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# ========= Copyright 2023-2024 @ 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 2023-2024 @ CAMEL-AI.org. All Rights Reserved. ========= | |
import os | |
import warnings | |
from typing import Any, Dict, List, Optional, Union | |
from openai import OpenAI, Stream | |
from retry import retry | |
from loguru import logger | |
import requests | |
import openai | |
from camel.configs import OPENAI_API_PARAMS, ChatGPTConfig | |
from camel.messages import OpenAIMessage | |
from camel.models import BaseModelBackend | |
from camel.types import ( | |
ChatCompletion, | |
ChatCompletionChunk, | |
ModelType, | |
) | |
from camel.utils import ( | |
BaseTokenCounter, | |
OpenAITokenCounter, | |
api_keys_required, | |
) | |
class OpenAIModel(BaseModelBackend): | |
r"""OpenAI API in a unified BaseModelBackend interface. | |
Args: | |
model_type (Union[ModelType, str]): Model for which a backend is | |
created, one of GPT_* series. | |
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary | |
that will be fed into:obj:`openai.ChatCompletion.create()`. If | |
:obj:`None`, :obj:`ChatGPTConfig().as_dict()` will be used. | |
(default: :obj:`None`) | |
api_key (Optional[str], optional): The API key for authenticating | |
with the OpenAI service. (default: :obj:`None`) | |
url (Optional[str], optional): The url to the OpenAI service. | |
(default: :obj:`None`) | |
token_counter (Optional[BaseTokenCounter], optional): Token counter to | |
use for the model. If not provided, :obj:`OpenAITokenCounter` will | |
be used. (default: :obj:`None`) | |
""" | |
def __init__( | |
self, | |
model_type: Union[ModelType, str], | |
model_config_dict: Optional[Dict[str, Any]] = None, | |
api_key: Optional[str] = None, | |
url: Optional[str] = None, | |
token_counter: Optional[BaseTokenCounter] = None, | |
) -> None: | |
if model_config_dict is None: | |
model_config_dict = ChatGPTConfig().as_dict() | |
api_key = api_key or os.environ.get("OPENAI_API_KEY") | |
url = url or os.environ.get("OPENAI_API_BASE_URL") | |
super().__init__( | |
model_type, model_config_dict, api_key, url, token_counter | |
) | |
self._client = OpenAI( | |
timeout=180, | |
max_retries=3, | |
base_url=self._url, | |
api_key=self._api_key, | |
) | |
def token_counter(self) -> BaseTokenCounter: | |
r"""Initialize the token counter for the model backend. | |
Returns: | |
BaseTokenCounter: The token counter following the model's | |
tokenization style. | |
""" | |
if not self._token_counter: | |
self._token_counter = OpenAITokenCounter(self.model_type) | |
return self._token_counter | |
def run( | |
self, | |
messages: List[OpenAIMessage], | |
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]: | |
r"""Runs inference of OpenAI chat completion. | |
Args: | |
messages (List[OpenAIMessage]): Message list with the chat history | |
in OpenAI API format. | |
Returns: | |
Union[ChatCompletion, Stream[ChatCompletionChunk]]: | |
`ChatCompletion` in the non-stream mode, or | |
`Stream[ChatCompletionChunk]` in the stream mode. | |
""" | |
# o1-preview and o1-mini have Beta limitations | |
# reference: https://platform.openai.com/docs/guides/reasoning | |
if self.model_type in [ | |
ModelType.O1, | |
ModelType.O1_MINI, | |
ModelType.O1_PREVIEW, | |
ModelType.O3_MINI, | |
]: | |
warnings.warn( | |
"Warning: You are using an O1 model (O1_MINI or O1_PREVIEW), " | |
"which has certain limitations, reference: " | |
"`https://platform.openai.com/docs/guides/reasoning`.", | |
UserWarning, | |
) | |
# Check and remove unsupported parameters and reset the fixed | |
# parameters | |
unsupported_keys = [ | |
"temperature", | |
"top_p", | |
"presence_penalty", | |
"frequency_penalty", | |
"logprobs", | |
"top_logprobs", | |
"logit_bias", | |
] | |
for key in unsupported_keys: | |
if key in self.model_config_dict: | |
del self.model_config_dict[key] | |
# ! O1 mini and O1 preview do not support tools and system message | |
if self.model_type in [ModelType.O1_MINI, ModelType.O1_PREVIEW]: | |
# convert system message into user message | |
if messages[0]['role'] == "system": | |
system_content = messages[0]['content'] | |
# delete the first message, and concat | |
messages = messages[1:] | |
messages[0]['content'] = f"{system_content}\n\n{messages[0]['content']}" | |
if 'tools' in self.model_config_dict: | |
del self.model_config_dict['tools'] | |
if self.model_config_dict.get("response_format"): | |
# stream is not supported in beta.chat.completions.parse | |
if "stream" in self.model_config_dict: | |
del self.model_config_dict["stream"] | |
response = self._client.beta.chat.completions.parse( | |
messages=messages, | |
model=self.model_type, | |
**self.model_config_dict, | |
) | |
return self._to_chat_completion(response) | |
response = self._client.chat.completions.create( | |
messages=messages, | |
model=self.model_type, | |
**self.model_config_dict, | |
) | |
return response | |
def check_model_config(self): | |
r"""Check whether the model configuration contains any | |
unexpected arguments to OpenAI API. | |
Raises: | |
ValueError: If the model configuration dictionary contains any | |
unexpected arguments to OpenAI API. | |
""" | |
for param in self.model_config_dict: | |
if param not in OPENAI_API_PARAMS: | |
raise ValueError( | |
f"Unexpected argument `{param}` is " | |
"input into OpenAI model backend." | |
) | |
def stream(self) -> bool: | |
r"""Returns whether the model is in stream mode, which sends partial | |
results each time. | |
Returns: | |
bool: Whether the model is in stream mode. | |
""" | |
return self.model_config_dict.get('stream', False) |