# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an “AS IS” BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
from __future__ import annotations
import ast
from typing import Dict, List, Optional
from colorama import Fore
from camel.agents.chat_agent import ChatAgent
from camel.messages.base import BaseMessage
from camel.societies import RolePlaying
from camel.tasks.task import Task, TaskState
from camel.utils import print_text_animated
from camel.workforce.prompts import (
ROLEPLAY_PROCESS_TASK_PROMPT,
ROLEPLAY_SUMMARIZE_PROMPT,
)
from camel.workforce.utils import TaskResult
from camel.workforce.worker import Worker
[docs]
class RolePlayingWorker(Worker):
r"""A worker node that contains a role playing.
Args:
description (str): Description of the node.
assistant_role_name (str): The role name of the assistant agent.
user_role_name (str): The role name of the user agent.
assistant_agent_kwargs (Optional[Dict], optional): The keyword
arguments to initialize the assistant agent in the role playing,
like the model name, etc. Defaults to None.
user_agent_kwargs (Optional[Dict], optional): The keyword arguments to
initialize the user agent in the role playing, like the model name,
etc. Defaults to None.
chat_turn_limit (int, optional): The maximum number of chat turns in
the role playing. Defaults to 3.
"""
def __init__(
self,
description: str,
assistant_role_name: str,
user_role_name: str,
assistant_agent_kwargs: Optional[Dict] = None,
user_agent_kwargs: Optional[Dict] = None,
chat_turn_limit: int = 3,
) -> None:
super().__init__(description)
summ_sys_msg = BaseMessage.make_assistant_message(
role_name="Summarizer",
content="You are a good summarizer. You will be presented with "
"scenarios where an assistant and a user with specific roles "
"are trying to solve a task. Your job is summarizing the result "
"of the task based on the chat history.",
)
self.summarize_agent = ChatAgent(summ_sys_msg)
self.chat_turn_limit = chat_turn_limit
self.assistant_role_name = assistant_role_name
self.user_role_name = user_role_name
self.assistant_agent_kwargs = assistant_agent_kwargs
self.user_agent_kwargs = user_agent_kwargs
async def _process_task(
self, task: Task, dependencies: List[Task]
) -> TaskState:
r"""Processes a task leveraging its dependencies through role-playing.
This method orchestrates a role-playing session between an AI
assistant and an AI user to process a given task. It initiates with a
generated prompt based on the task and its dependencies, conducts a
dialogue up to a specified chat turn limit, and then summarizes the
dialogue to determine the task's outcome.
Args:
task (Task): The task object to be processed, containing necessary
details like content and type.
dependencies (List[Task]): A list of task objects that the current
task depends on.
Returns:
TaskState: `TaskState.DONE` if processed successfully, otherwise
`TaskState.FAILED`.
"""
dependency_tasks_info = self._get_dep_tasks_info(dependencies)
prompt = ROLEPLAY_PROCESS_TASK_PROMPT.format(
content=task.content,
dependency_task_info=dependency_tasks_info,
additional_info=task.additional_info,
)
role_play_session = RolePlaying(
assistant_role_name=self.assistant_role_name,
user_role_name=self.user_role_name,
assistant_agent_kwargs=self.assistant_agent_kwargs,
user_agent_kwargs=self.user_agent_kwargs,
task_prompt=prompt,
with_task_specify=False,
)
n = 0
input_msg = role_play_session.init_chat()
chat_history = []
while n < self.chat_turn_limit:
n += 1
assistant_response, user_response = role_play_session.step(
input_msg
)
if assistant_response.terminated:
reason = assistant_response.info['termination_reasons']
print(
f"{Fore.GREEN}AI Assistant terminated. Reason: "
f"{reason}.{Fore.RESET}"
)
break
if user_response.terminated:
reason = user_response.info['termination_reasons']
print(
f"{Fore.GREEN}AI User terminated. Reason: {reason}."
f"{Fore.RESET}"
)
break
print_text_animated(
f"{Fore.BLUE}AI User:\n\n{user_response.msg.content}"
f"{Fore.RESET}\n",
delay=0.005,
)
chat_history.append(f"AI User: {user_response.msg.content}")
print_text_animated(
f"{Fore.GREEN}AI Assistant:{Fore.RESET}", delay=0.005
)
for func_record in assistant_response.info['tool_calls']:
print(func_record)
print_text_animated(
f"\n{Fore.GREEN}{assistant_response.msg.content}"
f"{Fore.RESET}\n",
delay=0.005,
)
chat_history.append(
f"AI Assistant: {assistant_response.msg.content}"
)
if "CAMEL_TASK_DONE" in user_response.msg.content:
break
input_msg = assistant_response.msg
chat_history_str = "\n".join(chat_history)
prompt = ROLEPLAY_SUMMARIZE_PROMPT.format(
user_role=self.user_role_name,
assistant_role=self.assistant_role_name,
content=task.content,
chat_history=chat_history_str,
additional_info=task.additional_info,
)
req = BaseMessage.make_user_message(
role_name="User",
content=prompt,
)
response = self.summarize_agent.step(req, output_schema=TaskResult)
result_dict = ast.literal_eval(response.msg.content)
task_result = TaskResult(**result_dict)
task.result = task_result.content
print(f"Task result: {task.result}\n")
return TaskState.DONE
