# ========= 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 Any, Dict, List, Optional, Type, Union
from openai import AsyncOpenAI, AsyncStream, OpenAI, Stream
from pydantic import BaseModel
from camel.configs import DEEPSEEK_API_PARAMS, DeepSeekConfig
from camel.logger import get_logger
from camel.messages import OpenAIMessage
from camel.models._utils import try_modify_message_with_format
from camel.models.base_model import BaseModelBackend
from camel.types import (
ChatCompletion,
ChatCompletionChunk,
ModelType,
)
from camel.utils import BaseTokenCounter, OpenAITokenCounter, api_keys_required
logger = get_logger(__name__)
REASONSER_UNSUPPORTED_PARAMS = [
"temperature",
"top_p",
"presence_penalty",
"frequency_penalty",
"logprobs",
"top_logprobs",
"tools",
]
[docs]
class DeepSeekModel(BaseModelBackend):
r"""DeepSeek API in a unified BaseModelBackend interface.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into:obj:`openai.ChatCompletion.create()`. If
:obj:`None`, :obj:`DeepSeekConfig().as_dict()` will be used.
(default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating with
the DeepSeek service. (default: :obj:`None`)
url (Optional[str], optional): The url to the DeepSeek service.
(default: :obj:`https://api.deepseek.com`)
token_counter (Optional[BaseTokenCounter], optional): Token counter to
use for the model. If not provided, :obj:`OpenAITokenCounter`
will be used. (default: :obj:`None`)
References:
https://api-docs.deepseek.com/
"""
@api_keys_required(
[
("api_key", "DEEPSEEK_API_KEY"),
]
)
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:
if model_config_dict is None:
model_config_dict = DeepSeekConfig().as_dict()
api_key = api_key or os.environ.get("DEEPSEEK_API_KEY")
url = url or os.environ.get(
"DEEPSEEK_API_BASE_URL",
"https://api.deepseek.com",
)
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self._client = OpenAI(
timeout=180,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
self._async_client = AsyncOpenAI(
timeout=180,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
@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 = OpenAITokenCounter(
model=ModelType.GPT_4O_MINI
)
return self._token_counter
def _prepare_request(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Dict[str, Any]:
request_config = self.model_config_dict.copy()
if self.model_type in [
ModelType.DEEPSEEK_REASONER,
]:
logger.warning(
"Warning: You are using an DeepSeek Reasoner model, "
"which has certain limitations, reference: "
"`https://api-docs.deepseek.com/guides/reasoning_model"
"#api-parameters`.",
)
request_config = {
key: value
for key, value in request_config.items()
if key not in REASONSER_UNSUPPORTED_PARAMS
}
if tools:
for tool in tools:
function_dict = tool.get('function', {})
function_dict.pop("strict", None)
request_config["tools"] = tools
elif response_format:
try_modify_message_with_format(messages[-1], response_format)
request_config["response_format"] = {"type": "json_object"}
return request_config
def _post_handle_response(
self, response: ChatCompletion
) -> ChatCompletion:
r"""Handle reasoning content with <think> tags at the beginning."""
if (
self.model_type in [ModelType.DEEPSEEK_REASONER]
and os.environ.get("GET_REASONING_CONTENT", "false").lower()
== "true"
):
reasoning_content = response.choices[0].message.reasoning_content # type: ignore[attr-defined]
combined_content = ( # type: ignore[operator]
f"<think>\n{reasoning_content}\n</think>\n"
if reasoning_content
else ""
) + response.choices[0].message.content
response = ChatCompletion.construct(
id=response.id,
choices=[
dict(
index=response.choices[0].index,
message={
"role": response.choices[0].message.role,
"content": combined_content,
"tool_calls": None,
},
finish_reason=response.choices[0].finish_reason
if response.choices[0].finish_reason
else None,
)
],
created=response.created,
model=response.model,
object="chat.completion",
usage=response.usage,
)
return response
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 inference of DeepSeek chat completion.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
Returns:
Union[ChatCompletion, Stream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`Stream[ChatCompletionChunk]` in the stream mode.
"""
request_config = self._prepare_request(
messages, response_format, tools
)
response = self._client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
return self._post_handle_response(response)
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 inference of DeepSeek chat completion.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
Returns:
Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`AsyncStream[ChatCompletionChunk]` in the stream mode.
"""
request_config = self._prepare_request(
messages, response_format, tools
)
response = await self._async_client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
return self._post_handle_response(response)
[docs]
def check_model_config(self):
r"""Check whether the model configuration contains any
unexpected arguments to DeepSeek API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments to DeepSeek API.
"""
for param in self.model_config_dict:
if param not in DEEPSEEK_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into DeepSeek model backend."
)
@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 self.model_config_dict.get("stream", False)
