158 lines
8.0 KiB
Python
158 lines
8.0 KiB
Python
from typing import List, Union
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import copy
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from loguru import logger
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from langchain.schema import BaseRetriever
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from coagent.connector.schema import (
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Memory, Task, Env, Role, Message, ActionStatus, PromptField, LogVerboseEnum
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)
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from coagent.connector.memory_manager import BaseMemoryManager
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from coagent.llm_models import LLMConfig, EmbedConfig
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from coagent.connector.memory_manager import LocalMemoryManager
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from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
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from .base_agent import BaseAgent
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class ExecutorAgent(BaseAgent):
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def __init__(
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self,
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role: Role,
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prompt_config: List[PromptField],
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prompt_manager_type: str= "PromptManager",
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task: Task = None,
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memory: Memory = None,
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chat_turn: int = 1,
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focus_agents: List[str] = [],
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focus_message_keys: List[str] = [],
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#
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llm_config: LLMConfig = None,
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embed_config: EmbedConfig = None,
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sandbox_server: dict = {},
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jupyter_work_path: str = JUPYTER_WORK_PATH,
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kb_root_path: str = KB_ROOT_PATH,
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doc_retrieval: Union[BaseRetriever] = None,
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code_retrieval = None,
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search_retrieval = None,
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log_verbose: str = "0"
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):
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super().__init__(role, prompt_config, prompt_manager_type, task, memory, chat_turn,
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focus_agents, focus_message_keys, llm_config, embed_config, sandbox_server,
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jupyter_work_path, kb_root_path, doc_retrieval, code_retrieval, search_retrieval, log_verbose
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)
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self.do_all_task = True # run all tasks
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def astep(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager=None) -> Message:
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'''agent reponse from multi-message'''
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# insert query into memory
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task_executor_memory = Memory(messages=[])
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# insert query
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output_message = Message(
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user_name=query.user_name,
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role_name=self.role.role_name,
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role_type="assistant", #self.role.role_type,
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role_content=query.input_query,
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step_content="",
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input_query=query.input_query,
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tools=query.tools,
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# parsed_output_list=[query.parsed_output],
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customed_kargs=query.customed_kargs
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)
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if memory_manager is None:
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memory_manager = LocalMemoryManager(
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unique_name=self.role.role_name,
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do_init=True,
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kb_root_path = self.kb_root_path,
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embed_config=self.embed_config,
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llm_config=self.embed_config
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)
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memory_manager.append(query)
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# self_memory = self.memory if self.do_use_self_memory else None
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plan_step = int(query.parsed_output.get("PLAN_STEP", 0))
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# 如果存在plan字段且plan字段为str的时候
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if "PLAN" not in query.parsed_output or isinstance(query.parsed_output.get("PLAN", []), str) or plan_step >= len(query.parsed_output.get("PLAN", [])):
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query_c = copy.deepcopy(query)
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query_c = self.start_action_step(query_c)
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query_c.parsed_output = {"CURRENT_STEP": query_c.input_query}
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task_executor_memory.append(query_c)
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for output_message, task_executor_memory in self._arun_step(output_message, query_c, self.memory, history, background, memory_manager, task_executor_memory):
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pass
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# task_executor_memory.append(query_c)
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# content = "the execution step of the plan is exceed the planned scope."
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# output_message.parsed_dict = {"Thought": content, "Action Status": "finished", "Action": content}
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# task_executor_memory.append(output_message)
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elif "PLAN" in query.parsed_output:
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if self.do_all_task:
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# run all tasks step by step
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for task_content in query.parsed_output["PLAN"][plan_step:]:
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# create your llm prompt
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query_c = copy.deepcopy(query)
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query_c.parsed_output = {"CURRENT_STEP": task_content}
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task_executor_memory.append(query_c)
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for output_message, task_executor_memory in self._arun_step(output_message, query_c, self.memory, history, background, memory_manager, task_executor_memory):
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pass
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yield output_message
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else:
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query_c = copy.deepcopy(query)
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query_c = self.start_action_step(query_c)
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task_content = query_c.parsed_output["PLAN"][plan_step]
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query_c.parsed_output = {"CURRENT_STEP": task_content}
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task_executor_memory.append(query_c)
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for output_message, task_executor_memory in self._arun_step(output_message, query_c, self.memory, history, background, memory_manager, task_executor_memory):
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pass
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output_message.parsed_output.update({"CURRENT_STEP": plan_step})
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# update self_memory
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self.append_history(query)
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self.append_history(output_message)
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output_message.input_query = output_message.role_content
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# end_action_step
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output_message = self.end_action_step(output_message)
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# update memory pool
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memory_manager.append(output_message)
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yield output_message
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def _arun_step(self, output_message: Message, query: Message, self_memory: Memory,
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history: Memory, background: Memory, memory_manager: BaseMemoryManager,
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task_memory: Memory) -> Union[Message, Memory]:
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'''execute the llm predict by created prompt'''
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memory_pool = memory_manager.get_memory_pool(query.user_name)
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prompt = self.prompt_manager.generate_full_prompt(
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previous_agent_message=query, agent_long_term_memory=self_memory, ui_history=history, chain_summary_messages=background, memory_pool=memory_pool,
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task_memory=task_memory)
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content = self.llm.predict(prompt)
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if LogVerboseEnum.ge(LogVerboseEnum.Log2Level, self.log_verbose):
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logger.debug(f"{self.role.role_name} prompt: {prompt}")
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if LogVerboseEnum.ge(LogVerboseEnum.Log1Level, self.log_verbose):
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logger.info(f"{self.role.role_name} content: {content}")
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output_message.role_content = content
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output_message.step_content += "\n"+output_message.role_content
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output_message = self.message_utils.parser(output_message)
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# according the output to choose one action for code_content or tool_content
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output_message, observation_message = self.message_utils.step_router(output_message)
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# update parserd_output_list
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output_message.parsed_output_list.append(output_message.parsed_output)
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react_message = copy.deepcopy(output_message)
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task_memory.append(react_message)
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if observation_message:
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task_memory.append(observation_message)
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output_message.parsed_output_list.append(observation_message.parsed_output)
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# logger.debug(f"{observation_message.role_name} content: {observation_message.role_content}")
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yield output_message, task_memory
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def pre_print(self, query: Message, history: Memory = None, background: Memory = None, memory_manager: BaseMemoryManager = None):
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task_memory = Memory(messages=[])
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prompt = self.prompt_manager.pre_print(
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previous_agent_message=query, agent_long_term_memory=self.memory, ui_history=history, chain_summary_messages=background, react_memory=None,
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memory_pool=memory_manager.current_memory, task_memory=task_memory)
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title = f"<<<<{self.role.role_name}'s prompt>>>>"
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print("#"*len(title) + f"\n{title}\n"+ "#"*len(title)+ f"\n\n{prompt}\n\n")
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