656 lines
23 KiB
Python
656 lines
23 KiB
Python
"""基于分享链接的聊天服务"""
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import asyncio
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import json
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import time
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import uuid
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from typing import Optional, Dict, Any, AsyncGenerator, Annotated
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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.schemas.prompt_schema import render_prompt_message, PromptMessageRole
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from app.services.conversation_service import ConversationService
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from app.services.draft_run_service import create_knowledge_retrieval_tool, create_long_term_memory_tool
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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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class AppChatService:
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"""基于分享链接的聊天服务"""
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def __init__(self, db: Session):
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self.db = db
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self.conversation_service = ConversationService(db)
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async def agnet_chat(
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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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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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) -> Dict[str, Any]:
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"""聊天（非流式）"""
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start_time = time.time()
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config_id = None
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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.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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# 添加知识库检索工具
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knowledge_retrieval = config.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.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.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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# 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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)
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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 agnet_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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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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try:
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start_time = time.time()
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config_id = None
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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.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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# 添加知识库检索工具
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knowledge_retrieval = config.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.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.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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)
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# 获取模型参数
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model_parameters = config.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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):
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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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elapsed_time = time.time() - start_time
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# 保存消息
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="user",
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content=message
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)
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=full_content,
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meta_data={
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"model": api_key_obj.model_name,
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"usage": {}
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}
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)
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# 发送结束事件
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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"
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logger.info(
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"流式聊天完成",
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extra={
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"conversation_id": str(conversation_id),
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"elapsed_time": elapsed_time,
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"message_length": len(full_content)
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}
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)
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except (GeneratorExit, asyncio.CancelledError):
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# 生成器被关闭或任务被取消，正常退出
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logger.debug("流式聊天被中断")
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raise
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except Exception as e:
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logger.error(f"流式聊天失败: {str(e)}", exc_info=True)
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# 发送错误事件
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yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
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async def multi_agent_chat(
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self,
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message: str,
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conversation_id: uuid.UUID,
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config: MultiAgentConfig,
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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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) -> Dict[str, Any]:
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"""多 Agent 聊天（非流式）"""
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start_time = time.time()
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actual_config_id = None
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config_id = actual_config_id
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if variables is None:
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variables = {}
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# 2. 创建编排器
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orchestrator = MultiAgentOrchestrator(self.db, config)
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# 3. 执行任务
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result = await orchestrator.execute(
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message=message,
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conversation_id=conversation_id,
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user_id=user_id,
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variables=variables,
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use_llm_routing=True, # 默认启用 LLM 路由
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web_search=web_search, # 网络搜索参数
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memory=memory # 记忆功能参数
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)
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elapsed_time = time.time() - start_time
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# 保存消息
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="user",
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content=message
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)
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=result.get("message", ""),
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meta_data={
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"mode": result.get("mode"),
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"elapsed_time": result.get("elapsed_time"),
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"sub_results": result.get("sub_results")
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}
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)
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return {
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"conversation_id": conversation_id,
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"message": result.get("message", ""),
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"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 multi_agent_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: MultiAgentConfig,
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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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"""多 Agent 聊天（流式）"""
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start_time = time.time()
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actual_config_id = None
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config_id = actual_config_id
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if variables is None:
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variables = {}
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try:
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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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full_content = ""
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# 2. 创建编排器
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orchestrator = MultiAgentOrchestrator(self.db, config)
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# 3. 流式执行任务
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async for event in orchestrator.execute_stream(
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message=message,
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conversation_id=conversation_id,
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user_id=user_id,
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variables=variables,
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use_llm_routing=True,
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web_search=web_search, # 网络搜索参数
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memory=memory, # 记忆功能参数
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storage_type=storage_type,
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user_rag_memory_id=user_rag_memory_id
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):
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yield event
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# 尝试提取内容（用于保存）
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if "data:" in event:
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try:
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data_line = event.split("data: ", 1)[1].strip()
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data = json.loads(data_line)
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if "content" in data:
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full_content += data["content"]
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except:
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pass
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elapsed_time = time.time() - start_time
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# 保存消息
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="user",
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content=message
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)
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self.conversation_service.add_message(
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conversation_id=conversation_id,
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role="assistant",
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content=full_content,
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meta_data={
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"elapsed_time": elapsed_time
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}
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)
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logger.info(
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"多 Agent 流式聊天完成",
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extra={
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"conversation_id": str(conversation_id),
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"elapsed_time": elapsed_time,
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"message_length": len(full_content)
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}
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)
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except (GeneratorExit, asyncio.CancelledError):
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# 生成器被关闭或任务被取消，正常退出
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logger.debug("多 Agent 流式聊天被中断")
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raise
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except Exception as e:
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logger.error(f"多 Agent 流式聊天失败: {str(e)}", exc_info=True)
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# 发送错误事件
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yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
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async def workflow_chat(
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self,
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message: str,
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conversation_id: uuid.UUID,
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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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) -> 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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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,
|
||
"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)
|