174 lines
8.0 KiB
Python
174 lines
8.0 KiB
Python
from pydantic import BaseModel
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from typing import List, Union
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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.agent_config import REACT_PROMPT_INPUT
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from .base_agent import BaseAgent
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class ReactAgent(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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def run(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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for message in self.arun(query, history, background, memory_pool):
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pass
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return message
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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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step_nums = copy.deepcopy(self.chat_turn)
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react_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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query_c = copy.deepcopy(query)
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query_c.parsed_output = {"Question": "\n".join([f"{v}" for k, v in query.parsed_output.items() if k not in ["Action Status"]])}
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react_memory.append(query_c)
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self_memory = self.memory if self.do_use_self_memory else None
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idx = 0
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# start to react
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while step_nums > 0:
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output_message.role_content = output_message.step_content
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prompt = self.create_prompt(query, self_memory, history, background, react_memory, memory_pool)
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try:
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content = self.llm.predict(prompt)
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except Exception as e:
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logger.warning(f"error prompt: {prompt}")
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raise Exception(traceback.format_exc())
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output_message.role_content = "\n"+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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yield output_message
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# logger.debug(f"{self.role.role_name}, {idx} iteration prompt: {prompt}")
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logger.info(f"{self.role.role_name}, {idx} iteration step_run: {output_message.role_content}")
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output_message = self.message_utils.parser(output_message)
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# when get finished signal can stop early
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if output_message.action_status == ActionStatus.FINISHED: break
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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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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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# logger.info(f"{self.role.role_name} currenct question: {output_message.input_query}\nllm_react_run: {output_message.role_content}")
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idx += 1
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step_nums -= 1
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yield output_message
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# react' self_memory saved at last
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self.append_history(output_message)
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# update memory pool
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# memory_pool.append(output_message)
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output_message.input_query = query.input_query
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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 create_prompt(
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self, query: Message, memory: Memory =None, history: Memory = None, background: Memory = None, react_memory: Memory = None, memory_pool: Memory= None,
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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)
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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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# 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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# 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
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# input_query = react_memory.to_tuple_messages(content_key="step_content")
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# # input_query = query.input_query + "\n" + "\n".join([f"{v}" for k, v in input_query if v])
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# input_query = "\n".join([f"{v}" for k, v in input_query if v])
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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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logger.debug(f"input_query: {input_query}")
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prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": 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 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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# prompt += f"\n知识库信息: {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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# prompt += f"\n代码库信息: {code_infos}"
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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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# if selfmemory_prompt:
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# prompt += "\n" + selfmemory_prompt
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# logger.debug(f"{self.role.role_name} extra_system_prompt: {self.role.role_prompt}")
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# logger.debug(f"{self.role.role_name} input_query: {input_query}")
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# logger.debug(f"{self.role.role_name} doc_infos: {doc_infos}")
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# logger.debug(f"{self.role.role_name} tool_names: {tool_names}")
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# prompt += "\n" + REACT_PROMPT_INPUT.format(**{"query": input_query})
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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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