diff --git a/.gitignore b/.gitignore index f07beb8..0f9499c 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ pa_data frontend/.env -.claude \ No newline at end of file +.claude +.DS_Store \ No newline at end of file diff --git a/backend/.python-version b/backend/.python-version index c8cfe39..6324d40 100644 --- a/backend/.python-version +++ b/backend/.python-version @@ -1 +1 @@ -3.10 +3.14 diff --git a/backend/ai_system/__init__.py b/backend/ai_system/__init__.py index d3ac935..449eff0 100644 --- a/backend/ai_system/__init__.py +++ b/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 diff --git a/backend/ai_system/config/environment.py b/backend/ai_system/config/environment.py index ddd284b..4ed6d08 100644 --- a/backend/ai_system/config/environment.py +++ b/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""" diff --git a/backend/ai_system/core_agents/__init__.py b/backend/ai_system/core_agents/__init__.py index d0f5961..0477157 100644 --- a/backend/ai_system/core_agents/__init__.py +++ b/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'] diff --git a/backend/ai_system/core_agents/agent_base.py b/backend/ai_system/core_agents/agent_base.py index b34f423..1a74a93 100644 --- a/backend/ai_system/core_agents/agent_base.py +++ b/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" \ No newline at end of file +# CodeAgent 已移至独立的 code_agent.py 文件 \ No newline at end of file diff --git a/backend/ai_system/core_agents/main_agent.py b/backend/ai_system/core_agents/main_agent.py index e2b7199..965b3c1 100644 --- a/backend/ai_system/core_agents/main_agent.py +++ b/backend/ai_system/core_agents/main_agent.py @@ -1,547 +1,228 @@ """ -主AI代理 - 重构版本 +主AI代理 - LangChain 重构版本 论文生成的中枢大脑，负责协调和规划整个论文生成过程 """ import logging -import json import os import asyncio -import shutil from typing import List, Dict, Any, Optional +from langchain.agents import create_agent +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.language_models import BaseLanguageModel +from langchain_core.messages import HumanMessage -from .agent_base import BaseAgent +from ..core_managers.langchain_tools import LangChainToolFactory logger = logging.getLogger(__name__) -class MainAgent(BaseAgent): +class MainAgent: """ 主LLM Agent (Orchestrator)，负责分析问题并委派任务 - 支持session上下文维护，保持对话连续性 + 基于 LangChain Agent，极简实现 """ - def __init__(self, llm_handler: 'LLMHandler', stream_manager: 'StreamOutputManager', - work_id: Optional[str] = None, template_id: Optional[int] = None): + def __init__(self, llm: BaseLanguageModel, stream_manager=None, + workspace_dir: str = None, work_id: Optional[str] = None, + template_id: Optional[int] = None, codeagent_llm=None): """ 初始化MainAgent Args: - llm_handler: LLM处理器 + llm: LangChain LLM 实例 stream_manager: 流式输出管理器 + workspace_dir: 工作空间目录路径 work_id: 工作ID template_id: 模板ID """ - # MainAgent特有属性必须在基类初始化之前设置 + logger.info(f"MainAgent初始化开始，codeagent_llm: {codeagent_llm}") + self.llm = llm + self.stream_manager = stream_manager + self.work_id = work_id self.template_id = template_id - - # 构建工作空间目录路径 - workspace_dir = None - if work_id: - workspace_dir = os.path.join( - os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), - "pa_data", "workspaces", work_id + self.workspace_dir = workspace_dir + + # 如果没有提供workspace_dir但有work_id，构建路径 + if not workspace_dir and work_id: + # 使用绝对路径，从backend目录向上一级到项目根目录 + backend_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + project_root = os.path.dirname(backend_dir) + self.workspace_dir = os.path.join( + project_root, "pa_data", "workspaces", work_id ) # 设置环境变量，供工具使用 - os.environ["WORKSPACE_DIR"] = workspace_dir + os.environ["WORKSPACE_DIR"] = self.workspace_dir - # 初始化基类 - super().__init__(llm_handler, stream_manager, workspace_dir, work_id) + # 创建所有需要的工具 + self.tools = LangChainToolFactory.create_all_tools( + self.workspace_dir, stream_manager, include_template=True + ) - # 如果有模板ID，复制模板文件到工作空间 - if self.template_id and work_id: - self._copy_template_to_workspace() + # 添加SmolAgents工具（使用codeagent_llm） + if codeagent_llm: + smolagents_tool = LangChainToolFactory.create_smolagents_tool( + self.workspace_dir, stream_manager, codeagent_llm + ) + if smolagents_tool: + self.tools.append(smolagents_tool) + logger.info("成功添加SmolAgents工具，使用codeagent配置") + else: + logger.warning("未提供codeagent_llm，跳过SmolAgents工具") - logger.info(f"MainAgent初始化完成，work_id: {work_id}, template_id: {template_id}") + # 创建 LangChain Agent + self.system_prompt = self._create_system_prompt() + self.agent = create_agent( + model=llm, + tools=self.tools + ) - def get_system_prompt(self) -> str: - """获取MainAgent的系统提示词""" + logger.info(f"MainAgent初始化完成，work_id: {work_id}, template_id: {template_id}, 工具数量: {len(self.tools)}") + + def _create_system_prompt(self) -> str: + """创建 MainAgent 的系统提示词""" # 基础系统提示 system_content = ( - "你是论文生成助手的中枢大脑，负责协调整个论文生成过程。**你使用的语言需要跟模板语言一致**\n" + "你是基于 LangChain Agent 的学术论文写作助手，负责协调整个论文生成过程。**你使用的语言需要跟模板语言一致**\n\n" "请你记住：论文尽可能使用图表等清晰表示！涉及图表等务必使用代码执行得到！\n" - "请你记住：如果最后tree发现没找到代码或者图片就重新调用CodeAgent生成！\n" + "请你记住：如果最后发现没找到代码或者图片就重新执行数据分析！\n\n" "你的职责：\n" - "0. 请你生成论文为paper.md文档！！！\n" "1. 分析用户需求，制定论文生成计划\n" - "2. **主动检查和分析附件**：当用户上传附件时，使用list_attachments工具查看所有附件，然后使用read_attachment工具读取相关内容\n" - "3. 当需要代码执行、数据分析、图表生成时，调用CodeAgent工具\n" - "4. 维护对话上下文，理解整个工作流程的连续性\n" - "5. 最终使用tree工具检查生成的文件\n\n" - "**附件处理能力**：\n" - "- 你可以读取和分析各种格式的附件文件（PDF、Word、Excel、CSV、文本文件、代码文件等）\n" - "- 使用list_attachments查看所有可用附件\n" - "- 使用read_attachment读取具体附件内容\n" - "- 使用get_attachment_info获取附件详细信息\n" - "- 使用search_attachments在附件中搜索关键词\n" - "- 基于附件内容进行论文写作和数据分析\n\n" - "重要原则：\n" - "- 保持对话连贯性，不重复询问已明确的信息\n" - "- 你是中枢大脑，负责规划和协调，不能直接编写、执行代码\n" - "- CodeAgent负责具体执行，你负责规划和协调\n" - "- **充分利用用户上传的附件内容，确保论文基于真实的资料和数据**\n" - "- 所有生成的文件都要在最终论文中引用\n" - "- 请自己执行迭代，直到任务完成\n" - "- 生成的论文不要杜撰，确保科学性" + "2. **主动检查和分析附件**：当用户上传附件时，使用read_attachment工具读取附件内容\n" + "3. 当需要代码执行、数据分析、图表生成时，使用smolagents_execute工具\n" + "4. 使用writemd工具保存论文草稿到paper.md\n\n" + "**你的工具集**：\n" + "- writemd: 保存论文草稿和内容到文件（推荐使用）\n" + "- update_template: 更新论文模板的特定章节\n" + "- read_attachment: 读取附件内容（PDF、Word、Excel等）\n" + "- list_attachments: 列出所有附件文件\n" + "- web_search: 搜索最新的学术资料和背景信息\n" + "- smolagents_execute: 使用SmolAgents执行复杂的代码任务，包括数据分析、图表生成、统计计算等（推荐用于复杂任务）\n" + "- tree: 显示工作空间目录结构\n" ) # 根据模板添加额外信息 if self.template_id: - system_content += f"\n\n**使用模板模式**：\n" - system_content += f"- 模板文件为 'paper.md'（这是最终论文文件）\n" - system_content += f"- 模板是一个大纲，你要填满大纲！\n" - system_content += f"- 生成论文时必须严格遵循模板的格式、结构和风格\n" - system_content += f"- 最终论文应该是一个完整的、格式规范的学术文档\n" + system_content += ( + f"\n\n**使用模板模式**（template_id: {self.template_id}）：\n" + f"- 模板文件为 'paper.md'（这是最终论文文件）\n" + f"- 模板是一个大纲，你要填满大纲！\n" + f"- 生成论文时必须严格遵循模板的格式、结构和风格\n" + f"- 优先使用update_template工具来更新特定章节\n" + f"- 最终论文应该是一个完整的、格式规范的学术文档\n" + ) else: - system_content += f"\n\n**不使用模板模式**：\n" - system_content += f"- 你需要从头开始创建完整的论文结构\n" - system_content += f"- 根据用户需求设计合适的论文章节结构\n" - system_content += f"- 确保论文结构完整、逻辑清晰\n" - - return system_content - - def _setup_tools(self): - """设置MainAgent的工具定义""" - # CodeAgent工具 - code_interpreter_tool = { - "type": "function", - "function": { - "name": "CodeAgent", - "description": "当需要数学计算、数据分析或执行编程任务时调用。提供清晰、具体的任务描述。不要提供代码。", - "parameters": { - "type": "object", - "properties": { - "task_prompt": {"type": "string", "description": "需要执行的具体任务描述。不要提供代码。"} - }, - "required": ["task_prompt"], - }, - }, - } - - # writemd工具 - writemd_tool = { - "type": "function", - "function": { - "name": "writemd", - "description": "将内容写入Markdown文件到workspace目录，支持多种写入模式", - "parameters": { - "type": "object", - "properties": { - "filename": {"type": "string", "description": "文件名（不需要.md后缀）"}, - "content": {"type": "string", "description": "Markdown格式的内容"}, - "mode": { - "type": "string", - "description": "写入模式：overwrite(覆盖), append(追加), modify(修改), insert(插入), smart_replace(智能替换), section_update(章节更新)", - "default": "overwrite" - } - }, - "required": ["filename", "content"], - }, - }, - } - - # update_template工具 - update_template_tool = { - "type": "function", - "function": { - "name": "update_template", - "description": "专门用于更新论文文件的工具，只支持章节级别更新，必须指定章节名称", - "parameters": { - "type": "object", - "properties": { - "template_name": {"type": "string", "description": "论文文件名，默认为paper.md"}, - "content": {"type": "string", "description": "要更新的内容"}, - "section": {"type": "string", "description": "要更新的章节名称（必需）"} - }, - "required": ["content", "section"], - }, - }, - } - - # tree工具 - tree_tool = { - "type": "function", - "function": { - "name": "tree", - "description": "显示workspace目录的树形结构", - "parameters": { - "type": "object", - "properties": { - "directory": {"type": "string", "description": "要显示的目录路径，默认为workspace目录"} - }, - "required": [], - }, - }, - } - - # 附件读取工具 - list_attachments_tool = { - "type": "function", - "function": { - "name": "list_attachments", - "description": "列出工作空间中所有上传的附件文件", - "parameters": { - "type": "object", - "properties": {}, - "required": [], - }, - }, - } - - read_attachment_tool = { - "type": "function", - "function": { - "name": "read_attachment", - "description": "读取指定附件文件的内容，支持txt、pdf、docx、csv、excel等格式", - "parameters": { - "type": "object", - "properties": { - "file_path": { - "type": "string", - "description": "附件文件路径（相对于attachment目录的路径）" - } - }, - "required": ["file_path"], - }, - }, - } - - get_attachment_info_tool = { - "type": "function", - "function": { - "name": "get_attachment_info", - "description": "获取附件文件的详细信息，包括文件大小、类型、创建时间等元数据", - "parameters": { - "type": "object", - "properties": { - "file_path": { - "type": "string", - "description": "附件文件路径（相对于attachment目录的路径）" - } - }, - "required": ["file_path"], - }, - }, - } - - search_attachments_tool = { - "type": "function", - "function": { - "name": "search_attachments", - "description": "在所有附件文件中搜索关键词，支持文件名和文件内容搜索", - "parameters": { - "type": "object", - "properties": { - "keyword": { - "type": "string", - "description": "要搜索的关键词" - }, - "file_type": { - "type": "string", - "description": "可选的文件类型过滤（如 'pdf', 'docx', 'txt' 等）" - } - }, - "required": ["keyword"], - }, - }, - } - - # 模板操作工具（仅在有模板时添加） - tools = [ - code_interpreter_tool, - writemd_tool, - update_template_tool, - tree_tool, - list_attachments_tool, - read_attachment_tool, - get_attachment_info_tool, - search_attachments_tool - ] - - if self.template_id: - # analyze_template工具 - analyze_template_tool = { - "type": "function", - "function": { - "name": "analyze_template", - "description": "分析当前工作目录中模板文件的模板结构，识别所有标题层级和内容，为AI提供模板概览", - "parameters": { - "type": "object", - "properties": {}, - "required": [], - }, - }, - } - - # get_section_content工具 - get_section_content_tool = { - "type": "function", - "function": { - "name": "get_section_content", - "description": "获取paper.md文件中指定章节的内容", - "parameters": { - "type": "object", - "properties": { - "section_title": {"type": "string", "description": "要查看的章节标题"} - }, - "required": ["section_title"], - }, - }, - } - - # update_section_content工具 - update_section_content_tool = { - "type": "function", - "function": { - "name": "update_section_content", - "description": "更新paper.md文件中指定章节的内容，支持多种更新模式", - "parameters": { - "type": "object", - "properties": { - "section_title": {"type": "string", "description": "要更新的章节标题"}, - "new_content": {"type": "string", "description": "新内容"}, - "mode": { - "type": "string", - "description": "更新模式：replace(替换), append(追加), prepend(插入), merge(合并)", - "default": "replace" - } - }, - "required": ["section_title", "new_content"], - }, - }, - } - - # add_section工具 - add_section_tool = { - "type": "function", - "function": { - "name": "add_section", - "description": "在paper.md文件末尾添加新章节", - "parameters": { - "type": "object", - "properties": { - "section_title": {"type": "string", "description": "新章节标题"}, - "content": {"type": "string", "description": "新章节内容", "default": ""} - }, - "required": ["section_title"], - }, - }, - } - - # rename_section_title工具 - rename_section_title_tool = { - "type": "function", - "function": { - "name": "rename_section_title", - "description": "修改paper.md文件中指定章节的标题，保持标题层级不变", - "parameters": { - "type": "object", - "properties": { - "old_title": {"type": "string", "description": "原章节标题（支持模糊匹配）"}, - "new_title": {"type": "string", "description": "新章节标题"} - }, - "required": ["old_title", "new_title"], - }, - }, - } - - tools.extend([ - analyze_template_tool, - get_section_content_tool, - update_section_content_tool, - add_section_tool, - rename_section_title_tool - ]) - - self.tools = tools - - def _register_tool_functions(self): - """注册MainAgent的工具函数""" - if not self.tool_manager: - raise ValueError("MainAgent需要工具管理器") - - # 获取工具实例 - file_tool = self.tool_manager.file() + system_content += ( + f"\n\n**不使用模板模式**：\n" + f"- 你需要从头开始创建完整的论文结构\n" + f"- 根据用户需求设计合适的论文章节结构\n" + f"- 使用writemd工具创建paper.md文件\n" + f"- 确保论文结构完整、逻辑清晰\n" + ) - # 基础工具函数 - self.available_functions = { - "writemd": file_tool.writemd, - "update_template": file_tool.update_template, - "tree": file_tool.tree, - "list_attachments": file_tool.list_attachments, - "read_attachment": file_tool.read_attachment, - "get_attachment_info": file_tool.get_attachment_info, - "search_attachments": file_tool.search_attachments, - "CodeAgent": self._execute_code_agent_wrapper - } + system_content += ( + "\n\n重要原则：\n" + "- 保持对话连贯性，按步骤执行任务\n" + "- **充分利用用户上传的附件内容，确保论文基于真实的资料和数据**\n" + "- 生成的图表要保存在outputs目录，并在论文中正确引用\n" + "- 论文不要杜撰，确保科学性和准确性\n" + "- 每完成一个重要章节，使用writemd保存一次\n" + "- 最终输出应该是完整的paper.md文件\n" + ) - # 模板工具函数（仅在有模板时注册） - if self.template_id: - template_tool = self.tool_manager.template() - self.available_functions.update({ - "analyze_template": template_tool.analyze_template, - "get_section_content": template_tool.get_section_content, - "update_section_content": template_tool.update_section_content, - "add_section": template_tool.add_section, - "rename_section_title": template_tool.rename_section_title - }) + return system_content - async def _execute_code_agent_wrapper(self, task_prompt: str) -> str: + async def run(self, user_input: str) -> str: """ - CodeAgent工具的包装函数 - - Args: - task_prompt: 任务提示词 - - Returns: - CodeAgent执行结果 + 执行主Agent逻辑，使用 LangChain Agent 处理 """ - return await self._execute_code_agent(task_prompt, "main_agent_call") + logger.info(f"MainAgent开始执行任务: {user_input[:100]}...") - def _copy_template_to_workspace(self): - """复制模板文件到工作空间""" try: - if not self.template_id: - logger.warning("模板ID为空，无法复制模板文件") - return - - # 获取模板信息 - from services.data_services.crud import get_paper_template - from database.database import get_db - - db = next(get_db()) - template = get_paper_template(db, self.template_id) - if not template: - logger.warning(f"模板 {self.template_id} 不存在") - return - - # 构建模板文件路径 - template_file_path = os.path.join( - os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), - "pa_data", "templates", f"{self.template_id}_template.md" - ) - - # 检查模板文件是否存在 - if not os.path.exists(template_file_path): - logger.warning(f"模板文件不存在: {template_file_path}") - return - - # 构建目标路径 - target_path = os.path.join(self.workspace_dir, "paper.md") - - # 复制模板文件到工作空间 - shutil.copy2(template_file_path, target_path) - logger.info(f"模板文件已复制到工作空间，重命名为paper.md: {target_path}") + # 发送开始通知 + if self.stream_manager: + try: + await self.stream_manager.send_json_block( + "main_agent_start", + f"MainAgent开始执行: {user_input[:100]}..." + ) + except Exception as e: + logger.warning(f"发送开始通知失败: {e}") + + # 使用 LangChain Agent 执行 + inputs = {"messages": [HumanMessage(content=user_input)]} + result = await self.agent.ainvoke(inputs) + + # 提取最后的AI回复 + messages = result.get("messages", []) + output = "" + for message in reversed(messages): + if hasattr(message, 'content') and message.content: + output = message.content + break + elif isinstance(message, dict) and message.get("role") == "assistant": + output = message.get("content", "") + break + + logger.info(f"MainAgent任务完成，结果长度: {len(output)}") + + # 发送完成通知 + if self.stream_manager: + try: + await self.stream_manager.send_json_block( + "main_agent_complete", + f"任务完成，结果长度: {len(output)} 字符" + ) + except Exception as e: + logger.warning(f"发送完成通知失败: {e}") + + return output except Exception as e: - logger.error(f"复制模板文件失败: {e}") + logger.error(f"MainAgent执行失败: {e}", exc_info=True) + error_msg = f"任务执行失败: {str(e)}" - async def run(self, user_problem: str) -> str: - """ - 执行主Agent逻辑，循环处理直到任务完成 + # 发送错误通知 + if self.stream_manager: + try: + await self.stream_manager.send_json_block("main_agent_error", error_msg) + except Exception as e: + logger.warning(f"发送错误通知失败: {e}") - Args: - user_problem: 用户问题 + return error_msg - Returns: - 执行结果 + async def stream_run(self, user_input: str): """ - logger.info(f"MainAgent开始执行，问题长度: {len(user_problem)} 字符") - - # 检查上下文状态 - self._check_and_compress_context() - - # 添加用户消息 - if not self.add_user_message(user_problem): - # 如果是重复消息，直接返回 - return "检测到重复消息，已跳过处理" - - iteration_count = 0 - while True: - iteration_count += 1 - logger.info(f"MainAgent第 {iteration_count} 次迭代") - - # 使用异步LLM处理 - assistant_message, tool_calls = await self.llm_handler.process_stream( - self.messages, self.tools) - self.messages.append(assistant_message) - - if not tool_calls: - logger.info("MainAgent没有工具调用，任务完成") - break - - logger.info(f"MainAgent执行 {len(tool_calls)} 个工具调用") - - # 并行执行工具调用 - tool_results = await self._execute_tool_calls_parallel(tool_calls) - - # 添加工具结果到消息历史 - for tool_call, tool_result in zip(tool_calls, tool_results): - self.messages.append({ - "role": "tool", - "tool_call_id": tool_call["id"], - "content": tool_result, - }) - - logger.info(f"MainAgent执行完成，总共 {iteration_count} 次迭代") - - # 确保在MainAgent执行完成后触发消息完成回调 - if self.stream_manager: - await self.stream_manager.finalize_message() - - return assistant_message.get('content', 'MainAgent任务完成') - - async def _execute_tool_calls_parallel(self, tool_calls: List[Dict[str, Any]]) -> List[str]: - """ - 并行执行多个工具调用 - - Args: - tool_calls: 工具调用列表 - - Returns: - 工具执行结果列表 + 流式执行，逐个输出Agent步骤 """ - tasks = [] - for i, tool_call in enumerate(tool_calls): - task = self._execute_tool_call(tool_call, i + 1, len(tool_calls)) - tasks.append(task) + logger.info(f"MainAgent开始流式执行: {user_input[:100]}...") - # 并行执行所有工具调用 - results = await asyncio.gather(*tasks, return_exceptions=True) - - # 处理异常结果 - processed_results = [] - for i, result in enumerate(results): - if isinstance(result, Exception): - logger.error(f"工具调用 {i+1} 执行失败: {result}") - processed_results.append(f"工具执行失败: {str(result)}") - else: - processed_results.append(result) + try: + # 使用异步流式执行 + inputs = {"messages": [HumanMessage(content=user_input)]} + async for chunk in self.agent.astream(inputs, stream_mode="updates"): + if self.stream_manager: + try: + await self.stream_manager.print_stream(str(chunk)) + except Exception as e: + logger.warning(f"流式输出失败: {e}") + else: + print(str(chunk)) - return processed_results + except Exception as e: + logger.error(f"流式执行失败: {e}") + error_msg = f"流式执行失败: {str(e)}" + if self.stream_manager: + await self.stream_manager.print_content(error_msg) def get_execution_summary(self) -> Dict[str, Any]: """获取执行摘要""" - base_summary = super().get_execution_summary() - - # 添加MainAgent特有的摘要信息 - main_agent_summary = { + return { + "agent_type": "MainAgent", "template_id": self.template_id, - "workspace_files": self._list_workspace_files() if self.tool_manager else [] - } - - base_summary.update(main_agent_summary) - return base_summary - - def _list_workspace_files(self) -> List[str]: - """列出工作空间中的文件""" - if not self.workspace_dir or not os.path.exists(self.workspace_dir): - return [] - - files = [] - for root, dirs, filenames in os.walk(self.workspace_dir): - for filename in filenames: - rel_path = os.path.relpath(os.path.join(root, filename), self.workspace_dir) - files.append(rel_path) - - return sorted(files) \ No newline at end of file + "work_id": self.work_id, + "workspace_dir": self.workspace_dir, + "tools_count": len(self.tools), + "tool_names": [tool.name for tool in self.tools], + "langchain_based": True + } \ No newline at end of file diff --git a/backend/ai_system/core_handlers/llm_factory.py b/backend/ai_system/core_handlers/llm_factory.py deleted file mode 100644 index 4e6a798..0000000 --- a/backend/ai_system/core_handlers/llm_factory.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -from typing import Optional -from sqlalchemy.orm import Session - -from ..config.environment import setup_environment_from_db -from ..core_handlers.llm_handler import LLMHandler -from ..core_managers.stream_manager import StreamOutputManager - - -class LLMFactory: - """统一创建 LLMHandler 的工厂，封装环境加载与模型选择。""" - - def __init__(self, db: Session): - self.db = db - self._env_manager = setup_environment_from_db(db) - - def initialize_system(self, system_type: str) -> None: - self._env_manager.initialize_system(system_type) - - def create_handler(self, system_type: str, stream_manager: Optional[StreamOutputManager] = None) -> LLMHandler: - config = self._env_manager.config_manager.get_model_config(system_type) - return LLMHandler(model=str(config.model_id), stream_manager=stream_manager) - - def get_workspace_path(self, work_id: str) -> str: - import os - workspace_path = os.path.join( - os.path.dirname(os.path.dirname(__file__)), - "..", - "pa_data", - "workspaces", - work_id, - ) - return workspace_path - - def setup_workspace(self, work_id: str) -> None: - workspace_path = self.get_workspace_path(work_id) - self._env_manager.setup_workspace(workspace_path) - - async def run_stream(self, system_type: str, messages, stream_manager: Optional[StreamOutputManager] = None, tools=None): - handler = self.create_handler(system_type, stream_manager=stream_manager) - assistant_message, tool_calls = await handler.process_stream(messages, tools) - return assistant_message, tool_calls - - async def run_sync(self, system_type: str, messages, tools=None): - handler = self.create_handler(system_type, stream_manager=None) - assistant_message, tool_calls = await handler.process_sync(messages, tools) - return assistant_message, tool_calls - - \ No newline at end of file diff --git a/backend/ai_system/core_handlers/llm_handler.py b/backend/ai_system/core_handlers/llm_handler.py index c8cfadf..fc954c1 100644 --- a/backend/ai_system/core_handlers/llm_handler.py +++ b/backend/ai_system/core_handlers/llm_handler.py @@ -1,351 +1,147 @@ """ -LLM通信处理器 -处理与litellm API的所有通信，包括流式响应和工具调用 +LLM处理器 - LangChain 重构版本 +简化的LLM处理器，专注于创建LLM实例和Agent，不再处理复杂的流式逻辑 """ import logging -import json -from typing import List, Dict, Any -import litellm -import asyncio -from concurrent.futures import ThreadPoolExecutor -import functools +from typing import Optional, Any, Dict +from langchain_core.language_models import BaseLanguageModel -# 使用字符串类型注解避免循环导入（类型仍用字符串） -# 运行期需要的配置模块安全地在模块级导入 -from ..config.async_config import AsyncConfig +from .llm_providers import LLMProviderFactory, create_llm_from_model_config +from ..core_agents.main_agent import MainAgent logger = logging.getLogger(__name__) class LLMHandler: """ - 处理与 litellm API 的所有通信，包括流式响应和工具调用。 - 完全异步化，避免阻塞事件循环。 + 简化的LLM处理器 + - 只负责创建LangChain LLM实例 + - 提供创建Agent的便捷方法 + - 不再处理复杂的流式输出逻辑 """ - def __init__(self, model: str, stream_manager: 'StreamOutputManager' = None): - self.model = model - self.stream_manager = stream_manager - # 创建线程池执行器，用于处理同步的litellm调用 - # 增加线程池大小以支持并发LLM处理 - import os - cpu = os.cpu_count() or 1 - max_workers = min(12, cpu * 3) # 动态调整，最多12个 - self.executor = ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix="LLMHandler") - logger.info(f"LLMHandler初始化完成，模型: {model}") - - async def process_stream(self, messages: List[Dict[str, Any]], tools: List[Dict[str, Any]] = None): + def __init__(self, + model: Optional[str] = None, + stream_manager=None, + provider: Optional[str] = None, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + model_config: Optional[Any] = None, + **llm_kwargs): """ - 异步调用 LLM API 并处理流式响应，返回完整的响应和工具调用信息。 - 使用线程池避免阻塞事件循环。 + 初始化LLM处理器 + + Args: + model: 模型ID（可选，如果提供了model_config则从中获取） + stream_manager: 流式输出管理器 + provider: AI提供商名称（可选，如果提供了model_config则从中获取） + api_key: API密钥（可选，如果提供了model_config则从中获取） + base_url: API基础URL（可选，如果提供了model_config则从中获取） + model_config: 数据库ModelConfig对象 + **llm_kwargs: 传递给LLM实例的额外参数 """ - logger.info(f"开始调用LLM API，消息数量: {len(messages)}") - - # 打印消息列表的详细信息，帮助调试 - logger.info("=== 消息列表详情 ===") - for i, msg in enumerate(messages): - role = msg.get('role', 'unknown') - content = msg.get('content', '')[:1000] # 只显示前100个字符 - logger.info(f"消息 {i}: role={role}, content={repr(content)}...") - logger.info("=== 消息列表结束 ===") - - if tools: - logger.info(f"使用工具数量: {len(tools)}") - - try: - # 在线程池中执行同步的litellm调用，避免阻塞事件循环 - loop = asyncio.get_event_loop() - response_stream = await loop.run_in_executor( - self.executor, - functools.partial( - litellm.completion, - model=self.model, - messages=messages, - tools=tools, - stream=True - ) - ) - - full_response_content = "" - tool_calls = [] - # 改为收集所有工具调用chunk，不进行实时解析 - tool_call_chunks = [] - chunk_count = 0 - - # 异步处理流式响应 - for chunk in response_stream: - chunk_count += 1 - delta = chunk.choices[0].delta - - # 1. 累积文本内容（保持不变） - if delta.content: - content = delta.content - if self.stream_manager: - await self.stream_manager.print_stream(content) - else: - print(content, end="", flush=True) - full_response_content += content - logger.debug( - f"接收到文本内容块 {chunk_count}: {repr(content[:30])}...") - - # 让出控制权，确保WebSocket能及时发送数据 - # 使用配置参数优化延迟 - config = AsyncConfig.get_llm_stream_config() - if chunk_count % config["yield_interval"] == 0: - await asyncio.sleep(config["yield_delay"]) - - # 2. 收集工具调用chunk，但不解析（改为完全等待模式） - if delta.tool_calls: - # 只收集chunk，不进行任何解析 - chunk_info = { - "chunk_id": chunk_count, - "delta": delta.tool_calls - } - tool_call_chunks.append(chunk_info) - - # 添加详细的调试日志 - tool_call_count = len(delta.tool_calls) - logger.debug(f"收集工具调用chunk {chunk_count}，包含 {tool_call_count} 个工具调用") - - # 如果chunk数量异常多，记录警告 - if chunk_count > 1000: - logger.warning(f"工具调用chunk数量异常多: {chunk_count}，可能存在无限循环") - - # 记录前几个chunk的详细信息 - if chunk_count <= 5: - for i, tool_call in enumerate(delta.tool_calls): - logger.debug(f" Chunk {chunk_count} 工具调用 {i}: id={tool_call.id}, name={getattr(tool_call.function, 'name', 'None') if tool_call.function else 'None'}") - - # 定期让出控制权，确保其他异步任务能够执行 - config = AsyncConfig.get_llm_stream_config() - if chunk_count % (config["yield_interval"] * 2) == 0: - await asyncio.sleep(config["yield_delay"]) - - # 流式响应结束后，统一处理所有工具调用（完全等待模式） - if tool_call_chunks: - logger.info(f"开始处理 {len(tool_call_chunks)} 个工具调用chunk") - try: - tool_calls = self._extract_tool_calls_from_chunks(tool_call_chunks) - logger.info(f"成功提取 {len(tool_calls)} 个完整工具调用") - except Exception as e: - logger.error(f"工具调用提取失败: {e}", exc_info=True) - tool_calls = [] - else: - logger.info("没有工具调用需要处理") - - if not self.stream_manager: - print() # 确保换行 - - logger.info(f"LLM API调用完成，总块数: {chunk_count}，工具调用数: {len(tool_calls)}") + self.stream_manager = stream_manager + self.llm_kwargs = { + 'temperature': llm_kwargs.get('temperature', 0.7), + 'max_tokens': llm_kwargs.get('max_tokens', 4000), + 'streaming': llm_kwargs.get('streaming', True) + } - # 确保触发消息完成回调 - if self.stream_manager: - await self.stream_manager.finalize_message() + # 构建配置字典并创建LLM实例 + if model_config: + # 从数据库配置对象创建LLM实例 + self.llm = create_llm_from_model_config(model_config, **self.llm_kwargs) + self.provider = getattr(model_config, 'provider', 'openai') + self.model = model_config.model_id + logger.info(f"LLMHandler初始化完成，提供商: {self.provider}, 模型: {self.model}") + else: + # 从直接参数创建LLM实例 + config = { + 'provider': provider or 'openai', + 'model_id': model, + 'api_key': api_key, + 'base_url': base_url, + 'is_active': True + } + self.llm = LLMProviderFactory.create_llm_instance(config, **self.llm_kwargs) + self.provider = provider or 'openai' + self.model = model + logger.info(f"LLMHandler初始化完成，提供商: {self.provider}, 模型: {self.model}") - # 构建完整的 assistant 消息 - assistant_message = {"role": "assistant", - "content": full_response_content} - if tool_calls: - assistant_message["tool_calls"] = tool_calls + logger.info(f"简化LLMHandler初始化完成，提供商: {self.provider}, 模型: {self.model}") - return assistant_message, tool_calls + def create_main_agent(self, workspace_dir: str = None, work_id: str = None, + template_id: int = None) -> MainAgent: + """ + 创建 MainAgent 实例的便捷方法 - except Exception as e: - logger.error(f"LLM API调用失败: {e}") - error_message = f"LLM API调用失败: {str(e)}" - if self.stream_manager: - await self.stream_manager.print_content(error_message) - - # 返回错误消息 - return {"role": "assistant", "content": error_message}, [] + Args: + workspace_dir: 工作空间目录 + work_id: 工作ID + template_id: 模板ID - async def process_sync(self, messages: List[Dict[str, Any]], tools: List[Dict[str, Any]] = None): - """ - 同步调用LLM API（在线程池中执行），用于不需要流式处理的场景 + Returns: + MainAgent 实例 """ - logger.info(f"开始同步调用LLM API，消息数量: {len(messages)}") - - try: - # 在线程池中执行同步调用 - loop = asyncio.get_event_loop() - response = await loop.run_in_executor( - self.executor, - functools.partial( - litellm.completion, - model=self.model, - messages=messages, - tools=tools, - stream=False - ) - ) - - content = response.choices[0].message.content - tool_calls = [] - - # 处理工具调用 - if hasattr(response.choices[0].message, 'tool_calls') and response.choices[0].message.tool_calls: - for tool_call in response.choices[0].message.tool_calls: - tool_calls.append({ - "id": tool_call.id, - "type": "function", - "function": { - "name": tool_call.function.name, - "arguments": tool_call.function.arguments - } - }) - - logger.info(f"同步LLM API调用完成，内容长度: {len(content)}，工具调用数: {len(tool_calls)}") - - return {"role": "assistant", "content": content}, tool_calls - - except Exception as e: - logger.error(f"同步LLM API调用失败: {e}") - error_message = f"LLM API调用失败: {str(e)}" - return {"role": "assistant", "content": error_message}, [] + return MainAgent( + llm=self.llm, + stream_manager=self.stream_manager, + workspace_dir=workspace_dir, + work_id=work_id, + template_id=template_id + ) - def _finalize_tool_call(self, tool_call_data: Dict) -> Dict: - """构建完整的工具调用对象""" - return { - "id": tool_call_data["id"], - "type": "function", - "function": { - "name": tool_call_data["name"], - "arguments": tool_call_data["arguments"] - } - } + # CodeAgent 已移除，直接使用 smolagents 库 - def _extract_tool_calls_from_chunks(self, tool_call_chunks: List[Dict]) -> List[Dict]: + def get_llm_instance(self) -> BaseLanguageModel: """ - 从完整的工具调用chunk列表中提取完整的工具调用 - 采用完全等待模式，确保所有参数都完整 + 获取原始的 LangChain LLM 实例 + 用于高级用例，直接使用 LangChain 功能 + + Returns: + LangChain LLM 实例 """ - if not tool_call_chunks: - return [] - - logger.info(f"开始从 {len(tool_call_chunks)} 个chunk中提取工具调用") - - # 按工具调用ID分组收集所有chunk - tool_call_groups = {} - current_tool_call_id = None - - for chunk_info in tool_call_chunks: - chunk_id = chunk_info["chunk_id"] - deltas = chunk_info["delta"] - - for delta in deltas: - # 处理工具调用ID - if delta.id: - # 新的工具调用开始 - current_tool_call_id = delta.id - if current_tool_call_id not in tool_call_groups: - tool_call_groups[current_tool_call_id] = { - "id": current_tool_call_id, - "name": "", - "arguments": "" - } - logger.debug(f"开始收集工具调用 {current_tool_call_id} 的chunk") - - # 只有在有有效工具调用ID时才处理function信息 - if current_tool_call_id and delta.function: - if delta.function.name: - tool_call_groups[current_tool_call_id]["name"] = delta.function.name - if delta.function.arguments: - tool_call_groups[current_tool_call_id]["arguments"] += delta.function.arguments - - # 构建完整的工具调用列表 - complete_tool_calls = [] - for tool_call_id, tool_call_data in tool_call_groups.items(): - # 验证工具调用是否完整 - if tool_call_data["name"] and tool_call_data["arguments"]: - # 验证JSON参数是否完整 - if self._is_valid_json(tool_call_data["arguments"]): - complete_tool_calls.append(self._finalize_tool_call(tool_call_data)) - logger.info(f"工具调用 {tool_call_data['name']} 参数完整，长度: {len(tool_call_data['arguments'])}") - else: - logger.warning(f"工具调用 {tool_call_data['name']} 参数不完整，跳过: {repr(tool_call_data['arguments'][:100])}") - else: - logger.warning(f"工具调用 {tool_call_id} 缺少必要信息，跳过") - - logger.info(f"成功提取 {len(complete_tool_calls)} 个完整工具调用") - return complete_tool_calls + return self.llm - def _is_valid_json(self, json_str: str) -> bool: - """检查字符串是否是有效的JSON""" - try: - json.loads(json_str) - return True - except json.JSONDecodeError: - return False - - def _try_fix_incomplete_json(self, json_str: str) -> str: - """尝试修复不完整的JSON字符串""" - if not json_str.strip(): - return json_str - - try: - # 尝试直接解析 - json.loads(json_str) - return json_str - except json.JSONDecodeError as e: - logger.debug(f"JSON解析失败，尝试修复: {e}") - - # 常见的修复策略 - fixed_str = json_str - - # 1. 检查未闭合的引号 - quote_count = json_str.count('"') - json_str.count('\\"') - if quote_count % 2 != 0: - # 找到最后一个未闭合的引号位置 - last_quote_pos = json_str.rfind('"') - if last_quote_pos > 0: - # 检查是否在字符串内部 - before_quote = json_str[:last_quote_pos] - if before_quote.count('"') % 2 == 0: - # 在字符串末尾添加引号 - fixed_str = json_str + '"' - logger.debug("修复未闭合的引号") - - # 2. 检查未闭合的大括号 - brace_count = json_str.count('{') - json_str.count('}') - if brace_count > 0: - fixed_str = json_str + '}' * brace_count - logger.debug(f"修复未闭合的大括号，添加 {brace_count} 个") - - # 3. 检查未闭合的方括号 - bracket_count = json_str.count('[') - json_str.count(']') - if bracket_count > 0: - fixed_str = json_str + ']' * bracket_count - logger.debug(f"修复未闭合的方括号，添加 {bracket_count} 个") - - # 4. 尝试解析修复后的字符串 - try: - json.loads(fixed_str) - logger.info(f"JSON修复成功，原始长度: {len(json_str)}, 修复后长度: {len(fixed_str)}") - return fixed_str - except json.JSONDecodeError: - logger.warning(f"JSON修复失败，原始字符串: {repr(json_str[:100])}") - return json_str + def set_model(self, model: str, provider: Optional[str] = None, **kwargs): + """ + 设置LLM模型和提供商 - def set_model(self, model: str): - """设置LLM模型""" + Args: + model: 模型ID + provider: AI提供商名称（可选，默认使用当前提供商） + **kwargs: 其他LLM参数 + """ self.model = model - logger.info(f"LLM模型已更新为: {model}") + if provider: + self.provider = provider + + # 构建配置并创建新的LLM实例 + config = { + 'provider': self.provider or 'openai', + 'model_id': model, + 'api_key': kwargs.get('api_key', ''), + 'base_url': kwargs.get('base_url', ''), + 'is_active': True + } + + # 更新LLM参数 + llm_params = {**self.llm_kwargs, **kwargs} + self.llm = LLMProviderFactory.create_llm_instance(config, **llm_params) + logger.info(f"LLM已更新，提供商: {self.provider}, 模型: {model}") def get_model_info(self) -> Dict[str, Any]: """获取当前模型信息""" return { + "provider": self.provider, "model": self.model, - "stream_manager_configured": self.stream_manager is not None + "stream_manager_configured": self.stream_manager is not None, + "langchain_based": True, + "simplified": True, + "supported_providers": LLMProviderFactory.get_supported_providers() } async def close(self): - """关闭线程池执行器""" - if self.executor: - self.executor.shutdown(wait=True) - logger.info("LLMHandler线程池已关闭") - - def __del__(self): - """析构函数，确保线程池被正确关闭""" - if hasattr(self, 'executor') and self.executor: - self.executor.shutdown(wait=False) + """清理资源""" + logger.info("简化 LLMHandler 资源清理完成") diff --git a/backend/ai_system/core_handlers/llm_providers.py b/backend/ai_system/core_handlers/llm_providers.py new file mode 100644 index 0000000..bb84812 --- /dev/null +++ b/backend/ai_system/core_handlers/llm_providers.py @@ -0,0 +1,274 @@ +""" +AI提供商抽象层 +支持多种AI提供商的统一接口 +""" + +import logging +from abc import ABC, abstractmethod +from typing import Dict, Any, Optional, List +from langchain_core.language_models import BaseLanguageModel +from langchain_openai import ChatOpenAI +from langchain_anthropic import ChatAnthropic +from langchain_google_genai import ChatGoogleGenerativeAI + +logger = logging.getLogger(__name__) + + +class BaseLLMProvider(ABC): + """AI提供商抽象基类""" + + def __init__(self, config: Dict[str, Any]): + self.config = config + self.provider_name = config.get('provider', 'unknown') + self.model_id = config.get('model_id', '') + self.api_key = config.get('api_key', '') + self.base_url = config.get('base_url', '') + self.is_active = config.get('is_active', True) + + @abstractmethod + def create_llm_instance(self, **kwargs) -> BaseLanguageModel: + """创建LLM实例""" + pass + + @abstractmethod + def validate_config(self) -> bool: + """验证配置是否有效""" + pass + + def get_model_info(self) -> Dict[str, Any]: + """获取模型信息""" + return { + "provider": self.provider_name, + "model_id": self.model_id, + "base_url": self.base_url, + "is_active": self.is_active + } + + +class OpenAIProvider(BaseLLMProvider): + """OpenAI提供商 - 支持OpenAI及兼容格式""" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.provider_name = "openai" + + def create_llm_instance(self, **kwargs) -> ChatOpenAI: + """创建ChatOpenAI实例""" + if not self.validate_config(): + raise ValueError(f"OpenAI提供商配置无效: {self.get_model_info()}") + + return ChatOpenAI( + model=self.model_id, + api_key=self.api_key, + base_url=self.base_url if self.base_url else None, + temperature=kwargs.get('temperature', 0.7), + max_tokens=kwargs.get('max_tokens', 4000), + streaming=kwargs.get('streaming', True) + ) + + def validate_config(self) -> bool: + """验证OpenAI配置""" + return bool(self.api_key and self.model_id) + + +class AnthropicProvider(BaseLLMProvider): + """Anthropic提供商 - 支持Claude系列""" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.provider_name = "anthropic" + + def create_llm_instance(self, **kwargs) -> ChatAnthropic: + """创建ChatAnthropic实例""" + if not self.validate_config(): + raise ValueError(f"Anthropic提供商配置无效: {self.get_model_info()}") + + return ChatAnthropic( + model=self.model_id, + api_key=self.api_key, + base_url=self.base_url if self.base_url else None, + temperature=kwargs.get('temperature', 0.7), + max_tokens=kwargs.get('max_tokens', 4000) + ) + + def validate_config(self) -> bool: + """验证Anthropic配置""" + return bool(self.api_key and self.model_id) + + +class GoogleProvider(BaseLLMProvider): + """Google提供商 - 支持Gemini系列""" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.provider_name = "google" + + def create_llm_instance(self, **kwargs) -> ChatGoogleGenerativeAI: + """创建ChatGoogleGenerativeAI实例""" + if not self.validate_config(): + raise ValueError(f"Google提供商配置无效: {self.get_model_info()}") + + return ChatGoogleGenerativeAI( + model=self.model_id, + google_api_key=self.api_key, + temperature=kwargs.get('temperature', 0.7), + max_tokens=kwargs.get('max_tokens', 4000) + ) + + def validate_config(self) -> bool: + """验证Google配置""" + return bool(self.api_key and self.model_id) + + +class LocalProvider(BaseLLMProvider): + """本地模型提供商 - 支持Ollama等本地服务""" + + def __init__(self, config: Dict[str, Any]): + super().__init__(config) + self.provider_name = "local" + + def create_llm_instance(self, **kwargs) -> ChatOpenAI: + """通过OpenAI兼容格式连接本地模型""" + if not self.validate_config(): + raise ValueError(f"本地模型提供商配置无效: {self.get_model_info()}") + + # 本地模型通常使用OpenAI兼容格式 + return ChatOpenAI( + model=self.model_id, + api_key=self.api_key or "fake-key", # 本地模型可能不需要真实API key + base_url=self.base_url or "http://localhost:11434/v1", # 默认Ollama地址 + temperature=kwargs.get('temperature', 0.7), + max_tokens=kwargs.get('max_tokens', 4000), + streaming=kwargs.get('streaming', True) + ) + + def validate_config(self) -> bool: + """验证本地模型配置""" + # 本地模型主要需要base_url，model_id通常也是必需的 + return bool(self.base_url or self.model_id) + + +class LLMProviderFactory: + """LLM提供商工厂类""" + + _providers = { + "openai": OpenAIProvider, + "anthropic": AnthropicProvider, + "google": GoogleProvider, + "local": LocalProvider + } + + @classmethod + def create_provider(cls, config: Dict[str, Any]) -> BaseLLMProvider: + """根据配置创建提供商实例""" + provider_name = config.get('provider', 'openai').lower() + + if provider_name not in cls._providers: + raise ValueError(f"不支持的AI提供商: {provider_name}，支持的提供商: {list(cls._providers.keys())}") + + provider_class = cls._providers[provider_name] + return provider_class(config) + + @classmethod + def create_llm_instance(cls, config: Dict[str, Any], **kwargs) -> BaseLanguageModel: + """直接创建LLM实例""" + provider = cls.create_provider(config) + return provider.create_llm_instance(**kwargs) + + @classmethod + def get_supported_providers(cls) -> List[str]: + """获取支持的提供商列表""" + return list(cls._providers.keys()) + + @classmethod + def register_provider(cls, name: str, provider_class: type): + """注册新的提供商""" + if not issubclass(provider_class, BaseLLMProvider): + raise ValueError("提供商类必须继承自BaseLLMProvider") + cls._providers[name.lower()] = provider_class + + +def create_llm_from_model_config(model_config, **kwargs) -> BaseLanguageModel: + """ + 从数据库ModelConfig对象创建LLM实例的便捷函数 + + Args: + model_config: 数据库ModelConfig对象 + **kwargs: 额外的LLM参数 + + Returns: + LLM实例 + """ + config = { + 'provider': getattr(model_config, 'provider', 'openai'), + 'model_id': model_config.model_id, + 'api_key': model_config.api_key, + 'base_url': model_config.base_url, + 'is_active': getattr(model_config, 'is_active', True) + } + + return LLMProviderFactory.create_llm_instance(config, **kwargs) + + +def create_smolagents_model_from_config(model_config, **kwargs): + """ + 从数据库ModelConfig对象创建SmolAgents原生模型实例 + + Args: + model_config: 数据库ModelConfig对象 + **kwargs: 额外的模型参数 + + Returns: + SmolAgents原生模型实例 + """ + try: + from smolagents.models import OpenAIServerModel + + provider = getattr(model_config, 'provider', 'openai').lower() + model_id = model_config.model_id + api_key = model_config.api_key + base_url = model_config.base_url + + logger.info(f"创建SmolAgents模型: {provider}/{model_id}") + + # 根据provider创建对应的SmolAgents模型 + if provider == 'openai' or provider == 'local': + # OpenAI兼容模型，包括本地服务 + return OpenAIServerModel( + model_id=model_id, + api_key=api_key, + api_base=base_url, + **kwargs + ) + elif provider == 'anthropic': + # Anthropic通过OpenAI兼容接口使用 + return OpenAIServerModel( + model_id=model_id, + api_key=api_key, + api_base="https://api.anthropic.com", + **kwargs + ) + elif provider == 'azure': + # Azure OpenAI + return OpenAIServerModel( + model_id=model_id, + api_key=api_key, + api_base=base_url, + **kwargs + ) + else: + # 默认使用OpenAI兼容格式 + logger.warning(f"提供商 {provider} 使用OpenAI兼容格式") + return OpenAIServerModel( + model_id=model_id, + api_key=api_key, + api_base=base_url, + **kwargs + ) + + except ImportError: + logger.error("SmolAgents未安装，无法创建模型实例") + raise ImportError("请安装smolagents库: pip install smolagents") + except Exception as e: + logger.error(f"创建SmolAgents模型失败: {e}") + raise ValueError(f"创建SmolAgents模型失败: {str(e)}") \ No newline at end of file diff --git a/backend/ai_system/core_managers/langchain_tools.py b/backend/ai_system/core_managers/langchain_tools.py new file mode 100644 index 0000000..f721190 --- /dev/null +++ b/backend/ai_system/core_managers/langchain_tools.py @@ -0,0 +1,370 @@ +""" +LangChain 工具工厂 +将现有自定义工具包装为 LangChain 标准格式，统一工具管理 +""" + +import os +import logging +from typing import List, Optional, Dict, Any +from langchain_core.tools import StructuredTool, BaseTool +from langchain_community.tools import DuckDuckGoSearchRun + +# 导入现有的自定义工具 +from ..core_tools.file_tools import FileTools +from ..core_tools.template_tools import TemplateAgentTools + +logger = logging.getLogger(__name__) + + +class LangChainToolFactory: + """ + LangChain 工具工厂 + 将现有的自定义工具包装为 LangChain 标准格式 + """ + + @staticmethod + def create_file_tools(workspace_dir: str, stream_manager=None) -> List[StructuredTool]: + """ + 基于现有 FileTools 创建 LangChain 工具 + + Args: + workspace_dir: 工作空间目录 + stream_manager: 流式输出管理器 + + Returns: + LangChain 格式的文件工具列表 + """ + try: + # 设置环境变量供 FileTools 使用 + os.environ["WORKSPACE_DIR"] = workspace_dir + file_tools = FileTools(stream_manager) + + tools = [ + StructuredTool.from_function( + func=file_tools.writemd, + name="writemd", + description="写入Markdown文件到工作空间。支持多种模式：append(追加)、overwrite(覆盖)、modify(修改)、insert(插入)、section_update(章节更新)", + args_schema={ + "filename": {"type": "string", "description": "文件名（不需要.md后缀）"}, + "content": {"type": "string", "description": "Markdown内容"}, + "mode": {"type": "string", "description": "写入模式：append/overwrite/modify/insert/section_update/smart_replace", "default": "overwrite"} + } + ), + StructuredTool.from_function( + func=file_tools.update_template, + name="update_template", + description="专门用于更新论文模板的特定章节，只支持章节级别的更新", + args_schema={ + "template_name": {"type": "string", "description": "论文文件名，默认为paper.md", "default": "paper.md"}, + "content": {"type": "string", "description": "要更新的章节内容"}, + "section": {"type": "string", "description": "要更新的章节名称（必需）"} + } + ), + StructuredTool.from_function( + func=file_tools.tree, + name="tree", + description="显示工作空间的目录树结构，帮助了解文件组织", + args_schema={ + "directory": {"type": "string", "description": "要显示的目录路径（可选，默认显示整个工作空间）"} + } + ), + StructuredTool.from_function( + func=file_tools.list_attachments, + name="list_attachments", + description="列出工作空间中所有附件文件，包括文件类型、大小等信息" + ), + StructuredTool.from_function( + func=file_tools.read_attachment, + name="read_attachment", + description="读取附件文件内容，支持多种格式（PDF、Word、Excel、文本等）", + args_schema={ + "file_path": {"type": "string", "description": "附件文件路径（相对于attachment目录）"} + } + ), + StructuredTool.from_function( + func=file_tools.search_attachments, + name="search_attachments", + description="在附件文件中搜索关键词", + args_schema={ + "keyword": {"type": "string", "description": "搜索关键词"}, + "file_type": {"type": "string", "description": "可选的文件类型过滤（如pdf、docx、txt等）"} + } + ) + ] + + logger.info(f"创建了 {len(tools)} 个文件工具") + return tools + + except Exception as e: + logger.error(f"创建文件工具失败: {e}") + return [] + + # CodeAgent已移除，直接使用smolagents库 + + @staticmethod + def create_smolagents_tool(workspace_dir: str, stream_manager=None, llm=None) -> Optional[StructuredTool]: + """ + 创建 SmolAgents 代码执行工具 + + Args: + workspace_dir: 工作空间目录 + stream_manager: 流式输出管理器 + llm: LLM实例 + + Returns: + SmolAgents 工具或 None + """ + try: + # 直接同步调用SmolAgents + def sync_smolagents_execute(task_description: str) -> str: + """同步调用SmolAgents""" + try: + # 直接使用smolagents，不通过异步包装 + from smolagents import CodeAgent + + # 检查模型类型 + if hasattr(llm, 'invoke'): + # 这是LangChain LLM，不兼容SmolAgents + logger.error("传入了LangChain LLM实例，但SmolAgents需要原生模型") + return "错误：传入了不兼容的LangChain LLM实例。请使用SmolAgents原生模型。" + + # 创建SmolAgents CodeAgent，使用传入的LLM + logger.info(f"使用模型创建SmolAgents CodeAgent: {llm}") + + # 配置额外的授权导入,支持科学计算和数据可视化 + additional_authorized_imports = [ + # 基础科学计算库 + 'numpy', + 'scipy', + 'pandas', + # 可视化库 + 'matplotlib.pyplot', + 'matplotlib', + 'seaborn' + ] + + # 配置 matplotlib 使用非交互式后端 + import os + os.environ['MPLBACKEND'] = 'Agg' + + agent = CodeAgent( + tools=[], + model=llm, # 使用传入的LLM实例，应该是SmolAgents原生模型 + max_steps=10, + additional_authorized_imports=additional_authorized_imports + ) + + # 直接运行任务 + result = agent.run(task_description) + + if hasattr(result, 'content'): + return result.content + else: + return str(result) + + except Exception as e: + logger.error(f"SmolAgents 执行失败: {e}") + error_msg = str(e) + if "'ChatMessage' object is not iterable" in error_msg: + return "错误：模型类型不兼容。请确保使用SmolAgents原生模型而不是LangChain LLM。" + return f"SmolAgents 执行失败: {error_msg}\n请检查任务描述是否清晰。" + + tool = StructuredTool.from_function( + func=sync_smolagents_execute, + name="smolagents_execute", + description="使用 SmolAgents 执行复杂的代码任务，包括数据分析、图表生成、统计计算等。这是处理复杂代码任务的强大工具。输入应该是一个详细描述要执行的代码任务的字符串。" + ) + + logger.info("创建了 SmolAgents 代码执行工具") + return tool + + except Exception as e: + logger.error(f"创建 SmolAgents 工具失败: {e}") + return None + + @staticmethod + def create_template_tools(workspace_dir: str, stream_manager=None) -> List[StructuredTool]: + """ + 基于现有 TemplateAgentTools 创建 LangChain 工具 + + Args: + workspace_dir: 工作空间目录 + stream_manager: 流式输出管理器 + + Returns: + LangChain 格式的模板工具列表 + """ + try: + template_tools = TemplateAgentTools(workspace_dir) + + tools = [ + StructuredTool.from_function( + func=template_tools.analyze_template, + name="analyze_template", + description="分析paper.md文件的模板结构，为AI提供模板概览" + ), + StructuredTool.from_function( + func=template_tools.get_section_content, + name="get_section_content", + description="获取paper.md文件中指定章节的内容", + args_schema={ + "section_title": {"type": "string", "description": "章节标题"} + } + ), + StructuredTool.from_function( + func=template_tools.update_section_content, + name="update_section_content", + description="更新paper.md文件中指定章节的内容", + args_schema={ + "section_title": {"type": "string", "description": "章节标题"}, + "new_content": {"type": "string", "description": "新的章节内容"} + } + ), + StructuredTool.from_function( + func=template_tools.add_section, + name="add_section", + description="在paper.md文件末尾添加新章节", + args_schema={ + "section_title": {"type": "string", "description": "新章节标题"}, + "content": {"type": "string", "description": "章节内容", "default": ""} + } + ), + StructuredTool.from_function( + func=template_tools.rename_section_title, + name="rename_section_title", + description="修改paper.md文件中指定章节的标题", + args_schema={ + "old_title": {"type": "string", "description": "原标题"}, + "new_title": {"type": "string", "description": "新标题"} + } + ) + ] + + logger.info(f"创建了 {len(tools)} 个模板工具") + return tools + + except Exception as e: + logger.error(f"创建模板工具失败: {e}") + return [] + + @staticmethod + def create_standard_tools() -> List[BaseTool]: + """ + 创建 LangChain 标准工具 + + Returns: + LangChain 标准工具列表 + """ + try: + tools = [ + DuckDuckGoSearchRun( + name="web_search", + description="在网络上搜索学术资料、背景信息和最新研究" + ) + ] + + logger.info(f"创建了 {len(tools)} 个标准工具") + return tools + + except Exception as e: + logger.error(f"创建标准工具失败: {e}") + return [] + + @staticmethod + def create_all_tools(workspace_dir: str, stream_manager=None, + include_template: bool = False) -> List[BaseTool]: + """ + 创建论文写作需要的所有工具 + + Args: + workspace_dir: 工作空间目录 + stream_manager: 流式输出管理器 + include_template: 是否包含模板工具 + + Returns: + 所有工具的列表 + """ + try: + all_tools = [] + + # 添加文件工具 + file_tools = LangChainToolFactory.create_file_tools(workspace_dir, stream_manager) + all_tools.extend(file_tools) + + # 添加标准工具 + standard_tools = LangChainToolFactory.create_standard_tools() + all_tools.extend(standard_tools) + + # 可选：添加模板工具 + if include_template: + template_tools = LangChainToolFactory.create_template_tools(workspace_dir, stream_manager) + all_tools.extend(template_tools) + + logger.info(f"总共创建了 {len(all_tools)} 个工具") + return all_tools + + except Exception as e: + logger.error(f"创建所有工具失败: {e}") + return [] + + @staticmethod + def get_tool_descriptions(tools: List[BaseTool]) -> Dict[str, str]: + """ + 获取工具描述字典 + + Args: + tools: 工具列表 + + Returns: + 工具名称到描述的映射 + """ + descriptions = {} + for tool in tools: + descriptions[tool.name] = tool.description + return descriptions + + @staticmethod + def create_custom_tool(func, name: str, description: str, + args_schema: Optional[Dict[str, Any]] = None) -> StructuredTool: + """ + 创建自定义 LangChain 工具的便捷方法 + + Args: + func: 工具函数 + name: 工具名称 + description: 工具描述 + args_schema: 参数模式（可选） + + Returns: + LangChain 工具 + """ + if args_schema: + return StructuredTool.from_function( + func=func, + name=name, + description=description, + args_schema=args_schema + ) + else: + return StructuredTool.from_function( + func=func, + name=name, + description=description + ) + + +# 便捷函数 +def create_paper_writing_tools(workspace_dir: str, stream_manager=None) -> List[BaseTool]: + """创建论文写作工具集的便捷函数""" + return LangChainToolFactory.create_all_tools(workspace_dir, stream_manager, include_template=True) + + +def create_data_analysis_tools(workspace_dir: str, stream_manager=None) -> List[BaseTool]: + """创建数据分析工具集的便捷函数""" + return LangChainToolFactory.create_file_tools(workspace_dir, stream_manager) + + +def create_research_tools(workspace_dir: str, stream_manager=None) -> List[BaseTool]: + """创建研究工具集的便捷函数""" + return LangChainToolFactory.create_standard_tools() + \ + LangChainToolFactory.create_file_tools(workspace_dir, stream_manager) \ No newline at end of file diff --git a/backend/ai_system/core_managers/tool_manager.py b/backend/ai_system/core_managers/tool_manager.py deleted file mode 100644 index fd55e0c..0000000 --- a/backend/ai_system/core_managers/tool_manager.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -统一工具管理器 -集中管理所有工具类，提供统一的初始化、获取和注册接口 -""" - -import os -import logging -from typing import Dict, Any, Optional -from ..core_tools.file_tools import FileTools -from ..core_tools.template_tools import TemplateAgentTools -from ..core_tools.code_executor import CodeExecutor - -logger = logging.getLogger(__name__) - - -class ToolRegistry: - """统一的工具注册表和管理器""" - - def __init__(self, workspace_dir: str, stream_manager=None): - """ - 初始化工具注册表 - - Args: - workspace_dir: 工作空间目录路径 - stream_manager: 流式输出管理器 - """ - self.workspace_dir = workspace_dir - self.stream_manager = stream_manager - self._tools: Dict[str, Any] = {} - self._initialize_tools() - logger.info(f"ToolRegistry初始化完成，工作空间: {workspace_dir}") - - def _initialize_tools(self): - """初始化所有可用的工具""" - try: - # 初始化文件工具 - self._tools['file'] = FileTools(self.stream_manager) - - # 初始化代码执行器 - self._tools['code_executor'] = CodeExecutor(self.stream_manager, self.workspace_dir) - - # 初始化模板工具（如果需要） - if self.workspace_dir: - self._tools['template'] = TemplateAgentTools(self.workspace_dir) - - logger.info(f"工具初始化完成，已注册工具: {list(self._tools.keys())}") - - except Exception as e: - logger.error(f"工具初始化失败: {e}") - raise - - def get_tool(self, tool_name: str): - """ - 获取指定工具实例 - - Args: - tool_name: 工具名称 - - Returns: - 工具实例 - - Raises: - ValueError: 工具不存在时抛出 - """ - if tool_name not in self._tools: - available_tools = list(self._tools.keys()) - raise ValueError(f"工具 '{tool_name}' 不存在。可用工具: {available_tools}") - - tool = self._tools[tool_name] - logger.debug(f"获取工具: {tool_name} -> {type(tool).__name__}") - return tool - - def register_tool(self, name: str, tool_instance: Any): - """ - 注册自定义工具 - - Args: - name: 工具名称 - tool_instance: 工具实例 - """ - if name in self._tools: - logger.warning(f"工具 '{name}' 已存在，将被覆盖") - - self._tools[name] = tool_instance - logger.info(f"注册自定义工具: {name} -> {type(tool_instance).__name__}") - - def list_tools(self) -> Dict[str, str]: - """ - 列出所有已注册的工具 - - Returns: - 工具名称到工具类名的映射 - """ - return {name: type(tool).__name__ for name, tool in self._tools.items()} - - def has_tool(self, tool_name: str) -> bool: - """ - 检查指定工具是否存在 - - Args: - tool_name: 工具名称 - - Returns: - 工具是否存在 - """ - return tool_name in self._tools - - def get_workspace_dir(self) -> str: - """获取工作空间目录路径""" - return self.workspace_dir - - def get_stream_manager(self): - """获取流式输出管理器""" - return self.stream_manager - - -class ToolManager: - """工具管理器的便捷接口""" - - def __init__(self, workspace_dir: str, stream_manager=None): - self.registry = ToolRegistry(workspace_dir, stream_manager) - - def get(self, tool_name: str): - """获取工具的便捷方法""" - return self.registry.get_tool(tool_name) - - def file(self): - """获取文件工具""" - return self.registry.get_tool('file') - - def template(self): - """获取模板工具""" - return self.registry.get_tool('template') - - def code_executor(self): - """获取代码执行器""" - return self.registry.get_tool('code_executor') - - def register(self, name: str, tool_instance: Any): - """注册工具的便捷方法""" - self.registry.register_tool(name, tool_instance) - - def list_all(self) -> Dict[str, str]: - """列出所有工具""" - return self.registry.list_tools() \ No newline at end of file diff --git a/backend/ai_system/core_tools/code_executor.py b/backend/ai_system/core_tools/code_executor.py index 55248b7..701cb56 100644 --- a/backend/ai_system/core_tools/code_executor.py +++ b/backend/ai_system/core_tools/code_executor.py @@ -282,6 +282,14 @@ def __del__(self): sys.stdout = TeeOutput(log_file) +# 禁用 plt.show() 以避免 GUI 线程问题 +# 在非交互式后端下，plt.show() 会被自动替换为无操作 +def _disabled_show(*args, **kwargs): + """在非交互式后端下禁用 plt.show()，避免 GUI 线程错误""" + pass + +plt.show = _disabled_show + # 用户代码开始 ''' diff --git a/backend/ai_system/core_tools/file_tools.py b/backend/ai_system/core_tools/file_tools.py index 89930fd..e6af4a9 100644 --- a/backend/ai_system/core_tools/file_tools.py +++ b/backend/ai_system/core_tools/file_tools.py @@ -29,7 +29,7 @@ def __init__(self, stream_manager: Optional[StreamOutputManager] = None): os.makedirs(self.workspace_dir, exist_ok=True) logger.info(f"FileTools初始化完成，workspace目录: {self.workspace_dir}") - async def writemd(self, filename: str, content: str, mode: str = "overwrite") -> str: + def writemd(self, filename: str, content: str, mode: str = "overwrite") -> str: """ 写入Markdown文件到workspace目录，支持多种写入模式 @@ -56,8 +56,6 @@ async def writemd(self, filename: str, content: str, mode: str = "overwrite") -> # 确保目录存在 os.makedirs(os.path.dirname(file_path), exist_ok=True) - logger.info(f"写入Markdown文件: {file_path}，模式: {mode}") - if mode == "append": # 附加模式：在文件末尾追加内容 with open(file_path, 'a', encoding='utf-8') as f: @@ -144,7 +142,7 @@ async def writemd(self, filename: str, content: str, mode: str = "overwrite") -> result += f"\n文件路径: {file_path}\n文件大小: {file_size} 字节" if self.stream_manager: - await self.stream_manager.send_json_block("writemd_result", result) + self._send_json_block_sync("writemd_result", result) return result @@ -153,11 +151,11 @@ async def writemd(self, filename: str, content: str, mode: str = "overwrite") -> logger.error(error_msg) if self.stream_manager: - await self.stream_manager.send_json_block("writemd_result", error_msg) + self._send_json_block_sync("writemd_result", error_msg) return error_msg - async def update_template(self, template_name: str = "paper.md", content: str = "", section: str = "") -> str: + def update_template(self, template_name: str = "paper.md", content: str = "", section: str = "") -> str: """ 专门用于更新论文文件的工具方法，只支持章节级别更新 @@ -193,7 +191,7 @@ async def update_template(self, template_name: str = "paper.md", content: str = result += f"\n文件路径: {file_path}\n文件大小: {file_size} 字节" if self.stream_manager: - await self.stream_manager.send_json_block("template_update_result", result) + self._send_json_block_sync("template_update_result", result) return result @@ -202,7 +200,7 @@ async def update_template(self, template_name: str = "paper.md", content: str = logger.error(error_msg) if self.stream_manager: - await self.stream_manager.send_json_block("template_update_result", error_msg) + self._send_json_block_sync("template_update_result", error_msg) return error_msg @@ -287,7 +285,7 @@ def _update_section_content(self, original_content: str, section_name: str, new_ return '\n'.join(updated_lines) - async def tree(self, directory: Optional[str] = None) -> str: + def tree(self, directory: Optional[str] = None) -> str: """显示目录树结构""" try: if directory is None: @@ -322,7 +320,7 @@ def _tree_helper(path: str, prefix: str = "", is_last: bool = True) -> str: _tree_helper(directory) if self.stream_manager: - await self.stream_manager.send_json_block("tree_result", tree_result) + self._send_json_block_sync("tree_result", tree_result) return tree_result @@ -364,7 +362,7 @@ def list_files(self, directory: Optional[str] = None) -> list: logger.error(f"列出目录失败: {e}") return [] - async def list_attachments(self) -> str: + def list_attachments(self) -> str: """ 列出工作空间中所有附件文件 @@ -426,7 +424,7 @@ async def list_attachments(self) -> str: "attachments": attachments } json_content = json.dumps(json_data, ensure_ascii=False) - await self.stream_manager.send_json_block("attachments_list", json_content) + self._send_json_block_sync("attachments_list", json_content) logger.info("JSON块发送完成") logger.info("准备返回结果") @@ -443,7 +441,7 @@ async def list_attachments(self) -> str: logger.error(error_msg) return error_msg - async def read_attachment(self, file_path: str) -> str: + def read_attachment(self, file_path: str) -> str: """ 读取附件文件内容 @@ -472,7 +470,7 @@ async def read_attachment(self, file_path: str) -> str: file_ext = Path(full_path).suffix.lower() # 根据文件类型选择合适的读取方法 - content = await self._extract_file_content(full_path, file_ext) + content = self._extract_file_content(full_path, file_ext) # 准备返回信息 result = f"**文件信息:**\n" @@ -492,7 +490,7 @@ async def read_attachment(self, file_path: str) -> str: "truncated": len(content) > 1000 } json_content = json.dumps(json_data, ensure_ascii=False) - await self.stream_manager.send_json_block("attachment_content", json_content) + self._send_json_block_sync("attachment_content", json_content) return result @@ -501,7 +499,7 @@ async def read_attachment(self, file_path: str) -> str: logger.error(error_msg) return error_msg - async def get_attachment_info(self, file_path: str) -> str: + def get_attachment_info(self, file_path: str) -> str: """ 获取附件文件的详细信息 @@ -553,7 +551,7 @@ async def get_attachment_info(self, file_path: str) -> str: logger.error(error_msg) return error_msg - async def search_attachments(self, keyword: str, file_type: Optional[str] = None) -> str: + def search_attachments(self, keyword: str, file_type: Optional[str] = None) -> str: """ 在附件文件中搜索关键词 @@ -596,7 +594,7 @@ async def search_attachments(self, keyword: str, file_type: Optional[str] = None # 文件内容匹配（仅对文本文件） if self._is_text_file(file_ext): try: - content = await self._extract_file_content(file_path, file_ext) + content = self._extract_file_content(file_path, file_ext) if keyword_lower in content.lower(): # 找到匹配的行 lines = content.split('\n') @@ -634,7 +632,7 @@ async def search_attachments(self, keyword: str, file_type: Optional[str] = None "results": search_results } json_content = json.dumps(json_data, ensure_ascii=False) - await self.stream_manager.send_json_block("search_results", json_content) + self._send_json_block_sync("search_results", json_content) return result.strip() @@ -711,7 +709,8 @@ def _is_text_file(self, file_ext: str) -> bool: } return file_ext in text_extensions - async def _extract_file_content(self, file_path: str, file_ext: str) -> str: + + def _extract_file_content(self, file_path: str, file_ext: str) -> str: """根据文件类型提取内容""" try: if file_ext in ['.txt', '.md', '.py', '.js', '.ts', '.java', '.cpp', '.c', '.h', '.hpp', @@ -777,3 +776,26 @@ async def _extract_file_content(self, file_path: str, file_ext: str) -> str: except Exception as e: logger.error(f"提取文件内容失败 {file_path}: {e}") return f"读取文件内容失败: {str(e)}" + + def _send_json_block_sync(self, block_type: str, data: Any): + """同步发送JSON块到stream_manager""" + try: + if self.stream_manager: + import asyncio + try: + # 尝试获取当前事件循环 + loop = asyncio.get_event_loop() + if loop.is_running(): + # 如果循环正在运行，使用create_task + asyncio.create_task(self.stream_manager.send_json_block(block_type, data)) + else: + # 如果循环没有运行，直接运行 + loop.run_until_complete(self.stream_manager.send_json_block(block_type, data)) + except RuntimeError: + # 如果没有事件循环，创建新的 + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(self.stream_manager.send_json_block(block_type, data)) + loop.close() + except Exception as e: + logger.warning(f"发送JSON块失败: {e}") diff --git a/backend/alembic/versions/8136c8a57cbb_add_provider_to_model_configs.py b/backend/alembic/versions/8136c8a57cbb_add_provider_to_model_configs.py new file mode 100644 index 0000000..cb72475 --- /dev/null +++ b/backend/alembic/versions/8136c8a57cbb_add_provider_to_model_configs.py @@ -0,0 +1,47 @@ +"""add_provider_to_model_configs + +Revision ID: 8136c8a57cbb +Revises: 2952aa9a98f3 +Create Date: 2025-10-26 21:24:30.986948 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '8136c8a57cbb' +down_revision: Union[str, Sequence[str], None] = '2952aa9a98f3' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + op.drop_table('homework') + op.add_column('model_configs', sa.Column('provider', sa.String(length=50), server_default='openai', nullable=False)) + # ### end Alembic commands ### + + +def downgrade() -> None: + """Downgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('model_configs', 'provider') + op.create_table('homework', + sa.Column('taskrefid', sa.INTEGER(), autoincrement=True, nullable=False), + sa.Column('subject', sa.VARCHAR(length=255), autoincrement=False, nullable=False), + sa.Column('homework_name', sa.TEXT(), autoincrement=False, nullable=False), + sa.Column('due_date', postgresql.TIMESTAMP(), autoincrement=False, nullable=True), + sa.Column('status', sa.TEXT(), autoincrement=False, nullable=False), + sa.Column('url', sa.TEXT(), autoincrement=False, nullable=False), + sa.Column('created_at', postgresql.TIMESTAMP(), server_default=sa.text('CURRENT_TIMESTAMP'), autoincrement=False, nullable=True), + sa.Column('updated_at', postgresql.TIMESTAMP(), server_default=sa.text('CURRENT_TIMESTAMP'), autoincrement=False, nullable=True), + sa.Column('schedule_task', sa.BOOLEAN(), autoincrement=False, nullable=True), + sa.Column('detail_url', sa.TEXT(), autoincrement=False, nullable=True), + sa.Column('detail_info', sa.TEXT(), autoincrement=False, nullable=True), + sa.PrimaryKeyConstraint('taskrefid', name=op.f('homework_pkey')) + ) + # ### end Alembic commands ### diff --git a/backend/models/models.py b/backend/models/models.py index f0e3b57..7858abb 100644 --- a/backend/models/models.py +++ b/backend/models/models.py @@ -29,6 +29,7 @@ class ModelConfig(Base): id = Column(Integer, primary_key=True, index=True) type = Column(String(50), nullable=False) # 模型种类：brain(中枢大脑), code(代码实验), writing(论文写作) + provider = Column(String(50), nullable=False, server_default="openai") # AI提供商：openai, anthropic, google, local model_id = Column(String(50), nullable=False) # 模型ID base_url = Column(String(100), nullable=False) # 模型URL api_key = Column(String(255), nullable=False) # API密钥 diff --git a/backend/pyproject.toml b/backend/pyproject.toml index bdb94d5..1f49677 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -3,7 +3,7 @@ name = "backend" version = "0.1.0" description = "PaperAgent Backend API" readme = "README.md" -requires-python = ">=3.10" +requires-python = ">=3.14" dependencies = [ "fastapi>=0.68.0", "requests>=2.32.4", @@ -12,8 +12,8 @@ dependencies = [ "bcrypt>=4.0.0", "python-multipart>=0.0.5", "sqlalchemy>=2.0.0", - "psycopg2-binary>=2.9.0", "asyncpg>=0.29.0", + "psycopg[binary]>=3.0.0", "aiosqlite>=0.19.0", "python-dotenv>=0.19.0", "pydantic[email]>=2.0.0", @@ -30,4 +30,11 @@ dependencies = [ "PyPDF2>=3.0.0", "pdfplumber>=0.7.0", "openpyxl>=3.0.0", + "langchain>=1.0.2", + "langchain-openai>=1.0.1", + "langchain-community>=0.4", + "smolagents>=1.22.0", + "ddgs>=9.6.1", + "langchain-anthropic>=1.0.0", + "langchain-google-genai>=3.0.0", ] diff --git a/backend/routers/chat_routes/chat.py b/backend/routers/chat_routes/chat.py index fc51c5a..2174988 100644 --- a/backend/routers/chat_routes/chat.py +++ b/backend/routers/chat_routes/chat.py @@ -17,7 +17,7 @@ from ai_system.core_managers.stream_manager import PersistentStreamManager, SimpleStreamCallback from ai_system.core_agents.main_agent import MainAgent from ai_system.core_handlers.llm_handler import LLMHandler -from ai_system.core_handlers.llm_factory import LLMFactory +from langchain_core.messages import HumanMessage from ..utils import route_guard logger = logging.getLogger(__name__) @@ -315,10 +315,16 @@ async def on_json_block(self, block: dict): except Exception as e: logger.error(f"发送JSON块失败: {e}") - # 初始化AI环境与工作空间（通过工厂） - llm_factory = LLMFactory(db) - llm_factory.initialize_system("brain") - llm_factory.setup_workspace(work_id) + # 初始化AI环境与工作空间 + env_manager = setup_environment_from_db(db, work_id) + model_config = env_manager.config_manager.get_model_config("brain") + codeagent_model_config = env_manager.config_manager.get_model_config("code") + + # 创建工作空间目录 - 使用绝对路径 + backend_dir = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + project_root = os.path.dirname(backend_dir) + workspace_dir = os.path.join(project_root, "pa_data", "workspaces", work_id) + os.makedirs(workspace_dir, exist_ok=True) # 创建流式回调和管理器 ws_callback = WebSocketStreamCallback( @@ -329,8 +335,38 @@ async def on_json_block(self, block: dict): session_id=str(session.session_id) ) - # 创建LLM处理器和主代理 - llm_handler = llm_factory.create_handler("brain", stream_manager=stream_manager) + # 创建支持多AI提供商的LLM处理器 + llm_handler = LLMHandler( + model_config=model_config, + stream_manager=stream_manager + ) + + # 获取codeagent的LLM实例 + codeagent_llm = None + if codeagent_model_config: + from ai_system.core_handlers.llm_providers import create_smolagents_model_from_config + try: + codeagent_llm = create_smolagents_model_from_config(codeagent_model_config) + logger.info(f"成功创建SmolAgents模型: {codeagent_llm}") + except Exception as e: + logger.warning(f"创建SmolAgents模型失败: {e}") + # 回退到LangChain LLM，但MainAgent会处理兼容性 + from ai_system.core_handlers.llm_providers import create_llm_from_model_config + try: + codeagent_llm = create_llm_from_model_config(codeagent_model_config) + logger.info(f"回退使用LangChain LLM: {codeagent_llm}") + except Exception as e2: + logger.error(f"创建LangChain LLM也失败: {e2}") + codeagent_llm = llm_handler.get_llm_instance() # 备用 + else: + logger.warning("未找到codeagent配置，使用brain LLM") + codeagent_llm = llm_handler.get_llm_instance() + + # 最终确保codeagent_llm不为None + if codeagent_llm is None: + logger.error("codeagent_llm仍然为None，强制使用brain LLM") + # brain LLM是LangChain格式，需要适配器处理 + codeagent_llm = llm_handler.get_llm_instance() # 获取工作的模板ID template_id = None @@ -343,8 +379,8 @@ async def on_json_block(self, block: dict): except Exception as e: logger.warning(f"获取工作模板ID失败: {e}") - # 创建MainAgent，传入work_id和template_id - main_agent = MainAgent(llm_handler, stream_manager, work_id, template_id) + # 创建MainAgent，传入workspace_dir、work_id、template_id和codeagent_llm + main_agent = MainAgent(llm_handler.get_llm_instance(), stream_manager, workspace_dir, work_id, template_id, codeagent_llm) # 先加载真正的历史消息（排除当前这次对话） history_messages = chat_service.get_work_chat_history( @@ -468,9 +504,9 @@ async def generate_work_title( if not question: raise HTTPException(status_code=400, detail="缺少问题内容") - # 初始化AI环境（通过工厂） - llm_factory = LLMFactory(db) - llm_factory.initialize_system("brain") + # 初始化AI环境 + env_manager = setup_environment_from_db(db) + model_config = env_manager.config_manager.get_model_config("brain") # 构建标题生成提示词 title_prompt = f"""请根据用户的研究问题生成一个简洁、专业的学术论文标题。 @@ -486,12 +522,18 @@ async def generate_work_title( # 调用AI生成标题 try: - # 使用现有的process_stream方法，但不使用流式处理 - messages = [{"role": "user", "content": title_prompt}] + # 使用新的LangChain方式生成标题 + llm_handler = LLMHandler( + model_config=model_config + ) + llm = llm_handler.get_llm_instance() + + # 使用LangChain标准消息格式 + messages = [HumanMessage(content=title_prompt)] - # 直接通过工厂执行同步（非流式）生成 - assistant_message, _ = await llm_factory.run_sync("brain", messages) - title = assistant_message.get("content", "") + # 同步调用LLM + response = await llm.ainvoke(messages) + title = response.content # 清理标题（移除可能的引号、换行等） title = title.strip().strip('"').strip("'").strip() diff --git a/backend/uv.lock b/backend/uv.lock index 19559df..a73c1a9 100644 --- a/backend/uv.lock +++ b/backend/uv.lock @@ -1,11 +1,6 @@ version = 1 -revision = 2 -requires-python = ">=3.10" -resolution-markers = [ - "python_full_version >= '3.12'", - "python_full_version == '3.11.*'", - "python_full_version < '3.11'", -] +revision = 3 +requires-python = ">=3.14" [[package]] name = "aiohappyeyeballs" @@ -18,88 +13,53 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.12.15" +version = "3.13.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - { name = "async-timeout", marker = "python_full_version < '3.11'" }, { name = "attrs" }, { name = "frozenlist" }, { name = "multidict" }, { name = "propcache" }, { name = "yarl" }, ] -sdist = { url = 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