two version of R2R are here HEAD master

14 files changed, 4544 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/assistants.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/assistants.py
new file mode 100644
index 00000000..2e8c78b2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/assistants.py
@@ -0,0 +1,1024 @@
+from typing import Any, Coroutine, Dict, Iterable, Literal, Optional, Union
+
+import httpx
+from openai import AsyncAzureOpenAI, AzureOpenAI
+from typing_extensions import overload
+
+from ...types.llms.openai import (
+    Assistant,
+    AssistantEventHandler,
+    AssistantStreamManager,
+    AssistantToolParam,
+    AsyncAssistantEventHandler,
+    AsyncAssistantStreamManager,
+    AsyncCursorPage,
+    OpenAICreateThreadParamsMessage,
+    OpenAIMessage,
+    Run,
+    SyncCursorPage,
+    Thread,
+)
+from .common_utils import BaseAzureLLM
+
+
+class AzureAssistantsAPI(BaseAzureLLM):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def get_azure_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> AzureOpenAI:
+        if client is None:
+            azure_client_params = self.initialize_azure_sdk_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                model_name="",
+                api_version=api_version,
+                is_async=False,
+            )
+            azure_openai_client = AzureOpenAI(**azure_client_params)  # type: ignore
+        else:
+            azure_openai_client = client
+
+        return azure_openai_client
+
+    def async_get_azure_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> AsyncAzureOpenAI:
+        if client is None:
+            azure_client_params = self.initialize_azure_sdk_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                model_name="",
+                api_version=api_version,
+                is_async=True,
+            )
+
+            azure_openai_client = AsyncAzureOpenAI(**azure_client_params)
+            # azure_openai_client = AsyncAzureOpenAI(**data)  # type: ignore
+        else:
+            azure_openai_client = client
+
+        return azure_openai_client
+
+    ### ASSISTANTS ###
+
+    async def async_get_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        litellm_params: Optional[dict] = None,
+    ) -> AsyncCursorPage[Assistant]:
+        azure_openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        response = await azure_openai_client.beta.assistants.list()
+
+        return response
+
+    # fmt: off
+
+    @overload
+    def get_assistants(
+        self, 
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        aget_assistants: Literal[True], 
+    ) -> Coroutine[None, None, AsyncCursorPage[Assistant]]:
+        ...
+
+    @overload
+    def get_assistants(
+        self, 
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI],
+        aget_assistants: Optional[Literal[False]], 
+    ) -> SyncCursorPage[Assistant]: 
+        ...
+
+    # fmt: on
+
+    def get_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client=None,
+        aget_assistants=None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                litellm_params=litellm_params,
+            )
+        azure_openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            api_version=api_version,
+            litellm_params=litellm_params,
+        )
+
+        response = azure_openai_client.beta.assistants.list()
+
+        return response
+
+    ### MESSAGES ###
+
+    async def a_add_message(
+        self,
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> OpenAIMessage:
+        openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        a_add_message: Literal[True],
+        litellm_params: Optional[dict] = None,
+    ) -> Coroutine[None, None, OpenAIMessage]:
+        ...
+
+    @overload
+    def add_message(
+        self,
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI],
+        a_add_message: Optional[Literal[False]],
+        litellm_params: Optional[dict] = None,
+    ) -> OpenAIMessage:
+        ...
+
+    # fmt: on
+
+    def add_message(
+        self,
+        thread_id: str,
+        message_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client=None,
+        a_add_message: Optional[bool] = None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                litellm_params=litellm_params,
+            )
+        openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> AsyncCursorPage[OpenAIMessage]:
+        openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        aget_messages: Literal[True],
+        litellm_params: Optional[dict] = None,
+    ) -> Coroutine[None, None, AsyncCursorPage[OpenAIMessage]]:
+        ...
+
+    @overload
+    def get_messages(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI],
+        aget_messages: Optional[Literal[False]],
+        litellm_params: Optional[dict] = None,
+    ) -> SyncCursorPage[OpenAIMessage]:
+        ...
+
+    # fmt: on
+
+    def get_messages(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client=None,
+        aget_messages=None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                litellm_params=litellm_params,
+            )
+        openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        litellm_params: Optional[dict] = None,
+    ) -> Thread:
+        openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        client: Optional[AsyncAzureOpenAI],
+        acreate_thread: Literal[True],
+        litellm_params: Optional[dict] = None,
+    ) -> Coroutine[None, None, Thread]:
+        ...
+
+    @overload
+    def create_thread(
+        self,
+        metadata: Optional[dict],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        client: Optional[AzureOpenAI],
+        acreate_thread: Optional[Literal[False]],
+        litellm_params: Optional[dict] = None,
+    ) -> Thread:
+        ...
+
+    # fmt: on
+
+    def create_thread(
+        self,
+        metadata: Optional[dict],
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        messages: Optional[Iterable[OpenAICreateThreadParamsMessage]],
+        client=None,
+        acreate_thread=None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                messages=messages,
+                litellm_params=litellm_params,
+            )
+        azure_openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        data = {}
+        if messages is not None:
+            data["messages"] = messages  # type: ignore
+        if metadata is not None:
+            data["metadata"] = metadata  # type: ignore
+
+        message_thread = azure_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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        litellm_params: Optional[dict] = None,
+    ) -> Thread:
+        openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        aget_thread: Literal[True],
+        litellm_params: Optional[dict] = None,
+    ) -> Coroutine[None, None, Thread]:
+        ...
+
+    @overload
+    def get_thread(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI],
+        aget_thread: Optional[Literal[False]],
+        litellm_params: Optional[dict] = None,
+    ) -> Thread:
+        ...
+
+    # fmt: on
+
+    def get_thread(
+        self,
+        thread_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client=None,
+        aget_thread=None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                litellm_params=litellm_params,
+            )
+        openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        litellm_params: Optional[dict] = None,
+    ) -> Run:
+        openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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,  # type: ignore
+            model=model,
+            tools=tools,
+        )
+
+        return response
+
+    def async_run_thread_stream(
+        self,
+        client: AsyncAzureOpenAI,
+        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],
+        litellm_params: Optional[dict] = None,
+    ) -> 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: AzureOpenAI,
+        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],
+        litellm_params: Optional[dict] = None,
+    ) -> 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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        arun_thread: Literal[True],
+    ) -> 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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI],
+        arun_thread: Optional[Literal[False]],
+    ) -> 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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client=None,
+        arun_thread=None,
+        event_handler: Optional[AssistantEventHandler] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        if arun_thread is not None and arun_thread is True:
+            if stream is not None and stream is True:
+                azure_client = self.async_get_azure_client(
+                    api_key=api_key,
+                    api_base=api_base,
+                    api_version=api_version,
+                    azure_ad_token=azure_ad_token,
+                    timeout=timeout,
+                    max_retries=max_retries,
+                    client=client,
+                    litellm_params=litellm_params,
+                )
+                return self.async_run_thread_stream(
+                    client=azure_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,
+                    litellm_params=litellm_params,
+                )
+            return self.arun_thread(
+                thread_id=thread_id,
+                assistant_id=assistant_id,
+                additional_instructions=additional_instructions,
+                instructions=instructions,
+                metadata=metadata,  # type: ignore
+                model=model,
+                stream=stream,
+                tools=tools,
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                litellm_params=litellm_params,
+            )
+        openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        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,
+                litellm_params=litellm_params,
+            )
+
+        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,  # type: ignore
+            model=model,
+            tools=tools,
+        )
+
+        return response
+
+    # Create Assistant
+    async def async_create_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        create_assistant_data: dict,
+        litellm_params: Optional[dict] = None,
+    ) -> Assistant:
+        azure_openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        response = await azure_openai_client.beta.assistants.create(
+            **create_assistant_data
+        )
+        return response
+
+    def create_assistants(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        create_assistant_data: dict,
+        client=None,
+        async_create_assistants=None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                create_assistant_data=create_assistant_data,
+                litellm_params=litellm_params,
+            )
+        azure_openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        response = azure_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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AsyncAzureOpenAI],
+        assistant_id: str,
+        litellm_params: Optional[dict] = None,
+    ):
+        azure_openai_client = self.async_get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        response = await azure_openai_client.beta.assistants.delete(
+            assistant_id=assistant_id
+        )
+        return response
+
+    def delete_assistant(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        assistant_id: str,
+        async_delete_assistants: Optional[bool] = None,
+        client=None,
+        litellm_params: Optional[dict] = 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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                timeout=timeout,
+                max_retries=max_retries,
+                client=client,
+                assistant_id=assistant_id,
+                litellm_params=litellm_params,
+            )
+        azure_openai_client = self.get_azure_client(
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            azure_ad_token=azure_ad_token,
+            timeout=timeout,
+            max_retries=max_retries,
+            client=client,
+            litellm_params=litellm_params,
+        )
+
+        response = azure_openai_client.beta.assistants.delete(assistant_id=assistant_id)
+        return response
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/audio_transcriptions.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/audio_transcriptions.py
new file mode 100644
index 00000000..be7d0fa3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/audio_transcriptions.py
@@ -0,0 +1,198 @@
+import uuid
+from typing import Any, Coroutine, Optional, Union
+
+from openai import AsyncAzureOpenAI, AzureOpenAI
+from pydantic import BaseModel
+
+from litellm.litellm_core_utils.audio_utils.utils import get_audio_file_name
+from litellm.types.utils import FileTypes
+from litellm.utils import (
+    TranscriptionResponse,
+    convert_to_model_response_object,
+    extract_duration_from_srt_or_vtt,
+)
+
+from .azure import AzureChatCompletion
+from .common_utils import AzureOpenAIError
+
+
+class AzureAudioTranscription(AzureChatCompletion):
+    def audio_transcriptions(
+        self,
+        model: str,
+        audio_file: FileTypes,
+        optional_params: dict,
+        logging_obj: Any,
+        model_response: TranscriptionResponse,
+        timeout: float,
+        max_retries: int,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        client=None,
+        azure_ad_token: Optional[str] = None,
+        atranscription: bool = False,
+        litellm_params: Optional[dict] = None,
+    ) -> Union[TranscriptionResponse, Coroutine[Any, Any, TranscriptionResponse]]:
+        data = {"model": model, "file": audio_file, **optional_params}
+
+        if atranscription is True:
+            return self.async_audio_transcriptions(
+                audio_file=audio_file,
+                data=data,
+                model_response=model_response,
+                timeout=timeout,
+                api_key=api_key,
+                api_base=api_base,
+                client=client,
+                max_retries=max_retries,
+                logging_obj=logging_obj,
+                model=model,
+                litellm_params=litellm_params,
+            )
+
+        azure_client = self.get_azure_openai_client(
+            api_version=api_version,
+            api_base=api_base,
+            api_key=api_key,
+            model=model,
+            _is_async=False,
+            client=client,
+            litellm_params=litellm_params,
+        )
+        if not isinstance(azure_client, AzureOpenAI):
+            raise AzureOpenAIError(
+                status_code=500,
+                message="azure_client is not an instance of AzureOpenAI",
+            )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=f"audio_file_{uuid.uuid4()}",
+            api_key=azure_client.api_key,
+            additional_args={
+                "headers": {"Authorization": f"Bearer {azure_client.api_key}"},
+                "api_base": azure_client._base_url._uri_reference,
+                "atranscription": True,
+                "complete_input_dict": data,
+            },
+        )
+
+        response = azure_client.audio.transcriptions.create(
+            **data, timeout=timeout  # type: ignore
+        )
+
+        if isinstance(response, BaseModel):
+            stringified_response = response.model_dump()
+        else:
+            stringified_response = TranscriptionResponse(text=response).model_dump()
+
+        ## LOGGING
+        logging_obj.post_call(
+            input=get_audio_file_name(audio_file),
+            api_key=api_key,
+            additional_args={"complete_input_dict": data},
+            original_response=stringified_response,
+        )
+        hidden_params = {"model": "whisper-1", "custom_llm_provider": "azure"}
+        final_response: TranscriptionResponse = convert_to_model_response_object(response_object=stringified_response, model_response_object=model_response, hidden_params=hidden_params, response_type="audio_transcription")  # type: ignore
+        return final_response
+
+    async def async_audio_transcriptions(
+        self,
+        audio_file: FileTypes,
+        model: str,
+        data: dict,
+        model_response: TranscriptionResponse,
+        timeout: float,
+        logging_obj: Any,
+        api_version: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        client=None,
+        max_retries=None,
+        litellm_params: Optional[dict] = None,
+    ) -> TranscriptionResponse:
+        response = None
+        try:
+            async_azure_client = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                _is_async=True,
+                client=client,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(async_azure_client, AsyncAzureOpenAI):
+                raise AzureOpenAIError(
+                    status_code=500,
+                    message="async_azure_client is not an instance of AsyncAzureOpenAI",
+                )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=f"audio_file_{uuid.uuid4()}",
+                api_key=async_azure_client.api_key,
+                additional_args={
+                    "headers": {
+                        "Authorization": f"Bearer {async_azure_client.api_key}"
+                    },
+                    "api_base": async_azure_client._base_url._uri_reference,
+                    "atranscription": True,
+                    "complete_input_dict": data,
+                },
+            )
+
+            raw_response = (
+                await async_azure_client.audio.transcriptions.with_raw_response.create(
+                    **data, timeout=timeout
+                )
+            )  # type: ignore
+
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+
+            if isinstance(response, BaseModel):
+                stringified_response = response.model_dump()
+            else:
+                stringified_response = TranscriptionResponse(text=response).model_dump()
+                duration = extract_duration_from_srt_or_vtt(response)
+                stringified_response["duration"] = duration
+
+            ## LOGGING
+            logging_obj.post_call(
+                input=get_audio_file_name(audio_file),
+                api_key=api_key,
+                additional_args={
+                    "headers": {
+                        "Authorization": f"Bearer {async_azure_client.api_key}"
+                    },
+                    "api_base": async_azure_client._base_url._uri_reference,
+                    "atranscription": True,
+                    "complete_input_dict": data,
+                },
+                original_response=stringified_response,
+            )
+            hidden_params = {"model": "whisper-1", "custom_llm_provider": "azure"}
+            response = convert_to_model_response_object(
+                _response_headers=headers,
+                response_object=stringified_response,
+                model_response_object=model_response,
+                hidden_params=hidden_params,
+                response_type="audio_transcription",
+            )
+            if not isinstance(response, TranscriptionResponse):
+                raise AzureOpenAIError(
+                    status_code=500,
+                    message="response is not an instance of TranscriptionResponse",
+                )
+            return response
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                original_response=str(e),
+            )
+            raise e
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/azure.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/azure.py
new file mode 100644
index 00000000..03c5cc09
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/azure.py
@@ -0,0 +1,1347 @@
+import asyncio
+import json
+import time
+from typing import Any, Callable, Coroutine, Dict, List, Optional, Union
+
+import httpx  # type: ignore
+from openai import APITimeoutError, AsyncAzureOpenAI, AzureOpenAI
+
+import litellm
+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.custom_httpx.http_handler import (
+    AsyncHTTPHandler,
+    HTTPHandler,
+    get_async_httpx_client,
+)
+from litellm.types.utils import (
+    EmbeddingResponse,
+    ImageResponse,
+    LlmProviders,
+    ModelResponse,
+)
+from litellm.utils import (
+    CustomStreamWrapper,
+    convert_to_model_response_object,
+    modify_url,
+)
+
+from ...types.llms.openai import HttpxBinaryResponseContent
+from ..base import BaseLLM
+from .common_utils import (
+    AzureOpenAIError,
+    BaseAzureLLM,
+    get_azure_ad_token_from_oidc,
+    process_azure_headers,
+    select_azure_base_url_or_endpoint,
+)
+
+
+class AzureOpenAIAssistantsAPIConfig:
+    """
+    Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/assistants-reference-messages?tabs=python#create-message
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        pass
+
+    def get_supported_openai_create_message_params(self):
+        return [
+            "role",
+            "content",
+            "attachments",
+            "metadata",
+        ]
+
+    def map_openai_params_create_message_params(
+        self, non_default_params: dict, optional_params: dict
+    ):
+        for param, value in non_default_params.items():
+            if param == "role":
+                optional_params["role"] = value
+            if param == "metadata":
+                optional_params["metadata"] = value
+            elif param == "content":  # only string accepted
+                if isinstance(value, str):
+                    optional_params["content"] = value
+                else:
+                    raise litellm.utils.UnsupportedParamsError(
+                        message="Azure only accepts content as a string.",
+                        status_code=400,
+                    )
+            elif (
+                param == "attachments"
+            ):  # this is a v2 param. Azure currently supports the old 'file_id's param
+                file_ids: List[str] = []
+                if isinstance(value, list):
+                    for item in value:
+                        if "file_id" in item:
+                            file_ids.append(item["file_id"])
+                        else:
+                            if litellm.drop_params is True:
+                                pass
+                            else:
+                                raise litellm.utils.UnsupportedParamsError(
+                                    message="Azure doesn't support {}. To drop it from the call, set `litellm.drop_params = True.".format(
+                                        value
+                                    ),
+                                    status_code=400,
+                                )
+                else:
+                    raise litellm.utils.UnsupportedParamsError(
+                        message="Invalid param. attachments should always be a list. Got={}, Expected=List. Raw value={}".format(
+                            type(value), value
+                        ),
+                        status_code=400,
+                    )
+        return optional_params
+
+
+def _check_dynamic_azure_params(
+    azure_client_params: dict,
+    azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]],
+) -> bool:
+    """
+    Returns True if user passed in client params != initialized azure client
+
+    Currently only implemented for api version
+    """
+    if azure_client is None:
+        return True
+
+    dynamic_params = ["api_version"]
+    for k, v in azure_client_params.items():
+        if k in dynamic_params and k == "api_version":
+            if v is not None and v != azure_client._custom_query["api-version"]:
+                return True
+
+    return False
+
+
+class AzureChatCompletion(BaseAzureLLM, BaseLLM):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def validate_environment(self, api_key, azure_ad_token, azure_ad_token_provider):
+        headers = {
+            "content-type": "application/json",
+        }
+        if api_key is not None:
+            headers["api-key"] = api_key
+        elif azure_ad_token is not None:
+            if azure_ad_token.startswith("oidc/"):
+                azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
+            headers["Authorization"] = f"Bearer {azure_ad_token}"
+        elif azure_ad_token_provider is not None:
+            azure_ad_token = azure_ad_token_provider()
+            headers["Authorization"] = f"Bearer {azure_ad_token}"
+
+        return headers
+
+    def make_sync_azure_openai_chat_completion_request(
+        self,
+        azure_client: AzureOpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+    ):
+        """
+        Helper to:
+        - call chat.completions.create.with_raw_response when litellm.return_response_headers is True
+        - call chat.completions.create by default
+        """
+        try:
+            raw_response = azure_client.chat.completions.with_raw_response.create(
+                **data, timeout=timeout
+            )
+
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+            return headers, response
+        except Exception as e:
+            raise e
+
+    @track_llm_api_timing()
+    async def make_azure_openai_chat_completion_request(
+        self,
+        azure_client: AsyncAzureOpenAI,
+        data: dict,
+        timeout: Union[float, httpx.Timeout],
+        logging_obj: LiteLLMLoggingObj,
+    ):
+        """
+        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 azure_client.chat.completions.with_raw_response.create(
+                **data, timeout=timeout
+            )
+
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+            return headers, response
+        except 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
+
+    def completion(  # noqa: PLR0915
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        api_key: str,
+        api_base: str,
+        api_version: str,
+        api_type: str,
+        azure_ad_token: str,
+        azure_ad_token_provider: Callable,
+        dynamic_params: bool,
+        print_verbose: Callable,
+        timeout: Union[float, httpx.Timeout],
+        logging_obj: LiteLLMLoggingObj,
+        optional_params,
+        litellm_params,
+        logger_fn,
+        acompletion: bool = False,
+        headers: Optional[dict] = None,
+        client=None,
+    ):
+        if headers:
+            optional_params["extra_headers"] = headers
+        try:
+            if model is None or messages is None:
+                raise AzureOpenAIError(
+                    status_code=422, message="Missing model or messages"
+                )
+
+            max_retries = optional_params.pop("max_retries", None)
+            if max_retries is None:
+                max_retries = DEFAULT_MAX_RETRIES
+            json_mode: Optional[bool] = optional_params.pop("json_mode", False)
+
+            ### CHECK IF CLOUDFLARE AI GATEWAY ###
+            ### if so - set the model as part of the base url
+            if "gateway.ai.cloudflare.com" in api_base:
+                client = self._init_azure_client_for_cloudflare_ai_gateway(
+                    api_base=api_base,
+                    model=model,
+                    api_version=api_version,
+                    max_retries=max_retries,
+                    timeout=timeout,
+                    api_key=api_key,
+                    azure_ad_token=azure_ad_token,
+                    azure_ad_token_provider=azure_ad_token_provider,
+                    acompletion=acompletion,
+                    client=client,
+                )
+
+                data = {"model": None, "messages": messages, **optional_params}
+            else:
+                data = litellm.AzureOpenAIConfig().transform_request(
+                    model=model,
+                    messages=messages,
+                    optional_params=optional_params,
+                    litellm_params=litellm_params,
+                    headers=headers or {},
+                )
+
+            if acompletion is True:
+                if optional_params.get("stream", False):
+                    return self.async_streaming(
+                        logging_obj=logging_obj,
+                        api_base=api_base,
+                        dynamic_params=dynamic_params,
+                        data=data,
+                        model=model,
+                        api_key=api_key,
+                        api_version=api_version,
+                        azure_ad_token=azure_ad_token,
+                        azure_ad_token_provider=azure_ad_token_provider,
+                        timeout=timeout,
+                        client=client,
+                        max_retries=max_retries,
+                        litellm_params=litellm_params,
+                    )
+                else:
+                    return self.acompletion(
+                        api_base=api_base,
+                        data=data,
+                        model_response=model_response,
+                        api_key=api_key,
+                        api_version=api_version,
+                        model=model,
+                        azure_ad_token=azure_ad_token,
+                        azure_ad_token_provider=azure_ad_token_provider,
+                        dynamic_params=dynamic_params,
+                        timeout=timeout,
+                        client=client,
+                        logging_obj=logging_obj,
+                        max_retries=max_retries,
+                        convert_tool_call_to_json_mode=json_mode,
+                        litellm_params=litellm_params,
+                    )
+            elif "stream" in optional_params and optional_params["stream"] is True:
+                return self.streaming(
+                    logging_obj=logging_obj,
+                    api_base=api_base,
+                    dynamic_params=dynamic_params,
+                    data=data,
+                    model=model,
+                    api_key=api_key,
+                    api_version=api_version,
+                    azure_ad_token=azure_ad_token,
+                    azure_ad_token_provider=azure_ad_token_provider,
+                    timeout=timeout,
+                    client=client,
+                    max_retries=max_retries,
+                    litellm_params=litellm_params,
+                )
+            else:
+                ## LOGGING
+                logging_obj.pre_call(
+                    input=messages,
+                    api_key=api_key,
+                    additional_args={
+                        "headers": {
+                            "api_key": api_key,
+                            "azure_ad_token": azure_ad_token,
+                        },
+                        "api_version": api_version,
+                        "api_base": api_base,
+                        "complete_input_dict": data,
+                    },
+                )
+                if not isinstance(max_retries, int):
+                    raise AzureOpenAIError(
+                        status_code=422, message="max retries must be an int"
+                    )
+                # init AzureOpenAI Client
+                azure_client = self.get_azure_openai_client(
+                    api_version=api_version,
+                    api_base=api_base,
+                    api_key=api_key,
+                    model=model,
+                    client=client,
+                    _is_async=False,
+                    litellm_params=litellm_params,
+                )
+                if not isinstance(azure_client, AzureOpenAI):
+                    raise AzureOpenAIError(
+                        status_code=500,
+                        message="azure_client is not an instance of AzureOpenAI",
+                    )
+
+                headers, response = self.make_sync_azure_openai_chat_completion_request(
+                    azure_client=azure_client, data=data, timeout=timeout
+                )
+                stringified_response = response.model_dump()
+                ## LOGGING
+                logging_obj.post_call(
+                    input=messages,
+                    api_key=api_key,
+                    original_response=stringified_response,
+                    additional_args={
+                        "headers": headers,
+                        "api_version": api_version,
+                        "api_base": api_base,
+                    },
+                )
+                return convert_to_model_response_object(
+                    response_object=stringified_response,
+                    model_response_object=model_response,
+                    convert_tool_call_to_json_mode=json_mode,
+                    _response_headers=headers,
+                )
+        except AzureOpenAIError as e:
+            raise e
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_response = getattr(e, "response", None)
+            error_body = getattr(e, "body", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise AzureOpenAIError(
+                status_code=status_code,
+                message=str(e),
+                headers=error_headers,
+                body=error_body,
+            )
+
+    async def acompletion(
+        self,
+        api_key: str,
+        api_version: str,
+        model: str,
+        api_base: str,
+        data: dict,
+        timeout: Any,
+        dynamic_params: bool,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        max_retries: int,
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        convert_tool_call_to_json_mode: Optional[bool] = None,
+        client=None,  # this is the AsyncAzureOpenAI
+        litellm_params: Optional[dict] = {},
+    ):
+        response = None
+        try:
+            # setting Azure client
+            azure_client = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                client=client,
+                _is_async=True,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(azure_client, AsyncAzureOpenAI):
+                raise ValueError("Azure client is not an instance of AsyncAzureOpenAI")
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data["messages"],
+                api_key=azure_client.api_key,
+                additional_args={
+                    "headers": {
+                        "api_key": api_key,
+                        "azure_ad_token": azure_ad_token,
+                    },
+                    "api_base": azure_client._base_url._uri_reference,
+                    "acompletion": True,
+                    "complete_input_dict": data,
+                },
+            )
+
+            headers, response = await self.make_azure_openai_chat_completion_request(
+                azure_client=azure_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=data["messages"],
+                api_key=api_key,
+                original_response=stringified_response,
+                additional_args={"complete_input_dict": data},
+            )
+
+            return convert_to_model_response_object(
+                response_object=stringified_response,
+                model_response_object=model_response,
+                hidden_params={"headers": headers},
+                _response_headers=headers,
+                convert_tool_call_to_json_mode=convert_tool_call_to_json_mode,
+            )
+        except AzureOpenAIError as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=data["messages"],
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            raise e
+        except asyncio.CancelledError as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=data["messages"],
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            raise AzureOpenAIError(status_code=500, message=str(e))
+        except Exception as e:
+            message = getattr(e, "message", str(e))
+            body = getattr(e, "body", None)
+            ## LOGGING
+            logging_obj.post_call(
+                input=data["messages"],
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            if hasattr(e, "status_code"):
+                raise e
+            else:
+                raise AzureOpenAIError(status_code=500, message=message, body=body)
+
+    def streaming(
+        self,
+        logging_obj,
+        api_base: str,
+        api_key: str,
+        api_version: str,
+        dynamic_params: bool,
+        data: dict,
+        model: str,
+        timeout: Any,
+        max_retries: int,
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        client=None,
+        litellm_params: Optional[dict] = {},
+    ):
+        # init AzureOpenAI Client
+        azure_client_params = {
+            "api_version": api_version,
+            "azure_endpoint": api_base,
+            "azure_deployment": model,
+            "http_client": litellm.client_session,
+            "max_retries": max_retries,
+            "timeout": timeout,
+        }
+        azure_client_params = select_azure_base_url_or_endpoint(
+            azure_client_params=azure_client_params
+        )
+        if api_key is not None:
+            azure_client_params["api_key"] = api_key
+        elif azure_ad_token is not None:
+            if azure_ad_token.startswith("oidc/"):
+                azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
+            azure_client_params["azure_ad_token"] = azure_ad_token
+        elif azure_ad_token_provider is not None:
+            azure_client_params["azure_ad_token_provider"] = azure_ad_token_provider
+
+        azure_client = self.get_azure_openai_client(
+            api_version=api_version,
+            api_base=api_base,
+            api_key=api_key,
+            model=model,
+            client=client,
+            _is_async=False,
+            litellm_params=litellm_params,
+        )
+        if not isinstance(azure_client, AzureOpenAI):
+            raise AzureOpenAIError(
+                status_code=500,
+                message="azure_client is not an instance of AzureOpenAI",
+            )
+        ## LOGGING
+        logging_obj.pre_call(
+            input=data["messages"],
+            api_key=azure_client.api_key,
+            additional_args={
+                "headers": {
+                    "api_key": api_key,
+                    "azure_ad_token": azure_ad_token,
+                },
+                "api_base": azure_client._base_url._uri_reference,
+                "acompletion": True,
+                "complete_input_dict": data,
+            },
+        )
+        headers, response = self.make_sync_azure_openai_chat_completion_request(
+            azure_client=azure_client, data=data, timeout=timeout
+        )
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=response,
+            model=model,
+            custom_llm_provider="azure",
+            logging_obj=logging_obj,
+            stream_options=data.get("stream_options", None),
+            _response_headers=process_azure_headers(headers),
+        )
+        return streamwrapper
+
+    async def async_streaming(
+        self,
+        logging_obj: LiteLLMLoggingObj,
+        api_base: str,
+        api_key: str,
+        api_version: str,
+        dynamic_params: bool,
+        data: dict,
+        model: str,
+        timeout: Any,
+        max_retries: int,
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        client=None,
+        litellm_params: Optional[dict] = {},
+    ):
+        try:
+            azure_client = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                client=client,
+                _is_async=True,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(azure_client, AsyncAzureOpenAI):
+                raise ValueError("Azure client is not an instance of AsyncAzureOpenAI")
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data["messages"],
+                api_key=azure_client.api_key,
+                additional_args={
+                    "headers": {
+                        "api_key": api_key,
+                        "azure_ad_token": azure_ad_token,
+                    },
+                    "api_base": azure_client._base_url._uri_reference,
+                    "acompletion": True,
+                    "complete_input_dict": data,
+                },
+            )
+
+            headers, response = await self.make_azure_openai_chat_completion_request(
+                azure_client=azure_client,
+                data=data,
+                timeout=timeout,
+                logging_obj=logging_obj,
+            )
+            logging_obj.model_call_details["response_headers"] = headers
+
+            # return response
+            streamwrapper = CustomStreamWrapper(
+                completion_stream=response,
+                model=model,
+                custom_llm_provider="azure",
+                logging_obj=logging_obj,
+                stream_options=data.get("stream_options", None),
+                _response_headers=headers,
+            )
+            return streamwrapper  ## DO NOT make this into an async for ... loop, it will yield an async generator, which won't raise errors if the response fails
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_response = getattr(e, "response", None)
+            message = getattr(e, "message", str(e))
+            error_body = getattr(e, "body", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise AzureOpenAIError(
+                status_code=status_code,
+                message=message,
+                headers=error_headers,
+                body=error_body,
+            )
+
+    async def aembedding(
+        self,
+        model: str,
+        data: dict,
+        model_response: EmbeddingResponse,
+        input: list,
+        logging_obj: LiteLLMLoggingObj,
+        api_base: str,
+        api_key: Optional[str] = None,
+        api_version: Optional[str] = None,
+        client: Optional[AsyncAzureOpenAI] = None,
+        timeout: Optional[Union[float, httpx.Timeout]] = None,
+        max_retries: Optional[int] = None,
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        litellm_params: Optional[dict] = {},
+    ) -> EmbeddingResponse:
+        response = None
+        try:
+
+            openai_aclient = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                _is_async=True,
+                client=client,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(openai_aclient, AsyncAzureOpenAI):
+                raise ValueError("Azure client is not an instance of AsyncAzureOpenAI")
+
+            raw_response = await openai_aclient.embeddings.with_raw_response.create(
+                **data, timeout=timeout
+            )
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+            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,
+            )
+            embedding_response = convert_to_model_response_object(
+                response_object=stringified_response,
+                model_response_object=model_response,
+                hidden_params={"headers": headers},
+                _response_headers=process_azure_headers(headers),
+                response_type="embedding",
+            )
+            if not isinstance(embedding_response, EmbeddingResponse):
+                raise AzureOpenAIError(
+                    status_code=500,
+                    message="embedding_response is not an instance of EmbeddingResponse",
+                )
+            return embedding_response
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            raise e
+
+    def embedding(
+        self,
+        model: str,
+        input: list,
+        api_base: str,
+        api_version: str,
+        timeout: float,
+        logging_obj: LiteLLMLoggingObj,
+        model_response: EmbeddingResponse,
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        max_retries: Optional[int] = None,
+        client=None,
+        aembedding=None,
+        headers: Optional[dict] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> Union[EmbeddingResponse, Coroutine[Any, Any, EmbeddingResponse]]:
+        if headers:
+            optional_params["extra_headers"] = headers
+        if self._client_session is None:
+            self._client_session = self.create_client_session()
+        try:
+            data = {"model": model, "input": input, **optional_params}
+            if max_retries is None:
+                max_retries = litellm.DEFAULT_MAX_RETRIES
+            ## LOGGING
+            logging_obj.pre_call(
+                input=input,
+                api_key=api_key,
+                additional_args={
+                    "complete_input_dict": data,
+                    "headers": {"api_key": api_key, "azure_ad_token": azure_ad_token},
+                },
+            )
+
+            if aembedding is True:
+                return self.aembedding(
+                    data=data,
+                    input=input,
+                    model=model,
+                    logging_obj=logging_obj,
+                    api_key=api_key,
+                    model_response=model_response,
+                    timeout=timeout,
+                    client=client,
+                    litellm_params=litellm_params,
+                    api_base=api_base,
+                )
+            azure_client = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                _is_async=False,
+                client=client,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(azure_client, AzureOpenAI):
+                raise AzureOpenAIError(
+                    status_code=500,
+                    message="azure_client is not an instance of AzureOpenAI",
+                )
+
+            ## COMPLETION CALL
+            raw_response = azure_client.embeddings.with_raw_response.create(**data, timeout=timeout)  # type: ignore
+            headers = dict(raw_response.headers)
+            response = raw_response.parse()
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data, "api_base": api_base},
+                original_response=response,
+            )
+
+            return convert_to_model_response_object(response_object=response.model_dump(), model_response_object=model_response, response_type="embedding", _response_headers=process_azure_headers(headers))  # type: ignore
+        except AzureOpenAIError as e:
+            raise e
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise AzureOpenAIError(
+                status_code=status_code, message=str(e), headers=error_headers
+            )
+
+    async def make_async_azure_httpx_request(
+        self,
+        client: Optional[AsyncHTTPHandler],
+        timeout: Optional[Union[float, httpx.Timeout]],
+        api_base: str,
+        api_version: str,
+        api_key: str,
+        data: dict,
+        headers: dict,
+    ) -> httpx.Response:
+        """
+        Implemented for azure dall-e-2 image gen calls
+
+        Alternative to needing a custom transport implementation
+        """
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    _httpx_timeout = httpx.Timeout(timeout)
+                    _params["timeout"] = _httpx_timeout
+            else:
+                _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+            async_handler = get_async_httpx_client(
+                llm_provider=LlmProviders.AZURE,
+                params=_params,
+            )
+        else:
+            async_handler = client  # type: ignore
+
+        if (
+            "images/generations" in api_base
+            and api_version
+            in [  # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
+                "2023-06-01-preview",
+                "2023-07-01-preview",
+                "2023-08-01-preview",
+                "2023-09-01-preview",
+                "2023-10-01-preview",
+            ]
+        ):  # CREATE + POLL for azure dall-e-2 calls
+
+            api_base = modify_url(
+                original_url=api_base, new_path="/openai/images/generations:submit"
+            )
+
+            data.pop(
+                "model", None
+            )  # REMOVE 'model' from dall-e-2 arg https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-a-generated-image-dall-e-2-preview
+            response = await async_handler.post(
+                url=api_base,
+                data=json.dumps(data),
+                headers=headers,
+            )
+            if "operation-location" in response.headers:
+                operation_location_url = response.headers["operation-location"]
+            else:
+                raise AzureOpenAIError(status_code=500, message=response.text)
+            response = await async_handler.get(
+                url=operation_location_url,
+                headers=headers,
+            )
+
+            await response.aread()
+
+            timeout_secs: int = 120
+            start_time = time.time()
+            if "status" not in response.json():
+                raise Exception(
+                    "Expected 'status' in response. Got={}".format(response.json())
+                )
+            while response.json()["status"] not in ["succeeded", "failed"]:
+                if time.time() - start_time > timeout_secs:
+
+                    raise AzureOpenAIError(
+                        status_code=408, message="Operation polling timed out."
+                    )
+
+                await asyncio.sleep(int(response.headers.get("retry-after") or 10))
+                response = await async_handler.get(
+                    url=operation_location_url,
+                    headers=headers,
+                )
+                await response.aread()
+
+            if response.json()["status"] == "failed":
+                error_data = response.json()
+                raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
+
+            result = response.json()["result"]
+            return httpx.Response(
+                status_code=200,
+                headers=response.headers,
+                content=json.dumps(result).encode("utf-8"),
+                request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
+            )
+        return await async_handler.post(
+            url=api_base,
+            json=data,
+            headers=headers,
+        )
+
+    def make_sync_azure_httpx_request(
+        self,
+        client: Optional[HTTPHandler],
+        timeout: Optional[Union[float, httpx.Timeout]],
+        api_base: str,
+        api_version: str,
+        api_key: str,
+        data: dict,
+        headers: dict,
+    ) -> httpx.Response:
+        """
+        Implemented for azure dall-e-2 image gen calls
+
+        Alternative to needing a custom transport implementation
+        """
+        if client is None:
+            _params = {}
+            if timeout is not None:
+                if isinstance(timeout, float) or isinstance(timeout, int):
+                    _httpx_timeout = httpx.Timeout(timeout)
+                    _params["timeout"] = _httpx_timeout
+            else:
+                _params["timeout"] = httpx.Timeout(timeout=600.0, connect=5.0)
+
+            sync_handler = HTTPHandler(**_params, client=litellm.client_session)  # type: ignore
+        else:
+            sync_handler = client  # type: ignore
+
+        if (
+            "images/generations" in api_base
+            and api_version
+            in [  # dall-e-3 starts from `2023-12-01-preview` so we should be able to avoid conflict
+                "2023-06-01-preview",
+                "2023-07-01-preview",
+                "2023-08-01-preview",
+                "2023-09-01-preview",
+                "2023-10-01-preview",
+            ]
+        ):  # CREATE + POLL for azure dall-e-2 calls
+
+            api_base = modify_url(
+                original_url=api_base, new_path="/openai/images/generations:submit"
+            )
+
+            data.pop(
+                "model", None
+            )  # REMOVE 'model' from dall-e-2 arg https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#request-a-generated-image-dall-e-2-preview
+            response = sync_handler.post(
+                url=api_base,
+                data=json.dumps(data),
+                headers=headers,
+            )
+            if "operation-location" in response.headers:
+                operation_location_url = response.headers["operation-location"]
+            else:
+                raise AzureOpenAIError(status_code=500, message=response.text)
+            response = sync_handler.get(
+                url=operation_location_url,
+                headers=headers,
+            )
+
+            response.read()
+
+            timeout_secs: int = 120
+            start_time = time.time()
+            if "status" not in response.json():
+                raise Exception(
+                    "Expected 'status' in response. Got={}".format(response.json())
+                )
+            while response.json()["status"] not in ["succeeded", "failed"]:
+                if time.time() - start_time > timeout_secs:
+                    raise AzureOpenAIError(
+                        status_code=408, message="Operation polling timed out."
+                    )
+
+                time.sleep(int(response.headers.get("retry-after") or 10))
+                response = sync_handler.get(
+                    url=operation_location_url,
+                    headers=headers,
+                )
+                response.read()
+
+            if response.json()["status"] == "failed":
+                error_data = response.json()
+                raise AzureOpenAIError(status_code=400, message=json.dumps(error_data))
+
+            result = response.json()["result"]
+            return httpx.Response(
+                status_code=200,
+                headers=response.headers,
+                content=json.dumps(result).encode("utf-8"),
+                request=httpx.Request(method="POST", url="https://api.openai.com/v1"),
+            )
+        return sync_handler.post(
+            url=api_base,
+            json=data,
+            headers=headers,
+        )
+
+    def create_azure_base_url(
+        self, azure_client_params: dict, model: Optional[str]
+    ) -> str:
+        api_base: str = azure_client_params.get(
+            "azure_endpoint", ""
+        )  # "https://example-endpoint.openai.azure.com"
+        if api_base.endswith("/"):
+            api_base = api_base.rstrip("/")
+        api_version: str = azure_client_params.get("api_version", "")
+        if model is None:
+            model = ""
+
+        if "/openai/deployments/" in api_base:
+            base_url_with_deployment = api_base
+        else:
+            base_url_with_deployment = api_base + "/openai/deployments/" + model
+
+        base_url_with_deployment += "/images/generations"
+        base_url_with_deployment += "?api-version=" + api_version
+
+        return base_url_with_deployment
+
+    async def aimage_generation(
+        self,
+        data: dict,
+        model_response: ModelResponse,
+        azure_client_params: dict,
+        api_key: str,
+        input: list,
+        logging_obj: LiteLLMLoggingObj,
+        headers: dict,
+        client=None,
+        timeout=None,
+    ) -> litellm.ImageResponse:
+        response: Optional[dict] = None
+        try:
+            # response = await azure_client.images.generate(**data, timeout=timeout)
+            api_base: str = azure_client_params.get(
+                "api_base", ""
+            )  # "https://example-endpoint.openai.azure.com"
+            if api_base.endswith("/"):
+                api_base = api_base.rstrip("/")
+            api_version: str = azure_client_params.get("api_version", "")
+            img_gen_api_base = self.create_azure_base_url(
+                azure_client_params=azure_client_params, model=data.get("model", "")
+            )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data["prompt"],
+                api_key=api_key,
+                additional_args={
+                    "complete_input_dict": data,
+                    "api_base": img_gen_api_base,
+                    "headers": headers,
+                },
+            )
+            httpx_response: httpx.Response = await self.make_async_azure_httpx_request(
+                client=None,
+                timeout=timeout,
+                api_base=img_gen_api_base,
+                api_version=api_version,
+                api_key=api_key,
+                data=data,
+                headers=headers,
+            )
+            response = httpx_response.json()
+
+            stringified_response = response
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=stringified_response,
+            )
+            return convert_to_model_response_object(  # type: ignore
+                response_object=stringified_response,
+                model_response_object=model_response,
+                response_type="image_generation",
+            )
+        except Exception as e:
+            ## LOGGING
+            logging_obj.post_call(
+                input=input,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=str(e),
+            )
+            raise e
+
+    def image_generation(
+        self,
+        prompt: str,
+        timeout: float,
+        optional_params: dict,
+        logging_obj: LiteLLMLoggingObj,
+        headers: dict,
+        model: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+        api_version: Optional[str] = None,
+        model_response: Optional[ImageResponse] = None,
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        client=None,
+        aimg_generation=None,
+        litellm_params: Optional[dict] = None,
+    ) -> ImageResponse:
+        try:
+            if model and len(model) > 0:
+                model = model
+            else:
+                model = None
+
+            ## BASE MODEL CHECK
+            if (
+                model_response is not None
+                and optional_params.get("base_model", None) is not None
+            ):
+                model_response._hidden_params["model"] = optional_params.pop(
+                    "base_model"
+                )
+
+            data = {"model": model, "prompt": prompt, **optional_params}
+            max_retries = data.pop("max_retries", 2)
+            if not isinstance(max_retries, int):
+                raise AzureOpenAIError(
+                    status_code=422, message="max retries must be an int"
+                )
+
+            # init AzureOpenAI Client
+            azure_client_params: Dict[str, Any] = self.initialize_azure_sdk_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                model_name=model or "",
+                api_version=api_version,
+                api_base=api_base,
+                is_async=False,
+            )
+            if aimg_generation is True:
+                return self.aimage_generation(data=data, input=input, logging_obj=logging_obj, model_response=model_response, api_key=api_key, client=client, azure_client_params=azure_client_params, timeout=timeout, headers=headers)  # type: ignore
+
+            img_gen_api_base = self.create_azure_base_url(
+                azure_client_params=azure_client_params, model=data.get("model", "")
+            )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data["prompt"],
+                api_key=api_key,
+                additional_args={
+                    "complete_input_dict": data,
+                    "api_base": img_gen_api_base,
+                    "headers": headers,
+                },
+            )
+            httpx_response: httpx.Response = self.make_sync_azure_httpx_request(
+                client=None,
+                timeout=timeout,
+                api_base=img_gen_api_base,
+                api_version=api_version or "",
+                api_key=api_key or "",
+                data=data,
+                headers=headers,
+            )
+            response = httpx_response.json()
+
+            ## LOGGING
+            logging_obj.post_call(
+                input=prompt,
+                api_key=api_key,
+                additional_args={"complete_input_dict": data},
+                original_response=response,
+            )
+            # return response
+            return convert_to_model_response_object(response_object=response, model_response_object=model_response, response_type="image_generation")  # type: ignore
+        except AzureOpenAIError as e:
+            raise e
+        except Exception as e:
+            error_code = getattr(e, "status_code", None)
+            if error_code is not None:
+                raise AzureOpenAIError(status_code=error_code, message=str(e))
+            else:
+                raise AzureOpenAIError(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],
+        api_version: Optional[str],
+        organization: Optional[str],
+        max_retries: int,
+        timeout: Union[float, httpx.Timeout],
+        azure_ad_token: Optional[str] = None,
+        azure_ad_token_provider: Optional[Callable] = None,
+        aspeech: Optional[bool] = None,
+        client=None,
+        litellm_params: Optional[dict] = None,
+    ) -> HttpxBinaryResponseContent:
+
+        max_retries = optional_params.pop("max_retries", 2)
+
+        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,
+                api_version=api_version,
+                azure_ad_token=azure_ad_token,
+                azure_ad_token_provider=azure_ad_token_provider,
+                max_retries=max_retries,
+                timeout=timeout,
+                client=client,
+                litellm_params=litellm_params,
+            )  # type: ignore
+
+        azure_client: AzureOpenAI = self.get_azure_openai_client(
+            api_base=api_base,
+            api_version=api_version,
+            api_key=api_key,
+            model=model,
+            _is_async=False,
+            client=client,
+            litellm_params=litellm_params,
+        )  # type: ignore
+
+        response = azure_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],
+        api_version: Optional[str],
+        azure_ad_token: Optional[str],
+        azure_ad_token_provider: Optional[Callable],
+        max_retries: int,
+        timeout: Union[float, httpx.Timeout],
+        client=None,
+        litellm_params: Optional[dict] = None,
+    ) -> HttpxBinaryResponseContent:
+
+        azure_client: AsyncAzureOpenAI = self.get_azure_openai_client(
+            api_base=api_base,
+            api_version=api_version,
+            api_key=api_key,
+            model=model,
+            _is_async=True,
+            client=client,
+            litellm_params=litellm_params,
+        )  # type: ignore
+
+        azure_response = await azure_client.audio.speech.create(
+            model=model,
+            voice=voice,  # type: ignore
+            input=input,
+            **optional_params,
+        )
+
+        return HttpxBinaryResponseContent(response=azure_response.response)
+
+    def get_headers(
+        self,
+        model: Optional[str],
+        api_key: str,
+        api_base: str,
+        api_version: str,
+        timeout: float,
+        mode: str,
+        messages: Optional[list] = None,
+        input: Optional[list] = None,
+        prompt: Optional[str] = None,
+    ) -> dict:
+        client_session = litellm.client_session or httpx.Client()
+        if "gateway.ai.cloudflare.com" in api_base:
+            ## build base url - assume api base includes resource name
+            if not api_base.endswith("/"):
+                api_base += "/"
+            api_base += f"{model}"
+            client = AzureOpenAI(
+                base_url=api_base,
+                api_version=api_version,
+                api_key=api_key,
+                timeout=timeout,
+                http_client=client_session,
+            )
+            model = None
+            # cloudflare ai gateway, needs model=None
+        else:
+            client = AzureOpenAI(
+                api_version=api_version,
+                azure_endpoint=api_base,
+                api_key=api_key,
+                timeout=timeout,
+                http_client=client_session,
+            )
+
+            # only run this check if it's not cloudflare ai gateway
+            if model is None and mode != "image_generation":
+                raise Exception("model is not set")
+
+        completion = None
+
+        if messages is None:
+            messages = [{"role": "user", "content": "Hey"}]
+        try:
+            completion = client.chat.completions.with_raw_response.create(
+                model=model,  # type: ignore
+                messages=messages,  # type: ignore
+            )
+        except Exception as e:
+            raise e
+        response = {}
+
+        if completion is None or not hasattr(completion, "headers"):
+            raise Exception("invalid completion response")
+
+        if (
+            completion.headers.get("x-ratelimit-remaining-requests", None) is not None
+        ):  # not provided for dall-e requests
+            response["x-ratelimit-remaining-requests"] = completion.headers[
+                "x-ratelimit-remaining-requests"
+            ]
+
+        if completion.headers.get("x-ratelimit-remaining-tokens", None) is not None:
+            response["x-ratelimit-remaining-tokens"] = completion.headers[
+                "x-ratelimit-remaining-tokens"
+            ]
+
+        if completion.headers.get("x-ms-region", None) is not None:
+            response["x-ms-region"] = completion.headers["x-ms-region"]
+
+        return response
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/batches/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/batches/handler.py
new file mode 100644
index 00000000..1b93c526
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/batches/handler.py
@@ -0,0 +1,210 @@
+"""
+Azure Batches API Handler
+"""
+
+from typing import Any, Coroutine, Optional, Union, cast
+
+import httpx
+
+from litellm.llms.azure.azure import AsyncAzureOpenAI, AzureOpenAI
+from litellm.types.llms.openai import (
+    Batch,
+    CancelBatchRequest,
+    CreateBatchRequest,
+    RetrieveBatchRequest,
+)
+from litellm.types.utils import LiteLLMBatch
+
+from ..common_utils import BaseAzureLLM
+
+
+class AzureBatchesAPI(BaseAzureLLM):
+    """
+    Azure methods to support for batches
+    - create_batch()
+    - retrieve_batch()
+    - cancel_batch()
+    - list_batch()
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    async def acreate_batch(
+        self,
+        create_batch_data: CreateBatchRequest,
+        azure_client: AsyncAzureOpenAI,
+    ) -> LiteLLMBatch:
+        response = await azure_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],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> Union[LiteLLMBatch, Coroutine[Any, Any, LiteLLMBatch]]:
+        azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+                litellm_params=litellm_params or {},
+            )
+        )
+        if azure_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(azure_client, AsyncAzureOpenAI):
+                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, azure_client=azure_client
+            )
+        response = cast(AzureOpenAI, azure_client).batches.create(**create_batch_data)
+        return LiteLLMBatch(**response.model_dump())
+
+    async def aretrieve_batch(
+        self,
+        retrieve_batch_data: RetrieveBatchRequest,
+        client: AsyncAzureOpenAI,
+    ) -> LiteLLMBatch:
+        response = await 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],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+                litellm_params=litellm_params or {},
+            )
+        )
+        if azure_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(azure_client, AsyncAzureOpenAI):
+                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, client=azure_client
+            )
+        response = cast(AzureOpenAI, azure_client).batches.retrieve(
+            **retrieve_batch_data
+        )
+        return LiteLLMBatch(**response.model_dump())
+
+    async def acancel_batch(
+        self,
+        cancel_batch_data: CancelBatchRequest,
+        client: AsyncAzureOpenAI,
+    ) -> Batch:
+        response = await 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],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[AzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+                litellm_params=litellm_params or {},
+            )
+        )
+        if azure_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."
+            )
+        response = azure_client.batches.cancel(**cancel_batch_data)
+        return response
+
+    async def alist_batches(
+        self,
+        client: AsyncAzureOpenAI,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        response = await 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],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+        client: Optional[AzureOpenAI] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        azure_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+                litellm_params=litellm_params or {},
+            )
+        )
+        if azure_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(azure_client, AsyncAzureOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.alist_batches(  # type: ignore
+                client=azure_client, after=after, limit=limit
+            )
+        response = azure_client.batches.list(after=after, limit=limit)  # type: ignore
+        return response
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/gpt_transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/gpt_transformation.py
new file mode 100644
index 00000000..ee85517e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/gpt_transformation.py
@@ -0,0 +1,294 @@
+from typing import TYPE_CHECKING, Any, List, Optional, Union
+
+from httpx._models import Headers, Response
+
+import litellm
+from litellm.litellm_core_utils.prompt_templates.factory import (
+    convert_to_azure_openai_messages,
+)
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.types.utils import ModelResponse
+from litellm.utils import supports_response_schema
+
+from ....exceptions import UnsupportedParamsError
+from ....types.llms.openai import AllMessageValues
+from ...base_llm.chat.transformation import BaseConfig
+from ..common_utils import AzureOpenAIError
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+    LoggingClass = LiteLLMLoggingObj
+else:
+    LoggingClass = Any
+
+
+class AzureOpenAIConfig(BaseConfig):
+    """
+    Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions
+
+    The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. Below are the parameters::
+
+    - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.
+
+    - `function_call` (string or object): This optional parameter controls how the model calls functions.
+
+    - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs.
+
+    - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
+
+    - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion.
+
+    - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message.
+
+    - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics.
+
+    - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.
+
+    - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2.
+
+    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
+    """
+
+    def __init__(
+        self,
+        frequency_penalty: Optional[int] = None,
+        function_call: Optional[Union[str, dict]] = None,
+        functions: Optional[list] = None,
+        logit_bias: Optional[dict] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[int] = None,
+        stop: Optional[Union[str, list]] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+    ) -> None:
+        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[str]:
+        return [
+            "temperature",
+            "n",
+            "stream",
+            "stream_options",
+            "stop",
+            "max_tokens",
+            "max_completion_tokens",
+            "tools",
+            "tool_choice",
+            "presence_penalty",
+            "frequency_penalty",
+            "logit_bias",
+            "user",
+            "function_call",
+            "functions",
+            "tools",
+            "tool_choice",
+            "top_p",
+            "logprobs",
+            "top_logprobs",
+            "response_format",
+            "seed",
+            "extra_headers",
+            "parallel_tool_calls",
+            "prediction",
+            "modalities",
+            "audio",
+        ]
+
+    def _is_response_format_supported_model(self, model: str) -> bool:
+        """
+        - all 4o models are supported
+        - check if 'supports_response_format' is True from get_model_info
+        - [TODO] support smart retries for 3.5 models (some supported, some not)
+        """
+        if "4o" in model:
+            return True
+        elif supports_response_schema(model):
+            return True
+
+        return False
+
+    def _is_response_format_supported_api_version(
+        self, api_version_year: str, api_version_month: str
+    ) -> bool:
+        """
+        - check if api_version is supported for response_format
+        """
+
+        is_supported = int(api_version_year) <= 2024 and int(api_version_month) >= 8
+
+        return is_supported
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+        api_version: str = "",
+    ) -> dict:
+        supported_openai_params = self.get_supported_openai_params(model)
+
+        api_version_times = api_version.split("-")
+        api_version_year = api_version_times[0]
+        api_version_month = api_version_times[1]
+        api_version_day = api_version_times[2]
+        for param, value in non_default_params.items():
+            if param == "tool_choice":
+                """
+                This parameter requires API version 2023-12-01-preview or later
+
+                tool_choice='required' is not supported as of 2024-05-01-preview
+                """
+                ## check if api version supports this param ##
+                if (
+                    api_version_year < "2023"
+                    or (api_version_year == "2023" and api_version_month < "12")
+                    or (
+                        api_version_year == "2023"
+                        and api_version_month == "12"
+                        and api_version_day < "01"
+                    )
+                ):
+                    if litellm.drop_params is True or (
+                        drop_params is not None and drop_params is True
+                    ):
+                        pass
+                    else:
+                        raise UnsupportedParamsError(
+                            status_code=400,
+                            message=f"""Azure does not support 'tool_choice', for api_version={api_version}. Bump your API version to '2023-12-01-preview' or later. This parameter requires 'api_version="2023-12-01-preview"' or later. Azure API Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions""",
+                        )
+                elif value == "required" and (
+                    api_version_year == "2024" and api_version_month <= "05"
+                ):  ## check if tool_choice value is supported ##
+                    if litellm.drop_params is True or (
+                        drop_params is not None and drop_params is True
+                    ):
+                        pass
+                    else:
+                        raise UnsupportedParamsError(
+                            status_code=400,
+                            message=f"Azure does not support '{value}' as a {param} param, for api_version={api_version}. To drop 'tool_choice=required' for calls with this Azure API version, set `litellm.drop_params=True` or for proxy:\n\n`litellm_settings:\n drop_params: true`\nAzure API Reference: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions",
+                        )
+                else:
+                    optional_params["tool_choice"] = value
+            elif param == "response_format" and isinstance(value, dict):
+                _is_response_format_supported_model = (
+                    self._is_response_format_supported_model(model)
+                )
+
+                is_response_format_supported_api_version = (
+                    self._is_response_format_supported_api_version(
+                        api_version_year, api_version_month
+                    )
+                )
+                is_response_format_supported = (
+                    is_response_format_supported_api_version
+                    and _is_response_format_supported_model
+                )
+                optional_params = self._add_response_format_to_tools(
+                    optional_params=optional_params,
+                    value=value,
+                    is_response_format_supported=is_response_format_supported,
+                )
+            elif param == "tools" and isinstance(value, list):
+                optional_params.setdefault("tools", [])
+                optional_params["tools"].extend(value)
+            elif param in supported_openai_params:
+                optional_params[param] = value
+
+        return optional_params
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        messages = convert_to_azure_openai_messages(messages)
+        return {
+            "model": model,
+            "messages": messages,
+            **optional_params,
+        }
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: Response,
+        model_response: ModelResponse,
+        logging_obj: LoggingClass,
+        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:
+        raise NotImplementedError(
+            "Azure OpenAI handler.py has custom logic for transforming response, as it uses the OpenAI SDK."
+        )
+
+    def get_mapped_special_auth_params(self) -> dict:
+        return {"token": "azure_ad_token"}
+
+    def map_special_auth_params(self, non_default_params: dict, optional_params: dict):
+        for param, value in non_default_params.items():
+            if param == "token":
+                optional_params["azure_ad_token"] = value
+        return optional_params
+
+    def get_eu_regions(self) -> List[str]:
+        """
+        Source: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-model-availability
+        """
+        return ["europe", "sweden", "switzerland", "france", "uk"]
+
+    def get_us_regions(self) -> List[str]:
+        """
+        Source: https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models#gpt-4-and-gpt-4-turbo-model-availability
+        """
+        return [
+            "us",
+            "eastus",
+            "eastus2",
+            "eastus2euap",
+            "eastus3",
+            "southcentralus",
+            "westus",
+            "westus2",
+            "westus3",
+            "westus4",
+        ]
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, Headers]
+    ) -> BaseLLMException:
+        return AzureOpenAIError(
+            message=error_message, status_code=status_code, headers=headers
+        )
+
+    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:
+        raise NotImplementedError(
+            "Azure OpenAI has custom logic for validating environment, as it uses the OpenAI SDK."
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/o_series_handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/o_series_handler.py
new file mode 100644
index 00000000..2f3e9e63
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/o_series_handler.py
@@ -0,0 +1,72 @@
+"""
+Handler file for calls to Azure OpenAI's o1/o3 family of models
+
+Written separately to handle faking streaming for o1 and o3 models.
+"""
+
+from typing import Any, Callable, Optional, Union
+
+import httpx
+
+from litellm.types.utils import ModelResponse
+
+from ...openai.openai import OpenAIChatCompletion
+from ..common_utils import BaseAzureLLM
+
+
+class AzureOpenAIO1ChatCompletion(BaseAzureLLM, OpenAIChatCompletion):
+    def completion(
+        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,
+    ):
+        client = self.get_azure_openai_client(
+            litellm_params=litellm_params,
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            client=client,
+            _is_async=acompletion,
+        )
+        return super().completion(
+            model_response=model_response,
+            timeout=timeout,
+            optional_params=optional_params,
+            litellm_params=litellm_params,
+            logging_obj=logging_obj,
+            model=model,
+            messages=messages,
+            print_verbose=print_verbose,
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            dynamic_params=dynamic_params,
+            azure_ad_token=azure_ad_token,
+            acompletion=acompletion,
+            logger_fn=logger_fn,
+            headers=headers,
+            custom_prompt_dict=custom_prompt_dict,
+            client=client,
+            organization=organization,
+            custom_llm_provider=custom_llm_provider,
+            drop_params=drop_params,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/o_series_transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/o_series_transformation.py
new file mode 100644
index 00000000..0ca3a28d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/chat/o_series_transformation.py
@@ -0,0 +1,75 @@
+"""
+Support for o1 and o3 model families
+
+https://platform.openai.com/docs/guides/reasoning
+
+Translations handled by LiteLLM:
+- modalities: image => drop param (if user opts in to dropping param)  
+- role: system ==> translate to role 'user' 
+- streaming => faked by LiteLLM 
+- Tools, response_format =>  drop param (if user opts in to dropping param) 
+- Logprobs => drop param (if user opts in to dropping param)
+- Temperature => drop param (if user opts in to dropping param)
+"""
+
+from typing import List, Optional
+
+from litellm import verbose_logger
+from litellm.types.llms.openai import AllMessageValues
+from litellm.utils import get_model_info
+
+from ...openai.chat.o_series_transformation import OpenAIOSeriesConfig
+
+
+class AzureOpenAIO1Config(OpenAIOSeriesConfig):
+    def should_fake_stream(
+        self,
+        model: Optional[str],
+        stream: Optional[bool],
+        custom_llm_provider: Optional[str] = None,
+    ) -> bool:
+        """
+        Currently no Azure O Series models support native streaming.
+        """
+
+        if stream is not True:
+            return False
+
+        if (
+            model and "o3" in model
+        ):  # o3 models support streaming - https://github.com/BerriAI/litellm/issues/8274
+            return False
+
+        if model is not None:
+            try:
+                model_info = get_model_info(
+                    model=model, custom_llm_provider=custom_llm_provider
+                )  # allow user to override default with model_info={"supports_native_streaming": true}
+
+                if (
+                    model_info.get("supports_native_streaming") is True
+                ):  # allow user to override default with model_info={"supports_native_streaming": true}
+                    return False
+            except Exception as e:
+                verbose_logger.debug(
+                    f"Error getting model info in AzureOpenAIO1Config: {e}"
+                )
+        return True
+
+    def is_o_series_model(self, model: str) -> bool:
+        return "o1" in model or "o3" in model or "o_series/" in model
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        model = model.replace(
+            "o_series/", ""
+        )  # handle o_series/my-random-deployment-name
+        return super().transform_request(
+            model, messages, optional_params, litellm_params, headers
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/common_utils.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/common_utils.py
new file mode 100644
index 00000000..71092c8b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/common_utils.py
@@ -0,0 +1,426 @@
+import json
+import os
+from typing import Any, Callable, Dict, Optional, Union
+
+import httpx
+from openai import AsyncAzureOpenAI, AzureOpenAI
+
+import litellm
+from litellm._logging import verbose_logger
+from litellm.caching.caching import DualCache
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.openai.common_utils import BaseOpenAILLM
+from litellm.secret_managers.get_azure_ad_token_provider import (
+    get_azure_ad_token_provider,
+)
+from litellm.secret_managers.main import get_secret_str
+
+azure_ad_cache = DualCache()
+
+
+class AzureOpenAIError(BaseLLMException):
+    def __init__(
+        self,
+        status_code,
+        message,
+        request: Optional[httpx.Request] = None,
+        response: Optional[httpx.Response] = None,
+        headers: Optional[Union[httpx.Headers, dict]] = None,
+        body: Optional[dict] = None,
+    ):
+        super().__init__(
+            status_code=status_code,
+            message=message,
+            request=request,
+            response=response,
+            headers=headers,
+            body=body,
+        )
+
+
+def process_azure_headers(headers: Union[httpx.Headers, dict]) -> dict:
+    openai_headers = {}
+    if "x-ratelimit-limit-requests" in headers:
+        openai_headers["x-ratelimit-limit-requests"] = headers[
+            "x-ratelimit-limit-requests"
+        ]
+    if "x-ratelimit-remaining-requests" in headers:
+        openai_headers["x-ratelimit-remaining-requests"] = headers[
+            "x-ratelimit-remaining-requests"
+        ]
+    if "x-ratelimit-limit-tokens" in headers:
+        openai_headers["x-ratelimit-limit-tokens"] = headers["x-ratelimit-limit-tokens"]
+    if "x-ratelimit-remaining-tokens" in headers:
+        openai_headers["x-ratelimit-remaining-tokens"] = headers[
+            "x-ratelimit-remaining-tokens"
+        ]
+    llm_response_headers = {
+        "{}-{}".format("llm_provider", k): v for k, v in headers.items()
+    }
+
+    return {**llm_response_headers, **openai_headers}
+
+
+def get_azure_ad_token_from_entrata_id(
+    tenant_id: str,
+    client_id: str,
+    client_secret: str,
+    scope: str = "https://cognitiveservices.azure.com/.default",
+) -> Callable[[], str]:
+    """
+    Get Azure AD token provider from `client_id`, `client_secret`, and `tenant_id`
+
+    Args:
+        tenant_id: str
+        client_id: str
+        client_secret: str
+        scope: str
+
+    Returns:
+        callable that returns a bearer token.
+    """
+    from azure.identity import ClientSecretCredential, get_bearer_token_provider
+
+    verbose_logger.debug("Getting Azure AD Token from Entrata ID")
+
+    if tenant_id.startswith("os.environ/"):
+        _tenant_id = get_secret_str(tenant_id)
+    else:
+        _tenant_id = tenant_id
+
+    if client_id.startswith("os.environ/"):
+        _client_id = get_secret_str(client_id)
+    else:
+        _client_id = client_id
+
+    if client_secret.startswith("os.environ/"):
+        _client_secret = get_secret_str(client_secret)
+    else:
+        _client_secret = client_secret
+
+    verbose_logger.debug(
+        "tenant_id %s, client_id %s, client_secret %s",
+        _tenant_id,
+        _client_id,
+        _client_secret,
+    )
+    if _tenant_id is None or _client_id is None or _client_secret is None:
+        raise ValueError("tenant_id, client_id, and client_secret must be provided")
+    credential = ClientSecretCredential(_tenant_id, _client_id, _client_secret)
+
+    verbose_logger.debug("credential %s", credential)
+
+    token_provider = get_bearer_token_provider(credential, scope)
+
+    verbose_logger.debug("token_provider %s", token_provider)
+
+    return token_provider
+
+
+def get_azure_ad_token_from_username_password(
+    client_id: str,
+    azure_username: str,
+    azure_password: str,
+    scope: str = "https://cognitiveservices.azure.com/.default",
+) -> Callable[[], str]:
+    """
+    Get Azure AD token provider from `client_id`, `azure_username`, and `azure_password`
+
+    Args:
+        client_id: str
+        azure_username: str
+        azure_password: str
+        scope: str
+
+    Returns:
+        callable that returns a bearer token.
+    """
+    from azure.identity import UsernamePasswordCredential, get_bearer_token_provider
+
+    verbose_logger.debug(
+        "client_id %s, azure_username %s, azure_password %s",
+        client_id,
+        azure_username,
+        azure_password,
+    )
+    credential = UsernamePasswordCredential(
+        client_id=client_id,
+        username=azure_username,
+        password=azure_password,
+    )
+
+    verbose_logger.debug("credential %s", credential)
+
+    token_provider = get_bearer_token_provider(credential, scope)
+
+    verbose_logger.debug("token_provider %s", token_provider)
+
+    return token_provider
+
+
+def get_azure_ad_token_from_oidc(azure_ad_token: str):
+    azure_client_id = os.getenv("AZURE_CLIENT_ID", None)
+    azure_tenant_id = os.getenv("AZURE_TENANT_ID", None)
+    azure_authority_host = os.getenv(
+        "AZURE_AUTHORITY_HOST", "https://login.microsoftonline.com"
+    )
+
+    if azure_client_id is None or azure_tenant_id is None:
+        raise AzureOpenAIError(
+            status_code=422,
+            message="AZURE_CLIENT_ID and AZURE_TENANT_ID must be set",
+        )
+
+    oidc_token = get_secret_str(azure_ad_token)
+
+    if oidc_token is None:
+        raise AzureOpenAIError(
+            status_code=401,
+            message="OIDC token could not be retrieved from secret manager.",
+        )
+
+    azure_ad_token_cache_key = json.dumps(
+        {
+            "azure_client_id": azure_client_id,
+            "azure_tenant_id": azure_tenant_id,
+            "azure_authority_host": azure_authority_host,
+            "oidc_token": oidc_token,
+        }
+    )
+
+    azure_ad_token_access_token = azure_ad_cache.get_cache(azure_ad_token_cache_key)
+    if azure_ad_token_access_token is not None:
+        return azure_ad_token_access_token
+
+    client = litellm.module_level_client
+    req_token = client.post(
+        f"{azure_authority_host}/{azure_tenant_id}/oauth2/v2.0/token",
+        data={
+            "client_id": azure_client_id,
+            "grant_type": "client_credentials",
+            "scope": "https://cognitiveservices.azure.com/.default",
+            "client_assertion_type": "urn:ietf:params:oauth:client-assertion-type:jwt-bearer",
+            "client_assertion": oidc_token,
+        },
+    )
+
+    if req_token.status_code != 200:
+        raise AzureOpenAIError(
+            status_code=req_token.status_code,
+            message=req_token.text,
+        )
+
+    azure_ad_token_json = req_token.json()
+    azure_ad_token_access_token = azure_ad_token_json.get("access_token", None)
+    azure_ad_token_expires_in = azure_ad_token_json.get("expires_in", None)
+
+    if azure_ad_token_access_token is None:
+        raise AzureOpenAIError(
+            status_code=422, message="Azure AD Token access_token not returned"
+        )
+
+    if azure_ad_token_expires_in is None:
+        raise AzureOpenAIError(
+            status_code=422, message="Azure AD Token expires_in not returned"
+        )
+
+    azure_ad_cache.set_cache(
+        key=azure_ad_token_cache_key,
+        value=azure_ad_token_access_token,
+        ttl=azure_ad_token_expires_in,
+    )
+
+    return azure_ad_token_access_token
+
+
+def select_azure_base_url_or_endpoint(azure_client_params: dict):
+    azure_endpoint = azure_client_params.get("azure_endpoint", None)
+    if azure_endpoint is not None:
+        # see : https://github.com/openai/openai-python/blob/3d61ed42aba652b547029095a7eb269ad4e1e957/src/openai/lib/azure.py#L192
+        if "/openai/deployments" in azure_endpoint:
+            # this is base_url, not an azure_endpoint
+            azure_client_params["base_url"] = azure_endpoint
+            azure_client_params.pop("azure_endpoint")
+
+    return azure_client_params
+
+
+class BaseAzureLLM(BaseOpenAILLM):
+    def get_azure_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str] = None,
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+        _is_async: bool = False,
+        model: Optional[str] = None,
+    ) -> Optional[Union[AzureOpenAI, AsyncAzureOpenAI]]:
+        openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None
+        client_initialization_params: dict = locals()
+        if client is None:
+            cached_client = self.get_cached_openai_client(
+                client_initialization_params=client_initialization_params,
+                client_type="azure",
+            )
+            if cached_client:
+                if isinstance(cached_client, AzureOpenAI) or isinstance(
+                    cached_client, AsyncAzureOpenAI
+                ):
+                    return cached_client
+
+            azure_client_params = self.initialize_azure_sdk_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                model_name=model,
+                api_version=api_version,
+                is_async=_is_async,
+            )
+            if _is_async is True:
+                openai_client = AsyncAzureOpenAI(**azure_client_params)
+            else:
+                openai_client = AzureOpenAI(**azure_client_params)  # type: ignore
+        else:
+            openai_client = client
+            if api_version is not None and isinstance(
+                openai_client._custom_query, dict
+            ):
+                # set api_version to version passed by user
+                openai_client._custom_query.setdefault("api-version", api_version)
+
+        # save client in-memory cache
+        self.set_cached_openai_client(
+            openai_client=openai_client,
+            client_initialization_params=client_initialization_params,
+            client_type="azure",
+        )
+        return openai_client
+
+    def initialize_azure_sdk_client(
+        self,
+        litellm_params: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        model_name: Optional[str],
+        api_version: Optional[str],
+        is_async: bool,
+    ) -> dict:
+
+        azure_ad_token_provider: Optional[Callable[[], str]] = None
+        # If we have api_key, then we have higher priority
+        azure_ad_token = litellm_params.get("azure_ad_token")
+        tenant_id = litellm_params.get("tenant_id")
+        client_id = litellm_params.get("client_id")
+        client_secret = litellm_params.get("client_secret")
+        azure_username = litellm_params.get("azure_username")
+        azure_password = litellm_params.get("azure_password")
+        max_retries = litellm_params.get("max_retries")
+        timeout = litellm_params.get("timeout")
+        if not api_key and tenant_id and client_id and client_secret:
+            verbose_logger.debug("Using Azure AD Token Provider for Azure Auth")
+            azure_ad_token_provider = get_azure_ad_token_from_entrata_id(
+                tenant_id=tenant_id,
+                client_id=client_id,
+                client_secret=client_secret,
+            )
+        if azure_username and azure_password and client_id:
+            azure_ad_token_provider = get_azure_ad_token_from_username_password(
+                azure_username=azure_username,
+                azure_password=azure_password,
+                client_id=client_id,
+            )
+
+        if azure_ad_token is not None and azure_ad_token.startswith("oidc/"):
+            azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
+        elif (
+            not api_key
+            and azure_ad_token_provider is None
+            and litellm.enable_azure_ad_token_refresh is True
+        ):
+            try:
+                azure_ad_token_provider = get_azure_ad_token_provider()
+            except ValueError:
+                verbose_logger.debug("Azure AD Token Provider could not be used.")
+        if api_version is None:
+            api_version = os.getenv(
+                "AZURE_API_VERSION", litellm.AZURE_DEFAULT_API_VERSION
+            )
+
+        _api_key = api_key
+        if _api_key is not None and isinstance(_api_key, str):
+            # only show first 5 chars of api_key
+            _api_key = _api_key[:8] + "*" * 15
+        verbose_logger.debug(
+            f"Initializing Azure OpenAI Client for {model_name}, Api Base: {str(api_base)}, Api Key:{_api_key}"
+        )
+        azure_client_params = {
+            "api_key": api_key,
+            "azure_endpoint": api_base,
+            "api_version": api_version,
+            "azure_ad_token": azure_ad_token,
+            "azure_ad_token_provider": azure_ad_token_provider,
+        }
+        # init http client + SSL Verification settings
+        if is_async is True:
+            azure_client_params["http_client"] = self._get_async_http_client()
+        else:
+            azure_client_params["http_client"] = self._get_sync_http_client()
+
+        if max_retries is not None:
+            azure_client_params["max_retries"] = max_retries
+        if timeout is not None:
+            azure_client_params["timeout"] = timeout
+
+        if azure_ad_token_provider is not None:
+            azure_client_params["azure_ad_token_provider"] = azure_ad_token_provider
+        # this decides if we should set azure_endpoint or base_url on Azure OpenAI Client
+        # required to support GPT-4 vision enhancements, since base_url needs to be set on Azure OpenAI Client
+
+        azure_client_params = select_azure_base_url_or_endpoint(
+            azure_client_params=azure_client_params
+        )
+
+        return azure_client_params
+
+    def _init_azure_client_for_cloudflare_ai_gateway(
+        self,
+        api_base: str,
+        model: str,
+        api_version: str,
+        max_retries: int,
+        timeout: Union[float, httpx.Timeout],
+        api_key: Optional[str],
+        azure_ad_token: Optional[str],
+        azure_ad_token_provider: Optional[Callable[[], str]],
+        acompletion: bool,
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+    ) -> Union[AzureOpenAI, AsyncAzureOpenAI]:
+        ## build base url - assume api base includes resource name
+        if client is None:
+            if not api_base.endswith("/"):
+                api_base += "/"
+            api_base += f"{model}"
+
+            azure_client_params: Dict[str, Any] = {
+                "api_version": api_version,
+                "base_url": f"{api_base}",
+                "http_client": litellm.client_session,
+                "max_retries": max_retries,
+                "timeout": timeout,
+            }
+            if api_key is not None:
+                azure_client_params["api_key"] = api_key
+            elif azure_ad_token is not None:
+                if azure_ad_token.startswith("oidc/"):
+                    azure_ad_token = get_azure_ad_token_from_oidc(azure_ad_token)
+
+                azure_client_params["azure_ad_token"] = azure_ad_token
+            if azure_ad_token_provider is not None:
+                azure_client_params["azure_ad_token_provider"] = azure_ad_token_provider
+
+            if acompletion is True:
+                client = AsyncAzureOpenAI(**azure_client_params)  # type: ignore
+            else:
+                client = AzureOpenAI(**azure_client_params)  # type: ignore
+        return client
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/completion/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/completion/handler.py
new file mode 100644
index 00000000..8301c4d6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/completion/handler.py
@@ -0,0 +1,378 @@
+from typing import Any, Callable, Optional
+
+from openai import AsyncAzureOpenAI, AzureOpenAI
+
+from litellm.litellm_core_utils.prompt_templates.factory import prompt_factory
+from litellm.utils import CustomStreamWrapper, ModelResponse, TextCompletionResponse
+
+from ...openai.completion.transformation import OpenAITextCompletionConfig
+from ..common_utils import AzureOpenAIError, BaseAzureLLM
+
+openai_text_completion_config = OpenAITextCompletionConfig()
+
+
+class AzureTextCompletion(BaseAzureLLM):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def validate_environment(self, api_key, azure_ad_token):
+        headers = {
+            "content-type": "application/json",
+        }
+        if api_key is not None:
+            headers["api-key"] = api_key
+        elif azure_ad_token is not None:
+            headers["Authorization"] = f"Bearer {azure_ad_token}"
+        return headers
+
+    def completion(  # noqa: PLR0915
+        self,
+        model: str,
+        messages: list,
+        model_response: ModelResponse,
+        api_key: str,
+        api_base: str,
+        api_version: str,
+        api_type: str,
+        azure_ad_token: str,
+        azure_ad_token_provider: Optional[Callable],
+        print_verbose: Callable,
+        timeout,
+        logging_obj,
+        optional_params,
+        litellm_params,
+        logger_fn,
+        acompletion: bool = False,
+        headers: Optional[dict] = None,
+        client=None,
+    ):
+        try:
+            if model is None or messages is None:
+                raise AzureOpenAIError(
+                    status_code=422, message="Missing model or messages"
+                )
+
+            max_retries = optional_params.pop("max_retries", 2)
+            prompt = prompt_factory(
+                messages=messages, model=model, custom_llm_provider="azure_text"
+            )
+
+            ### CHECK IF CLOUDFLARE AI GATEWAY ###
+            ### if so - set the model as part of the base url
+            if "gateway.ai.cloudflare.com" in api_base:
+                ## build base url - assume api base includes resource name
+                client = self._init_azure_client_for_cloudflare_ai_gateway(
+                    api_key=api_key,
+                    api_version=api_version,
+                    api_base=api_base,
+                    model=model,
+                    client=client,
+                    max_retries=max_retries,
+                    timeout=timeout,
+                    azure_ad_token=azure_ad_token,
+                    azure_ad_token_provider=azure_ad_token_provider,
+                    acompletion=acompletion,
+                )
+
+                data = {"model": None, "prompt": prompt, **optional_params}
+            else:
+                data = {
+                    "model": model,  # type: ignore
+                    "prompt": prompt,
+                    **optional_params,
+                }
+
+            if acompletion is True:
+                if optional_params.get("stream", False):
+                    return self.async_streaming(
+                        logging_obj=logging_obj,
+                        api_base=api_base,
+                        data=data,
+                        model=model,
+                        api_key=api_key,
+                        api_version=api_version,
+                        azure_ad_token=azure_ad_token,
+                        timeout=timeout,
+                        client=client,
+                        litellm_params=litellm_params,
+                    )
+                else:
+                    return self.acompletion(
+                        api_base=api_base,
+                        data=data,
+                        model_response=model_response,
+                        api_key=api_key,
+                        api_version=api_version,
+                        model=model,
+                        azure_ad_token=azure_ad_token,
+                        timeout=timeout,
+                        client=client,
+                        logging_obj=logging_obj,
+                        max_retries=max_retries,
+                        litellm_params=litellm_params,
+                    )
+            elif "stream" in optional_params and optional_params["stream"] is True:
+                return self.streaming(
+                    logging_obj=logging_obj,
+                    api_base=api_base,
+                    data=data,
+                    model=model,
+                    api_key=api_key,
+                    api_version=api_version,
+                    azure_ad_token=azure_ad_token,
+                    timeout=timeout,
+                    client=client,
+                )
+            else:
+                ## LOGGING
+                logging_obj.pre_call(
+                    input=prompt,
+                    api_key=api_key,
+                    additional_args={
+                        "headers": {
+                            "api_key": api_key,
+                            "azure_ad_token": azure_ad_token,
+                        },
+                        "api_version": api_version,
+                        "api_base": api_base,
+                        "complete_input_dict": data,
+                    },
+                )
+                if not isinstance(max_retries, int):
+                    raise AzureOpenAIError(
+                        status_code=422, message="max retries must be an int"
+                    )
+                # init AzureOpenAI Client
+                azure_client = self.get_azure_openai_client(
+                    api_key=api_key,
+                    api_base=api_base,
+                    api_version=api_version,
+                    client=client,
+                    litellm_params=litellm_params,
+                    _is_async=False,
+                    model=model,
+                )
+
+                if not isinstance(azure_client, AzureOpenAI):
+                    raise AzureOpenAIError(
+                        status_code=500,
+                        message="azure_client is not an instance of AzureOpenAI",
+                    )
+
+                raw_response = azure_client.completions.with_raw_response.create(
+                    **data, timeout=timeout
+                )
+                response = raw_response.parse()
+                stringified_response = response.model_dump()
+                ## LOGGING
+                logging_obj.post_call(
+                    input=prompt,
+                    api_key=api_key,
+                    original_response=stringified_response,
+                    additional_args={
+                        "headers": headers,
+                        "api_version": api_version,
+                        "api_base": api_base,
+                    },
+                )
+                return (
+                    openai_text_completion_config.convert_to_chat_model_response_object(
+                        response_object=TextCompletionResponse(**stringified_response),
+                        model_response_object=model_response,
+                    )
+                )
+        except AzureOpenAIError as e:
+            raise e
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise AzureOpenAIError(
+                status_code=status_code, message=str(e), headers=error_headers
+            )
+
+    async def acompletion(
+        self,
+        api_key: str,
+        api_version: str,
+        model: str,
+        api_base: str,
+        data: dict,
+        timeout: Any,
+        model_response: ModelResponse,
+        logging_obj: Any,
+        max_retries: int,
+        azure_ad_token: Optional[str] = None,
+        client=None,  # this is the AsyncAzureOpenAI
+        litellm_params: dict = {},
+    ):
+        response = None
+        try:
+            # init AzureOpenAI Client
+            # setting Azure client
+            azure_client = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                _is_async=True,
+                client=client,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(azure_client, AsyncAzureOpenAI):
+                raise AzureOpenAIError(
+                    status_code=500,
+                    message="azure_client is not an instance of AsyncAzureOpenAI",
+                )
+
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data["prompt"],
+                api_key=azure_client.api_key,
+                additional_args={
+                    "headers": {"Authorization": f"Bearer {azure_client.api_key}"},
+                    "api_base": azure_client._base_url._uri_reference,
+                    "acompletion": True,
+                    "complete_input_dict": data,
+                },
+            )
+            raw_response = await azure_client.completions.with_raw_response.create(
+                **data, timeout=timeout
+            )
+            response = raw_response.parse()
+            return openai_text_completion_config.convert_to_chat_model_response_object(
+                response_object=response.model_dump(),
+                model_response_object=model_response,
+            )
+        except AzureOpenAIError as e:
+            raise e
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise AzureOpenAIError(
+                status_code=status_code, message=str(e), headers=error_headers
+            )
+
+    def streaming(
+        self,
+        logging_obj,
+        api_base: str,
+        api_key: str,
+        api_version: str,
+        data: dict,
+        model: str,
+        timeout: Any,
+        azure_ad_token: Optional[str] = None,
+        client=None,
+        litellm_params: dict = {},
+    ):
+        max_retries = data.pop("max_retries", 2)
+        if not isinstance(max_retries, int):
+            raise AzureOpenAIError(
+                status_code=422, message="max retries must be an int"
+            )
+        # init AzureOpenAI Client
+        azure_client = self.get_azure_openai_client(
+            api_version=api_version,
+            api_base=api_base,
+            api_key=api_key,
+            model=model,
+            _is_async=False,
+            client=client,
+            litellm_params=litellm_params,
+        )
+        if not isinstance(azure_client, AzureOpenAI):
+            raise AzureOpenAIError(
+                status_code=500,
+                message="azure_client is not an instance of AzureOpenAI",
+            )
+
+        ## LOGGING
+        logging_obj.pre_call(
+            input=data["prompt"],
+            api_key=azure_client.api_key,
+            additional_args={
+                "headers": {"Authorization": f"Bearer {azure_client.api_key}"},
+                "api_base": azure_client._base_url._uri_reference,
+                "acompletion": True,
+                "complete_input_dict": data,
+            },
+        )
+        raw_response = azure_client.completions.with_raw_response.create(
+            **data, timeout=timeout
+        )
+        response = raw_response.parse()
+        streamwrapper = CustomStreamWrapper(
+            completion_stream=response,
+            model=model,
+            custom_llm_provider="azure_text",
+            logging_obj=logging_obj,
+        )
+        return streamwrapper
+
+    async def async_streaming(
+        self,
+        logging_obj,
+        api_base: str,
+        api_key: str,
+        api_version: str,
+        data: dict,
+        model: str,
+        timeout: Any,
+        azure_ad_token: Optional[str] = None,
+        client=None,
+        litellm_params: dict = {},
+    ):
+        try:
+            # init AzureOpenAI Client
+            azure_client = self.get_azure_openai_client(
+                api_version=api_version,
+                api_base=api_base,
+                api_key=api_key,
+                model=model,
+                _is_async=True,
+                client=client,
+                litellm_params=litellm_params,
+            )
+            if not isinstance(azure_client, AsyncAzureOpenAI):
+                raise AzureOpenAIError(
+                    status_code=500,
+                    message="azure_client is not an instance of AsyncAzureOpenAI",
+                )
+            ## LOGGING
+            logging_obj.pre_call(
+                input=data["prompt"],
+                api_key=azure_client.api_key,
+                additional_args={
+                    "headers": {"Authorization": f"Bearer {azure_client.api_key}"},
+                    "api_base": azure_client._base_url._uri_reference,
+                    "acompletion": True,
+                    "complete_input_dict": data,
+                },
+            )
+            raw_response = await azure_client.completions.with_raw_response.create(
+                **data, timeout=timeout
+            )
+            response = raw_response.parse()
+            # return response
+            streamwrapper = CustomStreamWrapper(
+                completion_stream=response,
+                model=model,
+                custom_llm_provider="azure_text",
+                logging_obj=logging_obj,
+            )
+            return streamwrapper  ## DO NOT make this into an async for ... loop, it will yield an async generator, which won't raise errors if the response fails
+        except Exception as e:
+            status_code = getattr(e, "status_code", 500)
+            error_headers = getattr(e, "headers", None)
+            error_response = getattr(e, "response", None)
+            if error_headers is None and error_response:
+                error_headers = getattr(error_response, "headers", None)
+            raise AzureOpenAIError(
+                status_code=status_code, message=str(e), headers=error_headers
+            )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/completion/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/completion/transformation.py
new file mode 100644
index 00000000..bc7b97c6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/completion/transformation.py
@@ -0,0 +1,53 @@
+from typing import Optional, Union
+
+from ...openai.completion.transformation import OpenAITextCompletionConfig
+
+
+class AzureOpenAITextConfig(OpenAITextCompletionConfig):
+    """
+    Reference: https://platform.openai.com/docs/api-reference/chat/create
+
+    The class `AzureOpenAIConfig` provides configuration for the OpenAI's Chat API interface, for use with Azure. It inherits from `OpenAIConfig`. Below are the parameters::
+
+    - `frequency_penalty` (number or null): Defaults to 0. Allows a value between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, thereby minimizing repetition.
+
+    - `function_call` (string or object): This optional parameter controls how the model calls functions.
+
+    - `functions` (array): An optional parameter. It is a list of functions for which the model may generate JSON inputs.
+
+    - `logit_bias` (map): This optional parameter modifies the likelihood of specified tokens appearing in the completion.
+
+    - `max_tokens` (integer or null): This optional parameter helps to set the maximum number of tokens to generate in the chat completion.
+
+    - `n` (integer or null): This optional parameter helps to set how many chat completion choices to generate for each input message.
+
+    - `presence_penalty` (number or null): Defaults to 0. It penalizes new tokens based on if they appear in the text so far, hence increasing the model's likelihood to talk about new topics.
+
+    - `stop` (string / array / null): Specifies up to 4 sequences where the API will stop generating further tokens.
+
+    - `temperature` (number or null): Defines the sampling temperature to use, varying between 0 and 2.
+
+    - `top_p` (number or null): An alternative to sampling with temperature, used for nucleus sampling.
+    """
+
+    def __init__(
+        self,
+        frequency_penalty: Optional[int] = None,
+        logit_bias: Optional[dict] = None,
+        max_tokens: Optional[int] = None,
+        n: Optional[int] = None,
+        presence_penalty: Optional[int] = None,
+        stop: Optional[Union[str, list]] = None,
+        temperature: Optional[int] = None,
+        top_p: Optional[int] = None,
+    ) -> None:
+        super().__init__(
+            frequency_penalty=frequency_penalty,
+            logit_bias=logit_bias,
+            max_tokens=max_tokens,
+            n=n,
+            presence_penalty=presence_penalty,
+            stop=stop,
+            temperature=temperature,
+            top_p=top_p,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/cost_calculation.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/cost_calculation.py
new file mode 100644
index 00000000..96c58d95
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/cost_calculation.py
@@ -0,0 +1,61 @@
+"""
+Helper util for handling azure openai-specific cost calculation
+- e.g.: prompt caching
+"""
+
+from typing import Optional, Tuple
+
+from litellm._logging import verbose_logger
+from litellm.types.utils import Usage
+from litellm.utils import get_model_info
+
+
+def cost_per_token(
+    model: str, usage: Usage, response_time_ms: Optional[float] = 0.0
+) -> Tuple[float, float]:
+    """
+    Calculates the cost per token for a given model, prompt tokens, and completion tokens.
+
+    Input:
+        - model: str, the model name without provider prefix
+        - usage: LiteLLM Usage block, containing anthropic caching information
+
+    Returns:
+        Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
+    """
+    ## GET MODEL INFO
+    model_info = get_model_info(model=model, custom_llm_provider="azure")
+    cached_tokens: Optional[int] = None
+    ## CALCULATE INPUT COST
+    non_cached_text_tokens = usage.prompt_tokens
+    if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
+        cached_tokens = usage.prompt_tokens_details.cached_tokens
+        non_cached_text_tokens = non_cached_text_tokens - cached_tokens
+    prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"]
+
+    ## CALCULATE OUTPUT COST
+    completion_cost: float = (
+        usage["completion_tokens"] * model_info["output_cost_per_token"]
+    )
+
+    ## Prompt Caching cost calculation
+    if model_info.get("cache_read_input_token_cost") is not None and cached_tokens:
+        # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens
+        prompt_cost += cached_tokens * (
+            model_info.get("cache_read_input_token_cost", 0) or 0
+        )
+
+    ## Speech / Audio cost calculation
+    if (
+        "output_cost_per_second" in model_info
+        and model_info["output_cost_per_second"] is not None
+        and response_time_ms is not None
+    ):
+        verbose_logger.debug(
+            f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; response time: {response_time_ms}"
+        )
+        ## COST PER SECOND ##
+        prompt_cost = 0
+        completion_cost = model_info["output_cost_per_second"] * response_time_ms / 1000
+
+    return prompt_cost, completion_cost
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/files/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/files/handler.py
new file mode 100644
index 00000000..d45ac9a3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/files/handler.py
@@ -0,0 +1,284 @@
+from typing import Any, Coroutine, Optional, Union, cast
+
+import httpx
+from openai import AsyncAzureOpenAI, AzureOpenAI
+from openai.types.file_deleted import FileDeleted
+
+from litellm._logging import verbose_logger
+from litellm.types.llms.openai import *
+
+from ..common_utils import BaseAzureLLM
+
+
+class AzureOpenAIFilesAPI(BaseAzureLLM):
+    """
+    AzureOpenAI methods to support for batches
+    - create_file()
+    - retrieve_file()
+    - list_files()
+    - delete_file()
+    - file_content()
+    - update_file()
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    async def acreate_file(
+        self,
+        create_file_data: CreateFileRequest,
+        openai_client: AsyncAzureOpenAI,
+    ) -> FileObject:
+        verbose_logger.debug("create_file_data=%s", create_file_data)
+        response = await openai_client.files.create(**create_file_data)
+        verbose_logger.debug("create_file_response=%s", response)
+        return response
+
+    def create_file(
+        self,
+        _is_async: bool,
+        create_file_data: CreateFileRequest,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> Union[FileObject, Coroutine[Any, Any, FileObject]]:
+
+        openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+            )
+        )
+        if openai_client is None:
+            raise ValueError(
+                "AzureOpenAI 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, AsyncAzureOpenAI):
+                raise ValueError(
+                    "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI 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: AsyncAzureOpenAI,
+    ) -> 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: Optional[str],
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        api_version: Optional[str] = None,
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> Union[
+        HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
+    ]:
+        openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+            )
+        )
+        if openai_client is None:
+            raise ValueError(
+                "AzureOpenAI 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, AsyncAzureOpenAI):
+                raise ValueError(
+                    "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client."
+                )
+            return self.afile_content(  # type: ignore
+                file_content_request=file_content_request,
+                openai_client=openai_client,
+            )
+        response = cast(AzureOpenAI, openai_client).files.content(
+            **file_content_request
+        )
+
+        return HttpxBinaryResponseContent(response=response.response)
+
+    async def aretrieve_file(
+        self,
+        file_id: str,
+        openai_client: AsyncAzureOpenAI,
+    ) -> 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: Optional[str],
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        api_version: Optional[str] = None,
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+            )
+        )
+        if openai_client is None:
+            raise ValueError(
+                "AzureOpenAI 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, AsyncAzureOpenAI):
+                raise ValueError(
+                    "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI 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: AsyncAzureOpenAI,
+    ) -> FileDeleted:
+        response = await openai_client.files.delete(file_id=file_id)
+
+        if not isinstance(response, FileDeleted):  # azure returns an empty string
+            return FileDeleted(id=file_id, deleted=True, object="file")
+        return response
+
+    def delete_file(
+        self,
+        _is_async: bool,
+        file_id: str,
+        api_base: Optional[str],
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str] = None,
+        api_version: Optional[str] = None,
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+            )
+        )
+        if openai_client is None:
+            raise ValueError(
+                "AzureOpenAI 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, AsyncAzureOpenAI):
+                raise ValueError(
+                    "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI client."
+                )
+            return self.adelete_file(  # type: ignore
+                file_id=file_id,
+                openai_client=openai_client,
+            )
+        response = openai_client.files.delete(file_id=file_id)
+
+        if not isinstance(response, FileDeleted):  # azure returns an empty string
+            return FileDeleted(id=file_id, deleted=True, object="file")
+
+        return response
+
+    async def alist_files(
+        self,
+        openai_client: AsyncAzureOpenAI,
+        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: Optional[str],
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        purpose: Optional[str] = None,
+        api_version: Optional[str] = None,
+        client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = None,
+        litellm_params: Optional[dict] = None,
+    ):
+        openai_client: Optional[Union[AzureOpenAI, AsyncAzureOpenAI]] = (
+            self.get_azure_openai_client(
+                litellm_params=litellm_params or {},
+                api_key=api_key,
+                api_base=api_base,
+                api_version=api_version,
+                client=client,
+                _is_async=_is_async,
+            )
+        )
+        if openai_client is None:
+            raise ValueError(
+                "AzureOpenAI 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, AsyncAzureOpenAI):
+                raise ValueError(
+                    "AzureOpenAI client is not an instance of AsyncAzureOpenAI. Make sure you passed an AsyncAzureOpenAI 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
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/fine_tuning/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/fine_tuning/handler.py
new file mode 100644
index 00000000..3d7cc336
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/fine_tuning/handler.py
@@ -0,0 +1,47 @@
+from typing import Optional, Union
+
+import httpx
+from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
+
+from litellm.llms.azure.common_utils import BaseAzureLLM
+from litellm.llms.openai.fine_tuning.handler import OpenAIFineTuningAPI
+
+
+class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
+    """
+    AzureOpenAI methods to support fine tuning, inherits from OpenAIFineTuningAPI.
+    """
+
+    def get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+        _is_async: bool = False,
+        api_version: Optional[str] = None,
+        litellm_params: Optional[dict] = None,
+    ) -> Optional[
+        Union[
+            OpenAI,
+            AsyncOpenAI,
+            AzureOpenAI,
+            AsyncAzureOpenAI,
+        ]
+    ]:
+        # Override to use Azure-specific client initialization
+        if isinstance(client, OpenAI) or isinstance(client, AsyncOpenAI):
+            client = None
+
+        return self.get_azure_openai_client(
+            litellm_params=litellm_params or {},
+            api_key=api_key,
+            api_base=api_base,
+            api_version=api_version,
+            client=client,
+            _is_async=_is_async,
+        )
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/azure/realtime/handler.py b/.venv/lib/python3.12/site-packages/litellm/llms/azure/realtime/handler.py
new file mode 100644
index 00000000..5a4865e7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/litellm/llms/azure/realtime/handler.py
@@ -0,0 +1,75 @@
+"""
+This file contains the calling Azure OpenAI's `/openai/realtime` endpoint.
+
+This requires websockets, and is currently only supported on LiteLLM Proxy.
+"""
+
+from typing import Any, Optional
+
+from ....litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
+from ....litellm_core_utils.realtime_streaming import RealTimeStreaming
+from ..azure import AzureChatCompletion
+
+# BACKEND_WS_URL = "ws://localhost:8080/v1/realtime?model=gpt-4o-realtime-preview-2024-10-01"
+
+
+async def forward_messages(client_ws: Any, backend_ws: Any):
+    import websockets
+
+    try:
+        while True:
+            message = await backend_ws.recv()
+            await client_ws.send_text(message)
+    except websockets.exceptions.ConnectionClosed:  # type: ignore
+        pass
+
+
+class AzureOpenAIRealtime(AzureChatCompletion):
+    def _construct_url(self, api_base: str, model: str, api_version: str) -> str:
+        """
+        Example output:
+        "wss://my-endpoint-sweden-berri992.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview";
+
+        """
+        api_base = api_base.replace("https://", "wss://")
+        return (
+            f"{api_base}/openai/realtime?api-version={api_version}&deployment={model}"
+        )
+
+    async def async_realtime(
+        self,
+        model: str,
+        websocket: Any,
+        api_base: Optional[str] = None,
+        api_key: Optional[str] = None,
+        api_version: Optional[str] = None,
+        azure_ad_token: Optional[str] = None,
+        client: Optional[Any] = None,
+        logging_obj: Optional[LiteLLMLogging] = None,
+        timeout: Optional[float] = None,
+    ):
+        import websockets
+
+        if api_base is None:
+            raise ValueError("api_base is required for Azure OpenAI calls")
+        if api_version is None:
+            raise ValueError("api_version is required for Azure OpenAI calls")
+
+        url = self._construct_url(api_base, model, api_version)
+
+        try:
+            async with websockets.connect(  # type: ignore
+                url,
+                extra_headers={
+                    "api-key": api_key,  # type: ignore
+                },
+            ) as backend_ws:
+                realtime_streaming = RealTimeStreaming(
+                    websocket, backend_ws, logging_obj
+                )
+                await realtime_streaming.bidirectional_forward()
+
+        except websockets.exceptions.InvalidStatusCode as e:  # type: ignore
+            await websocket.close(code=e.status_code, reason=str(e))
+        except Exception:
+            pass