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131 | class AzureOpenAIEmbedding(OpenAIEmbedding):
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."
)
api_base: str = Field(default="", description="The base URL for Azure deployment.")
api_version: str = Field(
default="", description="The version for Azure OpenAI API."
)
azure_ad_token_provider: AzureADTokenProvider = Field(
default=None, description="Callback function to provide Azure AD token."
)
_client: AzureOpenAI = PrivateAttr()
_aclient: AsyncAzureOpenAI = PrivateAttr()
def __init__(
self,
mode: str = OpenAIEmbeddingMode.TEXT_SEARCH_MODE,
model: str = OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
additional_kwargs: Optional[Dict[str, Any]] = None,
api_key: Optional[str] = None,
api_version: Optional[str] = None,
# azure specific
azure_endpoint: Optional[str] = None,
azure_deployment: Optional[str] = None,
azure_ad_token_provider: AzureADTokenProvider = None,
deployment_name: Optional[str] = None,
max_retries: int = 10,
reuse_client: bool = True,
callback_manager: Optional[CallbackManager] = None,
num_workers: Optional[int] = None,
# custom httpx client
http_client: Optional[httpx.Client] = None,
**kwargs: Any,
):
azure_endpoint = get_from_param_or_env(
"azure_endpoint", azure_endpoint, "AZURE_OPENAI_ENDPOINT", ""
)
azure_deployment = resolve_from_aliases(
azure_deployment,
deployment_name,
)
super().__init__(
mode=mode,
model=model,
embed_batch_size=embed_batch_size,
additional_kwargs=additional_kwargs,
api_key=api_key,
api_version=api_version,
azure_endpoint=azure_endpoint,
azure_deployment=azure_deployment,
azure_ad_token_provider=azure_ad_token_provider,
max_retries=max_retries,
reuse_client=reuse_client,
callback_manager=callback_manager,
http_client=http_client,
num_workers=num_workers,
**kwargs,
)
@root_validator(pre=True)
def validate_env(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Validate necessary credentials are set."""
if (
values["api_base"] == "https://api.openai.com/v1"
and values["azure_endpoint"] is None
):
raise ValueError(
"You must set OPENAI_API_BASE to your Azure endpoint. "
"It should look like https://YOUR_RESOURCE_NAME.openai.azure.com/"
)
if values["api_version"] is None:
raise ValueError("You must set OPENAI_API_VERSION for Azure OpenAI.")
return values
def _get_client(self) -> AzureOpenAI:
if not self.reuse_client:
return AzureOpenAI(**self._get_credential_kwargs())
if self._client is None:
self._client = AzureOpenAI(**self._get_credential_kwargs())
return self._client
def _get_aclient(self) -> AsyncAzureOpenAI:
if not self.reuse_client:
return AsyncAzureOpenAI(**self._get_credential_kwargs())
if self._aclient is None:
self._aclient = AsyncAzureOpenAI(**self._get_credential_kwargs())
return self._aclient
def _get_credential_kwargs(self) -> Dict[str, Any]:
return {
"api_key": self.api_key,
"azure_ad_token_provider": self.azure_ad_token_provider,
"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,
}
@classmethod
def class_name(cls) -> str:
return "AzureOpenAIEmbedding"
|