Spaces:
Runtime error
Runtime error
File size: 13,754 Bytes
594c559 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 |
# =========== 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. ===========
import copy
from typing import Dict, List, Optional, Sequence, Tuple
from camel.agents import (
ChatAgent,
TaskPlannerAgent,
TaskSpecifyAgent,
)
from camel.agents.chat_agent import ChatAgentResponse
from camel.messages import ChatMessage, UserChatMessage
from camel.messages import SystemMessage
from camel.typing import ModelType, RoleType, TaskType, PhaseType
from chatdev.utils import log_arguments, log_and_print_online
@log_arguments
class RolePlaying:
r"""Role playing between two agents.
Args:
assistant_role_name (str): The name of the role played by the
assistant.
user_role_name (str): The name of the role played by the user.
critic_role_name (str): The name of the role played by the critic.
(default: :obj:`"critic"`)
task_prompt (str, optional): A prompt for the task to be performed.
(default: :obj:`""`)
with_task_specify (bool, optional): Whether to use a task specify
agent. (default: :obj:`True`)
with_task_planner (bool, optional): Whether to use a task planner
agent. (default: :obj:`False`)
with_critic_in_the_loop (bool, optional): Whether to include a critic
in the loop. (default: :obj:`False`)
model_type (ModelType, optional): The type of backend model to use.
(default: :obj:`ModelType.GPT_3_5_TURBO`)
task_type (TaskType, optional): The type of task to perform.
(default: :obj:`TaskType.AI_SOCIETY`)
assistant_agent_kwargs (Dict, optional): Additional arguments to pass
to the assistant agent. (default: :obj:`None`)
user_agent_kwargs (Dict, optional): Additional arguments to pass to
the user agent. (default: :obj:`None`)
task_specify_agent_kwargs (Dict, optional): Additional arguments to
pass to the task specify agent. (default: :obj:`None`)
task_planner_agent_kwargs (Dict, optional): Additional arguments to
pass to the task planner agent. (default: :obj:`None`)
critic_kwargs (Dict, optional): Additional arguments to pass to the
critic. (default: :obj:`None`)
sys_msg_generator_kwargs (Dict, optional): Additional arguments to
pass to the system message generator. (default: :obj:`None`)
extend_sys_msg_meta_dicts (List[Dict], optional): A list of dicts to
extend the system message meta dicts with. (default: :obj:`None`)
extend_task_specify_meta_dict (Dict, optional): A dict to extend the
task specify meta dict with. (default: :obj:`None`)
"""
def __init__(
self,
assistant_role_name: str,
user_role_name: str,
critic_role_name: str = "critic",
task_prompt: str = "",
assistant_role_prompt: str = "",
user_role_prompt: str = "",
user_role_type: Optional[RoleType] = None,
assistant_role_type: Optional[RoleType] = None,
with_task_specify: bool = True,
with_task_planner: bool = False,
with_critic_in_the_loop: bool = False,
critic_criteria: Optional[str] = None,
model_type: ModelType = ModelType.GPT_3_5_TURBO,
task_type: TaskType = TaskType.AI_SOCIETY,
assistant_agent_kwargs: Optional[Dict] = None,
user_agent_kwargs: Optional[Dict] = None,
task_specify_agent_kwargs: Optional[Dict] = None,
task_planner_agent_kwargs: Optional[Dict] = None,
critic_kwargs: Optional[Dict] = None,
sys_msg_generator_kwargs: Optional[Dict] = None,
extend_sys_msg_meta_dicts: Optional[List[Dict]] = None,
extend_task_specify_meta_dict: Optional[Dict] = None,
) -> None:
self.with_task_specify = with_task_specify
self.with_task_planner = with_task_planner
self.with_critic_in_the_loop = with_critic_in_the_loop
self.model_type = model_type
self.task_type = task_type
if with_task_specify:
task_specify_meta_dict = dict()
if self.task_type in [TaskType.AI_SOCIETY, TaskType.MISALIGNMENT]:
task_specify_meta_dict.update(
dict(assistant_role=assistant_role_name,
user_role=user_role_name))
if extend_task_specify_meta_dict is not None:
task_specify_meta_dict.update(extend_task_specify_meta_dict)
task_specify_agent = TaskSpecifyAgent(
self.model_type,
task_type=self.task_type,
**(task_specify_agent_kwargs or {}),
)
self.specified_task_prompt = task_specify_agent.step(
task_prompt,
meta_dict=task_specify_meta_dict,
)
task_prompt = self.specified_task_prompt
else:
self.specified_task_prompt = None
if with_task_planner:
task_planner_agent = TaskPlannerAgent(
self.model_type,
**(task_planner_agent_kwargs or {}),
)
self.planned_task_prompt = task_planner_agent.step(task_prompt)
task_prompt = f"{task_prompt}\n{self.planned_task_prompt}"
else:
self.planned_task_prompt = None
self.task_prompt = task_prompt
chatdev_prompt_template = "ChatDev is a software company powered by multiple intelligent agents, such as chief executive officer, chief human resources officer, chief product officer, chief technology officer, etc, with a multi-agent organizational structure and the mission of \"changing the digital world through programming\"."
sys_msg_meta_dicts = [dict(chatdev_prompt=chatdev_prompt_template, task=task_prompt)] * 2
if (extend_sys_msg_meta_dicts is None and self.task_type in [TaskType.AI_SOCIETY, TaskType.MISALIGNMENT,
TaskType.CHATDEV]):
extend_sys_msg_meta_dicts = [dict(assistant_role=assistant_role_name, user_role=user_role_name)] * 2
if extend_sys_msg_meta_dicts is not None:
sys_msg_meta_dicts = [{**sys_msg_meta_dict, **extend_sys_msg_meta_dict} for
sys_msg_meta_dict, extend_sys_msg_meta_dict in
zip(sys_msg_meta_dicts, extend_sys_msg_meta_dicts)]
self.assistant_sys_msg = SystemMessage(role_name=assistant_role_name, role_type=RoleType.DEFAULT,
meta_dict=sys_msg_meta_dicts[0],
content=assistant_role_prompt.format(**sys_msg_meta_dicts[0]))
self.user_sys_msg = SystemMessage(role_name=user_role_name, role_type=RoleType.DEFAULT,
meta_dict=sys_msg_meta_dicts[1],
content=user_role_prompt.format(**sys_msg_meta_dicts[1]))
self.assistant_agent: ChatAgent = ChatAgent(self.assistant_sys_msg, model_type,
**(assistant_agent_kwargs or {}), )
self.user_agent: ChatAgent = ChatAgent(self.user_sys_msg, model_type, **(user_agent_kwargs or {}), )
if with_critic_in_the_loop:
raise ValueError("with_critic_in_the_loop not available")
# if critic_role_name.lower() == "human":
# self.critic = Human(**(critic_kwargs or {}))
# else:
# critic_criteria = (critic_criteria or "improving the task performance")
# critic_msg_meta_dict = dict(critic_role=critic_role_name, criteria=critic_criteria,
# **sys_msg_meta_dicts[0])
# self.critic_sys_msg = sys_msg_generator.from_dict(critic_msg_meta_dict,
# role_tuple=(critic_role_name, RoleType.CRITIC), )
# self.critic = CriticAgent(self.critic_sys_msg, model_type, **(critic_kwargs or {}), )
else:
self.critic = None
def init_chat(self, phase_type: PhaseType = None,
placeholders=None, phase_prompt=None):
r"""Initializes the chat by resetting both the assistant and user
agents, and sending the system messages again to the agents using
chat messages. Returns the assistant's introductory message and the
user's response messages.
Returns:
A tuple containing an `AssistantChatMessage` representing the
assistant's introductory message, and a list of `ChatMessage`s
representing the user's response messages.
"""
if placeholders is None:
placeholders = {}
self.assistant_agent.reset()
self.user_agent.reset()
# refactored ChatDev
content = phase_prompt.format(
**({"assistant_role": self.assistant_agent.role_name} | placeholders)
)
user_msg = UserChatMessage(
role_name=self.user_sys_msg.role_name,
role="user",
content=content
# content here will be concatenated with assistant role prompt (because we mock user and send msg to assistant) in the ChatAgent.step
)
pseudo_msg = copy.deepcopy(user_msg)
pseudo_msg.role = "assistant"
self.user_agent.update_messages(pseudo_msg)
# here we concatenate to store the real message in the log
log_and_print_online(self.user_agent.role_name,
"**[Start Chat]**\n\n[" + self.assistant_agent.system_message.content + "]\n\n" + content)
return None, user_msg
def process_messages(
self,
messages: Sequence[ChatMessage],
) -> ChatMessage:
r"""Processes a list of chat messages, returning the processed message.
If multiple messages are provided and `with_critic_in_the_loop`
is `False`, raises a `ValueError`. If no messages are provided, also
raises a `ValueError`.
Args:
messages:
Returns:
A single `ChatMessage` representing the processed message.
"""
if len(messages) == 0:
raise ValueError("No messages to process.")
if len(messages) > 1 and not self.with_critic_in_the_loop:
raise ValueError("Got than one message to process. "
f"Num of messages: {len(messages)}.")
elif self.with_critic_in_the_loop and self.critic is not None:
processed_msg = self.critic.step(messages)
else:
processed_msg = messages[0]
return processed_msg
def step(
self,
user_msg: ChatMessage,
assistant_only: bool,
) -> Tuple[ChatAgentResponse, ChatAgentResponse]:
assert isinstance(user_msg, ChatMessage), print("broken user_msg: " + str(user_msg))
# print("assistant...")
user_msg_rst = user_msg.set_user_role_at_backend()
assistant_response = self.assistant_agent.step(user_msg_rst)
if assistant_response.terminated or assistant_response.msgs is None:
return (
ChatAgentResponse([assistant_response.msgs], assistant_response.terminated, assistant_response.info),
ChatAgentResponse([], False, {}))
assistant_msg = self.process_messages(assistant_response.msgs)
if self.assistant_agent.info:
return (ChatAgentResponse([assistant_msg], assistant_response.terminated, assistant_response.info),
ChatAgentResponse([], False, {}))
self.assistant_agent.update_messages(assistant_msg)
if assistant_only:
return (
ChatAgentResponse([assistant_msg], assistant_response.terminated, assistant_response.info),
ChatAgentResponse([], False, {})
)
# print("user...")
assistant_msg_rst = assistant_msg.set_user_role_at_backend()
user_response = self.user_agent.step(assistant_msg_rst)
if user_response.terminated or user_response.msgs is None:
return (ChatAgentResponse([assistant_msg], assistant_response.terminated, assistant_response.info),
ChatAgentResponse([user_response], user_response.terminated, user_response.info))
user_msg = self.process_messages(user_response.msgs)
if self.user_agent.info:
return (ChatAgentResponse([assistant_msg], assistant_response.terminated, assistant_response.info),
ChatAgentResponse([user_msg], user_response.terminated, user_response.info))
self.user_agent.update_messages(user_msg)
return (
ChatAgentResponse([assistant_msg], assistant_response.terminated, assistant_response.info),
ChatAgentResponse([user_msg], user_response.terminated, user_response.info),
)
|