Refactor before imporve knowledge base.

doc/webui.drawio

@@ -0,0 +1,150 @@
+<mxfile host="Electron" modified="2023-10-10T07:47:12.198Z" agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/22.0.2 Chrome/114.0.5735.289 Electron/25.8.4 Safari/537.36" etag="ooen5M4lIGDak3P-0D5_" version="22.0.2" type="device" pages="5">
+  <diagram name="Init" id="eZ0Fsh2vPRb0vo3J8o-a">
+    <mxGraphModel dx="2286" dy="1168" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="IzO59fDZ9m1edTcSyALi-11" value="" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+          <mxGeometry x="40" y="90" width="155" height="60" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-9" value="" style="verticalLabelPosition=bottom;shadow=0;dashed=0;align=center;html=1;verticalAlign=top;strokeWidth=1;shape=mxgraph.mockup.containers.userFemale;strokeColor=#666666;strokeColor2=#008cff;" parent="1" vertex="1">
+          <mxGeometry x="45" y="95" width="50" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-12" value="Main Page" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+          <mxGeometry x="40" y="150" width="155" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-13" value="AIGC" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+          <mxGeometry x="40" y="210" width="155" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-20" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shape=mxgraph.basic.rect;fillColor2=none;strokeWidth=1;size=20;indent=5;" parent="1" vertex="1">
+          <mxGeometry x="195" y="90" width="565" height="420" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-21" value="My Devices" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+          <mxGeometry x="40" y="180" width="155" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-22" value="" style="whiteSpace=wrap;html=1;aspect=fixed;" parent="1" vertex="1">
+          <mxGeometry x="312.5" y="130" width="340" height="340" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-23" value="Config LLM Kernel:" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="1" vertex="1">
+          <mxGeometry x="320" y="145" width="110" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-24" value="Config Owner Info:" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" parent="1" vertex="1">
+          <mxGeometry x="320" y="220" width="110" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-25" value="Done" style="strokeWidth=1;shadow=0;dashed=0;align=center;html=1;shape=mxgraph.mockup.buttons.button;strokeColor=#666666;fontColor=#ffffff;mainText=;buttonStyle=round;fontSize=17;fontStyle=1;fillColor=#008cff;whiteSpace=wrap;" parent="1" vertex="1">
+          <mxGeometry x="420" y="410" width="150" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-26" value="" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+          <mxGeometry x="40" y="240" width="155" height="270" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-27" value="&lt;font style=&quot;font-size: 10px;&quot;&gt;Version 0.5.2&lt;br&gt;http://www.opendan.ai&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;" parent="1" vertex="1">
+          <mxGeometry x="40" y="480" width="155" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-28" value="" style="shape=mxgraph.signs.science.nuclear_2;html=1;pointerEvents=1;fillColor=#000000;strokeColor=none;verticalLabelPosition=bottom;verticalAlign=top;align=center;sketch=0;" parent="1" vertex="1">
+          <mxGeometry x="45" y="155.5" width="20" height="19" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-31" value="" style="shape=mxgraph.signs.tech.ethernet;html=1;pointerEvents=1;fillColor=#000000;strokeColor=none;verticalLabelPosition=bottom;verticalAlign=top;align=center;sketch=0;" parent="1" vertex="1">
+          <mxGeometry x="45" y="190" width="20.63" height="10" as="geometry" />
+        </mxCell>
+        <mxCell id="IzO59fDZ9m1edTcSyALi-32" value="" style="shape=mxgraph.signs.travel.information_2;html=1;pointerEvents=1;fillColor=#000000;strokeColor=none;verticalLabelPosition=bottom;verticalAlign=top;align=center;sketch=0;" parent="1" vertex="1">
+          <mxGeometry x="49.900000000000006" y="216" width="10.84" height="18" as="geometry" />
+        </mxCell>
+        <mxCell id="l8k4_rfZMrNWla8Lv80Y-1" value="AI Store" style="rounded=0;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="40" y="240" width="155" height="30" as="geometry" />
+        </mxCell>
+      </root>
+    </mxGraphModel>
+  </diagram>
+  <diagram id="EX4l-TP_pYMqB1o-9Pjg" name="MainPage">
+    <mxGraphModel dx="1577" dy="806" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-1" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shape=mxgraph.basic.rect;fillColor2=none;strokeWidth=1;size=20;indent=5;" vertex="1" parent="1">
+          <mxGeometry x="97.5" y="66" width="565" height="580" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-3" value="" style="whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="127.5" y="231" width="80" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-2" value="" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="142.5" y="201" width="50" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-4" value="" style="whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="227.5" y="231" width="80" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-5" value="" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="242.5" y="201" width="50" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-6" value="" style="whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="327.5" y="231" width="80" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-7" value="" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="342.5" y="201" width="50" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-8" value="" style="whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="422.5" y="231" width="80" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-9" value="" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="437.5" y="201" width="50" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-10" value="" style="whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="517.5" y="231" width="80" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-11" value="" style="ellipse;whiteSpace=wrap;html=1;aspect=fixed;" vertex="1" parent="1">
+          <mxGeometry x="532.5" y="201" width="50" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-12" value="My Agents:" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
+          <mxGeometry x="117.5" y="171" width="70" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-17" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shadow=0;dashed=0;strokeWidth=1;shape=mxgraph.android.quickscroll3;dy=0.5;fillColor=#33b5e5;strokeColor=#66D5F5;rotation=90;" vertex="1" parent="1">
+          <mxGeometry x="380.5" y="66" width="6" height="520" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-19" value="" style="shape=cross;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="610.5" y="175" width="20" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="A7pxehO7BnMPWpYIlLSW-20" value="System Stauts" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="120" y="80" width="520" height="80" as="geometry" />
+        </mxCell>
+        <mxCell id="38aqMLvnZxr78S6UhFXi-2" value="" style="rounded=1;whiteSpace=wrap;html=1;" vertex="1" parent="1">
+          <mxGeometry x="127.5" y="740" width="497" height="240" as="geometry" />
+        </mxCell>
+        <mxCell id="38aqMLvnZxr78S6UhFXi-3" value="Agent Detials:" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
+          <mxGeometry x="298.5" y="750" width="139" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="38aqMLvnZxr78S6UhFXi-4" value="My Knowledge Base:" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
+          <mxGeometry x="117.5" y="330" width="132.5" height="30" as="geometry" />
+        </mxCell>
+        <mxCell id="38aqMLvnZxr78S6UhFXi-6" value="Logs/Command Line" style="text;html=1;strokeColor=none;fillColor=none;align=center;verticalAlign=middle;whiteSpace=wrap;rounded=0;" vertex="1" parent="1">
+          <mxGeometry x="117.5" y="570" width="122.5" height="30" as="geometry" />
+        </mxCell>
+      </root>
+    </mxGraphModel>
+  </diagram>
+  <diagram id="x4dUm2vkSZsrNgmtw8U3" name="Devices">
+    <mxGraphModel dx="2286" dy="1168" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="nG_j26PwBAeOLUIgazIM-1" value="" style="verticalLabelPosition=bottom;verticalAlign=top;html=1;shape=mxgraph.basic.rect;fillColor2=none;strokeWidth=1;size=20;indent=5;" vertex="1" parent="1">
+          <mxGeometry x="130" y="80" width="565" height="580" as="geometry" />
+        </mxCell>
+      </root>
+    </mxGraphModel>
+  </diagram>
+  <diagram id="5KzlG5ec7zi2r4DX32yK" name="AIGC">
+    <mxGraphModel dx="2286" dy="1168" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+      </root>
+    </mxGraphModel>
+  </diagram>
+  <diagram id="qqyI6l9TxLyHAeHWymlS" name="AI Store">
+    <mxGraphModel dx="2286" dy="1168" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+      </root>
+    </mxGraphModel>
+  </diagram>
+</mxfile>

rootfs/agents/Lachlan/agent.toml

@@ -8,5 +8,4 @@ max_token_size=4000
 role = "system"
 content = """
 Your name is Lachlan, and you are my advanced private Spanish tutor.
-You are also a local guide familiar with the history of the Inca Empire. While teaching me Spanish, you will introduce some related historical and cultural origins.
 """

src/aios_kernel/__init__.py

@@ -1,7 +1,7 @@
 from .environment import Environment,EnvironmentEvent
-from .agent_message import AgentMsg,AgentMsgStatus,AgentMsgType
+from .agent_base import AgentMsg,AgentMsgStatus,AgentMsgType,AgentPrompt
 from .chatsession import AIChatSession
-from .agent import AIAgent,AIAgentTemplete,AgentPrompt
+from .agent import AIAgent,AIAgentTemplete
 from .compute_kernel import ComputeKernel,ComputeTask,ComputeTaskResult,ComputeTaskState,ComputeTaskType
 from .compute_node import ComputeNode,LocalComputeNode
 from .open_ai_node import OpenAI_ComputeNode

src/aios_kernel/agent.py

@@ -10,73 +10,20 @@ import shlex
 import datetime
 import copy
 
-from .agent_message import AgentMsg, AgentMsgStatus, AgentMsgType,FunctionItem,LLMResult
+from .agent_base import AgentMsg, AgentMsgStatus, AgentMsgType,FunctionItem,LLMResult,AgentPrompt
 from .chatsession import AIChatSession
 from .compute_task import ComputeTaskResult,ComputeTaskResultCode
 from .ai_function import AIFunction
 from .environment import Environment
 from .contact_manager import ContactManager,Contact,FamilyMember
+from .knowledge_base import KnowledgeBase
+from .compute_kernel import ComputeKernel
+from .bus import AIBus
+
+from knowledge import *
 
 logger = logging.getLogger(__name__)
 
-class AgentPrompt:
-    def __init__(self,prompt_str = None) -> None:
-        self.messages = []
-        if prompt_str:
-            self.messages.append({"role":"user","content":prompt_str})
-        self.system_message = None
-
-    def as_str(self)->str:
-        result_str = ""
-        if self.system_message:
-            result_str += self.system_message.get("role") + ":" + self.system_message.get("content") + "\n"
-        if self.messages:
-            for msg in self.messages:
-                result_str += msg.get("role") + ":" + msg.get("content") + "\n"
-
-        return result_str
-
-    def to_message_list(self):
-        result = []
-        if self.system_message:
-            result.append(self.system_message)
-        result.extend(self.messages)
-        return result
-
-    def append(self,prompt):
-        if prompt is None:
-            return
-
-        if prompt.system_message is not None:
-            if self.system_message is None:
-                self.system_message = copy.deepcopy(prompt.system_message)
-            else:
-                self.system_message["content"] += prompt.system_message.get("content")
-
-        self.messages.extend(prompt.messages)
-
-    def get_prompt_token_len(self):
-        result = 0
-
-        if self.system_message:
-            result += len(self.system_message.get("content"))
-        for msg in self.messages:
-            result += len(msg.get("content"))
-
-        return result
-
-    def load_from_config(self,config:list) -> bool:
-        if isinstance(config,list) is not True:
-            logger.error("prompt is not list!")
-            return False
-        self.messages = []
-        for msg in config:
-            if msg.get("role") == "system":
-                self.system_message = msg
-            else:
-                self.messages.append(msg)
-        return True
-
 
 class AIAgentTemplete:
     def __init__(self) -> None:
@@ -106,10 +53,13 @@ class AIAgentTemplete:
 
 class AIAgent:
     def __init__(self) -> None:
+        self.role_prompt:AgentPrompt = None
         self.agent_prompt:AgentPrompt = None
         self.agent_think_prompt:AgentPrompt = None
         self.llm_model_name:str = None
         self.max_token_size:int = 3600
+        
+
         self.agent_id:str = None
         self.template_id:str = None
         self.fullname:str = None
@@ -122,6 +72,9 @@ class AIAgent:
         self.contact_prompt_str = None
         self.history_len = 10
 
+        self.learn_token_limit = 500
+        self.learn_prompt = None
+
         self.chat_db = None
         self.unread_msg = Queue() # msg from other agent
         self.owner_env : Environment = None
@@ -189,77 +142,31 @@ 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
 
+    def get_fullname(self) -> str:
+        return self.fullname
 
-    def _get_llm_result_type(self,llm_result_str:str) -> LLMResult:
+    def get_template_id(self) -> str:
-        r = LLMResult()
+        return self.template_id
-        if llm_result_str is None:
-            r.state = "ignore"
-            return r
-        if llm_result_str == "ignore":
-            r.state = "ignore"
-            return r
 
-        lines = llm_result_str.splitlines()
+    def get_llm_model_name(self) -> str:
-        is_need_wait = False
+        return self.llm_model_name
 
-        def check_args(func_item:FunctionItem):
+    def get_max_token_size(self) -> int:
-            match func_name:
+        return self.max_token_size
-                case "send_msg":# sendmsg($target_id,$msg_content)
+    
-                    if len(func_args) != 1:
+    def get_llm_learn_token_limit(self) -> int:
-                        logger.error(f"parse sendmsg failed! {func_name}")
+        return self.learn_token_limit
-                        return False
+    
-                    new_msg = AgentMsg()
+    def get_learn_prompt(self) -> AgentPrompt:
-                    target_id = func_item.args[0]
+        return self.learn_prompt
-                    msg_content = func_item.body
+    
-                    new_msg.set(self.agent_id,target_id,msg_content)
+    def get_agent_role_prompt(self) -> AgentPrompt:
+        return self.role_prompt
 
-                    r.send_msgs.append(new_msg)
-                    is_need_wait = True
-
-                case "post_msg":# postmsg($target_id,$msg_content)
-                    if len(func_args) != 1:
-                        logger.error(f"parse postmsg failed! {func_name}")
-                        return False
-                    new_msg = AgentMsg()
-                    target_id = func_item.args[0]
-                    msg_content = func_item.body
-                    new_msg.set(self.agent_id,target_id,msg_content)
-                    r.post_msgs.append(new_msg)
-
-                case "call":# call($func_name,$args_str)
-                    r.calls.append(func_item)
-                    is_need_wait = True
-                    return True
-                case "post_call": # post_call($func_name,$args_str)
-                    r.post_calls.append(func_item)
-                    return True
-
-        current_func : FunctionItem = None
-        for line in lines:
-            if line.startswith("##/"):
-                if current_func:
-                    if check_args(current_func) is False:
-                        r.resp += current_func.dumps()
-
-                func_name,func_args = AgentMsg.parse_function_call(line[3:])
-                current_func = FunctionItem(func_name,func_args)
-            else:
-                if current_func:
-                    current_func.append_body(line + "\n")
-                else:
-                    r.resp += line + "\n"
-
-        if current_func:
-            if check_args(current_func) is False:
-                r.resp += current_func.dumps()
-
-        if len(r.send_msgs) > 0 or len(r.calls) > 0:
-            r.state = "waiting"
-        else:
-            r.state = "reponsed"
-
-        return r
     
     def _get_remote_user_prompt(self,remote_user:str) -> AgentPrompt:
         cm = ContactManager.get_instance()
@@ -314,18 +221,18 @@ class AIAgent:
 
         return result_func,result_len
 
-    async def _execute_func(self,inenr_func_call_node:dict,prompt:AgentPrompt,org_msg:AgentMsg,stack_limit = 5) -> [str,int]:
+    async def _execute_func(self,inner_func_call_node:dict,prompt:AgentPrompt,inner_functions,org_msg:AgentMsg=None,stack_limit = 5) -> ComputeTaskResult:
-        from .compute_kernel import ComputeKernel
+        func_name = inner_func_call_node.get("name")
- 
+        arguments = json.loads(inner_func_call_node.get("arguments"))
-        func_name = inenr_func_call_node.get("name")
-        arguments = json.loads(inenr_func_call_node.get("arguments"))
         logger.info(f"llm execute inner func:{func_name} ({json.dumps(arguments)})")
 
         func_node : AIFunction = self.owner_env.get_ai_function(func_name)
         if func_node is None:
             result_str = f"execute {func_name} error,function not found"
         else:
-            ineternal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
+            if org_msg:
+                ineternal_call_record = AgentMsg.create_internal_call_msg(func_name,arguments,org_msg.get_msg_id(),org_msg.target)
+
             try:
                 result_str:str = await func_node.execute(**arguments)
             except Exception as e:
@@ -334,27 +241,29 @@ class AIAgent:
 
 
         logger.info("llm execute inner func result:" + result_str)
-        inner_functions,inner_function_len = self._get_inner_functions()
+        
         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"llm compute error:{task_result.error_str}")
-            return task_result.error_str,1
+            return task_result
         
         ineternal_call_record.result_str = task_result.result_str
         ineternal_call_record.done_time = time.time()
-        org_msg.inner_call_chain.append(ineternal_call_record)
+        if org_msg:
+            org_msg.inner_call_chain.append(ineternal_call_record)
 
+        inner_func_call_node = None
         if stack_limit > 0:
-            result_message = task_result.result.get("message")
+            result_message : dict = task_result.result.get("message")
             if result_message:
                 inner_func_call_node = result_message.get("function_call")
 
         if inner_func_call_node:
             return await self._execute_func(inner_func_call_node,prompt,org_msg,stack_limit-1)
         else:
-            return task_result.result_str,0
+            return task_result
-
+        
     async def _get_agent_prompt(self) -> AgentPrompt:
         return self.agent_prompt
     
@@ -384,12 +293,12 @@ class AIAgent:
         #4) advanced: reload all chatrecord,and think the topic of message.
         #5)     some topic could be end(not be thinked in futured )
         return 
+    
         
     async def think_chatsession(self,session_id):
         if self.agent_think_prompt is None:
             return
         logger.info(f"agent {self.agent_id} think session {session_id}")
-        from .compute_kernel import ComputeKernel
         chatsession = AIChatSession.get_session_by_id(session_id,self.chat_db)
 
         while True:
@@ -420,10 +329,7 @@ class AIAgent:
         
         return 
 
-    async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg:
+    async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg:  
-        from .compute_kernel import ComputeKernel
-        from .bus import AIBus
-        
         session_topic = msg.target + "#" + msg.topic
         chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
         need_process = False
@@ -453,26 +359,13 @@ class AIAgent:
             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:
-                logger.error(f"llm compute error:{task_result.error_str}")
                 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)
-            result_message = task_result.result.get("message")
-            if result_message:
-                inner_func_call_node = result_message.get("function_call")
-            if inner_func_call_node:
-                #TODO to save more token ,can i use msg_prompt?
-                call_prompt : AgentPrompt = copy.deepcopy(prompt)
-                final_result,error_code = await self._execute_func(inner_func_call_node,call_prompt,msg)
-                if error_code != 0:
-                    error_resp = msg.create_error_resp(final_result)
-                    return error_resp
-
-            llm_result : LLMResult = self._get_llm_result_type(final_result)
             is_ignore = False
             result_prompt_str = ""
             match llm_result.state:
@@ -481,6 +374,7 @@ class AIAgent:
                 case "waiting":
                     for sendmsg in llm_result.send_msgs:
                         target = sendmsg.target
+                        sendmsg.sender = self.agent_id
                         sendmsg.topic = msg.topic
                         sendmsg.prev_msg_id = msg.get_msg_id()
                         send_resp = await AIBus.get_default_bus().send_message(sendmsg)
@@ -502,16 +396,12 @@ class AIAgent:
             return None
 
     async def _process_msg(self,msg:AgentMsg) -> AgentMsg:
-            from .compute_kernel import ComputeKernel
-            from .bus import AIBus
-            
             if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
                 return await self._process_group_chat_msg(msg)
 
             session_topic = msg.get_sender() + "#" + msg.topic
             chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
 
-                
             msg_prompt = AgentPrompt()
             msg_prompt.messages = [{"role":"user","content":msg.body}]
 
@@ -530,26 +420,15 @@ class AIAgent:
             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: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:
-                logger.error(f"llm compute error:{task_result.error_str}")
                 error_resp = msg.create_error_resp(task_result.error_str)
                 return error_resp
             
             final_result = task_result.result_str
 
-            result_message = task_result.result.get("message")
+            llm_result : LLMResult = LLMResult.from_str(final_result)
-            if result_message:
-                inner_func_call_node = result_message.get("function_call")
-            if inner_func_call_node:
-                #TODO to save more token ,can i use msg_prompt?
-                call_prompt : AgentPrompt = copy.deepcopy(prompt)
-                final_result,error_code = await self._execute_func(inner_func_call_node,call_prompt,msg)
-                if error_code != 0:
-                    error_resp = msg.create_error_resp(final_result)
-                    return error_resp
-
-            llm_result : LLMResult = self._get_llm_result_type(final_result)
             is_ignore = False
             result_prompt_str = ""
             match llm_result.state:
@@ -557,6 +436,7 @@ class AIAgent:
                     is_ignore = True
                 case "waiting":
                     for sendmsg in llm_result.send_msgs:
+                        sendmsg.sender = self.agent_id
                         target = sendmsg.target
                         sendmsg.topic = msg.topic
                         sendmsg.prev_msg_id = msg.get_msg_id()
@@ -578,20 +458,7 @@ class AIAgent:
 
             return None
 
-    def get_id(self) -> str:
-        return self.agent_id
 
-    def get_fullname(self) -> str:
-        return self.fullname
-
-    def get_template_id(self) -> str:
-        return self.template_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
     
     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
@@ -660,6 +527,74 @@ class AIAgent:
 
         return result_prompt,result_token_len
 
+    async def _do_llm_complection(self,prompt:AgentPrompt,inner_functions:dict,org_msg:AgentMsg=None) -> ComputeTaskResult:
+        from .compute_kernel import ComputeKernel
+        #logger.debug(f"Agent {self.agent_id} do llm token static system:{system_prompt_len},function:{function_token_len},history:{history_token_len},input:{input_len}, totoal prompt:{system_prompt_len + function_token_len + history_token_len} ")
+        task_result:ComputeTaskResult = await ComputeKernel.get_instance().do_llm_completion(prompt,self.llm_model_name,self.max_token_size,inner_functions)
+        if task_result.result_code != ComputeTaskResultCode.OK:
+            logger.error(f"llm compute error:{task_result.error_str}")
+            #error_resp = msg.create_error_resp(task_result.error_str)
+            return task_result
+
+        result_message = task_result.result.get("message")
+        inner_func_call_node = None
+        if result_message:
+            inner_func_call_node = result_message.get("function_call")
+
+        if inner_func_call_node:
+            call_prompt : AgentPrompt = copy.deepcopy(prompt)
+            task_result = await self._execute_func(inner_func_call_node,call_prompt,inner_functions,org_msg)
+            
+        return task_result
+
+    def parser_learn_llm_result(self,llm_result:str):
+        pass
+
+    async def _llm_read_article(self,kb:KnowledgeBase,item:KnowledgeObject) -> ComputeTaskResult:
+        #kb_env = KnowledgeBaseFileSystemEnvironment()
+        full_content = item.get_article_full_content()
+        full_content_len = ComputeKernel.llm_num_tokens_from_text(full_content,self.get_llm_model_name())
+        if full_content_len < self.get_llm_learn_token_limit():
+            
+            # 短文章不用总结catelog
+            #path_list,summary = llm_get_summary(summary,full_content)
+            prompt = self.get_agent_role_prompt()
+            learn_prompt = self.get_learn_prompt()
+            cotent_prompt = AgentPrompt(full_content)
+            prompt.append(learn_prompt)
+            prompt.append(cotent_prompt)
+            
+            env_functions = self._get_inner_functions()
+            
+            task_result:ComputeTaskResult = await self._do_llm_complection(prompt,env_functions)
+            if task_result.result_code != ComputeTaskResultCode.OK:
+                return task_result
+            path_list,summary = self.parser_learn_llm_result(task_result.result_str)
+
+        else:
+            # 用传统方法对文章进行一些处理，目的是尽可能减少LLM调用的次数
+            catelog = item.get_articl_catelog()
+            chunk_content = full_content.read(self.get_llm_learn_token_limit())
+            summary = kb.try_get_summary(catelog,full_content)
+        
+            while chunk_content is not None:
+                #path_list,summarycatelog = llm_get_summary(summary,chunk_content)
+                #learn_prompt = self.get_learn_prompt_with_summary()
+
+                prompt = AgentPrompt("summary")
+                learn_prompt.append(prompt)
+                prompt = AgentPrompt(chunk_content)
+                learn_prompt.append(prompt)
+                
+                #llm_result = self.do_llm_competion(learn_prompt)
+                #path_list,summary,catelog = parser_learn_llm_result(llm_result)
+
+                #chunk_content = full_content.read(self.get_llm_learn_token_limit())
+            
+        kb.insert_item(path_list,item,catelog,summary) 
+
+
+
     async def _get_prompt_from_session(self,chatsession:AIChatSession,system_token_len,input_token_len) -> AgentPrompt:
         # TODO: get prompt from group chat is different from single chat
         

src/aios_kernel/agent_base.py

@@ -0,0 +1,317 @@
+import copy
+import logging
+from enum import Enum
+import uuid
+import time 
+import re
+import shlex
+from typing import List
+from .ai_function import FunctionItem
+
+logger = logging.getLogger(__name__)
+
+class AgentMsgType(Enum):
+    TYPE_MSG = 0
+    TYPE_GROUPMSG = 1
+    TYPE_INTERNAL_CALL = 10
+    TYPE_ACTION = 20
+    TYPE_EVENT = 30
+    TYPE_SYSTEM = 40
+
+
+class AgentMsgStatus(Enum):
+    RESPONSED = 0
+    INIT = 1
+    SENDING = 2
+    PROCESSING = 3
+    ERROR = 4
+    RECVED = 5
+    EXECUTED = 6
+
+# msg is a msg / msg resp
+# msg body可以有内容类型（MIME标签），text, image, voice, video, file,以及富文本(html)
+# msg is a inner function call with result
+# msg is a Action with result
+
+# qutoe Msg
+# forword msg
+# reply msg
+
+# 逻辑上的同一个Message在同一个session中看到的msgid相同
+#       在不同的session中看到的msgid不同
+
+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.rely_msg_id:str = None # if not none means this is a respone msg
+        self.session_id:str = None
+
+        #forword info
+
+
+        self.create_time = 0
+        self.done_time = 0
+        self.topic:str = None # topic is use to find session, not store in db
+
+        self.sender:str = None # obj_id.sub_objid@tunnel_id
+        self.target:str = None
+        self.mentions:[] = None #use in group chat only
+        #self.title:str = None
+        self.body:str = None
+        self.body_mime:str = None #//default is "text/plain",encode is utf8
+
+        #type is call / action
+        self.func_name = None 
+        self.args = None
+        self.result_str = None
+
+        #type is event
+        self.event_name = None
+        self.event_args = None
+
+        self.status = AgentMsgStatus.INIT
+        self.inner_call_chain = []
+        self.resp_msg = None
+
+    @classmethod
+    def create_internal_call_msg(self,func_name:str,args:dict,prev_msg_id:str,caller:str):
+        msg = AgentMsg(AgentMsgType.TYPE_INTERNAL_CALL)
+        msg.create_time = time.time()
+        msg.func_name = func_name
+        msg.args = args
+        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()
+        msg.func_name = action_name
+        msg.args = args
+        msg.prev_msg_id = self.msg_id
+        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
+        resp_msg.sender = self.target
+        resp_msg.target = self.sender
+
+        return resp_msg
+
+    def create_resp_msg(self,resp_body):
+        resp_msg = AgentMsg()
+        resp_msg.create_time = time.time()
+
+        resp_msg.rely_msg_id = self.msg_id
+        resp_msg.sender = self.target
+        resp_msg.target = self.sender
+        resp_msg.body = resp_body
+        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()
+
+        resp_msg.rely_msg_id = self.msg_id
+        resp_msg.target = self.target
+        resp_msg.sender = sender_id
+        resp_msg.body = resp_body
+        resp_msg.topic = self.topic
+
+        return resp_msg
+
+    def set(self,sender:str,target:str,body:str,topic:str=None) -> None:
+        self.sender = sender
+        self.target = target
+        self.body = body
+        self.create_time = time.time()
+        if topic:
+            self.topic = topic
+
+    def get_msg_id(self) -> str:
+        return self.msg_id
+
+    def get_sender(self) -> str:
+        return self.sender
+
+    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)
+        func_name = str_list[0]
+        params = str_list[1:]
+        return func_name, params
+
+class AgentPrompt:
+    def __init__(self,prompt_str = None) -> None:
+        self.messages = []
+        if prompt_str:
+            self.messages.append({"role":"user","content":prompt_str})
+        self.system_message = None
+
+    def as_str(self)->str:
+        result_str = ""
+        if self.system_message:
+            result_str += self.system_message.get("role") + ":" + self.system_message.get("content") + "\n"
+        if self.messages:
+            for msg in self.messages:
+                result_str += msg.get("role") + ":" + msg.get("content") + "\n"
+
+        return result_str
+
+    def to_message_list(self):
+        result = []
+        if self.system_message:
+            result.append(self.system_message)
+        result.extend(self.messages)
+        return result
+
+    def append(self,prompt):
+        if prompt is None:
+            return
+
+        if prompt.system_message is not None:
+            if self.system_message is None:
+                self.system_message = copy.deepcopy(prompt.system_message)
+            else:
+                self.system_message["content"] += prompt.system_message.get("content")
+
+        self.messages.extend(prompt.messages)
+
+    def get_prompt_token_len(self):
+        result = 0
+
+        if self.system_message:
+            result += len(self.system_message.get("content"))
+        for msg in self.messages:
+            result += len(msg.get("content"))
+
+        return result
+
+    def load_from_config(self,config:list) -> bool:
+        if isinstance(config,list) is not True:
+            logger.error("prompt is not list!")
+            return False
+        self.messages = []
+        for msg in config:
+            if msg.get("role") == "system":
+                self.system_message = msg
+            else:
+                self.messages.append(msg)
+        return True
+        
+class LLMResult:
+    def __init__(self) -> None:
+        self.state : str = "ignore"
+        self.resp : str = ""
+        self.paragraphs : dict[str,FunctionItem] = []
+        self.post_msgs : List[AgentMsg] = []
+        self.send_msgs : List[AgentMsg] = []
+        self.calls : List[FunctionItem] = []
+        self.post_calls : List[FunctionItem] = []
+
+    @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
+
+        lines = llm_result_str.splitlines()
+        is_need_wait = False
+
+        def check_args(func_item:FunctionItem):
+            match func_name:
+                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
+                    new_msg.set("",target_id,msg_content)
+
+                    r.send_msgs.append(new_msg)
+                    is_need_wait = True
+                    return True
+
+                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
+                    new_msg.set("",target_id,msg_content)
+                    r.post_msgs.append(new_msg)
+                    return True
+
+                case "call":# /call $func_name $args_str
+                    r.calls.append(func_item)
+                    is_need_wait = True
+                    return True
+                case "post_call": # /post_call $func_name,$args_str
+                    r.post_calls.append(func_item)
+                    return True
+                case _:
+                    if valid_func is not None:
+                        if func_name in valid_func:
+                            r.paragraphs[func_name] = func_item
+                            return True
+            
+            return False
+                
+
+        current_func : FunctionItem = None
+        for line in lines:
+            if line.startswith("##/"):
+                if current_func:
+                    if check_args(current_func) is False:
+                        r.resp += current_func.dumps()
+
+                func_name,func_args = AgentMsg.parse_function_call(line[3:])
+                current_func = FunctionItem(func_name,func_args)
+            else:
+                if current_func:
+                    current_func.append_body(line + "\n")
+                else:
+                    r.resp += line + "\n"
+
+        if current_func:
+            if check_args(current_func) is False:
+                r.resp += current_func.dumps()
+
+        if len(r.send_msgs) > 0 or len(r.calls) > 0:
+            r.state = "waiting"
+        else:
+            r.state = "reponsed"
+
+        return r    
+
+class BaseAIAgent:
+    def __init__(self) -> None:
+        pass

src/aios_kernel/agent_message.py

@@ -1,169 +0,0 @@
-from enum import Enum
-import uuid
-import time 
-import re
-import shlex
-from typing import List
-from .ai_function import FunctionItem
-
-class AgentMsgType(Enum):
-    TYPE_MSG = 0
-    TYPE_GROUPMSG = 1
-    TYPE_INTERNAL_CALL = 10
-    TYPE_ACTION = 20
-    TYPE_EVENT = 30
-    TYPE_SYSTEM = 40
-
-
-class AgentMsgStatus(Enum):
-    RESPONSED = 0
-    INIT = 1
-    SENDING = 2
-    PROCESSING = 3
-    ERROR = 4
-    RECVED = 5
-    EXECUTED = 6
-
-# msg is a msg / msg resp
-# msg body可以有内容类型（MIME标签），text, image, voice, video, file,以及富文本(html)
-# msg is a inner function call with result
-# msg is a Action with result
-
-# qutoe Msg
-# forword msg
-# reply msg
-
-# 逻辑上的同一个Message在同一个session中看到的msgid相同
-#       在不同的session中看到的msgid不同
-
-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.rely_msg_id:str = None # if not none means this is a respone msg
-        self.session_id:str = None
-
-        #forword info
-
-
-        self.create_time = 0
-        self.done_time = 0
-        self.topic:str = None # topic is use to find session, not store in db
-
-        self.sender:str = None # obj_id.sub_objid@tunnel_id
-        self.target:str = None
-        self.mentions:[] = None #use in group chat only
-        #self.title:str = None
-        self.body:str = None
-        self.body_mime:str = None #//default is "text/plain",encode is utf8
-
-        #type is call / action
-        self.func_name = None 
-        self.args = None
-        self.result_str = None
-
-        #type is event
-        self.event_name = None
-        self.event_args = None
-
-        self.status = AgentMsgStatus.INIT
-        self.inner_call_chain = []
-        self.resp_msg = None
-
-    @classmethod
-    def create_internal_call_msg(self,func_name:str,args:dict,prev_msg_id:str,caller:str):
-        msg = AgentMsg(AgentMsgType.TYPE_INTERNAL_CALL)
-        msg.create_time = time.time()
-        msg.func_name = func_name
-        msg.args = args
-        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()
-        msg.func_name = action_name
-        msg.args = args
-        msg.prev_msg_id = self.msg_id
-        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
-        resp_msg.sender = self.target
-        resp_msg.target = self.sender
-
-        return resp_msg
-
-    def create_resp_msg(self,resp_body):
-        resp_msg = AgentMsg()
-        resp_msg.create_time = time.time()
-
-        resp_msg.rely_msg_id = self.msg_id
-        resp_msg.sender = self.target
-        resp_msg.target = self.sender
-        resp_msg.body = resp_body
-        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()
-
-        resp_msg.rely_msg_id = self.msg_id
-        resp_msg.target = self.target
-        resp_msg.sender = sender_id
-        resp_msg.body = resp_body
-        resp_msg.topic = self.topic
-
-        return resp_msg
-
-    def set(self,sender:str,target:str,body:str,topic:str=None) -> None:
-        self.sender = sender
-        self.target = target
-        self.body = body
-        self.create_time = time.time()
-        if topic:
-            self.topic = topic
-
-    def get_msg_id(self) -> str:
-        return self.msg_id
-
-    def get_sender(self) -> str:
-        return self.sender
-
-    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)
-        func_name = str_list[0]
-        params = str_list[1:]
-        return func_name, params
-        
-class LLMResult:
-    def __init__(self) -> None:
-        self.state : str = "ignore"
-        self.resp : str = ""
-        self.post_msgs : List[AgentMsg] = []
-        self.send_msgs : List[AgentMsg] = []
-        self.calls : List[FunctionItem] = []
-        self.post_calls : List[FunctionItem] = []

src/aios_kernel/bus.py

@@ -1,5 +1,5 @@
 from typing import Coroutine,Dict,Any
-from .agent_message import AgentMsg,AgentMsgStatus,AgentMsgType
+from .agent_base import AgentMsg,AgentMsgStatus,AgentMsgType
 import asyncio
 from asyncio import Queue
 

src/aios_kernel/chatsession.py

@@ -7,7 +7,7 @@ import datetime
 import uuid
 import json
 
-from .agent_message import AgentMsgType, AgentMsg, AgentMsgStatus
+from .agent_base import AgentMsgType, AgentMsg, AgentMsgStatus
 
 class ChatSessionDB:
     def __init__(self, db_file):

src/aios_kernel/compute_kernel.py

@@ -3,10 +3,12 @@ import random
 from typing import Optional
 import logging
 import asyncio
+import tiktoken
+
 from asyncio import Queue
 
 from knowledge import ObjectID
-from .agent import AgentPrompt
+from .agent_base import AgentPrompt
 from .compute_node import ComputeNode
 from .compute_task import ComputeTask, ComputeTaskState, ComputeTaskResult, ComputeTaskType,ComputeTaskResultCode
 
@@ -104,6 +106,18 @@ class ComputeKernel:
     def is_task_support(self, task: ComputeTask) -> bool:
         return True
 
+    @staticmethod
+    def llm_num_tokens_from_text(text:str,model:str) -> int:
+        try:
+            encoding = tiktoken.encoding_for_model(model)
+        except KeyError:
+            logger.debug("Warning: model not found. Using cl100k_base encoding.")
+            encoding = tiktoken.get_encoding("cl100k_base")
+
+        token_count = len(encoding.encode(text))
+        return token_count
+
+
     # friendly interface for use:
     def llm_completion(self, prompt: AgentPrompt, mode_name: Optional[str] = None, max_token: int = 0,inner_functions = None):
         # craete a llm_work_task ,push on queue's end

src/aios_kernel/email_tunnel.py

@@ -8,7 +8,7 @@ import logging
 import time
 import datetime
 from .tunnel import AgentTunnel
-from .agent_message import AgentMsg
+from .agent_base import AgentMsg
 
 from email.message import EmailMessage
 

src/aios_kernel/knowledge_base.py

@@ -1,7 +1,8 @@
 # define a knowledge base class
 import json
 import logging
-from .agent import AgentPrompt
+
+from .agent_base import AgentPrompt
 from .compute_kernel import ComputeKernel 
 from .storage import AIStorage
 from .environment import Environment

src/aios_kernel/open_ai_node.py

@@ -118,7 +118,7 @@ class OpenAI_ComputeNode(ComputeNode):
                                                         #max_tokens=result_token,
                                                         temperature=0.7)
                     else:
-                        logger.info(f"call openai {mode_name} prompts: {prompts} functions: {json.dumps(llm_inner_functions)}")
+                        logger.info(f"call openai {mode_name} prompts: \n\t {prompts} \nfunctions: \n\t{json.dumps(llm_inner_functions)}")
                         resp = openai.ChatCompletion.create(model=mode_name,
                                                             messages=prompts,
                                                             functions=llm_inner_functions,

src/aios_kernel/role.py

@@ -1,6 +1,6 @@
 import logging
 
-from .agent import AIAgent,AgentPrompt
+from .agent_base import AgentPrompt
 
 class AIRole:
     def __init__(self) -> None:

src/aios_kernel/tg_tunnel.py

@@ -17,7 +17,7 @@ from .knowledge_base import KnowledgeBase
 from .tunnel import AgentTunnel
 from .storage import AIStorage
 from .contact_manager import ContactManager,Contact,FamilyMember
-from .agent_message import AgentMsg,AgentMsgType
+from .agent_base import AgentMsg,AgentMsgType
 
 
 logger = logging.getLogger(__name__)

src/aios_kernel/tunnel.py

@@ -1,7 +1,7 @@
 from abc import ABC, abstractmethod
 import logging
 from typing import Coroutine
-from .agent_message import AgentMsg
+from .agent_base import AgentMsg
 from .bus import AIBus
 
 logger = logging.getLogger(__name__)

src/aios_kernel/workflow.py

@@ -8,8 +8,7 @@ from typing import Optional,Tuple,List
 from abc import ABC, abstractmethod
 
 from .environment import Environment,EnvironmentEvent
-from .agent_message import AgentMsg,AgentMsgStatus,FunctionItem,LLMResult
+from .agent_base import AgentMsg,AgentMsgStatus,FunctionItem,LLMResult,AgentPrompt
-from .agent import AgentPrompt,AgentMsg
 from .chatsession import AIChatSession
 from .role import AIRole,AIRoleGroup
 from .ai_function import AIFunction,FunctionItem
@@ -238,77 +237,6 @@ class Workflow:
         error_resp = msg.create_error_resp(err_str)
         return error_resp
 
-    @classmethod
-    def prase_llm_result(cls,llm_result_str:str)->LLMResult:
-        r = LLMResult()
-        if llm_result_str is None:
-            r.state = "ignore"
-            return r
-        if llm_result_str == "ignore":
-            r.state = "ignore"
-            return r
-
-        lines = llm_result_str.splitlines()
-        is_need_wait = False
-
-        def check_args(func_item:FunctionItem):
-            match func_name:
-                case "send_msg":# sendmsg($target_id,$msg_content)
-                    if len(func_item.args) != 1:
-                        logger.error(f"parse sendmsg failed! {func_item}")
-                        return False
-                    new_msg = AgentMsg()
-                    target_id = func_item.args[0]
-                    msg_content = func_item.body
-                    new_msg.set("_",target_id,msg_content)
-
-                    r.send_msgs.append(new_msg)
-                    is_need_wait = True
-
-                case "post_msg":# postmsg($target_id,$msg_content)
-                    if len(func_item.args) != 1:
-                        logger.error(f"parse postmsg failed! {func_item}")
-                        return False
-                    new_msg = AgentMsg()
-                    target_id = func_item.args[0]
-                    msg_content = func_item.body
-                    new_msg.set("_",target_id,msg_content)
-                    r.post_msgs.append(new_msg)
-
-                case "call":# call($func_name,$args_str)
-                    r.calls.append(func_item)
-                    is_need_wait = True
-                    return True
-                case "post_call": # post_call($func_name,$args_str)
-                    r.post_calls.append(func_item)
-                    return True
-
-        current_func : FunctionItem = None
-        for line in lines:
-            if line.startswith("##/"):
-                if current_func:
-                    if check_args(current_func) is False:
-                        r.resp += current_func.dumps()
-
-                func_name,func_args = AgentMsg.parse_function_call(line[3:])
-                current_func = FunctionItem(func_name,func_args)
-            else:
-                if current_func:
-                    current_func.append_body(line + "\n")
-                else:
-                    r.resp += line + "\n"
-
-        if current_func:
-            if check_args(current_func) is False:
-                r.resp += current_func.dumps()
-
-        if len(r.send_msgs) > 0 or len(r.calls) > 0:
-            r.state = "waiting"
-        else:
-            r.state = "reponsed"
-
-        return r
-
     async def role_post_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession):
         msg.sender = the_role.get_role_id()
 
@@ -395,7 +323,6 @@ class Workflow:
         return None
 
     async def _role_execute_func(self,the_role:AIRole,inenr_func_call_node:dict,prompt:AgentPrompt,org_msg:AgentMsg,stack_limit = 5) -> [str,int]:
-        from .compute_kernel import ComputeKernel
 
         func_name = inenr_func_call_node.get("name")
         arguments = json.loads(inenr_func_call_node.get("arguments"))
@@ -441,7 +368,6 @@ class Workflow:
     async def role_process_msg(self,msg:AgentMsg,the_role:AIRole,workflow_chat_session:AIChatSession) -> AgentMsg:
         msg.target = the_role.get_role_id()
 
-
         prompt = AgentPrompt()
         prompt.append(the_role.agent.agent_prompt)
         prompt.append(self.get_workflow_rule_prompt())
@@ -481,7 +407,7 @@ class Workflow:
                     error_resp = msg.create_error_resp(result_str)
                     return error_resp
 
-            result : LLMResult = Workflow.prase_llm_result(result_str)
+            result : LLMResult = LLMResult.from_str(result_str)
             for postmsg in result.post_msgs:
                 postmsg.prev_msg_id = msg.get_msg_id()
                 # might be craete a new msg.topic for this postmsg

src/aios_kernel/workspace_env.py

@@ -1,5 +1,6 @@
 # this env is designed for workflow owner filesystem, support file/directory operations
 
+import json
 import subprocess
 import tempfile
 import threading
@@ -9,6 +10,9 @@ import ast
 import sys
 import os
 import re
+import asyncio
+import aiofiles.os
+import chardet
 
 from .environment import Environment,EnvironmentEvent
 from .ai_function import AIFunction,SimpleAIFunction
@@ -170,4 +174,50 @@ class WorkspaceEnvironment(Environment):
     async def run_code(self,pycode:str) -> str:
         interpreter = CodeInterpreter("python",True)
         return interpreter.run(pycode)
+    
+
+
+class KnowledgeBaseFileSystemEnvironment(Environment):
+    def __init__(self, env_id: str) -> None:
+        super().__init__(env_id)
+        self.root_path = "."
+
+        operator_param = {
+            "path": "full path of target directory",
+        }
+        self.add_ai_function(SimpleAIFunction("list",
+                                        "list the files and sub directory in target directory,result is a json array",
+                                        self.list,operator_param))
+        
+        operator_param = {
+            "path": "full path of target file",
+        }
+        self.add_ai_function(SimpleAIFunction("cat",
+                                        "cat the file content in target path,result is a string",
+                                        self.cat,operator_param))
+    
+    def set_root_path(self,path:str):
+        self.root_path = path
+
+    
+    async def list(self,path:str) -> str:
+        directory_path = self.root_path + path
+        items = []
+
+        with await aiofiles.os.scandir(directory_path) as entries:
+            async for entry in entries:
+                item_type = "directory" if entry.is_dir() else "file"
+                items.append({"name": entry.name, "type": item_type})
+
+        return json.dumps(items)
+
+    async def cat(self,path:str) -> str:
+        file_path = self.root_path + path
+        cur_encode = "utf-8"
+        async with aiofiles.open(file_path,'rb') as f:
+            cur_encode = chardet.detect(await f.read())['encoding']
+
+        async with aiofiles.open(file_path, mode='r', encoding=cur_encode) as f:
+            content = await f.read(2048)
+        return content
 

src/knowledge/object/object.py

@@ -47,6 +47,17 @@ class KnowledgeObject(ABC):
 
     def get_body(self) -> dict:
         return self.body
+    
+    def get_summary(self) -> str:
+        return self.desc.get("summary")
+    
+    def get_articl_catelog(self) -> str:
+        assert self.object_type == ObjectType.Document
+        return self.desc.get("catelog")
+    
+    def get_article_full_content(self) -> str:
+        assert self.object_type == ObjectType.Document
+        return self.body
 
     def calculate_id(self):
         # Convert the object_type and desc to string and compute the SHA256 hash

src/knowledge/store.py

@@ -6,6 +6,8 @@ from .vector import ChromaVectorStore, VectorBase
 import logging
 
 
+
+
 # KnowledgeStore class, which aggregates ChunkStore, ChunkTracker, and ObjectStore, and is a global singleton that makes it easy to use these three built-in store examples
 class KnowledgeStore:
     _instance = None
@@ -41,6 +43,8 @@ class KnowledgeStore:
         self.chunk_list_writer = ChunkListWriter(self.chunk_store, self.chunk_tracker)
         self.chunk_reader = ChunkReader(self.chunk_store, self.chunk_tracker)
         self.vector_store = {}
+
+    
     
     def get_relation_store(self) -> ObjectRelationStore:
         return self.relation_store