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from typing import Optional, Union, Any
import types, requests
from .base import BaseLLM
from litellm.utils import (
ModelResponse,
Choices,
Message,
CustomStreamWrapper,
convert_to_model_response_object,
)
from typing import Callable, Optional
from litellm import OpenAIConfig
import litellm, json
import httpx
from .custom_httpx.azure_dall_e_2 import CustomHTTPTransport, AsyncCustomHTTPTransport
from openai import AzureOpenAI, AsyncAzureOpenAI
class AzureOpenAIError(Exception):
def __init__(
self,
status_code,
message,
request: Optional[httpx.Request] = None,
response: Optional[httpx.Response] = None,
):
self.status_code = status_code
self.message = message
if request:
self.request = request
else:
self.request = httpx.Request(method="POST", url="https://api.openai.com/v1")
if response:
self.response = response
else:
self.response = httpx.Response(
status_code=status_code, request=self.request
)
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
class AzureOpenAIConfig(OpenAIConfig):
"""
Reference: https://platform.openai.com/docs/api-reference/chat/create
The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters::
- `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.
- `function_call` (string or object): This optional parameter controls how the model calls functions.
- `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs.
- `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
- `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion.
- `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message.
- `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics.
- `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.
- `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2.
- `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
"""
def __init__(
self,
frequency_penalty: Optional[int] = None,
function_call: Optional[Union[str, dict]] = None,
functions: Optional[list] = None,
logit_bias: Optional[dict] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[int] = None,
stop: Optional[Union[str, list]] = None,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
) -> None:
super().__init__(
frequency_penalty,
function_call,
functions,
logit_bias,
max_tokens,
n,
presence_penalty,
stop,
temperature,
top_p,
)
class AzureChatCompletion(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(
self,
model: str,
messages: list,
model_response: ModelResponse,
api_key: str,
api_base: str,
api_version: str,
api_type: str,
azure_ad_token: str,
print_verbose: Callable,
timeout,
logging_obj,
optional_params,
litellm_params,
logger_fn,
acompletion: bool = False,
headers: Optional[dict] = None,
client=None,
):
super().completion()
exception_mapping_worked = False
try:
if model is None or messages is None:
raise AzureOpenAIError(
status_code=422, message=f"Missing model or messages"
)
max_retries = optional_params.pop("max_retries", 2)
### 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, "messages": messages, **optional_params}
else:
data = {
"model": model, # type: ignore
"messages": messages,
**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"] == 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=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_params = {
"api_version": api_version,
"azure_endpoint": api_base,
"azure_deployment": model,
"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 client is None:
azure_client = AzureOpenAI(**azure_client_params)
else:
azure_client = client
response = azure_client.chat.completions.create(**data, timeout=timeout) # type: ignore
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,
)
except AzureOpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise AzureOpenAIError(status_code=e.status_code, message=str(e))
else:
raise AzureOpenAIError(status_code=500, message=str(e))
async def acompletion(
self,
api_key: str,
api_version: str,
model: str,
api_base: str,
data: dict,
timeout: Any,
model_response: ModelResponse,
azure_ad_token: Optional[str] = None,
client=None, # this is the AsyncAzureOpenAI
logging_obj=None,
):
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,
}
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
## LOGGING
logging_obj.pre_call(
input=data["messages"],
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,
},
)
response = await azure_client.chat.completions.create(
**data, timeout=timeout
)
return convert_to_model_response_object(
response_object=response.model_dump(),
model_response_object=model_response,
)
except AzureOpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise e
else:
raise AzureOpenAIError(status_code=500, message=str(e))
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,
}
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
## LOGGING
logging_obj.pre_call(
input=data["messages"],
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,
},
)
response = azure_client.chat.completions.create(**data, timeout=timeout)
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="azure",
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,
}
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
## LOGGING
logging_obj.pre_call(
input=data["messages"],
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,
},
)
response = await azure_client.chat.completions.create(
**data, timeout=timeout
)
# return response
streamwrapper = CustomStreamWrapper(
completion_stream=response,
model=model,
custom_llm_provider="azure",
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:
if hasattr(e, "status_code"):
raise AzureOpenAIError(status_code=e.status_code, message=str(e))
else:
raise AzureOpenAIError(status_code=500, message=str(e))
async def aembedding(
self,
data: dict,
model_response: ModelResponse,
azure_client_params: dict,
api_key: str,
input: list,
client=None,
logging_obj=None,
timeout=None,
):
response = None
try:
if client is None:
openai_aclient = AsyncAzureOpenAI(**azure_client_params)
else:
openai_aclient = client
response = await openai_aclient.embeddings.create(**data, timeout=timeout)
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,
)
return convert_to_model_response_object(
response_object=stringified_response,
model_response_object=model_response,
response_type="embedding",
)
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_key: str,
api_base: str,
api_version: str,
timeout: float,
logging_obj=None,
model_response=None,
optional_params=None,
azure_ad_token: Optional[str] = None,
client=None,
aembedding=None,
):
super().embedding()
exception_mapping_worked = False
if self._client_session is None:
self._client_session = self.create_client_session()
try:
data = {"model": model, "input": input, **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 = {
"api_version": api_version,
"azure_endpoint": api_base,
"azure_deployment": model,
"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
## 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 == True:
response = self.aembedding(
data=data,
input=input,
logging_obj=logging_obj,
api_key=api_key,
model_response=model_response,
azure_client_params=azure_client_params,
timeout=timeout,
)
return response
if client is None:
azure_client = AzureOpenAI(**azure_client_params) # type: ignore
else:
azure_client = client
## COMPLETION CALL
response = azure_client.embeddings.create(**data, timeout=timeout) # type: ignore
## 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") # type: ignore
except AzureOpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise AzureOpenAIError(status_code=e.status_code, message=str(e))
else:
raise AzureOpenAIError(status_code=500, message=str(e))
async def aimage_generation(
self,
data: dict,
model_response: ModelResponse,
azure_client_params: dict,
api_key: str,
input: list,
client=None,
logging_obj=None,
timeout=None,
):
response = None
try:
if client is None:
client_session = litellm.aclient_session or httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(),
)
openai_aclient = AsyncAzureOpenAI(
http_client=client_session, **azure_client_params
)
else:
openai_aclient = client
response = await openai_aclient.images.generate(**data, timeout=timeout)
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,
)
return convert_to_model_response_object(
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,
model: Optional[str] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
api_version: Optional[str] = None,
model_response: Optional[litellm.utils.ImageResponse] = None,
azure_ad_token: Optional[str] = None,
logging_obj=None,
optional_params=None,
client=None,
aimg_generation=None,
):
exception_mapping_worked = False
try:
if model and len(model) > 0:
model = model
else:
model = None
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 = {
"api_version": api_version,
"azure_endpoint": api_base,
"azure_deployment": model,
"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 aimg_generation == True:
response = 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) # type: ignore
return response
if client is None:
client_session = litellm.client_session or httpx.Client(
transport=CustomHTTPTransport(),
)
azure_client = AzureOpenAI(http_client=client_session, **azure_client_params) # type: ignore
else:
azure_client = client
## LOGGING
logging_obj.pre_call(
input=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": False,
"complete_input_dict": data,
},
)
## COMPLETION CALL
response = azure_client.images.generate(**data, timeout=timeout) # type: ignore
## LOGGING
logging_obj.post_call(
input=input,
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_dump(), model_response_object=model_response, response_type="image_generation") # type: ignore
except AzureOpenAIError as e:
exception_mapping_worked = True
raise e
except Exception as e:
if hasattr(e, "status_code"):
raise AzureOpenAIError(status_code=e.status_code, message=str(e))
else:
raise AzureOpenAIError(status_code=500, message=str(e))
async def ahealth_check(
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,
):
client_session = litellm.aclient_session or httpx.AsyncClient(
transport=AsyncCustomHTTPTransport(), # handle dall-e-2 calls
)
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 = AsyncAzureOpenAI(
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 = AsyncAzureOpenAI(
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 mode == "completion":
completion = await client.completions.with_raw_response.create(
model=model, # type: ignore
prompt=prompt, # type: ignore
)
elif mode == "chat":
if messages is None:
raise Exception("messages is not set")
completion = await client.chat.completions.with_raw_response.create(
model=model, # type: ignore
messages=messages, # type: ignore
)
elif mode == "embedding":
if input is None:
raise Exception("input is not set")
completion = await client.embeddings.with_raw_response.create(
model=model, # type: ignore
input=input, # type: ignore
)
elif mode == "image_generation":
if prompt is None:
raise Exception("prompt is not set")
completion = await client.images.with_raw_response.generate(
model=model, # type: ignore
prompt=prompt, # type: ignore
)
else:
raise Exception("mode not set")
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"
]
return response
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