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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 time
from typing import Any, Dict, List, Optional, Union
from openai import Stream
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import (
ChatCompletion,
ChatCompletionChunk,
ChatCompletionMessage,
Choice,
CompletionUsage,
ModelType,
)
from camel.utils import BaseTokenCounter
class StubTokenCounter(BaseTokenCounter):
def count_tokens_from_messages(self, messages: List[OpenAIMessage]) -> int:
r"""Token counting for STUB models, directly returning a constant.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
Returns:
int: A constant to act as the number of the tokens in the
messages.
"""
return 10
class StubModel(BaseModelBackend):
r"""A dummy model used for unit tests."""
model_type = ModelType.STUB
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:
r"""All arguments are unused for the dummy model."""
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
@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 = StubTokenCounter()
return self._token_counter
def run(
self, messages: List[OpenAIMessage]
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
r"""Run fake inference by returning a fixed string.
All arguments are unused for the dummy model.
Returns:
Dict[str, Any]: Response in the OpenAI API format.
"""
ARBITRARY_STRING = "Lorem Ipsum"
response: ChatCompletion = ChatCompletion(
id="stub_model_id",
model="stub",
object="chat.completion",
created=int(time.time()),
choices=[
Choice(
finish_reason="stop",
index=0,
message=ChatCompletionMessage(
content=ARBITRARY_STRING,
role="assistant",
),
logprobs=None,
)
],
usage=CompletionUsage(
completion_tokens=10,
prompt_tokens=10,
total_tokens=20,
),
)
return response
def check_model_config(self):
r"""Directly pass the check on arguments to STUB model."""
pass
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