two version of R2R are hereHEADmaster

1 files changed, 2870 insertions, 0 deletions

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
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+++ 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