feat(router.py): Support Loadbalancing batch azure api endpoints (#5469)

* feat(router.py): initial commit for loadbalancing azure batch api endpoints

Closes https://github.com/BerriAI/litellm/issues/5396

* fix(router.py): working `router.acreate_file()`

* feat(router.py): working router.acreate_batch endpoint

* feat(router.py): expose router.aretrieve_batch function

Make it easy for user to retrieve the batch information

* feat(router.py): support 'router.alist_batches' endpoint

Adds support for getting all batches across all endpoints

* feat(router.py): working loadbalancing on `/v1/files`

* feat(proxy_server.py): working loadbalancing on `/v1/batches`

* feat(proxy_server.py): working loadbalancing on Retrieve + List batch

litellm/proxy/openai_files_endpoints/files_endpoints.py

@@ -31,6 +31,7 @@ from litellm._logging import verbose_proxy_logger
 from litellm.batches.main import FileObject
 from litellm.proxy._types import *
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+from litellm.router import Router
 
 router = APIRouter()
 
@@ -66,6 +67,41 @@ def get_files_provider_config(
     return None
 
 
+def get_first_json_object(file_content_bytes: bytes) -> Optional[dict]:
+    try:
+        # Decode the bytes to a string and split into lines
+        file_content = file_content_bytes.decode("utf-8")
+        first_line = file_content.splitlines()[0].strip()
+
+        # Parse the JSON object from the first line
+        json_object = json.loads(first_line)
+        return json_object
+    except (json.JSONDecodeError, UnicodeDecodeError) as e:
+        return None
+
+
+def get_model_from_json_obj(json_object: dict) -> Optional[str]:
+    body = json_object.get("body", {}) or {}
+    model = body.get("model")
+
+    return model
+
+
+def is_known_model(model: Optional[str], llm_router: Optional[Router]) -> bool:
+    """
+    Returns True if the model is in the llm_router model names
+    """
+    if model is None or llm_router is None:
+        return False
+    model_names = llm_router.get_model_names()
+
+    is_in_list = False
+    if model in model_names:
+        is_in_list = True
+
+    return is_in_list
+
+
 @router.post(
     "/{provider}/v1/files",
     dependencies=[Depends(user_api_key_auth)],
@@ -109,6 +145,7 @@ async def create_file(
         add_litellm_data_to_request,
         general_settings,
         get_custom_headers,
+        llm_router,
         proxy_config,
         proxy_logging_obj,
         version,
@@ -138,18 +175,46 @@ async def create_file(
         # Prepare the file data according to FileTypes
         file_data = (file.filename, file_content, file.content_type)
 
+        ## check if model is a loadbalanced model
+        router_model: Optional[str] = None
+        is_router_model = False
+        if litellm.enable_loadbalancing_on_batch_endpoints is True:
+            json_obj = get_first_json_object(file_content_bytes=file_content)
+            if json_obj:
+                router_model = get_model_from_json_obj(json_object=json_obj)
+                is_router_model = is_known_model(
+                    model=router_model, llm_router=llm_router
+                )
+
         _create_file_request = CreateFileRequest(file=file_data, **data)
 
-        # get configs for custom_llm_provider
-        llm_provider_config = get_files_provider_config(
-            custom_llm_provider=custom_llm_provider
-        )
+        if (
+            litellm.enable_loadbalancing_on_batch_endpoints is True
+            and is_router_model
+            and router_model is not None
+        ):
+            if llm_router is None:
+                raise HTTPException(
+                    status_code=500,
+                    detail={
+                        "error": "LLM Router not initialized. Ensure models added to proxy."
+                    },
+                )
 
-        # add llm_provider_config to data
-        _create_file_request.update(llm_provider_config)
+            response = await llm_router.acreate_file(
+                model=router_model, **_create_file_request
+            )
+        else:
+            # get configs for custom_llm_provider
+            llm_provider_config = get_files_provider_config(
+                custom_llm_provider=custom_llm_provider
+            )
 
-        # for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
-        response = await litellm.acreate_file(**_create_file_request)  # type: ignore
+            # add llm_provider_config to data
+            _create_file_request.update(llm_provider_config)
+
+            # for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
+            response = await litellm.acreate_file(**_create_file_request)  # type: ignore
 
         ### ALERTING ###
         asyncio.create_task(