import asyncio
import json
import time
from typing import Any, Callable, Coroutine, Dict, List, Optional, Union
import httpx # type: ignore
from openai import APITimeoutError, AsyncAzureOpenAI, AzureOpenAI
import litellm
from litellm.constants import AZURE_OPERATION_POLLING_TIMEOUT, DEFAULT_MAX_RETRIES
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
from litellm.llms.custom_httpx.http_handler import (
AsyncHTTPHandler,
HTTPHandler,
get_async_httpx_client,
)
from litellm.types.utils import (
EmbeddingResponse,
ImageResponse,
LlmProviders,
ModelResponse,
)
from litellm.utils import (
CustomStreamWrapper,
convert_to_model_response_object,
modify_url,
)
from ...types.llms.openai import HttpxBinaryResponseContent
from ..base import BaseLLM
from .common_utils import (
AzureOpenAIError,
BaseAzureLLM,
get_azure_ad_token_from_oidc,
process_azure_headers,
select_azure_base_url_or_endpoint,
)
class AzureOpenAIAssistantsAPIConfig:
"""
Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/assistants-reference-messages?tabs=python#create-message
"""
def __init__(
self,
) -> None:
pass
def get_supported_openai_create_message_params(self):
return [
"role",
"content",
"attachments",
"metadata",
]
def map_openai_params_create_message_params(
self, non_default_params: dict, optional_params: dict
):
for param, value in non_default_params.items():
if param == "role":
optional_params["role"] = value
if param == "metadata":
optional_params["metadata"] = value
elif param == "content": # only string accepted
if isinstance(value, str):
optional_params["content"] = value
else:
raise litellm.utils.UnsupportedParamsError(
message="Azure only accepts content as a string.",
status_code=400,
)
elif (
param == "attachments"
): # this is a v2 param. Azure currently supports the old 'file_id's param
file_ids: List[str] = []
if isinstance(value, list):
for item in value:
if "file_id" in item:
file_ids.append(item["file_id"])
else:
if litellm.drop_params is True:
pass
else:
raise litellm.utils.UnsupportedParamsError(
message="Azure doesn't support {}. To drop it from the call, set `litellm.drop_params = True.".format(
value
),
status_code=400,
)
else:
raise litellm.utils.UnsupportedParamsError(
message="Invalid param. attachments should always be a list. Got={}, Expected=List. Raw value={}".format(
type(value), value
),
status_code=400,
)
return optional_params
def _check_dynamic_azure_params(
azure_client_params: dict,
azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]],
) -> bool:
"""
Returns True if user passed in client params != initialized azure client
Currently only implemented for api version
"""
if azure_client is None:
return True
dynamic_params = ["api_version"]
for k, v in azure_client_params.items():
if k in dynamic_params and k == "api_version":
if v is not None and v != azure_client._custom_query["api-version"]:
return True
return False
class AzureChatCompletion(BaseAzureLLM, BaseLLM):
def __init__(self) -> None:
super().__init__()
def make_sync_azure_openai_chat_completion_request(
self,
azure_client: AzureOpenAI,
data: dict,
timeout: Union[float, httpx.Timeout],
):
"""
Helper to:
- call chat.completions.create.with_raw_response when litellm.return_response_headers is True
- call chat.completions.create by default
"""
try:
raw_response = azure_client.chat.completions.with_raw_response.create(
**data, timeout=timeout
)
headers = dict(raw_response.headers)
response = raw_response.parse()
return headers, response
except Exception as e:
raise e
@track_llm_api_timing()
async def make_azure_openai_chat_completion_request(
self,
azure_client: AsyncAzureOpenAI,
data: dict,
timeout: Union[float, httpx.Timeout],
logging_obj: LiteLLMLoggingObj,
):
"""
Helper to:
- call chat.completions.create.with_raw_response when litellm.return_response_headers is True
- call chat.completions.create by default
"""
start_time = time.time()
try:
raw_response = await azure_client.chat.completions.with_raw_response.create(
**data, timeout=timeout
)
headers = dict(raw_response.headers)
response = raw_response.parse()
return headers, response
except APITimeoutError as e:
end_time = time.time()
time_delta = round(end_time - start_time, 2)
e.message += f" - timeout value={timeout}, time taken={time_delta} seconds"
raise e
except Exception as e:
raise e
def completion( # noqa: PLR0915
self,
model: str,
messages: list,
model_response: ModelResponse,
api_key: str,
api_base: str,
api_version: str,
api_type: str,
azure_ad_token: str,
azure_ad_token_provider: Callable,
dynamic_params: bool,
print_verbose: Callable,
timeout: Union[float, httpx.Timeout],
logging_obj: LiteLLMLoggingObj,
optional_params,
litellm_params,
logger_fn,
acompletion: bool = False,
headers: Optional[dict] = None,
client=None,
):
if headers:
optional_params["extra_headers"] = headers
try:
if model is None or messages is None:
raise AzureOpenAIError(
status_code=422, message="Missing model or messages"
)
max_retries = optional_params.pop("max_retries", None)
if max_retries is None:
max_retries = DEFAULT_MAX_RETRIES
json_mode: Optional[bool] = optional_params.pop("json_mode", False)
### CHECK IF CLOUDFLARE AI GATEWAY ###
### if so - set the model as part of the base url
if "gateway.ai.cloudflare.com" in api_base:
client = self._init_azure_client_for_cloudflare_ai_gateway(
api_base=api_base,
model=model,
api_version=api_version,
max_retries=max_retries,
timeout=timeout,
api_key=api_key,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
acompletion=acompletion,
client=client,
litellm_params=litellm_params,
)
data = {"model": None, "messages": messages, **optional_params}
else:
data = litellm.AzureOpenAIConfig().transform_request(
model=model,
messages=messages,
optional_params=optional_params,
litellm_params=litellm_params,
headers=headers or {},
)
if acompletion is True:
if optional_params.get("stream", False):
return self.async_streaming(
logging_obj=logging_obj,
api_base=api_base,
dynamic_params=dynamic_params,
data=data,
model=model,
api_key=api_key,
api_version=api_version,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
timeout=timeout,
client=client,
max_retries=max_retries,
litellm_params=litellm_params,
)
else:
return self.acompletion(
api_base=api_base,
data=data,
model_response=model_response,
api_key=api_key,
api_version=api_version,
model=model,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
dynamic_params=dynamic_params,
timeout=timeout,
client=client,
logging_obj=logging_obj,
max_retries=max_retries,
convert_tool_call_to_json_mode=json_mode,
litellm_params=litellm_params,
)
elif "stream" in optional_params and optional_params["stream"] is True:
return self.streaming(
logging_obj=logging_obj,
api_base=api_base,
dynamic_params=dynamic_params,
data=data,
model=model,
api_key=api_key,
api_version=api_version,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
timeout=timeout,
client=client,
max_retries=max_retries,
litellm_params=litellm_params,
)
else:
## LOGGING
logging_obj.pre_call(
input=messages,
api_key=api_key,
additional_args={
"headers": {
"api_key": api_key,
"azure_ad_token": azure_ad_token,
},
"api_version": api_version,
"api_base": api_base,
"complete_input_dict": data,
},
)
if not isinstance(max_retries, int):
raise AzureOpenAIError(
status_code=422, message="max retries must be an int"
)
# init AzureOpenAI Client
azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
client=client,
_is_async=False,
litellm_params=litellm_params,
)
if not isinstance(azure_client, AzureOpenAI):
raise AzureOpenAIError(
status_code=500,
message="azure_client is not an instance of AzureOpenAI",
)
headers, response = self.make_sync_azure_openai_chat_completion_request(
azure_client=azure_client, data=data, timeout=timeout
)
stringified_response = response.model_dump()
## LOGGING
logging_obj.post_call(
input=messages,
api_key=api_key,
original_response=stringified_response,
additional_args={
"headers": headers,
"api_version": api_version,
"api_base": api_base,
},
)
return convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
convert_tool_call_to_json_mode=json_mode,
_response_headers=headers,
)
except AzureOpenAIError as e:
raise e
except Exception as e:
status_code = getattr(e, "status_code", 500)
error_headers = getattr(e, "headers", None)
error_response = getattr(e, "response", None)
error_body = getattr(e, "body", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
raise AzureOpenAIError(
status_code=status_code,
message=str(e),
headers=error_headers,
body=error_body,
)
async def acompletion(
self,
api_key: str,
api_version: str,
model: str,
api_base: str,
data: dict,
timeout: Any,
dynamic_params: bool,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
max_retries: int,
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
convert_tool_call_to_json_mode: Optional[bool] = None,
client=None, # this is the AsyncAzureOpenAI
litellm_params: Optional[dict] = {},
):
response = None
try:
# setting Azure client
azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
client=client,
_is_async=True,
litellm_params=litellm_params,
)
if not isinstance(azure_client, AsyncAzureOpenAI):
raise ValueError("Azure client is not an instance of AsyncAzureOpenAI")
## LOGGING
logging_obj.pre_call(
input=data["messages"],
api_key=azure_client.api_key,
additional_args={
"headers": {
"api_key": api_key,
"azure_ad_token": azure_ad_token,
},
"api_base": azure_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
headers, response = await self.make_azure_openai_chat_completion_request(
azure_client=azure_client,
data=data,
timeout=timeout,
logging_obj=logging_obj,
)
logging_obj.model_call_details["response_headers"] = headers
stringified_response = response.model_dump()
logging_obj.post_call(
input=data["messages"],
api_key=api_key,
original_response=stringified_response,
additional_args={"complete_input_dict": data},
)
return convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
hidden_params={"headers": headers},
_response_headers=headers,
convert_tool_call_to_json_mode=convert_tool_call_to_json_mode,
)
except AzureOpenAIError as e:
## LOGGING
logging_obj.post_call(
input=data["messages"],
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=str(e),
)
raise e
except asyncio.CancelledError as e:
## LOGGING
logging_obj.post_call(
input=data["messages"],
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=str(e),
)
raise AzureOpenAIError(status_code=500, message=str(e))
except Exception as e:
message = getattr(e, "message", str(e))
body = getattr(e, "body", None)
## LOGGING
logging_obj.post_call(
input=data["messages"],
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=str(e),
)
if hasattr(e, "status_code"):
raise e
else:
raise AzureOpenAIError(status_code=500, message=message, body=body)
def streaming(
self,
logging_obj,
api_base: str,
api_key: str,
api_version: str,
dynamic_params: bool,
data: dict,
model: str,
timeout: Any,
max_retries: int,
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
client=None,
litellm_params: Optional[dict] = {},
):
# init AzureOpenAI Client
azure_client_params = {
"api_version": api_version,
"azure_endpoint": api_base,
"azure_deployment": model,
"http_client": litellm.client_session,
"max_retries": max_retries,
"timeout": timeout,
}
azure_client_params = select_azure_base_url_or_endpoint(
azure_client_params=azure_client_params
)
if api_key is not None:
azure_client_params["api_key"] = api_key
elif azure_ad_token is not None:
if azure_ad_token.startswith("oidc/"):
azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
azure_client_params["azure_ad_token"] = azure_ad_token
elif azure_ad_token_provider is not None:
azure_client_params["azure_ad_token_provider"] = azure_ad_token_provider
azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
client=client,
_is_async=False,
litellm_params=litellm_params,
)
if not isinstance(azure_client, AzureOpenAI):
raise AzureOpenAIError(
status_code=500,
message="azure_client is not an instance of AzureOpenAI",
)
## LOGGING
logging_obj.pre_call(
input=data["messages"],
api_key=azure_client.api_key,
additional_args={
"headers": {
"api_key": api_key,
"azure_ad_token": azure_ad_token,
},
"api_base": azure_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
headers, response = self.make_sync_azure_openai_chat_completion_request(
azure_client=azure_client, data=data, timeout=timeout
)
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="azure",
logging_obj=logging_obj,
stream_options=data.get("stream_options", None),
_response_headers=process_azure_headers(headers),
)
return streamwrapper
async def async_streaming(
self,
logging_obj: LiteLLMLoggingObj,
api_base: str,
api_key: str,
api_version: str,
dynamic_params: bool,
data: dict,
model: str,
timeout: Any,
max_retries: int,
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
client=None,
litellm_params: Optional[dict] = {},
):
try:
azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
client=client,
_is_async=True,
litellm_params=litellm_params,
)
if not isinstance(azure_client, AsyncAzureOpenAI):
raise ValueError("Azure client is not an instance of AsyncAzureOpenAI")
## LOGGING
logging_obj.pre_call(
input=data["messages"],
api_key=azure_client.api_key,
additional_args={
"headers": {
"api_key": api_key,
"azure_ad_token": azure_ad_token,
},
"api_base": azure_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
headers, response = await self.make_azure_openai_chat_completion_request(
azure_client=azure_client,
data=data,
timeout=timeout,
logging_obj=logging_obj,
)
logging_obj.model_call_details["response_headers"] = headers
# return response
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="azure",
logging_obj=logging_obj,
stream_options=data.get("stream_options", None),
_response_headers=headers,
)
return streamwrapper ## DO NOT make this into an async for ... loop, it will yield an async generator, which won't raise errors if the response fails
except Exception as e:
status_code = getattr(e, "status_code", 500)
error_headers = getattr(e, "headers", None)
error_response = getattr(e, "response", None)
message = getattr(e, "message", str(e))
error_body = getattr(e, "body", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
raise AzureOpenAIError(
status_code=status_code,
message=message,
headers=error_headers,
body=error_body,
)
async def aembedding(
self,
model: str,
data: dict,
model_response: EmbeddingResponse,
input: list,
logging_obj: LiteLLMLoggingObj,
api_base: str,
api_key: Optional[str] = None,
api_version: Optional[str] = None,
client: Optional[AsyncAzureOpenAI] = None,
timeout: Optional[Union[float, httpx.Timeout]] = None,
max_retries: Optional[int] = None,
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
litellm_params: Optional[dict] = {},
) -> EmbeddingResponse:
response = None
try:
openai_aclient = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
_is_async=True,
client=client,
litellm_params=litellm_params,
)
if not isinstance(openai_aclient, AsyncAzureOpenAI):
raise ValueError("Azure client is not an instance of AsyncAzureOpenAI")
raw_response = await openai_aclient.embeddings.with_raw_response.create(
**data, timeout=timeout
)
headers = dict(raw_response.headers)
response = raw_response.parse()
stringified_response = response.model_dump()
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=stringified_response,
)
embedding_response = convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
hidden_params={"headers": headers},
_response_headers=process_azure_headers(headers),
response_type="embedding",
)
if not isinstance(embedding_response, EmbeddingResponse):
raise AzureOpenAIError(
status_code=500,
message="embedding_response is not an instance of EmbeddingResponse",
)
return embedding_response
except Exception as e:
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=str(e),
)
raise e
def embedding(
self,
model: str,
input: list,
api_base: str,
api_version: str,
timeout: float,
logging_obj: LiteLLMLoggingObj,
model_response: EmbeddingResponse,
optional_params: dict,
api_key: Optional[str] = None,
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
max_retries: Optional[int] = None,
client=None,
aembedding=None,
headers: Optional[dict] = None,
litellm_params: Optional[dict] = None,
) -> Union[EmbeddingResponse, Coroutine[Any, Any, EmbeddingResponse]]:
if headers:
optional_params["extra_headers"] = headers
if self._client_session is None:
self._client_session = self.create_client_session()
try:
data = {"model": model, "input": input, **optional_params}
if max_retries is None:
max_retries = litellm.DEFAULT_MAX_RETRIES
## LOGGING
logging_obj.pre_call(
input=input,
api_key=api_key,
additional_args={
"complete_input_dict": data,
"headers": {"api_key": api_key, "azure_ad_token": azure_ad_token},
},
)
if aembedding is True:
return self.aembedding(
data=data,
input=input,
model=model,
logging_obj=logging_obj,
api_key=api_key,
model_response=model_response,
timeout=timeout,
client=client,
litellm_params=litellm_params,
api_base=api_base,
)
azure_client = self.get_azure_openai_client(
api_version=api_version,
api_base=api_base,
api_key=api_key,
model=model,
_is_async=False,
client=client,
litellm_params=litellm_params,
)
if not isinstance(azure_client, AzureOpenAI):
raise AzureOpenAIError(
status_code=500,
message="azure_client is not an instance of AzureOpenAI",
)
## COMPLETION CALL
raw_response = azure_client.embeddings.with_raw_response.create(**data, timeout=timeout) # type: ignore
headers = dict(raw_response.headers)
response = raw_response.parse()
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data, "api_base": api_base},
original_response=response,
)
return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="embedding", _response_headers=process_azure_headers(headers)) # type: ignore
except AzureOpenAIError as e:
raise e
except Exception as e:
status_code = getattr(e, "status_code", 500)
error_headers = getattr(e, "headers", None)
error_response = getattr(e, "response", None)
if error_headers is None and error_response:
error_headers = getattr(error_response, "headers", None)
raise AzureOpenAIError(
status_code=status_code, message=str(e), headers=error_headers
)
async def make_async_azure_httpx_request(
self,
client: Optional[AsyncHTTPHandler],
timeout: Optional[Union[float, httpx.Timeout]],
api_base: str,
api_version: str,
api_key: str,
data: dict,
headers: dict,
) -> httpx.Response:
"""
Implemented for azure dall-e-2 image gen calls
Alternative to needing a custom transport implementation
"""
if client is None:
_params = {}
if timeout is not None:
if isinstance(timeout, float) or isinstance(timeout, int):
_httpx_timeout = httpx.Timeout(timeout)
_params["timeout"] = _httpx_timeout
else:
_params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
async_handler = get_async_httpx_client(
llm_provider=LlmProviders.AZURE,
params=_params,
)
else:
async_handler = client # type: ignore
if (
"images/generations" in api_base
and api_version
in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
"2023-06-01-preview",
"2023-07-01-preview",
"2023-08-01-preview",
"2023-09-01-preview",
"2023-10-01-preview",
]
): # CREATE + POLL for azure dall-e-2 calls
api_base = modify_url(
original_url=api_base, new_path="/openai/images/generations:submit"
)
data.pop(
"model", None
) # REMOVE 'model' from dall-e-2 arg https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-a-generated-image-dall-e-2-preview
response = await async_handler.post(
url=api_base,
data=json.dumps(data),
headers=headers,
)
if "operation-location" in response.headers:
operation_location_url = response.headers["operation-location"]
else:
raise AzureOpenAIError(status_code=500, message=response.text)
response = await async_handler.get(
url=operation_location_url,
headers=headers,
)
await response.aread()
timeout_secs: int = AZURE_OPERATION_POLLING_TIMEOUT
start_time = time.time()
if "status" not in response.json():
raise Exception(
"Expected 'status' in response. Got={}".format(response.json())
)
while response.json()["status"] not in ["succeeded", "failed"]:
if time.time() - start_time > timeout_secs:
raise AzureOpenAIError(
status_code=408, message="Operation polling timed out."
)
await asyncio.sleep(int(response.headers.get("retry-after") or 10))
response = await async_handler.get(
url=operation_location_url,
headers=headers,
)
await response.aread()
if response.json()["status"] == "failed":
error_data = response.json()
raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
result = response.json()["result"]
return httpx.Response(
status_code=200,
headers=response.headers,
content=json.dumps(result).encode("utf-8"),
request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
)
return await async_handler.post(
url=api_base,
json=data,
headers=headers,
)
def make_sync_azure_httpx_request(
self,
client: Optional[HTTPHandler],
timeout: Optional[Union[float, httpx.Timeout]],
api_base: str,
api_version: str,
api_key: str,
data: dict,
headers: dict,
) -> httpx.Response:
"""
Implemented for azure dall-e-2 image gen calls
Alternative to needing a custom transport implementation
"""
if client is None:
_params = {}
if timeout is not None:
if isinstance(timeout, float) or isinstance(timeout, int):
_httpx_timeout = httpx.Timeout(timeout)
_params["timeout"] = _httpx_timeout
else:
_params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
sync_handler = HTTPHandler(**_params, client=litellm.client_session) # type: ignore
else:
sync_handler = client # type: ignore
if (
"images/generations" in api_base
and api_version
in [ # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
"2023-06-01-preview",
"2023-07-01-preview",
"2023-08-01-preview",
"2023-09-01-preview",
"2023-10-01-preview",
]
): # CREATE + POLL for azure dall-e-2 calls
api_base = modify_url(
original_url=api_base, new_path="/openai/images/generations:submit"
)
data.pop(
"model", None
) # REMOVE 'model' from dall-e-2 arg https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-a-generated-image-dall-e-2-preview
response = sync_handler.post(
url=api_base,
data=json.dumps(data),
headers=headers,
)
if "operation-location" in response.headers:
operation_location_url = response.headers["operation-location"]
else:
raise AzureOpenAIError(status_code=500, message=response.text)
response = sync_handler.get(
url=operation_location_url,
headers=headers,
)
response.read()
timeout_secs: int = AZURE_OPERATION_POLLING_TIMEOUT
start_time = time.time()
if "status" not in response.json():
raise Exception(
"Expected 'status' in response. Got={}".format(response.json())
)
while response.json()["status"] not in ["succeeded", "failed"]:
if time.time() - start_time > timeout_secs:
raise AzureOpenAIError(
status_code=408, message="Operation polling timed out."
)
time.sleep(int(response.headers.get("retry-after") or 10))
response = sync_handler.get(
url=operation_location_url,
headers=headers,
)
response.read()
if response.json()["status"] == "failed":
error_data = response.json()
raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
result = response.json()["result"]
return httpx.Response(
status_code=200,
headers=response.headers,
content=json.dumps(result).encode("utf-8"),
request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
)
return sync_handler.post(
url=api_base,
json=data,
headers=headers,
)
def create_azure_base_url(
self, azure_client_params: dict, model: Optional[str]
) -> str:
api_base: str = azure_client_params.get(
"azure_endpoint", ""
) # "https://example-endpoint.openai.azure.com"
if api_base.endswith("/"):
api_base = api_base.rstrip("/")
api_version: str = azure_client_params.get("api_version", "")
if model is None:
model = ""
if "/openai/deployments/" in api_base:
base_url_with_deployment = api_base
else:
base_url_with_deployment = api_base + "/openai/deployments/" + model
base_url_with_deployment += "/images/generations"
base_url_with_deployment += "?api-version=" + api_version
return base_url_with_deployment
async def aimage_generation(
self,
data: dict,
model_response: ModelResponse,
azure_client_params: dict,
api_key: str,
input: list,
logging_obj: LiteLLMLoggingObj,
headers: dict,
client=None,
timeout=None,
) -> litellm.ImageResponse:
response: Optional[dict] = None
try:
# response = await azure_client.images.generate(**data, timeout=timeout)
api_base: str = azure_client_params.get(
"api_base", ""
) # "https://example-endpoint.openai.azure.com"
if api_base.endswith("/"):
api_base = api_base.rstrip("/")
api_version: str = azure_client_params.get("api_version", "")
img_gen_api_base = self.create_azure_base_url(
azure_client_params=azure_client_params, model=data.get("model", "")
)
## LOGGING
logging_obj.pre_call(
input=data["prompt"],
api_key=api_key,
additional_args={
"complete_input_dict": data,
"api_base": img_gen_api_base,
"headers": headers,
},
)
httpx_response: httpx.Response = await self.make_async_azure_httpx_request(
client=None,
timeout=timeout,
api_base=img_gen_api_base,
api_version=api_version,
api_key=api_key,
data=data,
headers=headers,
)
response = httpx_response.json()
stringified_response = response
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=stringified_response,
)
return convert_to_model_response_object( # type: ignore
response_object=stringified_response,
model_response_object=model_response,
response_type="image_generation",
)
except Exception as e:
## LOGGING
logging_obj.post_call(
input=input,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=str(e),
)
raise e
def image_generation(
self,
prompt: str,
timeout: float,
optional_params: dict,
logging_obj: LiteLLMLoggingObj,
headers: dict,
model: Optional[str] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
model_response: Optional[ImageResponse] = None,
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
client=None,
aimg_generation=None,
litellm_params: Optional[dict] = None,
) -> ImageResponse:
try:
if model and len(model) > 0:
model = model
else:
model = None
## BASE MODEL CHECK
if (
model_response is not None
and optional_params.get("base_model", None) is not None
):
model_response._hidden_params["model"] = optional_params.pop(
"base_model"
)
data = {"model": model, "prompt": prompt, **optional_params}
max_retries = data.pop("max_retries", 2)
if not isinstance(max_retries, int):
raise AzureOpenAIError(
status_code=422, message="max retries must be an int"
)
# init AzureOpenAI Client
azure_client_params: Dict[str, Any] = self.initialize_azure_sdk_client(
litellm_params=litellm_params or {},
api_key=api_key,
model_name=model or "",
api_version=api_version,
api_base=api_base,
is_async=False,
)
if aimg_generation is True:
return self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout, headers=headers) # type: ignore
img_gen_api_base = self.create_azure_base_url(
azure_client_params=azure_client_params, model=data.get("model", "")
)
## LOGGING
logging_obj.pre_call(
input=data["prompt"],
api_key=api_key,
additional_args={
"complete_input_dict": data,
"api_base": img_gen_api_base,
"headers": headers,
},
)
httpx_response: httpx.Response = self.make_sync_azure_httpx_request(
client=None,
timeout=timeout,
api_base=img_gen_api_base,
api_version=api_version or "",
api_key=api_key or "",
data=data,
headers=headers,
)
response = httpx_response.json()
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
additional_args={"complete_input_dict": data},
original_response=response,
)
# return response
return convert_to_model_response_object(response_object=response, model_response_object=model_response, response_type="image_generation") # type: ignore
except AzureOpenAIError as e:
raise e
except Exception as e:
error_code = getattr(e, "status_code", None)
if error_code is not None:
raise AzureOpenAIError(status_code=error_code, message=str(e))
else:
raise AzureOpenAIError(status_code=500, message=str(e))
def audio_speech(
self,
model: str,
input: str,
voice: str,
optional_params: dict,
api_key: Optional[str],
api_base: Optional[str],
api_version: Optional[str],
organization: Optional[str],
max_retries: int,
timeout: Union[float, httpx.Timeout],
azure_ad_token: Optional[str] = None,
azure_ad_token_provider: Optional[Callable] = None,
aspeech: Optional[bool] = None,
client=None,
litellm_params: Optional[dict] = None,
) -> HttpxBinaryResponseContent:
max_retries = optional_params.pop("max_retries", 2)
if aspeech is not None and aspeech is True:
return self.async_audio_speech(
model=model,
input=input,
voice=voice,
optional_params=optional_params,
api_key=api_key,
api_base=api_base,
api_version=api_version,
azure_ad_token=azure_ad_token,
azure_ad_token_provider=azure_ad_token_provider,
max_retries=max_retries,
timeout=timeout,
client=client,
litellm_params=litellm_params,
) # type: ignore
azure_client: AzureOpenAI = self.get_azure_openai_client(
api_base=api_base,
api_version=api_version,
api_key=api_key,
model=model,
_is_async=False,
client=client,
litellm_params=litellm_params,
) # type: ignore
response = azure_client.audio.speech.create(
model=model,
voice=voice, # type: ignore
input=input,
**optional_params,
)
return HttpxBinaryResponseContent(response=response.response)
async def async_audio_speech(
self,
model: str,
input: str,
voice: str,
optional_params: dict,
api_key: Optional[str],
api_base: Optional[str],
api_version: Optional[str],
azure_ad_token: Optional[str],
azure_ad_token_provider: Optional[Callable],
max_retries: int,
timeout: Union[float, httpx.Timeout],
client=None,
litellm_params: Optional[dict] = None,
) -> HttpxBinaryResponseContent:
azure_client: AsyncAzureOpenAI = self.get_azure_openai_client(
api_base=api_base,
api_version=api_version,
api_key=api_key,
model=model,
_is_async=True,
client=client,
litellm_params=litellm_params,
) # type: ignore
azure_response = await azure_client.audio.speech.create(
model=model,
voice=voice, # type: ignore
input=input,
**optional_params,
)
return HttpxBinaryResponseContent(response=azure_response.response)
def get_headers(
self,
model: Optional[str],
api_key: str,
api_base: str,
api_version: str,
timeout: float,
mode: str,
messages: Optional[list] = None,
input: Optional[list] = None,
prompt: Optional[str] = None,
) -> dict:
client_session = litellm.client_session or httpx.Client()
if "gateway.ai.cloudflare.com" in api_base:
## build base url - assume api base includes resource name
if not api_base.endswith("/"):
api_base += "/"
api_base += f"{model}"
client = AzureOpenAI(
base_url=api_base,
api_version=api_version,
api_key=api_key,
timeout=timeout,
http_client=client_session,
)
model = None
# cloudflare ai gateway, needs model=None
else:
client = AzureOpenAI(
api_version=api_version,
azure_endpoint=api_base,
api_key=api_key,
timeout=timeout,
http_client=client_session,
)
# only run this check if it's not cloudflare ai gateway
if model is None and mode != "image_generation":
raise Exception("model is not set")
completion = None
if messages is None:
messages = [{"role": "user", "content": "Hey"}]
try:
completion = client.chat.completions.with_raw_response.create(
model=model, # type: ignore
messages=messages, # type: ignore
)
except Exception as e:
raise e
response = {}
if completion is None or not hasattr(completion, "headers"):
raise Exception("invalid completion response")
if (
completion.headers.get("x-ratelimit-remaining-requests", None) is not None
): # not provided for dall-e requests
response["x-ratelimit-remaining-requests"] = completion.headers[
"x-ratelimit-remaining-requests"
]
if completion.headers.get("x-ratelimit-remaining-tokens", None) is not None:
response["x-ratelimit-remaining-tokens"] = completion.headers[
"x-ratelimit-remaining-tokens"
]
if completion.headers.get("x-ms-region", None) is not None:
response["x-ms-region"] = completion.headers["x-ms-region"]
return response