mirror of
https://github.com/OpenBMB/ChatDev.git
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230 lines
7.1 KiB
Python
230 lines
7.1 KiB
Python
# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
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# Licensed under the Apache License, Version 2.0 (the “License”);
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an “AS IS” BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
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import os
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import re
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import zipfile
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from functools import wraps
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from typing import Any, Callable, List, Optional, Set, TypeVar
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import requests
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import tiktoken
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from camel.messages import OpenAIMessage
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from camel.typing import ModelType, TaskType
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F = TypeVar('F', bound=Callable[..., Any])
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import time
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def count_tokens_openai_chat_models(
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messages: List[OpenAIMessage],
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encoding: Any,
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) -> int:
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r"""Counts the number of tokens required to generate an OpenAI chat based
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on a given list of messages.
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Args:
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messages (List[OpenAIMessage]): The list of messages.
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encoding (Any): The encoding method to use.
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Returns:
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int: The number of tokens required.
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"""
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num_tokens = 0
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for message in messages:
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# message follows <im_start>{role/name}\n{content}<im_end>\n
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num_tokens += 4
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for key, value in message.items():
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num_tokens += len(encoding.encode(value))
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if key == "name": # if there's a name, the role is omitted
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num_tokens += -1 # role is always 1 token
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num_tokens += 2 # every reply is primed with <im_start>assistant
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return num_tokens
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def num_tokens_from_messages(
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messages: List[OpenAIMessage],
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model: ModelType,
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) -> int:
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r"""Returns the number of tokens used by a list of messages.
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Args:
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messages (List[OpenAIMessage]): The list of messages to count the
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number of tokens for.
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model (ModelType): The OpenAI model used to encode the messages.
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Returns:
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int: The total number of tokens used by the messages.
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Raises:
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NotImplementedError: If the specified `model` is not implemented.
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References:
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- https://github.com/openai/openai-python/blob/main/chatml.md
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- https://platform.openai.com/docs/models/gpt-4
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- https://platform.openai.com/docs/models/gpt-3-5
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"""
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try:
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value_for_tiktoken = model.value_for_tiktoken
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encoding = tiktoken.encoding_for_model(value_for_tiktoken)
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except KeyError:
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encoding = tiktoken.get_encoding("cl100k_base")
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if model in {
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ModelType.GPT_3_5_TURBO,
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ModelType.GPT_3_5_TURBO_NEW,
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ModelType.GPT_4,
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ModelType.GPT_4_32k,
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ModelType.GPT_4_TURBO,
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ModelType.GPT_4_TURBO_V,
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ModelType.STUB
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}:
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return count_tokens_openai_chat_models(messages, encoding)
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else:
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raise NotImplementedError(
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f"`num_tokens_from_messages`` is not presently implemented "
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f"for model {model}. "
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f"See https://github.com/openai/openai-python/blob/main/chatml.md "
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f"for information on how messages are converted to tokens. "
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f"See https://platform.openai.com/docs/models/gpt-4"
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f"or https://platform.openai.com/docs/models/gpt-3-5"
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f"for information about openai chat models.")
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def get_model_token_limit(model: ModelType) -> int:
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r"""Returns the maximum token limit for a given model.
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Args:
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model (ModelType): The type of the model.
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Returns:
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int: The maximum token limit for the given model.
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"""
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if model == ModelType.GPT_3_5_TURBO:
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return 16384
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elif model == ModelType.GPT_3_5_TURBO_NEW:
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return 16384
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elif model == ModelType.GPT_4:
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return 8192
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elif model == ModelType.GPT_4_32k:
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return 32768
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elif model == ModelType.GPT_4_TURBO:
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return 128000
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elif model == ModelType.STUB:
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return 4096
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else:
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raise ValueError("Unknown model type")
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def openai_api_key_required(func: F) -> F:
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r"""Decorator that checks if the OpenAI API key is available in the
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environment variables.
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Args:
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func (callable): The function to be wrapped.
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Returns:
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callable: The decorated function.
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Raises:
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ValueError: If the OpenAI API key is not found in the environment
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variables.
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"""
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@wraps(func)
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def wrapper(self, *args, **kwargs):
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from camel.agents.chat_agent import ChatAgent
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if not isinstance(self, ChatAgent):
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raise ValueError("Expected ChatAgent")
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if self.model == ModelType.STUB:
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return func(self, *args, **kwargs)
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elif 'OPENAI_API_KEY' in os.environ:
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return func(self, *args, **kwargs)
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else:
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raise ValueError('OpenAI API key not found.')
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return wrapper
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def print_text_animated(text, delay: float = 0.005, end: str = ""):
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r"""Prints the given text with an animated effect.
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Args:
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text (str): The text to print.
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delay (float, optional): The delay between each character printed.
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(default: :obj:`0.02`)
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end (str, optional): The end character to print after the text.
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(default: :obj:`""`)
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"""
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for char in text:
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print(char, end=end, flush=True)
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time.sleep(delay)
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print('\n')
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def get_prompt_template_key_words(template: str) -> Set[str]:
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r"""Given a string template containing curly braces {}, return a set of
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the words inside the braces.
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Args:
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template (str): A string containing curly braces.
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Returns:
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List[str]: A list of the words inside the curly braces.
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Example:
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>>> get_prompt_template_key_words('Hi, {name}! How are you {status}?')
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{'name', 'status'}
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"""
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return set(re.findall(r'{([^}]*)}', template))
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def get_first_int(string: str) -> Optional[int]:
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r"""Returns the first integer number found in the given string.
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If no integer number is found, returns None.
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Args:
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string (str): The input string.
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Returns:
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int or None: The first integer number found in the string, or None if
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no integer number is found.
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"""
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match = re.search(r'\d+', string)
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if match:
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return int(match.group())
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else:
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return None
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def download_tasks(task: TaskType, folder_path: str) -> None:
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# Define the path to save the zip file
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zip_file_path = os.path.join(folder_path, "tasks.zip")
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# Download the zip file from the Google Drive link
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response = requests.get("https://huggingface.co/datasets/camel-ai/"
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f"metadata/resolve/main/{task.value}_tasks.zip")
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# Save the zip file
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with open(zip_file_path, "wb") as f:
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f.write(response.content)
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with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
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zip_ref.extractall(folder_path)
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# Delete the zip file
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os.remove(zip_file_path)
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