Use LLMProcess implement Agent.OnMessage

src/aios/agent/agent_memory.py

@@ -0,0 +1,104 @@
+from ast import Dict
+from datetime import timedelta
+from typing import List
+
+from ..frame.compute_kernel import ComputeKernel
+from ..proto.ai_function import SimpleAIOperation
+
+from .chatsession import *
+
+class AgentMemory:
+    def __init__(self,agent_id:str,db_path:str) -> None:
+        self.agent_id:str= agent_id
+        self.chat_db:str = db_path
+        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
+
+    def get_session_from_msg(self,msg:AgentMsg) -> AIChatSession:
+        if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
+            session_topic = msg.target + "#" + msg.topic
+            chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
+        else:
+            session_topic = msg.get_sender() + "#" + msg.topic
+            chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
+        return chatsession
+
+    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
+
+        messages_n = chatsession.read_history(n) # read
+        if len(messages_n) >= n:
+            messages_m = chatsession.read_history(m,n)
+        else:
+            messages_m = []
+
+        histroy_str = ""
+        read_count = 0
+        for msg in messages_n:
+            dt = datetime.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)
+            if token_limit <= 32:
+                break
+            read_count += 1
+            histroy_str = record_str + histroy_str
+
+        if len(messages_n) > 2:
+            if read_count < 3:
+                logging.warning(f"read history {read_count} < 3, will not load more")
+
+        now = datetime.datetime.now()
+        for msg in messages_m:
+            dt = datetime.datetime.fromtimestamp(float(msg.create_time))
+            time_diff = now - dt
+            if time_diff > timedelta(hours=self.threshold_hours):
+                break
+
+            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)
+            if token_limit <= 32:
+                break
+            read_count += 1
+            histroy_str = record_str + histroy_str
+
+        return histroy_str 
+    
+    async def action_chatlog_append(self,params:Dict) -> str:
+        # 使用params可以得到: LLM Process的输入，LLM Result,基于LLM Result构造的参数，当前actionItem
+        input_msg:AgentMsg = params.get("input").get("msg")
+        llm_result = params.get("llm_result")
+        chatsession = self.get_session_from_msg(input_msg)
+        resp_msg = params.get("resp_msg")
+        if resp_msg:
+            tags =  llm_result.raw_result.get("tags")
+            chatsession.append(input_msg,tags)
+            chatsession.append(resp_msg,tags)
+    
+        return "OK"
+    
+    async def get_contact_summary(self,contact_id:str) -> str:
+        if contact_id is None:
+            return None
+        
+        if contact_id == "lzc":
+            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 get_log_summary(self,msg:AgentMsg) -> str:
+        return None
+    
+