use native endpoints

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