rollback agent memory to "chat session history & session summary"
This commit is contained in:
Liu Zhicong
+85
-113
@@ -39,8 +39,9 @@ class BaseLLMProcess(ABC):
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#None means system default,
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# TODO: support abcstract model name like: local-hight,local-low,local-medium,remote-hight,remote-low,remote-medium
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self.model_name = None
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self.max_token = 1000 # result_token
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self.max_prompt_token = 1000 # not include input prompt
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self.max_token = 2000 # result_token
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self.max_prompt_token = 2000 # not include input prompt
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self.chat_summary_token_len = 500
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self.timeout = 1800 # 30 min
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self.llm_context:LLMProcessContext = None
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@@ -64,6 +65,9 @@ class BaseLLMProcess(ABC):
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async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
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pass
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def get_remain_prompt_length(self,prompt:LLMPrompt,will_append_str:str) -> int:
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return self.max_prompt_token - ComputeKernel.llm_num_tokens(prompt,self.model_name)
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@abstractmethod
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async def load_from_config(self,config:dict) -> bool:
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#self.behavior = config.get("behavior")
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@@ -166,6 +170,10 @@ class BaseLLMProcess(ABC):
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# Action define in prompt, will be execute after llm compute
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prompt = await self.prepare_prompt(input)
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if prompt is None:
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logger.warn(f"prepare_prompt return None, break llm_process")
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return LLMResult.from_error_str("prepare_prompt return None")
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max_result_token = self.max_token - ComputeKernel.llm_num_tokens(prompt,self.get_llm_model_name())
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#if max_result_token < MIN_PREDICT_TOKEN_LEN:
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# return LLMResult.from_error_str(f"prompt too long,can not predict")
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@@ -429,14 +437,15 @@ class AgentMessageProcess(LLMAgentBaseProcess):
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#TODO Is sender an agent?
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return await self.memory.get_contact_summary(sender_id)
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async def load_chatlogs(self,msg:AgentMsg)->str:
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async def load_chatlogs(self,msg:AgentMsg,max_length_by_token:int)->str:
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## like
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#sender,[2023-11-1 12:00:00]
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#content
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return await self.memory.load_chatlogs(msg)
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return await self.memory.load_chatlogs(msg,max_length_by_token)
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async def get_chat_summary(self,msg:AgentMsg)->str:
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return await self.memory.get_chat_summary(msg)
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async def get_log_summary(self,msg:AgentMsg)->str:
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return None
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async def get_extend_known_info(self,msg:AgentMsg,prompt:LLMPrompt)->str:
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@@ -466,18 +475,7 @@ class AgentMessageProcess(LLMAgentBaseProcess):
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### 信息发送者资料
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known_info["sender_info"] = await self.sender_info(msg)
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#prompt.append_system_message(await self.sender_info(self,msg))
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### 近期的聊天记录
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chat_record = await self.load_chatlogs(msg)
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if chat_record:
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if len(chat_record) > 4:
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known_info["chat_record"] = chat_record
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#prompt.append_system_message(await self.load_chatlogs(self,msg))
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### 交流总结
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summary = await self.get_log_summary(msg)
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if summary:
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if len(summary) > 4:
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known_info["summary"] = summary
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#prompt.append_system_message(await self.get_log_summary(self,msg))
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system_prompt_dict["known_info"] = known_info
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prompt.inner_functions =LLMProcessContext.aifunctions_to_inner_functions(self.llm_context.get_all_ai_functions())
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@@ -490,10 +488,24 @@ class AgentMessageProcess(LLMAgentBaseProcess):
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logger.info(f"enable kb")
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prompt.append_system_message(json.dumps(system_prompt_dict,ensure_ascii=False))
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## 扩展已知信息 (这可能是一个LLM过程)
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prompt.append_system_message(await self.get_extend_known_info(msg,prompt))
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### 根据Token Limit加载聊天记录
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remain_token = self.get_remain_prompt_length(prompt,json.dumps(system_prompt_dict,ensure_ascii=False))
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chat_record,is_all = await self.load_chatlogs(msg,remain_token - self.chat_summary_token_len)
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if chat_record:
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if len(chat_record) > 4:
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known_info["chat_record"] = chat_record
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if not is_all :
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### 如果出触发了Token Limit,则删除几条信息后，加载summary （summary的长度基本是固定的）
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summary = await self.get_chat_summary(msg)
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if summary:
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if len(summary) > 4:
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known_info["chat_summary"] = summary
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# TODO: extend known info
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#prompt.append_system_message(await self.get_extend_known_info(msg,prompt))
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prompt.append_system_message(json.dumps(system_prompt_dict,ensure_ascii=False))
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return prompt
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@@ -527,117 +539,77 @@ class AgentMessageProcess(LLMAgentBaseProcess):
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class AgentSelfThinking(LLMAgentBaseProcess):
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def __init__(self) -> None:
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super().__init__()
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async def load_from_config(self, config: dict) -> Coroutine[Any, Any, bool]:
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if await super().load_from_config(config) is False:
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return False
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async def _load_chat_history(self,token_limit:int):
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chat_history = {}
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session_list = AIChatSession.list_session(self.memory.agent_id ,self.memory.memory_db)
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total_read_msg = 0
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for session_id in session_list:
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chatsession = AIChatSession.get_session_by_id(session_id,self.memory.memory_db)
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session_history = {}
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session_history["summary"] = chatsession.summary
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session_history["id"] = chatsession.session_id
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token_limit -= ComputeKernel.llm_num_tokens_from_text(chatsession.summary,self.model_name)
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read_history_msg = 0
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if token_limit > 8:
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# load session chat history
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cur_pos = chatsession.summarize_pos
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messages = chatsession.read_history(0,cur_pos,"natural") # read
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history_str = ""
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for msg in messages:
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read_history_msg += 1
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total_read_msg += 1
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cur_pos += 1
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dt = datetime.fromtimestamp(float(msg.create_time))
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formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
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record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
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token_limit -= ComputeKernel.llm_num_tokens_from_text(record_str,self.model_name)
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if token_limit < 8:
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break
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async def _get_history_prompt_for_think(self,chatsession,summary:str,system_token_len:int,pos:int)->(LLMPrompt,int):
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history_len = (self.max_token_size * 0.7) - system_token_len
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history_str = history_str + record_str
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messages = chatsession.read_history(self.history_len,pos,"natural") # read
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result_token_len = 0
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result_prompt = LLMPrompt()
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have_summary = False
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if summary is not None:
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if len(summary) > 1:
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have_summary = True
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if read_history_msg >= 2:
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session_history["history"] = history_str
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chat_history[session_id] = session_history
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chatsession.summarize_pos = cur_pos
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if have_summary:
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result_prompt.messages.append({"role":"user","content":summary})
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result_token_len -= len(summary)
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else:
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result_prompt.messages.append({"role":"user","content":"There is no summary yet."})
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result_token_len -= 6
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read_history_msg = 0
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history_str : str = ""
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for msg in messages:
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read_history_msg += 1
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dt = datetime.datetime.fromtimestamp(float(msg.create_time))
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formatted_time = dt.strftime('%y-%m-%d %H:%M:%S')
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record_str = f"{msg.sender},[{formatted_time}]\n{msg.body}\n"
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history_str = history_str + record_str
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history_len -= len(msg.body)
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result_token_len += len(msg.body)
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if history_len < 0:
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logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
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break
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result_prompt.messages.append({"role":"user","content":history_str})
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return result_prompt,pos+read_history_msg
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async def _think_chatsession(self,session_id):
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if self.agent_think_prompt is None:
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return
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logger.info(f"agent {self.agent_id} think session {session_id}")
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chatsession = AIChatSession.get_session_by_id(session_id,self.chat_db)
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while True:
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cur_pos = chatsession.summarize_pos
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summary = chatsession.summary
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prompt:LLMPrompt = LLMPrompt()
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#prompt.append(self._get_agent_prompt())
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prompt.append(await self._get_agent_think_prompt())
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system_prompt_len = ComputeKernel.llm_num_tokens(prompt)
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#think env?
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history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
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prompt.append(history_prompt)
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is_finish = next_pos - cur_pos < 2
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if is_finish:
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logger.info(f"agent {self.agent_id} think session {session_id} is finished!,no more history")
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break
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#3) llm summarize chat history
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task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
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if task_result.result_code != ComputeTaskResultCode.OK:
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logger.error(f"think_chatsession llm compute error:{task_result.error_str}")
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break
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else:
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new_summary= task_result.result_str
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logger.info(f"agent {self.agent_id} think session {session_id} from {cur_pos} to {next_pos} summary:{new_summary}")
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chatsession.update_think_progress(next_pos,new_summary)
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return
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logger.info(f"load_chat_history reach token limit,load {total_read_msg} history messages.")
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return chat_history
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if total_read_msg < 2:
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logger.info(f"load_chat_history: no history messages,return NONE")
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return None
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return chat_history
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async def prepare_prompt(self,input:Dict) -> LLMPrompt:
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prompt = LLMPrompt()
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record_list = input.get("record_list")
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context_info = input.get("context_info")
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if record_list is None:
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logger.error(f"AgentSelfThinking prepare_prompt failed! input not found")
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return None
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prompt.append_user_message(json.dumps(record_list,ensure_ascii=False))
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system_prompt_dict = self.prepare_role_system_prompt(context_info)
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# Known_info is the SESSION summary of the existence, the current task work record summary,
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known_info = {}
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have_known_info = False
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known_session_list = input.get("known_session_list")
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known_task_list = input.get("known_task_list")
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known_contact_list = input.get("known_contact_list")
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known_experience_list = input.get("known_experience_list")
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if known_session_list:
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known_info["known_session_list"] = known_session_list
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have_known_info = True
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if known_task_list:
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known_info["known_task_list"] = known_task_list
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have_known_info = True
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if known_contact_list:
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known_info["known_contact_list"] = known_contact_list
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have_known_info = True
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if known_experience_list:
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known_info["known_experience_list"] = known_experience_list
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have_known_info = True
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if have_known_info:
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system_prompt_dict["known_info"] = known_info
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token_remain = self.get_remain_prompt_length(prompt,json.dumps(system_prompt_dict,ensure_ascii=False))
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chat_history = await self._load_chat_history(token_remain)
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if chat_history is None:
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logger.info(f"prepare_prompt: no history messages,return NONE")
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return None
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prompt.inner_functions =LLMProcessContext.aifunctions_to_inner_functions(self.llm_context.get_all_ai_functions())
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prompt.append_system_message(json.dumps(system_prompt_dict,ensure_ascii=False))
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prompt.append_user_message(json.dumps(chat_history,ensure_ascii=False))
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return prompt
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async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
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action_params = {}
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