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# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
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#
# 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,
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# limitations under the License.
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
import inspect
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Set,
Tuple,
TypeVar,
Union,
)
from camel.typing import RoleType
from camel.utils import PythonInterpreter
T = TypeVar('T')
[docs]def return_prompt_wrapper(
cls: Any,
func: Callable,
) -> Callable[..., Union[Any, tuple]]:
r"""Wrapper that converts the return value of a function to an input
class instance if it's a string.
Args:
cls (Any): The class to convert to.
func (Callable): The function to decorate.
Returns:
Callable[..., Union[Any, str]]: Decorated function that
returns the decorated class instance if the return value is a
string.
"""
def wrapper(*args: Any, **kwargs: Any) -> Union[Any, str]:
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[Any, str]: 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
[docs]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
[docs]@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 keywords in the prompt.
Attributes:
key_words (set): A set of strings representing the keywords in the
prompt.
"""
@property
def key_words(self) -> Set[str]:
r"""Returns a set of strings representing the keywords in the prompt.
"""
from camel.utils import get_prompt_template_key_words
return get_prompt_template_key_words(self)
[docs] 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))
[docs]@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.
Attributes:
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
[docs] 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
[docs] def execute(
self, interpreter: Optional[PythonInterpreter] = None,
user_variable: Optional[Dict[str, Any]] = None
) -> Tuple[Any, PythonInterpreter]:
r"""Executes the code string by a given python interpreter.
Args:
interpreter (PythonInterpreter, optional): interpreter to be used
during code execution. (default: :obj:`None`)
user_variable (Optional[Dict[str, Any]]): varibales that can be
used in the code, which applying fuzzy matching, such as images
or documents. (default: :obj:`None`)
Returns:
Tuple[Any, PythonInterpreter]: A tuple containing the execution
result and the used interpreter. The execution result
represents the value of the last statement (excluding "import")
in the code. This value could potentially be the desired result
of the LLM-generated code.
"""
# NOTE: Only supports Python code for now.
if not interpreter:
interpreter = PythonInterpreter(action_space=globals())
execution_res = interpreter.execute(self, fuzz_state=user_variable,
keep_state=True)
return execution_res, interpreter
# flake8: noqa :E501
[docs]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})