from pydantic import BaseModel from typing import List, Union import re import json import traceback import copy from loguru import logger from langchain.prompts.chat import ChatPromptTemplate from dev_opsgpt.connector.schema import ( Memory, Task, Env, Role, Message, ActionStatus ) from dev_opsgpt.llm_models import getChatModel from dev_opsgpt.connector.configs.agent_config import REACT_PROMPT_INPUT from .base_agent import BaseAgent class ReactAgent(BaseAgent): def __init__( self, role: Role, task: Task = None, memory: Memory = None, chat_turn: int = 1, do_search: bool = False, do_doc_retrieval: bool = False, do_tool_retrieval: bool = False, temperature: float = 0.2, stop: Union[List[str], str] = None, do_filter: bool = True, do_use_self_memory: bool = True, focus_agents: List[str] = [], focus_message_keys: List[str] = [], # prompt_mamnger: PromptManager ): super().__init__(role, task, memory, chat_turn, do_search, do_doc_retrieval, do_tool_retrieval, temperature, stop, do_filter,do_use_self_memory, focus_agents, focus_message_keys ) def run(self, query: Message, history: Memory = None, background: Memory = None, memory_pool: Memory = None) -> Message: '''agent reponse from multi-message''' for message in self.arun(query, history, background, memory_pool): pass return message def arun(self, query: Message, history: Memory = None, background: Memory = None, memory_pool: Memory = None) -> Message: '''agent reponse from multi-message''' step_nums = copy.deepcopy(self.chat_turn) react_memory = Memory(messages=[]) # insert query output_message = Message( role_name=self.role.role_name, role_type="ai", #self.role.role_type, role_content=query.input_query, step_content="", input_query=query.input_query, tools=query.tools, parsed_output_list=[query.parsed_output] ) query_c = copy.deepcopy(query) query_c.parsed_output = {"Question": "\n".join([f"{v}" for k, v in query.parsed_output.items() if k not in ["Action Status"]])} react_memory.append(query_c) self_memory = self.memory if self.do_use_self_memory else None idx = 0 # start to react while step_nums > 0: output_message.role_content = output_message.step_content prompt = self.create_prompt(query, self_memory, history, background, react_memory, memory_pool) try: content = self.llm.predict(prompt) except Exception as e: logger.warning(f"error prompt: {prompt}") raise Exception(traceback.format_exc()) output_message.role_content = "\n"+content output_message.role_contents += [content] output_message.step_content += "\n"+output_message.role_content output_message.step_contents + [output_message.role_content] yield output_message # logger.debug(f"{self.role.role_name}, {idx} iteration prompt: {prompt}") logger.info(f"{self.role.role_name}, {idx} iteration step_run: {output_message.role_content}") output_message = self.message_utils.parser(output_message) # when get finished signal can stop early if output_message.action_status == ActionStatus.FINISHED: break # according the output to choose one action for code_content or tool_content output_message, observation_message = self.message_utils.step_router(output_message) output_message.parsed_output_list.append(output_message.parsed_output) react_message = copy.deepcopy(output_message) react_memory.append(react_message) if observation_message: react_memory.append(observation_message) output_message.parsed_output_list.append(observation_message.parsed_output) # logger.debug(f"{observation_message.role_name} content: {observation_message.role_content}") # logger.info(f"{self.role.role_name} currenct question: {output_message.input_query}\nllm_react_run: {output_message.role_content}") idx += 1 step_nums -= 1 yield output_message # react' self_memory saved at last self.append_history(output_message) # update memory pool # memory_pool.append(output_message) output_message.input_query = query.input_query # update memory pool memory_pool.append(output_message) yield output_message def create_prompt( self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, react_memory: Memory = None, memory_pool: Memory= None, prompt_mamnger=None) -> str: ''' role\task\tools\docs\memory ''' # doc_infos = self.create_doc_prompt(query) code_infos = self.create_codedoc_prompt(query) # formatted_tools, tool_names = self.create_tools_prompt(query) task_prompt = self.create_task_prompt(query) background_prompt = self.create_background_prompt(background) history_prompt = self.create_history_prompt(history) selfmemory_prompt = self.create_selfmemory_prompt(memory, control_key="step_content") # # extra_system_prompt = self.role.role_prompt prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names}) # react 流程是自身迭代过程，另外二次触发的是需要作为历史对话信息 # input_query = react_memory.to_tuple_messages(content_key="step_content") # # input_query = query.input_query + "\n" + "\n".join([f"{v}" for k, v in input_query if v]) # input_query = "\n".join([f"{v}" for k, v in input_query if v]) input_query = "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()]) logger.debug(f"input_query: {input_query}") prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query}) task = query.task or self.task # if task_prompt is not None: # prompt += "\n" + task.task_prompt # if doc_infos is not None and doc_infos!="" and doc_infos!="不存在知识库辅助信息": # prompt += f"\n知识库信息: {doc_infos}" # if code_infos is not None and code_infos!="" and code_infos!="不存在代码库辅助信息": # prompt += f"\n代码库信息: {code_infos}" # if background_prompt: # prompt += "\n" + background_prompt # if history_prompt: # prompt += "\n" + history_prompt # if selfmemory_prompt: # prompt += "\n" + selfmemory_prompt # logger.debug(f"{self.role.role_name} extra_system_prompt: {self.role.role_prompt}") # logger.debug(f"{self.role.role_name} input_query: {input_query}") # logger.debug(f"{self.role.role_name} doc_infos: {doc_infos}") # logger.debug(f"{self.role.role_name} tool_names: {tool_names}") # prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query}) # prompt = extra_system_prompt.format(**{"query": input_query, "doc_infos": doc_infos, "formatted_tools": formatted_tools, "tool_names": tool_names}) while "{{" in prompt or "}}" in prompt: prompt = prompt.replace("{{", "{") prompt = prompt.replace("}}", "}") return prompt