diff --git a/src/aios_kernel/agent.py b/src/aios_kernel/agent.py index 133759e..82c9eb0 100644 --- a/src/aios_kernel/agent.py +++ b/src/aios_kernel/agent.py @@ -12,7 +12,8 @@ import datetime import copy import sys -from .agent_base import AgentMsg, AgentMsgStatus, AgentMsgType,FunctionItem,LLMResult,AgentPrompt,AgentReport,AgentTodo,AgentTodoResult,AgentWorkLog +from .agent_base import AgentMsg, AgentMsgStatus, AgentMsgType, FunctionItem, LLMResult, AgentPrompt, AgentReport, \ + AgentTodo, AgentTodoResult, AgentWorkLog, BaseAIAgent from .chatsession import AIChatSession from .compute_task import ComputeTaskResult,ComputeTaskResultCode from .ai_function import AIFunction @@ -115,7 +116,7 @@ class AIAgentTemplete: return True -class AIAgent: +class AIAgent(BaseAIAgent): def __init__(self) -> None: self.role_prompt:AgentPrompt = None self.agent_prompt:AgentPrompt = None @@ -127,7 +128,7 @@ class AIAgent: self.last_recover_time = time.time() self.enable_thread = False self.can_do_unassigned_task = True - + self.agent_id:str = None self.template_id:str = None @@ -136,7 +137,7 @@ class AIAgent: self.enable = True self.enable_kb = False self.enable_timestamp = False - self.guest_prompt_str = None + self.guest_prompt_str = None self.owner_promp_str = None self.contact_prompt_str = None self.history_len = 10 @@ -158,7 +159,7 @@ class AIAgent: self.owner_env : Environment = None self.owenr_bus = None self.enable_function_list = None - + @classmethod def create_from_templete(cls,templete:AIAgentTemplete, fullname:str): @@ -191,7 +192,7 @@ class AIAgent: if config.get("prompt") is not None: self.agent_prompt = AgentPrompt() self.agent_prompt.load_from_config(config["prompt"]) - + if config.get("think_prompt") is not None: self.agent_think_prompt = AgentPrompt() self.agent_think_prompt.load_from_config(config["think_prompt"]) @@ -206,13 +207,13 @@ class AIAgent: if config.get("owner_prompt") is not None: self.owner_promp_str = config["owner_prompt"] - + if config.get("contact_prompt") is not None: self.contact_prompt_str = config["contact_prompt"] if config.get("owner_env") is not None: self.owner_env = config.get("owner_env") - + if config.get("powerby") is not None: self.powerby = config["powerby"] @@ -231,7 +232,7 @@ class AIAgent: if config.get("history_len"): self.history_len = int(config.get("history_len")) return True - + def get_id(self) -> str: return self.agent_id @@ -244,22 +245,22 @@ class AIAgent: def get_llm_model_name(self) -> str: if self.llm_model_name is None: return AIStorage.get_instance().get_user_config().get_value("llm_model_name") - + return self.llm_model_name def get_max_token_size(self) -> int: return self.max_token_size - + def get_llm_learn_token_limit(self) -> int: return self.learn_token_limit - + def get_learn_prompt(self) -> AgentPrompt: return self.learn_prompt - + def get_agent_role_prompt(self) -> AgentPrompt: return self.role_prompt - + def _get_remote_user_prompt(self,remote_user:str) -> AgentPrompt: cm = ContactManager.get_instance() contact = cm.find_contact_by_name(remote_user) @@ -283,7 +284,7 @@ class AIAgent: prompt = AgentPrompt() prompt.system_message = {"role":"system","content":real_str} return prompt - + return None def _get_inner_functions(self) -> dict: @@ -333,13 +334,13 @@ class AIAgent: logger.info("llm execute inner func result:" + result_str) - + prompt.messages.append({"role":"function","content":result_str,"name":func_name}) 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"_execute_func llm compute error:{task_result.error_str}") return task_result - + ineternal_call_record.result_str = task_result.result_str ineternal_call_record.done_time = time.time() if org_msg: @@ -355,10 +356,10 @@ class AIAgent: return await self._execute_func(inner_func_call_node,prompt,org_msg,stack_limit-1) else: return task_result - + def get_agent_prompt(self) -> AgentPrompt: return self.agent_prompt - + async def _get_agent_think_prompt(self) -> AgentPrompt: return self.agent_think_prompt @@ -373,11 +374,11 @@ class AIAgent: async def _handle_event(self,event): if event.type == "AgentThink": return await self.do_self_think() - - # async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg: + + # async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg: # session_topic = msg.target + "#" + msg.topic # chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db) # workspace = self.get_current_workspace() @@ -409,7 +410,7 @@ class AIAgent: # self._format_msg_by_env_value(prompt) # inner_functions,function_token_len = self._get_inner_functions() - + # system_prompt_len = prompt.get_prompt_token_len() # input_len = len(msg.body) @@ -422,7 +423,7 @@ class AIAgent: # 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 # llm_result : LLMResult = LLMResult.from_str(final_result) # is_ignore = False @@ -455,12 +456,12 @@ class AIAgent: # return None def get_workspace_by_msg(self,msg:AgentMsg) -> WorkspaceEnvironment: return self.agent_workspace - + def need_session_summmary(self,msg:AgentMsg,session:AIChatSession) -> bool: return False - + async def _create_openai_thread(self) -> str: - return None + return None async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg: msg_prompt = AgentPrompt() @@ -474,7 +475,7 @@ class AIAgent: 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,"") @@ -490,7 +491,7 @@ class AIAgent: 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: @@ -507,12 +508,12 @@ class AIAgent: 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() @@ -525,25 +526,25 @@ class AIAgent: 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) + 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:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions) task_result = await self._do_llm_complection(prompt,inner_functions,msg) 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) @@ -593,11 +594,11 @@ class AIAgent: 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() @@ -605,7 +606,7 @@ class AIAgent: 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) @@ -627,10 +628,10 @@ class AIAgent: 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 @@ -647,7 +648,7 @@ class AIAgent: 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} "}) @@ -665,11 +666,11 @@ class AIAgent: async def _llm_summary_work(self,workspace:WorkspaceEnvironment): - # read report ,and update work summary of + # 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 @@ -690,7 +691,7 @@ class AIAgent: 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() @@ -709,7 +710,7 @@ class AIAgent: 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) @@ -722,7 +723,7 @@ class AIAgent: 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 @@ -739,7 +740,7 @@ class AIAgent: 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) + 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) @@ -747,7 +748,7 @@ class AIAgent: self.agent_energy -= 1 check_count += 1 elif await self.can_do(todo,workspace): - do_result : AgentTodoResult = await self._llm_do(todo,workspace) + do_result : AgentTodoResult = await self._llm_do(todo,workspace) todo.last_do_time = datetime.datetime.now().timestamp() todo.retry_count += 1 @@ -755,14 +756,14 @@ class AIAgent: 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) + 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) + 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: @@ -783,50 +784,50 @@ class AIAgent: if task_result.result_code != ComputeTaskResultCode.OK: logger.error(f"_llm_review_todos compute error:{task_result.error_str}") return - - 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: + 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 - + else: + todo.worker = self.agent_id + return True async def _llm_do(self,todo:AgentTodo,workspace:WorkspaceEnvironment) -> AgentTodoResult: @@ -834,7 +835,7 @@ class AIAgent: 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) @@ -853,7 +854,7 @@ class AIAgent: 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 @@ -873,19 +874,19 @@ class AIAgent: 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)) @@ -906,11 +907,11 @@ class AIAgent: 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？ + # 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: @@ -952,7 +953,7 @@ class AIAgent: # self.llm_read_book(kb,item) # learn_power -= 1 # case "article": - # + # # self.llm_read_article(kb,item) # learn_power -= 1 # case "video": @@ -981,8 +982,8 @@ class AIAgent: 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 @@ -991,7 +992,7 @@ class AIAgent: 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 @@ -1010,13 +1011,13 @@ class AIAgent: known_obj["summary"] = summary tags = knowledge_item.get("tags") if tags: - known_obj["tags"] = tags + known_obj["tags"] = tags if need_catalogs: catalogs = knowledge_item.get("catalogs") if catalogs: known_obj["catalogs"] = catalogs - if temp_meta: + if temp_meta: for key in temp_meta.keys(): known_obj[key] = temp_meta[key] @@ -1029,7 +1030,7 @@ class AIAgent: # 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: + # 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) @@ -1039,26 +1040,13 @@ class AIAgent: # 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) - try: - full_content = await workspace.load_knowledge_content(full_path) - if full_content is None: - return None - except Exception as e: - logger.error(f"llm_read_article: load knowledge {full_path} error:{e}") - return None - - if len(full_content) < 16: - logger.warning(f"llm_read_article: article {knowledge_item['path']} is too short,just read summary!") - return None - - str_len = len(full_content) 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() @@ -1075,7 +1063,7 @@ class AIAgent: result_obj = {} result_obj["error_str"] = task_result.error_str return result_obj - + result_obj = json.loads(task_result.result_str) return result_obj @@ -1110,14 +1098,14 @@ class AIAgent: 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) @@ -1134,8 +1122,8 @@ class AIAgent: used_energy = await self.think_todo_log(todo_log) self.agent_energy -= used_energy - return - + return + async def think_todo_log(self,todo_log:AgentWorkLog): pass @@ -1168,24 +1156,24 @@ class AIAgent: 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 - + 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: + 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 @@ -1204,13 +1192,13 @@ class AIAgent: 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 - + async def _do_llm_complection(self,prompt:AgentPrompt,inner_functions:dict=None,org_msg:AgentMsg=None,is_json_resp = False) -> 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} ") @@ -1231,28 +1219,28 @@ class AIAgent: 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 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 - + return False + def wake_up(self) -> None: if self.agent_task is None: self.agent_task = asyncio.create_task(self._on_timer()) @@ -1296,7 +1284,7 @@ class AIAgent: def token_len(self,text:str) -> int: return ComputeKernel.llm_num_tokens_from_text(text,self.get_llm_model_name()) - - - - + + + + diff --git a/src/aios_kernel/agent_base.py b/src/aios_kernel/agent_base.py index 9ecc604..95b5e9e 100644 --- a/src/aios_kernel/agent_base.py +++ b/src/aios_kernel/agent_base.py @@ -1,13 +1,16 @@ +import abc import copy +from abc import abstractmethod from datetime import datetime, timedelta import logging from enum import Enum import uuid -import time +import time import re import shlex import json from typing import List + from .ai_function import FunctionItem from .compute_task import ComputeTaskResult @@ -47,9 +50,9 @@ class AgentMsg: def __init__(self,msg_type=AgentMsgType.TYPE_MSG) -> None: self.msg_id = "msg#" + uuid.uuid4().hex self.msg_type:AgentMsgType = msg_type - + self.prev_msg_id:str = None - self.quote_msg_id:str = None + self.quote_msg_id:str = None self.rely_msg_id:str = None # if not none means this is a respone msg self.session_id:str = None @@ -68,7 +71,7 @@ class AgentMsg: self.body_mime:str = None #//default is "text/plain",encode is utf8 #type is call / action - self.func_name = None + self.func_name = None self.args = None self.result_str = None @@ -95,7 +98,7 @@ class AgentMsg: msg.prev_msg_id = prev_msg_id msg.sender = caller return msg - + def create_action_msg(self,action_name:str,args:dict,caller:str): msg = AgentMsg(AgentMsgType.TYPE_ACTION) msg.create_time = time.time() @@ -105,11 +108,11 @@ class AgentMsg: msg.topic = self.topic msg.sender = caller return msg - + def create_error_resp(self,error_msg:str): resp_msg = AgentMsg(AgentMsgType.TYPE_SYSTEM) resp_msg.create_time = time.time() - + resp_msg.rely_msg_id = self.msg_id resp_msg.body = error_msg resp_msg.topic = self.topic @@ -129,7 +132,7 @@ class AgentMsg: resp_msg.topic = self.topic return resp_msg - + def create_group_resp_msg(self,sender_id,resp_body): resp_msg = AgentMsg(AgentMsgType.TYPE_GROUPMSG) resp_msg.create_time = time.time() @@ -158,13 +161,13 @@ class AgentMsg: def get_target(self) -> str: return self.target - + def get_prev_msg_id(self) -> str: return self.prev_msg_id - + def get_quote_msg_id(self) -> str: return self.quote_msg_id - + @classmethod def parse_function_call(cls,func_string:str): str_list = shlex.split(func_string) @@ -237,9 +240,9 @@ class LLMResult: def __init__(self) -> None: self.state : str = "ignore" self.resp : str = "" - self.raw_resp = None + self.raw_resp = None self.paragraphs : dict[str,FunctionItem] = [] - + self.post_msgs : List[AgentMsg] = [] self.send_msgs : List[AgentMsg] = [] @@ -281,14 +284,14 @@ class LLMResult: @classmethod def from_str(self,llm_result_str:str,valid_func:List[str]=None) -> 'LLMResult': r = LLMResult() - + if llm_result_str is None: r.state = "ignore" return r if llm_result_str == "ignore": r.state = "ignore" return r - + if llm_result_str[0] == "{": return LLMResult.from_json_str(llm_result_str) @@ -300,7 +303,7 @@ class LLMResult: case "send_msg":# /send_msg $target_id if len(func_args) != 1: return False - + new_msg = AgentMsg() target_id = func_item.args[0] msg_content = func_item.body @@ -313,7 +316,7 @@ class LLMResult: case "post_msg":# /post_msg $target_id if len(func_args) != 1: return False - + new_msg = AgentMsg() target_id = func_item.args[0] msg_content = func_item.body @@ -333,9 +336,9 @@ class LLMResult: if func_name in valid_func: r.paragraphs[func_name] = func_item return True - + return False - + current_func : FunctionItem = None for line in lines: @@ -361,11 +364,11 @@ class LLMResult: else: r.state = "reponsed" - return r + return r class AgentReport: def __init__(self): - pass + pass class AgentTodoResult: TODO_RESULT_CODE_OK = 0, @@ -386,7 +389,7 @@ class AgentTodoResult: result["error_str"] = self.error_str result["op_list"] = self.op_list return result - + class AgentTodo: TODO_STATE_WAIT_ASSIGN = "wait_assign" @@ -400,7 +403,7 @@ class AgentTodo: TODO_STATE_CASNCEL = "cancel" TODO_STATE_DONE = "done" TODO_STATE_EXPIRED = "expired" - + def __init__(self): self.todo_id = "todo#" + uuid.uuid4().hex self.title = None @@ -409,9 +412,9 @@ class AgentTodo: #self.parent = None self.create_time = time.time() - self.state = "wait_assign" - self.worker = None - self.checker = None + self.state = "wait_assign" + self.worker = None + self.checker = None self.createor = None self.need_check = True @@ -433,7 +436,7 @@ class AgentTodo: todo = AgentTodo() if json_obj.get("id") is not None: todo.todo_id = json_obj.get("id") - + todo.title = json_obj.get("title") todo.state = json_obj.get("state") create_time = json_obj.get("create_time") @@ -448,7 +451,7 @@ class AgentTodo: last_do_time = json_obj.get("last_do_time") if last_do_time: todo.last_do_time = datetime.fromisoformat(last_do_time).timestamp() - last_check_time = json_obj.get("last_check_time") + last_check_time = json_obj.get("last_check_time") if last_check_time: todo.last_check_time = datetime.fromisoformat(last_check_time).timestamp() last_review_time = json_obj.get("last_review_time") @@ -492,21 +495,21 @@ class AgentTodo: result["createor"] = self.createor result["retry_count"] = self.retry_count - return result - + return result + def can_check(self)->bool: if self.state != AgentTodo.TODO_STATE_WAITING_CHECK: return False - + now = datetime.now().timestamp() if self.last_check_time: time_diff = now - self.last_check_time if time_diff < 60*15: logger.info(f"todo {self.title} is already checked, ignore") - return False - + return False + return True - + def can_do(self) -> bool: match self.state: case AgentTodo.TODO_STATE_DONE: @@ -522,32 +525,70 @@ class AgentTodo: if self.retry_count > 3: logger.info(f"todo {self.title} retry count ({self.retry_count}) is too many, ignore") return False - + now = datetime.now().timestamp() - time_diff = self.due_date - now + time_diff = self.due_date - now if time_diff < 0: logger.info(f"todo {self.title} is expired, ignore") self.state = AgentTodo.TODO_STATE_EXPIRED return False - + if time_diff > 7*24*3600: logger.info(f"todo {self.title} is far before due date, ignore") return False - + if self.last_do_time: time_diff = now - self.last_do_time if time_diff < 60*15: logger.info(f"todo {self.title} is already do ignore") - return False - + return False + logger.info(f"todo {self.title} can do.") return True - + class AgentWorkLog: def __init__(self) -> None: pass -class BaseAIAgent: - def __init__(self) -> None: - pass \ No newline at end of file + +class BaseAIAgent(abc.ABC): + @abstractmethod + def get_id(self) -> str: + pass + + @abstractmethod + def get_llm_model_name(self) -> str: + pass + + @abstractmethod + def get_max_token_size(self) -> int: + pass + + @abstractmethod + def get_llm_learn_token_limit(self) -> int: + pass + + @abstractmethod + async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg: + pass + + +class CustomAIAgent(BaseAIAgent): + def __init__(self, agent_id: str, llm_model_name: str, max_token_size: int, llm_learn_token_limit: int) -> None: + self.agent_id = agent_id + self.llm_model_name = llm_model_name + self.max_token_size = max_token_size + self.llm_learn_token_limit = llm_learn_token_limit + + def get_id(self) -> str: + return self.agent_id + + def get_llm_model_name(self) -> str: + return self.llm_model_name + + def get_max_token_size(self) -> int: + return self.max_token_size + + def get_llm_learn_token_limit(self) -> int: + return self.llm_learn_token_limit diff --git a/src/component/agent_manager/agent_manager.py b/src/component/agent_manager/agent_manager.py index 9b838fb..8f39f71 100644 --- a/src/component/agent_manager/agent_manager.py +++ b/src/component/agent_manager/agent_manager.py @@ -1,11 +1,13 @@ - +import importlib import logging import toml import os +import sys import runpy from typing import Any, Callable, Dict, List, Optional, Union from aios_kernel import AIAgent,AIAgentTemplete,AIStorage,Environment +from aios_kernel.agent_base import BaseAIAgent from package_manager import PackageEnv,PackageEnvManager,PackageMediaInfo,PackageInstallTask logger = logging.getLogger(__name__) @@ -17,19 +19,19 @@ cache = "./.agents" class AgentManager: _instance = None - + @classmethod def get_instance(cls)->'AgentManager': if cls._instance is None: cls._instance = AgentManager() return cls._instance - + def __init__(self) -> None: self.agent_templete_env : PackageEnv = None self.agent_env : PackageEnv = None - self.db_path : str = None - self.loaded_agent_instance : Dict[str,AIAgent] = None - + self.db_path : str = None + self.loaded_agent_instance : Dict[str,BaseAIAgent] = None + async def initial(self) -> None: system_app_dir = AIStorage.get_instance().get_system_app_dir() user_data_dir = AIStorage.get_instance().get_myai_dir() @@ -43,13 +45,13 @@ class AgentManager: self.db_path = f"{user_data_dir}/messages.db" self.loaded_agent_instance = {} - + return True - + async def scan_all_agent(self)->None: pass - - + + async def is_exist(self,agent_id:str) -> bool: the_aget = await self.get(agent_id) if the_aget: @@ -60,17 +62,17 @@ class AgentManager: the_agent = self.loaded_agent_instance.get(agent_id) if the_agent: return the_agent - + # try load from disk agent_media_info = self.agent_env.load(agent_id) if agent_media_info is None: return None - + the_agent : AIAgent = await self._load_agent_from_media(agent_media_info) if the_agent is None: logger.warn(f"load agent {agent_id} from media failed!") return None - + the_agent.chat_db = self.db_path return the_agent @@ -86,19 +88,19 @@ class AgentManager: def install(self,templete_id) -> PackageInstallTask: installer = self.agent_templete_env.get_installer() return installer.install(templete_id) - + def uninstall(self,templete_id) -> int: - pass - + pass + async def _load_templete_from_media(self,templete_media:PackageMediaInfo) -> AIAgentTemplete: pass - async def _load_agent_from_media(self,agent_media:PackageMediaInfo) -> AIAgent: + async def _load_agent_from_media(self,agent_media:PackageMediaInfo) -> BaseAIAgent: reader = self.agent_env._create_media_loader(agent_media) if reader is None: logger.error(f"create media loader for {agent_media} failed!") return None - + try: config_file = await reader.read("agent.toml","r") if config_file is None: @@ -125,11 +127,22 @@ class AgentManager: return None return result_agent except Exception as e: - logger.error(f"read agent.toml cfg from {agent_media} failed! unexpected error occurred: {str(e)}") - return None - + custom_agent = os.path.join(agent_media.full_path,"agent.py") + if not os.path.exists(custom_agent): + logger.error(f"read agent.toml cfg from {agent_media} failed! unexpected error occurred: {str(e)}") + return None + + agent_name = os.path.split(agent_media.full_path)[1] + spec = importlib.util.spec_from_file_location(agent_name, custom_agent) + the_api = importlib.util.module_from_spec(spec) + spec.loader.exec_module(the_api) + if not hasattr(the_api,"Agent"): + logger.error(f"read agent.toml cfg from {agent_media} failed! unexpected error occurred: {str(e)}") + return None + return the_api.Agent() + + - def create(self,template,agent_name,agent_last_name,agent_introduce) -> AIAgent: pass