adding deepkeep as custom guardrail

.gitignore

@@ -46,6 +46,7 @@ ui/litellm-dashboard/node_modules
 ui/litellm-dashboard/next-env.d.ts
 ui/litellm-dashboard/package.json
 ui/litellm-dashboard/package-lock.json
+ui/litellm-dashboard/out/
 deploy/charts/litellm/*.tgz
 deploy/charts/litellm/charts/*
 deploy/charts/*.tgz
@@ -100,4 +101,4 @@ STABILIZATION_TODO.md
 **/test-results
 **/playwright-report
 **/*.storageState.json
-**/coverage
+**/coverage

enterprise/litellm_enterprise/proxy/audit_logging_endpoints.py

@@ -153,11 +153,11 @@ async def get_audit_logs(
 
     # Return paginated response
     return PaginatedAuditLogResponse(
-        audit_logs=[
-            AuditLogResponse(**audit_log.model_dump()) for audit_log in audit_logs
-        ]
-        if audit_logs
-        else [],
+        audit_logs=(
+            [AuditLogResponse(**audit_log.model_dump()) for audit_log in audit_logs]
+            if audit_logs
+            else []
+        ),
         total=total_count,
         page=page,
         page_size=page_size,

enterprise/litellm_enterprise/proxy/auth/__init__.py

@@ -7,4 +7,4 @@ including custom SSO handlers and advanced authentication features.
 
 from .custom_sso_handler import EnterpriseCustomSSOHandler
 
-__all__ = ["EnterpriseCustomSSOHandler"] 
+__all__ = ["EnterpriseCustomSSOHandler"]

enterprise/litellm_enterprise/proxy/auth/custom_sso_handler.py

@@ -26,12 +26,12 @@ from litellm.proxy.management_endpoints.types import CustomOpenID
 class EnterpriseCustomSSOHandler:
     """
     Enterprise Custom SSO Handler for LiteLLM Proxy
-    
+
     This class provides methods for handling custom SSO authentication flows
     where users can implement their own authentication logic by processing
     request headers and returning user information in OpenID format.
     """
-    
+
     @staticmethod
     async def handle_custom_ui_sso_sign_in(
         request: Request,
@@ -40,16 +40,16 @@ class EnterpriseCustomSSOHandler:
         Allow a user to execute their custom code to parse incoming request headers and return a OpenID object
 
         Use this when you have an OAuth proxy in front of LiteLLM (where the OAuth proxy has already authenticated the user)
-        
+
         Args:
             request: The FastAPI request object containing headers and other request data
-            
+
         Returns:
             RedirectResponse: Redirect response that sends the user to the LiteLLM UI with authentication token
-            
+
         Raises:
             ValueError: If custom_ui_sso_sign_in_handler is not configured
-            
+
         Example:
             This method is typically called when a user has already been authenticated by an
             external OAuth proxy and the proxy has added custom headers containing user information.
@@ -63,24 +63,31 @@ class EnterpriseCustomSSOHandler:
             premium_user,
             user_custom_ui_sso_sign_in_handler,
         )
+
         if premium_user is not True:
             raise ValueError(CommonProxyErrors.not_premium_user.value)
-        
+
         if user_custom_ui_sso_sign_in_handler is None:
-            raise ValueError("custom_ui_sso_sign_in_handler is not configured. Please set it in general_settings.")
-        
-        custom_sso_login_handler = cast(CustomSSOLoginHandler, user_custom_ui_sso_sign_in_handler)
-        openid_response: OpenID = await custom_sso_login_handler.handle_custom_ui_sso_sign_in(
-            request=request,
+            raise ValueError(
+                "custom_ui_sso_sign_in_handler is not configured. Please set it in general_settings."
+            )
+
+        custom_sso_login_handler = cast(
+            CustomSSOLoginHandler, user_custom_ui_sso_sign_in_handler
         )
-        
+        openid_response: OpenID = (
+            await custom_sso_login_handler.handle_custom_ui_sso_sign_in(
+                request=request,
+            )
+        )
+
         # Import here to avoid circular imports
         from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler
-        
+
         return await SSOAuthenticationHandler.get_redirect_response_from_openid(
             result=openid_response,
             request=request,
             received_response=None,
             generic_client_id=None,
             ui_access_mode=None,
-        ) 
+        )

enterprise/litellm_enterprise/proxy/common_utils/check_batch_cost.py

@@ -53,7 +53,9 @@ class CheckBatchCost:
                 "user_api_key_alias": getattr(user_row, "user_alias", None),
             }
         except Exception as e:
-            verbose_proxy_logger.error(f"CheckBatchCost: could not look up user {user_id} for batch {batch_id}: {e}")
+            verbose_proxy_logger.error(
+                f"CheckBatchCost: could not look up user {user_id} for batch {batch_id}: {e}"
+            )
             return {}
 
     async def _cleanup_stale_managed_objects(self) -> None:
@@ -62,11 +64,22 @@ class CheckBatchCost:
         in non-terminal states as 'stale_expired'. These will never complete and
         should not be polled.
         """
-        cutoff = datetime.now(timezone.utc) - timedelta(days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS)
+        cutoff = datetime.now(timezone.utc) - timedelta(
+            days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS
+        )
         result = await self.prisma_client.db.litellm_managedobjecttable.update_many(
             where={
                 "file_purpose": "batch",
-                "status": {"not_in": ["completed", "complete", "failed", "expired", "cancelled", "stale_expired"]},
+                "status": {
+                    "not_in": [
+                        "completed",
+                        "complete",
+                        "failed",
+                        "expired",
+                        "cancelled",
+                        "stale_expired",
+                    ]
+                },
                 "created_at": {"lt": cutoff},
             },
             data={"status": "stale_expired"},
@@ -120,9 +133,12 @@ class CheckBatchCost:
 
         try:
             from litellm.integrations.prometheus import PrometheusLogger
+
             prom_logger = PrometheusLogger.get_instance()
         except Exception as e:
-            verbose_proxy_logger.error(f"CheckBatchCost: could not get Prometheus logger: {e}")
+            verbose_proxy_logger.error(
+                f"CheckBatchCost: could not get Prometheus logger: {e}"
+            )
             prom_logger = None
 
         processed_models: List[Tuple[Optional[str], Optional[str]]] = []
@@ -161,7 +177,11 @@ class CheckBatchCost:
                     order={"created_at": "asc"},
                 )
             except Exception as query_err:
-                if "batch_processed" not in str(query_err).lower() and "unknown column" not in str(query_err).lower() and "does not exist" not in str(query_err).lower():
+                if (
+                    "batch_processed" not in str(query_err).lower()
+                    and "unknown column" not in str(query_err).lower()
+                    and "does not exist" not in str(query_err).lower()
+                ):
                     raise
                 # Permanent schema gap — cache the result so future cycles skip straight to fallback
                 self._has_batch_processed_column = False
@@ -216,14 +236,13 @@ class CheckBatchCost:
                     f"Skipping job {unified_object_id} because of error querying model ID: {model_id} for cost and usage of batch ID: {batch_id}: {e}"
                 )
                 if prom_logger:
-                    prom_logger.record_check_batch_cost_error("provider_retrieval_error")
+                    prom_logger.record_check_batch_cost_error(
+                        "provider_retrieval_error"
+                    )
                 continue
 
             ## RETRIEVE THE BATCH JOB OUTPUT FILE
-            if (
-                response.status == "completed"
-                and response.output_file_id is not None
-            ):
+            if response.status == "completed" and response.output_file_id is not None:
                 verbose_proxy_logger.info(
                     f"Batch ID: {batch_id} is complete, tracking cost and usage"
                 )
@@ -250,20 +269,25 @@ class CheckBatchCost:
                 decoded = _is_base64_encoded_unified_file_id(raw_output_file_id)
                 if decoded:
                     try:
-                        raw_output_file_id = decoded.split("llm_output_file_id,")[1].split(";")[0]
+                        raw_output_file_id = decoded.split("llm_output_file_id,")[
+                            1
+                        ].split(";")[0]
                     except (IndexError, AttributeError):
                         pass
 
-                credentials = self.llm_router.get_deployment_credentials_with_provider(model_id) or {}
+                credentials = (
+                    self.llm_router.get_deployment_credentials_with_provider(model_id)
+                    or {}
+                )
                 _file_content = await afile_content(
                     file_id=raw_output_file_id,
                     **credentials,
                 )
 
                 # Access content - handle both direct attribute and method call
-                if hasattr(_file_content, 'content'):
+                if hasattr(_file_content, "content"):
                     content_bytes = _file_content.content  # type: ignore[union-attr]
-                elif hasattr(_file_content, 'read'):
+                elif hasattr(_file_content, "read"):
                     content_bytes = await _file_content.read()  # type: ignore[misc]
                 else:
                     content_bytes = _file_content  # type: ignore[assignment]
@@ -290,7 +314,9 @@ class CheckBatchCost:
                         f"Skipping job {unified_object_id} because it is not a valid deployment info"
                     )
                     if prom_logger:
-                        prom_logger.record_check_batch_cost_error("deployment_not_found")
+                        prom_logger.record_check_batch_cost_error(
+                            "deployment_not_found"
+                        )
                     continue
                 custom_llm_provider = deployment_info.litellm_params.custom_llm_provider
                 litellm_model_name = deployment_info.litellm_params.model
@@ -302,7 +328,11 @@ class CheckBatchCost:
 
                 # Pass deployment model_info so custom batch pricing
                 # (input_cost_per_token_batches etc.) is used for cost calc
-                deployment_model_info = deployment_info.model_info.model_dump() if deployment_info.model_info else {}
+                deployment_model_info = (
+                    deployment_info.model_info.model_dump()
+                    if deployment_info.model_info
+                    else {}
+                )
                 batch_cost, batch_usage, batch_models = (
                     await calculate_batch_cost_and_usage(
                         file_content_dictionary=file_content_as_dict,
@@ -349,7 +379,9 @@ class CheckBatchCost:
 
                 # Record batch duration (completed_at - created_at)
                 if prom_logger and response.completed_at and response.created_at:
-                    duration_seconds = float(response.completed_at - response.created_at)
+                    duration_seconds = float(
+                        response.completed_at - response.created_at
+                    )
                     if duration_seconds >= 0:
                         prom_logger.record_managed_batch_duration(
                             duration_seconds=duration_seconds,
@@ -358,7 +390,9 @@ class CheckBatchCost:
                         )
 
                 # Track this job for the final metrics summary
-                processed_models.append((model_name, str(llm_provider) if llm_provider else None))
+                processed_models.append(
+                    (model_name, str(llm_provider) if llm_provider else None)
+                )
 
                 # mark the job as complete
                 try:

enterprise/litellm_enterprise/proxy/common_utils/check_responses_cost.py

@@ -33,9 +33,7 @@ class CheckResponsesCost:
         self.prisma_client: PrismaClient = prisma_client
         self.llm_router: Router = llm_router
 
-    async def _expire_stale_rows(
-        self, cutoff: datetime, batch_size: int
-    ) -> int:
+    async def _expire_stale_rows(self, cutoff: datetime, batch_size: int) -> int:
         """Execute the bounded UPDATE that marks stale rows as 'stale_expired'.
 
         Isolated so it can be swapped / mocked in tests without touching the
@@ -74,7 +72,9 @@ class CheckResponsesCost:
         rows per invocation to avoid overwhelming the DB when there is a large
         backlog.
         """
-        cutoff = datetime.now(timezone.utc) - timedelta(days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS)
+        cutoff = datetime.now(timezone.utc) - timedelta(
+            days=MANAGED_OBJECT_STALENESS_CUTOFF_DAYS
+        )
         result = await self._expire_stale_rows(cutoff, STALE_OBJECT_CLEANUP_BATCH_SIZE)
         if result > 0:
             verbose_proxy_logger.warning(
@@ -105,7 +105,7 @@ class CheckResponsesCost:
             take=MAX_OBJECTS_PER_POLL_CYCLE,
             order={"created_at": "asc"},
         )
-        
+
         verbose_proxy_logger.debug(f"Found {len(jobs)} response jobs to check")
         completed_jobs = []
 
@@ -120,29 +120,33 @@ class CheckResponsesCost:
                 # Get the stored response object to extract model information
                 stored_response = job.file_object
                 model_name = stored_response.get("model", None)
-                
+
                 # Decrypt the response ID
-                responses_id_security, _, _ = ResponsesIDSecurity()._decrypt_response_id(unified_object_id)
-                
+                responses_id_security, _, _ = (
+                    ResponsesIDSecurity()._decrypt_response_id(unified_object_id)
+                )
+
                 # Prepare metadata with model information for cost tracking
                 litellm_metadata = {
                     "user_api_key_user_id": job.created_by or "default-user-id",
                 }
-                
+
                 # Add model information if available
                 if model_name:
                     litellm_metadata["model"] = model_name
-                    litellm_metadata["model_group"] = model_name  # Use same value for model_group
-                
+                    litellm_metadata["model_group"] = (
+                        model_name  # Use same value for model_group
+                    )
+
                 response = await litellm.aget_responses(
                     response_id=responses_id_security,
                     litellm_metadata=litellm_metadata,
                 )
-                
+
                 verbose_proxy_logger.debug(
                     f"Response {unified_object_id} status: {response.status}, model: {model_name}"
                 )
-                
+
             except Exception as e:
                 verbose_proxy_logger.info(
                     f"Skipping job {unified_object_id} due to error: {e}"
@@ -155,7 +159,7 @@ class CheckResponsesCost:
                     f"Response {unified_object_id} is complete. Cost automatically tracked by aget_responses."
                 )
                 completed_jobs.append(job)
-                
+
             elif response.status in ["failed", "cancelled"]:
                 verbose_proxy_logger.info(
                     f"Response {unified_object_id} has status {response.status}, marking as complete"
@@ -171,4 +175,3 @@ class CheckResponsesCost:
             verbose_proxy_logger.info(
                 f"Marked {len(completed_jobs)} response jobs as completed"
             )
-

enterprise/litellm_enterprise/proxy/hooks/managed_files.py

@@ -125,7 +125,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                 db_data["storage_backend"] = hidden_params["storage_backend"]
             if "storage_url" in hidden_params:
                 db_data["storage_url"] = hidden_params["storage_url"]
-            
+
             verbose_logger.debug(
                 f"Storage metadata: storage_backend={db_data.get('storage_backend')}, "
                 f"storage_url={db_data.get('storage_url')}"
@@ -285,28 +285,28 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             raise Exception(
                 "Filtering by 'target_model_names' is not supported when using managed batches."
             )
-        
+
         where_clause: Dict[str, Any] = {"file_purpose": "batch"}
-        
+
         # Filter by user who created the batch
         if user_api_key_dict.user_id:
             where_clause["created_by"] = user_api_key_dict.user_id
-        
+
         if after:
             where_clause["id"] = {"gt": after}
-        
+
         # Fetch more than needed to allow for post-fetch filtering
         fetch_limit = limit or 20
         if target_model_names:
             # Fetch extra to account for filtering
             fetch_limit = max(fetch_limit * 3, 100)
-        
+
         batches = await self.prisma_client.db.litellm_managedobjecttable.find_many(
             where=where_clause,
             take=fetch_limit,
             order={"created_at": "desc"},
         )
-                
+
         batch_objects: List[LiteLLMBatch] = []
         for batch in batches:
             try:
@@ -314,7 +314,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                 if len(batch_objects) >= (limit or 20):
                     break
 
-                batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object
+                batch_data = (
+                    json.loads(batch.file_object)
+                    if isinstance(batch.file_object, str)
+                    else batch.file_object
+                )
                 batch_obj = LiteLLMBatch(**batch_data)
                 batch_obj.id = batch.unified_object_id
                 batch_objects.append(batch_obj)
@@ -324,7 +328,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                     f"Failed to parse batch object {batch.unified_object_id}: {e}"
                 )
                 continue
-        
+
         return {
             "object": "list",
             "data": batch_objects,
@@ -377,11 +381,11 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         """
         Check if the user has access to a list of file IDs.
         Only checks managed (unified) file IDs.
-        
+
         Args:
             file_ids: List of file IDs to check access for
             user_api_key_dict: User API key authentication details
-            
+
         Raises:
             HTTPException: If user doesn't have access to any of the files
         """
@@ -419,10 +423,10 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         ### HANDLE TRANSFORMATIONS ###
         # Check both completion and acompletion call types
         is_completion_call = (
-            call_type == CallTypes.completion.value 
+            call_type == CallTypes.completion.value
             or call_type == CallTypes.acompletion.value
         )
-        
+
         if is_completion_call:
             messages = data.get("messages")
             model = data.get("model", "")
@@ -431,22 +435,27 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                 if file_ids:
                     # Check user has access to all managed files
                     await self.check_file_ids_access(file_ids, user_api_key_dict)
-                    
+
                     # Check if any files are stored in storage backends and need base64 conversion
                     # This is needed for Vertex AI/Gemini which requires base64 content
-                    is_vertex_ai = model and ("vertex_ai" in model or "gemini" in model.lower())
+                    is_vertex_ai = model and (
+                        "vertex_ai" in model or "gemini" in model.lower()
+                    )
                     if is_vertex_ai:
                         await self._convert_storage_files_to_base64(
                             messages=messages,
                             file_ids=file_ids,
                             litellm_parent_otel_span=user_api_key_dict.parent_otel_span,
                         )
-                    
+
                     model_file_id_mapping = await self.get_model_file_id_mapping(
                         file_ids, user_api_key_dict.parent_otel_span
                     )
                     data["model_file_id_mapping"] = model_file_id_mapping
-        elif call_type == CallTypes.aresponses.value or call_type == CallTypes.responses.value:
+        elif (
+            call_type == CallTypes.aresponses.value
+            or call_type == CallTypes.responses.value
+        ):
             # Handle managed files in responses API input and tools
             file_ids = []
 
@@ -611,7 +620,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             if model_id is None:
                 model_id = cast(
                     Optional[str],
-                    kwargs.get("litellm_metadata", {}).get("model_info", {}).get("id", None),
+                    kwargs.get("litellm_metadata", {})
+                    .get("model_info", {})
+                    .get("id", None),
                 )
             mapped_file_id: Optional[str] = None
             if input_file_id and model_file_id_mapping and model_id:
@@ -648,7 +659,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
     ) -> List[str]:
         """
         Gets file ids from responses API input.
-        
+
         The input can be:
         - A string (no files)
         - A list of input items, where each item can have:
@@ -656,32 +667,35 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
           - content: a list that can contain items with type: "input_file" and file_id
         """
         file_ids: List[str] = []
-        
+
         if isinstance(input, str):
             return file_ids
-        
+
         if not isinstance(input, list):
             return file_ids
-        
+
         for item in input:
             if not isinstance(item, dict):
                 continue
-            
+
             # Check for direct input_file type
             if item.get("type") == "input_file":
                 file_id = item.get("file_id")
                 if file_id:
                     file_ids.append(file_id)
-            
+
             # Check for input_file in content array
             content = item.get("content")
             if isinstance(content, list):
                 for content_item in content:
-                    if isinstance(content_item, dict) and content_item.get("type") == "input_file":
+                    if (
+                        isinstance(content_item, dict)
+                        and content_item.get("type") == "input_file"
+                    ):
                         file_id = content_item.get("file_id")
                         if file_id:
                             file_ids.append(file_id)
-        
+
         return file_ids
 
     def get_file_ids_from_responses_tools(
@@ -689,7 +703,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
     ) -> List[str]:
         """
         Gets file ids from responses API tools parameter.
-        
+
         The tools can contain code_interpreter with container.file_ids:
         [
             {
@@ -699,14 +713,14 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         ]
         """
         file_ids: List[str] = []
-        
+
         if not isinstance(tools, list):
             return file_ids
-        
+
         for tool in tools:
             if not isinstance(tool, dict):
                 continue
-            
+
             # Check for code_interpreter with container file_ids
             if tool.get("type") == "code_interpreter":
                 container = tool.get("container")
@@ -716,7 +730,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                         for file_id in container_file_ids:
                             if isinstance(file_id, str):
                                 file_ids.append(file_id)
-        
+
         return file_ids
 
     def get_vector_store_ids_from_file_search_tools(
@@ -916,10 +930,17 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         # Emit Prometheus metrics for managed file creation
         prom_logger = self._get_prometheus_logger()
         if prom_logger:
-            first_model = target_model_names_list[0] if target_model_names_list else None
+            first_model = (
+                target_model_names_list[0] if target_model_names_list else None
+            )
             first_provider = ""
             if responses:
-                first_provider = getattr(responses[0], "_hidden_params", {}).get("custom_llm_provider") or ""
+                first_provider = (
+                    getattr(responses[0], "_hidden_params", {}).get(
+                        "custom_llm_provider"
+                    )
+                    or ""
+                )
             prom_logger.record_managed_file_created(
                 model=first_model or "",
                 api_provider=first_provider,
@@ -1073,16 +1094,24 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                             model_name=resolved_model_name,
                         )
                         setattr(response, file_attr, unified_file_id)
-                        
+
                         # Use llm_router credentials when available. Without credentials,
                         # Azure and other auth-required providers return 500/401.
                         file_object = None
                         try:
                             # Import module and use getattr for better testability with mocks
                             import litellm.proxy.proxy_server as proxy_server_module
-                            _llm_router = getattr(proxy_server_module, 'llm_router', None)
+
+                            _llm_router = getattr(
+                                proxy_server_module, "llm_router", None
+                            )
                             if _llm_router is not None and model_id:
-                                _creds = _llm_router.get_deployment_credentials_with_provider(model_id) or {}
+                                _creds = (
+                                    _llm_router.get_deployment_credentials_with_provider(
+                                        model_id
+                                    )
+                                    or {}
+                                )
                                 file_object = await litellm.afile_retrieve(
                                     file_id=original_file_id,
                                     **_creds,
@@ -1099,7 +1128,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                             verbose_logger.warning(
                                 f"Failed to retrieve file object for {file_attr}={original_file_id}: {str(e)}. Storing with None and will fetch on-demand."
                             )
-                        
+
                         await self.store_unified_file_id(
                             file_id=unified_file_id,
                             file_object=file_object,
@@ -1128,6 +1157,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                             from litellm.litellm_core_utils.get_llm_provider_logic import (
                                 get_llm_provider,
                             )
+
                             _, batch_provider, _, _ = get_llm_provider(model=model_name)
                         except Exception:
                             if "/" in model_name:
@@ -1199,7 +1229,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         # Case 1 : This is not a managed file
         if not stored_file_object:
             raise Exception(f"LiteLLM Managed File object with id={file_id} not found")
-        
+
         # Case 2: Managed file and the file object exists in the database
         # The stored file_object has the raw provider ID. Replace with the unified ID
         # so callers see a consistent ID (matching Case 3 which does response.id = file_id).
@@ -1217,13 +1247,21 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             )
 
         try:
-            model_id, model_file_id = next(iter(stored_file_object.model_mappings.items()))
-            credentials = llm_router.get_deployment_credentials_with_provider(model_id) or {}
-            response = await litellm.afile_retrieve(file_id=model_file_id, **credentials)
+            model_id, model_file_id = next(
+                iter(stored_file_object.model_mappings.items())
+            )
+            credentials = (
+                llm_router.get_deployment_credentials_with_provider(model_id) or {}
+            )
+            response = await litellm.afile_retrieve(
+                file_id=model_file_id, **credentials
+            )
             response.id = file_id  # Replace with unified ID
             return response
         except Exception as e:
-            raise Exception(f"Failed to retrieve file {file_id} from provider: {str(e)}") from e
+            raise Exception(
+                f"Failed to retrieve file {file_id} from provider: {str(e)}"
+            ) from e
 
     async def afile_list(
         self,
@@ -1245,19 +1283,19 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             import litellm.proxy.proxy_server as proxy_server_module
 
             # Check if the scheduler has the batch cost checking job registered
-            scheduler = getattr(proxy_server_module, 'scheduler', None)
+            scheduler = getattr(proxy_server_module, "scheduler", None)
             if scheduler is None:
                 return False
-            
+
             # Check if the check_batch_cost_job exists in the scheduler
             try:
-                job = scheduler.get_job('check_batch_cost_job')
+                job = scheduler.get_job("check_batch_cost_job")
                 if job is not None:
                     return True
             except Exception:
                 # Job not found or scheduler doesn't support get_job
                 pass
-            
+
             return False
         except Exception as e:
             verbose_logger.warning(
@@ -1265,28 +1303,26 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
             )
             return False
 
-    async def _get_batches_referencing_file(
-        self, file_id: str
-    ) -> List[Dict[str, Any]]:
+    async def _get_batches_referencing_file(self, file_id: str) -> List[Dict[str, Any]]:
         """
         Find batches that reference this file and still need cost tracking.
         Find batches that are in non-terminal state and have not yet been processed by CheckBatchCost.
         Args:
             file_id: The unified file ID to check
-            
+
         Returns:
             List of batch objects referencing this file in non-terminal state
             (max 10 for error message display)
         """
         # Prepare list of file IDs to check (both unified and provider IDs)
         file_ids_to_check = [file_id]
-        
+
         # Get model-specific file IDs for this unified file ID if it's a managed file
         try:
             model_file_id_mapping = await self.get_model_file_id_mapping(
                 [file_id], litellm_parent_otel_span=None
             )
-            
+
             if model_file_id_mapping and file_id in model_file_id_mapping:
                 # Add all provider file IDs for this unified file
                 provider_file_ids = list(model_file_id_mapping[file_id].values())
@@ -1296,59 +1332,67 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                 f"Could not get model file ID mapping for {file_id}: {e}. "
                 f"Will only check unified file ID."
             )
-        MAX_MATCHES_TO_RETURN = 10 
-        
+        MAX_MATCHES_TO_RETURN = 10
+
         batches = await self.prisma_client.db.litellm_managedobjecttable.find_many(
             where={
                 "file_purpose": "batch",
                 "batch_processed": False,
-                "status": {"not_in": ["failed", "expired", "cancelled"]}
+                "status": {"not_in": ["failed", "expired", "cancelled"]},
             },
             take=MAX_MATCHES_TO_RETURN,
             order={"created_at": "desc"},
         )
-        
+
         referencing_batches = []
         for batch in batches:
             try:
                 # Parse the batch file_object to check for file references
-                batch_data = json.loads(batch.file_object) if isinstance(batch.file_object, str) else batch.file_object
-                
+                batch_data = (
+                    json.loads(batch.file_object)
+                    if isinstance(batch.file_object, str)
+                    else batch.file_object
+                )
+
                 # Extract file IDs from batch
                 # Batches typically reference the unified file ID in input_file_id
                 # Output and error files are generated by the provider
                 input_file_id = batch_data.get("input_file_id")
                 output_file_id = batch_data.get("output_file_id")
                 error_file_id = batch_data.get("error_file_id")
-                
-                referenced_file_ids = [fid for fid in [input_file_id, output_file_id, error_file_id] if fid]
-                
+
+                referenced_file_ids = [
+                    fid for fid in [input_file_id, output_file_id, error_file_id] if fid
+                ]
+
                 # Check if any referenced file ID matches the file we're trying to delete
                 if any(ref_id in file_ids_to_check for ref_id in referenced_file_ids):
-                    referencing_batches.append({
-                        "batch_id": batch.unified_object_id,
-                        "status": batch.status,
-                        "created_at": batch.created_at,
-                    })
+                    referencing_batches.append(
+                        {
+                            "batch_id": batch.unified_object_id,
+                            "status": batch.status,
+                            "created_at": batch.created_at,
+                        }
+                    )
             except Exception as e:
                 verbose_logger.warning(
                     f"Error parsing batch object {batch.unified_object_id}: {e}"
                 )
                 continue
-        
+
         return referencing_batches
 
     async def _check_file_deletion_allowed(self, file_id: str) -> None:
         """
         Check if file deletion should be blocked due to batch references.
-        
+
         Blocks deletion if:
         1. File is referenced by any batch in non-terminal state, AND
         2. Batch polling is configured (user wants cost tracking)
-        
+
         Args:
             file_id: The unified file ID to check
-            
+
         Raises:
             HTTPException: If file deletion should be blocked
         """
@@ -1356,39 +1400,45 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         if not self._is_batch_polling_enabled():
             # Batch polling not configured, allow deletion
             return
-        
+
         # Check if file is referenced by any non-terminal batches
         referencing_batches = await self._get_batches_referencing_file(file_id)
-        
+
         if referencing_batches:
             # File is referenced by non-terminal batches and polling is enabled
-            MAX_BATCHES_IN_ERROR = 5  # Limit batches shown in error message for readability
-            
+            MAX_BATCHES_IN_ERROR = (
+                5  # Limit batches shown in error message for readability
+            )
+
             # Show up to MAX_BATCHES_IN_ERROR in the error message
             batches_to_show = referencing_batches[:MAX_BATCHES_IN_ERROR]
-            batch_statuses = [f"{b['batch_id']}: {b['status']}" for b in batches_to_show]
-            
+            batch_statuses = [
+                f"{b['batch_id']}: {b['status']}" for b in batches_to_show
+            ]
+
             # Determine the count message
             count_message = f"{len(referencing_batches)}"
-            if len(referencing_batches) >= 10:  # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file
+            if (
+                len(referencing_batches) >= 10
+            ):  # MAX_MATCHES_TO_RETURN from _get_batches_referencing_file
                 count_message = "10+"
-            
+
             error_message = (
                 f"Cannot delete file {file_id}. "
                 f"The file is referenced by {count_message} batch(es) in non-terminal state"
             )
-            
+
             # Add specific batch details if not too many
             if len(referencing_batches) <= MAX_BATCHES_IN_ERROR:
                 error_message += f": {', '.join(batch_statuses)}. "
             else:
                 error_message += f" (showing {MAX_BATCHES_IN_ERROR} most recent): {', '.join(batch_statuses)}. "
-            
+
             error_message += (
                 f"To delete this file before complete cost tracking, please delete or cancel the referencing batch(es) first. "
                 f"Alternatively, wait for all batches to complete and for cost to be computed (batch_processed=true)."
             )
-            
+
             # Record blocked deletion metric
             prom_logger = self._get_prometheus_logger()
             if prom_logger:
@@ -1419,7 +1469,9 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         specific_model_file_id_mapping = model_file_id_mapping.get(file_id)
         if specific_model_file_id_mapping:
             # Remove conflicting keys from data to avoid duplicate keyword arguments
-            filtered_data = {k: v for k, v in data.items() if k not in ("model", "file_id")}
+            filtered_data = {
+                k: v for k, v in data.items() if k not in ("model", "file_id")
+            }
             for model_id, model_file_id in specific_model_file_id_mapping.items():
                 delete_response = await llm_router.afile_delete(model=model_id, file_id=model_file_id, **filtered_data)  # type: ignore
 
@@ -1480,7 +1532,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
     ) -> None:
         """
         Convert files stored in storage backends to base64 format for Vertex AI/Gemini.
-        
+
         This method checks if any managed files are stored in storage backends,
         downloads them, and converts them to base64 format in the messages.
         """
@@ -1488,29 +1540,29 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
         for file_id in file_ids:
             # Check if this is a base64 encoded unified file ID
             decoded_unified_file_id = _is_base64_encoded_unified_file_id(file_id)
-            
+
             if not decoded_unified_file_id:
                 continue
-            
+
             # Check database for storage backend info
             # IMPORTANT: The database stores the base64 encoded unified_file_id (not the decoded version)
             # So we query with the original file_id (which is base64 encoded)
             db_file = await self.prisma_client.db.litellm_managedfiletable.find_first(
                 where={"unified_file_id": file_id}
             )
-            
+
             if not db_file or not db_file.storage_backend or not db_file.storage_url:
                 continue
-            
+
             # File is stored in a storage backend, download and convert to base64
             try:
                 from litellm.llms.base_llm.files.storage_backend_factory import (
                     get_storage_backend,
                 )
-                
+
                 storage_backend_name = db_file.storage_backend
                 storage_url = db_file.storage_url
-                
+
                 # Get storage backend (uses same env vars as callback)
                 try:
                     storage_backend = get_storage_backend(storage_backend_name)
@@ -1519,18 +1571,22 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                         f"Storage backend '{storage_backend_name}' error for file {file_id}: {str(e)}"
                     )
                     continue
-                
+
                 file_content = await storage_backend.download_file(storage_url)
-                
+
                 # Determine content type from file object
-                content_type = self._get_content_type_from_file_object(db_file.file_object)
-                
+                content_type = self._get_content_type_from_file_object(
+                    db_file.file_object
+                )
+
                 # Convert to base64
                 base64_data = base64.b64encode(file_content).decode("utf-8")
                 base64_data_uri = f"data:{content_type};base64,{base64_data}"
-                
+
                 # Update messages to use base64 instead of file_id
-                self._update_messages_with_base64_data(messages, file_id, base64_data_uri, content_type)
+                self._update_messages_with_base64_data(
+                    messages, file_id, base64_data_uri, content_type
+                )
             except Exception as e:
                 verbose_logger.exception(
                     f"Error converting file {file_id} from storage backend to base64: {str(e)}"
@@ -1541,21 +1597,21 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
     def _get_content_type_from_file_object(self, file_object: Optional[Any]) -> str:
         """
         Determine content type from file object.
-        
+
         Uses the MIME type utility for consistent detection and normalization.
-        
+
         Args:
             file_object: The file object from the database (can be dict, JSON string, or None)
-        
+
         Returns:
             str: MIME type (defaults to "application/octet-stream" if cannot be determined)
         """
         # Use utility function for detection
         content_type = get_content_type_from_file_object(file_object)
-        
+
         # Normalize for Gemini/Vertex AI (requires image/jpeg, not image/jpg)
         content_type = normalize_mime_type_for_provider(content_type, provider="gemini")
-        
+
         return content_type
 
     def _update_messages_with_base64_data(
@@ -1567,7 +1623,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
     ) -> None:
         """
         Update messages to replace file_id with base64 data URI.
-        
+
         Args:
             messages: List of messages to update
             file_id: The file ID to replace
@@ -1582,7 +1638,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                         if element.get("type") == "file":
                             file_element = cast(ChatCompletionFileObject, element)
                             file_element_file = file_element.get("file", {})
-                            
+
                             if file_element_file.get("file_id") == file_id:
                                 # Replace file_id with base64 data
                                 file_element_file["file_data"] = base64_data_uri
@@ -1590,7 +1646,7 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints):
                                 file_element_file["format"] = content_type
                                 # Remove file_id to ensure only file_data is used
                                 file_element_file.pop("file_id", None)
-                                
+
                                 verbose_logger.debug(
                                     f"Converted file {file_id} from storage backend to base64 with format {content_type}"
                                 )

enterprise/litellm_enterprise/proxy/hooks/managed_vector_stores.py

@@ -41,7 +41,7 @@ class _PROXY_LiteLLMManagedVectorStores(
 ):
     """
     Managed vector stores with target_model_names support.
-    
+
     This class provides functionality to:
     - Create vector stores across multiple models
     - Retrieve vector stores by unified ID
@@ -77,14 +77,14 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> str:
         """
         Generate the format string for the unified vector store ID.
-        
+
         Format:
         litellm_proxy:vector_store;unified_id,<uuid>;target_model_names,<models>;resource_id,<vs_id>;model_id,<model_id>
         """
         # VectorStoreCreateResponse is a TypedDict, so resource_object is a dictionary
         # Extract provider resource ID from the response
         provider_resource_id = resource_object.get("id", "")
-        
+
         # Model ID is stored in hidden params if the response object supports it
         # For TypedDict responses, we need to check if _hidden_params was added
         hidden_params: Dict[str, Any] = {}
@@ -109,20 +109,18 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> VectorStoreCreateResponse:
         """
         Create a vector store for a specific model.
-        
+
         Args:
             llm_router: LiteLLM router instance
             model: Model name to create vector store for
             request_data: Request data for vector store creation
             litellm_parent_otel_span: OpenTelemetry span for tracing
-            
+
         Returns:
             VectorStoreCreateResponse from the provider
         """
         # Use the router to create the vector store
-        response = await llm_router.avector_store_create(
-            model=model, **request_data
-        )
+        response = await llm_router.avector_store_create(model=model, **request_data)
         return response
 
     # ============================================================================
@@ -139,14 +137,14 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> VectorStoreCreateResponse:
         """
         Create a vector store across multiple models.
-        
+
         Args:
             create_request: Vector store creation request parameters
             llm_router: LiteLLM router instance
             target_model_names_list: List of target model names
             litellm_parent_otel_span: OpenTelemetry span for tracing
             user_api_key_dict: User API key authentication details
-            
+
         Returns:
             VectorStoreCreateResponse with unified ID
         """
@@ -196,7 +194,7 @@ class _PROXY_LiteLLMManagedVectorStores(
         # VectorStoreCreateResponse is a TypedDict, so we need to create a new dict with the unified ID
         response = responses[0].copy()
         response["id"] = unified_id
-        
+
         verbose_logger.info(
             f"Successfully created managed vector store with unified ID: {unified_id}"
         )
@@ -212,13 +210,13 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> Dict[str, Any]:
         """
         List vector stores created by a user.
-        
+
         Args:
             user_api_key_dict: User API key authentication details
             limit: Maximum number of vector stores to return
             after: Cursor for pagination
             order: Sort order ('asc' or 'desc')
-            
+
         Returns:
             Dictionary with list of vector stores and pagination info
         """
@@ -238,23 +236,23 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> bool:
         """
         Check if user has access to a vector store.
-        
+
         Args:
             vector_store_id: The unified vector store ID
             user_api_key_dict: User API key authentication details
-            
+
         Returns:
             True if user has access, False otherwise
         """
         is_unified_id = is_base64_encoded_unified_id(vector_store_id)
-        
+
         if is_unified_id:
             # Check access for managed vector store
             return await self.can_user_access_unified_resource_id(
                 vector_store_id,
                 user_api_key_dict,
             )
-        
+
         # Not a managed vector store, allow access
         return True
 
@@ -263,24 +261,22 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> bool:
         """
         Check if user has access to a managed vector store in request data.
-        
+
         Args:
             data: Request data containing vector_store_id
             user_api_key_dict: User API key authentication details
-            
+
         Returns:
             True if this is a managed vector store and user has access
-            
+
         Raises:
             HTTPException: If user doesn't have access
         """
         vector_store_id = cast(Optional[str], data.get("vector_store_id"))
         is_unified_id = (
-            is_base64_encoded_unified_id(vector_store_id)
-            if vector_store_id
-            else False
+            is_base64_encoded_unified_id(vector_store_id) if vector_store_id else False
         )
-        
+
         if is_unified_id and vector_store_id:
             if await self.can_user_access_unified_resource_id(
                 vector_store_id, user_api_key_dict
@@ -291,7 +287,7 @@ class _PROXY_LiteLLMManagedVectorStores(
                     status_code=403,
                     detail=f"User {user_api_key_dict.user_id} does not have access to vector store {vector_store_id}",
                 )
-        
+
         return False
 
     # ============================================================================
@@ -307,18 +303,18 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> Union[Exception, str, Dict, None]:
         """
         Pre-call hook to handle vector store operations.
-        
+
         This hook intercepts vector store requests and:
         - Validates access for managed vector stores
         - Transforms unified IDs to provider-specific IDs
         - Adds model routing information
-        
+
         Args:
             user_api_key_dict: User API key authentication details
             cache: Cache instance
             data: Request data
             call_type: Type of call being made
-            
+
         Returns:
             Modified request data or None
         """
@@ -330,40 +326,40 @@ class _PROXY_LiteLLMManagedVectorStores(
         # Handle vector store search operations
         if call_type == "avector_store_search":
             vector_store_id = data.get("vector_store_id")
-            
+
             if vector_store_id:
                 # Check if it's a managed vector store ID
                 decoded_id = is_base64_encoded_unified_id(vector_store_id)
-                
+
                 if decoded_id:
                     verbose_logger.debug(
                         f"Processing managed vector store search: {vector_store_id}"
                     )
-                    
+
                     # Check access
                     has_access = await self.can_user_access_unified_resource_id(
                         vector_store_id, user_api_key_dict
                     )
-                    
+
                     if not has_access:
                         raise HTTPException(
                             status_code=403,
                             detail=f"User {user_api_key_dict.user_id} does not have access to vector store {vector_store_id}",
                         )
-                    
+
                     # Parse the unified ID to extract components
                     parsed_id = parse_unified_id(vector_store_id)
-                    
+
                     if parsed_id:
                         # Extract the model ID and provider resource ID
                         model_id = parsed_id.get("model_id")
                         provider_resource_id = parsed_id.get("provider_resource_id")
                         target_model_names = parsed_id.get("target_model_names", [])
-                        
+
                         verbose_logger.debug(
                             f"Decoded vector store - model_id: {model_id}, provider_resource_id: {provider_resource_id}, target_model_names: {target_model_names}"
                         )
-                        
+
                         # Determine which model to use for routing
                         # Priority: model_id (deployment ID) > first target_model_name
                         routing_model = None
@@ -371,28 +367,28 @@ class _PROXY_LiteLLMManagedVectorStores(
                             routing_model = model_id
                         elif target_model_names and len(target_model_names) > 0:
                             routing_model = target_model_names[0]
-                        
+
                         # Set the model for routing
                         if routing_model:
                             data["model"] = routing_model
                             verbose_logger.info(
                                 f"Routing vector store search to model: {routing_model}"
                             )
-                        
+
                         # Replace the unified ID with the provider-specific ID
                         if provider_resource_id:
                             data["vector_store_id"] = provider_resource_id
                             verbose_logger.debug(
                                 f"Replaced unified ID with provider resource ID: {provider_resource_id}"
                             )
-        
+
         # Handle vector store retrieve/delete operations
         elif call_type in ("avector_store_retrieve", "avector_store_delete"):
             await self.check_managed_vector_store_access(data, user_api_key_dict)
-            
+
             # If it's a managed vector store, we'll handle it in the endpoint
             # No need to transform here as the endpoint will route to the hook
-            
+
         return data
 
     # ============================================================================
@@ -407,15 +403,15 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> Any:
         """
         Post-call hook to transform responses.
-        
+
         This hook can be used to transform responses if needed.
         For now, it just passes through the response.
-        
+
         Args:
             data: Request data
             user_api_key_dict: User API key authentication details
             response: Response from the provider
-            
+
         Returns:
             Potentially modified response
         """
@@ -436,21 +432,21 @@ class _PROXY_LiteLLMManagedVectorStores(
     ) -> List[Dict]:
         """
         Filter deployments based on vector store availability.
-        
+
         This is used by the router to select only deployments that have
         the vector store available.
-        
+
         Note: This method signature is a compromise between CustomLogger and BaseManagedResource
         parent classes which have incompatible signatures. The type: ignore[override] is necessary
         due to this multiple inheritance conflict.
-        
+
         Args:
             model: Model name
             healthy_deployments: List of healthy deployments
             messages: Messages (unused for vector stores, required by CustomLogger interface)
             request_kwargs: Request kwargs containing vector_store_id and mappings
             parent_otel_span: OpenTelemetry span for tracing
-            
+
         Returns:
             Filtered list of deployments
         """

enterprise/litellm_enterprise/proxy/management_endpoints/internal_user_endpoints.py

@@ -2,7 +2,6 @@
 Enterprise internal user management endpoints
 """
 
-
 from fastapi import APIRouter, Depends, HTTPException
 
 from litellm.proxy._types import UserAPIKeyAuth

enterprise/litellm_enterprise/proxy/vector_stores/endpoints.py

@@ -147,12 +147,12 @@ async def list_vector_stores(
         vector_stores_from_db = await VectorStoreRegistry._get_vector_stores_from_db(
             prisma_client=prisma_client
         )
-        
+
         # Also clean up in-memory registry to remove any deleted vector stores
         if litellm.vector_store_registry is not None:
             db_vector_store_ids = {
-                vs.get("vector_store_id") 
-                for vs in vector_stores_from_db 
+                vs.get("vector_store_id")
+                for vs in vector_stores_from_db
                 if vs.get("vector_store_id")
             }
             # Remove any in-memory vector stores that no longer exist in database

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litellm/proxy/_experimental/out/__next._index.txt

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litellm/proxy/_experimental/out/__next._tree.txt

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litellm/proxy/_experimental/out/_next/static/6BWZ9pP30VkI_U10n-4Bg/_buildManifest.js

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+    "beforeFiles": [
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+        "destination": "/_next/:path+"
+      }
+    ],
+    "fallback": []
+  },
+  "sortedPages": [
+    "/_app",
+    "/_error"
+  ]
+};self.__BUILD_MANIFEST_CB && self.__BUILD_MANIFEST_CB()

litellm/proxy/_experimental/out/_next/static/6BWZ9pP30VkI_U10n-4Bg/_clientMiddlewareManifest.json

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+[]

litellm/proxy/_experimental/out/_next/static/6BWZ9pP30VkI_U10n-4Bg/_ssgManifest.js

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litellm/proxy/_experimental/out/_next/static/89xXu4HpcVi8vZ2tAZonL/_buildManifest.js

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+self.__BUILD_MANIFEST = {
+  "__rewrites": {
+    "afterFiles": [],
+    "beforeFiles": [
+      {
+        "source": "/litellm-asset-prefix/_next/:path+",
+        "destination": "/_next/:path+"
+      }
+    ],
+    "fallback": []
+  },
+  "sortedPages": [
+    "/_app",
+    "/_error"
+  ]
+};self.__BUILD_MANIFEST_CB && self.__BUILD_MANIFEST_CB()

litellm/proxy/_experimental/out/_next/static/89xXu4HpcVi8vZ2tAZonL/_clientMiddlewareManifest.json

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+[]

litellm/proxy/_experimental/out/_next/static/89xXu4HpcVi8vZ2tAZonL/_ssgManifest.js

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litellm/proxy/_experimental/out/_next/static/HIU5ZGw7S1xqtzfXTNFcq/_buildManifest.js

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+    "beforeFiles": [
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+    ],
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+  },
+  "sortedPages": [
+    "/_app",
+    "/_error"
+  ]
+};self.__BUILD_MANIFEST_CB && self.__BUILD_MANIFEST_CB()

litellm/proxy/_experimental/out/_next/static/HIU5ZGw7S1xqtzfXTNFcq/_clientMiddlewareManifest.json

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+[]

litellm/proxy/_experimental/out/_next/static/HIU5ZGw7S1xqtzfXTNFcq/_ssgManifest.js

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+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()

litellm/proxy/_experimental/out/_next/static/chunks/d0510af52e5b6373.js

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litellm/proxy/_experimental/out/_next/static/k7To-SVXi-A1OvORNVUmn/_buildManifest.js

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+self.__BUILD_MANIFEST = {
+  "__rewrites": {
+    "afterFiles": [],
+    "beforeFiles": [
+      {
+        "source": "/litellm-asset-prefix/_next/:path+",
+        "destination": "/_next/:path+"
+      }
+    ],
+    "fallback": []
+  },
+  "sortedPages": [
+    "/_app",
+    "/_error"
+  ]
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litellm/proxy/_experimental/out/_next/static/k7To-SVXi-A1OvORNVUmn/_clientMiddlewareManifest.json

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+[]

litellm/proxy/_experimental/out/_next/static/k7To-SVXi-A1OvORNVUmn/_ssgManifest.js

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+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()

litellm/proxy/_experimental/out/_not-found.txt

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litellm/proxy/_experimental/out/_not-found/__next._full.txt

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litellm/proxy/_experimental/out/api-reference/__next.!KGRhc2hib2FyZCk.txt

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litellm/proxy/_experimental/out/api-reference/__next._head.txt

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litellm/proxy/_experimental/out/api-reference/__next._index.txt

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litellm/proxy/_experimental/out/api-reference/__next._tree.txt

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litellm/proxy/_experimental/out/chat.txt

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litellm/proxy/_experimental/out/chat/__next.chat.__PAGE__.txt

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