tsukasa
636 lines
27 KiB
Python
636 lines
27 KiB
Python
from typing import Optional
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from asyncio import Queue
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import asyncio
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import logging
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import uuid
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import time
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import json
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import shlex
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import datetime
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import copy
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from .agent_base import AgentMsg, AgentMsgStatus, AgentMsgType,FunctionItem,LLMResult,AgentPrompt
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from .chatsession import AIChatSession
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from .compute_task import ComputeTaskResult,ComputeTaskResultCode
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from .ai_function import AIFunction
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from .environment import Environment
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from .contact_manager import ContactManager,Contact,FamilyMember
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from .compute_kernel import ComputeKernel
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from .bus import AIBus
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from knowledge import *
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logger = logging.getLogger(__name__)
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class AIAgentTemplete:
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def __init__(self) -> None:
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self.llm_model_name:str = "gpt-4-0613"
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self.max_token_size:int = 0
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self.template_id:str = None
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self.introduce:str = None
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self.author:str = None
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self.prompt:AgentPrompt = None
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def load_from_config(self,config:dict) -> bool:
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if config.get("llm_model_name") is not None:
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self.llm_model_name = config["llm_model_name"]
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if config.get("max_token_size") is not None:
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self.max_token_size = config["max_token_size"]
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if config.get("template_id") is not None:
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self.template_id = config["template_id"]
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if config.get("prompt") is not None:
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self.prompt = AgentPrompt()
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if self.prompt.load_from_config(config["prompt"]) is False:
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logger.error("load prompt from config failed!")
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return False
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return True
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class AIAgent:
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def __init__(self) -> None:
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self.role_prompt:AgentPrompt = None
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self.agent_prompt:AgentPrompt = None
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self.agent_think_prompt:AgentPrompt = None
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self.llm_model_name:str = None
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self.max_token_size:int = 3600
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self.agent_id:str = None
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self.template_id:str = None
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self.fullname:str = None
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self.powerby = None
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self.enable = True
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self.enable_kb = False
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self.enable_timestamp = False
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self.guest_prompt_str = None
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self.owner_promp_str = None
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self.contact_prompt_str = None
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self.history_len = 10
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self.learn_token_limit = 500
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self.learn_prompt = None
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self.chat_db = None
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self.unread_msg = Queue() # msg from other agent
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self.owner_env : Environment = None
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self.owenr_bus = None
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self.enable_function_list = None
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@classmethod
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def create_from_templete(cls,templete:AIAgentTemplete, fullname:str):
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# Agent just inherit from templete on craete,if template changed,agent will not change
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result_agent = AIAgent()
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result_agent.llm_model_name = templete.llm_model_name
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result_agent.max_token_size = templete.max_token_size
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result_agent.template_id = templete.template_id
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result_agent.agent_id = "agent#" + uuid.uuid4().hex
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result_agent.fullname = fullname
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result_agent.powerby = templete.author
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result_agent.agent_prompt = templete.prompt
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return result_agent
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def load_from_config(self,config:dict) -> bool:
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if config.get("instance_id") is None:
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logger.error("agent instance_id is None!")
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return False
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self.agent_id = config["instance_id"]
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if config.get("fullname") is None:
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logger.error(f"agent {self.agent_id} fullname is None!")
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return False
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self.fullname = config["fullname"]
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if config.get("prompt") is not None:
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self.agent_prompt = AgentPrompt()
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self.agent_prompt.load_from_config(config["prompt"])
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if config.get("think_prompt") is not None:
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self.agent_think_prompt = AgentPrompt()
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self.agent_think_prompt.load_from_config(config["think_prompt"])
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if config.get("guest_prompt") is not None:
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self.guest_prompt_str = config["guest_prompt"]
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if config.get("owner_prompt") is not None:
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self.owner_promp_str = config["owner_prompt"]
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if config.get("contact_prompt") is not None:
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self.contact_prompt_str = config["contact_prompt"]
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if config.get("owner_env") is not None:
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self.owner_env = Environment.get_env_by_id(config["owner_env"])
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if config.get("powerby") is not None:
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self.powerby = config["powerby"]
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if config.get("template_id") is not None:
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self.template_id = config["template_id"]
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if config.get("llm_model_name") is not None:
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self.llm_model_name = config["llm_model_name"]
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if config.get("max_token_size") is not None:
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self.max_token_size = config["max_token_size"]
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if config.get("enable_function") is not None:
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self.enable_function_list = config["enable_function"]
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if config.get("enable_kb") is not None:
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self.enable_kb = bool(config["enable_kb"])
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if config.get("enable_timestamp") is not None:
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self.enable_timestamp = bool(config["enable_timestamp"])
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if config.get("history_len"):
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self.history_len = int(config.get("history_len"))
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return True
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def get_id(self) -> str:
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return self.agent_id
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def get_fullname(self) -> str:
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return self.fullname
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def get_template_id(self) -> str:
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return self.template_id
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def get_llm_model_name(self) -> str:
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return self.llm_model_name
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def get_max_token_size(self) -> int:
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return self.max_token_size
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def get_llm_learn_token_limit(self) -> int:
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return self.learn_token_limit
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def get_learn_prompt(self) -> AgentPrompt:
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return self.learn_prompt
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def get_agent_role_prompt(self) -> AgentPrompt:
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return self.role_prompt
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def _get_remote_user_prompt(self,remote_user:str) -> AgentPrompt:
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cm = ContactManager.get_instance()
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contact = cm.find_contact_by_name(remote_user)
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if contact is None:
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#create guest prompt
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if self.guest_prompt_str is not None:
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prompt = AgentPrompt()
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prompt.system_message = {"role":"system","content":self.guest_prompt_str}
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return prompt
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return None
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else:
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if contact.is_family_member:
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if self.owner_promp_str is not None:
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real_str = self.owner_promp_str.format_map(contact.to_dict())
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prompt = AgentPrompt()
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prompt.system_message = {"role":"system","content":real_str}
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return prompt
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else:
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if self.contact_prompt_str is not None:
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real_str = self.contact_prompt_str.format_map(contact.to_dict())
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prompt = AgentPrompt()
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prompt.system_message = {"role":"system","content":real_str}
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return prompt
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return None
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def _get_inner_functions(self) -> dict:
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if self.owner_env is None:
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return None,0
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all_inner_function = self.owner_env.get_all_ai_functions()
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if all_inner_function is None:
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return None,0
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result_func = []
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result_len = 0
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for inner_func in all_inner_function:
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func_name = inner_func.get_name()
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if self.enable_function_list is not None:
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if len(self.enable_function_list) > 0:
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if func_name not in self.enable_function_list:
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logger.debug(f"ageint {self.agent_id} ignore inner func:{func_name}")
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continue
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this_func = {}
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this_func["name"] = func_name
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this_func["description"] = inner_func.get_description()
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this_func["parameters"] = inner_func.get_parameters()
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result_len += len(json.dumps(this_func)) / 4
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result_func.append(this_func)
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return result_func,result_len
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async def _execute_func(self,inner_func_call_node:dict,prompt:AgentPrompt,inner_functions,org_msg:AgentMsg=None,stack_limit = 5) -> ComputeTaskResult:
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func_name = inner_func_call_node.get("name")
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arguments = json.loads(inner_func_call_node.get("arguments"))
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logger.info(f"llm execute inner func:{func_name} ({json.dumps(arguments)})")
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func_node : AIFunction = self.owner_env.get_ai_function(func_name)
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if func_node is None:
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result_str = f"execute {func_name} error,function not found"
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else:
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if org_msg:
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ineternal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
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try:
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result_str:str = await func_node.execute(**arguments)
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except Exception as e:
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result_str = f"execute {func_name} error:{str(e)}"
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logger.error(f"llm execute inner func:{func_name} error:{e}")
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logger.info("llm execute inner func result:" + result_str)
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prompt.messages.append({"role":"function","content":result_str,"name":func_name})
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task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions)
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if task_result.result_code != ComputeTaskResultCode.OK:
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logger.error(f"llm compute error:{task_result.error_str}")
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return task_result
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ineternal_call_record.result_str = task_result.result_str
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ineternal_call_record.done_time = time.time()
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if org_msg:
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org_msg.inner_call_chain.append(ineternal_call_record)
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inner_func_call_node = None
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if stack_limit > 0:
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result_message : dict = task_result.result.get("message")
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if result_message:
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inner_func_call_node = result_message.get("function_call")
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if inner_func_call_node:
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return await self._execute_func(inner_func_call_node,prompt,org_msg,stack_limit-1)
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else:
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return task_result
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async def _get_agent_prompt(self) -> AgentPrompt:
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return self.agent_prompt
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async def _get_agent_think_prompt(self) -> AgentPrompt:
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return self.agent_think_prompt
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def _format_msg_by_env_value(self,prompt:AgentPrompt):
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if self.owner_env is None:
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return
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for msg in prompt.messages:
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old_content = msg.get("content")
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msg["content"] = old_content.format_map(self.owner_env)
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async def _handle_event(self,event):
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if event.type == "AgentThink":
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return await self._do_think()
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async def _do_think(self):
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#1) load all sessions
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session_id_list = AIChatSession.list_session(self.agent_id,self.chat_db)
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#2) get history from session in token limit
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for session_id in session_id_list:
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await self.think_chatsession(session_id)
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#4) advanced: reload all chatrecord,and think the topic of message.
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#5) some topic could be end(not be thinked in futured )
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return
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async def think_chatsession(self,session_id):
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if self.agent_think_prompt is None:
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return
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logger.info(f"agent {self.agent_id} think session {session_id}")
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chatsession = AIChatSession.get_session_by_id(session_id,self.chat_db)
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while True:
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cur_pos = chatsession.summarize_pos
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summary = chatsession.summary
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prompt:AgentPrompt = AgentPrompt()
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#prompt.append(self._get_agent_prompt())
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prompt.append(await self._get_agent_think_prompt())
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system_prompt_len = prompt.get_prompt_token_len()
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#think env?
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history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
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prompt.append(history_prompt)
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is_finish = next_pos - cur_pos < 2
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if is_finish:
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logger.info(f"agent {self.agent_id} think session {session_id} is finished!,no more history")
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break
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#3) llm summarize chat history
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task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,None)
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if task_result.result_code != ComputeTaskResultCode.OK:
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logger.error(f"llm compute error:{task_result.error_str}")
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break
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else:
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new_summary= task_result.result_str
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logger.info(f"agent {self.agent_id} think session {session_id} from {cur_pos} to {next_pos} summary:{new_summary}")
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chatsession.update_think_progress(next_pos,new_summary)
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return
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async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg:
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session_topic = msg.target + "#" + msg.topic
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chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
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need_process = False
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if msg.mentions is not None:
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if self.agent_id in msg.mentions:
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need_process = True
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logger.info(f"agent {self.agent_id} recv a group chat message from {msg.sender},but is not mentioned,ignore!")
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if need_process is not True:
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chatsession.append(msg)
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resp_msg = msg.create_group_resp_msg(self.agent_id,"")
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return resp_msg
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else:
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msg_prompt = AgentPrompt()
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msg_prompt.messages = [{"role":"user","content":f"{msg.sender}:{msg.body}"}]
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prompt = AgentPrompt()
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prompt.append(await self._get_agent_prompt())
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self._format_msg_by_env_value(prompt)
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inner_functions,function_token_len = self._get_inner_functions()
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system_prompt_len = prompt.get_prompt_token_len()
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input_len = len(msg.body)
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history_prmpt,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len)
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prompt.append(history_prmpt) # chat context
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prompt.append(msg_prompt)
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logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
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task_result = await self._do_llm_complection(prompt,inner_functions,msg)
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if task_result.result_code != ComputeTaskResultCode.OK:
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error_resp = msg.create_error_resp(task_result.error_str)
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return error_resp
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final_result = task_result.result_str
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llm_result : LLMResult = LLMResult.from_str(final_result)
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is_ignore = False
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result_prompt_str = ""
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match llm_result.state:
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case "ignore":
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is_ignore = True
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case "waiting":
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for sendmsg in llm_result.send_msgs:
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target = sendmsg.target
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sendmsg.sender = self.agent_id
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sendmsg.topic = msg.topic
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sendmsg.prev_msg_id = msg.get_msg_id()
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send_resp = await AIBus.get_default_bus().send_message(sendmsg)
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if send_resp is not None:
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result_prompt_str += f"\n{target} response is :{send_resp.body}"
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agent_sesion = AIChatSession.get_session(self.agent_id,f"{sendmsg.target}#{sendmsg.topic}",self.chat_db)
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agent_sesion.append(sendmsg)
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agent_sesion.append(send_resp)
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final_result = llm_result.resp + result_prompt_str
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if is_ignore is not True:
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resp_msg = msg.create_group_resp_msg(self.agent_id,final_result)
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chatsession.append(msg)
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chatsession.append(resp_msg)
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return resp_msg
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return None
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async def _process_msg(self,msg:AgentMsg) -> AgentMsg:
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if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
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return await self._process_group_chat_msg(msg)
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session_topic = msg.get_sender() + "#" + msg.topic
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chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
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msg_prompt = AgentPrompt()
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msg_prompt.messages = [{"role":"user","content":msg.body}]
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prompt = AgentPrompt()
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prompt.append(await self._get_agent_prompt())
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self._format_msg_by_env_value(prompt)
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prompt.append(self._get_remote_user_prompt(msg.sender))
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inner_functions,function_token_len = self._get_inner_functions()
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system_prompt_len = prompt.get_prompt_token_len()
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input_len = len(msg.body)
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history_prmpt,history_token_len = await self._get_prompt_from_session(chatsession,system_prompt_len + function_token_len,input_len)
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prompt.append(history_prmpt) # chat context
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prompt.append(msg_prompt)
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logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
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#task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions)
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task_result = await self._do_llm_complection(prompt,inner_functions,msg)
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if task_result.result_code != ComputeTaskResultCode.OK:
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error_resp = msg.create_error_resp(task_result.error_str)
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return error_resp
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final_result = task_result.result_str
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llm_result : LLMResult = LLMResult.from_str(final_result)
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is_ignore = False
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result_prompt_str = ""
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match llm_result.state:
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case "ignore":
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is_ignore = True
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case "waiting":
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for sendmsg in llm_result.send_msgs:
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sendmsg.sender = self.agent_id
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target = sendmsg.target
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sendmsg.topic = msg.topic
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sendmsg.prev_msg_id = msg.get_msg_id()
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send_resp = await AIBus.get_default_bus().send_message(sendmsg)
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if send_resp is not None:
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result_prompt_str += f"\n{target} response is :{send_resp.body}"
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agent_sesion = AIChatSession.get_session(self.agent_id,f"{sendmsg.target}#{sendmsg.topic}",self.chat_db)
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agent_sesion.append(sendmsg)
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agent_sesion.append(send_resp)
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final_result = llm_result.resp + result_prompt_str
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if is_ignore is not True:
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resp_msg = msg.create_resp_msg(final_result)
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chatsession.append(msg)
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chatsession.append(resp_msg)
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return resp_msg
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return None
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async def _get_history_prompt_for_think(self,chatsession:AIChatSession,summary:str,system_token_len:int,pos:int)->(AgentPrompt,int):
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history_len = (self.max_token_size * 0.7) - system_token_len
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messages = chatsession.read_history(self.history_len,pos,"natural") # read
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result_token_len = 0
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result_prompt = AgentPrompt()
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have_summary = False
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if summary is not None:
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if len(summary) > 1:
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have_summary = True
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if have_summary:
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result_prompt.messages.append({"role":"user","content":summary})
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result_token_len -= len(summary)
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else:
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result_prompt.messages.append({"role":"user","content":"There is no summary yet."})
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result_token_len -= 6
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|
|
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read_history_msg = 0
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history_str : str = ""
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for msg in messages:
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read_history_msg += 1
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dt = datetime.datetime.fromtimestamp(float(msg.create_time))
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formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
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record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
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history_str = history_str + record_str
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history_len -= len(msg.body)
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result_token_len += len(msg.body)
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if history_len < 0:
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logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
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break
|
|
|
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result_prompt.messages.append({"role":"user","content":history_str})
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return result_prompt,pos+read_history_msg
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|
|
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async def _get_prompt_from_session_for_groupchat(self,chatsession:AIChatSession,system_token_len,input_token_len,is_groupchat=False):
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history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len
|
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messages = chatsession.read_history(self.history_len) # read
|
|
result_token_len = 0
|
|
result_prompt = AgentPrompt()
|
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read_history_msg = 0
|
|
for msg in reversed(messages):
|
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read_history_msg += 1
|
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dt = datetime.datetime.fromtimestamp(float(msg.create_time))
|
|
formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
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|
|
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if msg.sender == self.agent_id:
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|
if self.enable_timestamp:
|
|
result_prompt.messages.append({"role":"assistant","content":f"(create on {formatted_time}) {msg.body} "})
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else:
|
|
result_prompt.messages.append({"role":"assistant","content":msg.body})
|
|
|
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else:
|
|
if self.enable_timestamp:
|
|
result_prompt.messages.append({"role":"user","content":f"(create on {formatted_time}) {msg.body} "})
|
|
else:
|
|
result_prompt.messages.append({"role":"user","content":f"{msg.sender}:{msg.body}"})
|
|
|
|
history_len -= len(msg.body)
|
|
result_token_len += len(msg.body)
|
|
if history_len < 0:
|
|
logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
|
|
break
|
|
|
|
return result_prompt,result_token_len
|
|
|
|
async def _do_llm_complection(self,prompt:AgentPrompt,inner_functions:dict,org_msg:AgentMsg=None) -> ComputeTaskResult:
|
|
from .compute_kernel import ComputeKernel
|
|
#logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
|
|
task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
logger.error(f"llm compute error:{task_result.error_str}")
|
|
#error_resp = msg.create_error_resp(task_result.error_str)
|
|
return task_result
|
|
|
|
result_message = task_result.result.get("message")
|
|
inner_func_call_node = None
|
|
if result_message:
|
|
inner_func_call_node = result_message.get("function_call")
|
|
|
|
if inner_func_call_node:
|
|
call_prompt : AgentPrompt = copy.deepcopy(prompt)
|
|
task_result = await self._execute_func(inner_func_call_node,call_prompt,inner_functions,org_msg)
|
|
|
|
return task_result
|
|
|
|
def parser_learn_llm_result(self,llm_result:str):
|
|
pass
|
|
|
|
async def _llm_read_article(self,item:KnowledgeObject) -> ComputeTaskResult:
|
|
full_content = item.get_article_full_content()
|
|
full_content_len = ComputeKernel.llm_num_tokens_from_text(full_content,self.get_llm_model_name())
|
|
if full_content_len < self.get_llm_learn_token_limit():
|
|
|
|
# 短文章不用总结catelog
|
|
#path_list,summary = llm_get_summary(summary,full_content)
|
|
prompt = self.get_agent_role_prompt()
|
|
learn_prompt = self.get_learn_prompt()
|
|
cotent_prompt = AgentPrompt(full_content)
|
|
prompt.append(learn_prompt)
|
|
prompt.append(cotent_prompt)
|
|
|
|
env_functions = self._get_inner_functions()
|
|
|
|
task_result:ComputeTaskResult = await self._do_llm_complection(prompt,env_functions)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
return task_result
|
|
path_list,summary = self.parser_learn_llm_result(task_result.result_str)
|
|
|
|
else:
|
|
# 用传统方法对文章进行一些处理，目的是尽可能减少LLM调用的次数
|
|
catelog = item.get_articl_catelog()
|
|
chunk_content = full_content.read(self.get_llm_learn_token_limit())
|
|
summary = kb.try_get_summary(catelog,full_content)
|
|
|
|
while chunk_content is not None:
|
|
#path_list,summarycatelog = llm_get_summary(summary,chunk_content)
|
|
#learn_prompt = self.get_learn_prompt_with_summary()
|
|
|
|
prompt = AgentPrompt("summary")
|
|
learn_prompt.append(prompt)
|
|
prompt = AgentPrompt(chunk_content)
|
|
learn_prompt.append(prompt)
|
|
|
|
#llm_result = self.do_llm_competion(learn_prompt)
|
|
#path_list,summary,catelog = parser_learn_llm_result(llm_result)
|
|
|
|
#chunk_content = full_content.read(self.get_llm_learn_token_limit())
|
|
|
|
kb.insert_item(path_list,item,catelog,summary)
|
|
|
|
|
|
|
|
async def _get_prompt_from_session(self,chatsession:AIChatSession,system_token_len,input_token_len) -> AgentPrompt:
|
|
# TODO: get prompt from group chat is different from single chat
|
|
|
|
history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len
|
|
messages = chatsession.read_history(self.history_len) # read
|
|
result_token_len = 0
|
|
result_prompt = AgentPrompt()
|
|
read_history_msg = 0
|
|
|
|
if chatsession.summary is not None:
|
|
if len(chatsession.summary) > 1:
|
|
result_prompt.messages.append({"role":"user","content":chatsession.summary})
|
|
result_token_len -= len(chatsession.summary)
|
|
|
|
for msg in reversed(messages):
|
|
read_history_msg += 1
|
|
dt = datetime.datetime.fromtimestamp(float(msg.create_time))
|
|
formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
|
|
|
|
if msg.sender == self.agent_id:
|
|
|
|
if self.enable_timestamp:
|
|
result_prompt.messages.append({"role":"assistant","content":f"(create on {formatted_time}) {msg.body} "})
|
|
else:
|
|
result_prompt.messages.append({"role":"assistant","content":msg.body})
|
|
|
|
else:
|
|
if self.enable_timestamp:
|
|
result_prompt.messages.append({"role":"user","content":f"(create on {formatted_time}) {msg.body} "})
|
|
else:
|
|
result_prompt.messages.append({"role":"user","content":msg.body})
|
|
|
|
history_len -= len(msg.body)
|
|
result_token_len += len(msg.body)
|
|
if history_len < 0:
|
|
logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
|
|
break
|
|
|
|
return result_prompt,result_token_len
|
|
|