mirror of
https://github.com/OpenBMB/ChatDev.git
synced 2024-09-16 04:37:11 +03:00
366 lines
16 KiB
Python
366 lines
16 KiB
Python
import importlib
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import json
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import logging
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import os
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import shutil
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import time
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from datetime import datetime
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from camel.agents import RolePlaying
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from camel.configs import ChatGPTConfig
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from camel.typing import TaskType, ModelType
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from chatdev.chat_env import ChatEnv, ChatEnvConfig
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from chatdev.statistics import get_info
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from camel.web_spider import modal_trans
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from chatdev.utils import log_visualize, now
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def check_bool(s):
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return s.lower() == "true"
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class ChatChain:
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def __init__(self,
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config_path: str = None,
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config_phase_path: str = None,
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config_role_path: str = None,
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task_prompt: str = None,
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project_name: str = None,
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org_name: str = None,
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model_type: ModelType = ModelType.GPT_3_5_TURBO,
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code_path: str = None) -> None:
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"""
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Args:
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config_path: path to the ChatChainConfig.json
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config_phase_path: path to the PhaseConfig.json
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config_role_path: path to the RoleConfig.json
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task_prompt: the user input prompt for software
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project_name: the user input name for software
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org_name: the organization name of the human user
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"""
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# load config file
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self.config_path = config_path
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self.config_phase_path = config_phase_path
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self.config_role_path = config_role_path
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self.project_name = project_name
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self.org_name = org_name
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self.model_type = model_type
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self.code_path = code_path
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with open(self.config_path, 'r', encoding="utf8") as file:
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self.config = json.load(file)
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with open(self.config_phase_path, 'r', encoding="utf8") as file:
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self.config_phase = json.load(file)
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with open(self.config_role_path, 'r', encoding="utf8") as file:
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self.config_role = json.load(file)
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# init chatchain config and recruitments
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self.chain = self.config["chain"]
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self.recruitments = self.config["recruitments"]
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self.web_spider = self.config["web_spider"]
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# init default max chat turn
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self.chat_turn_limit_default = 10
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# init ChatEnv
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self.chat_env_config = ChatEnvConfig(clear_structure=check_bool(self.config["clear_structure"]),
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gui_design=check_bool(self.config["gui_design"]),
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git_management=check_bool(self.config["git_management"]),
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incremental_develop=check_bool(self.config["incremental_develop"]),
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background_prompt=self.config["background_prompt"],
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with_memory=check_bool(self.config["with_memory"]))
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self.chat_env = ChatEnv(self.chat_env_config)
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# the user input prompt will be self-improved (if set "self_improve": "True" in ChatChainConfig.json)
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# the self-improvement is done in self.preprocess
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self.task_prompt_raw = task_prompt
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self.task_prompt = ""
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# init role prompts
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self.role_prompts = dict()
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for role in self.config_role:
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self.role_prompts[role] = "\n".join(self.config_role[role])
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# init log
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self.start_time, self.log_filepath = self.get_logfilepath()
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# init SimplePhase instances
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# import all used phases in PhaseConfig.json from chatdev.phase
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# note that in PhaseConfig.json there only exist SimplePhases
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# ComposedPhases are defined in ChatChainConfig.json and will be imported in self.execute_step
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self.compose_phase_module = importlib.import_module("chatdev.composed_phase")
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self.phase_module = importlib.import_module("chatdev.phase")
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self.phases = dict()
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for phase in self.config_phase:
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assistant_role_name = self.config_phase[phase]['assistant_role_name']
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user_role_name = self.config_phase[phase]['user_role_name']
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phase_prompt = "\n\n".join(self.config_phase[phase]['phase_prompt'])
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phase_class = getattr(self.phase_module, phase)
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phase_instance = phase_class(assistant_role_name=assistant_role_name,
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user_role_name=user_role_name,
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phase_prompt=phase_prompt,
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role_prompts=self.role_prompts,
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phase_name=phase,
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model_type=self.model_type,
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log_filepath=self.log_filepath)
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self.phases[phase] = phase_instance
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def make_recruitment(self):
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"""
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recruit all employees
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Returns: None
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"""
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for employee in self.recruitments:
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self.chat_env.recruit(agent_name=employee)
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def execute_step(self, phase_item: dict):
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"""
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execute single phase in the chain
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Args:
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phase_item: single phase configuration in the ChatChainConfig.json
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Returns:
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"""
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phase = phase_item['phase']
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phase_type = phase_item['phaseType']
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# For SimplePhase, just look it up from self.phases and conduct the "Phase.execute" method
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if phase_type == "SimplePhase":
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max_turn_step = phase_item['max_turn_step']
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need_reflect = check_bool(phase_item['need_reflect'])
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if phase in self.phases:
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self.chat_env = self.phases[phase].execute(self.chat_env,
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self.chat_turn_limit_default if max_turn_step <= 0 else max_turn_step,
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need_reflect)
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else:
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raise RuntimeError(f"Phase '{phase}' is not yet implemented in chatdev.phase")
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# For ComposedPhase, we create instance here then conduct the "ComposedPhase.execute" method
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elif phase_type == "ComposedPhase":
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cycle_num = phase_item['cycleNum']
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composition = phase_item['Composition']
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compose_phase_class = getattr(self.compose_phase_module, phase)
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if not compose_phase_class:
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raise RuntimeError(f"Phase '{phase}' is not yet implemented in chatdev.compose_phase")
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compose_phase_instance = compose_phase_class(phase_name=phase,
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cycle_num=cycle_num,
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composition=composition,
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config_phase=self.config_phase,
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config_role=self.config_role,
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model_type=self.model_type,
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log_filepath=self.log_filepath)
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self.chat_env = compose_phase_instance.execute(self.chat_env)
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else:
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raise RuntimeError(f"PhaseType '{phase_type}' is not yet implemented.")
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def execute_chain(self):
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"""
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execute the whole chain based on ChatChainConfig.json
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Returns: None
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"""
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for phase_item in self.chain:
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self.execute_step(phase_item)
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def get_logfilepath(self):
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"""
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get the log path (under the software path)
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Returns:
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start_time: time for starting making the software
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log_filepath: path to the log
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"""
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start_time = now()
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filepath = os.path.dirname(__file__)
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# root = "/".join(filepath.split("/")[:-1])
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root = os.path.dirname(filepath)
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# directory = root + "/WareHouse/"
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directory = os.path.join(root, "WareHouse")
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log_filepath = os.path.join(directory,
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"{}.log".format("_".join([self.project_name, self.org_name, start_time])))
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return start_time, log_filepath
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def pre_processing(self):
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"""
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remove useless files and log some global config settings
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Returns: None
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"""
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filepath = os.path.dirname(__file__)
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root = os.path.dirname(filepath)
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directory = os.path.join(root, "WareHouse")
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if self.chat_env.config.clear_structure:
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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# logs with error trials are left in WareHouse/
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if os.path.isfile(file_path) and not filename.endswith(".py") and not filename.endswith(".log"):
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os.remove(file_path)
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print("{} Removed.".format(file_path))
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software_path = os.path.join(directory, "_".join([self.project_name, self.org_name, self.start_time]))
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self.chat_env.set_directory(software_path)
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if self.chat_env.config.with_memory is True:
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self.chat_env.init_memory()
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# copy config files to software path
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shutil.copy(self.config_path, software_path)
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shutil.copy(self.config_phase_path, software_path)
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shutil.copy(self.config_role_path, software_path)
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# copy code files to software path in incremental_develop mode
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if check_bool(self.config["incremental_develop"]):
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for root, dirs, files in os.walk(self.code_path):
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relative_path = os.path.relpath(root, self.code_path)
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target_dir = os.path.join(software_path, 'base', relative_path)
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os.makedirs(target_dir, exist_ok=True)
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for file in files:
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source_file = os.path.join(root, file)
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target_file = os.path.join(target_dir, file)
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shutil.copy2(source_file, target_file)
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self.chat_env._load_from_hardware(os.path.join(software_path, 'base'))
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# write task prompt to software
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with open(os.path.join(software_path, self.project_name + ".prompt"), "w") as f:
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f.write(self.task_prompt_raw)
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preprocess_msg = "**[Preprocessing]**\n\n"
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chat_gpt_config = ChatGPTConfig()
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preprocess_msg += "**ChatDev Starts** ({})\n\n".format(self.start_time)
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preprocess_msg += "**Timestamp**: {}\n\n".format(self.start_time)
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preprocess_msg += "**config_path**: {}\n\n".format(self.config_path)
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preprocess_msg += "**config_phase_path**: {}\n\n".format(self.config_phase_path)
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preprocess_msg += "**config_role_path**: {}\n\n".format(self.config_role_path)
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preprocess_msg += "**task_prompt**: {}\n\n".format(self.task_prompt_raw)
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preprocess_msg += "**project_name**: {}\n\n".format(self.project_name)
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preprocess_msg += "**Log File**: {}\n\n".format(self.log_filepath)
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preprocess_msg += "**ChatDevConfig**:\n{}\n\n".format(self.chat_env.config.__str__())
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preprocess_msg += "**ChatGPTConfig**:\n{}\n\n".format(chat_gpt_config)
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log_visualize(preprocess_msg)
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# init task prompt
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if check_bool(self.config['self_improve']):
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self.chat_env.env_dict['task_prompt'] = self.self_task_improve(self.task_prompt_raw)
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else:
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self.chat_env.env_dict['task_prompt'] = self.task_prompt_raw
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if(check_bool(self.web_spider)):
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self.chat_env.env_dict['task_description'] = modal_trans(self.task_prompt_raw)
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def post_processing(self):
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"""
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summarize the production and move log files to the software directory
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Returns: None
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"""
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self.chat_env.write_meta()
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filepath = os.path.dirname(__file__)
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root = os.path.dirname(filepath)
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if self.chat_env_config.git_management:
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log_git_info = "**[Git Information]**\n\n"
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self.chat_env.codes.version += 1
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os.system("cd {}; git add .".format(self.chat_env.env_dict["directory"]))
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log_git_info += "cd {}; git add .\n".format(self.chat_env.env_dict["directory"])
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os.system("cd {}; git commit -m \"v{} Final Version\"".format(self.chat_env.env_dict["directory"],
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self.chat_env.codes.version))
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log_git_info += "cd {}; git commit -m \"v{} Final Version\"\n".format(self.chat_env.env_dict["directory"],
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self.chat_env.codes.version)
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log_visualize(log_git_info)
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git_info = "**[Git Log]**\n\n"
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import subprocess
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# execute git log
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command = "cd {}; git log".format(self.chat_env.env_dict["directory"])
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completed_process = subprocess.run(command, shell=True, text=True, stdout=subprocess.PIPE)
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if completed_process.returncode == 0:
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log_output = completed_process.stdout
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else:
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log_output = "Error when executing " + command
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git_info += log_output
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log_visualize(git_info)
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post_info = "**[Post Info]**\n\n"
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now_time = now()
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time_format = "%Y%m%d%H%M%S"
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datetime1 = datetime.strptime(self.start_time, time_format)
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datetime2 = datetime.strptime(now_time, time_format)
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duration = (datetime2 - datetime1).total_seconds()
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post_info += "Software Info: {}".format(
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get_info(self.chat_env.env_dict['directory'], self.log_filepath) + "\n\n🕑**duration**={:.2f}s\n\n".format(
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duration))
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post_info += "ChatDev Starts ({})".format(self.start_time) + "\n\n"
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post_info += "ChatDev Ends ({})".format(now_time) + "\n\n"
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directory = self.chat_env.env_dict['directory']
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if self.chat_env.config.clear_structure:
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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if os.path.isdir(file_path) and file_path.endswith("__pycache__"):
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shutil.rmtree(file_path, ignore_errors=True)
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post_info += "{} Removed.".format(file_path) + "\n\n"
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log_visualize(post_info)
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logging.shutdown()
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time.sleep(1)
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shutil.move(self.log_filepath,
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os.path.join(root + "/WareHouse", "_".join([self.project_name, self.org_name, self.start_time]),
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os.path.basename(self.log_filepath)))
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# @staticmethod
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def self_task_improve(self, task_prompt):
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"""
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ask agent to improve the user query prompt
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Args:
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task_prompt: original user query prompt
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Returns:
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revised_task_prompt: revised prompt from the prompt engineer agent
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"""
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self_task_improve_prompt = """I will give you a short description of a software design requirement,
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please rewrite it into a detailed prompt that can make large language model know how to make this software better based this prompt,
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the prompt should ensure LLMs build a software that can be run correctly, which is the most import part you need to consider.
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remember that the revised prompt should not contain more than 200 words,
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here is the short description:\"{}\".
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If the revised prompt is revised_version_of_the_description,
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then you should return a message in a format like \"<INFO> revised_version_of_the_description\", do not return messages in other formats.""".format(
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task_prompt)
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role_play_session = RolePlaying(
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assistant_role_name="Prompt Engineer",
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assistant_role_prompt="You are an professional prompt engineer that can improve user input prompt to make LLM better understand these prompts.",
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user_role_prompt="You are an user that want to use LLM to build software.",
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user_role_name="User",
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task_type=TaskType.CHATDEV,
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task_prompt="Do prompt engineering on user query",
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with_task_specify=False,
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model_type=self.model_type,
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)
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# log_visualize("System", role_play_session.assistant_sys_msg)
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# log_visualize("System", role_play_session.user_sys_msg)
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_, input_user_msg = role_play_session.init_chat(None, None, self_task_improve_prompt)
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assistant_response, user_response = role_play_session.step(input_user_msg, True)
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revised_task_prompt = assistant_response.msg.content.split("<INFO>")[-1].lower().strip()
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log_visualize(role_play_session.assistant_agent.role_name, assistant_response.msg.content)
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log_visualize(
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"**[Task Prompt Self Improvement]**\n**Original Task Prompt**: {}\n**Improved Task Prompt**: {}".format(
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task_prompt, revised_task_prompt))
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return revised_task_prompt
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