# ========= 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. =========
from typing import Any, Dict, List, Optional, Union
from camel.configs import LITELLM_API_PARAMS, LiteLLMConfig
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import ChatCompletion, ModelType
from camel.utils import (
BaseTokenCounter,
LiteLLMTokenCounter,
dependencies_required,
)
[docs]
class LiteLLMModel(BaseModelBackend):
r"""Constructor for LiteLLM backend with OpenAI compatibility.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created, such as GPT-3.5-turbo, Claude-2, etc.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into:obj:`openai.ChatCompletion.create()`.
If:obj:`None`, :obj:`LiteLLMConfig().as_dict()` will be used.
(default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating with
the model service. (default: :obj:`None`)
url (Optional[str], optional): The url to the model service.
(default: :obj:`None`)
token_counter (Optional[BaseTokenCounter], optional): Token counter to
use for the model. If not provided, :obj:`LiteLLMTokenCounter` will
be used. (default: :obj:`None`)
"""
# NOTE: Currently stream mode is not supported.
@dependencies_required('litellm')
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:
from litellm import completion
if model_config_dict is None:
model_config_dict = LiteLLMConfig().as_dict()
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self.client = completion
def _convert_response_from_litellm_to_openai(
self, response
) -> ChatCompletion:
r"""Converts a response from the LiteLLM format to the OpenAI format.
Parameters:
response (LiteLLMResponse): The response object from LiteLLM.
Returns:
ChatCompletion: The response object in OpenAI's format.
"""
return ChatCompletion.construct(
id=response.id,
choices=[
{
"index": response.choices[0].index,
"message": {
"role": response.choices[0].message.role,
"content": response.choices[0].message.content,
},
"finish_reason": response.choices[0].finish_reason,
}
],
created=response.created,
model=response.model,
object=response.object,
system_fingerprint=response.system_fingerprint,
usage=response.usage,
)
@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 = LiteLLMTokenCounter(self.model_type)
return self._token_counter
[docs]
def run(
self,
messages: List[OpenAIMessage],
) -> ChatCompletion:
r"""Runs inference of LiteLLM chat completion.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI format.
Returns:
ChatCompletion
"""
response = self.client(
api_key=self._api_key,
base_url=self._url,
model=self.model_type,
messages=messages,
**self.model_config_dict,
)
response = self._convert_response_from_litellm_to_openai(response)
return response
[docs]
def check_model_config(self):
r"""Check whether the model configuration contains any unexpected
arguments to LiteLLM API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments.
"""
for param in self.model_config_dict:
if param not in LITELLM_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into LiteLLM model backend."
)
