Refactor the Action/Function components, and refactor the basic architecture of Agent Task/Todo.
This commit is contained in:
Liu Zhicong
+5
-459
@@ -36,37 +36,6 @@ from ..proto.compute_task import ComputeTaskResult,ComputeTaskResultCode
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logger = logging.getLogger(__name__)
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# DEFAULT_AGENT_READ_REPORT_PROMPT = """
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# """
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# DEFAULT_AGENT_DO_PROMPT = """
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# You are a helpful AI assistant.
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# Solve tasks using your coding and language skills.
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# In the following cases, suggest python code (in a python coding block) for the user to execute.
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# 1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.
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# 2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.
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# Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.
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# When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.
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# If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.
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# If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can't be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
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# When you find an answer, verify the answer carefully. Include verifiable evidence in your response if possible.
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# Reply "TERMINATE" in the end when everything is done.
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# """
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# DEFAULT_AGENT_SELF_CHECK_PROMPT = """
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# """
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# DEFAULT_AGENT_GOAL_TO_TODO_PROMPT = """
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# 我会给你一个目标，你需要结合自己的角色思考如何将其拆解成多个TODO。请直接返回json来表达这些TODO
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# """
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# DEFAULT_AGENT_LEARN_LONG_CONENT_PROMPT = """
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# 我给你一段内容，尝试为期建立目录。目录的标题不能超过16个字，
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# 目录要指向正文的位置（用字符偏移即可），整个目录的文本长度不能超过256个字节。并用json表达这个目录
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# """
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class AIAgentTemplete:
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def __init__(self) -> None:
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self.llm_model_name:str = "gpt-4-0613"
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@@ -120,7 +89,7 @@ class AIAgent(BaseAIAgent):
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todo_prompts = {}
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todo_prompts[TodoListType.TO_WORK] = {
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"do": None,
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"check": None,
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"check": None,
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"review": None,
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}
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todo_prompts[TodoListType.TO_LEARN] = {
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@@ -133,10 +102,10 @@ class AIAgent(BaseAIAgent):
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self.chat_db = None
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self.unread_msg = Queue() # msg from other agent
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self.owenr_bus = None
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self.enable_function_list = None
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self.memory : AgentMemory = None
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self.prviate_workspace : AgentWorkspace = None
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self.behaviors:Dict[str,BaseLLMProcess] = {}
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@@ -161,7 +130,6 @@ class AIAgent(BaseAIAgent):
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logger.error("agent instance_id is None!")
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return False
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self.agent_id = config["instance_id"]
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self.agent_workspace = config["workspace"]
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if config.get("fullname") is None:
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logger.error(f"agent {self.agent_id} fullname is None!")
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@@ -171,43 +139,6 @@ class AIAgent(BaseAIAgent):
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if config.get("enable_thread") is not None:
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self.enable_thread = bool(config["enable_thread"])
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if config.get("prompt") is not None:
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self.agent_prompt = LLMPrompt()
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self.agent_prompt.load_from_config(config["prompt"])
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if config.get("think_prompt") is not None:
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self.agent_think_prompt = LLMPrompt()
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self.agent_think_prompt.load_from_config(config["think_prompt"])
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def load_todo_config(todo_type:str) -> bool:
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todo_config = config.get(todo_type)
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if todo_config is not None:
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if todo_config.get("do") is not None:
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prompt = LLMPrompt()
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prompt.load_from_config(todo_config["do"])
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self.todo_prompts[todo_type]["do"] = prompt
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if todo_config.get("check") is not None:
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prompt = LLMPrompt()
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prompt.load_from_config(todo_config["check"])
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self.todo_prompts[todo_type]["check"] = prompt
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if todo_config.get("review_prompt") is not None:
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prompt = LLMPrompt()
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prompt.load_from_config(todo_config["review_prompt"])
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self.todo_prompts[todo_type]["review"] = prompt
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load_todo_config(TodoListType.TO_WORK)
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load_todo_config(TodoListType.TO_LEARN)
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if config.get("guest_prompt") is not None:
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self.guest_prompt_str = config["guest_prompt"]
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if config.get("owner_prompt") is not None:
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self.owner_promp_str = config["owner_prompt"]
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if config.get("contact_prompt") is not None:
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self.contact_prompt_str = config["contact_prompt"]
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if config.get("powerby") is not None:
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self.powerby = config["powerby"]
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if config.get("template_id") is not None:
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@@ -265,62 +196,11 @@ class AIAgent(BaseAIAgent):
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def get_agent_role_prompt(self) -> LLMPrompt:
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return self.role_prompt
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def _get_remote_user_prompt(self,remote_user:str) -> LLMPrompt:
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cm = ContactManager.get_instance()
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contact = cm.find_contact_by_name(remote_user)
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if contact is None:
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#create guest prompt
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if self.guest_prompt_str is not None:
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prompt = LLMPrompt()
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prompt.system_message = {"role":"system","content":self.guest_prompt_str}
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return prompt
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return None
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else:
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if contact.is_family_member:
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if self.owner_promp_str is not None:
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real_str = self.owner_promp_str.format_map(contact.to_dict())
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prompt = LLMPrompt()
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prompt.system_message = {"role":"system","content":real_str}
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return prompt
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else:
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if self.contact_prompt_str is not None:
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real_str = self.contact_prompt_str.format_map(contact.to_dict())
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prompt = LLMPrompt()
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prompt.system_message = {"role":"system","content":real_str}
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return prompt
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return None
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def get_agent_prompt(self) -> LLMPrompt:
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return self.agent_prompt
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async def _get_agent_think_prompt(self) -> LLMPrompt:
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return self.agent_think_prompt
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def _format_msg_by_env_value(self,prompt:LLMPrompt):
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for msg in prompt.messages:
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old_content = msg.get("content")
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msg["content"] = old_content.format_map(self.agent_workspace)
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async def _handle_event(self,event):
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if event.type == "AgentThink":
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return await self.do_self_think()
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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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def need_session_summmary(self,msg:AgentMsg,session:AIChatSession) -> bool:
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return False
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async def _create_openai_thread(self) -> str:
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return None
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def check_and_to_base64(self, image_path: str) -> str:
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if image_utils.is_file(image_path):
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return image_utils.to_base64(image_path, (1024, 1024))
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else:
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return image_path
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async def llm_process_msg(self,msg:AgentMsg) -> AgentMsg:
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need_process:bool = True
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if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
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@@ -349,7 +229,7 @@ class AIAgent(BaseAIAgent):
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elif llm_result.state == LLMResultStates.IGNORE:
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return None
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else: # OK
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resp_msg = llm_result.raw_result.get("resp_msg")
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resp_msg = llm_result.raw_result.get("_resp_msg")
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return resp_msg
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async def _process_msg(self,msg:AgentMsg,workspace = None) -> AgentMsg:
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@@ -358,271 +238,8 @@ class AIAgent(BaseAIAgent):
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msg.context_info["now"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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msg.context_info["weather"] = "Partly Cloudy, 60°F"
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return await self.llm_process_msg(msg)
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msg_prompt = LLMPrompt()
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need_process = True
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if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
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need_process = False
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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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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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else:
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if msg.is_image_msg():
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image_prompt, images = msg.get_image_body()
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if image_prompt is None:
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msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images]}]
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else:
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content = [{"type": "text", "text": image_prompt}]
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content.extend([{"type": "image_url", "image_url": {"url": self.check_and_to_base64(image)}} for image in images])
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msg_prompt.messages = [{"role": "user", "content": content}]
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elif msg.is_video_msg():
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video_prompt, video = msg.get_video_body()
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frames = video_utils.extract_frames(video, (1024, 1024))
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if video_prompt is None:
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msg_prompt.messages = [{"role": "user", "content": [{"type": "image_url", "image_url": {"url": frame}} for frame in frames]}]
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else:
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content = [{"type": "text", "text": video_prompt}]
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content.extend([{"type": "image_url", "image_url": {"url": frame}} for frame in frames])
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msg_prompt.messages = [{"role": "user", "content": content}]
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elif msg.is_audio_msg():
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audio_file = msg.body
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resp = await (ComputeKernel.get_instance().do_speech_to_text(audio_file, None, prompt=None, response_format="text"))
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if resp.result_code != ComputeTaskResultCode.OK:
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error_resp = msg.create_error_resp(resp.error_str)
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return error_resp
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else:
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msg.body = resp.result_str
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msg_prompt.messages = [{"role":"user","content":resp.result_str}]
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else:
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msg_prompt.messages = [{"role":"user","content":msg.body}]
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session_topic = msg.get_sender() + "#" + msg.topic
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chatsession = AIChatSession.get_session(self.agent_id,session_topic,self.chat_db)
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if self.enable_thread:
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need_create_thread = False
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if chatsession.openai_thread_id is not None:
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if len(chatsession.openai_thread_id) < 1:
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need_create_thread = True
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else:
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need_create_thread = True
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if need_create_thread:
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openai_thread_id = await self._create_openai_thread()
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if openai_thread_id is not None:
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chatsession.update_openai_thread_id(openai_thread_id)
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workspace = self.get_workspace_by_msg(msg)
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prompt = LLMPrompt()
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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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prompt.append(self.get_agent_prompt())
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prompt.append(self._get_remote_user_prompt(msg.sender))
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self._format_msg_by_env_value(prompt)
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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(LLMPrompt(summary))
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known_info_str = "# Known information\n"
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have_known_info = False
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todos_str,todo_count = await workspace.todo_list[TodoListType.TO_WORK].get_todo_tree()
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if todo_count > 0:
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have_known_info = True
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known_info_str += f"## todo\n{todos_str}\n"
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inner_functions,function_token_len = BaseAIAgent.get_inner_functions(self.agent_workspace)
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system_prompt_len = ComputeKernel.llm_num_tokens(prompt)
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input_len = len(msg.body)
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if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
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history_str,history_token_len = await self._get_prompt_from_session_for_groupchat(chatsession,system_prompt_len + function_token_len,input_len)
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else:
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history_str,history_token_len = await self.get_prompt_from_session(chatsession,system_prompt_len + function_token_len,input_len)
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if history_str:
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have_known_info = True
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known_info_str += history_str
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if have_known_info:
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known_info_prompt = LLMPrompt(known_info_str)
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prompt.append(known_info_prompt) # 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,msg, inner_functions=inner_functions)
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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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if final_result is not None:
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llm_result : LLMResult = LLMResult.from_str(final_result)
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else:
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llm_result = LLMResult()
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llm_result.state = "ignore"
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if llm_result.resp is None:
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if llm_result.raw_resp:
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final_result = json.dumps(llm_result.raw_resp)
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else:
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final_result = llm_result.resp
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await workspace.exec_op_list(llm_result.action_list,self.agent_id)
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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": # like inner call
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for sendmsg in llm_result.send_msgs:
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sendmsg.sender = self.agent_id
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target = sendmsg.target
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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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if msg.msg_type == AgentMsgType.TYPE_GROUPMSG:
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resp_msg = msg.create_group_resp_msg(self.agent_id,final_result)
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else:
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resp_msg = msg.create_resp_msg(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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async def _get_history_prompt_for_think(self,chatsession:AIChatSession,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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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 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 _get_prompt_from_session_for_groupchat(self,chatsession:AIChatSession,system_token_len,input_token_len,is_groupchat=False):
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history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len
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messages = chatsession.read_history(self.history_len) # read
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result_token_len = 0
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result_prompt = LLMPrompt()
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read_history_msg = 0
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for msg in reversed(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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if msg.sender == self.agent_id:
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if self.enable_timestamp:
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result_prompt.messages.append({"role":"assistant","content":f"(create on {formatted_time}) {msg.body} "})
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else:
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result_prompt.messages.append({"role":"assistant","content":msg.body})
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|
||||
else:
|
||||
if self.enable_timestamp:
|
||||
result_prompt.messages.append({"role":"user","content":f"(create on {formatted_time}) {msg.body} "})
|
||||
else:
|
||||
result_prompt.messages.append({"role":"user","content":f"{msg.sender}:{msg.body}"})
|
||||
|
||||
history_len -= len(msg.body)
|
||||
result_token_len += len(msg.body)
|
||||
if history_len < 0:
|
||||
logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
|
||||
break
|
||||
|
||||
return result_prompt,result_token_len
|
||||
|
||||
|
||||
|
||||
async def _llm_summary_work(self,workspace:WorkspaceEnvironment):
|
||||
# read report ,and update work summary of
|
||||
# build todo list from work summary and goals
|
||||
#
|
||||
report_list = self.get_unread_reports()
|
||||
|
||||
for report in report_list:
|
||||
if self.agent_energy <= 0:
|
||||
break
|
||||
# merge report to work summary
|
||||
await self._llm_read_report(report,workspace)
|
||||
self.agent_energy -= 1
|
||||
|
||||
if workspace.is_mgr(self.agent_id):
|
||||
# manager can do more work
|
||||
await self._llm_review_team(workspace)
|
||||
self.agent_energy -= 5
|
||||
await self._llm_review_unassigned_todos(workspace)
|
||||
self.agent_energy -= 5
|
||||
|
||||
|
||||
async def _llm_review_team(self,workspace:WorkspaceEnvironment):
|
||||
pass
|
||||
|
||||
async def _llm_review_unassigned_todos(self,workspace:WorkspaceEnvironment):
|
||||
pass
|
||||
|
||||
async def _llm_read_report(self,report,worksapce:WorkspaceEnvironment):
|
||||
work_summary = worksapce.get_work_summary(self.agent_id)
|
||||
prompt : LLMPrompt = LLMPrompt()
|
||||
prompt.append(self.agent_prompt)
|
||||
prompt.append(worksapce.get_role_prompt(self.agent_id))
|
||||
prompt.append(self.read_report_prompt)
|
||||
# report is a message from other agent(human) about work
|
||||
prompt.append(LLMPrompt(work_summary))
|
||||
prompt.append(LLMPrompt(report.content))
|
||||
|
||||
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
|
||||
|
||||
if task_result.error_str is not None:
|
||||
logger.error(f"_llm_read_report compute error:{task_result.error_str}")
|
||||
return
|
||||
|
||||
worksapce.set_work_summary(self.agent_id,task_result.result_str)
|
||||
|
||||
async def _llm_run_todo_list(self, todo_list_type: TodoListType):
|
||||
workspace : WorkspaceEnvironment = self.get_workspace_by_msg(None)
|
||||
@@ -872,82 +489,11 @@ class AIAgent(BaseAIAgent):
|
||||
async def think_todo_log(self,todo_log:AgentWorkLog):
|
||||
pass
|
||||
|
||||
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}")
|
||||
chatsession = AIChatSession.get_session_by_id(session_id,self.chat_db)
|
||||
|
||||
while True:
|
||||
cur_pos = chatsession.summarize_pos
|
||||
summary = chatsession.summary
|
||||
prompt:LLMPrompt = LLMPrompt()
|
||||
#prompt.append(self._get_agent_prompt())
|
||||
prompt.append(await self._get_agent_think_prompt())
|
||||
system_prompt_len = ComputeKernel.llm_num_tokens(prompt)
|
||||
#think env?
|
||||
history_prompt,next_pos = await self._get_history_prompt_for_think(chatsession,summary,system_prompt_len,cur_pos)
|
||||
prompt.append(history_prompt)
|
||||
is_finish = next_pos - cur_pos < 2
|
||||
if is_finish:
|
||||
logger.info(f"agent {self.agent_id} think session {session_id} is finished!,no more history")
|
||||
break
|
||||
#3) llm summarize chat history
|
||||
task_result:ComputeTaskResult = await self.do_llm_complection(prompt)
|
||||
if task_result.result_code != ComputeTaskResultCode.OK:
|
||||
logger.error(f"think_chatsession llm compute error:{task_result.error_str}")
|
||||
break
|
||||
else:
|
||||
new_summary= task_result.result_str
|
||||
logger.info(f"agent {self.agent_id} think session {session_id} from {cur_pos} to {next_pos} summary:{new_summary}")
|
||||
chatsession.update_think_progress(next_pos,new_summary)
|
||||
return
|
||||
|
||||
async def get_prompt_from_session(self,chatsession:AIChatSession,system_token_len,input_token_len) -> LLMPrompt:
|
||||
# TODO: get prompt from group chat is different from single chat
|
||||
if self.enable_thread:
|
||||
return None
|
||||
|
||||
history_len = (self.max_token_size * 0.7) - system_token_len - input_token_len
|
||||
messages = chatsession.read_history(self.history_len) # read
|
||||
result_token_len = 0
|
||||
|
||||
read_history_msg = 0
|
||||
have_known_info = False
|
||||
|
||||
known_info = ""
|
||||
if chatsession.summary is not None:
|
||||
if len(chatsession.summary) > 1:
|
||||
known_info += f"## Recent conversation summary \n {chatsession.summary}\n"
|
||||
result_token_len -= len(chatsession.summary)
|
||||
have_known_info = True
|
||||
|
||||
histroy_str = ""
|
||||
for msg in reversed(messages):
|
||||
read_history_msg += 1
|
||||
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"
|
||||
have_known_info = True
|
||||
histroy_str = histroy_str + record_str
|
||||
|
||||
history_len -= len(msg.body)
|
||||
result_token_len += len(msg.body)
|
||||
if history_len < 0:
|
||||
logger.warning(f"_get_prompt_from_session reach limit of token,just read {read_history_msg} history message.")
|
||||
break
|
||||
|
||||
known_info += f"## Recent conversation history \n {histroy_str}\n"
|
||||
|
||||
if have_known_info:
|
||||
return known_info,result_token_len
|
||||
return None,0
|
||||
|
||||
|
||||
def need_self_think(self) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
def wake_up(self) -> None:
|
||||
if self.agent_task is None:
|
||||
self.agent_task = asyncio.create_task(self._on_timer())
|
||||
@@ -971,9 +517,9 @@ class AIAgent(BaseAIAgent):
|
||||
continue
|
||||
|
||||
# complete & check todo
|
||||
await self._llm_run_todo_list(TodoListType.TO_WORK)
|
||||
#await self._llm_run_todo_list(TodoListType.TO_WORK)
|
||||
|
||||
await self._llm_run_todo_list(TodoListType.TO_LEARN)
|
||||
##await self._llm_run_todo_list(TodoListType.TO_LEARN)
|
||||
|
||||
if self.need_self_think():
|
||||
await self.do_self_think()
|
||||
|
||||
Reference in New Issue
Block a user