Refactor the Action/Function components, and refactor the basic architecture of Agent Task/Todo.

src/aios/agent/agent_memory.py

@@ -1,11 +1,17 @@
-from ast import Dict
-from datetime import timedelta
-from typing import List
+# pylint:disable=E0402
+from datetime import datetime,timedelta
+from typing import Dict
 
 from ..frame.compute_kernel import ComputeKernel
-from ..proto.ai_function import SimpleAIOperation
+from ..proto.ai_function import SimpleAIAction
+from ..proto.agent_msg import AgentMsg, AgentMsgType
 
-from .chatsession import *
+from .llm_context import GlobaToolsLibrary
+from .chatsession import AIChatSession
+
+import logging
+
+logger = logging.getLogger(__name__)
 
 class AgentMemory:
     def __init__(self,agent_id:str,db_path:str) -> None:
@@ -14,12 +20,16 @@ class AgentMemory:
         self.model_name:str = "gp4-1106-preview"
         self.threshold_hours = 72
 
-        self.actions = {}
-        self.init_actions()
-
-    def init_actions(self) -> Dict:
-        chatlog_append_op = SimpleAIOperation("chatlog_append","Append request & reply message to chatlog. No params",self.action_chatlog_append)
-        self.actions[chatlog_append_op.get_name()] = chatlog_append_op
+    @classmethod
+    def register_actions(cls):
+        async def action_chatlog_append(parms:Dict):
+            memory = parms.get("_memory")
+            if memory:
+                return await memory.action_chatlog_append(parms)
+            
+        chatlog_append_action = SimpleAIAction("chatlog_append","Append request & reply message to chatlog. No params",action_chatlog_append)
+        GlobaToolsLibrary.get_instance().register_tool_function(chatlog_append_action,"agent.memory.chatlog.append")
+        
 
     def get_session_from_msg(self,msg:AgentMsg) -> AIChatSession:
         if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
@@ -32,8 +42,8 @@ class AgentMemory:
 
     async def load_chatlogs(self,msg:AgentMsg,n:int=6,m:int=64,token_limit=800)->str:
         chatsession = self.get_session_from_msg(msg)
-        # 必定加载n条(n>=2),期望加载m条
-        # m条里的信息逐步添加，知道距离现在的时间未72小时以上，且消耗了足够的Token
+        # Must load n (n> = 2), and hope to load the M
+        # The information in the # M is gradually added, knowing that it is less than 72 hours from the current time, and consumes enough tokens
 
         messages_n = chatsession.read_history(n) # read
         if len(messages_n) >= n:
@@ -44,7 +54,7 @@ class AgentMemory:
         histroy_str = ""
         read_count = 0
         for msg in messages_n:
-            dt = datetime.datetime.fromtimestamp(float(msg.create_time))
+            dt = datetime.fromtimestamp(float(msg.create_time))
             formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
             record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
             token_limit -= ComputeKernel.llm_num_tokens_from_text(record_str,self.model_name)
@@ -57,9 +67,9 @@ class AgentMemory:
             if read_count < 3:
                 logging.warning(f"read history {read_count} < 3, will not load more")
 
-        now = datetime.datetime.now()
+        now = datetime.now()
         for msg in messages_m:
-            dt = datetime.datetime.fromtimestamp(float(msg.create_time))
+            dt = datetime.fromtimestamp(float(msg.create_time))
             time_diff = now - dt
             if time_diff > timedelta(hours=self.threshold_hours):
                 break
@@ -95,10 +105,18 @@ class AgentMemory:
             return "lzc is your master. Male, 40 years old, Mother tongue is Chinese, senior software engineer."
         return None
     
-    def get_actions(self) -> Dict:
-        return self.actions
+    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
     
     async def get_log_summary(self,msg:AgentMsg) -> str:
         return None
     
+
+