1) Complete new Agent Behavior: triage_tasks
2) Fix bugs.
This commit is contained in:
Liu Zhicong
+95
-294
@@ -25,8 +25,6 @@ logger = logging.getLogger(__name__)
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MIN_PREDICT_TOKEN_LEN = 32
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class BaseLLMProcess(ABC):
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def __init__(self) -> None:
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self.behavior:str = None #行为名字
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@@ -42,6 +40,8 @@ class BaseLLMProcess(ABC):
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self.max_prompt_token = 1000 # not include input prompt
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self.timeout = 1800 # 30 min
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self.llm_context:LLMProcessContext = None
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@abstractmethod
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async def prepare_prompt(self,input:Dict) -> LLMPrompt:
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pass
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@@ -50,6 +50,10 @@ class BaseLLMProcess(ABC):
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
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pass
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@abstractmethod
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def prepare_inner_function_context_for_exec(self,inner_func_name:str,parameters:Dict):
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return
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@abstractmethod
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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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@@ -80,8 +84,6 @@ class BaseLLMProcess(ABC):
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def _format_content_by_env_value(self,content:str,env)->str:
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return content.format_map(env)
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def prepare_inner_function_context_for_exec(self,inner_func_name:str,parameters:Dict):
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return
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async def _execute_inner_func(self,inner_func_call_node:Dict,prompt: LLMPrompt,stack_limit = 1) -> ComputeTaskResult:
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arguments = None
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@@ -205,68 +207,12 @@ class LLMAgentBaseProcess(BaseLLMProcess):
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self.process_description:str = None
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self.reply_format:str = None
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self.context : str = None
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self.known_info_tips :str = None
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self.tools_tips:str = None
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self.workspace : AgentWorkspace = None # If Workspace is not none , enable Agent Tasklist
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self.memory : AgentMemory = None
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self.enable_kb : bool = False
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self.kb = None
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async def load_default_config(self) -> bool:
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return True
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async def load_from_config(self, config: dict,is_load_default=True) -> Coroutine[Any, Any, bool]:
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if is_load_default:
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await self.load_default_config()
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if await super().load_from_config(config) is False:
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return False
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self.role_description = config.get("role_desc")
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if self.role_description is None:
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logger.error(f"role_description not found in config")
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return False
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if config.get("process_description"):
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self.process_description = config.get("process_description")
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if config.get("reply_format"):
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self.reply_format = config.get("reply_format")
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if config.get("context"):
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self.context = config.get("context")
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if config.get("known_info_tips"):
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self.known_info_tips = config.get("known_info_tips")
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if config.get("tools_tips"):
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self.tools_tips = config.get("tools_tips")
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if config.get("knowledge_base"):
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self.kb = config.get("knowledge_base")
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class LLMAgentMessageProcess(BaseLLMProcess):
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def __init__(self) -> None:
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super().__init__()
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self.role_description:str = None
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self.process_description:str = None
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self.reply_format:str = None
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self.context : str = None
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self.known_info_tips :str = None
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self.tools_tips:str = None
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self.workspace : AgentWorkspace = None # If Workspace is not none , enable Agent Tasklist
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self.memory : AgentMemory = None
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self.enable_kb = False
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self.kb = None
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self.llm_context : LLMProcessContext = None
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async def initial(self,params:Dict = None) -> bool:
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self.memory = params.get("memory")
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if self.memory is None:
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@@ -274,9 +220,7 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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return False
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self.workspace = params.get("workspace")
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return True
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async def load_default_config(self) -> bool:
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return True
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@@ -302,27 +246,86 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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if config.get("context"):
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self.context = config.get("context")
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if config.get("known_info_tips"):
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self.known_info_tips = config.get("known_info_tips")
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if config.get("tools_tips"):
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self.tools_tips = config.get("tools_tips")
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if config.get("enable_kb"):
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self.enable_kb = config.get("enable_kb") == "true"
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self.llm_context = SimpleLLMContext()
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if config.get("llm_context"):
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self.llm_context.load_from_config(config.get("llm_context"))
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if config.get("enable_kb"):
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self.enable_kb = config.get("enable_kb") == "true"
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def prepare_role_system_prompt(self,context_info:Dict) -> Dict:
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system_prompt_dict = {}
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# System Prompt
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## LLM的身份说明
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system_prompt_dict["role_description"] = self.role_description
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#prompt.append_system_message(self.role_description)
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## 处理信息的流程说明
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system_prompt_dict["process_rule"] = self.process_description
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#prompt.append_system_message(self.process_description)
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### 回复的格式
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system_prompt_dict["reply_format"] = self.reply_format
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#prompt.append_system_message(self.reply_format)
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## Context
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context = self._format_content_by_env_value(self.context,context_info)
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system_prompt_dict["context"] = context
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#prompt.append_system_message(context)
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system_prompt_dict["support_actions"] = self.get_action_desc()
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return system_prompt_dict
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def prepare_inner_function_context_for_exec(self,inner_func_name:str,parameters:Dict):
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parameters["_workspace"] = self.workspace
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def get_action_desc(self) -> Dict:
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result = {}
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actions_list = self.llm_context.get_all_ai_action()
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for action in actions_list:
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result[action.get_name()] = action.get_description()
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return result
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
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return self.llm_context.get_ai_function(func_name)
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async def _execute_actions(self,actions:List[ActionNode],action_params:Dict):
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for action_item in actions:
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op : AIAction = self.llm_context.get_ai_action(action_item.name)
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if op:
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if action_item.parms is None:
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action_item.parms = {}
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real_parms = {**action_params,**action_item.parms}
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action_item.parms["_result"] = await op.execute(real_parms)
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action_item.parms["_end_at"] = datetime.now()
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else:
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logger.warn(f"action {action_item.name} not found")
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return False
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class AgentMessageProcess(LLMAgentBaseProcess):
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def __init__(self) -> None:
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super().__init__()
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async def load_default_config(self) -> bool:
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return True
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async def load_from_config(self, config: dict,is_load_default=True) -> Coroutine[Any, Any, bool]:
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if is_load_default:
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await self.load_default_config()
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if await super().load_from_config(config) is False:
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return False
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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 get_prompt_from_msg(self,msg:AgentMsg) -> LLMPrompt:
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msg_prompt = LLMPrompt()
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if msg.is_image_msg():
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@@ -356,13 +359,6 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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return msg_prompt
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async def get_action_desc(self) -> Dict:
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result = {}
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actions_list = self.llm_context.get_all_ai_action()
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for action in actions_list:
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result[action.get_name()] = action.get_description()
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return result
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async def sender_info(self,msg:AgentMsg)->str:
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sender_id = msg.sender
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#TODO Is sender an agent?
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@@ -386,6 +382,7 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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# User Prompt
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## Input Msg
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msg : AgentMsg = input.get("msg")
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context_info = input.get("context_info")
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if msg is None:
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logger.error(f"LLMAgeMessageProcess prepare_prompt failed! input msg not found")
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return None
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@@ -395,31 +392,8 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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return None
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prompt.append(msg_prompt)
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system_prompt_dict = {}
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# System Prompt
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## LLM的身份说明
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system_prompt_dict["role_description"] = self.role_description
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#prompt.append_system_message(self.role_description)
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## 处理信息的流程说明
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system_prompt_dict["process_rule"] = self.process_description
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#prompt.append_system_message(self.process_description)
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### 回复的格式
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system_prompt_dict["reply_format"] = self.reply_format
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#prompt.append_system_message(self.reply_format)
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### 修改chatlog的action
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### 修改todo/task的action
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### workspace提供的额外的action
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system_prompt_dict["support_actions"] = await self.get_action_desc()
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#prompt.append_system_message(await self.get_action_desc())
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## Context （文本替换）,是否应该覆盖全部消息
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context = self._format_content_by_env_value(self.context,msg.context_info)
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system_prompt_dict["context"] = context
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#prompt.append_system_message(context)
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## 通用的角色相关的系统提示词
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system_prompt_dict = self.prepare_role_system_prompt(context_info)
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## 已知信息
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known_info = {}
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@@ -441,10 +415,6 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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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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## 可以使用的tools(inner function)的解释，注意不定义该tips,则不会导入任何workspace中的tools
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if self.tools_tips:
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system_prompt_dict["tools_tips"] = self.tools_tips
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prompt.inner_functions =LLMProcessContext.aifunctions_to_inner_functions(self.llm_context.get_all_ai_functions())
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if self.workspace:
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#TODO eanble workspace functions?
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@@ -461,11 +431,6 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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return prompt
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def prepare_inner_function_context_for_exec(self,inner_func_name:str,parameters:Dict):
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parameters["_workspace"] = self.workspace
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
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return self.llm_context.get_ai_function(func_name)
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async def post_llm_process(self,actions:List[ActionNode],input:Dict,llm_result:LLMResult) -> bool:
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msg:AgentMsg = input.get("msg")
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@@ -476,137 +441,24 @@ class LLMAgentMessageProcess(BaseLLMProcess):
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llm_result.raw_result["_resp_msg"] = resp_msg
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for action_item in actions:
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op : AIAction = self.llm_context.get_ai_action(action_item.name)
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if op:
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if action_item.parms is None:
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action_item.parms = {}
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action_params = {}
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action_params["_input"] = input
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action_params["_memory"] = self.memory
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action_params["_workspace"] = self.workspace
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action_params["_resp_msg"] = resp_msg
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action_params["_llm_result"] = llm_result
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action_params["_agentid"] = self.memory.agent_id
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action_params["_start_at"] = datetime.now()
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action_item.parms["_input"] = input
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action_item.parms["_memory"] = self.memory
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action_item.parms["_workspace"] = self.workspace
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action_item.parms["_resp_msg"] = resp_msg
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action_item.parms["_llm_result"] = llm_result
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action_item.parms["_start_at"] = datetime.now()
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action_item.parms["_agentid"] = self.memory.agent_id
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action_item.parms["_result"] = await op.execute(action_item.parms)
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action_item.parms["_end_at"] = datetime.now()
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else:
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logger.warn(f"action {action_item.name} not found")
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return False
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await self._execute_actions(actions,action_params)
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chatsession = self.memory.get_session_from_msg(msg)
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chatsession.append(msg)
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chatsession.append(resp_msg)
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return True
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class ReviewTaskProcess(BaseLLMProcess):
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def __init__(self) -> None:
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super().__init__()
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self.role_description:str = None
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self.process_description:str = None
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self.reply_format = None
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# 虽然在架构上LLM Process可以很容易的去Call另一个Process，但实际应用中还是应该慎重的保持LLM Process的简单性
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#self.do_task_llm_process : BaseLLMProcess = None
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async def initial(self,params:Dict = None) -> bool:
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self.memory = params.get("memory")
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if self.memory is None:
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logger.error(f"LLMAgeMessageProcess initial failed! memory not found")
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return False
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self.workspace = params.get("workspace")
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return True
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async def load_from_config(self, config: dict,is_load_default=True) -> 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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self.role_description = config.get("role_desc")
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if self.role_description is None:
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logger.error(f"role_description not found in config")
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return False
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if config.get("process_description"):
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self.process_description = config.get("process_description")
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if config.get("reply_format"):
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self.reply_format = config.get("reply_format")
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if config.get("context"):
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self.context = config.get("context")
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self.llm_context = SimpleLLMContext()
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if config.get("llm_context"):
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self.llm_context.load_from_config(config.get("llm_context"))
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async def prepare_prompt(self,input:Dict) -> LLMPrompt:
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agent_task = input.get("task")
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prompt = LLMPrompt()
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system_prompt_dict = {}
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system_prompt_dict["role_description"] = self.role_description
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system_prompt_dict["process_rule"] = self.process_description
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system_prompt_dict["reply_format"] = self.reply_format
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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(agent_task.to_dict(),ensure_ascii=False))
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return prompt
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async def get_review_task_actions(self) -> Dict[str,Dict]:
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pass
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
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pass
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async def post_llm_process(self,actions:List[ActionNode]) -> bool:
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pass
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class QuickReviewTaskProcess(BaseLLMProcess):
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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):
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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 prepare_prompt(self) -> LLMPrompt:
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prompt = LLMPrompt()
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pass
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
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pass
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async def post_llm_process(self,actions:List[ActionNode]) -> bool:
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pass
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class DoTodoProcess(BaseLLMProcess):
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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):
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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 prepare_prompt(self) -> LLMPrompt:
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prompt = LLMPrompt()
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pass
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
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pass
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async def post_llm_process(self,actions:List[ActionNode]) -> bool:
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pass
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class CheckTodoProcess(BaseLLMProcess):
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class AgentSelfLearning(BaseLLMProcess):
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def __init__(self) -> None:
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super().__init__()
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@@ -624,25 +476,7 @@ class CheckTodoProcess(BaseLLMProcess):
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async def post_llm_process(self,actions:List[ActionNode]) -> bool:
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pass
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class SelfLearningProcess(BaseLLMProcess):
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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 prepare_prompt(self) -> LLMPrompt:
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prompt = LLMPrompt()
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pass
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async def get_inner_function_for_exec(self,func_name:str) -> AIFunction:
|
||||
pass
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class SelfThinkingProcess(BaseLLMProcess):
|
||||
class AgentSelfThinking(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
|
||||
@@ -727,43 +561,10 @@ class SelfThinkingProcess(BaseLLMProcess):
|
||||
|
||||
async def post_llm_process(self,actions:List[ActionNode]) -> bool:
|
||||
pass
|
||||
|
||||
class LLMProcessLoader:
|
||||
|
||||
class AgentSelfImprove(BaseLLMProcess):
|
||||
def __init__(self) -> None:
|
||||
self.loaders : Dict[str,Callable[[dict],Awaitable[BaseLLMProcess]]] = {}
|
||||
return
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls)->"LLMProcessLoader":
|
||||
if not hasattr(cls,"_instance"):
|
||||
cls._instance = LLMProcessLoader()
|
||||
return cls._instance
|
||||
|
||||
def register_loader(self, typename:str,loader:Callable[[dict],Awaitable[BaseLLMProcess]]):
|
||||
self.loaders[typename] = loader
|
||||
|
||||
async def load_from_config(self,config:dict) -> BaseLLMProcess:
|
||||
llm_type_name = config.get("type")
|
||||
if llm_type_name:
|
||||
loader = self.loaders.get(llm_type_name)
|
||||
if loader:
|
||||
return await loader(config)
|
||||
|
||||
selected_type = globals().get(llm_type_name)
|
||||
if selected_type:
|
||||
result : BaseLLMProcess = selected_type()
|
||||
load_result = await result.load_from_config(config)
|
||||
if load_result is False:
|
||||
logger.warn(f"load LLMProcess {llm_type_name} from config failed! load_from_config return False")
|
||||
return None
|
||||
else:
|
||||
return result
|
||||
|
||||
|
||||
logger.warn(f"load LLMProcess {llm_type_name} from config failed! type not found")
|
||||
return None
|
||||
|
||||
|
||||
super().__init__()
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user