use native endpoints

litellm/llms/fine_tuning_apis/vertex_ai.py

@@ -241,12 +241,13 @@ class VertexFineTuningAPI(VertexLLM):
         )
         return open_ai_response
 
-    async def pass_through_vertex_ai_fine_tuning_job(
+    async def pass_through_vertex_ai_POST_request(
         self,
         request_data: dict,
         vertex_project: str,
         vertex_location: str,
         vertex_credentials: str,
+        request_route: str,
     ):
         auth_header, _ = self._get_token_and_url(
             model="",
@@ -264,14 +265,16 @@ class VertexFineTuningAPI(VertexLLM):
             "Content-Type": "application/json",
         }
 
-        fine_tuning_url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs"
+        url = None
+        if request_route == "tuningJobs":
+            url = f"https://{vertex_location}-aiplatform.googleapis.com/v1/projects/{vertex_project}/locations/{vertex_location}/tuningJobs"
 
         if self.async_handler is None:
             raise ValueError("VertexAI Fine Tuning - async_handler is not initialized")
 
         response = await self.async_handler.post(
             headers=headers,
-            url=fine_tuning_url,
+            url=url,
             json=request_data,  # type: ignore
         )
 

litellm/proxy/fine_tuning_endpoints/endpoints.py

@@ -429,72 +429,3 @@ async def retrieve_fine_tuning_job(
                 param=getattr(e, "param", "None"),
                 code=getattr(e, "status_code", 500),
             )
-
-
-@router.post(
-    "/v1/projects/tuningJobs",
-    dependencies=[Depends(user_api_key_auth)],
-    tags=["fine-tuning"],
-    summary="✨ (Enterprise) Create Fine-Tuning Jobs",
-)
-async def vertex_create_fine_tuning_job(
-    request: Request,
-    fastapi_response: Response,
-    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
-):
-    """
-    this is a pass through endpoint for the Vertex AI API. /tuningJobs endpoint
-
-    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
-    """
-    try:
-        from litellm.fine_tuning.main import vertex_fine_tuning_apis_instance
-        from litellm.proxy.proxy_server import (
-            add_litellm_data_to_request,
-            general_settings,
-            get_custom_headers,
-            premium_user,
-            proxy_config,
-            proxy_logging_obj,
-            version,
-        )
-
-        # get configs for custom_llm_provider
-        llm_provider_config = get_fine_tuning_provider_config(
-            custom_llm_provider="vertex_ai"
-        )
-
-        vertex_project = llm_provider_config.get("vertex_project", None)
-        vertex_location = llm_provider_config.get("vertex_location", None)
-        vertex_credentials = llm_provider_config.get("vertex_credentials", None)
-        request_data_json = await request.json()
-        response = await vertex_fine_tuning_apis_instance.pass_through_vertex_ai_fine_tuning_job(
-            request_data=request_data_json,
-            vertex_project=vertex_project,
-            vertex_location=vertex_location,
-            vertex_credentials=vertex_credentials,
-        )
-
-        return response
-    except Exception as e:
-        verbose_proxy_logger.error(
-            "litellm.proxy.proxy_server.v1/projects/tuningJobs(): Exception occurred - {}".format(
-                str(e)
-            )
-        )
-        verbose_proxy_logger.debug(traceback.format_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_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),
-            )

litellm/proxy/proxy_config.yaml

@@ -48,6 +48,11 @@ files_settings:
   - custom_llm_provider: openai
     api_key: os.environ/OPENAI_API_KEY
 
+default_vertex_config:
+  vertex_project: "adroit-crow-413218"
+  vertex_location: "us-central1"
+  vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json"
+
 
 
 general_settings: 

litellm/proxy/vertex_ai_endpoints/vertex_endpoints.py

@@ -0,0 +1,120 @@
+import asyncio
+import traceback
+from datetime import datetime, timedelta, timezone
+from typing import List, Optional
+
+import fastapi
+import httpx
+from fastapi import (
+    APIRouter,
+    Depends,
+    File,
+    Form,
+    Header,
+    HTTPException,
+    Request,
+    Response,
+    UploadFile,
+    status,
+)
+
+import litellm
+from litellm._logging import verbose_proxy_logger
+from litellm.batches.main import FileObject
+from litellm.fine_tuning.main import vertex_fine_tuning_apis_instance
+from litellm.proxy._types import *
+from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
+
+router = APIRouter()
+default_vertex_config = None
+
+
+def set_default_vertex_config(config):
+    global default_vertex_config
+    if config is None:
+        return
+
+    if not isinstance(config, list):
+        raise ValueError("invalid files config, expected a list is not a list")
+
+    for element in config:
+        if isinstance(element, dict):
+            for key, value in element.items():
+                if isinstance(value, str) and value.startswith("os.environ/"):
+                    element[key] = litellm.get_secret(value)
+
+    default_vertex_config = config
+
+
+def exception_handler(e: Exception):
+    verbose_proxy_logger.error(
+        "litellm.proxy.proxy_server.v1/projects/tuningJobs(): Exception occurred - {}".format(
+            str(e)
+        )
+    )
+    verbose_proxy_logger.debug(traceback.format_exc())
+    if isinstance(e, HTTPException):
+        return 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_msg = f"{str(e)}"
+        return ProxyException(
+            message=getattr(e, "message", error_msg),
+            type=getattr(e, "type", "None"),
+            param=getattr(e, "param", "None"),
+            code=getattr(e, "status_code", 500),
+        )
+
+
+async def execute_post_vertex_ai_request(
+    request: Request,
+    route: str,
+):
+    from litellm.fine_tuning.main import vertex_fine_tuning_apis_instance
+
+    vertex_project = default_vertex_config.get("vertex_project", None)
+    vertex_location = default_vertex_config.get("vertex_location", None)
+    vertex_credentials = default_vertex_config.get("vertex_credentials", None)
+    request_data_json = await request.json()
+
+    response = (
+        await vertex_fine_tuning_apis_instance.pass_through_vertex_ai_POST_request(
+            request_data=request_data_json,
+            vertex_project=vertex_project,
+            vertex_location=vertex_location,
+            vertex_credentials=vertex_credentials,
+            request_route=route,
+        )
+    )
+
+    return response
+
+
+@router.post(
+    "/vertex-ai/tuningJobs",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["Vertex AI endpoints"],
+)
+async def vertex_create_fine_tuning_job(
+    request: Request,
+    fastapi_response: Response,
+    endpoint_name: str,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+):
+    """
+    this is a pass through endpoint for the Vertex AI API. /tuningJobs endpoint
+
+    it uses the vertex ai credentials on the proxy and forwards to vertex ai api
+    """
+    try:
+        response = await execute_post_vertex_ai_request(
+            request=request,
+            route="/tuningJobs",
+        )
+        return response
+    except Exception as e:
+        raise exception_handler(e) from e