From 31ecd4ce49c5d6eaa65b8323328ab3bf31ca11ca Mon Sep 17 00:00:00 2001
From: Ishaan Jaff <ishaanjaffer0324@gmail.com>
Date: Thu, 27 Nov 2025 09:12:44 -0800
Subject: [PATCH] Revert "Respect custom llm provider in header" (#17211)

---
 litellm/proxy/batches_endpoints/endpoints.py  |  6 +-
 .../test_openai_batches_endpoint.py           | 86 +------------------
 2 files changed, 3 insertions(+), 89 deletions(-)

diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py
index 03b9ac3dea..98492bcc2d 100644
--- a/litellm/proxy/batches_endpoints/endpoints.py
+++ b/litellm/proxy/batches_endpoints/endpoints.py
@@ -31,6 +31,7 @@ from litellm.proxy.openai_files_endpoints.common_utils import (
     get_original_file_id,
     prepare_data_with_credentials,
 )
+
 from litellm.proxy.utils import handle_exception_on_proxy, is_known_model
 from litellm.types.llms.openai import LiteLLMBatchCreateRequest
 
@@ -111,10 +112,7 @@ async def create_batch(  # noqa: PLR0915
             is_router_model = is_known_model(model=router_model, llm_router=llm_router)
 
         custom_llm_provider = (
-            provider
-            or data.pop("custom_llm_provider", None)
-            or get_custom_llm_provider_from_request_headers(request=request)
-            or "openai"
+            provider or data.pop("custom_llm_provider", None) or "openai"
         )
         _create_batch_data = LiteLLMBatchCreateRequest(**data)
         input_file_id = _create_batch_data.get("input_file_id", None)
diff --git a/tests/openai_endpoints_tests/test_openai_batches_endpoint.py b/tests/openai_endpoints_tests/test_openai_batches_endpoint.py
index be931f2f4b..5a84900f87 100644
--- a/tests/openai_endpoints_tests/test_openai_batches_endpoint.py
+++ b/tests/openai_endpoints_tests/test_openai_batches_endpoint.py
@@ -5,13 +5,12 @@ import asyncio
 import aiohttp, openai
 from openai import OpenAI, AsyncOpenAI
 from typing import Optional, List, Union
+from test_openai_files_endpoints import upload_file, delete_file
 import os
 import sys
 import time
 from unittest.mock import patch, MagicMock, AsyncMock
 
-from litellm.proxy.batches_endpoints.endpoints import create_batch
-
 
 BASE_URL = "http://localhost:4000"  # Replace with your actual base URL
 API_KEY = "sk-1234"  # Replace with your actual API key
@@ -223,89 +222,6 @@ def test_vertex_batches_endpoint():
     pass
 
 
-@pytest.mark.asyncio
-async def test_create_batch_respects_custom_llm_provider_header():
-    request_body = {
-        "input_file_id": "file-batch-123",
-        "endpoint": "/v1/chat/completions",
-        "completion_window": "24h",
-    }
-
-    response_object = MagicMock()
-    response_object._hidden_params = {}
-    response_object.id = "batch-id"
-    response_object.input_file_id = request_body["input_file_id"]
-
-    mock_user_api_key_dict = MagicMock(
-        tpm_limit=0,
-        rpm_limit=0,
-        max_budget=0,
-        spend=0,
-        allowed_model_region="",
-    )
-    mock_user_api_key_dict.metadata = {}
-
-    request = MagicMock()
-    request.headers = {"custom-llm-provider": "vertex_ai"}
-    request.query_params = {}
-    request.method = "POST"
-
-    fastapi_response = MagicMock()
-    fastapi_response.headers = {}
-
-    logging_obj = MagicMock()
-
-    mock_proxy_logging_obj = MagicMock()
-    mock_proxy_logging_obj.post_call_success_hook = AsyncMock(
-        return_value=response_object
-    )
-    mock_proxy_logging_obj.post_call_failure_hook = AsyncMock()
-    mock_proxy_logging_obj.update_request_status = AsyncMock()
-
-    with patch(
-        "litellm.proxy.batches_endpoints.endpoints._read_request_body",
-        new_callable=AsyncMock,
-    ) as mock_read_body, patch(
-        "litellm.proxy.batches_endpoints.endpoints.ProxyBaseLLMRequestProcessing.common_processing_pre_call_logic",
-        new_callable=AsyncMock,
-    ) as mock_common_processing, patch(
-        "litellm.proxy.batches_endpoints.endpoints.litellm.acreate_batch",
-        new_callable=AsyncMock,
-    ) as mock_acreate_batch, patch(
-        "litellm.proxy.proxy_server.general_settings",
-        new={},
-    ), patch(
-        "litellm.proxy.proxy_server.proxy_config",
-        new={},
-    ), patch(
-        "litellm.proxy.proxy_server.version",
-        new="test-version",
-    ), patch(
-        "litellm.proxy.proxy_server.llm_router",
-        new=None,
-    ), patch(
-        "litellm.proxy.proxy_server.proxy_logging_obj",
-        new=mock_proxy_logging_obj,
-    ), patch(
-        "litellm.proxy.batches_endpoints.endpoints.asyncio.create_task",
-        new=lambda coro: asyncio.ensure_future(coro),
-    ):
-        mock_read_body.return_value = dict(request_body)
-        mock_common_processing.return_value = (dict(request_body), logging_obj)
-        mock_acreate_batch.return_value = response_object
-
-        response = await create_batch(
-            request=request,
-            fastapi_response=fastapi_response,
-            user_api_key_dict=mock_user_api_key_dict,
-        )
-
-    mock_acreate_batch.assert_awaited_once()
-    called_kwargs = mock_acreate_batch.call_args.kwargs
-    assert called_kwargs["custom_llm_provider"] == "vertex_ai"
-    assert response is response_object
-
-
 @pytest.mark.skip(reason="Local only test to verify if things work well")
 @pytest.mark.asyncio
 async def test_list_batches_with_target_model_names():