learn knowledge design document
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tsukasa
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# learn todo list
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在workspace中分离出独立的两个work list；用于处理工作的work todo list， 和用于更新knowledge的 learn todo list；两种todo list在agent templete中单独的配置prompt，每个todo list都可以配置 do , check 和 review 三个prompt。
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learn todo list中的todo，可以在knowledge pipeline 生成。
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一个典型的例子是新的JarvisPlus agent，它可以依照自己的理解将某个本地目录中的文档归类到不同的逻辑目录中：
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+ 首先定义他的pipeline：扫描目录中没有读过的文档，传递给parser；
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+ 定义他的pipeline parser的实现：向自己所处的workspace 的learn todo list中添加一条todo， todo的内容是文档的全文；
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+ 定义他的 learn prompts：根据输入的文档内容，输出摘要，和归类后的目录，产生一组归类operation；
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+ workspace中嵌入了支持归类operation 的knowledage base environment，可以执行learn todo list产生的归类operation；
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# environments and workspace
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agent在独立的workspace中工作，目前默认的workspace是agent自己；除了自己的workspace，agent也可以进入其他的workspace；environment表示agent可以调用的function，和可以产生的operation；
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environment中的function或者operation输出的结果，应当应用在agent所处的workspace中，所以agent在不同workspace中执行work，结果应当是被隔离的。
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# Learn Todo List
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Separate two independent work lists in the workspace: a work todo list for handling work, and a learn todo list for updating knowledge. Both types of todo lists have their own configured prompts in the agent template, and each todo list can configure do, check, and review prompts.
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The todos in the learn todo list can be generated in the knowledge pipeline.
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A typical example is the new JarvisPlus agent, which can categorize documents from a local directory into different logical directories according to its understanding:
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+ First, define its pipeline: scan documents in the directory that have not been read and pass them to the parser;
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+ Define the implementation of its pipeline parser: add a todo to the learn todo list of the workspace it is in, the content of the todo is the full text of the document;
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+ Define its learn prompts: based on the input document content, output a summary and the directory after categorization, generating a set of categorization operations;
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+ The workspace embeds a knowledge base environment that supports categorization operations, which can execute the categorization operations generated by the learn todo list.
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# Environments and Workspace
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The agent works in an independent workspace, and the current default workspace is the agent itself. In addition to its own workspace, the agent can also enter other workspaces. The environment represents the functions that the agent can call and the operations it can generate.
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The results of the functions or operations in the environment should be applied in the workspace where the agent is located, so the results of the agent's work in different workspaces should be isolated.
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Please note that the translation might not be perfect due to the technical nature of the text and potential ambiguity in the original text.
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@@ -133,19 +133,6 @@ class AIAgent(BaseAIAgent):
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self.owenr_bus = None
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self.enable_function_list = None
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# @classmethod
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# def create_from_templete(cls,templete:AIAgentTemplete, fullname:str):
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# # Agent just inherit from templete on craete,if template changed,agent will not change
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# result_agent = AIAgent()
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# result_agent.llm_model_name = templete.llm_model_name
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# result_agent.max_token_size = templete.max_token_size
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# result_agent.template_id = templete.template_id
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# result_agent.agent_id = "agent#" + uuid.uuid4().hex
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# result_agent.fullname = fullname
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# result_agent.powerby = templete.author
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# result_agent.agent_prompt = templete.prompt
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# return result_agent
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def load_from_config(self,config:dict) -> bool:
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if config.get("instance_id") is None:
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logger.error("agent instance_id is None!")
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@@ -281,82 +268,6 @@ class AIAgent(BaseAIAgent):
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if event.type == "AgentThink":
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return await self.do_self_think()
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# async def _process_group_chat_msg(self,msg:AgentMsg) -> AgentMsg:
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# session_topic = msg.target + "#" + msg.topic
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# chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
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# workspace = self.get_current_workspace()
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# need_process = False
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# if msg.mentions is not None:
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# if self.agent_id in msg.mentions:
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# need_process = True
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# logger.info(f"agent {self.agent_id} recv a group chat message from {msg.sender},but is not mentioned,ignore!")
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# if need_process is not True:
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# chatsession.append(msg)
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# resp_msg = msg.create_group_resp_msg(self.agent_id,"")
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# return resp_msg
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# else:
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# msg_prompt = AgentPrompt()
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# msg_prompt.messages = [{"role":"user","content":f"{msg.sender}:{msg.body}"}]
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# prompt = AgentPrompt()
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# prompt.append(self.get_agent_prompt())
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# if workspace:
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# prompt.append(workspace.get_prompt())
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# prompt.append(workspace.get_role_prompt(self.agent_id))
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# if self.need_session_summmary(msg,chatsession):
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# # get relate session(todos) summary
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# summary = self.llm_select_session_summary(msg,chatsession)
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# prompt.append(AgentPrompt(summary))
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# self._format_msg_by_env_value(prompt)
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# inner_functions,function_token_len = self._get_inner_functions()
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# system_prompt_len = self.token_len(prompt=prompt)
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# input_len = len(msg.body)
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# history_prmpt,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len)
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# prompt.append(history_prmpt) # chat context
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# prompt.append(msg_prompt)
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# 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} ")
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# task_result = await self._do_llm_complection(prompt,inner_functions,msg)
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# if task_result.result_code != ComputeTaskResultCode.OK:
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# error_resp = msg.create_error_resp(task_result.error_str)
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# return error_resp
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# final_result = task_result.result_str
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# llm_result : LLMResult = LLMResult.from_str(final_result)
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# is_ignore = False
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# result_prompt_str = ""
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# match llm_result.state:
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# case "ignore":
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# is_ignore = True
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# case "waiting":
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# for sendmsg in llm_result.send_msgs:
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# target = sendmsg.target
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# sendmsg.sender = self.agent_id
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# sendmsg.topic = msg.topic
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# sendmsg.prev_msg_id = msg.get_msg_id()
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# send_resp = await AIBus.get_default_bus().send_message(sendmsg)
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# if send_resp is not None:
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# result_prompt_str += f"\n{target} response is :{send_resp.body}"
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# agent_sesion = AIChatSession.get_session(self.agent_id,f"{sendmsg.target}#{sendmsg.topic}",self.chat_db)
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# agent_sesion.append(sendmsg)
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# agent_sesion.append(send_resp)
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# final_result = llm_result.resp + result_prompt_str
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# if is_ignore is not True:
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# resp_msg = msg.create_group_resp_msg(self.agent_id,final_result)
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# chatsession.append(msg)
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# chatsession.append(resp_msg)
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# return resp_msg
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# return None
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def get_workspace_by_msg(self,msg:AgentMsg) -> WorkspaceEnvironment:
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return self.agent_workspace
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@@ -884,10 +795,6 @@ class AIAgent(BaseAIAgent):
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# if learn_power <= 0:
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# break
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def parser_learn_llm_result(self,llm_result:LLMResult):
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pass
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async def do_self_think(self):
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session_id_list = AIChatSession.list_session(self.agent_id,self.chat_db)
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for session_id in session_id_list:
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@@ -980,17 +887,6 @@ class AIAgent(BaseAIAgent):
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return None,0
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def need_work(self) -> bool:
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if self.do_prompt is not None:
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return True
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if self.check_prompt is not None:
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return True
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if self.agent_energy > 2:
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return True
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return False
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def need_self_think(self) -> bool:
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return False
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