# ========= 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, Type, Union
from openai import AsyncOpenAI, AsyncStream, OpenAI, Stream
from pydantic import BaseModel
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,
)
UNSUPPORTED_PARAMS = {
"temperature",
"top_p",
"presence_penalty",
"frequency_penalty",
"logprobs",
"top_logprobs",
"logit_bias",
}
[docs]
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`)
timeout (Optional[float], optional): The timeout value in seconds for
API calls. If not provided, will fall back to the MODEL_TIMEOUT
environment variable or default to 180 seconds.
(default: :obj:`None`)
"""
@api_keys_required(
[
("api_key", "OPENAI_API_KEY"),
]
)
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,
timeout: Optional[float] = 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")
timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
super().__init__(
model_type, model_config_dict, api_key, url, token_counter, timeout
)
self._client = OpenAI(
timeout=self._timeout,
max_retries=3,
base_url=self._url,
api_key=self._api_key,
)
self._async_client = AsyncOpenAI(
timeout=self._timeout,
max_retries=3,
base_url=self._url,
api_key=self._api_key,
)
def _sanitize_config(self, config_dict: Dict[str, Any]) -> Dict[str, Any]:
r"""Sanitize the model configuration for O1 models."""
if self.model_type in [
ModelType.O1,
ModelType.O1_MINI,
ModelType.O1_PREVIEW,
ModelType.O3_MINI,
]:
warnings.warn(
"Warning: You are using an reasoning model (O1 or O3), "
"which has certain limitations, reference: "
"`https://platform.openai.com/docs/guides/reasoning`.",
UserWarning,
)
return {
k: v
for k, v in config_dict.items()
if k not in UNSUPPORTED_PARAMS
}
return config_dict
def _adapt_messages_for_o1_models(
self, messages: List[OpenAIMessage]
) -> List[OpenAIMessage]:
r"""Adjust message roles to comply with O1 model requirements by
converting 'system' or 'developer' to 'user' role.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
Returns:
processed_messages (List[OpenAIMessage]): Return a new list of
messages to avoid mutating input.
"""
# Define supported O1 model types as a class constant would be better
O1_MODEL_TYPES = {ModelType.O1_MINI, ModelType.O1_PREVIEW}
if self.model_type not in O1_MODEL_TYPES:
return messages.copy()
# Issue warning only once using class state
if not hasattr(self, "_o1_warning_issued"):
warnings.warn(
"O1 models (O1_MINI/O1_PREVIEW) have role limitations: "
"System or Developer messages will be converted to user role."
"Reference: https://community.openai.com/t/"
"developer-role-not-accepted-for-o1-o1-mini-o3-mini/1110750/7",
UserWarning,
stacklevel=2,
)
self._o1_warning_issued = True
# Create new message list to avoid mutating input
processed_messages = []
for message in messages:
processed_message = message.copy()
if (
processed_message["role"] == "system"
or processed_message["role"] == "developer"
):
processed_message["role"] = "user" # type: ignore[arg-type]
processed_messages.append(processed_message)
return processed_messages
@property
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],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> 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.
response_format (Optional[Type[BaseModel]]): The format of the
response.
tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
use for the request.
Returns:
Union[ChatCompletion, Stream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`Stream[ChatCompletionChunk]` in the stream mode.
"""
messages = self._adapt_messages_for_o1_models(messages)
response_format = response_format or self.model_config_dict.get(
"response_format", None
)
if response_format:
return self._request_parse(messages, response_format, tools)
else:
return self._request_chat_completion(messages, tools)
async def _arun(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
r"""Runs inference of OpenAI chat completion in async mode.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
response_format (Optional[Type[BaseModel]]): The format of the
response.
tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
use for the request.
Returns:
Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`AsyncStream[ChatCompletionChunk]` in the stream mode.
"""
response_format = response_format or self.model_config_dict.get(
"response_format", None
)
if response_format:
return await self._arequest_parse(messages, response_format, tools)
else:
return await self._arequest_chat_completion(messages, tools)
def _request_chat_completion(
self,
messages: List[OpenAIMessage],
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
import copy
request_config = copy.deepcopy(self.model_config_dict)
if tools:
request_config["tools"] = tools
request_config = self._sanitize_config(request_config)
return self._client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
async def _arequest_chat_completion(
self,
messages: List[OpenAIMessage],
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
import copy
request_config = copy.deepcopy(self.model_config_dict)
if tools:
request_config["tools"] = tools
request_config = self._sanitize_config(request_config)
return await self._async_client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
def _request_parse(
self,
messages: List[OpenAIMessage],
response_format: Type[BaseModel],
tools: Optional[List[Dict[str, Any]]] = None,
) -> ChatCompletion:
import copy
request_config = copy.deepcopy(self.model_config_dict)
request_config["response_format"] = response_format
# Remove stream from request config since OpenAI does not support it
# with structured response
request_config.pop("stream", None)
if tools is not None:
request_config["tools"] = tools
request_config = self._sanitize_config(request_config)
return self._client.beta.chat.completions.parse(
messages=messages,
model=self.model_type,
**request_config,
)
async def _arequest_parse(
self,
messages: List[OpenAIMessage],
response_format: Type[BaseModel],
tools: Optional[List[Dict[str, Any]]] = None,
) -> ChatCompletion:
import copy
request_config = copy.deepcopy(self.model_config_dict)
request_config["response_format"] = response_format
# Remove stream from request config since OpenAI does not support it
# with structured response
request_config.pop("stream", None)
if tools is not None:
request_config["tools"] = tools
request_config = self._sanitize_config(request_config)
return await self._async_client.beta.chat.completions.parse(
messages=messages,
model=self.model_type,
**request_config,
)
[docs]
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."
)
@property
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)
