"""基于分享链接的聊天服务""" import asyncio import json import time import uuid from typing import Optional, Dict, Any, AsyncGenerator, Annotated from fastapi import Depends from sqlalchemy.orm import Session from app.core.agent.langchain_agent import LangChainAgent from app.core.error_codes import BizCode from app.core.exceptions import BusinessException from app.core.logging_config import get_business_logger from app.db import get_db, get_db_context from app.models import MultiAgentConfig, AgentConfig, WorkflowConfig from app.services.tool_service import ToolService from app.repositories.tool_repository import ToolRepository from app.db import get_db from app.models import MultiAgentConfig, AgentConfig from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole from app.services.conversation_service import ConversationService from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool from app.services.draft_run_service import create_web_search_tool from app.services.model_service import ModelApiKeyService from app.services.multi_agent_orchestrator import MultiAgentOrchestrator from app.services.workflow_service import WorkflowService logger = get_business_logger() class AppChatService: """基于分享链接的聊天服务""" def __init__(self, db: Session): self.db = db self.conversation_service = ConversationService(db) async def agnet_chat( self, message: str, conversation_id: uuid.UUID, config: AgentConfig, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, web_search: bool = False, memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, workspace_id: Optional[str] = None ) -> Dict[str, Any]: """聊天（非流式）""" start_time = time.time() config_id = None if variables is None: variables = {} # 获取模型配置ID model_config_id = config.default_model_config_id api_key_obj = ModelApiKeyService.get_a_api_key(self.db ,model_config_id) # 处理系统提示词（支持变量替换） system_prompt = config.system_prompt if variables: system_prompt_rendered = render_prompt_message( system_prompt, PromptMessageRole.USER, variables ) system_prompt = system_prompt_rendered.get_text_content() or system_prompt # 准备工具列表 tools = [] # 获取工具服务 tool_service = ToolService(self.db) # 从配置中获取启用的工具 if hasattr(config, 'tools') and config.tools and isinstance(config.tools, list): for tool_config in config.tools: if tool_config.get("enabled", False): # 根据工具名称查找工具实例 tool_instance = tool_service._get_tool_instance(tool_config.get("tool_id", ""), ToolRepository.get_tenant_id_by_workspace_id( self.db, workspace_id)) if tool_instance: if tool_instance.name == "baidu_search_tool" and not web_search: continue # 转换为LangChain工具 langchain_tool = tool_instance.to_langchain_tool(tool_config.get("operation", None)) tools.append(langchain_tool) # 添加知识库检索工具 knowledge_retrieval = config.knowledge_retrieval if knowledge_retrieval: knowledge_bases = knowledge_retrieval.get("knowledge_bases", []) kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")] if kb_ids: kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id) tools.append(kb_tool) # 添加长期记忆工具 memory_flag = False if memory == True: memory_config = config.memory if memory_config.get("enabled") and user_id: memory_flag = True memory_tool = create_long_term_memory_tool(memory_config, user_id) tools.append(memory_tool) if hasattr(config, 'tools') and config.tools and isinstance(config.tools, dict): web_tools = config.tools web_search_choice = web_tools.get("web_search", {}) web_search_enable = web_search_choice.get("enabled", False) if web_search == True: if web_search_enable == True: search_tool = create_web_search_tool({}) tools.append(search_tool) logger.debug( "已添加网络搜索工具", extra={ "tool_count": len(tools) } ) # 获取模型参数 model_parameters = config.model_parameters # 创建 LangChain Agent agent = LangChainAgent( model_name=api_key_obj.model_name, api_key=api_key_obj.api_key, provider=api_key_obj.provider, api_base=api_key_obj.api_base, temperature=model_parameters.get("temperature", 0.7), max_tokens=model_parameters.get("max_tokens", 2000), system_prompt=system_prompt, tools=tools, ) # 加载历史消息 history = [] memory_config = {"enabled": True, 'max_history': 10} if memory_config.get("enabled"): messages = self.conversation_service.get_messages( conversation_id=conversation_id, limit=memory_config.get("max_history", 10) ) history = [ {"role": msg.role, "content": msg.content} for msg in messages ] # 调用 Agent result = await agent.chat( message=message, history=history, context=None, end_user_id=user_id, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, config_id=config_id, memory_flag=memory_flag ) # 保存消息 self.conversation_service.save_conversation_messages( conversation_id=conversation_id, user_message=message, assistant_message=result["content"] ) elapsed_time = time.time() - start_time return { "conversation_id": conversation_id, "message": result["content"], "usage": result.get("usage", { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 }), "elapsed_time": elapsed_time } async def agnet_chat_stream( self, message: str, conversation_id: uuid.UUID, config: AgentConfig, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, web_search: bool = False, memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, workspace_id: Optional[str] = None, ) -> AsyncGenerator[str, None]: """聊天（流式）""" try: start_time = time.time() config_id = None if variables is None: variables = {} # 获取模型配置ID model_config_id = config.default_model_config_id api_key_obj = ModelApiKeyService.get_a_api_key(self.db ,model_config_id) # 处理系统提示词（支持变量替换） system_prompt = config.system_prompt if variables: system_prompt_rendered = render_prompt_message( system_prompt, PromptMessageRole.USER, variables ) system_prompt = system_prompt_rendered.get_text_content() or system_prompt # 准备工具列表 tools = [] # 获取工具服务 tool_service = ToolService(self.db) if hasattr(config, 'tools') and config.tools and isinstance(config.tools, list): for tool_config in config.tools: if tool_config.get("enabled", False): # 根据工具名称查找工具实例 tool_instance = tool_service._get_tool_instance(tool_config.get("tool_id", ""), ToolRepository.get_tenant_id_by_workspace_id( self.db, workspace_id)) if tool_instance: if tool_instance.name == "baidu_search_tool" and not web_search: continue # 转换为LangChain工具 langchain_tool = tool_instance.to_langchain_tool(tool_config.get("operation", None)) tools.append(langchain_tool) # 添加知识库检索工具 knowledge_retrieval = config.knowledge_retrieval if knowledge_retrieval: knowledge_bases = knowledge_retrieval.get("knowledge_bases", []) kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")] if kb_ids: kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id) tools.append(kb_tool) # 添加长期记忆工具 memory_flag = False if memory: memory_config = config.memory if memory_config.get("enabled") and user_id: memory_flag = True memory_tool = create_long_term_memory_tool(memory_config, user_id) tools.append(memory_tool) if hasattr(config, 'tools') and config.tools and isinstance(config.tools, dict): web_tools = config.tools web_search_choice = web_tools.get("web_search", {}) web_search_enable = web_search_choice.get("enabled", False) if web_search == True: if web_search_enable == True: search_tool = create_web_search_tool({}) tools.append(search_tool) logger.debug( "已添加网络搜索工具", extra={ "tool_count": len(tools) } ) # 获取模型参数 model_parameters = config.model_parameters # 创建 LangChain Agent agent = LangChainAgent( model_name=api_key_obj.model_name, api_key=api_key_obj.api_key, provider=api_key_obj.provider, api_base=api_key_obj.api_base, temperature=model_parameters.get("temperature", 0.7), max_tokens=model_parameters.get("max_tokens", 2000), system_prompt=system_prompt, tools=tools, streaming=True ) # 加载历史消息 history = [] memory_config = {"enabled": True, 'max_history': 10} if memory_config.get("enabled"): messages = self.conversation_service.get_messages( conversation_id=conversation_id, limit=memory_config.get("max_history", 10) ) history = [ {"role": msg.role, "content": msg.content} for msg in messages ] # 发送开始事件 yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n" # 流式调用 Agent full_content = "" async for chunk in agent.chat_stream( message=message, history=history, context=None, end_user_id=user_id, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id, config_id=config_id, memory_flag=memory_flag ): full_content += chunk # 发送消息块事件 yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n" elapsed_time = time.time() - start_time # 保存消息 self.conversation_service.add_message( conversation_id=conversation_id, role="user", content=message ) self.conversation_service.add_message( conversation_id=conversation_id, role="assistant", content=full_content, meta_data={ "model": api_key_obj.model_name, "usage": {} } ) # 发送结束事件 end_data = {"elapsed_time": elapsed_time, "message_length": len(full_content)} yield f"event: end\ndata: {json.dumps(end_data, ensure_ascii=False)}\n\n" logger.info( "流式聊天完成", extra={ "conversation_id": str(conversation_id), "elapsed_time": elapsed_time, "message_length": len(full_content) } ) except (GeneratorExit, asyncio.CancelledError): # 生成器被关闭或任务被取消，正常退出 logger.debug("流式聊天被中断") raise except Exception as e: logger.error(f"流式聊天失败: {str(e)}", exc_info=True) # 发送错误事件 yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n" async def multi_agent_chat( self, message: str, conversation_id: uuid.UUID, config: MultiAgentConfig, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, web_search: bool = False, memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, ) -> Dict[str, Any]: """多 Agent 聊天（非流式）""" start_time = time.time() actual_config_id = None config_id = actual_config_id if variables is None: variables = {} # 2. 创建编排器 orchestrator = MultiAgentOrchestrator(self.db, config) # 3. 执行任务 result = await orchestrator.execute( message=message, conversation_id=conversation_id, user_id=user_id, variables=variables, use_llm_routing=True, # 默认启用 LLM 路由 web_search=web_search, # 网络搜索参数 memory=memory # 记忆功能参数 ) elapsed_time = time.time() - start_time # 保存消息 self.conversation_service.add_message( conversation_id=conversation_id, role="user", content=message ) self.conversation_service.add_message( conversation_id=conversation_id, role="assistant", content=result.get("message", ""), meta_data={ "mode": result.get("mode"), "elapsed_time": result.get("elapsed_time"), "sub_results": result.get("sub_results") } ) return { "conversation_id": conversation_id, "message": result.get("message", ""), "usage": { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 }, "elapsed_time": elapsed_time } async def multi_agent_chat_stream( self, message: str, conversation_id: uuid.UUID, config: MultiAgentConfig, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, web_search: bool = False, memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, ) -> AsyncGenerator[str, None]: """多 Agent 聊天（流式）""" start_time = time.time() actual_config_id = None config_id = actual_config_id if variables is None: variables = {} try: # 发送开始事件 yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n" full_content = "" # 2. 创建编排器 orchestrator = MultiAgentOrchestrator(self.db, config) # 3. 流式执行任务 async for event in orchestrator.execute_stream( message=message, conversation_id=conversation_id, user_id=user_id, variables=variables, use_llm_routing=True, web_search=web_search, # 网络搜索参数 memory=memory, # 记忆功能参数 storage_type=storage_type, user_rag_memory_id=user_rag_memory_id ): yield event # 尝试提取内容（用于保存） if "data:" in event: try: data_line = event.split("data: ", 1)[1].strip() data = json.loads(data_line) if "content" in data: full_content += data["content"] except: pass elapsed_time = time.time() - start_time # 保存消息 self.conversation_service.add_message( conversation_id=conversation_id, role="user", content=message ) self.conversation_service.add_message( conversation_id=conversation_id, role="assistant", content=full_content, meta_data={ "elapsed_time": elapsed_time } ) logger.info( "多 Agent 流式聊天完成", extra={ "conversation_id": str(conversation_id), "elapsed_time": elapsed_time, "message_length": len(full_content) } ) except (GeneratorExit, asyncio.CancelledError): # 生成器被关闭或任务被取消，正常退出 logger.debug("多 Agent 流式聊天被中断") raise except Exception as e: logger.error(f"多 Agent 流式聊天失败: {str(e)}", exc_info=True) # 发送错误事件 yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n" async def workflow_chat( self, message: str, conversation_id: uuid.UUID, config: WorkflowConfig, app_id: uuid.UUID, workspace_id: uuid.UUID, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, web_search: bool = False, memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, ) -> Dict[str, Any]: """聊天（非流式）""" workflow_service = WorkflowService(self.db) input_data = {"message":message, "variables": variables, "conversation_id": str(conversation_id)} inconfig = workflow_service.get_workflow_config(app_id) # 2. 创建执行记录 execution = workflow_service.create_execution( workflow_config_id=inconfig.id, app_id=app_id, trigger_type="manual", triggered_by=None, conversation_id=conversation_id, input_data=input_data ) # 3. 构建工作流配置字典 workflow_config_dict = { "nodes": config.nodes, "edges": config.edges, "variables": config.variables, "execution_config": config.execution_config } # 4. 获取工作空间 ID（从 app 获取） # 5. 执行工作流 from app.core.workflow.executor import execute_workflow try: # 更新状态为运行中 workflow_service.update_execution_status(execution.execution_id, "running") result = await execute_workflow( workflow_config=workflow_config_dict, input_data=input_data, execution_id=execution.execution_id, workspace_id=str(workspace_id), user_id=user_id ) # 更新执行结果 if result.get("status") == "completed": workflow_service.update_execution_status( execution.execution_id, "completed", output_data=result.get("node_outputs", {}) ) else: workflow_service.update_execution_status( execution.execution_id, "failed", error_message=result.get("error") ) # 返回增强的响应结构 return { "execution_id": execution.execution_id, "status": result.get("status"), "output": result.get("output"), # 最终输出（字符串） "output_data": result.get("node_outputs", {}), # 所有节点输出（详细数据） "conversation_id": result.get("conversation_id"), # 所有节点输出（详细数据）payload., # 会话 ID "error_message": result.get("error"), "elapsed_time": result.get("elapsed_time"), "token_usage": result.get("token_usage") } except Exception as e: logger.error(f"工作流执行失败: execution_id={execution.execution_id}, error={e}", exc_info=True) workflow_service.update_execution_status( execution.execution_id, "failed", error_message=str(e) ) raise BusinessException( code=BizCode.INTERNAL_ERROR, message=f"工作流执行失败: {str(e)}" ) async def workflow_chat_stream( self, message: str, conversation_id: uuid.UUID, config: WorkflowConfig, app_id: uuid.UUID, workspace_id: uuid.UUID, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, web_search: bool = False, memory: bool = True, storage_type: Optional[str] = None, user_rag_memory_id: Optional[str] = None, ) -> AsyncGenerator[str, None]: """聊天（流式）""" workflow_service = WorkflowService(self.db) input_data = {"message": message, "variables": variables, "conversation_id": str(conversation_id)} inconfig = workflow_service.get_workflow_config(app_id) # 2. 创建执行记录 execution = workflow_service.create_execution( workflow_config_id=inconfig.id, app_id=app_id, trigger_type="manual", triggered_by=None, conversation_id=conversation_id, input_data=input_data ) # 3. 构建工作流配置字典 workflow_config_dict = { "nodes": config.nodes, "edges": config.edges, "variables": config.variables, "execution_config": config.execution_config } # 4. 获取工作空间 ID（从 app 获取） # 5. 流式执行工作流 try: # 更新状态为运行中 workflow_service.update_execution_status(execution.execution_id, "running") # 调用流式执行（executor 会发送 workflow_start 和 workflow_end 事件） async for event in workflow_service._run_workflow_stream( workflow_config=workflow_config_dict, input_data=input_data, execution_id=execution.execution_id, workspace_id=str(workspace_id), user_id=user_id ): # 直接转发 executor 的事件（已经是正确的格式） yield event except Exception as e: logger.error(f"工作流流式执行失败: execution_id={execution.execution_id}, error={e}", exc_info=True) workflow_service.update_execution_status( execution.execution_id, "failed", error_message=str(e) ) # 发送错误事件 yield { "event": "error", "data": { "execution_id": execution.execution_id, "error": str(e) } } # ==================== 依赖注入函数 ==================== def get_app_chat_service( db: Annotated[Session, Depends(get_db)] ) -> AppChatService: """获取工作流服务（依赖注入）""" return AppChatService(db)