Merge branch 'refs/heads/develop' into feature/20260105_xjn
# Conflicts: # api/app/services/app_chat_service.py
This commit is contained in:
谢俊男
@@ -9,7 +9,11 @@ from fastapi import Depends
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from sqlalchemy.orm import Session
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from app.core.agent.langchain_agent import LangChainAgent
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from app.core.error_codes import BizCode
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from app.core.exceptions import BusinessException
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from app.core.logging_config import get_business_logger
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from app.db import get_db, get_db_context
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from app.models import MultiAgentConfig, AgentConfig, WorkflowConfig
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from app.services.tool_service import ToolService
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from app.repositories.tool_repository import ToolRepository
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from app.db import get_db
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@@ -20,6 +24,7 @@ from app.services.draft_run_service import create_knowledge_retrieval_tool, crea
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from app.services.draft_run_service import create_web_search_tool
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from app.services.model_service import ModelApiKeyService
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from app.services.multi_agent_orchestrator import MultiAgentOrchestrator
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from app.services.workflow_service import WorkflowService
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logger = get_business_logger()
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@@ -67,10 +72,10 @@ class AppChatService:
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# 准备工具列表
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tools = []
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# 获取工具服务
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tool_service = ToolService(self.db)
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# 从配置中获取启用的工具
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if hasattr(config, 'tools') and config.tools:
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for tool_id, tool_config in config.tools.items():
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@@ -100,6 +105,21 @@ class AppChatService:
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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# web_tools = config.tools
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# web_search_choice = web_tools.get("web_search", {})
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# web_search_enable = web_search_choice.get("enabled", False)
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# if web_search == True:
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# if web_search_enable == True:
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# search_tool = create_web_search_tool({})
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# tools.append(search_tool)
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#
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# logger.debug(
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# "已添加网络搜索工具",
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# extra={
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# "tool_count": len(tools)
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# }
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# )
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# 获取模型参数
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model_parameters = config.model_parameters
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@@ -482,7 +502,9 @@ class AppChatService:
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: AgentConfig,
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config: WorkflowConfig,
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app_id: uuid.UUID,
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workspace_id: uuid.UUID,
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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web_search: bool = False,
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@@ -491,280 +513,158 @@ class AppChatService:
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user_rag_memory_id: Optional[str] = None,
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) -> Dict[str, Any]:
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"""聊天（非流式）"""
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workflow_service = WorkflowService(self.db)
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start_time = time.time()
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config_id = None
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input_data = {"message":message, "variables": variables,
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"conversation_id": str(conversation_id)}
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inconfig = workflow_service.get_workflow_config(app_id)
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if variables is None:
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variables = {}
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# 2. 创建执行记录
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execution = workflow_service.create_execution(
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workflow_config_id=inconfig.id,
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app_id=app_id,
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trigger_type="manual",
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triggered_by=None,
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conversation_id=conversation_id,
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input_data=input_data
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)
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# 获取模型配置ID
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model_config_id = config.default_model_config_id
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api_key_obj = ModelApiKeyService.get_a_api_key(self.db ,model_config_id)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.get("system_prompt", "")
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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# 3. 构建工作流配置字典
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workflow_config_dict = {
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"nodes": config.nodes,
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"edges": config.edges,
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"variables": config.variables,
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"execution_config": config.execution_config
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}
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# 4. 获取工作空间 ID（从 app 获取）
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# 5. 执行工作流
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from app.core.workflow.executor import execute_workflow
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try:
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# 更新状态为运行中
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workflow_service.update_execution_status(execution.execution_id, "running")
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result = await execute_workflow(
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workflow_config=workflow_config_dict,
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input_data=input_data,
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execution_id=execution.execution_id,
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workspace_id=str(workspace_id),
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user_id=user_id
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 添加知识库检索工具
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knowledge_retrieval = config.get("knowledge_retrieval")
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if knowledge_retrieval:
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knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
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kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
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if kb_ids:
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kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id)
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tools.append(kb_tool)
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# 添加长期记忆工具
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memory_flag = False
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if memory == True:
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memory_config = config.get("memory", {})
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if memory_config.get("enabled") and user_id:
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memory_flag = True
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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web_tools = config.get("tools")
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web_search_choice = web_tools.get("web_search", {})
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web_search_enable = web_search_choice.get("enabled", False)
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if web_search == True:
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if web_search_enable == True:
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search_tool = create_web_search_tool({})
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tools.append(search_tool)
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logger.debug(
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"已添加网络搜索工具",
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extra={
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"tool_count": len(tools)
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}
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# 更新执行结果
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if result.get("status") == "completed":
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workflow_service.update_execution_status(
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execution.execution_id,
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"completed",
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output_data=result.get("node_outputs", {})
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)
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else:
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workflow_service.update_execution_status(
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execution.execution_id,
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"failed",
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error_message=result.get("error")
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)
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# 获取模型参数
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model_parameters = config.get("model_parameters", {})
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# 返回增强的响应结构
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return {
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"execution_id": execution.execution_id,
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"status": result.get("status"),
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"output": result.get("output"), # 最终输出（字符串）
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"output_data": result.get("node_outputs", {}), # 所有节点输出（详细数据）
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"conversation_id": result.get("conversation_id"), # 所有节点输出（详细数据）payload., # 会话 ID
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"error_message": result.get("error"),
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"elapsed_time": result.get("elapsed_time"),
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"token_usage": result.get("token_usage")
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}
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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)
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# 加载历史消息
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history = []
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memory_config = {"enabled": True, 'max_history': 10}
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if memory_config.get("enabled"):
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messages = self.conversation_service.get_messages(
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conversation_id=conversation_id,
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limit=memory_config.get("max_history", 10)
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except Exception as e:
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logger.error(f"工作流执行失败: execution_id={execution.execution_id}, error={e}", exc_info=True)
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workflow_service.update_execution_status(
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execution.execution_id,
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"failed",
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error_message=str(e)
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)
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raise BusinessException(
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code=BizCode.INTERNAL_ERROR,
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message=f"工作流执行失败: {str(e)}"
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)
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history = [
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{"role": msg.role, "content": msg.content}
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for msg in messages
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]
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# 调用 Agent
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result = await agent.chat(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id,
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id,
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config_id=config_id,
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memory_flag=memory_flag
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)
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# 保存消息
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self.conversation_service.save_conversation_messages(
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conversation_id=conversation_id,
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user_message=message,
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assistant_message=result["content"]
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)
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elapsed_time = time.time() - start_time
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return {
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"conversation_id": conversation_id,
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"message": result["content"],
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"usage": result.get("usage", {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}),
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"elapsed_time": elapsed_time
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}
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async def workflow_chat_stream(
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: AgentConfig,
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config: WorkflowConfig,
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app_id: uuid.UUID,
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workspace_id: uuid.UUID,
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user_id: Optional[str] = None,
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variables: Optional[Dict[str, Any]] = None,
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web_search: bool = False,
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memory: bool = True,
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storage_type: Optional[str] = None,
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user_rag_memory_id: Optional[str] = None,
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) -> AsyncGenerator[str, None]:
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"""聊天（流式）"""
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workflow_service = WorkflowService(self.db)
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input_data = {"message": message, "variables": variables,
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"conversation_id": str(conversation_id)}
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inconfig = workflow_service.get_workflow_config(app_id)
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# 2. 创建执行记录
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execution = workflow_service.create_execution(
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workflow_config_id=inconfig.id,
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app_id=app_id,
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trigger_type="manual",
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triggered_by=None,
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conversation_id=conversation_id,
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input_data=input_data
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)
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# 3. 构建工作流配置字典
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workflow_config_dict = {
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"nodes": config.nodes,
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"edges": config.edges,
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"variables": config.variables,
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"execution_config": config.execution_config
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}
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# 4. 获取工作空间 ID（从 app 获取）
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# 5. 流式执行工作流
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try:
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start_time = time.time()
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config_id = None
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# 更新状态为运行中
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workflow_service.update_execution_status(execution.execution_id, "running")
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if variables is None:
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variables = {}
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# 获取模型配置ID
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model_config_id = config.default_model_config_id
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api_key_obj = ModelApiKeyService.get_a_api_key(self.db ,model_config_id)
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# 处理系统提示词（支持变量替换）
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system_prompt = config.get("system_prompt", "")
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if variables:
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system_prompt_rendered = render_prompt_message(
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system_prompt,
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PromptMessageRole.USER,
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variables
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)
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system_prompt = system_prompt_rendered.get_text_content() or system_prompt
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# 准备工具列表
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tools = []
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# 获取工具服务
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tool_service = ToolService(self.db)
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# 从配置中获取启用的工具
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if hasattr(config, 'tools') and config.tools:
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for tool_id, tool_config in config.tools.items():
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if tool_config.get("enabled", False):
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# 根据工具名称查找工具实例
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tool_instance = tool_service._get_tool_instance(tool_id, ToolRepository.get_tenant_id_by_workspace_id(self.db, workspace_id))
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if tool_instance:
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# 转换为LangChain工具
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langchain_tool = tool_instance.to_langchain_tool(tool_config.get("config", {}).get("operation", None))
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tools.append(langchain_tool)
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# 添加知识库检索工具
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knowledge_retrieval = config.get("knowledge_retrieval")
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if knowledge_retrieval:
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knowledge_bases = knowledge_retrieval.get("knowledge_bases", [])
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kb_ids = [kb.get("kb_id") for kb in knowledge_bases if kb.get("kb_id")]
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if kb_ids:
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kb_tool = create_knowledge_retrieval_tool(knowledge_retrieval, kb_ids, user_id)
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tools.append(kb_tool)
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# 添加长期记忆工具
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memory_flag = False
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if memory:
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memory_config = config.get("memory", {})
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if memory_config.get("enabled") and user_id:
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memory_flag = True
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memory_tool = create_long_term_memory_tool(memory_config, user_id)
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tools.append(memory_tool)
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# 获取模型参数
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model_parameters = config.get("model_parameters", {})
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# 创建 LangChain Agent
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agent = LangChainAgent(
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model_name=api_key_obj.model_name,
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api_key=api_key_obj.api_key,
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provider=api_key_obj.provider,
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api_base=api_key_obj.api_base,
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temperature=model_parameters.get("temperature", 0.7),
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max_tokens=model_parameters.get("max_tokens", 2000),
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system_prompt=system_prompt,
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tools=tools,
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streaming=True
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)
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# 加载历史消息
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history = []
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memory_config = {"enabled": True, 'max_history': 10}
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if memory_config.get("enabled"):
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messages = self.conversation_service.get_messages(
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conversation_id=conversation_id,
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limit=memory_config.get("max_history", 10)
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)
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history = [
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{"role": msg.role, "content": msg.content}
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for msg in messages
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]
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# 发送开始事件
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yield f"event: start\ndata: {json.dumps({'conversation_id': str(conversation_id)}, ensure_ascii=False)}\n\n"
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# 流式调用 Agent
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full_content = ""
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async for chunk in agent.chat_stream(
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message=message,
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history=history,
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context=None,
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end_user_id=user_id,
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storage_type=storage_type,
|
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user_rag_memory_id=user_rag_memory_id,
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config_id=config_id,
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memory_flag=memory_flag
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# 调用流式执行（executor 会发送 workflow_start 和 workflow_end 事件）
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async for event in workflow_service._run_workflow_stream(
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workflow_config=workflow_config_dict,
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input_data=input_data,
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execution_id=execution.execution_id,
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workspace_id=str(workspace_id),
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user_id=user_id
|
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):
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full_content += chunk
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# 发送消息块事件
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yield f"event: message\ndata: {json.dumps({'content': chunk}, ensure_ascii=False)}\n\n"
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# 直接转发 executor 的事件（已经是正确的格式）
|
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yield event
|
||||
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elapsed_time = time.time() - start_time
|
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|
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# 保存消息
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||||
self.conversation_service.add_message(
|
||||
conversation_id=conversation_id,
|
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role="user",
|
||||
content=message
|
||||
)
|
||||
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||||
self.conversation_service.add_message(
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conversation_id=conversation_id,
|
||||
role="assistant",
|
||||
content=full_content,
|
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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)}
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||||
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)
|
||||
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 f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
|
||||
yield {
|
||||
"event": "error",
|
||||
"data": {
|
||||
"execution_id": execution.execution_id,
|
||||
"error": str(e)
|
||||
}
|
||||
}
|
||||
|
||||
# ==================== 依赖注入函数 ====================
|
||||
|
||||
|
||||
Reference in New Issue
Block a user