"""基于分享链接的聊天服务""" import uuid import time import asyncio from typing import Optional, Dict, Any, AsyncGenerator from sqlalchemy.orm import Session from app.repositories.model_repository import ModelApiKeyRepository from app.services.memory_konwledges_server import write_rag from app.models import ReleaseShare, AppRelease, Conversation from app.services.conversation_service import ConversationService from app.services.draft_run_service import create_web_search_tool from app.services.model_service import ModelApiKeyService from app.services.release_share_service import ReleaseShareService from app.core.exceptions import BusinessException, ResourceNotFoundException from app.core.error_codes import BizCode from app.core.logging_config import get_business_logger from app.services.multi_agent_service import MultiAgentService from app.models import MultiAgentConfig from app.repositories import knowledge_repository import json from app.services.task_service import get_task_memory_write_result from app.tasks import write_message_task logger = get_business_logger() class SharedChatService: """基于分享链接的聊天服务""" def __init__(self, db: Session): self.db = db self.conversation_service = ConversationService(db) self.share_service = ReleaseShareService(db) def _get_release_by_share_token( self, share_token: str, password: Optional[str] = None ) -> tuple[ReleaseShare, AppRelease]: """通过 share_token 获取发布版本""" # 获取分享配置 share = self.share_service.repo.get_by_share_token(share_token) if not share: raise ResourceNotFoundException("分享链接", share_token) # 验证分享是否启用 if not share.is_enabled: raise BusinessException("该分享链接已被禁用", BizCode.SHARE_DISABLED) # 验证密码 if share.require_password: if not password: raise BusinessException("需要提供访问密码", BizCode.PASSWORD_REQUIRED) if not self.share_service.verify_password(share_token, password): raise BusinessException("访问密码错误", BizCode.INVALID_PASSWORD) # 获取发布版本 release = self.db.get(AppRelease, share.release_id) if not release: raise ResourceNotFoundException("发布版本", str(share.release_id)) # 更新访问统计 try: self.share_service.repo.increment_view_count(share.id) except Exception as e: logger.warning(f"更新访问统计失败: {str(e)}") return share, release def create_or_get_conversation( self, share_token: str, conversation_id: Optional[uuid.UUID] = None, user_id: Optional[str] = None, password: Optional[str] = None ) -> Conversation: """创建或获取会话""" share, release = self._get_release_by_share_token(share_token, password) # 如果提供了 conversation_id，尝试获取现有会话 if conversation_id: try: conversation = self.conversation_service.get_conversation( conversation_id=conversation_id, workspace_id=release.app.workspace_id ) # 验证会话是否属于该应用 if conversation.app_id != release.app_id: raise BusinessException("会话不属于该应用", BizCode.INVALID_CONVERSATION) return conversation except ResourceNotFoundException: logger.warning( "会话不存在，将创建新会话", extra={"conversation_id": str(conversation_id)} ) # 创建新会话（使用发布版本的配置） conversation = self.conversation_service.create_conversation( app_id=release.app_id, workspace_id=release.app.workspace_id, user_id=user_id, is_draft=False, # 分享链接使用发布版本 config_snapshot=release.config ) logger.info( "为分享链接创建新会话", extra={ "conversation_id": str(conversation.id), "share_token": share_token, "release_id": str(release.id) } ) return conversation async def chat( self, share_token: str, message: str, conversation_id: Optional[uuid.UUID] = None, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, password: Optional[str] = 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）""" actual_config_id = None config_id=actual_config_id from app.core.agent.langchain_agent import LangChainAgent from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool from app.services.model_parameter_merger import ModelParameterMerger from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole from sqlalchemy import select from app.models import ModelApiKey start_time = time.time() actual_config_id=None config_id=actual_config_id if variables is None: variables = {} # 获取发布版本和配置 share, release = self._get_release_by_share_token(share_token, password) # 获取 Agent 配置 config = release.config or {} # 获取模型配置ID model_config_id = release.default_model_config_id if not model_config_id: raise BusinessException("发布版本未配置模型", BizCode.AGENT_CONFIG_MISSING) # 获取模型配置 from app.models import ModelConfig model_config = self.db.get(ModelConfig, model_config_id) if not model_config: raise ResourceNotFoundException("模型配置", str(model_config_id)) # 获取 API Key # stmt = ( # select(ModelApiKey).join( # ModelConfig, ModelApiKey.model_configs # ) # .where( # ModelConfig.id == model_config_id, # ModelApiKey.is_active.is_(True) # ) # .order_by(ModelApiKey.priority.desc()) # .limit(1) # ) # api_key_obj = self.db.scalars(stmt).first() # api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config_id) # api_key_obj = api_keys[0] if api_keys else None api_key_obj = ModelApiKeyService.get_available_api_key(self.db, model_config_id) if not api_key_obj: raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING) # 获取或创建会话 conversation = self.create_or_get_conversation( share_token=share_token, conversation_id=conversation_id, user_id=user_id, password=password ) # 处理系统提示词（支持变量替换） system_prompt = config.get("system_prompt", "你是一个专业的AI助手") 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 = [] # 添加知识库检索工具 knowledge_retrieval = config.get("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.get("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) web_tools=config.get("tools") web_search_choice = web_tools.get("web_search", {}) web_search_enable = web_search_choice.get("enabled",False) if web_search: if web_search_enable: 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.get("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"], meta_data={ "usage": result.get("usage", { "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0 }) } ) # 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["content"], # meta_data={ # "model": api_key_obj.model_name, # "usage": result.get("usage", {}) # } # ) elapsed_time = time.time() - start_time ModelApiKeyService.record_api_key_usage(self.db, api_key_obj.id) 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 chat_stream( self, share_token: str, message: str, conversation_id: Optional[uuid.UUID] = None, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, password: Optional[str] = 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）""" from app.core.agent.langchain_agent import LangChainAgent from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole from sqlalchemy import select from app.models import ModelApiKey import json start_time = time.time() actual_config_id=None config_id=actual_config_id if variables is None: variables = {} # 兼容新旧字段名：使用 memory_config_id memory_config = {"enabled": memory, "memory_config_id": "17", "max_history": 10} try: # 获取发布版本和配置 share, release = self._get_release_by_share_token(share_token, password) # 获取 Agent 配置 config = release.config or {} agent_config_data = config.get("agent_config", {}) # 获取模型配置ID model_config_id = release.default_model_config_id if not model_config_id: raise BusinessException("发布版本未配置模型", BizCode.AGENT_CONFIG_MISSING) # 获取模型配置 from app.models import ModelConfig model_config = self.db.get(ModelConfig, model_config_id) if not model_config: raise ResourceNotFoundException("模型配置", str(model_config_id)) # 获取 API Key # stmt = ( # select(ModelApiKey).join( # ModelConfig, ModelApiKey.model_configs # ) # .where( # ModelConfig.id == model_config_id, # ModelApiKey.is_active.is_(True) # ) # .order_by(ModelApiKey.priority.desc()) # .limit(1) # ) # api_key_obj = self.db.scalars(stmt).first() # api_keys = ModelApiKeyRepository.get_by_model_config(self.db, model_config_id) # api_key_obj = api_keys[0] if api_keys else None api_key_obj = ModelApiKeyService.get_available_api_key(self.db, model_config_id) if not api_key_obj: raise BusinessException("没有可用的 API Key", BizCode.AGENT_CONFIG_MISSING) # 获取或创建会话 conversation = self.create_or_get_conversation( share_token=share_token, conversation_id=conversation_id, user_id=user_id, password=password ) # 处理系统提示词（支持变量替换） system_prompt = config.get("system_prompt", "你是一个专业的AI助手") 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 = [] # 添加知识库检索工具 knowledge_retrieval = config.get("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.get("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) web_tools = config.get("tools") web_search_choice = web_tools.get("web_search", {}) web_search_enable = web_search_choice.get("enabled", False) if web_search: if web_search_enable: 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.get("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 = "" total_tokens = 0 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 ): if isinstance(chunk, int): total_tokens = chunk else: 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": {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": total_tokens} } ) ModelApiKeyService.record_api_key_usage(self.db, api_key_obj.id) # 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" def get_conversation_messages( self, share_token: str, conversation_id: uuid.UUID, password: Optional[str] = None ) -> Conversation: """获取会话消息""" share, release = self._get_release_by_share_token(share_token, password) # 获取会话 conversation = self.conversation_service.get_conversation( conversation_id=conversation_id, workspace_id=release.app.workspace_id ) # 验证会话是否属于该应用 if conversation.app_id != release.app_id: raise BusinessException("会话不属于该应用", BizCode.INVALID_CONVERSATION) return conversation def list_conversations( self, share_token: str, user_id: Optional[str] = None, password: Optional[str] = None, page: int = 1, pagesize: int = 20 ) -> tuple[list[Conversation], int]: """列出会话""" share, release = self._get_release_by_share_token(share_token, password) conversations, total = self.conversation_service.list_conversations( app_id=release.app_id, workspace_id=release.app.workspace_id, user_id=user_id, is_draft=False, # 只显示发布版本的会话 page=page, pagesize=pagesize ) return conversations, total async def multi_agent_chat( self, share_token: str, message: str, conversation_id: Optional[uuid.UUID] = None, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, password: Optional[str] = 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）""" from app.services.multi_agent_service import MultiAgentService from app.models import MultiAgentConfig start_time = time.time() actual_config_id=None config_id=actual_config_id if variables is None: variables = {} # 获取发布版本和配置 share, release = self._get_release_by_share_token(share_token, password) # 获取或创建会话 conversation = self.create_or_get_conversation( share_token=share_token, conversation_id=conversation_id, user_id=user_id, password=password ) # 获取多 Agent 配置 multi_agent_config = self.db.query(MultiAgentConfig).filter( MultiAgentConfig.app_id == release.app_id, MultiAgentConfig.is_active.is_(True) ).first() if not multi_agent_config: raise BusinessException("多 Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING) # 构建多 Agent 运行请求 from app.schemas.multi_agent_schema import MultiAgentRunRequest multi_agent_request = MultiAgentRunRequest( message=message, conversation_id=conversation.id, user_id=user_id, variables=variables, use_llm_routing=True, web_search=web_search, memory=memory ) # 使用多 Agent 服务执行 multi_agent_service = MultiAgentService(self.db) result = await multi_agent_service.run( app_id=release.app_id, request=multi_agent_request ) 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, share_token: str, message: str, conversation_id: Optional[uuid.UUID] = None, user_id: Optional[str] = None, variables: Optional[Dict[str, Any]] = None, password: Optional[str] = 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: # 获取发布版本和配置 share, release = self._get_release_by_share_token(share_token, password) # 获取或创建会话 conversation = self.create_or_get_conversation( share_token=share_token, conversation_id=conversation_id, user_id=user_id, password=password ) # 获取多 Agent 配置 multi_agent_config = self.db.query(MultiAgentConfig).filter( MultiAgentConfig.app_id == release.app_id, MultiAgentConfig.is_active.is_(True) ).first() if not multi_agent_config: raise BusinessException("多 Agent 配置不存在", BizCode.AGENT_CONFIG_MISSING) # 获取 storage_type 和 user_rag_memory_id workspace_id = release.app.workspace_id storage_type = 'neo4j' # 默认值 user_rag_memory_id = '' try: # 获取工作空间的存储类型（不需要用户权限检查，因为是公开分享） from app.models import Workspace workspace = self.db.get(Workspace, workspace_id) if workspace and workspace.storage_type: storage_type = workspace.storage_type # 获取 USER_RAG_MERORY 知识库 ID knowledge = knowledge_repository.get_knowledge_by_name( db=self.db, name="USER_RAG_MERORY", workspace_id=workspace_id ) if knowledge: user_rag_memory_id = str(knowledge.id) except Exception as e: logger.warning(f"获取 storage_type 或 user_rag_memory_id 失败，使用默认值: {str(e)}") # 发送开始事件 yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation.id)}, ensure_ascii=False)}\n\n" # 构建多 Agent 运行请求 from app.schemas.multi_agent_schema import MultiAgentRunRequest multi_agent_request = MultiAgentRunRequest( message=message, conversation_id=conversation.id, user_id=user_id, variables=variables, use_llm_routing=True, web_search=web_search, memory=memory ) # 使用多 Agent 服务流式执行 multi_agent_service = MultiAgentService(self.db) full_content = "" async for event in multi_agent_service.run_stream( app_id=release.app_id, request=multi_agent_request, storage_type=storage_type, user_rag_memory_id=user_rag_memory_id ): # 直接转发事件 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"