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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
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."
)
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