add AgentConfig

agent/deep_assistant.py

@@ -2,7 +2,7 @@ import json
 import logging
 import os
 import sqlite3
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, List
 from langchain.chat_models import init_chat_model
 # from deepagents import create_deep_agent
 from langchain.agents import create_agent
@@ -15,6 +15,8 @@ from utils.fastapi_utils import detect_provider
 from .guideline_middleware import GuidelineMiddleware
 from .tool_output_length_middleware import ToolOutputLengthMiddleware
 from utils.settings import SUMMARIZATION_MAX_TOKENS, TOOL_OUTPUT_MAX_LENGTH
+from utils.agent_config import AgentConfig
+
 
 class LoggingCallbackHandler(BaseCallbackHandler):
     """自定义的 CallbackHandler，使用项目的 logger 来记录日志"""
@@ -22,17 +24,6 @@ class LoggingCallbackHandler(BaseCallbackHandler):
     def __init__(self, logger_name: str = 'app'):
         self.logger = logging.getLogger(logger_name)
 
-    # def on_llm_start(
-    #     self, serialized: Optional[Dict[str, Any]], prompts: list[str], **kwargs: Any
-    # ) -> None:
-    #     """当 LLM 开始时调用"""
-    #     self.logger.info("🤖 LLM Start - Input Messages:")
-    #     if prompts:
-    #         for i, prompt in enumerate(prompts):
-    #             self.logger.info(f"  Message {i+1}:\n{prompt}")
-    #     else:
-    #         self.logger.info("  No prompts")
-
     def on_llm_end(self, response, **kwargs: Any) -> None:
         """当 LLM 结束时调用"""
         self.logger.info("✅ LLM End - Output:")
@@ -78,7 +69,6 @@ class LoggingCallbackHandler(BaseCallbackHandler):
         """当工具调用出错时调用"""
         self.logger.error(f"❌ Tool Error: {error}")
 
-
     def on_agent_action(self, action, **kwargs: Any) -> None:
         """当 Agent 执行动作时调用"""
         self.logger.info(f"🎯 Agent Action: {action.log}")
@@ -97,6 +87,7 @@ def read_mcp_settings():
         mcp_settings_json = json.load(f)
     return mcp_settings_json
 
+
 async def get_tools_from_mcp(mcp):
     """从MCP配置中提取工具"""
     # 防御式处理：确保 mcp 是列表且长度大于 0，且包含 mcpServers
@@ -123,66 +114,60 @@ async def get_tools_from_mcp(mcp):
         # 发生异常时返回空列表，避免上层调用报错
         return []
 
-async def init_agent(bot_id: str, model_name="qwen3-next", api_key=None,
+
-                    model_server=None, generate_cfg=None,
+async def init_agent(config: AgentConfig):
-                    system_prompt=None, mcp=None, robot_type=None, language="jp", user_identifier=None,
-                    session_id=None):
     """
     初始化 Agent，支持持久化内存和对话摘要
 
     Args:
-        bot_id: Bot ID
+        config: AgentConfig 对象，包含所有初始化参数
-        model_name: 模型名称
+        mcp: MCP配置（如果为None则使用配置中的mcp_settings）
-        api_key: API密钥
-        model_server: 模型服务器地址
-        generate_cfg: 生成配置
-        system_prompt: 系统提示
-        mcp: MCP配置
-        robot_type: 机器人类型
-        language: 语言
-        user_identifier: 用户标识
-        session_id: 会话ID（如果为None，则不启用持久化内存）
     """
-    system = system_prompt if system_prompt else read_system_prompt()
+    # 如果没有提供mcp，使用config中的mcp_settings
-    mcp = mcp if mcp else read_mcp_settings()
+    mcp = config.mcp_settings if config.mcp_settings else read_mcp_settings()
+    system = config.system_prompt if config.system_prompt else read_system_prompt()
     mcp_tools = await get_tools_from_mcp(mcp)
 
     # 检测或使用指定的提供商
-    model_provider,base_url = detect_provider(model_name,model_server)
+    model_provider,base_url = detect_provider(config.model_name, config.model_server)
 
     # 构建模型参数
     model_kwargs = {
-        "model": model_name,
+        "model": config.model_name,
         "model_provider": model_provider,
         "temperature": 0.8,
         "base_url": base_url,
-        "api_key": api_key
+        "api_key": config.api_key
     }
-    if generate_cfg:
+    if config.generate_cfg:
-        model_kwargs.update(generate_cfg)
+        model_kwargs.update(config.generate_cfg)
     llm_instance = init_chat_model(**model_kwargs)
 
     # 创建自定义的日志处理器
     logging_handler = LoggingCallbackHandler()
 
     # 构建中间件列表
-    middleware = [GuidelineMiddleware(bot_id, llm_instance, system, robot_type, language, user_identifier)]
+    middleware = []
+
+    # 只有在 enable_thinking 为 True 时才添加 GuidelineMiddleware
+    if config.enable_thinking:
+        middleware.append(GuidelineMiddleware(config.bot_id, llm_instance, system, config.robot_type, config.language, config.user_identifier))
 
     # 添加工具输出长度控制中间件
     tool_output_middleware = ToolOutputLengthMiddleware(
-        max_length=getattr(generate_cfg, 'tool_output_max_length', None) or TOOL_OUTPUT_MAX_LENGTH,
+        max_length=getattr(config.generate_cfg, 'tool_output_max_length', None) if config.generate_cfg else None or TOOL_OUTPUT_MAX_LENGTH,
-        truncation_strategy=getattr(generate_cfg, 'tool_output_truncation_strategy', 'smart'),
+        truncation_strategy=getattr(config.generate_cfg, 'tool_output_truncation_strategy', 'smart') if config.generate_cfg else 'smart',
-        tool_filters=getattr(generate_cfg, 'tool_output_filters', None),  # 可配置特定工具
+        tool_filters=getattr(config.generate_cfg, 'tool_output_filters', None) if config.generate_cfg else None,  # 可配置特定工具
-        exclude_tools=getattr(generate_cfg, 'tool_output_exclude', []),  # 排除的工具
+        exclude_tools=getattr(config.generate_cfg, 'tool_output_exclude', []) if config.generate_cfg else [],  # 排除的工具
-        preserve_code_blocks=getattr(generate_cfg, 'preserve_code_blocks', True),
+        preserve_code_blocks=getattr(config.generate_cfg, 'preserve_code_blocks', True) if config.generate_cfg else True,
-        preserve_json=getattr(generate_cfg, 'preserve_json', True)
+        preserve_json=getattr(config.generate_cfg, 'preserve_json', True) if config.generate_cfg else True
     )
     middleware.append(tool_output_middleware)
 
     # 初始化 checkpointer 和中间件
     checkpointer = None
 
-    if session_id:
+    if config.session_id:
         checkpointer = MemorySaver()
         summarization_middleware = SummarizationMiddleware(
             model=llm_instance,
@@ -203,6 +188,6 @@ async def init_agent(bot_id: str, model_name="qwen3-next", api_key=None,
     # 将 handler 和 checkpointer 存储在 agent 的属性中，方便在调用时使用
     agent.logging_handler = logging_handler
     agent.checkpointer = checkpointer
-    agent.bot_id = bot_id
+    agent.bot_id = config.bot_id
-    agent.session_id = session_id
+    agent.session_id = config.session_id
     return agent

agent/sharded_agent_manager.py

@@ -26,6 +26,7 @@ logger = logging.getLogger('app')
 
 from agent.deep_assistant import init_agent
 from agent.prompt_loader import load_system_prompt_async, load_mcp_settings_async
+from utils.agent_config import AgentConfig
 
 
 class ShardedAgentManager:
@@ -67,7 +68,8 @@ class ShardedAgentManager:
     
     def _get_cache_key(self, bot_id: str, model_name: str = None, api_key: str = None,
                        model_server: str = None, generate_cfg: Dict = None,
-                       system_prompt: str = None, mcp_settings: List[Dict] = None) -> str:
+                       system_prompt: str = None, mcp_settings: List[Dict] = None,
+                       enable_thinking: bool = False) -> str:
         """获取包含所有相关参数的哈希值作为缓存键"""
         cache_data = {
             'bot_id': bot_id,
@@ -76,7 +78,8 @@ class ShardedAgentManager:
             'model_server': model_server or '',
             'generate_cfg': json.dumps(generate_cfg or {}, sort_keys=True),
             'system_prompt': system_prompt or '',
-            'mcp_settings': json.dumps(mcp_settings or [], sort_keys=True)
+            'mcp_settings': json.dumps(mcp_settings or [], sort_keys=True),
+            'enable_thinking': enable_thinking
         }
         
         cache_str = json.dumps(cache_data, sort_keys=True)
@@ -116,20 +119,12 @@ class ShardedAgentManager:
         if removed_count > 0:
             logger.info(f"分片已清理 {removed_count} 个过期的助手实例缓存")
     
-    async def get_or_create_agent(self, 
+    async def get_or_create_agent(self, config: AgentConfig):
-                                 bot_id: str,
+        """获取或创建文件预加载的助手实例
-                                 project_dir: Optional[str],
+
-                                 model_name: str = "qwen3-next",
+        Args:
-                                 api_key: Optional[str] = None,
+            config: AgentConfig 对象，包含所有初始化参数
-                                 model_server: Optional[str] = None,
+        """
-                                 generate_cfg: Optional[Dict] = None,
-                                 language: Optional[str] = None,
-                                 system_prompt: Optional[str] = None,
-                                 mcp_settings: Optional[List[Dict]] = None,
-                                 robot_type: Optional[str] = "general_agent",
-                                 user_identifier: Optional[str] = None,
-                                 session_id: Optional[str] = None):
-        """获取或创建文件预加载的助手实例"""
 
         # 更新请求统计
         with self._stats_lock:
@@ -137,14 +132,16 @@ class ShardedAgentManager:
 
         # 异步加载配置文件（带缓存）
         final_system_prompt = await load_system_prompt_async(
-            project_dir, language, system_prompt, robot_type, bot_id, user_identifier
+            config.project_dir, config.language, config.system_prompt, config.robot_type, config.bot_id, config.user_identifier
         )
         final_mcp_settings = await load_mcp_settings_async(
-            project_dir, mcp_settings, bot_id, robot_type
+            config.project_dir, config.mcp_settings, config.bot_id, config.robot_type
         )
-        
+        config.system_prompt = final_system_prompt
-        cache_key = self._get_cache_key(bot_id, model_name, api_key, model_server,
+        config.mcp_settings = final_mcp_settings
-                                        generate_cfg, final_system_prompt, final_mcp_settings)
+        cache_key = self._get_cache_key(config.bot_id, config.model_name, config.api_key, config.model_server,
+                                        config.generate_cfg, final_system_prompt, final_mcp_settings,
+                                        config.enable_thinking)
         
         # 获取分片
         shard_index = self._get_shard_index(cache_key)
@@ -160,7 +157,7 @@ class ShardedAgentManager:
                 with self._stats_lock:
                     self._global_stats['cache_hits'] += 1
                 
-                logger.info(f"分片复用现有的助手实例缓存: {cache_key} (bot_id: {bot_id}, shard: {shard_index})")
+                logger.info(f"分片复用现有的助手实例缓存: {cache_key} (bot_id: {config.bot_id}, shard: {shard_index})")
                 return agent
             
             # 更新缓存未命中统计
@@ -188,27 +185,15 @@ class ShardedAgentManager:
                 self._cleanup_old_agents(shard)
             
             # 创建新的助手实例
-            logger.info(f"分片创建新的助手实例缓存: {cache_key}, bot_id: {bot_id}, shard: {shard_index}")
+            logger.info(f"分片创建新的助手实例缓存: {cache_key}, bot_id: {config.bot_id}, shard: {shard_index}")
             current_time = time.time()
 
-            agent = await init_agent(
+            agent = await init_agent(config)
-                bot_id=bot_id,
-                model_name=model_name, 
-                api_key=api_key, 
-                model_server=model_server, 
-                generate_cfg=generate_cfg,
-                system_prompt=final_system_prompt,
-                mcp=final_mcp_settings,
-                robot_type=robot_type,
-                language=language,
-                user_identifier=user_identifier,
-                session_id=session_id
-            )
             
             # 缓存实例
             async with shard['lock']:
                 shard['agents'][cache_key] = agent
-                shard['unique_ids'][cache_key] = bot_id
+                shard['unique_ids'][cache_key] = config.bot_id
                 shard['access_times'][cache_key] = current_time
                 shard['creation_times'][cache_key] = current_time
             

routes/chat.py

@@ -14,13 +14,14 @@ from utils import (
 from agent.sharded_agent_manager import init_global_sharded_agent_manager
 from utils.api_models import ChatRequestV2
 from utils.fastapi_utils import (
-    process_messages, format_messages_to_chat_history,
+    process_messages,
     create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config,
-    call_preamble_llm, get_preamble_text, get_user_last_message_content,
+    call_preamble_llm, get_preamble_text,
     create_stream_chunk
 )
 from langchain_core.messages import AIMessageChunk, HumanMessage, ToolMessage, AIMessage
 from utils.settings import MAX_OUTPUT_TOKENS, MAX_CACHED_AGENTS, SHARD_COUNT
+from utils.agent_config import AgentConfig
 
 router = APIRouter()
 
@@ -69,30 +70,45 @@ def append_assistant_last_message(messages: list, content: str) -> bool:
         messages.append({"role":"assistant","content":content})
     return messages
 
+
+def get_user_last_message_content(messages: list) -> str:
+    """获取最后一条用户消息的内容"""
+    if not messages:
+        return ""
+
+    for msg in reversed(messages):
+        if msg.get('role') == 'user':
+            return msg.get('content', '')
+
+    return ""
+
+
+def format_messages_to_chat_history(messages: list) -> str:
+    """将消息格式化为聊天历史字符串"""
+    chat_history = ""
+    for msg in messages:
+        role = msg.get('role', '')
+        content = msg.get('content', '')
+        if role == 'user':
+            chat_history += f"用户: {content}\n"
+        elif role == 'assistant':
+            chat_history += f"助手: {content}\n"
+    return chat_history
+
+
 async def enhanced_generate_stream_response(
     agent_manager,
-    bot_id: str,
-    api_key: str,
     messages: list,
-    tool_response: bool,
+    config: AgentConfig
-    model_name: str,
-    model_server: str,
-    language: str,
-    system_prompt: str,
-    mcp_settings: Optional[list],
-    robot_type: str,
-    project_dir: Optional[str],
-    generate_cfg: Optional[dict],
-    user_identifier: Optional[str],
-    session_id: Optional[str] = None,
 ):
-    """增强的渐进式流式响应生成器 - 并发优化版本"""
+    """增强的渐进式流式响应生成器 - 并发优化版本
-    try:
-        # 准备参数
-        query_text = get_user_last_message_content(messages)
-        chat_history = format_messages_to_chat_history(messages)
-        preamble_text, system_prompt = get_preamble_text(language, system_prompt)
 
+    Args:
+        agent_manager: agent管理器
+        messages: 消息列表
+        config: AgentConfig 对象，包含所有参数
+    """
+    try:
         # 创建输出队列和控制事件
         output_queue = asyncio.Queue()
         preamble_completed = asyncio.Event()
@@ -100,11 +116,11 @@ async def enhanced_generate_stream_response(
         # Preamble 任务
         async def preamble_task():
             try:
-                preamble_result = await call_preamble_llm(chat_history, query_text, preamble_text, language, model_name, api_key, model_server)
+                preamble_result = await call_preamble_llm(messages,config)
                 # 只有当preamble_text不为空且不为"<empty>"时才输出
                 if preamble_result and preamble_result.strip() and preamble_result != "<empty>":
                     preamble_content = f"[PREAMBLE]\n{preamble_result}\n"
-                    chunk_data = create_stream_chunk(f"chatcmpl-preamble", model_name, preamble_content)
+                    chunk_data = create_stream_chunk(f"chatcmpl-preamble", config.model_name, preamble_content)
                     await output_queue.put(("preamble", f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"))
                     logger.info(f"Stream mode: Generated preamble text ({len(preamble_result)} chars)")
                 else:
@@ -124,32 +140,19 @@ async def enhanced_generate_stream_response(
         async def agent_task():
             try:
                 # 准备 agent
-                agent = await agent_manager.get_or_create_agent(
+                agent = await agent_manager.get_or_create_agent(config)
-                    bot_id=bot_id,
-                    project_dir=project_dir,
-                    model_name=model_name,
-                    api_key=api_key,
-                    model_server=model_server,
-                    generate_cfg=generate_cfg,
-                    language=language,
-                    system_prompt=system_prompt,
-                    mcp_settings=mcp_settings,
-                    robot_type=robot_type,
-                    user_identifier=user_identifier,
-                    session_id=session_id,
-                )
 
                 # 开始流式处理
                 logger.info(f"Starting agent stream response")
                 chunk_id = 0
                 message_tag = ""
 
-                config = {}
+                stream_config = {}
-                if session_id:
+                if config.session_id:
-                    config["configurable"] = {"thread_id": session_id}
+                    stream_config["configurable"] = {"thread_id": config.session_id}
                 if hasattr(agent, 'logging_handler'):
-                    config["callbacks"] = [agent.logging_handler]
+                    stream_config["callbacks"] = [agent.logging_handler]
-                async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages", config=config, max_tokens=MAX_OUTPUT_TOKENS):
+                async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages", config=stream_config, max_tokens=MAX_OUTPUT_TOKENS):
                     new_content = ""
 
                     if isinstance(msg, AIMessageChunk):
@@ -174,7 +177,7 @@ async def enhanced_generate_stream_response(
                                 new_content += msg.text
 
                     # 处理工具响应
-                    elif isinstance(msg, ToolMessage) and tool_response and msg.content:
+                    elif isinstance(msg, ToolMessage) and config.tool_response and msg.content:
                         message_tag = "TOOL_RESPONSE"
                         new_content = f"[{message_tag}] {msg.name}\n{msg.text}\n"
 
@@ -183,11 +186,11 @@ async def enhanced_generate_stream_response(
                         if chunk_id == 0:
                             logger.info(f"Agent首个Token已生成, 开始流式输出")
                         chunk_id += 1
-                        chunk_data = create_stream_chunk(f"chatcmpl-{chunk_id}", model_name, new_content)
+                        chunk_data = create_stream_chunk(f"chatcmpl-{chunk_id}", config.model_name, new_content)
                         await output_queue.put(("agent", f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n"))
 
                 # 发送最终chunk
-                final_chunk = create_stream_chunk(f"chatcmpl-{chunk_id + 1}", model_name, finish_reason="stop")
+                final_chunk = create_stream_chunk(f"chatcmpl-{chunk_id + 1}", config.model_name, finish_reason="stop")
                 await output_queue.put(("agent", f"data: {json.dumps(final_chunk, ensure_ascii=False)}\n\n"))
                 await output_queue.put(("agent_done", None))
 
@@ -196,7 +199,15 @@ async def enhanced_generate_stream_response(
                 await output_queue.put(("agent_done", None))
 
         # 并发执行任务
-        preamble_task_handle = asyncio.create_task(preamble_task())
+        # 只有在 enable_thinking 为 True 时才执行 preamble 任务
+        if config.enable_thinking:
+            preamble_task_handle = asyncio.create_task(preamble_task())
+        else:
+            # 如果不启用 thinking，创建一个空的已完成任务
+            preamble_task_handle = asyncio.create_task(asyncio.sleep(0))
+            # 直接标记 preamble 完成
+            preamble_completed.set()
+
         agent_task_handle = asyncio.create_task(agent_task())
 
         # 输出控制器：确保 preamble 先输出，然后是 agent stream
@@ -214,7 +225,7 @@ async def enhanced_generate_stream_response(
                     preamble_output_done = True
 
                 elif item_type == "preamble_done":
-                    # Preamble 已完成，标记并继续处理
+                    # Preamble 已完成，标记并继续
                     preamble_output_done = True
 
                 elif item_type == "agent":
@@ -259,77 +270,43 @@ async def enhanced_generate_stream_response(
 
 
 async def create_agent_and_generate_response(
-    bot_id: str,
-    api_key: str,
     messages: list,
-    stream: bool,
+    config: AgentConfig
-    tool_response: bool,
-    model_name: str,
-    model_server: str,
-    language: str,
-    system_prompt: Optional[str],
-    mcp_settings: Optional[list],
-    robot_type: str,
-    project_dir: Optional[str] = None,
-    generate_cfg: Optional[dict] = None,
-    user_identifier: Optional[str] = None,
-    session_id: Optional[str] = None
 ) -> Union[ChatResponse, StreamingResponse]:
-    """创建agent并生成响应的公共逻辑"""
+    """创建agent并生成响应的公共逻辑
-    if generate_cfg is None:
+
-        generate_cfg = {}
+    Args:
+        messages: 消息列表
+        config: AgentConfig 对象，包含所有参数
+    """
+    config.preamble_text, config.system_prompt = get_preamble_text(config.language, config.system_prompt)
 
     # 如果是流式模式，使用增强的流式响应生成器
-    if stream:
+    if config.stream:
         return StreamingResponse(
             enhanced_generate_stream_response(
                 agent_manager=agent_manager,
-                bot_id=bot_id,
-                api_key=api_key,
                 messages=messages,
-                tool_response=tool_response,
+                config=config
-                model_name=model_name,
-                model_server=model_server,
-                language=language,
-                system_prompt=system_prompt or "",
-                mcp_settings=mcp_settings,
-                robot_type=robot_type,
-                project_dir=project_dir,
-                generate_cfg=generate_cfg,
-                user_identifier=user_identifier,
-                session_id=session_id
             ),
             media_type="text/event-stream",
             headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
         )
 
-    _, system_prompt = get_preamble_text(language, system_prompt)
+    
     # 使用公共函数处理所有逻辑
-    agent = await agent_manager.get_or_create_agent(
+    agent = await agent_manager.get_or_create_agent(config)
-        bot_id=bot_id,
-        project_dir=project_dir,
-        model_name=model_name,
-        api_key=api_key,
-        model_server=model_server,
-        generate_cfg=generate_cfg,
-        language=language,
-        system_prompt=system_prompt,
-        mcp_settings=mcp_settings,
-        robot_type=robot_type,
-        user_identifier=user_identifier,
-        session_id=session_id,
-    )
 
     # 准备最终的消息
     final_messages = messages.copy()
 
     # 非流式响应
-    config = {}
+    agent_config = {}
-    if session_id:
+    if config.session_id:
-        config["configurable"] = {"thread_id": session_id}
+        agent_config["configurable"] = {"thread_id": config.session_id}
     if hasattr(agent, 'logging_handler'):
-        config["callbacks"] = [agent.logging_handler]
+        agent_config["callbacks"] = [agent.logging_handler]
-    agent_responses = await agent.ainvoke({"messages": final_messages}, config=config, max_tokens=MAX_OUTPUT_TOKENS)
+    agent_responses = await agent.ainvoke({"messages": final_messages}, config=agent_config, max_tokens=MAX_OUTPUT_TOKENS)
     append_messages = agent_responses["messages"][len(final_messages):]
     response_text = ""
     for msg in append_messages:
@@ -340,7 +317,7 @@ async def create_agent_and_generate_response(
                 response_text += f"[{meta_message_tag}]\n"+output_text+ "\n"
             if len(msg.tool_calls)>0:
                 response_text += "".join([f"[TOOL_CALL] {tool['name']}\n{json.dumps(tool["args"]) if isinstance(tool["args"],dict) else tool["args"]}\n" for tool in msg.tool_calls])
-        elif isinstance(msg,ToolMessage) and tool_response:
+        elif isinstance(msg,ToolMessage) and config.tool_response:
             response_text += f"[TOOL_RESPONSE] {msg.name}\n{msg.text}\n"
 
     if len(response_text) > 0:
@@ -410,29 +387,16 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
         project_dir = create_project_directory(request.dataset_ids, bot_id, request.robot_type)
 
         # 收集额外参数作为 generate_cfg
-        exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id'}
+        exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id', 'enable_thinking'}
         generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
-        
         # 处理消息
         messages = process_messages(request.messages, request.language)
-
+        # 创建 AgentConfig 对象
+        config = AgentConfig.from_v1_request(request, api_key, project_dir, generate_cfg)
         # 调用公共的agent创建和响应生成逻辑
         return await create_agent_and_generate_response(
-            bot_id=bot_id,
-            api_key=api_key,
             messages=messages,
-            stream=request.stream,
+            config=config
-            tool_response=request.tool_response,
-            model_name=request.model,
-            model_server=request.model_server,
-            language=request.language,
-            system_prompt=request.system_prompt,
-            mcp_settings=request.mcp_settings,
-            robot_type=request.robot_type,
-            project_dir=project_dir,
-            generate_cfg=generate_cfg,
-            user_identifier=request.user_identifier,
-            session_id=request.session_id
         )
 
     except Exception as e:
@@ -496,38 +460,20 @@ async def chat_completions_v2(request: ChatRequestV2, authorization: Optional[st
 
         # 从后端API获取机器人配置（使用v2的鉴权方式）
         bot_config = await fetch_bot_config(bot_id)
-
-        # v2接口：API密钥优先从后端配置获取，其次才从Authorization header获取
-        # 注意：这里的Authorization header已经用于鉴权，不再作为API key使用
-        api_key = bot_config.get("api_key")
-
         # 创建项目目录（从后端配置获取dataset_ids）
         project_dir = create_project_directory(
             bot_config.get("dataset_ids", []),
             bot_id,
             bot_config.get("robot_type", "general_agent")
         )
-
         # 处理消息
         messages = process_messages(request.messages, request.language)
-
+        # 创建 AgentConfig 对象
+        config = AgentConfig.from_v2_request(request, bot_config, project_dir)
         # 调用公共的agent创建和响应生成逻辑
         return await create_agent_and_generate_response(
-            bot_id=bot_id,
-            api_key=api_key,
             messages=messages,
-            stream=request.stream,
+            config=config
-            tool_response=request.tool_response,
-            model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"),
-            model_server=bot_config.get("model_server", ""),
-            language=request.language or bot_config.get("language", "zh"),
-            system_prompt=bot_config.get("system_prompt"),
-            mcp_settings=bot_config.get("mcp_settings", []),
-            robot_type=bot_config.get("robot_type", "general_agent"),
-            project_dir=project_dir,
-            generate_cfg={},  # v2接口不传递额外的generate_cfg
-            user_identifier=request.user_identifier,
-            session_id=request.session_id
         )
 
     except HTTPException:

utils/agent_config.py

@@ -0,0 +1,97 @@
+"""Agent配置类，用于管理所有Agent相关的参数"""
+
+from typing import Optional, List, Dict, Any
+from pydantic import BaseModel, Field
+from dataclasses import dataclass
+
+
+@dataclass
+class AgentConfig:
+    """Agent配置类，包含创建和管理Agent所需的所有参数"""
+
+    # 基础参数
+    bot_id: str
+    api_key: Optional[str] = None
+    model_name: str = "qwen3-next"
+    model_server: Optional[str] = None
+    language: Optional[str] = "jp"
+
+    # 配置参数
+    system_prompt: Optional[str] = None
+    mcp_settings: Optional[List[Dict]] = None
+    robot_type: Optional[str] = "general_agent"
+    generate_cfg: Optional[Dict] = None
+    enable_thinking: bool = True
+
+    # 上下文参数
+    project_dir: Optional[str] = None
+    user_identifier: Optional[str] = None
+    session_id: Optional[str] = None
+
+    # 响应控制参数
+    stream: bool = False
+    tool_response: bool = True
+    preamble_text: Optional[str] = None
+
+    def to_dict(self) -> Dict[str, Any]:
+        """转换为字典格式，用于传递给需要**kwargs的函数"""
+        return {
+            'bot_id': self.bot_id,
+            'api_key': self.api_key,
+            'model_name': self.model_name,
+            'model_server': self.model_server,
+            'language': self.language,
+            'system_prompt': self.system_prompt,
+            'mcp_settings': self.mcp_settings,
+            'robot_type': self.robot_type,
+            'generate_cfg': self.generate_cfg,
+            'enable_thinking': self.enable_thinking,
+            'project_dir': self.project_dir,
+            'user_identifier': self.user_identifier,
+            'session_id': self.session_id,
+            'stream': self.stream,
+            'tool_response': self.tool_response,
+            'preamble_text': self.preamble_text
+        }
+
+    @classmethod
+    def from_v1_request(cls, request, api_key: str, project_dir: Optional[str] = None, generate_cfg: Optional[Dict] = None):
+        """从v1请求创建配置"""
+        return cls(
+            bot_id=request.bot_id,
+            api_key=api_key,
+            model_name=request.model,
+            model_server=request.model_server,
+            language=request.language,
+            system_prompt=request.system_prompt,
+            mcp_settings=request.mcp_settings,
+            robot_type=request.robot_type,
+            user_identifier=request.user_identifier,
+            session_id=request.session_id,
+            enable_thinking=request.enable_thinking,
+            project_dir=project_dir,
+            stream=request.stream,
+            tool_response=request.tool_response,
+            generate_cfg=generate_cfg
+        )
+
+    @classmethod
+    def from_v2_request(cls, request, bot_config: Dict, project_dir: Optional[str] = None):
+        """从v2请求创建配置"""
+        return cls(
+            bot_id=request.bot_id,
+            api_key=bot_config.get("api_key"),
+            model_name=bot_config.get("model", "qwen/qwen3-next-80b-a3b-instruct"),
+            model_server=bot_config.get("model_server", ""),
+            language=request.language or bot_config.get("language", "zh"),
+            system_prompt=bot_config.get("system_prompt"),
+            mcp_settings=bot_config.get("mcp_settings", []),
+            robot_type=bot_config.get("robot_type", "general_agent"),
+            user_identifier=request.user_identifier,
+            session_id=request.session_id,
+            enable_thinking=request.enable_thinking,
+            project_dir=project_dir,
+            stream=request.stream,
+            tool_response=request.tool_response,
+            generate_cfg={}  # v2接口不传递额外的generate_cfg
+        )

utils/api_models.py

@@ -53,6 +53,7 @@ class ChatRequest(BaseModel):
     robot_type: Optional[str] = "general_agent"
     user_identifier: Optional[str] = ""
     session_id: Optional[str] = None
+    enable_thinking: Optional[bool] = False
 
 
 class ChatRequestV2(BaseModel):
@@ -63,6 +64,7 @@ class ChatRequestV2(BaseModel):
     language: Optional[str] = "zh"
     user_identifier: Optional[str] = ""
     session_id: Optional[str] = None
+    enable_thinking: Optional[bool] = False
 
 
 class FileProcessRequest(BaseModel):

utils/fastapi_utils.py

@@ -11,6 +11,7 @@ import logging
 from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
 from langchain.chat_models import init_chat_model
 from utils.settings import MASTERKEY, BACKEND_HOST
+from utils.agent_config import AgentConfig
 
 USER = "user"
 ASSISTANT = "assistant"
@@ -500,9 +501,10 @@ def get_preamble_text(language: str, system_prompt: str):
         if preamble_matches:
             # 提取preamble内容
             preamble_content = preamble_matches[0].strip()
-            # 从system_prompt中删除preamble代码块
+            if preamble_content:
-            cleaned_system_prompt = re.sub(preamble_pattern, '', system_prompt, flags=re.DOTALL)
+                # 从system_prompt中删除preamble代码块
-            return preamble_content, cleaned_system_prompt
+                cleaned_system_prompt = re.sub(preamble_pattern, '', system_prompt, flags=re.DOTALL)
+                return preamble_content, cleaned_system_prompt
 
     # 如果没有找到preamble代码块，使用默认的preamble选择
     if language == "jp":
@@ -559,12 +561,12 @@ def get_preamble_text(language: str, system_prompt: str):
     return default_preamble, system_prompt  # 返回默认preamble和原始system_prompt
 
 
-async def call_preamble_llm(chat_history: str, last_message: str, preamble_choices_text: str, language: str, model_name: str, api_key: str, model_server: str) -> str:
+async def call_preamble_llm(messages: list, config: AgentConfig) -> str:
     """调用大语言模型处理guideline分析
 
     Args:
-        chat_history: 聊天历史记录
+        messages: 消息列表
-        guidelines_text: 指导原则文本
+        preamble_choices_text: 指导原则文本
         model_name: 模型名称
         api_key: API密钥
         model_server: 模型服务器地址
@@ -580,6 +582,14 @@ async def call_preamble_llm(chat_history: str, last_message: str, preamble_choic
         logger.error(f"Error reading guideline prompt template: {e}")
         return ""
     
+    api_key = config.api_key
+    model_name = config.model_name
+    model_server = config.model_server
+    language = config.language
+    preamble_choices_text = config.preamble_text
+    last_message = get_user_last_message_content(messages)
+    chat_history = format_messages_to_chat_history(messages)
+
     # 替换模板中的占位符
     system_prompt = preamble_template.replace('{preamble_choices_text}', preamble_choices_text).replace('{chat_history}', chat_history).replace('{last_message}', last_message).replace('{language}', get_language_text(language))
     # 配置LLM