Complete self_thinking llm process and Agent Memory

2024-02-04 17:28:25 -08:00
parent ddb17756eb
commit 906c3e791a
21 changed files with 886 additions and 222 deletions
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 ## LLM / AI 相关框架
 ### LLM Process
 LLM调用封装的最小单元，提供了一系列最基础的支持
 流程上 inpurt, prepare promot, llm_function_call_loop, post_llm , llmresult parser, AI Action
 功能上 动态类型系统 load_from_config,llm_process_loader
 ### Agent
 从Agent的视角定义了Agent的LLM行为逻辑
 Process behavior  (响应)
 Task/Todo Loop (自主)
 Self Loop (自省)
 #### Agent.Memory
 #### Agent.Workspace
 #### Agent.behavior
 ### Workflow
 一组Agent共享Work space后的流程
 Task可以分配给不同的Agent
 Todo的Do和Check可以分配给不同的Agent
 ## Knowledge Base （sisi)
 AI First的未来文件系统
 ## Agent 能力扩展框架
 ### AI Function / Action 
 最重要的扩展框架
 ### Environment
 可以通过 {environment.xxx} 读取
 ### Code Interpreter  
 Agent 能不能写代码是一个重要的理念之争
 能写代码的Agent想象空间大，是通往AGI的必然之路，但不够稳定可预期
 不能写代码的Agent可以专注于组合使用基础的能力，稳定可靠的
 ## AI系统组件
 ### AI Compute Kernel
 通过AI Compute Kernel对 LLM, AIGC等新一代的AI基础能力进行抽象
 通过Compute Node可以对这些基础能力进行不同的实现 
 ### AI Models
 模型的fine-tune Pipeline
 LoRA的Pipeline
 ### Contact Manage
 基于Contact的自然语言权限控制
 ### Tunnel
 可以使用开放API的通信软件，于自己的AI时刻保持沟通
 ### Spider
 持续的导入用户在旧时代的数据。
 从Web2->web3
 ### Calendar (Calendar是否应该是Agent.Worksapce的一部分）
 ### 基础的pkg_loader
 支持一系列可安装的扩展
 可扩展的扩展是AIOS的开发者需要重点关注的
 Agent （用自然语言扩展）
 Workflow （用自然语言扩展）
 Plugin:(需要会写代码）
 	AI Function / Action
 	Environment
 	Knowledge Pipeline
 	LLM Process
 	Compute Node
 ### System Config Manage
 Zone Config-> System Config
 ## UI 
 ### Installer 
 图形化的安装界面，帮助用户能快速的安装使用
 我们也会在这里讨论面向用户的AIOS的过渡性安装逻辑
 ### WebUI & OS Desktop
 系统控制面板
 Agent/Workflow管理   
 新Outlook
 	会话管理 （于Agent会话）
 	日程管理
 	Todo管理
 新Dropbox
 	Knowledge Base浏览
 	Knowledge Base查询
 应用商店
 ### AIOS Shell
 ### Personal Station (新个人主页）
 内容的发布/联系人内容的查看/个人日历的公开
 ## Frame Service （完全未开始）
 通过Frame Service，让AIOS成为一个典型的网络系统（Personal Server OS）
 这一块会复用很多CYFS/Bucky OS 的基础设计
 这一层AI不会直接使用，这一层支持AI系统组件的实现
 在用户看来，这一层的功能都是高级的，偏向系统维护的。很少会直接使用
 ### zone & node-daemon
 NOS的booter
 ### Runtime (Container) Manage
 这里抽象了系统的运行时模型
 通过容器技术对可扩展组件的权限进行控制，保护系统的隐私安全
 ### d-Storage & Named Object
 Named- Object File System
 D-RDB
 D-VDB
 ### BUS
 系统消息总线，在不同的系统组件中路由消息
 ### CYFS (httpv4) Gateway
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@@ -892,4 +892,98 @@
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@@ -2,19 +2,20 @@ instance_id = "Jarvis"
 fullname = "Jarvis"
 max_token = 4000
 #timeout = 1800
-model_name = "gpt-4-1106-preview"
+model_name = "gpt-4-turbo-preview"
 #enable_kb = "true"
 enable_timestamp = "true"
 enable_json_resp = "true"
 role_desc = """
-Your name is Jarvis, the super personal assistant to the master. Help the Master do a good job of schedule.Reminder before the start of the important schedule, and you should bring useful information as much as possible when reminding.
+Your name is Jarvis, the super personal assistant to the Principal. Help the Principal do a good job of schedule.Reminder before the start of the important schedule, and you should bring useful information as much as possible when reminding.
 Only clearly specifying the task you completed can be completed independently.
 """
 [behavior.on_message]
 type="AgentMessageProcess"
 # TODO: 是否应该自动记录 inner function和action的执行细节
 mutil_model="gpt-4-vision-preview"
 process_description="""
 1. Based on your role and the existing information, please think and then make a brief and efficient reply.
@@ -68,7 +69,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
    }]
 }
 """
-context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
+context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
 llm_context.actions.enable = ["agent.workspace.confirm_task","agent.workspace.update_task","agent.workspace.cancel_task","post_message"]
@@ -97,7 +98,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
 llm_context.actions.enable = ["agent.workspace.create_task","agent.workspace.update_task","agent.workspace.set_todos","agent.workspace.cancel_task","post_message"]
 #llm_context.functions.enable = ["agent.workspace.list_task"]
-context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
+context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
 [behavior.review_task]
 ## 当task的所有todo/subtask都完成后（不敢成功或是失败），进行一次review
@@ -122,7 +123,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
 llm_context.actions.enable = ["agent.workspace.cancel_task","agent.workspace.update_task"]
-context="Your master now in {location}, time: {now}, weather: {weather}."
+context="Your Principal now in {location}, time: {now}, weather: {weather}."
 [behavior.do] 
 # do TODO
 type="AgentDo"
@@ -147,7 +148,7 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
    ]
 }
 """
-context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
+context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
 # 对于DO操作来说，让Agent查询自己的能力集合是否更合适？
 llm_context.actions.enable = ["agent.workspace.update_todo","post_message","agent.workspace.write_file","agent.workspace.append_file"]
 llm_context.functions.enable = ["agent.workspace.read_file","agent.workspace.list_dir","system.shell.exec","aigc.text_2_image","aigc.text_2_voice","web.search.duckduckgo"]
@@ -171,14 +172,39 @@ The Response must be directly parsed by `python json.loads`. Here is an example:
    ]
 }
 """
-context="Your master is {owner}, now in {location}, time: {now}, weather: {weather}."
+context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
 llm_context.actions.enable = ["agent.workspace.update_todo"]
 llm_context.functions.enable =  ["agent.workspace.read_file","agent.workspace.list_dir","system.shell.exec","system.shell.run_code","aigc.image_2_text","aigc.voice_2_text","web.search.duckduckgo"]
-#[behavior.self_thinking]
+[behavior.self_thinking2]
 # self thing的主要目的是对各种chatlog,worklog进行分析，并更新面向人和事的summary。
-#type="AgentSelfThinking"
+type="AgentSelfThinking"
 process_description="""
 You are very good at thinking and summarizing what you have already happened。Your input is a chat history and work record,After you think about it, you will follow the requirements below to generate abstract.
 1. Try to understand the theme of each sentence, and call the relevant operation to record the relationship between the dialogue and the theme
 2. Try to analyze the personality of different people involved in information
 3. Try to summarize important events in the information and record it
 4. Try to understand the attitude of different people on different topics or events
 5. Pay attention to the time order when summarizing, and combine the summary you have done to update Summary
 6. The summary of the generation cannot exceed 400 token
 7. 思考的目的是让自己未来的工作更加高效
 8. 总结中只包含有长期价值和未完成的事情，已经完成的事情不需要总结
 """
 reply_format = """
 The Response must be directly parsed by `python json.loads`. Here is an example:
 {
    resp:'$Summary in one sentence',
    name: '$action1_name',
    $param_name: '$parm' #Optional, fill in only if the action has parameters.
    }, ... 
    ]
 }
 """
 context="Your Principal is {owner}, now in {location}, time: {now}, weather: {weather}."
 llm_context.actions.enable = ["agent.memory.update_summary","agent.memory.update_contact_summary","agent.memory.update_relation_summary","agent.memory.set_experience"]
 llm_context.functions.enable =  ["agent.memory.get_summary","agent.memory.get_contact_summary","agent.memory.list_summary","agent.memory.get_relation_summary","agent.memory.get_experience"]
 #[behavior.self_improve]
 # self_improve 是最后的行为，允许Agent结合自己的工作经验，改进自己的提示词（注意保留历史版本）
@@ -19,7 +19,7 @@ from .frame.compute_kernel import ComputeKernel,ComputeTask,ComputeTaskResult,Co
 from .frame.compute_node import ComputeNode,LocalComputeNode
 from .frame.bus import AIBus
 from .frame.tunnel import AgentTunnel
-from .frame.contact_manager import ContactManager,Contact,FamilyMember
+from .frame.contact_manager import ContactManager,Contact
 from .frame.queue_compute_node import Queue_ComputeNode
 from .environment.environment import BaseEnvironment,SimpleEnvironment,CompositeEnvironment
@@ -24,21 +24,16 @@ from .llm_do_task import *
 from .chatsession import *
 from ..environment.workspace_env import WorkspaceEnvironment, TodoListType
 from ..frame.contact_manager import ContactManager,Contact,FamilyMember
 from ..frame.compute_kernel import ComputeKernel
 from ..frame.bus import AIBus
 from ..environment.environment import *
 from ..environment.workspace_env import WorkspaceEnvironment
 from ..storage.storage import AIStorage
 from ..knowledge import *
-from ..utils import video_utils, image_utils
+from ..proto.compute_task import LLMPrompt,LLMResult
 from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode,LLMPrompt,LLMResult
 logger = logging.getLogger(__name__)
 class AIAgentTemplete:
    def __init__(self) -> None:
-        self.llm_model_name:str = "gpt-4-0613"
+        self.llm_model_name:str = "gpt-4-turbo-preview"
        self.max_token_size:int = 0
        self.template_id:str = None
        self.introduce:str = None
@@ -99,7 +94,8 @@ class AIAgent(BaseAIAgent):
        }
        self.todo_prompts = todo_prompts
-        self.memory_db = None
+        self.base_dir = None
        #self.memory_db = None
        self.unread_msg = Queue() # msg from other agent
        self.owenr_bus = None
@@ -109,7 +105,9 @@ class AIAgent(BaseAIAgent):
        self.behaviors:Dict[str,BaseLLMProcess] = {}
    async def initial(self,params:Dict = None):
-        self.memory = AgentMemory(self.agent_id,self.memory_db)
+        self.base_dir = f"{AIStorage.get_instance().get_myai_dir()}/agent_data/{self.agent_id}"
        memory_base_dir = f"{self.base_dir}/memory"
        self.memory = AgentMemory(self.agent_id,memory_base_dir)
        self.prviate_workspace = AgentWorkspace(self.agent_id) 
        init_params = {}
        init_params["memory"] = self.memory
@@ -241,6 +239,34 @@ class AIAgent(BaseAIAgent):
        return await self.llm_process_msg(msg)
    async def llm_self_think(self):
        llm_process : BaseLLMProcess = self.behaviors.get("self_thinking")
        if llm_process:
            logger.info(f"agent {self.agent_id} self thinking start!")
            context_info = await self._get_context_info()
            known_session_list = AIChatSession.list_session(self.agent_id,self.memory.memory_db)
            known_experience_list = await self.memory.list_experience()
            record_list = await self.memory.load_records(await self.memory.get_last_think_time())
            input_parms = {
                "record_list":record_list,
                "known_session_list":known_session_list,
                "known_experience_list":known_experience_list,
                "context_info":context_info
            }
            llm_result : LLMResult = await llm_process.process(input_parms)
            if llm_result.state == LLMResultStates.ERROR:
                logger.error(f"llm process self thinking error:{llm_result.compute_error_str}")
            elif llm_result.state == LLMResultStates.IGNORE:
                logger.info(f"llm process self thinking  ignore!")
            else:
                logger.info(f"llm process self thinking  ok!,think is:{llm_result.resp}")
                self.memory.set_last_think_time(time.time())
            self.agent_energy -= 2  
            return
    async def llm_triage_tasklist(self):
        llm_process : BaseLLMProcess = self.behaviors.get("triage_tasks")
        if llm_process:
@@ -361,21 +387,8 @@ class AIAgent(BaseAIAgent):
            self.agent_energy -= 1
    async def do_self_think(self):
        session_id_list = AIChatSession.list_session(self.agent_id,self.memory_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
        return
    def need_self_think(self) -> bool:
        return False
    async def _self_imporve(self):
-        if self.need_self_think():
+        await self.llm_self_think()
            await self.do_self_think()
    def wake_up(self) -> None:
        if self.agent_task is None:
@@ -446,10 +459,11 @@ class AIAgent(BaseAIAgent):
                await self._self_imporve()
            except Exception as e:
                tb_str = traceback.format_exc()
                logger.error(f"agent {self.agent_id} on timer error:{e},{tb_str}")
                continue
            # Because the LLM itself is very slow, the accuracy of the system processing task is in minutes.
            await asyncio.sleep(30) 
@@ -458,3 +472,5 @@ class AIAgent(BaseAIAgent):
@@ -1,12 +1,18 @@
 # pylint:disable=E0402
 from datetime import datetime,timedelta
 import json
 import os
 import threading
 from typing import Dict, List
 import sqlite3
 import aiofiles
 from ..storage.storage import AIStorage
 from ..frame.compute_kernel import ComputeKernel
-from ..proto.ai_function import SimpleAIAction
+from ..frame.contact_manager import ContactManager
 from ..frame.contact import Contact
 from ..proto.ai_function import ParameterDefine, SimpleAIAction, SimpleAIFunction
 from ..proto.agent_msg import AgentMsg, AgentMsgType
 from ..proto.agent_task import AgentWorkLog
@@ -17,12 +23,34 @@ import logging
 logger = logging.getLogger(__name__)
 #class ObjectSummary:
 #    def __init__(self) -> None:
 #        self.summary : str = None
 #        self.object_name : str = None
 #        self.priority : int = 5
        # [info_source, info]
 #        self.infos : Dict[str,str] = {}
 class AgentMemory:
-    def __init__(self,agent_id:str,db_path:str) -> None:
+    def __init__(self,agent_id:str,base_dir:str) -> None:
        self.agent_memory_base_dir = base_dir
        self.agent_id:str= agent_id
-        self.memory_db:str = db_path
+
        AIStorage.get_instance().ensure_directory_exists(self.agent_memory_base_dir)
        AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/experience")
        AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/contacts")
        AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/relations")
        AIStorage.get_instance().ensure_directory_exists(f"{self.agent_memory_base_dir}/summary")
        self.memory_db:str = f"{self.agent_memory_base_dir}/memory.db"
        self.model_name:str = "gp4-1106-preview"
        self.threshold_hours = 72
        self.last_think_time : float = 0.0
        self.load_memory_meta()
    def _get_conn(self):
@@ -55,6 +83,15 @@ class AgentMemory:
            chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.memory_db)
        return chatsession
    # return last record time
    async def load_records(self,starttime,tokenlimit=8000)->float:
        # 专用思路：做聊天记录/工作经验的整理
        # 通用思路：没有具体的目的，让Agent根据提示词自己工作（可能效果很差也可能很好）
        # 先实现通用思路
        msg_records = AIChatSession.load_message_records_by_agentid(self.agent_id,starttime,32,self.memory_db)
        work_records = self.load_worklogs(self.agent_id,token_limit=tokenlimit)
        pass
    async def load_chatlogs(self,msg:AgentMsg,n:int=6,m:int=64,token_limit=800)->str:
        chatsession = self.get_session_from_msg(msg)
        # Must load n (n> = 2), and hope to load the M
@@ -176,23 +213,295 @@ class AgentMemory:
        conn.commit()
        #conn.close()
    def memory_meta_to_dict(self) -> Dict:
        return {
            "last_think_time" : self.last_think_time
        }
    def load_meta(self,Dict):
        self.last_think_time = Dict.get("last_think_time",0.0)
    def load_memory_meta(self):
        meta_file_path = f"{self.agent_memory_base_dir}/meta.json"
        try:
            with open(meta_file_path, mode='r') as file:
                meta = json.load(file)
                self.load_meta(meta)
        except Exception as e:
            logger.error(f"load memory meta failed: {e}")
            self.last_think_time = 0.0
    def save_memory_meta(self):
        meta_file_path = f"{self.agent_memory_base_dir}/meta.json"
        try:
            with open(meta_file_path, mode='w') as file:
                meta = self.memory_meta_to_dict()
                json.dump(meta,file)
        except Exception as e:
            logger.error(f"save memory meta failed: {e}")
    async def get_last_think_time(self)->float:
        return self.last_think_time
    async def set_last_think_time(self,last_time:float):
        self.last_think_time = last_time    
        self.save_memory_meta()
    async def get_contact_summary(self,contact_id:str) -> str:
        if contact_id is None:
-            return None
+            return "Contact id is None"
        result = {}
        contact_info:Contact = ContactManager.get_instance().find_contact_by_name(contact_id)
        if contact_info:
            result["name"] = contact_info.name
            result["relation"] = contact_info.relationship
            result["notes"]  = contact_info.notes
        summary_path = f"{self.agent_memory_base_dir}/contacts/{contact_id}.summary"
        try:
            async with aiofiles.open(summary_path, mode='r') as file:
                result["summary"] =  await file.read()
        except Exception as e:
            logger.error(f"read contact summary failed: {e}")
        return json.dumps(result,ensure_ascii=False)
    async def update_contact_summary(self,contact_id:str,summary:str):
        summary_path = f"{self.agent_memory_base_dir}/contacts/{contact_id}.summary"
        try:
            async with aiofiles.open(summary_path, mode='w') as file:
                await file.write(summary)
                return "OK"
        except Exception as e:
            logger.error(f"write contact summary failed: {e}")
            return "write contact summary failed: {e}"
    async def get_summary(self,object_name:str) -> str:
        summary_path = f"{self.agent_memory_base_dir}/{object_name}.summary"
        try:
            async with aiofiles.open(summary_path, mode='r') as file:
                return await file.read()
        except Exception as e:
            logger.error(f"read summary failed: {e}")
            return f"read summary failed: {e}"
    async def update_summary(self,object_name:str,summary:str) -> str:
        summary_path = f"{self.agent_memory_base_dir}/{object_name}.summary"
        try:
            async with aiofiles.open(summary_path, mode='w') as file:
                await file.write(summary)
                return "OK"
        except Exception as e:
            logger.error(f"write summary failed: {e}")
            return f"write summary failed: {e}"
    async def list_summary_object_names(self) -> List[str]:
        # list dir
        try:
            contents = os.listdir(self.agent_memory_base_dir)
            return [x for x in contents if x.endswith(".summary")]
        except Exception as e:
            logger.error(f"list summary object names failed: {e}")
            return []
    # means object1 feel object2 is ... 
    async def get_relation_summary(self,object_name1:str,object_name2:str) -> str:
        summary_path = f"{self.agent_memory_base_dir}/relations/{object_name1}.relation.{object_name2}.summary"
        try:
            async with aiofiles.open(summary_path, mode='r') as file:
                await file.read()
        except FileNotFoundError:
            return "no summary"
        except Exception as e:
            logger.error(f"read relation summary failed: {e}")
            return f"read relation summary failed: {e}"
    async def update_relation_summary(self,object_name1:str,object_name2:str,summary:Dict):
        summary_path = f"{self.agent_memory_base_dir}/relations/{object_name1}.relation.{object_name2}.summary"
        try:
            async with aiofiles.open(summary_path, mode='w') as file:
                await file.write(json.dumps(summary))
                return "OK"
        except Exception as e:
            logger.error(f"write relation summary failed: {e}")
            return "write relation summary failed: {e}"
    async def get_experience(self,topic_name:str) -> str:
        experience_path = f"{self.agent_memory_base_dir}/experience/{topic_name}.experience"
        try:
            async with aiofiles.open(experience_path, mode='r') as file:
                await file.read()
        except FileNotFoundError:
            return "no experience"
        except Exception as e:
            logger.error(f"read experience failed: {e}")
            return f"read experience failed: {e}"
    async def set_experience(self,topic_name:str,summary:str) -> str:
        experience_path = f"{self.agent_memory_base_dir}/experience/{topic_name}.experience"
        try:
            async with aiofiles.open(experience_path, mode='w') as file:
                await file.write(summary)
                return "OK"
        except Exception as e:
            logger.error(f"write experience failed: {e}")
            return "write experience failed: {e}"
    async def list_experience(self) -> List[str]:
        dir_path = f"{self.agent_memory_base_dir}/experience"
        try:
            contents = os.listdir(dir_path)
            return [x for x in contents if x.endswith(".experience")]
        except Exception as e:
            logger.error(f"list experience failed: {e}")
            return []
    @staticmethod
    def register_ai_functions():
        async def get_contact_summary(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            contact_name = parameters.get("contact_name")
            return await agent_memory.get_contact_summary(contact_name)
        parameters = ParameterDefine.create_parameters({
            "contact_name": {"type": "string", "description": "contact name"}
        })
        get_contact_summary_func = SimpleAIFunction("agent.memory.get_contact_summary",
                                                    "get contact summary",
                                                    get_contact_summary,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(get_contact_summary_func)
        async def update_contact_summary(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            contact_name = parameters.get("contact_name")
            summary = parameters.get("summary")
            return await agent_memory.update_contact_summary(contact_name,summary)
        parameters = ParameterDefine.create_parameters({
            "contact_name": {"type": "string", "description": "contact name"},
            "summary": {"type": "string", "description": "new summary"}
        })
        update_contact_summary_func = SimpleAIFunction("agent.memory.update_contact_summary",
                                                    "update contact summary",
                                                    update_contact_summary,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(update_contact_summary_func)
        async def get_summary(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            object_name = parameters.get("object_name")
            return await agent_memory.get_summary(object_name)
        parameters = ParameterDefine.create_parameters({
            "object_name": {"type": "string", "description": "object name"}
        })
        get_summary_func = SimpleAIFunction("agent.memory.get_summary",
                                                    "get summary of sth",
                                                    get_summary,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(get_summary_func)
        async def update_summary(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            object_name = parameters.get("object_name")
            summary = parameters.get("summary")
            return await agent_memory.update_summary(object_name,summary)
        parameters = ParameterDefine.create_parameters({
            "object_name": {"type": "string", "description": "object name"},
            "summary": {"type": "string", "description": "new summary"}
        })
        update_summary_func = SimpleAIFunction("agent.memory.update_summary",
                                                    "update summary of sth",
                                                    update_summary,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(update_summary_func)
        async def list_summary_object_names(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            return await agent_memory.list_summary_object_names()
        parameters = ParameterDefine.create_parameters({})
        list_summary_object_names_func = SimpleAIFunction("agent.memory.list_summary",
                                                    "list summary object names",
                                                    list_summary_object_names,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(list_summary_object_names_func)
        async def get_relation_summary(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            object_name1 = parameters.get("object1_name")
            object_name2 = parameters.get("object2_name")
            return await agent_memory.get_relation_summary(object_name1,object_name2)
        parameters = ParameterDefine.create_parameters({
            "object1_name": {"type": "string", "description": "object name1"},
            "object2_name": {"type": "string", "description": "object name2"}
        })
        get_relation_summary_func = SimpleAIFunction("agent.memory.get_relation_summary",
                                                    "object1 feel object2 is ...",
                                                    get_relation_summary,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(get_relation_summary_func)
        async def update_relation_summary(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            object_name1 = parameters.get("object1_name")
            object_name2 = parameters.get("object2_name")
            summary = parameters.get("summary")
            return await agent_memory.update_relation_summary(object_name1,object_name2,summary)
        parameters = ParameterDefine.create_parameters({
            "object1_name": {"type": "string", "description": "object name1"},
            "object2_name": {"type": "string", "description": "object name2"},
            "summary": {"type": "string", "description": "new summary"}
        })
        update_relation_summary_func = SimpleAIFunction("agent.memory.update_relation_summary",
                                                    "object1 feel object2 is ...",
                                                    update_relation_summary,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(update_relation_summary_func)
        async def get_experience(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            topic_name = parameters.get("topic_name")
            return await agent_memory.get_experience(topic_name)
        parameters = ParameterDefine.create_parameters({
            "topic_name": {"type": "string", "description": "topic name"}
        })
        get_experience_func = SimpleAIFunction("agent.memory.get_experience",
                                                    "get experience",
                                                    get_experience,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(get_experience_func)
        async def set_experience(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            topic_name = parameters.get("topic_name")
            summary = parameters.get("summary")
            return await agent_memory.set_experience(topic_name,summary)
        parameters = ParameterDefine.create_parameters({
            "topic_name": {"type": "string", "description": "topic name"},
            "summary": {"type": "string", "description": "new summary"}
        })
        set_experience_func = SimpleAIFunction("agent.memory.set_experience",
                                                    "set experience",
                                                    set_experience,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(set_experience_func)
        async def list_experience(parameters):
            agent_memory:AgentMemory = parameters.get("_agent_memory")
            return await agent_memory.list_experience()
        parameters = ParameterDefine.create_parameters({})
        list_experience_func = SimpleAIFunction("agent.memory.list_experience",
                                                    "list exist experience topics",
                                                    list_experience,
                                                    parameters)
        GlobaToolsLibrary.register_tool_function(list_experience_func)
        # TODO : fix this by system config.
        if contact_id == "lzc":
            return "lzc is your master. Male, 40 years old, Mother tongue is Chinese, senior software engineer."
        return None
    async def update_contact_summary(self,contact_id:str,summary:str) -> str:
        return "OK"
    async def get_sth_summary(self,sth_id:str) -> str:
        return None
    async def update_sth_summary(self,sth_id:str,summary:str) -> str:
        return None
@@ -211,6 +211,23 @@ class ChatSessionDB:
            logging.error("Error occurred while getting messages: %s", e)
            return -1, None  # return -1 and None if an error occurs
    def load_message_by_agentid(self,agent_id,limit,start_time="1970-01-01 00:00:00"):
        try:
            conn = self._get_conn()
            cursor = conn.cursor()
            cursor.execute("""
                SELECT MessageID, SessionID, MsgType, PrevMsgID, SenderID, ReceiverID, Timestamp, Topic,Mentions,ContentMIME,Content,ActionName,ActionParams,ActionResult,DoneTime,Status FROM Messages
                WHERE SenderID = ? or ReceiverID =? AND Timestamp > ?
                ORDER BY Timestamp 
                LIMIT ? 
            """, (agent_id, agent_id, start_time,limit))
            results = cursor.fetchall()
            #self.close()
            return results  # return 0 and the result if successful
        except Error as e:
            logging.error("Error occurred while getting messages: %s", e)
            return -1, None
    # read message from  now->beign    
    def get_messages(self, session_id, limit, offset):
        """ retrieve messages of a session with pagination """
@@ -287,6 +304,38 @@ class AIChatSession:
    #        cls._dbs[db_path] = db
    #    db.get_chatsession_by_id(session_id)
    #    #result = AIChatSession()
    @classmethod
    # start_time is a string like "2021-01-01 00:00:00"
    def load_message_records_by_agentid(cls,agent_id:str,start_time:str,limit:int,db_path:str)->List[AgentMsg]:
        db = cls._dbs.get(db_path)
        if db is None:
            db = ChatSessionDB(db_path)
            cls._dbs[db_path] = db
        msgs = db.load_message_by_agentid(agent_id,start_time,limit)
        result = []
        for msg in msgs:
            agent_msg = AgentMsg()
            agent_msg.msg_id = msg[0]
            agent_msg.session_id = msg[1]
            agent_msg.msg_type = AgentMsgType(msg[2])
            agent_msg.prev_msg_id = msg[3]
            agent_msg.sender = msg[4]
            agent_msg.target = msg[5]
            agent_msg.create_time = msg[6]
            agent_msg.topic = msg[7]
            if msg[8] is not None:
                agent_msg.mentions = json.loads(msg[8])
            agent_msg.body_mime = msg[9]
            agent_msg.body = msg[10]
            agent_msg.func_name = msg[11]
            if msg[12] is not None:
                agent_msg.args = json.loads(msg[12])
            agent_msg.result_str = msg[13]
            agent_msg.done_time = msg[14]
            agent_msg.status = AgentMsgStatus(msg[15])
            result.append(agent_msg)
        return result
    @classmethod
    def get_session(cls,owner_id:str,session_topic:str,db_path:str,auto_create = True) -> 'AIChatSession':
@@ -42,6 +42,9 @@ class BaseLLMProcess(ABC):
        self.llm_context:LLMProcessContext = None
    def get_llm_model_name(self) -> str:
        return self.model_name
    @abstractmethod
    async def prepare_prompt(self,input:Dict) -> LLMPrompt:
        pass
@@ -123,10 +126,11 @@ class BaseLLMProcess(ABC):
        else:
            inner_functions = None
        task_result: ComputeTaskResult = await (ComputeKernel.get_instance().do_llm_completion(
            prompt,
            resp_mode=resp_mode,
-            mode_name=self.model_name,
+            mode_name=self.get_llm_model_name(),
            max_token=max_result_token,
            inner_functions=inner_functions, #NOTICE: inner_function in prompt can be a subset of get_inner_function
            timeout=self.timeout))
@@ -166,7 +170,7 @@ class BaseLLMProcess(ABC):
        task_result: ComputeTaskResult = await (ComputeKernel.get_instance().do_llm_completion(
                prompt,
                resp_mode=resp_mode,
-                mode_name=self.model_name,
+                mode_name=self.get_llm_model_name(),
                max_token=max_result_token,
                inner_functions=prompt.inner_functions, #NOTICE: inner_function in prompt can be a subset of get_inner_function
                timeout=self.timeout))
@@ -309,6 +313,9 @@ class LLMAgentBaseProcess(BaseLLMProcess):
 class AgentMessageProcess(LLMAgentBaseProcess):
    def __init__(self) -> None:
        super().__init__()
        self.mutil_model = None
        self.enable_media2text = False
        self.is_mutil_model = False
    async def load_default_config(self) -> bool:
        return True
@@ -320,6 +327,16 @@ class AgentMessageProcess(LLMAgentBaseProcess):
        if await super().load_from_config(config) is False:
            return False
        self.enable_media2text = config.get('enable_media2text', 'false').lower() in ('true', '1', 't', 'y', 'yes')
        if config.get("mutil_model"):
            self.mutil_model = config.get("mutil_model")
    def get_llm_model_name(self) -> str:
        if self.is_mutil_model:
            return self.mutil_model
        else:
            return self.model_name
    def check_and_to_base64(self, image_path: str) -> str:
        if image_utils.is_file(image_path):
@@ -329,14 +346,24 @@ class AgentMessageProcess(LLMAgentBaseProcess):
    async def get_prompt_from_msg(self,msg:AgentMsg) -> LLMPrompt:
        msg_prompt = LLMPrompt()
        self.is_mutil_model = False
        if msg.is_image_msg():  
-            image_prompt, images = msg.get_image_body()
+            if self.enable_media2text:
-            if image_prompt is None:
+                logger.error(f"enable_media2text is not supported yet")
                msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images]}]
            else:
-                content = [{"type": "text", "text": image_prompt}]
+                image_prompt, images = msg.get_image_body()
-                content.extend([{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images])
+                if image_prompt is None:
-                msg_prompt.messages = [{"role": "user", "content": content}]
+                    msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images]}]
                else:
                    content = [{"type": "text", "text": image_prompt}]
                    content.extend([{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images])
                    msg_prompt.messages = [{"role": "user", "content": content}]
                if self.mutil_model:
                    self.is_mutil_model = True
                else:
                    logger.warning(f"mutil_model is not set!")
        elif msg.is_video_msg():
            video_prompt, video = msg.get_video_body()
            frames = video_utils.extract_frames(video, (1024, 1024))
@@ -459,25 +486,7 @@ class AgentMessageProcess(LLMAgentBaseProcess):
        return True
-class AgentSelfLearning(BaseLLMProcess):
+class AgentSelfThinking(LLMAgentBaseProcess):
    def __init__(self) -> None:
        super().__init__()
    async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
        if await super().load_from_config(config) is False:
            return False
    async def prepare_prompt(self) -> LLMPrompt:
        prompt = LLMPrompt()
        pass  
    async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
        pass
    async def post_llm_process(self,actions:List[ActionNode]) -> bool:
        pass
 class AgentSelfThinking(BaseLLMProcess):
    def __init__(self) -> None:
        super().__init__()
@@ -553,6 +562,68 @@ class AgentSelfThinking(BaseLLMProcess):
                chatsession.update_think_progress(next_pos,new_summary)
        return
    async def prepare_prompt(self,input:Dict) -> LLMPrompt:
        prompt = LLMPrompt()
        record_list = input.get("record_list")
        context_info = input.get("context_info")
        if record_list is None:
            logger.error(f"AgentSelfThinking prepare_prompt failed! input  not found")
            return None
        prompt.append_user_message(json.dumps(record_list,ensure_ascii=False))
        system_prompt_dict = self.prepare_role_system_prompt(context_info)
        # Known_info is the SESSION summary of the existence, the current task work record summary,
        known_info = {}
        have_known_info = False
        known_session_list = input.get("known_session_list")
        known_task_list = input.get("known_task_list")
        known_contact_list = input.get("known_contact_list")
        known_experience_list = input.get("known_experience_list")
        if known_session_list:
            known_info["known_session_list"] = known_session_list
            have_known_info = True
        if known_task_list:
            known_info["known_task_list"] = known_task_list
            have_known_info = True
        if known_contact_list:
            known_info["known_contact_list"] = known_contact_list
            have_known_info = True
        if known_experience_list:
            known_info["known_experience_list"] = known_experience_list
            have_known_info = True
        if have_known_info:
            system_prompt_dict["known_info"] = known_info
        prompt.inner_functions =LLMProcessContext.aifunctions_to_inner_functions(self.llm_context.get_all_ai_functions())
        prompt.append_system_message(json.dumps(system_prompt_dict,ensure_ascii=False))
    async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
        action_params = {}
        action_params["_input"] = input
        action_params["_memory"] = self.memory
        action_params["_workspace"] = self.workspace
        action_params["_llm_result"] = llm_result
        action_params["_agentid"] = self.memory.agent_id
        action_params["_start_at"] = datetime.now()
        try:
            if await self._execute_actions(actions,action_params) is False:
                result_str = "execute action failed!"
        except Exception as e:
            logger.error(f"execute action failed! {e}")
            result_str = "execute action failed!,error:" + str(e)
 class AgentSelfLearning(BaseLLMProcess):
    def __init__(self) -> None:
        super().__init__()
    async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
        if await super().load_from_config(config) is False:
            return False
    async def prepare_prompt(self) -> LLMPrompt:
        prompt = LLMPrompt()
        pass  
@@ -21,7 +21,7 @@ logger = logging.getLogger(__name__)
 class LocalAgentTaskManger(AgentTaskManager):
    def __init__(self, owner_id):
        super().__init__() 
-        self.root_path = f"{AIStorage.get_instance().get_myai_dir()}/workspaces/{owner_id}_workspace"
+        self.root_path = f"{AIStorage.get_instance().get_myai_dir()}/agent_data/{owner_id}/workspace/"
        #self.root_path = os.path.join(workspace, list_type)
        if not os.path.exists(self.root_path):
            os.makedirs(self.root_path)
@@ -13,7 +13,7 @@ from ..agent.llm_context import GlobaToolsLibrary
 from ..proto.compute_task import *
 from ..proto.ai_function import *
 from ..frame.compute_kernel import ComputeKernel
-from ..frame.contact_manager import ContactManager,Contact,FamilyMember
+from ..frame.contact_manager import ContactManager,Contact
 from ..storage.storage import AIStorage
 from .environment import SimpleEnvironment, CompositeEnvironment
@@ -107,7 +107,7 @@ class ComputeKernel:
    @staticmethod
    def llm_num_tokens_from_text(text:str,model:str) -> int:
        if model is None:
-            model = "gpt4"
+            model = "gpt-4-turbo-preview"
        try:
            encoding = tiktoken.encoding_for_model(model)
@@ -18,6 +18,7 @@ class Contact:
        self.notes = notes
        self.is_family_member = False
        self.active_tunnels = {}
        self.relationship = "friends"
    def to_dict(self):
        return {
@@ -25,11 +26,13 @@ class Contact:
            "phone": self.phone,
            "email": self.email,
            "telegram" : self.telegram,
            "is_family_member": self.is_family_member,
            "added_by": self.added_by,
            "tags": self.tags,
            "notes": self.notes,
-            "now" : datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+            "now" : datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
            "relationship" : self.relationship
        }
    async def _process_msg(self,msg:AgentMsg):
@@ -55,6 +58,7 @@ class Contact:
        self.active_tunnels[agent_id] = tunnel
    async def create_default_tunnel(self,agent_id:str) -> AgentTunnel:
        #TODO:fix this
        from .email_tunnel import EmailTunnel
        result_tunnels = AgentTunnel.get_tunnel_by_agentid(agent_id)
@@ -66,20 +70,7 @@ class Contact:
    @classmethod
    def from_dict(cls, data):
-        return Contact(data.get("name"), data.get("phone"), data.get("email"), data.get("telegram"),data.get("added_by"), data.get("tags"), data.get("notes"))
+        result = Contact(data.get("name"), data.get("phone"), data.get("email"), data.get("telegram"),data.get("added_by"), data.get("tags"), data.get("notes"))
-
+        if data.get("relationship") is not None:
-class FamilyMember(Contact):
+            result.relationship = data.get("relationship")
    def __init__(self, name, relationship,phone=None, email=None,telegram=None):
        super().__init__(name, phone, email, telegram)
        self.name = name
        self.relationship = relationship  
        self.is_family_member = True
    def to_dict(self):
        result = super().to_dict()
        result["relationship"] = self.relationship
        return result
    @classmethod
    def from_dict(cls, data):
        return FamilyMember(data.get("name"),data.get("relationship"),data.get("phone"), data.get("email"),data.get("telegram"))
@@ -8,7 +8,7 @@ from ..proto.agent_msg import AgentMsg
 from ..proto.ai_function import ParameterDefine, SimpleAIFunction
 from ..agent.llm_context import GlobaToolsLibrary
 from .tunnel import AgentTunnel
-from .contact import Contact,FamilyMember
+from .contact import Contact
 logger = logging.getLogger(__name__)    
@@ -25,11 +25,12 @@ class ContactManager:
    def register_global_functions(self):
        gl = GlobaToolsLibrary.get_instance()
-        get_parameters = ParameterDefine.create_parameters({"name":"name"})
+        get_parameters = ParameterDefine.create_parameters({"name":"contact name name"})
        gl.register_tool_function(SimpleAIFunction("system.contacts.get",
                                        "get contact info",
                                        self._get_contact,get_parameters))
        # todo: use json to save contact info
        update_parameters = ParameterDefine.create_parameters({"name":"name","contact_info":"A json to descrpit contact"})
        gl.register_tool_function(SimpleAIFunction("system.contacts.set",
                                        "set contact info",
@@ -42,7 +43,6 @@ class ContactManager:
    def __init__(self, filename="contacts.toml"):
        self.filename = filename
        self.contacts = []
        self.family_members = []    
        self.is_auto_create_contact_from_telegram = True
@@ -56,12 +56,10 @@ class ContactManager:
    def load_from_config(self,config_data:dict):
        self.contacts = [Contact.from_dict(item) for item in config_data.get("contacts", [])]
        self.family_members = [FamilyMember.from_dict(item) for item in config_data.get("family_members", [])]
    def save_data(self):
        data = {
            "contacts": [contact.to_dict() for contact in self.contacts],
            "family_members": [member.to_dict() for member in self.family_members]
        }
        with open(self.filename, "w") as f:
            toml.dump(data, f)
@@ -95,54 +93,28 @@ class ContactManager:
            if contact.name == name:
                return contact
        for member in self.family_members:
            if member.name == name:
                return member
        return None
    def find_contact_by_telegram(self, telegram:str):
        for contact in self.contacts:
            if contact.telegram == telegram:
                return contact
-        for member in self.family_members:
+         
            if member.telegram == telegram:
                return member            
        return None
    def find_contact_by_email(self, email:str):
        for contact in self.contacts:
            if contact.email == email:
                return contact
-        for member in self.family_members:
+
            if member.email == email:
                return member   
        return None
    def find_contact_by_phone(self, phone:str):
        for contact in self.contacts:
            if contact.phone == phone:
                return contact
-        for member in self.family_members:
+
            if member.phone == phone:
                return member   
        return None
    def add_family_member(self, name, new_member:FamilyMember):
        assert name == new_member.name
        self.family_members.append(new_member)
        self.save_data()
    def list_contacts(self):
        return self.contacts
    def list_family_members(self):
        return self.family_members
    #def register_to_ai_bus(self, ai_bus:AIBus):
    #    ai_bus.register_message_handler("contact_manager", self.process_msg)
    #async def process_msg(self,msg:AgentMsg):
    #    # forword message to contact    
    #    pass
@@ -15,7 +15,7 @@ class KnowledgeStore:
        if cls._instance is None:
            cls._instance = super().__new__(cls)
-            knowledge_dir = AIStorage.get_instance().get_myai_dir() / "knowledge" / "objects"
+            knowledge_dir = f"{AIStorage.get_instance().get_myai_dir()}/knowledge/objects"
            if not os.path.exists(knowledge_dir):
                os.makedirs(knowledge_dir)
@@ -40,7 +40,7 @@ class UserConfig:
        self.config_table = {}
        self.user_config_path:str = None
-        self._init_default_value("llm_model_name","gpt-4-1106-preview")
+        self._init_default_value("llm_model_name","gpt-4-turbo-preview")
    def _init_default_value(self,key:str,value:Any) -> None:
        if self.config_table.get(key) is not None:
@@ -92,7 +92,7 @@ class UserConfig:
                    os.makedirs(directory)
                async with aiofiles.open(self.user_config_path,"w") as f:
-                    toml_str = toml.dumps(will_save_config,ensure_ascii=False)
+                    toml_str = toml.dumps(will_save_config)
                    await f.write(toml_str)
            except Exception as e:
                logger.error(f"save user config to {self.user_config_path} failed!")
@@ -156,7 +156,7 @@ class AIStorage:
        self.feature_init_results = {}
    async def initial(self)->bool:
-        self.user_config.user_config_path = str(self.get_myai_dir() / "etc/system.cfg.toml")
+        self.user_config.user_config_path = str(self.get_myai_dir() + "/etc/system.cfg.toml")
        await self.user_config.load_value_from_file(self.get_system_dir() + "/system.cfg.toml")
        await self.user_config.load_value_from_file(self.user_config.user_config_path,True)
@@ -215,7 +215,7 @@ class AIStorage:
        my ai dir is the dir for user to store their ai app and data
        ~/myai/
        """
-        return Path.home() / "myai"
+        return f"{Path.home()}/myai"
    def get_download_dir(self) -> str:
        """
@@ -249,3 +249,10 @@ class AIStorage:
        except Exception as e:
            logger.error(f"open or create file {path} failed! {str(e)}")
    @staticmethod
    def ensure_directory_exists(directory_path):
        if not os.path.exists(directory_path):
            os.makedirs(directory_path)
            logger.info(f"Directory created: {directory_path}")
@@ -72,8 +72,6 @@ class AgentManager:
            logger.warn(f"load agent {agent_id} from media failed!")
            return None
        the_agent.memory_db = f"{self.agent_memory_base_dir}/{agent_id}/{agent_id}_memory.db"
        os.makedirs(os.path.dirname(the_agent.memory_db),exist_ok=True)
        if await the_agent.initial():
            return the_agent
        else:
@@ -453,7 +453,7 @@ class ParseLocalDocument:
        return f"# Known information:\n## Current directory structure:\n{kb_tree}\n## Knowlege Metadata:\n{json.dumps(known_obj,ensure_ascii=False)}\n"
    def _token_len(self, text: str) -> int:
-        return CustomAIAgent("", "gpt-4-1106-preview", self.token_limit).token_len(text=text)
+        return CustomAIAgent("", "gpt-4-turbo-preview", self.token_limit).token_len(text=text)
    async def _learn_by_agent(self, meta:dict) -> dict:
@@ -64,7 +64,7 @@ class DallEComputeNode(ComputeNode):
            self.output_dir = "./"
            self.output_dir = os.path.abspath(self.output_dir)
-        self.start()
+        await self.start()
        return True
@@ -209,7 +209,7 @@ class OpenAI_ComputeNode(ComputeNode):
                    if max_token_size > 4096:
                        result_token = 4096
                    else:
-                        result_token = max_token_size
+                        result_token = -1
                else:
                    result_token = NOT_GIVEN
@@ -12,7 +12,7 @@ from telegram import Bot
 from telegram.ext import Updater
 from telegram.error import Forbidden, NetworkError
-from aios import ObjectType, KnowledgeStore,AgentTunnel,AIStorage,ContactManager,Contact,FamilyMember,AgentMsg,AgentMsgType
+from aios import ObjectType, KnowledgeStore,AgentTunnel,AIStorage,ContactManager,Contact,AgentMsg,AgentMsgType
 logger = logging.getLogger(__name__)
@@ -76,8 +76,9 @@ class AIOS_Shell:
        user_config = AIStorage.get_instance().get_user_config()
        user_config.add_user_config("username","username is your full name when using AIOS",False,None)
-        user_config.add_user_config("telegram","Your telgram username",False,None)
+        user_config.add_user_config("user_telegram","Your telgram username",False,None)
-        user_config.add_user_config("email","Your email",False,None)
+        user_config.add_user_config("user_email","Your email",False,None)
        user_config.add_user_config("user_notes","Introduce yourself to your Agent!",False,None)
        user_config.add_user_config("feature.llama","enable Local-llama feature",True,"False")
        user_config.add_user_config("feature.aigc","enable AIGC feature",True,"False")
@@ -126,15 +127,16 @@ class AIOS_Shell:
    async def initial(self) -> bool:
        cm = ContactManager.get_instance()
-        owenr = cm.find_contact_by_name(self.username)
+        owner = cm.find_contact_by_name(self.username)
-        if owenr is None:
+        if owner is None:
-            owenr = Contact(self.username)
+            owner = Contact(self.username)
-            owenr.added_by = self.username
+            owner.added_by = self.username
-            owenr.is_family_member = True
+            owner.relationship = "Principal"
-            owenr.email = AIStorage.get_instance().get_user_config().get_value("email")
+            owner.email = AIStorage.get_instance().get_user_config().get_value("user_email")
-            owenr.telegram = AIStorage.get_instance().get_user_config().get_value("telegram")
+            owner.telegram = AIStorage.get_instance().get_user_config().get_value("user_telegram")
            owner.notes = AIStorage.get_instance().get_user_config().get_value("user_notes")
-            cm.add_family_member(self.username,owenr)
+            cm.add_contact(self.username,owner)
        # cal_env = CalenderEnvironment("calender")
        # await cal_env.start()
@@ -247,7 +249,7 @@ class AIOS_Shell:
            if tunnel_config is not None:
                await AgentTunnel.load_all_tunnels_from_config(tunnel_config)
        except Exception as e:
-            logger.warning(f"load tunnels config from {tunnels_config_path} failed!")
+            logger.warning(f"load tunnels config from {tunnels_config_path} failed! {e}")
        return True
@@ -385,7 +387,7 @@ class AIOS_Shell:
        contact_note = await try_get_input(f"Input {contact_name}'s note (optional):")
        if contact_note is not None:
-            contact.note = contact_note
+            contact.notes = contact_note
        contact.added_by = self.username
        if is_update:
@@ -712,9 +714,9 @@ async def get_user_config_from_input(check_result:dict) -> bool:
                continue
            else:
                True
-
+        if user_input:
-        if len(user_input) > 0:
+            if len(user_input) > 0:
-            AIStorage.get_instance().get_user_config().set_value(key,user_input)
+                AIStorage.get_instance().get_user_config().set_value(key,user_input)
    await AIStorage.get_instance().get_user_config().save_to_user_config()
    return True