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from typing import Any, Callable, Optional
from openai import AsyncAzureOpenAI, AzureOpenAI
import litellm
from litellm.litellm_core_utils.prompt_templates.factory import prompt_factory
from litellm.utils import CustomStreamWrapper, ModelResponse, TextCompletionResponse
from ...base import BaseLLM
from ...openai.completion.transformation import OpenAITextCompletionConfig
from ..common_utils import AzureOpenAIError
openai_text_completion_config = OpenAITextCompletionConfig()
def select_azure_base_url_or_endpoint(azure_client_params: dict):
azure_endpoint = azure_client_params.get("azure_endpoint", None)
if azure_endpoint is not None:
# see : https://github.com/openai/openai-python/blob/3d61ed42aba652b547029095a7eb269ad4e1e957/src/openai/lib/azure.py#L192
if "/openai/deployments" in azure_endpoint:
# this is base_url, not an azure_endpoint
azure_client_params["base_url"] = azure_endpoint
azure_client_params.pop("azure_endpoint")
return azure_client_params
class AzureTextCompletion(BaseLLM):
def __init__(self) -> None:
super().__init__()
def validate_environment(self, api_key, azure_ad_token):
headers = {
"content-type": "application/json",
}
if api_key is not None:
headers["api-key"] = api_key
elif azure_ad_token is not None:
headers["Authorization"] = f"Bearer {azure_ad_token}"
return headers
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: Optional[Callable],
print_verbose: Callable,
timeout,
logging_obj,
optional_params,
litellm_params,
logger_fn,
acompletion: bool = False,
headers: Optional[dict] = None,
client=None,
):
super().completion()
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", 2)
prompt = prompt_factory(
messages=messages, model=model, custom_llm_provider="azure_text"
)
### CHECK IF CLOUDFLARE AI GATEWAY ###
### if so - set the model as part of the base url
if "gateway.ai.cloudflare.com" in api_base:
## build base url - assume api base includes resource name
if client is None:
if not api_base.endswith("/"):
api_base += "/"
api_base += f"{model}"
azure_client_params = {
"api_version": api_version,
"base_url": f"{api_base}",
"http_client": litellm.client_session,
"max_retries": max_retries,
"timeout": timeout,
}
if api_key is not None:
azure_client_params["api_key"] = api_key
elif azure_ad_token is not None:
azure_client_params["azure_ad_token"] = azure_ad_token
if acompletion is True:
client = AsyncAzureOpenAI(**azure_client_params)
else:
client = AzureOpenAI(**azure_client_params)
data = {"model": None, "prompt": prompt, **optional_params}
else:
data = {
"model": model, # type: ignore
"prompt": prompt,
**optional_params,
}
if acompletion is True:
if optional_params.get("stream", False):
return self.async_streaming(
logging_obj=logging_obj,
api_base=api_base,
data=data,
model=model,
api_key=api_key,
api_version=api_version,
azure_ad_token=azure_ad_token,
timeout=timeout,
client=client,
)
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,
timeout=timeout,
client=client,
logging_obj=logging_obj,
)
elif "stream" in optional_params and optional_params["stream"] is True:
return self.streaming(
logging_obj=logging_obj,
api_base=api_base,
data=data,
model=model,
api_key=api_key,
api_version=api_version,
azure_ad_token=azure_ad_token,
timeout=timeout,
client=client,
)
else:
## LOGGING
logging_obj.pre_call(
input=prompt,
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_params = {
"api_version": api_version,
"azure_endpoint": api_base,
"azure_deployment": model,
"http_client": litellm.client_session,
"max_retries": max_retries,
"timeout": timeout,
"azure_ad_token_provider": azure_ad_token_provider,
}
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:
azure_client_params["azure_ad_token"] = azure_ad_token
if client is None:
azure_client = AzureOpenAI(**azure_client_params)
else:
azure_client = client
if api_version is not None and isinstance(
azure_client._custom_query, dict
):
# set api_version to version passed by user
azure_client._custom_query.setdefault(
"api-version", api_version
)
raw_response = azure_client.completions.with_raw_response.create(
**data, timeout=timeout
)
response = raw_response.parse()
stringified_response = response.model_dump()
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
original_response=stringified_response,
additional_args={
"headers": headers,
"api_version": api_version,
"api_base": api_base,
},
)
return (
openai_text_completion_config.convert_to_chat_model_response_object(
response_object=TextCompletionResponse(**stringified_response),
model_response_object=model_response,
)
)
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 acompletion(
self,
api_key: str,
api_version: str,
model: str,
api_base: str,
data: dict,
timeout: Any,
model_response: ModelResponse,
logging_obj: Any,
azure_ad_token: Optional[str] = None,
client=None, # this is the AsyncAzureOpenAI
):
response = None
try:
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 = {
"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:
azure_client_params["azure_ad_token"] = azure_ad_token
# setting Azure client
if client is None:
azure_client = AsyncAzureOpenAI(**azure_client_params)
else:
azure_client = client
if api_version is not None and isinstance(
azure_client._custom_query, dict
):
# set api_version to version passed by user
azure_client._custom_query.setdefault("api-version", api_version)
## LOGGING
logging_obj.pre_call(
input=data["prompt"],
api_key=azure_client.api_key,
additional_args={
"headers": {"Authorization": f"Bearer {azure_client.api_key}"},
"api_base": azure_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
raw_response = await azure_client.completions.with_raw_response.create(
**data, timeout=timeout
)
response = raw_response.parse()
return openai_text_completion_config.convert_to_chat_model_response_object(
response_object=response.model_dump(),
model_response_object=model_response,
)
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
)
def streaming(
self,
logging_obj,
api_base: str,
api_key: str,
api_version: str,
data: dict,
model: str,
timeout: Any,
azure_ad_token: Optional[str] = None,
client=None,
):
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 = {
"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:
azure_client_params["azure_ad_token"] = azure_ad_token
if client is None:
azure_client = AzureOpenAI(**azure_client_params)
else:
azure_client = client
if api_version is not None and isinstance(azure_client._custom_query, dict):
# set api_version to version passed by user
azure_client._custom_query.setdefault("api-version", api_version)
## LOGGING
logging_obj.pre_call(
input=data["prompt"],
api_key=azure_client.api_key,
additional_args={
"headers": {"Authorization": f"Bearer {azure_client.api_key}"},
"api_base": azure_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
raw_response = azure_client.completions.with_raw_response.create(
**data, timeout=timeout
)
response = raw_response.parse()
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="azure_text",
logging_obj=logging_obj,
)
return streamwrapper
async def async_streaming(
self,
logging_obj,
api_base: str,
api_key: str,
api_version: str,
data: dict,
model: str,
timeout: Any,
azure_ad_token: Optional[str] = None,
client=None,
):
try:
# init AzureOpenAI Client
azure_client_params = {
"api_version": api_version,
"azure_endpoint": api_base,
"azure_deployment": model,
"http_client": litellm.client_session,
"max_retries": data.pop("max_retries", 2),
"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:
azure_client_params["azure_ad_token"] = azure_ad_token
if client is None:
azure_client = AsyncAzureOpenAI(**azure_client_params)
else:
azure_client = client
if api_version is not None and isinstance(
azure_client._custom_query, dict
):
# set api_version to version passed by user
azure_client._custom_query.setdefault("api-version", api_version)
## LOGGING
logging_obj.pre_call(
input=data["prompt"],
api_key=azure_client.api_key,
additional_args={
"headers": {"Authorization": f"Bearer {azure_client.api_key}"},
"api_base": azure_client._base_url._uri_reference,
"acompletion": True,
"complete_input_dict": data,
},
)
raw_response = await azure_client.completions.with_raw_response.create(
**data, timeout=timeout
)
response = raw_response.parse()
# return response
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="azure_text",
logging_obj=logging_obj,
)
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)
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
)
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