Merge pull request #3888 from BerriAI/litellm_add_files_proxy

[Feat] LiteLLM Proxy Add `POST /v1/files` and `GET /v1/files`

litellm/batches/main.py

@@ -30,6 +30,8 @@ from ..types.llms.openai import (
     FileTypes,
     FileObject,
     Batch,
+    FileContentRequest,
+    HttpxBinaryResponseContent,
 )
 
 ####### ENVIRONMENT VARIABLES ###################
@@ -170,6 +172,134 @@ def create_file(
         raise e
 
 
+async def afile_content(
+    file_id: str,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> Coroutine[Any, Any, HttpxBinaryResponseContent]:
+    """
+    Async: Get file contents
+
+    LiteLLM Equivalent of GET https://api.openai.com/v1/files
+    """
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["afile_content"] = True
+
+        # Use a partial function to pass your keyword arguments
+        func = partial(
+            file_content,
+            file_id,
+            custom_llm_provider,
+            extra_headers,
+            extra_body,
+            **kwargs,
+        )
+
+        # Add the context to the function
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
+        init_response = await loop.run_in_executor(None, func_with_context)
+        if asyncio.iscoroutine(init_response):
+            response = await init_response
+        else:
+            response = init_response  # type: ignore
+
+        return response
+    except Exception as e:
+        raise e
+
+
+def file_content(
+    file_id: str,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> Union[HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]]:
+    """
+    Returns the contents of the specified file.
+
+    LiteLLM Equivalent of POST: POST https://api.openai.com/v1/files
+    """
+    try:
+        optional_params = GenericLiteLLMParams(**kwargs)
+        if custom_llm_provider == "openai":
+            # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+            api_base = (
+                optional_params.api_base
+                or litellm.api_base
+                or os.getenv("OPENAI_API_BASE")
+                or "https://api.openai.com/v1"
+            )
+            organization = (
+                optional_params.organization
+                or litellm.organization
+                or os.getenv("OPENAI_ORGANIZATION", None)
+                or None  # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+            )
+            # set API KEY
+            api_key = (
+                optional_params.api_key
+                or litellm.api_key  # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+                or litellm.openai_key
+                or os.getenv("OPENAI_API_KEY")
+            )
+            ### TIMEOUT LOGIC ###
+            timeout = (
+                optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+            )
+            # set timeout for 10 minutes by default
+
+            if (
+                timeout is not None
+                and isinstance(timeout, httpx.Timeout)
+                and supports_httpx_timeout(custom_llm_provider) == False
+            ):
+                read_timeout = timeout.read or 600
+                timeout = read_timeout  # default 10 min timeout
+            elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+                timeout = float(timeout)  # type: ignore
+            elif timeout is None:
+                timeout = 600.0
+
+            _file_content_request = FileContentRequest(
+                file_id=file_id,
+                extra_headers=extra_headers,
+                extra_body=extra_body,
+            )
+
+            _is_async = kwargs.pop("afile_content", False) is True
+
+            response = openai_files_instance.file_content(
+                _is_async=_is_async,
+                file_content_request=_file_content_request,
+                api_base=api_base,
+                api_key=api_key,
+                timeout=timeout,
+                max_retries=optional_params.max_retries,
+                organization=organization,
+            )
+        else:
+            raise litellm.exceptions.BadRequestError(
+                message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+                    custom_llm_provider
+                ),
+                model="n/a",
+                llm_provider=custom_llm_provider,
+                response=httpx.Response(
+                    status_code=400,
+                    content="Unsupported provider",
+                    request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"),  # type: ignore
+                ),
+            )
+        return response
+    except Exception as e:
+        raise e
+
+
 async def acreate_batch(
     completion_window: Literal["24h"],
     endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"],

litellm/llms/openai.py

@@ -1585,6 +1585,54 @@ class OpenAIFilesAPI(BaseLLM):
         response = openai_client.files.create(**create_file_data)
         return response
 
+    async def afile_content(
+        self,
+        file_content_request: FileContentRequest,
+        openai_client: AsyncOpenAI,
+    ) -> HttpxBinaryResponseContent:
+        response = await openai_client.files.content(**file_content_request)
+        return response
+
+    def file_content(
+        self,
+        _is_async: bool,
+        file_content_request: FileContentRequest,
+        api_base: str,
+        api_key: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ) -> Union[
+        HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
+    ]:
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.afile_content(  # type: ignore
+                file_content_request=file_content_request,
+                openai_client=openai_client,
+            )
+        response = openai_client.files.content(**file_content_request)
+
+        return response
+
 
 class OpenAIBatchesAPI(BaseLLM):
     """

litellm/proxy/_types.py

@@ -99,6 +99,14 @@ class LiteLLMRoutes(enum.Enum):
         # moderations
         "/moderations",
         "/v1/moderations",
+        # batches
+        "/v1/batches",
+        "/batches",
+        "/v1/batches{batch_id}",
+        "/batches{batch_id}",
+        # files
+        "/v1/files",
+        "/files",
         # models
         "/models",
         "/v1/models",
@@ -1215,6 +1223,7 @@ class InvitationModel(LiteLLMBase):
     updated_at: datetime
     updated_by: str
 
+
 class ConfigFieldInfo(LiteLLMBase):
     field_name: str
     field_value: Any

litellm/proxy/proxy_server.py

@@ -100,6 +100,13 @@ from litellm.proxy.utils import (
     encrypt_value,
     decrypt_value,
 )
+from litellm import (
+    CreateBatchRequest,
+    RetrieveBatchRequest,
+    ListBatchRequest,
+    CancelBatchRequest,
+    CreateFileRequest,
+)
 from litellm.proxy.secret_managers.google_kms import load_google_kms
 from litellm.proxy.secret_managers.aws_secret_manager import load_aws_secret_manager
 import pydantic
@@ -142,6 +149,7 @@ from fastapi import (
     Request,
     HTTPException,
     status,
+    Path,
     Depends,
     Header,
     Response,
@@ -5027,6 +5035,447 @@ async def audio_transcriptions(
             )
 
 
+######################################################################
+
+#                          /v1/batches Endpoints
+
+
+######################################################################
+@router.post(
+    "/v1/batches",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["batch"],
+)
+@router.post(
+    "/batches",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["batch"],
+)
+async def create_batch(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Create large batches of API requests for asynchronous processing.
+    This is the equivalent of POST https://api.openai.com/v1/batch
+    Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch
+
+    Example Curl
+    ```
+    curl http://localhost:4000/v1/batches \
+        -H "Authorization: Bearer sk-1234" \
+        -H "Content-Type: application/json" \
+        -d '{
+            "input_file_id": "file-abc123",
+            "endpoint": "/v1/chat/completions",
+            "completion_window": "24h"
+    }'
+    ```
+    """
+    global proxy_logging_obj
+    data: Dict = {}
+    try:
+        # Use orjson to parse JSON data, orjson speeds up requests significantly
+        form_data = await request.form()
+        data = {key: value for key, value in form_data.items() if key != "file"}
+
+        # Include original request and headers in the data
+        data["proxy_server_request"] = {  # type: ignore
+            "url": str(request.url),
+            "method": request.method,
+            "headers": dict(request.headers),
+            "body": copy.copy(data),  # use copy instead of deepcopy
+        }
+
+        if data.get("user", None) is None and user_api_key_dict.user_id is not None:
+            data["user"] = user_api_key_dict.user_id
+
+        if "metadata" not in data:
+            data["metadata"] = {}
+        data["metadata"]["user_api_key"] = user_api_key_dict.api_key
+        data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
+        _headers = dict(request.headers)
+        _headers.pop(
+            "authorization", None
+        )  # do not store the original `sk-..` api key in the db
+        data["metadata"]["headers"] = _headers
+        data["metadata"]["user_api_key_alias"] = getattr(
+            user_api_key_dict, "key_alias", None
+        )
+        data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id
+        data["metadata"]["user_api_key_team_id"] = getattr(
+            user_api_key_dict, "team_id", None
+        )
+        data["metadata"]["global_max_parallel_requests"] = general_settings.get(
+            "global_max_parallel_requests", None
+        )
+        data["metadata"]["user_api_key_team_alias"] = getattr(
+            user_api_key_dict, "team_alias", None
+        )
+        data["metadata"]["endpoint"] = str(request.url)
+
+        ### TEAM-SPECIFIC PARAMS ###
+        if user_api_key_dict.team_id is not None:
+            team_config = await proxy_config.load_team_config(
+                team_id=user_api_key_dict.team_id
+            )
+            if len(team_config) == 0:
+                pass
+            else:
+                team_id = team_config.pop("team_id", None)
+                data["metadata"]["team_id"] = team_id
+                data = {
+                    **team_config,
+                    **data,
+                }  # add the team-specific configs to the completion call
+
+        _create_batch_data = CreateBatchRequest(**data)
+
+        # for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
+        response = await litellm.acreate_batch(
+            custom_llm_provider="openai", **_create_batch_data
+        )
+
+        ### ALERTING ###
+        data["litellm_status"] = "success"  # used for alerting
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers.update(
+            get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            )
+        )
+
+        return response
+    except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
+        )
+        traceback.print_exc()
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "message", str(e.detail)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+            )
+        else:
+            error_traceback = traceback.format_exc()
+            error_msg = f"{str(e)}"
+            raise ProxyException(
+                message=getattr(e, "message", error_msg),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", 500),
+            )
+
+
+@router.get(
+    "/v1/batches{batch_id}",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["Batch"],
+)
+@router.get(
+    "/batches{batch_id}",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["Batch"],
+)
+async def retrieve_batch(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+    batch_id: str = Path(
+        title="Batch ID to retrieve", description="The ID of the batch to retrieve"
+    ),
+):
+    """
+    Retrieves a batch.
+    This is the equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+    Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/retrieve
+
+    Example Curl
+    ```
+    curl http://localhost:4000/v1/batches/batch_abc123 \
+    -H "Authorization: Bearer sk-1234" \
+    -H "Content-Type: application/json" \
+
+    ```
+    """
+    global proxy_logging_obj
+    data: Dict = {}
+    try:
+        # Use orjson to parse JSON data, orjson speeds up requests significantly
+        form_data = await request.form()
+        data = {key: value for key, value in form_data.items() if key != "file"}
+
+        # Include original request and headers in the data
+        data["proxy_server_request"] = {  # type: ignore
+            "url": str(request.url),
+            "method": request.method,
+            "headers": dict(request.headers),
+            "body": copy.copy(data),  # use copy instead of deepcopy
+        }
+
+        if data.get("user", None) is None and user_api_key_dict.user_id is not None:
+            data["user"] = user_api_key_dict.user_id
+
+        if "metadata" not in data:
+            data["metadata"] = {}
+        data["metadata"]["user_api_key"] = user_api_key_dict.api_key
+        data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
+        _headers = dict(request.headers)
+        _headers.pop(
+            "authorization", None
+        )  # do not store the original `sk-..` api key in the db
+        data["metadata"]["headers"] = _headers
+        data["metadata"]["user_api_key_alias"] = getattr(
+            user_api_key_dict, "key_alias", None
+        )
+        data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id
+        data["metadata"]["user_api_key_team_id"] = getattr(
+            user_api_key_dict, "team_id", None
+        )
+        data["metadata"]["global_max_parallel_requests"] = general_settings.get(
+            "global_max_parallel_requests", None
+        )
+        data["metadata"]["user_api_key_team_alias"] = getattr(
+            user_api_key_dict, "team_alias", None
+        )
+        data["metadata"]["endpoint"] = str(request.url)
+
+        ### TEAM-SPECIFIC PARAMS ###
+        if user_api_key_dict.team_id is not None:
+            team_config = await proxy_config.load_team_config(
+                team_id=user_api_key_dict.team_id
+            )
+            if len(team_config) == 0:
+                pass
+            else:
+                team_id = team_config.pop("team_id", None)
+                data["metadata"]["team_id"] = team_id
+                data = {
+                    **team_config,
+                    **data,
+                }  # add the team-specific configs to the completion call
+
+        _retrieve_batch_request = RetrieveBatchRequest(
+            batch_id=batch_id,
+        )
+
+        # for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
+        response = await litellm.aretrieve_batch(
+            custom_llm_provider="openai", **_retrieve_batch_request
+        )
+
+        ### ALERTING ###
+        data["litellm_status"] = "success"  # used for alerting
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers.update(
+            get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            )
+        )
+
+        return response
+    except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
+        )
+        traceback.print_exc()
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "message", str(e.detail)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+            )
+        else:
+            error_traceback = traceback.format_exc()
+            error_msg = f"{str(e)}"
+            raise ProxyException(
+                message=getattr(e, "message", error_msg),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", 500),
+            )
+
+
+######################################################################
+
+#            END OF  /v1/batches Endpoints Implementation
+
+######################################################################
+
+
+######################################################################
+
+#                          /v1/files Endpoints
+
+
+######################################################################
+@router.post(
+    "/v1/files",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["files"],
+)
+@router.post(
+    "/files",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["files"],
+)
+async def create_file(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    Upload a file that can be used across - Assistants API, Batch API 
+    This is the equivalent of POST https://api.openai.com/v1/files
+
+    Supports Identical Params as: https://platform.openai.com/docs/api-reference/files/create
+
+    Example Curl
+    ```
+    curl https://api.openai.com/v1/files \
+        -H "Authorization: Bearer sk-1234" \
+        -F purpose="batch" \
+        -F file="@mydata.jsonl"
+
+    ```
+    """
+    global proxy_logging_obj
+    data: Dict = {}
+    try:
+        # Use orjson to parse JSON data, orjson speeds up requests significantly
+        form_data = await request.form()
+        data = {key: value for key, value in form_data.items() if key != "file"}
+
+        # Include original request and headers in the data
+        data["proxy_server_request"] = {  # type: ignore
+            "url": str(request.url),
+            "method": request.method,
+            "headers": dict(request.headers),
+            "body": copy.copy(data),  # use copy instead of deepcopy
+        }
+
+        if data.get("user", None) is None and user_api_key_dict.user_id is not None:
+            data["user"] = user_api_key_dict.user_id
+
+        if "metadata" not in data:
+            data["metadata"] = {}
+        data["metadata"]["user_api_key"] = user_api_key_dict.api_key
+        data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
+        _headers = dict(request.headers)
+        _headers.pop(
+            "authorization", None
+        )  # do not store the original `sk-..` api key in the db
+        data["metadata"]["headers"] = _headers
+        data["metadata"]["user_api_key_alias"] = getattr(
+            user_api_key_dict, "key_alias", None
+        )
+        data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id
+        data["metadata"]["user_api_key_team_id"] = getattr(
+            user_api_key_dict, "team_id", None
+        )
+        data["metadata"]["global_max_parallel_requests"] = general_settings.get(
+            "global_max_parallel_requests", None
+        )
+        data["metadata"]["user_api_key_team_alias"] = getattr(
+            user_api_key_dict, "team_alias", None
+        )
+        data["metadata"]["endpoint"] = str(request.url)
+
+        ### TEAM-SPECIFIC PARAMS ###
+        if user_api_key_dict.team_id is not None:
+            team_config = await proxy_config.load_team_config(
+                team_id=user_api_key_dict.team_id
+            )
+            if len(team_config) == 0:
+                pass
+            else:
+                team_id = team_config.pop("team_id", None)
+                data["metadata"]["team_id"] = team_id
+                data = {
+                    **team_config,
+                    **data,
+                }  # add the team-specific configs to the completion call
+
+        _create_file_request = CreateFileRequest()
+
+        # for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
+        response = await litellm.acreate_file(
+            custom_llm_provider="openai", **_create_file_request
+        )
+
+        ### ALERTING ###
+        data["litellm_status"] = "success"  # used for alerting
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers.update(
+            get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            )
+        )
+
+        return response
+    except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
+        )
+        traceback.print_exc()
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "message", str(e.detail)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+            )
+        else:
+            error_traceback = traceback.format_exc()
+            error_msg = f"{str(e)}"
+            raise ProxyException(
+                message=getattr(e, "message", error_msg),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", 500),
+            )
+
+
 @router.post(
     "/v1/moderations",
     dependencies=[Depends(user_api_key_auth)],

litellm/tests/test_openai_batches.py

@@ -60,8 +60,6 @@ def test_create_batch():
         create_batch_response.input_file_id == batch_input_file_id
     ), f"Failed to create batch, expected input_file_id to be {batch_input_file_id} but got {create_batch_response.input_file_id}"
 
-    time.sleep(30)
-
     retrieved_batch = litellm.retrieve_batch(
         batch_id=create_batch_response.id, custom_llm_provider="openai"
     )
@@ -70,6 +68,17 @@ def test_create_batch():
 
     assert retrieved_batch.id == create_batch_response.id
 
+    file_content = litellm.file_content(
+        file_id=batch_input_file_id, custom_llm_provider="openai"
+    )
+
+    result = file_content.content
+
+    result_file_name = "batch_job_results_furniture.jsonl"
+
+    with open(result_file_name, "wb") as file:
+        file.write(result)
+
     pass
 
 
@@ -127,6 +136,18 @@ async def test_async_create_batch():
 
     assert retrieved_batch.id == create_batch_response.id
 
+    # try to get file content for our original file
+
+    file_content = await litellm.afile_content(
+        file_id=batch_input_file_id, custom_llm_provider="openai"
+    )
+
+    print("file content = ", file_content)
+
+    # # write this file content to a file
+    # with open("file_content.json", "w") as f:
+    #     json.dump(file_content, f)
+
 
 def test_retrieve_batch():
     pass

litellm/types/llms/openai.py

@@ -20,6 +20,7 @@ from openai.types.beta.assistant import Assistant
 from openai.pagination import SyncCursorPage
 from os import PathLike
 from openai.types import FileObject, Batch
+from openai._legacy_response import HttpxBinaryResponseContent
 
 from typing import TypedDict, List, Optional, Tuple, Mapping, IO
 
@@ -186,6 +187,26 @@ class CreateFileRequest(TypedDict, total=False):
     timeout: Optional[float]
 
 
+class FileContentRequest(TypedDict, total=False):
+    """
+    FileContentRequest
+    Used by Assistants API, Batches API, and Fine-Tunes API
+
+    Required Params:
+        file_id: str
+
+    Optional Params:
+        extra_headers: Optional[Dict[str, str]]
+        extra_body: Optional[Dict[str, str]] = None
+        timeout: Optional[float] = None
+    """
+
+    file_id: str
+    extra_headers: Optional[Dict[str, str]]
+    extra_body: Optional[Dict[str, str]]
+    timeout: Optional[float]
+
+
 # OpenAI Batches Types
 class CreateBatchRequest(TypedDict, total=False):
     """