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| # =========== 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 typing import Any, Dict, List, Optional, Tuple | |
| from colorama import Fore | |
| from camel.agents import BaseToolAgent, ChatAgent, HuggingFaceToolAgent | |
| from camel.messages import ChatMessage, SystemMessage | |
| from camel.typing import ModelType | |
| from camel.utils import print_text_animated | |
| class EmbodiedAgent(ChatAgent): | |
| r"""Class for managing conversations of CAMEL Embodied Agents. | |
| Args: | |
| system_message (SystemMessage): 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. | |
| (default: :obj:`None`) | |
| message_window_size (int, optional): The maximum number of previous | |
| messages to include in the context window. If `None`, no windowing | |
| is performed. (default: :obj:`None`) | |
| action_space (List[Any], optional): The action space for the embodied | |
| agent. (default: :obj:`None`) | |
| verbose (bool, optional): Whether to print the critic's messages. | |
| logger_color (Any): The color of the logger displayed to the user. | |
| (default: :obj:`Fore.MAGENTA`) | |
| """ | |
| def __init__( | |
| self, | |
| system_message: SystemMessage, | |
| model: ModelType = ModelType.GPT_4, | |
| model_config: Optional[Any] = None, | |
| message_window_size: Optional[int] = None, | |
| action_space: Optional[List[BaseToolAgent]] = None, | |
| verbose: bool = False, | |
| logger_color: Any = Fore.MAGENTA, | |
| ) -> None: | |
| default_action_space = [ | |
| HuggingFaceToolAgent('hugging_face_tool_agent', model=model.value), | |
| ] | |
| self.action_space = action_space or default_action_space | |
| action_space_prompt = self.get_action_space_prompt() | |
| system_message.content = system_message.content.format( | |
| action_space=action_space_prompt) | |
| self.verbose = verbose | |
| self.logger_color = logger_color | |
| super().__init__( | |
| system_message=system_message, | |
| model=model, | |
| model_config=model_config, | |
| message_window_size=message_window_size, | |
| ) | |
| def get_action_space_prompt(self) -> str: | |
| r"""Returns the action space prompt. | |
| Returns: | |
| str: The action space prompt. | |
| """ | |
| return "\n".join([ | |
| f"*** {action.name} ***:\n {action.description}" | |
| for action in self.action_space | |
| ]) | |
| def step( | |
| self, | |
| input_message: ChatMessage, | |
| ) -> Tuple[ChatMessage, bool, Dict[str, Any]]: | |
| r"""Performs a step in the conversation. | |
| Args: | |
| input_message (ChatMessage): The input message. | |
| Returns: | |
| Tuple[ChatMessage, bool, Dict[str, Any]]: A tuple | |
| containing the output messages, termination status, and | |
| additional information. | |
| """ | |
| response = super().step(input_message) | |
| if response.msgs is None or len(response.msgs) == 0: | |
| raise RuntimeError("Got None output messages.") | |
| if response.terminated: | |
| raise RuntimeError(f"{self.__class__.__name__} step failed.") | |
| # NOTE: Only single output messages are supported | |
| explanations, codes = response.msg.extract_text_and_code_prompts() | |
| if self.verbose: | |
| for explanation, code in zip(explanations, codes): | |
| print_text_animated(self.logger_color + | |
| f"> Explanation:\n{explanation}") | |
| print_text_animated(self.logger_color + f"> Code:\n{code}") | |
| if len(explanations) > len(codes): | |
| print_text_animated(self.logger_color + | |
| f"> Explanation:\n{explanations}") | |
| content = response.msg.content | |
| if codes is not None: | |
| content = "\n> Executed Results:" | |
| global_vars = {action.name: action for action in self.action_space} | |
| for code in codes: | |
| executed_outputs = code.execute(global_vars) | |
| content += ( | |
| f"- Python standard output:\n{executed_outputs[0]}\n" | |
| f"- Local variables:\n{executed_outputs[1]}\n") | |
| content += "*" * 50 + "\n" | |
| # 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) | |
| return message, response.terminated, response.info | |