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462 lines
18 KiB
Python
462 lines
18 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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import warnings
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from typing import Any, Callable, Dict, List, Optional, Type, Union
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from openai import AsyncAzureOpenAI, AsyncStream, AzureOpenAI, 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
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from camel.configs import ChatGPTConfig
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from camel.messages import OpenAIMessage
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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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AzureADTokenProvider = Callable[[], str]
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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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class AzureOpenAIModel(BaseModelBackend):
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r"""Azure OpenAI API in a unified BaseModelBackend interface.
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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, Should be the deployment name you chose when you deployed
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an azure model.
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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:`ChatGPTConfig().as_dict()` will be used.
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(default: :obj:`None`)
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api_key (Optional[str], optional): The API key for authenticating with
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the OpenAI service. (default: :obj:`None`)
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url (Optional[str], optional): The url to the OpenAI service.
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(default: :obj:`None`)
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api_version (Optional[str], optional): The api version for the model.
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(default: :obj:`None`)
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azure_ad_token (Optional[str], optional): Your Azure Active Directory
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token, https://www.microsoft.com/en-us/security/business/
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identity-access/microsoft-entra-id. (default: :obj:`None`)
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azure_ad_token_provider (Optional[AzureADTokenProvider], optional): A
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function that returns an Azure Active Directory token, will be
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invoked on every request. (default: :obj:`None`)
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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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will be used. (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 AzureOpenAI
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client instance. If provided, this client will be used instead of
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creating a new one. Useful for RL frameworks like AReaL or rLLM
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that provide Azure OpenAI-compatible clients. The client should
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implement the AzureOpenAI client interface with
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`.chat.completions.create()` and `.beta.chat.completions.parse()`
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methods. (default: :obj:`None`)
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async_client (Optional[Any], optional): A custom asynchronous
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AzureOpenAI client instance. If provided, this client will be
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used instead of creating a new one. The client should implement
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the AsyncAzureOpenAI client interface. (default: :obj:`None`)
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azure_deployment_name (Optional[str], optional): **Deprecated**.
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Use `model_type` parameter instead. This parameter is kept for
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backward compatibility and will be removed in a future version.
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(default: :obj:`None`)
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**kwargs (Any): Additional arguments to pass to the client
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initialization. Ignored if custom clients are provided.
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References:
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https://learn.microsoft.com/en-us/azure/ai-services/openai/
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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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timeout: Optional[float] = None,
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token_counter: Optional[BaseTokenCounter] = None,
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api_version: Optional[str] = None,
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azure_ad_token_provider: Optional["AzureADTokenProvider"] = None,
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azure_ad_token: Optional[str] = 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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azure_deployment_name: Optional[str] = None,
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**kwargs: Any,
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) -> None:
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# Handle deprecated azure_deployment_name parameter
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if azure_deployment_name is not None:
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warnings.warn(
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"The 'azure_deployment_name' parameter is deprecated. "
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"Please use 'model_type' parameter instead. "
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"The 'azure_deployment_name' parameter is being ignored.",
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DeprecationWarning,
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stacklevel=2,
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)
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# Handle deprecated AZURE_DEPLOYMENT_NAME environment variable
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if os.environ.get("AZURE_DEPLOYMENT_NAME") is not None:
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warnings.warn(
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"The 'AZURE_DEPLOYMENT_NAME' environment variable is "
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"deprecated. Please use the 'model_type' parameter "
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"instead. The 'AZURE_DEPLOYMENT_NAME' environment "
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"variable is being ignored.",
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DeprecationWarning,
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stacklevel=2,
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)
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if model_config_dict is None:
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model_config_dict = ChatGPTConfig().as_dict()
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api_key = api_key or os.environ.get("AZURE_OPENAI_API_KEY")
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url = url or os.environ.get("AZURE_OPENAI_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, model_config_dict, api_key, url, token_counter, timeout
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)
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self.api_version = api_version or os.environ.get("AZURE_API_VERSION")
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self._azure_ad_token = azure_ad_token or os.environ.get(
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"AZURE_AD_TOKEN"
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)
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self.azure_ad_token_provider = azure_ad_token_provider
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if self.api_version is None:
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raise ValueError(
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"Must provide either the `api_version` argument "
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"or `AZURE_API_VERSION` environment variable."
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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 AzureOpenAI as LangfuseOpenAI
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self._client = LangfuseOpenAI(
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azure_endpoint=str(self._url),
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azure_deployment=str(self.model_type),
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api_version=self.api_version,
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api_key=self._api_key,
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azure_ad_token=self._azure_ad_token,
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azure_ad_token_provider=self.azure_ad_token_provider,
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timeout=self._timeout,
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max_retries=max_retries,
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**kwargs,
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)
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else:
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self._client = AzureOpenAI(
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azure_endpoint=str(self._url),
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azure_deployment=str(self.model_type),
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api_version=self.api_version,
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api_key=self._api_key,
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azure_ad_token=self._azure_ad_token,
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azure_ad_token_provider=self.azure_ad_token_provider,
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timeout=self._timeout,
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max_retries=max_retries,
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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 (
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AsyncAzureOpenAI as LangfuseAsyncOpenAI,
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)
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self._async_client = LangfuseAsyncOpenAI(
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azure_endpoint=str(self._url),
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azure_deployment=str(self.model_type),
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api_version=self.api_version,
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api_key=self._api_key,
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azure_ad_token=self._azure_ad_token,
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azure_ad_token_provider=self.azure_ad_token_provider,
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timeout=self._timeout,
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max_retries=max_retries,
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**kwargs,
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)
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else:
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self._async_client = AsyncAzureOpenAI(
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azure_endpoint=str(self._url),
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azure_deployment=str(self.model_type),
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api_version=self.api_version,
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api_key=self._api_key,
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azure_ad_token=self._azure_ad_token,
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azure_ad_token_provider=self.azure_ad_token_provider,
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timeout=self._timeout,
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max_retries=max_retries,
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**kwargs,
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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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BaseTokenCounter: 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(self.model_type)
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return self._token_counter
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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 Azure 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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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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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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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 Azure 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, AsyncStream[ChatCompletionChunk],
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AsyncChatCompletionStreamManager[BaseModel]]:
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`ChatCompletion` in the non-stream mode, or
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`AsyncStream[ChatCompletionChunk]` in the stream mode.
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`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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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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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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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=str(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=str(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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request_config["response_format"] = response_format
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# Remove stream from request config since OpenAI does not support it
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# with structured response
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request_config.pop("stream", None)
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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=str(self.model_type),
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**request_config,
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)
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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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request_config["response_format"] = response_format
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# Remove stream from request config since OpenAI does not support it
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# with structured response
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request_config.pop("stream", None)
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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=str(self.model_type),
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**request_config,
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)
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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=str(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=str(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 stream(self) -> bool:
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r"""Returns whether the model is in stream mode,
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which sends partial 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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