wugren
1275 lines
53 KiB
Python
1275 lines
53 KiB
Python
import traceback
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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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import sys
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from ..proto.agent_msg import AgentMsg
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from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode
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from .agent_base import *
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from .chatsession import *
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from .ai_function import *
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from ..frame.contact_manager import ContactManager,Contact,FamilyMember
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from ..frame.compute_kernel import ComputeKernel
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from ..frame.bus import AIBus
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from ..environment.environment import *
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from ..environment.workspace_env import WorkspaceEnvironment
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from ..storage.storage import AIStorage
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from ..knowledge import *
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from . import video_utils, image_utils
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logger = logging.getLogger(__name__)
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DEFAULT_AGENT_READ_REPORT_PROMPT = """
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"""
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DEFAULT_AGENT_DO_PROMPT = """
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You are a helpful AI assistant.
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Solve tasks using your coding and language skills.
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In the following cases, suggest python code (in a python coding block) for the user to execute.
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1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
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2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
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Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
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When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
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If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
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If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
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When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
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Reply "TERMINATE" in the end when everything is done.
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"""
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DEFAULT_AGENT_SELF_CHECK_PROMPT = """
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"""
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DEFAULT_AGENT_GOAL_TO_TODO_PROMPT = """
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我会给你一个目标，你需要结合自己的角色思考如何将其拆解成多个TODO。请直接返回json来表达这些TODO
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"""
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DEFAULT_AGENT_LEARN_PROMPT = """
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我是一名软件工程师，拥有非常优秀的资料学习能力。下面是我学习和整理资料的方法
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1. 由于LLM的Token限制，我学习的可能只是资料的部分内容，此时我应能产生合适的学习中间结果，中间结果保存在metadata中。我要么产生中间结果，要么产生最终结果。
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2. 当存在已知信息时，需参考已知信息的内容来思考结果。
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3. 当我收到最后一部分内容时，我能结合已知的中间结果产生最终结果。
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4. 现有资料库以文件系统的形式组织，我未来借助资料的摘要来浏览知识库
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5. 我将学习过的资料另存在资料库的合适位置（以/开始的完整路径）。保存位置的目录深度不超过5层，文件夹名称长度不超过16个字符。
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6. 总是以json格式返回思考结果，json格式如下
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{
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think:"$think_result",
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metadata:{...} , # temp result for long content
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tags:["tag1","tag2"...],
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path:["/graphic/opengl","/database/mysql"], # list of directories to save to.
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title:"$article_title",
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summary:"$summary",
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catalogs: [{ # optional,catalogs is a tree
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title:"$catalog_name1",
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pos:"$pos:$length"
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children:[
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{
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title:"$catalog_name 1.1",
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pos:"$pos:$length"
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}
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]},
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{
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title:"$catalog_name2",
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pos:"$pos:$length"
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}
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]
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}
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"""
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DEFAULT_AGENT_LEARN_LONG_CONENT_PROMPT = """
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我给你一段内容，尝试为期建立目录。目录的标题不能超过16个字，
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目录要指向正文的位置（用字符偏移即可），整个目录的文本长度不能超过256个字节。并用json表达这个目录
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"""
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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(BaseAIAgent):
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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 = 128000
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self.agent_energy = 15
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self.agent_task = None
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self.last_recover_time = time.time()
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self.enable_thread = False
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self.can_do_unassigned_task = True
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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.review_todo_prompt = None
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self.read_report_prompt = None
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self.do_prompt = None
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self.check_prompt = None
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self.goal_to_todo_prompt = None
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self.learn_token_limit = 4000
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self.learn_prompt = AgentPrompt(DEFAULT_AGENT_LEARN_PROMPT)
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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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self.agent_workspace = WorkspaceEnvironment(self.agent_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("enable_thread") is not None:
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self.enable_thread = bool(config["enable_thread"])
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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("do_prompt") is not None:
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self.do_prompt = AgentPrompt()
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self.do_prompt.load_from_config(config["do_prompt"])
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self.wake_up()
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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 = config.get("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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if self.llm_model_name is None:
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return AIStorage.get_instance().get_user_config().get_value("llm_model_name")
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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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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_self_think()
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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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# workspace = self.get_current_workspace()
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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(self.get_agent_prompt())
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# if workspace:
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# prompt.append(workspace.get_prompt())
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# prompt.append(workspace.get_role_prompt(self.agent_id))
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# if self.need_session_summmary(msg,chatsession):
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# # get relate session(todos) summary
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# summary = self.llm_select_session_summary(msg,chatsession)
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# prompt.append(AgentPrompt(summary))
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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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def get_workspace_by_msg(self,msg:AgentMsg) -> WorkspaceEnvironment:
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return self.agent_workspace
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def need_session_summmary(self,msg:AgentMsg,session:AIChatSession) -> bool:
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return False
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async def _create_openai_thread(self) -> str:
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return None
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def check_and_to_base64(self, image_path: str) -> str:
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if image_utils.is_file(image_path):
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return image_utils.image_to_base64(image_path)
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else:
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return image_path
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async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg:
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msg_prompt = AgentPrompt()
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if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
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need_process = False
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if msg.is_image_msg():
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image_prompt, images = msg.get_image_body()
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if image_prompt is None:
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content = [[{"type": "text", "text": f"{msg.sender}'s message"}]]
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content.extend([{"type": "image_url", "url": self.check_and_to_base64(image)} for image in images])
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msg_prompt.messages = [{"role": "user", "content": content}]
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else:
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content = [{"type": "text", "text": f"{msg.sender}:{image_prompt}"}]
|
|
content.extend([{"type": "image_url", "url": self.check_and_to_base64(image)} for image in images])
|
|
msg_prompt.messages = [{"role": "user", "content": content}]
|
|
elif msg.is_video_msg():
|
|
video_prompt, video = msg.get_video_body()
|
|
frames = video_utils.extract_frames(video)
|
|
if video_prompt is None:
|
|
content = [{"type": "text", "text": f"{msg.sender}'s message"}]
|
|
content.extend([{"type": "image_url", "url": frame} for frame in frames])
|
|
msg_prompt.messages = [{"role": "user", "content": content}]
|
|
else:
|
|
content = [{"type": "text", "text": f"{msg.sender}:{video_prompt}"}]
|
|
content.extend([{"type": "image_url", "url": frame} for frame in frames])
|
|
msg_prompt.messages = [{"role": "user", "content": content}]
|
|
else:
|
|
msg_prompt.messages = [{"role":"user","content":f"{msg.sender}:{msg.body}"}]
|
|
session_topic = msg.target + "#" + msg.topic
|
|
chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
|
|
|
|
if msg.mentions is not None:
|
|
if self.agent_id in msg.mentions:
|
|
need_process = True
|
|
logger.info(f"agent {self.agent_id} recv a group chat message from {msg.sender},but is not mentioned,ignore!")
|
|
|
|
if need_process is not True:
|
|
chatsession.append(msg)
|
|
resp_msg = msg.create_group_resp_msg(self.agent_id,"")
|
|
return resp_msg
|
|
else:
|
|
if msg.is_image_msg():
|
|
image_prompt, images = msg.get_image_body()
|
|
if image_prompt is None:
|
|
msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "url": image} for image in images]}]
|
|
else:
|
|
content = [{"type": "text", "text": image_prompt}]
|
|
content.extend([{"type": "image_url", "url": image} for image in images])
|
|
msg_prompt.messages = [{"role": "user", "content": content}]
|
|
elif msg.is_video_msg():
|
|
video_prompt, video = msg.get_video_body()
|
|
frames = video_utils.extract_frames(video)
|
|
if video_prompt is None:
|
|
msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "url": frame} for frame in frames]}]
|
|
else:
|
|
content = [{"type": "text", "text": video_prompt}]
|
|
content.extend([{"type": "image_url", "url": frame} for frame in frames])
|
|
msg_prompt.messages = [{"role": "user", "content": content}]
|
|
else:
|
|
msg_prompt.messages = [{"role":"user","content":msg.body}]
|
|
session_topic = msg.get_sender() + "#" + msg.topic
|
|
chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
|
|
if self.enable_thread:
|
|
need_create_thread = False
|
|
if chatsession.openai_thread_id is not None:
|
|
if len(chatsession.openai_thread_id) < 1:
|
|
need_create_thread = True
|
|
else:
|
|
need_create_thread = True
|
|
|
|
if need_create_thread:
|
|
openai_thread_id = await self._create_openai_thread()
|
|
if openai_thread_id is not None:
|
|
chatsession.update_openai_thread_id(openai_thread_id)
|
|
|
|
|
|
workspace = self.get_workspace_by_msg(msg)
|
|
|
|
prompt = AgentPrompt()
|
|
if workspace:
|
|
prompt.append(workspace.get_prompt())
|
|
prompt.append(workspace.get_role_prompt(self.agent_id))
|
|
|
|
prompt.append(self.get_agent_prompt())
|
|
prompt.append(self._get_remote_user_prompt(msg.sender))
|
|
self._format_msg_by_env_value(prompt)
|
|
|
|
if self.need_session_summmary(msg,chatsession):
|
|
# get relate session(todos) summary
|
|
summary = self.llm_select_session_summary(msg,chatsession)
|
|
prompt.append(AgentPrompt(summary))
|
|
|
|
known_info_str = "# Known information\n"
|
|
have_known_info = False
|
|
todos_str,todo_count = await workspace.get_todo_tree()
|
|
if todo_count > 0:
|
|
have_known_info = True
|
|
known_info_str += f"## todo\n{todos_str}\n"
|
|
inner_functions,function_token_len = BaseAIAgent.get_inner_functions(self.owner_env)
|
|
system_prompt_len = prompt.get_prompt_token_len()
|
|
input_len = len(msg.body)
|
|
if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
|
|
history_str,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len)
|
|
else:
|
|
history_str,history_token_len = await self.get_prompt_from_session(chatsession,system_prompt_len + function_token_len,input_len)
|
|
if history_str:
|
|
have_known_info = True
|
|
known_info_str += history_str
|
|
|
|
if have_known_info:
|
|
known_info_prompt = AgentPrompt(known_info_str)
|
|
prompt.append(known_info_prompt) # chat context
|
|
|
|
prompt.append(msg_prompt)
|
|
|
|
|
|
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 = await self.do_llm_complection(prompt,msg, env=self.owner_env,inner_functions=inner_functions)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
error_resp = msg.create_error_resp(task_result.error_str)
|
|
return error_resp
|
|
|
|
final_result = task_result.result_str
|
|
if final_result is not None:
|
|
llm_result : LLMResult = LLMResult.from_str(final_result)
|
|
else:
|
|
llm_result = LLMResult()
|
|
llm_result.state = "ignore"
|
|
|
|
if llm_result.resp is None:
|
|
if llm_result.raw_resp:
|
|
final_result = json.dumps(llm_result.raw_resp)
|
|
else:
|
|
final_result = llm_result.resp
|
|
|
|
|
|
await workspace.exec_op_list(llm_result.op_list,self.agent_id)
|
|
|
|
is_ignore = False
|
|
result_prompt_str = ""
|
|
match llm_result.state:
|
|
case "ignore":
|
|
is_ignore = True
|
|
case "waiting": # like inner call
|
|
for sendmsg in llm_result.send_msgs:
|
|
sendmsg.sender = self.agent_id
|
|
target = sendmsg.target
|
|
sendmsg.topic = msg.topic
|
|
sendmsg.prev_msg_id = msg.get_msg_id()
|
|
send_resp = await AIBus.get_default_bus().send_message(sendmsg)
|
|
if send_resp is not None:
|
|
result_prompt_str += f"\n{target} response is :{send_resp.body}"
|
|
agent_sesion = AIChatSession.get_session(self.agent_id,f"{sendmsg.target}#{sendmsg.topic}",self.chat_db)
|
|
agent_sesion.append(sendmsg)
|
|
agent_sesion.append(send_resp)
|
|
|
|
final_result = llm_result.resp + result_prompt_str
|
|
|
|
if is_ignore is not True:
|
|
if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
|
|
resp_msg = msg.create_group_resp_msg(self.agent_id,final_result)
|
|
else:
|
|
resp_msg = msg.create_resp_msg(final_result)
|
|
chatsession.append(msg)
|
|
chatsession.append(resp_msg)
|
|
|
|
return resp_msg
|
|
|
|
return None
|
|
|
|
|
|
|
|
async def _get_history_prompt_for_think(self,chatsession:AIChatSession,summary:str,system_token_len:int,pos:int)->(AgentPrompt,int):
|
|
|
|
history_len = (self.max_token_size * 0.7) - system_token_len
|
|
|
|
messages = chatsession.read_history(self.history_len,pos,"natural") # read
|
|
result_token_len = 0
|
|
result_prompt = AgentPrompt()
|
|
have_summary = False
|
|
if summary is not None:
|
|
if len(summary) > 1:
|
|
have_summary = True
|
|
|
|
if have_summary:
|
|
result_prompt.messages.append({"role":"user","content":summary})
|
|
result_token_len -= len(summary)
|
|
else:
|
|
result_prompt.messages.append({"role":"user","content":"There is no summary yet."})
|
|
result_token_len -= 6
|
|
|
|
read_history_msg = 0
|
|
history_str : str = ""
|
|
for msg in messages:
|
|
read_history_msg += 1
|
|
dt = datetime.datetime.fromtimestamp(float(msg.create_time))
|
|
formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
|
|
record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
|
|
history_str = history_str + record_str
|
|
|
|
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
|
|
|
|
result_prompt.messages.append({"role":"user","content":history_str})
|
|
return result_prompt,pos+read_history_msg
|
|
|
|
async def _get_prompt_from_session_for_groupchat(self,chatsession:AIChatSession,system_token_len,input_token_len,is_groupchat=False):
|
|
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
|
|
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":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 _llm_summary_work(self,workspace:WorkspaceEnvironment):
|
|
# read report ,and update work summary of
|
|
# build todo list from work summary and goals
|
|
#
|
|
report_list = self.get_unread_reports()
|
|
|
|
for report in report_list:
|
|
if self.agent_energy <= 0:
|
|
break
|
|
# merge report to work summary
|
|
await self._llm_read_report(report,workspace)
|
|
self.agent_energy -= 1
|
|
|
|
if workspace.is_mgr(self.agent_id):
|
|
# manager can do more work
|
|
await self._llm_review_team(workspace)
|
|
self.agent_energy -= 5
|
|
await self._llm_review_unassigned_todos(workspace)
|
|
self.agent_energy -= 5
|
|
|
|
|
|
async def _llm_review_team(self,workspace:WorkspaceEnvironment):
|
|
pass
|
|
|
|
async def _llm_review_unassigned_todos(self,workspace:WorkspaceEnvironment):
|
|
pass
|
|
|
|
async def _llm_read_report(self,report:AgentReport,worksapce:WorkspaceEnvironment):
|
|
work_summary = worksapce.get_work_summary(self.agent_id)
|
|
prompt : AgentPrompt = AgentPrompt()
|
|
prompt.append(self.agent_prompt)
|
|
prompt.append(worksapce.get_role_prompt(self.agent_id))
|
|
prompt.append(self.read_report_prompt)
|
|
# report is a message from other agent(human) about work
|
|
prompt.append(AgentPrompt(work_summary))
|
|
prompt.append(AgentPrompt(report.content))
|
|
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
|
|
|
|
if task_result.error_str is not None:
|
|
logger.error(f"_llm_read_report compute error:{task_result.error_str}")
|
|
return
|
|
|
|
worksapce.set_work_summary(self.agent_id,task_result.result_str)
|
|
|
|
|
|
# 尝试完成自己的TOOD （不依赖任何其他Agnet）
|
|
async def do_my_work(self) -> None:
|
|
workspace : WorkspaceEnvironment = self.get_workspace_by_msg(None)
|
|
logger.info(f"agent {self.agent_id} do my work start!")
|
|
|
|
# review todolist
|
|
#if await self.need_review_todolist():
|
|
# await self._llm_review_todolist(workspace)
|
|
|
|
todo_list = await workspace.get_todo_list(self.agent_id)
|
|
check_count = 0
|
|
do_count = 0
|
|
|
|
for todo in todo_list:
|
|
if self.agent_energy <= 0:
|
|
break
|
|
|
|
if await self.need_review_todo(todo,workspace):
|
|
review_result = await self._llm_review_todo(todo,workspace)
|
|
todo.last_review_time = datetime.datetime.now().timestamp()
|
|
|
|
elif await self.can_check(todo,workspace):
|
|
check_result : AgentTodoResult = await self._llm_check_todo(todo,workspace)
|
|
todo.last_check_time = datetime.datetime.now().timestamp()
|
|
|
|
match check_result.result_code:
|
|
case AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR:
|
|
continue
|
|
case AgentTodoResult.TODO_RESULT_CODE_OK:
|
|
await workspace.update_todo(todo.todo_id,AgentTodo.TODO_STATE_DONE)
|
|
case AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR:
|
|
await workspace.update_todo(todo.todo_id,AgentTodo.TDDO_STATE_CHECKFAILED)
|
|
|
|
await workspace.append_worklog(todo,check_result)
|
|
self.agent_energy -= 1
|
|
check_count += 1
|
|
elif await self.can_do(todo,workspace):
|
|
do_result : AgentTodoResult = await self._llm_do(todo,workspace)
|
|
todo.last_do_time = datetime.datetime.now().timestamp()
|
|
todo.retry_count += 1
|
|
|
|
match do_result.result_code:
|
|
case AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR:
|
|
continue
|
|
case AgentTodoResult.TODO_RESULT_CODE_OK:
|
|
await workspace.update_todo(todo.todo_id,AgentTodo.TODO_STATE_WAITING_CHECK)
|
|
case AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR:
|
|
await workspace.update_todo(todo.todo_id,AgentTodo.TODO_STATE_EXEC_FAILED)
|
|
|
|
await workspace.append_worklog(todo,do_result)
|
|
self.agent_energy -= 2
|
|
do_count += 1
|
|
|
|
logger.info(f"agent {self.agent_id} ,check:{check_count} todo,do:{do_count} todo.")
|
|
|
|
def get_review_todo_prompt(self,todo:AgentTodo) -> AgentPrompt:
|
|
return self.review_todo_prompt
|
|
|
|
async def _llm_review_todo(self,todo:AgentTodo,workspace:WorkspaceEnvironment):
|
|
prompt = AgentPrompt()
|
|
|
|
prompt.append(workspace.get_prompt())
|
|
prompt.append(workspace.get_role_prompt(self.agent_id))
|
|
prompt.append(self.get_review_todo_prompt(todo))
|
|
|
|
todo_tree = workspace.get_todo_tree("/")
|
|
prompt.append(AgentPrompt(todo_tree))
|
|
inner_functions,_ = BaseAIAgent.get_inner_functions(self.owner_env)
|
|
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
logger.error(f"_llm_review_todos compute error:{task_result.error_str}")
|
|
return
|
|
|
|
return
|
|
|
|
def get_do_prompt(self,todo:AgentTodo) -> AgentPrompt:
|
|
return self.do_prompt
|
|
|
|
def get_prompt_from_todo(self,todo:AgentTodo) -> AgentPrompt:
|
|
json_str = json.dumps(todo.raw_obj)
|
|
return AgentPrompt(json_str)
|
|
|
|
async def need_review_todo(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> bool:
|
|
return False
|
|
|
|
async def can_check(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> bool:
|
|
if self.get_check_prompt(todo) is None:
|
|
return False
|
|
|
|
if todo.can_check() is False:
|
|
return False
|
|
|
|
if todo.checker is not None:
|
|
if todo.checker != self.agent_id:
|
|
return False
|
|
else:
|
|
if self.can_do_unassigned_task is False:
|
|
return False
|
|
else:
|
|
todo.checker = self.agent_id
|
|
|
|
return True
|
|
|
|
async def can_do(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> bool:
|
|
if todo.can_do() is False:
|
|
return False
|
|
|
|
if todo.worker is not None:
|
|
if todo.worker != self.agent_id:
|
|
return False
|
|
else:
|
|
if self.can_do_unassigned_task is False:
|
|
return False
|
|
else:
|
|
todo.worker = self.agent_id
|
|
|
|
return True
|
|
|
|
async def _llm_do(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> AgentTodoResult:
|
|
result = AgentTodoResult()
|
|
prompt : AgentPrompt = AgentPrompt()
|
|
#prompt.append(self.agent_prompt)
|
|
prompt.append(workspace.get_role_prompt(self.agent_id))
|
|
|
|
do_prompt = workspace.get_do_prompt(todo)
|
|
if do_prompt is None:
|
|
do_prompt = self.get_do_prompt(todo)
|
|
|
|
prompt.append(do_prompt)
|
|
|
|
# There are general methods for executing todos, as well as customized ones that are more efficient for specific types of TODOS.
|
|
# Based on experience, an Agent can autonomously master/organize execution methods for a greater variety of TODO types.
|
|
|
|
#prompt.append(work_log_prompt)
|
|
prompt.append(self.get_prompt_from_todo(todo))
|
|
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
|
|
if task_result.error_str is not None:
|
|
logger.error(f"_llm_do compute error:{task_result.error_str}")
|
|
result.result_code = AgentTodoResult.TODO_RESULT_CODE_LLM_ERROR
|
|
result.error_str = task_result.error_str
|
|
return result
|
|
|
|
llm_result = LLMResult.from_str(task_result.result_str)
|
|
# result_str is the explain of how to do this todo
|
|
result.result_str = llm_result.resp
|
|
result.op_list = llm_result.op_list
|
|
if llm_result.post_msgs is not None:
|
|
for msg in llm_result.post_msgs:
|
|
msg.sender = self.agent_id
|
|
msg.topic = f"{todo.title}##{todo.todo_id}"
|
|
#msg.prev_msg_id = todo.todo_id
|
|
chatsession = AIChatSession.get_session(self.agent_id,f"{msg.target}#{msg.topic}",self.chat_db)
|
|
chatsession.append(msg)
|
|
resp = await AIBus.get_default_bus().post_message(msg)
|
|
logging.info(f"agent {self.agent_id} send msg to {msg.target} result:{resp}")
|
|
|
|
op_errors,have_error = await workspace.exec_op_list(llm_result.op_list,self.agent_id)
|
|
if have_error:
|
|
result.result_code = AgentTodoResult.TODO_RESULT_CODE_EXEC_OP_ERROR
|
|
#result.error_str = error_str
|
|
return result
|
|
|
|
return result
|
|
|
|
async def append_toddo_result(self,todo,worksapce,llm_result,result_str):
|
|
pass
|
|
|
|
def get_check_prompt(self,todo:AgentTodo) -> AgentPrompt:
|
|
return self.check_prompt
|
|
|
|
async def _llm_check_todo(self, todo:AgentTodo,workspace:WorkspaceEnvironment) :
|
|
if self.get_check_prompt(todo) is None:
|
|
return None
|
|
|
|
prompt : AgentPrompt = AgentPrompt()
|
|
prompt.append(self.agent_prompt)
|
|
prompt.append(workspace.get_role_prompt(self.agent_id))
|
|
prompt.append(self.get_check_prompt(todo))
|
|
if todo.last_check_result:
|
|
prompt.append(AgentPrompt(todo.last_check_result))
|
|
|
|
prompt.append(todo.detail)
|
|
prompt.append(todo.result)
|
|
|
|
inner_functions,_ = BaseAIAgent.get_inner_functions(workspace)
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,inner_functions=inner_functions,is_json_resp=True)
|
|
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
logger.error(f"_llm_check_todo compute error:{task_result.error_str}")
|
|
return False
|
|
|
|
if task_result.result_str == "OK":
|
|
return True
|
|
todo.last_check_result = task_result.result_str
|
|
return False
|
|
|
|
# 尝试自我学习，会主动获取、读取资料并进行整理
|
|
# LLM的本质能力是处理海量知识，应该让LLM能基于知识把自己的工作处理的更好
|
|
async def do_self_learn(self) -> None:
|
|
# 不同的workspace是否应该有不同的学习方法？
|
|
workspace = self.get_workspace_by_msg(None)
|
|
hash_list = workspace.kb_db.get_knowledge_without_llm_title()
|
|
for hash in hash_list:
|
|
if self.agent_energy <= 0:
|
|
break
|
|
|
|
knowledge = workspace.kb_db.get_knowledge(hash)
|
|
if knowledge is None:
|
|
continue
|
|
|
|
full_path = knowledge.get("full_path")
|
|
if full_path is None:
|
|
continue
|
|
|
|
if os.path.exists(full_path) is False:
|
|
logger.warning(f"do_self_learn: knowledge {full_path} is not exists!")
|
|
continue
|
|
|
|
#TODO 可以用v-db 对不同目录的名字进行选择后，先进行一次快速的插入。有时间再慢慢用LLM整理
|
|
result_obj = await self._llm_read_article(knowledge,full_path)
|
|
|
|
#根据结果更新knowledge
|
|
if result_obj is not None:
|
|
workspace.kb_db.set_knowledge_llm_result(hash,result_obj)
|
|
# 在知识库中创建软链接
|
|
path_list = result_obj.get("path")
|
|
new_title = result_obj.get("title")
|
|
if path_list:
|
|
for new_path in path_list:
|
|
full_new_path = f"/knowledge{new_path}/{new_title}"
|
|
await workspace.symlink(full_path,full_new_path)
|
|
logger.info(f"create soft link {full_path} -> {full_new_path}")
|
|
|
|
|
|
self.agent_energy -= 1
|
|
|
|
# match item.type():
|
|
# case "book":
|
|
# self.llm_read_book(kb,item)
|
|
# learn_power -= 1
|
|
# case "article":
|
|
#
|
|
# self.llm_read_article(kb,item)
|
|
# learn_power -= 1
|
|
# case "video":
|
|
# self.llm_watch_video(kb,item)
|
|
# learn_power -= 1
|
|
# case "audio":
|
|
# self.llm_listen_audio(kb,item)
|
|
# learn_power -= 1
|
|
# case "code_project":
|
|
# self.llm_read_code_project(kb,item)
|
|
# learn_power -= 1
|
|
# case "image":
|
|
# self.llm_view_image(kb,item)
|
|
# learn_power -= 1
|
|
# case "other":
|
|
# self.llm_read_other(kb,item)
|
|
# learn_power -= 1
|
|
# case _:
|
|
# self.llm_learn_any(kb,item)
|
|
# pass
|
|
|
|
|
|
async def do_blance_knowledge_base(selft):
|
|
# 整理自己的知识库(让分类更平衡，更由于自己以后的工作)，并尝试更新学习目标
|
|
current_path = "/"
|
|
current_list = kb.get_list(current_path)
|
|
self_assessment_with_goal = self.get_self_assessment_with_goal()
|
|
learn_goal = {}
|
|
|
|
|
|
llm_blance_knowledge_base(current_path,current_list,self_assessment_with_goal,learn_goal,learn_power)
|
|
|
|
# 主动学习
|
|
# 方法目前只有使用搜索引擎一种？
|
|
for goal in learn_goal.items():
|
|
self.llm_learn_with_search_engine(kb,goal,learn_power)
|
|
if learn_power <= 0:
|
|
break
|
|
|
|
|
|
def parser_learn_llm_result(self,llm_result:LLMResult):
|
|
pass
|
|
|
|
async def gen_known_info_for_knowledge_prompt(self,knowledge_item:dict,temp_meta = None,need_catalogs = False) -> AgentPrompt:
|
|
workspace =self.get_workspace_by_msg(None)
|
|
kb_tree = await workspace.get_knowledege_catalog()
|
|
|
|
|
|
known_obj = {}
|
|
title = knowledge_item.get("title")
|
|
if title:
|
|
known_obj["title"] = title
|
|
summary = knowledge_item.get("summary")
|
|
if summary:
|
|
known_obj["summary"] = summary
|
|
tags = knowledge_item.get("tags")
|
|
if tags:
|
|
known_obj["tags"] = tags
|
|
if need_catalogs:
|
|
catalogs = knowledge_item.get("catalogs")
|
|
if catalogs:
|
|
known_obj["catalogs"] = catalogs
|
|
|
|
if temp_meta:
|
|
for key in temp_meta.keys():
|
|
known_obj[key] = temp_meta[key]
|
|
|
|
org_path = knowledge_item.get("full_path")
|
|
known_obj["orginal_path"] = org_path
|
|
know_info_str = f"# Known information:\n## Current directory structure:\n{kb_tree}\n## Knowlege Metadata:\n{json.dumps(known_obj)}\n"
|
|
return AgentPrompt(know_info_str)
|
|
|
|
async def _llm_read_article(self,knowledge_item:dict,full_path:str) -> ComputeTaskResult:
|
|
# Objectives:
|
|
# Obtain better titles, abstracts, table of contents (if necessary), tags
|
|
# Determine the appropriate place to put it (in line with the organization's goals)
|
|
# Known information:
|
|
# The reason why the target service's learn_prompt is being sorted
|
|
# Summary of the organization's work (if any)
|
|
# The current structure of the knowledge base (note the size control) gen_kb_tree_prompt (when empty, LLM should generate an appropriate initial directory structure)
|
|
# Original path, current title, abstract, table of contents
|
|
|
|
# Sorting long files (general tricks)
|
|
# Indicate that the input is part of the content, let LLM generate intermediate results for the task
|
|
# Enter the content in sequence, when the last content block is input, LLM gets the result
|
|
|
|
|
|
#full_content = item.get_article_full_content()
|
|
workspace = self.get_workspace_by_msg(None)
|
|
full_content_len = self.token_len(full_content)
|
|
|
|
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()
|
|
prompt = AgentPrompt()
|
|
prompt.append(self.get_learn_prompt())
|
|
known_info_prompt = await self.gen_known_info_for_knowledge_prompt(knowledge_item)
|
|
prompt.append(known_info_prompt)
|
|
content_prompt = AgentPrompt(full_content)
|
|
prompt.append(content_prompt)
|
|
env_functions = None
|
|
#env_functions,function_len = workspace.get_knowledge_base_ai_functions()
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,is_json_resp=True)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
result_obj = {}
|
|
result_obj["error_str"] = task_result.error_str
|
|
return result_obj
|
|
|
|
result_obj = json.loads(task_result.result_str)
|
|
return result_obj
|
|
|
|
else:
|
|
logger.warning(f"llm_read_article: article {full_path} use LLM loop learn!")
|
|
pos = 0
|
|
read_len = int(self.get_llm_learn_token_limit() * 1.2)
|
|
|
|
temp_meta_data = {}
|
|
is_final = False
|
|
while pos < str_len:
|
|
_content = full_content[pos:pos+read_len]
|
|
part_cotent_len = len(_content)
|
|
if part_cotent_len < read_len:
|
|
# last chunk
|
|
is_final = True
|
|
part_content = f"<<Final Part:start at {pos}>>\n{_content}"
|
|
else:
|
|
part_content = f"<<Part:start at {pos}>>\n{_content}"
|
|
|
|
pos = pos + read_len
|
|
prompt = AgentPrompt()
|
|
prompt.append(self.get_learn_prompt())
|
|
known_info_prompt = await self.gen_known_info_for_knowledge_prompt(knowledge_item,temp_meta_data)
|
|
prompt.append(known_info_prompt)
|
|
content_prompt = AgentPrompt(part_content)
|
|
prompt.append(content_prompt)
|
|
#env_functions,function_len = workspace.get_knowledge_base_ai_functions()
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt,is_json_resp=True)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
result_obj = {}
|
|
result_obj["error_str"] = task_result.error_str
|
|
return result_obj
|
|
|
|
result_obj = json.loads(task_result.result_str)
|
|
temp_meta_data = result_obj
|
|
if is_final:
|
|
return result_obj
|
|
|
|
return None
|
|
|
|
|
|
async def do_self_think(self):
|
|
session_id_list = AIChatSession.list_session(self.agent_id,self.chat_db)
|
|
for session_id in session_id_list:
|
|
if self.agent_energy <= 0:
|
|
break
|
|
used_energy = await self.think_chatsession(session_id)
|
|
self.agent_energy -= used_energy
|
|
|
|
todo_logs = await self.get_todo_logs()
|
|
for todo_log in todo_logs:
|
|
if self.agent_energy <= 0:
|
|
break
|
|
used_energy = await self.think_todo_log(todo_log)
|
|
self.agent_energy -= used_energy
|
|
|
|
return
|
|
|
|
|
|
async def think_todo_log(self,todo_log:AgentWorkLog):
|
|
pass
|
|
|
|
async def think_chatsession(self,session_id):
|
|
if self.agent_think_prompt is None:
|
|
return
|
|
logger.info(f"agent {self.agent_id} think session {session_id}")
|
|
chatsession = AIChatSession.get_session_by_id(session_id,self.chat_db)
|
|
|
|
while True:
|
|
cur_pos = chatsession.summarize_pos
|
|
summary = chatsession.summary
|
|
prompt:AgentPrompt = AgentPrompt()
|
|
#prompt.append(self._get_agent_prompt())
|
|
prompt.append(await self._get_agent_think_prompt())
|
|
system_prompt_len = prompt.get_prompt_token_len()
|
|
#think env?
|
|
history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
|
|
prompt.append(history_prompt)
|
|
is_finish = next_pos - cur_pos < 2
|
|
if is_finish:
|
|
logger.info(f"agent {self.agent_id} think session {session_id} is finished!,no more history")
|
|
break
|
|
#3) llm summarize chat history
|
|
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
|
|
if task_result.result_code != ComputeTaskResultCode.OK:
|
|
logger.error(f"think_chatsession llm compute error:{task_result.error_str}")
|
|
break
|
|
else:
|
|
new_summary= task_result.result_str
|
|
logger.info(f"agent {self.agent_id} think session {session_id} from {cur_pos} to {next_pos} summary:{new_summary}")
|
|
chatsession.update_think_progress(next_pos,new_summary)
|
|
return
|
|
|
|
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
|
|
if self.enable_thread:
|
|
return None
|
|
|
|
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
|
|
|
|
read_history_msg = 0
|
|
have_known_info = False
|
|
|
|
known_info = ""
|
|
if chatsession.summary is not None:
|
|
if len(chatsession.summary) > 1:
|
|
known_info += f"## Recent conversation summary \n {chatsession.summary}\n"
|
|
result_token_len -= len(chatsession.summary)
|
|
have_known_info = True
|
|
|
|
histroy_str = ""
|
|
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')
|
|
record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
|
|
have_known_info = True
|
|
histroy_str = histroy_str + record_str
|
|
|
|
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
|
|
|
|
known_info += f"## Recent conversation history \n {histroy_str}\n"
|
|
|
|
if have_known_info:
|
|
return known_info,result_token_len
|
|
return None,0
|
|
|
|
|
|
def need_work(self) -> bool:
|
|
if self.do_prompt is not None:
|
|
return True
|
|
if self.check_prompt is not None:
|
|
return True
|
|
|
|
if self.agent_energy > 2:
|
|
return True
|
|
|
|
return False
|
|
|
|
def need_self_think(self) -> bool:
|
|
return False
|
|
|
|
def need_self_learn(self) -> bool:
|
|
if self.learn_prompt is not None:
|
|
return True
|
|
return False
|
|
|
|
def wake_up(self) -> None:
|
|
if self.agent_task is None:
|
|
self.agent_task = asyncio.create_task(self._on_timer())
|
|
else:
|
|
logger.warning(f"agent {self.agent_id} is already wake up!")
|
|
|
|
# agent loop
|
|
async def _on_timer(self):
|
|
while True:
|
|
await asyncio.sleep(15)
|
|
try:
|
|
now = time.time()
|
|
if self.last_recover_time is None:
|
|
self.last_recover_time = now
|
|
else:
|
|
if now - self.last_recover_time > 60:
|
|
self.agent_energy += (now - self.last_recover_time) / 60
|
|
self.last_recover_time = now
|
|
|
|
if self.agent_energy <= 1:
|
|
continue
|
|
|
|
# complete & check todo
|
|
if self.need_work():
|
|
await self.do_my_work()
|
|
|
|
# review other's todo
|
|
# self.review_other_works()
|
|
|
|
if self.need_self_think():
|
|
await self.do_self_think()
|
|
|
|
if self.need_self_learn():
|
|
await self.do_self_learn()
|
|
|
|
except Exception as e:
|
|
tb_str = traceback.format_exc()
|
|
logger.error(f"agent {self.agent_id} on timer error:{e},{tb_str}")
|
|
continue
|
|
|
|
def token_len(self,text:str) -> int:
|
|
return ComputeKernel.llm_num_tokens_from_text(text,self.get_llm_model_name())
|
|
|
|
|
|
|
|
|
|
|