two version of R2R are hereHEADmaster

16 files changed, 5444 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/gpt_audio_transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/gpt_audio_transformation.py
new file mode 100644
index 00000000..581ffea2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/gpt_audio_transformation.py
@@ -0,0 +1,48 @@
+"""
+Support for GPT-4o audio Family
+
+OpenAI Doc: https://platform.openai.com/docs/guides/audio/quickstart?audio-generation-quickstart-example=audio-in&lang=python
+"""
+
+import litellm
+
+from .gpt_transformation import OpenAIGPTConfig
+
+
+class OpenAIGPTAudioConfig(OpenAIGPTConfig):
+    """
+    Reference: https://platform.openai.com/docs/guides/audio
+    """
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get the supported OpenAI params for the `gpt-audio` models
+
+        """
+
+        all_openai_params = super().get_supported_openai_params(model=model)
+        audio_specific_params = ["audio"]
+        return all_openai_params + audio_specific_params
+
+    def is_model_gpt_audio_model(self, model: str) -> bool:
+        if model in litellm.open_ai_chat_completion_models and "audio" in model:
+            return True
+        return False
+
+    def _map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return super()._map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/gpt_transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/gpt_transformation.py
new file mode 100644
index 00000000..c765f979
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/gpt_transformation.py
@@ -0,0 +1,405 @@
+"""
+Support for gpt model family 
+"""
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterator,
+    Iterator,
+    List,
+    Optional,
+    Union,
+    cast,
+)
+
+import httpx
+
+import litellm
+from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.llms.base_llm.base_utils import BaseLLMModelInfo
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    ChatCompletionImageObject,
+    ChatCompletionImageUrlObject,
+)
+from litellm.types.utils import ModelResponse, ModelResponseStream
+from litellm.utils import convert_to_model_response_object
+
+from ..common_utils import OpenAIError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class OpenAIGPTConfig(BaseLLMModelInfo, BaseConfig):
+    """
+    Reference: https://platform.openai.com/docs/api-reference/chat/create
+
+    The class `OpenAIConfig` provides configuration for the OpenAI's Chat API interface. 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.
+    """
+
+    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
+    response_format: Optional[dict] = None
+
+    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,
+        response_format: Optional[dict] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str) -> list:
+        base_params = [
+            "frequency_penalty",
+            "logit_bias",
+            "logprobs",
+            "top_logprobs",
+            "max_tokens",
+            "max_completion_tokens",
+            "modalities",
+            "prediction",
+            "n",
+            "presence_penalty",
+            "seed",
+            "stop",
+            "stream",
+            "stream_options",
+            "temperature",
+            "top_p",
+            "tools",
+            "tool_choice",
+            "function_call",
+            "functions",
+            "max_retries",
+            "extra_headers",
+            "parallel_tool_calls",
+            "audio",
+        ]  # works across all models
+
+        model_specific_params = []
+        if (
+            model != "gpt-3.5-turbo-16k" and model != "gpt-4"
+        ):  # gpt-4 does not support 'response_format'
+            model_specific_params.append("response_format")
+
+        if (
+            model in litellm.open_ai_chat_completion_models
+        ) or model in litellm.open_ai_text_completion_models:
+            model_specific_params.append(
+                "user"
+            )  # user is not a param supported by all openai-compatible endpoints - e.g. azure ai
+        return base_params + model_specific_params
+
+    def _map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """
+        If any supported_openai_params are in non_default_params, add them to optional_params, so they are use in API call
+
+        Args:
+            non_default_params (dict): Non-default parameters to filter.
+            optional_params (dict): Optional parameters to update.
+            model (str): Model name for parameter support check.
+
+        Returns:
+            dict: Updated optional_params with supported non-default parameters.
+        """
+        supported_openai_params = self.get_supported_openai_params(model)
+        for param, value in non_default_params.items():
+            if param in supported_openai_params:
+                optional_params[param] = value
+        return optional_params
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        return self._map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        """OpenAI no longer supports image_url as a string, so we need to convert it to a dict"""
+        for message in messages:
+            message_content = message.get("content")
+            if message_content and isinstance(message_content, list):
+                for content_item in message_content:
+                    if content_item.get("type") == "image_url":
+                        content_item = cast(ChatCompletionImageObject, content_item)
+                        if isinstance(content_item["image_url"], str):
+                            content_item["image_url"] = {
+                                "url": content_item["image_url"],
+                            }
+                        elif isinstance(content_item["image_url"], dict):
+                            litellm_specific_params = {"format"}
+                            new_image_url_obj = ChatCompletionImageUrlObject(
+                                **{  # type: ignore
+                                    k: v
+                                    for k, v in content_item["image_url"].items()
+                                    if k not in litellm_specific_params
+                                }
+                            )
+                            content_item["image_url"] = new_image_url_obj
+        return messages
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        """
+        Transform the overall request to be sent to the API.
+
+        Returns:
+            dict: The transformed request. Sent as the body of the API call.
+        """
+        messages = self._transform_messages(messages=messages, model=model)
+        return {
+            "model": model,
+            "messages": messages,
+            **optional_params,
+        }
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        """
+        Transform the response from the API.
+
+        Returns:
+            dict: The transformed response.
+        """
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=messages,
+            api_key=api_key,
+            original_response=raw_response.text,
+            additional_args={"complete_input_dict": request_data},
+        )
+
+        ## RESPONSE OBJECT
+        try:
+            completion_response = raw_response.json()
+        except Exception as e:
+            response_headers = getattr(raw_response, "headers", None)
+            raise OpenAIError(
+                message="Unable to get json response - {}, Original Response: {}".format(
+                    str(e), raw_response.text
+                ),
+                status_code=raw_response.status_code,
+                headers=response_headers,
+            )
+        raw_response_headers = dict(raw_response.headers)
+        final_response_obj = convert_to_model_response_object(
+            response_object=completion_response,
+            model_response_object=model_response,
+            hidden_params={"headers": raw_response_headers},
+            _response_headers=raw_response_headers,
+        )
+
+        return cast(ModelResponse, final_response_obj)
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return OpenAIError(
+            status_code=status_code,
+            message=error_message,
+            headers=cast(httpx.Headers, headers),
+        )
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        model: str,
+        optional_params: dict,
+        litellm_params: dict,
+        stream: Optional[bool] = None,
+    ) -> str:
+        """
+        Get the complete URL for the API call.
+
+        Returns:
+            str: The complete URL for the API call.
+        """
+        if api_base is None:
+            api_base = "https://api.openai.com"
+        endpoint = "chat/completions"
+
+        # Remove trailing slash from api_base if present
+        api_base = api_base.rstrip("/")
+
+        # Check if endpoint is already in the api_base
+        if endpoint in api_base:
+            return api_base
+
+        return f"{api_base}/{endpoint}"
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        if api_key is not None:
+            headers["Authorization"] = f"Bearer {api_key}"
+
+        # Ensure Content-Type is set to application/json
+        if "content-type" not in headers and "Content-Type" not in headers:
+            headers["Content-Type"] = "application/json"
+
+        return headers
+
+    def get_models(
+        self, api_key: Optional[str] = None, api_base: Optional[str] = None
+    ) -> List[str]:
+        """
+        Calls OpenAI's `/v1/models` endpoint and returns the list of models.
+        """
+
+        if api_base is None:
+            api_base = "https://api.openai.com"
+        if api_key is None:
+            api_key = get_secret_str("OPENAI_API_KEY")
+
+        response = litellm.module_level_client.get(
+            url=f"{api_base}/v1/models",
+            headers={"Authorization": f"Bearer {api_key}"},
+        )
+
+        if response.status_code != 200:
+            raise Exception(f"Failed to get models: {response.text}")
+
+        models = response.json()["data"]
+        return [model["id"] for model in models]
+
+    @staticmethod
+    def get_api_key(api_key: Optional[str] = None) -> Optional[str]:
+        return (
+            api_key
+            or litellm.api_key
+            or litellm.openai_key
+            or get_secret_str("OPENAI_API_KEY")
+        )
+
+    @staticmethod
+    def get_api_base(api_base: Optional[str] = None) -> Optional[str]:
+        return (
+            api_base
+            or litellm.api_base
+            or get_secret_str("OPENAI_API_BASE")
+            or "https://api.openai.com/v1"
+        )
+
+    @staticmethod
+    def get_base_model(model: str) -> str:
+        return model
+
+    def get_model_response_iterator(
+        self,
+        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+    ) -> Any:
+        return OpenAIChatCompletionStreamingHandler(
+            streaming_response=streaming_response,
+            sync_stream=sync_stream,
+            json_mode=json_mode,
+        )
+
+
+class OpenAIChatCompletionStreamingHandler(BaseModelResponseIterator):
+
+    def chunk_parser(self, chunk: dict) -> ModelResponseStream:
+        try:
+            return ModelResponseStream(
+                id=chunk["id"],
+                object="chat.completion.chunk",
+                created=chunk["created"],
+                model=chunk["model"],
+                choices=chunk["choices"],
+            )
+        except Exception as e:
+            raise e
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/o_series_handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/o_series_handler.py
new file mode 100644
index 00000000..d141498c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/o_series_handler.py
@@ -0,0 +1,3 @@
+"""
+LLM Calling done in `openai/openai.py`
+"""
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/o_series_transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/o_series_transformation.py
new file mode 100644
index 00000000..b2ffda6e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/chat/o_series_transformation.py
@@ -0,0 +1,156 @@
+"""
+Support for o1/o3 model family 
+
+https://platform.openai.com/docs/guides/reasoning
+
+Translations handled by LiteLLM:
+- modalities: image => drop param (if user opts in to dropping param)  
+- role: system ==> translate to role 'user' 
+- streaming => faked by LiteLLM 
+- Tools, response_format =>  drop param (if user opts in to dropping param) 
+- Logprobs => drop param (if user opts in to dropping param) 
+"""
+
+from typing import List, Optional
+
+import litellm
+from litellm import verbose_logger
+from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
+from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage
+from litellm.utils import (
+    supports_function_calling,
+    supports_parallel_function_calling,
+    supports_response_schema,
+    supports_system_messages,
+)
+
+from .gpt_transformation import OpenAIGPTConfig
+
+
+class OpenAIOSeriesConfig(OpenAIGPTConfig):
+    """
+    Reference: https://platform.openai.com/docs/guides/reasoning
+    """
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def translate_developer_role_to_system_role(
+        self, messages: List[AllMessageValues]
+    ) -> List[AllMessageValues]:
+        """
+        O-series models support `developer` role.
+        """
+        return messages
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        Get the supported OpenAI params for the given model
+
+        """
+
+        all_openai_params = super().get_supported_openai_params(model=model)
+        non_supported_params = [
+            "logprobs",
+            "top_p",
+            "presence_penalty",
+            "frequency_penalty",
+            "top_logprobs",
+        ]
+
+        o_series_only_param = ["reasoning_effort"]
+
+        all_openai_params.extend(o_series_only_param)
+
+        try:
+            model, custom_llm_provider, api_base, api_key = get_llm_provider(
+                model=model
+            )
+        except Exception:
+            verbose_logger.debug(
+                f"Unable to infer model provider for model={model}, defaulting to openai for o1 supported param check"
+            )
+            custom_llm_provider = "openai"
+
+        _supports_function_calling = supports_function_calling(
+            model, custom_llm_provider
+        )
+        _supports_response_schema = supports_response_schema(model, custom_llm_provider)
+        _supports_parallel_tool_calls = supports_parallel_function_calling(
+            model, custom_llm_provider
+        )
+
+        if not _supports_function_calling:
+            non_supported_params.append("tools")
+            non_supported_params.append("tool_choice")
+            non_supported_params.append("function_call")
+            non_supported_params.append("functions")
+
+        if not _supports_parallel_tool_calls:
+            non_supported_params.append("parallel_tool_calls")
+
+        if not _supports_response_schema:
+            non_supported_params.append("response_format")
+
+        return [
+            param for param in all_openai_params if param not in non_supported_params
+        ]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ):
+        if "max_tokens" in non_default_params:
+            optional_params["max_completion_tokens"] = non_default_params.pop(
+                "max_tokens"
+            )
+        if "temperature" in non_default_params:
+            temperature_value: Optional[float] = non_default_params.pop("temperature")
+            if temperature_value is not None:
+                if temperature_value == 1:
+                    optional_params["temperature"] = temperature_value
+                else:
+                    ## UNSUPPORTED TOOL CHOICE VALUE
+                    if litellm.drop_params is True or drop_params is True:
+                        pass
+                    else:
+                        raise litellm.utils.UnsupportedParamsError(
+                            message="O-series models don't support temperature={}. Only temperature=1 is supported. To drop unsupported openai params from the call, set `litellm.drop_params = True`".format(
+                                temperature_value
+                            ),
+                            status_code=400,
+                        )
+
+        return super()._map_openai_params(
+            non_default_params, optional_params, model, drop_params
+        )
+
+    def is_model_o_series_model(self, model: str) -> bool:
+        if model in litellm.open_ai_chat_completion_models and (
+            "o1" in model or "o3" in model
+        ):
+            return True
+        return False
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        """
+        Handles limitations of O-1 model family.
+        - modalities: image => drop param (if user opts in to dropping param)
+        - role: system ==> translate to role 'user'
+        """
+        _supports_system_messages = supports_system_messages(model, "openai")
+        for i, message in enumerate(messages):
+            if message["role"] == "system" and not _supports_system_messages:
+                new_message = ChatCompletionUserMessage(
+                    content=message["content"], role="user"
+                )
+                messages[i] = new_message  # Replace the old message with the new one
+
+        messages = super()._transform_messages(messages, model)
+        return messages
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/common_utils.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/common_utils.py
new file mode 100644
index 00000000..55da16d6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/common_utils.py
@@ -0,0 +1,208 @@
+"""
+Common helpers / utils across al OpenAI endpoints
+"""
+
+import hashlib
+import json
+from typing import Any, Dict, List, Literal, Optional, Union
+
+import httpx
+import openai
+from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
+
+import litellm
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.custom_httpx.http_handler import _DEFAULT_TTL_FOR_HTTPX_CLIENTS
+
+
+class OpenAIError(BaseLLMException):
+    def __init__(
+        self,
+        status_code: int,
+        message: str,
+        request: Optional[httpx.Request] = None,
+        response: Optional[httpx.Response] = None,
+        headers: Optional[Union[dict, httpx.Headers]] = None,
+        body: Optional[dict] = None,
+    ):
+        self.status_code = status_code
+        self.message = message
+        self.headers = headers
+        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__(
+            status_code=status_code,
+            message=self.message,
+            headers=self.headers,
+            request=self.request,
+            response=self.response,
+            body=body,
+        )
+
+
+####### Error Handling Utils for OpenAI API #######################
+###################################################################
+def drop_params_from_unprocessable_entity_error(
+    e: Union[openai.UnprocessableEntityError, httpx.HTTPStatusError],
+    data: Dict[str, Any],
+) -> Dict[str, Any]:
+    """
+    Helper function to read OpenAI UnprocessableEntityError and drop the params that raised an error from the error message.
+
+    Args:
+    e (UnprocessableEntityError): The UnprocessableEntityError exception
+    data (Dict[str, Any]): The original data dictionary containing all parameters
+
+    Returns:
+    Dict[str, Any]: A new dictionary with invalid parameters removed
+    """
+    invalid_params: List[str] = []
+    if isinstance(e, httpx.HTTPStatusError):
+        error_json = e.response.json()
+        error_message = error_json.get("error", {})
+        error_body = error_message
+    else:
+        error_body = e.body
+    if (
+        error_body is not None
+        and isinstance(error_body, dict)
+        and error_body.get("message")
+    ):
+        message = error_body.get("message", {})
+        if isinstance(message, str):
+            try:
+                message = json.loads(message)
+            except json.JSONDecodeError:
+                message = {"detail": message}
+        detail = message.get("detail")
+
+        if isinstance(detail, List) and len(detail) > 0 and isinstance(detail[0], dict):
+            for error_dict in detail:
+                if (
+                    error_dict.get("loc")
+                    and isinstance(error_dict.get("loc"), list)
+                    and len(error_dict.get("loc")) == 2
+                ):
+                    invalid_params.append(error_dict["loc"][1])
+
+    new_data = {k: v for k, v in data.items() if k not in invalid_params}
+
+    return new_data
+
+
+class BaseOpenAILLM:
+    """
+    Base class for OpenAI LLMs for getting their httpx clients and SSL verification settings
+    """
+
+    @staticmethod
+    def get_cached_openai_client(
+        client_initialization_params: dict, client_type: Literal["openai", "azure"]
+    ) -> Optional[Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]]:
+        """Retrieves the OpenAI client from the in-memory cache based on the client initialization parameters"""
+        _cache_key = BaseOpenAILLM.get_openai_client_cache_key(
+            client_initialization_params=client_initialization_params,
+            client_type=client_type,
+        )
+        _cached_client = litellm.in_memory_llm_clients_cache.get_cache(_cache_key)
+        return _cached_client
+
+    @staticmethod
+    def set_cached_openai_client(
+        openai_client: Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI],
+        client_type: Literal["openai", "azure"],
+        client_initialization_params: dict,
+    ):
+        """Stores the OpenAI client in the in-memory cache for _DEFAULT_TTL_FOR_HTTPX_CLIENTS SECONDS"""
+        _cache_key = BaseOpenAILLM.get_openai_client_cache_key(
+            client_initialization_params=client_initialization_params,
+            client_type=client_type,
+        )
+        litellm.in_memory_llm_clients_cache.set_cache(
+            key=_cache_key,
+            value=openai_client,
+            ttl=_DEFAULT_TTL_FOR_HTTPX_CLIENTS,
+        )
+
+    @staticmethod
+    def get_openai_client_cache_key(
+        client_initialization_params: dict, client_type: Literal["openai", "azure"]
+    ) -> str:
+        """Creates a cache key for the OpenAI client based on the client initialization parameters"""
+        hashed_api_key = None
+        if client_initialization_params.get("api_key") is not None:
+            hash_object = hashlib.sha256(
+                client_initialization_params.get("api_key", "").encode()
+            )
+            # Hexadecimal representation of the hash
+            hashed_api_key = hash_object.hexdigest()
+
+        # Create a more readable cache key using a list of key-value pairs
+        key_parts = [
+            f"hashed_api_key={hashed_api_key}",
+            f"is_async={client_initialization_params.get('is_async')}",
+        ]
+
+        LITELLM_CLIENT_SPECIFIC_PARAMS = [
+            "timeout",
+            "max_retries",
+            "organization",
+            "api_base",
+        ]
+        openai_client_fields = (
+            BaseOpenAILLM.get_openai_client_initialization_param_fields(
+                client_type=client_type
+            )
+            + LITELLM_CLIENT_SPECIFIC_PARAMS
+        )
+
+        for param in openai_client_fields:
+            key_parts.append(f"{param}={client_initialization_params.get(param)}")
+
+        _cache_key = ",".join(key_parts)
+        return _cache_key
+
+    @staticmethod
+    def get_openai_client_initialization_param_fields(
+        client_type: Literal["openai", "azure"]
+    ) -> List[str]:
+        """Returns a list of fields that are used to initialize the OpenAI client"""
+        import inspect
+
+        from openai import AzureOpenAI, OpenAI
+
+        if client_type == "openai":
+            signature = inspect.signature(OpenAI.__init__)
+        else:
+            signature = inspect.signature(AzureOpenAI.__init__)
+
+        # Extract parameter names, excluding 'self'
+        param_names = [param for param in signature.parameters if param != "self"]
+        return param_names
+
+    @staticmethod
+    def _get_async_http_client() -> Optional[httpx.AsyncClient]:
+        if litellm.aclient_session is not None:
+            return litellm.aclient_session
+
+        return httpx.AsyncClient(
+            limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100),
+            verify=litellm.ssl_verify,
+        )
+
+    @staticmethod
+    def _get_sync_http_client() -> Optional[httpx.Client]:
+        if litellm.client_session is not None:
+            return litellm.client_session
+        return httpx.Client(
+            limits=httpx.Limits(max_connections=1000, max_keepalive_connections=100),
+            verify=litellm.ssl_verify,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/handler.py
new file mode 100644
index 00000000..2e60f55b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/handler.py
@@ -0,0 +1,319 @@
+import json
+from typing import Callable, List, Optional, Union
+
+from openai import AsyncOpenAI, OpenAI
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+from litellm.llms.base import BaseLLM
+from litellm.types.llms.openai import AllMessageValues, OpenAITextCompletionUserMessage
+from litellm.types.utils import LlmProviders, ModelResponse, TextCompletionResponse
+from litellm.utils import ProviderConfigManager
+
+from ..common_utils import OpenAIError
+from .transformation import OpenAITextCompletionConfig
+
+
+class OpenAITextCompletion(BaseLLM):
+    openai_text_completion_global_config = OpenAITextCompletionConfig()
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def validate_environment(self, api_key):
+        headers = {
+            "content-type": "application/json",
+        }
+        if api_key:
+            headers["Authorization"] = f"Bearer {api_key}"
+        return headers
+
+    def completion(
+        self,
+        model_response: ModelResponse,
+        api_key: str,
+        model: str,
+        messages: Union[List[AllMessageValues], List[OpenAITextCompletionUserMessage]],
+        timeout: float,
+        custom_llm_provider: str,
+        logging_obj: LiteLLMLoggingObj,
+        optional_params: dict,
+        print_verbose: Optional[Callable] = None,
+        api_base: Optional[str] = None,
+        acompletion: bool = False,
+        litellm_params=None,
+        logger_fn=None,
+        client=None,
+        organization: Optional[str] = None,
+        headers: Optional[dict] = None,
+    ):
+        try:
+            if headers is None:
+                headers = self.validate_environment(api_key=api_key)
+            if model is None or messages is None:
+                raise OpenAIError(status_code=422, message="Missing model or messages")
+
+            # don't send max retries to the api, if set
+
+            provider_config = ProviderConfigManager.get_provider_text_completion_config(
+                model=model,
+                provider=LlmProviders(custom_llm_provider),
+            )
+
+            data = provider_config.transform_text_completion_request(
+                model=model,
+                messages=messages,
+                optional_params=optional_params,
+                headers=headers,
+            )
+            max_retries = data.pop("max_retries", 2)
+            ## LOGGING
+            logging_obj.pre_call(
+                input=messages,
+                api_key=api_key,
+                additional_args={
+                    "headers": headers,
+                    "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,
+                        api_key=api_key,
+                        data=data,
+                        headers=headers,
+                        model_response=model_response,
+                        model=model,
+                        timeout=timeout,
+                        max_retries=max_retries,
+                        client=client,
+                        organization=organization,
+                    )
+                else:
+                    return self.acompletion(api_base=api_base, data=data, headers=headers, model_response=model_response, api_key=api_key, logging_obj=logging_obj, model=model, timeout=timeout, max_retries=max_retries, organization=organization, client=client)  # type: ignore
+            elif optional_params.get("stream", False):
+                return self.streaming(
+                    logging_obj=logging_obj,
+                    api_base=api_base,
+                    api_key=api_key,
+                    data=data,
+                    headers=headers,
+                    model_response=model_response,
+                    model=model,
+                    timeout=timeout,
+                    max_retries=max_retries,  # type: ignore
+                    client=client,
+                    organization=organization,
+                )
+            else:
+                if client is None:
+                    openai_client = OpenAI(
+                        api_key=api_key,
+                        base_url=api_base,
+                        http_client=litellm.client_session,
+                        timeout=timeout,
+                        max_retries=max_retries,  # type: ignore
+                        organization=organization,
+                    )
+                else:
+                    openai_client = client
+
+                raw_response = openai_client.completions.with_raw_response.create(**data)  # type: ignore
+                response = raw_response.parse()
+                response_json = response.model_dump()
+
+                ## LOGGING
+                logging_obj.post_call(
+                    api_key=api_key,
+                    original_response=response_json,
+                    additional_args={
+                        "headers": headers,
+                        "api_base": api_base,
+                    },
+                )
+
+                ## RESPONSE OBJECT
+                return TextCompletionResponse(**response_json)
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+
+    async def acompletion(
+        self,
+        logging_obj,
+        api_base: str,
+        data: dict,
+        headers: dict,
+        model_response: ModelResponse,
+        api_key: str,
+        model: str,
+        timeout: float,
+        max_retries: int,
+        organization: Optional[str] = None,
+        client=None,
+    ):
+        try:
+            if client is None:
+                openai_aclient = AsyncOpenAI(
+                    api_key=api_key,
+                    base_url=api_base,
+                    http_client=litellm.aclient_session,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                )
+            else:
+                openai_aclient = client
+
+            raw_response = await openai_aclient.completions.with_raw_response.create(
+                **data
+            )
+            response = raw_response.parse()
+            response_json = response.model_dump()
+
+            ## LOGGING
+            logging_obj.post_call(
+                api_key=api_key,
+                original_response=response,
+                additional_args={
+                    "headers": headers,
+                    "api_base": api_base,
+                },
+            )
+            ## RESPONSE OBJECT
+            response_obj = TextCompletionResponse(**response_json)
+            response_obj._hidden_params.original_response = json.dumps(response_json)
+            return response_obj
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+
+    def streaming(
+        self,
+        logging_obj,
+        api_key: str,
+        data: dict,
+        headers: dict,
+        model_response: ModelResponse,
+        model: str,
+        timeout: float,
+        api_base: Optional[str] = None,
+        max_retries=None,
+        client=None,
+        organization=None,
+    ):
+
+        if client is None:
+            openai_client = OpenAI(
+                api_key=api_key,
+                base_url=api_base,
+                http_client=litellm.client_session,
+                timeout=timeout,
+                max_retries=max_retries,  # type: ignore
+                organization=organization,
+            )
+        else:
+            openai_client = client
+
+        try:
+            raw_response = openai_client.completions.with_raw_response.create(**data)
+            response = raw_response.parse()
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=response,
+            model=model,
+            custom_llm_provider="text-completion-openai",
+            logging_obj=logging_obj,
+            stream_options=data.get("stream_options", None),
+        )
+
+        try:
+            for chunk in streamwrapper:
+                yield chunk
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+
+    async def async_streaming(
+        self,
+        logging_obj,
+        api_key: str,
+        data: dict,
+        headers: dict,
+        model_response: ModelResponse,
+        model: str,
+        timeout: float,
+        max_retries: int,
+        api_base: Optional[str] = None,
+        client=None,
+        organization=None,
+    ):
+        if client is None:
+            openai_client = AsyncOpenAI(
+                api_key=api_key,
+                base_url=api_base,
+                http_client=litellm.aclient_session,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+            )
+        else:
+            openai_client = client
+
+        raw_response = await openai_client.completions.with_raw_response.create(**data)
+        response = raw_response.parse()
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=response,
+            model=model,
+            custom_llm_provider="text-completion-openai",
+            logging_obj=logging_obj,
+            stream_options=data.get("stream_options", None),
+        )
+
+        try:
+            async for transformed_chunk in streamwrapper:
+                yield transformed_chunk
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py
new file mode 100644
index 00000000..1aef72d3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/transformation.py
@@ -0,0 +1,158 @@
+"""
+Support for gpt model family 
+"""
+
+from typing import List, Optional, Union
+
+from litellm.llms.base_llm.completion.transformation import BaseTextCompletionConfig
+from litellm.types.llms.openai import AllMessageValues, OpenAITextCompletionUserMessage
+from litellm.types.utils import Choices, Message, ModelResponse, TextCompletionResponse
+
+from ..chat.gpt_transformation import OpenAIGPTConfig
+from .utils import _transform_prompt
+
+
+class OpenAITextCompletionConfig(BaseTextCompletionConfig, OpenAIGPTConfig):
+    """
+    Reference: https://platform.openai.com/docs/api-reference/completions/create
+
+    The class `OpenAITextCompletionConfig` provides configuration for the OpenAI's text completion API interface. Below are the parameters:
+
+    - `best_of` (integer or null): This optional parameter generates server-side completions and returns the one with the highest log probability per token.
+
+    - `echo` (boolean or null): This optional parameter will echo back the prompt in addition to the completion.
+
+    - `frequency_penalty` (number or null): Defaults to 0. It is a numbers from -2.0 to 2.0, where positive values decrease the model's likelihood to repeat the same line.
+
+    - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
+
+    - `logprobs` (integer or null): This optional parameter includes the log probabilities on the most likely tokens as well as the chosen tokens.
+
+    - `max_tokens` (integer or null): This optional parameter sets the maximum number of tokens to generate in the completion.
+
+    - `n` (integer or null): This optional parameter sets how many completions to generate for each prompt.
+
+    - `presence_penalty` (number or null): Defaults to 0 and can be between -2.0 and 2.0. Positive values increase 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.
+
+    - `suffix` (string or null): Defines the suffix that comes after a completion of inserted text.
+
+    - `temperature` (number or null): This optional parameter defines the sampling temperature to use.
+
+    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
+    """
+
+    best_of: Optional[int] = None
+    echo: Optional[bool] = None
+    frequency_penalty: Optional[int] = None
+    logit_bias: Optional[dict] = None
+    logprobs: Optional[int] = None
+    max_tokens: Optional[int] = None
+    n: Optional[int] = None
+    presence_penalty: Optional[int] = None
+    stop: Optional[Union[str, list]] = None
+    suffix: Optional[str] = None
+
+    def __init__(
+        self,
+        best_of: Optional[int] = None,
+        echo: Optional[bool] = None,
+        frequency_penalty: Optional[int] = None,
+        logit_bias: Optional[dict] = None,
+        logprobs: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[int] = None,
+        stop: Optional[Union[str, list]] = None,
+        suffix: Optional[str] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def convert_to_chat_model_response_object(
+        self,
+        response_object: Optional[TextCompletionResponse] = None,
+        model_response_object: Optional[ModelResponse] = None,
+    ):
+        try:
+            ## RESPONSE OBJECT
+            if response_object is None or model_response_object is None:
+                raise ValueError("Error in response object format")
+            choice_list = []
+            for idx, choice in enumerate(response_object["choices"]):
+                message = Message(
+                    content=choice["text"],
+                    role="assistant",
+                )
+                choice = Choices(
+                    finish_reason=choice["finish_reason"],
+                    index=idx,
+                    message=message,
+                    logprobs=choice.get("logprobs", None),
+                )
+                choice_list.append(choice)
+            model_response_object.choices = choice_list
+
+            if "usage" in response_object:
+                setattr(model_response_object, "usage", response_object["usage"])
+
+            if "id" in response_object:
+                model_response_object.id = response_object["id"]
+
+            if "model" in response_object:
+                model_response_object.model = response_object["model"]
+
+            model_response_object._hidden_params["original_response"] = (
+                response_object  # track original response, if users make a litellm.text_completion() request, we can return the original response
+            )
+            return model_response_object
+        except Exception as e:
+            raise e
+
+    def get_supported_openai_params(self, model: str) -> List:
+        return [
+            "functions",
+            "function_call",
+            "temperature",
+            "top_p",
+            "n",
+            "stream",
+            "stream_options",
+            "stop",
+            "max_tokens",
+            "presence_penalty",
+            "frequency_penalty",
+            "logit_bias",
+            "user",
+            "response_format",
+            "seed",
+            "tools",
+            "tool_choice",
+            "max_retries",
+            "logprobs",
+            "top_logprobs",
+            "extra_headers",
+        ]
+
+    def transform_text_completion_request(
+        self,
+        model: str,
+        messages: Union[List[AllMessageValues], List[OpenAITextCompletionUserMessage]],
+        optional_params: dict,
+        headers: dict,
+    ) -> dict:
+        prompt = _transform_prompt(messages)
+        return {
+            "model": model,
+            "prompt": prompt,
+            **optional_params,
+        }
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/utils.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/utils.py
new file mode 100644
index 00000000..8b3efb4c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/completion/utils.py
@@ -0,0 +1,50 @@
+from typing import List, Union, cast
+
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    convert_content_list_to_str,
+)
+from litellm.types.llms.openai import (
+    AllMessageValues,
+    AllPromptValues,
+    OpenAITextCompletionUserMessage,
+)
+
+
+def is_tokens_or_list_of_tokens(value: List):
+    # Check if it's a list of integers (tokens)
+    if isinstance(value, list) and all(isinstance(item, int) for item in value):
+        return True
+    # Check if it's a list of lists of integers (list of tokens)
+    if isinstance(value, list) and all(
+        isinstance(item, list) and all(isinstance(i, int) for i in item)
+        for item in value
+    ):
+        return True
+    return False
+
+
+def _transform_prompt(
+    messages: Union[List[AllMessageValues], List[OpenAITextCompletionUserMessage]],
+) -> AllPromptValues:
+    if len(messages) == 1:  # base case
+        message_content = messages[0].get("content")
+        if (
+            message_content
+            and isinstance(message_content, list)
+            and is_tokens_or_list_of_tokens(message_content)
+        ):
+            openai_prompt: AllPromptValues = cast(AllPromptValues, message_content)
+        else:
+            openai_prompt = ""
+            content = convert_content_list_to_str(cast(AllMessageValues, messages[0]))
+            openai_prompt += content
+    else:
+        prompt_str_list: List[str] = []
+        for m in messages:
+            try:  # expect list of int/list of list of int to be a 1 message array only.
+                content = convert_content_list_to_str(cast(AllMessageValues, m))
+                prompt_str_list.append(content)
+            except Exception as e:
+                raise e
+        openai_prompt = prompt_str_list
+    return openai_prompt
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/cost_calculation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/cost_calculation.py
new file mode 100644
index 00000000..0c26fd74
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/cost_calculation.py
@@ -0,0 +1,120 @@
+"""
+Helper util for handling openai-specific cost calculation
+- e.g.: prompt caching
+"""
+
+from typing import Literal, Optional, Tuple
+
+from litellm._logging import verbose_logger
+from litellm.types.utils import CallTypes, Usage
+from litellm.utils import get_model_info
+
+
+def cost_router(call_type: CallTypes) -> Literal["cost_per_token", "cost_per_second"]:
+    if call_type == CallTypes.atranscription or call_type == CallTypes.transcription:
+        return "cost_per_second"
+    else:
+        return "cost_per_token"
+
+
+def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
+    """
+    Calculates the cost per token for a given model, prompt tokens, and completion tokens.
+
+    Input:
+        - model: str, the model name without provider prefix
+        - usage: LiteLLM Usage block, containing anthropic caching information
+
+    Returns:
+        Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+    """
+    ## GET MODEL INFO
+    model_info = get_model_info(model=model, custom_llm_provider="openai")
+    ## CALCULATE INPUT COST
+    ### Non-cached text tokens
+    non_cached_text_tokens = usage.prompt_tokens
+    cached_tokens: Optional[int] = None
+    if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
+        cached_tokens = usage.prompt_tokens_details.cached_tokens
+        non_cached_text_tokens = non_cached_text_tokens - cached_tokens
+    prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"]
+    ## Prompt Caching cost calculation
+    if model_info.get("cache_read_input_token_cost") is not None and cached_tokens:
+        # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens
+        prompt_cost += cached_tokens * (
+            model_info.get("cache_read_input_token_cost", 0) or 0
+        )
+
+    _audio_tokens: Optional[int] = (
+        usage.prompt_tokens_details.audio_tokens
+        if usage.prompt_tokens_details is not None
+        else None
+    )
+    _audio_cost_per_token: Optional[float] = model_info.get(
+        "input_cost_per_audio_token"
+    )
+    if _audio_tokens is not None and _audio_cost_per_token is not None:
+        audio_cost: float = _audio_tokens * _audio_cost_per_token
+        prompt_cost += audio_cost
+
+    ## CALCULATE OUTPUT COST
+    completion_cost: float = (
+        usage["completion_tokens"] * model_info["output_cost_per_token"]
+    )
+    _output_cost_per_audio_token: Optional[float] = model_info.get(
+        "output_cost_per_audio_token"
+    )
+    _output_audio_tokens: Optional[int] = (
+        usage.completion_tokens_details.audio_tokens
+        if usage.completion_tokens_details is not None
+        else None
+    )
+    if _output_cost_per_audio_token is not None and _output_audio_tokens is not None:
+        audio_cost = _output_audio_tokens * _output_cost_per_audio_token
+        completion_cost += audio_cost
+
+    return prompt_cost, completion_cost
+
+
+def cost_per_second(
+    model: str, custom_llm_provider: Optional[str], duration: float = 0.0
+) -> Tuple[float, float]:
+    """
+    Calculates the cost per second for a given model, prompt tokens, and completion tokens.
+
+    Input:
+        - model: str, the model name without provider prefix
+        - custom_llm_provider: str, the custom llm provider
+        - duration: float, the duration of the response in seconds
+
+    Returns:
+        Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+    """
+    ## GET MODEL INFO
+    model_info = get_model_info(
+        model=model, custom_llm_provider=custom_llm_provider or "openai"
+    )
+    prompt_cost = 0.0
+    completion_cost = 0.0
+    ## Speech / Audio cost calculation
+    if (
+        "output_cost_per_second" in model_info
+        and model_info["output_cost_per_second"] is not None
+    ):
+        verbose_logger.debug(
+            f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; duration: {duration}"
+        )
+        ## COST PER SECOND ##
+        completion_cost = model_info["output_cost_per_second"] * duration
+    elif (
+        "input_cost_per_second" in model_info
+        and model_info["input_cost_per_second"] is not None
+    ):
+        verbose_logger.debug(
+            f"For model={model} - input_cost_per_second: {model_info.get('input_cost_per_second')}; duration: {duration}"
+        )
+        ## COST PER SECOND ##
+        prompt_cost = model_info["input_cost_per_second"] * duration
+        completion_cost = 0.0
+
+    return prompt_cost, completion_cost
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/fine_tuning/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/fine_tuning/handler.py
new file mode 100644
index 00000000..97b237c7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/fine_tuning/handler.py
@@ -0,0 +1,282 @@
+from typing import Any, Coroutine, Optional, Union
+
+import httpx
+from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
+from openai.types.fine_tuning import FineTuningJob
+
+from litellm._logging import verbose_logger
+
+
+class OpenAIFineTuningAPI:
+    """
+    OpenAI methods to support for batches
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+        _is_async: bool = False,
+        api_version: Optional[str] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> Optional[
+        Union[
+            OpenAI,
+            AsyncOpenAI,
+            AzureOpenAI,
+            AsyncAzureOpenAI,
+        ]
+    ]:
+        received_args = locals()
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None
+        if client is None:
+            data = {}
+            for k, v in received_args.items():
+                if k == "self" or k == "client" or k == "_is_async":
+                    pass
+                elif k == "api_base" and v is not None:
+                    data["base_url"] = v
+                elif v is not None:
+                    data[k] = v
+            if _is_async is True:
+                openai_client = AsyncOpenAI(**data)
+            else:
+                openai_client = OpenAI(**data)  # type: ignore
+        else:
+            openai_client = client
+
+        return openai_client
+
+    async def acreate_fine_tuning_job(
+        self,
+        create_fine_tuning_job_data: dict,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> FineTuningJob:
+        response = await openai_client.fine_tuning.jobs.create(
+            **create_fine_tuning_job_data
+        )
+        return response
+
+    def create_fine_tuning_job(
+        self,
+        _is_async: bool,
+        create_fine_tuning_job_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]:
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acreate_fine_tuning_job(  # type: ignore
+                create_fine_tuning_job_data=create_fine_tuning_job_data,
+                openai_client=openai_client,
+            )
+        verbose_logger.debug(
+            "creating fine tuning job, args= %s", create_fine_tuning_job_data
+        )
+        response = openai_client.fine_tuning.jobs.create(**create_fine_tuning_job_data)
+        return response
+
+    async def acancel_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> FineTuningJob:
+        response = await openai_client.fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return response
+
+    def cancel_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ):
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acancel_fine_tuning_job(  # type: ignore
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+        verbose_logger.debug("canceling fine tuning job, args= %s", fine_tuning_job_id)
+        response = openai_client.fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return response
+
+    async def alist_fine_tuning_jobs(
+        self,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        response = await openai_client.fine_tuning.jobs.list(after=after, limit=limit)  # type: ignore
+        return response
+
+    def list_fine_tuning_jobs(
+        self,
+        _is_async: bool,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.alist_fine_tuning_jobs(  # type: ignore
+                after=after,
+                limit=limit,
+                openai_client=openai_client,
+            )
+        verbose_logger.debug("list fine tuning job, after= %s, limit= %s", after, limit)
+        response = openai_client.fine_tuning.jobs.list(after=after, limit=limit)  # type: ignore
+        return response
+
+    async def aretrieve_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> FineTuningJob:
+        response = await openai_client.fine_tuning.jobs.retrieve(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return response
+
+    def retrieve_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ):
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.aretrieve_fine_tuning_job(  # type: ignore
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+        verbose_logger.debug("retrieving fine tuning job, id= %s", fine_tuning_job_id)
+        response = openai_client.fine_tuning.jobs.retrieve(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return response
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/image_variations/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/image_variations/handler.py
new file mode 100644
index 00000000..f738115a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/image_variations/handler.py
@@ -0,0 +1,244 @@
+"""
+OpenAI Image Variations Handler
+"""
+
+from typing import Callable, Optional
+
+import httpx
+from openai import AsyncOpenAI, OpenAI
+
+import litellm
+from litellm.types.utils import FileTypes, ImageResponse, LlmProviders
+from litellm.utils import ProviderConfigManager
+
+from ...base_llm.image_variations.transformation import BaseImageVariationConfig
+from ...custom_httpx.llm_http_handler import LiteLLMLoggingObj
+from ..common_utils import OpenAIError
+
+
+class OpenAIImageVariationsHandler:
+    def get_sync_client(
+        self,
+        client: Optional[OpenAI],
+        init_client_params: dict,
+    ):
+        if client is None:
+            openai_client = OpenAI(
+                **init_client_params,
+            )
+        else:
+            openai_client = client
+        return openai_client
+
+    def get_async_client(
+        self, client: Optional[AsyncOpenAI], init_client_params: dict
+    ) -> AsyncOpenAI:
+        if client is None:
+            openai_client = AsyncOpenAI(
+                **init_client_params,
+            )
+        else:
+            openai_client = client
+        return openai_client
+
+    async def async_image_variations(
+        self,
+        api_key: str,
+        api_base: str,
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        data: dict,
+        headers: dict,
+        model: Optional[str],
+        timeout: float,
+        max_retries: int,
+        logging_obj: LiteLLMLoggingObj,
+        model_response: ImageResponse,
+        optional_params: dict,
+        litellm_params: dict,
+        image: FileTypes,
+        provider_config: BaseImageVariationConfig,
+    ) -> ImageResponse:
+        try:
+            init_client_params = {
+                "api_key": api_key,
+                "base_url": api_base,
+                "http_client": litellm.client_session,
+                "timeout": timeout,
+                "max_retries": max_retries,  # type: ignore
+                "organization": organization,
+            }
+
+            client = self.get_async_client(
+                client=client, init_client_params=init_client_params
+            )
+
+            raw_response = await client.images.with_raw_response.create_variation(**data)  # type: ignore
+            response = raw_response.parse()
+            response_json = response.model_dump()
+
+            ## LOGGING
+            logging_obj.post_call(
+                api_key=api_key,
+                original_response=response_json,
+                additional_args={
+                    "headers": headers,
+                    "api_base": api_base,
+                },
+            )
+
+            ## RESPONSE OBJECT
+            return provider_config.transform_response_image_variation(
+                model=model,
+                model_response=ImageResponse(**response_json),
+                raw_response=httpx.Response(
+                    status_code=200,
+                    request=httpx.Request(
+                        method="GET", url="https://litellm.ai"
+                    ),  # mock request object
+                ),
+                logging_obj=logging_obj,
+                request_data=data,
+                image=image,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                encoding=None,
+                api_key=api_key,
+            )
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+
+    def image_variations(
+        self,
+        model_response: ImageResponse,
+        api_key: str,
+        api_base: str,
+        model: Optional[str],
+        image: FileTypes,
+        timeout: float,
+        custom_llm_provider: str,
+        logging_obj: LiteLLMLoggingObj,
+        optional_params: dict,
+        litellm_params: dict,
+        print_verbose: Optional[Callable] = None,
+        logger_fn=None,
+        client=None,
+        organization: Optional[str] = None,
+        headers: Optional[dict] = None,
+    ) -> ImageResponse:
+        try:
+            provider_config = ProviderConfigManager.get_provider_image_variation_config(
+                model=model or "",  # openai defaults to dall-e-2
+                provider=LlmProviders.OPENAI,
+            )
+
+            if provider_config is None:
+                raise ValueError(
+                    f"image variation provider not found: {custom_llm_provider}."
+                )
+
+            max_retries = optional_params.pop("max_retries", 2)
+
+            data = provider_config.transform_request_image_variation(
+                model=model,
+                image=image,
+                optional_params=optional_params,
+                headers=headers or {},
+            )
+            json_data = data.get("data")
+            if not json_data:
+                raise ValueError(
+                    f"data field is required, for openai image variations. Got={data}"
+                )
+            ## LOGGING
+            logging_obj.pre_call(
+                input="",
+                api_key=api_key,
+                additional_args={
+                    "headers": headers,
+                    "api_base": api_base,
+                    "complete_input_dict": data,
+                },
+            )
+            if litellm_params.get("async_call", False):
+                return self.async_image_variations(
+                    api_base=api_base,
+                    data=json_data,
+                    headers=headers or {},
+                    model_response=model_response,
+                    api_key=api_key,
+                    logging_obj=logging_obj,
+                    model=model,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                    client=client,
+                    provider_config=provider_config,
+                    image=image,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                )  # type: ignore
+
+            init_client_params = {
+                "api_key": api_key,
+                "base_url": api_base,
+                "http_client": litellm.client_session,
+                "timeout": timeout,
+                "max_retries": max_retries,  # type: ignore
+                "organization": organization,
+            }
+
+            client = self.get_sync_client(
+                client=client, init_client_params=init_client_params
+            )
+
+            raw_response = client.images.with_raw_response.create_variation(**json_data)  # type: ignore
+            response = raw_response.parse()
+            response_json = response.model_dump()
+
+            ## LOGGING
+            logging_obj.post_call(
+                api_key=api_key,
+                original_response=response_json,
+                additional_args={
+                    "headers": headers,
+                    "api_base": api_base,
+                },
+            )
+
+            ## RESPONSE OBJECT
+            return provider_config.transform_response_image_variation(
+                model=model,
+                model_response=ImageResponse(**response_json),
+                raw_response=httpx.Response(
+                    status_code=200,
+                    request=httpx.Request(
+                        method="GET", url="https://litellm.ai"
+                    ),  # mock request object
+                ),
+                logging_obj=logging_obj,
+                request_data=json_data,
+                image=image,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                encoding=None,
+                api_key=api_key,
+            )
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/image_variations/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/image_variations/transformation.py
new file mode 100644
index 00000000..96d1a302
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/image_variations/transformation.py
@@ -0,0 +1,82 @@
+from typing import Any, List, Optional, Union
+
+from aiohttp import ClientResponse
+from httpx import Headers, Response
+
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.image_variations.transformation import LiteLLMLoggingObj
+from litellm.types.llms.openai import OpenAIImageVariationOptionalParams
+from litellm.types.utils import FileTypes, HttpHandlerRequestFields, ImageResponse
+
+from ...base_llm.image_variations.transformation import BaseImageVariationConfig
+from ..common_utils import OpenAIError
+
+
+class OpenAIImageVariationConfig(BaseImageVariationConfig):
+    def get_supported_openai_params(
+        self, model: str
+    ) -> List[OpenAIImageVariationOptionalParams]:
+        return ["n", "size", "response_format", "user"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        optional_params.update(non_default_params)
+        return optional_params
+
+    def transform_request_image_variation(
+        self,
+        model: Optional[str],
+        image: FileTypes,
+        optional_params: dict,
+        headers: dict,
+    ) -> HttpHandlerRequestFields:
+        return {
+            "data": {
+                "image": image,
+                **optional_params,
+            }
+        }
+
+    async def async_transform_response_image_variation(
+        self,
+        model: Optional[str],
+        raw_response: ClientResponse,
+        model_response: ImageResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        image: FileTypes,
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+    ) -> ImageResponse:
+        return model_response
+
+    def transform_response_image_variation(
+        self,
+        model: Optional[str],
+        raw_response: Response,
+        model_response: ImageResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        image: FileTypes,
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+    ) -> ImageResponse:
+        return model_response
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, Headers]
+    ) -> BaseLLMException:
+        return OpenAIError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/openai.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/openai.py
new file mode 100644
index 00000000..deb70b48
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/openai.py
@@ -0,0 +1,2870 @@
+import time
+import types
+from typing import (
+    Any,
+    AsyncIterator,
+    Callable,
+    Coroutine,
+    Iterable,
+    Iterator,
+    List,
+    Literal,
+    Optional,
+    Union,
+    cast,
+)
+from urllib.parse import urlparse
+
+import httpx
+import openai
+from openai import AsyncOpenAI, OpenAI
+from openai.types.beta.assistant_deleted import AssistantDeleted
+from openai.types.file_deleted import FileDeleted
+from pydantic import BaseModel
+from typing_extensions import overload
+
+import litellm
+from litellm import LlmProviders
+from litellm._logging import verbose_logger
+from litellm.constants import 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.base_llm.base_model_iterator import BaseModelResponseIterator
+from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
+from litellm.llms.bedrock.chat.invoke_handler import MockResponseIterator
+from litellm.types.utils import (
+    EmbeddingResponse,
+    ImageResponse,
+    LiteLLMBatch,
+    ModelResponse,
+    ModelResponseStream,
+)
+from litellm.utils import (
+    CustomStreamWrapper,
+    ProviderConfigManager,
+    convert_to_model_response_object,
+)
+
+from ...types.llms.openai import *
+from ..base import BaseLLM
+from .chat.o_series_transformation import OpenAIOSeriesConfig
+from .common_utils import (
+    BaseOpenAILLM,
+    OpenAIError,
+    drop_params_from_unprocessable_entity_error,
+)
+
+openaiOSeriesConfig = OpenAIOSeriesConfig()
+
+
+class MistralEmbeddingConfig:
+    """
+    Reference: https://docs.mistral.ai/api/#operation/createEmbedding
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return {
+            k: v
+            for k, v in cls.__dict__.items()
+            if not k.startswith("__")
+            and not isinstance(
+                v,
+                (
+                    types.FunctionType,
+                    types.BuiltinFunctionType,
+                    classmethod,
+                    staticmethod,
+                ),
+            )
+            and v is not None
+        }
+
+    def get_supported_openai_params(self):
+        return [
+            "encoding_format",
+        ]
+
+    def map_openai_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "encoding_format":
+                optional_params["encoding_format"] = value
+        return optional_params
+
+
+class OpenAIConfig(BaseConfig):
+    """
+    Reference: https://platform.openai.com/docs/api-reference/chat/create
+
+    The class `OpenAIConfig` provides configuration for the OpenAI's Chat API interface. 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. OpenAI has now deprecated in favor of max_completion_tokens, and is not compatible with o1 series models.
+
+    - `max_completion_tokens` (integer or null): An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens.
+
+    - `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.
+    """
+
+    frequency_penalty: Optional[int] = None
+    function_call: Optional[Union[str, dict]] = None
+    functions: Optional[list] = None
+    logit_bias: Optional[dict] = None
+    max_completion_tokens: Optional[int] = 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
+    response_format: Optional[dict] = None
+
+    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_completion_tokens: Optional[int] = 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,
+        response_format: Optional[dict] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        This function returns the list
+        of supported openai parameters for a given OpenAI Model
+
+        - If O1 model, returns O1 supported params
+        - If gpt-audio model, returns gpt-audio supported params
+        - Else, returns gpt supported params
+
+        Args:
+            model (str): OpenAI model
+
+        Returns:
+            list: List of supported openai parameters
+        """
+        if openaiOSeriesConfig.is_model_o_series_model(model=model):
+            return openaiOSeriesConfig.get_supported_openai_params(model=model)
+        elif litellm.openAIGPTAudioConfig.is_model_gpt_audio_model(model=model):
+            return litellm.openAIGPTAudioConfig.get_supported_openai_params(model=model)
+        else:
+            return litellm.openAIGPTConfig.get_supported_openai_params(model=model)
+
+    def _map_openai_params(
+        self, non_default_params: dict, optional_params: dict, model: str
+    ) -> dict:
+        supported_openai_params = self.get_supported_openai_params(model)
+        for param, value in non_default_params.items():
+            if param in supported_openai_params:
+                optional_params[param] = value
+        return optional_params
+
+    def _transform_messages(
+        self, messages: List[AllMessageValues], model: str
+    ) -> List[AllMessageValues]:
+        return messages
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        """ """
+        if openaiOSeriesConfig.is_model_o_series_model(model=model):
+            return openaiOSeriesConfig.map_openai_params(
+                non_default_params=non_default_params,
+                optional_params=optional_params,
+                model=model,
+                drop_params=drop_params,
+            )
+        elif litellm.openAIGPTAudioConfig.is_model_gpt_audio_model(model=model):
+            return litellm.openAIGPTAudioConfig.map_openai_params(
+                non_default_params=non_default_params,
+                optional_params=optional_params,
+                model=model,
+                drop_params=drop_params,
+            )
+
+        return litellm.openAIGPTConfig.map_openai_params(
+            non_default_params=non_default_params,
+            optional_params=optional_params,
+            model=model,
+            drop_params=drop_params,
+        )
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers]
+    ) -> BaseLLMException:
+        return OpenAIError(
+            status_code=status_code,
+            message=error_message,
+            headers=headers,
+        )
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        messages = self._transform_messages(messages=messages, model=model)
+        return {"model": model, "messages": messages, **optional_params}
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+
+        logging_obj.post_call(original_response=raw_response.text)
+        logging_obj.model_call_details["response_headers"] = raw_response.headers
+        final_response_obj = cast(
+            ModelResponse,
+            convert_to_model_response_object(
+                response_object=raw_response.json(),
+                model_response_object=model_response,
+                hidden_params={"headers": raw_response.headers},
+                _response_headers=dict(raw_response.headers),
+            ),
+        )
+
+        return final_response_obj
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        return {
+            "Authorization": f"Bearer {api_key}",
+            **headers,
+        }
+
+    def get_model_response_iterator(
+        self,
+        streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
+        sync_stream: bool,
+        json_mode: Optional[bool] = False,
+    ) -> Any:
+        return OpenAIChatCompletionResponseIterator(
+            streaming_response=streaming_response,
+            sync_stream=sync_stream,
+            json_mode=json_mode,
+        )
+
+
+class OpenAIChatCompletionResponseIterator(BaseModelResponseIterator):
+    def chunk_parser(self, chunk: dict) -> ModelResponseStream:
+        """
+        {'choices': [{'delta': {'content': '', 'role': 'assistant'}, 'finish_reason': None, 'index': 0, 'logprobs': None}], 'created': 1735763082, 'id': 'a83a2b0fbfaf4aab9c2c93cb8ba346d7', 'model': 'mistral-large', 'object': 'chat.completion.chunk'}
+        """
+        try:
+            return ModelResponseStream(**chunk)
+        except Exception as e:
+            raise e
+
+
+class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def _set_dynamic_params_on_client(
+        self,
+        client: Union[OpenAI, AsyncOpenAI],
+        organization: Optional[str] = None,
+        max_retries: Optional[int] = None,
+    ):
+        if organization is not None:
+            client.organization = organization
+        if max_retries is not None:
+            client.max_retries = max_retries
+
+    def _get_openai_client(
+        self,
+        is_async: bool,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
+        max_retries: Optional[int] = DEFAULT_MAX_RETRIES,
+        organization: Optional[str] = None,
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+        client_initialization_params: Dict = locals()
+        if client is None:
+            if not isinstance(max_retries, int):
+                raise OpenAIError(
+                    status_code=422,
+                    message="max retries must be an int. Passed in value: {}".format(
+                        max_retries
+                    ),
+                )
+            cached_client = self.get_cached_openai_client(
+                client_initialization_params=client_initialization_params,
+                client_type="openai",
+            )
+
+            if cached_client:
+                if isinstance(cached_client, OpenAI) or isinstance(
+                    cached_client, AsyncOpenAI
+                ):
+                    return cached_client
+            if is_async:
+                _new_client: Union[OpenAI, AsyncOpenAI] = AsyncOpenAI(
+                    api_key=api_key,
+                    base_url=api_base,
+                    http_client=OpenAIChatCompletion._get_async_http_client(),
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                )
+            else:
+                _new_client = OpenAI(
+                    api_key=api_key,
+                    base_url=api_base,
+                    http_client=OpenAIChatCompletion._get_sync_http_client(),
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                )
+
+            ## SAVE CACHE KEY
+            self.set_cached_openai_client(
+                openai_client=_new_client,
+                client_initialization_params=client_initialization_params,
+                client_type="openai",
+            )
+            return _new_client
+
+        else:
+            self._set_dynamic_params_on_client(
+                client=client,
+                organization=organization,
+                max_retries=max_retries,
+            )
+            return client
+
+    @track_llm_api_timing()
+    async def make_openai_chat_completion_request(
+        self,
+        openai_aclient: AsyncOpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+        logging_obj: LiteLLMLoggingObj,
+    ) -> Tuple[dict, BaseModel]:
+        """
+        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 openai_aclient.chat.completions.with_raw_response.create(
+                    **data, timeout=timeout
+                )
+            )
+            end_time = time.time()
+
+            if hasattr(raw_response, "headers"):
+                headers = dict(raw_response.headers)
+            else:
+                headers = {}
+            response = raw_response.parse()
+            return headers, response
+        except openai.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
+
+    @track_llm_api_timing()
+    def make_sync_openai_chat_completion_request(
+        self,
+        openai_client: OpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+        logging_obj: LiteLLMLoggingObj,
+    ) -> Tuple[dict, BaseModel]:
+        """
+        Helper to:
+        - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
+        - call chat.completions.create by default
+        """
+        raw_response = None
+        try:
+            raw_response = openai_client.chat.completions.with_raw_response.create(
+                **data, timeout=timeout
+            )
+
+            if hasattr(raw_response, "headers"):
+                headers = dict(raw_response.headers)
+            else:
+                headers = {}
+            response = raw_response.parse()
+            return headers, response
+        except Exception as e:
+            if raw_response is not None:
+                raise Exception(
+                    "error - {}, Received response - {}, Type of response - {}".format(
+                        e, raw_response, type(raw_response)
+                    )
+                )
+            else:
+                raise e
+
+    def mock_streaming(
+        self,
+        response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        model: str,
+        stream_options: Optional[dict] = None,
+    ) -> CustomStreamWrapper:
+        completion_stream = MockResponseIterator(model_response=response)
+        streaming_response = CustomStreamWrapper(
+            completion_stream=completion_stream,
+            model=model,
+            custom_llm_provider="openai",
+            logging_obj=logging_obj,
+            stream_options=stream_options,
+        )
+
+        return streaming_response
+
+    def completion(  # type: ignore # noqa: PLR0915
+        self,
+        model_response: ModelResponse,
+        timeout: Union[float, httpx.Timeout],
+        optional_params: dict,
+        litellm_params: dict,
+        logging_obj: Any,
+        model: Optional[str] = None,
+        messages: Optional[list] = None,
+        print_verbose: Optional[Callable] = None,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        dynamic_params: Optional[bool] = None,
+        azure_ad_token: Optional[str] = None,
+        acompletion: bool = False,
+        logger_fn=None,
+        headers: Optional[dict] = None,
+        custom_prompt_dict: dict = {},
+        client=None,
+        organization: Optional[str] = None,
+        custom_llm_provider: Optional[str] = None,
+        drop_params: Optional[bool] = None,
+    ):
+
+        super().completion()
+        try:
+            fake_stream: bool = False
+            inference_params = optional_params.copy()
+            stream_options: Optional[dict] = inference_params.pop(
+                "stream_options", None
+            )
+            stream: Optional[bool] = inference_params.pop("stream", False)
+            provider_config: Optional[BaseConfig] = None
+
+            if custom_llm_provider is not None and model is not None:
+                provider_config = ProviderConfigManager.get_provider_chat_config(
+                    model=model, provider=LlmProviders(custom_llm_provider)
+                )
+
+            if provider_config:
+                fake_stream = provider_config.should_fake_stream(
+                    model=model, custom_llm_provider=custom_llm_provider, stream=stream
+                )
+
+            if headers:
+                inference_params["extra_headers"] = headers
+            if model is None or messages is None:
+                raise OpenAIError(status_code=422, message="Missing model or messages")
+
+            if not isinstance(timeout, float) and not isinstance(
+                timeout, httpx.Timeout
+            ):
+                raise OpenAIError(
+                    status_code=422,
+                    message="Timeout needs to be a float or httpx.Timeout",
+                )
+
+            if custom_llm_provider is not None and custom_llm_provider != "openai":
+                model_response.model = f"{custom_llm_provider}/{model}"
+
+            for _ in range(
+                2
+            ):  # if call fails due to alternating messages, retry with reformatted message
+
+                if provider_config is not None:
+                    data = provider_config.transform_request(
+                        model=model,
+                        messages=messages,
+                        optional_params=inference_params,
+                        litellm_params=litellm_params,
+                        headers=headers or {},
+                    )
+                else:
+                    data = OpenAIConfig().transform_request(
+                        model=model,
+                        messages=messages,
+                        optional_params=inference_params,
+                        litellm_params=litellm_params,
+                        headers=headers or {},
+                    )
+                try:
+                    max_retries = data.pop("max_retries", 2)
+                    if acompletion is True:
+                        if stream is True and fake_stream is False:
+                            return self.async_streaming(
+                                logging_obj=logging_obj,
+                                headers=headers,
+                                data=data,
+                                model=model,
+                                api_base=api_base,
+                                api_key=api_key,
+                                api_version=api_version,
+                                timeout=timeout,
+                                client=client,
+                                max_retries=max_retries,
+                                organization=organization,
+                                drop_params=drop_params,
+                                stream_options=stream_options,
+                            )
+                        else:
+                            return self.acompletion(
+                                data=data,
+                                headers=headers,
+                                model=model,
+                                logging_obj=logging_obj,
+                                model_response=model_response,
+                                api_base=api_base,
+                                api_key=api_key,
+                                api_version=api_version,
+                                timeout=timeout,
+                                client=client,
+                                max_retries=max_retries,
+                                organization=organization,
+                                drop_params=drop_params,
+                                fake_stream=fake_stream,
+                            )
+                    elif stream is True and fake_stream is False:
+                        return self.streaming(
+                            logging_obj=logging_obj,
+                            headers=headers,
+                            data=data,
+                            model=model,
+                            api_base=api_base,
+                            api_key=api_key,
+                            api_version=api_version,
+                            timeout=timeout,
+                            client=client,
+                            max_retries=max_retries,
+                            organization=organization,
+                            stream_options=stream_options,
+                        )
+                    else:
+                        if not isinstance(max_retries, int):
+                            raise OpenAIError(
+                                status_code=422, message="max retries must be an int"
+                            )
+                        openai_client: OpenAI = self._get_openai_client(  # type: ignore
+                            is_async=False,
+                            api_key=api_key,
+                            api_base=api_base,
+                            api_version=api_version,
+                            timeout=timeout,
+                            max_retries=max_retries,
+                            organization=organization,
+                            client=client,
+                        )
+
+                        ## LOGGING
+                        logging_obj.pre_call(
+                            input=messages,
+                            api_key=openai_client.api_key,
+                            additional_args={
+                                "headers": headers,
+                                "api_base": openai_client._base_url._uri_reference,
+                                "acompletion": acompletion,
+                                "complete_input_dict": data,
+                            },
+                        )
+
+                        headers, response = (
+                            self.make_sync_openai_chat_completion_request(
+                                openai_client=openai_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=messages,
+                            api_key=api_key,
+                            original_response=stringified_response,
+                            additional_args={"complete_input_dict": data},
+                        )
+
+                        final_response_obj = convert_to_model_response_object(
+                            response_object=stringified_response,
+                            model_response_object=model_response,
+                            _response_headers=headers,
+                        )
+                        if fake_stream is True:
+                            return self.mock_streaming(
+                                response=cast(ModelResponse, final_response_obj),
+                                logging_obj=logging_obj,
+                                model=model,
+                                stream_options=stream_options,
+                            )
+
+                        return final_response_obj
+                except openai.UnprocessableEntityError as e:
+                    ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
+                    if litellm.drop_params is True or drop_params is True:
+                        inference_params = drop_params_from_unprocessable_entity_error(
+                            e, inference_params
+                        )
+                    else:
+                        raise e
+                    # e.message
+                except Exception as e:
+                    if print_verbose is not None:
+                        print_verbose(f"openai.py: Received openai error - {str(e)}")
+                    if (
+                        "Conversation roles must alternate user/assistant" in str(e)
+                        or "user and assistant roles should be alternating" in str(e)
+                    ) and messages is not None:
+                        if print_verbose is not None:
+                            print_verbose("openai.py: REFORMATS THE MESSAGE!")
+                        # reformat messages to ensure user/assistant are alternating, if there's either 2 consecutive 'user' messages or 2 consecutive 'assistant' message, add a blank 'user' or 'assistant' message to ensure compatibility
+                        new_messages = []
+                        for i in range(len(messages) - 1):  # type: ignore
+                            new_messages.append(messages[i])
+                            if messages[i]["role"] == messages[i + 1]["role"]:
+                                if messages[i]["role"] == "user":
+                                    new_messages.append(
+                                        {"role": "assistant", "content": ""}
+                                    )
+                                else:
+                                    new_messages.append({"role": "user", "content": ""})
+                        new_messages.append(messages[-1])
+                        messages = new_messages
+                    elif (
+                        "Last message must have role `user`" in str(e)
+                    ) and messages is not None:
+                        new_messages = messages
+                        new_messages.append({"role": "user", "content": ""})
+                        messages = new_messages
+                    elif "unknown field: parameter index is not a valid field" in str(
+                        e
+                    ):
+                        litellm.remove_index_from_tool_calls(messages=messages)
+                    else:
+                        raise e
+        except OpenAIError as e:
+            raise e
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            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 OpenAIError(
+                status_code=status_code,
+                message=error_text,
+                headers=error_headers,
+                body=error_body,
+            )
+
+    async def acompletion(
+        self,
+        data: dict,
+        model: str,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        timeout: Union[float, httpx.Timeout],
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        organization: Optional[str] = None,
+        client=None,
+        max_retries=None,
+        headers=None,
+        drop_params: Optional[bool] = None,
+        stream_options: Optional[dict] = None,
+        fake_stream: bool = False,
+    ):
+        response = None
+        for _ in range(
+            2
+        ):  # if call fails due to alternating messages, retry with reformatted message
+
+            try:
+                openai_aclient: AsyncOpenAI = self._get_openai_client(  # type: ignore
+                    is_async=True,
+                    api_key=api_key,
+                    api_base=api_base,
+                    api_version=api_version,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                    client=client,
+                )
+
+                ## LOGGING
+                logging_obj.pre_call(
+                    input=data["messages"],
+                    api_key=openai_aclient.api_key,
+                    additional_args={
+                        "headers": {
+                            "Authorization": f"Bearer {openai_aclient.api_key}"
+                        },
+                        "api_base": openai_aclient._base_url._uri_reference,
+                        "acompletion": True,
+                        "complete_input_dict": data,
+                    },
+                )
+
+                headers, response = await self.make_openai_chat_completion_request(
+                    openai_aclient=openai_aclient,
+                    data=data,
+                    timeout=timeout,
+                    logging_obj=logging_obj,
+                )
+                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},
+                )
+                logging_obj.model_call_details["response_headers"] = headers
+                final_response_obj = convert_to_model_response_object(
+                    response_object=stringified_response,
+                    model_response_object=model_response,
+                    hidden_params={"headers": headers},
+                    _response_headers=headers,
+                )
+
+                if fake_stream is True:
+                    return self.mock_streaming(
+                        response=cast(ModelResponse, final_response_obj),
+                        logging_obj=logging_obj,
+                        model=model,
+                        stream_options=stream_options,
+                    )
+
+                return final_response_obj
+            except openai.UnprocessableEntityError as e:
+                ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
+                if litellm.drop_params is True or drop_params is True:
+                    data = drop_params_from_unprocessable_entity_error(e, data)
+                else:
+                    raise e
+                # e.message
+            except Exception as e:
+                exception_response = getattr(e, "response", None)
+                status_code = getattr(e, "status_code", 500)
+                exception_body = getattr(e, "body", None)
+                error_headers = getattr(e, "headers", None)
+                if error_headers is None and exception_response:
+                    error_headers = getattr(exception_response, "headers", None)
+                message = getattr(e, "message", str(e))
+
+                raise OpenAIError(
+                    status_code=status_code,
+                    message=message,
+                    headers=error_headers,
+                    body=exception_body,
+                )
+
+    def streaming(
+        self,
+        logging_obj,
+        timeout: Union[float, httpx.Timeout],
+        data: dict,
+        model: str,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        organization: Optional[str] = None,
+        client=None,
+        max_retries=None,
+        headers=None,
+        stream_options: Optional[dict] = None,
+    ):
+        data["stream"] = True
+        data.update(
+            self.get_stream_options(stream_options=stream_options, api_base=api_base)
+        )
+
+        openai_client: OpenAI = self._get_openai_client(  # type: ignore
+            is_async=False,
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+        ## LOGGING
+        logging_obj.pre_call(
+            input=data["messages"],
+            api_key=api_key,
+            additional_args={
+                "headers": {"Authorization": f"Bearer {openai_client.api_key}"},
+                "api_base": openai_client._base_url._uri_reference,
+                "acompletion": False,
+                "complete_input_dict": data,
+            },
+        )
+        headers, response = self.make_sync_openai_chat_completion_request(
+            openai_client=openai_client,
+            data=data,
+            timeout=timeout,
+            logging_obj=logging_obj,
+        )
+
+        logging_obj.model_call_details["response_headers"] = headers
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=response,
+            model=model,
+            custom_llm_provider="openai",
+            logging_obj=logging_obj,
+            stream_options=data.get("stream_options", None),
+            _response_headers=headers,
+        )
+        return streamwrapper
+
+    async def async_streaming(
+        self,
+        timeout: Union[float, httpx.Timeout],
+        data: dict,
+        model: str,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        organization: Optional[str] = None,
+        client=None,
+        max_retries=None,
+        headers=None,
+        drop_params: Optional[bool] = None,
+        stream_options: Optional[dict] = None,
+    ):
+        response = None
+        data["stream"] = True
+        data.update(
+            self.get_stream_options(stream_options=stream_options, api_base=api_base)
+        )
+        for _ in range(2):
+            try:
+                openai_aclient: AsyncOpenAI = self._get_openai_client(  # type: ignore
+                    is_async=True,
+                    api_key=api_key,
+                    api_base=api_base,
+                    api_version=api_version,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                    client=client,
+                )
+                ## LOGGING
+                logging_obj.pre_call(
+                    input=data["messages"],
+                    api_key=api_key,
+                    additional_args={
+                        "headers": headers,
+                        "api_base": api_base,
+                        "acompletion": True,
+                        "complete_input_dict": data,
+                    },
+                )
+
+                headers, response = await self.make_openai_chat_completion_request(
+                    openai_aclient=openai_aclient,
+                    data=data,
+                    timeout=timeout,
+                    logging_obj=logging_obj,
+                )
+                logging_obj.model_call_details["response_headers"] = headers
+                streamwrapper = CustomStreamWrapper(
+                    completion_stream=response,
+                    model=model,
+                    custom_llm_provider="openai",
+                    logging_obj=logging_obj,
+                    stream_options=data.get("stream_options", None),
+                    _response_headers=headers,
+                )
+                return streamwrapper
+            except openai.UnprocessableEntityError as e:
+                ## check if body contains unprocessable params - related issue https://github.com/BerriAI/litellm/issues/4800
+                if litellm.drop_params is True or drop_params is True:
+                    data = drop_params_from_unprocessable_entity_error(e, data)
+                else:
+                    raise e
+            except (
+                Exception
+            ) as e:  # need to exception handle here. async exceptions don't get caught in sync functions.
+
+                if isinstance(e, OpenAIError):
+                    raise e
+
+                error_headers = getattr(e, "headers", None)
+                status_code = getattr(e, "status_code", 500)
+                error_response = getattr(e, "response", None)
+                exception_body = getattr(e, "body", None)
+                if error_headers is None and error_response:
+                    error_headers = getattr(error_response, "headers", None)
+                if response is not None and hasattr(response, "text"):
+                    raise OpenAIError(
+                        status_code=status_code,
+                        message=f"{str(e)}\n\nOriginal Response: {response.text}",  # type: ignore
+                        headers=error_headers,
+                        body=exception_body,
+                    )
+                else:
+                    if type(e).__name__ == "ReadTimeout":
+                        raise OpenAIError(
+                            status_code=408,
+                            message=f"{type(e).__name__}",
+                            headers=error_headers,
+                            body=exception_body,
+                        )
+                    elif hasattr(e, "status_code"):
+                        raise OpenAIError(
+                            status_code=getattr(e, "status_code", 500),
+                            message=str(e),
+                            headers=error_headers,
+                            body=exception_body,
+                        )
+                    else:
+                        raise OpenAIError(
+                            status_code=500,
+                            message=f"{str(e)}",
+                            headers=error_headers,
+                            body=exception_body,
+                        )
+
+    def get_stream_options(
+        self, stream_options: Optional[dict], api_base: Optional[str]
+    ) -> dict:
+        """
+        Pass `stream_options` to the data dict for OpenAI requests
+        """
+        if stream_options is not None:
+            return {"stream_options": stream_options}
+        else:
+            # by default litellm will include usage for openai endpoints
+            if api_base is None or urlparse(api_base).hostname == "api.openai.com":
+                return {"stream_options": {"include_usage": True}}
+        return {}
+
+    # Embedding
+    @track_llm_api_timing()
+    async def make_openai_embedding_request(
+        self,
+        openai_aclient: AsyncOpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+        logging_obj: LiteLLMLoggingObj,
+    ):
+        """
+        Helper to:
+        - call embeddings.create.with_raw_response when litellm.return_response_headers is True
+        - call embeddings.create by default
+        """
+        try:
+            raw_response = await openai_aclient.embeddings.with_raw_response.create(
+                **data, timeout=timeout
+            )  # type: ignore
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+            return headers, response
+        except Exception as e:
+            raise e
+
+    @track_llm_api_timing()
+    def make_sync_openai_embedding_request(
+        self,
+        openai_client: OpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+        logging_obj: LiteLLMLoggingObj,
+    ):
+        """
+        Helper to:
+        - call embeddings.create.with_raw_response when litellm.return_response_headers is True
+        - call embeddings.create by default
+        """
+        try:
+            raw_response = openai_client.embeddings.with_raw_response.create(
+                **data, timeout=timeout
+            )  # type: ignore
+
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+            return headers, response
+        except Exception as e:
+            raise e
+
+    async def aembedding(
+        self,
+        input: list,
+        data: dict,
+        model_response: EmbeddingResponse,
+        timeout: float,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client: Optional[AsyncOpenAI] = None,
+        max_retries=None,
+    ):
+        try:
+            openai_aclient: AsyncOpenAI = self._get_openai_client(  # type: ignore
+                is_async=True,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+            )
+            headers, response = await self.make_openai_embedding_request(
+                openai_aclient=openai_aclient,
+                data=data,
+                timeout=timeout,
+                logging_obj=logging_obj,
+            )
+            logging_obj.model_call_details["response_headers"] = headers
+            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,
+            )
+            returned_response: EmbeddingResponse = convert_to_model_response_object(
+                response_object=stringified_response,
+                model_response_object=model_response,
+                response_type="embedding",
+                _response_headers=headers,
+            )  # type: ignore
+            return returned_response
+        except OpenAIError 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
+        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),
+            )
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+
+    def embedding(  # type: ignore
+        self,
+        model: str,
+        input: list,
+        timeout: float,
+        logging_obj,
+        model_response: EmbeddingResponse,
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client=None,
+        aembedding=None,
+        max_retries: Optional[int] = None,
+    ) -> EmbeddingResponse:
+        super().embedding()
+        try:
+            model = model
+            data = {"model": model, "input": input, **optional_params}
+            max_retries = max_retries or litellm.DEFAULT_MAX_RETRIES
+            if not isinstance(max_retries, int):
+                raise OpenAIError(status_code=422, message="max retries must be an int")
+            ## LOGGING
+            logging_obj.pre_call(
+                input=input,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data, "api_base": api_base},
+            )
+
+            if aembedding is True:
+                return self.aembedding(  # type: ignore
+                    data=data,
+                    input=input,
+                    logging_obj=logging_obj,
+                    model_response=model_response,
+                    api_base=api_base,
+                    api_key=api_key,
+                    timeout=timeout,
+                    client=client,
+                    max_retries=max_retries,
+                )
+
+            openai_client: OpenAI = self._get_openai_client(  # type: ignore
+                is_async=False,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+            )
+
+            ## embedding CALL
+            headers: Optional[Dict] = None
+            headers, sync_embedding_response = self.make_sync_openai_embedding_request(
+                openai_client=openai_client,
+                data=data,
+                timeout=timeout,
+                logging_obj=logging_obj,
+            )  # type: ignore
+
+            ## LOGGING
+            logging_obj.model_call_details["response_headers"] = headers
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=sync_embedding_response,
+            )
+            response: EmbeddingResponse = convert_to_model_response_object(
+                response_object=sync_embedding_response.model_dump(),
+                model_response_object=model_response,
+                _response_headers=headers,
+                response_type="embedding",
+            )  # type: ignore
+            return response
+        except OpenAIError as e:
+            raise e
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_text = getattr(e, "text", str(e))
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise OpenAIError(
+                status_code=status_code, message=error_text, headers=error_headers
+            )
+
+    async def aimage_generation(
+        self,
+        prompt: str,
+        data: dict,
+        model_response: ModelResponse,
+        timeout: float,
+        logging_obj: Any,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client=None,
+        max_retries=None,
+    ):
+        response = None
+        try:
+
+            openai_aclient = self._get_openai_client(
+                is_async=True,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+            )
+
+            response = await openai_aclient.images.generate(**data, timeout=timeout)  # type: ignore
+            stringified_response = response.model_dump()
+            ## LOGGING
+            logging_obj.post_call(
+                input=prompt,
+                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")  # type: ignore
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=prompt,
+                api_key=api_key,
+                original_response=str(e),
+            )
+            raise e
+
+    def image_generation(
+        self,
+        model: Optional[str],
+        prompt: str,
+        timeout: float,
+        optional_params: dict,
+        logging_obj: Any,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        model_response: Optional[ImageResponse] = None,
+        client=None,
+        aimg_generation=None,
+    ) -> ImageResponse:
+        data = {}
+        try:
+            model = model
+            data = {"model": model, "prompt": prompt, **optional_params}
+            max_retries = data.pop("max_retries", 2)
+            if not isinstance(max_retries, int):
+                raise OpenAIError(status_code=422, message="max retries must be an int")
+
+            if aimg_generation is True:
+                return self.aimage_generation(data=data, prompt=prompt, logging_obj=logging_obj, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries)  # type: ignore
+
+            openai_client: OpenAI = self._get_openai_client(  # type: ignore
+                is_async=False,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+            )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=prompt,
+                api_key=openai_client.api_key,
+                additional_args={
+                    "headers": {"Authorization": f"Bearer {openai_client.api_key}"},
+                    "api_base": openai_client._base_url._uri_reference,
+                    "acompletion": True,
+                    "complete_input_dict": data,
+                },
+            )
+
+            ## COMPLETION CALL
+            _response = openai_client.images.generate(**data, timeout=timeout)  # type: ignore
+
+            response = _response.model_dump()
+            ## LOGGING
+            logging_obj.post_call(
+                input=prompt,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=response,
+            )
+            return convert_to_model_response_object(response_object=response, model_response_object=model_response, response_type="image_generation")  # type: ignore
+        except OpenAIError as e:
+
+            ## LOGGING
+            logging_obj.post_call(
+                input=prompt,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            raise e
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=prompt,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            if hasattr(e, "status_code"):
+                raise OpenAIError(
+                    status_code=getattr(e, "status_code", 500), message=str(e)
+                )
+            else:
+                raise OpenAIError(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],
+        organization: Optional[str],
+        project: Optional[str],
+        max_retries: int,
+        timeout: Union[float, httpx.Timeout],
+        aspeech: Optional[bool] = None,
+        client=None,
+    ) -> HttpxBinaryResponseContent:
+
+        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,
+                organization=organization,
+                project=project,
+                max_retries=max_retries,
+                timeout=timeout,
+                client=client,
+            )  # type: ignore
+
+        openai_client = self._get_openai_client(
+            is_async=False,
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+        )
+
+        response = cast(OpenAI, openai_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],
+        organization: Optional[str],
+        project: Optional[str],
+        max_retries: int,
+        timeout: Union[float, httpx.Timeout],
+        client=None,
+    ) -> HttpxBinaryResponseContent:
+
+        openai_client = cast(
+            AsyncOpenAI,
+            self._get_openai_client(
+                is_async=True,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+            ),
+        )
+
+        response = await openai_client.audio.speech.create(
+            model=model,
+            voice=voice,  # type: ignore
+            input=input,
+            **optional_params,
+        )
+
+        return HttpxBinaryResponseContent(response=response.response)
+
+
+class OpenAIFilesAPI(BaseLLM):
+    """
+    OpenAI methods to support for batches
+    - create_file()
+    - retrieve_file()
+    - list_files()
+    - delete_file()
+    - file_content()
+    - update_file()
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+        _is_async: bool = False,
+    ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+        received_args = locals()
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+        if client is None:
+            data = {}
+            for k, v in received_args.items():
+                if k == "self" or k == "client" or k == "_is_async":
+                    pass
+                elif k == "api_base" and v is not None:
+                    data["base_url"] = v
+                elif v is not None:
+                    data[k] = v
+            if _is_async is True:
+                openai_client = AsyncOpenAI(**data)
+            else:
+                openai_client = OpenAI(**data)  # type: ignore
+        else:
+            openai_client = client
+
+        return openai_client
+
+    async def acreate_file(
+        self,
+        create_file_data: CreateFileRequest,
+        openai_client: AsyncOpenAI,
+    ) -> FileObject:
+        response = await openai_client.files.create(**create_file_data)
+        return response
+
+    def create_file(
+        self,
+        _is_async: bool,
+        create_file_data: CreateFileRequest,
+        api_base: str,
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ) -> Union[FileObject, Coroutine[Any, Any, FileObject]]:
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acreate_file(  # type: ignore
+                create_file_data=create_file_data, openai_client=openai_client
+            )
+        response = openai_client.files.create(**create_file_data)
+        return response
+
+    async def afile_content(
+        self,
+        file_content_request: FileContentRequest,
+        openai_client: AsyncOpenAI,
+    ) -> HttpxBinaryResponseContent:
+        response = await openai_client.files.content(**file_content_request)
+        return HttpxBinaryResponseContent(response=response.response)
+
+    def file_content(
+        self,
+        _is_async: bool,
+        file_content_request: FileContentRequest,
+        api_base: str,
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ) -> Union[
+        HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
+    ]:
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.afile_content(  # type: ignore
+                file_content_request=file_content_request,
+                openai_client=openai_client,
+            )
+        response = cast(OpenAI, openai_client).files.content(**file_content_request)
+
+        return HttpxBinaryResponseContent(response=response.response)
+
+    async def aretrieve_file(
+        self,
+        file_id: str,
+        openai_client: AsyncOpenAI,
+    ) -> FileObject:
+        response = await openai_client.files.retrieve(file_id=file_id)
+        return response
+
+    def retrieve_file(
+        self,
+        _is_async: bool,
+        file_id: str,
+        api_base: str,
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.aretrieve_file(  # type: ignore
+                file_id=file_id,
+                openai_client=openai_client,
+            )
+        response = openai_client.files.retrieve(file_id=file_id)
+
+        return response
+
+    async def adelete_file(
+        self,
+        file_id: str,
+        openai_client: AsyncOpenAI,
+    ) -> FileDeleted:
+        response = await openai_client.files.delete(file_id=file_id)
+        return response
+
+    def delete_file(
+        self,
+        _is_async: bool,
+        file_id: str,
+        api_base: str,
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.adelete_file(  # type: ignore
+                file_id=file_id,
+                openai_client=openai_client,
+            )
+        response = openai_client.files.delete(file_id=file_id)
+
+        return response
+
+    async def alist_files(
+        self,
+        openai_client: AsyncOpenAI,
+        purpose: Optional[str] = None,
+    ):
+        if isinstance(purpose, str):
+            response = await openai_client.files.list(purpose=purpose)
+        else:
+            response = await openai_client.files.list()
+        return response
+
+    def list_files(
+        self,
+        _is_async: bool,
+        api_base: str,
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        purpose: Optional[str] = None,
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.alist_files(  # type: ignore
+                purpose=purpose,
+                openai_client=openai_client,
+            )
+
+        if isinstance(purpose, str):
+            response = openai_client.files.list(purpose=purpose)
+        else:
+            response = openai_client.files.list()
+
+        return response
+
+
+class OpenAIBatchesAPI(BaseLLM):
+    """
+    OpenAI methods to support for batches
+    - create_batch()
+    - retrieve_batch()
+    - cancel_batch()
+    - list_batch()
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+        _is_async: bool = False,
+    ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+        received_args = locals()
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+        if client is None:
+            data = {}
+            for k, v in received_args.items():
+                if k == "self" or k == "client" or k == "_is_async":
+                    pass
+                elif k == "api_base" and v is not None:
+                    data["base_url"] = v
+                elif v is not None:
+                    data[k] = v
+            if _is_async is True:
+                openai_client = AsyncOpenAI(**data)
+            else:
+                openai_client = OpenAI(**data)  # type: ignore
+        else:
+            openai_client = client
+
+        return openai_client
+
+    async def acreate_batch(
+        self,
+        create_batch_data: CreateBatchRequest,
+        openai_client: AsyncOpenAI,
+    ) -> LiteLLMBatch:
+        response = await openai_client.batches.create(**create_batch_data)
+        return LiteLLMBatch(**response.model_dump())
+
+    def create_batch(
+        self,
+        _is_async: bool,
+        create_batch_data: CreateBatchRequest,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acreate_batch(  # type: ignore
+                create_batch_data=create_batch_data, openai_client=openai_client
+            )
+        response = cast(OpenAI, openai_client).batches.create(**create_batch_data)
+
+        return LiteLLMBatch(**response.model_dump())
+
+    async def aretrieve_batch(
+        self,
+        retrieve_batch_data: RetrieveBatchRequest,
+        openai_client: AsyncOpenAI,
+    ) -> LiteLLMBatch:
+        verbose_logger.debug("retrieving batch, args= %s", retrieve_batch_data)
+        response = await openai_client.batches.retrieve(**retrieve_batch_data)
+        return LiteLLMBatch(**response.model_dump())
+
+    def retrieve_batch(
+        self,
+        _is_async: bool,
+        retrieve_batch_data: RetrieveBatchRequest,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.aretrieve_batch(  # type: ignore
+                retrieve_batch_data=retrieve_batch_data, openai_client=openai_client
+            )
+        response = cast(OpenAI, openai_client).batches.retrieve(**retrieve_batch_data)
+        return LiteLLMBatch(**response.model_dump())
+
+    async def acancel_batch(
+        self,
+        cancel_batch_data: CancelBatchRequest,
+        openai_client: AsyncOpenAI,
+    ) -> Batch:
+        verbose_logger.debug("async cancelling batch, args= %s", cancel_batch_data)
+        response = await openai_client.batches.cancel(**cancel_batch_data)
+        return response
+
+    def cancel_batch(
+        self,
+        _is_async: bool,
+        cancel_batch_data: CancelBatchRequest,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acancel_batch(  # type: ignore
+                cancel_batch_data=cancel_batch_data, openai_client=openai_client
+            )
+
+        response = openai_client.batches.cancel(**cancel_batch_data)
+        return response
+
+    async def alist_batches(
+        self,
+        openai_client: AsyncOpenAI,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        verbose_logger.debug("listing batches, after= %s, limit= %s", after, limit)
+        response = await openai_client.batches.list(after=after, limit=limit)  # type: ignore
+        return response
+
+    def list_batches(
+        self,
+        _is_async: bool,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+        client: Optional[OpenAI] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.alist_batches(  # type: ignore
+                openai_client=openai_client, after=after, limit=limit
+            )
+        response = openai_client.batches.list(after=after, limit=limit)  # type: ignore
+        return response
+
+
+class OpenAIAssistantsAPI(BaseLLM):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI] = None,
+    ) -> OpenAI:
+        received_args = locals()
+        if client is None:
+            data = {}
+            for k, v in received_args.items():
+                if k == "self" or k == "client":
+                    pass
+                elif k == "api_base" and v is not None:
+                    data["base_url"] = v
+                elif v is not None:
+                    data[k] = v
+            openai_client = OpenAI(**data)  # type: ignore
+        else:
+            openai_client = client
+
+        return openai_client
+
+    def async_get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI] = None,
+    ) -> AsyncOpenAI:
+        received_args = locals()
+        if client is None:
+            data = {}
+            for k, v in received_args.items():
+                if k == "self" or k == "client":
+                    pass
+                elif k == "api_base" and v is not None:
+                    data["base_url"] = v
+                elif v is not None:
+                    data[k] = v
+            openai_client = AsyncOpenAI(**data)  # type: ignore
+        else:
+            openai_client = client
+
+        return openai_client
+
+    ### ASSISTANTS ###
+
+    async def async_get_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        order: Optional[str] = "desc",
+        limit: Optional[int] = 20,
+        before: Optional[str] = None,
+        after: Optional[str] = None,
+    ) -> AsyncCursorPage[Assistant]:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+        request_params = {
+            "order": order,
+            "limit": limit,
+        }
+        if before:
+            request_params["before"] = before
+        if after:
+            request_params["after"] = after
+
+        response = await openai_client.beta.assistants.list(**request_params)  # type: ignore
+
+        return response
+
+    # fmt: off
+
+    @overload
+    def get_assistants(
+        self, 
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        aget_assistants: Literal[True], 
+    ) -> Coroutine[None, None, AsyncCursorPage[Assistant]]:
+        ...
+
+    @overload
+    def get_assistants(
+        self, 
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI],
+        aget_assistants: Optional[Literal[False]], 
+    ) -> SyncCursorPage[Assistant]: 
+        ...
+
+    # fmt: on
+
+    def get_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client=None,
+        aget_assistants=None,
+        order: Optional[str] = "desc",
+        limit: Optional[int] = 20,
+        before: Optional[str] = None,
+        after: Optional[str] = None,
+    ):
+        if aget_assistants is not None and aget_assistants is True:
+            return self.async_get_assistants(
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        request_params = {
+            "order": order,
+            "limit": limit,
+        }
+
+        if before:
+            request_params["before"] = before
+        if after:
+            request_params["after"] = after
+
+        response = openai_client.beta.assistants.list(**request_params)  # type: ignore
+
+        return response
+
+    # Create Assistant
+    async def async_create_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        create_assistant_data: dict,
+    ) -> Assistant:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = await openai_client.beta.assistants.create(**create_assistant_data)
+
+        return response
+
+    def create_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        create_assistant_data: dict,
+        client=None,
+        async_create_assistants=None,
+    ):
+        if async_create_assistants is not None and async_create_assistants is True:
+            return self.async_create_assistants(
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+                create_assistant_data=create_assistant_data,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = openai_client.beta.assistants.create(**create_assistant_data)
+        return response
+
+    # Delete Assistant
+    async def async_delete_assistant(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        assistant_id: str,
+    ) -> AssistantDeleted:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = await openai_client.beta.assistants.delete(assistant_id=assistant_id)
+
+        return response
+
+    def delete_assistant(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        assistant_id: str,
+        client=None,
+        async_delete_assistants=None,
+    ):
+        if async_delete_assistants is not None and async_delete_assistants is True:
+            return self.async_delete_assistant(
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+                assistant_id=assistant_id,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = openai_client.beta.assistants.delete(assistant_id=assistant_id)
+        return response
+
+    ### MESSAGES ###
+
+    async def a_add_message(
+        self,
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI] = None,
+    ) -> OpenAIMessage:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        thread_message: OpenAIMessage = await openai_client.beta.threads.messages.create(  # type: ignore
+            thread_id, **message_data  # type: ignore
+        )
+
+        response_obj: Optional[OpenAIMessage] = None
+        if getattr(thread_message, "status", None) is None:
+            thread_message.status = "completed"
+            response_obj = OpenAIMessage(**thread_message.dict())
+        else:
+            response_obj = OpenAIMessage(**thread_message.dict())
+        return response_obj
+
+    # fmt: off
+
+    @overload
+    def add_message(
+        self, 
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        a_add_message: Literal[True], 
+    ) -> Coroutine[None, None, OpenAIMessage]:
+        ...
+
+    @overload
+    def add_message(
+        self, 
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI],
+        a_add_message: Optional[Literal[False]], 
+    ) -> OpenAIMessage: 
+        ...
+
+    # fmt: on
+
+    def add_message(
+        self,
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client=None,
+        a_add_message: Optional[bool] = None,
+    ):
+        if a_add_message is not None and a_add_message is True:
+            return self.a_add_message(
+                thread_id=thread_id,
+                message_data=message_data,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        thread_message: OpenAIMessage = openai_client.beta.threads.messages.create(  # type: ignore
+            thread_id, **message_data  # type: ignore
+        )
+
+        response_obj: Optional[OpenAIMessage] = None
+        if getattr(thread_message, "status", None) is None:
+            thread_message.status = "completed"
+            response_obj = OpenAIMessage(**thread_message.dict())
+        else:
+            response_obj = OpenAIMessage(**thread_message.dict())
+        return response_obj
+
+    async def async_get_messages(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI] = None,
+    ) -> AsyncCursorPage[OpenAIMessage]:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = await openai_client.beta.threads.messages.list(thread_id=thread_id)
+
+        return response
+
+    # fmt: off
+
+    @overload
+    def get_messages(
+        self, 
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        aget_messages: Literal[True], 
+    ) -> Coroutine[None, None, AsyncCursorPage[OpenAIMessage]]:
+        ...
+
+    @overload
+    def get_messages(
+        self, 
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI],
+        aget_messages: Optional[Literal[False]], 
+    ) -> SyncCursorPage[OpenAIMessage]: 
+        ...
+
+    # fmt: on
+
+    def get_messages(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client=None,
+        aget_messages=None,
+    ):
+        if aget_messages is not None and aget_messages is True:
+            return self.async_get_messages(
+                thread_id=thread_id,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = openai_client.beta.threads.messages.list(thread_id=thread_id)
+
+        return response
+
+    ### THREADS ###
+
+    async def async_create_thread(
+        self,
+        metadata: Optional[dict],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+    ) -> Thread:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        data = {}
+        if messages is not None:
+            data["messages"] = messages  # type: ignore
+        if metadata is not None:
+            data["metadata"] = metadata  # type: ignore
+
+        message_thread = await openai_client.beta.threads.create(**data)  # type: ignore
+
+        return Thread(**message_thread.dict())
+
+    # fmt: off
+
+    @overload
+    def create_thread(
+        self, 
+        metadata: Optional[dict],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        client: Optional[AsyncOpenAI],
+        acreate_thread: Literal[True], 
+    ) -> Coroutine[None, None, Thread]:
+        ...
+
+    @overload
+    def create_thread(
+        self, 
+        metadata: Optional[dict],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        client: Optional[OpenAI],
+        acreate_thread: Optional[Literal[False]], 
+    ) -> Thread: 
+        ...
+
+    # fmt: on
+
+    def create_thread(
+        self,
+        metadata: Optional[dict],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        client=None,
+        acreate_thread=None,
+    ):
+        """
+        Here's an example:
+        ```
+        from litellm.llms.openai.openai import OpenAIAssistantsAPI, MessageData
+
+        # create thread
+        message: MessageData = {"role": "user", "content": "Hey, how's it going?"}
+        openai_api.create_thread(messages=[message])
+        ```
+        """
+        if acreate_thread is not None and acreate_thread is True:
+            return self.async_create_thread(
+                metadata=metadata,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+                messages=messages,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        data = {}
+        if messages is not None:
+            data["messages"] = messages  # type: ignore
+        if metadata is not None:
+            data["metadata"] = metadata  # type: ignore
+
+        message_thread = openai_client.beta.threads.create(**data)  # type: ignore
+
+        return Thread(**message_thread.dict())
+
+    async def async_get_thread(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+    ) -> Thread:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = await openai_client.beta.threads.retrieve(thread_id=thread_id)
+
+        return Thread(**response.dict())
+
+    # fmt: off
+
+    @overload
+    def get_thread(
+        self, 
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+        aget_thread: Literal[True], 
+    ) -> Coroutine[None, None, Thread]:
+        ...
+
+    @overload
+    def get_thread(
+        self, 
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[OpenAI],
+        aget_thread: Optional[Literal[False]], 
+    ) -> Thread: 
+        ...
+
+    # fmt: on
+
+    def get_thread(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client=None,
+        aget_thread=None,
+    ):
+        if aget_thread is not None and aget_thread is True:
+            return self.async_get_thread(
+                thread_id=thread_id,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = openai_client.beta.threads.retrieve(thread_id=thread_id)
+
+        return Thread(**response.dict())
+
+    def delete_thread(self):
+        pass
+
+    ### RUNS ###
+
+    async def arun_thread(
+        self,
+        thread_id: str,
+        assistant_id: str,
+        additional_instructions: Optional[str],
+        instructions: Optional[str],
+        metadata: Optional[Dict],
+        model: Optional[str],
+        stream: Optional[bool],
+        tools: Optional[Iterable[AssistantToolParam]],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[AsyncOpenAI],
+    ) -> Run:
+        openai_client = self.async_get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        response = await openai_client.beta.threads.runs.create_and_poll(  # type: ignore
+            thread_id=thread_id,
+            assistant_id=assistant_id,
+            additional_instructions=additional_instructions,
+            instructions=instructions,
+            metadata=metadata,
+            model=model,
+            tools=tools,
+        )
+
+        return response
+
+    def async_run_thread_stream(
+        self,
+        client: AsyncOpenAI,
+        thread_id: str,
+        assistant_id: str,
+        additional_instructions: Optional[str],
+        instructions: Optional[str],
+        metadata: Optional[Dict],
+        model: Optional[str],
+        tools: Optional[Iterable[AssistantToolParam]],
+        event_handler: Optional[AssistantEventHandler],
+    ) -> AsyncAssistantStreamManager[AsyncAssistantEventHandler]:
+        data: Dict[str, Any] = {
+            "thread_id": thread_id,
+            "assistant_id": assistant_id,
+            "additional_instructions": additional_instructions,
+            "instructions": instructions,
+            "metadata": metadata,
+            "model": model,
+            "tools": tools,
+        }
+        if event_handler is not None:
+            data["event_handler"] = event_handler
+        return client.beta.threads.runs.stream(**data)  # type: ignore
+
+    def run_thread_stream(
+        self,
+        client: OpenAI,
+        thread_id: str,
+        assistant_id: str,
+        additional_instructions: Optional[str],
+        instructions: Optional[str],
+        metadata: Optional[Dict],
+        model: Optional[str],
+        tools: Optional[Iterable[AssistantToolParam]],
+        event_handler: Optional[AssistantEventHandler],
+    ) -> AssistantStreamManager[AssistantEventHandler]:
+        data: Dict[str, Any] = {
+            "thread_id": thread_id,
+            "assistant_id": assistant_id,
+            "additional_instructions": additional_instructions,
+            "instructions": instructions,
+            "metadata": metadata,
+            "model": model,
+            "tools": tools,
+        }
+        if event_handler is not None:
+            data["event_handler"] = event_handler
+        return client.beta.threads.runs.stream(**data)  # type: ignore
+
+    # fmt: off
+
+    @overload
+    def run_thread(
+        self, 
+        thread_id: str,
+        assistant_id: str,
+        additional_instructions: Optional[str],
+        instructions: Optional[str],
+        metadata: Optional[Dict],
+        model: Optional[str],
+        stream: Optional[bool],
+        tools: Optional[Iterable[AssistantToolParam]],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client,
+        arun_thread: Literal[True], 
+        event_handler: Optional[AssistantEventHandler],
+    ) -> Coroutine[None, None, Run]:
+        ...
+
+    @overload
+    def run_thread(
+        self, 
+        thread_id: str,
+        assistant_id: str,
+        additional_instructions: Optional[str],
+        instructions: Optional[str],
+        metadata: Optional[Dict],
+        model: Optional[str],
+        stream: Optional[bool],
+        tools: Optional[Iterable[AssistantToolParam]],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client,
+        arun_thread: Optional[Literal[False]], 
+        event_handler: Optional[AssistantEventHandler],
+    ) -> Run: 
+        ...
+
+    # fmt: on
+
+    def run_thread(
+        self,
+        thread_id: str,
+        assistant_id: str,
+        additional_instructions: Optional[str],
+        instructions: Optional[str],
+        metadata: Optional[Dict],
+        model: Optional[str],
+        stream: Optional[bool],
+        tools: Optional[Iterable[AssistantToolParam]],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client=None,
+        arun_thread=None,
+        event_handler: Optional[AssistantEventHandler] = None,
+    ):
+        if arun_thread is not None and arun_thread is True:
+            if stream is not None and stream is True:
+                _client = self.async_get_openai_client(
+                    api_key=api_key,
+                    api_base=api_base,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    organization=organization,
+                    client=client,
+                )
+                return self.async_run_thread_stream(
+                    client=_client,
+                    thread_id=thread_id,
+                    assistant_id=assistant_id,
+                    additional_instructions=additional_instructions,
+                    instructions=instructions,
+                    metadata=metadata,
+                    model=model,
+                    tools=tools,
+                    event_handler=event_handler,
+                )
+            return self.arun_thread(
+                thread_id=thread_id,
+                assistant_id=assistant_id,
+                additional_instructions=additional_instructions,
+                instructions=instructions,
+                metadata=metadata,
+                model=model,
+                stream=stream,
+                tools=tools,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                organization=organization,
+                client=client,
+            )
+        openai_client = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+        )
+
+        if stream is not None and stream is True:
+            return self.run_thread_stream(
+                client=openai_client,
+                thread_id=thread_id,
+                assistant_id=assistant_id,
+                additional_instructions=additional_instructions,
+                instructions=instructions,
+                metadata=metadata,
+                model=model,
+                tools=tools,
+                event_handler=event_handler,
+            )
+
+        response = openai_client.beta.threads.runs.create_and_poll(  # type: ignore
+            thread_id=thread_id,
+            assistant_id=assistant_id,
+            additional_instructions=additional_instructions,
+            instructions=instructions,
+            metadata=metadata,
+            model=model,
+            tools=tools,
+        )
+
+        return response
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/realtime/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/realtime/handler.py
new file mode 100644
index 00000000..83398ad1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/realtime/handler.py
@@ -0,0 +1,73 @@
+"""
+This file contains the calling Azure OpenAI's `/openai/realtime` endpoint.
+
+This requires websockets, and is currently only supported on LiteLLM Proxy.
+"""
+
+from typing import Any, Optional
+
+from ....litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+from ....litellm_core_utils.realtime_streaming import RealTimeStreaming
+from ..openai import OpenAIChatCompletion
+
+
+class OpenAIRealtime(OpenAIChatCompletion):
+    def _construct_url(self, api_base: str, model: str) -> str:
+        """
+        Example output:
+        "BACKEND_WS_URL = "wss://localhost:8080/v1/realtime?model=gpt-4o-realtime-preview-2024-10-01"";
+        """
+        api_base = api_base.replace("https://", "wss://")
+        api_base = api_base.replace("http://", "ws://")
+        return f"{api_base}/v1/realtime?model={model}"
+
+    async def async_realtime(
+        self,
+        model: str,
+        websocket: Any,
+        logging_obj: LiteLLMLogging,
+        api_base: Optional[str] = None,
+        api_key: Optional[str] = None,
+        client: Optional[Any] = None,
+        timeout: Optional[float] = None,
+    ):
+        import websockets
+
+        if api_base is None:
+            raise ValueError("api_base is required for Azure OpenAI calls")
+        if api_key is None:
+            raise ValueError("api_key is required for Azure OpenAI calls")
+
+        url = self._construct_url(api_base, model)
+
+        try:
+            async with websockets.connect(  # type: ignore
+                url,
+                extra_headers={
+                    "Authorization": f"Bearer {api_key}",  # type: ignore
+                    "OpenAI-Beta": "realtime=v1",
+                },
+            ) as backend_ws:
+                realtime_streaming = RealTimeStreaming(
+                    websocket, backend_ws, logging_obj
+                )
+                await realtime_streaming.bidirectional_forward()
+
+        except websockets.exceptions.InvalidStatusCode as e:  # type: ignore
+            await websocket.close(code=e.status_code, reason=str(e))
+        except Exception as e:
+            try:
+                await websocket.close(
+                    code=1011, reason=f"Internal server error: {str(e)}"
+                )
+            except RuntimeError as close_error:
+                if "already completed" in str(close_error) or "websocket.close" in str(
+                    close_error
+                ):
+                    # The WebSocket is already closed or the response is completed, so we can ignore this error
+                    pass
+                else:
+                    # If it's a different RuntimeError, we might want to log it or handle it differently
+                    raise Exception(
+                        f"Unexpected error while closing WebSocket: {close_error}"
+                    )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/responses/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/responses/transformation.py
new file mode 100644
index 00000000..e062c0c9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/responses/transformation.py
@@ -0,0 +1,216 @@
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union, cast
+
+import httpx
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig
+from litellm.secret_managers.main import get_secret_str
+from litellm.types.llms.openai import *
+from litellm.types.router import GenericLiteLLMParams
+
+from ..common_utils import OpenAIError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
+
+    LiteLLMLoggingObj = _LiteLLMLoggingObj
+else:
+    LiteLLMLoggingObj = Any
+
+
+class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig):
+    def get_supported_openai_params(self, model: str) -> list:
+        """
+        All OpenAI Responses API params are supported
+        """
+        return [
+            "input",
+            "model",
+            "include",
+            "instructions",
+            "max_output_tokens",
+            "metadata",
+            "parallel_tool_calls",
+            "previous_response_id",
+            "reasoning",
+            "store",
+            "stream",
+            "temperature",
+            "text",
+            "tool_choice",
+            "tools",
+            "top_p",
+            "truncation",
+            "user",
+            "extra_headers",
+            "extra_query",
+            "extra_body",
+            "timeout",
+        ]
+
+    def map_openai_params(
+        self,
+        response_api_optional_params: ResponsesAPIOptionalRequestParams,
+        model: str,
+        drop_params: bool,
+    ) -> Dict:
+        """No mapping applied since inputs are in OpenAI spec already"""
+        return dict(response_api_optional_params)
+
+    def transform_responses_api_request(
+        self,
+        model: str,
+        input: Union[str, ResponseInputParam],
+        response_api_optional_request_params: Dict,
+        litellm_params: GenericLiteLLMParams,
+        headers: dict,
+    ) -> Dict:
+        """No transform applied since inputs are in OpenAI spec already"""
+        return dict(
+            ResponsesAPIRequestParams(
+                model=model, input=input, **response_api_optional_request_params
+            )
+        )
+
+    def transform_response_api_response(
+        self,
+        model: str,
+        raw_response: httpx.Response,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> ResponsesAPIResponse:
+        """No transform applied since outputs are in OpenAI spec already"""
+        try:
+            raw_response_json = raw_response.json()
+        except Exception:
+            raise OpenAIError(
+                message=raw_response.text, status_code=raw_response.status_code
+            )
+        return ResponsesAPIResponse(**raw_response_json)
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        api_key: Optional[str] = None,
+    ) -> dict:
+        api_key = (
+            api_key
+            or litellm.api_key
+            or litellm.openai_key
+            or get_secret_str("OPENAI_API_KEY")
+        )
+        headers.update(
+            {
+                "Authorization": f"Bearer {api_key}",
+            }
+        )
+        return headers
+
+    def get_complete_url(
+        self,
+        api_base: Optional[str],
+        model: str,
+        stream: Optional[bool] = None,
+    ) -> str:
+        """
+        Get the endpoint for OpenAI responses API
+        """
+        api_base = (
+            api_base
+            or litellm.api_base
+            or get_secret_str("OPENAI_API_BASE")
+            or "https://api.openai.com/v1"
+        )
+
+        # Remove trailing slashes
+        api_base = api_base.rstrip("/")
+
+        return f"{api_base}/responses"
+
+    def transform_streaming_response(
+        self,
+        model: str,
+        parsed_chunk: dict,
+        logging_obj: LiteLLMLoggingObj,
+    ) -> ResponsesAPIStreamingResponse:
+        """
+        Transform a parsed streaming response chunk into a ResponsesAPIStreamingResponse
+        """
+        # Convert the dictionary to a properly typed ResponsesAPIStreamingResponse
+        verbose_logger.debug("Raw OpenAI Chunk=%s", parsed_chunk)
+        event_type = str(parsed_chunk.get("type"))
+        event_pydantic_model = OpenAIResponsesAPIConfig.get_event_model_class(
+            event_type=event_type
+        )
+        return event_pydantic_model(**parsed_chunk)
+
+    @staticmethod
+    def get_event_model_class(event_type: str) -> Any:
+        """
+        Returns the appropriate event model class based on the event type.
+
+        Args:
+            event_type (str): The type of event from the response chunk
+
+        Returns:
+            Any: The corresponding event model class
+
+        Raises:
+            ValueError: If the event type is unknown
+        """
+        event_models = {
+            ResponsesAPIStreamEvents.RESPONSE_CREATED: ResponseCreatedEvent,
+            ResponsesAPIStreamEvents.RESPONSE_IN_PROGRESS: ResponseInProgressEvent,
+            ResponsesAPIStreamEvents.RESPONSE_COMPLETED: ResponseCompletedEvent,
+            ResponsesAPIStreamEvents.RESPONSE_FAILED: ResponseFailedEvent,
+            ResponsesAPIStreamEvents.RESPONSE_INCOMPLETE: ResponseIncompleteEvent,
+            ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED: OutputItemAddedEvent,
+            ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE: OutputItemDoneEvent,
+            ResponsesAPIStreamEvents.CONTENT_PART_ADDED: ContentPartAddedEvent,
+            ResponsesAPIStreamEvents.CONTENT_PART_DONE: ContentPartDoneEvent,
+            ResponsesAPIStreamEvents.OUTPUT_TEXT_DELTA: OutputTextDeltaEvent,
+            ResponsesAPIStreamEvents.OUTPUT_TEXT_ANNOTATION_ADDED: OutputTextAnnotationAddedEvent,
+            ResponsesAPIStreamEvents.OUTPUT_TEXT_DONE: OutputTextDoneEvent,
+            ResponsesAPIStreamEvents.REFUSAL_DELTA: RefusalDeltaEvent,
+            ResponsesAPIStreamEvents.REFUSAL_DONE: RefusalDoneEvent,
+            ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DELTA: FunctionCallArgumentsDeltaEvent,
+            ResponsesAPIStreamEvents.FUNCTION_CALL_ARGUMENTS_DONE: FunctionCallArgumentsDoneEvent,
+            ResponsesAPIStreamEvents.FILE_SEARCH_CALL_IN_PROGRESS: FileSearchCallInProgressEvent,
+            ResponsesAPIStreamEvents.FILE_SEARCH_CALL_SEARCHING: FileSearchCallSearchingEvent,
+            ResponsesAPIStreamEvents.FILE_SEARCH_CALL_COMPLETED: FileSearchCallCompletedEvent,
+            ResponsesAPIStreamEvents.WEB_SEARCH_CALL_IN_PROGRESS: WebSearchCallInProgressEvent,
+            ResponsesAPIStreamEvents.WEB_SEARCH_CALL_SEARCHING: WebSearchCallSearchingEvent,
+            ResponsesAPIStreamEvents.WEB_SEARCH_CALL_COMPLETED: WebSearchCallCompletedEvent,
+            ResponsesAPIStreamEvents.ERROR: ErrorEvent,
+        }
+
+        model_class = event_models.get(cast(ResponsesAPIStreamEvents, event_type))
+        if not model_class:
+            raise ValueError(f"Unknown event type: {event_type}")
+
+        return model_class
+
+    def should_fake_stream(
+        self,
+        model: Optional[str],
+        stream: Optional[bool],
+        custom_llm_provider: Optional[str] = None,
+    ) -> bool:
+        if stream is not True:
+            return False
+        if model is not None:
+            try:
+                if (
+                    litellm.utils.supports_native_streaming(
+                        model=model,
+                        custom_llm_provider=custom_llm_provider,
+                    )
+                    is False
+                ):
+                    return True
+            except Exception as e:
+                verbose_logger.debug(
+                    f"Error getting model info in OpenAIResponsesAPIConfig: {e}"
+                )
+        return False
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/openai/transcriptions/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/openai/transcriptions/handler.py
new file mode 100644
index 00000000..d9dd3c12
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/openai/transcriptions/handler.py
@@ -0,0 +1,210 @@
+from typing import Optional, Union
+
+import httpx
+from openai import AsyncOpenAI, OpenAI
+from pydantic import BaseModel
+
+import litellm
+from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_name
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.types.utils import FileTypes
+from litellm.utils import (
+    TranscriptionResponse,
+    convert_to_model_response_object,
+    extract_duration_from_srt_or_vtt,
+)
+
+from ..openai import OpenAIChatCompletion
+
+
+class OpenAIAudioTranscription(OpenAIChatCompletion):
+    # Audio Transcriptions
+    async def make_openai_audio_transcriptions_request(
+        self,
+        openai_aclient: AsyncOpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+    ):
+        """
+        Helper to:
+        - call openai_aclient.audio.transcriptions.with_raw_response when litellm.return_response_headers is True
+        - call openai_aclient.audio.transcriptions.create by default
+        """
+        try:
+            raw_response = (
+                await openai_aclient.audio.transcriptions.with_raw_response.create(
+                    **data, timeout=timeout
+                )
+            )  # type: ignore
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+
+            return headers, response
+        except Exception as e:
+            raise e
+
+    def make_sync_openai_audio_transcriptions_request(
+        self,
+        openai_client: OpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+    ):
+        """
+        Helper to:
+        - call openai_aclient.audio.transcriptions.with_raw_response when litellm.return_response_headers is True
+        - call openai_aclient.audio.transcriptions.create by default
+        """
+        try:
+            if litellm.return_response_headers is True:
+                raw_response = (
+                    openai_client.audio.transcriptions.with_raw_response.create(
+                        **data, timeout=timeout
+                    )
+                )  # type: ignore
+                headers = dict(raw_response.headers)
+                response = raw_response.parse()
+                return headers, response
+            else:
+                response = openai_client.audio.transcriptions.create(**data, timeout=timeout)  # type: ignore
+                return None, response
+        except Exception as e:
+            raise e
+
+    def audio_transcriptions(
+        self,
+        model: str,
+        audio_file: FileTypes,
+        optional_params: dict,
+        model_response: TranscriptionResponse,
+        timeout: float,
+        max_retries: int,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        client=None,
+        atranscription: bool = False,
+    ) -> TranscriptionResponse:
+        data = {"model": model, "file": audio_file, **optional_params}
+
+        if "response_format" not in data or (
+            data["response_format"] == "text" or data["response_format"] == "json"
+        ):
+            data["response_format"] = (
+                "verbose_json"  # ensures 'duration' is received - used for cost calculation
+            )
+
+        if atranscription is True:
+            return self.async_audio_transcriptions(  # type: ignore
+                audio_file=audio_file,
+                data=data,
+                model_response=model_response,
+                timeout=timeout,
+                api_key=api_key,
+                api_base=api_base,
+                client=client,
+                max_retries=max_retries,
+                logging_obj=logging_obj,
+            )
+
+        openai_client: OpenAI = self._get_openai_client(  # type: ignore
+            is_async=False,
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+        )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=None,
+            api_key=openai_client.api_key,
+            additional_args={
+                "api_base": openai_client._base_url._uri_reference,
+                "atranscription": True,
+                "complete_input_dict": data,
+            },
+        )
+        _, response = self.make_sync_openai_audio_transcriptions_request(
+            openai_client=openai_client,
+            data=data,
+            timeout=timeout,
+        )
+
+        if isinstance(response, BaseModel):
+            stringified_response = response.model_dump()
+        else:
+            stringified_response = TranscriptionResponse(text=response).model_dump()
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=get_audio_file_name(audio_file),
+            api_key=api_key,
+            additional_args={"complete_input_dict": data},
+            original_response=stringified_response,
+        )
+        hidden_params = {"model": "whisper-1", "custom_llm_provider": "openai"}
+        final_response: TranscriptionResponse = convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, hidden_params=hidden_params, response_type="audio_transcription")  # type: ignore
+        return final_response
+
+    async def async_audio_transcriptions(
+        self,
+        audio_file: FileTypes,
+        data: dict,
+        model_response: TranscriptionResponse,
+        timeout: float,
+        logging_obj: LiteLLMLoggingObj,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client=None,
+        max_retries=None,
+    ):
+        try:
+            openai_aclient: AsyncOpenAI = self._get_openai_client(  # type: ignore
+                is_async=True,
+                api_key=api_key,
+                api_base=api_base,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+            )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=None,
+                api_key=openai_aclient.api_key,
+                additional_args={
+                    "api_base": openai_aclient._base_url._uri_reference,
+                    "atranscription": True,
+                    "complete_input_dict": data,
+                },
+            )
+            headers, response = await self.make_openai_audio_transcriptions_request(
+                openai_aclient=openai_aclient,
+                data=data,
+                timeout=timeout,
+            )
+            logging_obj.model_call_details["response_headers"] = headers
+            if isinstance(response, BaseModel):
+                stringified_response = response.model_dump()
+            else:
+                duration = extract_duration_from_srt_or_vtt(response)
+                stringified_response = TranscriptionResponse(text=response).model_dump()
+                stringified_response["duration"] = duration
+            ## LOGGING
+            logging_obj.post_call(
+                input=get_audio_file_name(audio_file),
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=stringified_response,
+            )
+            hidden_params = {"model": "whisper-1", "custom_llm_provider": "openai"}
+            return convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, hidden_params=hidden_params, response_type="audio_transcription")  # type: ignore
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                original_response=str(e),
+            )
+            raise e