# ========= 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 abc
import re
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Type, Union
from openai import AsyncStream, Stream
from pydantic import BaseModel
from camel.messages import OpenAIMessage
from camel.types import (
ChatCompletion,
ChatCompletionChunk,
ModelType,
ParsedChatCompletion,
UnifiedModelType,
)
from camel.utils import BaseTokenCounter
[docs]
class BaseModelBackend(ABC, metaclass=ModelBackendMeta):
r"""Base class for different model backends.
It may be OpenAI API, a local LLM, a stub for unit tests, etc.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created.
model_config_dict (Optional[Dict[str, Any]], optional): A config
dictionary. (default: :obj:`{}`)
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:`OpenAITokenCounter` 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.model_type: UnifiedModelType = UnifiedModelType(model_type)
if model_config_dict is None:
model_config_dict = {}
self.model_config_dict = model_config_dict
self._api_key = api_key
self._url = url
self._token_counter = token_counter
self.check_model_config()
@property
@abstractmethod
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.
"""
pass
[docs]
def preprocess_messages(
self, messages: List[OpenAIMessage]
) -> List[OpenAIMessage]:
r"""Preprocess messages before sending to model API.
Removes thinking content from assistant and user messages.
Args:
messages (List[OpenAIMessage]): Original messages
Returns:
List[OpenAIMessage]: Preprocessed messages
"""
def should_process_thinking(msg: OpenAIMessage) -> bool:
# Only process thinking content for assistant and user messages
return msg['role'] in ['assistant', 'user'] and isinstance(
msg['content'], str
)
def remove_thinking(content: str) -> str:
# Only remove thinking content if the tags are present
if '<think>' in content and '</think>' in content:
return re.sub(
r'<think>.*?</think>',
'',
content,
flags=re.DOTALL,
).strip()
return content
return [
{ # type: ignore[misc]
**msg,
'content': (
remove_thinking(msg['content']) # type: ignore[arg-type]
if should_process_thinking(msg)
else msg['content']
),
}
for msg in messages
]
@abstractmethod
def _run(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
pass
@abstractmethod
async def _arun(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
pass
[docs]
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 the query to the backend model.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
response_format (Optional[Type[BaseModel]]): The response format
to use for the model. (default: :obj:`None`)
tools (Optional[List[Tool]]): The schema of tools to use for the
model for this request. Will override the tools specified in
the model configuration (but not change the configuration).
(default: :obj:`None`)
Returns:
Union[ChatCompletion, Stream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`Stream[ChatCompletionChunk]` in the stream mode.
"""
# None -> use default tools
if tools is None:
tools = self.model_config_dict.get("tools", None)
# Empty -> use no tools
elif not tools:
tools = None
return self._run(messages, response_format, tools)
[docs]
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 the query to the backend model asynchronously.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
response_format (Optional[Type[BaseModel]]): The response format
to use for the model. (default: :obj:`None`)
tools (Optional[List[Tool]]): The schema of tools to use for the
model for this request. Will override the tools specified in
the model configuration (but not change the configuration).
(default: :obj:`None`)
Returns:
Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`AsyncStream[ChatCompletionChunk]` in the stream mode.
"""
if tools is None:
tools = self.model_config_dict.get("tools", None)
elif not tools:
tools = None
return await self._arun(messages, response_format, tools)
[docs]
@abstractmethod
def check_model_config(self):
r"""Check whether the input model configuration contains unexpected
arguments
Raises:
ValueError: If the model configuration dictionary contains any
unexpected argument for this model class.
"""
pass
[docs]
def count_tokens_from_messages(self, messages: List[OpenAIMessage]) -> int:
r"""Count the number of tokens in the messages using the specific
tokenizer.
Args:
messages (List[Dict]): message list with the chat history
in OpenAI API format.
Returns:
int: Number of tokens in the messages.
"""
return self.token_counter.count_tokens_from_messages(messages)
def _to_chat_completion(
self, response: ParsedChatCompletion
) -> ChatCompletion:
if len(response.choices) > 1:
print("Warning: Multiple response choices detected")
choice = dict(
index=response.choices[0].index,
message={
"role": response.choices[0].message.role,
"content": response.choices[0].message.content,
"tool_calls": response.choices[0].message.tool_calls,
"parsed": response.choices[0].message.parsed,
},
finish_reason=response.choices[0].finish_reason,
)
obj = ChatCompletion.construct(
id=response.id,
choices=[choice],
created=response.created,
model=response.model,
object="chat.completion",
usage=response.usage,
)
return obj
@property
def token_limit(self) -> int:
r"""Returns the maximum token limit for a given model.
This method retrieves the maximum token limit either from the
`model_config_dict` or from the model's default token limit.
Returns:
int: The maximum token limit for the given model.
"""
return (
self.model_config_dict.get("max_tokens")
or self.model_type.token_limit
)
@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 False
