# ========= 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
from typing import Any, Dict, List, Optional, Type, Union
from openai import AsyncOpenAI, AsyncStream, OpenAI, Stream
from pydantic import BaseModel
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import (
ChatCompletion,
ChatCompletionChunk,
ModelType,
)
from camel.utils import (
BaseTokenCounter,
OpenAITokenCounter,
)
[docs]
class OpenAICompatibleModel(BaseModelBackend):
r"""Constructor for model backend supporting OpenAI compatibility.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into:obj:`openai.ChatCompletion.create()`. If
:obj:`None`, :obj:`{}` will be used. (default: :obj:`None`)
api_key (str): The API key for authenticating with the model service.
url (str): The url to the model service.
token_counter (Optional[BaseTokenCounter], optional): Token counter to
use for the model. If not provided, :obj:`OpenAITokenCounter(
ModelType.GPT_4O_MINI)` 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:
self.api_key = api_key or os.environ.get(
"OPENAI_COMPATIBILITY_API_KEY"
)
self.url = url or os.environ.get("OPENAI_COMPATIBILITY_API_BASE_URL")
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self._client = OpenAI(
timeout=180,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
self._async_client = AsyncOpenAI(
timeout=180,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
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.
"""
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]]:
request_config = self.model_config_dict.copy()
if tools:
request_config["tools"] = tools
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]]:
request_config = self.model_config_dict.copy()
if tools:
request_config["tools"] = tools
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:
request_config = self.model_config_dict.copy()
request_config["response_format"] = response_format
request_config.pop("stream", None)
if tools is not None:
request_config["tools"] = tools
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:
request_config = self.model_config_dict.copy()
request_config["response_format"] = response_format
request_config.pop("stream", None)
if tools is not None:
request_config["tools"] = tools
return await self._async_client.beta.chat.completions.parse(
messages=messages,
model=self.model_type,
**request_config,
)
@property
def token_counter(self) -> BaseTokenCounter:
r"""Initialize the token counter for the model backend.
Returns:
OpenAITokenCounter: The token counter following the model's
tokenization style.
"""
if not self._token_counter:
self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
return self._token_counter
@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)
[docs]
def check_model_config(self):
pass
