Support custom agent

2023-11-15 18:43:27 +08:00
parent 87fdba9714
commit bddeebdad0
3 changed files with 232 additions and 177 deletions
@@ -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
@@ -116,7 +117,7 @@ class AIAgentTemplete:
        return True


-class AIAgent:
+class AIAgent(BaseAIAgent):
    def __init__(self) -> None:
        self.role_prompt:AgentPrompt = None
        self.agent_prompt:AgentPrompt = None
@@ -128,7 +129,7 @@ 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
@@ -137,7 +138,7 @@ 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
@@ -159,7 +160,7 @@ 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):
@@ -192,7 +193,7 @@ 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"])
@@ -207,13 +208,13 @@ 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"]
@@ -232,7 +233,7 @@ 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

@@ -245,22 +246,22 @@ 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)
@@ -284,7 +285,7 @@ class AIAgent:
                    prompt = AgentPrompt()
                    prompt.system_message = {"role":"system","content":real_str}
                    return prompt
-                
+
        return None

    def _get_inner_functions(self) -> dict:
@@ -334,13 +335,13 @@ 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:
@@ -356,10 +357,10 @@ 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

@@ -374,11 +375,11 @@ 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()
@@ -410,7 +411,7 @@ 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)

@@ -423,7 +424,7 @@ 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
@@ -456,12 +457,12 @@ 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()
@@ -475,7 +476,7 @@ 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,"")
@@ -491,7 +492,7 @@ 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:
@@ -508,12 +509,12 @@ 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()
@@ -526,25 +527,25 @@ 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)
@@ -594,11 +595,11 @@ 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()
@@ -606,7 +607,7 @@ 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)
@@ -628,10 +629,10 @@ 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
@@ -648,7 +649,7 @@ 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} "})
@@ -666,11 +667,11 @@ 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
@@ -691,7 +692,7 @@ 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()
@@ -710,7 +711,7 @@ 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)
@@ -723,7 +724,7 @@ 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
@@ -740,7 +741,7 @@ 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)

@@ -748,7 +749,7 @@ 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

@@ -756,14 +757,14 @@ 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:
@@ -784,50 +785,50 @@ 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:
@@ -835,7 +836,7 @@ 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)
@@ -854,7 +855,7 @@ 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
@@ -874,19 +875,19 @@ 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))
@@ -907,11 +908,11 @@ 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:
@@ -953,7 +954,7 @@ class AIAgent:
            #         self.llm_read_book(kb,item)
            #         learn_power -= 1
            #     case "article":
-            #        
+            #
            #         self.llm_read_article(kb,item)
            #         learn_power -= 1
            #     case "video":
@@ -982,8 +983,8 @@ 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
@@ -992,7 +993,7 @@ 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
@@ -1011,13 +1012,13 @@ 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]

@@ -1030,7 +1031,7 @@ 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)
@@ -1040,20 +1041,20 @@ 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)
        full_content = await workspace.load_knowledge_content(full_path)
        if full_content is None:
            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 
-        
+            return 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()
@@ -1069,7 +1070,7 @@ 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

@@ -1102,14 +1103,14 @@ 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.get("metadata")
                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)
@@ -1126,8 +1127,8 @@ 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
@@ -1160,24 +1161,24 @@ 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
@@ -1196,13 +1197,13 @@ 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} ")
@@ -1223,28 +1224,28 @@ 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())
@@ -1288,7 +1289,7 @@ class AIAgent:
    def token_len(self,text:str) -> int:
        return ComputeKernel.llm_num_tokens_from_text(text,self.get_llm_model_name())

-        
-     
-        
-    
+
+
+
+