Use LangChain to refactor PaperAgent

.gitignore

@@ -1,3 +1,4 @@
 pa_data
 frontend/.env
-.claude
+.claude
+.DS_Store

backend/.python-version

@@ -1 +1 @@
-3.10
+3.14

backend/ai_system/__init__.py

@@ -25,8 +25,8 @@
     'ContextManager',
     'CodeExecutor',
     'FileTools',
-    'TemplateAgentTools',
-    'ToolManager'
+    'TemplateAgentTools'
+    # 'ToolManager'  # 已移除
 ]
 
 def _import_core_components():
@@ -36,7 +36,7 @@ def _import_core_components():
     from .core_handlers.llm_factory import LLMFactory
     from .core_managers.stream_manager import StreamOutputManager
     from .core_managers.context_manager import ContextManager
-    from .core_managers.tool_manager import ToolManager
+    # ToolManager 已移除，不再导入
     from .core_tools.code_executor import CodeExecutor
     from .core_tools.file_tools import FileTools
     from .core_tools.template_tools import TemplateAgentTools
@@ -49,7 +49,7 @@ def _import_core_components():
         'LLMFactory': LLMFactory,
         'StreamOutputManager': StreamOutputManager,
         'ContextManager': ContextManager,
-        'ToolManager': ToolManager,
+        # 'ToolManager': ToolManager,  # 已移除
         'CodeExecutor': CodeExecutor,
         'FileTools': FileTools,
         'TemplateAgentTools': TemplateAgentTools

backend/ai_system/config/environment.py

@@ -18,8 +18,18 @@ class DatabaseConfigManager:
     def __init__(self, db_session: Session):
         self.db_session = db_session
 
-    def get_model_config(self, system_type: str, user_id: int = None):
-        """获取指定系统类型的模型配置"""
+    def get_model_config(self, system_type: str, user_id: int = None, provider: str = None):
+        """
+        获取指定系统类型的模型配置
+
+        Args:
+            system_type: 系统类型（brain, code, writing）
+            user_id: 用户ID（可选）
+            provider: AI提供商（可选，用于筛选特定提供商的配置）
+
+        Returns:
+            ModelConfig对象
+        """
         from models.models import ModelConfig
 
         query = self.db_session.query(ModelConfig).filter(
@@ -31,26 +41,55 @@ def get_model_config(self, system_type: str, user_id: int = None):
         if user_id is not None:
             query = query.filter(ModelConfig.created_by == user_id)
 
+        # 如果指定了提供商，只获取该提供商的配置
+        if provider is not None:
+            query = query.filter(ModelConfig.provider == provider)
+
         config = query.first()
 
         if not config:
-            raise ValueError(f"未找到系统类型 {system_type} 的配置")
+            raise ValueError(f"未找到系统类型 {system_type} 的配置" +
+                           (f"，提供商: {provider}" if provider else ""))
         return config
 
-    def get_api_key(self, system_type: str, user_id: int = None) -> str:
+    def get_api_key(self, system_type: str, user_id: int = None, provider: str = None) -> str:
         """获取指定系统类型的API密钥"""
-        config = self.get_model_config(system_type, user_id)
+        config = self.get_model_config(system_type, user_id, provider)
         return config.api_key
 
-    def get_model_info(self, system_type: str, user_id: int = None) -> Dict[str, Any]:
+    def get_model_info(self, system_type: str, user_id: int = None, provider: str = None) -> Dict[str, Any]:
         """获取模型信息（不包含敏感信息）"""
-        config = self.get_model_config(system_type, user_id)
+        config = self.get_model_config(system_type, user_id, provider)
         return {
+            "provider": getattr(config, 'provider', 'openai'),
             "model_id": config.model_id,
             "base_url": config.base_url,
             "is_active": config.is_active
         }
 
+    def get_available_providers(self, system_type: str, user_id: int = None) -> List[str]:
+        """获取指定系统类型可用的AI提供商列表"""
+        from models.models import ModelConfig
+
+        query = self.db_session.query(ModelConfig.provider).filter(
+            ModelConfig.type == system_type,
+            ModelConfig.is_active == True
+        )
+
+        if user_id is not None:
+            query = query.filter(ModelConfig.created_by == user_id)
+
+        providers = [row.provider for row in query.distinct()]
+        return providers
+
+    def validate_provider_config(self, system_type: str, provider: str, user_id: int = None) -> bool:
+        """验证指定提供商的配置是否有效"""
+        try:
+            config = self.get_model_config(system_type, user_id, provider)
+            return bool(config.api_key and config.model_id)
+        except ValueError:
+            return False
+
 
 class AIEnvironmentManager:
     """AI环境管理器"""

backend/ai_system/core_agents/__init__.py

@@ -1,9 +1,8 @@
 """
-AI系统核心代理模块 - 重构版本
-包含BaseAgent、MainAgent、CodeAgent等核心代理类
+AI系统核心代理模块 - LangChain 重构版本
+包含MainAgent、CodeAgent等核心代理类
 """
 
-from .agent_base import BaseAgent, CodeAgent
 from .main_agent import MainAgent
 
-__all__ = ['BaseAgent', 'CodeAgent', 'MainAgent']
+__all__ = ['MainAgent']

backend/ai_system/core_agents/agent_base.py

@@ -9,8 +9,9 @@
 from typing import List, Dict, Any, Callable, Optional
 from abc import ABC, abstractmethod
 from datetime import datetime
+from langchain.agents import create_openai_tools_agent, AgentExecutor
+from langchain_core.prompts import ChatPromptTemplate
 
-from ..core_managers.tool_manager import ToolManager
 from ..core_managers.context_manager import ContextManager
 
 logger = logging.getLogger(__name__)
@@ -37,11 +38,10 @@ def __init__(self, llm_handler: 'LLMHandler', stream_manager: 'StreamOutputManag
         self.work_id = work_id
         self.workspace_dir = workspace_dir
 
-        # 初始化工具管理器
-        if workspace_dir:
-            self.tool_manager = ToolManager(workspace_dir, stream_manager)
-        else:
-            self.tool_manager = None
+        # 工具管理器已移除，BaseAgent 不再直接管理工具
+        # 子类可以自行实现工具管理逻辑
+        self.tool_manager = None
+        self.smolagent_manager = None
 
         # 消息和工具管理
         self.messages: List[Dict[str, Any]] = []
@@ -67,11 +67,8 @@ def _initialize(self):
             "content": system_prompt
         }]
 
-        # 设置工具
-        self._setup_tools()
-
-        # 注册工具函数
-        self._register_tool_functions()
+        # 工具设置由子类实现
+        self.tools = []
 
     @abstractmethod
     def get_system_prompt(self) -> str:
@@ -357,213 +354,4 @@ def get_execution_summary(self) -> Dict[str, Any]:
         }
 
 
-class CodeAgent(BaseAgent):
-    """
-    代码生成和执行Agent，专注于代码相关任务
-    """
-
-    def __init__(self, llm_handler: 'LLMHandler', stream_manager: 'StreamOutputManager', workspace_dir: str, work_id: Optional[str] = None):
-        """
-        初始化CodeAgent
-
-        Args:
-            llm_handler: LLM处理器
-            stream_manager: 流式输出管理器
-            workspace_dir: 工作空间目录（必需）
-            work_id: 工作ID
-        """
-        if not workspace_dir:
-            raise ValueError("CodeAgent必须提供workspace_dir参数")
-
-        super().__init__(llm_handler, stream_manager, workspace_dir, work_id)
-
-    def get_system_prompt(self) -> str:
-        """获取CodeAgent的系统提示词"""
-        return (
-            "你是一个专业的代码生成和执行助手。**务必确保成功产出所需文件再交付**，工作完成之前一定要调用工具！重复执行直到成功。\n\n"
-            "你的工作流程：\n"
-            "1. 分析用户任务，生成相应的Python代码\n"
-            "2. 使用 save_and_execute 工具保存代码并立即执行\n"
-            "3. 仔细分析执行结果和错误信息\n"
-            "4. 如果代码有错误或需要优化，使用 edit_code_file 修改后重新执行\n"
-            "5. 重复修改和执行，直到得到正确结果\n\n"
-            "**重要策略：**\n"
-            "- 推荐使用 save_and_execute 工具，一次性完成保存和执行\n"
-            "- 代码执行失败时，仔细分析错误信息，然后修改代码重试\n"
-            "- 代码应该包含必要的导入语句和完整的逻辑\n"
-            "- 文件操作使用相对路径，例如：plt.savefig('outputs/filename.png')\n"
-            "- 必须要保证保存图片且图片名字含有时间戳，否则会覆盖之前的图片\n\n"
-            "**重复执行直到成功**"
-        )
-
-    def _setup_tools(self):
-        """设置CodeAgent的工具定义"""
-        # 保存并执行代码工具
-        save_and_execute_tool = {
-            "type": "function",
-            "function": {
-                "name": "save_and_execute",
-                "description": "保存Python代码到文件并立即执行",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "code_content": {"type": "string", "description": "要保存和执行的Python代码内容"},
-                        "filename": {"type": "string", "description": "文件名（不需要.py后缀）"}
-                    },
-                    "required": ["code_content", "filename"],
-                },
-            },
-        }
-
-        # 直接执行代码工具
-        execute_code_tool = {
-            "type": "function",
-            "function": {
-                "name": "execute_code",
-                "description": "直接执行Python代码内容，不保存到文件",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "code_content": {"type": "string", "description": "要执行的Python代码内容"}
-                    },
-                    "required": ["code_content"],
-                },
-            },
-        }
-
-        # 执行文件工具
-        execute_file_tool = {
-            "type": "function",
-            "function": {
-                "name": "execute_file",
-                "description": "执行指定的Python代码文件",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "file_path": {"type": "string", "description": "要执行的代码文件路径"}
-                    },
-                    "required": ["file_path"],
-                },
-            },
-        }
-
-        # 修改代码文件工具
-        edit_code_tool = {
-            "type": "function",
-            "function": {
-                "name": "edit_code_file",
-                "description": "修改已存在的Python代码文件",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "filename": {"type": "string", "description": "要修改的文件名（不需要.py后缀）"},
-                        "new_code_content": {"type": "string", "description": "修复后的完整代码内容"}
-                    },
-                    "required": ["filename", "new_code_content"],
-                },
-            },
-        }
-
-        # 列出代码文件工具
-        list_files_tool = {
-            "type": "function",
-            "function": {
-                "name": "list_code_files",
-                "description": "列出工作空间中的所有代码文件",
-                "parameters": {
-                    "type": "object",
-                    "properties": {},
-                    "required": [],
-                },
-            },
-        }
-
-        # 注册工具定义
-        self.tools = [
-            save_and_execute_tool,
-            execute_code_tool,
-            execute_file_tool,
-            edit_code_tool,
-            list_files_tool
-        ]
-
-    def _register_tool_functions(self):
-        """注册CodeAgent的工具函数"""
-        if not self.tool_manager:
-            raise ValueError("CodeAgent需要工具管理器")
-
-        code_executor = self.tool_manager.code_executor()
-
-        self.available_functions = {
-            "save_and_execute": code_executor.save_and_execute,
-            "execute_code": code_executor.execute_code,
-            "execute_file": code_executor.execute_file,
-            "edit_code_file": code_executor.edit_code_file,
-            "list_code_files": code_executor.list_code_files
-        }
-
-    async def run(self, task_prompt: str) -> str:
-        """执行代码生成和执行任务"""
-        logger.info(f"CodeAgent开始执行任务: {repr(task_prompt[:50])}...")
-
-        if self.stream_manager:
-            await self.stream_manager.send_json_block("code_agent_start", f"开始执行代码任务: {task_prompt[:100]}...")
-
-        self.add_user_message(task_prompt)
-
-        max_iterations = 10
-        iteration = 0
-        last_tool_result = None
-
-        while iteration < max_iterations:
-            iteration += 1
-            logger.info(f"CodeAgent第{iteration}次迭代")
-
-            # 调用LLM
-            assistant_message, tool_calls = await self.llm_handler.process_stream(self.messages, self.tools)
-            self.messages.append(assistant_message)
-
-            if not tool_calls:
-                # 任务完成
-                if last_tool_result:
-                    result = f"任务完成！\n\n执行结果：\n{last_tool_result}\n\nLLM总结：{assistant_message.get('content', '')}"
-                else:
-                    result = assistant_message.get("content", "代码任务完成。")
-
-                logger.info(f"CodeAgent在第{iteration}次迭代完成")
-                if self.stream_manager:
-                    await self.stream_manager.send_json_block("code_agent_result", f"任务完成，最终结果: {result[:200]}...")
-                return result
-
-            # 执行工具调用 - 使用统一的执行方法
-            for tool_call in tool_calls:
-                function_name = tool_call["function"]["name"]
-                logger.info(f"CodeAgent执行工具调用: {function_name}")
-
-                try:
-                    tool_result = await self._execute_tool_call(tool_call)
-                    last_tool_result = tool_result
-
-                    # 添加工具结果到消息历史
-                    self.messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call["id"],
-                        "content": tool_result,
-                    })
-
-                except Exception as e:
-                    logger.error(f"工具 {function_name} 执行失败: {e}")
-                    error_result = f"工具 {function_name} 执行失败: {str(e)}"
-                    self.messages.append({
-                        "role": "tool",
-                        "tool_call_id": tool_call["id"],
-                        "content": error_result,
-                    })
-                    last_tool_result = error_result
-
-        # 达到最大迭代次数
-        logger.warning(f"CodeAgent达到最大迭代次数({max_iterations})，强制结束")
-        if last_tool_result:
-            return f"任务完成（达到最大迭代次数）！\n\n最终执行结果：\n{last_tool_result}"
-        else:
-            return "代码任务完成（达到最大迭代次数），但未获得有效结果"
+# CodeAgent 已移至独立的 code_agent.py 文件