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_and_print_online, 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.timeout_seconds = 1.0
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 TODO: only for employee detection, can be deleted
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,
)
# log_and_print_online("System", role_play_session.assistant_sys_msg)
# log_and_print_online("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 "<INFO>" mark
# 1.1 get seminar conclusion flag (ChatAgent.info) from assistant or user role, which means there exist special "<INFO>" mark in the conversation
# 1.2 add "<INFO>" to the reflected content of the chat (which may be terminated chat without "<INFO>" mark)
# 2. without "<INFO>" 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_and_print_online(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_and_print_online(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 = "<INFO> " + 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 = "<INFO> " + self.self_reflection(task_prompt, role_play_session,
phase_name,
chat_env)
elif seminar_conclusion in [None, ""]:
seminar_conclusion = "<INFO> " + self.self_reflection(task_prompt, role_play_session, phase_name,
chat_env)
else:
seminar_conclusion = assistant_response.msg.content
log_and_print_online("**[Seminar Conclusion]**:\n\n {}".format(seminar_conclusion))
seminar_conclusion = seminar_conclusion.split("<INFO>")[-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 in [PhaseType.BRAINSTORMING]:
# question = """Conclude three most creative and imaginative brainstorm ideas from the whole discussion, in the format: "1) *; 2) *; 3) *; where '*' represents a suggestion." """
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("<INFO>")[-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'],
"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 "<INFO>" in self.seminar_conclusion:
chat_env.env_dict['language'] = self.seminar_conclusion.split("<INFO>")[-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'],
"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()
log_and_print_online("**[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'],
"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_and_print_online("**[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()
# chat_env.generate_images_from_codes()
log_and_print_online("**[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()
log_and_print_online("**[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()
log_and_print_online("**[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):
print(
f"You can participate in the development of the software {chat_env.env_dict['task_prompt']}. Please input your feedback. (\"End\" to quit the involvement.)")
provided_comments = input()
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": provided_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()
log_and_print_online("**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'],self.log_filepath)))
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_and_print_online("**[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_and_print_online(
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_and_print_online(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()
log_and_print_online("**[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_and_print_online("**[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