import os import re from abc import ABC, abstractmethod from camel.agents import RolePlaying from camel.messages import ChatMessage from camel.typing import TaskType, ModelType from chatdev.chat_env import ChatEnv from chatdev.statistics import get_info from chatdev.utils import log_visualize, log_arguments class Phase(ABC): def __init__(self, assistant_role_name, user_role_name, phase_prompt, role_prompts, phase_name, model_type, log_filepath): """ Args: assistant_role_name: who receives chat in a phase user_role_name: who starts the chat in a phase phase_prompt: prompt of this phase role_prompts: prompts of all roles phase_name: name of this phase """ self.seminar_conclusion = None self.assistant_role_name = assistant_role_name self.user_role_name = user_role_name self.phase_prompt = phase_prompt self.phase_env = dict() self.phase_name = phase_name self.assistant_role_prompt = role_prompts[assistant_role_name] self.user_role_prompt = role_prompts[user_role_name] self.ceo_prompt = role_prompts["Chief Executive Officer"] self.counselor_prompt = role_prompts["Counselor"] self.max_retries = 3 self.reflection_prompt = """Here is a conversation between two roles: {conversations} {question}""" self.model_type = model_type self.log_filepath = log_filepath @log_arguments def chatting( self, chat_env, task_prompt: str, assistant_role_name: str, user_role_name: str, phase_prompt: str, phase_name: str, assistant_role_prompt: str, user_role_prompt: str, task_type=TaskType.CHATDEV, need_reflect=False, with_task_specify=False, model_type=ModelType.GPT_3_5_TURBO, placeholders=None, chat_turn_limit=10 ) -> str: """ Args: chat_env: global chatchain environment task_prompt: user query prompt for building the software assistant_role_name: who receives the chat user_role_name: who starts the chat phase_prompt: prompt of the phase phase_name: name of the phase assistant_role_prompt: prompt of assistant role user_role_prompt: prompt of user role task_type: task type need_reflect: flag for checking reflection with_task_specify: with task specify model_type: model type placeholders: placeholders for phase environment to generate phase prompt chat_turn_limit: turn limits in each chat Returns: """ if placeholders is None: placeholders = {} assert 1 <= chat_turn_limit <= 100 if not chat_env.exist_employee(assistant_role_name): raise ValueError(f"{assistant_role_name} not recruited in ChatEnv.") if not chat_env.exist_employee(user_role_name): raise ValueError(f"{user_role_name} not recruited in ChatEnv.") # init role play role_play_session = RolePlaying( assistant_role_name=assistant_role_name, user_role_name=user_role_name, assistant_role_prompt=assistant_role_prompt, user_role_prompt=user_role_prompt, task_prompt=task_prompt, task_type=task_type, with_task_specify=with_task_specify, model_type=model_type, background_prompt=chat_env.config.background_prompt ) # log_visualize("System", role_play_session.assistant_sys_msg) # log_visualize("System", role_play_session.user_sys_msg) # start the chat _, input_user_msg = role_play_session.init_chat(None, placeholders, phase_prompt) seminar_conclusion = None # handle chats # the purpose of the chatting in one phase is to get a seminar conclusion # there are two types of conclusion # 1. with "" mark # 1.1 get seminar conclusion flag (ChatAgent.info) from assistant or user role, which means there exist special "" mark in the conversation # 1.2 add "" to the reflected content of the chat (which may be terminated chat without "" mark) # 2. without "" mark, which means the chat is terminated or normally ended without generating a marked conclusion, and there is no need to reflect for i in range(chat_turn_limit): # start the chat, we represent the user and send msg to assistant # 1. so the input_user_msg should be assistant_role_prompt + phase_prompt # 2. then input_user_msg send to LLM and get assistant_response # 3. now we represent the assistant and send msg to user, so the input_assistant_msg is user_role_prompt + assistant_response # 4. then input_assistant_msg send to LLM and get user_response # all above are done in role_play_session.step, which contains two interactions with LLM # the first interaction is logged in role_play_session.init_chat assistant_response, user_response = role_play_session.step(input_user_msg, chat_turn_limit == 1) conversation_meta = "**" + assistant_role_name + "<->" + user_role_name + " on : " + str( phase_name) + ", turn " + str(i) + "**\n\n" # TODO: max_tokens_exceeded errors here if isinstance(assistant_response.msg, ChatMessage): # we log the second interaction here log_visualize(role_play_session.assistant_agent.role_name, conversation_meta + "[" + role_play_session.user_agent.system_message.content + "]\n\n" + assistant_response.msg.content) if role_play_session.assistant_agent.info: seminar_conclusion = assistant_response.msg.content break if assistant_response.terminated: break if isinstance(user_response.msg, ChatMessage): # here is the result of the second interaction, which may be used to start the next chat turn log_visualize(role_play_session.user_agent.role_name, conversation_meta + "[" + role_play_session.assistant_agent.system_message.content + "]\n\n" + user_response.msg.content) if role_play_session.user_agent.info: seminar_conclusion = user_response.msg.content break if user_response.terminated: break # continue the chat if chat_turn_limit > 1 and isinstance(user_response.msg, ChatMessage): input_user_msg = user_response.msg else: break # conduct self reflection if need_reflect: if seminar_conclusion in [None, ""]: seminar_conclusion = " " + self.self_reflection(task_prompt, role_play_session, phase_name, chat_env) if "recruiting" in phase_name: if "Yes".lower() not in seminar_conclusion.lower() and "No".lower() not in seminar_conclusion.lower(): seminar_conclusion = " " + self.self_reflection(task_prompt, role_play_session, phase_name, chat_env) elif seminar_conclusion in [None, ""]: seminar_conclusion = " " + self.self_reflection(task_prompt, role_play_session, phase_name, chat_env) else: seminar_conclusion = assistant_response.msg.content log_visualize("**[Seminar Conclusion]**:\n\n {}".format(seminar_conclusion)) seminar_conclusion = seminar_conclusion.split("")[-1] return seminar_conclusion def self_reflection(self, task_prompt: str, role_play_session: RolePlaying, phase_name: str, chat_env: ChatEnv) -> str: """ Args: task_prompt: user query prompt for building the software role_play_session: role play session from the chat phase which needs reflection phase_name: name of the chat phase which needs reflection chat_env: global chatchain environment Returns: reflected_content: str, reflected results """ messages = role_play_session.assistant_agent.stored_messages if len( role_play_session.assistant_agent.stored_messages) >= len( role_play_session.user_agent.stored_messages) else role_play_session.user_agent.stored_messages messages = ["{}: {}".format(message.role_name, message.content.replace("\n\n", "\n")) for message in messages] messages = "\n\n".join(messages) if "recruiting" in phase_name: question = """Answer their final discussed conclusion (Yes or No) in the discussion without any other words, e.g., "Yes" """ elif phase_name == "DemandAnalysis": question = """Answer their final product modality in the discussion without any other words, e.g., "PowerPoint" """ elif phase_name == "LanguageChoose": question = """Conclude the programming language being discussed for software development, in the format: "*" where '*' represents a programming language." """ elif phase_name == "EnvironmentDoc": question = """According to the codes and file format listed above, write a requirements.txt file to specify the dependencies or packages required for the project to run properly." """ else: raise ValueError(f"Reflection of phase {phase_name}: Not Assigned.") # Reflections actually is a special phase between CEO and counselor # They read the whole chatting history of this phase and give refined conclusion of this phase reflected_content = \ self.chatting(chat_env=chat_env, task_prompt=task_prompt, assistant_role_name="Chief Executive Officer", user_role_name="Counselor", phase_prompt=self.reflection_prompt, phase_name="Reflection", assistant_role_prompt=self.ceo_prompt, user_role_prompt=self.counselor_prompt, placeholders={"conversations": messages, "question": question}, need_reflect=False, chat_turn_limit=1, model_type=self.model_type) if "recruiting" in phase_name: if "Yes".lower() in reflected_content.lower(): return "Yes" return "No" else: return reflected_content @abstractmethod def update_phase_env(self, chat_env): """ update self.phase_env (if needed) using chat_env, then the chatting will use self.phase_env to follow the context and fill placeholders in phase prompt must be implemented in customized phase the usual format is just like: ``` self.phase_env.update({key:chat_env[key]}) ``` Args: chat_env: global chat chain environment Returns: None """ pass @abstractmethod def update_chat_env(self, chat_env) -> ChatEnv: """ update chan_env based on the results of self.execute, which is self.seminar_conclusion must be implemented in customized phase the usual format is just like: ``` chat_env.xxx = some_func_for_postprocess(self.seminar_conclusion) ``` Args: chat_env:global chat chain environment Returns: chat_env: updated global chat chain environment """ pass def execute(self, chat_env, chat_turn_limit, need_reflect) -> ChatEnv: """ execute the chatting in this phase 1. receive information from environment: update the phase environment from global environment 2. execute the chatting 3. change the environment: update the global environment using the conclusion Args: chat_env: global chat chain environment chat_turn_limit: turn limit in each chat need_reflect: flag for reflection Returns: chat_env: updated global chat chain environment using the conclusion from this phase execution """ self.update_phase_env(chat_env) self.seminar_conclusion = \ self.chatting(chat_env=chat_env, task_prompt=chat_env.env_dict['task_prompt'], need_reflect=need_reflect, assistant_role_name=self.assistant_role_name, user_role_name=self.user_role_name, phase_prompt=self.phase_prompt, phase_name=self.phase_name, assistant_role_prompt=self.assistant_role_prompt, user_role_prompt=self.user_role_prompt, chat_turn_limit=chat_turn_limit, placeholders=self.phase_env, model_type=self.model_type) chat_env = self.update_chat_env(chat_env) return chat_env class DemandAnalysis(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): pass def update_chat_env(self, chat_env) -> ChatEnv: if len(self.seminar_conclusion) > 0: chat_env.env_dict['modality'] = self.seminar_conclusion.split("")[-1].lower().replace(".", "").strip() return chat_env class LanguageChoose(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "description": chat_env.env_dict['task_description'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas']}) def update_chat_env(self, chat_env) -> ChatEnv: if len(self.seminar_conclusion) > 0 and "" in self.seminar_conclusion: chat_env.env_dict['language'] = self.seminar_conclusion.split("")[-1].lower().replace(".", "").strip() elif len(self.seminar_conclusion) > 0: chat_env.env_dict['language'] = self.seminar_conclusion else: chat_env.env_dict['language'] = "Python" return chat_env class Coding(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): gui = "" if not chat_env.config.gui_design \ else "The software should be equipped with graphical user interface (GUI) so that user can visually and graphically use it; so you must choose a GUI framework (e.g., in Python, you can implement GUI via tkinter, Pygame, Flexx, PyGUI, etc,)." self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "description": chat_env.env_dict['task_description'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "gui": gui}) def update_chat_env(self, chat_env) -> ChatEnv: chat_env.update_codes(self.seminar_conclusion) if len(chat_env.codes.codebooks.keys()) == 0: raise ValueError("No Valid Codes.") chat_env.rewrite_codes("Finish Coding") log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env class ArtDesign(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env = {"task": chat_env.env_dict['task_prompt'], "description": chat_env.env_dict['task_description'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes()} def update_chat_env(self, chat_env) -> ChatEnv: chat_env.proposed_images = chat_env.get_proposed_images_from_message(self.seminar_conclusion) log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env class ArtIntegration(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env = {"task": chat_env.env_dict['task_prompt'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes(), "images": "\n".join( ["{}: {}".format(filename, chat_env.proposed_images[filename]) for filename in sorted(list(chat_env.proposed_images.keys()))])} def update_chat_env(self, chat_env) -> ChatEnv: chat_env.update_codes(self.seminar_conclusion) chat_env.rewrite_codes("Finish Art Integration") # chat_env.generate_images_from_codes() log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env class CodeComplete(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes(), "unimplemented_file": ""}) unimplemented_file = "" for filename in self.phase_env['pyfiles']: code_content = open(os.path.join(chat_env.env_dict['directory'], filename)).read() lines = [line.strip() for line in code_content.split("\n") if line.strip() == "pass"] if len(lines) > 0 and self.phase_env['num_tried'][filename] < self.phase_env['max_num_implement']: unimplemented_file = filename break self.phase_env['num_tried'][unimplemented_file] += 1 self.phase_env['unimplemented_file'] = unimplemented_file def update_chat_env(self, chat_env) -> ChatEnv: chat_env.update_codes(self.seminar_conclusion) if len(chat_env.codes.codebooks.keys()) == 0: raise ValueError("No Valid Codes.") chat_env.rewrite_codes("Code Complete #" + str(self.phase_env["cycle_index"]) + " Finished") log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env class CodeReviewComment(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update( {"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes(), "images": ", ".join(chat_env.incorporated_images)}) def update_chat_env(self, chat_env) -> ChatEnv: chat_env.env_dict['review_comments'] = self.seminar_conclusion return chat_env class CodeReviewModification(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes(), "comments": chat_env.env_dict['review_comments']}) def update_chat_env(self, chat_env) -> ChatEnv: if "```".lower() in self.seminar_conclusion.lower(): chat_env.update_codes(self.seminar_conclusion) chat_env.rewrite_codes("Review #" + str(self.phase_env["cycle_index"]) + " Finished") log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) self.phase_env['modification_conclusion'] = self.seminar_conclusion return chat_env class CodeReviewHuman(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes()}) def update_chat_env(self, chat_env) -> ChatEnv: if "```".lower() in self.seminar_conclusion.lower(): chat_env.update_codes(self.seminar_conclusion) chat_env.rewrite_codes("Human Review #" + str(self.phase_env["cycle_index"]) + " Finished") log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env def execute(self, chat_env, chat_turn_limit, need_reflect) -> ChatEnv: self.update_phase_env(chat_env) log_visualize( f"**[Human-Agent-Interaction]**\n\n" f"Now you can participate in the development of the software!\n" f"The task is: {chat_env.env_dict['task_prompt']}\n" f"Please input your feedback (in multiple lines). It can be bug report or new feature requirement.\n" f"You are currently in the #{self.phase_env['cycle_index']} human feedback with a total of {self.phase_env['cycle_num']} feedbacks\n" f"Type 'end' on a separate line to submit.\n" f"You can type \"Exit\" to quit this mode at any time.\n" ) provided_comments = [] while True: user_input = input(">>>>>>") if user_input.strip().lower() == "end": break if user_input.strip().lower() == "exit": provided_comments = ["exit"] break provided_comments.append(user_input) self.phase_env["comments"] = '\n'.join(provided_comments) log_visualize( f"**[User Provided Comments]**\n\n In the #{self.phase_env['cycle_index']} of total {self.phase_env['cycle_num']} comments: \n\n" + self.phase_env["comments"]) if self.phase_env["comments"].strip().lower() == "exit": return chat_env self.seminar_conclusion = \ self.chatting(chat_env=chat_env, task_prompt=chat_env.env_dict['task_prompt'], need_reflect=need_reflect, assistant_role_name=self.assistant_role_name, user_role_name=self.user_role_name, phase_prompt=self.phase_prompt, phase_name=self.phase_name, assistant_role_prompt=self.assistant_role_prompt, user_role_prompt=self.user_role_prompt, chat_turn_limit=chat_turn_limit, placeholders=self.phase_env, model_type=self.model_type) chat_env = self.update_chat_env(chat_env) return chat_env class TestErrorSummary(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): chat_env.generate_images_from_codes() (exist_bugs_flag, test_reports) = chat_env.exist_bugs() self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes(), "test_reports": test_reports, "exist_bugs_flag": exist_bugs_flag}) log_visualize("**[Test Reports]**:\n\n{}".format(test_reports)) def update_chat_env(self, chat_env) -> ChatEnv: chat_env.env_dict['error_summary'] = self.seminar_conclusion chat_env.env_dict['test_reports'] = self.phase_env['test_reports'] return chat_env def execute(self, chat_env, chat_turn_limit, need_reflect) -> ChatEnv: self.update_phase_env(chat_env) if "ModuleNotFoundError" in self.phase_env['test_reports']: chat_env.fix_module_not_found_error(self.phase_env['test_reports']) log_visualize( f"Software Test Engineer found ModuleNotFoundError:\n{self.phase_env['test_reports']}\n") pip_install_content = "" for match in re.finditer(r"No module named '(\S+)'", self.phase_env['test_reports'], re.DOTALL): module = match.group(1) pip_install_content += "{}\n```{}\n{}\n```\n".format("cmd", "bash", f"pip install {module}") log_visualize(f"Programmer resolve ModuleNotFoundError by:\n{pip_install_content}\n") self.seminar_conclusion = "nothing need to do" else: self.seminar_conclusion = \ self.chatting(chat_env=chat_env, task_prompt=chat_env.env_dict['task_prompt'], need_reflect=need_reflect, assistant_role_name=self.assistant_role_name, user_role_name=self.user_role_name, phase_prompt=self.phase_prompt, phase_name=self.phase_name, assistant_role_prompt=self.assistant_role_prompt, user_role_prompt=self.user_role_prompt, chat_turn_limit=chat_turn_limit, placeholders=self.phase_env) chat_env = self.update_chat_env(chat_env) return chat_env class TestModification(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "test_reports": chat_env.env_dict['test_reports'], "error_summary": chat_env.env_dict['error_summary'], "codes": chat_env.get_codes() }) def update_chat_env(self, chat_env) -> ChatEnv: if "```".lower() in self.seminar_conclusion.lower(): chat_env.update_codes(self.seminar_conclusion) chat_env.rewrite_codes("Test #" + str(self.phase_env["cycle_index"]) + " Finished") log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env class EnvironmentDoc(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes()}) def update_chat_env(self, chat_env) -> ChatEnv: chat_env._update_requirements(self.seminar_conclusion) chat_env.rewrite_requirements() log_visualize( "**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath))) return chat_env class Manual(Phase): def __init__(self, **kwargs): super().__init__(**kwargs) def update_phase_env(self, chat_env): self.phase_env.update({"task": chat_env.env_dict['task_prompt'], "modality": chat_env.env_dict['modality'], "ideas": chat_env.env_dict['ideas'], "language": chat_env.env_dict['language'], "codes": chat_env.get_codes(), "requirements": chat_env.get_requirements()}) def update_chat_env(self, chat_env) -> ChatEnv: chat_env._update_manuals(self.seminar_conclusion) chat_env.rewrite_manuals() return chat_env