dialogue_history(
+        staff_id, user_id, recent_minutes
+    )
+    return jsonify({"code": 200, "data": dialogue_history})
 
-@app.route('/api/listModels', methods=['GET'])
+
+@app.route("/api/listModels", methods=["GET"])
 def list_models():
     models = [
         {
-            'model_type': 'openai_compatible',
-            'model_name': chat_service.VOLCENGINE_MODEL_DEEPSEEK_V3,
-            'display_name': 'DeepSeek V3 on 火山'
+            "model_type": "openai_compatible",
+            "model_name": chat_service.VOLCENGINE_MODEL_DEEPSEEK_V3,
+            "display_name": "DeepSeek V3 on 火山",
         },
         {
-            'model_type': 'openai_compatible',
-            'model_name': chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
-            'display_name': '豆包Pro 32K'
+            "model_type": "openai_compatible",
+            "model_name": chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
+            "display_name": "豆包Pro 32K",
         },
         {
-            'model_type': 'openai_compatible',
-            'model_name': chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
-            'display_name': '豆包Pro 1.5'
+            "model_type": "openai_compatible",
+            "model_name": chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
+            "display_name": "豆包Pro 1.5",
         },
         {
-            'model_type': 'openai_compatible',
-            'model_name': chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH,
-            'display_name': 'DeepSeek V3联网 on 火山'
+            "model_type": "openai_compatible",
+            "model_name": chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH,
+            "display_name": "DeepSeek V3联网 on 火山",
         },
         {
-            'model_type': 'openai_compatible',
-            'model_name': chat_service.VOLCENGINE_MODEL_DOUBAO_1_5_VISION_PRO,
-            'display_name': '豆包1.5视觉理解Pro'
+            "model_type": "openai_compatible",
+            "model_name": chat_service.VOLCENGINE_MODEL_DOUBAO_1_5_VISION_PRO,
+            "display_name": "豆包1.5视觉理解Pro",
         },
     ]
     return wrap_response(200, data=models)
 
-@app.route('/api/listScenes', methods=['GET'])
+
+@app.route("/api/listScenes", methods=["GET"])
 def list_scenes():
     scenes = [
-        {'scene': 'greeting', 'display_name': '问候'},
-        {'scene': 'chitchat', 'display_name': '闲聊'},
-        {'scene': 'profile_extractor', 'display_name': '画像提取'},
-        {'scene': 'response_type_detector', 'display_name': '回复模态判断'},
-        {'scene': 'custom_debugging', 'display_name': '自定义调试场景'}
+        {"scene": "greeting", "display_name": "问候"},
+        {"scene": "chitchat", "display_name": "闲聊"},
+        {"scene": "profile_extractor", "display_name": "画像提取"},
+        {"scene": "response_type_detector", "display_name": "回复模态判断"},
+        {"scene": "custom_debugging", "display_name": "自定义调试场景"},
     ]
     return wrap_response(200, data=scenes)
 
-@app.route('/api/getBasePrompt', methods=['GET'])
+
+@app.route("/api/getBasePrompt", methods=["GET"])
 def get_base_prompt():
-    scene = request.args['scene']
+    scene = request.args["scene"]
     prompt_map = {
-        'greeting': prompt_templates.GENERAL_GREETING_PROMPT,
-        'chitchat': prompt_templates.CHITCHAT_PROMPT_COZE,
-        'profile_extractor': prompt_templates.USER_PROFILE_EXTRACT_PROMPT,
-        'response_type_detector': prompt_templates.RESPONSE_TYPE_DETECT_PROMPT,
-        'custom_debugging': '',
+        "greeting": prompt_templates.GENERAL_GREETING_PROMPT,
+        "chitchat": prompt_templates.CHITCHAT_PROMPT_COZE,
+        "profile_extractor": prompt_templates.USER_PROFILE_EXTRACT_PROMPT,
+        "response_type_detector": prompt_templates.RESPONSE_TYPE_DETECT_PROMPT,
+        "custom_debugging": "",
     }
     model_map = {
-        'greeting': chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
-        'chitchat': chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
-        'profile_extractor': chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
-        'response_type_detector': chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
-        'custom_debugging': chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH
+        "greeting": chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
+        "chitchat": chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
+        "profile_extractor": chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
+        "response_type_detector": chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
+        "custom_debugging": chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH,
     }
     if scene not in prompt_map:
-        return wrap_response(404, msg='scene not found')
-    data = {
-        'model_name': model_map[scene],
-        'content': prompt_map[scene]
-    }
+        return wrap_response(404, msg="scene not found")
+    data = {"model_name": model_map[scene], "content": prompt_map[scene]}
     return wrap_response(200, data=data)
 
-def run_openai_chat(messages, model_name, **kwargs):
-    volcengine_models = [
-        chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
-        chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
-        chat_service.VOLCENGINE_MODEL_DOUBAO_1_5_VISION_PRO,
-        chat_service.VOLCENGINE_MODEL_DEEPSEEK_V3
-    ]
-    deepseek_models = [
-        chat_service.DEEPSEEK_CHAT_MODEL,
-    ]
-    volcengine_bots = [
-        chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH,
-    ]
-    if model_name in volcengine_models:
-        llm_client = OpenAI(api_key=chat_service.VOLCENGINE_API_TOKEN, base_url=chat_service.VOLCENGINE_BASE_URL)
-    elif model_name in volcengine_bots:
-        llm_client = OpenAI(api_key=chat_service.VOLCENGINE_API_TOKEN, base_url=chat_service.VOLCENGINE_BOT_BASE_URL)
-    elif model_name in deepseek_models:
-        llm_client = OpenAI(api_key=chat_service.DEEPSEEK_API_TOKEN, base_url=chat_service.DEEPSEEK_BASE_URL)
-    else:
-        raise Exception('model not supported')
-    response = llm_client.chat.completions.create(
-        messages=messages, model=model_name, **kwargs)
-    logger.debug(response)
-    return response
-
-def run_extractor_prompt(req_data):
-    prompt = req_data['prompt']
-    user_profile = req_data['user_profile']
-    staff_profile = req_data['staff_profile']
-    dialogue_history = req_data['dialogue_history']
-    model_name = req_data['model_name']
-    prompt_context = {**staff_profile,
-                      **user_profile,
-                      'dialogue_history': UserProfileExtractor.compose_dialogue(dialogue_history)}
-    prompt = prompt.format(**prompt_context)
-    messages = [
-        {"role": "system", "content": '你是一个专业的用户画像分析助手。'},
-        {"role": "user", "content": prompt}
-    ]
-    tools = [UserProfileExtractor.get_extraction_function()]
-    response = run_openai_chat(messages, model_name, tools=tools, temperature=0)
-    tool_calls = response.choices[0].message.tool_calls
-    if tool_calls:
-        function_call = tool_calls[0]
-        if function_call.function.name == 'update_user_profile':
-            profile_info = json.loads(function_call.function.arguments)
-            return {k: v for k, v in profile_info.items() if v}
-        else:
-            logger.error("llm does not return update_user_profile")
-            return {}
-    else:
-        return {}
-
-def run_chat_prompt(req_data):
-    prompt = req_data['prompt']
-    staff_profile = req_data.get('staff_profile', {})
-    user_profile = req_data.get('user_profile', {})
-    dialogue_history = req_data.get('dialogue_history', [])
-    model_name = req_data['model_name']
-    current_timestamp = req_data['current_timestamp'] / 1000
-    prompt_context = {**staff_profile, **user_profile}
-    current_hour = datetime.fromtimestamp(current_timestamp).hour
-    prompt_context['last_interaction_interval'] = 0
-    prompt_context['current_time_period'] = DialogueManager.get_time_context(current_hour)
-    prompt_context['current_hour'] = current_hour
-    prompt_context['if_first_interaction'] = False if dialogue_history else True
-    last_message = dialogue_history[-1] if dialogue_history else {'role': 'assistant'}
-    prompt_context['if_active_greeting'] = False if last_message['role'] == 'user' else True
-
-    current_time_str = datetime.fromtimestamp(current_timestamp).strftime('%Y-%m-%d %H:%M:%S')
-    system_prompt = {
-        'role': 'system',
-        'content': prompt.format(**prompt_context)
-    }
-    messages = [system_prompt]
-    if req_data['scene'] == 'custom_debugging':
-        messages.extend(compose_openai_chat_messages_no_time(dialogue_history))
-        if '头像' in system_prompt['content']:
-            messages.append({
-                "role": 'user',
-                "content": [
-                    {"type": "image_url", "image_url": {"url": user_profile['avatar']}}
-                ]
-            })
-    else:
-        messages.extend(DialogueManager.compose_chat_messages_openai_compatible(dialogue_history, current_time_str))
-    return run_openai_chat(messages, model_name, temperature=1, top_p=0.7, max_tokens=1024)
-
-def run_response_type_prompt(req_data):
-    prompt = req_data['prompt']
-    dialogue_history = req_data['dialogue_history']
-    model_name = req_data['model_name']
-
-    composed_dialogue = ResponseTypeDetector.compose_dialogue(dialogue_history[:-1])
-    next_message = DialogueManager.format_dialogue_content(dialogue_history[-1])
-    prompt = prompt.format(
-        dialogue_history=composed_dialogue,
-        message=next_message
-    )
-    messages = [
-        {'role': 'system', 'content': '你是一个专业的智能助手'},
-        {'role': 'user', 'content': prompt}
-    ]
-    return run_openai_chat(messages, model_name,temperature=0.2, max_tokens=128)
 
-
-@app.route('/api/runPrompt', methods=['POST'])
+@app.route("/api/runPrompt", methods=["POST"])
 def run_prompt():
     try:
         req_data = request.json
         logger.debug(req_data)
-        scene = req_data['scene']
-        if scene == 'profile_extractor':
+        scene = req_data["scene"]
+        if scene == "profile_extractor":
             response = run_extractor_prompt(req_data)
             return wrap_response(200, data=response)
-        elif scene == 'response_type_detector':
+        elif scene == "response_type_detector":
             response = run_response_type_prompt(req_data)
             return wrap_response(200, data=response.choices[0].message.content)
         else:
@@ -303,43 +155,49 @@ def run_prompt():
             return wrap_response(200, data=response.choices[0].message.content)
     except Exception as e:
         logger.error(e)
-        return wrap_response(500, msg='Error: {}'.format(e))
+        return wrap_response(500, msg="Error: {}".format(e))
+
 
 @app.errorhandler(werkzeug.exceptions.BadRequest)
 def handle_bad_request(e):
     logger.error(e)
-    return wrap_response(400, msg='Bad Request: {}'.format(e.description))
+    return wrap_response(400, msg="Bad Request: {}".format(e.description))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     parser = ArgumentParser()
-    parser.add_argument('--prod', action='store_true')
-    parser.add_argument('--host', default='127.0.0.1')
-    parser.add_argument('--port', type=int, default=8083)
-    parser.add_argument('--log-level', default='INFO')
+    parser.add_argument("--prod", action="store_true")
+    parser.add_argument("--host", default="127.0.0.1")
+    parser.add_argument("--port", type=int, default=8083)
+    parser.add_argument("--log-level", default="INFO")
     args = parser.parse_args()
 
     config = configs.get()
     logging_level = logging.getLevelName(args.log_level)
-    logging_service.setup_root_logger(level=logging_level, logfile_name='agent_api_server.log')
+    logging_service.setup_root_logger(
+        level=logging_level, logfile_name="agent_api_server.log"
+    )
 
-    user_db_config = config['storage']['user']
-    staff_db_config = config['storage']['staff']
-    user_manager = MySQLUserManager(user_db_config['mysql'], user_db_config['table'], staff_db_config['table'])
+    user_db_config = config["storage"]["user"]
+    staff_db_config = config["storage"]["staff"]
+    user_manager = MySQLUserManager(
+        user_db_config["mysql"], user_db_config["table"], staff_db_config["table"]
+    )
     app.user_manager = user_manager
 
-    wecom_db_config = config['storage']['user_relation']
+    wecom_db_config = config["storage"]["user_relation"]
     user_relation_manager = MySQLUserRelationManager(
-        user_db_config['mysql'], wecom_db_config['mysql'],
-        config['storage']['staff']['table'],
-        user_db_config['table'],
-        wecom_db_config['table']['staff'],
-        wecom_db_config['table']['relation'],
-        wecom_db_config['table']['user']
+        user_db_config["mysql"],
+        wecom_db_config["mysql"],
+        config["storage"]["staff"]["table"],
+        user_db_config["table"],
+        wecom_db_config["table"]["staff"],
+        wecom_db_config["table"]["relation"],
+        wecom_db_config["table"]["user"],
     )
     app.user_relation_manager = user_relation_manager
     app.history_dialogue_service = HistoryDialogueService(
-        config['storage']['history_dialogue']['api_base_url']
+        config["storage"]["history_dialogue"]["api_base_url"]
     )
 
     app.run(debug=not args.prod, host=args.host, port=args.port)

pqai_agent_server/utils.py

@@ -0,0 +1,183 @@
+import json
+
+from flask import jsonify
+from datetime import datetime
+
+from openai import OpenAI
+
+from pqai_agent import logging_service, chat_service
+from pqai_agent.dialogue_manager import DialogueManager
+from pqai_agent.message import MessageType
+from pqai_agent.response_type_detector import ResponseTypeDetector
+from pqai_agent.user_profile_extractor import UserProfileExtractor
+
+logger = logging_service.logger
+
+
+def wrap_response(code, msg=None, data=None):
+    resp = {"code": code, "msg": msg}
+    if code == 200 and not msg:
+        resp["msg"] = "success"
+    if data:
+        resp["data"] = data
+    return jsonify(resp)
+
+
+def compose_openai_chat_messages_no_time(dialogue_history, multimodal=False):
+    messages = []
+    for entry in dialogue_history:
+        role = entry["role"]
+        msg_type = entry.get("type", MessageType.TEXT)
+        fmt_time = DialogueManager.format_timestamp(entry["timestamp"])
+        if msg_type in (MessageType.IMAGE_GW, MessageType.IMAGE_QW, MessageType.GIF):
+            if multimodal:
+                messages.append(
+                    {
+                        "role": role,
+                        "content": [
+                            {
+                                "type": "image_url",
+                                "image_url": {"url": entry["content"]},
+                            }
+                        ],
+                    }
+                )
+            else:
+                logger.warning("Image in non-multimodal mode")
+                messages.append({"role": role, "content": "[图片]"})
+        else:
+            messages.append({"role": role, "content": f'{entry["content"]}'})
+    return messages
+
+
+def run_openai_chat(messages, model_name, **kwargs):
+    volcengine_models = [
+        chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_32K,
+        chat_service.VOLCENGINE_MODEL_DOUBAO_PRO_1_5,
+        chat_service.VOLCENGINE_MODEL_DOUBAO_1_5_VISION_PRO,
+        chat_service.VOLCENGINE_MODEL_DEEPSEEK_V3,
+    ]
+    deepseek_models = [
+        chat_service.DEEPSEEK_CHAT_MODEL,
+    ]
+    volcengine_bots = [
+        chat_service.VOLCENGINE_BOT_DEEPSEEK_V3_SEARCH,
+    ]
+    if model_name in volcengine_models:
+        llm_client = OpenAI(
+            api_key=chat_service.VOLCENGINE_API_TOKEN,
+            base_url=chat_service.VOLCENGINE_BASE_URL,
+        )
+    elif model_name in volcengine_bots:
+        llm_client = OpenAI(
+            api_key=chat_service.VOLCENGINE_API_TOKEN,
+            base_url=chat_service.VOLCENGINE_BOT_BASE_URL,
+        )
+    elif model_name in deepseek_models:
+        llm_client = OpenAI(
+            api_key=chat_service.DEEPSEEK_API_TOKEN,
+            base_url=chat_service.DEEPSEEK_BASE_URL,
+        )
+    else:
+        raise Exception("model not supported")
+    response = llm_client.chat.completions.create(
+        messages=messages, model=model_name, **kwargs
+    )
+    logger.debug(response)
+    return response
+
+
+def run_extractor_prompt(req_data):
+    prompt = req_data["prompt"]
+    user_profile = req_data["user_profile"]
+    staff_profile = req_data["staff_profile"]
+    dialogue_history = req_data["dialogue_history"]
+    model_name = req_data["model_name"]
+    prompt_context = {
+        **staff_profile,
+        **user_profile,
+        "dialogue_history": UserProfileExtractor.compose_dialogue(dialogue_history),
+    }
+    prompt = prompt.format(**prompt_context)
+    messages = [
+        {"role": "system", "content": "你是一个专业的用户画像分析助手。"},
+        {"role": "user", "content": prompt},
+    ]
+    tools = [UserProfileExtractor.get_extraction_function()]
+    response = run_openai_chat(messages, model_name, tools=tools, temperature=0)
+    tool_calls = response.choices[0].message.tool_calls
+    if tool_calls:
+        function_call = tool_calls[0]
+        if function_call.function.name == "update_user_profile":
+            profile_info = json.loads(function_call.function.arguments)
+            return {k: v for k, v in profile_info.items() if v}
+        else:
+            logger.error("llm does not return update_user_profile")
+            return {}
+    else:
+        return {}
+
+
+def run_chat_prompt(req_data):
+    prompt = req_data["prompt"]
+    staff_profile = req_data.get("staff_profile", {})
+    user_profile = req_data.get("user_profile", {})
+    dialogue_history = req_data.get("dialogue_history", [])
+    model_name = req_data["model_name"]
+    current_timestamp = req_data["current_timestamp"] / 1000
+    prompt_context = {**staff_profile, **user_profile}
+    current_hour = datetime.fromtimestamp(current_timestamp).hour
+    prompt_context["last_interaction_interval"] = 0
+    prompt_context["current_time_period"] = DialogueManager.get_time_context(
+        current_hour
+    )
+    prompt_context["current_hour"] = current_hour
+    prompt_context["if_first_interaction"] = False if dialogue_history else True
+    last_message = dialogue_history[-1] if dialogue_history else {"role": "assistant"}
+    prompt_context["if_active_greeting"] = (
+        False if last_message["role"] == "user" else True
+    )
+
+    current_time_str = datetime.fromtimestamp(current_timestamp).strftime(
+        "%Y-%m-%d %H:%M:%S"
+    )
+    system_prompt = {"role": "system", "content": prompt.format(**prompt_context)}
+    messages = [system_prompt]
+    if req_data["scene"] == "custom_debugging":
+        messages.extend(compose_openai_chat_messages_no_time(dialogue_history))
+        if "头像" in system_prompt["content"]:
+            messages.append(
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": {"url": user_profile["avatar"]},
+                        }
+                    ],
+                }
+            )
+    else:
+        messages.extend(
+            DialogueManager.compose_chat_messages_openai_compatible(
+                dialogue_history, current_time_str
+            )
+        )
+    return run_openai_chat(
+        messages, model_name, temperature=1, top_p=0.7, max_tokens=1024
+    )
+
+
+def run_response_type_prompt(req_data):
+    prompt = req_data["prompt"]
+    dialogue_history = req_data["dialogue_history"]
+    model_name = req_data["model_name"]
+
+    composed_dialogue = ResponseTypeDetector.compose_dialogue(dialogue_history[:-1])
+    next_message = DialogueManager.format_dialogue_content(dialogue_history[-1])
+    prompt = prompt.format(dialogue_history=composed_dialogue, message=next_message)
+    messages = [
+        {"role": "system", "content": "你是一个专业的智能助手"},
+        {"role": "user", "content": prompt},
+    ]
+    return run_openai_chat(messages, model_name, temperature=0.2, max_tokens=128)