# ========= 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
import time
from typing import Any, Dict, List, Optional, Union
from openai import AsyncStream, Stream
from camel.configs import QWEN_API_PARAMS, QwenConfig
from camel.messages import OpenAIMessage
from camel.models.openai_compatible_model import OpenAICompatibleModel
from camel.types import (
ChatCompletion,
ChatCompletionChunk,
ModelType,
)
from camel.utils import (
BaseTokenCounter,
api_keys_required,
)
[docs]
class QwenModel(OpenAICompatibleModel):
r"""Qwen API in a unified OpenAICompatibleModel interface.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created, one of Qwen series.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into:obj:`openai.ChatCompletion.create()`. If
:obj:`None`, :obj:`QwenConfig().as_dict()` will be used.
(default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating with
the Qwen service. (default: :obj:`None`)
url (Optional[str], optional): The url to the Qwen service.
(default: :obj:`https://dashscope.aliyuncs.com/compatible-mode/v1`)
token_counter (Optional[BaseTokenCounter], optional): Token counter to
use for the model. If not provided, :obj:`OpenAITokenCounter(
ModelType.GPT_4O_MINI)` will be used.
(default: :obj:`None`)
timeout (Optional[float], optional): The timeout value in seconds for
API calls. If not provided, will fall back to the MODEL_TIMEOUT
environment variable or default to 180 seconds.
(default: :obj:`None`)
"""
@api_keys_required(
[
("api_key", "QWEN_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,
timeout: Optional[float] = None,
) -> None:
if model_config_dict is None:
model_config_dict = QwenConfig().as_dict()
api_key = api_key or os.environ.get("QWEN_API_KEY")
url = url or os.environ.get(
"QWEN_API_BASE_URL",
"https://dashscope.aliyuncs.com/compatible-mode/v1",
)
timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
super().__init__(
model_type=model_type,
model_config_dict=model_config_dict,
api_key=api_key,
url=url,
token_counter=token_counter,
timeout=timeout,
)
def _post_handle_response(
self, response: Union[ChatCompletion, Stream[ChatCompletionChunk]]
) -> ChatCompletion:
r"""Handle reasoning content with <think> tags at the beginning."""
if not isinstance(response, Stream):
# Handle non-streaming response (existing logic)
if self.model_config_dict.get("extra_body", {}).get(
"enable_thinking", False
):
reasoning_content = response.choices[
0
].message.reasoning_content # type: ignore[attr-defined]
combined_content = (
f"<think>\n{reasoning_content}\n</think>\n"
if reasoning_content
else ""
)
response_content = response.choices[0].message.content or ""
combined_content += response_content
# Construct a new ChatCompletion with combined content
return ChatCompletion.construct(
id=response.id,
choices=[
dict(
finish_reason=response.choices[0].finish_reason,
index=response.choices[0].index,
logprobs=response.choices[0].logprobs,
message=dict(
role=response.choices[0].message.role,
content=combined_content,
),
)
],
created=response.created,
model=response.model,
object="chat.completion",
system_fingerprint=response.system_fingerprint,
usage=response.usage,
)
else:
return response # Return original if no thinking enabled
# Handle streaming response
accumulated_reasoning = ""
accumulated_content = ""
final_chunk = None
usage_data = None # Initialize usage data
role = "assistant" # Default role
for chunk in response:
final_chunk = chunk # Keep track of the last chunk for metadata
if chunk.choices:
delta = chunk.choices[0].delta
if delta.role:
role = delta.role # Update role if provided
if (
hasattr(delta, 'reasoning_content')
and delta.reasoning_content
):
accumulated_reasoning += delta.reasoning_content
if delta.content:
accumulated_content += delta.content
if hasattr(chunk, 'usage') and chunk.usage:
usage_data = chunk.usage
combined_content = (
f"<think>\n{accumulated_reasoning}\n</think>\n"
if accumulated_reasoning
else ""
) + accumulated_content
# Construct the final ChatCompletion object from accumulated
# stream data
if final_chunk:
finish_reason = "stop" # Default finish reason
logprobs = None
if final_chunk.choices:
finish_reason = (
final_chunk.choices[0].finish_reason or finish_reason
)
if hasattr(final_chunk.choices[0], 'logprobs'):
logprobs = final_chunk.choices[0].logprobs
return ChatCompletion.construct(
# Use data from the final chunk or defaults
id=final_chunk.id
if hasattr(final_chunk, 'id')
else "streamed-completion",
choices=[
dict(
finish_reason=finish_reason,
index=0,
logprobs=logprobs,
message=dict(
role=role,
content=combined_content,
),
)
],
created=final_chunk.created
if hasattr(final_chunk, 'created')
else int(time.time()),
model=final_chunk.model
if hasattr(final_chunk, 'model')
else self.model_type,
object="chat.completion",
system_fingerprint=final_chunk.system_fingerprint
if hasattr(final_chunk, 'system_fingerprint')
else None,
usage=usage_data,
)
else:
# Handle cases where the stream was empty or invalid
return ChatCompletion.construct(
id="empty-stream",
choices=[
dict(
finish_reason="error",
index=0,
message=dict(role="assistant", content=""),
)
],
created=int(time.time()),
model=self.model_type,
object="chat.completion",
usage=usage_data,
)
def _request_chat_completion(
self,
messages: List[OpenAIMessage],
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
request_config = self.model_config_dict.copy()
if tools:
request_config["tools"] = tools
return self._post_handle_response(
self._client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
)
async def _arequest_chat_completion(
self,
messages: List[OpenAIMessage],
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
request_config = self.model_config_dict.copy()
if tools:
request_config["tools"] = 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 Qwen API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments to Qwen API.
"""
for param in self.model_config_dict:
if param not in QWEN_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into Qwen model backend."
)
