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446 lines
16 KiB
Python
446 lines
16 KiB
Python
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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import os
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from json import JSONDecodeError
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from typing import Any, Dict, List, Optional, Type, Union
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from openai import AsyncOpenAI, AsyncStream, BadRequestError, OpenAI, Stream
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from openai.lib.streaming.chat import (
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AsyncChatCompletionStreamManager,
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ChatCompletionStreamManager,
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)
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from pydantic import BaseModel, ValidationError
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from camel.logger import get_logger
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from camel.messages import OpenAIMessage
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from camel.models._utils import try_modify_message_with_format
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from camel.models.base_model import BaseModelBackend
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from camel.types import (
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ChatCompletion,
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ChatCompletionChunk,
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ModelType,
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)
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from camel.utils import (
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BaseTokenCounter,
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OpenAITokenCounter,
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is_langfuse_available,
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)
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if os.environ.get("LANGFUSE_ENABLED", "False").lower() == "true":
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try:
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from langfuse.decorators import observe
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except ImportError:
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from camel.utils import observe
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elif os.environ.get("TRACEROOT_ENABLED", "False").lower() == "true":
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try:
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from traceroot import trace as observe # type: ignore[import]
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except ImportError:
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from camel.utils import observe
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else:
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from camel.utils import observe
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logger = get_logger(__name__)
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class OpenAICompatibleModel(BaseModelBackend):
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r"""Constructor for model backend supporting OpenAI compatibility.
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Args:
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model_type (Union[ModelType, str]): Model for which a backend is
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created.
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model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
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that will be fed into:obj:`openai.ChatCompletion.create()`. If
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:obj:`None`, :obj:`{}` will be used. (default: :obj:`None`)
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api_key (str): The API key for authenticating with the model service.
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url (str): The url to the model service.
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token_counter (Optional[BaseTokenCounter], optional): Token counter to
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use for the model. If not provided, :obj:`OpenAITokenCounter(
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ModelType.GPT_4O_MINI)` will be used.
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(default: :obj:`None`)
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timeout (Optional[float], optional): The timeout value in seconds for
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API calls. If not provided, will fall back to the MODEL_TIMEOUT
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environment variable or default to 180 seconds.
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(default: :obj:`None`)
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max_retries (int, optional): Maximum number of retries for API calls.
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(default: :obj:`3`)
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client (Optional[Any], optional): A custom synchronous
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OpenAI-compatible client instance. If provided, this client will
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be used instead of creating a new one. Useful for RL frameworks
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like AReaL or rLLM that provide OpenAI-compatible clients (e.g.,
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ArealOpenAI). The client should implement the OpenAI client
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interface with `.chat.completions.create()` and `.beta.chat.
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completions.parse()` methods. (default: :obj:`None`)
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async_client (Optional[Any], optional): A custom asynchronous
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OpenAI-compatible client instance. If provided, this client will
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be used instead of creating a new one. The client should implement
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the AsyncOpenAI client interface. (default: :obj:`None`)
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**kwargs (Any): Additional arguments to pass to the
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OpenAI client initialization. These can include parameters like
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'organization', 'default_headers', 'http_client', etc.
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Ignored if custom clients are provided.
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"""
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def __init__(
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self,
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model_type: Union[ModelType, str],
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model_config_dict: Optional[Dict[str, Any]] = None,
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api_key: Optional[str] = None,
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url: Optional[str] = None,
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token_counter: Optional[BaseTokenCounter] = None,
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timeout: Optional[float] = None,
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max_retries: int = 3,
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client: Optional[Any] = None,
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async_client: Optional[Any] = None,
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**kwargs: Any,
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) -> None:
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api_key = api_key or os.environ.get("OPENAI_COMPATIBILITY_API_KEY")
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url = url or os.environ.get("OPENAI_COMPATIBILITY_API_BASE_URL")
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timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
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super().__init__(
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model_type,
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model_config_dict,
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api_key,
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url,
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token_counter,
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timeout,
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max_retries,
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)
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# Use custom clients if provided, otherwise create new ones
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if client is not None:
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# Use the provided custom sync client
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self._client = client
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else:
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# Create default sync client
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if is_langfuse_available():
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from langfuse.openai import OpenAI as LangfuseOpenAI
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self._client = LangfuseOpenAI(
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timeout=self._timeout,
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max_retries=max_retries,
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base_url=self._url,
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api_key=self._api_key,
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**kwargs,
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)
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else:
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self._client = OpenAI(
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timeout=self._timeout,
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max_retries=max_retries,
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base_url=self._url,
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api_key=self._api_key,
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**kwargs,
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)
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if async_client is not None:
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# Use the provided custom async client
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self._async_client = async_client
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else:
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# Create default async client
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if is_langfuse_available():
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from langfuse.openai import AsyncOpenAI as LangfuseAsyncOpenAI
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self._async_client = LangfuseAsyncOpenAI(
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timeout=self._timeout,
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max_retries=max_retries,
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base_url=self._url,
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api_key=self._api_key,
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**kwargs,
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)
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else:
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self._async_client = AsyncOpenAI(
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timeout=self._timeout,
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max_retries=max_retries,
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base_url=self._url,
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api_key=self._api_key,
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**kwargs,
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)
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@observe()
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def _run(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[
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ChatCompletion,
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Stream[ChatCompletionChunk],
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ChatCompletionStreamManager[BaseModel],
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]:
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r"""Runs inference of OpenAI chat completion.
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Args:
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messages (List[OpenAIMessage]): Message list with the chat history
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in OpenAI API format.
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response_format (Optional[Type[BaseModel]]): The format of the
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response.
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tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
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use for the request.
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Returns:
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Union[ChatCompletion, Stream[ChatCompletionChunk]]:
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`ChatCompletion` in the non-stream mode, or
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`Stream[ChatCompletionChunk]` in the stream mode.
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`ChatCompletionStreamManager[BaseModel]` for
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structured output streaming.
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"""
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self._log_and_trace()
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response_format = response_format or self.model_config_dict.get(
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"response_format", None
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)
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# Check if streaming is enabled
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is_streaming = self.model_config_dict.get("stream", False)
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if response_format:
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if is_streaming:
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# Use streaming parse for structured output
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return self._request_stream_parse(
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messages, response_format, tools
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)
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else:
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# Use non-streaming parse for structured output
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return self._request_parse(messages, response_format, tools)
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else:
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result = self._request_chat_completion(messages, tools)
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return result
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@observe()
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async def _arun(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[
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ChatCompletion,
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AsyncStream[ChatCompletionChunk],
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AsyncChatCompletionStreamManager[BaseModel],
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]:
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r"""Runs inference of OpenAI chat completion in async mode.
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Args:
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messages (List[OpenAIMessage]): Message list with the chat history
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in OpenAI API format.
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response_format (Optional[Type[BaseModel]]): The format of the
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response.
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tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
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use for the request.
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Returns:
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Union[ChatCompletion, AsyncStream[ChatCompletionChunk],
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AsyncChatCompletionStreamManager[BaseModel]]:
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`ChatCompletion` in the non-stream mode,
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`AsyncStream[ChatCompletionChunk]` in the stream mode,
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or `AsyncChatCompletionStreamManager[BaseModel]` for
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structured output streaming.
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"""
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self._log_and_trace()
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response_format = response_format or self.model_config_dict.get(
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"response_format", None
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)
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# Check if streaming is enabled
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is_streaming = self.model_config_dict.get("stream", False)
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if response_format:
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if is_streaming:
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# Use streaming parse for structured output
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return await self._arequest_stream_parse(
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messages, response_format, tools
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)
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else:
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# Use non-streaming parse for structured output
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return await self._arequest_parse(
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messages, response_format, tools
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)
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else:
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result = await self._arequest_chat_completion(messages, tools)
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return result
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def _request_chat_completion(
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self,
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messages: List[OpenAIMessage],
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
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request_config = self._prepare_request_config(tools)
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return self._call_client(
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self._client.chat.completions.create,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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async def _arequest_chat_completion(
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self,
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messages: List[OpenAIMessage],
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
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request_config = self._prepare_request_config(tools)
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return await self._acall_client(
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self._async_client.chat.completions.create,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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def _request_parse(
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self,
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messages: List[OpenAIMessage],
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response_format: Type[BaseModel],
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> ChatCompletion:
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request_config = self._prepare_request_config(tools)
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# Remove stream from request_config since OpenAI does not support it
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# when structured response is used
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request_config["response_format"] = response_format
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request_config.pop("stream", None)
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try:
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return self._call_client(
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self._client.beta.chat.completions.parse,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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except (ValidationError, JSONDecodeError, BadRequestError) as e:
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logger.warning(
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f"Format validation error: {e}. "
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f"Attempting fallback with JSON format."
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)
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try_modify_message_with_format(messages[-1], response_format)
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request_config["response_format"] = {"type": "json_object"}
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try:
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return self._call_client(
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self._client.beta.chat.completions.parse,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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except Exception as e:
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logger.error(f"Fallback attempt also failed: {e}")
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raise
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async def _arequest_parse(
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self,
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messages: List[OpenAIMessage],
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response_format: Type[BaseModel],
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> ChatCompletion:
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request_config = self._prepare_request_config(tools)
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# Remove stream from request_config since OpenAI does not support it
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# when structured response is used
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request_config["response_format"] = response_format
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request_config.pop("stream", None)
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try:
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return await self._acall_client(
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self._async_client.beta.chat.completions.parse,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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except (ValidationError, JSONDecodeError, BadRequestError) as e:
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logger.warning(
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f"Format validation error: {e}. "
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f"Attempting fallback with JSON format."
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)
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try_modify_message_with_format(messages[-1], response_format)
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request_config["response_format"] = {"type": "json_object"}
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try:
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return await self._acall_client(
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self._async_client.beta.chat.completions.parse,
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messages=messages,
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model=self.model_type,
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**request_config,
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)
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except Exception as e:
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logger.error(f"Fallback attempt also failed: {e}")
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raise
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def _request_stream_parse(
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self,
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messages: List[OpenAIMessage],
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response_format: Type[BaseModel],
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> ChatCompletionStreamManager[BaseModel]:
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r"""Request streaming structured output parsing.
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Note: This uses OpenAI's beta streaming API for structured outputs.
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"""
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request_config = self._prepare_request_config(tools)
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# Remove stream from config as it's handled by the stream method
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request_config.pop("stream", None)
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# Use the beta streaming API for structured outputs
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return self._call_client(
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self._client.beta.chat.completions.stream,
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messages=messages,
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model=self.model_type,
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response_format=response_format,
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**request_config,
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)
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async def _arequest_stream_parse(
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self,
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messages: List[OpenAIMessage],
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response_format: Type[BaseModel],
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> AsyncChatCompletionStreamManager[BaseModel]:
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r"""Request async streaming structured output parsing.
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Note: This uses OpenAI's beta streaming API for structured outputs.
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"""
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request_config = self._prepare_request_config(tools)
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# Remove stream from config as it's handled by the stream method
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request_config.pop("stream", None)
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# Use the beta streaming API for structured outputs
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return self._call_client(
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self._async_client.beta.chat.completions.stream,
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messages=messages,
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model=self.model_type,
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response_format=response_format,
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**request_config,
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)
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@property
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def token_counter(self) -> BaseTokenCounter:
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r"""Initialize the token counter for the model backend.
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Returns:
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OpenAITokenCounter: The token counter following the model's
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tokenization style.
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"""
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if not self._token_counter:
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self._token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
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return self._token_counter
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@property
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def stream(self) -> bool:
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r"""Returns whether the model is in stream mode, which sends partial
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results each time.
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Returns:
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bool: Whether the model is in stream mode.
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"""
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return self.model_config_dict.get('stream', False)
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