Removed all derived from BaseMessage. Removed role field from BaseMes…

…sage

apps/agents/agents.py

@@ -29,7 +29,7 @@
 
 from apps.agents.text_utils import split_markdown_code
 from camel.agents import TaskSpecifyAgent
-from camel.messages import AssistantChatMessage
+from camel.messages import BaseMessage
 from camel.societies import RolePlaying
 
 REPO_ROOT = os.path.realpath(
@@ -43,17 +43,16 @@ class State:
     session: Optional[RolePlaying]
     max_messages: int
     chat: ChatBotHistory
-    saved_assistant_msg: Optional[AssistantChatMessage]
+    saved_assistant_msg: Optional[BaseMessage]
 
     @classmethod
     def empty(cls) -> 'State':
         return cls(None, 0, [], None)
 
     @staticmethod
-    def construct_inplace(
-            state: 'State', session: Optional[RolePlaying], max_messages: int,
-            chat: ChatBotHistory,
-            saved_assistant_msg: Optional[AssistantChatMessage]) -> None:
+    def construct_inplace(state: 'State', session: Optional[RolePlaying],
+                          max_messages: int, chat: ChatBotHistory,
+                          saved_assistant_msg: Optional[BaseMessage]) -> None:
         state.session = session
         state.max_messages = max_messages
         state.chat = chat
@@ -216,7 +215,7 @@ def role_playing_chat_init(state) -> \
 
     try:
         init_assistant_msg, _ = session.init_chat()
-        init_assistant_msg: AssistantChatMessage
+        init_assistant_msg: BaseMessage
     except (openai.error.RateLimitError, tenacity.RetryError,
             RuntimeError) as ex:
         print("OpenAI API exception 1 " + str(ex))

camel/agents/chat_agent.py

@@ -20,7 +20,7 @@
 
 from camel.agents import BaseAgent
 from camel.configs import ChatGPTConfig
-from camel.messages import ChatMessage, MessageType, SystemMessage
+from camel.messages import BaseMessage
 from camel.models import BaseModelBackend, ModelFactory
 from camel.typing import ModelType, RoleType
 from camel.utils import num_tokens_from_messages, openai_api_key_required
@@ -31,15 +31,15 @@ class ChatAgentResponse:
     r"""Response of a ChatAgent.
 
     Attributes:
-        msgs (List[ChatMessage]): A list of zero, one or several messages.
+        msgs (List[BaseMessage]): A list of zero, one or several messages.
             If the list is empty, there is some error in message generation.
             If the list has one message, this is normal mode.
             If the list has several messages, this is the critic mode.
         terminated (bool): A boolean indicating whether the agent decided
             to terminate the chat session.
         info (Dict[str, Any]): Extra information about the chat message.
     """
-    msgs: List[ChatMessage]
+    msgs: List[BaseMessage]
     terminated: bool
     info: Dict[str, Any]
 
@@ -51,11 +51,17 @@ def msg(self):
         return self.msgs[0]
 
 
+@dataclass(frozen=True)
+class ChatRecord:
+    role_at_backend: str
+    message: BaseMessage
+
+
 class ChatAgent(BaseAgent):
     r"""Class for managing conversations of CAMEL Chat Agents.
 
     Args:
-        system_message (SystemMessage): The system message for the chat agent.
+        system_message (BaseMessage): The system message for the chat agent.
         model (ModelType, optional): The LLM model to use for generating
             responses. (default :obj:`ModelType.GPT_3_5_TURBO`)
         model_config (Any, optional): Configuration options for the LLM model.
@@ -67,13 +73,13 @@ class ChatAgent(BaseAgent):
 
     def __init__(
         self,
-        system_message: SystemMessage,
+        system_message: BaseMessage,
         model: Optional[ModelType] = None,
         model_config: Optional[Any] = None,
         message_window_size: Optional[int] = None,
     ) -> None:
 
-        self.system_message: SystemMessage = system_message
+        self.system_message: BaseMessage = system_message
         self.role_name: str = system_message.role_name
         self.role_type: RoleType = system_message.role_type
 
@@ -87,14 +93,15 @@ def __init__(
         self.model_token_limit: int = self.model_backend.token_limit
 
         self.terminated: bool = False
+        self.stored_messages: List[ChatRecord]
         self.init_messages()
 
-    def reset(self) -> List[MessageType]:
+    def reset(self) -> List[ChatRecord]:
         r"""Resets the :obj:`ChatAgent` to its initial state and returns the
         stored messages.
 
         Returns:
-            List[MessageType]: The stored messages.
+            List[BaseMessage]: The stored messages.
         """
         self.terminated = False
         self.init_messages()
@@ -131,32 +138,38 @@ def init_messages(self) -> None:
         r"""Initializes the stored messages list with the initial system
         message.
         """
-        self.stored_messages: List[MessageType] = [self.system_message]
+        self.stored_messages = [ChatRecord('system', self.system_message)]
 
-    def update_messages(self, message: ChatMessage) -> List[MessageType]:
+    def update_messages(self, role: str,
+                        message: BaseMessage) -> List[ChatRecord]:
         r"""Updates the stored messages list with a new message.
 
         Args:
-            message (ChatMessage): The new message to add to the stored
+            message (BaseMessage): The new message to add to the stored
                 messages.
 
         Returns:
-            List[ChatMessage]: The updated stored messages.
+            List[BaseMessage]: The updated stored messages.
         """
-        self.stored_messages.append(message)
+        if role not in {'system', 'user', 'assistant'}:
+            raise ValueError(f"Unsupported role {role}")
+        self.stored_messages.append(ChatRecord(role, message))
         return self.stored_messages
 
+    def submit_critic_choice(self, message: BaseMessage) -> None:
+        self.stored_messages.append(ChatRecord('assistant', message))
+
     @retry(wait=wait_exponential(min=5, max=60), stop=stop_after_attempt(5))
     @openai_api_key_required
     def step(
         self,
-        input_message: ChatMessage,
+        input_message: BaseMessage,
     ) -> ChatAgentResponse:
         r"""Performs a single step in the chat session by generating a response
         to the input message.
 
         Args:
-            input_message (ChatMessage): The input message to the agent.
+            input_message (BaseMessage): The input message to the agent.
             Its `role` field that specifies the role at backen may be either
             `user` or `assistant` but it will be set to `user` anyway since
             for the self agent any incoming message is external.
@@ -167,25 +180,28 @@ def step(
                 the chat session has terminated, and information about the chat
                 session.
         """
-        msg_user_at_backend = input_message.set_user_role_at_backend()
-        messages = self.update_messages(msg_user_at_backend)
+        messages = self.update_messages('user', input_message)
         if self.message_window_size is not None and len(
                 messages) > self.message_window_size:
-            messages = [self.system_message
+            messages = [ChatRecord('system', self.system_message)
                         ] + messages[-self.message_window_size:]
-        openai_messages = [message.to_openai_message() for message in messages]
+        openai_messages = [
+            record.message.to_openai_message(record.role_at_backend)
+            for record in messages
+        ]
         num_tokens = num_tokens_from_messages(openai_messages, self.model)
 
-        output_messages: Optional[List[ChatMessage]]
+        output_messages: Optional[List[BaseMessage]]
         info: Dict[str, Any]
 
         if num_tokens < self.model_token_limit:
             response = self.model_backend.run(openai_messages)
             if not isinstance(response, dict):
                 raise RuntimeError("OpenAI returned unexpected struct")
             output_messages = [
-                ChatMessage(role_name=self.role_name, role_type=self.role_type,
-                            meta_dict=dict(), **dict(choice["message"]))
+                BaseMessage(role_name=self.role_name, role_type=self.role_type,
+                            meta_dict=dict(),
+                            content=choice["message"]['content'])
                 for choice in response["choices"]
             ]
             info = self.get_info(

camel/agents/critic_agent.py

@@ -19,7 +19,7 @@
 from colorama import Fore
 
 from camel.agents import ChatAgent
-from camel.messages import ChatMessage, SystemMessage
+from camel.messages import BaseMessage
 from camel.typing import ModelType
 from camel.utils import get_first_int, print_text_animated
 
@@ -28,7 +28,7 @@ class CriticAgent(ChatAgent):
     r"""A class for the critic agent that assists in selecting an option.
 
     Args:
-        system_message (SystemMessage): The system message for the critic
+        system_message (BaseMessage): The system message for the critic
             agent.
         model (ModelType, optional): The LLM model to use for generating
             responses. (default :obj:`ModelType.GPT_3_5_TURBO`)
@@ -46,7 +46,7 @@ class CriticAgent(ChatAgent):
 
     def __init__(
         self,
-        system_message: SystemMessage,
+        system_message: BaseMessage,
         model: ModelType = ModelType.GPT_3_5_TURBO,
         model_config: Optional[Any] = None,
         message_window_size: int = 6,
@@ -61,11 +61,11 @@ def __init__(
         self.verbose = verbose
         self.logger_color = logger_color
 
-    def flatten_options(self, messages: Sequence[ChatMessage]) -> str:
+    def flatten_options(self, messages: Sequence[BaseMessage]) -> str:
         r"""Flattens the options to the critic.
 
         Args:
-            messages (Sequence[ChatMessage]): A list of `ChatMessage` objects.
+            messages (Sequence[BaseMessage]): A list of `BaseMessage` objects.
 
         Returns:
             str: A string containing the flattened options to the critic.
@@ -83,11 +83,11 @@ def flatten_options(self, messages: Sequence[ChatMessage]) -> str:
             "and then your explanation and comparison: ")
         return flatten_options + format
 
-    def get_option(self, input_message: ChatMessage) -> str:
+    def get_option(self, input_message: BaseMessage) -> str:
         r"""Gets the option selected by the critic.
 
         Args:
-            input_message (ChatMessage): A `ChatMessage` object representing
+            input_message (BaseMessage): A `BaseMessage` object representing
                 the input message.
 
         Returns:
@@ -104,8 +104,8 @@ def get_option(self, input_message: ChatMessage) -> str:
             if critic_response.terminated:
                 raise RuntimeError("Critic step failed.")
 
-            critic_msg = critic_response.msgs[0]
-            self.update_messages(critic_msg)
+            critic_msg = critic_response.msg
+            self.update_messages('assistant', critic_msg)
             if self.verbose:
                 print_text_animated(self.logger_color + "\n> Critic response: "
                                     f"\x1b[3m{critic_msg.content}\x1b[0m\n")
@@ -114,11 +114,10 @@ def get_option(self, input_message: ChatMessage) -> str:
             if choice in self.options_dict:
                 return self.options_dict[choice]
             else:
-                input_message = ChatMessage(
+                input_message = BaseMessage(
                     role_name=input_message.role_name,
                     role_type=input_message.role_type,
                     meta_dict=input_message.meta_dict,
-                    role=input_message.role,
                     content="> Invalid choice. Please choose again.\n" +
                     msg_content,
                 )
@@ -128,11 +127,11 @@ def get_option(self, input_message: ChatMessage) -> str:
                       "Returning a random option.")
         return random.choice(list(self.options_dict.values()))
 
-    def parse_critic(self, critic_msg: ChatMessage) -> Optional[str]:
+    def parse_critic(self, critic_msg: BaseMessage) -> Optional[str]:
         r"""Parses the critic's message and extracts the choice.
 
         Args:
-            critic_msg (ChatMessage): A `ChatMessage` object representing the
+            critic_msg (BaseMessage): A `BaseMessage` object representing the
                 critic's response.
 
         Returns:
@@ -142,22 +141,21 @@ def parse_critic(self, critic_msg: ChatMessage) -> Optional[str]:
         choice = str(get_first_int(critic_msg.content))
         return choice
 
-    def step(self, messages: Sequence[ChatMessage]) -> ChatMessage:
+    def step(self, messages: Sequence[BaseMessage]) -> BaseMessage:
         r"""Performs one step of the conversation by flattening options to the
         critic, getting the option, and parsing the choice.
 
         Args:
-            messages (Sequence[ChatMessage]): A list of ChatMessage objects.
+            messages (Sequence[BaseMessage]): A list of BaseMessage objects.
 
         Returns:
-            ChatMessage: A `ChatMessage` object representing the critic's
+            BaseMessage: A `BaseMessage` object representing the critic's
                 choice.
         """
-        meta_chat_message = ChatMessage(
+        meta_chat_message = BaseMessage(
             role_name=messages[0].role_name,
             role_type=messages[0].role_type,
             meta_dict=messages[0].meta_dict,
-            role=messages[0].role,
             content="",
         )
 

camel/agents/embodied_agent.py

@@ -16,7 +16,7 @@
 from colorama import Fore
 
 from camel.agents import BaseToolAgent, ChatAgent, HuggingFaceToolAgent
-from camel.messages import ChatMessage, SystemMessage
+from camel.messages import BaseMessage
 from camel.typing import ModelType
 from camel.utils import print_text_animated
 
@@ -25,7 +25,7 @@ class EmbodiedAgent(ChatAgent):
     r"""Class for managing conversations of CAMEL Embodied Agents.
 
     Args:
-        system_message (SystemMessage): The system message for the chat agent.
+        system_message (BaseMessage): The system message for the chat agent.
         model (ModelType, optional): The LLM model to use for generating
             responses. (default :obj:`ModelType.GPT_4`)
         model_config (Any, optional): Configuration options for the LLM model.
@@ -42,7 +42,7 @@ class EmbodiedAgent(ChatAgent):
 
     def __init__(
         self,
-        system_message: SystemMessage,
+        system_message: BaseMessage,
         model: ModelType = ModelType.GPT_4,
         model_config: Optional[Any] = None,
         message_window_size: Optional[int] = None,
@@ -79,15 +79,15 @@ def get_action_space_prompt(self) -> str:
 
     def step(
         self,
-        input_message: ChatMessage,
-    ) -> Tuple[ChatMessage, bool, Dict[str, Any]]:
+        input_message: BaseMessage,
+    ) -> Tuple[BaseMessage, bool, Dict[str, Any]]:
         r"""Performs a step in the conversation.
 
         Args:
-            input_message (ChatMessage): The input message.
+            input_message (BaseMessage): The input message.
 
         Returns:
-            Tuple[ChatMessage, bool, Dict[str, Any]]: A tuple
+            Tuple[BaseMessage, bool, Dict[str, Any]]: A tuple
                 containing the output messages, termination status, and
                 additional information.
         """
@@ -126,7 +126,6 @@ def step(
         # TODO: Handle errors
         content = input_message.content + (Fore.RESET +
                                            f"\n> Embodied Actions:\n{content}")
-        message = ChatMessage(input_message.role_name, input_message.role_type,
-                              input_message.meta_dict, input_message.role,
-                              content)
+        message = BaseMessage(input_message.role_name, input_message.role_type,
+                              input_message.meta_dict, content)
         return message, response.terminated, response.info