214 lines
11 KiB
Python
214 lines
11 KiB
Python
from pydantic import BaseModel
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from typing import List, Union, Tuple, Any
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import re
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import json
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import traceback
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import copy
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from loguru import logger
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from langchain.prompts.chat import ChatPromptTemplate
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from dev_opsgpt.connector.schema import (
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Memory, Task, Env, Role, Message, ActionStatus
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)
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from dev_opsgpt.llm_models import getChatModel
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from dev_opsgpt.connector.configs.prompts import EXECUTOR_PROMPT_INPUT, BEGIN_PROMPT_INPUT
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from dev_opsgpt.connector.utils import parse_section
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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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task: Task = None,
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memory: Memory = None,
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chat_turn: int = 1,
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do_search: bool = False,
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do_doc_retrieval: bool = False,
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do_tool_retrieval: bool = False,
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temperature: float = 0.2,
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stop: Union[List[str], str] = None,
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do_filter: bool = True,
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do_use_self_memory: bool = True,
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focus_agents: List[str] = [],
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focus_message_keys: List[str] = [],
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# prompt_mamnger: PromptManager
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):
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super().__init__(role, task, memory, chat_turn, do_search, do_doc_retrieval,
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do_tool_retrieval, temperature, stop, do_filter,do_use_self_memory,
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focus_agents, focus_message_keys
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)
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self.do_all_task = True # run all tasks
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def arun(self, query: Message, history: Memory = None, background: Memory = None, memory_pool: Memory=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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role_name=self.role.role_name,
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role_type="ai", #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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)
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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.parsed_output = {"Question": 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_pool, 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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logger.debug(f"{query.parsed_output['PLAN']}")
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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 = {"Question": 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_pool, 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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task_content = query_c.parsed_output["PLAN"][plan_step]
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query_c.parsed_output = {"Question": 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_pool, 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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# logger.info(f"{self.role.role_name} currenct question: {output_message.input_query}\nllm_executor_run: {output_message.step_content}")
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# logger.info(f"{self.role.role_name} currenct parserd_output_list: {output_message.parserd_output_list}")
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output_message.input_query = output_message.role_content
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# update memory pool
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memory_pool.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_pool: Memory,
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react_memory: Memory) -> Union[Message, Memory]:
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'''execute the llm predict by created prompt'''
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prompt = self.create_prompt(query, self_memory, history, background, memory_pool=memory_pool, react_memory=react_memory)
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content = self.llm.predict(prompt)
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# logger.debug(f"{self.role.role_name} prompt: {prompt}")
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logger.debug(f"{self.role.role_name} content: {content}")
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output_message.role_content = content
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output_message.role_contents += [content]
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output_message.step_content += "\n"+output_message.role_content
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output_message.step_contents + [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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# logger.debug(f"{self.role.role_name} content: {content}")
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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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react_memory.append(react_message)
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if observation_message:
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react_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, react_memory
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def create_prompt(
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self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, memory_pool: Memory=None, react_memory: Memory = None, prompt_mamnger=None) -> str:
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'''
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role\task\tools\docs\memory
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'''
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#
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doc_infos = self.create_doc_prompt(query)
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code_infos = self.create_codedoc_prompt(query)
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#
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formatted_tools, tool_names = self.create_tools_prompt(query)
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task_prompt = self.create_task_prompt(query)
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background_prompt = self.create_background_prompt(background, control_key="step_content")
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history_prompt = self.create_history_prompt(history)
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selfmemory_prompt = self.create_selfmemory_prompt(memory, control_key="step_content")
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#
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memory_pool_select_by_agent_key = self.select_memory_by_agent_key(memory_pool)
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memory_pool_select_by_agent_key_context = '\n\n'.join([
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f"*{k}*\n{v}" for parsed_output in memory_pool_select_by_agent_key.get_parserd_output_list() for k, v in parsed_output.items() if k not in ['Action Status']
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])
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DocInfos = ""
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if doc_infos is not None and doc_infos!="" and doc_infos!="不存在知识库辅助信息":
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DocInfos += f"\nDocument Information: {doc_infos}"
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if code_infos is not None and code_infos!="" and code_infos!="不存在代码库辅助信息":
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DocInfos += f"\nCodeBase Infomation: {code_infos}"
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# extra_system_prompt = self.role.role_prompt
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prompt = self.role.role_prompt.format(**{"formatted_tools": formatted_tools, "tool_names": tool_names})
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# input_query = react_memory.to_tuple_messages(content_key="role_content")
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# logger.debug(f"get_parserd_dict {react_memory.get_parserd_output()}")
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input_query = "\n".join(["\n".join([f"**{k}:**\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()])
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# input_query = query.input_query + "\n".join([f"{v}" for k, v in input_query if v])
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last_agent_parsed_output = "\n".join(["\n".join([f"*{k}*\n{v}" for k,v in _dict.items()]) for _dict in query.parsed_output_list])
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react_parsed_output = "\n".join(["\n".join([f"*{k}_context*\n{v}" for k,v in _dict.items()]) for _dict in react_memory.get_parserd_output()[:-1]])
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#
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prompt += "\n" + BEGIN_PROMPT_INPUT
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input_keys = parse_section(self.role.role_prompt, 'Input Format')
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if input_keys:
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for input_key in input_keys:
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if input_key == "Origin Query":
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prompt += "\n**Origin Query:**\n" + query.origin_query
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elif input_key == "DocInfos":
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prompt += "\n**DocInfos:**\n" + DocInfos
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elif input_key == "Context":
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if self.focus_agents and memory_pool_select_by_agent_key_context:
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context = memory_pool_select_by_agent_key_context
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else:
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context = last_agent_parsed_output
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prompt += "\n**Context:**\n" + context + f"\n{react_parsed_output}"
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elif input_key == "Question":
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prompt += "\n**Question:**\n" + query.parsed_output.get("Question")
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else:
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prompt += "\n" + input_query
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task = query.task or self.task
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# if task_prompt is not None:
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# prompt += "\n" + task.task_prompt
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# if selfmemory_prompt:
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# prompt += "\n" + selfmemory_prompt
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# if background_prompt:
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# prompt += "\n" + background_prompt
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# if history_prompt:
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# prompt += "\n" + history_prompt
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# prompt = extra_system_prompt.format(**{"query": input_query, "doc_infos": doc_infos, "formatted_tools": formatted_tools, "tool_names": tool_names})
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while "{{" in prompt or "}}" in prompt:
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prompt = prompt.replace("{{", "{")
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prompt = prompt.replace("}}", "}")
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return prompt
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def set_task(self, do_all_task):
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'''set task exec type'''
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self.do_all_task = do_all_task |