from typing import Optional, Union 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 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 if 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, logging_obj, optional_params, litellm_params, logger_fn, acompletion: bool = False, headers: Optional[dict]=None): super().completion() if self._client_session is None: self._client_session = self.create_client_session() exception_mapping_worked = False try: if model is None or messages is None: raise AzureOpenAIError(status_code=422, message=f"Missing model or messages") data = { "model": model, "messages": messages, **optional_params } ## LOGGING logging_obj.pre_call( input=messages, api_key=api_key, additional_args={ "headers": headers, "api_version": api_version, "api_base": api_base, "complete_input_dict": data, }, ) 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) 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) 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) else: azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) response = azure_client.chat.completions.create(**data) # type: ignore return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) except AzureOpenAIError as e: exception_mapping_worked = True raise e except Exception as e: raise e async def acompletion(self, api_key: str, api_version: str, model: str, api_base: str, data: dict, model_response: ModelResponse, azure_ad_token: Optional[str]=None, ): try: azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) response = await azure_client.chat.completions.create(**data) return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response) except Exception as e: if isinstance(e,httpx.TimeoutException): raise AzureOpenAIError(status_code=500, message="Request Timeout Error") elif response and hasattr(response, "text"): raise AzureOpenAIError(status_code=500, message=f"{str(e)}\n\nOriginal Response: {response.text}") else: raise AzureOpenAIError(status_code=500, message=f"{str(e)}") def streaming(self, logging_obj, api_base: str, api_key: str, api_version: str, data: dict, model: str, azure_ad_token: Optional[str]=None, ): azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) response = azure_client.chat.completions.create(**data) streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj) for transformed_chunk in streamwrapper: yield transformed_chunk async def async_streaming(self, logging_obj, api_base: str, api_key: str, api_version: str, data: dict, model: str, azure_ad_token: Optional[str]=None): azure_client = AsyncAzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) response = await azure_client.chat.completions.create(**data) streamwrapper = CustomStreamWrapper(completion_stream=response, model=model, custom_llm_provider="azure",logging_obj=logging_obj) async for transformed_chunk in streamwrapper: yield transformed_chunk def embedding(self, model: str, input: list, api_key: str, api_base: str, api_version: str, logging_obj=None, model_response=None, optional_params=None, azure_ad_token: Optional[str]=None): super().embedding() exception_mapping_worked = False if self._client_session is None: self._client_session = self.create_client_session() try: azure_client = AzureOpenAI(api_key=api_key, api_version=api_version, azure_endpoint=api_base, azure_deployment=model, azure_ad_token=azure_ad_token) data = { "model": model, "input": input, **optional_params } ## LOGGING logging_obj.pre_call( input=input, api_key=api_key, additional_args={"complete_input_dict": data}, ) ## COMPLETION CALL response = azure_client.embeddings.create(**data) # type: ignore ## LOGGING logging_obj.post_call( input=input, api_key=api_key, additional_args={"complete_input_dict": data}, original_response=response, ) embedding_response = json.loads(response.model_dump_json()) output_data = [] for idx, embedding in enumerate(embedding_response["data"]): output_data.append( { "object": embedding["object"], "index": embedding["index"], "embedding": embedding["embedding"] } ) model_response["object"] = "list" model_response["data"] = output_data model_response["model"] = model model_response["usage"] = embedding_response["usage"] return model_response except AzureOpenAIError as e: exception_mapping_worked = True raise e except Exception as e: if exception_mapping_worked: raise e else: import traceback raise AzureOpenAIError(status_code=500, message=traceback.format_exc())