# =========== 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 inspect from typing import Any, Callable, Dict, Optional, Set, Tuple, TypeVar, Union from camel.typing import RoleType T = TypeVar('T') def return_prompt_wrapper( cls: T, func: Callable, ) -> Callable[..., Union[T, tuple]]: r"""Wrapper that converts the return value of a function to an input class instance if it's a string. Args: cls (type): The class to convert to. func (Callable): The function to decorate. Returns: Callable[..., Union[T, tuple]]: Decorated function that returns the decorated class instance if the return value is a string. """ def wrapper(*args: Any, **kwargs: Any) -> Union[T, tuple]: r"""Wrapper function that performs the conversion to :obj:`TextPrompt` instance. Args: *args (Any): Variable length argument list. **kwargs (Any): Arbitrary keyword arguments. Returns: Union[TextPrompt, tuple]: The converted return value. """ result = func(*args, **kwargs) if isinstance(result, str) and not isinstance(result, cls): return cls(result) elif isinstance(result, tuple): new_result = tuple( cls(item) if isinstance(item, str) and not isinstance(item, cls) else item for item in result) return new_result return result # # Preserve the original function's attributes wrapper.__name__ = func.__name__ wrapper.__doc__ = func.__doc__ return wrapper def wrap_prompt_functions(cls: T) -> T: r"""Decorator that wraps functions of a class inherited from :obj:`str` with the :obj:`return_text_prompt` decorator. Args: cls (type): The class to decorate. Returns: type: Decorated class with wrapped functions. """ excluded_attrs = {'__init__', '__new__', '__str__', '__repr__'} for attr_name in dir(cls): attr_value = getattr(cls, attr_name) if callable(attr_value) and attr_name not in excluded_attrs: if inspect.isroutine(attr_value): setattr(cls, attr_name, return_prompt_wrapper(cls, attr_value)) return cls @wrap_prompt_functions class TextPrompt(str): r"""A class that represents a text prompt. The :obj:`TextPrompt` class extends the built-in :obj:`str` class to provide a property for retrieving the set of key words in the prompt. Attributes: key_words (set): A set of strings representing the key words in the prompt. """ @property def key_words(self) -> Set[str]: r"""Returns a set of strings representing the key words in the prompt. """ from camel.utils import get_prompt_template_key_words return get_prompt_template_key_words(self) def format(self, *args: Any, **kwargs: Any) -> 'TextPrompt': r"""Overrides the built-in :obj:`str.format` method to allow for default values in the format string. This is used to allow formatting the partial string. Args: *args (Any): Variable length argument list. **kwargs (Any): Arbitrary keyword arguments. Returns: TextPrompt: A new :obj:`TextPrompt` object with the format string replaced with the formatted string. """ default_kwargs = {key: '{' + f'{key}' + '}' for key in self.key_words} default_kwargs.update(kwargs) return TextPrompt(super().format(*args, **default_kwargs)) @wrap_prompt_functions class CodePrompt(TextPrompt): r"""A class that represents a code prompt. It extends the :obj:`TextPrompt` class with a :obj:`code_type` property. Args: code_string (str): The code string for the prompt. code_type (str, optional): The type of code. Defaults to None. """ def __new__(cls, *args: Any, **kwargs: Any) -> 'CodePrompt': r"""Creates a new instance of the :obj:`CodePrompt` class. Args: *args (Any): Positional arguments. **kwargs (Any): Keyword arguments. Returns: CodePrompt: The created :obj:`CodePrompt` instance. """ code_type = kwargs.pop('code_type', None) instance = super().__new__(cls, *args, **kwargs) instance._code_type = code_type return instance @property def code_type(self) -> Optional[str]: r"""Returns the type of code. Returns: Optional[str]: The type of code. """ return self._code_type def set_code_type(self, code_type: str) -> None: r"""Sets the type of code. Args: code_type (str): The type of code. """ self._code_type = code_type def execute( self, global_vars: Optional[Dict] = None) -> Tuple[str, Optional[Dict]]: r"""Executes the code string. If there is an error, the error is caught and the traceback is returned. Otherwise, the output string and local variables are returned. Args: global_vars (Dict, optional): Global variables to be used during code execution. (default: :obj:`None`) Returns: Tuple[str, Optional[Dict]]: A tuple containing the output string and local variables. """ # NOTE: Only supports Python code for now. try: # Execute the code string import io import sys output_str = io.StringIO() sys.stdout = output_str global_vars = global_vars or globals() local_vars = {} exec( self, global_vars, local_vars, ) sys.stdout = sys.__stdout__ output_str.seek(0) # If there was no error, return the output and local variables return output_str.read(), local_vars except Exception: import traceback traceback_str = traceback.format_exc() sys.stdout = sys.__stdout__ # If there was an error, return the traceback return traceback_str, None # flake8: noqa :E501 class TextPromptDict(Dict[Any, TextPrompt]): r"""A dictionary class that maps from key to :obj:`TextPrompt` object. """ EMBODIMENT_PROMPT = TextPrompt( """You are the physical embodiment of the {role} who is working on solving a task: {task}. You can do things in the physical world including browsing the Internet, reading documents, drawing images, creating videos, executing code and so on. Your job is to perform the physical actions necessary to interact with the physical world. You will receive thoughts from the {role} and you will need to perform the actions described in the thoughts. You can write a series of simple commands in Python to act. You can perform a set of actions by calling the available Python functions. You should perform actions based on the descriptions of the functions. Here is your action space: {action_space} You should only perform actions in the action space. You can perform multiple actions. You can perform actions in any order. First, explain the actions you will perform and your reasons, then write Python code to implement your actions. If you decide to perform actions, you must write Python code to implement the actions. You may print intermediate results if necessary.""") def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) self.update({RoleType.EMBODIMENT: self.EMBODIMENT_PROMPT})