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158 | class AzureOpenAIMultiModal(OpenAIMultiModal):
"""
Azure OpenAI.
To use this, you must first deploy a model on Azure OpenAI.
Unlike OpenAI, you need to specify a `engine` parameter to identify
your deployment (called "model deployment name" in Azure portal).
- model: Name of the model (e.g. `text-davinci-003`)
This in only used to decide completion vs. chat endpoint.
- engine: This will correspond to the custom name you chose
for your deployment when you deployed a model.
You must have the following environment variables set:
- `OPENAI_API_VERSION`: set this to `2023-05-15`
This may change in the future.
- `AZURE_OPENAI_ENDPOINT`: your endpoint should look like the following
https://YOUR_RESOURCE_NAME.openai.azure.com/
- `AZURE_OPENAI_API_KEY`: your API key if the api type is `azure`
More information can be found here:
https://learn.microsoft.com/en-us/azure/cognitive-services/openai/quickstart?tabs=command-line&pivots=programming-language-python
"""
engine: str = Field(description="The name of the deployed azure engine.")
azure_endpoint: Optional[str] = Field(
default=None, description="The Azure endpoint to use."
)
azure_deployment: Optional[str] = Field(
default=None, description="The Azure deployment to use."
)
use_azure_ad: bool = Field(
description="Indicates if Microsoft Entra ID (former Azure AD) is used for token authentication"
)
_azure_ad_token: Any = PrivateAttr(default=None)
def __init__(
self,
model: str = "gpt-4-vision-preview",
engine: Optional[str] = None,
temperature: float = DEFAULT_TEMPERATURE,
max_new_tokens: Optional[int] = 300,
additional_kwargs: Optional[Dict[str, Any]] = None,
context_window: Optional[int] = DEFAULT_CONTEXT_WINDOW,
max_retries: int = 3,
timeout: float = 60.0,
image_detail: str = "low",
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
# azure specific
azure_endpoint: Optional[str] = None,
azure_deployment: Optional[str] = None,
use_azure_ad: bool = False,
# aliases for engine
deployment_name: Optional[str] = None,
deployment_id: Optional[str] = None,
deployment: Optional[str] = None,
messages_to_prompt: Optional[Callable] = None,
completion_to_prompt: Optional[Callable] = None,
callback_manager: Optional[CallbackManager] = None,
default_headers: Optional[Dict[str, str]] = None,
http_client: Optional[httpx.Client] = None,
**kwargs: Any,
) -> None:
engine = resolve_from_aliases(
engine, deployment_name, deployment_id, deployment, azure_deployment
)
if engine is None:
raise ValueError("You must specify an `engine` parameter.")
azure_endpoint = get_from_param_or_env(
"azure_endpoint", azure_endpoint, "AZURE_OPENAI_ENDPOINT", ""
)
super().__init__(
engine=engine,
model=model,
temperature=temperature,
max_new_tokens=max_new_tokens,
additional_kwargs=additional_kwargs,
context_window=context_window,
max_retries=max_retries,
timeout=timeout,
image_detail=image_detail,
api_key=api_key,
api_base=api_base,
api_version=api_version,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
callback_manager=callback_manager,
azure_endpoint=azure_endpoint,
azure_deployment=azure_deployment,
use_azure_ad=use_azure_ad,
default_headers=default_headers,
http_client=http_client,
**kwargs,
)
def _get_clients(self, **kwargs: Any) -> Tuple[SyncAzureOpenAI, AsyncAzureOpenAI]:
client = SyncAzureOpenAI(**self._get_credential_kwargs())
aclient = AsyncAzureOpenAI(**self._get_credential_kwargs())
return client, aclient
@classmethod
def class_name(cls) -> str:
return "azure_openai_multi_modal_llm"
@property
def metadata(self) -> MultiModalLLMMetadata:
"""Multi Modal LLM metadata."""
return MultiModalLLMMetadata(
num_output=self.max_new_tokens or DEFAULT_NUM_OUTPUTS,
model_name=self.engine,
)
def _get_credential_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
if self.use_azure_ad:
self._azure_ad_token = refresh_openai_azuread_token(self._azure_ad_token)
self.api_key = self._azure_ad_token.token
return {
"api_key": self.api_key or None,
"max_retries": self.max_retries,
"azure_endpoint": self.azure_endpoint,
"azure_deployment": self.azure_deployment,
"api_version": self.api_version,
"default_headers": self.default_headers,
"http_client": self._http_client,
"timeout": self.timeout,
}
def _get_model_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
model_kwargs = super()._get_model_kwargs(**kwargs)
model_kwargs["model"] = self.engine
return model_kwargs
|