109 lines
4.4 KiB
Python
109 lines
4.4 KiB
Python
import asyncio
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import os
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import time
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from typing import Dict, Any, List
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from app.core.logging_config import get_business_logger
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from app.schemas.prompt_schema import render_prompt_message, PromptMessageRole
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from app.services.api_resquests_server import messages_type, write_messages
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from app.services.agent_server import ChatRequest, tool_memory, create_dynamic_agent, tool_Retrieval
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logger = get_business_logger()
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class Agent_chat:
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def __init__(self,config_data: dict):
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self.prompt_message = render_prompt_message(
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config_data.template_str,
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PromptMessageRole.USER,
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config_data.params
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)
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self.prompt = self.prompt_message.get_text_content()
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self.model_configs = config_data.model_configs
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self.history_memory = config_data.history_memory
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self.knowledge_base = config_data.knowledge_base
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logger.info(f"渲染结果：{self.prompt_message.get_text_content()}" )
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async def run_agent(self,agent, end_user_id:str, user_prompt:str, model_name:str):
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response = agent.invoke(
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{
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"messages": [
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{
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"role": "user",
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"content": user_prompt
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}
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]
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},
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{"configurable": {"thread_id": f'{model_name}_{end_user_id}'}},
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)
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outputs = []
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for msg in response["messages"]:
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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outputs.append({
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"role": "assistant",
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"tool_calls": [
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{"name": t["name"], "arguments": t["args"]}
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for t in msg.tool_calls
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]
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})
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elif hasattr(msg, "content") and msg.content:
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outputs.append({
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"role": msg.__class__.__name__.lower().replace("message", ""),
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"content": msg.content
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})
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ai_messages=[msg['content'] for msg in outputs if msg["role"] == "ai"]
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return {"model_name": model_name, "end_user_id": end_user_id, "response": ai_messages}
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async def chat(self,req: ChatRequest) -> Dict[str, Any]:
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end_user_id = req.end_user_id # 用 user_id 作为对话线程标识
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start=time.time()
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user_prompt = req.message
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'''判断是都写入redis数据库'''
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messags_type = await messages_type(req.message,end_user_id)
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messags_type=messags_type['data']
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if messags_type=='question':
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writer_result=await write_messages(f'{end_user_id}', req.message)
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logger.info(f'判断类型写入耗时：{time.time() - start},{writer_result}')
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'''history_memory'''
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if self.history_memory==True:
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tool_result =await tool_memory(req)
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if tool_result!='' :tool_result=tool_result['data']
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if tool_result!='' :self.prompt=self.prompt+f''',历史消息：{tool_result},结合历史消息'''
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logger.info(f"记忆科学消耗时间：{time.time()-start},工具调用结果:{tool_result}")
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'''baidu'''
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'''knowledge_base'''
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if self.knowledge_base == True:
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retrieval_result=await tool_Retrieval(req)
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retrieval_knowledge = [i['page_content'] for i in retrieval_result['data']]
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retrieval_knowledge=','.join(retrieval_knowledge)
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logger.info(f"检索消耗时间：{time.time()-start},{retrieval_knowledge}")
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if retrieval_knowledge!='' :self.prompt=self.prompt+f",知识库检索内容：{retrieval_knowledge},结合检索结果"
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self.prompt=self.prompt+f'给出最合适的答案，确保答案的完整性，只保留用户的问题的回答，不额外输出提示语'
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logger.info(f"用户输入：{user_prompt}")
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logger.info(f"系统prompt：{self.prompt}")
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AGENTS = {
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cfg["name"]: await create_dynamic_agent(cfg["name"], cfg["moder_id"], self.prompt, req.token)
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for cfg in self.model_configs
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}
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tasks=[
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self.run_agent(agent, end_user_id, user_prompt, model_name)
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for model_name, agent in AGENTS.items()
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]
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# 并行运行
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results = await asyncio.gather(*tasks)
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result=[]
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for i in results:
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result.append(i)
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chat_result=(f"最终耗时：{time.time()-start},{result}")
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return chat_result |