# ========= 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 List, Optional, Union from openai import OpenAI from camel.messages import OpenAIMessage from camel.models import BaseModelBackend from camel.types import ChatCompletion, ModelType from camel.utils import ( BaseTokenCounter, api_keys_required, ) class NemotronModel(BaseModelBackend): r"""Nemotron model API backend with OpenAI compatibility. Args: model_type (Union[ModelType, str]): Model for which a backend is created. api_key (Optional[str], optional): The API key for authenticating with the Nvidia service. (default: :obj:`None`) url (Optional[str], optional): The url to the Nvidia service. (default: :obj:`https://integrate.api.nvidia.com/v1`) Notes: Nemotron model doesn't support additional model config like OpenAI. """ def __init__( self, model_type: Union[ModelType, str], api_key: Optional[str] = None, url: Optional[str] = None, ) -> None: url = url or os.environ.get( "NVIDIA_API_BASE_URL", "https://integrate.api.nvidia.com/v1" ) api_key = api_key or os.environ.get("NVIDIA_API_KEY") super().__init__(model_type, {}, api_key, url) self._client = OpenAI( timeout=60, max_retries=3, base_url=self._url, api_key=self._api_key, ) @api_keys_required("NVIDIA_API_KEY") def run( self, messages: List[OpenAIMessage], ) -> ChatCompletion: r"""Runs inference of OpenAI chat completion. Args: messages (List[OpenAIMessage]): Message list. Returns: ChatCompletion. """ response = self._client.chat.completions.create( messages=messages, model=self.model_type, ) return response @property def token_counter(self) -> BaseTokenCounter: raise NotImplementedError( "Nemotron model doesn't support token counter." ) def check_model_config(self): raise NotImplementedError( "Nemotron model doesn't support model config." )