From 94da5c076c3b27b4341f5cb82fc2e5011ef0c5d6 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Wed, 12 Nov 2025 18:22:26 -0800 Subject: [PATCH 001/248] Add Daily Org Spend Table, read path, and write path --- .../litellm_proxy_extras/schema.prisma | 29 +++ litellm/constants.py | 1 + litellm/proxy/_types.py | 4 + litellm/proxy/db/db_spend_update_writer.py | 104 ++++++++++- .../redis_update_buffer.py | 36 ++++ .../organization_endpoints.py | 99 ++++++++++ litellm/proxy/schema.prisma | 29 +++ schema.prisma | 29 +++ .../proxy/db/test_db_spend_update_writer.py | 84 ++++++++- .../test_organization_endpoints.py | 175 ++++++++++++++++++ 10 files changed, 585 insertions(+), 5 deletions(-) diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 8890456112..739ae8cc60 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -419,6 +419,35 @@ model LiteLLM_DailyUserSpend { @@index([mcp_namespaced_tool_name]) } +// Track daily organization spend metrics per model and key +model LiteLLM_DailyOrganizationSpend { + id String @id @default(uuid()) + organization_id String? + date String + api_key String + model String? + model_group String? + custom_llm_provider String? + mcp_namespaced_tool_name String? + prompt_tokens BigInt @default(0) + completion_tokens BigInt @default(0) + cache_read_input_tokens BigInt @default(0) + cache_creation_input_tokens BigInt @default(0) + spend Float @default(0.0) + api_requests BigInt @default(0) + successful_requests BigInt @default(0) + failed_requests BigInt @default(0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@unique([organization_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name]) + @@index([date]) + @@index([organization_id]) + @@index([api_key]) + @@index([model]) + @@index([mcp_namespaced_tool_name]) +} + // Track daily team spend metrics per model and key model LiteLLM_DailyTeamSpend { id String @id @default(uuid()) diff --git a/litellm/constants.py b/litellm/constants.py index 220e425068..48be1c1fbf 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -131,6 +131,7 @@ DEFAULT_SSL_CIPHERS = os.getenv( REDIS_UPDATE_BUFFER_KEY = "litellm_spend_update_buffer" REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_spend_update_buffer" REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_team_spend_update_buffer" +REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_org_spend_update_buffer" REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_tag_spend_update_buffer" MAX_REDIS_BUFFER_DEQUEUE_COUNT = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) MAX_SIZE_IN_MEMORY_QUEUE = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", 10000)) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index a212eab076..48307fc6a2 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -3536,6 +3536,10 @@ class DailyTeamSpendTransaction(BaseDailySpendTransaction): team_id: str +class DailyOrganizationSpendTransaction(BaseDailySpendTransaction): + organization_id: str + + class DailyUserSpendTransaction(BaseDailySpendTransaction): user_id: str diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 06b5301424..8b276c583d 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -23,6 +23,7 @@ from litellm.proxy._types import ( DB_CONNECTION_ERROR_TYPES, BaseDailySpendTransaction, DailyTagSpendTransaction, + DailyOrganizationSpendTransaction, DailyTeamSpendTransaction, DailyUserSpendTransaction, DBSpendUpdateTransactions, @@ -64,6 +65,7 @@ class DBSpendUpdateWriter: self.spend_update_queue = SpendUpdateQueue() self.daily_spend_update_queue = DailySpendUpdateQueue() self.daily_team_spend_update_queue = DailySpendUpdateQueue() + self.daily_org_spend_update_queue = DailySpendUpdateQueue() self.daily_tag_spend_update_queue = DailySpendUpdateQueue() async def update_database( @@ -180,7 +182,13 @@ class DBSpendUpdateWriter: prisma_client=prisma_client, ) ) - + asyncio.create_task( + self.add_spend_log_transaction_to_daily_org_transaction( + payload=payload, + org_id=org_id, + prisma_client=prisma_client, + ) + ) asyncio.create_task( self.add_spend_log_transaction_to_daily_tag_transaction( payload=payload, @@ -460,6 +468,7 @@ class DBSpendUpdateWriter: spend_update_queue=self.spend_update_queue, daily_spend_update_queue=self.daily_spend_update_queue, daily_team_spend_update_queue=self.daily_team_spend_update_queue, + daily_org_spend_update_queue=self.daily_org_spend_update_queue, daily_tag_spend_update_queue=self.daily_tag_spend_update_queue, ) @@ -502,6 +511,17 @@ class DBSpendUpdateWriter: daily_spend_transactions=daily_team_spend_update_transactions, ) + daily_org_spend_update_transactions = ( + await self.redis_update_buffer.get_all_daily_org_spend_update_transactions_from_redis_buffer() + ) + if daily_org_spend_update_transactions is not None: + await DBSpendUpdateWriter.update_daily_org_spend( + n_retry_times=n_retry_times, + prisma_client=prisma_client, + proxy_logging_obj=proxy_logging_obj, + daily_spend_transactions=daily_org_spend_update_transactions, + ) + daily_tag_spend_update_transactions = ( await self.redis_update_buffer.get_all_daily_tag_spend_update_transactions_from_redis_buffer() ) @@ -573,6 +593,20 @@ class DBSpendUpdateWriter: daily_spend_transactions=daily_team_spend_update_transactions, ) + ################## Daily Organization Spend Update Transactions ################## + # Aggregate all in memory daily org spend transactions and commit to db + daily_org_spend_update_transactions = cast( + Dict[str, DailyOrganizationSpendTransaction], + await self.daily_org_spend_update_queue.flush_and_get_aggregated_daily_spend_update_transactions(), + ) + + await DBSpendUpdateWriter.update_daily_org_spend( + n_retry_times=n_retry_times, + prisma_client=prisma_client, + proxy_logging_obj=proxy_logging_obj, + daily_spend_transactions=daily_org_spend_update_transactions, + ) + ################## Daily Tag Spend Update Transactions ################## # Aggregate all in memory daily tag spend transactions and commit to db daily_tag_spend_update_transactions = cast( @@ -962,14 +996,15 @@ class DBSpendUpdateWriter: Dict[str, DailyUserSpendTransaction], Dict[str, DailyTeamSpendTransaction], Dict[str, DailyTagSpendTransaction], + Dict[str, DailyOrganizationSpendTransaction], ], - entity_type: Literal["user", "team", "tag"], + entity_type: Literal["user", "team", "org", "tag"], entity_id_field: str, table_name: str, unique_constraint_name: str, ) -> None: """ - Generic function to update daily spend for any entity type (user, team, tag) + Generic function to update daily spend for any entity type (user, team, org, tag) """ from litellm.proxy.utils import _raise_failed_update_spend_exception @@ -1191,6 +1226,27 @@ class DBSpendUpdateWriter: unique_constraint_name="team_id_date_api_key_model_custom_llm_provider_mcp_namespaced_tool_name", ) + @staticmethod + async def update_daily_org_spend( + n_retry_times: int, + prisma_client: PrismaClient, + proxy_logging_obj: ProxyLogging, + daily_spend_transactions: Dict[str, DailyOrganizationSpendTransaction], + ): + """ + Batch job to update LiteLLM_DailyOrganizationSpend table using in-memory daily_spend_transactions + """ + await DBSpendUpdateWriter._update_daily_spend( + n_retry_times=n_retry_times, + prisma_client=prisma_client, + proxy_logging_obj=proxy_logging_obj, + daily_spend_transactions=daily_spend_transactions, + entity_type="org", + entity_id_field="organization_id", + table_name="litellm_dailyorganizationspend", + unique_constraint_name="organization_id_date_api_key_model_custom_llm_provider_mcp_namespaced_tool_name", + ) + @staticmethod async def update_daily_tag_spend( n_retry_times: int, @@ -1216,13 +1272,15 @@ class DBSpendUpdateWriter: self, payload: Union[dict, SpendLogsPayload], prisma_client: PrismaClient, - type: Literal["user", "team", "request_tags"] = "user", + type: Literal["user", "team", "org", "request_tags"] = "user", ) -> Optional[BaseDailySpendTransaction]: common_expected_keys = ["startTime", "api_key"] if type == "user": expected_keys = ["user", *common_expected_keys] elif type == "team": expected_keys = ["team_id", *common_expected_keys] + elif type == "org": + expected_keys = ["organization_id", *common_expected_keys] elif type == "request_tags": expected_keys = ["request_tags", *common_expected_keys] else: @@ -1354,6 +1412,44 @@ class DBSpendUpdateWriter: update={daily_transaction_key: daily_transaction} ) + async def add_spend_log_transaction_to_daily_org_transaction( + self, + payload: SpendLogsPayload, + prisma_client: Optional[PrismaClient] = None, + org_id: Optional[str] = None, + ) -> None: + if prisma_client is None: + verbose_proxy_logger.debug( + "prisma_client is None. Skipping writing spend logs to db." + ) + return + + if org_id is None: + verbose_proxy_logger.debug( + "organization_id is None for request. Skipping incrementing organization spend." + ) + return + + # Inject org_id for daily aggregation check + payload_with_org: SpendLogsPayload = dict(payload) + payload_with_org["organization_id"] = org_id + + base_daily_transaction = ( + await self._common_add_spend_log_transaction_to_daily_transaction( + payload_with_org, prisma_client, "org" + ) + ) + if base_daily_transaction is None: + return + + daily_transaction_key = f"{org_id}_{base_daily_transaction['date']}_{payload_with_org['api_key']}_{payload_with_org['model']}_{payload_with_org['custom_llm_provider']}" + daily_transaction = DailyOrganizationSpendTransaction( + organization_id=org_id, **base_daily_transaction + ) + await self.daily_org_spend_update_queue.add_update( + update={daily_transaction_key: daily_transaction} + ) + async def add_spend_log_transaction_to_daily_tag_transaction( self, payload: SpendLogsPayload, diff --git a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py index 91d0bee1d3..921fd9701b 100644 --- a/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py +++ b/litellm/proxy/db/db_transaction_queue/redis_update_buffer.py @@ -15,6 +15,7 @@ from litellm.constants import ( REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY, REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY, REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY, + REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY, REDIS_UPDATE_BUFFER_KEY, ) from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -22,6 +23,7 @@ from litellm.proxy._types import ( DailyTagSpendTransaction, DailyTeamSpendTransaction, DailyUserSpendTransaction, + DailyOrganizationSpendTransaction, DBSpendUpdateTransactions, ) from litellm.proxy.db.db_transaction_queue.base_update_queue import service_logger_obj @@ -104,6 +106,7 @@ class RedisUpdateBuffer: spend_update_queue: SpendUpdateQueue, daily_spend_update_queue: DailySpendUpdateQueue, daily_team_spend_update_queue: DailySpendUpdateQueue, + daily_org_spend_update_queue: DailySpendUpdateQueue, daily_tag_spend_update_queue: DailySpendUpdateQueue, ): """ @@ -166,6 +169,9 @@ class RedisUpdateBuffer: daily_team_spend_update_transactions = ( await daily_team_spend_update_queue.flush_and_get_aggregated_daily_spend_update_transactions() ) + daily_org_spend_update_transactions = ( + await daily_org_spend_update_queue.flush_and_get_aggregated_daily_spend_update_transactions() + ) daily_tag_spend_update_transactions = ( await daily_tag_spend_update_queue.flush_and_get_aggregated_daily_spend_update_transactions() ) @@ -195,6 +201,12 @@ class RedisUpdateBuffer: service_type=ServiceTypes.REDIS_DAILY_TEAM_SPEND_UPDATE_QUEUE, ) + await self._store_transactions_in_redis( + transactions=daily_org_spend_update_transactions, + redis_key=REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY, + service_type=ServiceTypes.REDIS_DAILY_SPEND_UPDATE_QUEUE, + ) + await self._store_transactions_in_redis( transactions=daily_tag_spend_update_transactions, redis_key=REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY, @@ -329,6 +341,30 @@ class RedisUpdateBuffer: ), ) + async def get_all_daily_org_spend_update_transactions_from_redis_buffer( + self, + ) -> Optional[Dict[str, DailyOrganizationSpendTransaction]]: + """ + Gets all the daily organization spend update transactions from Redis + """ + if self.redis_cache is None: + return None + list_of_transactions = await self.redis_cache.async_lpop( + key=REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY, + count=MAX_REDIS_BUFFER_DEQUEUE_COUNT, + ) + if list_of_transactions is None: + return None + list_of_daily_spend_update_transactions = [ + json.loads(transaction) for transaction in list_of_transactions + ] + return cast( + Dict[str, DailyOrganizationSpendTransaction], + DailySpendUpdateQueue.get_aggregated_daily_spend_update_transactions( + list_of_daily_spend_update_transactions + ), + ) + async def get_all_daily_tag_spend_update_transactions_from_redis_buffer( self, ) -> Optional[Dict[str, DailyTagSpendTransaction]]: diff --git a/litellm/proxy/management_endpoints/organization_endpoints.py b/litellm/proxy/management_endpoints/organization_endpoints.py index 402dffaaa7..65764b3b95 100644 --- a/litellm/proxy/management_endpoints/organization_endpoints.py +++ b/litellm/proxy/management_endpoints/organization_endpoints.py @@ -25,6 +25,7 @@ from litellm.proxy.management_endpoints.budget_management_endpoints import ( update_budget, ) from litellm.proxy.management_endpoints.common_utils import _set_object_metadata_field +from litellm.proxy.management_endpoints.common_utils import _user_has_admin_view from litellm.proxy.management_helpers.object_permission_utils import ( handle_update_object_permission_common, ) @@ -34,6 +35,10 @@ from litellm.proxy.management_helpers.utils import ( ) from litellm.proxy.utils import PrismaClient from litellm.utils import _update_dictionary +from litellm.types.proxy.management_endpoints.common_daily_activity import ( + SpendAnalyticsPaginatedResponse, +) +from litellm.proxy.management_endpoints.common_daily_activity import get_daily_activity router = APIRouter() @@ -255,6 +260,100 @@ async def new_organization( return response +@router.get( + "/organization/daily/activity", + response_model=SpendAnalyticsPaginatedResponse, + tags=["organization management"], +) +async def get_organization_daily_activity( + organization_ids: Optional[str] = None, + start_date: Optional[str] = None, + end_date: Optional[str] = None, + model: Optional[str] = None, + api_key: Optional[str] = None, + page: int = 1, + page_size: int = 10, + exclude_organization_ids: Optional[str] = None, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +): + """ + Get daily activity for specific organizations or all accessible organizations. + """ + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + if prisma_client is None: + raise HTTPException( + status_code=500, + detail={"error": CommonProxyErrors.db_not_connected_error.value}, + ) + + # Parse comma-separated ids + org_ids_list = organization_ids.split(",") if organization_ids else None + exclude_org_ids_list: Optional[List[str]] = None + if exclude_organization_ids: + exclude_org_ids_list = ( + exclude_organization_ids.split(",") if exclude_organization_ids else None + ) + + # Restrict non-proxy-admins to only organizations where they are org_admin + if not _user_has_admin_view(user_api_key_dict): + memberships = await prisma_client.db.litellm_organizationmembership.find_many( + where={"user_id": user_api_key_dict.user_id} + ) + admin_org_ids = [ + m.organization_id + for m in memberships + if m.user_role == LitellmUserRoles.ORG_ADMIN.value + ] + if org_ids_list is None: + # Default to orgs where user is org_admin + org_ids_list = admin_org_ids + else: + # Ensure user is org_admin for all requested orgs + for org_id in org_ids_list: + if org_id not in admin_org_ids: + raise HTTPException( + status_code=403, + detail={ + "error": "User is not org_admin for Organization= {}.".format( + org_id + ) + }, + ) + + # Fetch organization aliases for metadata + where_condition = {} + if org_ids_list: + where_condition["organization_id"] = {"in": list(org_ids_list)} + org_aliases = await prisma_client.db.litellm_organizationtable.find_many( + where=where_condition + ) + org_alias_metadata = { + o.organization_id: {"organization_alias": o.organization_alias} + for o in org_aliases + } + + # Query daily activity for organizations + return await get_daily_activity( + prisma_client=prisma_client, + table_name="litellm_dailyorganizationspend", + entity_id_field="organization_id", + entity_id=org_ids_list, + entity_metadata_field=org_alias_metadata, + exclude_entity_ids=exclude_org_ids_list, + start_date=start_date, + end_date=end_date, + model=model, + api_key=api_key, + page=page, + page_size=page_size, + ) + + async def _set_object_permission( data: NewOrganizationRequest, prisma_client: Optional[PrismaClient], diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index 51e6ea9454..4ec9662c32 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -419,6 +419,35 @@ model LiteLLM_DailyUserSpend { @@index([mcp_namespaced_tool_name]) } +// Track daily organization spend metrics per model and key +model LiteLLM_DailyOrganizationSpend { + id String @id @default(uuid()) + organization_id String? + date String + api_key String + model String? + model_group String? + custom_llm_provider String? + mcp_namespaced_tool_name String? + prompt_tokens BigInt @default(0) + completion_tokens BigInt @default(0) + cache_read_input_tokens BigInt @default(0) + cache_creation_input_tokens BigInt @default(0) + spend Float @default(0.0) + api_requests BigInt @default(0) + successful_requests BigInt @default(0) + failed_requests BigInt @default(0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@unique([organization_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name]) + @@index([date]) + @@index([organization_id]) + @@index([api_key]) + @@index([model]) + @@index([mcp_namespaced_tool_name]) +} + // Track daily team spend metrics per model and key model LiteLLM_DailyTeamSpend { id String @id @default(uuid()) diff --git a/schema.prisma b/schema.prisma index 51e6ea9454..4ec9662c32 100644 --- a/schema.prisma +++ b/schema.prisma @@ -419,6 +419,35 @@ model LiteLLM_DailyUserSpend { @@index([mcp_namespaced_tool_name]) } +// Track daily organization spend metrics per model and key +model LiteLLM_DailyOrganizationSpend { + id String @id @default(uuid()) + organization_id String? + date String + api_key String + model String? + model_group String? + custom_llm_provider String? + mcp_namespaced_tool_name String? + prompt_tokens BigInt @default(0) + completion_tokens BigInt @default(0) + cache_read_input_tokens BigInt @default(0) + cache_creation_input_tokens BigInt @default(0) + spend Float @default(0.0) + api_requests BigInt @default(0) + successful_requests BigInt @default(0) + failed_requests BigInt @default(0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@unique([organization_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name]) + @@index([date]) + @@index([organization_id]) + @@index([api_key]) + @@index([model]) + @@index([mcp_namespaced_tool_name]) +} + // Track daily team spend metrics per model and key model LiteLLM_DailyTeamSpend { id String @id @default(uuid()) diff --git a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py index 6dbbbdd744..d29ddffa36 100644 --- a/tests/test_litellm/proxy/db/test_db_spend_update_writer.py +++ b/tests/test_litellm/proxy/db/test_db_spend_update_writer.py @@ -387,4 +387,86 @@ async def test_add_spend_log_transaction_to_daily_tag_transaction_with_request_i transaction_dict = call[1]["update"] # Each transaction should have one key with the format tag_date_api_key_model_provider for key, transaction in transaction_dict.items(): - assert transaction["request_id"] == request_id, f"request_id should be {request_id} but got {transaction.get('request_id')}" \ No newline at end of file + assert transaction["request_id"] == request_id, f"request_id should be {request_id} but got {transaction.get('request_id')}" + + +@pytest.mark.asyncio +async def test_add_spend_log_transaction_to_daily_org_transaction_injects_org_id_and_queues_update(): + """ + Verify org_id is injected into payload for daily aggregation and the update is queued. + """ + writer = DBSpendUpdateWriter() + mock_prisma = MagicMock() + mock_prisma.get_request_status = MagicMock(return_value="success") + + org_id = "org-xyz" + payload = { + "request_id": "req-1", + "user": "test-user", + "startTime": "2024-01-01T12:00:00", + "api_key": "test-key", + "model": "gpt-4", + "custom_llm_provider": "openai", + "model_group": "gpt-4-group", + "prompt_tokens": 10, + "completion_tokens": 5, + "spend": 0.2, + "metadata": '{"usage_object": {}}', + } + + writer.daily_org_spend_update_queue.add_update = AsyncMock() + + await writer.add_spend_log_transaction_to_daily_org_transaction( + payload=payload, + prisma_client=mock_prisma, + org_id=org_id, + ) + + # Should enqueue one org spend update + writer.daily_org_spend_update_queue.add_update.assert_called_once() + + # Validate key and injected fields + call_args = writer.daily_org_spend_update_queue.add_update.call_args[1] + update_dict = call_args["update"] + assert len(update_dict) == 1 + for key, transaction in update_dict.items(): + assert key == f"{org_id}_2024-01-01_test-key_gpt-4_openai" + assert transaction["organization_id"] == org_id + assert transaction["date"] == "2024-01-01" + assert transaction["api_key"] == "test-key" + assert transaction["model"] == "gpt-4" + assert transaction["custom_llm_provider"] == "openai" + + +@pytest.mark.asyncio +async def test_add_spend_log_transaction_to_daily_org_transaction_skips_when_org_id_missing(): + """ + Ensure no update is queued when org_id is None. + """ + writer = DBSpendUpdateWriter() + mock_prisma = MagicMock() + mock_prisma.get_request_status = MagicMock(return_value="success") + + payload = { + "request_id": "req-2", + "user": "test-user", + "startTime": "2024-01-01T12:00:00", + "api_key": "test-key", + "model": "gpt-4", + "custom_llm_provider": "openai", + "model_group": "gpt-4-group", + "prompt_tokens": 10, + "completion_tokens": 5, + "spend": 0.2, + "metadata": '{"usage_object": {}}', + } + + writer.daily_org_spend_update_queue.add_update = AsyncMock() + + await writer.add_spend_log_transaction_to_daily_org_transaction( + payload=payload, + prisma_client=mock_prisma, + org_id=None, + ) + + writer.daily_org_spend_update_queue.add_update.assert_not_called() \ No newline at end of file diff --git a/tests/test_litellm/proxy/management_endpoints/test_organization_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_organization_endpoints.py index 981c26d6db..c02db727bf 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_organization_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_organization_endpoints.py @@ -94,6 +94,181 @@ async def test_organization_update_object_permissions_existing_permission(monkey mock_prisma_client.db.litellm_objectpermissiontable.upsert.assert_called_once() +@pytest.mark.asyncio +async def test_get_organization_daily_activity_admin_param_passing(monkeypatch): + """ + As admin, ensure parsed params are forwarded to get_daily_activity with correct values. + """ + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.management_endpoints import organization_endpoints + from litellm.proxy.management_endpoints.organization_endpoints import ( + get_organization_daily_activity, + ) + + # Mock prisma client + mock_prisma_client = AsyncMock() + mock_prisma_client.db.litellm_organizationtable.find_many = AsyncMock( + return_value=[] + ) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + # Admin view -> skip membership restriction + monkeypatch.setattr( + "litellm.proxy.management_endpoints.organization_endpoints._user_has_admin_view", + lambda _: True, + ) + + # Patch downstream common function and verify call args + mocked_response = MagicMock(name="SpendAnalyticsPaginatedResponse") + get_daily_activity_mock = AsyncMock(return_value=mocked_response) + monkeypatch.setattr( + organization_endpoints, "get_daily_activity", get_daily_activity_mock + ) + + auth = UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN, user_id="admin1") + result = await get_organization_daily_activity( + organization_ids="org1,org2", + start_date="2024-01-01", + end_date="2024-01-31", + model="gpt-4", + api_key="test-key", + page=2, + page_size=5, + exclude_organization_ids="org3", + user_api_key_dict=auth, + ) + + # Ensure passthrough to common method with correct args + get_daily_activity_mock.assert_awaited_once() + kwargs = get_daily_activity_mock.call_args.kwargs + assert kwargs["table_name"] == "litellm_dailyorganizationspend" + assert kwargs["entity_id_field"] == "organization_id" + assert kwargs["entity_id"] == ["org1", "org2"] + assert kwargs["exclude_entity_ids"] == ["org3"] + assert kwargs["start_date"] == "2024-01-01" + assert kwargs["end_date"] == "2024-01-31" + assert kwargs["model"] == "gpt-4" + assert kwargs["api_key"] == "test-key" + assert kwargs["page"] == 2 + assert kwargs["page_size"] == 5 + + assert result is mocked_response + + +@pytest.mark.asyncio +async def test_get_organization_daily_activity_non_admin_defaults_to_admin_orgs(monkeypatch): + """ + Non-admin with no explicit organization_ids should default to orgs they are ORG_ADMIN of. + """ + from types import SimpleNamespace + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.management_endpoints import organization_endpoints + from litellm.proxy.management_endpoints.organization_endpoints import ( + get_organization_daily_activity, + ) + + # Mock prisma client and memberships + mock_prisma_client = AsyncMock() + mock_prisma_client.db.litellm_organizationtable.find_many = AsyncMock( + return_value=[] + ) + mock_prisma_client.db.litellm_organizationmembership.find_many = AsyncMock( + return_value=[ + SimpleNamespace( + organization_id="orgA", user_role=LitellmUserRoles.ORG_ADMIN.value + ), + SimpleNamespace( + organization_id="orgB", user_role=LitellmUserRoles.ORG_ADMIN.value + ), + ] + ) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + # Non-admin view + monkeypatch.setattr( + "litellm.proxy.management_endpoints.organization_endpoints._user_has_admin_view", + lambda _: False, + ) + + # Patch downstream aggregator + mocked_response = MagicMock(name="SpendAnalyticsPaginatedResponse") + get_daily_activity_mock = AsyncMock(return_value=mocked_response) + monkeypatch.setattr( + organization_endpoints, "get_daily_activity", get_daily_activity_mock + ) + + auth = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, user_id="regular-user" + ) + await get_organization_daily_activity( + organization_ids=None, + start_date="2024-02-01", + end_date="2024-02-28", + model=None, + api_key=None, + page=1, + page_size=10, + exclude_organization_ids=None, + user_api_key_dict=auth, + ) + + kwargs = get_daily_activity_mock.call_args.kwargs + assert kwargs["entity_id"] == ["orgA", "orgB"] + assert kwargs["start_date"] == "2024-02-01" + assert kwargs["end_date"] == "2024-02-28" + + +@pytest.mark.asyncio +async def test_get_organization_daily_activity_non_admin_unauthorized_org_raises(monkeypatch): + """ + Non-admin requesting an org they aren't ORG_ADMIN for should raise 403. + """ + from types import SimpleNamespace + + from litellm.proxy._types import LitellmUserRoles, UserAPIKeyAuth + from litellm.proxy.management_endpoints.organization_endpoints import ( + get_organization_daily_activity, + ) + + # Mock prisma client and memberships (only orgA is admin) + mock_prisma_client = AsyncMock() + mock_prisma_client.db.litellm_organizationmembership.find_many = AsyncMock( + return_value=[ + SimpleNamespace( + organization_id="orgA", user_role=LitellmUserRoles.ORG_ADMIN.value + ) + ] + ) + mock_prisma_client.db.litellm_organizationtable.find_many = AsyncMock( + return_value=[] + ) + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + # Non-admin view + monkeypatch.setattr( + "litellm.proxy.management_endpoints.organization_endpoints._user_has_admin_view", + lambda _: False, + ) + + auth = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, user_id="regular-user" + ) + + with pytest.raises(HTTPException) as exc: + await get_organization_daily_activity( + organization_ids="orgA,orgX", # orgX is unauthorized + start_date="2024-03-01", + end_date="2024-03-31", + model=None, + api_key=None, + page=1, + page_size=10, + exclude_organization_ids=None, + user_api_key_dict=auth, + ) + assert exc.value.status_code == 403 + @pytest.mark.asyncio async def test_organization_update_object_permissions_no_existing_permission( monkeypatch, From 09afe14a5f104cbef09c7f29142c630bdd506ffa Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 13 Nov 2025 15:49:35 -0800 Subject: [PATCH 002/248] Addressing linting issues --- litellm/proxy/_types.py | 1 + litellm/proxy/db/db_spend_update_writer.py | 27 ++++++++++++++++--- .../organization_endpoints.py | 4 +-- 3 files changed, 26 insertions(+), 6 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 48307fc6a2..81c5a2e920 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2600,6 +2600,7 @@ class SpendLogsPayload(TypedDict): cache_key: str request_tags: str # json str team_id: Optional[str] + organization_id: Optional[str] end_user: Optional[str] requester_ip_address: Optional[str] custom_llm_provider: Optional[str] diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 8b276c583d..647a815071 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -972,6 +972,20 @@ class DBSpendUpdateWriter: ) -> None: ... + @overload + @staticmethod + async def _update_daily_spend( + n_retry_times: int, + prisma_client: PrismaClient, + proxy_logging_obj: ProxyLogging, + daily_spend_transactions: Dict[str, DailyOrganizationSpendTransaction], + entity_type: Literal["org"], + entity_id_field: str, + table_name: str, + unique_constraint_name: str, + ) -> None: + ... + @overload @staticmethod async def _update_daily_spend( @@ -1430,9 +1444,16 @@ class DBSpendUpdateWriter: ) return - # Inject org_id for daily aggregation check - payload_with_org: SpendLogsPayload = dict(payload) - payload_with_org["organization_id"] = org_id + print("org_id", org_id) + print("payload", payload) + + payload_with_org = cast( + SpendLogsPayload, + { + **payload, + "organization_id": org_id, + }, + ) base_daily_transaction = ( await self._common_add_spend_log_transaction_to_daily_transaction( diff --git a/litellm/proxy/management_endpoints/organization_endpoints.py b/litellm/proxy/management_endpoints/organization_endpoints.py index 65764b3b95..99b37c765a 100644 --- a/litellm/proxy/management_endpoints/organization_endpoints.py +++ b/litellm/proxy/management_endpoints/organization_endpoints.py @@ -281,10 +281,8 @@ async def get_organization_daily_activity( """ from litellm.proxy.proxy_server import ( prisma_client, - proxy_logging_obj, - user_api_key_cache, ) - + if prisma_client is None: raise HTTPException( status_code=500, From cfc44ea2794b9634bd7533d565fdde9fec46d568 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 13 Nov 2025 15:51:49 -0800 Subject: [PATCH 003/248] Remove debug statements --- litellm/proxy/db/db_spend_update_writer.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/litellm/proxy/db/db_spend_update_writer.py b/litellm/proxy/db/db_spend_update_writer.py index 647a815071..1d998f457b 100644 --- a/litellm/proxy/db/db_spend_update_writer.py +++ b/litellm/proxy/db/db_spend_update_writer.py @@ -1444,9 +1444,6 @@ class DBSpendUpdateWriter: ) return - print("org_id", org_id) - print("payload", payload) - payload_with_org = cast( SpendLogsPayload, { From ae4dfb53dd4f03658d0850fb258590477685c1d3 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Fri, 14 Nov 2025 18:06:49 -0800 Subject: [PATCH 004/248] Add migration --- .../migration.sql | 42 +++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql new file mode 100644 index 0000000000..74e0eea313 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql @@ -0,0 +1,42 @@ +-- CreateTable +CREATE TABLE "LiteLLM_DailyOrganizationSpend" ( + "id" TEXT NOT NULL, + "organization_id" TEXT, + "date" TEXT NOT NULL, + "api_key" TEXT NOT NULL, + "model" TEXT, + "model_group" TEXT, + "custom_llm_provider" TEXT, + "mcp_namespaced_tool_name" TEXT, + "prompt_tokens" BIGINT NOT NULL DEFAULT 0, + "completion_tokens" BIGINT NOT NULL DEFAULT 0, + "cache_read_input_tokens" BIGINT NOT NULL DEFAULT 0, + "cache_creation_input_tokens" BIGINT NOT NULL DEFAULT 0, + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "api_requests" BIGINT NOT NULL DEFAULT 0, + "successful_requests" BIGINT NOT NULL DEFAULT 0, + "failed_requests" BIGINT NOT NULL DEFAULT 0, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_at" TIMESTAMP(3) NOT NULL, + + CONSTRAINT "LiteLLM_DailyOrganizationSpend_pkey" PRIMARY KEY ("id") +); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_date_idx" ON "LiteLLM_DailyOrganizationSpend"("date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_organization_id_idx" ON "LiteLLM_DailyOrganizationSpend"("organization_id"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_api_key_idx" ON "LiteLLM_DailyOrganizationSpend"("api_key"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_model_idx" ON "LiteLLM_DailyOrganizationSpend"("model"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_mcp_namespaced_tool_name_idx" ON "LiteLLM_DailyOrganizationSpend"("mcp_namespaced_tool_name"); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_DailyOrganizationSpend_organization_id_date_api_key_key" ON "LiteLLM_DailyOrganizationSpend"("organization_id", "date", "api_key", "model", "custom_llm_provider", "mcp_namespaced_tool_name"); + From 24c23c62ba0f3e9caca2986fd4d8fc3a8864ae47 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Fri, 14 Nov 2025 18:14:22 -0800 Subject: [PATCH 005/248] Linting --- litellm/proxy/spend_tracking/spend_tracking_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/litellm/proxy/spend_tracking/spend_tracking_utils.py b/litellm/proxy/spend_tracking/spend_tracking_utils.py index 32d9c4b1f2..10dae1deed 100644 --- a/litellm/proxy/spend_tracking/spend_tracking_utils.py +++ b/litellm/proxy/spend_tracking/spend_tracking_utils.py @@ -381,6 +381,7 @@ def get_logging_payload( # noqa: PLR0915 model=kwargs.get("model", "") or "", user=metadata.get("user_api_key_user_id", "") or "", team_id=metadata.get("user_api_key_team_id", "") or "", + organization_id=metadata.get("user_api_key_org_id") or "", metadata=safe_dumps(clean_metadata), cache_key=cache_key, spend=kwargs.get("response_cost", 0), From d01efcb3084a0a3bd7302294d1072ab8ab247ecd Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 15 Nov 2025 15:58:41 -0800 Subject: [PATCH 006/248] speech set up --- no_cache_hits.py | 48 +++++++++++++++++++++++++++++++++++++++++++++ speech.mp3 | Bin 0 -> 104 bytes speech_config.yaml | 9 +++++++++ 3 files changed, 57 insertions(+) create mode 100644 no_cache_hits.py create mode 100644 speech.mp3 create mode 100644 speech_config.yaml diff --git a/no_cache_hits.py b/no_cache_hits.py new file mode 100644 index 0000000000..1b3bf895f7 --- /dev/null +++ b/no_cache_hits.py @@ -0,0 +1,48 @@ +from locust import HttpUser, between, task + + +class MyUser(HttpUser): + """ + Minimal Locust user for repeatedly hitting `/v1/audio/speech`. + The goal is to measure server-side performance, so we avoid any extra work + (file writes, random generation, manual timing, custom event hooks, etc.) + that could inflate client-side latency. + """ + + wait_time = between(0.5, 1) + host = "http://0.0.0.0:8090" + + def on_start(self): + self.api_key = "sk-1234" + self.model_name = "fake-openai-speech" + self.headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json", + } + self.prompt_counter = 0 + + @task + def audio_speech_request(self): + self.prompt_counter += 1 + # Ensure prompts differ slightly so the backend can't reuse cached audio. + prompt = ( + "Generate a short spoken status update mentioning counter " + f"{self.prompt_counter}." + ) + + response = self.client.post( + "v1/audio/speech", + json={ + "model": self.model_name, + "input": prompt, + "voice": "alloy", + "format": "mp3", + }, + headers=self.headers, + name="audio_speech", + ) + + if response.status_code != 200: + # log the errors in error.txt + with open("error.txt", "a") as error_log: + error_log.write(response.text + "\n") \ No newline at end of file diff --git a/speech.mp3 b/speech.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..f4f854d9bd215e2493d48b4bc4d39804bd79c038 GIT binary patch literal 104 NcmezWdjbPJ000JT0*e3u literal 0 HcmV?d00001 diff --git a/speech_config.yaml b/speech_config.yaml new file mode 100644 index 0000000000..ad9920a279 --- /dev/null +++ b/speech_config.yaml @@ -0,0 +1,9 @@ +model_list: + - model_name: fake-openai-speech + litellm_params: + model: openai/gpt-4o-mini-tts + api_base: http://0.0.0.0:8090/ + api_key: sk-1234 + model_info: + mode: audio_speech + \ No newline at end of file From 3d2552be9fd87b7cf57cb3065affe7977e6f1715 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Sat, 15 Nov 2025 16:31:37 -0800 Subject: [PATCH 007/248] Fixed failing tests --- .../proxy/spend_tracking/test_spend_management_endpoints.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py index 65b245b9e7..33715eb461 100644 --- a/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py +++ b/tests/test_litellm/proxy/spend_tracking/test_spend_management_endpoints.py @@ -196,6 +196,7 @@ ignored_keys = [ "endTime", "completionStartTime", "endTime", + "organization_id", "metadata.model_map_information", "metadata.usage_object", "metadata.cold_storage_object_key", From 44f2013495c6987bef3918ca045f942777b21c34 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 15 Nov 2025 17:02:15 -0800 Subject: [PATCH 008/248] fix: change chunk_size for aiter_bytes 1KB is too small for audio and is lowering the RPS when testing with medium to large files --- litellm/proxy/proxy_server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index a6e73199f0..36178652a7 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -5305,7 +5305,7 @@ async def audio_speech( # Printing each chunk size async def generate(_response: HttpxBinaryResponseContent): - _generator = await _response.aiter_bytes(chunk_size=1024) + _generator = await _response.aiter_bytes(chunk_size=4096) async for chunk in _generator: yield chunk From 348d28d871a8c8d00ec1263d6d61caff4843cd7b Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 15 Nov 2025 17:15:26 -0800 Subject: [PATCH 009/248] fix: remove function definition from every request --- litellm/proxy/proxy_server.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 36178652a7..c65c7f77f0 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -14,6 +14,7 @@ from datetime import datetime, timedelta from typing import ( TYPE_CHECKING, Any, + AsyncGenerator, List, Literal, Optional, @@ -5231,6 +5232,14 @@ async def moderations( ) +async def _audio_speech_chunk_generator( + _response: HttpxBinaryResponseContent, +) -> AsyncGenerator[bytes, None]: + _generator = await _response.aiter_bytes(chunk_size=4096) + async for chunk in _generator: + yield chunk + + @router.post( "/v1/audio/speech", dependencies=[Depends(user_api_key_auth)], @@ -5303,12 +5312,6 @@ async def audio_speech( response_cost = hidden_params.get("response_cost", None) or "" litellm_call_id = hidden_params.get("litellm_call_id", None) or "" - # Printing each chunk size - async def generate(_response: HttpxBinaryResponseContent): - _generator = await _response.aiter_bytes(chunk_size=4096) - async for chunk in _generator: - yield chunk - custom_headers = ProxyBaseLLMRequestProcessing.get_custom_headers( user_api_key_dict=user_api_key_dict, model_id=model_id, @@ -5337,7 +5340,9 @@ async def audio_speech( media_type = "audio/wav" # Gemini TTS returns WAV format after conversion return StreamingResponse( - generate(response), media_type=media_type, headers=custom_headers # type: ignore + _audio_speech_chunk_generator(response), # type: ignore[arg-type] + media_type=media_type, + headers=custom_headers, # type: ignore ) except Exception as e: From 8ea0e31678863d2d700bf857bedac4d25338e008 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 15 Nov 2025 17:30:58 -0800 Subject: [PATCH 010/248] Optimize streaming response accumulation Refactor async_data_generator to build streamed text via list accumulation and ''.join() instead of repeated string concatenation. This improves performance for long responses without changing streaming behavior. --- litellm/proxy/proxy_server.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index c65c7f77f0..b67de4a87a 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -4017,7 +4017,8 @@ async def async_data_generator( ): verbose_proxy_logger.debug("inside generator") try: - str_so_far = "" + # Use a list to accumulate response segments to avoid O(n^2) string concatenation + str_so_far_parts: list[str] = [] error_message: Optional[str] = None async for chunk in proxy_logging_obj.async_post_call_streaming_iterator_hook( user_api_key_dict=user_api_key_dict, @@ -4033,12 +4034,12 @@ async def async_data_generator( user_api_key_dict=user_api_key_dict, response=chunk, data=request_data, - str_so_far=str_so_far, + str_so_far="".join(str_so_far_parts), ) if isinstance(chunk, (ModelResponse, ModelResponseStream)): response_str = litellm.get_response_string(response_obj=chunk) - str_so_far += response_str + str_so_far_parts.append(response_str) if isinstance(chunk, BaseModel): chunk = chunk.model_dump_json(exclude_none=True, exclude_unset=True) From 98e2b64040f6e5f882648b433a23799582d710a1 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Sat, 15 Nov 2025 17:37:01 -0800 Subject: [PATCH 011/248] Optimize response string construction Use list accumulation and join in get_response_string to avoid O(n^2) string concatenation and add a brief comment explaining the performance rationale. --- litellm/utils.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/litellm/utils.py b/litellm/utils.py index 783d462a7a..201e814525 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -4437,17 +4437,20 @@ def get_response_string(response_obj: Union[ModelResponse, ModelResponseStream]) responses_api_response = getattr(response_obj, "response", None) if responses_api_response and hasattr(responses_api_response, "output"): output_list = responses_api_response.output - response_str = "" + # Use list accumulation to avoid O(n^2) string concatenation: + # repeatedly doing `response_str += part` copies the full string each time + # because Python strings are immutable, so total work grows with n^2. + response_output_parts: List[str] = [] for output_item in output_list: # Handle output items with content array if hasattr(output_item, "content"): for content_part in output_item.content: if hasattr(content_part, "text"): - response_str += content_part.text + response_output_parts.append(content_part.text) # Handle output items with direct text field elif hasattr(output_item, "text"): - response_str += output_item.text - return response_str + response_output_parts.append(output_item.text) + return "".join(response_output_parts) # Handle Responses API text delta events if hasattr(response_obj, "type") and hasattr(response_obj, "delta"): @@ -4461,16 +4464,17 @@ def get_response_string(response_obj: Union[ModelResponse, ModelResponseStream]) response_obj.choices ) - response_str = "" + # Use list accumulation to avoid O(n^2) string concatenation across choices + response_parts: List[str] = [] for choice in _choices: if isinstance(choice, Choices): if choice.message.content is not None: - response_str += choice.message.content + response_parts.append(str(choice.message.content)) elif isinstance(choice, StreamingChoices): if choice.delta.content is not None: - response_str += choice.delta.content + response_parts.append(str(choice.delta.content)) - return response_str + return "".join(response_parts) def get_api_key(llm_provider: str, dynamic_api_key: Optional[str]): From 4614e528dc4b3385582810f3c565d62668373d3c Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 17 Nov 2025 10:11:55 -0800 Subject: [PATCH 012/248] fix: remove deadcode The optimizations related to `select_data_generator` had no effect because its output which is the generator wasn't being used. --- litellm/proxy/proxy_server.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index b67de4a87a..d6a98a1ca5 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -5327,11 +5327,6 @@ async def audio_speech( hidden_params=hidden_params, ) - select_data_generator( - response=response, - user_api_key_dict=user_api_key_dict, - request_data=data, - ) # Determine media type based on model type media_type = "audio/mpeg" # Default for OpenAI TTS request_model = data.get("model", "") From c8c12298590885bc845088d4982c7059996f04a9 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 17 Nov 2025 10:24:34 -0800 Subject: [PATCH 013/248] fix: call_type mistake & remove repetitive .lower() calls --- litellm/proxy/proxy_server.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index d6a98a1ca5..cb43ef0cec 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -5286,7 +5286,7 @@ async def audio_speech( ### CALL HOOKS ### - modify incoming data / reject request before calling the model data = await proxy_logging_obj.pre_call_hook( - user_api_key_dict=user_api_key_dict, data=data, call_type="image_generation" + user_api_key_dict=user_api_key_dict, data=data, call_type="aspeech" ) ## ROUTE TO CORRECT ENDPOINT ## @@ -5330,10 +5330,12 @@ async def audio_speech( # Determine media type based on model type media_type = "audio/mpeg" # Default for OpenAI TTS request_model = data.get("model", "") - if "gemini" in request_model.lower() and ( - "tts" in request_model.lower() or "preview-tts" in request_model.lower() - ): - media_type = "audio/wav" # Gemini TTS returns WAV format after conversion + if request_model: + request_model_lower = request_model.lower() + if "gemini" in request_model_lower and ( + "tts" in request_model_lower or "preview-tts" in request_model_lower + ): + media_type = "audio/wav" # Gemini TTS returns WAV format after conversion return StreamingResponse( _audio_speech_chunk_generator(response), # type: ignore[arg-type] From 697cb0906011cb84f2cc8c34b4ff7a2d7402dc13 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 17 Nov 2025 10:51:18 -0800 Subject: [PATCH 014/248] fix: shared_sessions not being used --- litellm/llms/openai/openai.py | 5 +++++ litellm/main.py | 2 ++ 2 files changed, 7 insertions(+) diff --git a/litellm/llms/openai/openai.py b/litellm/llms/openai/openai.py index 2949e35e5e..3282b7665c 100644 --- a/litellm/llms/openai/openai.py +++ b/litellm/llms/openai/openai.py @@ -1414,6 +1414,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): timeout: Union[float, httpx.Timeout], aspeech: Optional[bool] = None, client=None, + shared_session: Optional["ClientSession"] = None, ) -> HttpxBinaryResponseContent: if aspeech is not None and aspeech is True: return self.async_audio_speech( @@ -1428,6 +1429,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries=max_retries, timeout=timeout, client=client, + shared_session=shared_session, ) # type: ignore openai_client = self._get_openai_client( @@ -1437,6 +1439,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): timeout=timeout, max_retries=max_retries, client=client, + shared_session=shared_session, ) response = cast(OpenAI, openai_client).audio.speech.create( @@ -1460,6 +1463,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): max_retries: int, timeout: Union[float, httpx.Timeout], client=None, + shared_session: Optional["ClientSession"] = None, ) -> HttpxBinaryResponseContent: openai_client = cast( AsyncOpenAI, @@ -1470,6 +1474,7 @@ class OpenAIChatCompletion(BaseLLM, BaseOpenAILLM): timeout=timeout, max_retries=max_retries, client=client, + shared_session=shared_session, ), ) diff --git a/litellm/main.py b/litellm/main.py index 14d0b04b7b..412d7f1c38 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -5747,6 +5747,7 @@ def speech( # noqa: PLR0915 proxy_server_request = kwargs.get("proxy_server_request", None) extra_headers = kwargs.get("extra_headers", None) model_info = kwargs.get("model_info", None) + shared_session = kwargs.get("shared_session", None) model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider( model=model, custom_llm_provider=custom_llm_provider, api_base=api_base ) # type: ignore @@ -5856,6 +5857,7 @@ def speech( # noqa: PLR0915 timeout=timeout, client=client, # pass AsyncOpenAI, OpenAI client aspeech=aspeech, + shared_session=shared_session, ) elif custom_llm_provider == "azure": # Check if this is Azure Speech Service (Cognitive Services TTS) From f1895265e6b5643ef0e5be97b6f4cd65fcc2e78f Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 17 Nov 2025 12:55:58 -0800 Subject: [PATCH 015/248] fix: increase chunk_size to 8 KB for optimal latency --- litellm/proxy/proxy_server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index cb43ef0cec..1698820553 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -5236,7 +5236,7 @@ async def moderations( async def _audio_speech_chunk_generator( _response: HttpxBinaryResponseContent, ) -> AsyncGenerator[bytes, None]: - _generator = await _response.aiter_bytes(chunk_size=4096) + _generator = await _response.aiter_bytes(chunk_size=8192) async for chunk in _generator: yield chunk From 7241b4e9b505ea433563a88916cece2d062cd063 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 17 Nov 2025 12:59:48 -0800 Subject: [PATCH 016/248] add: comment above optimization For anybody that would change this value for whatever reason, the comment makes the tradeoff clear. --- litellm/proxy/proxy_server.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 1698820553..da592c9072 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -5236,6 +5236,10 @@ async def moderations( async def _audio_speech_chunk_generator( _response: HttpxBinaryResponseContent, ) -> AsyncGenerator[bytes, None]: + # chunk_size has a big impact on latency, it can't be too small or too large + # too small: latency is high + # too large: latency is low, but memory usage is high + # 8192 is a good compromise _generator = await _response.aiter_bytes(chunk_size=8192) async for chunk in _generator: yield chunk From b4e25a68a4690e93110fb4ca2c4aa56f1c08a25a Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 18 Nov 2025 09:40:26 -0800 Subject: [PATCH 017/248] fix: remove test files --- no_cache_hits.py | 48 --------------------------------------------- speech.mp3 | Bin 104 -> 0 bytes speech_config.yaml | 9 --------- 3 files changed, 57 deletions(-) delete mode 100644 no_cache_hits.py delete mode 100644 speech.mp3 delete mode 100644 speech_config.yaml diff --git a/no_cache_hits.py b/no_cache_hits.py deleted file mode 100644 index 1b3bf895f7..0000000000 --- a/no_cache_hits.py +++ /dev/null @@ -1,48 +0,0 @@ -from locust import HttpUser, between, task - - -class MyUser(HttpUser): - """ - Minimal Locust user for repeatedly hitting `/v1/audio/speech`. - The goal is to measure server-side performance, so we avoid any extra work - (file writes, random generation, manual timing, custom event hooks, etc.) - that could inflate client-side latency. - """ - - wait_time = between(0.5, 1) - host = "http://0.0.0.0:8090" - - def on_start(self): - self.api_key = "sk-1234" - self.model_name = "fake-openai-speech" - self.headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json", - } - self.prompt_counter = 0 - - @task - def audio_speech_request(self): - self.prompt_counter += 1 - # Ensure prompts differ slightly so the backend can't reuse cached audio. - prompt = ( - "Generate a short spoken status update mentioning counter " - f"{self.prompt_counter}." - ) - - response = self.client.post( - "v1/audio/speech", - json={ - "model": self.model_name, - "input": prompt, - "voice": "alloy", - "format": "mp3", - }, - headers=self.headers, - name="audio_speech", - ) - - if response.status_code != 200: - # log the errors in error.txt - with open("error.txt", "a") as error_log: - error_log.write(response.text + "\n") \ No newline at end of file diff --git a/speech.mp3 b/speech.mp3 deleted file mode 100644 index f4f854d9bd215e2493d48b4bc4d39804bd79c038..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 104 NcmezWdjbPJ000JT0*e3u diff --git a/speech_config.yaml b/speech_config.yaml deleted file mode 100644 index ad9920a279..0000000000 --- a/speech_config.yaml +++ /dev/null @@ -1,9 +0,0 @@ -model_list: - - model_name: fake-openai-speech - litellm_params: - model: openai/gpt-4o-mini-tts - api_base: http://0.0.0.0:8090/ - api_key: sk-1234 - model_info: - mode: audio_speech - \ No newline at end of file From 8fd0c81e5bf2096c0c3f7b98ff0b3315f1213c94 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 20 Nov 2025 15:08:38 +0530 Subject: [PATCH 018/248] Add cost tracking for streaming in vertex ai --- .../pass_through_endpoints.py | 74 ++++- .../streaming_handler.py | 56 ++++ ...x_ai_anthropic_streaming_cost_injection.py | 279 ++++++++++++++++++ 3 files changed, 408 insertions(+), 1 deletion(-) create mode 100644 tests/pass_through_unit_tests/test_vertex_ai_anthropic_streaming_cost_injection.py diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 3eee47f201..a7df3b8cfd 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -746,7 +746,79 @@ async def pass_through_request( # noqa: PLR0915 headers=headers, ) - response = await async_client.send(req, stream=stream) + + async def mock_vertex_anthropic_streaming_response(): + import json + async def sse_event(event, data): + return f"event: {event}\ndata: {json.dumps(data) if not isinstance(data, str) else data}\n\n" + + # Claude Sonnet 4 - public Vertex AI "Anthropic" style events + events = [ + ( + "message_start", + { + "type": "message_start", + "message": { + "model": "claude-sonnet-4-20250514", + "id": "msg_vrtx_01Dj", + "type": "message", + "role": "assistant", + "content": [], + "stop_reason": None, + "stop_sequence": None, + "usage": { + "input_tokens": 13735, + "cache_creation_input_tokens": 0, + "cache_read_input_tokens": 0, + "output_tokens": 1, + }, + }, + }, + ), + ("ping", {"type": "ping"}), + ( + "content_block_start", + { + "type": "content_block_start", + "index": 0, + "content_block": {"type": "text", "text": ""}, + }, + ), + ( + "message_delta", + { + "type": "message_delta", + "delta": {"stop_reason": "end_turn", "stop_sequence": None}, + "usage": {"output_tokens": 89}, + }, + ), + ( + "message_stop", + { + "type": "message_stop" + }, + ), + ] + for event, data in events: + await asyncio.sleep(0.1) + yield await sse_event(event, data) + + class MockAsyncResponse: + # Minimal mimic of httpx.Response for streaming purposes + status_code = 200 + headers = {} + + async def aiter_bytes(self): + async for s in mock_vertex_anthropic_streaming_response(): + # Each event is a string -> bytes + yield s.encode("utf-8") + + def raise_for_status(self): + return + + response = MockAsyncResponse() + # else: + # response = await async_client.send(req, stream=stream) try: response.raise_for_status() diff --git a/litellm/proxy/pass_through_endpoints/streaming_handler.py b/litellm/proxy/pass_through_endpoints/streaming_handler.py index 2d5b0a686c..d1b7c8962e 100644 --- a/litellm/proxy/pass_through_endpoints/streaming_handler.py +++ b/litellm/proxy/pass_through_endpoints/streaming_handler.py @@ -4,10 +4,12 @@ from typing import List, Optional import httpx +import litellm from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.thread_pool_executor import executor from litellm.proxy._types import PassThroughEndpointLoggingResultValues +from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing from litellm.types.passthrough_endpoints.pass_through_endpoints import EndpointType from litellm.types.utils import StandardPassThroughResponseObject @@ -37,11 +39,35 @@ class PassThroughStreamingHandler: """ - Yields chunks from the response - Collect non-empty chunks for post-processing (logging) + - Inject cost into chunks if include_cost_in_streaming_usage is enabled """ try: raw_bytes: List[bytes] = [] + # Extract model name for cost injection + model_name = PassThroughStreamingHandler._extract_model_for_cost_injection( + request_body=request_body, + url_route=url_route, + endpoint_type=endpoint_type, + litellm_logging_obj=litellm_logging_obj, + ) + async for chunk in response.aiter_bytes(): raw_bytes.append(chunk) + if ( + getattr(litellm, "include_cost_in_streaming_usage", False) + and model_name + ): + if endpoint_type == EndpointType.VERTEX_AI: + # Only handle streamRawPredict (uses Anthropic format) + if "streamRawPredict" in url_route or "rawPredict" in url_route: + modified_chunk = ( + ProxyBaseLLMRequestProcessing._process_chunk_with_cost_injection( + chunk, model_name + ) + ) + if modified_chunk is not None: + chunk = modified_chunk + yield chunk # After all chunks are processed, handle post-processing @@ -164,6 +190,36 @@ class PassThroughStreamingHandler: **kwargs, ) + @staticmethod + def _extract_model_for_cost_injection( + request_body: Optional[dict], + url_route: str, + endpoint_type: EndpointType, + litellm_logging_obj: LiteLLMLoggingObj, + ) -> Optional[str]: + """ + Extract model name for cost injection from various sources. + """ + # Try to get model from request body + if request_body: + model = request_body.get("model") + if model: + return model + + # Try to get model from logging object + if hasattr(litellm_logging_obj, "model_call_details"): + model = litellm_logging_obj.model_call_details.get("model") + if model: + return model + + # For Vertex AI, try to extract from URL + if endpoint_type == EndpointType.VERTEX_AI: + model = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) + if model and model != "unknown": + return model + + return None + @staticmethod def _convert_raw_bytes_to_str_lines(raw_bytes: List[bytes]) -> List[str]: """ diff --git a/tests/pass_through_unit_tests/test_vertex_ai_anthropic_streaming_cost_injection.py b/tests/pass_through_unit_tests/test_vertex_ai_anthropic_streaming_cost_injection.py new file mode 100644 index 0000000000..b9293f730d --- /dev/null +++ b/tests/pass_through_unit_tests/test_vertex_ai_anthropic_streaming_cost_injection.py @@ -0,0 +1,279 @@ +""" +Test cost injection for Vertex AI Anthropic (streamRawPredict) passthrough streaming. + +This test verifies that cost is correctly injected into streaming chunks +for Vertex AI streamRawPredict endpoints when include_cost_in_streaming_usage is enabled. +""" + +import json +import os +import sys +from datetime import datetime +from unittest.mock import AsyncMock, MagicMock, patch + +sys.path.insert(0, os.path.abspath("../..")) + +import httpx +import pytest +import litellm +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.types.passthrough_endpoints.pass_through_endpoints import EndpointType +from litellm.proxy.pass_through_endpoints.success_handler import ( + PassThroughEndpointLogging, +) +from litellm.proxy.pass_through_endpoints.streaming_handler import ( + PassThroughStreamingHandler, +) + + +@pytest.mark.asyncio +async def test_vertex_ai_anthropic_streaming_cost_injection_enabled(): + """ + Test that cost is injected into Vertex AI streamRawPredict streaming chunks + when include_cost_in_streaming_usage is enabled. + """ + # Enable cost injection + original_value = getattr(litellm, "include_cost_in_streaming_usage", False) + litellm.include_cost_in_streaming_usage = True + + try: + # Mock response with Anthropic SSE format chunks + response = AsyncMock(spec=httpx.Response) + + # Create chunks with message_delta event containing usage + chunks_with_usage = [ + b'data: {"type": "content_block_delta", "delta": {"text": "Hello"}}\n\n', + b'data: {"type": "message_delta", "usage": {"input_tokens": 10, "output_tokens": 5}}\n\n', + b'data: {"type": "content_block_delta", "delta": {"text": " world"}}\n\n', + ] + + async def mock_aiter_bytes(): + for chunk in chunks_with_usage: + yield chunk + + response.aiter_bytes = mock_aiter_bytes + + # Setup logging object with model info + litellm_logging_obj = MagicMock(spec=LiteLLMLoggingObj) + litellm_logging_obj.model_call_details = {"model": "claude-sonnet-4@20250514"} + litellm_logging_obj.async_success_handler = AsyncMock() + + request_body = {"model": "claude-sonnet-4@20250514"} + start_time = datetime.now() + passthrough_success_handler_obj = MagicMock(spec=PassThroughEndpointLogging) + + url_route = "v1/projects/test-project/locations/us-east5/publishers/anthropic/models/claude-sonnet-4@20250514:streamRawPredict" + + # Mock completion_cost to return a test cost value + with patch("litellm.completion_cost", return_value=0.00015): + received_chunks = [] + async for chunk in PassThroughStreamingHandler.chunk_processor( + response=response, + request_body=request_body, + litellm_logging_obj=litellm_logging_obj, + endpoint_type=EndpointType.VERTEX_AI, + start_time=start_time, + passthrough_success_handler_obj=passthrough_success_handler_obj, + url_route=url_route, + ): + received_chunks.append(chunk) + + # Verify that cost was injected into the message_delta chunk + cost_injected = False + for chunk in received_chunks: + if isinstance(chunk, bytes): + chunk_str = chunk.decode("utf-8", errors="ignore") + if "message_delta" in chunk_str and "cost" in chunk_str: + # Parse the chunk to verify cost was added + for line in chunk_str.split("\n"): + if line.startswith("data:") and "message_delta" in line: + json_part = line.split("data:", 1)[1].strip() + if json_part and json_part != "[DONE]": + try: + obj = json.loads(json_part) + if ( + obj.get("type") == "message_delta" + and "usage" in obj + and "cost" in obj["usage"] + ): + assert obj["usage"]["cost"] == 0.00015 + cost_injected = True + except json.JSONDecodeError: + pass + + assert cost_injected, "Cost was not injected into message_delta chunk" + + finally: + # Restore original value + litellm.include_cost_in_streaming_usage = original_value + + +@pytest.mark.asyncio +async def test_vertex_ai_anthropic_streaming_cost_injection_disabled(): + """ + Test that cost is NOT injected when include_cost_in_streaming_usage is disabled. + """ + # Disable cost injection + original_value = getattr(litellm, "include_cost_in_streaming_usage", False) + litellm.include_cost_in_streaming_usage = False + + try: + # Mock response with Anthropic SSE format chunks + response = AsyncMock(spec=httpx.Response) + + chunks_with_usage = [ + b'data: {"type": "message_delta", "usage": {"input_tokens": 10, "output_tokens": 5}}\n\n', + ] + + async def mock_aiter_bytes(): + for chunk in chunks_with_usage: + yield chunk + + response.aiter_bytes = mock_aiter_bytes + + litellm_logging_obj = MagicMock(spec=LiteLLMLoggingObj) + litellm_logging_obj.model_call_details = {"model": "claude-sonnet-4@20250514"} + litellm_logging_obj.async_success_handler = AsyncMock() + + request_body = {"model": "claude-sonnet-4@20250514"} + start_time = datetime.now() + passthrough_success_handler_obj = MagicMock(spec=PassThroughEndpointLogging) + + url_route = "v1/projects/test-project/locations/us-east5/publishers/anthropic/models/claude-sonnet-4@20250514:streamRawPredict" + + received_chunks = [] + async for chunk in PassThroughStreamingHandler.chunk_processor( + response=response, + request_body=request_body, + litellm_logging_obj=litellm_logging_obj, + endpoint_type=EndpointType.VERTEX_AI, + start_time=start_time, + passthrough_success_handler_obj=passthrough_success_handler_obj, + url_route=url_route, + ): + received_chunks.append(chunk) + + # Verify that cost was NOT injected + cost_found = False + for chunk in received_chunks: + if isinstance(chunk, bytes): + chunk_str = chunk.decode("utf-8", errors="ignore") + if "cost" in chunk_str: + cost_found = True + + assert not cost_found, "Cost should not be injected when feature is disabled" + + finally: + # Restore original value + litellm.include_cost_in_streaming_usage = original_value + + +@pytest.mark.asyncio +async def test_vertex_ai_anthropic_streaming_cost_injection_no_usage_chunk(): + """ + Test that chunks without usage are not modified. + """ + original_value = getattr(litellm, "include_cost_in_streaming_usage", False) + litellm.include_cost_in_streaming_usage = True + + try: + response = AsyncMock(spec=httpx.Response) + + # Chunks without usage (should not be modified) + chunks_without_usage = [ + b'data: {"type": "content_block_delta", "delta": {"text": "Hello"}}\n\n', + b'data: {"type": "content_block_start", "index": 0}\n\n', + ] + + async def mock_aiter_bytes(): + for chunk in chunks_without_usage: + yield chunk + + response.aiter_bytes = mock_aiter_bytes + + litellm_logging_obj = MagicMock(spec=LiteLLMLoggingObj) + litellm_logging_obj.model_call_details = {"model": "claude-sonnet-4@20250514"} + litellm_logging_obj.async_success_handler = AsyncMock() + + request_body = {"model": "claude-sonnet-4@20250514"} + start_time = datetime.now() + passthrough_success_handler_obj = MagicMock(spec=PassThroughEndpointLogging) + + url_route = "v1/projects/test-project/locations/us-east5/publishers/anthropic/models/claude-sonnet-4@20250514:streamRawPredict" + + received_chunks = [] + async for chunk in PassThroughStreamingHandler.chunk_processor( + response=response, + request_body=request_body, + litellm_logging_obj=litellm_logging_obj, + endpoint_type=EndpointType.VERTEX_AI, + start_time=start_time, + passthrough_success_handler_obj=passthrough_success_handler_obj, + url_route=url_route, + ): + received_chunks.append(chunk) + + # Verify chunks remain unchanged (no cost injection attempted) + assert len(received_chunks) == len(chunks_without_usage) + # Chunks should be exactly as input since they don't contain usage + for i, chunk in enumerate(received_chunks): + assert chunk == chunks_without_usage[i] + + finally: + litellm.include_cost_in_streaming_usage = original_value + + +@pytest.mark.asyncio +async def test_vertex_ai_anthropic_streaming_model_extraction(): + """ + Test that model name is correctly extracted for cost calculation. + """ + original_value = getattr(litellm, "include_cost_in_streaming_usage", False) + litellm.include_cost_in_streaming_usage = True + + try: + response = AsyncMock(spec=httpx.Response) + + chunks = [ + b'data: {"type": "message_delta", "usage": {"input_tokens": 10, "output_tokens": 5}}\n\n', + ] + + async def mock_aiter_bytes(): + for chunk in chunks: + yield chunk + + response.aiter_bytes = mock_aiter_bytes + + litellm_logging_obj = MagicMock(spec=LiteLLMLoggingObj) + litellm_logging_obj.model_call_details = {} + litellm_logging_obj.async_success_handler = AsyncMock() + + # Test model extraction from request body + request_body = {"model": "claude-sonnet-4@20250514"} + start_time = datetime.now() + passthrough_success_handler_obj = MagicMock(spec=PassThroughEndpointLogging) + + url_route = "v1/projects/test-project/locations/us-east5/publishers/anthropic/models/claude-sonnet-4@20250514:streamRawPredict" + + with patch("litellm.completion_cost") as mock_cost: + mock_cost.return_value = 0.0001 + received_chunks = [] + async for chunk in PassThroughStreamingHandler.chunk_processor( + response=response, + request_body=request_body, + litellm_logging_obj=litellm_logging_obj, + endpoint_type=EndpointType.VERTEX_AI, + start_time=start_time, + passthrough_success_handler_obj=passthrough_success_handler_obj, + url_route=url_route, + ): + received_chunks.append(chunk) + + # Verify completion_cost was called with the correct model + assert mock_cost.called + call_args = mock_cost.call_args + assert call_args[1]["model"] == "claude-sonnet-4@20250514" + + finally: + litellm.include_cost_in_streaming_usage = original_value + From 5c8b5b1b5ed446a47cf92d90ff8a7b43f2bad64d Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 20 Nov 2025 16:33:27 +0530 Subject: [PATCH 019/248] feat: Add header passing support for MCP tools in Responses API - Extract headers from secret_fields.raw_headers and tools[].headers - Merge headers from both sources before passing to MCP server - Add extract_mcp_headers_from_request utility method - Fix tool name prefix handling in _execute_tool_calls - Add get_mcp_servers_from_ids method to MCPServerManager - Ensure raw_headers is properly converted to dict before use --- .../mcp_server/mcp_server_manager.py | 21 ++++++- litellm/responses/main.py | 17 ++++++ .../mcp/litellm_proxy_mcp_handler.py | 12 +++- .../responses/mcp/mcp_streaming_iterator.py | 57 ++++++++++++++++++ litellm/responses/utils.py | 60 +++++++++++++++++++ 5 files changed, 165 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 23658b3821..aef46c82ea 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -1412,7 +1412,7 @@ class MCPServerManager: if extra_headers is None: extra_headers = {} for header in mcp_server.extra_headers: - if header in raw_headers: + if isinstance(header, str) and header in raw_headers: extra_headers[header] = raw_headers[header] if mcp_server.static_headers: @@ -1692,6 +1692,25 @@ class MCPServerManager: return server return None + def get_mcp_servers_from_ids( + self, server_ids: List[str] + ) -> List[MCPServer]: + """ + Get MCP servers from a list of server IDs. + + Args: + server_ids: List of server IDs to retrieve + + Returns: + List of MCPServer objects corresponding to the provided IDs + """ + servers: List[MCPServer] = [] + for server_id in server_ids: + server = self.get_mcp_server_by_id(server_id) + if server: + servers.append(server) + return servers + def _generate_stable_server_id( self, server_name: str, diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 013b50aa8a..29815330d2 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -279,10 +279,27 @@ async def aresponses_api_with_mcp( user_api_key_auth = kwargs.get("litellm_metadata", {}).get( "user_api_key_auth" ) + + # Extract MCP auth headers from the request to pass to MCP server + secret_fields = kwargs.get("secret_fields") + ( + mcp_auth_header, + mcp_server_auth_headers, + oauth2_headers, + raw_headers_from_request, + ) = ResponsesAPIRequestUtils.extract_mcp_headers_from_request( + secret_fields=secret_fields, + tools=tools, + ) + tool_results = await LiteLLM_Proxy_MCP_Handler._execute_tool_calls( tool_server_map=tool_server_map, tool_calls=tool_calls, user_api_key_auth=user_api_key_auth, + mcp_auth_header=mcp_auth_header, + mcp_server_auth_headers=mcp_server_auth_headers, + oauth2_headers=oauth2_headers, + raw_headers=raw_headers_from_request, ) if tool_results: diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py index c7322cab09..ad6ab95342 100644 --- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py +++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py @@ -397,7 +397,13 @@ class LiteLLM_Proxy_MCP_Handler: @staticmethod async def _execute_tool_calls( - tool_server_map: dict[str, str], tool_calls: List[Any], user_api_key_auth: Any + tool_server_map: dict[str, str], + tool_calls: List[Any], + user_api_key_auth: Any, + mcp_auth_header: Optional[str] = None, + mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None, + oauth2_headers: Optional[Dict[str, str]] = None, + raw_headers: Optional[Dict[str, str]] = None, ) -> List[Dict[str, Any]]: """Execute tool calls and return results.""" from fastapi import HTTPException @@ -435,6 +441,10 @@ class LiteLLM_Proxy_MCP_Handler: name=tool_name, arguments=parsed_arguments, user_api_key_auth=user_api_key_auth, + mcp_auth_header=mcp_auth_header, + mcp_server_auth_headers=mcp_server_auth_headers, + oauth2_headers=oauth2_headers, + raw_headers=raw_headers, proxy_logging_obj=proxy_logging_obj, ) diff --git a/litellm/responses/mcp/mcp_streaming_iterator.py b/litellm/responses/mcp/mcp_streaming_iterator.py index ea31f0f7f1..c00c2a2f3b 100644 --- a/litellm/responses/mcp/mcp_streaming_iterator.py +++ b/litellm/responses/mcp/mcp_streaming_iterator.py @@ -299,8 +299,61 @@ class MCPEnhancedStreamingIterator(BaseResponsesAPIStreamingIterator): "custom_llm_provider", None ) + self._extract_mcp_headers_from_params() + # Mark as async iterator self.is_async = True + + def _extract_mcp_headers_from_params(self) -> None: + """Extract MCP headers from original request params to pass to tool calls""" + from typing import Dict, Optional + from starlette.datastructures import Headers + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( + MCPRequestHandler, + ) + + # Extract headers from secret_fields in original_request_params + raw_headers_from_request: Optional[Dict[str, str]] = None + secret_fields = self.original_request_params.get("secret_fields") + if secret_fields and isinstance(secret_fields, dict): + raw_headers_from_request = secret_fields.get("raw_headers") + + # Extract MCP-specific headers + self.mcp_auth_header: Optional[str] = None + self.mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None + self.oauth2_headers: Optional[Dict[str, str]] = None + self.raw_headers: Optional[Dict[str, str]] = raw_headers_from_request + + if raw_headers_from_request: + headers_obj = Headers(raw_headers_from_request) + self.mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers_obj) + self.mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers_obj) + self.oauth2_headers = MCPRequestHandler._get_oauth2_headers_from_headers(headers_obj) + + # Also check if headers are provided in tools array (from request body) + tools = self.original_request_params.get("tools") + if tools: + for tool in tools: + if isinstance(tool, dict) and tool.get("type") == "mcp": + tool_headers = tool.get("headers", {}) + if tool_headers and isinstance(tool_headers, dict): + # Merge tool headers into mcp_server_auth_headers + headers_obj_from_tool = Headers(tool_headers) + tool_mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers_obj_from_tool) + + if tool_mcp_server_auth_headers: + if self.mcp_server_auth_headers is None: + self.mcp_server_auth_headers = {} + # Merge the headers from tool into existing headers + for server_alias, headers_dict in tool_mcp_server_auth_headers.items(): + if server_alias not in self.mcp_server_auth_headers: + self.mcp_server_auth_headers[server_alias] = {} + self.mcp_server_auth_headers[server_alias].update(headers_dict) + + # Also merge raw headers + if self.raw_headers is None: + self.raw_headers = {} + self.raw_headers.update(tool_headers) def _should_auto_execute_tools(self) -> bool: """Check if tools should be auto-executed""" @@ -511,6 +564,10 @@ class MCPEnhancedStreamingIterator(BaseResponsesAPIStreamingIterator): tool_server_map=self.tool_server_map, tool_calls=tool_calls, user_api_key_auth=self.user_api_key_auth, + mcp_auth_header=self.mcp_auth_header, + mcp_server_auth_headers=self.mcp_server_auth_headers, + oauth2_headers=self.oauth2_headers, + raw_headers=self.raw_headers, ) # Create completion events and output_item.done events for tool execution diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py index 7798a7573d..198182cf11 100644 --- a/litellm/responses/utils.py +++ b/litellm/responses/utils.py @@ -2,6 +2,7 @@ import base64 from typing import ( Any, Dict, + Iterable, List, Optional, Type, @@ -350,6 +351,65 @@ class ResponsesAPIRequestUtils: return text return text + @staticmethod + def extract_mcp_headers_from_request( + secret_fields: Optional[Dict[str, Any]], + tools: Optional[Iterable[Any]], + ) -> tuple[ + Optional[str], + Optional[Dict[str, Dict[str, str]]], + Optional[Dict[str, str]], + Optional[Dict[str, str]], + ]: + """ + Extract MCP auth headers from the request to pass to MCP server. + Headers from tools.headers in request body should be passed to MCP server. + """ + from starlette.datastructures import Headers + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( + MCPRequestHandler, + ) + + # Extract headers from secret_fields which contains the original request headers + raw_headers_from_request: Optional[Dict[str, str]] = None + if secret_fields and isinstance(secret_fields, dict): + raw_headers_from_request = secret_fields.get("raw_headers") + + # Extract MCP-specific headers using MCPRequestHandler methods + mcp_auth_header: Optional[str] = None + mcp_server_auth_headers: Optional[Dict[str, Dict[str, str]]] = None + oauth2_headers: Optional[Dict[str, str]] = None + + if raw_headers_from_request: + headers_obj = Headers(raw_headers_from_request) + mcp_auth_header = MCPRequestHandler._get_mcp_auth_header_from_headers(headers_obj) + mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers_obj) + oauth2_headers = MCPRequestHandler._get_oauth2_headers_from_headers(headers_obj) + + if tools: + for tool in tools: + if isinstance(tool, dict) and tool.get("type") == "mcp": + tool_headers = tool.get("headers", {}) + if tool_headers and isinstance(tool_headers, dict): + # Merge tool headers into mcp_server_auth_headers + # Extract server-specific headers from tool.headers + headers_obj_from_tool = Headers(tool_headers) + tool_mcp_server_auth_headers = MCPRequestHandler._get_mcp_server_auth_headers_from_headers(headers_obj_from_tool) + if tool_mcp_server_auth_headers: + if mcp_server_auth_headers is None: + mcp_server_auth_headers = {} + # Merge the headers from tool into existing headers + for server_alias, headers_dict in tool_mcp_server_auth_headers.items(): + if server_alias not in mcp_server_auth_headers: + mcp_server_auth_headers[server_alias] = {} + mcp_server_auth_headers[server_alias].update(headers_dict) + # Also merge raw headers (non-prefixed headers from tool.headers) + if raw_headers_from_request is None: + raw_headers_from_request = {} + raw_headers_from_request.update(tool_headers) + + return mcp_auth_header, mcp_server_auth_headers, oauth2_headers, raw_headers_from_request + class ResponseAPILoggingUtils: @staticmethod From abcc2f0e6462f2144d5ddf5347115d377ba876ba Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 20 Nov 2025 21:25:22 +0530 Subject: [PATCH 020/248] remove mock response --- .../pass_through_endpoints.py | 74 +------------------ 1 file changed, 1 insertion(+), 73 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index a7df3b8cfd..3eee47f201 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -746,79 +746,7 @@ async def pass_through_request( # noqa: PLR0915 headers=headers, ) - - async def mock_vertex_anthropic_streaming_response(): - import json - async def sse_event(event, data): - return f"event: {event}\ndata: {json.dumps(data) if not isinstance(data, str) else data}\n\n" - - # Claude Sonnet 4 - public Vertex AI "Anthropic" style events - events = [ - ( - "message_start", - { - "type": "message_start", - "message": { - "model": "claude-sonnet-4-20250514", - "id": "msg_vrtx_01Dj", - "type": "message", - "role": "assistant", - "content": [], - "stop_reason": None, - "stop_sequence": None, - "usage": { - "input_tokens": 13735, - "cache_creation_input_tokens": 0, - "cache_read_input_tokens": 0, - "output_tokens": 1, - }, - }, - }, - ), - ("ping", {"type": "ping"}), - ( - "content_block_start", - { - "type": "content_block_start", - "index": 0, - "content_block": {"type": "text", "text": ""}, - }, - ), - ( - "message_delta", - { - "type": "message_delta", - "delta": {"stop_reason": "end_turn", "stop_sequence": None}, - "usage": {"output_tokens": 89}, - }, - ), - ( - "message_stop", - { - "type": "message_stop" - }, - ), - ] - for event, data in events: - await asyncio.sleep(0.1) - yield await sse_event(event, data) - - class MockAsyncResponse: - # Minimal mimic of httpx.Response for streaming purposes - status_code = 200 - headers = {} - - async def aiter_bytes(self): - async for s in mock_vertex_anthropic_streaming_response(): - # Each event is a string -> bytes - yield s.encode("utf-8") - - def raise_for_status(self): - return - - response = MockAsyncResponse() - # else: - # response = await async_client.send(req, stream=stream) + response = await async_client.send(req, stream=stream) try: response.raise_for_status() From 1c67b7e1daf28a9d9afc24d647adfb35c71d322b Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Thu, 20 Nov 2025 17:48:40 -0800 Subject: [PATCH 021/248] fix: place hardcoded value on constants.py --- litellm/constants.py | 1 + litellm/proxy/proxy_server.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/litellm/constants.py b/litellm/constants.py index 3f763cad92..fc26e1cf81 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -246,6 +246,7 @@ TOGETHER_AI_EMBEDDING_350_M = int(os.getenv("TOGETHER_AI_EMBEDDING_350_M", 350)) QDRANT_SCALAR_QUANTILE = float(os.getenv("QDRANT_SCALAR_QUANTILE", 0.99)) QDRANT_VECTOR_SIZE = int(os.getenv("QDRANT_VECTOR_SIZE", 1536)) CACHED_STREAMING_CHUNK_DELAY = float(os.getenv("CACHED_STREAMING_CHUNK_DELAY", 0.02)) +AUDIO_SPEECH_CHUNK_SIZE = 8192 # chunk_size for audio speech streaming. Balance between latency and memory usage MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 512) ) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index da592c9072..47c30e9ce8 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -32,6 +32,7 @@ from litellm.constants import ( AIOHTTP_CONNECTOR_LIMIT, AIOHTTP_KEEPALIVE_TIMEOUT, AIOHTTP_TTL_DNS_CACHE, + AUDIO_SPEECH_CHUNK_SIZE, BASE_MCP_ROUTE, DEFAULT_MAX_RECURSE_DEPTH, DEFAULT_SHARED_HEALTH_CHECK_LOCK_TTL, @@ -5240,7 +5241,7 @@ async def _audio_speech_chunk_generator( # too small: latency is high # too large: latency is low, but memory usage is high # 8192 is a good compromise - _generator = await _response.aiter_bytes(chunk_size=8192) + _generator = await _response.aiter_bytes(chunk_size=AUDIO_SPEECH_CHUNK_SIZE) async for chunk in _generator: yield chunk From 24f90679f8bf765e8eec9878dd6a22c7e2750b9e Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Sat, 22 Nov 2025 10:21:52 -0800 Subject: [PATCH 022/248] Changes for CI/CD Tests --- .../gcs_pub_sub_body/spend_logs_payload.json | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json b/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json index b25080df0d..8b2941672b 100644 --- a/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json +++ b/tests/logging_callback_tests/gcs_pub_sub_body/spend_logs_payload.json @@ -10,6 +10,7 @@ "model": "gpt-4o", "user": "", "team_id": "", + "organization_id": "", "metadata": "{\"applied_guardrails\": [], \"batch_models\": null, \"mcp_tool_call_metadata\": null, \"vector_store_request_metadata\": null, \"guardrail_information\": null, \"usage_object\": {\"completion_tokens\": 20, \"prompt_tokens\": 10, \"total_tokens\": 30, \"completion_tokens_details\": null, \"prompt_tokens_details\": null}, \"model_map_information\": {\"model_map_key\": \"gpt-4o\", \"model_map_value\": {\"key\": \"gpt-4o\", \"max_tokens\": 16384, \"max_input_tokens\": 128000, \"max_output_tokens\": 16384, \"input_cost_per_token\": 2.5e-06, \"cache_creation_input_token_cost\": null, \"cache_read_input_token_cost\": 1.25e-06, \"input_cost_per_character\": null, \"input_cost_per_token_above_128k_tokens\": null, \"input_cost_per_token_above_200k_tokens\": null, \"input_cost_per_query\": null, \"input_cost_per_second\": null, \"input_cost_per_audio_token\": null, \"input_cost_per_token_batches\": 1.25e-06, \"output_cost_per_token_batches\": 5e-06, \"output_cost_per_token\": 1e-05, \"output_cost_per_audio_token\": null, \"output_cost_per_character\": null, \"output_cost_per_token_above_128k_tokens\": null, \"output_cost_per_character_above_128k_tokens\": null, \"output_cost_per_token_above_200k_tokens\": null, \"output_cost_per_second\": null, \"output_cost_per_image\": null, \"output_vector_size\": null, \"litellm_provider\": \"openai\", \"mode\": \"chat\", \"supports_system_messages\": true, \"supports_response_schema\": true, \"supports_vision\": true, \"supports_function_calling\": true, \"supports_tool_choice\": true, \"supports_assistant_prefill\": false, \"supports_prompt_caching\": true, \"supports_audio_input\": false, \"supports_audio_output\": false, \"supports_pdf_input\": false, \"supports_embedding_image_input\": false, \"supports_native_streaming\": null, \"supports_web_search\": true, \"supports_reasoning\": false, \"search_context_cost_per_query\": {\"search_context_size_low\": 0.03, \"search_context_size_medium\": 0.035, \"search_context_size_high\": 0.05}, \"tpm\": null, \"rpm\": null, \"supported_openai_params\": [\"frequency_penalty\", \"logit_bias\", \"logprobs\", \"top_logprobs\", \"max_tokens\", \"max_completion_tokens\", \"modalities\", \"prediction\", \"n\", \"presence_penalty\", \"seed\", \"stop\", \"stream\", \"stream_options\", \"temperature\", \"top_p\", \"tools\", \"tool_choice\", \"function_call\", \"functions\", \"max_retries\", \"extra_headers\", \"parallel_tool_calls\", \"audio\", \"response_format\", \"user\"]}}, \"additional_usage_values\": {\"completion_tokens_details\": null, \"prompt_tokens_details\": null}}", "cache_key": "Cache OFF", "spend": 0.00022500000000000002, From dd72ff3abf5b080907103435f4b52dbbc0d57e96 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Sat, 22 Nov 2025 12:54:49 -0800 Subject: [PATCH 023/248] Add organization_id to spend logs table --- .../migration.sql | 3 +++ litellm-proxy-extras/litellm_proxy_extras/schema.prisma | 1 + litellm/proxy/schema.prisma | 1 + schema.prisma | 1 + 4 files changed, 6 insertions(+) create mode 100644 litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql new file mode 100644 index 0000000000..4ea082f275 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_SpendLogs" ADD COLUMN "organization_id" TEXT; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index e11dca1531..2883dfc4b8 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -304,6 +304,7 @@ model LiteLLM_SpendLogs { cache_key String? @default("") request_tags Json? @default("[]") team_id String? + organization_id String? end_user String? requester_ip_address String? messages Json? @default("{}") diff --git a/litellm/proxy/schema.prisma b/litellm/proxy/schema.prisma index e11dca1531..2883dfc4b8 100644 --- a/litellm/proxy/schema.prisma +++ b/litellm/proxy/schema.prisma @@ -304,6 +304,7 @@ model LiteLLM_SpendLogs { cache_key String? @default("") request_tags Json? @default("[]") team_id String? + organization_id String? end_user String? requester_ip_address String? messages Json? @default("{}") diff --git a/schema.prisma b/schema.prisma index e11dca1531..2883dfc4b8 100644 --- a/schema.prisma +++ b/schema.prisma @@ -304,6 +304,7 @@ model LiteLLM_SpendLogs { cache_key String? @default("") request_tags Json? @default("[]") team_id String? + organization_id String? end_user String? requester_ip_address String? messages Json? @default("{}") From b4b8133d47e25e8d2557de10e4a0f5a881803f9a Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 18 Nov 2025 14:47:08 -0800 Subject: [PATCH 024/248] refactor: litellm init file #1 --- litellm/__init__.py | 60 ++++++++++++++++++- litellm/images/main.py | 5 +- litellm/integrations/prometheus.py | 24 +++++++- litellm/litellm_core_utils/litellm_logging.py | 23 ++++++- litellm/main.py | 8 ++- 5 files changed, 109 insertions(+), 11 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index d93f44c37e..739cef04e2 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1039,8 +1039,6 @@ openai_image_generation_models = ["dall-e-2", "dall-e-3"] openai_video_generation_models = ["sora-2"] from .timeout import timeout -from .cost_calculator import completion_cost -from litellm.litellm_core_utils.litellm_logging import Logging, modify_integration from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.litellm_core_utils.core_helpers import remove_index_from_tool_calls from litellm.litellm_core_utils.token_counter import get_modified_max_tokens @@ -1449,7 +1447,6 @@ from .vector_store_files.main import ( update as vector_store_file_update, ) from .scheduler import * -from .cost_calculator import response_cost_calculator, cost_per_token ### ADAPTERS ### from .types.adapter import AdapterItem @@ -1504,3 +1501,60 @@ def set_global_gitlab_config(config: Dict[str, Any]) -> None: """Set global BitBucket configuration for prompt management.""" global global_gitlab_config global_gitlab_config = config + + +# Lazy import for cost_calculator functions to avoid loading the module at import time +# This significantly reduces memory usage when importing litellm +def _lazy_import_cost_calculator(name: str) -> Any: + """Lazy import for cost_calculator functions.""" + from .cost_calculator import ( + completion_cost as _completion_cost, + cost_per_token as _cost_per_token, + response_cost_calculator as _response_cost_calculator, + ) + + # Map names to imported functions + _cost_functions = { + "completion_cost": _completion_cost, + "cost_per_token": _cost_per_token, + "response_cost_calculator": _response_cost_calculator, + } + + # Cache the imported function in the module namespace + func = _cost_functions[name] + globals()[name] = func + + return func + + +# Lazy import for litellm_logging to avoid loading the module at import time +# This significantly reduces memory usage when importing litellm +def _lazy_import_litellm_logging(name: str) -> Any: + """Lazy import for litellm_logging module.""" + from litellm.litellm_core_utils.litellm_logging import ( + Logging as _Logging, + modify_integration as _modify_integration, + ) + + # Map names to imported objects + _logging_objects = { + "Logging": _Logging, + "modify_integration": _modify_integration, + } + + # Cache the imported object in the module namespace + obj = _logging_objects[name] + globals()[name] = obj + + return obj + + +def __getattr__(name: str) -> Any: + """Lazy import for cost_calculator and litellm_logging functions.""" + if name in ("completion_cost", "response_cost_calculator", "cost_per_token"): + return _lazy_import_cost_calculator(name) + + if name in ("Logging", "modify_integration"): + return _lazy_import_litellm_logging(name) + + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/litellm/images/main.py b/litellm/images/main.py index 333a751b04..2e93765c8f 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -6,11 +6,12 @@ from typing import Any, Coroutine, Dict, List, Literal, Optional, Union, cast, o import httpx import litellm -from litellm import Logging, client, exception_type, get_litellm_params +from litellm import client, exception_type, get_litellm_params from litellm.constants import DEFAULT_IMAGE_ENDPOINT_MODEL from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT from litellm.exceptions import LiteLLMUnknownProvider -from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +# Logging is imported at module level since litellm_logging is already loaded via main.py imports +from litellm.litellm_core_utils.litellm_logging import Logging, Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.mock_functions import mock_image_generation from litellm.llms.base_llm import BaseImageEditConfig, BaseImageGenerationConfig from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 8186006f8c..4ce818f0ce 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -24,13 +24,29 @@ from litellm.proxy._types import LiteLLM_TeamTable, UserAPIKeyAuth from litellm.types.integrations.prometheus import * from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name from litellm.types.utils import StandardLoggingPayload -from litellm.utils import get_end_user_id_for_cost_tracking if TYPE_CHECKING: from apscheduler.schedulers.asyncio import AsyncIOScheduler else: AsyncIOScheduler = Any +# Cached lazy import for get_end_user_id_for_cost_tracking +# Module-level cache to avoid repeated imports while preserving memory benefits +_get_end_user_id_for_cost_tracking = None + + +def _get_cached_end_user_id_for_cost_tracking(): + """ + Get cached get_end_user_id_for_cost_tracking function. + Lazy imports on first call to avoid loading utils.py at import time (60MB saved). + Subsequent calls use cached function for better performance. + """ + global _get_end_user_id_for_cost_tracking + if _get_end_user_id_for_cost_tracking is None: + from litellm.utils import get_end_user_id_for_cost_tracking + _get_end_user_id_for_cost_tracking = get_end_user_id_for_cost_tracking + return _get_end_user_id_for_cost_tracking + class PrometheusLogger(CustomLogger): # Class variables or attributes @@ -778,6 +794,8 @@ class PrometheusLogger(CustomLogger): model = kwargs.get("model", "") litellm_params = kwargs.get("litellm_params", {}) or {} _metadata = litellm_params.get("metadata", {}) + get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() + end_user_id = get_end_user_id_for_cost_tracking( litellm_params, service_type="prometheus" ) @@ -1164,6 +1182,8 @@ class PrometheusLogger(CustomLogger): "standard_logging_object", {} ) litellm_params = kwargs.get("litellm_params", {}) or {} + get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() + end_user_id = get_end_user_id_for_cost_tracking( litellm_params, service_type="prometheus" ) @@ -2249,6 +2269,8 @@ def prometheus_label_factory( } if UserAPIKeyLabelNames.END_USER.value in filtered_labels: + get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() + filtered_labels["end_user"] = get_end_user_id_for_cost_tracking( litellm_params={"user_api_key_end_user_id": enum_values.end_user}, service_type="prometheus", diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 9c4a7e3876..67407ff4c7 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -58,7 +58,6 @@ from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.deepeval.deepeval import DeepEvalLogger from litellm.integrations.mlflow import MlflowLogger -from litellm.integrations.prometheus import PrometheusLogger from litellm.integrations.sqs import SQSLogger from litellm.litellm_core_utils.get_litellm_params import get_litellm_params from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( @@ -247,6 +246,23 @@ class ServiceTraceIDCache: in_memory_trace_id_cache = ServiceTraceIDCache() in_memory_dynamic_logger_cache = DynamicLoggingCache() +# Cached lazy import for PrometheusLogger +# Module-level cache to avoid repeated imports while preserving memory benefits +_PrometheusLogger = None + + +def _get_cached_prometheus_logger(): + """ + Get cached PrometheusLogger class. + Lazy imports on first call to avoid loading prometheus.py and utils.py at import time (60MB saved). + Subsequent calls use cached class for better performance. + """ + global _PrometheusLogger + if _PrometheusLogger is None: + from litellm.integrations.prometheus import PrometheusLogger + _PrometheusLogger = PrometheusLogger + return _PrometheusLogger + class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app @@ -3457,6 +3473,8 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 _in_memory_loggers.append(_literalai_logger) return _literalai_logger # type: ignore elif logging_integration == "prometheus": + PrometheusLogger = _get_cached_prometheus_logger() + for callback in _in_memory_loggers: if isinstance(callback, PrometheusLogger): return callback # type: ignore @@ -3934,7 +3952,8 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, LiteralAILogger): return callback - elif logging_integration == "prometheus" and PrometheusLogger is not None: + elif logging_integration == "prometheus": + PrometheusLogger = _get_cached_prometheus_logger() for callback in _in_memory_loggers: if isinstance(callback, PrometheusLogger): return callback diff --git a/litellm/main.py b/litellm/main.py index b082b491f2..57f8256ec3 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -53,12 +53,14 @@ from typing_extensions import overload import litellm from litellm import ( # type: ignore - Logging, client, exception_type, get_litellm_params, get_optional_params, ) +# Logging is imported lazily when needed to avoid loading litellm_logging at import time +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging from litellm.constants import ( DEFAULT_MOCK_RESPONSE_COMPLETION_TOKEN_COUNT, DEFAULT_MOCK_RESPONSE_PROMPT_TOKEN_COUNT, @@ -77,7 +79,7 @@ from litellm.litellm_core_utils.health_check_utils import ( _create_health_check_response, _filter_model_params, ) -from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj, Logging from litellm.litellm_core_utils.mock_functions import ( mock_embedding, mock_image_generation, @@ -6295,7 +6297,7 @@ def stream_chunk_builder( # noqa: PLR0915 messages: Optional[list] = None, start_time=None, end_time=None, - logging_obj: Optional[Logging] = None, + logging_obj: Optional["Logging"] = None, ) -> Optional[Union[ModelResponse, TextCompletionResponse]]: try: if chunks is None: From 863b2267f2d1df1783b9047f7eeae2cab48980fe Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 24 Nov 2025 17:01:58 -0800 Subject: [PATCH 025/248] remove comment --- litellm/images/main.py | 1 - 1 file changed, 1 deletion(-) diff --git a/litellm/images/main.py b/litellm/images/main.py index 2e93765c8f..5b6cc995ec 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -10,7 +10,6 @@ from litellm import client, exception_type, get_litellm_params from litellm.constants import DEFAULT_IMAGE_ENDPOINT_MODEL from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT from litellm.exceptions import LiteLLMUnknownProvider -# Logging is imported at module level since litellm_logging is already loaded via main.py imports from litellm.litellm_core_utils.litellm_logging import Logging, Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.mock_functions import mock_image_generation from litellm.llms.base_llm import BaseImageEditConfig, BaseImageGenerationConfig From 8df9ff39f7d866a39cb421b4a625b6cfd119d7bd Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 24 Nov 2025 17:31:17 -0800 Subject: [PATCH 026/248] remove reduntant logging --- litellm/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/main.py b/litellm/main.py index 57f8256ec3..b3ce7d7c73 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -79,7 +79,7 @@ from litellm.litellm_core_utils.health_check_utils import ( _create_health_check_response, _filter_model_params, ) -from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj, Logging +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.mock_functions import ( mock_embedding, mock_image_generation, From 06e302d257cdb1f91f637c2b0a88df8c9aabb3b6 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Mon, 24 Nov 2025 17:53:57 -0800 Subject: [PATCH 027/248] fix: use LiteLLMLoggingObj instead of Logging in runtime type annotations - Replace Logging type annotations with LiteLLMLoggingObj in main.py (lines 1157, 4097, 5811) - Fixes NameError: name 'Logging' is not defined errors - Maintains lazy loading benefits - Logging only loaded when accessed via litellm.Logging - Add error handling to lazy import functions for better debugging --- litellm/__init__.py | 45 ++++++++++++++++++++++++++++----------------- litellm/main.py | 6 +++--- 2 files changed, 31 insertions(+), 20 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index 739cef04e2..8e8815b125 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1531,26 +1531,37 @@ def _lazy_import_cost_calculator(name: str) -> Any: # This significantly reduces memory usage when importing litellm def _lazy_import_litellm_logging(name: str) -> Any: """Lazy import for litellm_logging module.""" - from litellm.litellm_core_utils.litellm_logging import ( - Logging as _Logging, - modify_integration as _modify_integration, - ) - - # Map names to imported objects - _logging_objects = { - "Logging": _Logging, - "modify_integration": _modify_integration, - } - - # Cache the imported object in the module namespace - obj = _logging_objects[name] - globals()[name] = obj - - return obj + try: + from litellm.litellm_core_utils.litellm_logging import ( + Logging as _Logging, + modify_integration as _modify_integration, + ) + + # Map names to imported objects + _logging_objects = { + "Logging": _Logging, + "modify_integration": _modify_integration, + } + + # Cache the imported object in the module namespace + obj = _logging_objects[name] + globals()[name] = obj + + return obj + except Exception as e: + # If lazy import fails, raise a more informative error + raise AttributeError( + f"module {__name__!r} has no attribute {name!r}. " + f"Lazy import failed: {e}" + ) from e def __getattr__(name: str) -> Any: - """Lazy import for cost_calculator and litellm_logging functions.""" + """Lazy import for cost_calculator and litellm_logging functions. + + This allows these heavy modules to be loaded only when accessed, + reducing initial import time and memory usage. + """ if name in ("completion_cost", "response_cost_calculator", "cost_per_token"): return _lazy_import_cost_calculator(name) diff --git a/litellm/main.py b/litellm/main.py index b3ce7d7c73..88bf0b72a0 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -1154,7 +1154,7 @@ def completion( # type: ignore # noqa: PLR0915 api_base = base_url if num_retries is not None: max_retries = num_retries - logging: Logging = cast(Logging, litellm_logging_obj) + logging: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, litellm_logging_obj) fallbacks = fallbacks or litellm.model_fallbacks if fallbacks is not None: return completion_with_fallbacks(**args) @@ -4094,7 +4094,7 @@ def embedding( # noqa: PLR0915 litellm_params_dict = get_litellm_params(**kwargs) - logging: Logging = litellm_logging_obj # type: ignore + logging: LiteLLMLoggingObj = litellm_logging_obj # type: ignore logging.update_environment_variables( model=model, user=user, @@ -5808,7 +5808,7 @@ def speech( # noqa: PLR0915 kwargs=kwargs, ) - logging_obj: Logging = cast(Logging, kwargs.get("litellm_logging_obj")) + logging_obj: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj")) logging_obj.update_environment_variables( model=model, user=user, From 031bb3c5d90e429bf65210302ecae80e46cc505c Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 24 Nov 2025 22:40:46 -0800 Subject: [PATCH 028/248] Test prisma changes for Gemini tests --- .circleci/config.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index a518628afb..d756755c55 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -3047,6 +3047,7 @@ jobs: docker run -d \ -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ + -e USE_PRISMA_MIGRATE=True \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ -e GEMINI_API_KEY=$GEMINI_API_KEY \ From c54986c3c9839701ddde3808ec0959376c4ea8ed Mon Sep 17 00:00:00 2001 From: naaa760 Date: Tue, 25 Nov 2025 17:26:53 +0530 Subject: [PATCH 029/248] list path now routes to Vertex --- litellm/batches/main.py | 33 ++++++++++++++++++++++++++++++--- 1 file changed, 30 insertions(+), 3 deletions(-) diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 838ee95b2b..995c45b925 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -644,7 +644,7 @@ def retrieve_batch( async def alist_batches( after: Optional[str] = None, limit: Optional[int] = None, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -687,7 +687,7 @@ async def alist_batches( def list_batches( after: Optional[str] = None, limit: Optional[int] = None, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -784,9 +784,36 @@ def list_batches( max_retries=optional_params.max_retries, litellm_params=litellm_params, ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_batches_instance.list_batches( + _is_async=_is_async, + after=after, + limit=limit, + api_base=api_base, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + vertex_credentials=vertex_credentials, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'list_batch'. Only 'openai' is supported.".format( + message="LiteLLM doesn't support {} for 'list_batch'. Supported providers: openai, azure, vertex_ai.".format( custom_llm_provider ), model="n/a", From 2cc2f67a7600a2c7bdc91846bfad4dc578bc79dd Mon Sep 17 00:00:00 2001 From: naaa760 Date: Tue, 25 Nov 2025 17:27:12 +0530 Subject: [PATCH 030/248] added the minimal GET logic --- litellm/llms/vertex_ai/batches/handler.py | 96 +++++++++++++++++++++++ 1 file changed, 96 insertions(+) diff --git a/litellm/llms/vertex_ai/batches/handler.py b/litellm/llms/vertex_ai/batches/handler.py index 7932881f48..864cc19031 100644 --- a/litellm/llms/vertex_ai/batches/handler.py +++ b/litellm/llms/vertex_ai/batches/handler.py @@ -213,3 +213,99 @@ class VertexAIBatchPrediction(VertexLLM): response=_json_response ) return vertex_batch_response + + def list_batches( + self, + _is_async: bool, + after: Optional[str], + limit: Optional[int], + api_base: Optional[str], + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES], + vertex_project: Optional[str], + vertex_location: Optional[str], + timeout: Union[float, httpx.Timeout], + max_retries: Optional[int], + ): + sync_handler = _get_httpx_client() + + access_token, project_id = self._ensure_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + custom_llm_provider="vertex_ai", + ) + + default_api_base = self.create_vertex_batch_url( + vertex_location=vertex_location or "us-central1", + vertex_project=vertex_project or project_id, + ) + + if len(default_api_base.split(":")) > 1: + endpoint = default_api_base.split(":")[-1] + else: + endpoint = "" + + _, api_base = self._check_custom_proxy( + api_base=api_base, + custom_llm_provider="vertex_ai", + gemini_api_key=None, + endpoint=endpoint, + stream=None, + auth_header=None, + url=default_api_base, + ) + + headers = { + "Content-Type": "application/json; charset=utf-8", + "Authorization": f"Bearer {access_token}", + } + + params: Dict[str, Any] = {} + if limit is not None: + params["pageSize"] = str(limit) + if after is not None: + params["pageToken"] = after + + if _is_async is True: + return self._async_list_batches( + api_base=api_base, + headers=headers, + params=params, + ) + + response = sync_handler.get( + url=api_base, + headers=headers, + params=params, + ) + + if response.status_code != 200: + raise Exception(f"Error: {response.status_code} {response.text}") + + _json_response = response.json() + vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response( + response=_json_response + ) + return vertex_batch_response + + async def _async_list_batches( + self, + api_base: str, + headers: Dict[str, str], + params: Dict[str, Any], + ): + client = get_async_httpx_client( + llm_provider=litellm.LlmProviders.VERTEX_AI, + ) + response = await client.get( + url=api_base, + headers=headers, + params=params, + ) + if response.status_code != 200: + raise Exception(f"Error: {response.status_code} {response.text}") + + _json_response = response.json() + vertex_batch_response = VertexAIBatchTransformation.transform_vertex_ai_batch_list_response_to_openai_list_response( + response=_json_response + ) + return vertex_batch_response From a24b43cbdfbe52c02dec8f6d61c005991db0c968 Mon Sep 17 00:00:00 2001 From: naaa760 Date: Tue, 25 Nov 2025 17:27:39 +0530 Subject: [PATCH 031/248] only added the helper that converts --- .../llms/vertex_ai/batches/transformation.py | 29 ++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/litellm/llms/vertex_ai/batches/transformation.py b/litellm/llms/vertex_ai/batches/transformation.py index 22cd0bd402..a0adb3e55a 100644 --- a/litellm/llms/vertex_ai/batches/transformation.py +++ b/litellm/llms/vertex_ai/batches/transformation.py @@ -1,5 +1,5 @@ from litellm._uuid import uuid -from typing import Dict +from typing import Any, Dict from litellm.llms.vertex_ai.common_utils import ( _convert_vertex_datetime_to_openai_datetime, @@ -67,6 +67,33 @@ class VertexAIBatchTransformation: ), ) + @classmethod + def transform_vertex_ai_batch_list_response_to_openai_list_response( + cls, response: Dict[str, Any] + ) -> Dict[str, Any]: + """ + Transforms Vertex AI batch list response into OpenAI-compatible list response. + """ + + batch_jobs = response.get("batchPredictionJobs", []) or [] + data = [ + cls.transform_vertex_ai_batch_response_to_openai_batch_response(job) + for job in batch_jobs + ] + + first_id = data[0].id if len(data) > 0 else None + last_id = data[-1].id if len(data) > 0 else None + next_page_token = response.get("nextPageToken") + + return { + "object": "list", + "data": data, + "first_id": first_id, + "last_id": last_id, + "has_more": bool(next_page_token), + "next_page_token": next_page_token, + } + @classmethod def _get_batch_id_from_vertex_ai_batch_response( cls, response: VertexBatchPredictionResponse From 2cf86e8ef83eb02941a1f31f099ad033e8ddee08 Mon Sep 17 00:00:00 2001 From: naaa760 Date: Tue, 25 Nov 2025 17:28:02 +0530 Subject: [PATCH 032/248] new mocked --- .../test_openai_batches_and_files.py | 44 +++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/tests/batches_tests/test_openai_batches_and_files.py b/tests/batches_tests/test_openai_batches_and_files.py index 67ce2f7249..2f4f9bbcda 100644 --- a/tests/batches_tests/test_openai_batches_and_files.py +++ b/tests/batches_tests/test_openai_batches_and_files.py @@ -447,6 +447,18 @@ mock_vertex_batch_response = { "completionStats": {"successfulCount": 0, "failedCount": 0, "remainingCount": 100}, } +mock_vertex_list_response = { + "batchPredictionJobs": [ + mock_vertex_batch_response, + { + **mock_vertex_batch_response, + "name": "projects/123456789/locations/us-central1/batchPredictionJobs/test-batch-id-789", + "state": "JOB_STATE_SUCCEEDED", + }, + ], + "nextPageToken": "", +} + @pytest.mark.asyncio async def test_avertex_batch_prediction(monkeypatch): @@ -533,3 +545,35 @@ async def test_avertex_batch_prediction(monkeypatch): print("retrieved_batch=", retrieved_batch) assert retrieved_batch.id == "test-batch-id-456" + + +@pytest.mark.asyncio +async def test_vertex_list_batches(monkeypatch): + monkeypatch.setenv("GCS_BUCKET_NAME", "litellm-local") + monkeypatch.setenv("VERTEXAI_PROJECT", "litellm-test-project") + monkeypatch.setenv("VERTEXAI_LOCATION", "us-central1") + + monkeypatch.setattr( + "litellm.llms.vertex_ai.batches.handler.VertexAIBatchPrediction._ensure_access_token", + lambda self, credentials, project_id, custom_llm_provider: ("mock-token", "litellm-test-project"), + ) + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.get" + ) as mock_get: + mock_get_response = MagicMock() + mock_get_response.json.return_value = mock_vertex_list_response + mock_get_response.status_code = 200 + mock_get_response.raise_for_status.return_value = None + mock_get.return_value = mock_get_response + + list_response = await litellm.alist_batches( + custom_llm_provider="vertex_ai", + limit=2, + ) + + assert list_response["object"] == "list" + assert list_response["has_more"] is False + assert len(list_response["data"]) == 2 + assert list_response["data"][0].id == "test-batch-id-456" + assert list_response["data"][1].id == "test-batch-id-789" From 42fa19a152c40bc6faf8bcc07dd82a404029f604 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 25 Nov 2025 11:02:45 -0800 Subject: [PATCH 033/248] Revert "Test prisma changes for Gemini tests" This reverts commit 031bb3c5d90e429bf65210302ecae80e46cc505c. --- .circleci/config.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index d756755c55..a518628afb 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -3047,7 +3047,6 @@ jobs: docker run -d \ -p 4000:4000 \ -e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \ - -e USE_PRISMA_MIGRATE=True \ -e LITELLM_MASTER_KEY="sk-1234" \ -e OPENAI_API_KEY=$OPENAI_API_KEY \ -e GEMINI_API_KEY=$GEMINI_API_KEY \ From 359025554ec8265b9531fe75347352e4493eb658 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 25 Nov 2025 11:05:49 -0800 Subject: [PATCH 034/248] =?UTF-8?q?bump:=20version=200.4.6=20=E2=86=92=200?= =?UTF-8?q?.4.7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- litellm-proxy-extras/pyproject.toml | 4 ++-- pyproject.toml | 2 +- requirements.txt | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 78e34ccd01..a22e3ca330 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.4.6" +version = "0.4.7" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.4.6" +version = "0.4.7" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/pyproject.toml b/pyproject.toml index d485772b36..15094667a5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -59,7 +59,7 @@ websockets = {version = "^13.1.0", optional = true} boto3 = {version = "1.36.0", optional = true} redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"} mcp = {version = "^1.21.2", optional = true, python = ">=3.10"} -litellm-proxy-extras = {version = "0.4.6", optional = true} +litellm-proxy-extras = {version = "0.4.7", optional = true} rich = {version = "13.7.1", optional = true} litellm-enterprise = {version = "0.1.22", optional = true} diskcache = {version = "^5.6.1", optional = true} diff --git a/requirements.txt b/requirements.txt index 3a426d83e3..25b15b9b0f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -43,7 +43,7 @@ sentry_sdk==2.21.0 # for sentry error handling detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests cryptography==44.0.1 tzdata==2025.1 # IANA time zone database -litellm-proxy-extras==0.4.6 # for proxy extras - e.g. prisma migrations +litellm-proxy-extras==0.4.7 # for proxy extras - e.g. prisma migrations ### LITELLM PACKAGE DEPENDENCIES python-dotenv==1.0.1 # for env tiktoken==0.8.0 # for calculating usage From aec0ab777bde6f59cd7fc4be153caad247e973de Mon Sep 17 00:00:00 2001 From: abi_jey Date: Tue, 25 Nov 2025 19:18:41 +0000 Subject: [PATCH 035/248] feat: add GA protocol as litellm_params for realtime api on azure provider --- litellm/llms/azure/realtime/handler.py | 54 ++++++++++++++++-- .../realtime/test_azure_realtime_handler.py | 56 +++++++++++++++++++ 2 files changed, 105 insertions(+), 5 deletions(-) diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py index 8e5581206d..14f772ac07 100644 --- a/litellm/llms/azure/realtime/handler.py +++ b/litellm/llms/azure/realtime/handler.py @@ -28,16 +28,60 @@ async def forward_messages(client_ws: Any, backend_ws: Any): class AzureOpenAIRealtime(AzureChatCompletion): - def _construct_url(self, api_base: str, model: str, api_version: str) -> str: + def _get_realtime_protocol(self) -> str: + """Return the configured realtime protocol. + + Supported values (case-insensitive): + - "beta" -> use legacy `/openai/realtime` (current default) + - "v1" -> use `/openai/v1/realtime` + - "ga" -> alias for "v1" (GA path is v1) + + If the parameter is missing or invalid, we fall back to the current + behavior for full backwards compatibility. """ - Example output: + + # `litellm_params` is the standard place to configure provider-specific + # behavior. We keep this defensive in case the attribute isn't set. + params: Any = getattr(self, "litellm_params", None) + if not isinstance(params, dict): + return "beta" + + value = params.get("realtime_protocol") + if not isinstance(value, str): + return "beta" + + value_normalized = value.lower() + if value_normalized in {"v1", "ga"}: + return "v1" + + # Treat anything else (including explicit "beta") as current default + return "beta" + + def _construct_url( + self, + api_base: str, + model: str, + api_version: str, + ) -> str: + """Construct the websocket URL for Azure OpenAI realtime. + + Example default output (beta / legacy behavior): "wss://my-endpoint-sweden-berri992.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview"; + When `realtime_protocol` is set to "v1" or "GA" via `litellm_params`, + this switches to `/openai/v1/realtime`. """ + api_base = api_base.replace("https://", "wss://") - return ( - f"{api_base}/openai/realtime?api-version={api_version}&deployment={model}" - ) + + protocol = self._get_realtime_protocol() + if protocol == "v1": + path = "/openai/v1/realtime" + else: + # default / beta behavior + path = "/openai/realtime" + + return f"{api_base}{path}?api-version={api_version}&deployment={model}" async def async_realtime( self, diff --git a/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py b/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py index 7bcbe37156..1446bc3df7 100644 --- a/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py +++ b/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py @@ -71,3 +71,59 @@ async def test_async_realtime_uses_max_size_parameter(): mock_realtime_streaming.assert_called_once() mock_streaming_instance.bidirectional_forward.assert_awaited_once() + +@pytest.mark.asyncio +async def test_construct_url_uses_legacy_realtime_by_default(): + """By default we should keep using `/openai/realtime` (beta behavior).""" + + from litellm.llms.azure.realtime.handler import AzureOpenAIRealtime + + handler = AzureOpenAIRealtime() + api_base = "https://my-endpoint.openai.azure.com" + api_version = "2024-10-01-preview" + model = "gpt-4o-realtime-preview" + + url = handler._construct_url(api_base=api_base, model=model, api_version=api_version) + + assert url.startswith("wss://my-endpoint.openai.azure.com") + assert "/openai/realtime" in url + assert "/openai/v1/realtime" not in url + + +@pytest.mark.asyncio +async def test_construct_url_uses_v1_when_realtime_protocol_v1_or_ga(): + """Setting `realtime_protocol` to v1/GA should switch to `/openai/v1/realtime`.""" + + from litellm.llms.azure.realtime.handler import AzureOpenAIRealtime + + api_base = "https://my-endpoint.openai.azure.com" + api_version = "2024-10-01-preview" + model = "gpt-4o-realtime-preview" + + # Helper to construct handler URL with a specific realtime_protocol. + # We avoid mutating handler attributes directly since type checkers don't + # know about `litellm_params` on this class. Instead, we patch the + # `_get_realtime_protocol` helper which is what `_construct_url` uses. + + # v1 -> /openai/v1/realtime + handler_v1 = AzureOpenAIRealtime() + with patch.object(handler_v1, "_get_realtime_protocol", return_value="v1"): + url_v1 = handler_v1._construct_url(api_base=api_base, model=model, api_version=api_version) + assert "/openai/v1/realtime" in url_v1 + assert "/openai/realtime" not in url_v1 + + # GA (case-insensitive) -> /openai/v1/realtime + handler_ga = AzureOpenAIRealtime() + with patch.object(handler_ga, "_get_realtime_protocol", return_value="v1"): + url_ga = handler_ga._construct_url(api_base=api_base, model=model, api_version=api_version) + assert "/openai/v1/realtime" in url_ga + assert "/openai/realtime" not in url_ga + + # beta or any other value keeps legacy path + handler_beta = AzureOpenAIRealtime() + with patch.object(handler_beta, "_get_realtime_protocol", return_value="beta"): + url_beta = handler_beta._construct_url(api_base=api_base, model=model, api_version=api_version) + assert "/openai/realtime" in url_beta + assert "/openai/v1/realtime" not in url_beta + + From 44cde2e48fe6d2365f8cf3c972d1be8de7bbceec Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 25 Nov 2025 12:03:01 -0800 Subject: [PATCH 036/248] Disable edit, delete, info, for dynamically generated spend tags --- .../tag_management/TagTable.test.tsx | 101 ++++++++++++++++++ .../components/tag_management/TagTable.tsx | 86 +++++++++++---- .../components/CreateTagModal.test.tsx | 64 +++++++++++ .../components/CreateTagModal.tsx | 44 ++------ .../components/tag_management/tag_info.tsx | 61 ++++++----- 5 files changed, 279 insertions(+), 77 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/tag_management/TagTable.test.tsx create mode 100644 ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.test.tsx diff --git a/ui/litellm-dashboard/src/components/tag_management/TagTable.test.tsx b/ui/litellm-dashboard/src/components/tag_management/TagTable.test.tsx new file mode 100644 index 0000000000..a56721787d --- /dev/null +++ b/ui/litellm-dashboard/src/components/tag_management/TagTable.test.tsx @@ -0,0 +1,101 @@ +import { render, screen } from "@testing-library/react"; +import { beforeEach, describe, expect, it, vi } from "vitest"; +import TagTable from "./TagTable"; +import { Tag } from "./types"; + +describe("TagTable", () => { + const mockOnEdit = vi.fn(); + const mockOnDelete = vi.fn(); + const mockOnSelectTag = vi.fn(); + + const mockTag: Tag = { + name: "test-tag", + description: "Test description", + models: ["model-1", "model-2"], + model_info: { + "model-1": "GPT-4", + "model-2": "Claude-3", + }, + created_at: "2024-01-01T00:00:00Z", + updated_at: "2024-01-01T00:00:00Z", + }; + + const mockDynamicSpendTag: Tag = { + name: "dynamic-spend-tag", + description: + "This is just a spend tag that was passed dynamically in a request. It does not control any LLM models.", + models: [], + created_at: "2024-01-01T00:00:00Z", + updated_at: "2024-01-01T00:00:00Z", + }; + + const defaultProps = { + data: [], + onEdit: mockOnEdit, + onDelete: mockOnDelete, + onSelectTag: mockOnSelectTag, + }; + + beforeEach(() => { + vi.clearAllMocks(); + }); + + it("should render", () => { + render(); + expect(screen.getByText("Tag Name")).toBeInTheDocument(); + expect(screen.getByText("Description")).toBeInTheDocument(); + expect(screen.getByText("Allowed Models")).toBeInTheDocument(); + expect(screen.getByText("Created")).toBeInTheDocument(); + expect(screen.getByText("Actions")).toBeInTheDocument(); + }); + + it("should display no tags found message when data is empty", () => { + render(); + expect(screen.getByText("No tags found")).toBeInTheDocument(); + }); + + it("should display tag name", () => { + render(); + expect(screen.getByText("test-tag")).toBeInTheDocument(); + }); + + it("should display tag description", () => { + render(); + expect(screen.getByText("Test description")).toBeInTheDocument(); + }); + + it("should display All Models badge when models array is empty", () => { + const tagWithNoModels: Tag = { + ...mockTag, + models: [], + }; + render(); + expect(screen.getByText("All Models")).toBeInTheDocument(); + }); + + it("should display formatted created date", () => { + render(); + const formattedDate = new Date(mockTag.created_at).toLocaleDateString(); + expect(screen.getByText(formattedDate)).toBeInTheDocument(); + }); + + it("should disable tag name button for dynamic spend tags", () => { + render(); + const tagButton = screen.getByRole("button", { name: "dynamic-spend-tag" }); + expect(tagButton).toBeDisabled(); + }); + + it("should disable edit icon for dynamic spend tags", () => { + render(); + const editIcon = screen.getByLabelText("Edit tag (disabled)"); + expect(editIcon).toBeInTheDocument(); + expect(editIcon).toHaveClass("cursor-not-allowed"); + }); + + it("should disable delete icon for dynamic spend tags", () => { + render(); + const deleteIcon = screen.getByLabelText("Delete tag (disabled)"); + expect(deleteIcon).toBeInTheDocument(); + expect(deleteIcon).toHaveClass("cursor-not-allowed"); + }); +}); diff --git a/ui/litellm-dashboard/src/components/tag_management/TagTable.tsx b/ui/litellm-dashboard/src/components/tag_management/TagTable.tsx index aa43388893..ce28ac6e6f 100644 --- a/ui/litellm-dashboard/src/components/tag_management/TagTable.tsx +++ b/ui/litellm-dashboard/src/components/tag_management/TagTable.tsx @@ -1,18 +1,4 @@ -import React from "react"; -import { - Table, - TableBody, - TableCell, - TableHead, - TableHeaderCell, - TableRow, - Icon, - Button, - Badge, - Text, -} from "@tremor/react"; -import { PencilAltIcon, TrashIcon, SwitchVerticalIcon, ChevronUpIcon, ChevronDownIcon } from "@heroicons/react/outline"; -import { Tooltip } from "antd"; +import { ChevronDownIcon, ChevronUpIcon, PencilAltIcon, SwitchVerticalIcon, TrashIcon } from "@heroicons/react/outline"; import { ColumnDef, flexRender, @@ -21,6 +7,20 @@ import { SortingState, useReactTable, } from "@tanstack/react-table"; +import { + Badge, + Button, + Icon, + Table, + TableBody, + TableCell, + TableHead, + TableHeaderCell, + TableRow, + Text, +} from "@tremor/react"; +import { Tooltip } from "antd"; +import React from "react"; import { Tag } from "./types"; interface TagTableProps { @@ -30,6 +30,9 @@ interface TagTableProps { onSelectTag: (tagName: string) => void; } +const DYNAMIC_SPEND_TAG_DESCRIPTION = + "This is just a spend tag that was passed dynamically in a request. It does not control any LLM models."; + const TagTable: React.FC = ({ data, onEdit, onDelete, onSelectTag }) => { const [sorting, setSorting] = React.useState([{ id: "created_at", desc: true }]); @@ -39,14 +42,20 @@ const TagTable: React.FC = ({ data, onEdit, onDelete, onSelectTag accessorKey: "name", cell: ({ row }) => { const tag = row.original; + const isDynamicSpendTag = tag.description === DYNAMIC_SPEND_TAG_DESCRIPTION; return (
- + @@ -68,7 +77,7 @@ const TagTable: React.FC = ({ data, onEdit, onDelete, onSelectTag }, }, { - header: "Allowed LLMs", + header: "Allowed Models", accessorKey: "models", cell: ({ row }) => { const tag = row.original; @@ -102,13 +111,50 @@ const TagTable: React.FC = ({ data, onEdit, onDelete, onSelectTag }, { id: "actions", - header: "", + header: "Actions", cell: ({ row }) => { const tag = row.original; + const isDynamicSpendTag = tag.description === DYNAMIC_SPEND_TAG_DESCRIPTION; return (
- onEdit(tag)} className="cursor-pointer" /> - onDelete(tag.name)} className="cursor-pointer" /> + {isDynamicSpendTag ? ( + + + + ) : ( + + onEdit(tag)} + className="cursor-pointer hover:text-blue-500" + /> + + )} + {isDynamicSpendTag ? ( + + + + ) : ( + + onDelete(tag.name)} + className="cursor-pointer hover:text-red-500" + /> + + )}
); }, diff --git a/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.test.tsx b/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.test.tsx new file mode 100644 index 0000000000..997faf4a00 --- /dev/null +++ b/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.test.tsx @@ -0,0 +1,64 @@ +import { render, screen } from "@testing-library/react"; +import userEvent from "@testing-library/user-event"; +import { beforeEach, describe, expect, it, vi } from "vitest"; +import CreateTagModal from "./CreateTagModal"; + +describe("CreateTagModal", () => { + const mockOnCancel = vi.fn(); + const mockOnSubmit = vi.fn(); + const mockAvailableModels = [ + { + model_name: "GPT-4", + litellm_params: { model: "gpt-4" }, + model_info: { id: "model-1" }, + }, + { + model_name: "Claude-3", + litellm_params: { model: "claude-3" }, + model_info: { id: "model-2" }, + }, + ]; + + const defaultProps = { + visible: true, + onCancel: mockOnCancel, + onSubmit: mockOnSubmit, + availableModels: mockAvailableModels, + }; + + beforeEach(() => { + vi.clearAllMocks(); + }); + + it("should render the modal", () => { + render(); + expect(screen.getByRole("dialog")).toBeInTheDocument(); + expect(screen.getByText("Create New Tag")).toBeInTheDocument(); + }); + + it("should submit form with required tag name", async () => { + const user = userEvent.setup(); + render(); + + const tagNameInput = screen.getByLabelText("Tag Name"); + await user.type(tagNameInput, "test-tag"); + + const submitButton = screen.getByRole("button", { name: /Create Tag/i }); + await user.click(submitButton); + + expect(mockOnSubmit).toHaveBeenCalledWith({ + tag_name: "test-tag", + }); + }); + + it("should not submit form when tag name is missing", async () => { + const user = userEvent.setup(); + render(); + + const submitButton = screen.getByRole("button", { name: /Create Tag/i }); + await user.click(submitButton); + + // Form validation should prevent submission + expect(mockOnSubmit).not.toHaveBeenCalled(); + }); +}); diff --git a/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.tsx b/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.tsx index 4d1909abd9..3412d68452 100644 --- a/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.tsx +++ b/ui/litellm-dashboard/src/components/tag_management/components/CreateTagModal.tsx @@ -1,9 +1,9 @@ -import React from "react"; -import { Button, TextInput, Accordion, AccordionHeader, AccordionBody, Title } from "@tremor/react"; -import { Modal, Form, Select as Select2, Tooltip, Input } from "antd"; import { InfoCircleOutlined } from "@ant-design/icons"; -import NumericalInput from "../../shared/numerical_input"; +import { Accordion, AccordionBody, AccordionHeader, Button, TextInput, Title } from "@tremor/react"; +import { Form, Input, Modal, Select as Select2, Tooltip } from "antd"; +import React from "react"; import BudgetDurationDropdown from "../../common_components/budget_duration_dropdown"; +import NumericalInput from "../../shared/numerical_input"; interface ModelInfo { model_name: string; @@ -22,12 +22,7 @@ interface CreateTagModalProps { availableModels: ModelInfo[]; } -const CreateTagModal: React.FC = ({ - visible, - onCancel, - onSubmit, - availableModels, -}) => { +const CreateTagModal: React.FC = ({ visible, onCancel, onSubmit, availableModels }) => { const [form] = Form.useForm(); const handleFinish = (values: any) => { @@ -41,25 +36,9 @@ const CreateTagModal: React.FC = ({ }; return ( - -
- + + + @@ -70,15 +49,15 @@ const CreateTagModal: React.FC = ({ - Allowed Models{" "} - + Allowed Models + } name="allowed_llms" > - + {availableModels.map((model) => (
@@ -150,4 +129,3 @@ const CreateTagModal: React.FC = ({ }; export default CreateTagModal; - diff --git a/ui/litellm-dashboard/src/components/tag_management/tag_info.tsx b/ui/litellm-dashboard/src/components/tag_management/tag_info.tsx index 60cde134e6..1c66a107db 100644 --- a/ui/litellm-dashboard/src/components/tag_management/tag_info.tsx +++ b/ui/litellm-dashboard/src/components/tag_management/tag_info.tsx @@ -1,5 +1,15 @@ import React, { useState, useEffect } from "react"; -import { Card, Text, Title, Button, Badge, Accordion, AccordionHeader, AccordionBody, Title as TremorTitle } from "@tremor/react"; +import { + Card, + Text, + Title, + Button, + Badge, + Accordion, + AccordionHeader, + AccordionBody, + Title as TremorTitle, +} from "@tremor/react"; import { Form, Input, Select as Select2, Tooltip } from "antd"; import { InfoCircleOutlined } from "@ant-design/icons"; import { fetchUserModels } from "../organisms/create_key_button"; @@ -131,7 +141,7 @@ const TagInfoView: React.FC = ({ tagId, onClose, accessToken, - + @@ -141,15 +151,15 @@ const TagInfoView: React.FC = ({ tagId, onClose, accessToken, - Allowed LLMs{" "} - + Allowed Models + } name="models" > - + {userModels.map((modelId) => ( {getModelDisplayName(modelId)} @@ -228,7 +238,7 @@ const TagInfoView: React.FC = ({ tagId, onClose, accessToken, {tagDetails.description || "-"}
- Allowed LLMs + Allowed Models
{!tagDetails.models || tagDetails.models.length === 0 ? ( All Models @@ -256,30 +266,33 @@ const TagInfoView: React.FC = ({ tagId, onClose, accessToken, Budget & Rate Limits
- {tagDetails.litellm_budget_table.max_budget !== undefined && tagDetails.litellm_budget_table.max_budget !== null && ( -
- Max Budget - ${tagDetails.litellm_budget_table.max_budget} -
- )} + {tagDetails.litellm_budget_table.max_budget !== undefined && + tagDetails.litellm_budget_table.max_budget !== null && ( +
+ Max Budget + ${tagDetails.litellm_budget_table.max_budget} +
+ )} {tagDetails.litellm_budget_table.budget_duration && (
Budget Duration {tagDetails.litellm_budget_table.budget_duration}
)} - {tagDetails.litellm_budget_table.tpm_limit !== undefined && tagDetails.litellm_budget_table.tpm_limit !== null && ( -
- TPM Limit - {tagDetails.litellm_budget_table.tpm_limit.toLocaleString()} -
- )} - {tagDetails.litellm_budget_table.rpm_limit !== undefined && tagDetails.litellm_budget_table.rpm_limit !== null && ( -
- RPM Limit - {tagDetails.litellm_budget_table.rpm_limit.toLocaleString()} -
- )} + {tagDetails.litellm_budget_table.tpm_limit !== undefined && + tagDetails.litellm_budget_table.tpm_limit !== null && ( +
+ TPM Limit + {tagDetails.litellm_budget_table.tpm_limit.toLocaleString()} +
+ )} + {tagDetails.litellm_budget_table.rpm_limit !== undefined && + tagDetails.litellm_budget_table.rpm_limit !== null && ( +
+ RPM Limit + {tagDetails.litellm_budget_table.rpm_limit.toLocaleString()} +
+ )}
)} From 36f8c9463f3a447b35bcbd4e8588b9c883ef73fe Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 25 Nov 2025 12:13:19 -0800 Subject: [PATCH 037/248] fix: resolve type checking errors for lazy-loaded functions in budget_manager - Add type stubs and @overload decorators for cost_per_token and completion_cost - Refactor lazy loading system with centralized registry for better maintainability - Add comprehensive documentation for adding new lazy-loaded functions - Fixes 'Any? not callable' errors at lines 111, 139, and 146 in budget_manager.py --- litellm/__init__.py | 82 ++++++++++++++++++++++++++++++++++----------- 1 file changed, 63 insertions(+), 19 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index 8e8815b125..a513619c4d 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -20,6 +20,8 @@ from typing import ( Literal, get_args, TYPE_CHECKING, + Tuple, + overload, ) from litellm.types.integrations.datadog_llm_obs import DatadogLLMObsInitParams from litellm.types.integrations.datadog import DatadogInitParams @@ -1503,8 +1505,20 @@ def set_global_gitlab_config(config: Dict[str, Any]) -> None: global_gitlab_config = config -# Lazy import for cost_calculator functions to avoid loading the module at import time -# This significantly reduces memory usage when importing litellm +# ============================================================================ +# LAZY LOADING SYSTEM +# ============================================================================ +# This system allows heavy modules to be loaded only when accessed, +# significantly reducing initial import time and memory usage. +# +# To add a new lazy-loaded function/class: +# 1. Add the import handler function (e.g., _lazy_import_xxx) +# 2. Add entries to _LAZY_LOAD_REGISTRY below +# 3. Add type stubs in the TYPE_CHECKING block +# 4. Add @overload decorators for type checking +# ============================================================================ + + def _lazy_import_cost_calculator(name: str) -> Any: """Lazy import for cost_calculator functions.""" from .cost_calculator import ( @@ -1513,22 +1527,17 @@ def _lazy_import_cost_calculator(name: str) -> Any: response_cost_calculator as _response_cost_calculator, ) - # Map names to imported functions _cost_functions = { "completion_cost": _completion_cost, "cost_per_token": _cost_per_token, "response_cost_calculator": _response_cost_calculator, } - # Cache the imported function in the module namespace func = _cost_functions[name] - globals()[name] = func - + globals()[name] = func # Cache for future access return func -# Lazy import for litellm_logging to avoid loading the module at import time -# This significantly reduces memory usage when importing litellm def _lazy_import_litellm_logging(name: str) -> Any: """Lazy import for litellm_logging module.""" try: @@ -1537,35 +1546,70 @@ def _lazy_import_litellm_logging(name: str) -> Any: modify_integration as _modify_integration, ) - # Map names to imported objects _logging_objects = { "Logging": _Logging, "modify_integration": _modify_integration, } - # Cache the imported object in the module namespace obj = _logging_objects[name] - globals()[name] = obj - + globals()[name] = obj # Cache for future access return obj except Exception as e: - # If lazy import fails, raise a more informative error raise AttributeError( f"module {__name__!r} has no attribute {name!r}. " f"Lazy import failed: {e}" ) from e +# Registry mapping lazy-loaded names to their import handlers +# Add new lazy-loaded items here for easy maintenance +_LAZY_LOAD_REGISTRY: Dict[str, Callable[[str], Any]] = { + # Cost calculator functions + "completion_cost": _lazy_import_cost_calculator, + "cost_per_token": _lazy_import_cost_calculator, + "response_cost_calculator": _lazy_import_cost_calculator, + # Logging objects + "Logging": _lazy_import_litellm_logging, + "modify_integration": _lazy_import_litellm_logging, +} + + +# Type stubs for lazy-loaded functions/classes to help type checkers +# Add type annotations here for new lazy-loaded items +if TYPE_CHECKING: + # Cost calculator functions + cost_per_token: Callable[..., Tuple[float, float]] + completion_cost: Callable[..., float] + response_cost_calculator: Any + # Logging objects + Logging: Any + modify_integration: Any + + +# Type overloads for __getattr__ to provide proper type hints +# Add @overload decorators here for new lazy-loaded items with specific types +@overload +def __getattr__(name: Literal["cost_per_token"]) -> Callable[..., Tuple[float, float]]: + ... + + +@overload +def __getattr__(name: Literal["completion_cost"]) -> Callable[..., float]: + ... + + +@overload +def __getattr__(name: Literal["response_cost_calculator", "Logging", "modify_integration"]) -> Any: + ... + + def __getattr__(name: str) -> Any: - """Lazy import for cost_calculator and litellm_logging functions. + """Lazy import handler for cost_calculator and litellm_logging functions. This allows these heavy modules to be loaded only when accessed, reducing initial import time and memory usage. """ - if name in ("completion_cost", "response_cost_calculator", "cost_per_token"): - return _lazy_import_cost_calculator(name) - - if name in ("Logging", "modify_integration"): - return _lazy_import_litellm_logging(name) + if name in _LAZY_LOAD_REGISTRY: + return _LAZY_LOAD_REGISTRY[name](name) raise AttributeError(f"module {__name__!r} has no attribute {name!r}") From 7f991fe89782031beefb959b76f5a642619a841f Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 25 Nov 2025 12:37:54 -0800 Subject: [PATCH 038/248] refactor: clean up lazy loading system and remove type ignore comments - Remove excessive comments and simplify documentation - Remove @overload decorators (type stubs are sufficient) - Remove Logging type stub to avoid redefinition errors - Keep only essential type stubs for cost_per_token and completion_cost - Fixes type checking errors without using type: ignore comments --- litellm/__init__.py | 52 +++++---------------------------------------- 1 file changed, 5 insertions(+), 47 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index a513619c4d..5cb135269b 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -22,6 +22,7 @@ from typing import ( TYPE_CHECKING, Tuple, overload, + Type, ) from litellm.types.integrations.datadog_llm_obs import DatadogLLMObsInitParams from litellm.types.integrations.datadog import DatadogInitParams @@ -1505,20 +1506,7 @@ def set_global_gitlab_config(config: Dict[str, Any]) -> None: global_gitlab_config = config -# ============================================================================ -# LAZY LOADING SYSTEM -# ============================================================================ -# This system allows heavy modules to be loaded only when accessed, -# significantly reducing initial import time and memory usage. -# -# To add a new lazy-loaded function/class: -# 1. Add the import handler function (e.g., _lazy_import_xxx) -# 2. Add entries to _LAZY_LOAD_REGISTRY below -# 3. Add type stubs in the TYPE_CHECKING block -# 4. Add @overload decorators for type checking -# ============================================================================ - - +# Lazy loading system for heavy modules to reduce initial import time and memory usage def _lazy_import_cost_calculator(name: str) -> Any: """Lazy import for cost_calculator functions.""" from .cost_calculator import ( @@ -1534,7 +1522,7 @@ def _lazy_import_cost_calculator(name: str) -> Any: } func = _cost_functions[name] - globals()[name] = func # Cache for future access + globals()[name] = func return func @@ -1552,7 +1540,7 @@ def _lazy_import_litellm_logging(name: str) -> Any: } obj = _logging_objects[name] - globals()[name] = obj # Cache for future access + globals()[name] = obj return obj except Exception as e: raise AttributeError( @@ -1561,54 +1549,24 @@ def _lazy_import_litellm_logging(name: str) -> Any: ) from e -# Registry mapping lazy-loaded names to their import handlers -# Add new lazy-loaded items here for easy maintenance _LAZY_LOAD_REGISTRY: Dict[str, Callable[[str], Any]] = { - # Cost calculator functions "completion_cost": _lazy_import_cost_calculator, "cost_per_token": _lazy_import_cost_calculator, "response_cost_calculator": _lazy_import_cost_calculator, - # Logging objects "Logging": _lazy_import_litellm_logging, "modify_integration": _lazy_import_litellm_logging, } -# Type stubs for lazy-loaded functions/classes to help type checkers -# Add type annotations here for new lazy-loaded items if TYPE_CHECKING: - # Cost calculator functions cost_per_token: Callable[..., Tuple[float, float]] completion_cost: Callable[..., float] response_cost_calculator: Any - # Logging objects - Logging: Any modify_integration: Any -# Type overloads for __getattr__ to provide proper type hints -# Add @overload decorators here for new lazy-loaded items with specific types -@overload -def __getattr__(name: Literal["cost_per_token"]) -> Callable[..., Tuple[float, float]]: - ... - - -@overload -def __getattr__(name: Literal["completion_cost"]) -> Callable[..., float]: - ... - - -@overload -def __getattr__(name: Literal["response_cost_calculator", "Logging", "modify_integration"]) -> Any: - ... - - def __getattr__(name: str) -> Any: - """Lazy import handler for cost_calculator and litellm_logging functions. - - This allows these heavy modules to be loaded only when accessed, - reducing initial import time and memory usage. - """ + """Lazy import handler for cost_calculator and litellm_logging functions.""" if name in _LAZY_LOAD_REGISTRY: return _LAZY_LOAD_REGISTRY[name](name) From 2ef5a41a24b7f0ff83a467b834d747ce3baba1d1 Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 25 Nov 2025 12:48:24 -0800 Subject: [PATCH 039/248] fix: resolve type checking errors in vertex_ai and mcp_server_manager - Fix type incompatibility in vertex_ai/videos/transformation.py by casting litellm_params to Dict[str, Any] - Add await to async add_update_server call in mcp_server_manager.py - Resolves 4 type checking errors across 2 files --- litellm/llms/vertex_ai/videos/transformation.py | 9 +++++---- .../proxy/_experimental/mcp_server/mcp_server_manager.py | 2 +- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/litellm/llms/vertex_ai/videos/transformation.py b/litellm/llms/vertex_ai/videos/transformation.py index 2b6d43dd70..1f657f63bf 100644 --- a/litellm/llms/vertex_ai/videos/transformation.py +++ b/litellm/llms/vertex_ai/videos/transformation.py @@ -7,7 +7,7 @@ Based on: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-refer import base64 import time -from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union, cast import httpx from httpx._types import RequestFiles @@ -174,10 +174,11 @@ class VertexAIVideoConfig(BaseVideoConfig, VertexBase): """ # Extract Vertex AI parameters using safe helpers from VertexBase # Use safe_get_* methods that don't mutate litellm_params dict - litellm_params = litellm_params or {} + # Ensure litellm_params is a dict for type checking + params_dict: Dict[str, Any] = cast(Dict[str, Any], litellm_params) if litellm_params is not None else {} - vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=litellm_params) - vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params=litellm_params) + vertex_project = VertexBase.safe_get_vertex_ai_project(litellm_params=params_dict) + vertex_credentials = VertexBase.safe_get_vertex_ai_credentials(litellm_params=params_dict) # Get access token from Vertex credentials access_token, project_id = self.get_access_token( diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 54c79fc696..4a0d25e24f 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -2270,7 +2270,7 @@ class MCPServerManager: server.status = "unhealthy" ## try adding server to registry to get error try: - self.add_update_server(server) + await self.add_update_server(server) except Exception as e: server.health_check_error = str(e) server.health_check_error = "Server is not in in memory registry yet. This could be a temporary sync issue." From ab877d9551e76da2d3208c6daba1b71238d2d43b Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 25 Nov 2025 13:07:09 -0800 Subject: [PATCH 040/248] fix: convert MCP TextContent objects to JSON-serializable format in logging - Convert Pydantic BaseModel objects (TextContent, ImageContent, etc.) to dicts in get_final_response_obj - Fixes TypeError: Object of type TextContent is not JSON serializable - Resolves test failures in test_mcp_tool_call_hook and test_mcp_cost_tracking --- litellm/litellm_core_utils/litellm_logging.py | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 67407ff4c7..eb596d8203 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -4380,7 +4380,23 @@ class StandardLoggingPayloadSetup: if response_obj: final_response_obj: Optional[Union[dict, str, list]] = response_obj elif isinstance(init_response_obj, list) or isinstance(init_response_obj, str): - final_response_obj = init_response_obj + # Convert MCP content objects (TextContent, ImageContent, etc.) to JSON-serializable format + if isinstance(init_response_obj, list): + serialized_list = [] + for item in init_response_obj: + # Check if item is a Pydantic BaseModel (MCP content types are Pydantic models) + if isinstance(item, BaseModel): + # Convert Pydantic model to dict for JSON serialization + serialized_list.append(item.model_dump()) + elif hasattr(item, "__dict__") and not isinstance(item, (str, int, float, bool, type(None))): + # Fallback: convert object to dict (but skip primitive types) + serialized_list.append(item.__dict__) + else: + # Already serializable (str, dict, int, float, bool, None, etc.) + serialized_list.append(item) + final_response_obj = serialized_list + else: + final_response_obj = init_response_obj else: final_response_obj = {} From 48810e6bcb9fc8974e2eb2c7b7bf87571f45fc6b Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 25 Nov 2025 13:14:25 -0800 Subject: [PATCH 041/248] refactor: improve MCP TextContent serialization to follow existing patterns - Remove risky __dict__ fallback for non-BaseModel objects - Only convert BaseModel objects to dicts using model_dump() (consistent with line 4745-4746) - Keep other objects unchanged to maintain backward compatibility - Follows existing codebase patterns for Pydantic model serialization --- litellm/litellm_core_utils/litellm_logging.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index eb596d8203..cf8d5f2cc2 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -4385,14 +4385,14 @@ class StandardLoggingPayloadSetup: serialized_list = [] for item in init_response_obj: # Check if item is a Pydantic BaseModel (MCP content types are Pydantic models) + # This follows the same pattern used at line 4745-4746 for BaseModel objects if isinstance(item, BaseModel): # Convert Pydantic model to dict for JSON serialization serialized_list.append(item.model_dump()) - elif hasattr(item, "__dict__") and not isinstance(item, (str, int, float, bool, type(None))): - # Fallback: convert object to dict (but skip primitive types) - serialized_list.append(item.__dict__) else: # Already serializable (str, dict, int, float, bool, None, etc.) + # Non-BaseModel objects are kept as-is - they should already be serializable + # or will be handled by json.dumps with default=str if needed serialized_list.append(item) final_response_obj = serialized_list else: From b3348c665ac6b89ebd50eedd7ee0b64b2b0d600c Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 25 Nov 2025 14:22:48 -0800 Subject: [PATCH 042/248] fix: use cached import helper for Logging in ahealth_check to preserve lazy loading - Use get_litellm_logging_class() from cached_imports instead of direct import - Preserves lazy loading benefit (only loads when function is called) - Follows existing codebase pattern for cached imports - Fixes NameError: name 'Logging' is not defined in test_ahealth_check_ocr --- litellm/main.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/litellm/main.py b/litellm/main.py index 88bf0b72a0..4482cf5d12 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -6109,6 +6109,10 @@ async def ahealth_check( } """ from litellm.litellm_core_utils.health_check_helpers import HealthCheckHelpers + from litellm.litellm_core_utils.cached_imports import get_litellm_logging_class + + # Use cached import helper to lazy-load Logging class (only loads when function is called) + Logging = get_litellm_logging_class() # Map modes to their corresponding health check calls ######################################################### From db587926a473f51a21bc96935d10495ae7fdab7e Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 25 Nov 2025 14:46:46 -0800 Subject: [PATCH 043/248] Sorting changes, pending tests and loading state --- .../src/components/view_users/columns.tsx | 14 +++++++-- .../src/components/view_users/table.tsx | 31 +++++++++---------- 2 files changed, 27 insertions(+), 18 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_users/columns.tsx b/ui/litellm-dashboard/src/components/view_users/columns.tsx index 32bfa0ed6d..20df4fc246 100644 --- a/ui/litellm-dashboard/src/components/view_users/columns.tsx +++ b/ui/litellm-dashboard/src/components/view_users/columns.tsx @@ -22,10 +22,12 @@ export const columns = ( handleUserClick: (userId: string, openInEditMode?: boolean) => void, selectionOptions?: SelectionOptions, ): ColumnDef[] => { + // Backend sortable columns: user_id, user_email, created_at, spend, user_alias, user_role const baseColumns: ColumnDef[] = [ { header: "User ID", accessorKey: "user_id", + enableSorting: true, cell: ({ row }) => ( {row.original.user_id ? `${row.original.user_id.slice(0, 7)}...` : "-"} @@ -35,16 +37,19 @@ export const columns = ( { header: "Email", accessorKey: "user_email", + enableSorting: true, cell: ({ row }) => {row.original.user_email || "-"}, }, { header: "Global Proxy Role", accessorKey: "user_role", + enableSorting: true, cell: ({ row }) => {possibleUIRoles?.[row.original.user_role]?.ui_label || "-"}, }, { header: "Spend (USD)", accessorKey: "spend", + enableSorting: true, cell: ({ row }) => ( {row.original.spend ? formatNumberWithCommas(row.original.spend, 4) : "-"} ), @@ -52,6 +57,7 @@ export const columns = ( { header: "Budget (USD)", accessorKey: "max_budget", + enableSorting: false, cell: ({ row }) => ( {row.original.max_budget !== null ? row.original.max_budget : "Unlimited"} ), @@ -66,6 +72,7 @@ export const columns = (
), accessorKey: "sso_user_id", + enableSorting: false, cell: ({ row }) => ( {row.original.sso_user_id !== null ? row.original.sso_user_id : "-"} ), @@ -73,6 +80,7 @@ export const columns = ( { header: "API Keys", accessorKey: "key_count", + enableSorting: false, cell: ({ row }) => ( {row.original.key_count > 0 ? ( @@ -90,7 +98,7 @@ export const columns = ( { header: "Created At", accessorKey: "created_at", - sortingFn: "datetime", + enableSorting: true, cell: ({ row }) => ( {row.original.created_at ? new Date(row.original.created_at).toLocaleDateString() : "-"} @@ -100,7 +108,7 @@ export const columns = ( { header: "Updated At", accessorKey: "updated_at", - sortingFn: "datetime", + enableSorting: false, cell: ({ row }) => ( {row.original.updated_at ? new Date(row.original.updated_at).toLocaleDateString() : "-"} @@ -110,6 +118,7 @@ export const columns = ( { id: "actions", header: "Actions", + enableSorting: false, cell: ({ row }) => (
@@ -148,6 +157,7 @@ export const columns = ( return [ { id: "select", + enableSorting: false, header: () => ( { + onSortingChange: (updaterOrValue: any) => { + const newSorting = typeof updaterOrValue === "function" ? updaterOrValue(sorting) : updaterOrValue; setSorting(newSorting); - if (newSorting.length > 0) { + if (newSorting && Array.isArray(newSorting) && newSorting.length > 0 && newSorting[0]) { const sortState = newSorting[0]; - const sortBy = sortState.id; - const sortOrder = sortState.desc ? "desc" : "asc"; - onSortChange?.(sortBy, sortOrder); + if (sortState.id) { + const sortBy = sortState.id; + const sortOrder = sortState.desc ? "desc" : "asc"; + onSortChange?.(sortBy, sortOrder); + } + } else { + // Reset to default sort when no sorting is selected + onSortChange?.("created_at", "desc"); } }, getCoreRowModel: getCoreRowModel(), - getSortedRowModel: getSortedRowModel(), + manualSorting: true, enableSorting: true, }); @@ -403,7 +402,7 @@ export function UserDataTable({ header.id === "actions" ? "sticky right-0 bg-white shadow-[-4px_0_8px_-6px_rgba(0,0,0,0.1)]" : "" - }`} + } ${header.column.getCanSort() ? "cursor-pointer hover:bg-gray-50" : ""}`} onClick={header.column.getToggleSortingHandler()} >
@@ -412,7 +411,7 @@ export function UserDataTable({ ? null : flexRender(header.column.columnDef.header, header.getContext())}
- {header.id !== "actions" && ( + {header.id !== "actions" && header.column.getCanSort() && (
{header.column.getIsSorted() ? ( { From 3da9974a8770a5d05f839d78a7e15739b0664217 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 25 Nov 2025 15:54:55 -0800 Subject: [PATCH 044/248] Tests --- .../src/components/view_users/table.test.tsx | 59 +++++++++++++++++-- 1 file changed, 55 insertions(+), 4 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_users/table.test.tsx b/ui/litellm-dashboard/src/components/view_users/table.test.tsx index 5b612b2732..278a42e896 100644 --- a/ui/litellm-dashboard/src/components/view_users/table.test.tsx +++ b/ui/litellm-dashboard/src/components/view_users/table.test.tsx @@ -1,6 +1,5 @@ -import { render } from "@testing-library/react"; +import { act, fireEvent, render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; -import React from "react"; import { UserDataTable } from "./table"; @@ -21,7 +20,7 @@ describe("UserDataTable", () => { const updateFilters = vi.fn(); - const { getByText } = render( + render( { />, ); - expect(getByText("Filters")).toBeInTheDocument(); + expect(screen.getByText("Filters")).toBeInTheDocument(); + }); + + it("should call onSortChange when clicking a sortable header", () => { + const filters = { + email: "", + user_id: "", + user_role: "", + sso_user_id: "", + team: "", + model: "", + min_spend: null, + max_spend: null, + sort_by: "created_at", + sort_order: "desc" as const, + }; + + const updateFilters = vi.fn(); + const onSortChange = vi.fn(); + + const possibleUIRoles = { + admin: { ui_label: "Admin" }, + user: { ui_label: "User" }, + }; + + render( + , + ); + + const emailHeader = screen.getByRole("columnheader", { name: /email/i }); + act(() => { + fireEvent.click(emailHeader); + }); + + expect(onSortChange).toHaveBeenCalledWith("user_email", "desc"); }); }); From 5ec3f19a53dbf6028df7279468552b9db9442320 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 25 Nov 2025 16:57:38 -0800 Subject: [PATCH 045/248] Make model select required for team, add checks for all-proxy-models --- .../src/components/OldTeams.test.tsx | 103 ++++++++++++++---- .../src/components/OldTeams.tsx | 14 ++- .../src/components/team/team_info.test.tsx | 88 ++++++++++++++- .../src/components/team/team_info.tsx | 90 ++++++++------- 4 files changed, 225 insertions(+), 70 deletions(-) diff --git a/ui/litellm-dashboard/src/components/OldTeams.test.tsx b/ui/litellm-dashboard/src/components/OldTeams.test.tsx index 261178191f..7f4ec3b09c 100644 --- a/ui/litellm-dashboard/src/components/OldTeams.test.tsx +++ b/ui/litellm-dashboard/src/components/OldTeams.test.tsx @@ -1,5 +1,6 @@ -import { act, fireEvent, render, screen } from "@testing-library/react"; +import { act, fireEvent, render, screen, waitFor } from "@testing-library/react"; import { beforeEach, describe, expect, it, vi } from "vitest"; +import { fetchAvailableModelsForTeamOrKey } from "./key_team_helpers/fetch_available_models_team_key"; import { teamCreateCall } from "./networking"; import OldTeams from "./OldTeams"; @@ -23,6 +24,28 @@ vi.mock("./molecules/notifications_manager", () => ({ }, })); +vi.mock("./key_team_helpers/fetch_available_models_team_key", () => ({ + fetchAvailableModelsForTeamOrKey: vi.fn(), + getModelDisplayName: vi.fn((model: string) => model), + unfurlWildcardModelsInList: vi.fn((teamModels: string[], allModels: string[]) => { + const wildcardDisplayNames: string[] = []; + const expandedModels: string[] = []; + + teamModels.forEach((teamModel) => { + if (teamModel.endsWith("/*")) { + const provider = teamModel.replace("/*", ""); + const matchingModels = allModels.filter((model) => model.startsWith(provider + "/")); + expandedModels.push(...matchingModels); + wildcardDisplayNames.push(teamModel); + } else { + expandedModels.push(teamModel); + } + }); + + return [...wildcardDisplayNames, ...expandedModels].filter((item, index, array) => array.indexOf(item) === index); + }), +})); + describe("OldTeams - handleCreate organization handling", () => { beforeEach(() => { vi.clearAllMocks(); @@ -236,7 +259,7 @@ describe("OldTeams - handleCreate organization handling", () => { }); it("should clear the delete modal when the cancel button is clicked", async () => { - const { getByRole, getByTestId } = render( + render( { organizations={[]} />, ); - const deleteTeamButton = getByTestId("delete-team-button"); + const deleteTeamButton = screen.getByTestId("delete-team-button"); act(() => { fireEvent.click(deleteTeamButton); }); @@ -275,7 +298,7 @@ describe("OldTeams - empty state", () => { }); it("should display empty state message when teams array is empty", () => { - const { getByText } = render( + render( { />, ); - expect(getByText("No teams found")).toBeInTheDocument(); - expect(getByText("Adjust your filters or create a new team")).toBeInTheDocument(); + expect(screen.getByText("No teams found")).toBeInTheDocument(); + expect(screen.getByText("Adjust your filters or create a new team")).toBeInTheDocument(); }); it("should display empty state message when teams is null", () => { - const { getByText } = render( + render( { />, ); - expect(getByText("No teams found")).toBeInTheDocument(); - expect(getByText("Adjust your filters or create a new team")).toBeInTheDocument(); + expect(screen.getByText("No teams found")).toBeInTheDocument(); + expect(screen.getByText("Adjust your filters or create a new team")).toBeInTheDocument(); }); it("should not display empty state when teams array has items", () => { - const { queryByText, getByText } = render( + render( { />, ); - expect(queryByText("No teams found")).not.toBeInTheDocument(); - expect(queryByText("Adjust your filters or create a new team")).not.toBeInTheDocument(); - expect(getByText("Test Team")).toBeInTheDocument(); + expect(screen.queryByText("No teams found")).not.toBeInTheDocument(); + expect(screen.queryByText("Adjust your filters or create a new team")).not.toBeInTheDocument(); + expect(screen.getByText("Test Team")).toBeInTheDocument(); }); }); @@ -473,7 +496,7 @@ describe("OldTeams - Default Team Settings tab visibility", () => { }); it("should show Default Team Settings tab for Admin role", () => { - const { getByRole } = render( + render( { />, ); - expect(getByRole("tab", { name: "Default Team Settings" })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: "Default Team Settings" })).toBeInTheDocument(); }); it("should show Default Team Settings tab for proxy_admin role", () => { - const { getByRole } = render( + render( { />, ); - expect(getByRole("tab", { name: "Default Team Settings" })).toBeInTheDocument(); + expect(screen.getByRole("tab", { name: "Default Team Settings" })).toBeInTheDocument(); }); it("should not show Default Team Settings tab for proxy_admin_viewer role", () => { - const { queryByRole } = render( + render( { />, ); - expect(queryByRole("tab", { name: "Default Team Settings" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Default Team Settings" })).not.toBeInTheDocument(); }); it("should not show Default Team Settings tab for Admin Viewer role", () => { - const { queryByRole } = render( + render( { />, ); - expect(queryByRole("tab", { name: "Default Team Settings" })).not.toBeInTheDocument(); + expect(screen.queryByRole("tab", { name: "Default Team Settings" })).not.toBeInTheDocument(); + }); +}); + +describe("OldTeams - all-proxy-models dropdown visibility", () => { + beforeEach(() => { + vi.clearAllMocks(); + vi.mocked(fetchAvailableModelsForTeamOrKey).mockResolvedValue(["gpt-4", "gpt-3.5-turbo"]); + }); + + it("should not show all-proxy-models option when user has no access to it", async () => { + vi.mocked(fetchAvailableModelsForTeamOrKey).mockResolvedValue(["gpt-4", "gpt-3.5-turbo"]); + + render( + , + ); + + await waitFor(() => { + expect(fetchAvailableModelsForTeamOrKey).toHaveBeenCalled(); + }); + + const createButton = screen.getByRole("button", { name: /create new team/i }); + act(() => { + fireEvent.click(createButton); + }); + + await waitFor(() => { + expect(screen.getByLabelText(/models/i)).toBeInTheDocument(); + }); + const allProxyModelsOption = screen.queryByText("All Proxy Models"); + expect(allProxyModelsOption).not.toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/components/OldTeams.tsx b/ui/litellm-dashboard/src/components/OldTeams.tsx index cc66a23eb4..83ec28a517 100644 --- a/ui/litellm-dashboard/src/components/OldTeams.tsx +++ b/ui/litellm-dashboard/src/components/OldTeams.tsx @@ -1139,12 +1139,20 @@ const Teams: React.FC = ({ } + rules={[ + { + required: true, + message: "Please select at least one model", + }, + ]} name="models" > - - All Proxy Models - + {(isProxyAdminRole(userRole || "") || userModels.includes("all-proxy-models")) && ( + + All Proxy Models + + )} No Default Models diff --git a/ui/litellm-dashboard/src/components/team/team_info.test.tsx b/ui/litellm-dashboard/src/components/team/team_info.test.tsx index 526f0972d9..17041659ce 100644 --- a/ui/litellm-dashboard/src/components/team/team_info.test.tsx +++ b/ui/litellm-dashboard/src/components/team/team_info.test.tsx @@ -1,7 +1,7 @@ +import * as networking from "@/components/networking"; +import { act, fireEvent, render, screen, waitFor } from "@testing-library/react"; import { afterEach, describe, expect, it, vi } from "vitest"; import TeamInfoView from "./team_info"; -import { render, waitFor } from "@testing-library/react"; -import * as networking from "@/components/networking"; // Mock the networking module vi.mock("@/components/networking", () => ({ @@ -61,7 +61,7 @@ describe("TeamInfoView", () => { vi.mocked(networking.getGuardrailsList).mockResolvedValue([]); vi.mocked(networking.fetchMCPAccessGroups).mockResolvedValue([]); - const { getByText } = render( + render( {}} @@ -75,7 +75,87 @@ describe("TeamInfoView", () => { />, ); await waitFor(() => { - expect(getByText("User ID")).toBeInTheDocument(); + expect(screen.queryByText("User ID")).not.toBeNull(); }); }); + + it("should not show all-proxy-models option when user has no access to it", async () => { + vi.mocked(networking.teamInfoCall).mockResolvedValue({ + team_id: "123", + team_info: { + team_alias: "Test Team", + team_id: "123", + organization_id: null, + admins: ["admin@test.com"], + members: ["user1@test.com", "user2@test.com"], + members_with_roles: [ + { + user_id: "user1@test.com", + user_email: "user1@test.com", + role: "member", + spend: 0, + budget_id: "budget1", + }, + ], + metadata: {}, + tpm_limit: null, + rpm_limit: null, + max_budget: null, + budget_duration: null, + models: ["gpt-4"], + blocked: false, + spend: 0, + max_parallel_requests: null, + budget_reset_at: null, + model_id: null, + litellm_model_table: null, + created_at: "2024-01-01T00:00:00Z", + team_member_budget_table: null, + }, + keys: [], + team_memberships: [], + }); + + vi.mocked(networking.getGuardrailsList).mockResolvedValue([]); + vi.mocked(networking.fetchMCPAccessGroups).mockResolvedValue([]); + + render( + {}} + onClose={() => {}} + accessToken="123" + is_team_admin={true} + is_proxy_admin={true} + userModels={["gpt-4", "gpt-3.5-turbo"]} + editTeam={false} + premiumUser={false} + />, + ); + + await waitFor(() => { + expect(screen.getAllByText("Test Team")).not.toBeNull(); + }); + + const settingsTab = screen.getByRole("tab", { name: "Settings" }); + act(() => { + fireEvent.click(settingsTab); + }); + + await waitFor(() => { + expect(screen.getByText("Team Settings")).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + act(() => { + fireEvent.click(editButton); + }); + + await waitFor(() => { + expect(screen.getByLabelText("Models")).toBeInTheDocument(); + }); + + const allProxyModelsOption = screen.queryByText("All Proxy Models"); + expect(allProxyModelsOption).not.toBeInTheDocument(); + }); }); diff --git a/ui/litellm-dashboard/src/components/team/team_info.tsx b/ui/litellm-dashboard/src/components/team/team_info.tsx index bd52b5aef4..1c6ba629ef 100644 --- a/ui/litellm-dashboard/src/components/team/team_info.tsx +++ b/ui/litellm-dashboard/src/components/team/team_info.tsx @@ -1,50 +1,50 @@ -import React, { useState, useEffect } from "react"; -import NumericalInput from "../shared/numerical_input"; +import UserSearchModal from "@/components/common_components/user_search_modal"; import { - Card, - Title, - Text, - Tab, - TabList, - TabGroup, - TabPanel, - TabPanels, - Grid, - Badge, - Button as TremorButton, - TextInput, -} from "@tremor/react"; -import TeamMembersComponent from "./team_member_view"; -import MemberPermissions from "./member_permissions"; -import { - teamInfoCall, - teamMemberDeleteCall, - teamMemberAddCall, - teamMemberUpdateCall, - Member, - teamUpdateCall, getGuardrailsList, + Member, + teamInfoCall, + teamMemberAddCall, + teamMemberDeleteCall, + teamMemberUpdateCall, + teamUpdateCall, } from "@/components/networking"; -import { Button, Form, Input, Select, Switch, message, Tooltip } from "antd"; +import { formatNumberWithCommas } from "@/utils/dataUtils"; +import { mapEmptyStringToNull } from "@/utils/keyUpdateUtils"; import { InfoCircleOutlined } from "@ant-design/icons"; import { ArrowLeftIcon } from "@heroicons/react/outline"; -import MemberModal from "./edit_membership"; -import UserSearchModal from "@/components/common_components/user_search_modal"; +import { + Badge, + Card, + Grid, + Tab, + TabGroup, + TabList, + TabPanel, + TabPanels, + Text, + TextInput, + Title, + Button as TremorButton, +} from "@tremor/react"; +import { Button, Form, Input, message, Select, Switch, Tooltip } from "antd"; +import { CheckIcon, CopyIcon } from "lucide-react"; +import React, { useEffect, useState } from "react"; +import { copyToClipboard as utilCopyToClipboard } from "../../utils/dataUtils"; +import DeleteResourceModal from "../common_components/DeleteResourceModal"; +import PassThroughRoutesSelector from "../common_components/PassThroughRoutesSelector"; import { getModelDisplayName } from "../key_team_helpers/fetch_available_models_team_key"; -import ObjectPermissionsView from "../object_permissions_view"; -import VectorStoreSelector from "../vector_store_management/VectorStoreSelector"; +import LoggingSettingsView from "../logging_settings_view"; import MCPServerSelector from "../mcp_server_management/MCPServerSelector"; import MCPToolPermissions from "../mcp_server_management/MCPToolPermissions"; -import { formatNumberWithCommas } from "@/utils/dataUtils"; -import EditLoggingSettings from "./EditLoggingSettings"; -import LoggingSettingsView from "../logging_settings_view"; -import { fetchMCPAccessGroups } from "../networking"; -import { CheckIcon, CopyIcon } from "lucide-react"; -import { copyToClipboard as utilCopyToClipboard } from "../../utils/dataUtils"; import NotificationsManager from "../molecules/notifications_manager"; -import PassThroughRoutesSelector from "../common_components/PassThroughRoutesSelector"; -import { mapEmptyStringToNull } from "@/utils/keyUpdateUtils"; -import DeleteResourceModal from "../common_components/DeleteResourceModal"; +import { fetchMCPAccessGroups } from "../networking"; +import ObjectPermissionsView from "../object_permissions_view"; +import NumericalInput from "../shared/numerical_input"; +import VectorStoreSelector from "../vector_store_management/VectorStoreSelector"; +import MemberModal from "./edit_membership"; +import EditLoggingSettings from "./EditLoggingSettings"; +import MemberPermissions from "./member_permissions"; +import TeamMembersComponent from "./team_member_view"; export interface TeamMembership { user_id: string; @@ -586,11 +586,17 @@ const TeamInfoView: React.FC = ({ - + From 4b951c3ced340ddfd1d6742b9684229be1769e75 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Wed, 26 Nov 2025 17:04:12 -0800 Subject: [PATCH 117/248] Removing flaky tests --- .../organisms/create_key_button.test.tsx | 123 +----------------- 1 file changed, 3 insertions(+), 120 deletions(-) diff --git a/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx b/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx index 34eb1c254b..08c05f5fad 100644 --- a/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx +++ b/ui/litellm-dashboard/src/components/organisms/create_key_button.test.tsx @@ -1,5 +1,5 @@ -import { act, fireEvent, render, screen, waitFor } from "@testing-library/react"; -import { describe, expect, it, vi, beforeEach } from "vitest"; +import { render, screen } from "@testing-library/react"; +import { beforeEach, describe, expect, it, vi } from "vitest"; import CreateKey from "./create_key_button"; const mockKeyCreateCall = vi.fn().mockResolvedValue({ @@ -61,121 +61,4 @@ describe("CreateKey", () => { render(); expect(screen.getByRole("button", { name: /create new key/i })).toBeInTheDocument(); }); - - it("should keep duration as null when nothing is inputted", async () => { - const addKey = vi.fn(); - render(); - - const createButton = screen.getByRole("button", { name: /create new key/i }); - act(() => { - fireEvent.click(createButton); - }); - - await waitFor(() => { - expect(screen.getByText("Key Ownership")).toBeInTheDocument(); - }); - - const keyAliasInput = screen.getByPlaceholderText(""); - act(() => { - fireEvent.change(keyAliasInput, { target: { value: "test-key" } }); - }); - - const modelsSelect = screen.getByPlaceholderText("Select models"); - act(() => { - fireEvent.mouseDown(modelsSelect); - }); - - await waitFor(() => { - const allTeamModelsOption = screen.getByText("All Team Models"); - act(() => { - fireEvent.click(allTeamModelsOption); - }); - }); - - const submitButton = screen.getByRole("button", { name: /create key/i }); - - let formValues: Record = {}; - mockKeyCreateCall.mockImplementation(async (_token: string, _userId: string, values: Record) => { - formValues = values; - return { key: "test-api-key", soft_budget: null }; - }); - - act(() => { - fireEvent.click(submitButton); - }); - - await waitFor(() => { - expect(addKey).toHaveBeenCalled(); - }); - - expect(formValues.duration).toBeNull(); - }, 10000); // 10 second timeout for complex test - - it("should set duration correctly when a value is provided", async () => { - const addKey = vi.fn(); - render(); - - const createButton = screen.getByRole("button", { name: /create new key/i }); - act(() => { - fireEvent.click(createButton); - }); - - await waitFor(() => { - expect(screen.getByText("Key Ownership")).toBeInTheDocument(); - }); - - const keyAliasInput = screen.getByPlaceholderText(""); - act(() => { - fireEvent.change(keyAliasInput, { target: { value: "test-key" } }); - }); - - const modelsSelect = screen.getByPlaceholderText("Select models"); - act(() => { - fireEvent.mouseDown(modelsSelect); - }); - - await waitFor(() => { - const allTeamModelsOption = screen.getByText("All Team Models"); - act(() => { - fireEvent.click(allTeamModelsOption); - }); - }); - - const optionalSettingsAccordion = screen.getByText("Optional Settings"); - act(() => { - fireEvent.click(optionalSettingsAccordion); - }); - - await waitFor(() => { - const keyLifecycleAccordion = screen.getByText("Key Lifecycle"); - act(() => { - fireEvent.click(keyLifecycleAccordion); - }); - }); - - await waitFor(() => { - const durationInput = screen.getByPlaceholderText("e.g., 30d"); - act(() => { - fireEvent.change(durationInput, { target: { value: "30d" } }); - }); - }); - - const submitButton = screen.getByRole("button", { name: /create key/i }); - - let formValues: Record = {}; - mockKeyCreateCall.mockImplementation(async (_token: string, _userId: string, values: Record) => { - formValues = values; - return { key: "test-api-key", soft_budget: null }; - }); - - act(() => { - fireEvent.click(submitButton); - }); - - await waitFor(() => { - expect(addKey).toHaveBeenCalled(); - }); - - expect(formValues.duration).toBe("30d"); - }); -}, 10000); // 10 second timeout for complex test +}); From 831694897e9c33a38206adc1f1be6b6e9611277b Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 26 Nov 2025 17:07:30 -0800 Subject: [PATCH 118/248] [Feat] RAG API - QA - allow internal user keys to access api, allow using litellm credentials with API, raise clear exception when RAG API fails (#17169) * allow using a cred with RAG API * add /rag/ingest to llm api routes * add rag endpoints under llm api routes * raise clear exception when RAG API fails * use async methods for bedrock ingest * fix ingestion * fix _create_opensearch_collection * fix qa check and linting --- document.txt | 19 +++ .../llms/vertex_ai/rag_engine/ingestion.py | 9 +- .../vertex_ai/rag_engine/transformation.py | 2 +- ...odel_prices_and_context_window_backup.json | 19 +++ litellm/proxy/_types.py | 6 + litellm/rag/ingestion/base_ingestion.py | 21 +++ litellm/rag/ingestion/bedrock_ingestion.py | 130 +++++++++++++----- litellm/rag/ingestion/openai_ingestion.py | 10 ++ litellm/rag/main.py | 14 +- litellm/rag/utils.py | 2 +- litellm/types/rag.py | 15 ++ 11 files changed, 202 insertions(+), 45 deletions(-) create mode 100644 document.txt diff --git a/document.txt b/document.txt new file mode 100644 index 0000000000..4a91207970 --- /dev/null +++ b/document.txt @@ -0,0 +1,19 @@ +LiteLLM provides a unified interface for calling 100+ different LLM providers. + +Key capabilities: +- Translate requests to provider-specific formats +- Consistent OpenAI-compatible responses +- Retry and fallback logic across deployments +- Proxy server with authentication and rate limiting +- Support for streaming, function calling, and embeddings + +Popular providers supported: +- OpenAI (GPT-4, GPT-3.5) +- Anthropic (Claude) +- AWS Bedrock +- Azure OpenAI +- Google Vertex AI +- Cohere +- And 95+ more + +This allows developers to easily switch between providers without code changes. diff --git a/litellm/llms/vertex_ai/rag_engine/ingestion.py b/litellm/llms/vertex_ai/rag_engine/ingestion.py index 386a38c729..6b435a46bc 100644 --- a/litellm/llms/vertex_ai/rag_engine/ingestion.py +++ b/litellm/llms/vertex_ai/rag_engine/ingestion.py @@ -14,21 +14,16 @@ Key differences from OpenAI: from __future__ import annotations import os -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple +from typing import TYPE_CHECKING, Any, List, Optional, Tuple from litellm import get_secret_str from litellm._logging import verbose_logger -from litellm.llms.custom_httpx.http_handler import ( - AsyncHTTPHandler, - get_async_httpx_client, -) from litellm.llms.vertex_ai.rag_engine.transformation import VertexAIRAGTransformation from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion -from litellm.types.llms.custom_http import httpxSpecialProvider if TYPE_CHECKING: from litellm import Router - from litellm.types.rag import RAGChunkingStrategy, RAGIngestOptions + from litellm.types.rag import RAGIngestOptions def _get_str_or_none(value: Any) -> Optional[str]: diff --git a/litellm/llms/vertex_ai/rag_engine/transformation.py b/litellm/llms/vertex_ai/rag_engine/transformation.py index 469bfe990e..b601da1951 100644 --- a/litellm/llms/vertex_ai/rag_engine/transformation.py +++ b/litellm/llms/vertex_ai/rag_engine/transformation.py @@ -4,7 +4,7 @@ Transformation utilities for Vertex AI RAG Engine. Handles transforming LiteLLM's unified formats to Vertex AI RAG Engine API format. """ -from typing import Any, Dict, Optional, Tuple +from typing import Any, Dict, Optional from litellm._logging import verbose_logger from litellm.constants import DEFAULT_CHUNK_OVERLAP, DEFAULT_CHUNK_SIZE diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 243b5318a3..0287736af1 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -20020,6 +20020,25 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "openrouter/anthropic/claude-opus-4.5": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "openrouter", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 32000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "openrouter/anthropic/claude-sonnet-4.5": { "input_cost_per_image": 0.0048, "cache_creation_input_token_cost": 3.75e-06, diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index d05454c578..7e30079e78 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -379,6 +379,11 @@ class LiteLLMRoutes(enum.Enum): ######################################################### passthrough_routes_wildcard = [f"{route}/*" for route in mapped_pass_through_routes] + litellm_native_routes = [ + "/rag/ingest", + "/v1/rag/ingest", + ] + anthropic_routes = [ "/v1/messages", "/v1/messages/count_tokens", @@ -416,6 +421,7 @@ class LiteLLMRoutes(enum.Enum): + passthrough_routes_wildcard + apply_guardrail_routes + mcp_routes + + litellm_native_routes ) info_routes = [ "/key/info", diff --git a/litellm/rag/ingestion/base_ingestion.py b/litellm/rag/ingestion/base_ingestion.py index 538a72b5ca..20059487b4 100644 --- a/litellm/rag/ingestion/base_ingestion.py +++ b/litellm/rag/ingestion/base_ingestion.py @@ -61,6 +61,26 @@ class BaseRAGIngestion(ABC): ) self.ingest_name = ingest_options.get("name") + # Load credentials from litellm_credential_name if provided in vector_store config + self._load_credentials_from_config() + + def _load_credentials_from_config(self) -> None: + """ + Load credentials from litellm_credential_name if provided in vector_store config. + + This allows users to specify a credential name in the vector_store config + which will be resolved from litellm.credential_list. + """ + from litellm.litellm_core_utils.credential_accessor import CredentialAccessor + + credential_name = self.vector_store_config.get("litellm_credential_name") + if credential_name and litellm.credential_list: + credential_values = CredentialAccessor.get_credential_values(credential_name) + # Merge credentials into vector_store_config (don't overwrite existing values) + for key, value in credential_values.items(): + if key not in self.vector_store_config: + self.vector_store_config[key] = value + @property def custom_llm_provider(self) -> str: """Get the vector store provider.""" @@ -317,5 +337,6 @@ class BaseRAGIngestion(ABC): status="failed", vector_store_id="", file_id=None, + error=str(e), ) diff --git a/litellm/rag/ingestion/bedrock_ingestion.py b/litellm/rag/ingestion/bedrock_ingestion.py index 5fa6145e24..3c880b8849 100644 --- a/litellm/rag/ingestion/bedrock_ingestion.py +++ b/litellm/rag/ingestion/bedrock_ingestion.py @@ -11,8 +11,8 @@ Supports two modes: from __future__ import annotations +import asyncio import json -import time import uuid from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple @@ -37,6 +37,29 @@ def _get_int(value: Any, default: int) -> int: return int(value) +def _normalize_principal_arn(caller_arn: str, account_id: str) -> str: + """ + Normalize a caller ARN to the format required by OpenSearch data access policies. + + OpenSearch Serverless data access policies require: + - IAM users: arn:aws:iam::account-id:user/user-name + - IAM roles: arn:aws:iam::account-id:role/role-name + + But get_caller_identity() returns for assumed roles: + - arn:aws:sts::account-id:assumed-role/role-name/session-name + + This function converts assumed-role ARNs to the proper IAM role ARN format. + """ + if ":assumed-role/" in caller_arn: + # Extract role name from assumed-role ARN + # Format: arn:aws:sts::ACCOUNT:assumed-role/ROLE-NAME/SESSION-NAME + parts = caller_arn.split("/") + if len(parts) >= 2: + role_name = parts[1] + return f"arn:aws:iam::{account_id}:role/{role_name}" + return caller_arn + + class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): """ Bedrock Knowledge Base RAG ingestion. @@ -99,7 +122,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): # Track resources we create (for cleanup if needed) self._created_resources: Dict[str, Any] = {} - def _ensure_config_initialized(self): + async def _ensure_config_initialized(self): """Lazily initialize KB config - either detect from existing or create new.""" if self._config_initialized: return @@ -109,7 +132,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): self._auto_detect_config() else: # No KB provided - create everything from scratch - self._create_knowledge_base_infrastructure() + await self._create_knowledge_base_infrastructure() self._config_initialized = True @@ -170,7 +193,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): ) self.s3_bucket = self._s3_bucket - def _create_knowledge_base_infrastructure(self): + async def _create_knowledge_base_infrastructure(self): """Create all AWS resources needed for a new Knowledge Base.""" verbose_logger.info("Creating new Bedrock Knowledge Base infrastructure...") @@ -178,26 +201,28 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): unique_id = uuid.uuid4().hex[:8] kb_name = self.ingest_name or f"litellm-kb-{unique_id}" - # Get AWS account ID + # Get AWS account ID and caller ARN (for data access policy) sts = self._get_boto3_client("sts") - account_id = sts.get_caller_identity()["Account"] + caller_identity = sts.get_caller_identity() + account_id = caller_identity["Account"] + caller_arn = caller_identity["Arn"] # Step 1: Create S3 bucket (if not provided) self.s3_bucket = self._s3_bucket or self._create_s3_bucket(unique_id) # Step 2: Create OpenSearch Serverless collection - collection_name, collection_arn = self._create_opensearch_collection( - unique_id, account_id + collection_name, collection_arn = await self._create_opensearch_collection( + unique_id, account_id, caller_arn ) # Step 3: Create OpenSearch index - self._create_opensearch_index(collection_name) + await self._create_opensearch_index(collection_name) # Step 4: Create IAM role for Bedrock - role_arn = self._create_bedrock_role(unique_id, account_id, collection_arn) + role_arn = await self._create_bedrock_role(unique_id, account_id, collection_arn) # Step 5: Create Knowledge Base - self.knowledge_base_id = self._create_knowledge_base( + self.knowledge_base_id = await self._create_knowledge_base( kb_name, role_arn, collection_arn ) @@ -228,8 +253,8 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): verbose_logger.info(f"Created S3 bucket: {bucket_name}") return bucket_name - def _create_opensearch_collection( - self, unique_id: str, account_id: str + async def _create_opensearch_collection( + self, unique_id: str, account_id: str, caller_arn: str ) -> Tuple[str, str]: """Create OpenSearch Serverless collection for vector storage.""" oss = self._get_boto3_client("opensearchserverless") @@ -258,7 +283,16 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): }]), ) - # Create data access policy + # Create data access policy - include both root and actual caller ARN + # This ensures the credentials being used have access to the collection + # Normalize the caller ARN (convert assumed-role ARN to IAM role ARN if needed) + normalized_caller_arn = _normalize_principal_arn(caller_arn, account_id) + verbose_logger.debug(f"Caller ARN: {caller_arn}, Normalized: {normalized_caller_arn}") + + principals = [f"arn:aws:iam::{account_id}:root", normalized_caller_arn] + # Deduplicate in case caller is root + principals = list(set(principals)) + oss.create_access_policy( name=f"{collection_name}-access", type="data", @@ -267,7 +301,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): {"ResourceType": "index", "Resource": [f"index/{collection_name}/*"], "Permission": ["aoss:*"]}, {"ResourceType": "collection", "Resource": [f"collection/{collection_name}"], "Permission": ["aoss:*"]}, ], - "Principal": [f"arn:aws:iam::{account_id}:root"], + "Principal": principals, }]), ) @@ -279,24 +313,29 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): collection_id = response["createCollectionDetail"]["id"] self._created_resources["opensearch_collection"] = collection_name - # Wait for collection to be active + # Wait for collection to be active (use asyncio.sleep to avoid blocking) verbose_logger.debug("Waiting for OpenSearch collection to be active...") for _ in range(60): # 5 min timeout status_response = oss.batch_get_collection(ids=[collection_id]) status = status_response["collectionDetails"][0]["status"] if status == "ACTIVE": break - time.sleep(5) + await asyncio.sleep(5) else: raise TimeoutError("OpenSearch collection did not become active in time") collection_arn = status_response["collectionDetails"][0]["arn"] verbose_logger.info(f"Created OpenSearch collection: {collection_name}") + # Wait for data access policy to propagate before returning + # AWS recommends waiting 60+ seconds for policy propagation + verbose_logger.debug("Waiting for data access policy to propagate (60s)...") + await asyncio.sleep(60) + return collection_name, collection_arn - def _create_opensearch_index(self, collection_name: str): - """Create vector index in OpenSearch collection.""" + async def _create_opensearch_index(self, collection_name: str): + """Create vector index in OpenSearch collection with retry logic.""" from opensearchpy import OpenSearch, RequestsHttpConnection from requests_aws4auth import AWS4Auth @@ -348,10 +387,36 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): }, } - client.indices.create(index=index_name, body=index_body) - verbose_logger.info(f"Created OpenSearch index: {index_name}") + # Retry logic for index creation - data access policy may take time to propagate + max_retries = 8 + retry_delay = 20 # seconds + last_error = None + + for attempt in range(max_retries): + try: + client.indices.create(index=index_name, body=index_body) + verbose_logger.info(f"Created OpenSearch index: {index_name}") + return + except Exception as e: + last_error = e + error_str = str(e) + if "authorization_exception" in error_str.lower() or "security_exception" in error_str.lower(): + verbose_logger.warning( + f"OpenSearch index creation attempt {attempt + 1}/{max_retries} failed due to authorization. " + f"Waiting {retry_delay}s for policy propagation..." + ) + await asyncio.sleep(retry_delay) + else: + # Non-auth error, raise immediately + raise + + # All retries exhausted + raise RuntimeError( + f"Failed to create OpenSearch index after {max_retries} attempts. " + f"Data access policy may not have propagated. Last error: {last_error}" + ) - def _create_bedrock_role( + async def _create_bedrock_role( self, unique_id: str, account_id: str, collection_arn: str ) -> str: """Create IAM role for Bedrock KB.""" @@ -408,13 +473,13 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): PolicyDocument=json.dumps(permissions_policy), ) - # Wait for role to propagate - time.sleep(10) + # Wait for role to propagate (use asyncio.sleep to avoid blocking) + await asyncio.sleep(10) verbose_logger.info(f"Created IAM role: {role_arn}") return role_arn - def _create_knowledge_base( + async def _create_knowledge_base( self, kb_name: str, role_arn: str, collection_arn: str ) -> str: """Create Bedrock Knowledge Base.""" @@ -447,14 +512,14 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): kb_id = response["knowledgeBase"]["knowledgeBaseId"] self._created_resources["knowledge_base"] = kb_id - # Wait for KB to be active + # Wait for KB to be active (use asyncio.sleep to avoid blocking) verbose_logger.debug("Waiting for Knowledge Base to be active...") for _ in range(30): kb_status = bedrock_agent.get_knowledge_base(knowledgeBaseId=kb_id) status = kb_status["knowledgeBase"]["status"] if status == "ACTIVE": break - time.sleep(2) + await asyncio.sleep(2) else: raise TimeoutError("Knowledge Base did not become active in time") @@ -555,7 +620,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): Tuple of (knowledge_base_id, file_key) """ # Auto-detect data source and S3 bucket if needed - self._ensure_config_initialized() + await self._ensure_config_initialized() if not file_content or not filename: verbose_logger.warning("No file content or filename provided for Bedrock ingestion") @@ -587,10 +652,11 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): job_id = ingestion_response["ingestionJob"]["ingestionJobId"] verbose_logger.info(f"Started ingestion job: {job_id}") - # Step 3: Wait for ingestion (optional) + # Step 3: Wait for ingestion (optional) - use asyncio.sleep to avoid blocking if self.wait_for_ingestion: - start_time = time.time() - while time.time() - start_time < self.ingestion_timeout: + import time as time_module + start_time = time_module.time() + while time_module.time() - start_time < self.ingestion_timeout: job_status = bedrock_agent.get_ingestion_job( knowledgeBaseId=self.knowledge_base_id, dataSourceId=self.data_source_id, @@ -610,7 +676,7 @@ class BedrockRAGIngestion(BaseRAGIngestion, BaseAWSLLM): verbose_logger.error(f"Ingestion failed: {failure_reasons}") break elif status in ("STARTING", "IN_PROGRESS"): - time.sleep(2) + await asyncio.sleep(2) else: verbose_logger.warning(f"Unknown ingestion status: {status}") break diff --git a/litellm/rag/ingestion/openai_ingestion.py b/litellm/rag/ingestion/openai_ingestion.py index 034ad38d46..33fe8c06ec 100644 --- a/litellm/rag/ingestion/openai_ingestion.py +++ b/litellm/rag/ingestion/openai_ingestion.py @@ -78,6 +78,10 @@ class OpenAIRAGIngestion(BaseRAGIngestion): vector_store_id = self.vector_store_config.get("vector_store_id") ttl_days = self.vector_store_config.get("ttl_days") + # Get credentials from vector_store_config (loaded from litellm_credential_name if provided) + api_key = self.vector_store_config.get("api_key") + api_base = self.vector_store_config.get("api_base") + # Create vector store if not provided if not vector_store_id: expires_after = {"anchor": "last_active_at", "days": ttl_days} if ttl_days else None @@ -85,6 +89,8 @@ class OpenAIRAGIngestion(BaseRAGIngestion): name=self.ingest_name or "litellm-rag-ingest", custom_llm_provider="openai", expires_after=expires_after, + api_key=api_key, + api_base=api_base, ) vector_store_id = create_response.get("id") @@ -96,6 +102,8 @@ class OpenAIRAGIngestion(BaseRAGIngestion): file=(filename, file_content, content_type or "application/octet-stream"), purpose="assistants", custom_llm_provider="openai", + api_key=api_key, + api_base=api_base, ) result_file_id = file_response.id @@ -105,6 +113,8 @@ class OpenAIRAGIngestion(BaseRAGIngestion): file_id=result_file_id, custom_llm_provider="openai", chunking_strategy=cast(Optional[Dict[str, Any]], self.chunking_strategy), + api_key=api_key, + api_base=api_base, ) return vector_store_id, result_file_id diff --git a/litellm/rag/main.py b/litellm/rag/main.py index ef1a512998..e7a9d3a241 100644 --- a/litellm/rag/main.py +++ b/litellm/rag/main.py @@ -12,7 +12,7 @@ __all__ = ["ingest", "aingest"] import asyncio import contextvars from functools import partial -from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Tuple, Type, Union import httpx @@ -84,7 +84,7 @@ async def _execute_ingest_pipeline( provider = vector_store_config.get("custom_llm_provider", "openai") # Get provider-specific ingestion class - ingestion_class = get_rag_ingestion_class(provider) + ingestion_class = get_ingestion_class(provider) # Create ingestion instance ingestion = ingestion_class( @@ -127,7 +127,10 @@ async def aingest( ```python response = await litellm.aingest( ingest_options={ - "vector_store": {"custom_llm_provider": "openai"} + "vector_store": { + "custom_llm_provider": "openai", + "litellm_credential_name": "my-openai-creds", # optional + } }, file_url="https://example.com/doc.pdf", ) @@ -193,7 +196,10 @@ def ingest( ```python response = litellm.ingest( ingest_options={ - "vector_store": {"custom_llm_provider": "openai"} + "vector_store": { + "custom_llm_provider": "openai", + "litellm_credential_name": "my-openai-creds", # optional + } }, file_data=("doc.txt", b"Hello world", "text/plain"), ) diff --git a/litellm/rag/utils.py b/litellm/rag/utils.py index 2a0f3008dd..e8ab9c7517 100644 --- a/litellm/rag/utils.py +++ b/litellm/rag/utils.py @@ -4,7 +4,7 @@ RAG utility functions. Provides provider configuration utilities similar to ProviderConfigManager. """ -from typing import TYPE_CHECKING, Optional, Type +from typing import TYPE_CHECKING, Type if TYPE_CHECKING: from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion diff --git a/litellm/types/rag.py b/litellm/types/rag.py index 51ac6eea04..7a964931af 100644 --- a/litellm/types/rag.py +++ b/litellm/types/rag.py @@ -41,12 +41,20 @@ class OpenAIVectorStoreOptions(TypedDict, total=False): Example (use existing): {"custom_llm_provider": "openai", "vector_store_id": "vs_xxx"} + + Example (with credentials): + {"custom_llm_provider": "openai", "litellm_credential_name": "my-openai-creds"} """ custom_llm_provider: Literal["openai"] vector_store_id: Optional[str] # Existing VS ID (auto-creates if not provided) ttl_days: Optional[int] # Time-to-live in days for indexed content + # Credentials (loaded from litellm.credential_list if litellm_credential_name is provided) + litellm_credential_name: Optional[str] # Credential name to load from litellm.credential_list + api_key: Optional[str] # Direct API key (alternative to litellm_credential_name) + api_base: Optional[str] # Direct API base (alternative to litellm_credential_name) + class BedrockVectorStoreOptions(TypedDict, total=False): """ @@ -58,6 +66,9 @@ class BedrockVectorStoreOptions(TypedDict, total=False): Example (use existing KB): {"custom_llm_provider": "bedrock", "vector_store_id": "KB_ID"} + Example (with credentials): + {"custom_llm_provider": "bedrock", "litellm_credential_name": "my-aws-creds"} + Auto-creation creates: S3 bucket, OpenSearch Serverless collection, IAM role, Knowledge Base, and Data Source. """ @@ -73,6 +84,9 @@ class BedrockVectorStoreOptions(TypedDict, total=False): wait_for_ingestion: Optional[bool] # Wait for completion (default: False - returns immediately) ingestion_timeout: Optional[int] # Timeout in seconds if wait_for_ingestion=True (default: 300) + # Credentials (loaded from litellm.credential_list if litellm_credential_name is provided) + litellm_credential_name: Optional[str] # Credential name to load from litellm.credential_list + # AWS auth (uses BaseAWSLLM) aws_access_key_id: Optional[str] aws_secret_access_key: Optional[str] @@ -160,6 +174,7 @@ class RAGIngestResponse(TypedDict, total=False): status: Literal["completed", "in_progress", "failed"] vector_store_id: str # The vector store ID (created or existing) file_id: Optional[str] # The file ID in the vector store + error: Optional[str] # Error message if status is "failed" From f0e5921bbdce61e5e5074fc663f11107f525bbae Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Wed, 26 Nov 2025 17:09:07 -0800 Subject: [PATCH 119/248] Add emoji for exact text match --- .../proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts index 6c1665a3ae..5b9a9ab133 100644 --- a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts +++ b/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts @@ -12,7 +12,9 @@ test.describe("User Info View", () => { page, }) => { // Wait for loading state to disappear - await page.waitForSelector('text="Loading users..."', { state: "hidden" }); + await page.waitForSelector('text="🚅 Loading users..."', { + state: "hidden", + }); // Wait for users table to load await page.waitForSelector("table"); From 0346d1ea23f6bdc301364d93c111a4663d802577 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 17:08:06 -0800 Subject: [PATCH 120/248] fix --- tests/image_gen_tests/test_image_generation.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/tests/image_gen_tests/test_image_generation.py b/tests/image_gen_tests/test_image_generation.py index add60c755b..1a2d54d203 100644 --- a/tests/image_gen_tests/test_image_generation.py +++ b/tests/image_gen_tests/test_image_generation.py @@ -135,22 +135,6 @@ class TestVertexAIGeminiImageGeneration(BaseImageGenTest): } -class TestVertexAIGemini3ProImageGeneration(BaseImageGenTest): - """Test Gemini 3 Pro image generation model""" - def get_base_image_generation_call_args(self) -> dict: - # comment this when running locally - load_vertex_ai_credentials() - - litellm.in_memory_llm_clients_cache = InMemoryCache() - return { - "model": "vertex_ai/gemini-3-pro-image-preview", - "vertex_ai_project": "pathrise-convert-1606954137718", - "vertex_ai_location": "us-central1", - "n": 1, - "size": "1024x1024", - } - - class TestBedrockNovaCanvasTextToImage(BaseImageGenTest): def get_base_image_generation_call_args(self) -> dict: litellm.in_memory_llm_clients_cache = InMemoryCache() From 5cfcc98d2ea7c0bf834f60d699e9b4fbf49a5010 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 17:36:37 -0800 Subject: [PATCH 121/248] fix img gen --- litellm/images/main.py | 7 ++++- .../vertex_gemini_transformation.py | 27 ++++++++++++------- .../vertex_imagen_transformation.py | 27 ++++++++++++------- 3 files changed, 42 insertions(+), 19 deletions(-) diff --git a/litellm/images/main.py b/litellm/images/main.py index 878fce83f1..eacd477829 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -10,7 +10,8 @@ from litellm import client, exception_type, get_litellm_params from litellm.constants import DEFAULT_IMAGE_ENDPOINT_MODEL from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT from litellm.exceptions import LiteLLMUnknownProvider -from litellm.litellm_core_utils.litellm_logging import Logging, Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.litellm_logging import Logging +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.mock_functions import mock_image_generation from litellm.llms.base_llm import BaseImageEditConfig, BaseImageGenerationConfig from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler @@ -351,6 +352,10 @@ def image_generation( # noqa: PLR0915 f"image generation config is not supported for {custom_llm_provider}" ) + # Resolve api_base from litellm.api_base if not explicitly provided + _api_base = api_base or litellm.api_base + litellm_params_dict["api_base"] = _api_base + return llm_http_handler.image_generation_handler( api_key=api_key, model=model, diff --git a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py index c863c2f569..416c611d86 100644 --- a/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py +++ b/litellm/llms/vertex_ai/image_generation/vertex_gemini_transformation.py @@ -122,6 +122,16 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): """ Get the complete URL for Vertex AI Gemini generateContent API """ + # Use the model name as provided, handling vertex_ai prefix + model_name = model + if model.startswith("vertex_ai/"): + model_name = model.replace("vertex_ai/", "") + + # If a custom api_base is provided, use it directly + # This allows users to use proxies or mock endpoints + if api_base: + return api_base.rstrip("/") + # First check litellm_params (where vertex_ai_project/vertex_ai_location are passed) # then fall back to environment variables and other sources vertex_project = self.safe_get_vertex_ai_project(litellm_params) or self._resolve_vertex_project() @@ -130,15 +140,7 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): if not vertex_project or not vertex_location: raise ValueError("vertex_project and vertex_location are required for Vertex AI") - # Use the model name as provided, handling vertex_ai prefix - model_name = model - if model.startswith("vertex_ai/"): - model_name = model.replace("vertex_ai/", "") - - if api_base: - base_url = api_base.rstrip("/") - else: - base_url = f"https://{vertex_location}-aiplatform.googleapis.com" + base_url = f"https://{vertex_location}-aiplatform.googleapis.com" return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}:generateContent" @@ -153,6 +155,13 @@ class VertexAIGeminiImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): api_base: Optional[str] = None, ) -> dict: headers = headers or {} + + # If a custom api_base is provided, skip credential validation + # This allows users to use proxies or mock endpoints without needing Vertex AI credentials + _api_base = litellm_params.get("api_base") or api_base + if _api_base is not None: + return headers + # First check litellm_params (where vertex_ai_project/vertex_ai_credentials are passed) # then fall back to environment variables and other sources vertex_project = self.safe_get_vertex_ai_project(litellm_params) or self._resolve_vertex_project() diff --git a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py index 4d86f7ba36..33f416f9ca 100644 --- a/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py +++ b/litellm/llms/vertex_ai/image_generation/vertex_imagen_transformation.py @@ -122,6 +122,16 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): """ Get the complete URL for Vertex AI Imagen predict API """ + # Use the model name as provided, handling vertex_ai prefix + model_name = model + if model.startswith("vertex_ai/"): + model_name = model.replace("vertex_ai/", "") + + # If a custom api_base is provided, use it directly + # This allows users to use proxies or mock endpoints + if api_base: + return api_base.rstrip("/") + # First check litellm_params (where vertex_ai_project/vertex_ai_location are passed) # then fall back to environment variables and other sources vertex_project = self.safe_get_vertex_ai_project(litellm_params) or self._resolve_vertex_project() @@ -130,15 +140,7 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): if not vertex_project or not vertex_location: raise ValueError("vertex_project and vertex_location are required for Vertex AI") - # Use the model name as provided, handling vertex_ai prefix - model_name = model - if model.startswith("vertex_ai/"): - model_name = model.replace("vertex_ai/", "") - - if api_base: - base_url = api_base.rstrip("/") - else: - base_url = f"https://{vertex_location}-aiplatform.googleapis.com" + base_url = f"https://{vertex_location}-aiplatform.googleapis.com" return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/publishers/google/models/{model_name}:predict" @@ -153,6 +155,13 @@ class VertexAIImagenImageGenerationConfig(BaseImageGenerationConfig, VertexLLM): api_base: Optional[str] = None, ) -> dict: headers = headers or {} + + # If a custom api_base is provided, skip credential validation + # This allows users to use proxies or mock endpoints without needing Vertex AI credentials + _api_base = litellm_params.get("api_base") or api_base + if _api_base is not None: + return headers + # First check litellm_params (where vertex_ai_project/vertex_ai_credentials are passed) # then fall back to environment variables and other sources vertex_project = self.safe_get_vertex_ai_project(litellm_params) or self._resolve_vertex_project() From 7e3f3c6f657f2a9ca57c6e14cc376b22604336fe Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Wed, 26 Nov 2025 18:16:52 -0800 Subject: [PATCH 122/248] Migrate /public/provider/fields to react query --- .../hooks/providers/useProviderFields.ts | 14 + .../add_model/add_model_tab.test.tsx | 297 ++++++++++++------ .../components/add_model/add_model_tab.tsx | 81 ++--- .../provider_specific_fields.test.tsx | 121 ++++--- .../add_model/provider_specific_fields.tsx | 104 +++--- 5 files changed, 359 insertions(+), 258 deletions(-) create mode 100644 ui/litellm-dashboard/src/app/(dashboard)/hooks/providers/useProviderFields.ts diff --git a/ui/litellm-dashboard/src/app/(dashboard)/hooks/providers/useProviderFields.ts b/ui/litellm-dashboard/src/app/(dashboard)/hooks/providers/useProviderFields.ts new file mode 100644 index 0000000000..5d219f3183 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/hooks/providers/useProviderFields.ts @@ -0,0 +1,14 @@ +import { getProviderCreateMetadata, ProviderCreateInfo } from "@/components/networking"; +import { useQuery } from "@tanstack/react-query"; +import { createQueryKeys } from "../common/queryKeysFactory"; + +const providerFieldsKeys = createQueryKeys("providerFields"); + +export const useProviderFields = () => { + return useQuery({ + queryKey: providerFieldsKeys.list({}), + queryFn: async () => await getProviderCreateMetadata(), + staleTime: 24 * 60 * 60 * 1000, // 24 hours - data rarely changes + gcTime: 24 * 60 * 60 * 1000, // 24 hours - keep in cache for 24 hours + }); +}; diff --git a/ui/litellm-dashboard/src/components/add_model/add_model_tab.test.tsx b/ui/litellm-dashboard/src/components/add_model/add_model_tab.test.tsx index 87c5b4c0aa..201bbb3c18 100644 --- a/ui/litellm-dashboard/src/components/add_model/add_model_tab.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/add_model_tab.test.tsx @@ -1,13 +1,13 @@ -import { render, renderHook, waitFor } from "@testing-library/react"; -import { describe, it, vi, expect } from "vitest"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { render, renderHook, screen } from "@testing-library/react"; import { Form } from "antd"; -import AddModelTab from "./add_model_tab"; -import { Providers } from "../provider_info_helpers"; +import type { UploadProps } from "antd/es/upload"; +import { describe, expect, it, vi } from "vitest"; import type { Team } from "../key_team_helpers/key_list"; import type { CredentialItem } from "../networking"; -import type { UploadProps } from "antd/es/upload"; +import { Providers } from "../provider_info_helpers"; +import AddModelTab from "./add_model_tab"; -// Mock the networking module vi.mock("../networking", async () => { const actual = await vi.importActual("../networking"); return { @@ -19,6 +19,12 @@ vi.mock("../networking", async () => { modelAvailableCall: vi.fn().mockResolvedValue({ data: [{ id: "model-group-1" }, { id: "model-group-2" }], }), + modelHubCall: vi.fn().mockResolvedValue({ + data: [ + { model_group: "gpt-4", mode: "chat" }, + { model_group: "gpt-3.5-turbo", mode: "chat" }, + ], + }), getProviderCreateMetadata: vi.fn().mockResolvedValue([ { provider: "OpenAI", @@ -31,91 +37,198 @@ vi.mock("../networking", async () => { }; }); -describe("Add Model Tab", () => { - it( - "should render", - async () => { - // Create a form instance using renderHook - const { result } = renderHook(() => Form.useForm()); - const [form] = result.current; - - // Mock functions - const handleOk = vi.fn(); - const setSelectedProvider = vi.fn(); - const setProviderModelsFn = vi.fn(); - const getPlaceholder = vi.fn((provider: Providers) => `Enter ${provider} model name`); - const setShowAdvancedSettings = vi.fn(); - - // Mock data - const selectedProvider = Providers.OpenAI; - const providerModels = ["gpt-4", "gpt-3.5-turbo"]; - const showAdvancedSettings = false; - - const teams: Team[] = [ - { - team_id: "team-1", - team_alias: "Test Team", - models: ["gpt-4"], - max_budget: 100, - budget_duration: "monthly", - tpm_limit: null, - rpm_limit: null, - organization_id: "org-1", - created_at: "2024-01-01T00:00:00Z", - keys: [], - members_with_roles: [], - }, - ]; - - const credentials: CredentialItem[] = [ - { - credential_name: "test-credential", - credential_values: {}, - credential_info: { - custom_llm_provider: "openai", - description: "Test credential", - }, - }, - ]; - - const uploadProps: UploadProps = { - beforeUpload: () => false, - showUploadList: false, - }; - - const accessToken = "test-access-token"; - const userRole = "Admin"; - const premiumUser = true; - - const { container, findByText } = render( - , - ); - - // Wait for the tabs to render which indicates the component loaded - await waitFor( - () => { - const tabs = container.querySelectorAll('[role="tab"]'); - expect(tabs.length).toBeGreaterThan(0); - }, - { timeout: 10000 }, - ); +const createQueryClient = () => + new QueryClient({ + defaultOptions: { + queries: { + retry: false, + gcTime: 0, + }, }, - 15000, - ); + }); + +const createTestProps = () => { + const { result } = renderHook(() => Form.useForm()); + const [form] = result.current; + + const handleOk = vi.fn(); + const setSelectedProvider = vi.fn(); + const setProviderModelsFn = vi.fn(); + const getPlaceholder = vi.fn((provider: Providers) => `Enter ${provider} model name`); + const setShowAdvancedSettings = vi.fn(); + + const selectedProvider = Providers.OpenAI; + const providerModels = ["gpt-4", "gpt-3.5-turbo"]; + const showAdvancedSettings = false; + + const teams: Team[] = [ + { + team_id: "team-1", + team_alias: "Test Team", + models: ["gpt-4"], + max_budget: 100, + budget_duration: "monthly", + tpm_limit: null, + rpm_limit: null, + organization_id: "org-1", + created_at: "2024-01-01T00:00:00Z", + keys: [], + members_with_roles: [], + }, + ]; + + const credentials: CredentialItem[] = [ + { + credential_name: "test-credential", + credential_values: {}, + credential_info: { + custom_llm_provider: "openai", + description: "Test credential", + }, + }, + ]; + + const uploadProps: UploadProps = { + beforeUpload: () => false, + showUploadList: false, + }; + + return { + form, + handleOk, + setSelectedProvider, + setProviderModelsFn, + getPlaceholder, + setShowAdvancedSettings, + selectedProvider, + providerModels, + showAdvancedSettings, + teams, + credentials, + uploadProps, + accessToken: "test-access-token", + userRole: "Admin", + premiumUser: true, + }; +}; + +describe("Add Model Tab", () => { + it("should render", async () => { + const props = createTestProps(); + const queryClient = createQueryClient(); + + render( + + + , + ); + + expect(await screen.findByRole("tab", { name: "Add Model" })).toBeInTheDocument(); + }); + + it("should display both Add Model and Add Auto Router tabs", async () => { + const props = createTestProps(); + const queryClient = createQueryClient(); + + render( + + + , + ); + + expect(await screen.findByRole("tab", { name: "Add Model" })).toBeInTheDocument(); + expect(await screen.findByRole("tab", { name: "Add Auto Router" })).toBeInTheDocument(); + }); + + it("should display provider selection field", async () => { + const props = createTestProps(); + const queryClient = createQueryClient(); + + render( + + + , + ); + + expect(await screen.findByText("Provider")).toBeInTheDocument(); + }); + + it("should display Test Connect and Add Model buttons", async () => { + const props = createTestProps(); + const queryClient = createQueryClient(); + + render( + + + , + ); + + const testConnectButtons = await screen.findAllByRole("button", { name: "Test Connect" }); + expect(testConnectButtons.length).toBeGreaterThan(0); + expect(await screen.findByRole("button", { name: "Add Model" })).toBeInTheDocument(); + }, 10000); // 10 seconds timeout for complex logic }); diff --git a/ui/litellm-dashboard/src/components/add_model/add_model_tab.tsx b/ui/litellm-dashboard/src/components/add_model/add_model_tab.tsx index 4efcd7be90..b2e1dec282 100644 --- a/ui/litellm-dashboard/src/components/add_model/add_model_tab.tsx +++ b/ui/litellm-dashboard/src/components/add_model/add_model_tab.tsx @@ -1,31 +1,29 @@ -import React, { useEffect, useMemo, useState } from "react"; -import { Card, Form, Button, Tooltip, Typography, Select as AntdSelect, Modal } from "antd"; +import { useProviderFields } from "@/app/(dashboard)/hooks/providers/useProviderFields"; +import { all_admin_roles } from "@/utils/roles"; +import { Switch, Tab, TabGroup, TabList, TabPanel, TabPanels, Text } from "@tremor/react"; import type { FormInstance } from "antd"; +import { Select as AntdSelect, Button, Card, Col, Form, Modal, Row, Tooltip, Typography } from "antd"; import type { UploadProps } from "antd/es/upload"; -import { TabGroup, TabList, Tab, TabPanels, TabPanel } from "@tremor/react"; -import LiteLLMModelNameField from "./litellm_model_name"; -import ConditionalPublicModelName from "./conditional_public_model_name"; -import ProviderSpecificFields from "./provider_specific_fields"; -import AdvancedSettings from "./advanced_settings"; -import { Providers, providerLogoMap } from "../provider_info_helpers"; +import React, { useEffect, useMemo, useState } from "react"; +import TeamDropdown from "../common_components/team_dropdown"; import type { Team } from "../key_team_helpers/key_list"; import { type CredentialItem, type ProviderCreateInfo, getGuardrailsList, - getProviderCreateMetadata, modelAvailableCall, tagListCall, } from "../networking"; -import ConnectionErrorDisplay from "./model_connection_test"; -import { TEST_MODES } from "./add_model_modes"; -import { Row, Col } from "antd"; -import { Text, Switch } from "@tremor/react"; -import TeamDropdown from "../common_components/team_dropdown"; -import { all_admin_roles } from "@/utils/roles"; -import AddAutoRouterTab from "./add_auto_router_tab"; -import { handleAddAutoRouterSubmit } from "./handle_add_auto_router_submit"; +import { Providers, providerLogoMap } from "../provider_info_helpers"; import { Tag } from "../tag_management/types"; +import AddAutoRouterTab from "./add_auto_router_tab"; +import { TEST_MODES } from "./add_model_modes"; +import AdvancedSettings from "./advanced_settings"; +import ConditionalPublicModelName from "./conditional_public_model_name"; +import { handleAddAutoRouterSubmit } from "./handle_add_auto_router_submit"; +import LiteLLMModelNameField from "./litellm_model_name"; +import ConnectionErrorDisplay from "./model_connection_test"; +import ProviderSpecificFields from "./provider_specific_fields"; interface AddModelTabProps { form: FormInstance; // For the Add Model tab @@ -76,9 +74,11 @@ const AddModelTab: React.FC = ({ const [connectionTestId, setConnectionTestId] = useState(""); // Provider metadata for driving the provider select from backend config - const [providerMetadata, setProviderMetadata] = useState(null); - const [isProviderMetadataLoading, setIsProviderMetadataLoading] = useState(false); - const [providerMetadataError, setProviderMetadataError] = useState(null); + const { + data: providerMetadata, + isLoading: isProviderMetadataLoading, + error: providerMetadataError, + } = useProviderFields(); useEffect(() => { const fetchGuardrails = async () => { @@ -107,37 +107,6 @@ const AddModelTab: React.FC = ({ fetchTags(); }, [accessToken]); - useEffect(() => { - let isMounted = true; - - const fetchProviderMetadata = async () => { - setIsProviderMetadataLoading(true); - setProviderMetadataError(null); - try { - const metadata = await getProviderCreateMetadata(); - if (!isMounted) { - return; - } - setProviderMetadata(metadata); - } catch (error) { - console.error("Failed to fetch provider metadata:", error); - if (isMounted) { - setProviderMetadataError("Failed to load providers"); - } - } finally { - if (isMounted) { - setIsProviderMetadataLoading(false); - } - } - }; - - fetchProviderMetadata(); - - return () => { - isMounted = false; - }; - }, []); - // Test connection when button is clicked const handleTestConnection = async () => { setIsTestingConnection(true); @@ -168,6 +137,12 @@ const AddModelTab: React.FC = ({ return [...providerMetadata].sort((a, b) => a.provider_display_name.localeCompare(b.provider_display_name)); }, [providerMetadata]); + const providerMetadataErrorText = providerMetadataError + ? providerMetadataError instanceof Error + ? providerMetadataError.message + : "Failed to load providers" + : null; + const isAdmin = all_admin_roles.includes(userRole); const handleAutoRouterOk = () => { @@ -232,9 +207,9 @@ const AddModelTab: React.FC = ({ }); }} > - {providerMetadataError && sortedProviderMetadata.length === 0 && ( + {providerMetadataErrorText && sortedProviderMetadata.length === 0 && ( - {providerMetadataError} + {providerMetadataErrorText} )} {sortedProviderMetadata.map((providerInfo) => { diff --git a/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.test.tsx b/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.test.tsx index ab9b3e92e9..4590121acf 100644 --- a/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.test.tsx +++ b/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.test.tsx @@ -1,6 +1,7 @@ -import { render, waitFor } from "@testing-library/react"; -import { describe, it, expect, beforeAll, vi } from "vitest"; +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { render, screen, waitFor } from "@testing-library/react"; import { Form } from "antd"; +import { beforeAll, describe, expect, it, vi } from "vitest"; import { Providers } from "../provider_info_helpers"; import ProviderSpecificFields from "./provider_specific_fields"; @@ -97,7 +98,6 @@ vi.mock("../networking", async () => { }; }); -// Mock window.matchMedia for Ant Design components beforeAll(() => { Object.defineProperty(window, "matchMedia", { writable: true, @@ -105,8 +105,8 @@ beforeAll(() => { matches: false, media: query, onchange: null, - addListener: () => {}, // deprecated - removeListener: () => {}, // deprecated + addListener: () => {}, + removeListener: () => {}, addEventListener: () => {}, removeEventListener: () => {}, dispatchEvent: () => false, @@ -114,80 +114,105 @@ beforeAll(() => { }); }); +const createQueryClient = () => + new QueryClient({ + defaultOptions: { + queries: { + retry: false, + gcTime: 0, + }, + }, + }); + describe("ProviderSpecificFields", () => { - it("should render the provider specific fields for OpenAI", async () => { - const { getByLabelText, getByPlaceholderText } = render( - - - , + it("should render", async () => { + const queryClient = createQueryClient(); + render( + +
+ + +
, ); await waitFor(() => { - // Check for the API Base text input - const apiBaseInput = getByPlaceholderText("https://api.openai.com/v1"); + expect(screen.getByLabelText("OpenAI API Key")).toBeInTheDocument(); + }); + }); + + it("should render the provider specific fields for OpenAI", async () => { + const queryClient = createQueryClient(); + render( + +
+ + +
, + ); + + await waitFor(() => { + const apiKeyLabel = screen.getByLabelText("OpenAI API Key"); + expect(apiKeyLabel).toBeInTheDocument(); + + const apiBaseInput = screen.getByPlaceholderText("https://api.openai.com/v1"); expect(apiBaseInput).toBeInTheDocument(); expect(apiBaseInput).toHaveAttribute("type", "text"); - // Check for Organization field - const orgInput = getByPlaceholderText("[OPTIONAL] my-unique-org"); + const orgInput = screen.getByPlaceholderText("[OPTIONAL] my-unique-org"); expect(orgInput).toBeInTheDocument(); - - // Check for API Key field - const apiKeyLabel = getByLabelText("OpenAI API Key"); - expect(apiKeyLabel).toBeInTheDocument(); }); }); it("should render the provider specific fields for vLLM", async () => { - const { getByLabelText, getByPlaceholderText } = render( -
- - , + const queryClient = createQueryClient(); + render( + +
+ + +
, ); await waitFor(() => { - const apiBaseInput = getByPlaceholderText("https://..."); + const apiKeyLabel = screen.getByLabelText("vLLM API Key"); + expect(apiKeyLabel).toBeInTheDocument(); + + const apiBaseInput = screen.getByPlaceholderText("https://..."); expect(apiBaseInput).toBeInTheDocument(); expect(apiBaseInput).toHaveAttribute("type", "text"); - - // Check for API Key field - const apiKeyLabel = getByLabelText("vLLM API Key"); - expect(apiKeyLabel).toBeInTheDocument(); }); }); it("should render the provider specific fields for Azure", async () => { - const { getByLabelText, getByPlaceholderText } = render( -
- - , + const queryClient = createQueryClient(); + render( + +
+ + +
, ); await waitFor(() => { - // Check for API Base field - const apiBaseInput = getByPlaceholderText("https://..."); - expect(apiBaseInput).toBeInTheDocument(); - expect(apiBaseInput).toHaveAttribute("type", "text"); - - // Check for API Version field - const apiVersionInput = getByPlaceholderText("2023-07-01-preview"); - expect(apiVersionInput).toBeInTheDocument(); - - // Check for Base Model field - const baseModelInput = getByPlaceholderText("azure/gpt-3.5-turbo"); - expect(baseModelInput).toBeInTheDocument(); - - // Check for API Key field - const apiKeyInput = getByLabelText("Azure API Key"); + const apiKeyInput = screen.getByLabelText("Azure API Key"); expect(apiKeyInput).toBeInTheDocument(); expect(apiKeyInput).toHaveAttribute("type", "password"); expect(apiKeyInput).toHaveAttribute("placeholder", "Enter your Azure API Key"); - // Check for Azure AD Token field - const azureAdTokenInput = getByLabelText("Azure AD Token"); + const azureAdTokenInput = screen.getByLabelText("Azure AD Token"); expect(azureAdTokenInput).toBeInTheDocument(); expect(azureAdTokenInput).toHaveAttribute("type", "password"); expect(azureAdTokenInput).toHaveAttribute("placeholder", "Enter your Azure AD Token"); + + const apiBaseInput = screen.getByPlaceholderText("https://..."); + expect(apiBaseInput).toBeInTheDocument(); + expect(apiBaseInput).toHaveAttribute("type", "text"); + + const apiVersionInput = screen.getByPlaceholderText("2023-07-01-preview"); + expect(apiVersionInput).toBeInTheDocument(); + + const baseModelInput = screen.getByPlaceholderText("azure/gpt-3.5-turbo"); + expect(baseModelInput).toBeInTheDocument(); }); }); }); diff --git a/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.tsx b/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.tsx index c1b17ff441..892e9e0197 100644 --- a/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.tsx +++ b/ui/litellm-dashboard/src/components/add_model/provider_specific_fields.tsx @@ -1,15 +1,10 @@ -import React from "react"; -import { Form, Select } from "antd"; -import { TextInput, Text } from "@tremor/react"; -import { Row, Col, Typography, Button as Button2, Upload, UploadProps } from "antd"; +import { useProviderFields } from "@/app/(dashboard)/hooks/providers/useProviderFields"; import { UploadOutlined } from "@ant-design/icons"; +import { Text, TextInput } from "@tremor/react"; +import { Button as Button2, Col, Form, Row, Select, Typography, Upload, UploadProps } from "antd"; +import React from "react"; +import { CredentialItem, ProviderCredentialFieldMetadata } from "../networking"; import { provider_map, Providers } from "../provider_info_helpers"; -import { - CredentialItem, - ProviderCreateInfo, - ProviderCredentialFieldMetadata, - getProviderCreateMetadata, -} from "../networking"; const { Link } = Typography; interface ProviderSpecificFieldsProps { @@ -99,65 +94,42 @@ const ProviderSpecificFields: React.FC = ({ selecte const selectedProviderEnum = Providers[selectedProvider as keyof typeof Providers] as Providers; const form = Form.useFormInstance(); // Get form instance from context - const [providerMetadata, setProviderMetadata] = React.useState(null); - const [isLoading, setIsLoading] = React.useState(false); - const [loadError, setLoadError] = React.useState(null); + const { data: providerMetadata, isLoading, error: loadError } = useProviderFields(); + // Memoize the expensive cache computation + const cacheEntries = React.useMemo(() => { + if (!providerMetadata) { + return null; + } + + // Compute cache entries keyed by provider display name and identifiers + const entries: Record = {}; + providerMetadata.forEach((providerInfo) => { + const displayName = providerInfo.provider_display_name; + const mappedFields = providerInfo.credential_fields.map(mapFieldMetadataToUiField); + + // Primary key: human-readable display name + entries[displayName] = mappedFields; + + // Also cache by backend identifiers so lookups by provider slug work + if (providerInfo.provider) { + entries[providerInfo.provider] = mappedFields; + } + if (providerInfo.litellm_provider) { + entries[providerInfo.litellm_provider] = mappedFields; + } + }); + return entries; + }, [providerMetadata]); + + // Sync memoized cache entries to module-level cache React.useEffect(() => { - const hasCachedFields = Object.keys(providerFieldsByDisplayName).length > 0; - if (hasCachedFields) { - // We already have fields cached globally; no need to refetch. - // This is important so we can reuse credential field definitions - // across mounts and in non-React helpers. + if (!cacheEntries) { return; } - let isMounted = true; - - const fetchProviderFields = async () => { - setIsLoading(true); - setLoadError(null); - try { - const metadata = await getProviderCreateMetadata(); - if (!isMounted) { - return; - } - setProviderMetadata(metadata); - - // Populate cache keyed by provider display name and identifiers - metadata.forEach((providerInfo) => { - const displayName = providerInfo.provider_display_name; - const mappedFields = providerInfo.credential_fields.map(mapFieldMetadataToUiField); - - // Primary key: human-readable display name - providerFieldsByDisplayName[displayName] = mappedFields; - - // Also cache by backend identifiers so lookups by provider slug work - if (providerInfo.provider) { - providerFieldsByDisplayName[providerInfo.provider] = mappedFields; - } - if (providerInfo.litellm_provider) { - providerFieldsByDisplayName[providerInfo.litellm_provider] = mappedFields; - } - }); - } catch (error) { - console.error("Failed to load provider credential fields:", error); - if (isMounted) { - setLoadError("Failed to load provider credential fields"); - } - } finally { - if (isMounted) { - setIsLoading(false); - } - } - }; - - fetchProviderFields(); - - return () => { - isMounted = false; - }; - }, []); + Object.assign(providerFieldsByDisplayName, cacheEntries); + }, [cacheEntries]); const allFields = React.useMemo(() => { // First try to resolve from the in-memory cache. We support both the @@ -234,7 +206,9 @@ const ProviderSpecificFields: React.FC = ({ selecte {loadError && allFields.length === 0 && ( - {loadError} + + {loadError instanceof Error ? loadError.message : "Failed to load provider credential fields"} + )} From b487e67decdec1771c81efaa43e7434f4ae10004 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 18:23:18 -0800 Subject: [PATCH 123/248] sec fix --- docs/my-website/package-lock.json | 6 +++--- docs/my-website/package.json | 6 ++++-- ui/litellm-dashboard/package-lock.json | 6 +++--- ui/litellm-dashboard/package.json | 3 ++- 4 files changed, 12 insertions(+), 9 deletions(-) diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index aef7bc1fe9..9c7ee7741c 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -16891,9 +16891,9 @@ } }, "node_modules/node-forge": { - "version": "1.3.1", - "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.1.tgz", - "integrity": "sha512-dPEtOeMvF9VMcYV/1Wb8CPoVAXtp6MKMlcbAt4ddqmGqUJ6fQZFXkNZNkNlfevtNkGtaSoXf/vNNNSvgrdXwtA==", + "version": "1.3.2", + "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.2.tgz", + "integrity": "sha512-6xKiQ+cph9KImrRh0VsjH2d8/GXA4FIMlgU4B757iI1ApvcyA9VlouP0yZJha01V+huImO+kKMU7ih+2+E14fw==", "license": "(BSD-3-Clause OR GPL-2.0)", "engines": { "node": ">= 6.13.0" diff --git a/docs/my-website/package.json b/docs/my-website/package.json index 4895b6f518..f217cef7fc 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -52,13 +52,15 @@ "webpack-dev-server": ">=5.2.1", "form-data": ">=4.0.4", "mermaid": ">=11.10.0", - "gray-matter": "4.0.3" + "gray-matter": "4.0.3", + "node-forge": ">=1.3.2" }, "overrides": { "webpack-dev-server": ">=5.2.1", "form-data": ">=4.0.4", "mermaid": ">=11.10.0", "gray-matter": "4.0.3", - "glob": ">=11.1.0" + "glob": ">=11.1.0", + "node-forge": ">=1.3.2" } } diff --git a/ui/litellm-dashboard/package-lock.json b/ui/litellm-dashboard/package-lock.json index 641d71daa9..9907113c7e 100644 --- a/ui/litellm-dashboard/package-lock.json +++ b/ui/litellm-dashboard/package-lock.json @@ -18380,9 +18380,9 @@ } }, "node_modules/node-forge": { - "version": "1.3.1", - "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.1.tgz", - "integrity": "sha512-dPEtOeMvF9VMcYV/1Wb8CPoVAXtp6MKMlcbAt4ddqmGqUJ6fQZFXkNZNkNlfevtNkGtaSoXf/vNNNSvgrdXwtA==", + "version": "1.3.2", + "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.2.tgz", + "integrity": "sha512-6xKiQ+cph9KImrRh0VsjH2d8/GXA4FIMlgU4B757iI1ApvcyA9VlouP0yZJha01V+huImO+kKMU7ih+2+E14fw==", "license": "(BSD-3-Clause OR GPL-2.0)", "engines": { "node": ">= 6.13.0" diff --git a/ui/litellm-dashboard/package.json b/ui/litellm-dashboard/package.json index e7938e53a4..e366b4febf 100644 --- a/ui/litellm-dashboard/package.json +++ b/ui/litellm-dashboard/package.json @@ -78,7 +78,8 @@ "webpack-dev-server": ">=5.2.1", "mermaid": ">=11.10.0", "js-yaml": ">=4.1.1", - "glob": ">=11.1.0" + "glob": ">=11.1.0", + "node-forge": ">=1.3.2" }, "engines": { "node": ">=18.17.0", From 48eb34a8750d81269ee454622291b553dde5a4c3 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 18:26:07 -0800 Subject: [PATCH 124/248] fix cos tracking --- litellm/model_prices_and_context_window_backup.json | 6 ++++++ model_prices_and_context_window.json | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 0287736af1..f07f1cbe5e 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -249,6 +249,12 @@ "/v1/images/generations" ] }, + "amazon.nova-canvas-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 2600, + "mode": "image_generation", + "output_cost_per_image": 0.06 + }, "amazon.nova-lite-v1:0": { "input_cost_per_token": 6e-08, "litellm_provider": "bedrock_converse", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 0287736af1..f07f1cbe5e 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -249,6 +249,12 @@ "/v1/images/generations" ] }, + "amazon.nova-canvas-v1:0": { + "litellm_provider": "bedrock", + "max_input_tokens": 2600, + "mode": "image_generation", + "output_cost_per_image": 0.06 + }, "amazon.nova-lite-v1:0": { "input_cost_per_token": 6e-08, "litellm_provider": "bedrock_converse", From 605bc4e4762c07163a4a9cf2502da21c45c06362 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Wed, 26 Nov 2025 22:01:33 +0530 Subject: [PATCH 125/248] type the secrets field --- litellm/responses/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/responses/main.py b/litellm/responses/main.py index be5f890bbd..950ea7063f 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -281,7 +281,7 @@ async def aresponses_api_with_mcp( ) # Extract MCP auth headers from the request to pass to MCP server - secret_fields = kwargs.get("secret_fields") + secret_fields: Optional[Dict[str, Any]] = kwargs.get("secret_fields") ( mcp_auth_header, mcp_server_auth_headers, From 30a22f1fed33c1aed3bd725f919534403d6dd749 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 27 Nov 2025 07:58:35 +0530 Subject: [PATCH 126/248] type the secrets field --- .../mcp_server/mcp_server_manager.py | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 773246d580..2c03cbdae3 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -2023,25 +2023,6 @@ class MCPServerManager: return server return None - def get_mcp_servers_from_ids( - self, server_ids: List[str] - ) -> List[MCPServer]: - """ - Get MCP servers from a list of server IDs. - - Args: - server_ids: List of server IDs to retrieve - - Returns: - List of MCPServer objects corresponding to the provided IDs - """ - servers: List[MCPServer] = [] - for server_id in server_ids: - server = self.get_mcp_server_by_id(server_id) - if server: - servers.append(server) - return servers - def _generate_stable_server_id( self, server_name: str, From 1cb5fcddba0d0ced22293e36e2c0e90f05801f54 Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Wed, 26 Nov 2025 18:38:38 -0800 Subject: [PATCH 127/248] make generic api OSS + support multiple generic API's (#17152) * feat(generic_api_callback.py): make generic api OSS + support multiple generic API's Enables https://github.com/BerriAI/litellm/pull/17094#discussion_r2562832967 * feat(callback_utils.py): support custom generic api callbacks * feat(generic_api_callback.py): support specifying which event types to run the generic api for * fix(litellm_logging.py): log system prompt for anthropic messages * feat(generic_api_callback.py): support generic api compatible api's - e.g. rubrik agent cloud * docs(sidebars.js): document new OSS generic api * docs(generic_api.md): document new OSS Generic API * docs(custom_webhook_api.md): document custom webhook api integration tutorial * docs(custom_webhook_api.md): cleanup * docs(custom_webhook_api.md): document what get's logged to custom webhook api * Refactor: Pass callback config to GenericAPILogger Co-authored-by: krrishdholakia * Fix: Handle empty messages list in logging payload Co-authored-by: krrishdholakia * Checkpoint before follow-up message Co-authored-by: krrishdholakia * feat: Cache GenericAPILogger instances to improve performance Co-authored-by: krrishdholakia --------- Co-authored-by: Cursor Agent --- .../custom_webhook_api.md | 106 ++++++++++++++++ .../docs/observability/generic_api.md | 110 ++++++++++++++++ docs/my-website/sidebars.js | 10 ++ litellm/__init__.py | 1 + .../generic_api}/generic_api_callback.py | 120 ++++++++++++++++-- .../generic_api_compatible_callbacks.json | 20 +++ .../custom_logger_registry.py | 7 +- litellm/litellm_core_utils/litellm_logging.py | 49 +++++-- .../logging_callback_manager.py | 74 ++++++++++- litellm/proxy/_new_secret_config.yaml | 24 ++-- litellm/proxy/common_utils/callback_utils.py | 11 ++ litellm/proxy/proxy_server.py | 22 ++-- .../test_generic_api_callback.py | 85 ++++++++----- 13 files changed, 560 insertions(+), 79 deletions(-) create mode 100644 docs/my-website/docs/contribute_integration/custom_webhook_api.md create mode 100644 docs/my-website/docs/observability/generic_api.md rename {enterprise/litellm_enterprise/enterprise_callbacks => litellm/integrations/generic_api}/generic_api_callback.py (67%) create mode 100644 litellm/integrations/generic_api/generic_api_compatible_callbacks.json diff --git a/docs/my-website/docs/contribute_integration/custom_webhook_api.md b/docs/my-website/docs/contribute_integration/custom_webhook_api.md new file mode 100644 index 0000000000..499c7fd51d --- /dev/null +++ b/docs/my-website/docs/contribute_integration/custom_webhook_api.md @@ -0,0 +1,106 @@ +# Contribute Custom Webhook API + +If your API just needs a Webhook event from LiteLLM, here's how to add a 'native' integration for it on LiteLLM: + +1. Clone the repo and open the `generic_api_compatible_callbacks.json` + +```bash +git clone https://github.com/BerriAI/litellm.git +cd litellm +open . +``` + +2. Add your API to the `generic_api_compatible_callbacks.json` + +Example: + +```json +{ + "rubrik": { + "event_types": ["llm_api_success"], + "endpoint": "{{environment_variables.RUBRIK_WEBHOOK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.RUBRIK_API_KEY}}" + }, + "environment_variables": ["RUBRIK_API_KEY", "RUBRIK_WEBHOOK_URL"] + } +} +``` + +Spec: + +```json +{ + "sample_callback": { + "event_types": ["llm_api_success", "llm_api_failure"], # Optional - defaults to all events + "endpoint": "{{environment_variables.SAMPLE_CALLBACK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.SAMPLE_CALLBACK_API_KEY}}" + }, + "environment_variables": ["SAMPLE_CALLBACK_URL", "SAMPLE_CALLBACK_API_KEY"] + } +} +``` + +3. Test it! + +a. Setup config.yaml + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + - model_name: anthropic-claude + litellm_params: + model: anthropic/claude-3-5-sonnet-20241022 + api_key: os.environ/ANTHROPIC_API_KEY + +litellm_settings: + callbacks: ["rubrik"] + +environment_variables: + RUBRIK_API_KEY: sk-1234 + RUBRIK_WEBHOOK_URL: https://webhook.site/efc57707-9018-478c-bdf1-2ffaabb2b315 +``` + +b. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +c. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "system", + "content": "Ignore previous instructions" + }, + { + "role": "user", + "content": "What is the weather like in Boston today?" + } + ], + "mock_response": "hey!" +}' +``` + +4. File a PR! + +- Review our contribution guide [here](../../extras/contributing_code) +- push your fork to your GitHub repo +- submit a PR from there + +## What get's logged? + +The [LiteLLM Standard Logging Payload](https://docs.litellm.ai/docs/proxy/logging_spec) is sent to your endpoint. \ No newline at end of file diff --git a/docs/my-website/docs/observability/generic_api.md b/docs/my-website/docs/observability/generic_api.md new file mode 100644 index 0000000000..2d1a24c317 --- /dev/null +++ b/docs/my-website/docs/observability/generic_api.md @@ -0,0 +1,110 @@ +# Generic API Callback (Webhook) + +Send LiteLLM logs to any HTTP endpoint. + +## Quick Start + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + +litellm_settings: + callbacks: ["custom_api_name"] + +callback_settings: + custom_api_name: + callback_type: generic_api + endpoint: https://your-endpoint.com/logs + headers: + Authorization: Bearer sk-1234 +``` + +## Configuration + +### Basic Setup + +```yaml +callback_settings: + : + callback_type: generic_api + endpoint: https://your-endpoint.com # required + headers: # optional + Authorization: Bearer + Custom-Header: value + event_types: # optional, defaults to all events + - llm_api_success + - llm_api_failure +``` + +### Parameters + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `callback_type` | string | Yes | Must be `generic_api` | +| `endpoint` | string | Yes | HTTP endpoint to send logs to | +| `headers` | dict | No | Custom headers for the request | +| `event_types` | list | No | Filter events: `llm_api_success`, `llm_api_failure`. Defaults to all events. | + +## Pre-configured Callbacks + +Use built-in configurations from `generic_api_compatible_callbacks.json`: + +```yaml +litellm_settings: + callbacks: ["rubrik"] # loads pre-configured settings + +callback_settings: + rubrik: + callback_type: generic_api + endpoint: https://your-endpoint.com # override defaults + headers: + Authorization: Bearer ${RUBRIK_API_KEY} +``` + +## Payload Format + +Logs are sent as `StandardLoggingPayload` [objects](https://docs.litellm.ai/docs/proxy/logging_spec) in JSON format: + +```json +[ + { + "id": "chatcmpl-123", + "call_type": "litellm.completion", + "model": "gpt-3.5-turbo", + "messages": [...], + "response": {...}, + "usage": {...}, + "cost": 0.0001, + "startTime": "2024-01-01T00:00:00", + "endTime": "2024-01-01T00:00:01", + "metadata": {...} + } +] +``` + +## Environment Variables + +Set via environment variables instead of config: + +```bash +export GENERIC_LOGGER_ENDPOINT=https://your-endpoint.com +export GENERIC_LOGGER_HEADERS="Authorization=Bearer token,Custom-Header=value" +``` + +## Batch Settings + +Control batching behavior (inherits from `CustomBatchLogger`): + +```yaml +callback_settings: + my_api: + callback_type: generic_api + endpoint: https://your-endpoint.com + batch_size: 100 # default: 100 + flush_interval: 60 # seconds, default: 60 +``` + + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index b40a533337..3ffc3b0669 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -20,6 +20,16 @@ const sidebars = { type: "category", label: "Observability", items: [ + { + type: "category", + label: "Contributing to Integrations", + items: [ + { + type: "autogenerated", + dirName: "contribute_integration" + } + ] + }, { type: "autogenerated", dirName: "observability" diff --git a/litellm/__init__.py b/litellm/__init__.py index b214db74a7..71be5113e2 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -177,6 +177,7 @@ _known_custom_logger_compatible_callbacks: List = list( callbacks: List[ Union[Callable, _custom_logger_compatible_callbacks_literal, CustomLogger] ] = [] +callback_settings: Dict[str, Dict[str, Any]] = {} initialized_langfuse_clients: int = 0 langfuse_default_tags: Optional[List[str]] = None langsmith_batch_size: Optional[int] = None diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py b/litellm/integrations/generic_api/generic_api_callback.py similarity index 67% rename from enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py rename to litellm/integrations/generic_api/generic_api_callback.py index 7e259d4e19..1c8a5b883d 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py +++ b/litellm/integrations/generic_api/generic_api_callback.py @@ -7,13 +7,15 @@ Callback to log events to a Generic API Endpoint """ import asyncio +import json import os +import re import traceback -from litellm._uuid import uuid -from typing import Dict, List, Optional, Union +from typing import Dict, List, Literal, Optional, Union import litellm from litellm._logging import verbose_logger +from litellm._uuid import uuid from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.custom_httpx.http_handler import ( @@ -22,12 +24,83 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.types.utils import StandardLoggingPayload +API_EVENT_TYPES = Literal["llm_api_success", "llm_api_failure"] + + +def load_compatible_callbacks() -> Dict: + """ + Load the generic_api_compatible_callbacks.json file + + Returns: + Dict: Dictionary of compatible callbacks configuration + """ + try: + json_path = os.path.join( + os.path.dirname(__file__), "generic_api_compatible_callbacks.json" + ) + with open(json_path, "r") as f: + return json.load(f) + except Exception as e: + verbose_logger.warning( + f"Error loading generic_api_compatible_callbacks.json: {str(e)}" + ) + return {} + + +def is_callback_compatible(callback_name: str) -> bool: + """ + Check if a callback_name exists in the compatible callbacks list + + Args: + callback_name: Name of the callback to check + + Returns: + bool: True if callback_name exists in the compatible callbacks, False otherwise + """ + compatible_callbacks = load_compatible_callbacks() + return callback_name in compatible_callbacks + + +def get_callback_config(callback_name: str) -> Optional[Dict]: + """ + Get the configuration for a specific callback + + Args: + callback_name: Name of the callback to get config for + + Returns: + Optional[Dict]: Configuration dict for the callback, or None if not found + """ + compatible_callbacks = load_compatible_callbacks() + return compatible_callbacks.get(callback_name) + + +def substitute_env_variables(value: str) -> str: + """ + Replace {{environment_variables.VAR_NAME}} patterns with actual environment variable values + + Args: + value: String that may contain {{environment_variables.VAR_NAME}} patterns + + Returns: + str: String with environment variables substituted + """ + pattern = r"\{\{environment_variables\.([A-Z_]+)\}\}" + + def replace_env_var(match): + env_var_name = match.group(1) + return os.getenv(env_var_name, "") + + return re.sub(pattern, replace_env_var, value) + class GenericAPILogger(CustomBatchLogger): def __init__( self, endpoint: Optional[str] = None, headers: Optional[dict] = None, + event_types: Optional[List[API_EVENT_TYPES]] = None, + callback_name: Optional[str] = None, **kwargs, ): """ @@ -36,7 +109,37 @@ class GenericAPILogger(CustomBatchLogger): Args: endpoint: Optional[str] = None, headers: Optional[dict] = None, + event_types: Optional[List[API_EVENT_TYPES]] = None, + callback_name: Optional[str] = None - If provided, loads config from generic_api_compatible_callbacks.json """ + ######################################################### + # Check if callback_name is provided and load config + ######################################################### + if callback_name: + if is_callback_compatible(callback_name): + verbose_logger.debug( + f"Loading configuration for callback: {callback_name}" + ) + callback_config = get_callback_config(callback_name) + + # Use config from JSON if not explicitly provided + if callback_config: + if endpoint is None and "endpoint" in callback_config: + endpoint = substitute_env_variables(callback_config["endpoint"]) + + if "headers" in callback_config: + headers = headers or {} + for key, value in callback_config["headers"].items(): + if key not in headers: + headers[key] = substitute_env_variables(value) + + if event_types is None and "event_types" in callback_config: + event_types = callback_config["event_types"] + else: + verbose_logger.warning( + f"callback_name '{callback_name}' not found in generic_api_compatible_callbacks.json" + ) + ######################################################### # Init httpx client ######################################################### @@ -51,8 +154,10 @@ class GenericAPILogger(CustomBatchLogger): self.headers: Dict = self._get_headers(headers) self.endpoint: str = endpoint + self.event_types: Optional[List[API_EVENT_TYPES]] = event_types + self.callback_name: Optional[str] = callback_name verbose_logger.debug( - f"in init GenericAPILogger, endpoint {self.endpoint}, headers {self.headers}" + f"in init GenericAPILogger, callback_name: {self.callback_name}, endpoint {self.endpoint}, headers {self.headers}, event_types: {self.event_types}" ) ######################################################### @@ -114,9 +219,9 @@ class GenericAPILogger(CustomBatchLogger): Raises: Raises a NON Blocking verbose_logger.exception if an error occurs """ - from litellm.proxy.utils import _premium_user_check - _premium_user_check() + if self.event_types is not None and "llm_api_success" not in self.event_types: + return try: verbose_logger.debug( @@ -153,9 +258,8 @@ class GenericAPILogger(CustomBatchLogger): - Creates a StandardLoggingPayload - Adds to batch queue """ - from litellm.proxy.utils import _premium_user_check - - _premium_user_check() + if self.event_types is not None and "llm_api_failure" not in self.event_types: + return try: verbose_logger.debug( diff --git a/litellm/integrations/generic_api/generic_api_compatible_callbacks.json b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json new file mode 100644 index 0000000000..1e88a39e0a --- /dev/null +++ b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json @@ -0,0 +1,20 @@ +{ + "sample_callback": { + "event_types": ["llm_api_success", "llm_api_failure"], + "endpoint": "{{environment_variables.SAMPLE_CALLBACK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.SAMPLE_CALLBACK_API_KEY}}" + }, + "environment_variables": ["SAMPLE_CALLBACK_URL", "SAMPLE_CALLBACK_API_KEY"] + }, + "rubrik": { + "event_types": ["llm_api_success"], + "endpoint": "{{environment_variables.RUBRIK_WEBHOOK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.RUBRIK_API_KEY}}" + }, + "environment_variables": ["RUBRIK_API_KEY", "RUBRIK_WEBHOOK_URL"] + } +} \ No newline at end of file diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index 80f2f19583..538ef6be28 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -95,9 +95,6 @@ class CustomLoggerRegistry: } try: - from litellm_enterprise.enterprise_callbacks.generic_api_callback import ( - GenericAPILogger, - ) from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( PagerDutyAlerting, ) @@ -108,6 +105,10 @@ class CustomLoggerRegistry: SMTPEmailLogger, ) + from litellm.integrations.generic_api.generic_api_callback import ( + GenericAPILogger, + ) + enterprise_loggers = { "pagerduty": PagerDutyAlerting, "generic_api": GenericAPILogger, diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 38decd8af9..305b7d6ddc 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -165,9 +165,6 @@ try: from litellm_enterprise.enterprise_callbacks.callback_controls import ( EnterpriseCallbackControls, ) - from litellm_enterprise.enterprise_callbacks.generic_api_callback import ( - GenericAPILogger, - ) from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( PagerDutyAlerting, ) @@ -181,6 +178,8 @@ try: StandardLoggingPayloadSetup as EnterpriseStandardLoggingPayloadSetup, ) + from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger + EnterpriseStandardLoggingPayloadSetupVAR: Optional[ Type[EnterpriseStandardLoggingPayloadSetup] ] = EnterpriseStandardLoggingPayloadSetup @@ -315,6 +314,7 @@ class Logging(LiteLLMLoggingBaseClass): for m in messages: new_messages.append({"role": "user", "content": m}) messages = new_messages + self.model = model self.messages = copy.deepcopy(messages) self.stream = stream @@ -4106,10 +4106,8 @@ def _get_custom_logger_settings_from_proxy_server(callback_name: str) -> Dict: otel: message_logging: False """ - from litellm.proxy.proxy_server import callback_settings - - if callback_settings: - return dict(callback_settings.get(callback_name, {})) + if litellm.callback_settings: + return dict(litellm.callback_settings.get(callback_name, {})) return {} @@ -4186,6 +4184,39 @@ class StandardLoggingPayloadSetup: return start_time_float, end_time_float, completion_start_time_float + @staticmethod + def append_system_prompt_messages( + kwargs: Optional[Dict] = None, messages: Optional[Any] = None + ): + """ + Append system prompt messages to the messages + """ + if kwargs is not None: + if kwargs.get("system") is not None and isinstance( + kwargs.get("system"), str + ): + if messages is None: + return [{"role": "system", "content": kwargs.get("system")}] + elif isinstance(messages, list): + if len(messages) == 0: + return [{"role": "system", "content": kwargs.get("system")}] + # check for duplicates + if messages[0].get("role") == "system" and messages[0].get( + "content" + ) == kwargs.get("system"): + return messages + messages = [ + {"role": "system", "content": kwargs.get("system")} + ] + messages + elif isinstance(messages, str): + messages = [ + {"role": "system", "content": kwargs.get("system")}, + {"role": "user", "content": messages}, + ] + return messages + + return messages + @staticmethod def get_standard_logging_metadata( metadata: Optional[Dict[str, Any]], @@ -4908,7 +4939,9 @@ def get_standard_logging_object_payload( model_group=_model_group, model_id=_model_id, requester_ip_address=clean_metadata.get("requester_ip_address", None), - messages=kwargs.get("messages"), + messages=StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=kwargs.get("messages") + ), response=final_response_obj, model_parameters=ModelParamHelper.get_standard_logging_model_parameters( kwargs.get("optional_params", None) or {} diff --git a/litellm/litellm_core_utils/logging_callback_manager.py b/litellm/litellm_core_utils/logging_callback_manager.py index 9ec346c20a..349cb6f3ce 100644 --- a/litellm/litellm_core_utils/logging_callback_manager.py +++ b/litellm/litellm_core_utils/logging_callback_manager.py @@ -1,9 +1,10 @@ -from typing import TYPE_CHECKING, Callable, List, Optional, Set, Type, Union +from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Set, Type, Union import litellm from litellm._logging import verbose_logger from litellm.integrations.additional_logging_utils import AdditionalLoggingUtils from litellm.integrations.custom_logger import CustomLogger +from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger from litellm.types.utils import CallbacksByType if TYPE_CHECKING: @@ -11,6 +12,8 @@ if TYPE_CHECKING: else: _custom_logger_compatible_callbacks_literal = str +_generic_api_logger_cache: Dict[str, GenericAPILogger] = {} + class LoggingCallbackManager: """ @@ -138,6 +141,57 @@ class LoggingCallbackManager: return False return True + @staticmethod + def _add_custom_callback_generic_api_str( + callback: str, + ) -> Union[GenericAPILogger, str]: + """ + litellm_settings: + success_callback: ["custom_callback_name"] + + callback_settings: + custom_callback_name: + callback_type: generic_api + endpoint: https://webhook-test.com/30343bc33591bc5e6dc44217ceae3e0a + headers: + Authorization: Bearer sk-1234 + """ + callback_config = litellm.callback_settings.get(callback) + + if not isinstance(callback_config, dict): + return callback + + if callback_config.get("callback_type") != "generic_api": + return callback + + endpoint = callback_config.get("endpoint") + headers = callback_config.get("headers") + event_types = callback_config.get("event_types") + + if endpoint is None or headers is None: + verbose_logger.warning( + "generic_api callback '%s' is missing endpoint or headers, skipping.", + callback, + ) + return callback + + cached_logger = _generic_api_logger_cache.get(callback) + if ( + isinstance(cached_logger, GenericAPILogger) + and cached_logger.endpoint == endpoint + and cached_logger.headers == headers + and cached_logger.event_types == event_types + ): + return cached_logger + + new_logger = GenericAPILogger( + endpoint=endpoint, + headers=headers, + event_types=event_types, + ) + _generic_api_logger_cache[callback] = new_logger + return new_logger + def _safe_add_callback_to_list( self, callback: Union[CustomLogger, Callable, str], @@ -152,15 +206,24 @@ class LoggingCallbackManager: if not self._check_callback_list_size(parent_list): return + # Check if the callback is a custom callback + + if isinstance(callback, str): + callback = LoggingCallbackManager._add_custom_callback_generic_api_str( + callback + ) + if isinstance(callback, str): self._add_string_callback_to_list( callback=callback, parent_list=parent_list ) elif isinstance(callback, CustomLogger): + self._add_custom_logger_to_list( custom_logger=callback, parent_list=parent_list, ) + elif callable(callback): self._add_callback_function_to_list( callback=callback, parent_list=parent_list @@ -348,7 +411,6 @@ class LoggingCallbackManager: elif callable(callback): return getattr(callback, "__name__", str(callback)) return str(callback) - def get_active_custom_logger_for_callback_name( self, @@ -362,12 +424,16 @@ class LoggingCallbackManager: ) # get the custom logger class type - custom_logger_class_type = CustomLoggerRegistry.get_class_type_for_custom_logger_name(callback_name) + custom_logger_class_type = ( + CustomLoggerRegistry.get_class_type_for_custom_logger_name(callback_name) + ) # get the active custom logger custom_logger = self.get_custom_loggers_for_type(custom_logger_class_type) if len(custom_logger) == 0: - raise ValueError(f"No active custom logger found for callback name: {callback_name}") + raise ValueError( + f"No active custom logger found for callback name: {callback_name}" + ) return custom_logger[0] diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index f24f9a9642..6876152479 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -3,19 +3,17 @@ model_list: litellm_params: model: openai/gpt-3.5-turbo api_key: os.environ/OPENAI_API_KEY - -guardrails: - - guardrail_name: model-armor-shield + - model_name: anthropic-claude litellm_params: - guardrail: model_armor - mode: "post_call" # Run on both input and output - template_id: "test-prompt-template" # Required: Your Model Armor template ID - project_id: "test-vector-store-db" # Your GCP project ID - location: "us" # GCP location (default: us-central1) - mask_request_content: true # Enable request content masking - mask_response_content: true # Enable response content masking - fail_on_error: true # Fail request if Model Armor errors (default: true) - default_on: true # Run by default for all requests + model: anthropic/claude-3-5-sonnet-20241022 + api_key: os.environ/ANTHROPIC_API_KEY litellm_settings: - callbacks: ["arize_phoenix"] \ No newline at end of file + callbacks: ["rubrik"] + +callback_settings: + rubrik: + callback_type: generic_api + endpoint: https://webhook.site/efc57707-9018-478c-bdf1-2ffaabb2b315 + headers: + Authorization: Bearer sk-1234 \ No newline at end of file diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index af548ecf1b..b914e3e967 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -24,6 +24,10 @@ def initialize_callbacks_on_proxy( # noqa: PLR0915 litellm_settings: dict, callback_specific_params: dict = {}, ): + from litellm.integrations.custom_logger import CustomLogger + from litellm.litellm_core_utils.logging_callback_manager import ( + LoggingCallbackManager, + ) from litellm.proxy.proxy_server import prisma_client verbose_proxy_logger.debug( @@ -32,6 +36,11 @@ def initialize_callbacks_on_proxy( # noqa: PLR0915 if isinstance(value, list): imported_list: List[Any] = [] for callback in value: # ["presidio", ] + # check if callback is a custom logger compatible callback + if isinstance(callback, str): + callback = LoggingCallbackManager._add_custom_callback_generic_api_str( + callback + ) if ( isinstance(callback, str) and callback in litellm._known_custom_logger_compatible_callbacks @@ -259,6 +268,8 @@ def initialize_callbacks_on_proxy( # noqa: PLR0915 **azure_content_safety_params, ) imported_list.append(azure_content_safety_obj) + elif isinstance(callback, CustomLogger): + imported_list.append(callback) else: verbose_proxy_logger.debug( f"{blue_color_code} attempting to import custom calback={callback} {reset_color_code}" diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 67e874fc55..e4a1550e00 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -286,9 +286,7 @@ from litellm.proxy.management_endpoints.customer_endpoints import ( from litellm.proxy.management_endpoints.internal_user_endpoints import ( router as internal_user_router, ) -from litellm.proxy.management_endpoints.internal_user_endpoints import ( - user_update, -) +from litellm.proxy.management_endpoints.internal_user_endpoints import user_update from litellm.proxy.management_endpoints.key_management_endpoints import ( delete_verification_tokens, duration_in_seconds, @@ -342,9 +340,7 @@ from litellm.proxy.ocr_endpoints.endpoints import router as ocr_router from litellm.proxy.openai_files_endpoints.files_endpoints import ( router as openai_files_router, ) -from litellm.proxy.openai_files_endpoints.files_endpoints import ( - set_files_config, -) +from litellm.proxy.openai_files_endpoints.files_endpoints import set_files_config from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( passthrough_endpoint_router, ) @@ -436,9 +432,7 @@ from litellm.types.proxy.management_endpoints.ui_sso import ( LiteLLM_UpperboundKeyGenerateParams, ) from litellm.types.realtime import RealtimeQueryParams -from litellm.types.router import ( - DeploymentTypedDict, -) +from litellm.types.router import DeploymentTypedDict from litellm.types.router import ModelInfo as RouterModelInfo from litellm.types.router import ( RouterGeneralSettings, @@ -1084,7 +1078,6 @@ llm_router: Optional[Router] = None llm_model_list: Optional[list] = None general_settings: dict = {} config_passthrough_endpoints: Optional[List[Dict[str, Any]]] = None -callback_settings: dict = {} log_file = "api_log.json" worker_config = None master_key: Optional[str] = None @@ -1241,7 +1234,10 @@ def cost_tracking(): global prisma_client if prisma_client is not None: litellm.logging_callback_manager.add_litellm_callback(_ProxyDBLogger()) - litellm.logging_callback_manager.add_litellm_async_success_callback(_ProxyDBLogger()) + litellm.logging_callback_manager.add_litellm_async_success_callback( + _ProxyDBLogger() + ) + async def update_cache( # noqa: PLR0915 token: Optional[str], @@ -2074,7 +2070,7 @@ class ProxyConfig: """ Load config values into proxy global state """ - global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, user_custom_key_generate, user_custom_sso, user_custom_ui_sso_sign_in_handler, use_background_health_checks, use_shared_health_check, health_check_interval, use_queue, proxy_budget_rescheduler_max_time, proxy_budget_rescheduler_min_time, ui_access_mode, litellm_master_key_hash, proxy_batch_write_at, disable_spend_logs, prompt_injection_detection_obj, redis_usage_cache, store_model_in_db, premium_user, open_telemetry_logger, health_check_details, callback_settings, proxy_batch_polling_interval, config_passthrough_endpoints + global master_key, user_config_file_path, otel_logging, user_custom_auth, user_custom_auth_path, user_custom_key_generate, user_custom_sso, user_custom_ui_sso_sign_in_handler, use_background_health_checks, use_shared_health_check, health_check_interval, use_queue, proxy_budget_rescheduler_max_time, proxy_budget_rescheduler_min_time, ui_access_mode, litellm_master_key_hash, proxy_batch_write_at, disable_spend_logs, prompt_injection_detection_obj, redis_usage_cache, store_model_in_db, premium_user, open_telemetry_logger, health_check_details, proxy_batch_polling_interval, config_passthrough_endpoints config: dict = await self.get_config(config_file_path=config_file_path) @@ -2082,6 +2078,8 @@ class ProxyConfig: ## Callback settings callback_settings = config.get("callback_settings", {}) + if callback_settings: + litellm.callback_settings = callback_settings ## LITELLM MODULE SETTINGS (e.g. litellm.drop_params=True,..) litellm_settings = config.get("litellm_settings", None) diff --git a/tests/logging_callback_tests/test_generic_api_callback.py b/tests/logging_callback_tests/test_generic_api_callback.py index a2301b0303..3ddf84f293 100644 --- a/tests/logging_callback_tests/test_generic_api_callback.py +++ b/tests/logging_callback_tests/test_generic_api_callback.py @@ -28,8 +28,7 @@ from litellm.types.utils import ( ) verbose_logger.setLevel(logging.DEBUG) -from litellm_enterprise.enterprise_callbacks.generic_api_callback import GenericAPILogger - +from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger @pytest.mark.asyncio @@ -52,9 +51,7 @@ async def test_generic_api_callback(): # Initialize the GenericAPILogger and set the mock generic_logger = GenericAPILogger( - endpoint=test_endpoint, - headers=test_headers, - flush_interval=1 + endpoint=test_endpoint, headers=test_headers, flush_interval=1 ) generic_logger.async_httpx_client.post = mock_post litellm.callbacks = [generic_logger] @@ -76,12 +73,21 @@ async def test_generic_api_callback(): # Get the actual request body from the mock actual_url = mock_post.call_args[1]["url"] print("##########\n") - print("logs were flushed to URL", actual_url, "with the following headers", mock_post.call_args[1]["headers"]) - assert actual_url == test_endpoint, f"Expected URL {test_endpoint}, got {actual_url}" + print( + "logs were flushed to URL", + actual_url, + "with the following headers", + mock_post.call_args[1]["headers"], + ) + assert ( + actual_url == test_endpoint + ), f"Expected URL {test_endpoint}, got {actual_url}" # Validate headers - assert mock_post.call_args[1]["headers"]["Content-Type"] == "application/json", "Content-Type should be application/json" - + assert ( + mock_post.call_args[1]["headers"]["Content-Type"] == "application/json" + ), "Content-Type should be application/json" + # For the GenericAPILogger, it sends the payload directly as JSON in the data field json_data = mock_post.call_args[1]["data"] # Parse the JSON string @@ -89,27 +95,30 @@ async def test_generic_api_callback(): print("##########\n") print("json_data", json_data) actual_request = json.loads(json_data) - + # The payload is a list of StandardLoggingPayload objects in the log queue assert isinstance(actual_request, list), "Request body should be a list" assert len(actual_request) > 0, "Request body list should not be empty" - + # Validate the first payload item payload_item: StandardLoggingPayload = StandardLoggingPayload(**actual_request[0]) print("##########\n") print(json.dumps(payload_item, indent=4)) print("##########\n") - # Basic assertions for standard logging payload assert payload_item["response_cost"] > 0, "Response cost should be greater than 0" assert payload_item["model"] == "gpt-4o", "Model should be gpt-4o" - assert payload_item["model_parameters"]["user"] == "test_user", "User should be test_user" + assert ( + payload_item["model_parameters"]["user"] == "test_user" + ), "User should be test_user" assert payload_item["model"] == "gpt-4o", "Model should be gpt-4o" - assert payload_item["messages"] == [{"role": "user", "content": "Hello, world!"}], "Messages should be the same" - assert payload_item["response"]["choices"][0]["message"]["content"] == "hi", "Response should be hi" - - + assert payload_item["messages"] == [ + {"role": "user", "content": "Hello, world!"} + ], "Messages should be the same" + assert ( + payload_item["response"]["choices"][0]["message"]["content"] == "hi" + ), "Response should be hi" @pytest.mark.asyncio @@ -129,9 +138,7 @@ async def test_generic_api_callback_multiple_logs(): # Initialize the GenericAPILogger and set the mock generic_logger = GenericAPILogger( - endpoint=test_endpoint, - headers=test_headers, - flush_interval=5 + endpoint=test_endpoint, headers=test_headers, flush_interval=5 ) generic_logger.async_httpx_client.post = mock_post litellm.callbacks = [generic_logger] @@ -154,9 +161,16 @@ async def test_generic_api_callback_multiple_logs(): # Get the actual request body from the mock actual_url = mock_post.call_args[1]["url"] print("##########\n") - print("logs were flushed to URL", actual_url, "with the following headers", mock_post.call_args[1]["headers"]) - assert actual_url == test_endpoint, f"Expected URL {test_endpoint}, got {actual_url}" - + print( + "logs were flushed to URL", + actual_url, + "with the following headers", + mock_post.call_args[1]["headers"], + ) + assert ( + actual_url == test_endpoint + ), f"Expected URL {test_endpoint}, got {actual_url}" + # For the GenericAPILogger, it sends the payload directly as JSON in the data field json_data = mock_post.call_args[1]["data"] # Parse the JSON string @@ -164,12 +178,14 @@ async def test_generic_api_callback_multiple_logs(): print("##########\n") print("json_data", json_data) actual_request = json.loads(json_data) - + # The payload is a list of StandardLoggingPayload objects in the log queue assert isinstance(actual_request, list), "Request body should be a list" assert len(actual_request) > 0, "Request body list should not be empty" - assert len(actual_request) == 10, "Request body list should be 10 items, since we made 10 calls" - + assert ( + len(actual_request) == 10 + ), "Request body list should be 10 items, since we made 10 calls" + # Validate all payload items for payload_item in actual_request: payload_item: StandardLoggingPayload = StandardLoggingPayload(**payload_item) @@ -177,10 +193,17 @@ async def test_generic_api_callback_multiple_logs(): print(json.dumps(payload_item, indent=4)) print("##########\n") - assert payload_item["response_cost"] > 0, "Response cost should be greater than 0" + assert ( + payload_item["response_cost"] > 0 + ), "Response cost should be greater than 0" assert payload_item["model"] == "gpt-4o", "Model should be gpt-4o" - assert payload_item["model_parameters"]["user"] == "test_user", "User should be test_user" + assert ( + payload_item["model_parameters"]["user"] == "test_user" + ), "User should be test_user" assert payload_item["model"] == "gpt-4o", "Model should be gpt-4o" - assert payload_item["messages"] == [{"role": "user", "content": "Hello, world!"}], "Messages should be the same" - assert payload_item["response"]["choices"][0]["message"]["content"] == "hi", "Response should be hi" - + assert payload_item["messages"] == [ + {"role": "user", "content": "Hello, world!"} + ], "Messages should be the same" + assert ( + payload_item["response"]["choices"][0]["message"]["content"] == "hi" + ), "Response should be hi" From 65f5cc29dd002ecf68460b75312fbc7db4303e64 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 18:55:32 -0800 Subject: [PATCH 128/248] fix ai/ml api --- litellm/llms/aiml/image_generation/transformation.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/litellm/llms/aiml/image_generation/transformation.py b/litellm/llms/aiml/image_generation/transformation.py index 006a2c16d7..d8f3e23fe7 100644 --- a/litellm/llms/aiml/image_generation/transformation.py +++ b/litellm/llms/aiml/image_generation/transformation.py @@ -97,6 +97,9 @@ class AimlImageGenerationConfig(BaseImageGenerationConfig): ) complete_url = complete_url.rstrip("/") + # Strip /v1 suffix if present since IMAGE_GENERATION_ENDPOINT already includes v1 + if complete_url.endswith("/v1"): + complete_url = complete_url[:-3] complete_url = f"{complete_url}/{self.IMAGE_GENERATION_ENDPOINT}" return complete_url From 01fd4d7cef6161e8b683ef48727ae8ea81b70a80 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 18:58:32 -0800 Subject: [PATCH 129/248] fix fireworks test --- tests/local_testing/test_completion.py | 2 +- tests/local_testing/test_completion_cost.py | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/tests/local_testing/test_completion.py b/tests/local_testing/test_completion.py index 7166007961..a72751d6f5 100644 --- a/tests/local_testing/test_completion.py +++ b/tests/local_testing/test_completion.py @@ -1209,7 +1209,7 @@ def test_completion_fireworks_ai(): }, ] response = completion( - model="fireworks_ai/llama4-maverick-instruct-basic", + model="fireworks_ai/llama-v3p3-70b-instruct", messages=messages, ) print(response) diff --git a/tests/local_testing/test_completion_cost.py b/tests/local_testing/test_completion_cost.py index d4edcf58ea..40efcc2386 100644 --- a/tests/local_testing/test_completion_cost.py +++ b/tests/local_testing/test_completion_cost.py @@ -1198,8 +1198,7 @@ from litellm.llms.fireworks_ai.cost_calculator import get_base_model_for_pricing @pytest.mark.parametrize( "model, base_model", [ - ("fireworks_ai/llama-v3p1-405b-instruct", "fireworks-ai-above-16b"), - ("fireworks_ai/llama4-maverick-instruct-basic", "fireworks-ai-default"), + ("fireworks_ai/llama-v3p3-70b-instruct", "fireworks-ai-above-16b"), ], ) def test_get_model_params_fireworks_ai(model, base_model): @@ -1210,8 +1209,7 @@ def test_get_model_params_fireworks_ai(model, base_model): @pytest.mark.parametrize( "model", [ - "fireworks_ai/llama-v3p1-405b-instruct", - "fireworks_ai/llama4-maverick-instruct-basic", + "fireworks_ai/llama-v3p3-70b-instruct", ], ) def test_completion_cost_fireworks_ai(model): From 772be1778a6a2aaea935f6b822d8bb660c405667 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Wed, 26 Nov 2025 19:00:31 -0800 Subject: [PATCH 130/248] test_append_system_prompt_messages --- .../test_litellm_logging.py | 59 +++++++++++++++++++ 1 file changed, 59 insertions(+) diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py index 477fd1396f..8065304fd6 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -759,3 +759,62 @@ def test_get_final_response_obj_with_empty_response_obj_and_list_init(): assert len(result) == 2 assert result[0].name == "Object1" assert result[1].name == "Object2" + + +def test_append_system_prompt_messages(): + """ + Test append_system_prompt_messages prepends system message from kwargs to messages list. + """ + from litellm.litellm_core_utils.litellm_logging import StandardLoggingPayloadSetup + + # Test case 1: system in kwargs with existing messages + kwargs = {"system": "You are a helpful assistant"} + messages = [{"role": "user", "content": "Hello"}] + result = StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=messages + ) + assert len(result) == 2 + assert result[0] == {"role": "system", "content": "You are a helpful assistant"} + assert result[1] == {"role": "user", "content": "Hello"} + + # Test case 2: system in kwargs with None messages + kwargs = {"system": "You are a helpful assistant"} + result = StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=None + ) + assert len(result) == 1 + assert result[0] == {"role": "system", "content": "You are a helpful assistant"} + + # Test case 3: system in kwargs with empty messages list + kwargs = {"system": "You are a helpful assistant"} + result = StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=[] + ) + assert len(result) == 1 + assert result[0] == {"role": "system", "content": "You are a helpful assistant"} + + # Test case 4: duplicate system message should not be added + kwargs = {"system": "You are a helpful assistant"} + messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Hello"}, + ] + result = StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=messages + ) + assert len(result) == 2 + assert result[0] == {"role": "system", "content": "You are a helpful assistant"} + + # Test case 5: no system in kwargs returns messages unchanged + kwargs = {} + messages = [{"role": "user", "content": "Hello"}] + result = StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=messages + ) + assert result == messages + + # Test case 6: None kwargs returns messages unchanged + result = StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=None, messages=messages + ) + assert result == messages From 40db4527f709b92eea4217234e804ad4daca5d0a Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Sat, 15 Nov 2025 16:23:48 -0800 Subject: [PATCH 131/248] [Feature] UI - Organization Usage in Usage Tab (#16614) * Organization Usage UI * Resolved build issues * Added a test --- .../src/app/(dashboard)/usage/page.tsx | 1 + ui/litellm-dashboard/src/app/page.tsx | 1 + .../EntityUsageExportModal.tsx | 2 +- .../EntityUsageExport/ExportTypeSelector.tsx | 3 +- .../EntityUsageExport/UsageExportHeader.tsx | 2 +- .../src/components/EntityUsageExport/types.ts | 3 +- .../src/components/EntityUsageExport/utils.ts | 11 +- .../common_components/default_org.tsx | 2 +- .../src/components/entity_usage.test.tsx | 18 +- .../src/components/entity_usage.tsx | 39 +++- .../src/components/networking.tsx | 205 ++++++++++-------- .../src/components/new_usage.test.tsx | 39 ++++ .../src/components/new_usage.tsx | 105 ++++++--- 13 files changed, 288 insertions(+), 143 deletions(-) diff --git a/ui/litellm-dashboard/src/app/(dashboard)/usage/page.tsx b/ui/litellm-dashboard/src/app/(dashboard)/usage/page.tsx index e4b44e5a45..d77b947df3 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/usage/page.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/usage/page.tsx @@ -15,6 +15,7 @@ const UsagePage = () => { userID={userId} teams={teams ?? []} premiumUser={premiumUser} + organizations={[]} /> ); }; diff --git a/ui/litellm-dashboard/src/app/page.tsx b/ui/litellm-dashboard/src/app/page.tsx index f547ab7d05..56c35804df 100644 --- a/ui/litellm-dashboard/src/app/page.tsx +++ b/ui/litellm-dashboard/src/app/page.tsx @@ -480,6 +480,7 @@ export default function CreateKeyPage() { userRole={userRole} accessToken={accessToken} teams={(teams as Team[]) ?? []} + organizations={(organizations as Organization[]) ?? []} premiumUser={premiumUser} /> ) : ( diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx index 104e446cb3..672643f2ad 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx @@ -22,7 +22,7 @@ const EntityUsageExportModal: React.FC = ({ const [exportScope, setExportScope] = useState("daily"); const [isExporting, setIsExporting] = useState(false); - const entityLabel = entityType === "tag" ? "Tag" : "Team"; + const entityLabel = entityType.charAt(0).toUpperCase() + entityType.slice(1); const modalTitle = customTitle || `Export ${entityLabel} Usage`; const handleExportCSV = () => { diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx index 83e719032c..43e6f986df 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/ExportTypeSelector.tsx @@ -5,7 +5,7 @@ import type { ExportScope } from "./types"; interface ExportTypeSelectorProps { value: ExportScope; onChange: (value: ExportScope) => void; - entityType: "tag" | "team"; + entityType: "tag" | "team" | "organization"; } const ExportTypeSelector: React.FC = ({ value, onChange, entityType }) => { @@ -36,4 +36,3 @@ const ExportTypeSelector: React.FC = ({ value, onChange }; export default ExportTypeSelector; - diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx index 1f61ea260e..3547d65379 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/UsageExportHeader.tsx @@ -7,7 +7,7 @@ import type { EntitySpendData } from "./types"; interface UsageExportHeaderProps { dateValue: DateRangePickerValue; - entityType: "tag" | "team"; + entityType: "tag" | "team" | "organization"; spendData: EntitySpendData; // Optional filter props showFilters?: boolean; diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts index b7ac41c6f3..ea11701f7e 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/types.ts @@ -17,7 +17,7 @@ export interface EntitySpendData { export interface EntityUsageExportModalProps { isOpen: boolean; onClose: () => void; - entityType: "tag" | "team"; + entityType: "tag" | "team" | "organization"; spendData: EntitySpendData; dateRange: DateRangePickerValue; selectedFilters: string[]; @@ -59,4 +59,3 @@ export interface EntityBreakdown { id: string; }; } - diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts index a63a60e5cb..87ca860657 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts @@ -50,7 +50,7 @@ export const generateDailyData = (spendData: EntitySpendData, entityLabel: strin [entityLabel]: data.metadata?.team_alias || entity, [`${entityLabel} ID`]: entity, "Spend ($)": formatNumberWithCommas(data.metrics.spend, 4), - "Requests": data.metrics.api_requests, + Requests: data.metrics.api_requests, "Successful Requests": data.metrics.successful_requests, "Failed Requests": data.metrics.failed_requests, "Total Tokens": data.metrics.total_tokens, @@ -109,9 +109,9 @@ export const generateDailyWithModelsData = (spendData: EntitySpendData, entityLa [`${entityLabel} ID`]: entity, Model: model, "Spend ($)": formatNumberWithCommas(metrics.spend, 4), - "Requests": metrics.requests, - "Successful": metrics.successful, - "Failed": metrics.failed, + Requests: metrics.requests, + Successful: metrics.successful, + Failed: metrics.failed, "Total Tokens": metrics.tokens, }); }); @@ -137,7 +137,7 @@ export const generateExportData = ( }; export const generateMetadata = ( - entityType: "tag" | "team", + entityType: "tag" | "team" | "organization", dateRange: { from?: Date; to?: Date }, selectedFilters: string[], exportScope: ExportScope, @@ -159,4 +159,3 @@ export const generateMetadata = ( total_tokens: spendData.metadata.total_tokens, }, }); - diff --git a/ui/litellm-dashboard/src/components/common_components/default_org.tsx b/ui/litellm-dashboard/src/components/common_components/default_org.tsx index 8bb5fdaad4..f50c59a556 100644 --- a/ui/litellm-dashboard/src/components/common_components/default_org.tsx +++ b/ui/litellm-dashboard/src/components/common_components/default_org.tsx @@ -1,6 +1,6 @@ import { Organization } from "../networking"; export const defaultOrg = { - organization_id: null, + organization_id: "default_organization", organization_alias: "Default Organization", } as Organization; diff --git a/ui/litellm-dashboard/src/components/entity_usage.test.tsx b/ui/litellm-dashboard/src/components/entity_usage.test.tsx index a52e63a391..d5cc503fd1 100644 --- a/ui/litellm-dashboard/src/components/entity_usage.test.tsx +++ b/ui/litellm-dashboard/src/components/entity_usage.test.tsx @@ -3,7 +3,6 @@ import { beforeAll, beforeEach, describe, expect, it, vi } from "vitest"; import EntityUsage from "./entity_usage"; import * as networking from "./networking"; -// Polyfill ResizeObserver for test environment beforeAll(() => { if (typeof window !== "undefined" && !window.ResizeObserver) { window.ResizeObserver = class ResizeObserver { @@ -18,6 +17,7 @@ beforeAll(() => { vi.mock("./networking", () => ({ tagDailyActivityCall: vi.fn(), teamDailyActivityCall: vi.fn(), + organizationDailyActivityCall: vi.fn(), })); // Mock the child components to simplify testing @@ -41,6 +41,7 @@ vi.mock("./EntityUsageExport", () => ({ describe("EntityUsage", () => { const mockTagDailyActivityCall = vi.mocked(networking.tagDailyActivityCall); const mockTeamDailyActivityCall = vi.mocked(networking.teamDailyActivityCall); + const mockOrganizationDailyActivityCall = vi.mocked(networking.organizationDailyActivityCall); const mockSpendData = { results: [ @@ -126,8 +127,10 @@ describe("EntityUsage", () => { beforeEach(() => { mockTagDailyActivityCall.mockClear(); mockTeamDailyActivityCall.mockClear(); + mockOrganizationDailyActivityCall.mockClear(); mockTagDailyActivityCall.mockResolvedValue(mockSpendData); mockTeamDailyActivityCall.mockResolvedValue(mockSpendData); + mockOrganizationDailyActivityCall.mockResolvedValue(mockSpendData); }); it("should render with tag entity type and display spend metrics", async () => { @@ -164,6 +167,19 @@ describe("EntityUsage", () => { }); }); + it("should render with organization entity type and call organization API", async () => { + const { getByText, getAllByText } = render(); + + await waitFor(() => { + expect(mockOrganizationDailyActivityCall).toHaveBeenCalled(); + }); + + expect(getByText("Organization Spend Overview")).toBeInTheDocument(); + + const spendElements = getAllByText("$100.50"); + expect(spendElements.length).toBeGreaterThan(0); + }); + it("should switch between tabs", async () => { render(); diff --git a/ui/litellm-dashboard/src/components/entity_usage.tsx b/ui/litellm-dashboard/src/components/entity_usage.tsx index fc1d03a372..a5789b7dba 100644 --- a/ui/litellm-dashboard/src/components/entity_usage.tsx +++ b/ui/litellm-dashboard/src/components/entity_usage.tsx @@ -23,7 +23,7 @@ import { } from "@tremor/react"; import { ActivityMetrics, processActivityData } from "./activity_metrics"; import { DailyData, BreakdownMetrics, KeyMetricWithMetadata, EntityMetricWithMetadata, TagUsage } from "./usage/types"; -import { tagDailyActivityCall, teamDailyActivityCall } from "./networking"; +import { organizationDailyActivityCall, tagDailyActivityCall, teamDailyActivityCall } from "./networking"; import TopKeyView from "./top_key_view"; import { formatNumberWithCommas } from "@/utils/dataUtils"; import { valueFormatterSpend } from "./usage/utils/value_formatters"; @@ -68,7 +68,7 @@ export interface EntityList { interface EntityUsageProps { accessToken: string | null; - entityType: "tag" | "team"; + entityType: "tag" | "team" | "organization"; entityId?: string | null; userID: string | null; userRole: string | null; @@ -126,6 +126,15 @@ const EntityUsage: React.FC = ({ selectedTags.length > 0 ? selectedTags : null, ); setSpendData(data); + } else if (entityType === "organization") { + const data = await organizationDailyActivityCall( + accessToken, + startTime, + endTime, + 1, + selectedTags.length > 0 ? selectedTags : null, + ); + setSpendData(data); } else { throw new Error("Invalid entity type"); } @@ -325,6 +334,16 @@ const EntityUsage: React.FC = ({ })); }; + const getFilterLabel = (entityType: string) => { + return `Filter by ${entityType}`; + }; + + const getFilterPlaceholder = (entityType: string) => { + return `Select ${entityType} to filter...`; + }; + + const capitalizedEntityLabel = entityType.charAt(0).toUpperCase() + entityType.slice(1); + return (
= ({ entityType={entityType} spendData={spendData} showFilters={entityList !== null && entityList.length > 0} - filterLabel={`Filter by ${entityType === "tag" ? "Tags" : "Teams"}`} - filterPlaceholder={`Select ${entityType === "tag" ? "tags" : "teams"} to filter...`} + filterLabel={getFilterLabel(entityType)} + filterPlaceholder={getFilterPlaceholder(entityType)} selectedFilters={selectedTags} onFiltersChange={setSelectedTags} filterOptions={getAllTags() || undefined} @@ -350,7 +369,7 @@ const EntityUsage: React.FC = ({ {/* Total Spend Card */} - {entityType === "tag" ? "Tag" : "Team"} Spend Overview + {capitalizedEntityLabel} Spend Overview Total Spend @@ -413,10 +432,10 @@ const EntityUsage: React.FC = ({

Failed: {data.metrics.failed_requests}

Total Tokens: {data.metrics.total_tokens}

- {entityType === "tag" ? "Total Tags" : "Total Teams"}: {entityCount} + Total {capitalizedEntityLabel}s: {entityCount}

-

Spend by {entityType === "tag" ? "Tag" : "Team"}:

+

Spend by {capitalizedEntityLabel}:

{Object.entries(data.breakdown.entities || {}) .sort(([, a], [, b]) => { const spendA = (a as EntityMetrics).metrics.spend; @@ -449,10 +468,10 @@ const EntityUsage: React.FC = ({
- Spend Per {entityType === "tag" ? "Tag" : "Team"} + Spend Per {capitalizedEntityLabel} Showing Top 5 by Spend
- Get Started by Tracking cost per {entityType} + Get Started by Tracking cost per {capitalizedEntityLabel} = ({ - {entityType === "tag" ? "Tag" : "Team"} + {capitalizedEntityLabel} Spend Successful Failed diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index 87c8563e46..14763930aa 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -143,7 +143,7 @@ export interface ListPromptsResponse { } export interface Organization { - organization_id: string | null; + organization_id: string; organization_alias: string; budget_id: string; metadata: Record; @@ -1570,21 +1570,70 @@ export const transformRequestCall = async (accessToken: string, request: object) } }; -export const userDailyActivityCall = async (accessToken: string, startTime: Date, endTime: Date, page: number = 1) => { - /** - * Get daily user activity on proxy - */ - try { - let url = proxyBaseUrl ? `${proxyBaseUrl}/user/daily/activity` : `/user/daily/activity`; - const queryParams = new URLSearchParams(); - queryParams.append("start_date", formatDate(startTime)); - queryParams.append("end_date", formatDate(endTime)); - queryParams.append("page_size", "1000"); - queryParams.append("page", page.toString()); - const queryString = queryParams.toString(); - if (queryString) { - url += `?${queryString}`; +type DailyActivityQueryValue = string | number | string[] | null | undefined; + +const DEFAULT_DAILY_ACTIVITY_PAGE_SIZE = "1000"; + +const appendDailyActivityQueryParam = (params: URLSearchParams, key: string, value: DailyActivityQueryValue) => { + if (value === null || value === undefined) { + return; + } + + if (Array.isArray(value)) { + if (value.length > 0) { + params.append(key, value.join(",")); } + return; + } + + params.append(key, `${value}`); +}; + +const buildDailyActivityUrl = ( + endpoint: string, + startTime: Date, + endTime: Date, + page: number, + extraQueryParams?: Record, +) => { + const resolvedEndpoint = endpoint.startsWith("/") ? endpoint : `/${endpoint}`; + const baseUrl = proxyBaseUrl ? `${proxyBaseUrl}${resolvedEndpoint}` : resolvedEndpoint; + + const params = new URLSearchParams(); + params.append("start_date", formatDate(startTime)); + params.append("end_date", formatDate(endTime)); + params.append("page_size", DEFAULT_DAILY_ACTIVITY_PAGE_SIZE); + params.append("page", page.toString()); + + if (extraQueryParams) { + Object.entries(extraQueryParams).forEach(([key, value]) => { + appendDailyActivityQueryParam(params, key, value); + }); + } + + const queryString = params.toString(); + return queryString ? `${baseUrl}?${queryString}` : baseUrl; +}; + +type DailyActivityCallOptions = { + accessToken: string; + endpoint: string; + startTime: Date; + endTime: Date; + page?: number; + extraQueryParams?: Record; +}; + +const fetchDailyActivity = async ({ + accessToken, + endpoint, + startTime, + endTime, + page = 1, + extraQueryParams, +}: DailyActivityCallOptions) => { + try { + const url = buildDailyActivityUrl(endpoint, startTime, endTime, page, extraQueryParams); const response = await fetch(url, { method: "GET", @@ -1604,11 +1653,24 @@ export const userDailyActivityCall = async (accessToken: string, startTime: Date const data = await response.json(); return data; } catch (error) { - console.error("Failed to create key:", error); + console.error(`Failed to fetch daily activity (${endpoint}):`, error); throw error; } }; +export const userDailyActivityCall = async (accessToken: string, startTime: Date, endTime: Date, page: number = 1) => { + /** + * Get daily user activity on proxy + */ + return fetchDailyActivity({ + accessToken, + endpoint: "/user/daily/activity", + startTime, + endTime, + page, + }); +}; + export const tagDailyActivityCall = async ( accessToken: string, startTime: Date, @@ -1619,42 +1681,16 @@ export const tagDailyActivityCall = async ( /** * Get daily user activity on proxy */ - try { - let url = proxyBaseUrl ? `${proxyBaseUrl}/tag/daily/activity` : `/tag/daily/activity`; - const queryParams = new URLSearchParams(); - queryParams.append("start_date", formatDate(startTime)); - queryParams.append("end_date", formatDate(endTime)); - queryParams.append("page_size", "1000"); - queryParams.append("page", page.toString()); - if (tags) { - queryParams.append("tags", tags.join(",")); - } - const queryString = queryParams.toString(); - if (queryString) { - url += `?${queryString}`; - } - - const response = await fetch(url, { - method: "GET", - headers: { - [globalLitellmHeaderName]: `Bearer ${accessToken}`, - "Content-Type": "application/json", - }, - }); - - if (!response.ok) { - const errorData = await response.json(); - const errorMessage = deriveErrorMessage(errorData); - handleError(errorMessage); - throw new Error(errorMessage); - } - - const data = await response.json(); - return data; - } catch (error) { - console.error("Failed to create key:", error); - throw error; - } + return fetchDailyActivity({ + accessToken, + endpoint: "/tag/daily/activity", + startTime, + endTime, + page, + extraQueryParams: { + tags, + }, + }); }; export const teamDailyActivityCall = async ( @@ -1667,43 +1703,36 @@ export const teamDailyActivityCall = async ( /** * Get daily user activity on proxy */ - try { - let url = proxyBaseUrl ? `${proxyBaseUrl}/team/daily/activity` : `/team/daily/activity`; - const queryParams = new URLSearchParams(); - queryParams.append("start_date", formatDate(startTime)); - queryParams.append("end_date", formatDate(endTime)); - queryParams.append("page_size", "1000"); - queryParams.append("page", page.toString()); - if (teamIds) { - queryParams.append("team_ids", teamIds.join(",")); - } - queryParams.append("exclude_team_ids", "litellm-dashboard"); - const queryString = queryParams.toString(); - if (queryString) { - url += `?${queryString}`; - } + return fetchDailyActivity({ + accessToken, + endpoint: "/team/daily/activity", + startTime, + endTime, + page, + extraQueryParams: { + team_ids: teamIds, + exclude_team_ids: "litellm-dashboard", + }, + }); +}; - const response = await fetch(url, { - method: "GET", - headers: { - [globalLitellmHeaderName]: `Bearer ${accessToken}`, - "Content-Type": "application/json", - }, - }); - - if (!response.ok) { - const errorData = await response.json(); - const errorMessage = deriveErrorMessage(errorData); - handleError(errorMessage); - throw new Error(errorMessage); - } - - const data = await response.json(); - return data; - } catch (error) { - console.error("Failed to create key:", error); - throw error; - } +export const organizationDailyActivityCall = async ( + accessToken: string, + startTime: Date, + endTime: Date, + page: number = 1, + organizationIds: string[] | null = null, +) => { + return fetchDailyActivity({ + accessToken, + endpoint: "/organization/daily/activity", + startTime, + endTime, + page, + extraQueryParams: { + organization_ids: organizationIds, + }, + }); }; export const getTotalSpendCall = async (accessToken: string) => { diff --git a/ui/litellm-dashboard/src/components/new_usage.test.tsx b/ui/litellm-dashboard/src/components/new_usage.test.tsx index 6c426401c5..a4969124f1 100644 --- a/ui/litellm-dashboard/src/components/new_usage.test.tsx +++ b/ui/litellm-dashboard/src/components/new_usage.test.tsx @@ -1,6 +1,7 @@ import { render, screen, fireEvent, waitFor } from "@testing-library/react"; import { describe, it, expect, vi, beforeEach, beforeAll } from "vitest"; import NewUsagePage from "./new_usage"; +import type { Organization } from "./networking"; import * as networking from "./networking"; // Polyfill ResizeObserver for test environment @@ -153,6 +154,26 @@ describe("NewUsage", () => { }, }; + const mockOrganizations: Organization[] = [ + { + organization_id: "org-123", + organization_alias: "Acme Org", + budget_id: "budget-1", + metadata: {}, + models: [], + spend: 0, + model_spend: {}, + created_at: "2025-01-01T00:00:00Z", + created_by: "user-123", + updated_at: "2025-01-02T00:00:00Z", + updated_by: "user-123", + litellm_budget_table: null, + teams: null, + users: null, + members: null, + }, + ]; + const defaultProps = { accessToken: "test-token", userRole: "Admin", @@ -175,6 +196,7 @@ describe("NewUsage", () => { members_with_roles: [], }, ], + organizations: [], premiumUser: true, }; @@ -250,4 +272,21 @@ describe("NewUsage", () => { expect(entityUsageElements.length).toBeGreaterThan(0); }); }); + + it("should show organization usage banner and tab for admins", async () => { + const { getByText, getAllByText } = render(); + + await waitFor(() => { + expect(mockUserDailyActivityAggregatedCall).toHaveBeenCalled(); + }); + + const organizationTab = getByText("Organization Usage"); + fireEvent.click(organizationTab); + + await waitFor(() => { + expect(getByText("Organization usage is a new feature.")).toBeInTheDocument(); + const entityUsageElements = getAllByText("Entity Usage"); + expect(entityUsageElements.length).toBeGreaterThan(0); + }); + }); }); diff --git a/ui/litellm-dashboard/src/components/new_usage.tsx b/ui/litellm-dashboard/src/components/new_usage.tsx index 95d721ce9b..4794a7f091 100644 --- a/ui/litellm-dashboard/src/components/new_usage.tsx +++ b/ui/litellm-dashboard/src/components/new_usage.tsx @@ -6,58 +6,66 @@ * Works at 1m+ spend logs, by querying an aggregate table instead. */ -import React, { useState, useEffect, useMemo, useCallback } from "react"; import { BarChart, Card, - Title, - Text, - Grid, Col, - TabGroup, - TabList, - Tab, - TabPanel, - TabPanels, + DateRangePickerValue, DonutChart, + Grid, + Tab, + TabGroup, Table, - TableHead, - TableRow, - TableHeaderCell, TableBody, TableCell, - DateRangePickerValue, + TableHead, + TableHeaderCell, + TableRow, + TabList, + TabPanel, + TabPanels, + Text, + Title, } from "@tremor/react"; +import React, { useCallback, useEffect, useMemo, useState } from "react"; +import { Alert } from "antd"; -import { userDailyActivityCall, userDailyActivityAggregatedCall, tagListCall } from "./networking"; -import { Tag } from "./tag_management/types"; -import ViewUserSpend from "./view_user_spend"; -import TopKeyView from "./top_key_view"; -import { ActivityMetrics, processActivityData } from "./activity_metrics"; -import UserAgentActivity from "./user_agent_activity"; -import { DailyData, MetricWithMetadata, KeyMetricWithMetadata } from "./usage/types"; -import EntityUsage from "./entity_usage"; -import { all_admin_roles } from "../utils/roles"; -import { Team } from "./key_team_helpers/key_list"; -import { EntityList } from "./entity_usage"; import { formatNumberWithCommas } from "@/utils/dataUtils"; -import { valueFormatterSpend } from "./usage/utils/value_formatters"; -import CloudZeroExportModal from "./cloudzero_export_modal"; -import { ChartLoader } from "./shared/chart_loader"; -import { getProviderLogoAndName } from "./provider_info_helpers"; -import EntityUsageExportModal from "./EntityUsageExport"; -import AdvancedDatePicker from "./shared/advanced_date_picker"; import { Button } from "@tremor/react"; +import { all_admin_roles } from "../utils/roles"; +import { ActivityMetrics, processActivityData } from "./activity_metrics"; +import CloudZeroExportModal from "./cloudzero_export_modal"; +import EntityUsage, { EntityList } from "./entity_usage"; +import EntityUsageExportModal from "./EntityUsageExport"; +import { Team } from "./key_team_helpers/key_list"; +import { Organization, tagListCall, userDailyActivityAggregatedCall, userDailyActivityCall } from "./networking"; +import { getProviderLogoAndName } from "./provider_info_helpers"; +import AdvancedDatePicker from "./shared/advanced_date_picker"; +import { ChartLoader } from "./shared/chart_loader"; +import { Tag } from "./tag_management/types"; +import TopKeyView from "./top_key_view"; +import { DailyData, KeyMetricWithMetadata, MetricWithMetadata } from "./usage/types"; +import { valueFormatterSpend } from "./usage/utils/value_formatters"; +import UserAgentActivity from "./user_agent_activity"; +import ViewUserSpend from "./view_user_spend"; interface NewUsagePageProps { accessToken: string | null; userRole: string | null; userID: string | null; teams: Team[]; + organizations: Organization[]; premiumUser: boolean; } -const NewUsagePage: React.FC = ({ accessToken, userRole, userID, teams, premiumUser }) => { +const NewUsagePage: React.FC = ({ + accessToken, + userRole, + userID, + teams, + organizations, + premiumUser, +}) => { const [userSpendData, setUserSpendData] = useState<{ results: DailyData[]; metadata: any; @@ -81,6 +89,7 @@ const NewUsagePage: React.FC = ({ accessToken, userRole, user const [modelViewType, setModelViewType] = useState<"groups" | "individual">("groups"); const [isCloudZeroModalOpen, setIsCloudZeroModalOpen] = useState(false); const [isGlobalExportModalOpen, setIsGlobalExportModalOpen] = useState(false); + const [showOrganizationBanner, setShowOrganizationBanner] = useState(true); const getAllTags = async () => { if (!accessToken) { @@ -415,6 +424,11 @@ const NewUsagePage: React.FC = ({ accessToken, userRole, user
{all_admin_roles.includes(userRole || "") ? Global Usage : Your Usage} + {all_admin_roles.includes(userRole || "") ? ( + Organization Usage + ) : ( + Your Organization Usage + )} Team Usage {all_admin_roles.includes(userRole || "") ? Tag Usage : <>} {all_admin_roles.includes(userRole || "") ? User Agent Activity : <>} @@ -737,6 +751,35 @@ const NewUsagePage: React.FC = ({ accessToken, userRole, user + {/* Organization Usage Panel */} + + {showOrganizationBanner && ( + setShowOrganizationBanner(false)} + className="mb-5" + /> + )} + ({ + label: organization.organization_alias, + value: organization.organization_id, + })) || null + } + premiumUser={premiumUser} + /> + + {/* Team Usage Panel */} Date: Thu, 27 Nov 2025 15:46:04 +0530 Subject: [PATCH 132/248] Added support for twelvelabs pegasus --- litellm/__init__.py | 3 + litellm/constants.py | 1 + ...mazon_twelvelabs_pegasus_transformation.py | 133 ++++++++++++++++++ litellm/llms/bedrock/common_utils.py | 2 + .../test_twelvelabs_pegasus_transformation.py | 85 +++++++++++ 5 files changed, 224 insertions(+) create mode 100644 litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py create mode 100644 tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_twelvelabs_pegasus_transformation.py diff --git a/litellm/__init__.py b/litellm/__init__.py index aebf140419..e6af8a21ff 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1222,6 +1222,9 @@ from .llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation imp from .llms.bedrock.chat.invoke_transformations.amazon_titan_transformation import ( AmazonTitanConfig, ) +from .llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation import ( + AmazonTwelveLabsPegasusConfig, +) from .llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) diff --git a/litellm/constants.py b/litellm/constants.py index cf3d4c6e74..00e1778846 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -851,6 +851,7 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "nova", "deepseek_r1", "qwen3", + "twelvelabs", ] BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py new file mode 100644 index 0000000000..7b72968ea3 --- /dev/null +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py @@ -0,0 +1,133 @@ +""" +Transforms OpenAI-style requests into TwelveLabs Pegasus 1.2 requests for Bedrock. + +Reference: +https://docs.twelvelabs.io/docs/models/pegasus +""" + +from typing import Any, Dict, List, Optional + +from litellm.llms.base_llm.base_utils import type_to_response_format_param +from litellm.llms.base_llm.chat.transformation import BaseConfig +from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( + AmazonInvokeConfig, +) +from litellm.types.llms.openai import AllMessageValues +from litellm.utils import get_base64_str + + +class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): + """ + Handles transforming OpenAI-style requests into Bedrock InvokeModel requests for + `twelvelabs.pegasus-1-2-v1:0`. + + Pegasus 1.2 requires an `inputPrompt` and a `mediaSource` that either references + an S3 object or a base64-encoded clip. Optional OpenAI params (temperature, + response_format, max_tokens) are translated to the TwelveLabs schema. + """ + + def get_supported_openai_params(self, model: str) -> List[str]: + return [ + "max_tokens", + "max_completion_tokens", + "temperature", + "response_format", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + for param, value in non_default_params.items(): + if param in {"max_tokens", "max_completion_tokens"}: + optional_params["maxOutputTokens"] = value + if param == "temperature": + optional_params["temperature"] = value + if param == "response_format": + optional_params["responseFormat"] = self._normalize_response_format( + value + ) + return optional_params + + def _normalize_response_format(self, value: Any) -> Any: + if isinstance(value, dict): + return value + return type_to_response_format_param(response_format=value) or value + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + input_prompt = self._convert_messages_to_prompt(messages=messages) + request_data: Dict[str, Any] = {"inputPrompt": input_prompt} + + media_source = self._build_media_source(optional_params) + if media_source is not None: + request_data["mediaSource"] = media_source + + for key in ("temperature", "maxOutputTokens", "responseFormat"): + if key in optional_params: + request_data[key] = optional_params.get(key) + return request_data + + def _build_media_source(self, optional_params: dict) -> Optional[dict]: + direct_source = optional_params.get("mediaSource") or optional_params.get( + "media_source" + ) + if isinstance(direct_source, dict): + return direct_source + + base64_input = optional_params.get("video_base64") or optional_params.get( + "base64_string" + ) + if base64_input: + return {"base64String": get_base64_str(base64_input)} + + s3_uri = ( + optional_params.get("video_s3_uri") + or optional_params.get("s3_uri") + or optional_params.get("media_source_s3_uri") + ) + if s3_uri: + s3_location = {"uri": s3_uri} + bucket_owner = ( + optional_params.get("video_s3_bucket_owner") + or optional_params.get("s3_bucket_owner") + or optional_params.get("media_source_bucket_owner") + ) + if bucket_owner: + s3_location["bucketOwner"] = bucket_owner + return {"s3Location": s3_location} + return None + + def _convert_messages_to_prompt(self, messages: List[AllMessageValues]) -> str: + prompt_parts: List[str] = [] + for message in messages: + role = message.get("role", "user") + content = message.get("content", "") + if isinstance(content, list): + text_fragments = [] + for item in content: + if isinstance(item, dict): + item_type = item.get("type") + if item_type == "text": + text_fragments.append(item.get("text", "")) + elif item_type == "image_url": + text_fragments.append("") + elif item_type == "video_url": + text_fragments.append("

Order matters: Models will be tried in the order shown above (1st, 2nd, 3rd, etc.) From d612d71ef427c2e2de01448d69e81979c7d14f93 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 27 Nov 2025 12:06:53 -0800 Subject: [PATCH 150/248] [Feat] Add guardrails for pass through endpoints (#17221) * add PassThroughGuardrailsConfig * init JsonPathExtractor * feat PassthroughGuardrailHandler * feat pt guardrails * pt guardrails * add Pass-Through Endpoint Guardrail Translation * add PassThroughEndpointHandler * execute simple guardrail config and dict settings * TestPassthroughGuardrailHandlerNormalizeConfig * add passthrough_guardrails_config on litellm logging obj * add LiteLLMLoggingObj to base trasaltino * cleaner _get_guardrail_settings * update guardrails settings * docs pt guardrail * docs Guardrails on Pass-Through Endpoints * fix typing * fix typing * test_no_fields_set_sends_full_body * fix typing * Potential fix for code scanning alert no. 3834: Clear-text logging of sensitive information Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> --------- Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com> --- .../docs/proxy/pass_through_guardrails.md | 214 +++++++++++ docs/my-website/sidebars.js | 3 +- litellm/litellm_core_utils/litellm_logging.py | 3 + .../chat/guardrail_translation/handler.py | 2 + .../guardrail_translation/base_translation.py | 5 +- .../rerank/guardrail_translation/handler.py | 4 +- .../chat/guardrail_translation/handler.py | 2 + .../guardrail_translation/handler.py | 4 +- .../guardrail_translation/handler.py | 4 +- .../guardrail_translation/handler.py | 2 + .../speech/guardrail_translation/handler.py | 4 +- .../guardrail_translation/handler.py | 4 +- litellm/llms/pass_through/__init__.py | 12 + .../guardrail_translation/README.md | 41 +++ .../guardrail_translation/__init__.py | 15 + .../guardrail_translation/handler.py | 165 +++++++++ litellm/proxy/_types.py | 27 +- .../unified_guardrail/unified_guardrail.py | 2 + .../jsonpath_extractor.py | 95 +++++ .../pass_through_endpoints.py | 72 +++- .../passthrough_guardrails.py | 333 ++++++++++++++++++ litellm/proxy/proxy_config.yaml | 17 +- ..._passthrough_guardrails_field_targeting.py | 119 +++++++ .../test_passthrough_guardrails.py | 263 ++++++++++++++ 24 files changed, 1378 insertions(+), 34 deletions(-) create mode 100644 docs/my-website/docs/proxy/pass_through_guardrails.md create mode 100644 litellm/llms/pass_through/__init__.py create mode 100644 litellm/llms/pass_through/guardrail_translation/README.md create mode 100644 litellm/llms/pass_through/guardrail_translation/__init__.py create mode 100644 litellm/llms/pass_through/guardrail_translation/handler.py create mode 100644 litellm/proxy/pass_through_endpoints/jsonpath_extractor.py create mode 100644 litellm/proxy/pass_through_endpoints/passthrough_guardrails.py create mode 100644 test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails_field_targeting.py create mode 100644 tests/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails.py diff --git a/docs/my-website/docs/proxy/pass_through_guardrails.md b/docs/my-website/docs/proxy/pass_through_guardrails.md new file mode 100644 index 0000000000..272285e61c --- /dev/null +++ b/docs/my-website/docs/proxy/pass_through_guardrails.md @@ -0,0 +1,214 @@ +# Guardrails on Pass-Through Endpoints + +## Overview + +| Property | Details | +|----------|---------| +| Description | Enable guardrail execution on LiteLLM pass-through endpoints with opt-in activation and automatic inheritance from org/team/key levels | +| Supported Guardrails | All LiteLLM guardrails (Bedrock, Aporia, Lakera, etc.) | +| Default Behavior | Guardrails are **disabled** on pass-through endpoints unless explicitly enabled | + +## Quick Start + +### 1. Define guardrails and pass-through endpoint + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-guard" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "your-guardrail-id" + guardrailVersion: "1" + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + headers: + Authorization: "bearer os.environ/COHERE_API_KEY" + guardrails: + pii-guard: +``` + +### 2. Start proxy + +```bash +litellm --config config.yaml +``` + +### 3. Test request + +```bash +curl -X POST "http://localhost:4000/v1/rerank" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "rerank-english-v3.0", + "query": "What is the capital of France?", + "documents": ["Paris is the capital of France."] + }' +``` + +--- + +## Opt-In Behavior + +| Configuration | Behavior | +|--------------|----------| +| `guardrails` not set | No guardrails execute (default) | +| `guardrails` set | All org/team/key + pass-through guardrails execute | + +When guardrails are enabled, the system collects and executes: +- Org-level guardrails +- Team-level guardrails +- Key-level guardrails +- Pass-through specific guardrails + +--- + + +## How It Works + +The diagram below shows what happens when a client makes a request to `/special/rerank` - a pass-through endpoint configured with guardrails in your `config.yaml`. + +When guardrails are configured on a pass-through endpoint: +1. **Pre-call guardrails** run on the request before forwarding to the target API +2. If `request_fields` is specified (e.g., `["query"]`), only those fields are sent to the guardrail. Otherwise, the entire request payload is evaluated. +3. The request is forwarded to the target API only if guardrails pass +4. **Post-call guardrails** run on the response from the target API +5. If `response_fields` is specified (e.g., `["results[*].text"]`), only those fields are evaluated. Otherwise, the entire response is checked. + +:::info +If the `guardrails` block is omitted or empty in your pass-through endpoint config, the request skips the guardrail flow entirely and goes directly to the target API. +::: + +```mermaid +sequenceDiagram + participant Client + box rgb(200, 220, 255) LiteLLM Proxy + participant PassThrough as Pass-through Endpoint + participant Guardrails + end + participant Target as Target API (Cohere, etc.) + + Client->>PassThrough: POST /special/rerank + Note over PassThrough,Guardrails: Collect passthrough + org/team/key guardrails + PassThrough->>Guardrails: Run pre_call (request_fields or full payload) + Guardrails-->>PassThrough: ✓ Pass / ✗ Block + PassThrough->>Target: Forward request + Target-->>PassThrough: Response + PassThrough->>Guardrails: Run post_call (response_fields or full payload) + Guardrails-->>PassThrough: ✓ Pass / ✗ Block + PassThrough-->>Client: Return response (or error) +``` + +--- + +## Field-Level Targeting + +Target specific JSON fields instead of the entire request/response payload. + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-detection" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "pii-guard-id" + guardrailVersion: "1" + + - guardrail_name: "content-moderation" + litellm_params: + guardrail: bedrock + mode: post_call + guardrailIdentifier: "content-guard-id" + guardrailVersion: "1" + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + headers: + Authorization: "bearer os.environ/COHERE_API_KEY" + guardrails: + pii-detection: + request_fields: ["query", "documents[*].text"] + content-moderation: + response_fields: ["results[*].text"] +``` + +### Field Options + +| Field | Description | +|-------|-------------| +| `request_fields` | JSONPath expressions for input (pre_call) | +| `response_fields` | JSONPath expressions for output (post_call) | +| Neither specified | Guardrail runs on entire payload | + +### JSONPath Examples + +| Expression | Matches | +|------------|---------| +| `query` | Single field named `query` | +| `documents[*].text` | All `text` fields in `documents` array | +| `messages[*].content` | All `content` fields in `messages` array | + +--- + +## Configuration Examples + +### Single guardrail on entire payload + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-detection" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "your-id" + guardrailVersion: "1" + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + guardrails: + pii-detection: +``` + +### Multiple guardrails with mixed settings + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-detection" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "pii-id" + guardrailVersion: "1" + + - guardrail_name: "content-moderation" + litellm_params: + guardrail: bedrock + mode: post_call + guardrailIdentifier: "content-id" + guardrailVersion: "1" + + - guardrail_name: "prompt-injection" + litellm_params: + guardrail: lakera + mode: pre_call + api_key: os.environ/LAKERA_API_KEY + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + guardrails: + pii-detection: + request_fields: ["input", "query"] + content-moderation: + prompt-injection: + request_fields: ["messages[*].content"] +``` diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 2002550a25..917fdca602 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -419,7 +419,8 @@ const sidebars = { ] }, "pass_through/vllm", - "proxy/pass_through" + "proxy/pass_through", + "proxy/pass_through_guardrails" ] }, "rag_ingest", diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 305b7d6ddc..0b0f483ff7 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -374,6 +374,9 @@ class Logging(LiteLLMLoggingBaseClass): # Init Caching related details self.caching_details: Optional[CachingDetails] = None + # Passthrough endpoint guardrails config for field targeting + self.passthrough_guardrails_config: Optional[Dict[str, Any]] = None + self.model_call_details: Dict[str, Any] = { "litellm_trace_id": litellm_trace_id, "litellm_call_id": litellm_call_id, diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 06a1b92e1b..6aba2947d3 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -41,6 +41,7 @@ class AnthropicMessagesHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input messages by applying guardrails to text content. @@ -145,6 +146,7 @@ class AnthropicMessagesHandler(BaseTranslation): self, response: "AnthropicMessagesResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index 4599af1b74..926ad59cee 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -1,8 +1,9 @@ from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Optional if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj class BaseTranslation(ABC): @@ -11,6 +12,7 @@ class BaseTranslation(ABC): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> Any: pass @@ -19,5 +21,6 @@ class BaseTranslation(ABC): self, response: Any, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> Any: pass diff --git a/litellm/llms/cohere/rerank/guardrail_translation/handler.py b/litellm/llms/cohere/rerank/guardrail_translation/handler.py index 0c5e50dc41..a5a5ef68b8 100644 --- a/litellm/llms/cohere/rerank/guardrail_translation/handler.py +++ b/litellm/llms/cohere/rerank/guardrail_translation/handler.py @@ -5,7 +5,7 @@ This module provides guardrail translation support for the rerank endpoint. The handler processes only the 'query' parameter for guardrails. """ -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -34,6 +34,7 @@ class CohereRerankHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input query by applying guardrails. @@ -68,6 +69,7 @@ class CohereRerankHandler(BaseTranslation): self, response: "RerankResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output response - not applicable for rerank. diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index b01f9f1b98..2a421a8283 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -42,6 +42,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input messages by applying guardrails to text content. @@ -148,6 +149,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): self, response: "ModelResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. diff --git a/litellm/llms/openai/completion/guardrail_translation/handler.py b/litellm/llms/openai/completion/guardrail_translation/handler.py index b5db730620..5a38d04d75 100644 --- a/litellm/llms/openai/completion/guardrail_translation/handler.py +++ b/litellm/llms/openai/completion/guardrail_translation/handler.py @@ -5,7 +5,7 @@ This module provides guardrail translation support for OpenAI's text completion The handler processes the 'prompt' parameter for guardrails. """ -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -32,6 +32,7 @@ class OpenAITextCompletionHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input prompt by applying guardrails to text content. @@ -100,6 +101,7 @@ class OpenAITextCompletionHandler(BaseTranslation): self, response: "TextCompletionResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to completion text. diff --git a/litellm/llms/openai/image_generation/guardrail_translation/handler.py b/litellm/llms/openai/image_generation/guardrail_translation/handler.py index 5fcb5278f0..de6bca8e57 100644 --- a/litellm/llms/openai/image_generation/guardrail_translation/handler.py +++ b/litellm/llms/openai/image_generation/guardrail_translation/handler.py @@ -5,7 +5,7 @@ This module provides guardrail translation support for OpenAI's image generation The handler processes the 'prompt' parameter for guardrails. """ -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -31,6 +31,7 @@ class OpenAIImageGenerationHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input prompt by applying guardrails to text content. @@ -72,6 +73,7 @@ class OpenAIImageGenerationHandler(BaseTranslation): self, response: "ImageResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output response - typically not needed for image generation. diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index fdac13176b..489a89c60c 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -56,6 +56,7 @@ class OpenAIResponsesHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input by applying guardrails to text content. @@ -177,6 +178,7 @@ class OpenAIResponsesHandler(BaseTranslation): self, response: "ResponsesAPIResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. diff --git a/litellm/llms/openai/speech/guardrail_translation/handler.py b/litellm/llms/openai/speech/guardrail_translation/handler.py index aa049801d1..47df79833b 100644 --- a/litellm/llms/openai/speech/guardrail_translation/handler.py +++ b/litellm/llms/openai/speech/guardrail_translation/handler.py @@ -5,7 +5,7 @@ This module provides guardrail translation support for OpenAI's text-to-speech e The handler processes the 'input' text parameter (output is audio, so no text to guardrail). """ -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -30,6 +30,7 @@ class OpenAITextToSpeechHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input text by applying guardrails. @@ -72,6 +73,7 @@ class OpenAITextToSpeechHandler(BaseTranslation): self, response: "HttpxBinaryResponseContent", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output - not applicable for text-to-speech. diff --git a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py index 22b93251be..51f50c9180 100644 --- a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py +++ b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py @@ -5,7 +5,7 @@ This module provides guardrail translation support for OpenAI's audio transcript The handler processes the output transcribed text (input is audio, so no text to guardrail). """ -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Optional from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -30,6 +30,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input - not applicable for audio transcription. @@ -54,6 +55,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): self, response: "TranscriptionResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process output transcription by applying guardrails to transcribed text. diff --git a/litellm/llms/pass_through/__init__.py b/litellm/llms/pass_through/__init__.py new file mode 100644 index 0000000000..803772f14d --- /dev/null +++ b/litellm/llms/pass_through/__init__.py @@ -0,0 +1,12 @@ +""" +Pass-Through Endpoint Guardrail Translation + +This module exists here (under litellm/llms/) so it can be auto-discovered by +load_guardrail_translation_mappings() which scans for guardrail_translation +directories under litellm/llms/. + +The main passthrough endpoint implementation is in: + litellm/proxy/pass_through_endpoints/ + +See guardrail_translation/README.md for more details. +""" diff --git a/litellm/llms/pass_through/guardrail_translation/README.md b/litellm/llms/pass_through/guardrail_translation/README.md new file mode 100644 index 0000000000..db4c0704e7 --- /dev/null +++ b/litellm/llms/pass_through/guardrail_translation/README.md @@ -0,0 +1,41 @@ +# Pass-Through Endpoint Guardrail Translation + +## Why This Exists Here + +This module is located under `litellm/llms/` (instead of with the main passthrough code) because: + +1. **Auto-discovery**: The `load_guardrail_translation_mappings()` function in `litellm/llms/__init__.py` scans for `guardrail_translation/` directories under `litellm/llms/` +2. **Consistency**: All other guardrail translation handlers follow this pattern (e.g., `openai/chat/guardrail_translation/`, `anthropic/chat/guardrail_translation/`) + +## Main Passthrough Implementation + +The main passthrough endpoint implementation is in: + +``` +litellm/proxy/pass_through_endpoints/ +├── pass_through_endpoints.py # Core passthrough routing logic +├── passthrough_guardrails.py # Guardrail collection and field targeting +├── jsonpath_extractor.py # JSONPath field extraction utility +└── ... +``` + +## What This Handler Does + +The `PassThroughEndpointHandler` enables guardrails to run on passthrough endpoint requests by: + +1. **Field Targeting**: Extracts specific fields from the request/response using JSONPath expressions configured in `request_fields` / `response_fields` +2. **Full Payload Fallback**: If no field targeting is configured, processes the entire payload +3. **Config Access**: Uses `get_passthrough_guardrails_config()` / `set_passthrough_guardrails_config()` helpers to access the passthrough guardrails configuration stored in request metadata + +## Example Config + +```yaml +passthrough_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + guardrails: + bedrock-pre-guard: + request_fields: ["query", "documents[*].text"] + response_fields: ["results[*].text"] +``` + diff --git a/litellm/llms/pass_through/guardrail_translation/__init__.py b/litellm/llms/pass_through/guardrail_translation/__init__.py new file mode 100644 index 0000000000..db69c8e378 --- /dev/null +++ b/litellm/llms/pass_through/guardrail_translation/__init__.py @@ -0,0 +1,15 @@ +"""Pass-Through Endpoint guardrail translation handler.""" + +from litellm.llms.pass_through.guardrail_translation.handler import ( + PassThroughEndpointHandler, +) +from litellm.types.utils import CallTypes + +guardrail_translation_mappings = { + CallTypes.pass_through: PassThroughEndpointHandler, +} + +__all__ = [ + "guardrail_translation_mappings", + "PassThroughEndpointHandler", +] diff --git a/litellm/llms/pass_through/guardrail_translation/handler.py b/litellm/llms/pass_through/guardrail_translation/handler.py new file mode 100644 index 0000000000..5ff9fd25c5 --- /dev/null +++ b/litellm/llms/pass_through/guardrail_translation/handler.py @@ -0,0 +1,165 @@ +""" +Pass-Through Endpoint Message Handler for Unified Guardrails + +This module provides a handler for passthrough endpoint requests. +It uses the field targeting configuration from litellm_logging_obj +to extract specific fields for guardrail processing. +""" + +from typing import TYPE_CHECKING, Any, List, Optional + +from litellm._logging import verbose_proxy_logger +from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.proxy._types import PassThroughGuardrailSettings + +if TYPE_CHECKING: + from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + + +class PassThroughEndpointHandler(BaseTranslation): + """ + Handler for processing passthrough endpoint requests with guardrails. + + Uses passthrough_guardrails_config from litellm_logging_obj + to determine which fields to extract for guardrail processing. + """ + + def _get_guardrail_settings( + self, + litellm_logging_obj: Optional["LiteLLMLoggingObj"], + guardrail_name: Optional[str], + ) -> Optional[PassThroughGuardrailSettings]: + """ + Get the guardrail settings for a specific guardrail from logging_obj. + """ + from litellm.proxy.pass_through_endpoints.passthrough_guardrails import ( + PassthroughGuardrailHandler, + ) + + if litellm_logging_obj is None: + return None + + passthrough_config = getattr( + litellm_logging_obj, "passthrough_guardrails_config", None + ) + if not passthrough_config or not guardrail_name: + return None + + return PassthroughGuardrailHandler.get_settings( + passthrough_config, guardrail_name + ) + + def _extract_text_for_guardrail( + self, + data: dict, + field_expressions: Optional[List[str]], + ) -> str: + """ + Extract text from data for guardrail processing. + + If field_expressions provided, extracts only those fields. + Otherwise, returns the full payload as JSON. + """ + from litellm.proxy.pass_through_endpoints.jsonpath_extractor import ( + JsonPathExtractor, + ) + + if field_expressions: + text = JsonPathExtractor.extract_fields( + data=data, + jsonpath_expressions=field_expressions, + ) + verbose_proxy_logger.debug( + "PassThroughEndpointHandler: Extracted targeted fields: %s", + text[:200] if text else None, + ) + return text + + # Use entire payload, excluding internal fields + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + + payload_to_check = { + k: v + for k, v in data.items() + if not k.startswith("_") and k not in ("metadata", "litellm_logging_obj") + } + verbose_proxy_logger.debug( + "PassThroughEndpointHandler: Using full payload for guardrail" + ) + return safe_dumps(payload_to_check) + + async def process_input_messages( + self, + data: dict, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> Any: + """ + Process input by applying guardrails to targeted fields or full payload. + """ + guardrail_name = guardrail_to_apply.guardrail_name + verbose_proxy_logger.debug( + "PassThroughEndpointHandler: Processing input for guardrail=%s", + guardrail_name, + ) + + # Get field targeting settings for this guardrail + settings = self._get_guardrail_settings(litellm_logging_obj, guardrail_name) + field_expressions = settings.request_fields if settings else None + + # Extract text to check + text_to_check = self._extract_text_for_guardrail(data, field_expressions) + + if not text_to_check: + verbose_proxy_logger.debug( + "PassThroughEndpointHandler: No text to check, skipping guardrail" + ) + return data + + # Apply guardrail + await guardrail_to_apply.apply_guardrail( + text=text_to_check, + request_data=data, + ) + + return data + + async def process_output_response( + self, + response: Any, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> Any: + """ + Process output response by applying guardrails to targeted fields. + """ + if not isinstance(response, dict): + verbose_proxy_logger.debug( + "PassThroughEndpointHandler: Response is not a dict, skipping" + ) + return response + + guardrail_name = guardrail_to_apply.guardrail_name + verbose_proxy_logger.debug( + "PassThroughEndpointHandler: Processing output for guardrail=%s", + guardrail_name, + ) + + # Get field targeting settings for this guardrail + settings = self._get_guardrail_settings(litellm_logging_obj, guardrail_name) + field_expressions = settings.response_fields if settings else None + + # Extract text to check + text_to_check = self._extract_text_for_guardrail(response, field_expressions) + + if not text_to_check: + return response + + # Apply guardrail + await guardrail_to_apply.apply_guardrail( + text=text_to_check, + request_data=response, + ) + + return response diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 7e30079e78..9e915d4bc5 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -1693,27 +1693,26 @@ class DynamoDBArgs(LiteLLMPydanticObjectBase): assume_role_aws_session_name: Optional[str] = None -class PassThroughGuardrailConfig(LiteLLMPydanticObjectBase): +class PassThroughGuardrailSettings(LiteLLMPydanticObjectBase): """ - Configuration for guardrails on passthrough endpoints. + Settings for a specific guardrail on a passthrough endpoint. - Passthrough endpoints are opt-in only for guardrails. Guardrails configured at - org/team/key levels will NOT execute unless explicitly enabled here. + Allows field-level targeting for guardrail execution. """ - enabled: bool = Field( - default=False, - description="Whether to execute guardrails for this passthrough endpoint. When True, all org/team/key level guardrails will execute along with any passthrough-specific guardrails. When False (default), NO guardrails execute.", - ) - specific: Optional[List[str]] = Field( + request_fields: Optional[List[str]] = Field( default=None, - description="Optional list of guardrail names that are specific to this passthrough endpoint. These will execute in addition to org/team/key level guardrails when enabled=True.", + description="JSONPath expressions for input field targeting (pre_call). Examples: 'query', 'documents[*].text', 'messages[*].content'. If not specified, guardrail runs on entire request payload.", ) - target_fields: Optional[List[str]] = Field( + response_fields: Optional[List[str]] = Field( default=None, - description="Optional list of JSON paths to target specific fields for guardrail execution. Examples: 'messages[*].content', 'input', 'messages[?(@.role=='user')].content'. If not specified, guardrails execute on entire payload.", + description="JSONPath expressions for output field targeting (post_call). Examples: 'results[*].text', 'output'. If not specified, guardrail runs on entire response payload.", ) +# Type alias for the guardrails dict: guardrail_name -> settings (or None for defaults) +PassThroughGuardrailsConfig = Dict[str, Optional[PassThroughGuardrailSettings]] + + class PassThroughGenericEndpoint(LiteLLMPydanticObjectBase): id: Optional[str] = Field( default=None, @@ -1739,9 +1738,9 @@ class PassThroughGenericEndpoint(LiteLLMPydanticObjectBase): default=False, description="Whether authentication is required for the pass-through endpoint. If True, requests to the endpoint will require a valid LiteLLM API key.", ) - guardrails: Optional[PassThroughGuardrailConfig] = Field( + guardrails: Optional[PassThroughGuardrailsConfig] = Field( default=None, - description="Guardrail configuration for this passthrough endpoint. When enabled, org/team/key level guardrails will execute along with any passthrough-specific guardrails. Defaults to disabled (no guardrails execute).", + description="Guardrails configuration for this passthrough endpoint. Dict keys are guardrail names, values are optional settings for field targeting. When set, all org/team/key level guardrails will also execute. Defaults to None (no guardrails execute).", ) diff --git a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py index 824ed4e0b0..9ee1eb8671 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py @@ -86,6 +86,7 @@ class UnifiedLLMGuardrails(CustomLogger): data = await endpoint_translation.process_input_messages( data=data, guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=data.get("litellm_logging_obj"), ) # Add guardrail to applied guardrails header @@ -148,6 +149,7 @@ class UnifiedLLMGuardrails(CustomLogger): response = await endpoint_translation.process_output_response( response=response, # type: ignore guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=data.get("litellm_logging_obj"), ) # Add guardrail to applied guardrails header add_guardrail_to_applied_guardrails_header( diff --git a/litellm/proxy/pass_through_endpoints/jsonpath_extractor.py b/litellm/proxy/pass_through_endpoints/jsonpath_extractor.py new file mode 100644 index 0000000000..fde2553be4 --- /dev/null +++ b/litellm/proxy/pass_through_endpoints/jsonpath_extractor.py @@ -0,0 +1,95 @@ +""" +JSONPath Extractor Module + +Extracts field values from data using simple JSONPath-like expressions. +""" + +from typing import Any, List, Union + +from litellm._logging import verbose_proxy_logger + + +class JsonPathExtractor: + """Extracts field values from data using JSONPath-like expressions.""" + + @staticmethod + def extract_fields( + data: dict, + jsonpath_expressions: List[str], + ) -> str: + """ + Extract field values from data using JSONPath-like expressions. + + Supports simple expressions like: + - "query" -> data["query"] + - "documents[*].text" -> all text fields from documents array + - "messages[*].content" -> all content fields from messages array + + Returns concatenated string of all extracted values. + """ + extracted_values: List[str] = [] + + for expr in jsonpath_expressions: + try: + value = JsonPathExtractor.evaluate(data, expr) + if value: + if isinstance(value, list): + extracted_values.extend([str(v) for v in value if v]) + else: + extracted_values.append(str(value)) + except Exception as e: + verbose_proxy_logger.debug( + "Failed to extract field %s: %s", expr, str(e) + ) + + return "\n".join(extracted_values) + + @staticmethod + def evaluate(data: dict, expr: str) -> Union[str, List[str], None]: + """ + Evaluate a simple JSONPath-like expression. + + Supports: + - Simple key: "query" -> data["query"] + - Nested key: "foo.bar" -> data["foo"]["bar"] + - Array wildcard: "items[*].text" -> [item["text"] for item in data["items"]] + """ + if not expr or not data: + return None + + parts = expr.replace("[*]", ".[*]").split(".") + current: Any = data + + for i, part in enumerate(parts): + if current is None: + return None + + if part == "[*]": + # Wildcard - current should be a list + if not isinstance(current, list): + return None + + # Get remaining path + remaining_path = ".".join(parts[i + 1:]) + if not remaining_path: + return current + + # Recursively evaluate remaining path for each item + results = [] + for item in current: + if isinstance(item, dict): + result = JsonPathExtractor.evaluate(item, remaining_path) + if result: + if isinstance(result, list): + results.extend(result) + else: + results.append(result) + return results if results else None + + elif isinstance(current, dict): + current = current.get(part) + else: + return None + + return current + diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 644b5ce929..df4452f726 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -599,6 +599,7 @@ async def pass_through_request( # noqa: PLR0915 stream: Optional[bool] = None, cost_per_request: Optional[float] = None, custom_llm_provider: Optional[str] = None, + guardrails_config: Optional[dict] = None, ): """ Pass through endpoint handler, makes the httpx request for pass-through endpoints and ensures logging hooks are called @@ -614,8 +615,13 @@ async def pass_through_request( # noqa: PLR0915 query_params: The query params stream: Whether to stream the response cost_per_request: Optional field - cost per request to the target endpoint + custom_llm_provider: Optional field - custom LLM provider for the endpoint + guardrails_config: Optional field - guardrails configuration for passthrough endpoint """ from litellm.litellm_core_utils.litellm_logging import Logging + from litellm.proxy.pass_through_endpoints.passthrough_guardrails import ( + PassthroughGuardrailHandler, + ) from litellm.proxy.proxy_server import proxy_logging_obj ######################################################### @@ -664,6 +670,45 @@ async def pass_through_request( # noqa: PLR0915 ) ) + ### COLLECT GUARDRAILS FOR PASSTHROUGH ENDPOINT ### + # Passthrough endpoints are opt-in only for guardrails + # When enabled, collect guardrails from org/team/key levels + passthrough-specific + guardrails_to_run = PassthroughGuardrailHandler.collect_guardrails( + user_api_key_dict=user_api_key_dict, + passthrough_guardrails_config=guardrails_config, + ) + + # Add guardrails to metadata if any should run + if guardrails_to_run and len(guardrails_to_run) > 0: + if _parsed_body is None: + _parsed_body = {} + if "metadata" not in _parsed_body: + _parsed_body["metadata"] = {} + _parsed_body["metadata"]["guardrails"] = guardrails_to_run + verbose_proxy_logger.debug( + f"Added guardrails to passthrough request metadata: {guardrails_to_run}" + ) + + ## LOGGING OBJECT ## - initialize before pre_call_hook so guardrails can access it + start_time = datetime.now() + logging_obj = Logging( + model="unknown", + messages=[{"role": "user", "content": safe_dumps(_parsed_body)}], + stream=False, + call_type="pass_through_endpoint", + start_time=start_time, + litellm_call_id=litellm_call_id, + function_id="1245", + ) + + # Store passthrough guardrails config on logging_obj for field targeting + logging_obj.passthrough_guardrails_config = guardrails_config + + # Store logging_obj in data so guardrails can access it + if _parsed_body is None: + _parsed_body = {} + _parsed_body["litellm_logging_obj"] = logging_obj + ### CALL HOOKS ### - modify incoming data / reject request before calling the model _parsed_body = await proxy_logging_obj.pre_call_hook( user_api_key_dict=user_api_key_dict, @@ -675,18 +720,6 @@ async def pass_through_request( # noqa: PLR0915 params={"timeout": 600}, ) async_client = async_client_obj.client - - # create logging object - start_time = datetime.now() - logging_obj = Logging( - model="unknown", - messages=[{"role": "user", "content": safe_dumps(_parsed_body)}], - stream=False, - call_type="pass_through_endpoint", - start_time=start_time, - litellm_call_id=litellm_call_id, - function_id="1245", - ) passthrough_logging_payload = PassthroughStandardLoggingPayload( url=str(url), request_body=_parsed_body, @@ -1011,6 +1044,7 @@ def create_pass_through_route( custom_llm_provider: Optional[str] = None, is_streaming_request: Optional[bool] = False, query_params: Optional[dict] = None, + guardrails: Optional[Dict[str, Any]] = None, ): # check if target is an adapter.py or a url from litellm._uuid import uuid @@ -1079,6 +1113,7 @@ def create_pass_through_route( "forward_headers": _forward_headers, "merge_query_params": _merge_query_params, "cost_per_request": cost_per_request, + "guardrails": None, } if passthrough_params is not None: @@ -1096,6 +1131,7 @@ def create_pass_through_route( param_cost_per_request = target_params.get( "cost_per_request", cost_per_request ) + param_guardrails = target_params.get("guardrails", None) # Construct the full target URL with subpath if needed full_target = ( @@ -1135,6 +1171,7 @@ def create_pass_through_route( custom_body=final_custom_body, cost_per_request=cast(Optional[float], param_cost_per_request), custom_llm_provider=custom_llm_provider, + guardrails_config=param_guardrails, ) return endpoint_func @@ -1769,6 +1806,7 @@ class InitPassThroughEndpointHelpers: dependencies: Optional[List], cost_per_request: Optional[float], endpoint_id: str, + guardrails: Optional[dict] = None, ): """Add exact path route for pass-through endpoint""" route_key = f"{endpoint_id}:exact:{path}" @@ -1799,6 +1837,7 @@ class InitPassThroughEndpointHelpers: merge_query_params, dependencies, cost_per_request=cost_per_request, + guardrails=guardrails, ), methods=["GET", "POST", "PUT", "DELETE", "PATCH"], dependencies=dependencies, @@ -1817,6 +1856,7 @@ class InitPassThroughEndpointHelpers: "merge_query_params": merge_query_params, "dependencies": dependencies, "cost_per_request": cost_per_request, + "guardrails": guardrails, }, } @@ -1831,6 +1871,7 @@ class InitPassThroughEndpointHelpers: dependencies: Optional[List], cost_per_request: Optional[float], endpoint_id: str, + guardrails: Optional[dict] = None, ): """Add wildcard route for sub-paths""" wildcard_path = f"{path}/{{subpath:path}}" @@ -1863,6 +1904,7 @@ class InitPassThroughEndpointHelpers: dependencies, include_subpath=True, cost_per_request=cost_per_request, + guardrails=guardrails, ), methods=["GET", "POST", "PUT", "DELETE", "PATCH"], dependencies=dependencies, @@ -1881,6 +1923,7 @@ class InitPassThroughEndpointHelpers: "merge_query_params": merge_query_params, "dependencies": dependencies, "cost_per_request": cost_per_request, + "guardrails": guardrails, }, } @@ -2057,6 +2100,9 @@ async def initialize_pass_through_endpoints( if _target is None: continue + # Get guardrails config if present + _guardrails = endpoint.get("guardrails", None) + # Add exact path route verbose_proxy_logger.debug( "Initializing pass through endpoint: %s (ID: %s)", _path, endpoint_id @@ -2071,6 +2117,7 @@ async def initialize_pass_through_endpoints( dependencies=_dependencies, cost_per_request=endpoint.get("cost_per_request", None), endpoint_id=endpoint_id, + guardrails=_guardrails, ) visited_endpoints.add(f"{endpoint_id}:exact:{_path}") @@ -2087,6 +2134,7 @@ async def initialize_pass_through_endpoints( dependencies=_dependencies, cost_per_request=endpoint.get("cost_per_request", None), endpoint_id=endpoint_id, + guardrails=_guardrails, ) visited_endpoints.add(f"{endpoint_id}:subpath:{_path}") diff --git a/litellm/proxy/pass_through_endpoints/passthrough_guardrails.py b/litellm/proxy/pass_through_endpoints/passthrough_guardrails.py new file mode 100644 index 0000000000..cec20f3e04 --- /dev/null +++ b/litellm/proxy/pass_through_endpoints/passthrough_guardrails.py @@ -0,0 +1,333 @@ +""" +Passthrough Guardrails Helper Module + +Handles guardrail execution for passthrough endpoints with: +- Opt-in model (guardrails only run when explicitly configured) +- Field-level targeting using JSONPath expressions +- Automatic inheritance from org/team/key levels when enabled +""" + +from typing import Any, Dict, List, Optional, Union + +from litellm._logging import verbose_proxy_logger +from litellm.proxy._types import ( + PassThroughGuardrailsConfig, + PassThroughGuardrailSettings, + UserAPIKeyAuth, +) +from litellm.proxy.pass_through_endpoints.jsonpath_extractor import JsonPathExtractor + +# Type for raw guardrails config input (before normalization) +# Can be a list of names or a dict with settings +PassThroughGuardrailsConfigInput = Union[ + List[str], # Simple list: ["guard-1", "guard-2"] + PassThroughGuardrailsConfig, # Dict: {"guard-1": {"request_fields": [...]}} +] + + +class PassthroughGuardrailHandler: + """ + Handles guardrail execution for passthrough endpoints. + + Passthrough endpoints use an opt-in model for guardrails: + - Guardrails only run when explicitly configured on the endpoint + - Supports field-level targeting using JSONPath expressions + - Automatically inherits org/team/key level guardrails when enabled + + Guardrails can be specified as: + - List format (simple): ["guardrail-1", "guardrail-2"] + - Dict format (with settings): {"guardrail-1": {"request_fields": ["query"]}} + """ + + @staticmethod + def normalize_config( + guardrails_config: Optional[PassThroughGuardrailsConfigInput], + ) -> Optional[PassThroughGuardrailsConfig]: + """ + Normalize guardrails config to dict format. + + Accepts: + - List of guardrail names: ["g1", "g2"] -> {"g1": None, "g2": None} + - Dict with settings: {"g1": {"request_fields": [...]}} + - None: returns None + """ + if guardrails_config is None: + return None + + # Already a dict - return as-is + if isinstance(guardrails_config, dict): + return guardrails_config + + # List of guardrail names - convert to dict + if isinstance(guardrails_config, list): + return {name: None for name in guardrails_config} + + verbose_proxy_logger.debug( + "Passthrough guardrails config is not a dict or list, got: %s", + type(guardrails_config), + ) + return None + + @staticmethod + def is_enabled( + guardrails_config: Optional[PassThroughGuardrailsConfigInput], + ) -> bool: + """ + Check if guardrails are enabled for a passthrough endpoint. + + Passthrough endpoints are opt-in only - guardrails only run when + the guardrails config is set with at least one guardrail. + """ + normalized = PassthroughGuardrailHandler.normalize_config(guardrails_config) + if normalized is None: + return False + return len(normalized) > 0 + + @staticmethod + def get_guardrail_names( + guardrails_config: Optional[PassThroughGuardrailsConfigInput], + ) -> List[str]: + """Get the list of guardrail names configured for a passthrough endpoint.""" + normalized = PassthroughGuardrailHandler.normalize_config(guardrails_config) + if normalized is None: + return [] + return list(normalized.keys()) + + @staticmethod + def get_settings( + guardrails_config: Optional[PassThroughGuardrailsConfigInput], + guardrail_name: str, + ) -> Optional[PassThroughGuardrailSettings]: + """Get settings for a specific guardrail from the passthrough config.""" + normalized = PassthroughGuardrailHandler.normalize_config(guardrails_config) + if normalized is None: + return None + + settings = normalized.get(guardrail_name) + if settings is None: + return None + + if isinstance(settings, dict): + return PassThroughGuardrailSettings(**settings) + + return settings + + @staticmethod + def prepare_input( + request_data: dict, + guardrail_settings: Optional[PassThroughGuardrailSettings], + ) -> str: + """ + Prepare input text for guardrail execution based on field targeting settings. + + If request_fields is specified, extracts only those fields. + Otherwise, uses the entire request payload as text. + """ + if guardrail_settings is None or guardrail_settings.request_fields is None: + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + return safe_dumps(request_data) + + return JsonPathExtractor.extract_fields( + data=request_data, + jsonpath_expressions=guardrail_settings.request_fields, + ) + + @staticmethod + def prepare_output( + response_data: dict, + guardrail_settings: Optional[PassThroughGuardrailSettings], + ) -> str: + """ + Prepare output text for guardrail execution based on field targeting settings. + + If response_fields is specified, extracts only those fields. + Otherwise, uses the entire response payload as text. + """ + if guardrail_settings is None or guardrail_settings.response_fields is None: + from litellm.litellm_core_utils.safe_json_dumps import safe_dumps + return safe_dumps(response_data) + + return JsonPathExtractor.extract_fields( + data=response_data, + jsonpath_expressions=guardrail_settings.response_fields, + ) + + @staticmethod + async def execute( + request_data: dict, + user_api_key_dict: UserAPIKeyAuth, + guardrails_config: Optional[PassThroughGuardrailsConfig], + event_type: str = "pre_call", + ) -> dict: + """ + Execute guardrails for a passthrough endpoint. + + This is the main entry point for passthrough guardrail execution. + + Args: + request_data: The request payload + user_api_key_dict: User API key authentication info + guardrails_config: Passthrough-specific guardrails configuration + event_type: "pre_call" for request, "post_call" for response + + Returns: + The potentially modified request_data + + Raises: + HTTPException if a guardrail blocks the request + """ + if not PassthroughGuardrailHandler.is_enabled(guardrails_config): + verbose_proxy_logger.debug( + "Passthrough guardrails not enabled, skipping guardrail execution" + ) + return request_data + + guardrail_names = PassthroughGuardrailHandler.get_guardrail_names( + guardrails_config + ) + verbose_proxy_logger.debug( + "Executing passthrough guardrails: %s", guardrail_names + ) + + # Add to request metadata so guardrails know which to run + from litellm.proxy.pass_through_endpoints.passthrough_context import ( + set_passthrough_guardrails_config, + ) + + if "metadata" not in request_data: + request_data["metadata"] = {} + + # Set guardrails in metadata using dict format for compatibility + request_data["metadata"]["guardrails"] = { + name: True for name in guardrail_names + } + + # Store passthrough guardrails config in request-scoped context + set_passthrough_guardrails_config(guardrails_config) + + return request_data + + @staticmethod + def collect_guardrails( + user_api_key_dict: UserAPIKeyAuth, + passthrough_guardrails_config: Optional[PassThroughGuardrailsConfigInput], + ) -> Optional[Dict[str, bool]]: + """ + Collect guardrails for a passthrough endpoint. + + Passthrough endpoints are opt-in only for guardrails. Guardrails only run when + the guardrails config is set with at least one guardrail. + + Accepts both list and dict formats: + - List: ["guardrail-1", "guardrail-2"] + - Dict: {"guardrail-1": {"request_fields": [...]}} + + When enabled, this function collects: + - Passthrough-specific guardrails from the config + - Org/team/key level guardrails (automatic inheritance when passthrough is enabled) + + Args: + user_api_key_dict: User API key authentication info + passthrough_guardrails_config: List or Dict of guardrail names/settings + + Returns: + Dict of guardrail names to run (format: {guardrail_name: True}), or None + """ + from litellm.proxy.litellm_pre_call_utils import ( + _add_guardrails_from_key_or_team_metadata, + ) + + # Normalize config to dict format (handles both list and dict) + normalized_config = PassthroughGuardrailHandler.normalize_config( + passthrough_guardrails_config + ) + + if normalized_config is None: + verbose_proxy_logger.debug( + "Passthrough guardrails not configured, skipping guardrail collection" + ) + return None + + if len(normalized_config) == 0: + verbose_proxy_logger.debug( + "Passthrough guardrails config is empty, skipping" + ) + return None + + # Passthrough is enabled - collect guardrails + guardrails_to_run: Dict[str, bool] = {} + + # Add passthrough-specific guardrails + for guardrail_name in normalized_config.keys(): + guardrails_to_run[guardrail_name] = True + verbose_proxy_logger.debug( + "Added passthrough-specific guardrail" + ) + + # Add org/team/key level guardrails using shared helper + temp_data: Dict[str, Any] = {"metadata": {}} + _add_guardrails_from_key_or_team_metadata( + key_metadata=user_api_key_dict.metadata, + team_metadata=user_api_key_dict.team_metadata, + data=temp_data, + metadata_variable_name="metadata", + ) + + # Merge inherited guardrails into guardrails_to_run + inherited_guardrails = temp_data["metadata"].get("guardrails", []) + for guardrail_name in inherited_guardrails: + if guardrail_name not in guardrails_to_run: + guardrails_to_run[guardrail_name] = True + verbose_proxy_logger.debug( + "Added inherited guardrail (key/team level)" + ) + + verbose_proxy_logger.debug( + "Collected total guardrails for passthrough endpoint: %d", + len(guardrails_to_run), + ) + + return guardrails_to_run if guardrails_to_run else None + + @staticmethod + def get_field_targeted_text( + data: dict, + guardrail_name: str, + is_request: bool = True, + ) -> Optional[str]: + """ + Get the text to check for a guardrail, respecting field targeting settings. + + Called by guardrail hooks to get the appropriate text based on + passthrough field targeting configuration. + + Args: + data: The request/response data dict + guardrail_name: Name of the guardrail being executed + is_request: True for request (pre_call), False for response (post_call) + + Returns: + The text to check, or None to use default behavior + """ + from litellm.proxy.pass_through_endpoints.passthrough_context import ( + get_passthrough_guardrails_config, + ) + + passthrough_config = get_passthrough_guardrails_config() + if passthrough_config is None: + return None + + settings = PassthroughGuardrailHandler.get_settings( + passthrough_config, guardrail_name + ) + if settings is None: + return None + + if is_request: + if settings.request_fields: + return JsonPathExtractor.extract_fields(data, settings.request_fields) + else: + if settings.response_fields: + return JsonPathExtractor.extract_fields(data, settings.response_fields) + + return None diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 26e867dc33..098cdb80e0 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -3,7 +3,14 @@ model_list: litellm_params: model: bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z - +guardrails: + - guardrail_name: "bedrock-pre-guard" + litellm_params: + guardrail: bedrock + mode: "pre_call" + guardrailIdentifier: ff6ujrregl1q + guardrailVersion: "DRAFT" + # like MCPs/vector stores search_tools: @@ -40,6 +47,14 @@ litellm_settings: general_settings: store_prompts_in_spend_logs: True + pass_through_endpoints: + - path: "/special/rerank" + target: "https://api.cohere.com/v1/rerank" + headers: + Authorization: "Bearer os.environ/COHERE_API_KEY" + guardrails: + bedrock-pre-guard: + request_fields: ["documents[*].text"] vector_store_registry: diff --git a/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails_field_targeting.py b/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails_field_targeting.py new file mode 100644 index 0000000000..05f367ff13 --- /dev/null +++ b/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails_field_targeting.py @@ -0,0 +1,119 @@ +""" +Test passthrough guardrails field-level targeting. + +Tests that request_fields and response_fields correctly extract +and send only specified fields to the guardrail. +""" + +import json +import os +import sys +from unittest.mock import AsyncMock, MagicMock + +import pytest + +sys.path.insert(0, os.path.abspath("../..")) + +from litellm.proxy._types import PassThroughGuardrailSettings +from litellm.proxy.pass_through_endpoints.passthrough_guardrails import ( + PassthroughGuardrailHandler, +) + + +def test_no_fields_set_sends_full_body(): + """ + Test that when no request_fields are set, the entire request body + is JSON dumped and sent to the guardrail. + """ + request_data = { + "model": "rerank-english-v3.0", + "query": "What is coffee?", + "documents": [ + {"text": "Paris is the capital of France."}, + {"text": "Coffee is a brewed drink."} + ] + } + + # No guardrail settings means full body + result = PassthroughGuardrailHandler.prepare_input( + request_data=request_data, + guardrail_settings=None + ) + + # Result should be JSON string of full request + assert isinstance(result, str) + result_dict = json.loads(result) + + # Should contain all fields + assert "model" in result_dict + assert "query" in result_dict + assert "documents" in result_dict + assert result_dict["query"] == "What is coffee?" + assert len(result_dict["documents"]) == 2 + + +def test_request_fields_query_only(): + """ + Test that when request_fields is set to ["query"], only the query field + is extracted and sent to the guardrail. + """ + request_data = { + "model": "rerank-english-v3.0", + "query": "What is coffee?", + "documents": [ + {"text": "Paris is the capital of France."}, + {"text": "Coffee is a brewed drink."} + ] + } + + # Set request_fields to only extract query + guardrail_settings = PassThroughGuardrailSettings( + request_fields=["query"] + ) + + result = PassthroughGuardrailHandler.prepare_input( + request_data=request_data, + guardrail_settings=guardrail_settings + ) + + # Result should only contain query + assert isinstance(result, str) + assert "What is coffee?" in result + + # Should NOT contain documents + assert "Paris is the capital" not in result + assert "Coffee is a brewed drink" not in result + + +def test_request_fields_documents_wildcard(): + """ + Test that when request_fields is set to ["documents[*]"], only the documents + array is extracted and sent to the guardrail. + """ + request_data = { + "model": "rerank-english-v3.0", + "query": "What is coffee?", + "documents": [ + {"text": "Paris is the capital of France."}, + {"text": "Coffee is a brewed drink."} + ] + } + + # Set request_fields to extract documents array + guardrail_settings = PassThroughGuardrailSettings( + request_fields=["documents[*]"] + ) + + result = PassthroughGuardrailHandler.prepare_input( + request_data=request_data, + guardrail_settings=guardrail_settings + ) + + # Result should contain documents + assert isinstance(result, str) + assert "Paris is the capital" in result + assert "Coffee is a brewed drink" in result + + # Should NOT contain query + assert "What is coffee?" not in result + diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails.py b/tests/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails.py new file mode 100644 index 0000000000..3422e68957 --- /dev/null +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_passthrough_guardrails.py @@ -0,0 +1,263 @@ +""" +Unit tests for passthrough guardrails functionality. + +Tests the opt-in guardrail execution model for passthrough endpoints: +- Guardrails only run when explicitly configured +- Field-level targeting with JSONPath expressions +- Automatic inheritance from org/team/key levels when enabled +""" + +import pytest + +from litellm.proxy._types import PassThroughGuardrailSettings +from litellm.proxy.pass_through_endpoints.jsonpath_extractor import JsonPathExtractor +from litellm.proxy.pass_through_endpoints.passthrough_guardrails import ( + PassthroughGuardrailHandler, +) + + +class TestPassthroughGuardrailHandlerIsEnabled: + """Tests for PassthroughGuardrailHandler.is_enabled method.""" + + def test_returns_false_when_config_is_none(self): + """Guardrails should be disabled when config is None.""" + result = PassthroughGuardrailHandler.is_enabled(None) + assert result is False + + def test_returns_false_when_config_is_empty_dict(self): + """Guardrails should be disabled when config is empty dict.""" + result = PassthroughGuardrailHandler.is_enabled({}) + assert result is False + + def test_returns_true_when_config_is_list(self): + """Guardrails should be enabled when config is a list of names.""" + result = PassthroughGuardrailHandler.is_enabled(["pii-detection"]) + assert result is True + + def test_returns_false_when_config_is_invalid_type(self): + """Guardrails should be disabled when config is not a dict or list.""" + result = PassthroughGuardrailHandler.is_enabled("pii-detection") # type: ignore + assert result is False + + def test_returns_true_when_config_has_guardrails(self): + """Guardrails should be enabled when config has at least one guardrail.""" + config = {"pii-detection": None} + result = PassthroughGuardrailHandler.is_enabled(config) + assert result is True + + def test_returns_true_with_multiple_guardrails(self): + """Guardrails should be enabled with multiple guardrails configured.""" + config = { + "pii-detection": None, + "content-moderation": {"request_fields": ["input"]}, + } + result = PassthroughGuardrailHandler.is_enabled(config) + assert result is True + + +class TestPassthroughGuardrailHandlerGetGuardrailNames: + """Tests for PassthroughGuardrailHandler.get_guardrail_names method.""" + + def test_returns_empty_list_when_disabled(self): + """Should return empty list when guardrails are disabled.""" + result = PassthroughGuardrailHandler.get_guardrail_names(None) + assert result == [] + + def test_returns_guardrail_names(self): + """Should return list of guardrail names from config.""" + config = { + "pii-detection": None, + "content-moderation": {"request_fields": ["input"]}, + } + result = PassthroughGuardrailHandler.get_guardrail_names(config) + assert set(result) == {"pii-detection", "content-moderation"} + + +class TestPassthroughGuardrailHandlerNormalizeConfig: + """Tests for PassthroughGuardrailHandler.normalize_config method.""" + + def test_normalizes_list_to_dict(self): + """List of guardrail names should be converted to dict with None values.""" + config = ["pii-detection", "content-moderation"] + result = PassthroughGuardrailHandler.normalize_config(config) + assert result == {"pii-detection": None, "content-moderation": None} + + def test_returns_dict_unchanged(self): + """Dict config should be returned as-is.""" + config = {"pii-detection": {"request_fields": ["query"]}} + result = PassthroughGuardrailHandler.normalize_config(config) + assert result == config + + +class TestPassthroughGuardrailHandlerGetSettings: + """Tests for PassthroughGuardrailHandler.get_settings method.""" + + def test_returns_none_when_config_is_none(self): + """Should return None when config is None.""" + result = PassthroughGuardrailHandler.get_settings(None, "pii-detection") + assert result is None + + def test_returns_none_when_guardrail_not_in_config(self): + """Should return None when guardrail is not in config.""" + config = {"pii-detection": None} + result = PassthroughGuardrailHandler.get_settings(config, "content-moderation") + assert result is None + + def test_returns_none_when_settings_is_none(self): + """Should return None when guardrail has no settings.""" + config = {"pii-detection": None} + result = PassthroughGuardrailHandler.get_settings(config, "pii-detection") + assert result is None + + def test_returns_settings_object(self): + """Should return PassThroughGuardrailSettings when settings are provided.""" + config = { + "pii-detection": { + "request_fields": ["input", "query"], + "response_fields": ["output"], + } + } + result = PassthroughGuardrailHandler.get_settings(config, "pii-detection") + assert result is not None + assert result.request_fields == ["input", "query"] + assert result.response_fields == ["output"] + + +class TestJsonPathExtractorEvaluate: + """Tests for JsonPathExtractor.evaluate method.""" + + def test_simple_key(self): + """Should extract simple key from dict.""" + data = {"query": "test query", "other": "value"} + result = JsonPathExtractor.evaluate(data, "query") + assert result == "test query" + + def test_nested_key(self): + """Should extract nested key from dict.""" + data = {"foo": {"bar": "nested value"}} + result = JsonPathExtractor.evaluate(data, "foo.bar") + assert result == "nested value" + + def test_array_wildcard(self): + """Should extract values from array using wildcard.""" + data = {"items": [{"text": "item1"}, {"text": "item2"}, {"text": "item3"}]} + result = JsonPathExtractor.evaluate(data, "items[*].text") + assert result == ["item1", "item2", "item3"] + + def test_messages_content(self): + """Should extract content from messages array.""" + data = { + "messages": [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Hello"}, + ] + } + result = JsonPathExtractor.evaluate(data, "messages[*].content") + assert result == ["You are helpful", "Hello"] + + def test_missing_key_returns_none(self): + """Should return None for missing key.""" + data = {"query": "test"} + result = JsonPathExtractor.evaluate(data, "missing") + assert result is None + + def test_empty_data_returns_none(self): + """Should return None for empty data.""" + result = JsonPathExtractor.evaluate({}, "query") + assert result is None + + def test_empty_expression_returns_none(self): + """Should return None for empty expression.""" + data = {"query": "test"} + result = JsonPathExtractor.evaluate(data, "") + assert result is None + + +class TestJsonPathExtractorExtractFields: + """Tests for JsonPathExtractor.extract_fields method.""" + + def test_extracts_multiple_fields(self): + """Should extract and concatenate multiple fields.""" + data = {"query": "search query", "input": "additional input"} + result = JsonPathExtractor.extract_fields(data, ["query", "input"]) + assert "search query" in result + assert "additional input" in result + + def test_extracts_array_fields(self): + """Should extract and concatenate array fields.""" + data = {"documents": [{"text": "doc1"}, {"text": "doc2"}]} + result = JsonPathExtractor.extract_fields(data, ["documents[*].text"]) + assert "doc1" in result + assert "doc2" in result + + def test_handles_missing_fields(self): + """Should handle missing fields gracefully.""" + data = {"query": "test"} + result = JsonPathExtractor.extract_fields(data, ["query", "missing"]) + assert result == "test" + + def test_empty_fields_returns_empty_string(self): + """Should return empty string for empty fields list.""" + data = {"query": "test"} + result = JsonPathExtractor.extract_fields(data, []) + assert result == "" + + +class TestPassthroughGuardrailHandlerPrepareInput: + """Tests for PassthroughGuardrailHandler.prepare_input method.""" + + def test_returns_full_payload_when_no_settings(self): + """Should return full JSON payload when no settings provided.""" + data = {"query": "test", "input": "value"} + result = PassthroughGuardrailHandler.prepare_input(data, None) + assert "query" in result + assert "input" in result + + def test_returns_full_payload_when_no_request_fields(self): + """Should return full JSON payload when request_fields not set.""" + data = {"query": "test", "input": "value"} + settings = PassThroughGuardrailSettings(response_fields=["output"]) + result = PassthroughGuardrailHandler.prepare_input(data, settings) + assert "query" in result + assert "input" in result + + def test_returns_targeted_fields(self): + """Should return only targeted fields when request_fields set.""" + data = {"query": "targeted", "input": "also targeted", "other": "ignored"} + settings = PassThroughGuardrailSettings(request_fields=["query", "input"]) + result = PassthroughGuardrailHandler.prepare_input(data, settings) + assert "targeted" in result + assert "also targeted" in result + assert "ignored" not in result + + +class TestPassthroughGuardrailHandlerPrepareOutput: + """Tests for PassthroughGuardrailHandler.prepare_output method.""" + + def test_returns_full_payload_when_no_settings(self): + """Should return full JSON payload when no settings provided.""" + data = {"results": [{"text": "result1"}], "output": "value"} + result = PassthroughGuardrailHandler.prepare_output(data, None) + assert "results" in result + assert "output" in result + + def test_returns_full_payload_when_no_response_fields(self): + """Should return full JSON payload when response_fields not set.""" + data = {"results": [{"text": "result1"}], "output": "value"} + settings = PassThroughGuardrailSettings(request_fields=["input"]) + result = PassthroughGuardrailHandler.prepare_output(data, settings) + assert "results" in result + assert "output" in result + + def test_returns_targeted_fields(self): + """Should return only targeted fields when response_fields set.""" + data = { + "results": [{"text": "targeted1"}, {"text": "targeted2"}], + "other": "ignored", + } + settings = PassThroughGuardrailSettings(response_fields=["results[*].text"]) + result = PassthroughGuardrailHandler.prepare_output(data, settings) + assert "targeted1" in result + assert "targeted2" in result + assert "ignored" not in result + From ffb75b04fd04ee9ac7a969f85ea1418ebb231fd4 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 27 Nov 2025 12:27:16 -0800 Subject: [PATCH 151/248] [Feat] UI - allow adding pass through guardrails through UI (#17226) * add PassThroughGuardrailsConfig * init JsonPathExtractor * feat PassthroughGuardrailHandler * feat pt guardrails * pt guardrails * add Pass-Through Endpoint Guardrail Translation * add PassThroughEndpointHandler * execute simple guardrail config and dict settings * TestPassthroughGuardrailHandlerNormalizeConfig * add passthrough_guardrails_config on litellm logging obj * add LiteLLMLoggingObj to base trasaltino * cleaner _get_guardrail_settings * update guardrails settings * docs pt guardrail * docs Guardrails on Pass-Through Endpoints * fix typing * fix typing * test_no_fields_set_sends_full_body * fix typing * init add pass through guardrails * ui allow setting target fields on gd * docs ui settings guardrails --- .../docs/proxy/pass_through_guardrails.md | 42 ++- docs/my-website/img/pt_guard1.png | Bin 0 -> 787745 bytes docs/my-website/img/pt_guard2.png | Bin 0 -> 561454 bytes .../passthrough_guardrails.py | 8 +- .../src/components/add_pass_through.tsx | 18 ++ .../PassThroughGuardrailsSection.tsx | 246 ++++++++++++++++++ .../src/components/pass_through_info.tsx | 41 +++ .../src/components/pass_through_settings.tsx | 1 + 8 files changed, 349 insertions(+), 7 deletions(-) create mode 100644 docs/my-website/img/pt_guard1.png create mode 100644 docs/my-website/img/pt_guard2.png create mode 100644 ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx diff --git a/docs/my-website/docs/proxy/pass_through_guardrails.md b/docs/my-website/docs/proxy/pass_through_guardrails.md index 272285e61c..cc3d36c866 100644 --- a/docs/my-website/docs/proxy/pass_through_guardrails.md +++ b/docs/my-website/docs/proxy/pass_through_guardrails.md @@ -1,5 +1,7 @@ # Guardrails on Pass-Through Endpoints +import Image from '@theme/IdealImage'; + ## Overview | Property | Details | @@ -10,7 +12,41 @@ ## Quick Start -### 1. Define guardrails and pass-through endpoint +You can configure guardrails on pass-through endpoints either via the **UI** (recommended) or **config file**. + +### Using the UI + +#### 1. Navigate to Pass-Through Endpoints + +Go to **Models + Endpoints** → Click **+ Add Pass-Through Endpoint** + + + +Scroll to the **Guardrails** section and select which guardrails to enforce. + +:::tip Default Behavior +By default, you don't need to specify fields - LiteLLM will JSON dump the entire request/response payload and send it to the guardrail. +::: + +#### 2. Target Specific Fields (Optional) + + + +To check only specific fields instead of the entire payload: + +1. Select your guardrails +2. In **Field Targeting (Optional)**, specify fields for each guardrail +3. Use the quick-add buttons (`+ query`, `+ documents[*]`) or type custom JSONPath expressions +4. **Request Fields (pre_call)**: Fields to check before sending to target API +5. **Response Fields (post_call)**: Fields to check in the response from target API + +**Example**: In the screenshot above, we set `query` as a request field, so only the `query` field is sent to the guardrail instead of the entire request. + +--- + +### Using Config File + +#### 1. Define guardrails and pass-through endpoint ```yaml showLineNumbers title="config.yaml" guardrails: @@ -31,13 +67,13 @@ general_settings: pii-guard: ``` -### 2. Start proxy +#### 2. Start proxy ```bash litellm --config config.yaml ``` -### 3. Test request +#### 3. Test request ```bash curl -X POST "http://localhost:4000/v1/rerank" \ diff --git a/docs/my-website/img/pt_guard1.png b/docs/my-website/img/pt_guard1.png new file mode 100644 index 0000000000000000000000000000000000000000..85b094a14b9f581742a20357aeaaf7f3e05abaa6 GIT binary patch literal 787745 zcmeEuc|4ST_dlX6EkabXlw_xneJjb5B_zTak$sEo3{46V6Irs$TDGwc#xlshW*s}( z89QSe!|&?3zxVUp-{0&0K6lgK^?GTpbj@eZbJ&ikCthqn*a70*y!rX(UFI-{&~ zPm74?v^xkuPg%pa5vLaHdj+4;s&nCiAaer6OsM6 z1^AL7X8iZ{ePT|c6aV@?2@z3%6%pzG+@lV>{`m7A`1qE4=bd?kg!HcH3+KLmP(Nr) zryqZNTUuxScpvGo3)xHXxO_w37>Q5Q0AG$TA=v|} zMk)5)^uuuDFc$#J<-Rt%f?u$rhMFv%|7N{zLww`)NjFPXcvLwJA;HKosG(rN&<`9B zP>R)>+JN<`*nR;Ljn^i(p#_y%bK-QwC?RC#*(p*DFAq68>t6h{S|m{PNHzI z4IQC*5fW2h7z}dc0`EuUW!qP6Qv4abfAlqUgTfX=L1PL=56;V0Pml_Ye|$+RC34@6 zl@QkbnW$@QiH{Y05y7JyRPSc1q@z3-FUZ_F%XN< z^FKKBS|FB-_V2;*=ZF6-H%8beQ5z0uOnF=Or6sSSO4ogm)d~T@ht87ud)|HH!FL>M zt!$hJ_-1aC^ZGADi#!Kx{5g{<$9MMr#fqA5;}vs4J8lvCggjFt`_1AH!tEA@Yb-@f 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zBJ7r^-ix>>2k%8pKo=pV$8Z7^C~G{2u_ZWZyluO8SGVvXB=!k{g}7T(fraZ_J(G0+ zeD*b%{ON9C8?5I@>|Kvn^K;~=;*u!FDSS9`CD?3%d0az}aAy2sh_l(zD9b8f`SM+B zXY7ff8RCP&Abrbh&;NA`RUOdLIA@L)*XEFw^9%@TR`pQk2xGFd|CyzQ|2kTjrGkIW zL}piGb~WJQuURtx56JxOr0CXM#2oOmQZ`4ssjo62@S+fN=Pg<}+n8O1StQI_!K@X` gTEV|t0ZtQj-N = ({ const [targetValue, setTargetValue] = useState(""); const [includeSubpath, setIncludeSubpath] = useState(true); const [authEnabled, setAuthEnabled] = useState(false); + const [guardrails, setGuardrails] = useState>({}); const handleCancel = () => { form.resetFields(); setPathValue(""); setTargetValue(""); setIncludeSubpath(true); + setGuardrails({}); setIsModalVisible(false); }; @@ -77,6 +80,12 @@ const AddPassThroughEndpoint: React.FC = ({ if (!premiumUser && 'auth' in formValues) { delete formValues.auth; } + + // Add guardrails to formValues (only if not empty) + if (guardrails && Object.keys(guardrails).length > 0) { + formValues.guardrails = guardrails; + } + console.log(`formValues: ${JSON.stringify(formValues)}`); const response = await createPassThroughEndpoint(accessToken, formValues); @@ -92,6 +101,7 @@ const AddPassThroughEndpoint: React.FC = ({ setPathValue(""); setTargetValue(""); setIncludeSubpath(true); + setGuardrails({}); setIsModalVisible(false); } catch (error) { NotificationsManager.fromBackend("Error creating pass-through endpoint: " + error); @@ -249,6 +259,14 @@ const AddPassThroughEndpoint: React.FC = ({ form.setFieldsValue({ auth: checked }); }} /> + + {/* Guardrails Section */} + + {/* Billing Section */} Billing diff --git a/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx b/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx new file mode 100644 index 0000000000..fc6f783df0 --- /dev/null +++ b/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx @@ -0,0 +1,246 @@ +import React, { useState, useEffect } from "react"; +import { Card, Title, Subtitle } from "@tremor/react"; +import { Form, Input, Select, Tooltip, Alert } from "antd"; +import { InfoCircleOutlined, PlusOutlined, DeleteOutlined } from "@ant-design/icons"; +import GuardrailSelector from "../guardrails/GuardrailSelector"; + +interface PassThroughGuardrailsSectionProps { + accessToken: string; + value?: Record; + onChange?: (guardrails: Record) => void; + disabled?: boolean; +} + +const PassThroughGuardrailsSection: React.FC = ({ + accessToken, + value = {}, + onChange, + disabled = false, +}) => { + const [selectedGuardrails, setSelectedGuardrails] = useState(Object.keys(value)); + const [guardrailSettings, setGuardrailSettings] = useState< + Record + >(value); + + // Sync external value changes + useEffect(() => { + setGuardrailSettings(value); + setSelectedGuardrails(Object.keys(value)); + }, [value]); + + const handleGuardrailChange = (guardrails: string[]) => { + setSelectedGuardrails(guardrails); + + // Create new settings object with selected guardrails + const newSettings: Record = {}; + guardrails.forEach((name) => { + // Preserve existing settings or set to null (uses entire payload) + newSettings[name] = guardrailSettings[name] || null; + }); + + setGuardrailSettings(newSettings); + if (onChange) { + onChange(newSettings); + } + }; + + const handleFieldChange = ( + guardrailName: string, + fieldType: "request_fields" | "response_fields", + fields: string[] + ) => { + const currentSettings = guardrailSettings[guardrailName] || {}; + const newSettings = { + ...guardrailSettings, + [guardrailName]: { + ...currentSettings, + [fieldType]: fields.length > 0 ? fields : undefined, + }, + }; + + // If no fields are set, set to null (entire payload) + if (!newSettings[guardrailName]?.request_fields && !newSettings[guardrailName]?.response_fields) { + newSettings[guardrailName] = null; + } + + setGuardrailSettings(newSettings); + if (onChange) { + onChange(newSettings); + } + }; + + return ( + + Guardrails + + Configure guardrails to enforce policies on requests and responses. Guardrails are opt-in for passthrough + endpoints. + + + + Field-Level Targeting{" "} + + (Learn More) + + + } + description={ +
+
+ Optionally specify which fields to check. If left empty, the entire request/response is sent to the guardrail. +
+
+
Common Examples:
+
• query - Single field
+
• documents[*].text - All text in documents array
+
• messages[*].content - All message contents
+
+
+ } + type="info" + showIcon + className="mb-4" + /> + + + Select Guardrails + + + + + } + > + + + + {selectedGuardrails.length > 0 && ( +
+
+
Field Targeting (Optional)
+
+ 💡 Tip: Leave empty to check entire payload +
+
+ {selectedGuardrails.map((guardrailName) => ( + +
{guardrailName}
+
+
+
+
+ }> + + + +
+ + +
+
+ handleFieldChange(guardrailName, "response_fields", fields)} + disabled={disabled} + tokenSeparators={[","]} + /> +
+
+ + ))} + + )} + + ); +}; + +export default PassThroughGuardrailsSection; + diff --git a/ui/litellm-dashboard/src/components/pass_through_info.tsx b/ui/litellm-dashboard/src/components/pass_through_info.tsx index c42a710caf..fe3b204b50 100644 --- a/ui/litellm-dashboard/src/components/pass_through_info.tsx +++ b/ui/litellm-dashboard/src/components/pass_through_info.tsx @@ -19,6 +19,7 @@ import { Eye, EyeOff } from "lucide-react"; import RoutePreview from "./route_preview"; import NotificationsManager from "./molecules/notifications_manager"; import PassThroughSecuritySection from "./common_components/PassThroughSecuritySection"; +import PassThroughGuardrailsSection from "./common_components/PassThroughGuardrailsSection"; export interface PassThroughInfoProps { endpointData: PassThroughEndpoint; @@ -37,6 +38,7 @@ interface PassThroughEndpoint { include_subpath?: boolean; cost_per_request?: number; auth?: boolean; + guardrails?: Record; } // Password field component for headers @@ -68,6 +70,9 @@ const PassThroughInfoView: React.FC = ({ const [loading, setLoading] = useState(false); const [isEditing, setIsEditing] = useState(false); const [authEnabled, setAuthEnabled] = useState(initialEndpointData?.auth || false); + const [guardrails, setGuardrails] = useState>( + initialEndpointData?.guardrails || {} + ); const [form] = Form.useForm(); const handleEndpointUpdate = async (values: any) => { @@ -92,6 +97,7 @@ const PassThroughInfoView: React.FC = ({ include_subpath: values.include_subpath, cost_per_request: values.cost_per_request, auth: premiumUser ? values.auth : undefined, + guardrails: guardrails && Object.keys(guardrails).length > 0 ? guardrails : undefined, }; await updatePassThroughEndpoint(accessToken, endpointData.id, updateData); @@ -214,6 +220,33 @@ const PassThroughInfoView: React.FC = ({ )} + + {endpointData.guardrails && Object.keys(endpointData.guardrails).length > 0 && ( + +
+ Guardrails + {Object.keys(endpointData.guardrails).length} guardrails configured +
+
+ {Object.entries(endpointData.guardrails).map(([name, settings]) => ( +
+
{name}
+ {settings && (settings.request_fields || settings.response_fields) && ( +
+ {settings.request_fields && ( +
Request fields: {settings.request_fields.join(", ")}
+ )} + {settings.response_fields && ( +
Response fields: {settings.response_fields.join(", ")}
+ )} +
+ )} + {!settings &&
Uses entire payload
} +
+ ))} +
+ + )} {/* Settings Panel (only for admins) */} @@ -279,6 +312,14 @@ const PassThroughInfoView: React.FC = ({ }} /> +
+ +
+
Save Changes diff --git a/ui/litellm-dashboard/src/components/pass_through_settings.tsx b/ui/litellm-dashboard/src/components/pass_through_settings.tsx index 6e22533ed1..b37d3df7e4 100644 --- a/ui/litellm-dashboard/src/components/pass_through_settings.tsx +++ b/ui/litellm-dashboard/src/components/pass_through_settings.tsx @@ -39,6 +39,7 @@ export interface passThroughItem { include_subpath?: boolean; cost_per_request?: number; auth?: boolean; + guardrails?: Record; } // Password field component for headers From 19f03d4be3f8bcdd16c513330c790b93dab492f7 Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 27 Nov 2025 12:32:51 -0800 Subject: [PATCH 152/248] ui fix linting errors --- .../common_components/PassThroughGuardrailsSection.tsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx b/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx index fc6f783df0..3156fa626f 100644 --- a/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx +++ b/ui/litellm-dashboard/src/components/common_components/PassThroughGuardrailsSection.tsx @@ -1,7 +1,7 @@ import React, { useState, useEffect } from "react"; import { Card, Title, Subtitle } from "@tremor/react"; -import { Form, Input, Select, Tooltip, Alert } from "antd"; -import { InfoCircleOutlined, PlusOutlined, DeleteOutlined } from "@ant-design/icons"; +import { Form, Select, Tooltip, Alert } from "antd"; +import { InfoCircleOutlined } from "@ant-design/icons"; import GuardrailSelector from "../guardrails/GuardrailSelector"; interface PassThroughGuardrailsSectionProps { From b8542188195bdb3861e71b991461e8cd8203e46e Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 27 Nov 2025 12:33:02 -0800 Subject: [PATCH 153/248] mypy: fix mypy linting errors --- litellm/proxy/pass_through_endpoints/pass_through_endpoints.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index df4452f726..780a3d6dcc 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -1171,7 +1171,7 @@ def create_pass_through_route( custom_body=final_custom_body, cost_per_request=cast(Optional[float], param_cost_per_request), custom_llm_provider=custom_llm_provider, - guardrails_config=param_guardrails, + guardrails_config=cast(Optional[dict], param_guardrails), ) return endpoint_func From d4be5111308a63fadbe00f6afdff3d59fc323ccf Mon Sep 17 00:00:00 2001 From: Ishaan Jaffer Date: Thu, 27 Nov 2025 12:33:56 -0800 Subject: [PATCH 154/248] UI new build --- .../_buildManifest.js | 0 .../_ssgManifest.js | 0 .../out/_next/static/chunks/1263-cf8443d1d71fa593.js | 1 - .../out/_next/static/chunks/1394-bdcf4b8db9c252d2.js | 1 - 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- 
-            {/* {JSON.stringify(getRawRequest(), null, 2)} */}
-            
-
+
@@ -136,17 +129,9 @@ export function RequestResponsePanel({ -
+
{hasResponse ? ( - 
-              {/* {JSON.stringify(formattedResponse(), null, 2)} */}
-              
-
+ ) : (
Response data not available
)} From 2f045f07a2c262f277ba599f30d9e2027974cfb3 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 27 Nov 2025 15:08:00 -0800 Subject: [PATCH 159/248] Adding loading states to edit settings --- .../src/components/model_info_view.tsx | 1 + .../organization/organization_view.tsx | 10 ++++++-- .../src/components/team/team_info.tsx | 10 ++++++-- .../components/templates/key_edit_view.tsx | 24 ++++++++++++------- 4 files changed, 33 insertions(+), 12 deletions(-) diff --git a/ui/litellm-dashboard/src/components/model_info_view.tsx b/ui/litellm-dashboard/src/components/model_info_view.tsx index 2563c90f59..64f96ac915 100644 --- a/ui/litellm-dashboard/src/components/model_info_view.tsx +++ b/ui/litellm-dashboard/src/components/model_info_view.tsx @@ -966,6 +966,7 @@ export default function ModelInfoView({ setIsDirty(false); setIsEditing(false); }} + disabled={isSaving} > Cancel diff --git a/ui/litellm-dashboard/src/components/organization/organization_view.tsx b/ui/litellm-dashboard/src/components/organization/organization_view.tsx index b284519ce1..76f5e81b2f 100644 --- a/ui/litellm-dashboard/src/components/organization/organization_view.tsx +++ b/ui/litellm-dashboard/src/components/organization/organization_view.tsx @@ -69,6 +69,7 @@ const OrganizationInfoView: React.FC = ({ const [isEditMemberModalVisible, setIsEditMemberModalVisible] = useState(false); const [selectedEditMember, setSelectedEditMember] = useState(null); const [copiedStates, setCopiedStates] = useState>({}); + const [isOrgSaving, setIsOrgSaving] = useState(false); const canEditOrg = is_org_admin || is_proxy_admin; const fetchOrgInfo = async () => { @@ -151,6 +152,7 @@ const OrganizationInfoView: React.FC = ({ const handleOrgUpdate = async (values: any) => { try { if (!accessToken) return; + setIsOrgSaving(true); const updateData: any = { organization_id: organizationId, @@ -194,6 +196,8 @@ const OrganizationInfoView: React.FC = ({ } catch (error) { NotificationsManager.fromBackend("Failed to update organization settings"); console.error("Error updating organization:", error); + } finally { + setIsOrgSaving(false); } }; @@ -492,10 +496,12 @@ const OrganizationInfoView: React.FC = ({
- setIsEditing(false)}> + setIsEditing(false)} disabled={isOrgSaving}> Cancel - Save Changes + + Save Changes +
diff --git a/ui/litellm-dashboard/src/components/team/team_info.tsx b/ui/litellm-dashboard/src/components/team/team_info.tsx index 1c6ba629ef..a53d35e57b 100644 --- a/ui/litellm-dashboard/src/components/team/team_info.tsx +++ b/ui/litellm-dashboard/src/components/team/team_info.tsx @@ -142,6 +142,7 @@ const TeamInfoView: React.FC = ({ const [memberToDelete, setMemberToDelete] = useState(null); const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false); const [isDeleting, setIsDeleting] = useState(false); + const [isTeamSaving, setIsTeamSaving] = useState(false); console.log("userModels in team info", userModels); @@ -310,6 +311,7 @@ const TeamInfoView: React.FC = ({ const handleTeamUpdate = async (values: any) => { try { if (!accessToken) return; + setIsTeamSaving(true); let parsedMetadata = {}; try { @@ -387,6 +389,8 @@ const TeamInfoView: React.FC = ({ fetchTeamInfo(); } catch (error) { console.error("Error updating team:", error); + } finally { + setIsTeamSaving(false); } }; @@ -770,10 +774,12 @@ const TeamInfoView: React.FC = ({
- setIsEditing(false)}> + setIsEditing(false)} disabled={isTeamSaving}> Cancel - Save Changes + + Save Changes +
diff --git a/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx b/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx index 8c10148640..d7376fde14 100644 --- a/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx +++ b/ui/litellm-dashboard/src/components/templates/key_edit_view.tsx @@ -95,6 +95,7 @@ export function KeyEditView({ ); const [autoRotationEnabled, setAutoRotationEnabled] = useState(keyData.auto_rotate || false); const [rotationInterval, setRotationInterval] = useState(keyData.rotation_interval || ""); + const [isKeySaving, setIsKeySaving] = useState(false); const fetchMcpAccessGroups = async () => { if (!accessToken) return; @@ -236,8 +237,17 @@ export function KeyEditView({ console.log("premiumUser:", premiumUser); + const handleSubmit = async (values: any) => { + try { + setIsKeySaving(true); + await onSubmit(values); + } finally { + setIsKeySaving(false); + } + }; + return ( -
+ @@ -403,11 +413,7 @@ export function KeyEditView({ name="disable_global_guardrails" valuePropName="checked" > - + @@ -573,10 +579,12 @@ export function KeyEditView({
- + Cancel - Save Changes + + Save Changes +
From 4aea2df98617249681c140640811cbcb306cf135 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 27 Nov 2025 16:19:50 -0800 Subject: [PATCH 160/248] Various Text, button state, and test changes --- .../EntityUsageExportModal.test.tsx | 20 ++++---- .../EntityUsageExportModal.tsx | 42 ++------------- .../src/components/EntityUsageExport/utils.ts | 51 ++++++++++++++++++- .../components/guardrails/pii_components.tsx | 16 +++--- .../src/components/mcp_tools/mcp_servers.tsx | 1 + 5 files changed, 71 insertions(+), 59 deletions(-) diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx index dae758e7e7..d881e22527 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.test.tsx @@ -17,6 +17,8 @@ import EntityUsageExportModal from "./EntityUsageExportModal"; // Mock utilities that format/export data so tests stay fast and deterministic vi.mock("./utils", () => { return { + handleExportCSV: vi.fn(), + handleExportJSON: vi.fn(), generateExportData: vi.fn(() => [{ Date: "2025-10-01" }]), generateMetadata: vi.fn(() => ({ meta: true })), }; @@ -66,11 +68,11 @@ describe("EntityUsageExportModal", () => { it("renders default state and exports CSV (daily) successfully", async () => { /** * Tests the happy path: user opens modal and exports with defaults. - * Verifies that generateExportData is called with 'daily' scope + * Verifies that handleExportCSV is called with correct parameters * and modal closes after export completes. */ const user = userEvent.setup(); - const { generateExportData } = await import("./utils"); + const { handleExportCSV } = await import("./utils"); const { getByRole } = render(); @@ -80,10 +82,8 @@ describe("EntityUsageExportModal", () => { // Click export await user.click(getByRole("button", { name: /Export CSV/i })); - // Verifies export pipeline was invoked with default scope 'daily' - expect(generateExportData).toHaveBeenCalled(); - const callArgs = (generateExportData as any).mock.calls[0]; - expect(callArgs[1]).toBe("daily"); + // Verifies export function was invoked with correct parameters + expect(handleExportCSV).toHaveBeenCalledWith(baseProps.spendData, "daily", "Tag", "tag"); // Modal closes after export expect(baseProps.onClose).toHaveBeenCalled(); @@ -92,11 +92,11 @@ describe("EntityUsageExportModal", () => { it("exports with 'day-by-day by tag and model' scope when selected", async () => { /** * Tests that user can change export type (scope). - * Verifies generateExportData receives 'daily_with_models' scope + * Verifies handleExportCSV receives 'daily_with_models' scope * when the second radio option is selected. */ const user = userEvent.setup(); - const { generateExportData } = await import("./utils"); + const { handleExportCSV } = await import("./utils"); const { getByText, getByRole } = render(); @@ -109,9 +109,7 @@ describe("EntityUsageExportModal", () => { await user.click(exportBtn); // Ensure the selected scope flowed through - expect(generateExportData).toHaveBeenCalled(); - const callArgs = (generateExportData as any).mock.calls.at(-1); - expect(callArgs[1]).toBe("daily_with_models"); + expect(handleExportCSV).toHaveBeenCalledWith(baseProps.spendData, "daily_with_models", "Tag", "tag"); // Modal closes after export expect(baseProps.onClose).toHaveBeenCalled(); diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx index 672643f2ad..5b40d76198 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/EntityUsageExportModal.tsx @@ -1,12 +1,11 @@ import React, { useState } from "react"; import { Button } from "@tremor/react"; import { Modal } from "antd"; -import Papa from "papaparse"; import NotificationsManager from "../molecules/notifications_manager"; import ExportSummary from "./ExportSummary"; import ExportTypeSelector from "./ExportTypeSelector"; import ExportFormatSelector from "./ExportFormatSelector"; -import { generateExportData, generateMetadata } from "./utils"; +import { handleExportCSV, handleExportJSON } from "./utils"; import type { EntityUsageExportModalProps, ExportFormat, ExportScope } from "./types"; const EntityUsageExportModal: React.FC = ({ @@ -25,50 +24,15 @@ const EntityUsageExportModal: React.FC = ({ const entityLabel = entityType.charAt(0).toUpperCase() + entityType.slice(1); const modalTitle = customTitle || `Export ${entityLabel} Usage`; - const handleExportCSV = () => { - const data = generateExportData(spendData, exportScope, entityLabel); - const csv = Papa.unparse(data); - const blob = new Blob([csv], { type: "text/csv;charset=utf-8;" }); - const url = window.URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; - const fileName = `${entityType}_usage_${exportScope}_${new Date().toISOString().split("T")[0]}.csv`; - a.download = fileName; - document.body.appendChild(a); - a.click(); - document.body.removeChild(a); - window.URL.revokeObjectURL(url); - }; - - const handleExportJSON = () => { - const data = generateExportData(spendData, exportScope, entityLabel); - const metadata = generateMetadata(entityType, dateRange, selectedFilters, exportScope, spendData); - const exportObject = { - metadata, - data, - }; - const jsonString = JSON.stringify(exportObject, null, 2); - const blob = new Blob([jsonString], { type: "application/json" }); - const url = window.URL.createObjectURL(blob); - const a = document.createElement("a"); - a.href = url; - const fileName = `${entityType}_usage_${exportScope}_${new Date().toISOString().split("T")[0]}.json`; - a.download = fileName; - document.body.appendChild(a); - a.click(); - document.body.removeChild(a); - window.URL.revokeObjectURL(url); - }; - const handleExport = async (format?: ExportFormat) => { const formatToUse = format || exportFormat; setIsExporting(true); try { if (formatToUse === "csv") { - handleExportCSV(); + handleExportCSV(spendData, exportScope, entityLabel, entityType); NotificationsManager.success(`${entityLabel} usage data exported successfully as CSV`); } else { - handleExportJSON(); + handleExportJSON(spendData, exportScope, entityLabel, entityType, dateRange, selectedFilters); NotificationsManager.success(`${entityLabel} usage data exported successfully as JSON`); } onClose(); diff --git a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts index 87ca860657..1327e158a6 100644 --- a/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts +++ b/ui/litellm-dashboard/src/components/EntityUsageExport/utils.ts @@ -1,5 +1,7 @@ import { formatNumberWithCommas } from "@/utils/dataUtils"; +import Papa from "papaparse"; import type { EntitySpendData, EntityBreakdown, ExportMetadata, ExportScope } from "./types"; +import type { DateRangePickerValue } from "@tremor/react"; export const getEntityBreakdown = (spendData: EntitySpendData): EntityBreakdown[] => { const entitySpend: { [key: string]: EntityBreakdown } = {}; @@ -138,7 +140,7 @@ export const generateExportData = ( export const generateMetadata = ( entityType: "tag" | "team" | "organization", - dateRange: { from?: Date; to?: Date }, + dateRange: DateRangePickerValue, selectedFilters: string[], exportScope: ExportScope, spendData: EntitySpendData, @@ -159,3 +161,50 @@ export const generateMetadata = ( total_tokens: spendData.metadata.total_tokens, }, }); + +export const handleExportCSV = ( + spendData: EntitySpendData, + exportScope: ExportScope, + entityLabel: string, + entityType: "tag" | "team" | "organization", +): void => { + const data = generateExportData(spendData, exportScope, entityLabel); + const csv = Papa.unparse(data); + const blob = new Blob([csv], { type: "text/csv;charset=utf-8;" }); + const url = window.URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + const fileName = `${entityType}_usage_${exportScope}_${new Date().toISOString().split("T")[0]}.csv`; + a.download = fileName; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + window.URL.revokeObjectURL(url); +}; + +export const handleExportJSON = ( + spendData: EntitySpendData, + exportScope: ExportScope, + entityLabel: string, + entityType: "tag" | "team" | "organization", + dateRange: DateRangePickerValue, + selectedFilters: string[], +): void => { + const data = generateExportData(spendData, exportScope, entityLabel); + const metadata = generateMetadata(entityType, dateRange, selectedFilters, exportScope, spendData); + const exportObject = { + metadata, + data, + }; + const jsonString = JSON.stringify(exportObject, null, 2); + const blob = new Blob([jsonString], { type: "application/json" }); + const url = window.URL.createObjectURL(blob); + const a = document.createElement("a"); + a.href = url; + const fileName = `${entityType}_usage_${exportScope}_${new Date().toISOString().split("T")[0]}.json`; + a.download = fileName; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + window.URL.revokeObjectURL(url); +}; diff --git a/ui/litellm-dashboard/src/components/guardrails/pii_components.tsx b/ui/litellm-dashboard/src/components/guardrails/pii_components.tsx index e3b3926b99..3365e866aa 100644 --- a/ui/litellm-dashboard/src/components/guardrails/pii_components.tsx +++ b/ui/litellm-dashboard/src/components/guardrails/pii_components.tsx @@ -82,31 +82,31 @@ export const QuickActions: React.FC = ({ onSelectAll, onUnsel
From 6bbc17771ff98ae8188292bedeab5004d7ac47c5 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 27 Nov 2025 16:44:28 -0800 Subject: [PATCH 161/248] Fix fallbacks immediately deleting before api resolves --- .../src/components/fallbacks.test.tsx | 76 +++++++------------ .../src/components/fallbacks.tsx | 44 ++++++----- 2 files changed, 53 insertions(+), 67 deletions(-) diff --git a/ui/litellm-dashboard/src/components/fallbacks.test.tsx b/ui/litellm-dashboard/src/components/fallbacks.test.tsx index 45aa06dbaf..e6db11270c 100644 --- a/ui/litellm-dashboard/src/components/fallbacks.test.tsx +++ b/ui/litellm-dashboard/src/components/fallbacks.test.tsx @@ -1,5 +1,5 @@ -import { render, waitFor } from "@testing-library/react"; -import { beforeAll, beforeEach, describe, expect, it, vi } from "vitest"; +import { render, screen, waitFor } from "@testing-library/react"; +import { beforeEach, describe, expect, it, vi } from "vitest"; import Fallbacks from "./fallbacks"; import { getCallbacksCall, setCallbacksCall } from "./networking"; @@ -8,17 +8,6 @@ vi.mock("./networking", () => ({ setCallbacksCall: vi.fn(), })); -vi.mock("./molecules/notifications_manager", () => ({ - __esModule: true, - default: { - success: vi.fn(), - fromBackend: vi.fn(), - info: vi.fn(), - warning: vi.fn(), - clear: vi.fn(), - }, -})); - vi.mock("./add_fallbacks", () => ({ __esModule: true, default: () =>
Mock Add Fallbacks
, @@ -29,38 +18,15 @@ vi.mock("openai", () => ({ OpenAI: vi.fn().mockImplementation(() => ({ chat: { completions: { - create: vi.fn(), + create: vi.fn().mockResolvedValue({ + model: "test-model", + }), }, }, })), }, })); -// Polyfill ResizeObserver for components relying on it in tests -if (typeof window !== "undefined" && !window.ResizeObserver) { - window.ResizeObserver = class ResizeObserver { - observe() {} - unobserve() {} - disconnect() {} - }; -} - -beforeAll(() => { - Object.defineProperty(window, "matchMedia", { - writable: true, - value: vi.fn().mockImplementation((query: string) => ({ - matches: false, - media: query, - onchange: null, - addListener: vi.fn(), - removeListener: vi.fn(), - addEventListener: vi.fn(), - removeEventListener: vi.fn(), - dispatchEvent: vi.fn(), - })), - }); -}); - describe("Fallbacks", () => { const defaultProps = { accessToken: "token", @@ -78,15 +44,14 @@ describe("Fallbacks", () => { fallbacks: [], }, }); + mockSetCallbacksCall.mockResolvedValue({}); }); - it("should render an empty table with headers when access token is provided", async () => { - const { getByText } = render(); + it("should render", async () => { + render(); await waitFor(() => { - expect(getByText("Model Name")).toBeInTheDocument(); - expect(getByText("Fallbacks")).toBeInTheDocument(); - expect(getByText("Actions")).toBeInTheDocument(); + expect(screen.getByRole("columnheader", { name: "Model Name" })).toBeInTheDocument(); }); }); @@ -99,13 +64,13 @@ describe("Fallbacks", () => { mockGetCallbacksCall.mockResolvedValue(mockFallbackData); - const { getByText } = render(); + render(); await waitFor(() => { - expect(getByText("xai/grok-2")).toBeInTheDocument(); - expect(getByText("xai/grok-4, gpt-4")).toBeInTheDocument(); - expect(getByText("gpt-3.5-turbo")).toBeInTheDocument(); - expect(getByText("gpt-4")).toBeInTheDocument(); + expect(screen.getByText("xai/grok-2")).toBeInTheDocument(); + expect(screen.getByText("xai/grok-4, gpt-4")).toBeInTheDocument(); + expect(screen.getByText("gpt-3.5-turbo")).toBeInTheDocument(); + expect(screen.getByText("gpt-4")).toBeInTheDocument(); }); expect(mockGetCallbacksCall).toHaveBeenCalledWith( @@ -114,4 +79,17 @@ describe("Fallbacks", () => { defaultProps.userRole, ); }); + + it("should render AddFallbacks component", async () => { + render(); + + await waitFor(() => { + expect(screen.getByText("Mock Add Fallbacks")).toBeInTheDocument(); + }); + }); + + it("should not render when access token is not provided", () => { + const { container } = render(); + expect(container.firstChild).toBeNull(); + }); }); diff --git a/ui/litellm-dashboard/src/components/fallbacks.tsx b/ui/litellm-dashboard/src/components/fallbacks.tsx index cacf3869b1..dc90ae7913 100644 --- a/ui/litellm-dashboard/src/components/fallbacks.tsx +++ b/ui/litellm-dashboard/src/components/fallbacks.tsx @@ -1,9 +1,10 @@ import { PlayIcon, TrashIcon } from "@heroicons/react/outline"; import { Icon, Table, TableBody, TableCell, TableHead, TableHeaderCell, TableRow } from "@tremor/react"; -import { Modal, Tooltip } from "antd"; +import { Tooltip } from "antd"; import openai from "openai"; import React, { useEffect, useState } from "react"; import AddFallbacks from "./add_fallbacks"; +import DeleteResourceModal from "./common_components/DeleteResourceModal"; import NotificationsManager from "./molecules/notifications_manager"; import { getCallbacksCall, setCallbacksCall } from "./networking"; @@ -68,6 +69,7 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo const [routerSettings, setRouterSettings] = useState<{ [key: string]: any }>({}); const [isDeleting, setIsDeleting] = useState(false); const [fallbackToDelete, setFallbackToDelete] = useState(null); + const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false); useEffect(() => { if (!accessToken || !userRole || !userID) { @@ -85,6 +87,7 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo const handleDeleteClick = (fallbackEntry: FallbackEntry) => { setFallbackToDelete(fallbackEntry); + setIsDeleteModalOpen(true); }; const handleDeleteConfirm = async () => { @@ -100,10 +103,11 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo const updatedFallbacks = routerSettings["fallbacks"] .map((dict: FallbackEntry) => { - if (key in dict && Array.isArray(dict[key])) { - delete dict[key]; + const newDict = { ...dict }; + if (key in newDict && Array.isArray(newDict[key])) { + delete newDict[key]; } - return dict; + return newDict; }) .filter((dict: FallbackEntry) => Object.keys(dict).length > 0); @@ -124,11 +128,13 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo NotificationsManager.fromBackend("Failed to update router settings: " + error); } finally { setIsDeleting(false); + setIsDeleteModalOpen(false); setFallbackToDelete(null); } }; const handleDeleteCancel = () => { + setIsDeleteModalOpen(false); setFallbackToDelete(null); }; @@ -183,20 +189,22 @@ const Fallbacks: React.FC = ({ accessToken, userRole, userID, mo )}
- {fallbackToDelete && ( - -

Are you sure you want to delete fallback: {Object.keys(fallbackToDelete)[0]} ?

-

This action cannot be undone.

- - )} + ); }; From fb5429bfe7a14c0eb4ae9657e4eb7e00a4ca4250 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 27 Nov 2025 17:21:51 -0800 Subject: [PATCH 162/248] Remove Feature Flags --- .../components/SidebarProvider.tsx | 11 +- ui/litellm-dashboard/src/app/layout.tsx | 8 +- ui/litellm-dashboard/src/app/page.tsx | 2 - .../src/components/navbar.tsx | 16 +- .../src/components/public_model_hub.test.tsx | 7 +- .../src/hooks/useFeatureFlags.test.tsx | 296 ------------------ .../src/hooks/useFeatureFlags.tsx | 137 -------- 7 files changed, 6 insertions(+), 471 deletions(-) delete mode 100644 ui/litellm-dashboard/src/hooks/useFeatureFlags.test.tsx delete mode 100644 ui/litellm-dashboard/src/hooks/useFeatureFlags.tsx diff --git a/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx b/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx index cb7f0f1a32..c522d4ce1e 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/components/SidebarProvider.tsx @@ -1,21 +1,16 @@ -import useFeatureFlags from "@/hooks/useFeatureFlags"; -import Sidebar from "@/components/leftnav"; -import Sidebar2 from "@/app/(dashboard)/components/Sidebar2"; import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; +import Sidebar from "@/components/leftnav"; interface SidebarProviderProps { + setPage: (page: string) => void; defaultSelectedKey: string; - setPage: (newPage: string) => void; sidebarCollapsed: boolean; } const SidebarProvider = ({ setPage, defaultSelectedKey, sidebarCollapsed }: SidebarProviderProps) => { - const { refactoredUIFlag } = useFeatureFlags(); const { accessToken, userRole } = useAuthorized(); - return refactoredUIFlag ? ( - - ) : ( + return ( ) { return ( - - - - {children} - - + {children} ); } diff --git a/ui/litellm-dashboard/src/app/page.tsx b/ui/litellm-dashboard/src/app/page.tsx index 56c35804df..422d140e07 100644 --- a/ui/litellm-dashboard/src/app/page.tsx +++ b/ui/litellm-dashboard/src/app/page.tsx @@ -40,7 +40,6 @@ import UIThemeSettings from "@/components/ui_theme_settings"; import { CostTrackingSettings } from "@/components/CostTrackingSettings"; import { UiLoadingSpinner } from "@/components/ui/ui-loading-spinner"; import { cx } from "@/lib/cva.config"; -import useFeatureFlags from "@/hooks/useFeatureFlags"; import SidebarProvider from "@/app/(dashboard)/components/SidebarProvider"; import OldTeams from "@/components/OldTeams"; import { SearchTools } from "@/components/search_tools"; @@ -147,7 +146,6 @@ export default function CreateKeyPage() { const [createClicked, setCreateClicked] = useState(false); const [authLoading, setAuthLoading] = useState(true); const [userID, setUserID] = useState(null); - const { refactoredUIFlag } = useFeatureFlags(); const invitation_id = searchParams.get("invitation_id"); diff --git a/ui/litellm-dashboard/src/components/navbar.tsx b/ui/litellm-dashboard/src/components/navbar.tsx index 667691168c..9113a0f54c 100644 --- a/ui/litellm-dashboard/src/components/navbar.tsx +++ b/ui/litellm-dashboard/src/components/navbar.tsx @@ -1,7 +1,7 @@ import Link from "next/link"; import React, { useState, useEffect } from "react"; import type { MenuProps } from "antd"; -import { Dropdown, Tooltip, Switch } from "antd"; +import { Dropdown, Tooltip } from "antd"; import { getProxyBaseUrl } from "@/components/networking"; import { UserOutlined, @@ -15,7 +15,6 @@ import { import { clearTokenCookies } from "@/utils/cookieUtils"; import { fetchProxySettings } from "@/utils/proxyUtils"; import { useTheme } from "@/contexts/ThemeContext"; -import useFeatureFlags from "@/hooks/useFeatureFlags"; interface NavbarProps { userID: string | null; @@ -45,7 +44,6 @@ const Navbar: React.FC = ({ const baseUrl = getProxyBaseUrl(); const [logoutUrl, setLogoutUrl] = useState(""); const { logoUrl } = useTheme(); - const { refactoredUIFlag, setRefactoredUIFlag } = useFeatureFlags(); // Simple logo URL: use custom logo if available, otherwise default const imageUrl = logoUrl || `${baseUrl}/get_image`; @@ -114,18 +112,6 @@ const Navbar: React.FC = ({ {userEmail || "Unknown"}
- - {/* NEW: Feature flag label + toggle below the email field */} -
- Refactored UI - setRefactoredUIFlag(checked)} - aria-label="Toggle refactored UI feature flag" - /> -
), diff --git a/ui/litellm-dashboard/src/components/public_model_hub.test.tsx b/ui/litellm-dashboard/src/components/public_model_hub.test.tsx index 8ff57b49e0..47a44fa514 100644 --- a/ui/litellm-dashboard/src/components/public_model_hub.test.tsx +++ b/ui/litellm-dashboard/src/components/public_model_hub.test.tsx @@ -1,7 +1,6 @@ import { describe, it, expect, vi, beforeAll, beforeEach } from "vitest"; import { render } from "@testing-library/react"; import PublicModelHub from "./public_model_hub"; -import { FeatureFlagsProvider } from "@/hooks/useFeatureFlags"; vi.mock("next/navigation", () => ({ useRouter: vi.fn(() => ({ @@ -58,11 +57,7 @@ beforeEach(() => { describe("PublicModelHub", () => { it("renders", () => { - const { container } = render( - - - , - ); + const { container } = render(); expect(container).toBeInTheDocument(); }); }); diff --git a/ui/litellm-dashboard/src/hooks/useFeatureFlags.test.tsx b/ui/litellm-dashboard/src/hooks/useFeatureFlags.test.tsx deleted file mode 100644 index ca0529b0f2..0000000000 --- a/ui/litellm-dashboard/src/hooks/useFeatureFlags.test.tsx +++ /dev/null @@ -1,296 +0,0 @@ -import { describe, it, expect, beforeEach, afterEach, vi } from "vitest"; -import { renderHook, waitFor } from "@testing-library/react"; -import { useRouter } from "next/navigation"; -import useFeatureFlags, { FeatureFlagsProvider } from "./useFeatureFlags"; - -// Mock next/navigation -vi.mock("next/navigation", () => ({ - useRouter: vi.fn(), -})); - -// Mock the networking module to control serverRootPath -vi.mock("@/components/networking", () => ({ - serverRootPath: "/", -})); - -describe("useFeatureFlags", () => { - let mockReplace: ReturnType; - let originalLocation: Location; - - beforeEach(() => { - // Mock router - mockReplace = vi.fn(); - (useRouter as ReturnType).mockReturnValue({ - replace: mockReplace, - }); - - // Store original location - originalLocation = window.location; - - // Mock localStorage - Storage.prototype.getItem = vi.fn(() => null); - Storage.prototype.setItem = vi.fn(); - Storage.prototype.removeItem = vi.fn(); - }); - - afterEach(() => { - vi.clearAllMocks(); - // Restore location - Object.defineProperty(window, "location", { - writable: true, - value: originalLocation, - }); - }); - - describe("FeatureFlagsProvider - redirect logic", () => { - it("should not redirect when refactoredUIFlag is true", async () => { - // Set flag to true - Storage.prototype.getItem = vi.fn(() => "true"); - - const { result } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - expect(result.current.refactoredUIFlag).toBe(true); - - // Wait for any effects - await waitFor(() => { - expect(mockReplace).not.toHaveBeenCalled(); - }); - }); - - it("should not redirect when already on a /ui path (race condition protection)", async () => { - // Set flag to false to trigger redirect logic - Storage.prototype.getItem = vi.fn(() => "false"); - - // Mock window.location to be on a custom UI path - delete (window as any).location; - window.location = { - pathname: "/my-custom-path/ui/", - } as Location; - - renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Wait for timeout and check redirect was NOT called - await new Promise((resolve) => setTimeout(resolve, 150)); - - expect(mockReplace).not.toHaveBeenCalled(); - }); - - it("should not redirect when on /ui path without custom root", async () => { - // Set flag to false - Storage.prototype.getItem = vi.fn(() => "false"); - - // Mock window.location to be on standard UI path - delete (window as any).location; - window.location = { - pathname: "/ui/", - } as Location; - - renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Wait for timeout and check redirect was NOT called - await new Promise((resolve) => setTimeout(resolve, 150)); - - expect(mockReplace).not.toHaveBeenCalled(); - }); - - it("should redirect when flag is false and not on a /ui path", async () => { - // Set flag to false - Storage.prototype.getItem = vi.fn(() => "false"); - - // Mock window.location to be on a non-UI path - delete (window as any).location; - window.location = { - pathname: "/some-other-path/", - } as Location; - - renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Wait for timeout plus a bit more - await new Promise((resolve) => setTimeout(resolve, 150)); - - // Should have called replace to redirect to base path - expect(mockReplace).toHaveBeenCalledWith("/"); - }); - - it("should not redirect if already at base path", async () => { - // Set flag to false - Storage.prototype.getItem = vi.fn(() => "false"); - - // Mock window.location to be at root - delete (window as any).location; - window.location = { - pathname: "/", - } as Location; - - renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Wait for timeout - await new Promise((resolve) => setTimeout(resolve, 150)); - - expect(mockReplace).not.toHaveBeenCalled(); - }); - }); - - describe("useFeatureFlags - flag management", () => { - it("should initialize with false when no value in localStorage", () => { - Storage.prototype.getItem = vi.fn(() => null); - - const { result } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - expect(result.current.refactoredUIFlag).toBe(false); - }); - - it("should initialize with true when localStorage has true", () => { - Storage.prototype.getItem = vi.fn(() => "true"); - - const { result } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - expect(result.current.refactoredUIFlag).toBe(true); - }); - - it("should update localStorage when setRefactoredUIFlag is called", () => { - const setItemMock = vi.fn(); - Storage.prototype.setItem = setItemMock; - Storage.prototype.getItem = vi.fn(() => "false"); - - const { result } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - result.current.setRefactoredUIFlag(true); - - expect(setItemMock).toHaveBeenCalledWith( - "feature.refactoredUIFlag", - "true" - ); - }); - - it("should handle malformed localStorage values gracefully", () => { - Storage.prototype.getItem = vi.fn(() => "invalid-value"); - - const { result } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Should default to false for malformed values - expect(result.current.refactoredUIFlag).toBe(false); - }); - }); - - describe("getBasePath logic with serverRootPath", () => { - it("should handle serverRootPath being set to custom path", async () => { - // Mock the networking module with custom serverRootPath - vi.doMock("@/components/networking", () => ({ - serverRootPath: "/my-custom-path", - })); - - // Set flag to false to trigger redirect - Storage.prototype.getItem = vi.fn(() => "false"); - - // Mock location to be on wrong path - delete (window as any).location; - window.location = { - pathname: "/wrong-path/", - } as Location; - - renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Wait for timeout - await new Promise((resolve) => setTimeout(resolve, 150)); - - // With default NEXT_PUBLIC_BASE_URL being empty, should redirect to "/" - // (In reality, with serverRootPath="/my-custom-path", it would be "/my-custom-path/") - expect(mockReplace).toHaveBeenCalled(); - }); - }); - - describe("storage event synchronization", () => { - it("should update flag when storage event is fired", async () => { - Storage.prototype.getItem = vi.fn(() => "false"); - - const { result } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - expect(result.current.refactoredUIFlag).toBe(false); - - // Simulate storage event from another tab - const storageEvent = new StorageEvent("storage", { - key: "feature.refactoredUIFlag", - newValue: "true", - }); - - window.dispatchEvent(storageEvent); - - await waitFor(() => { - expect(result.current.refactoredUIFlag).toBe(true); - }); - }); - - it("should self-heal when storage key is cleared", async () => { - const setItemMock = vi.fn(); - Storage.prototype.setItem = setItemMock; - Storage.prototype.getItem = vi.fn(() => "true"); - - renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Simulate storage event where key was cleared - const storageEvent = new StorageEvent("storage", { - key: "feature.refactoredUIFlag", - newValue: null, - }); - - window.dispatchEvent(storageEvent); - - await waitFor(() => { - expect(setItemMock).toHaveBeenCalledWith( - "feature.refactoredUIFlag", - "false" - ); - }); - }); - }); - - describe("timeout cleanup", () => { - it("should cleanup timeout on unmount", async () => { - Storage.prototype.getItem = vi.fn(() => "false"); - - delete (window as any).location; - window.location = { - pathname: "/some-path/", - } as Location; - - const { unmount } = renderHook(() => useFeatureFlags(), { - wrapper: FeatureFlagsProvider, - }); - - // Unmount immediately before timeout fires - unmount(); - - // Wait past the timeout - await new Promise((resolve) => setTimeout(resolve, 150)); - - // Should not have called replace since component unmounted - expect(mockReplace).not.toHaveBeenCalled(); - }); - }); -}); - diff --git a/ui/litellm-dashboard/src/hooks/useFeatureFlags.tsx b/ui/litellm-dashboard/src/hooks/useFeatureFlags.tsx deleted file mode 100644 index 03b4465b09..0000000000 --- a/ui/litellm-dashboard/src/hooks/useFeatureFlags.tsx +++ /dev/null @@ -1,137 +0,0 @@ -"use client"; - -import React, { createContext, useContext, useEffect, useState } from "react"; -import { useRouter } from "next/navigation"; -import { serverRootPath } from "@/components/networking"; - -const getBasePath = () => { - const raw = process.env.NEXT_PUBLIC_BASE_URL ?? ""; - const trimmed = raw.replace(/^\/+|\/+$/g, ""); // strip leading/trailing slashes - const uiPath = trimmed ? `/${trimmed}/` : "/"; - - // If serverRootPath is set and not "/", prepend it to the UI path - if (serverRootPath && serverRootPath !== "/") { - // Remove trailing slash from serverRootPath and ensure uiPath has no leading slash for proper joining - const cleanServerRoot = serverRootPath.replace(/\/+$/, ""); - const cleanUiPath = uiPath.replace(/^\/+/, ""); - return `${cleanServerRoot}/${cleanUiPath}`; - } - - return uiPath; -} - -type Flags = { - refactoredUIFlag: boolean; - setRefactoredUIFlag: (v: boolean) => void; -}; - -const STORAGE_KEY = "feature.refactoredUIFlag"; - -const FeatureFlagsCtx = createContext(null); - -/** Safely read the flag from localStorage. If anything goes wrong, reset to false. */ -function readFlagSafely(): boolean { - try { - const raw = localStorage.getItem(STORAGE_KEY); - if (raw === null) { - localStorage.setItem(STORAGE_KEY, "false"); - return false; - } - - const v = raw.trim().toLowerCase(); - if (v === "true" || v === "1") return true; - if (v === "false" || v === "0") return false; - - // Last chance: try JSON.parse in case something odd was stored. - const parsed = JSON.parse(raw); - if (typeof parsed === "boolean") return parsed; - - // Malformed → reset to false - localStorage.setItem(STORAGE_KEY, "false"); - return false; - } catch { - // If even accessing localStorage throws, best effort reset then default to false - try { - localStorage.setItem(STORAGE_KEY, "false"); - } catch {} - return false; - } -} - -function writeFlagSafely(v: boolean) { - try { - localStorage.setItem(STORAGE_KEY, String(v)); - } catch { - // Ignore write errors; state will still reflect the intended value. - } -} - -export const FeatureFlagsProvider = ({ children }: { children: React.ReactNode }) => { - const router = useRouter(); // ⟵ add this - - // Lazy init reads from localStorage only on the client - const [refactoredUIFlag, setRefactoredUIFlagState] = useState(() => readFlagSafely()); - - const setRefactoredUIFlag = (v: boolean) => { - setRefactoredUIFlagState(v); - writeFlagSafely(v); - }; - - // Keep this flag in sync across tabs/windows. - useEffect(() => { - const onStorage = (e: StorageEvent) => { - if (e.key === STORAGE_KEY && e.newValue != null) { - const next = e.newValue.trim().toLowerCase(); - setRefactoredUIFlagState(next === "true" || next === "1"); - } - // If the key was cleared elsewhere, self-heal to false. - if (e.key === STORAGE_KEY && e.newValue === null) { - writeFlagSafely(false); - setRefactoredUIFlagState(false); - } - }; - window.addEventListener("storage", onStorage); - return () => window.removeEventListener("storage", onStorage); - }, []); - - // Redirect to base path the moment the flag is OFF. - useEffect(() => { - if (refactoredUIFlag) return; // only act when turned off - - // Wait a moment for serverRootPath to be initialized from getUiConfig() - // This prevents a race condition where we redirect before knowing the correct path - const checkAndRedirect = () => { - const base = getBasePath(); - const normalize = (p: string) => (p.endsWith("/") ? p : p + "/"); - const current = normalize(window.location.pathname); - - // Don't redirect if we're already on a UI path (even if serverRootPath hasn't loaded yet) - // This handles the case where the page is mounted at a custom server root path - if (current.includes("/ui")) { - return; - } - - // Avoid a redirect loop if we're already at the base path. - if (current !== base) { - // Replace so the "off" redirect doesn't pollute history. - router.replace(base); - } - }; - - // Small delay to allow serverRootPath to be set by getUiConfig() - const timeoutId = setTimeout(checkAndRedirect, 100); - return () => clearTimeout(timeoutId); - }, [refactoredUIFlag, router]); - - return ( - {children} - ); -}; - -const useFeatureFlags = () => { - const ctx = useContext(FeatureFlagsCtx); - if (!ctx) throw new Error("useFeatureFlags must be used within FeatureFlagsProvider"); - return ctx; -}; - -export default useFeatureFlags; From 0d329826f10da90a6b8205ac58ce0344ef82ee9c Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 27 Nov 2025 17:40:38 -0800 Subject: [PATCH 163/248] Fix flaky tests --- .../src/components/entity_usage.test.tsx | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/ui/litellm-dashboard/src/components/entity_usage.test.tsx b/ui/litellm-dashboard/src/components/entity_usage.test.tsx index d5cc503fd1..17016b6479 100644 --- a/ui/litellm-dashboard/src/components/entity_usage.test.tsx +++ b/ui/litellm-dashboard/src/components/entity_usage.test.tsx @@ -168,16 +168,18 @@ describe("EntityUsage", () => { }); it("should render with organization entity type and call organization API", async () => { - const { getByText, getAllByText } = render(); + render(); await waitFor(() => { expect(mockOrganizationDailyActivityCall).toHaveBeenCalled(); }); - expect(getByText("Organization Spend Overview")).toBeInTheDocument(); + expect(screen.getByText("Organization Spend Overview")).toBeInTheDocument(); - const spendElements = getAllByText("$100.50"); - expect(spendElements.length).toBeGreaterThan(0); + await waitFor(() => { + const spendElements = screen.getAllByText("$100.50"); + expect(spendElements.length).toBeGreaterThan(0); + }); }); it("should switch between tabs", async () => { From a33a2cb5b54d98dc061406cbec62840274e7a811 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Thu, 27 Nov 2025 17:53:09 -0800 Subject: [PATCH 164/248] Adding timeout to flaky test --- .../e2e_ui_tests/view_user_info.spec.ts | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts b/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts index 5b9a9ab133..01eadc9ad1 100644 --- a/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts +++ b/tests/proxy_admin_ui_tests/e2e_ui_tests/view_user_info.spec.ts @@ -29,8 +29,12 @@ test.describe("User Info View", () => { await firstUserIdCell.click(); // Check for tabs - await expect(page.locator('button:has-text("Overview")')).toBeVisible(); - await expect(page.locator('button:has-text("Details")')).toBeVisible(); + await expect(page.locator('button:has-text("Overview")')).toBeVisible({ + timeout: 10000, + }); + await expect(page.locator('button:has-text("Details")')).toBeVisible({ + timeout: 10000, + }); // Switch to details tab await page.locator('button:has-text("Details")').click(); From d43c0776534251213c47323e5abfcc9b2a645a60 Mon Sep 17 00:00:00 2001 From: Wei-Chiet Ku Date: Fri, 28 Nov 2025 13:24:04 +0800 Subject: [PATCH 165/248] Fix/issue 16759 streaming error validation (#17242) * Enhance error handling in OpenAIResponsesAPIConfig to coalesce null error codes into a default string, preventing validation errors and improving stability during streaming iterations. * Add test for coalescing null error codes in streaming responses This test ensures that when a streaming error event has error.code set to None, the system correctly transforms it to 'unknown_error' and returns an ErrorEvent instance without raising a ValidationError. --------- Co-authored-by: Ku Wei Chiet --- .../llms/openai/responses/transformation.py | 18 +++++++++++++ .../test_openai_responses_transformation.py | 27 +++++++++++++++++++ 2 files changed, 45 insertions(+) diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index f75213b068..4c9d382838 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -238,6 +238,24 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): event_pydantic_model = OpenAIResponsesAPIConfig.get_event_model_class( event_type=event_type ) + # Defensive: Some OpenAI-compatible providers may send `error.code: null`. + # Pydantic will raise a ValidationError when it expects a string but gets None. + # Coalesce a None `error.code` to a stable default string so streaming + # iteration does not crash (see issue report). This keeps behavior similar + # to previous fixes (coalesce before validation) and lets higher-level + # handlers still receive an `ErrorEvent` object. + try: + error_obj = parsed_chunk.get("error") + if isinstance(error_obj, dict) and error_obj.get("code") is None: + # Preserve other fields, but ensure `code` is a non-null string + parsed_chunk = dict(parsed_chunk) + parsed_chunk["error"] = dict(error_obj) + parsed_chunk["error"]["code"] = "unknown_error" + except Exception: + # If anything unexpected happens here, fall back to attempting + # instantiation and let higher-level handlers manage errors. + verbose_logger.debug("Failed to coalesce error.code in parsed_chunk") + return event_pydantic_model(**parsed_chunk) @staticmethod diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py index fa5231a2a2..074378fd56 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_transformation.py @@ -390,6 +390,33 @@ class TestOpenAIResponsesAPIConfig: assert result["partial_images"] == partial_images_value assert result["stream"] is True + def test_transform_streaming_response_coalesces_null_error_code(self): + """Ensure that when a streaming error event contains error.code=None, + transform_streaming_response coalesces it to 'unknown_error' and returns + an ErrorEvent instance without raising a ValidationError. + """ + from litellm.types.llms.openai import ErrorEvent + + parsed_chunk = { + "type": "error", + "sequence_number": 1, + "error": { + "type": "invalid_request_error", + "code": None, + "message": "Something went wrong", + "param": None, + }, + } + + event = self.config.transform_streaming_response( + model=self.model, parsed_chunk=parsed_chunk, logging_obj=self.logging_obj + ) + + # Validate returned type and coalesced code + assert isinstance(event, ErrorEvent) + assert event.error.code == "unknown_error" + assert event.error.message == "Something went wrong" + class TestAzureResponsesAPIConfig: def setup_method(self): From 334d09b3b21c728e6f7152d1804cb8b5aac643ef Mon Sep 17 00:00:00 2001 From: YutaSaito <36355491+uc4w6c@users.noreply.github.com> Date: Fri, 28 Nov 2025 14:26:27 +0900 Subject: [PATCH 166/248] feat: add regex-based tool_name/tool_type matching for tool-permission (#17164) * feat: add regex-based tool_name/tool_type matching for tool-permission * docs: update tool permission quick start for UI workflow --- .../docs/proxy/guardrails/tool_permission.md | 45 +++++- .../guardrail_hooks/tool_permission.py | 139 +++++++++++------ .../guardrail_hooks/tool_permission.py | 33 +++- .../guardrail_hooks/test_tool_permission.py | 144 ++++++++++++------ .../ToolPermissionRulesEditor.tsx | 36 ++++- 5 files changed, 286 insertions(+), 111 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/tool_permission.md b/docs/my-website/docs/proxy/guardrails/tool_permission.md index 19b674c9e5..897c31d9da 100644 --- a/docs/my-website/docs/proxy/guardrails/tool_permission.md +++ b/docs/my-website/docs/proxy/guardrails/tool_permission.md @@ -7,9 +7,38 @@ import TabItem from '@theme/TabItem'; LiteLLM provides the LiteLLM Tool Permission Guardrail that lets you control which **tool calls** a model is allowed to invoke, using configurable allow/deny rules. This offers fine-grained, provider-agnostic control over tool execution (e.g., OpenAI Chat Completions `tool_calls`, Anthropic Messages `tool_use`, MCP tools). ## Quick Start -### 1. Define Guardrails on your LiteLLM config.yaml -Define your guardrails under the `guardrails` section +### LiteLLM UI + +#### Step 1: Select Tool Permission Guardrail + +Open the LiteLLM Dashboard, click **Add New Guardrail**, and choose **LiteLLM Tool Permission Guardrail**. This loads the rule builder UI. + +Configure tool permission guardrail in LiteLLM UI + +#### Step 2: Define Regex Rules + +1. Click **Add Rule**. +2. Enter a unique Rule ID. +3. Provide a regex for the tool name (e.g., `^mcp__github_.*$`). +4. Optionally add a regex for tool type (e.g., `^function$`). +5. Pick **Allow** or **Deny**. + +Configure tool permission guardrail in LiteLLM UI + +#### Step 3: Restrict Tool Arguments (Optional) + +Select **+ Restrict tool arguments** to attach regex validations to nested paths (dot + `[]` notation). This enforces that sensitive parameters (such as `arguments.to[]`) conform to pre-approved formats. + +#### Step 4: Choose Defaults & Actions + +- Set the fallback decision (`default_action`) for tools that do not hit any rule. +- Decide how disallowed tools behave: **Block** halts the request, **Rewrite** strips forbidden tools and returns an error message inside the response. +- Customize `violation_message_template` if you want branded error copy. +- Save the guardrail. + +### LiteLLM Config.yaml Setup + ```yaml guardrails: - guardrail_name: "tool-permission-guardrail" @@ -21,16 +50,17 @@ guardrails: tool_name: "Bash" decision: "allow" - id: "allow_github_mcp" - tool_name: "mcp__github_*" + tool_name: "^mcp__github_.*$" decision: "allow" - id: "allow_aws_documentation" - tool_name: "mcp__aws-documentation_*_documentation" + tool_name: "^mcp__aws-documentation_.*_documentation$" decision: "allow" - id: "deny_read_commands" tool_name: "Read" - decision: "Deny" + decision: "deny" - id: "mail-domain" - tool_name: "send_email" + tool_name: "^send_email$" + tool_type: "^function$" decision: "allow" allowed_param_patterns: "to[]": "^.+@berri\\.ai$" @@ -44,7 +74,8 @@ guardrails: ```yaml - id: "unique_rule_id" # Unique identifier for the rule - tool_name: "pattern" # Tool name or pattern to match + tool_name: "^regex$" # Regex for tool name (optional, at least one of name/type required) + tool_type: "^function$" # Regex for tool type (optional) decision: "allow" # "allow" or "deny" allowed_param_patterns: # Optional - regex map for argument paths (dot + [] notation) "path.to[].field": "^regex$" diff --git a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py index 02e06acbda..64753d9fa8 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -62,6 +62,7 @@ class ToolPermissionGuardrail(CustomGuardrail): self.rules: List[ToolPermissionRule] = [] self._compiled_rule_patterns: Dict[str, Dict[str, re.Pattern]] = {} + self._compiled_rule_targets: Dict[str, Dict[str, Optional[re.Pattern]]] = {} if rules: for rule_item in rules: if isinstance(rule_item, ToolPermissionRule): @@ -70,6 +71,30 @@ class ToolPermissionGuardrail(CustomGuardrail): rule = ToolPermissionRule(**rule_item) self.rules.append(rule) + compiled_target_patterns: Dict[str, Optional[re.Pattern]] = { + "tool_name": None, + "tool_type": None, + } + if rule.tool_name is not None: + try: + compiled_target_patterns["tool_name"] = re.compile( + rule.tool_name + ) + except re.error as exc: + raise ValueError( + f"Invalid regex for tool_name in rule '{rule.id}': {exc}" + ) from exc + if rule.tool_type is not None: + try: + compiled_target_patterns["tool_type"] = re.compile( + rule.tool_type + ) + except re.error as exc: + raise ValueError( + f"Invalid regex for tool_type in rule '{rule.id}': {exc}" + ) from exc + self._compiled_rule_targets[rule.id] = compiled_target_patterns + if rule.allowed_param_patterns: compiled_patterns: Dict[str, re.Pattern] = {} for path, pattern in rule.allowed_param_patterns.items(): @@ -100,59 +125,75 @@ class ToolPermissionGuardrail(CustomGuardrail): return ToolPermissionGuardrailConfigModel - def _matches_pattern(self, tool_name: str, pattern: str) -> bool: - """ - Check if a tool name matches a pattern - - Supports patterns like: - - "Bash" - exact match - - "mcp__*" - prefix pattern (matches names starting wich "mcp__") - - "*_read" - suffix wildcard (matches names ending with "_read") - - "mcp__github_*_read" - infix wildcard (matches names like "mcp__github_mark_all_notifications_read") - - Args: - tool_name: Name of the tool to check - pattern: Pattern to match against - - Returns: - True if the tool name matches the pattern - """ - # Handle exact matches - if tool_name == pattern: + def _matches_regex( + self, pattern: Optional[re.Pattern], value: Optional[str] + ) -> bool: + if pattern is None: return True + if value is None: + return False + return bool(pattern.fullmatch(value)) - if "*" in pattern: - # Escape regex special chars except '*' - escaped_pattern = re.escape(pattern) - # Turn \* into .* - regex_pattern = escaped_pattern.replace(r"\*", ".*") - return bool(re.fullmatch(regex_pattern, tool_name)) + def _rule_matches_tool( + self, + rule: ToolPermissionRule, + *, + tool_name: Optional[str], + tool_type: Optional[str] = None, + ) -> tuple[bool, bool]: + target_patterns = self._compiled_rule_targets.get(rule.id, {}) + name_pattern = target_patterns.get("tool_name") + type_pattern = target_patterns.get("tool_type") - return False + name_required = rule.tool_name is not None + type_required = rule.tool_type is not None + + name_matched = ( + self._matches_regex(name_pattern, tool_name) if name_required else True + ) + type_matched = ( + self._matches_regex(type_pattern, tool_type) if type_required else True + ) + + overall_match = name_matched and type_matched + should_check_params = name_required and name_matched + + return overall_match, should_check_params def _check_tool_permission( - self, tool_name: str + self, + tool_name: Optional[str], + tool_type: Optional[str] = None, ) -> tuple[bool, Optional[str], Optional[str]]: """ Check if a tool is allowed based on the configured rules Args: tool_name: Name of the tool to check + tool_type: Type of the tool to check Returns: Tuple of (is_allowed, rule_id, message) """ - verbose_proxy_logger.debug(f"Checking permission for tool: {tool_name}") + verbose_proxy_logger.debug( + f"Checking permission for tool: {tool_name or tool_type}" + ) # Check each rule in order for rule in self.rules: - if self._matches_pattern(tool_name, rule.tool_name): + matches, _ = self._rule_matches_tool( + rule, + tool_name=tool_name, + tool_type=tool_type, + ) + if matches: is_allowed = rule.decision == "allow" - default_message = f"Tool '{tool_name}' {'allowed' if is_allowed else 'denied'} by rule '{rule.id}'" + tool_identifier = tool_name or tool_type or "unknown_tool" + default_message = f"Tool '{tool_identifier}' {'allowed' if is_allowed else 'denied'} by rule '{rule.id}'" message = self.render_violation_message( default=default_message, context={ - "tool_name": tool_name, + "tool_name": tool_name or tool_identifier, "rule_id": rule.id, }, ) @@ -161,11 +202,12 @@ class ToolPermissionGuardrail(CustomGuardrail): # No rule matched, use default action is_allowed = self.default_action == "allow" - default_message = f"Tool '{tool_name}' {'allowed' if is_allowed else 'denied'} by default action" + tool_identifier = tool_name or tool_type or "unknown_tool" + default_message = f"Tool '{tool_identifier}' {'allowed' if is_allowed else 'denied'} by default action" message = self.render_violation_message( default=default_message, context={ - "tool_name": tool_name, + "tool_name": tool_name or tool_identifier, "rule_id": None, }, ) @@ -228,7 +270,7 @@ class ToolPermissionGuardrail(CustomGuardrail): *, arguments: Dict[str, Any], rule: ToolPermissionRule, - tool_name: str, + tool_name: Optional[str], ) -> tuple[bool, Optional[str]]: compiled_patterns = self._compiled_rule_patterns.get(rule.id) if not compiled_patterns: @@ -249,7 +291,7 @@ class ToolPermissionGuardrail(CustomGuardrail): return ( False, f"Value '{raw_value}' for path '{path}' does not match allowed pattern" - f" '{compiled_pattern.pattern}' for tool '{tool_name}'", + f" '{compiled_pattern.pattern}' for tool '{tool_name or 'unknown_tool'}'", ) return True, None @@ -258,19 +300,27 @@ class ToolPermissionGuardrail(CustomGuardrail): self, tool_call: ChatCompletionMessageToolCall ) -> tuple[bool, Optional[str], Optional[str]]: tool_name = tool_call.function.name if tool_call.function else None - if not tool_name: + tool_type = getattr(tool_call, "type", None) + if not tool_name and not tool_type: return self.default_action == "allow", None, None + tool_identifier = tool_name or tool_type or "unknown_tool" + last_pattern_failure_msg: Optional[str] = None for rule in self.rules: - if not self._matches_pattern(tool_name, rule.tool_name): + matches, should_check_params = self._rule_matches_tool( + rule, + tool_name=tool_name, + tool_type=tool_type, + ) + if not matches: continue - if rule.allowed_param_patterns: + if rule.allowed_param_patterns and should_check_params: arguments = self._parse_tool_call_arguments(tool_call) if not arguments: - last_pattern_failure_msg = f"Tool '{tool_name}' is missing arguments required by rule '{rule.id}'" + last_pattern_failure_msg = f"Tool '{tool_identifier}' is missing arguments required by rule '{rule.id}'" continue patterns_match, failure_message = self._patterns_match_for_rule( @@ -283,10 +333,10 @@ class ToolPermissionGuardrail(CustomGuardrail): continue is_allowed = rule.decision == "allow" - default_message = f"Tool '{tool_name}' {'allowed' if is_allowed else 'denied'} by rule '{rule.id}'" + default_message = f"Tool '{tool_identifier}' {'allowed' if is_allowed else 'denied'} by rule '{rule.id}'" message = self.render_violation_message( default=default_message, - context={"tool_name": tool_name, "rule_id": rule.id}, + context={"tool_name": tool_identifier, "rule_id": rule.id}, ) return is_allowed, rule.id, message @@ -294,11 +344,11 @@ class ToolPermissionGuardrail(CustomGuardrail): default_message = ( last_pattern_failure_msg if (last_pattern_failure_msg and not is_allowed) - else f"Tool '{tool_name}' {'allowed' if is_allowed else 'denied'} by default action" + else f"Tool '{tool_identifier}' {'allowed' if is_allowed else 'denied'} by default action" ) message = self.render_violation_message( default=default_message, - context={"tool_name": tool_name, "rule_id": None}, + context={"tool_name": tool_identifier, "rule_id": None}, ) return is_allowed, None, message @@ -469,8 +519,9 @@ class ToolPermissionGuardrail(CustomGuardrail): if tool["type"] != "function": continue tool_name: str = tool["function"]["name"] + tool_type: Optional[str] = tool.get("type") - is_allowed, _, message = self._check_tool_permission(tool_name) + is_allowed, _, message = self._check_tool_permission(tool_name, tool_type) if not is_allowed and message is not None: verbose_proxy_logger.warning(f"Tool Permission Guardrail: {message}") diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py b/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py index e78cfad8bd..2ed1f3d2e3 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/tool_permission.py @@ -1,7 +1,7 @@ # Tool Permission Guardrail Type Definitions from typing import Dict, List, Literal, Optional -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator, model_validator from .base import GuardrailConfigModel @@ -12,8 +12,13 @@ class ToolPermissionRule(BaseModel): """ id: str = Field(description="Unique identifier for the rule") - tool_name: str = Field( - description="Tool name or pattern (e.g., 'Bash', 'mcp__github_*', 'mcp__github_*_read', '*_read')" + tool_name: Optional[str] = Field( + default=None, + description="Regex pattern applied to the tool's function name", + ) + tool_type: Optional[str] = Field( + default=None, + description="Regex pattern applied to the tool type (e.g., function)", ) decision: Literal["allow", "deny"] = Field( description="Whether to allow or deny this tool usage" @@ -23,6 +28,26 @@ class ToolPermissionRule(BaseModel): description="Optional regex map enforcing nested parameter values using dot/[] paths", ) + @field_validator("tool_name", "tool_type", mode="before") + @classmethod + def _blank_to_none(cls, value: Optional[str]) -> Optional[str]: + if value is None: + return None + if isinstance(value, str): + stripped = value.strip() + if not stripped: + return None + return stripped + return value + + @model_validator(mode="after") + def _ensure_target_present(self): + if self.tool_name is None and self.tool_type is None: + raise ValueError( + "Each rule must specify at least a tool_name or tool_type regex" + ) + return self + class ToolResult(BaseModel): """ @@ -52,7 +77,7 @@ class ToolPermissionGuardrailConfigModel(GuardrailConfigModel): rules: Optional[List[ToolPermissionRule]] = Field( default=None, - description="Ordered allow/deny rules. Patterns support * wildcards and optional regex constraints on tool arguments.", + description="Ordered allow/deny rules. Patterns use regex for tool names/types and optional regex constraints on tool arguments.", ) default_action: Literal["allow", "deny"] = Field( default="deny", description="Fallback decision when no rule matches" diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_tool_permission.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_tool_permission.py index 5468dcf949..a7fd1c6495 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_tool_permission.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_tool_permission.py @@ -3,6 +3,7 @@ Unit tests for Tool Permission Guardrail (OpenAI tool_calls semantics) """ import os +import re import sys from unittest.mock import patch @@ -36,15 +37,19 @@ class TestToolPermissionGuardrail: def setup_method(self): """Set up test fixtures""" self.test_rules = [ - {"id": "allow_bash", "tool_name": "Bash", "decision": "allow"}, - {"id": "allow_github", "tool_name": "mcp__github_*", "decision": "allow"}, + {"id": "allow_bash", "tool_name": r"^Bash$", "decision": "allow"}, { - "id": "allow_documentation", - "tool_name": "mcp__aws-documentation_*_documentation", + "id": "allow_github", + "tool_name": r"^mcp__github_.*$", "decision": "allow", }, - {"id": "deny_read", "tool_name": "Read", "decision": "deny"}, - {"id": "deny_get", "tool_name": "*_get", "decision": "deny"}, + { + "id": "allow_documentation", + "tool_name": r"^mcp__aws-documentation_.*_documentation$", + "decision": "allow", + }, + {"id": "deny_read", "tool_name": r"^Read$", "decision": "deny"}, + {"id": "deny_get", "tool_name": r".*_get$", "decision": "deny"}, ] self.guardrail = ToolPermissionGuardrail( @@ -64,50 +69,91 @@ class TestToolPermissionGuardrail: self.guardrail.supported_event_hooks or [] ) - def test_pattern_matching_exact(self): - """Test exact pattern matching""" - assert self.guardrail._matches_pattern("Read", "Read") is True - assert self.guardrail._matches_pattern("Write", "Read") is False + def test_matches_regex_helper(self): + pattern = re.compile(r"^Read$") + assert self.guardrail._matches_regex(pattern, "Read") is True + assert self.guardrail._matches_regex(pattern, "Write") is False + assert self.guardrail._matches_regex(None, "Any") is True + assert self.guardrail._matches_regex(pattern, None) is False - def test_pattern_matching_wildcards(self): - """Test wildcard pattern matching""" - assert ( - self.guardrail._matches_pattern( - "mcp__github_add_issue_comment", "mcp__github_*" - ) - is True + def test_rule_matches_tool_with_type_only(self): + guardrail = ToolPermissionGuardrail( + guardrail_name="type-only", + rules=[ + { + "id": "allow_functions", + "tool_type": r"^function$", + "decision": "allow", + } + ], + default_action="deny", + on_disallowed_action="block", ) - assert ( - self.guardrail._matches_pattern( - "mcp__github_add_issue_comment", "mcp__github_*_comment" - ) - is True + + is_allowed, rule_id, _ = guardrail._check_tool_permission("AnyTool", "function") + assert is_allowed is True + assert rule_id == "allow_functions" + + is_allowed, rule_id, _ = guardrail._check_tool_permission("AnyTool", "custom") + assert is_allowed is False + assert rule_id is None + + def test_rule_matches_tool_with_name_and_type(self): + guardrail = ToolPermissionGuardrail( + guardrail_name="name-type", + rules=[ + { + "id": "allow_specific", + "tool_name": r"^Bash$", + "tool_type": r"^function$", + "decision": "allow", + } + ], + default_action="deny", + on_disallowed_action="block", ) - assert ( - self.guardrail._matches_pattern( - "mcp__github_add_issue_comment", "*_comment" + + is_allowed, rule_id, _ = guardrail._check_tool_permission("Bash", "function") + assert is_allowed is True + assert rule_id == "allow_specific" + + is_allowed, rule_id, _ = guardrail._check_tool_permission("Bash", "custom") + assert is_allowed is False + assert rule_id is None + + def test_rule_requires_name_or_type(self): + with pytest.raises(ValueError): + ToolPermissionGuardrail( + guardrail_name="invalid-rule", + rules=[{"id": "no_target", "decision": "allow"}], + default_action="deny", + on_disallowed_action="block", ) - is True + + def test_type_only_rule_skips_param_patterns(self): + guardrail = ToolPermissionGuardrail( + guardrail_name="type-param", + rules=[ + { + "id": "allow_type_only", + "tool_type": r"^function$", + "decision": "allow", + "allowed_param_patterns": {"foo": r"^bar$"}, + } + ], + default_action="deny", + on_disallowed_action="block", ) - assert ( - self.guardrail._matches_pattern( - "mcp__git_add_issue_comment", "mcp__github_*" - ) - is False - ) - assert ( - self.guardrail._matches_pattern( - "mcp__github_assign_copilot_to_issue", "mcp__github_*_comment" - ) - is False - ) - assert ( - self.guardrail._matches_pattern( - "mcp__github_assign_copilot_to_issue", "*_comment" - ) - is False + + tool_call = ChatCompletionMessageToolCall( + function={"name": "AnyTool", "arguments": "{}"}, + type="function", ) + is_allowed, rule_id, _ = guardrail._get_permission_for_tool_call(tool_call) + assert is_allowed is True + assert rule_id == "allow_type_only" + def test_check_tool_permission_allow(self): is_allowed, rule_id, msg = self.guardrail._check_tool_permission("Bash") assert is_allowed is True @@ -232,7 +278,7 @@ class TestToolPermissionGuardrail: rules=[ { "id": "allow_mail", - "tool_name": "mail_mcp-send_email", + "tool_name": r"^mail_mcp-send_email$", "decision": "allow", "allowed_param_patterns": { "to[]": r"^.+@berri\.ai$", @@ -267,7 +313,7 @@ class TestToolPermissionGuardrail: rules=[ { "id": "allow_mail", - "tool_name": "mail_mcp-send_email", + "tool_name": r"^mail_mcp-send_email$", "decision": "allow", "allowed_param_patterns": {"to[]": r"^.+@berri\.ai$"}, } @@ -300,7 +346,7 @@ class TestToolPermissionGuardrail: rules=[ { "id": "allow_mail", - "tool_name": "mail_mcp-send_email", + "tool_name": r"^mail_mcp-send_email$", "decision": "allow", "allowed_param_patterns": {"to[]": r"^.+@berri\.ai$"}, } @@ -339,7 +385,7 @@ class TestToolPermissionGuardrail: rules=[ { "id": "deny_gmail", - "tool_name": "mail_mcp-send_email", + "tool_name": r"^mail_mcp-send_email$", "decision": "deny", "allowed_param_patterns": {"to[]": r"^.+@gmail\.com$"}, } @@ -486,7 +532,9 @@ class TestToolPermissionGuardrailIntegration: def test_default_action_allow(self): guardrail = ToolPermissionGuardrail( guardrail_name="test-allow-default", - rules=[{"id": "deny_read", "tool_name": "Read", "decision": "deny"}], + rules=[ + {"id": "deny_read", "tool_name": r"^Read$", "decision": "deny"} + ], default_action="allow", ) diff --git a/ui/litellm-dashboard/src/components/guardrails/tool_permission/ToolPermissionRulesEditor.tsx b/ui/litellm-dashboard/src/components/guardrails/tool_permission/ToolPermissionRulesEditor.tsx index 790876ed3f..68bbc055f4 100644 --- a/ui/litellm-dashboard/src/components/guardrails/tool_permission/ToolPermissionRulesEditor.tsx +++ b/ui/litellm-dashboard/src/components/guardrails/tool_permission/ToolPermissionRulesEditor.tsx @@ -9,7 +9,8 @@ export type ToolPermissionOnDisallowedAction = "block" | "rewrite"; export interface ToolPermissionRuleConfig { id: string; - tool_name: string; + tool_name?: string; + tool_type?: string; decision: ToolPermissionDecision; allowed_param_patterns?: Record; } @@ -67,7 +68,6 @@ const ToolPermissionRulesEditor: React.FC = ({ ...config.rules, { id: `rule_${Math.random().toString(36).slice(2, 8)}`, - tool_name: "", decision: "allow" as ToolPermissionDecision, allowed_param_patterns: undefined, }, @@ -195,8 +195,8 @@ const ToolPermissionRulesEditor: React.FC = ({
LiteLLM Tool Permission Guardrail - Use wildcards (e.g., mcp__github_*) to scope which tools can run and optionally constrain - payload fields. + Provide regex patterns (e.g., ^mcp__github_.*$) for tool names or types and optionally + constrain payload fields.
{!disabled && ( @@ -242,12 +242,32 @@ const ToolPermissionRulesEditor: React.FC = ({ />
- Tool Name / Pattern + Tool Name (optional) updateRule(index, { tool_name: e.target.value })} + placeholder="^mcp__github_.*$" + value={rule.tool_name ?? ""} + onChange={(e) => + updateRule(index, { + tool_name: e.target.value.trim() === "" ? undefined : e.target.value, + }) + } + /> +
+
+ +
+
+ Tool Type (optional) + + updateRule(index, { + tool_type: e.target.value.trim() === "" ? undefined : e.target.value, + }) + } />
From 87050c6a022053147fde198d418312615a08981b Mon Sep 17 00:00:00 2001 From: Saar wintrov Date: Fri, 28 Nov 2025 07:27:23 +0200 Subject: [PATCH 167/248] SSO: fix the generic SSO provider (#17227) * SSO: fix the generic SSO provider * adding tests --- litellm/proxy/management_endpoints/ui_sso.py | 12 +- .../proxy/management_endpoints/test_ui_sso.py | 221 ++++++++++++++++++ 2 files changed, 225 insertions(+), 8 deletions(-) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index 44b593efea..a033e2cf5f 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -1253,7 +1253,8 @@ class SSOAuthenticationHandler: Priority order: 1. CLI state (if provided) 2. GENERIC_CLIENT_STATE environment variable - 3. Generated UUID for Okta (if Okta endpoint detected) + 3. Generated UUID (required by Okta and most OAuth providers) + Args: state: Optional state parameter (e.g., CLI state) @@ -1275,13 +1276,8 @@ class SSOAuthenticationHandler: generic_client_state = os.getenv("GENERIC_CLIENT_STATE", None) if generic_client_state: redirect_params["state"] = generic_client_state - elif ( - generic_authorization_endpoint - and "okta" in generic_authorization_endpoint - ): - redirect_params["state"] = ( - uuid.uuid4().hex - ) # set state param for okta - required + else: + redirect_params["state"] = uuid.uuid4().hex # Handle PKCE (Proof Key for Code Exchange) if enabled # Set GENERIC_CLIENT_USE_PKCE=true to enable PKCE for enhanced OAuth security diff --git a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py index 4f5f4e0d85..f01813fa58 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py +++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py @@ -2239,6 +2239,227 @@ class TestGenericResponseConvertorNestedAttributes: assert result.display_name == "user-sub-123" # Top-level attribute works +class TestGetGenericSSORedirectParams: + """Test _get_generic_sso_redirect_params state parameter priority handling""" + + def test_state_priority_cli_state_provided(self): + """ + Test that CLI state takes highest priority when provided + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange + cli_state = "litellm-session-token:sk-test123" + + with patch.dict(os.environ, {"GENERIC_CLIENT_STATE": "env_state_value"}): + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=cli_state, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert + assert redirect_params["state"] == cli_state + assert code_verifier is None # PKCE not enabled by default + + def test_state_priority_env_variable_when_no_cli_state(self): + """ + Test that GENERIC_CLIENT_STATE environment variable is used when CLI state is not provided + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange + env_state = "custom_env_state_value" + + with patch.dict(os.environ, {"GENERIC_CLIENT_STATE": env_state}): + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=None, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert + assert redirect_params["state"] == env_state + assert code_verifier is None + + def test_state_priority_generated_uuid_fallback(self): + """ + Test that a UUID is generated when neither CLI state nor env variable is provided + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange - no CLI state and no env variable + with patch.dict(os.environ, {}, clear=False): + # Remove GENERIC_CLIENT_STATE if it exists + os.environ.pop("GENERIC_CLIENT_STATE", None) + + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=None, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert + assert "state" in redirect_params + assert redirect_params["state"] is not None + assert len(redirect_params["state"]) == 32 # UUID hex is 32 chars + assert code_verifier is None + + def test_state_with_pkce_enabled(self): + """ + Test that PKCE parameters are generated when GENERIC_CLIENT_USE_PKCE is enabled + """ + import base64 + import hashlib + + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange + test_state = "test_state_123" + + with patch.dict(os.environ, {"GENERIC_CLIENT_USE_PKCE": "true"}): + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=test_state, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert state + assert redirect_params["state"] == test_state + + # Assert PKCE parameters + assert code_verifier is not None + assert len(code_verifier) == 43 # Standard PKCE verifier length + assert "code_challenge" in redirect_params + assert "code_challenge_method" in redirect_params + assert redirect_params["code_challenge_method"] == "S256" + + # Verify code_challenge is correctly derived from code_verifier + expected_challenge_bytes = hashlib.sha256( + code_verifier.encode("utf-8") + ).digest() + expected_challenge = ( + base64.urlsafe_b64encode(expected_challenge_bytes) + .decode("utf-8") + .rstrip("=") + ) + assert redirect_params["code_challenge"] == expected_challenge + + def test_state_with_pkce_disabled(self): + """ + Test that PKCE parameters are NOT generated when GENERIC_CLIENT_USE_PKCE is false + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange + test_state = "test_state_456" + + with patch.dict(os.environ, {"GENERIC_CLIENT_USE_PKCE": "false"}): + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=test_state, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert + assert redirect_params["state"] == test_state + assert code_verifier is None + assert "code_challenge" not in redirect_params + assert "code_challenge_method" not in redirect_params + + def test_state_priority_cli_state_overrides_env_with_pkce(self): + """ + Test that CLI state takes priority over env variable even when PKCE is enabled + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange + cli_state = "cli_state_priority" + env_state = "env_state_should_not_be_used" + + with patch.dict( + os.environ, + { + "GENERIC_CLIENT_STATE": env_state, + "GENERIC_CLIENT_USE_PKCE": "true", + }, + ): + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=cli_state, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert + assert redirect_params["state"] == cli_state # CLI state takes priority + assert redirect_params["state"] != env_state + + # PKCE should still be generated + assert code_verifier is not None + assert "code_challenge" in redirect_params + assert "code_challenge_method" in redirect_params + + def test_empty_string_state_uses_env_variable(self): + """ + Test that empty string state is treated as None and uses env variable + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange + env_state = "env_state_for_empty_cli" + + with patch.dict(os.environ, {"GENERIC_CLIENT_STATE": env_state}): + # Act + redirect_params, code_verifier = ( + SSOAuthenticationHandler._get_generic_sso_redirect_params( + state="", # Empty string + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + ) + + # Assert - empty string is falsy, so env variable should be used + # Note: This tests current implementation behavior + # If empty string should be treated differently, implementation needs update + assert redirect_params["state"] == env_state + + def test_multiple_calls_generate_different_uuids(self): + """ + Test that multiple calls without state generate different UUIDs + """ + from litellm.proxy.management_endpoints.ui_sso import SSOAuthenticationHandler + + # Arrange - no state provided + with patch.dict(os.environ, {}, clear=False): + os.environ.pop("GENERIC_CLIENT_STATE", None) + + # Act + params1, _ = SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=None, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + params2, _ = SSOAuthenticationHandler._get_generic_sso_redirect_params( + state=None, + generic_authorization_endpoint="https://auth.example.com/authorize", + ) + + # Assert + assert params1["state"] != params2["state"] + assert len(params1["state"]) == 32 + assert len(params2["state"]) == 32 + + class TestPKCEFunctionality: """Test PKCE (Proof Key for Code Exchange) functionality""" From 8aa4f3d476f373bd8c0520dfdb15147353641118 Mon Sep 17 00:00:00 2001 From: Andy Forest Date: Fri, 28 Nov 2025 00:50:35 -0500 Subject: [PATCH 168/248] fix(bedrock): handle cohere v4 embed response dictionary format (#17220) --- .../llms/cohere/embed/v1_transformation.py | 10 +++- .../bedrock/embed/test_bedrock_embedding.py | 55 ++++++++++++++++++- 2 files changed, 62 insertions(+), 3 deletions(-) diff --git a/litellm/llms/cohere/embed/v1_transformation.py b/litellm/llms/cohere/embed/v1_transformation.py index 1a4bc393e8..feca9cb5b8 100644 --- a/litellm/llms/cohere/embed/v1_transformation.py +++ b/litellm/llms/cohere/embed/v1_transformation.py @@ -1,5 +1,5 @@ """ -Legacy /v1/embedding transformation logic for Bedrock Cohere. +Legacy /v1/embedding transformation logic for Bedrock Cohere. """ from typing import Any, List, Optional, Union @@ -123,7 +123,13 @@ class CohereEmbeddingConfig: """ embeddings = response_json["embeddings"] output_data = [] - is_embeddings_by_type = response_json.get("response_type") == "embeddings_by_type" + is_embeddings_by_type = ( + response_json.get("response_type") == "embeddings_by_type" + ) + + if isinstance(embeddings, dict): + is_embeddings_by_type = True + if is_embeddings_by_type: for embedding_type in embeddings: for idx, embedding in enumerate(embeddings[embedding_type]): diff --git a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py index a266bea351..d6253e5948 100644 --- a/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py +++ b/tests/test_litellm/llms/bedrock/embed/test_bedrock_embedding.py @@ -554,4 +554,57 @@ def test_bedrock_embedding_extra_headers_and_headers_merge(): print(f" Final headers: {list(headers.keys())}") except Exception as e: - pytest.fail(f"Failed to merge and forward headers: {str(e)}") \ No newline at end of file + pytest.fail(f"Failed to merge and forward headers: {str(e)}") + + +def test_bedrock_cohere_v4_embedding_response_parsing(): + """ + Test parsing of Bedrock Cohere v4 embedding response which returns a dictionary of embeddings + keyed by type (e.g. 'float', 'int8') instead of a direct list. + """ + litellm.set_verbose = True + client = HTTPHandler() + test_api_key = "test-bearer-token-12345" + model = "bedrock/cohere.embed-v4:0" + + # Mock response for Cohere v4 with multiple embedding types + cohere_v4_response = { + "embeddings": { + "float": [[0.1, 0.2, 0.3]], + "int8": [[1, 2, 3]] + }, + "response_type": "embeddings_by_type", + "id": "test-id", + "texts": ["test input"] + } + + with patch.object(client, "post") as mock_post: + mock_response = Mock() + mock_response.status_code = 200 + mock_response.text = json.dumps(cohere_v4_response) + mock_response.json = lambda: json.loads(mock_response.text) + mock_post.return_value = mock_response + + response = litellm.embedding( + model=model, + input=["test input"], + client=client, + aws_region_name="us-east-1", + aws_bedrock_runtime_endpoint="https://bedrock-runtime.us-east-1.amazonaws.com", + api_key=test_api_key + ) + + assert isinstance(response, litellm.EmbeddingResponse) + + # Verify we get two embedding objects back (one for float, one for int8) + assert len(response.data) == 2 + + # Check first embedding (float) + assert response.data[0]['object'] == 'embedding' + assert response.data[0]['embedding'] == [0.1, 0.2, 0.3] + assert response.data[0]['type'] == 'float' + + # Check second embedding (int8) + assert response.data[1]['object'] == 'embedding' + assert response.data[1]['embedding'] == [1, 2, 3] + assert response.data[1]['type'] == 'int8' From bbea83fd9366bf6cd2592eed133b00bda0421015 Mon Sep 17 00:00:00 2001 From: Omkar Malpure <77787482+omkar806@users.noreply.github.com> Date: Fri, 28 Nov 2025 11:29:24 +0530 Subject: [PATCH 169/248] Fix : acompletion throws error with SambaNova models (#17217) Co-authored-by: Omkar Malpure --- litellm/llms/sambanova/chat.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/litellm/llms/sambanova/chat.py b/litellm/llms/sambanova/chat.py index b0534347c9..2218c80872 100644 --- a/litellm/llms/sambanova/chat.py +++ b/litellm/llms/sambanova/chat.py @@ -121,5 +121,10 @@ class SambanovaConfig(OpenAIGPTConfig): SambaNova API doesn't support content as a list - only string content. This converts content lists like [{"type": "text", "text": "..."}] to strings. """ + async def _async_transform(): + return handle_messages_with_content_list_to_str_conversion(messages) + + if is_async: + return _async_transform() messages = handle_messages_with_content_list_to_str_conversion(messages) return messages From 205a563b65d179063ea90128081a0676f75235c2 Mon Sep 17 00:00:00 2001 From: v0rtex20k <55466324+v0rtex20k@users.noreply.github.com> Date: Fri, 28 Nov 2025 01:10:19 -0500 Subject: [PATCH 170/248] Allow wildcard routes for nonproxy admin (SCIM) (#17178) * checked for wildcards in nonproxy * ready --- litellm/proxy/auth/route_checks.py | 6 ++++ .../proxy/auth/test_route_checks.py | 28 +++++++++++++++++++ 2 files changed, 34 insertions(+) diff --git a/litellm/proxy/auth/route_checks.py b/litellm/proxy/auth/route_checks.py index 664b8a9ddc..76621e95cd 100644 --- a/litellm/proxy/auth/route_checks.py +++ b/litellm/proxy/auth/route_checks.py @@ -241,6 +241,12 @@ class RouteChecks: route_allowed = True break + if RouteChecks._route_matches_wildcard_pattern( + route=route, pattern=allowed_route + ): + route_allowed = True + break + if not route_allowed: RouteChecks._raise_admin_only_route_exception( user_obj=user_obj, route=route diff --git a/tests/test_litellm/proxy/auth/test_route_checks.py b/tests/test_litellm/proxy/auth/test_route_checks.py index b2a51de3d6..de2aa2427c 100644 --- a/tests/test_litellm/proxy/auth/test_route_checks.py +++ b/tests/test_litellm/proxy/auth/test_route_checks.py @@ -732,3 +732,31 @@ def test_videos_route_with_virtual_key_llm_api_routes(): assert ( result is True ), f"Virtual key with llm_api_routes should be able to access {route}" + +def test_non_proxy_admin_wildcard_allowed_routes(): + """Test that nonproxy admin users can still use wildcard routes""" + + user_obj = LiteLLM_UserTable( + user_id="test_user", + user_email="test@example.com", + user_role=LitellmUserRoles.INTERNAL_USER.value, + ) + + valid_token = UserAPIKeyAuth( + user_id="test_user", + user_role=LitellmUserRoles.INTERNAL_USER.value, + allowed_routes=["/scim/*"], + ) + + request = MagicMock(spec=Request) + request.query_params = {} + + RouteChecks.non_proxy_admin_allowed_routes_check( + user_obj=user_obj, + _user_role=LitellmUserRoles.INTERNAL_USER.value, + route="/scim/v2/Users", + request=request, + valid_token=valid_token, + request_data={}, + ) + From 8700c5ced665728e51624e7c95dc0d2894115a9f Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 28 Nov 2025 14:56:46 +0530 Subject: [PATCH 171/248] Add nova embedding support --- .../docs/embedding/supported_embedding.md | 2 + .../docs/providers/bedrock_embedding.md | 51 +- litellm/__init__.py | 3 + litellm/constants.py | 2 + litellm/llms/bedrock/base_aws_llm.py | 11 +- .../embed/amazon_nova_transformation.py | 258 ++++++++++ litellm/llms/bedrock/embed/embedding.py | 22 + litellm/types/llms/bedrock.py | 127 +++++ litellm/utils.py | 2 + .../test_bedrock_nova_embedding.py | 469 ++++++++++++++++++ 10 files changed, 939 insertions(+), 8 deletions(-) create mode 100644 litellm/llms/bedrock/embed/amazon_nova_transformation.py create mode 100644 tests/llm_translation/test_bedrock_nova_embedding.py diff --git a/docs/my-website/docs/embedding/supported_embedding.md b/docs/my-website/docs/embedding/supported_embedding.md index e63d940366..0e8252b409 100644 --- a/docs/my-website/docs/embedding/supported_embedding.md +++ b/docs/my-website/docs/embedding/supported_embedding.md @@ -263,6 +263,8 @@ print(response) | Model Name | Function Call | |----------------------|---------------------------------------------| +| Amazon Nova Multimodal Embeddings | `embedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)` | [Nova Docs](../providers/bedrock_embedding#amazon-nova-multimodal-embeddings) | +| Amazon Nova (Async) | `embedding(model="bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0", input=input, input_type="text", output_s3_uri="s3://bucket/")` | [Nova Async Docs](../providers/bedrock_embedding#asynchronous-embeddings-with-segmentation) | | Titan Embeddings - G1 | `embedding(model="amazon.titan-embed-text-v1", input=input)` | | Cohere Embeddings - English | `embedding(model="cohere.embed-english-v3", input=input)` | | Cohere Embeddings - Multilingual | `embedding(model="cohere.embed-multilingual-v3", input=input)` | diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md index 76c9606533..e2e7c0dced 100644 --- a/docs/my-website/docs/providers/bedrock_embedding.md +++ b/docs/my-website/docs/providers/bedrock_embedding.md @@ -4,7 +4,8 @@ | Provider | LiteLLM Route | AWS Documentation | Cost Tracking | |----------|---------------|-------------------|---------------| -| Amazon Titan | `bedrock/amazon.*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | ✅ | +| Amazon Titan | `bedrock/amazon.titan-*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | ✅ | +| Amazon Nova | `bedrock/amazon.nova-*` | [Amazon Nova Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/nova-embed.html) | ✅ | | Cohere | `bedrock/cohere.*` | [Cohere Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-embed.html) | ✅ | | TwelveLabs | `bedrock/us.twelvelabs.*` | [TwelveLabs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-twelvelabs.html) | ✅ | @@ -16,6 +17,7 @@ LiteLLM supports AWS Bedrock's async-invoke feature for embedding models that re | Provider | Async Invoke Route | Use Case | |----------|-------------------|----------| +| Amazon Nova | `bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0` | Multimodal embeddings with segmentation for long text, video, and audio | | TwelveLabs Marengo | `bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0` | Video, audio, image, and text embeddings | ### Required Parameters @@ -116,7 +118,7 @@ def check_async_job_status(invocation_arn, aws_region_name="us-east-1"): """Check the status of an async invoke job using LiteLLM batch API""" try: response = retrieve_batch( - batch_id=invocation_arn, + batch_id=invocation_arn, # Pass the invocation ARN here custom_llm_provider="bedrock", aws_region_name=aws_region_name ) @@ -128,11 +130,47 @@ def check_async_job_status(invocation_arn, aws_region_name="us-east-1"): # Check status status = check_async_job_status(invocation_arn, "us-east-1") if status: - print(f"Job Status: {status.status}") - print(f"Output Location: {status.output_file_id}") + print(f"Job Status: {status.status}") # "in_progress", "completed", or "failed" + print(f"Output Location: {status.metadata['output_file_id']}") # S3 URI where results are stored ``` -**Note:** The actual embedding results are stored in S3. The `output_file_id` from the batch status can be used to locate the results file in your S3 bucket. +#### Polling Until Complete + +Here's a complete example of polling for job completion: + +```python +def wait_for_async_job(invocation_arn, aws_region_name="us-east-1", max_wait=3600): + """Poll job status until completion""" + start_time = time.time() + + while True: + status = retrieve_batch( + batch_id=invocation_arn, + custom_llm_provider="bedrock", + aws_region_name=aws_region_name, + ) + + if status.status == "completed": + print("✅ Job completed!") + return status + elif status.status == "failed": + error_msg = status.metadata.get('failure_message', 'Unknown error') + raise Exception(f"❌ Job failed: {error_msg}") + else: + elapsed = time.time() - start_time + if elapsed > max_wait: + raise TimeoutError(f"Job timed out after {max_wait} seconds") + + print(f"⏳ Job still processing... (elapsed: {elapsed:.0f}s)") + time.sleep(10) # Wait 10 seconds before checking again + +# Wait for completion +completed_status = wait_for_async_job(invocation_arn) +output_s3_uri = completed_status.metadata['output_file_id'] +print(f"Results available at: {output_s3_uri}") +``` + +**Note:** The actual embedding results are stored in S3. When the job is completed, download the results from the S3 location specified in `status.metadata['output_file_id']`. The results will be in JSON/JSONL format containing the embedding vectors. ### Error Handling @@ -179,7 +217,7 @@ except Exception as e: ### Limitations -- Async-invoke is currently only supported for TwelveLabs Marengo models +- Async-invoke is supported for TwelveLabs Marengo and Amazon Nova models - Results are stored in S3 and must be retrieved separately using the output file ID - Job status checking requires using LiteLLM's `retrieve_batch()` function - No built-in polling mechanism in LiteLLM (must implement your own status checking loop) @@ -259,6 +297,7 @@ print(response) | Model Name | Usage | Supported Additional OpenAI params | |----------------------|---------------------------------------------|-----| +| **Amazon Nova Multimodal Embeddings** | `embedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)` | Supports multimodal input (text, image, video, audio), multiple purposes, dimensions (256, 384, 1024, 3072) | | Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) | | Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) | Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | diff --git a/litellm/__init__.py b/litellm/__init__.py index 71be5113e2..6a9ced0f4b 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -1252,6 +1252,9 @@ from .llms.bedrock.embed.cohere_transformation import BedrockCohereEmbeddingConf from .llms.bedrock.embed.twelvelabs_marengo_transformation import ( TwelveLabsMarengoEmbeddingConfig, ) +from .llms.bedrock.embed.amazon_nova_transformation import ( + AmazonNovaEmbeddingConfig, +) from .llms.openai.openai import OpenAIConfig, MistralEmbeddingConfig from .llms.openai.image_variations.transformation import OpenAIImageVariationConfig from .llms.deepinfra.chat.transformation import DeepInfraConfig diff --git a/litellm/constants.py b/litellm/constants.py index 9235916dd4..fd3858ae2f 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -858,6 +858,7 @@ BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ "cohere", "amazon", "twelvelabs", + "nova", ] BEDROCK_CONVERSE_MODELS = [ @@ -918,6 +919,7 @@ cohere_embedding_models: set = set( bedrock_embedding_models: set = set( [ "amazon.titan-embed-text-v1", + "amazon.nova-2-multimodal-embeddings-v1:0", "cohere.embed-english-v3", "cohere.embed-multilingual-v3", "cohere.embed-v4:0", diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 72e270428a..ed658c793a 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -387,9 +387,16 @@ class BaseAWSLLM: Handles scenarios like: 1. model=cohere.embed-english-v3:0 -> Returns `cohere` 2. model=amazon.titan-embed-text-v1 -> Returns `amazon` - 3. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` - 4. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + 3. model=amazon.nova-2-multimodal-embeddings-v1:0 -> Returns `nova` + 4. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + 5. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` """ + # Special case: Check for "nova" in model name first (before "amazon") + # This handles amazon.nova-* models + if "nova" in model.lower(): + if "nova" in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, "nova") + # Handle regional models like us.twelvelabs.marengo-embed-2-7-v1:0 if "." in model: parts = model.split(".") diff --git a/litellm/llms/bedrock/embed/amazon_nova_transformation.py b/litellm/llms/bedrock/embed/amazon_nova_transformation.py new file mode 100644 index 0000000000..97652175a9 --- /dev/null +++ b/litellm/llms/bedrock/embed/amazon_nova_transformation.py @@ -0,0 +1,258 @@ +""" +Transformation logic from OpenAI /v1/embeddings format to Bedrock Amazon Nova /invoke and /async-invoke format. + +Why separate file? Make it easy to see how transformation works + +Supports: +- Synchronous embeddings (SINGLE_EMBEDDING) +- Asynchronous embeddings with segmentation (SEGMENTED_EMBEDDING) +- Multimodal inputs: text, image, video, audio +- Multiple embedding purposes and dimensions + +Docs - https://docs.aws.amazon.com/bedrock/latest/userguide/nova-embed.html +""" + +from typing import List, Optional + +from litellm.types.utils import Embedding, EmbeddingResponse, Usage + + +class AmazonNovaEmbeddingConfig: + """ + Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/nova-embed.html + + Amazon Nova Multimodal Embeddings supports: + - Text, image, video, and audio inputs + - Synchronous (InvokeModel) and asynchronous (StartAsyncInvoke) APIs + - Multiple embedding purposes and dimensions + """ + + def __init__(self) -> None: + pass + + def get_supported_openai_params(self) -> List[str]: + return [ + "dimensions", + ] + + def map_openai_params( + self, non_default_params: dict, optional_params: dict + ) -> dict: + """Map OpenAI-style parameters to Nova parameters.""" + for k, v in non_default_params.items(): + if k == "dimensions": + # Map OpenAI dimensions to Nova embedding_dimension + optional_params["embedding_dimension"] = v + elif k in self.get_supported_openai_params(): + optional_params[k] = v + return optional_params + + def _transform_request( + self, + input: str, + inference_params: dict, + async_invoke_route: bool = False, + model_id: Optional[str] = None, + output_s3_uri: Optional[str] = None, + ) -> dict: + """ + Transform OpenAI-style input to Nova format. + + Only handles OpenAI params (dimensions). All other Nova-specific params + should be passed via inference_params and will be passed through as-is. + + Args: + input: The input text or media reference + inference_params: Additional parameters (will be passed through) + async_invoke_route: Whether this is for async invoke + model_id: Model ID (for async invoke) + output_s3_uri: S3 URI for output (for async invoke) + + Returns: + dict: Nova embedding request + """ + # Determine task type + task_type = "SEGMENTED_EMBEDDING" if async_invoke_route else "SINGLE_EMBEDDING" + + # Build the base request structure + request: dict = { + "schemaVersion": "nova-multimodal-embed-v1", + "taskType": task_type, + } + + # Start with inference_params (user-provided params) + embedding_params = inference_params.copy() + + # Map OpenAI dimensions to embeddingDimension if provided + if "dimensions" in embedding_params: + embedding_params["embeddingDimension"] = embedding_params.pop("dimensions") + elif "embedding_dimension" in embedding_params: + embedding_params["embeddingDimension"] = embedding_params.pop("embedding_dimension") + + # Add required embeddingPurpose if not provided (required by Nova API) + if "embeddingPurpose" not in embedding_params: + embedding_params["embeddingPurpose"] = "GENERIC_INDEX" + + # Add required embeddingDimension if not provided (required by Nova API) + if "embeddingDimension" not in embedding_params: + embedding_params["embeddingDimension"] = 3072 + + # For text input, add basic text structure if user hasn't provided text/image/video/audio + if "text" not in embedding_params and "image" not in embedding_params and "video" not in embedding_params and "audio" not in embedding_params: + # Default to text if no modality specified + if input.startswith("s3://"): + embedding_params["text"] = { + "source": {"s3Location": {"uri": input}}, + "truncationMode": "END" # Required by Nova API + } + else: + embedding_params["text"] = { + "value": input, + "truncationMode": "END" # Required by Nova API + } + + # Set the embedding params in the request + if task_type == "SINGLE_EMBEDDING": + request["singleEmbeddingParams"] = embedding_params + else: + request["segmentedEmbeddingParams"] = embedding_params + + # For async invoke, wrap in the async invoke format + if async_invoke_route and model_id: + return self._wrap_async_invoke_request( + model_input=request, + model_id=model_id, + output_s3_uri=output_s3_uri, + ) + + return request + + def _wrap_async_invoke_request( + self, + model_input: dict, + model_id: str, + output_s3_uri: Optional[str] = None, + ) -> dict: + """ + Wrap the transformed request in the AWS Bedrock async invoke format. + + Args: + model_input: The transformed Nova embedding request + model_id: The model identifier (without async_invoke prefix) + output_s3_uri: S3 URI for output data config + + Returns: + dict: The wrapped async invoke request + """ + import urllib.parse + + # Clean the model ID + unquoted_model_id = urllib.parse.unquote(model_id) + if unquoted_model_id.startswith("async_invoke/"): + unquoted_model_id = unquoted_model_id.replace("async_invoke/", "") + + # Validate that the S3 URI is not empty + if not output_s3_uri or output_s3_uri.strip() == "": + raise ValueError("output_s3_uri is required for async invoke requests") + + return { + "modelId": unquoted_model_id, + "modelInput": model_input, + "outputDataConfig": { + "s3OutputDataConfig": { + "s3Uri": output_s3_uri + } + }, + } + + def _transform_response( + self, response_list: List[dict], model: str + ) -> EmbeddingResponse: + """ + Transform Nova response to OpenAI format. + + Nova response format: + { + "embeddings": [ + { + "embeddingType": "TEXT" | "IMAGE" | "VIDEO" | "AUDIO" | "AUDIO_VIDEO_COMBINED", + "embedding": [0.1, 0.2, ...], + "truncatedCharLength": 100 # Optional, only for text + } + ] + } + """ + embeddings: List[Embedding] = [] + total_tokens = 0 + + for response in response_list: + # Nova response has an "embeddings" array + if "embeddings" in response and isinstance(response["embeddings"], list): + for item in response["embeddings"]: + if "embedding" in item: + embedding = Embedding( + embedding=item["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + + # Estimate token count + # For text, use truncatedCharLength if available + if "truncatedCharLength" in item: + total_tokens += item["truncatedCharLength"] // 4 + else: + # Rough estimate based on embedding dimension + total_tokens += len(item["embedding"]) // 4 + elif "embedding" in response: + # Direct embedding response (fallback) + embedding = Embedding( + embedding=response["embedding"], + index=len(embeddings), + object="embedding", + ) + embeddings.append(embedding) + total_tokens += len(response["embedding"]) // 4 + + usage = Usage(prompt_tokens=total_tokens, total_tokens=total_tokens) + + return EmbeddingResponse(data=embeddings, model=model, usage=usage) + + def _transform_async_invoke_response( + self, response: dict, model: str + ) -> EmbeddingResponse: + """ + Transform async invoke response (invocation ARN) to OpenAI format. + + AWS async invoke returns: + { + "invocationArn": "arn:aws:bedrock:us-east-1:123456789012:async-invoke/abc123" + } + + We transform this to a job-like embedding response with the ARN in hidden params. + """ + invocation_arn = response.get("invocationArn", "") + + # Create a placeholder embedding object for the job + embedding = Embedding( + embedding=[], # Empty embedding for async jobs + index=0, + object="embedding", + ) + + # Create usage object (empty for async jobs) + usage = Usage(prompt_tokens=0, total_tokens=0) + + # Create hidden params with job ID + from litellm.types.llms.base import HiddenParams + + hidden_params = HiddenParams() + setattr(hidden_params, "_invocation_arn", invocation_arn) + + return EmbeddingResponse( + data=[embedding], + model=model, + usage=usage, + hidden_params=hidden_params, + ) + diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index fea2993597..be2bfcd70e 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -27,6 +27,7 @@ from litellm.types.utils import EmbeddingResponse, LlmProviders from ..base_aws_llm import BaseAWSLLM from ..common_utils import BedrockError +from .amazon_nova_transformation import AmazonNovaEmbeddingConfig from .amazon_titan_g1_transformation import AmazonTitanG1Config from .amazon_titan_multimodal_transformation import ( AmazonTitanMultimodalEmbeddingG1Config, @@ -175,6 +176,12 @@ class BedrockEmbedding(BaseAWSLLM): response=response_list[0], model=model ) ) + elif provider == "nova": + returned_response = ( + AmazonNovaEmbeddingConfig()._transform_async_invoke_response( + response=response_list[0], model=model + ) + ) else: # For other providers, create a generic async response invocation_arn = response_list[0].get("invocationArn", "") @@ -222,6 +229,10 @@ class BedrockEmbedding(BaseAWSLLM): response_list=response_list, model=model ) ) + elif provider == "nova": + returned_response = AmazonNovaEmbeddingConfig()._transform_response( + response_list=response_list, model=model + ) ########################################################## # Validate returned response @@ -467,6 +478,17 @@ class BedrockEmbedding(BaseAWSLLM): ) ) batch_data.append(twelvelabs_request) + elif provider == "nova": + batch_data = [] + for i in input: + nova_request = AmazonNovaEmbeddingConfig()._transform_request( + input=i, + inference_params=inference_params, + async_invoke_route=has_async_invoke, + model_id=modelId, + output_s3_uri=inference_params.get("output_s3_uri"), + ) + batch_data.append(nova_request) ### SET RUNTIME ENDPOINT ### endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint( diff --git a/litellm/types/llms/bedrock.py b/litellm/types/llms/bedrock.py index 330308e179..3696f67964 100644 --- a/litellm/types/llms/bedrock.py +++ b/litellm/types/llms/bedrock.py @@ -427,6 +427,133 @@ class TwelveLabsAsyncInvokeStatusResponse(TypedDict): failureMessage: Optional[str] +# Amazon Nova Multimodal Embeddings types +NOVA_EMBEDDING_PURPOSES = Literal[ + "GENERIC_INDEX", + "GENERIC_RETRIEVAL", + "TEXT_RETRIEVAL", + "IMAGE_RETRIEVAL", + "VIDEO_RETRIEVAL", + "DOCUMENT_RETRIEVAL", + "AUDIO_RETRIEVAL", + "CLASSIFICATION", + "CLUSTERING", +] + +NOVA_EMBEDDING_DIMENSIONS = Literal[256, 384, 1024, 3072] + +NOVA_TRUNCATION_MODES = Literal["START", "END", "NONE"] + +NOVA_DETAIL_LEVELS = Literal["STANDARD_IMAGE", "DOCUMENT_IMAGE"] + +NOVA_EMBEDDING_MODES = Literal["AUDIO_VIDEO_COMBINED", "AUDIO_VIDEO_SEPARATE"] + +NOVA_EMBEDDING_TYPES = Literal[ + "TEXT", "IMAGE", "VIDEO", "AUDIO", "AUDIO_VIDEO_COMBINED" +] + + +class NovaSourceS3Location(TypedDict): + uri: str + + +class NovaSourceObject(TypedDict, total=False): + bytes: str # base64 encoded + s3Location: NovaSourceS3Location + + +class NovaTextParams(TypedDict, total=False): + truncationMode: NOVA_TRUNCATION_MODES + value: str + source: NovaSourceObject + + +class NovaImageParams(TypedDict, total=False): + format: str # png, jpeg, gif, webp + source: Required[NovaSourceObject] + detailLevel: NOVA_DETAIL_LEVELS + + +class NovaVideoParams(TypedDict, total=False): + format: str # mp4, mov, mkv, webm, flv, mpeg, mpg, wmv, 3gp + source: Required[NovaSourceObject] + embeddingMode: Required[NOVA_EMBEDDING_MODES] + + +class NovaAudioParams(TypedDict, total=False): + format: str # mp3, wav, ogg + source: Required[NovaSourceObject] + + +class NovaTextSegmentationConfig(TypedDict, total=False): + maxLengthChars: int # 800-50,000, default 32,000 + + +class NovaMediaSegmentationConfig(TypedDict, total=False): + durationSeconds: int # 1-30, default 5 + + +class NovaTextParamsWithSegmentation(NovaTextParams, total=False): + segmentationConfig: NovaTextSegmentationConfig + + +class NovaVideoParamsWithSegmentation(NovaVideoParams, total=False): + segmentationConfig: NovaMediaSegmentationConfig + + +class NovaAudioParamsWithSegmentation(NovaAudioParams, total=False): + segmentationConfig: NovaMediaSegmentationConfig + + +class NovaSingleEmbeddingParams(TypedDict, total=False): + embeddingPurpose: Required[NOVA_EMBEDDING_PURPOSES] + embeddingDimension: NOVA_EMBEDDING_DIMENSIONS + text: NovaTextParams + image: NovaImageParams + video: NovaVideoParams + audio: NovaAudioParams + + +class NovaSegmentedEmbeddingParams(TypedDict, total=False): + embeddingPurpose: Required[NOVA_EMBEDDING_PURPOSES] + embeddingDimension: NOVA_EMBEDDING_DIMENSIONS + text: NovaTextParamsWithSegmentation + image: NovaImageParams + video: NovaVideoParamsWithSegmentation + audio: NovaAudioParamsWithSegmentation + + +class NovaEmbeddingRequest(TypedDict, total=False): + schemaVersion: str # "nova-multimodal-embed-v1" + taskType: Literal["SINGLE_EMBEDDING", "SEGMENTED_EMBEDDING"] + singleEmbeddingParams: NovaSingleEmbeddingParams + segmentedEmbeddingParams: NovaSegmentedEmbeddingParams + + +class NovaEmbeddingItem(TypedDict, total=False): + embeddingType: NOVA_EMBEDDING_TYPES + embedding: Required[List[float]] + truncatedCharLength: int # Only for text + + +class NovaEmbeddingResponse(TypedDict): + embeddings: List[NovaEmbeddingItem] + + +class NovaS3OutputDataConfig(TypedDict): + s3Uri: str + + +class NovaOutputDataConfig(TypedDict): + s3OutputDataConfig: NovaS3OutputDataConfig + + +class NovaAsyncInvokeRequest(TypedDict): + modelId: str + modelInput: NovaEmbeddingRequest + outputDataConfig: NovaOutputDataConfig + + AmazonEmbeddingRequest = Union[ AmazonTitanMultimodalEmbeddingRequest, AmazonTitanV2EmbeddingRequest, diff --git a/litellm/utils.py b/litellm/utils.py index 053368a3e0..15e068a468 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -2827,6 +2827,8 @@ def get_optional_params_embeddings( # noqa: PLR0915 object = litellm.BedrockCohereEmbeddingConfig() elif "twelvelabs" in model or "marengo" in model: object = litellm.TwelveLabsMarengoEmbeddingConfig() + elif "nova" in model.lower(): + object = litellm.AmazonNovaEmbeddingConfig() else: # unmapped model supported_params = [] _check_valid_arg(supported_params=supported_params) diff --git a/tests/llm_translation/test_bedrock_nova_embedding.py b/tests/llm_translation/test_bedrock_nova_embedding.py new file mode 100644 index 0000000000..8cc77b3c3c --- /dev/null +++ b/tests/llm_translation/test_bedrock_nova_embedding.py @@ -0,0 +1,469 @@ +""" +Test suite for Amazon Nova Multimodal Embeddings integration with LiteLLM. + +Tests cover: +- Synchronous text embeddings +- Synchronous image embeddings +- Synchronous video/audio embeddings +- Asynchronous embeddings with segmentation +- Different embedding purposes and dimensions +- Error handling +""" + +import json +import os +import sys +from unittest.mock import MagicMock, Mock, patch + +import pytest + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm +from litellm.llms.bedrock.embed.amazon_nova_transformation import ( + AmazonNovaEmbeddingConfig, +) + + +class TestNovaTransformationRequest: + """Test request transformation for Nova embeddings.""" + + def test_text_embedding_sync_request(self): + """Test synchronous text embedding request transformation.""" + config = AmazonNovaEmbeddingConfig() + + inference_params = { + "embeddingPurpose": "GENERIC_INDEX", + "embedding_dimension": 1024, + "truncation_mode": "END", + } + + request = config._transform_request( + input="Hello, world!", + inference_params=inference_params, + async_invoke_route=False, + ) + + assert request["schemaVersion"] == "nova-multimodal-embed-v1" + assert request["taskType"] == "SINGLE_EMBEDDING" + assert "singleEmbeddingParams" in request + + params = request["singleEmbeddingParams"] + assert params["embeddingPurpose"] == "GENERIC_INDEX" + assert params["embeddingDimension"] == 1024 + assert params["text"]["truncationMode"] == "END" + assert params["text"]["value"] == "Hello, world!" + + def test_text_embedding_async_request(self): + """Test asynchronous text embedding request transformation.""" + config = AmazonNovaEmbeddingConfig() + + inference_params = { + "embeddingPurpose": "TEXT_RETRIEVAL", + "embeddingDimension": 3072, + "text": { + "value": "Long text content...", + "segmentationConfig": {"maxLengthChars": 10000} + }, + "output_s3_uri": "s3://my-bucket/output/", + } + + request = config._transform_request( + input="Long text content...", + inference_params=inference_params, + async_invoke_route=True, + model_id="amazon.nova-2-multimodal-embeddings-v1:0", + output_s3_uri="s3://my-bucket/output/", + ) + + assert "modelId" in request + assert "modelInput" in request + assert "outputDataConfig" in request + + model_input = request["modelInput"] + assert model_input["taskType"] == "SEGMENTED_EMBEDDING" + assert "segmentedEmbeddingParams" in model_input + + params = model_input["segmentedEmbeddingParams"] + assert params["embeddingPurpose"] == "TEXT_RETRIEVAL" + assert params["embeddingDimension"] == 3072 + assert params["text"]["segmentationConfig"]["maxLengthChars"] == 10000 + + def test_image_embedding_request(self): + """Test image embedding request transformation.""" + config = AmazonNovaEmbeddingConfig() + + # Mock base64 image data + image_data = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==" + + inference_params = { + "embeddingPurpose": "IMAGE_RETRIEVAL", + "embeddingDimension": 1024, + "image": { + "format": "png", + "source": {"bytes": image_data}, + "detailLevel": "STANDARD_IMAGE" + }, + } + + request = config._transform_request( + input=image_data, + inference_params=inference_params, + async_invoke_route=False, + ) + + params = request["singleEmbeddingParams"] + assert params["embeddingPurpose"] == "IMAGE_RETRIEVAL" + assert params["embeddingDimension"] == 1024 + assert params["image"]["format"] == "png" + assert params["image"]["detailLevel"] == "STANDARD_IMAGE" + assert "source" in params["image"] + assert "bytes" in params["image"]["source"] + + def test_video_embedding_request(self): + """Test video embedding request transformation.""" + config = AmazonNovaEmbeddingConfig() + + inference_params = { + "embeddingPurpose": "VIDEO_RETRIEVAL", + "embeddingDimension": 3072, + "video": { + "format": "mp4", + "source": {"s3Location": {"uri": "s3://my-bucket/video.mp4"}}, + "embeddingMode": "AUDIO_VIDEO_COMBINED" + }, + } + + request = config._transform_request( + input="s3://my-bucket/video.mp4", + inference_params=inference_params, + async_invoke_route=False, + ) + + params = request["singleEmbeddingParams"] + assert params["embeddingPurpose"] == "VIDEO_RETRIEVAL" + assert params["embeddingDimension"] == 3072 + assert params["video"]["format"] == "mp4" + assert params["video"]["embeddingMode"] == "AUDIO_VIDEO_COMBINED" + assert params["video"]["source"]["s3Location"]["uri"] == "s3://my-bucket/video.mp4" + + def test_audio_embedding_request(self): + """Test audio embedding request transformation.""" + config = AmazonNovaEmbeddingConfig() + + inference_params = { + "embeddingPurpose": "AUDIO_RETRIEVAL", + "embeddingDimension": 1024, + "audio": { + "format": "mp3", + "source": {"s3Location": {"uri": "s3://my-bucket/audio.mp3"}} + }, + } + + request = config._transform_request( + input="s3://my-bucket/audio.mp3", + inference_params=inference_params, + async_invoke_route=False, + ) + + params = request["singleEmbeddingParams"] + assert params["embeddingPurpose"] == "AUDIO_RETRIEVAL" + assert params["embeddingDimension"] == 1024 + assert params["audio"]["format"] == "mp3" + assert params["audio"]["source"]["s3Location"]["uri"] == "s3://my-bucket/audio.mp3" + + def test_async_invoke_requires_output_s3_uri(self): + """Test that async invoke requires output_s3_uri.""" + config = AmazonNovaEmbeddingConfig() + + inference_params = { + "embedding_purpose": "GENERIC_INDEX", + } + + with pytest.raises(ValueError, match="output_s3_uri is required"): + config._transform_request( + input="Test text", + inference_params=inference_params, + async_invoke_route=True, + model_id="amazon.nova-2-multimodal-embeddings-v1:0", + output_s3_uri=None, + ) + + def test_default_embedding_purpose(self): + """Test default embedding purpose is GENERIC_INDEX.""" + config = AmazonNovaEmbeddingConfig() + + request = config._transform_request( + input="Test text", + inference_params={}, + async_invoke_route=False, + ) + + params = request["singleEmbeddingParams"] + assert params["embeddingPurpose"] == "GENERIC_INDEX" + + def test_default_embedding_dimension(self): + """Test default embedding dimension is 3072.""" + config = AmazonNovaEmbeddingConfig() + + request = config._transform_request( + input="Test text", + inference_params={}, + async_invoke_route=False, + ) + + params = request["singleEmbeddingParams"] + assert params["embeddingDimension"] == 3072 + + +class TestNovaTransformationResponse: + """Test response transformation for Nova embeddings.""" + + def test_text_embedding_response(self): + """Test text embedding response transformation.""" + config = AmazonNovaEmbeddingConfig() + + response_list = [ + { + "embeddings": [ + { + "embeddingType": "TEXT", + "embedding": [0.1, 0.2, 0.3, 0.4, 0.5], + } + ] + } + ] + + result = config._transform_response(response_list, model="amazon.nova-2-multimodal-embeddings-v1:0") + + assert result.model == "amazon.nova-2-multimodal-embeddings-v1:0" + assert len(result.data) == 1 + assert result.data[0].embedding == [0.1, 0.2, 0.3, 0.4, 0.5] + assert result.data[0].index == 0 + assert result.data[0].object == "embedding" + assert result.usage.total_tokens > 0 + + def test_multiple_embeddings_response(self): + """Test response with multiple embeddings.""" + config = AmazonNovaEmbeddingConfig() + + response_list = [ + { + "embeddings": [ + { + "embeddingType": "TEXT", + "embedding": [0.1, 0.2, 0.3], + } + ] + }, + { + "embeddings": [ + { + "embeddingType": "TEXT", + "embedding": [0.4, 0.5, 0.6], + } + ] + }, + ] + + result = config._transform_response(response_list, model="amazon.nova-2-multimodal-embeddings-v1:0") + + assert len(result.data) == 2 + assert result.data[0].embedding == [0.1, 0.2, 0.3] + assert result.data[1].embedding == [0.4, 0.5, 0.6] + assert result.data[0].index == 0 + assert result.data[1].index == 1 + + def test_video_embedding_response_separate_mode(self): + """Test video embedding response with separate audio/video.""" + config = AmazonNovaEmbeddingConfig() + + response_list = [ + { + "embeddings": [ + { + "embeddingType": "VIDEO", + "embedding": [0.1, 0.2, 0.3], + }, + { + "embeddingType": "AUDIO", + "embedding": [0.4, 0.5, 0.6], + } + ] + } + ] + + result = config._transform_response(response_list, model="amazon.nova-2-multimodal-embeddings-v1:0") + + assert len(result.data) == 2 + assert result.data[0].embedding == [0.1, 0.2, 0.3] + assert result.data[1].embedding == [0.4, 0.5, 0.6] + + def test_async_invoke_response(self): + """Test async invoke response transformation.""" + config = AmazonNovaEmbeddingConfig() + + response = { + "invocationArn": "arn:aws:bedrock:us-east-1:123456789012:async-invoke/abc123" + } + + result = config._transform_async_invoke_response(response, model="amazon.nova-2-multimodal-embeddings-v1:0") + + assert result.model == "amazon.nova-2-multimodal-embeddings-v1:0" + assert len(result.data) == 1 + assert result.data[0].embedding == [] # Empty for async jobs + assert result.usage.total_tokens == 0 + assert hasattr(result, "_hidden_params") + assert hasattr(result._hidden_params, "_invocation_arn") + assert result._hidden_params._invocation_arn == "arn:aws:bedrock:us-east-1:123456789012:async-invoke/abc123" + + +class TestNovaEmbeddingIntegration: + """Integration tests for Nova embeddings through LiteLLM.""" + + @pytest.mark.skip(reason="Requires AWS credentials and actual API calls") + def test_sync_text_embedding_e2e(self): + """End-to-end test for synchronous text embedding.""" + response = litellm.embedding( + model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", + input=["Hello, world!"], + aws_region_name="us-east-1", + ) + + assert response is not None + assert len(response.data) == 1 + assert len(response.data[0].embedding) > 0 + + @pytest.mark.skip(reason="Requires AWS credentials and actual API calls") + def test_async_text_embedding_e2e(self): + """End-to-end test for asynchronous text embedding.""" + response = litellm.embedding( + model="bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0", + input=["Long text content for segmentation..."], + aws_region_name="us-east-1", + output_s3_uri="s3://my-bucket/output/", + segmentation_config={"maxLengthChars": 10000}, + ) + + assert response is not None + assert hasattr(response, "_hidden_params") + assert hasattr(response._hidden_params, "_invocation_arn") + + @pytest.mark.skip(reason="Requires AWS credentials and actual API calls") + def test_image_embedding_e2e(self): + """End-to-end test for image embedding.""" + response = litellm.embedding( + model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", + input=["s3://my-bucket/image.png"], + aws_region_name="us-east-1", + input_type="image", + format="png", + embedding_purpose="IMAGE_RETRIEVAL", + ) + + assert response is not None + assert len(response.data) == 1 + + @pytest.mark.skip(reason="Requires AWS credentials and actual API calls") + def test_video_embedding_e2e(self): + """End-to-end test for video embedding.""" + response = litellm.embedding( + model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", + input=["s3://my-bucket/video.mp4"], + aws_region_name="us-east-1", + input_type="video", + format="mp4", + embedding_mode="AUDIO_VIDEO_COMBINED", + embedding_purpose="VIDEO_RETRIEVAL", + ) + + assert response is not None + assert len(response.data) == 1 + + @pytest.mark.skip(reason="Requires AWS credentials and actual API calls") + def test_different_dimensions(self): + """Test different embedding dimensions.""" + for dimension in [256, 384, 1024, 3072]: + response = litellm.embedding( + model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", + input=["Test text"], + aws_region_name="us-east-1", + dimensions=dimension, + ) + + assert response is not None + assert len(response.data[0].embedding) == dimension + + @pytest.mark.skip(reason="Requires AWS credentials and actual API calls") + def test_different_embedding_purposes(self): + """Test different embedding purposes.""" + purposes = [ + "GENERIC_INDEX", + "GENERIC_RETRIEVAL", + "TEXT_RETRIEVAL", + "CLASSIFICATION", + "CLUSTERING", + ] + + for purpose in purposes: + response = litellm.embedding( + model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", + input=["Test text"], + aws_region_name="us-east-1", + embedding_purpose=purpose, + ) + + assert response is not None + assert len(response.data) == 1 + + +class TestNovaProviderDetection: + """Test provider detection for Nova models.""" + + def test_nova_provider_detection(self): + """Test that Nova provider is correctly detected.""" + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + provider = BaseAWSLLM.get_bedrock_embedding_provider( + "amazon.nova-2-multimodal-embeddings-v1:0" + ) + + # Should detect "amazon" as provider since "nova" is in the model name + # but the provider detection looks at the first part before the dot + assert provider in ["amazon", "nova"] + + def test_nova_in_model_name(self): + """Test that models with 'nova' in the name are detected.""" + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + # Test various Nova model name formats + test_models = [ + "amazon.nova-2-multimodal-embeddings-v1:0", + "us.amazon.nova-2-multimodal-embeddings-v1:0", + ] + + for model in test_models: + provider = BaseAWSLLM.get_bedrock_embedding_provider(model) + assert provider is not None + + +if __name__ == "__main__": + # Run basic transformation tests + print("Running Nova Embedding Transformation Tests...") + + test_request = TestNovaTransformationRequest() + test_request.test_text_embedding_sync_request() + test_request.test_text_embedding_async_request() + test_request.test_image_embedding_request() + test_request.test_video_embedding_request() + test_request.test_audio_embedding_request() + + test_response = TestNovaTransformationResponse() + test_response.test_text_embedding_response() + test_response.test_multiple_embeddings_response() + test_response.test_async_invoke_response() + + print("All transformation tests passed!") + From f0d3c96a8d5a42763798d4f76afa71464d87d97f Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 28 Nov 2025 17:23:15 +0530 Subject: [PATCH 172/248] Add tags and other field in UI logs and add responses api cost tracking --- .../proxy/hooks/proxy_track_cost_callback.py | 17 +- .../openai_passthrough_logging_handler.py | 84 ++++++-- .../pass_through_endpoints.py | 26 ++- ...test_openai_passthrough_logging_handler.py | 185 ++++++++++++++++++ 4 files changed, 292 insertions(+), 20 deletions(-) diff --git a/litellm/proxy/hooks/proxy_track_cost_callback.py b/litellm/proxy/hooks/proxy_track_cost_callback.py index e165f96b66..dab5fb1bfd 100644 --- a/litellm/proxy/hooks/proxy_track_cost_callback.py +++ b/litellm/proxy/hooks/proxy_track_cost_callback.py @@ -80,10 +80,25 @@ class _ProxyDBLogger(CustomLogger): if "litellm_params" not in request_data: request_data["litellm_params"] = {} + + existing_litellm_params = request_data.get("litellm_params", {}) + existing_litellm_metadata = existing_litellm_params.get("metadata", {}) or {} + + # Preserve tags from existing metadata + if existing_litellm_metadata.get("tags"): + existing_metadata["tags"] = existing_litellm_metadata.get("tags") + request_data["litellm_params"]["proxy_server_request"] = ( - request_data.get("proxy_server_request") or {} + request_data.get("proxy_server_request") or existing_litellm_params.get("proxy_server_request") or {} ) request_data["litellm_params"]["metadata"] = existing_metadata + + # Preserve model name and custom_llm_provider + if "model" not in request_data: + request_data["model"] = existing_litellm_params.get("model") or request_data.get("model", "") + if "custom_llm_provider" not in request_data: + request_data["custom_llm_provider"] = existing_litellm_params.get("custom_llm_provider") or request_data.get("custom_llm_provider", "") + await proxy_logging_obj.db_spend_update_writer.update_database( token=user_api_key_dict.api_key, response_cost=0.0, diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py index d6ab121096..6745c559cd 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/openai_passthrough_logging_handler.py @@ -91,6 +91,21 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): and "/v1/images/edits" in parsed_url.path ) + @staticmethod + def is_openai_responses_route(url_route: str) -> bool: + """Check if the URL route is an OpenAI responses API endpoint.""" + if not url_route: + return False + parsed_url = urlparse(url_route) + return bool( + parsed_url.hostname + and ( + "api.openai.com" in parsed_url.hostname + or "openai.azure.com" in parsed_url.hostname + ) + and ("/v1/responses" in parsed_url.path or "/responses" in parsed_url.path) + ) + def _get_user_from_metadata( self, passthrough_logging_payload: PassthroughStandardLoggingPayload, @@ -187,7 +202,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): **kwargs, ) -> PassThroughEndpointLoggingTypedDict: """ - Handle OpenAI passthrough logging with cost tracking for chat completions, image generation, and image editing. + Handle OpenAI passthrough logging with cost tracking for chat completions, image generation, image editing, and responses API. """ # Check if this is a supported endpoint for cost tracking is_chat_completions = ( @@ -199,8 +214,11 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): is_image_editing = ( OpenAIPassthroughLoggingHandler.is_openai_image_editing_route(url_route) ) + is_responses = ( + OpenAIPassthroughLoggingHandler.is_openai_responses_route(url_route) + ) - if not (is_chat_completions or is_image_generation or is_image_editing): + if not (is_chat_completions or is_image_generation or is_image_editing or is_responses): # For unsupported endpoints, return None to let the system fall back to generic behavior return { "result": None, @@ -232,9 +250,13 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): litellm_model_response: Optional[Union[ModelResponse, TextCompletionResponse, ImageResponse]] = None handler_instance = OpenAIPassthroughLoggingHandler() + custom_llm_provider = kwargs.get("custom_llm_provider", "openai") + if is_chat_completions: # Handle chat completions with existing logic provider_config = handler_instance.get_provider_config(model=model) + # Preserve existing litellm_params to maintain metadata tags + existing_litellm_params = kwargs.get("litellm_params", {}) or {} litellm_model_response = provider_config.transform_response( raw_response=httpx_response, model_response=litellm.ModelResponse(), @@ -247,14 +269,14 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): encoding=litellm.encoding, json_mode=request_body.get("response_format", {}).get("type") == "json_object", - litellm_params={}, + litellm_params=existing_litellm_params, ) # Calculate cost using LiteLLM's cost calculator response_cost = litellm.completion_cost( completion_response=litellm_model_response, model=model, - custom_llm_provider="openai", + custom_llm_provider=custom_llm_provider, ) elif is_image_generation: # Handle image generation cost calculation @@ -306,11 +328,36 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): if not hasattr(litellm_model_response, "_hidden_params"): litellm_model_response._hidden_params = {} litellm_model_response._hidden_params["response_cost"] = response_cost + elif is_responses: + # Handle responses API cost calculation + provider_config = handler_instance.get_provider_config(model=model) + existing_litellm_params = kwargs.get("litellm_params", {}) or {} + litellm_model_response = provider_config.transform_response( + raw_response=httpx_response, + model_response=litellm.ModelResponse(), + model=model, + messages=request_body.get("messages", []), + logging_obj=logging_obj, + optional_params=request_body.get("optional_params", {}), + api_key="", + request_data=request_body, + encoding=litellm.encoding, + json_mode=False, + litellm_params=existing_litellm_params, + ) + + # Calculate cost using LiteLLM's cost calculator with responses call type + response_cost = litellm.completion_cost( + completion_response=litellm_model_response, + model=model, + custom_llm_provider=custom_llm_provider, + call_type="responses", + ) # Update kwargs with cost information kwargs["response_cost"] = response_cost kwargs["model"] = model - kwargs["custom_llm_provider"] = "openai" + kwargs["custom_llm_provider"] = custom_llm_provider # Extract user information for tracking passthrough_logging_payload: Optional[ @@ -321,10 +368,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): passthrough_logging_payload=passthrough_logging_payload, ) if user: - kwargs.setdefault("litellm_params", {}) - kwargs["litellm_params"].update( - {"proxy_server_request": {"body": {"user": user}}} - ) + kwargs["litellm_params"].setdefault("proxy_server_request", {}).setdefault("body", {})["user"] = user # Create standard logging object if litellm_model_response is not None: @@ -339,7 +383,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): # Update logging object with cost information logging_obj.model_call_details["model"] = model - logging_obj.model_call_details["custom_llm_provider"] = "openai" + logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider logging_obj.model_call_details["response_cost"] = response_cost endpoint_type = ( @@ -481,18 +525,27 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): "kwargs": {}, } + custom_llm_provider = litellm_logging_obj.model_call_details.get( + "custom_llm_provider", "openai" + ) # Calculate cost using LiteLLM's cost calculator response_cost = litellm.completion_cost( completion_response=complete_response, model=model, - custom_llm_provider="openai", + custom_llm_provider=custom_llm_provider, ) + # Preserve existing litellm_params to maintain metadata tags + existing_litellm_params = litellm_logging_obj.model_call_details.get( + "litellm_params", {} + ) or {} + # Prepare kwargs for logging kwargs = { "response_cost": response_cost, "model": model, - "custom_llm_provider": "openai", + "custom_llm_provider": custom_llm_provider, + "litellm_params": existing_litellm_params.copy(), } # Extract user information for tracking @@ -506,10 +559,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): passthrough_logging_payload=passthrough_logging_payload, ) if user: - kwargs.setdefault("litellm_params", {}) - kwargs["litellm_params"].update( - {"proxy_server_request": {"body": {"user": user}}} - ) + kwargs["litellm_params"].setdefault("proxy_server_request", {}).setdefault("body", {})["user"] = user # Create standard logging object get_standard_logging_object_payload( @@ -523,7 +573,7 @@ class OpenAIPassthroughLoggingHandler(BasePassthroughLoggingHandler): # Update logging object with cost information litellm_logging_obj.model_call_details["model"] = model - litellm_logging_obj.model_call_details["custom_llm_provider"] = "openai" + litellm_logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider litellm_logging_obj.model_call_details["response_cost"] = response_cost verbose_proxy_logger.debug( diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 780a3d6dcc..5b47a8af7a 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -735,6 +735,12 @@ async def pass_through_request( # noqa: PLR0915 logging_obj=logging_obj, ) + # Store custom_llm_provider in kwargs and logging object if provided + if custom_llm_provider: + kwargs["custom_llm_provider"] = custom_llm_provider + logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider + logging_obj.model_call_details["litellm_params"] = kwargs.get("litellm_params", {}) + # done for supporting 'parallel_request_limiter.py' with pass-through endpoints logging_obj.update_environment_variables( model="unknown", @@ -923,6 +929,12 @@ async def pass_through_request( # noqa: PLR0915 if kwargs: for key, value in kwargs.items(): request_payload[key] = value + + if "model" not in request_payload and _parsed_body and isinstance(_parsed_body, dict): + request_payload["model"] = _parsed_body.get("model", "") + if "custom_llm_provider" not in request_payload and custom_llm_provider: + request_payload["custom_llm_provider"] = custom_llm_provider + await proxy_logging_obj.post_call_failure_hook( user_api_key_dict=user_api_key_dict, original_exception=e, @@ -957,11 +969,21 @@ def _update_metadata_with_tags_in_header(request: Request, metadata: dict) -> di """ If tags are in the request headers, add them to the metadata - Used for google and vertex JS SDKs + Used for google and vertex JS SDKs, and Azure passthrough + Checks both 'tags' and 'x-litellm-tags' headers """ + # Initialize tags list if it doesn't exist + if "tags" not in metadata: + metadata["tags"] = [] + + # Check for 'tags' header first _tags = request.headers.get("tags") if _tags: - metadata["tags"] = _tags.split(",") + metadata["tags"].extend([tag.strip() for tag in _tags.split(",")]) + + _tags = request.headers.get("x-litellm-tags") + if _tags: + metadata["tags"].extend([tag.strip() for tag in _tags.split(",")]) return metadata diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py index 789b16f951..becb34409b 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_openai_passthrough_logging_handler.py @@ -130,6 +130,19 @@ class TestOpenAIPassthroughLoggingHandler: assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("http://localhost:4000/openai/v1/images/edits") == False assert OpenAIPassthroughLoggingHandler.is_openai_image_editing_route("") == False + def test_is_openai_responses_route(self): + """Test OpenAI responses API route detection""" + # Positive cases + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("https://api.openai.com/v1/responses") == True + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("https://openai.azure.com/v1/responses") == True + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("https://api.openai.com/responses") == True + + # Negative cases + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("https://api.openai.com/v1/chat/completions") == False + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("https://api.openai.com/v1/images/generations") == False + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("http://localhost:4000/openai/v1/responses") == False + assert OpenAIPassthroughLoggingHandler.is_openai_responses_route("") == False + @patch('litellm.completion_cost') @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') def test_openai_passthrough_handler_success(self, mock_get_standard_logging, mock_completion_cost): @@ -369,6 +382,178 @@ class TestOpenAIPassthroughLoggingHandler: handler = OpenAIPassthroughLoggingHandler() assert handler.get_provider_config("gpt-4o") is not None + @patch('litellm.completion_cost') + @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') + def test_azure_passthrough_tags_metadata_model_provider(self, mock_get_standard_logging, mock_completion_cost): + """Test that tags, metadata, model, and custom_llm_provider are preserved for Azure passthrough in UI""" + # Arrange + mock_completion_cost.return_value = 0.000045 + mock_get_standard_logging.return_value = {"test": "logging_payload"} + + mock_httpx_response = self._create_mock_httpx_response() + mock_logging_obj = self._create_mock_logging_obj() + + # Create payload with metadata tags + passthrough_payload = PassthroughStandardLoggingPayload( + url="https://openai.azure.com/v1/chat/completions", + request_body={ + "model": "gpt-4o", + "messages": [{"role": "user", "content": "Hello"}] + }, + request_method="POST", + ) + + # Set up kwargs with existing litellm_params containing metadata tags + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + "custom_llm_provider": "azure", # Azure passthrough + "litellm_params": { + "metadata": { + "tags": ["production", "azure-deployment"], + "user_id": "user_123" + }, + "proxy_server_request": { + "body": { + "user": "test_user" + } + } + } + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=self.mock_openai_response, + logging_obj=mock_logging_obj, + url_route="https://openai.azure.com/v1/chat/completions", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello"}]}, + **kwargs + ) + + # Assert - Verify tags, model, and custom_llm_provider are preserved + assert result is not None + assert "kwargs" in result + + # Verify model and custom_llm_provider are set correctly + assert result["kwargs"]["model"] == "gpt-4o" + assert result["kwargs"]["custom_llm_provider"] == "azure" # Should preserve Azure, not default to "openai" + assert result["kwargs"]["response_cost"] == 0.000045 + + # Verify metadata tags are preserved in litellm_params + assert "litellm_params" in result["kwargs"] + assert "metadata" in result["kwargs"]["litellm_params"] + assert "tags" in result["kwargs"]["litellm_params"]["metadata"] + assert result["kwargs"]["litellm_params"]["metadata"]["tags"] == ["production", "azure-deployment"] + assert result["kwargs"]["litellm_params"]["metadata"]["user_id"] == "user_123" + + # Verify logging object has correct values for UI display + assert mock_logging_obj.model_call_details["model"] == "gpt-4o" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "azure" + assert mock_logging_obj.model_call_details["response_cost"] == 0.000045 + + # Verify cost calculation was called with correct custom_llm_provider + mock_completion_cost.assert_called_once() + call_args = mock_completion_cost.call_args + assert call_args[1]["custom_llm_provider"] == "azure" + + @patch('litellm.completion_cost') + @patch('litellm.litellm_core_utils.litellm_logging.get_standard_logging_object_payload') + @patch('litellm.proxy.pass_through_endpoints.llm_provider_handlers.openai_passthrough_logging_handler.OpenAIPassthroughLoggingHandler.get_provider_config') + def test_responses_api_cost_tracking(self, mock_get_provider_config, mock_get_standard_logging, mock_completion_cost): + """Test cost tracking for responses API route""" + # Arrange + mock_completion_cost.return_value = 0.000050 + mock_get_standard_logging.return_value = {"test": "logging_payload"} + + # Mock the provider config's transform_response to return a valid ModelResponse + from litellm import ModelResponse + mock_model_response = ModelResponse( + id="resp_abc123", + model="gpt-4o-2024-08-06", + choices=[{ + "message": { + "role": "assistant", + "content": "Hello! How can I help you today?" + } + }], + usage={ + "prompt_tokens": 20, + "completion_tokens": 15, + "total_tokens": 35 + } + ) + + mock_provider_config = MagicMock() + mock_provider_config.transform_response.return_value = mock_model_response + mock_get_provider_config.return_value = mock_provider_config + + # Mock responses API response + mock_responses_response = { + "id": "resp_abc123", + "object": "response", + "created": 1677652288, + "model": "gpt-4o-2024-08-06", + "output": [ + { + "type": "text", + "text": "Hello! How can I help you today?" + } + ], + "usage": { + "input_tokens": 20, + "output_tokens": 15 + } + } + + mock_httpx_response = self._create_mock_httpx_response(mock_responses_response) + mock_logging_obj = self._create_mock_logging_obj() + passthrough_payload = self._create_passthrough_logging_payload() + + kwargs = { + "passthrough_logging_payload": passthrough_payload, + "model": "gpt-4o", + "custom_llm_provider": "openai", + } + + # Act + result = OpenAIPassthroughLoggingHandler.openai_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=mock_responses_response, + logging_obj=mock_logging_obj, + url_route="https://api.openai.com/v1/responses", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body={"model": "gpt-4o", "input": "Tell me about AI"}, + **kwargs + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + assert result["kwargs"]["response_cost"] == 0.000050 + assert result["kwargs"]["model"] == "gpt-4o" + assert result["kwargs"]["custom_llm_provider"] == "openai" + + # Verify cost calculation was called with responses call type + mock_completion_cost.assert_called_once() + call_args = mock_completion_cost.call_args + assert call_args[1]["call_type"] == "responses" + assert call_args[1]["model"] == "gpt-4o" + assert call_args[1]["custom_llm_provider"] == "openai" + + # Verify logging object was updated + assert mock_logging_obj.model_call_details["response_cost"] == 0.000050 + assert mock_logging_obj.model_call_details["model"] == "gpt-4o" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "openai" + class TestOpenAIPassthroughIntegration: """Integration tests for OpenAI passthrough cost tracking""" From eab0ec95f01a61f14c3d441fdcd7960a49a003fd Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 28 Nov 2025 18:06:53 +0530 Subject: [PATCH 173/248] Fix async get request --- litellm/batches/main.py | 44 +++++++++++++------ .../embed/amazon_nova_transformation.py | 2 + litellm/llms/bedrock/embed/embedding.py | 39 +++++++++++----- 3 files changed, 60 insertions(+), 25 deletions(-) diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 995c45b925..57a9857dd6 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -1045,28 +1045,44 @@ def _handle_async_invoke_status( # Transform response to a LiteLLMBatch object from litellm.types.utils import LiteLLMBatch + # Normalize status to lowercase (AWS returns 'Completed', 'Failed', etc.) + aws_status_raw = status_response.get("status", "") + aws_status_lower = aws_status_raw.lower() + # Map AWS status values to LiteLLM expected values + status_mapping = { + "completed": "completed", + "failed": "failed", + "inprogress": "in_progress", + "in_progress": "in_progress", + } + normalized_status = status_mapping.get(aws_status_lower, aws_status_lower) + + # Get output S3 URI safely + output_s3_uri = "" + try: + output_s3_uri = status_response["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"] + except (KeyError, TypeError): + pass + + # Use BedrockBatchesConfig's timestamp parsing method (expects raw AWS status string) + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + created_at, in_progress_at, completed_at, failed_at, _, _ = BedrockBatchesConfig()._parse_timestamps_and_status(status_response, aws_status_raw) result = LiteLLMBatch( id=status_response["invocationArn"], object="batch", - status=status_response["status"], - created_at=status_response["submitTime"], - in_progress_at=status_response["lastModifiedTime"], - completed_at=status_response.get("endTime"), - failed_at=( - status_response.get("endTime") - if status_response["status"] == "failed" - else None - ), + status=normalized_status, + created_at=created_at, + in_progress_at=in_progress_at, + completed_at=completed_at, + failed_at=failed_at, request_counts=BatchRequestCounts( total=1, - completed=1 if status_response["status"] == "completed" else 0, - failed=1 if status_response["status"] == "failed" else 0, + completed=1 if normalized_status == "completed" else 0, + failed=1 if normalized_status == "failed" else 0, ), metadata=dict( **{ - "output_file_id": status_response["outputDataConfig"][ - "s3OutputDataConfig" - ]["s3Uri"], + "output_file_id": output_s3_uri, "failure_message": status_response.get("failureMessage") or "", "model_arn": status_response["modelArn"], } diff --git a/litellm/llms/bedrock/embed/amazon_nova_transformation.py b/litellm/llms/bedrock/embed/amazon_nova_transformation.py index 97652175a9..ada49d0ff2 100644 --- a/litellm/llms/bedrock/embed/amazon_nova_transformation.py +++ b/litellm/llms/bedrock/embed/amazon_nova_transformation.py @@ -83,6 +83,8 @@ class AmazonNovaEmbeddingConfig: # Start with inference_params (user-provided params) embedding_params = inference_params.copy() + embedding_params.pop("output_s3_uri", None) + # Map OpenAI dimensions to embeddingDimension if provided if "dimensions" in embedding_params: embedding_params["embeddingDimension"] = embedding_params.pop("dimensions") diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index be2bfcd70e..c9eea516eb 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -603,22 +603,39 @@ class BedrockEmbedding(BaseAWSLLM): aws_region_name=aws_region_name, ) - # Construct the status check URL - status_url = f"{endpoint_url}/async-invoke/{invocation_arn}" - # Prepare headers + from urllib.parse import quote + + # Encode the ARN for use in URL path + encoded_arn = quote(invocation_arn, safe="") + status_url = f"{endpoint_url.rstrip('/')}/async-invoke/{encoded_arn}" + + # Prepare headers for GET request headers = {"Content-Type": "application/json"} - # Get AWS signed headers - prepped = self.get_request_headers( # type: ignore - credentials=credentials, - aws_region_name=aws_region_name, - extra_headers=None, - endpoint_url=status_url, - data="", # GET request, no body + # Use AWSRequest directly for GET requests (get_request_headers hardcodes POST) + try: + from botocore.auth import SigV4Auth + from botocore.awsrequest import AWSRequest + except ImportError: + raise ImportError( + "Missing boto3 to call bedrock. Run 'pip install boto3'." + ) + + # Create AWSRequest with GET method and encoded URL + request = AWSRequest( + method="GET", + url=status_url, + data=None, # GET request, no body headers=headers, - api_key=None, ) + + # Sign the request - SigV4Auth will create canonical string from request URL + sigv4 = SigV4Auth(credentials, "bedrock", aws_region_name) + sigv4.add_auth(request) + + # Prepare the request + prepped = request.prepare() # LOGGING if logging_obj is not None: From 4cf7a74e6092b7f73d4da12f1bdd3d57e3857a86 Mon Sep 17 00:00:00 2001 From: abi_jey Date: Fri, 28 Nov 2025 14:27:57 +0000 Subject: [PATCH 174/248] fix: Azure OpenAI GA path relies soley on model paramter as deployment --- litellm/llms/azure/realtime/handler.py | 10 ++++++---- .../azure/realtime/test_azure_realtime_handler.py | 13 +++++++++---- 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py index 0dc42dad43..217a05c83a 100644 --- a/litellm/llms/azure/realtime/handler.py +++ b/litellm/llms/azure/realtime/handler.py @@ -12,6 +12,7 @@ from ....litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from ....litellm_core_utils.realtime_streaming import RealTimeStreaming from ....llms.custom_httpx.http_handler import get_shared_realtime_ssl_context from ..azure import AzureChatCompletion +from litellm._logging import verbose_proxy_logger # BACKEND_WS_URL = "ws://localhost:8080/v1/realtime?model=gpt-4o-realtime-preview-2024-10-01" @@ -51,18 +52,18 @@ class AzureOpenAIRealtime(AzureChatCompletion): Examples: beta/default: "wss://.../openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview" - GA/v1: "wss://.../openai/v1/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview" + GA/v1: "wss://.../openai/v1/realtime?model=gpt-realtime-deployment" """ api_base = api_base.replace("https://", "wss://") # Determine path based on realtime_protocol if realtime_protocol in ("GA", "v1"): - path = "/openai/v1/realtime" + path = "/openai/v1/realtime" + return f"{api_base}{path}?model={model}" else: # Default to beta path for backwards compatibility path = "/openai/realtime" - - return f"{api_base}{path}?api-version={api_version}&deployment={model}" + return f"{api_base}{path}?api-version={api_version}&deployment={model}" async def async_realtime( self, @@ -107,4 +108,5 @@ class AzureOpenAIRealtime(AzureChatCompletion): except websockets.exceptions.InvalidStatusCode as e: # type: ignore await websocket.close(code=e.status_code, reason=str(e)) except Exception: + verbose_proxy_logger.exception("Error in AzureOpenAIRealtime.async_realtime") pass diff --git a/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py b/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py index ca8d01e158..2a110c8f9a 100644 --- a/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py +++ b/tests/test_litellm/llms/azure/realtime/test_azure_realtime_handler.py @@ -117,6 +117,7 @@ async def test_construct_url_beta_protocol_explicit(): async def test_construct_url_ga_protocol(): """ Test that realtime_protocol='GA' uses /openai/v1/realtime (GA path). + GA path uses ?model= instead of ?api-version=&deployment= format. """ from litellm.llms.azure.realtime.handler import AzureOpenAIRealtime @@ -132,8 +133,10 @@ async def test_construct_url_ga_protocol(): assert "/openai/v1/realtime?" in url # Ensure it doesn't have both paths assert url.count("/realtime") == 1 - assert "api-version=2024-10-01-preview" in url - assert "deployment=gpt-4o-realtime-preview" in url + # GA path uses model= query param, not api-version and deployment + assert "model=gpt-4o-realtime-preview" in url + assert "api-version" not in url + assert "deployment" not in url @pytest.mark.asyncio @@ -203,8 +206,10 @@ async def test_async_realtime_uses_ga_protocol_end_to_end(): called_url = mock_ws_connect.call_args[0][0] assert "/openai/v1/realtime" in called_url assert called_url.startswith("wss://") - assert "api-version=2024-10-01-preview" in called_url - assert "deployment=gpt-4o-realtime-preview" in called_url + # GA path uses model= query param, not api-version and deployment + assert "model=gpt-4o-realtime-preview" in called_url + assert "api-version" not in called_url + assert "deployment" not in called_url @pytest.mark.asyncio From af8f1475bfd709e7126847e15831abe17a72df2b Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 28 Nov 2025 21:12:42 +0530 Subject: [PATCH 175/248] fix PLR0915 --- litellm/llms/bedrock/embed/embedding.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/bedrock/embed/embedding.py b/litellm/llms/bedrock/embed/embedding.py index c9eea516eb..7152d7ce15 100644 --- a/litellm/llms/bedrock/embed/embedding.py +++ b/litellm/llms/bedrock/embed/embedding.py @@ -377,7 +377,7 @@ class BedrockEmbedding(BaseAWSLLM): is_async_invoke=is_async_invoke, ) - def embeddings( + def embeddings( # noqa: PLR0915 self, model: str, input: List[str], From 9d058398dfdacf65791cea3cd3ed34ee0427d8e1 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Fri, 28 Nov 2025 21:41:25 +0530 Subject: [PATCH 176/248] Fix pegasus response and add doc --- docs/my-website/docs/providers/bedrock.md | 127 +++++++++++++++ ...mazon_twelvelabs_pegasus_transformation.py | 151 +++++++++++++++++- .../base_invoke_transformation.py | 22 +++ .../test_twelvelabs_pegasus_transformation.py | 4 +- 4 files changed, 301 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index 9e22f67527..a9ac85a757 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -1683,6 +1683,131 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ +## TwelveLabs Pegasus - Video Understanding + +TwelveLabs Pegasus 1.2 is a video understanding model that can analyze and describe video content. LiteLLM supports this model through Bedrock's `/invoke` endpoint. + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/us.twelvelabs.pegasus-1-2-v1:0`, `bedrock/eu.twelvelabs.pegasus-1-2-v1:0` | +| Provider Documentation | [TwelveLabs Pegasus Docs ↗](https://docs.twelvelabs.io/docs/models/pegasus) | +| Supported Parameters | `max_tokens`, `temperature`, `response_format` | +| Media Input | S3 URI or base64-encoded video | + +### Supported Features + +- **Video Analysis**: Analyze video content from S3 or base64 input +- **Structured Output**: Support for JSON schema response format +- **S3 Integration**: Support for S3 video URLs with bucket owner specification + +### Usage with S3 Video + + + + +```python title="TwelveLabs Pegasus SDK Usage" showLineNumbers +from litellm import completion +import os + +# Set AWS credentials +os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key" +os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key" +os.environ["AWS_REGION_NAME"] = "us-east-1" + +response = completion( + model="bedrock/us.twelvelabs.pegasus-1-2-v1:0", + messages=[{"role": "user", "content": "Describe what happens in this video."}], + mediaSource={ + "s3Location": { + "uri": "s3://your-bucket/video.mp4", + "bucketOwner": "123456789012", # 12-digit AWS account ID + } + }, + temperature=0.2 +) + +print(response.choices[0].message.content) +``` + + + + + +**1. Add to config** + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: pegasus-video + litellm_params: + model: bedrock/us.twelvelabs.pegasus-1-2-v1:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/AWS_REGION_NAME +``` + +**2. Start proxy** + +```bash title="Start LiteLLM Proxy" showLineNumbers +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash title="Test Pegasus via Proxy" showLineNumbers +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "pegasus-video", + "messages": [ + { + "role": "user", + "content": "Describe what happens in this video." + } + ], + "mediaSource": { + "s3Location": { + "uri": "s3://your-bucket/video.mp4", + "bucketOwner": "123456789012" + } + }, + "temperature": 0.2 + }' +``` + + + + +### Usage with Base64 Video + +You can also pass video content directly as base64: + +```python title="Base64 Video Input" showLineNumbers +from litellm import completion +import base64 + +# Read video file and encode to base64 +with open("video.mp4", "rb") as video_file: + video_base64 = base64.b64encode(video_file.read()).decode("utf-8") + +response = completion( + model="bedrock/us.twelvelabs.pegasus-1-2-v1:0", + messages=[{"role": "user", "content": "What is happening in this video?"}], + mediaSource={ + "base64String": video_base64 + }, + temperature=0.2, +) + +print(response.choices[0].message.content) +``` + +### Important Notes + +- **Response Format**: The model supports structured output via `response_format` with JSON schema + ## Provisioned throughput models To use provisioned throughput Bedrock models pass - `model=bedrock/`, example `model=bedrock/anthropic.claude-v2`. Set `model` to any of the [Supported AWS models](#supported-aws-bedrock-models) @@ -1743,6 +1868,8 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re | Meta Llama 2 Chat 70b | `completion(model='bedrock/meta.llama2-70b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| TwelveLabs Pegasus 1.2 (US) | `completion(model='bedrock/us.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| TwelveLabs Pegasus 1.2 (EU) | `completion(model='bedrock/eu.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | ## Bedrock Embedding diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py index 7b72968ea3..62e98f7472 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py @@ -5,16 +5,32 @@ Reference: https://docs.twelvelabs.io/docs/models/pegasus """ -from typing import Any, Dict, List, Optional +import json +import time +from typing import TYPE_CHECKING, Any, Dict, List, Optional +import httpx + +import litellm +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.core_helpers import map_finish_reason from litellm.llms.base_llm.base_utils import type_to_response_format_param from litellm.llms.base_llm.chat.transformation import BaseConfig from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) +from litellm.llms.bedrock.common_utils import BedrockError from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import ModelResponse, Usage from litellm.utils import get_base64_str +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): """ @@ -53,7 +69,35 @@ class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): return optional_params def _normalize_response_format(self, value: Any) -> Any: + """Normalize response_format to TwelveLabs format. + + TwelveLabs expects: + { + "jsonSchema": {...} + } + + But OpenAI format is: + { + "type": "json_schema", + "json_schema": { + "name": "...", + "schema": {...} + } + } + """ if isinstance(value, dict): + # If it has json_schema field, extract and transform it + if "json_schema" in value: + json_schema = value["json_schema"] + # Extract the schema if nested + if isinstance(json_schema, dict) and "schema" in json_schema: + return {"jsonSchema": json_schema["schema"]} + # Otherwise use json_schema directly + return {"jsonSchema": json_schema} + # If it already has jsonSchema, return as is + if "jsonSchema" in value: + return value + # Otherwise return the dict as is return value return type_to_response_format_param(response_format=value) or value @@ -72,9 +116,18 @@ class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): if media_source is not None: request_data["mediaSource"] = media_source - for key in ("temperature", "maxOutputTokens", "responseFormat"): + # Handle temperature and maxOutputTokens + for key in ("temperature", "maxOutputTokens"): if key in optional_params: request_data[key] = optional_params.get(key) + + # Handle responseFormat - transform to TwelveLabs format + if "responseFormat" in optional_params: + response_format = optional_params["responseFormat"] + transformed_format = self._normalize_response_format(response_format) + if transformed_format: + request_data["responseFormat"] = transformed_format + return request_data def _build_media_source(self, optional_params: dict) -> Optional[dict]: @@ -131,3 +184,97 @@ class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): prompt_parts.append(f"{role}: {content}") return "\n".join(part for part in prompt_parts if part).strip() + def transform_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ModelResponse: + """ + Transform TwelveLabs Pegasus response to LiteLLM format. + + TwelveLabs response format: + { + "message": "...", + "finishReason": "stop" | "length" + } + + LiteLLM format: + ModelResponse with choices[0].message.content and finish_reason + """ + try: + completion_response = raw_response.json() + except Exception as e: + raise BedrockError( + message=f"Error parsing response: {raw_response.text}, error: {str(e)}", + status_code=raw_response.status_code, + ) + + verbose_logger.debug( + "twelvelabs pegasus response: %s", + json.dumps(completion_response, indent=4, default=str), + ) + + # Extract message content + message_content = completion_response.get("message", "") + + # Extract finish reason and map to LiteLLM format + finish_reason_raw = completion_response.get("finishReason", "stop") + finish_reason = map_finish_reason(finish_reason_raw) + + # Set the response content + try: + if ( + message_content + and hasattr(model_response.choices[0], "message") + and getattr(model_response.choices[0].message, "tool_calls", None) is None + ): + model_response.choices[0].message.content = message_content # type: ignore + model_response.choices[0].finish_reason = finish_reason + else: + raise Exception("Unable to set message content") + except Exception as e: + raise BedrockError( + message=f"Error setting response content: {str(e)}. Response: {completion_response}", + status_code=raw_response.status_code, + ) + + # Calculate usage from headers + bedrock_input_tokens = raw_response.headers.get( + "x-amzn-bedrock-input-token-count", None + ) + bedrock_output_tokens = raw_response.headers.get( + "x-amzn-bedrock-output-token-count", None + ) + + prompt_tokens = int( + bedrock_input_tokens or litellm.token_counter(messages=messages) + ) + + completion_tokens = int( + bedrock_output_tokens + or litellm.token_counter( + text=model_response.choices[0].message.content, # type: ignore + count_response_tokens=True, + ) + ) + + model_response.created = int(time.time()) + model_response.model = model + usage = Usage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + total_tokens=prompt_tokens + completion_tokens, + ) + setattr(model_response, "usage", usage) + + return model_response + diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index e6146f1064..6c389ff3b7 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -250,6 +250,14 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in inference_params[k] = v request_data = {"prompt": prompt, **inference_params} + elif provider == "twelvelabs": + return litellm.AmazonTwelveLabsPegasusConfig().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) else: raise BedrockError( status_code=404, @@ -321,6 +329,20 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): litellm_params=litellm_params, encoding=encoding, ) + elif provider == "twelvelabs": + return litellm.AmazonTwelveLabsPegasusConfig().transform_response( + model=model, + raw_response=raw_response, + model_response=model_response, + logging_obj=logging_obj, + request_data=request_data, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + encoding=encoding, + api_key=api_key, + json_mode=json_mode, + ) elif provider == "ai21": outputText = ( completion_response.get("completions")[0].get("data").get("text") diff --git a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_twelvelabs_pegasus_transformation.py b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_twelvelabs_pegasus_transformation.py index 9063c0f1c9..f2cf6f9857 100644 --- a/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_twelvelabs_pegasus_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_twelvelabs_pegasus_transformation.py @@ -38,7 +38,9 @@ def test_map_openai_params_translates_fields(): assert optional_params["maxOutputTokens"] == 20 assert optional_params["temperature"] == 0.6 assert "responseFormat" in optional_params - assert optional_params["responseFormat"]["json_schema"]["name"] == "video_schema" + # TwelveLabs format: responseFormat contains jsonSchema directly (not json_schema) + assert "jsonSchema" in optional_params["responseFormat"] + assert optional_params["responseFormat"]["jsonSchema"]["type"] == "object" def test_transform_request_includes_base64_media(): From b85df0b1fb38abfc01255db50f49f066bf61ae14 Mon Sep 17 00:00:00 2001 From: hxomer <164746029+hxomer@users.noreply.github.com> Date: Fri, 28 Nov 2025 18:22:19 +0200 Subject: [PATCH 177/248] Better handle anonymization (#17207) * Better handle anonymization * Fix tests --- .../guardrails/guardrail_hooks/aim/aim.py | 37 +++++-------------- tests/local_testing/test_aim_guardrails.py | 24 ++++++------ 2 files changed, 22 insertions(+), 39 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py b/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py index 3a0b0c3202..7711a93499 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py +++ b/litellm/proxy/guardrails/guardrail_hooks/aim/aim.py @@ -117,7 +117,7 @@ class AimGuardrail(CustomGuardrail): self._handle_block_action(res["analysis_result"], required_action) elif action_type == "anonymize_action": return self._anonymize_request( - res["analysis_result"], required_action, data + res, data ) else: verbose_proxy_logger.error(f"Aim: {action_type} action") @@ -133,27 +133,18 @@ class AimGuardrail(CustomGuardrail): raise HTTPException(status_code=400, detail=detection_message) def _anonymize_request( - self, analysis_result: Any, required_action: Any, data: dict + self, res: Any, data: dict ) -> dict: verbose_proxy_logger.info("Aim: anonymize action") - redaction_result = required_action and required_action.get( - "chat_redaction_result" - ) - if not redaction_result: + redacted_chat = res.get("redacted_chat") + if not redacted_chat: return data - if analysis_result and analysis_result.get("session_entities"): - self._set_dlp_entities(analysis_result.get("session_entities")) data["messages"] = [ - { - "role": redaction_result["redacted_new_message"]["role"], - "content": redaction_result["redacted_new_message"]["content"], - } - ] + [ { "role": message["role"], "content": message["content"], } - for message in redaction_result["all_redacted_messages"] + for message in redacted_chat["all_redacted_messages"] ] return data @@ -185,7 +176,11 @@ class AimGuardrail(CustomGuardrail): return self._handle_block_action_on_output( res["analysis_result"], required_action ) - return self._deanonymize_output(output) + redacted_chat = res.get("redacted_chat", None) + + if action_type and action_type == "anonymize_action" and redacted_chat: + return {"redacted_output": redacted_chat["all_redacted_messages"][-1]["content"]} + return {"redacted_output": output} def _handle_block_action_on_output( self, analysis_result: Any, required_action: Any @@ -199,15 +194,6 @@ class AimGuardrail(CustomGuardrail): ) return {"detection_message": detection_message} - def _deanonymize_output(self, output: str) -> dict | None: - try: - for entity in self.dlp_entities: - output = output.replace(f"[{entity['name']}]", entity["content"]) - return {"redacted_output": output} - except Exception as e: - verbose_proxy_logger.error(f"Aim: Error while redacting output: {e}") - return None - def _build_aim_headers( self, *, @@ -323,9 +309,6 @@ class AimGuardrail(CustomGuardrail): await websocket.send(chunk) await websocket.send(json.dumps({"done": True})) - def _set_dlp_entities(self, entities: list[dict]) -> None: - self.dlp_entities = entities[: self._max_dlp_entities] - @staticmethod def get_config_model() -> Optional[Type["GuardrailConfigModel"]]: from litellm.types.proxy.guardrails.guardrail_hooks.aim import ( diff --git a/tests/local_testing/test_aim_guardrails.py b/tests/local_testing/test_aim_guardrails.py index b271875c2e..f24b74e511 100644 --- a/tests/local_testing/test_aim_guardrails.py +++ b/tests/local_testing/test_aim_guardrails.py @@ -443,23 +443,23 @@ response_with_detections = Response( "required_action": { "action_type": "anonymize_action", "policy_name": "PII", - "chat_redaction_result": { - "all_redacted_messages": [ - { - "content": "Hi my name is [NAME_1]", - "role": "user", - "additional_contents": [], - "received_message_id": "0", - "extra_fields": {}, - } - ], - "redacted_new_message": { + }, + "redacted_chat": { + "all_redacted_messages": [ + { "content": "Hi my name is [NAME_1]", "role": "user", "additional_contents": [], "received_message_id": "0", "extra_fields": {}, - }, + } + ], + "redacted_new_message": { + "content": "Hi my name is [NAME_1]", + "role": "user", + "additional_contents": [], + "received_message_id": "0", + "extra_fields": {}, }, }, }, From f7380a51de3aa1823194a8b23ed1a60f2818f190 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 1 Dec 2025 08:36:29 +0530 Subject: [PATCH 178/248] Respect custom llm provider in header --- litellm/proxy/batches_endpoints/endpoints.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/batches_endpoints/endpoints.py b/litellm/proxy/batches_endpoints/endpoints.py index 98492bcc2d..03b9ac3dea 100644 --- a/litellm/proxy/batches_endpoints/endpoints.py +++ b/litellm/proxy/batches_endpoints/endpoints.py @@ -31,7 +31,6 @@ 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 @@ -112,7 +111,10 @@ 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 "openai" + provider + or data.pop("custom_llm_provider", None) + or get_custom_llm_provider_from_request_headers(request=request) + or "openai" ) _create_batch_data = LiteLLMBatchCreateRequest(**data) input_file_id = _create_batch_data.get("input_file_id", None) From 9edc50efbd117b38e54c038cbc1af1dd32572206 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 1 Dec 2025 10:21:44 +0530 Subject: [PATCH 179/248] Fix 500 error for malformed request --- litellm/proxy/common_request_processing.py | 19 ++++++++- tests/proxy_unit_tests/test_proxy_server.py | 44 +++++++++++++++++++++ 2 files changed, 62 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index d2b0441002..1c6c9b9717 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -809,7 +809,24 @@ class ProxyBaseLLMRequestProcessing: status_code=e.response.status_code, detail={"error": error_text}, ) - error_msg = f"{str(e)}" + error_msg = f"{str(e)}" + # Check for AttributeError in various places: + # 1. Direct AttributeError (already handled above) + # 2. In underlying exception (__cause__, __context__, original_exception) + has_attribute_error = ( + (isinstance(e, Exception) and isinstance(getattr(e, "__cause__", None), AttributeError)) + or (isinstance(e, Exception) and isinstance(getattr(e, "__context__", None), AttributeError)) + or (isinstance(e, Exception) and isinstance(getattr(e, "original_exception", None), AttributeError)) + ) + + if has_attribute_error: + raise ProxyException( + message=f"Invalid request format: {error_msg}", + type="invalid_request_error", + param=None, + code=status.HTTP_400_BAD_REQUEST, + headers=headers, + ) raise ProxyException( message=getattr(e, "message", error_msg), type=getattr(e, "type", "None"), diff --git a/tests/proxy_unit_tests/test_proxy_server.py b/tests/proxy_unit_tests/test_proxy_server.py index 6dad7cb08d..dc34a50f87 100644 --- a/tests/proxy_unit_tests/test_proxy_server.py +++ b/tests/proxy_unit_tests/test_proxy_server.py @@ -175,6 +175,50 @@ def test_chat_completion(mock_acompletion, client_no_auth): pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") +def test_chat_completion_malformed_messages_returns_400(client_no_auth): + """ + Test that malformed messages (strings instead of dicts) return 400 instead of 500. + + This test verifies that when a client sends messages as raw strings instead of + {role, content} objects, LiteLLM returns a 400 invalid_request_error instead + of a 500 Internal Server Error. + """ + global headers + try: + # Test data with malformed messages (string instead of dict) + test_data = { + "model": "gpt-3.5-turbo", + "messages": ["hi how are you"], # Invalid: should be [{"role": "user", "content": "hi how are you"}] + } + + print("testing proxy server with malformed messages") + response = client_no_auth.post("/v1/chat/completions", json=test_data, headers=headers) + + print(f"response status: {response.status_code}") + print(f"response text: {response.text}") + + # Should return 400, not 500 + assert response.status_code == 400, f"Expected 400, got {response.status_code}. Response: {response.text}" + + # Verify error format + result = response.json() + assert "error" in result, "Response should contain 'error' key" + error = result["error"] + + # Verify error type and message + assert error.get("type") == "invalid_request_error" or error.get("type") is None, \ + f"Expected invalid_request_error or None, got {error.get('type')}" + assert error.get("code") == "400" or error.get("code") == 400, \ + f"Expected code 400, got {error.get('code')}" + + # Error message should indicate invalid request format + error_message = error.get("message", "") + assert len(error_message) > 0, "Error message should not be empty" + + except Exception as e: + pytest.fail(f"LiteLLM Proxy test failed. Exception - {str(e)}") + + def test_get_settings_request_timeout(client_no_auth): """ When no timeout is set, it should use the litellm.request_timeout value From 02510a908f389055ccf55db0a59a141a49bc222c Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 1 Dec 2025 10:42:38 +0530 Subject: [PATCH 180/248] Add better handling image generation for gemini models --- .../llms/gemini/image_generation/transformation.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/litellm/llms/gemini/image_generation/transformation.py b/litellm/llms/gemini/image_generation/transformation.py index e79414394f..2d8d82e6ad 100644 --- a/litellm/llms/gemini/image_generation/transformation.py +++ b/litellm/llms/gemini/image_generation/transformation.py @@ -21,12 +21,6 @@ else: LiteLLMLoggingObj = Any -FLASH_IMAGE_PREVIEW_MODEL_IDENTIFIERS = ( - "2.0-flash-preview-image", - "2.0-flash-preview-image-generation", - "2.5-flash-image-preview", - "3-pro-image-preview", -) class GoogleImageGenConfig(BaseImageGenerationConfig): DEFAULT_BASE_URL: str = "https://generativelanguage.googleapis.com/v1beta" @@ -104,7 +98,7 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): complete_url = complete_url.rstrip("/") # Gemini Flash Image Preview models use generateContent endpoint - if any(identifier in model for identifier in FLASH_IMAGE_PREVIEW_MODEL_IDENTIFIERS): + if "gemini" in model: complete_url = f"{complete_url}/models/{model}:generateContent" else: # All other Imagen models use predict endpoint @@ -159,7 +153,7 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): } """ # For Gemini Flash Image Preview models, use standard Gemini format - if any(identifier in model for identifier in FLASH_IMAGE_PREVIEW_MODEL_IDENTIFIERS): + if "gemini" in model: request_body: dict = { "contents": [ { @@ -218,7 +212,7 @@ class GoogleImageGenConfig(BaseImageGenerationConfig): model_response.data = [] # Handle different response formats based on model - if any(identifier in model for identifier in FLASH_IMAGE_PREVIEW_MODEL_IDENTIFIERS): + if "gemini" in model: # Gemini Flash Image Preview models return in candidates format candidates = response_data.get("candidates", []) for candidate in candidates: From 7dac498efbaf9142c44ba560e736baa28f1223c2 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 1 Dec 2025 14:33:03 +0530 Subject: [PATCH 181/248] Add passthrough cost tracking for veo --- litellm/proxy/_types.py | 2 + .../llm_passthrough_endpoints.py | 9 +-- .../gemini_passthrough_logging_handler.py | 38 +++++++++ .../vertex_passthrough_logging_handler.py | 43 +++++++++- .../pass_through_endpoints.py | 26 +++++- .../pass_through_endpoints/success_handler.py | 4 +- ...test_gemini_passthrough_logging_handler.py | 79 ++++++++++++++++++- 7 files changed, 191 insertions(+), 10 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 9e915d4bc5..fe87a70b24 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -47,6 +47,7 @@ from litellm.types.utils import ( StandardPassThroughResponseObject, TextCompletionResponse, ) +from litellm.types.videos.main import VideoObject from .types_utils.utils import get_instance_fn, validate_custom_validate_return_type @@ -3275,6 +3276,7 @@ PassThroughEndpointLoggingResultValues = Union[ TextCompletionResponse, ImageResponse, EmbeddingResponse, + VideoObject, StandardPassThroughResponseObject, ] diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index 7afb6868c7..d1294f996e 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -187,13 +187,10 @@ async def gemini_proxy_route( """ [Docs](https://docs.litellm.ai/docs/pass_through/google_ai_studio) """ - ## CHECK FOR LITELLM API KEY IN THE QUERY PARAMS - ?..key=LITELLM_API_KEY - google_ai_studio_api_key = request.query_params.get("key") or request.headers.get( - "x-goog-api-key" - ) - + # Get LiteLLM API key from Authorization header for authentication + api_key_to_use = get_litellm_virtual_key(request=request) user_api_key_dict = await user_api_key_auth( - request=request, api_key=f"Bearer {google_ai_studio_api_key}" + request=request, api_key=api_key_to_use ) base_target_url = ( diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py index 16e8d5b434..2bda9ba485 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/gemini_passthrough_logging_handler.py @@ -7,6 +7,7 @@ import httpx import litellm from litellm._logging import verbose_proxy_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.llms.gemini.videos.transformation import GeminiVideoConfig from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( ModelResponseIterator as GeminiModelResponseIterator, ) @@ -39,6 +40,43 @@ class GeminiPassthroughLoggingHandler: request_body: dict, **kwargs, ) -> PassThroughEndpointLoggingTypedDict: + if "predictLongRunning" in url_route: + model = GeminiPassthroughLoggingHandler.extract_model_from_url(url_route) + + gemini_video_config = GeminiVideoConfig() + litellm_video_response = gemini_video_config.transform_video_create_response( + model=model, + raw_response=httpx_response, + logging_obj=logging_obj, + custom_llm_provider="gemini", + request_data=request_body, + ) + logging_obj.model = model + logging_obj.model_call_details["model"] = model + logging_obj.model_call_details["custom_llm_provider"] = "gemini" + logging_obj.custom_llm_provider = "gemini" + + response_cost = litellm.completion_cost( + completion_response=litellm_video_response, + model=model, + custom_llm_provider="gemini", + call_type="create_video", + ) + + # Set response_cost in _hidden_params to prevent recalculation + if not hasattr(litellm_video_response, "_hidden_params"): + litellm_video_response._hidden_params = {} + litellm_video_response._hidden_params["response_cost"] = response_cost + + kwargs["response_cost"] = response_cost + kwargs["model"] = model + kwargs["custom_llm_provider"] = "gemini" + logging_obj.model_call_details["response_cost"] = response_cost + return { + "result": litellm_video_response, + "kwargs": kwargs, + } + if "generateContent" in url_route: model = GeminiPassthroughLoggingHandler.extract_model_from_url(url_route) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py index b34a6f455c..0962fafe3f 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/vertex_passthrough_logging_handler.py @@ -14,6 +14,7 @@ from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import ( from litellm.llms.vertex_ai.vector_stores.search_api.transformation import ( VertexSearchAPIVectorStoreConfig, ) +from litellm.llms.vertex_ai.videos.transformation import VertexAIVideoConfig from litellm.proxy._types import PassThroughEndpointLoggingTypedDict from litellm.types.utils import ( Choices, @@ -49,9 +50,49 @@ class VertexPassthroughLoggingHandler: start_time: datetime, end_time: datetime, cache_hit: bool, + request_body: Optional[dict] = None, **kwargs, ) -> PassThroughEndpointLoggingTypedDict: - if "generateContent" in url_route: + if "predictLongRunning" in url_route: + model = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) + + vertex_video_config = VertexAIVideoConfig() + litellm_video_response = vertex_video_config.transform_video_create_response( + model=model, + raw_response=httpx_response, + logging_obj=logging_obj, + custom_llm_provider="vertex_ai", + request_data=request_body, + ) + + logging_obj.model = model + logging_obj.model_call_details["model"] = model + logging_obj.model_call_details["custom_llm_provider"] = "vertex_ai" + logging_obj.custom_llm_provider = "vertex_ai" + + response_cost = litellm.completion_cost( + completion_response=litellm_video_response, + model=model, + custom_llm_provider="vertex_ai", + call_type="create_video", + ) + + # Set response_cost in _hidden_params to prevent recalculation + if not hasattr(litellm_video_response, "_hidden_params"): + litellm_video_response._hidden_params = {} + litellm_video_response._hidden_params["response_cost"] = response_cost + + kwargs["response_cost"] = response_cost + kwargs["model"] = model + kwargs["custom_llm_provider"] = "vertex_ai" + logging_obj.model_call_details["response_cost"] = response_cost + + return { + "result": litellm_video_response, + "kwargs": kwargs, + } + + elif "generateContent" in url_route: model = VertexPassthroughLoggingHandler.extract_model_from_url(url_route) instance_of_vertex_llm = litellm.VertexGeminiConfig() diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 5b47a8af7a..8e297f645c 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -412,6 +412,31 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): params=requested_query_params, json=_parsed_body, ) + # Mock httpx response emulating a Google AI video generation operation status + # Attach a dummy request with headers set, so response.request.headers is always present + dummy_request = httpx.Request( + method=request.method, + url=str(url), + headers=headers or {}, + params=requested_query_params, + json=_parsed_body, + ) + # Ensure the .headers attribute exists and is a dict (httpx will normalize it) + mock_headers = httpx.Headers({"content-type": "application/json"}) + response = httpx.Response( + status_code=200, + headers=mock_headers, + json={ + "name": "operations/1234567890123456789", + "metadata": { + "@type": "type.googleapis.com/google.ai.generativelanguage.v1beta.GenerateVideoMetadata", + "state": "RUNNING", + "createTime": "2025-01-01T12:00:00Z" + }, + "done": False + }, + request=dummy_request + ) return response @staticmethod @@ -737,7 +762,6 @@ async def pass_through_request( # noqa: PLR0915 # Store custom_llm_provider in kwargs and logging object if provided if custom_llm_provider: - kwargs["custom_llm_provider"] = custom_llm_provider logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider logging_obj.model_call_details["litellm_params"] = kwargs.get("litellm_params", {}) diff --git a/litellm/proxy/pass_through_endpoints/success_handler.py b/litellm/proxy/pass_through_endpoints/success_handler.py index cc50d2c2d8..6d93ef68df 100644 --- a/litellm/proxy/pass_through_endpoints/success_handler.py +++ b/litellm/proxy/pass_through_endpoints/success_handler.py @@ -42,6 +42,7 @@ class PassThroughEndpointLogging: "streamRawPredict", "search", "batchPredictionJobs", + "predictLongRunning", ] # Anthropic @@ -57,7 +58,7 @@ class PassThroughEndpointLogging: self.TRACKED_LANGFUSE_ROUTES = ["/langfuse/"] # Gemini - self.TRACKED_GEMINI_ROUTES = ["generateContent", "streamGenerateContent"] + self.TRACKED_GEMINI_ROUTES = ["generateContent", "streamGenerateContent", "predictLongRunning"] # Vertex AI Live API WebSocket self.TRACKED_VERTEX_AI_LIVE_ROUTES = ["/vertex_ai/live"] @@ -149,6 +150,7 @@ class PassThroughEndpointLogging: start_time=start_time, end_time=end_time, cache_hit=cache_hit, + request_body=request_body, **kwargs, ) ) diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py index 6f87d8f6ab..2c3bbc0e6e 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_gemini_passthrough_logging_handler.py @@ -75,7 +75,9 @@ class TestGeminiPassthroughLoggingHandler: def test_is_gemini_route(self): """Test that Gemini routes are correctly identified""" - from litellm.proxy.pass_through_endpoints.success_handler import PassThroughEndpointLogging + from litellm.proxy.pass_through_endpoints.success_handler import ( + PassThroughEndpointLogging, + ) handler = PassThroughEndpointLogging() @@ -285,3 +287,78 @@ class TestGeminiPassthroughLoggingHandler: assert call_kwargs["response_cost"] is not None assert call_kwargs["model"] == "gemini-1.5-flash" assert call_kwargs["custom_llm_provider"] == "gemini" + + @patch("litellm.completion_cost") + def test_veo3_passthrough_cost_tracking(self, mock_completion_cost): + """Test Veo3 video generation cost tracking for passthrough requests""" + # Mock the completion_cost to return the expected video generation cost + # For veo-2.0-generate-001 with 8 seconds: 0.35 * 8 = 2.8 + expected_cost = 0.35 * 8.0 # $2.80 + mock_completion_cost.return_value = expected_cost + + # Mock Veo3 predictLongRunning response + mock_veo_response = { + "name": "operations/1234567890123456789" + } + + mock_httpx_response = MagicMock(spec=httpx.Response) + mock_httpx_response.status_code = 200 + mock_httpx_response.json.return_value = mock_veo_response + mock_httpx_response.headers = {"content-type": "application/json"} + + mock_logging_obj = self._create_mock_logging_obj() + + # Request body with durationSeconds + request_body = { + "instances": [{"prompt": "A close up of two people staring at a cryptic drawing on a wall,"}], + "parameters": {"durationSeconds": 8} + } + + kwargs = { + "passthrough_logging_payload": PassthroughStandardLoggingPayload( + url="https://generativelanguage.googleapis.com/v1beta/models/veo-2.0-generate-001:predictLongRunning", + request_body=request_body, + request_method="POST", + ), + } + + # Act + result = GeminiPassthroughLoggingHandler.gemini_passthrough_handler( + httpx_response=mock_httpx_response, + response_body=mock_veo_response, + logging_obj=mock_logging_obj, + url_route="https://generativelanguage.googleapis.com/v1beta/models/veo-2.0-generate-001:predictLongRunning", + result="", + start_time=self.start_time, + end_time=self.end_time, + cache_hit=False, + request_body=request_body, + **kwargs, + ) + + # Assert + assert result is not None + assert "result" in result + assert "kwargs" in result + + # Verify the cost is calculated correctly + assert result["kwargs"]["response_cost"] == expected_cost + assert result["kwargs"]["model"] == "veo-2.0-generate-001" + assert result["kwargs"]["custom_llm_provider"] == "gemini" + + # Verify completion_cost was called with create_video call_type + mock_completion_cost.assert_called_once() + call_args = mock_completion_cost.call_args + assert call_args.kwargs.get("call_type") == "create_video" + assert call_args.kwargs.get("custom_llm_provider") == "gemini" + assert call_args.kwargs.get("model") == "veo-2.0-generate-001" + + # Verify the response object has _hidden_params with response_cost + video_response = result["result"] + assert hasattr(video_response, "_hidden_params") + assert video_response._hidden_params.get("response_cost") == expected_cost + + # Verify logging object was updated + assert mock_logging_obj.model_call_details["response_cost"] == expected_cost + assert mock_logging_obj.model_call_details["model"] == "veo-2.0-generate-001" + assert mock_logging_obj.model_call_details["custom_llm_provider"] == "gemini" From ae132abff42cf7a3e65a28e40e7811bedc2b9a04 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 1 Dec 2025 14:36:30 +0530 Subject: [PATCH 182/248] Revert auth change --- .../llm_passthrough_endpoints.py | 9 ++++--- .../pass_through_endpoints.py | 25 ------------------- 2 files changed, 6 insertions(+), 28 deletions(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index d1294f996e..7afb6868c7 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -187,10 +187,13 @@ async def gemini_proxy_route( """ [Docs](https://docs.litellm.ai/docs/pass_through/google_ai_studio) """ - # Get LiteLLM API key from Authorization header for authentication - api_key_to_use = get_litellm_virtual_key(request=request) + ## CHECK FOR LITELLM API KEY IN THE QUERY PARAMS - ?..key=LITELLM_API_KEY + google_ai_studio_api_key = request.query_params.get("key") or request.headers.get( + "x-goog-api-key" + ) + user_api_key_dict = await user_api_key_auth( - request=request, api_key=api_key_to_use + request=request, api_key=f"Bearer {google_ai_studio_api_key}" ) base_target_url = ( diff --git a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py index 8e297f645c..a447dad0b1 100644 --- a/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/pass_through_endpoints.py @@ -412,31 +412,6 @@ class HttpPassThroughEndpointHelpers(BasePassthroughUtils): params=requested_query_params, json=_parsed_body, ) - # Mock httpx response emulating a Google AI video generation operation status - # Attach a dummy request with headers set, so response.request.headers is always present - dummy_request = httpx.Request( - method=request.method, - url=str(url), - headers=headers or {}, - params=requested_query_params, - json=_parsed_body, - ) - # Ensure the .headers attribute exists and is a dict (httpx will normalize it) - mock_headers = httpx.Headers({"content-type": "application/json"}) - response = httpx.Response( - status_code=200, - headers=mock_headers, - json={ - "name": "operations/1234567890123456789", - "metadata": { - "@type": "type.googleapis.com/google.ai.generativelanguage.v1beta.GenerateVideoMetadata", - "state": "RUNNING", - "createTime": "2025-01-01T12:00:00Z" - }, - "done": False - }, - request=dummy_request - ) return response @staticmethod From 983ba7aa0f2e4e7f3e6ffe6082fcd93c366a76ce Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Mon, 1 Dec 2025 17:22:04 +0530 Subject: [PATCH 183/248] Remove not compatible beta header from claude code --- .../anthropic_claude3_transformation.py | 2 +- .../anthropic_claude3_transformation.py | 2 +- .../bedrock/test_anthropic_beta_support.py | 138 +++++++++++++++++- 3 files changed, 133 insertions(+), 9 deletions(-) diff --git a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py index f003c0ed95..53e0822979 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/anthropic_claude3_transformation.py @@ -103,7 +103,7 @@ class AmazonAnthropicClaudeConfig(AmazonInvokeConfig, AnthropicConfig): model=model, optional_params=optional_params, computer_tool_used=self.is_computer_tool_used(tools), - prompt_caching_set=self.is_cache_control_set(messages), + prompt_caching_set=False, file_id_used=self.is_file_id_used(messages), mcp_server_used=self.is_mcp_server_used(optional_params.get("mcp_servers")), ) diff --git a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py index aea8a4b5a8..32be1a780a 100644 --- a/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py +++ b/litellm/llms/bedrock/messages/invoke_transformations/anthropic_claude3_transformation.py @@ -157,7 +157,7 @@ class AmazonAnthropicClaudeMessagesConfig( model=model, optional_params=anthropic_messages_optional_request_params, computer_tool_used=anthropic_model_info.is_computer_tool_used(tools), - prompt_caching_set=anthropic_model_info.is_cache_control_set(messages_typed), + prompt_caching_set=False, file_id_used=anthropic_model_info.is_file_id_used(messages_typed), mcp_server_used=anthropic_model_info.is_mcp_server_used( anthropic_messages_optional_request_params.get("mcp_servers") diff --git a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py index 1b9e1b5284..bd64670517 100644 --- a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py +++ b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py @@ -5,14 +5,19 @@ Tests that anthropic-beta headers are correctly processed and passed to AWS Bedr for enabling beta features like 1M context window, computer use tools, etc. """ -import pytest -from unittest.mock import patch, MagicMock import json +from unittest.mock import MagicMock, patch + +import pytest -from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig -from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import AmazonAnthropicClaudeConfig -from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import AmazonAnthropicClaudeMessagesConfig +from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeConfig, +) +from litellm.llms.bedrock.common_utils import get_anthropic_beta_from_headers +from litellm.llms.bedrock.messages.invoke_transformations.anthropic_claude3_transformation import ( + AmazonAnthropicClaudeMessagesConfig, +) class TestAnthropicBetaHeaderSupport: @@ -56,7 +61,8 @@ class TestAnthropicBetaHeaderSupport: ) assert "anthropic_beta" in result - assert result["anthropic_beta"] == ["context-1m-2025-08-07", "computer-use-2024-10-22"] + # Beta flags are stored as sets, so order may vary + assert set(result["anthropic_beta"]) == {"context-1m-2025-08-07", "computer-use-2024-10-22"} def test_converse_transformation_anthropic_beta(self): """Test that Converse API transformation includes anthropic_beta in additionalModelRequestFields.""" @@ -163,4 +169,122 @@ class TestAnthropicBetaHeaderSupport: ) assert "anthropic_beta" in result - assert result["anthropic_beta"] == supported_features \ No newline at end of file + # Beta flags are stored as sets, so order may vary + assert set(result["anthropic_beta"]) == set(supported_features) + + def test_prompt_caching_no_beta_header_messages_api(self): + """Test that prompt caching (cache_control) does NOT add prompt-caching-2024-07-31 beta header for Bedrock. + + Bedrock recognizes prompt caching via the request body (cache_control field), + not through beta headers. This test verifies the fix. + """ + config = AmazonAnthropicClaudeMessagesConfig() + headers = {} + + # Messages with cache_control set (prompt caching enabled) + messages = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Hello", + "cache_control": {"type": "ephemeral"} + } + ] + } + ] + + result = config.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=messages, + anthropic_messages_optional_request_params={"max_tokens": 100}, + litellm_params={}, + headers=headers + ) + + # Verify prompt-caching-2024-07-31 is NOT in anthropic_beta + if "anthropic_beta" in result: + assert "prompt-caching-2024-07-31" not in result["anthropic_beta"], ( + "prompt-caching-2024-07-31 should not be added as a beta header for Bedrock. " + "Bedrock recognizes prompt caching via cache_control in the request body, not beta headers." + ) + else: + # It's also valid if anthropic_beta is not present at all + assert True + + def test_prompt_caching_no_beta_header_chat_api(self): + """Test that prompt caching (cache_control) does NOT add prompt-caching-2024-07-31 beta header for Bedrock Chat API. + + Bedrock recognizes prompt caching via the request body (cache_control field), + not through beta headers. This test verifies the fix. + """ + config = AmazonAnthropicClaudeConfig() + headers = {} + + # Messages with cache_control set (prompt caching enabled) + messages = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Hello", + "cache_control": {"type": "ephemeral"} + } + ] + } + ] + + result = config.transform_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=messages, + optional_params={}, + litellm_params={}, + headers=headers + ) + + # Verify prompt-caching-2024-07-31 is NOT in anthropic_beta + if "anthropic_beta" in result: + assert "prompt-caching-2024-07-31" not in result["anthropic_beta"], ( + "prompt-caching-2024-07-31 should not be added as a beta header for Bedrock. " + "Bedrock recognizes prompt caching via cache_control in the request body, not beta headers." + ) + else: + # It's also valid if anthropic_beta is not present at all + assert True + + def test_prompt_caching_with_other_beta_headers(self): + """Test that prompt caching doesn't interfere with other valid beta headers.""" + config = AmazonAnthropicClaudeMessagesConfig() + headers = {"anthropic-beta": "context-1m-2025-08-07"} + + # Messages with cache_control set + messages = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Hello", + "cache_control": {"type": "ephemeral"} + } + ] + } + ] + + result = config.transform_anthropic_messages_request( + model="us.anthropic.claude-sonnet-4-5-20250929-v1:0", + messages=messages, + anthropic_messages_optional_request_params={"max_tokens": 100}, + litellm_params={}, + headers=headers + ) + + # Should have the user-provided beta header but NOT prompt-caching + if "anthropic_beta" in result: + assert "context-1m-2025-08-07" in result["anthropic_beta"] + assert "prompt-caching-2024-07-31" not in result["anthropic_beta"] + else: + # If no beta headers, that's also fine + assert True \ No newline at end of file From 6de610767340cadd6df1c5508325128045c8fae5 Mon Sep 17 00:00:00 2001 From: YutaSaito <36355491+uc4w6c@users.noreply.github.com> Date: Tue, 2 Dec 2025 02:59:01 +0900 Subject: [PATCH 184/248] fix: respect guardrail mock_response during during_call to return blocked output (#17247) --- litellm/proxy/common_request_processing.py | 23 +++-- .../proxy/test_common_request_processing.py | 99 ++++++++++++++++++- 2 files changed, 111 insertions(+), 11 deletions(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index d2b0441002..ed4c451f8d 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -536,7 +536,11 @@ class ProxyBaseLLMRequestProcessing: responses = await llm_responses - response = responses[1] + # Guardrails (pre/during-call) can inject a mock response to short-circuit the LLM call. + # Prefer it when present so blocked/filtered output is returned instead of the model response. + response = self.data.get("mock_response") + if response is None: + response = responses[1] hidden_params = getattr(response, "_hidden_params", {}) or {} model_id = hidden_params.get("model_id", None) or "" @@ -804,7 +808,7 @@ class ProxyBaseLLMRequestProcessing: # This matches the original behavior before the refactor in commit 511d435f6f error_body = await e.response.aread() error_text = error_body.decode("utf-8") - + raise HTTPException( status_code=e.response.status_code, detail={"error": error_text}, @@ -1072,9 +1076,9 @@ class ProxyBaseLLMRequestProcessing: # Add cache-related fields to **params (handled by Usage.__init__) if cache_creation_input_tokens is not None: - usage_kwargs["cache_creation_input_tokens"] = ( - cache_creation_input_tokens - ) + usage_kwargs[ + "cache_creation_input_tokens" + ] = cache_creation_input_tokens if cache_read_input_tokens is not None: usage_kwargs["cache_read_input_tokens"] = cache_read_input_tokens @@ -1093,7 +1097,9 @@ class ProxyBaseLLMRequestProcessing: return obj return None - def maybe_get_model_id(self, _logging_obj: Optional[LiteLLMLoggingObj]) -> Optional[str]: + def maybe_get_model_id( + self, _logging_obj: Optional[LiteLLMLoggingObj] + ) -> Optional[str]: """ Get model_id from logging object or request metadata. @@ -1103,10 +1109,7 @@ class ProxyBaseLLMRequestProcessing: model_id = None if _logging_obj: # 1. Try getting from litellm_params (updated during call) - if ( - hasattr(_logging_obj, "litellm_params") - and _logging_obj.litellm_params - ): + if hasattr(_logging_obj, "litellm_params") and _logging_obj.litellm_params: # First check direct model_info path (set by router.py with selected deployment) model_info = _logging_obj.litellm_params.get("model_info") or {} model_id = model_info.get("id", None) diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index 4768ec42ff..8f5f182f42 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -1,11 +1,13 @@ import copy +from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock import pytest -from fastapi import Request, status +from fastapi import Request, Response, status from fastapi.responses import StreamingResponse import litellm +import litellm.proxy.common_request_processing as common_request_processing from litellm._uuid import uuid from litellm.integrations.opentelemetry import UserAPIKeyAuth from litellm.proxy.common_request_processing import ( @@ -75,6 +77,101 @@ class TestProxyBaseLLMRequestProcessing: pytest.fail("litellm_call_id is not a valid UUID") assert data_passed["litellm_call_id"] == returned_data["litellm_call_id"] + @pytest.mark.asyncio + async def test_base_process_llm_request_prefers_guardrail_mock_response( + self, monkeypatch + ): + processing_obj = ProxyBaseLLMRequestProcessing( + data={ + "messages": [], + "metadata": {}, + "litellm_metadata": {"model_info": {"id": "fallback-model"}}, + } + ) + + guardrail_response = litellm.ModelResponse( + model="bedrock-guardrail", + hidden_params={"model_id": "guardrail-model"}, + ) + llm_response = litellm.ModelResponse( + model="real-model", + hidden_params={"model_id": "real-model"}, + ) + + async def mock_common_processing(self, *args, **kwargs): + logging_obj = SimpleNamespace(litellm_call_id="test-call-id") + self.data["litellm_call_id"] = "test-call-id" + self.data["litellm_logging_obj"] = logging_obj + return self.data, logging_obj + + monkeypatch.setattr( + ProxyBaseLLMRequestProcessing, + "common_processing_pre_call_logic", + mock_common_processing, + ) + + async def mock_route_request(*args, **kwargs): + async def _inner(): + return llm_response + + return _inner() + + monkeypatch.setattr( + common_request_processing, + "route_request", + mock_route_request, + ) + + check_response_size_is_safe_mock = AsyncMock() + monkeypatch.setattr( + common_request_processing, + "check_response_size_is_safe", + check_response_size_is_safe_mock, + ) + + async def mock_during_call_hook(*args, **kwargs): + kwargs["data"]["mock_response"] = guardrail_response + + proxy_logging_obj = MagicMock(spec=ProxyLogging) + proxy_logging_obj.during_call_hook = AsyncMock( + side_effect=mock_during_call_hook + ) + proxy_logging_obj.update_request_status = AsyncMock(return_value=None) + proxy_logging_obj.post_call_success_hook = AsyncMock( + return_value=guardrail_response + ) + + user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) + user_api_key_dict.tpm_limit = None + user_api_key_dict.rpm_limit = None + user_api_key_dict.max_budget = None + user_api_key_dict.spend = 0 + user_api_key_dict.allowed_model_region = None + + fastapi_response = Response() + proxy_config = MagicMock(spec=ProxyConfig) + + result = await processing_obj.base_process_llm_request( + request=MagicMock(spec=Request), + fastapi_response=fastapi_response, + user_api_key_dict=user_api_key_dict, + route_type="acompletion", + proxy_logging_obj=proxy_logging_obj, + general_settings={}, + proxy_config=proxy_config, + select_data_generator=lambda **kwargs: None, + ) + + assert result is guardrail_response + assert ( + proxy_logging_obj.post_call_success_hook.await_args.kwargs["response"] + is guardrail_response + ) + assert ( + check_response_size_is_safe_mock.await_args.kwargs["response"] + is guardrail_response + ) + @pytest.mark.asyncio async def test_stream_timeout_header_processing(self): """ From 7808a610f8a95ddb4449eae1b0af67d4e5b2e50d Mon Sep 17 00:00:00 2001 From: orgersh92 Date: Mon, 1 Dec 2025 20:03:51 +0200 Subject: [PATCH 185/248] Fix session consistency, move Lasso API version away from source code (#17316) * store and fetch lasso-conversation id from cache * include gateway/v# in the baseUrl to allow simpler version migrations in the future * add tests for cached conversation ID --- .../docs/proxy/guardrails/lasso_security.md | 4 +- .../guardrails/guardrail_hooks/lasso/lasso.py | 68 +++++++------------ .../guardrails/guardrail_hooks/test_lasso.py | 29 +++++--- 3 files changed, 47 insertions(+), 54 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/lasso_security.md b/docs/my-website/docs/proxy/guardrails/lasso_security.md index 21528790af..113e3f8974 100644 --- a/docs/my-website/docs/proxy/guardrails/lasso_security.md +++ b/docs/my-website/docs/proxy/guardrails/lasso_security.md @@ -35,7 +35,7 @@ guardrails: guardrail: lasso mode: "pre_call" api_key: os.environ/LASSO_API_KEY - api_base: "https://server.lasso.security" + api_base: "https://server.lasso.security/gateway/v3" - guardrail_name: "lasso-post-guard" litellm_params: guardrail: lasso @@ -228,7 +228,7 @@ Expected response: ## PII Masking with Lasso -Lasso supports automatic PII detection and masking using the `/gateway/v1/classifix` endpoint. When enabled, sensitive information like emails, phone numbers, and other PII will be automatically masked with appropriate placeholders. +Lasso supports automatic PII detection and masking using the `/classifix` endpoint. When enabled, sensitive information like emails, phone numbers, and other PII will be automatically masked with appropriate placeholders. ### Enabling PII Masking diff --git a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py index 99d2b82400..ea8f1b0a97 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py +++ b/litellm/proxy/guardrails/guardrail_hooks/lasso/lasso.py @@ -33,6 +33,8 @@ from litellm.integrations.custom_guardrail import ( CustomGuardrail, log_guardrail_information, ) +from litellm.integrations.custom_guardrail import dc as global_cache + from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, @@ -100,7 +102,7 @@ class LassoGuardrail(CustomGuardrail): ) self.api_base = ( - api_base or os.getenv("LASSO_API_BASE") or "https://server.lasso.security" + api_base or os.getenv("LASSO_API_BASE") or "https://server.lasso.security/gateway/v3" ) verbose_proxy_logger.debug( @@ -125,7 +127,7 @@ class LassoGuardrail(CustomGuardrail): async def async_pre_call_hook( self, user_api_key_dict: UserAPIKeyAuth, - cache: DualCache, + cache: DualCache, # Deprecated, use global_cache instead (kept to align with CustomGuardrail interface) data: dict, call_type: Literal[ "completion", @@ -150,10 +152,10 @@ class LassoGuardrail(CustomGuardrail): return data # Get or generate conversation_id and store it in data for post-call consistency - conversation_id = self._get_or_generate_conversation_id(data, cache) - data.setdefault("_lasso_internal", {})["conversation_id"] = conversation_id + # The conversation_id is being stored in the cache so it can be used by the post_call hook + self._get_or_generate_conversation_id(data, global_cache) - return await self._run_lasso_guardrail(data, cache, message_type="PROMPT") + return await self._run_lasso_guardrail(data, global_cache, message_type="PROMPT") @log_guardrail_information async def async_moderation_hook( @@ -213,17 +215,12 @@ class LassoGuardrail(CustomGuardrail): "litellm_call_id": data.get("litellm_call_id"), } - # Copy stored conversation_id from pre-call hook - if data.get("_lasso_internal", {}).get("conversation_id") and isinstance(response_data, dict): - response_data.setdefault("_lasso_internal", {})["conversation_id"] = data["_lasso_internal"][ - "conversation_id" - ] # Handle masking for post-call if self.mask: - headers = self._prepare_headers(response_data) - payload = self._prepare_payload(response_messages, "COMPLETION", response_data) - api_url = f"{self.api_base}/gateway/v3/classifix" + headers = self._prepare_headers(response_data, global_cache) + payload = self._prepare_payload(response_messages, response_data, global_cache, "COMPLETION") + api_url = f"{self.api_base}/classifix" try: lasso_response = await self._call_lasso_api(headers=headers, payload=payload, api_url=api_url) @@ -241,7 +238,7 @@ class LassoGuardrail(CustomGuardrail): raise LassoGuardrailAPIError(f"Failed to apply post-call masking: {str(e)}") else: # Use the same data for conversation_id consistency (no cache access needed) - await self._run_lasso_guardrail(response_data, cache=None, message_type="COMPLETION") + await self._run_lasso_guardrail(response_data, cache=global_cache, message_type="COMPLETION") verbose_proxy_logger.debug("Post-call Lasso validation completed") else: verbose_proxy_logger.warning("No response messages found to validate") @@ -306,7 +303,7 @@ class LassoGuardrail(CustomGuardrail): async def _run_lasso_guardrail( self, data: dict, - cache: Optional[DualCache], + cache: DualCache, message_type: Literal["PROMPT", "COMPLETION"] = "PROMPT", ): """ @@ -345,14 +342,14 @@ class LassoGuardrail(CustomGuardrail): async def _handle_classification( self, data: dict, - cache: Optional[DualCache], + cache: DualCache, message_type: Literal["PROMPT", "COMPLETION"], messages: List[Dict[str, str]], ) -> dict: """Handle classification without masking.""" try: headers = self._prepare_headers(data, cache) - payload = self._prepare_payload(messages, message_type, data, cache) + payload = self._prepare_payload(messages, data, cache, message_type) response = await self._call_lasso_api(headers=headers, payload=payload) self._process_lasso_response(response) return data @@ -363,15 +360,15 @@ class LassoGuardrail(CustomGuardrail): async def _handle_masking( self, data: dict, - cache: Optional[DualCache], + cache: DualCache, message_type: Literal["PROMPT", "COMPLETION"], messages: List[Dict[str, str]], ) -> dict: """Handle masking with classifix endpoint.""" try: headers = self._prepare_headers(data, cache) - payload = self._prepare_payload(messages, message_type, data, cache) - api_url = f"{self.api_base}/gateway/v3/classifix" + payload = self._prepare_payload(messages, data, cache, message_type) + api_url = f"{self.api_base}/classifix" response = await self._call_lasso_api(headers=headers, payload=payload, api_url=api_url) self._process_lasso_response(response) @@ -437,7 +434,7 @@ class LassoGuardrail(CustomGuardrail): }, ) - def _prepare_headers(self, data: dict, cache: Optional[DualCache] = None) -> Dict[str, str]: + def _prepare_headers(self, data: dict, cache: DualCache) -> Dict[str, str]: """Prepare headers for the Lasso API request.""" if not self.lasso_api_key: raise LassoGuardrailMissingSecrets( @@ -455,13 +452,7 @@ class LassoGuardrail(CustomGuardrail): headers["lasso-user-id"] = self.user_id # Always include conversation_id (generated or provided) - if cache is not None: - conversation_id = self._get_or_generate_conversation_id(data, cache) - else: - # For post-call hook, use stored conversation_id or generate a new one - conversation_id = ( - data.get("_lasso_internal", {}).get("conversation_id") or self.conversation_id or self._generate_ulid() - ) + conversation_id = self._get_or_generate_conversation_id(data, cache) headers["lasso-conversation-id"] = conversation_id @@ -470,9 +461,9 @@ class LassoGuardrail(CustomGuardrail): def _prepare_payload( self, messages: List[Dict[str, str]], + data: dict, + cache: DualCache, message_type: Literal["PROMPT", "COMPLETION"] = "PROMPT", - data: Optional[dict] = None, - cache: Optional[DualCache] = None, ) -> Dict[str, Any]: """ Prepare the payload for the Lasso API request. @@ -490,20 +481,9 @@ class LassoGuardrail(CustomGuardrail): payload["userId"] = self.user_id # Always include sessionId (conversation_id - generated or provided) - if data is not None: - if cache is not None: - conversation_id = self._get_or_generate_conversation_id(data, cache) - else: - # For post-call hook, use stored conversation_id or fallback - conversation_id = ( - data.get("_lasso_internal", {}).get("conversation_id") - or self.conversation_id - or self._generate_ulid() - ) + conversation_id = self._get_or_generate_conversation_id(data, cache) - payload["sessionId"] = conversation_id - elif self.conversation_id: - payload["sessionId"] = self.conversation_id + payload["sessionId"] = conversation_id return payload @@ -514,7 +494,7 @@ class LassoGuardrail(CustomGuardrail): api_url: Optional[str] = None, ) -> LassoResponse: """Call the Lasso API and return the response.""" - url = api_url or f"{self.api_base}/gateway/v3/classify" + url = api_url or f"{self.api_base}/classify" verbose_proxy_logger.debug(f"Calling Lasso API with messageType: {payload.get('messageType')}") response = await self.async_handler.post( url=url, diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lasso.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lasso.py index c63974ac3f..87542c974a 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lasso.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_lasso.py @@ -1,6 +1,7 @@ import os import sys import pytest +import uuid from unittest.mock import patch, MagicMock from httpx import Response, Request from fastapi import HTTPException @@ -77,10 +78,11 @@ class TestLassoGuardrail: assert guardrail.lasso_api_key == "test-api-key" assert guardrail.user_id == "test-user" assert guardrail.conversation_id == "test-conversation" - assert guardrail.api_base == "https://server.lasso.security" + assert guardrail.api_base == "https://server.lasso.security/gateway/v3" @pytest.mark.asyncio async def test_pre_call_no_violations(self): + from litellm.integrations.custom_guardrail import dc as global_cache """Test pre-call hook with no violations detected.""" # Setup guardrail guardrail = LassoGuardrail( @@ -90,12 +92,16 @@ class TestLassoGuardrail: default_on=True ) + test_call_id = str(uuid.uuid4()) + assert global_cache.get_cache(f"lasso_conversation_id:{test_call_id}") is None + # Test data data = { "messages": [ {"role": "user", "content": "Hello, how are you?"} ], - "metadata": {} + "metadata": {}, + "litellm_call_id": test_call_id } # Mock successful API response with no violations @@ -118,13 +124,14 @@ class TestLassoGuardrail: request=Request(method="POST", url="https://server.lasso.security/gateway/v3/classify"), ) + local_cache = DualCache() with patch( "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", return_value=mock_response ): result = await guardrail.async_pre_call_hook( user_api_key_dict=UserAPIKeyAuth(), - cache=DualCache(), + cache=local_cache, data=data, call_type="completion" ) @@ -132,6 +139,11 @@ class TestLassoGuardrail: # Should return original data when no violations detected assert result == data + # Verify that the conversation_id is stored in the global cache but not the local cache + cache_key = f"lasso_conversation_id:{test_call_id}" + assert global_cache.get_cache(cache_key) is not None + assert local_cache.get_cache(cache_key) is None + @pytest.mark.asyncio async def test_pre_call_with_violations(self): """Test pre-call hook with violations detected.""" @@ -466,9 +478,10 @@ class TestLassoGuardrail: ) messages = [{"role": "user", "content": "Test message"}] + cache = DualCache() # Test PROMPT payload - prompt_payload = guardrail._prepare_payload(messages, "PROMPT") + prompt_payload = guardrail._prepare_payload(messages, {}, cache, "PROMPT") assert prompt_payload["messageType"] == "PROMPT" assert prompt_payload["messages"] == messages assert prompt_payload["userId"] == "test-user" @@ -476,7 +489,7 @@ class TestLassoGuardrail: # Test COMPLETION payload completion_messages = [{"role": "assistant", "content": "Test response"}] - completion_payload = guardrail._prepare_payload(completion_messages, "COMPLETION") + completion_payload = guardrail._prepare_payload(completion_messages, {}, cache, "COMPLETION") assert completion_payload["messageType"] == "COMPLETION" assert completion_payload["messages"] == completion_messages assert completion_payload["userId"] == "test-user" @@ -489,9 +502,9 @@ class TestLassoGuardrail: user_id="test-user", conversation_id="test-conversation" ) - + cache = DualCache() data = {"litellm_call_id": "test-call-id"} - headers = guardrail._prepare_headers(data) + headers = guardrail._prepare_headers(data, cache) assert headers["lasso-api-key"] == "test-api-key" assert headers["Content-Type"] == "application/json" assert headers["lasso-user-id"] == "test-user" @@ -499,7 +512,7 @@ class TestLassoGuardrail: # Test without optional fields guardrail_minimal = LassoGuardrail(lasso_api_key="test-api-key") - headers_minimal = guardrail_minimal._prepare_headers(data) + headers_minimal = guardrail_minimal._prepare_headers(data, cache) assert headers_minimal["lasso-api-key"] == "test-api-key" assert headers_minimal["Content-Type"] == "application/json" assert "lasso-user-id" not in headers_minimal From c588e7854d7a8d03363b1b203af8650d37ab9b57 Mon Sep 17 00:00:00 2001 From: Colin Lin Date: Thu, 27 Nov 2025 16:36:04 -0500 Subject: [PATCH 186/248] use kwargs --- litellm/llms/custom_httpx/llm_http_handler.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index fdd504e2f5..10353c68b9 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -1804,15 +1804,23 @@ class BaseLLMHTTPHandler: Optional[litellm.types.utils.ProviderSpecificHeader], kwargs.get("provider_specific_header", None), ) - extra_headers = ProviderSpecificHeaderUtils.get_provider_specific_headers( + provider_specific_headers = ProviderSpecificHeaderUtils.get_provider_specific_headers( provider_specific_header=provider_specific_header, custom_llm_provider=custom_llm_provider, ) forwarded_headers = kwargs.get("headers", None) - if forwarded_headers and extra_headers: - merged_headers = {**forwarded_headers, **extra_headers} - else: - merged_headers = forwarded_headers or extra_headers + # Also check for extra_headers in kwargs (from config or direct calls) + extra_headers_from_kwargs = kwargs.get("extra_headers", None) + print("extra_headers_from_kwargs", extra_headers_from_kwargs) + print("provider_specific_headers", provider_specific_headers) + # Merge all header sources: forwarded < extra_headers < provider_specific + merged_headers = {} + if forwarded_headers: + merged_headers.update(forwarded_headers) + if extra_headers_from_kwargs: + merged_headers.update(extra_headers_from_kwargs) + if provider_specific_headers: + merged_headers.update(provider_specific_headers) ( headers, api_base, From 2a5082e6cf2e00c64faea984e587c314fac2731d Mon Sep 17 00:00:00 2001 From: Colin Lin Date: Thu, 27 Nov 2025 16:58:32 -0500 Subject: [PATCH 187/248] remove logs --- litellm/llms/custom_httpx/llm_http_handler.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 10353c68b9..701cefb771 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -1811,8 +1811,6 @@ class BaseLLMHTTPHandler: forwarded_headers = kwargs.get("headers", None) # Also check for extra_headers in kwargs (from config or direct calls) extra_headers_from_kwargs = kwargs.get("extra_headers", None) - print("extra_headers_from_kwargs", extra_headers_from_kwargs) - print("provider_specific_headers", provider_specific_headers) # Merge all header sources: forwarded < extra_headers < provider_specific merged_headers = {} if forwarded_headers: From e420b633a1ac8eaaf33fc2024d4ad70e7b8d688a Mon Sep 17 00:00:00 2001 From: Colin Lin Date: Fri, 28 Nov 2025 17:06:10 -0500 Subject: [PATCH 188/248] add tests --- .../custom_httpx/test_llm_http_handler.py | 150 +++++++++++++++++- 1 file changed, 145 insertions(+), 5 deletions(-) diff --git a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py index 26fc18de16..17b4243da1 100644 --- a/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_llm_http_handler.py @@ -1,17 +1,14 @@ -import io import os -import pathlib -import ssl import sys -from unittest.mock import MagicMock +from unittest.mock import AsyncMock, Mock, patch import pytest sys.path.insert( 0, os.path.abspath("../../../..") ) # Adds the parent directory to the system path -import litellm from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler +from litellm.types.router import GenericLiteLLMParams def test_prepare_fake_stream_request(): @@ -75,3 +72,146 @@ def test_prepare_fake_stream_request(): assert "stream" not in result_data assert result_data["model"] == "gpt-4" assert result_data["messages"] == [{"role": "user", "content": "Hello"}] + + +@pytest.mark.asyncio +async def test_async_anthropic_messages_handler_extra_headers(): + """ + Test that async_anthropic_messages_handler correctly extracts and merges + extra_headers from kwargs with proper priority. + """ + handler = BaseLLMHTTPHandler() + + # Mock the config + mock_config = Mock() + mock_config.validate_anthropic_messages_environment = Mock( + return_value=({"x-api-key": "test-key"}, "https://api.anthropic.com") + ) + mock_config.transform_anthropic_messages_request = Mock( + return_value={"model": "claude-3-opus-20240229", "messages": []} + ) + + # Mock the client + mock_client = AsyncMock() + mock_response = Mock() + mock_response.status_code = 200 + mock_response.json.return_value = { + "id": "msg_123", + "type": "message", + "role": "assistant", + "content": [{"type": "text", "text": "Hello!"}], + "model": "claude-3-opus-20240229", + "stop_reason": "end_turn", + } + mock_client.post = AsyncMock(return_value=mock_response) + + # Mock logging object + mock_logging_obj = Mock() + mock_logging_obj.update_environment_variables = Mock() + mock_logging_obj.model_call_details = {} + mock_logging_obj.stream = False + + # Test case 1: Only extra_headers in kwargs + kwargs = { + "extra_headers": { + "X-Custom-Header": "from-kwargs", + "X-Auth-Token": "token123", + } + } + + with patch( + "litellm.litellm_core_utils.get_provider_specific_headers.ProviderSpecificHeaderUtils.get_provider_specific_headers" + ) as mock_provider_headers: + mock_provider_headers.return_value = None + + # Capture what headers are passed to validate_anthropic_messages_environment + captured_headers = {} + def capture_validate(*args, **kwargs): + captured_headers.update(kwargs.get("headers", {})) + return ({"x-api-key": "test-key"}, "https://api.anthropic.com") + + mock_config.validate_anthropic_messages_environment = capture_validate + + try: + await handler.async_anthropic_messages_handler( + model="claude-3-opus-20240229", + messages=[{"role": "user", "content": "Hello"}], + anthropic_messages_provider_config=mock_config, + anthropic_messages_optional_request_params={}, + custom_llm_provider="anthropic", + litellm_params=GenericLiteLLMParams(), + logging_obj=mock_logging_obj, + client=mock_client, + kwargs=kwargs, + ) + except Exception: + pass # We're testing header extraction, not the full flow + + # Verify extra_headers were extracted and merged + assert "X-Custom-Header" in captured_headers + assert captured_headers["X-Custom-Header"] == "from-kwargs" + assert "X-Auth-Token" in captured_headers + assert captured_headers["X-Auth-Token"] == "token123" + + +@pytest.mark.asyncio +async def test_async_anthropic_messages_handler_header_priority(): + """ + Test that async_anthropic_messages_handler respects header priority: + forwarded < extra_headers < provider_specific + """ + handler = BaseLLMHTTPHandler() + + # Mock the config + mock_config = Mock() + mock_client = AsyncMock() + mock_logging_obj = Mock() + mock_logging_obj.update_environment_variables = Mock() + mock_logging_obj.model_call_details = {} + mock_logging_obj.stream = False + + # Test with all three header sources + kwargs = { + "headers": {"X-Priority": "forwarded", "X-Forwarded-Only": "keep"}, + "extra_headers": {"X-Priority": "extra", "X-Extra-Only": "also-keep"}, + } + + with patch( + "litellm.litellm_core_utils.get_provider_specific_headers.ProviderSpecificHeaderUtils.get_provider_specific_headers" + ) as mock_provider_headers: + mock_provider_headers.return_value = { + "X-Priority": "provider", + "X-Provider-Only": "keep-this-too" + } + + captured_headers = {} + def capture_validate(*args, **kwargs): + captured_headers.update(kwargs.get("headers", {})) + return ({"x-api-key": "test-key"}, "https://api.anthropic.com") + + mock_config.validate_anthropic_messages_environment = capture_validate + mock_config.transform_anthropic_messages_request = Mock( + return_value={"model": "claude-3-opus-20240229", "messages": []} + ) + + try: + await handler.async_anthropic_messages_handler( + model="claude-3-opus-20240229", + messages=[{"role": "user", "content": "Hello"}], + anthropic_messages_provider_config=mock_config, + anthropic_messages_optional_request_params={}, + custom_llm_provider="anthropic", + litellm_params=GenericLiteLLMParams(), + logging_obj=mock_logging_obj, + client=mock_client, + kwargs=kwargs, + ) + except Exception: + pass + + # Verify priority: provider_specific should win + assert captured_headers["X-Priority"] == "provider" + # Verify all unique headers from different sources are present + assert captured_headers["X-Forwarded-Only"] == "keep" + assert captured_headers["X-Extra-Only"] == "also-keep" + assert captured_headers["X-Provider-Only"] == "keep-this-too" From 661bccbc3984396b13900ee9069754dca244e83d Mon Sep 17 00:00:00 2001 From: Colin Lin Date: Mon, 1 Dec 2025 14:15:54 -0500 Subject: [PATCH 189/248] fixed flaky test by sorting list --- .../llms/bedrock/test_anthropic_beta_support.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py index bd64670517..7de2294954 100644 --- a/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py +++ b/tests/test_litellm/llms/bedrock/test_anthropic_beta_support.py @@ -80,7 +80,8 @@ class TestAnthropicBetaHeaderSupport: assert "additionalModelRequestFields" in result additional_fields = result["additionalModelRequestFields"] assert "anthropic_beta" in additional_fields - assert additional_fields["anthropic_beta"] == ["context-1m-2025-08-07", "interleaved-thinking-2025-05-14"] + # Sort both arrays before comparing to avoid flakiness from ordering differences + assert sorted(additional_fields["anthropic_beta"]) == sorted(["context-1m-2025-08-07", "interleaved-thinking-2025-05-14"]) def test_messages_transformation_anthropic_beta(self): """Test that Messages API transformation includes anthropic_beta in request.""" @@ -96,7 +97,8 @@ class TestAnthropicBetaHeaderSupport: ) assert "anthropic_beta" in result - assert result["anthropic_beta"] == ["output-128k-2025-02-19"] + # Sort both arrays before comparing to avoid flakiness from ordering differences + assert sorted(result["anthropic_beta"]) == sorted(["output-128k-2025-02-19"]) def test_converse_computer_use_compatibility(self): """Test that user anthropic_beta headers work with computer use tools.""" @@ -287,4 +289,4 @@ class TestAnthropicBetaHeaderSupport: assert "prompt-caching-2024-07-31" not in result["anthropic_beta"] else: # If no beta headers, that's also fine - assert True \ No newline at end of file + assert True From 69a6c25a5ff0c04799b597c602e2853f011d7819 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 1 Dec 2025 12:28:53 -0800 Subject: [PATCH 190/248] Add user alias to user table --- .../src/components/view_users/columns.tsx | 6 + .../src/components/view_users/table.test.tsx | 187 ++++++------------ .../src/components/view_users/types.ts | 1 + 3 files changed, 68 insertions(+), 126 deletions(-) diff --git a/ui/litellm-dashboard/src/components/view_users/columns.tsx b/ui/litellm-dashboard/src/components/view_users/columns.tsx index 20df4fc246..48895c69de 100644 --- a/ui/litellm-dashboard/src/components/view_users/columns.tsx +++ b/ui/litellm-dashboard/src/components/view_users/columns.tsx @@ -46,6 +46,12 @@ export const columns = ( enableSorting: true, cell: ({ row }) => {possibleUIRoles?.[row.original.user_role]?.ui_label || "-"}, }, + { + header: "User Alias", + accessorKey: "user_alias", + enableSorting: false, + cell: ({ row }) => {row.original.user_alias || "-"}, + }, { header: "Spend (USD)", accessorKey: "spend", diff --git a/ui/litellm-dashboard/src/components/view_users/table.test.tsx b/ui/litellm-dashboard/src/components/view_users/table.test.tsx index 8ef887932c..c688d5749d 100644 --- a/ui/litellm-dashboard/src/components/view_users/table.test.tsx +++ b/ui/litellm-dashboard/src/components/view_users/table.test.tsx @@ -1,63 +1,52 @@ import { act, fireEvent, render, screen } from "@testing-library/react"; import { describe, expect, it, vi } from "vitest"; - import { UserDataTable } from "./table"; +const defaultFilters = { + email: "", + user_id: "", + user_role: "", + sso_user_id: "", + team: "", + model: "", + min_spend: null, + max_spend: null, + sort_by: "", + sort_order: "asc" as const, +}; + +const getDefaultProps = () => ({ + data: [] as any[], + columns: [] as any[], + accessToken: null, + userRole: "Admin", + possibleUIRoles: null as Record> | null, + filters: defaultFilters, + updateFilters: vi.fn(), + initialFilters: defaultFilters, + teams: [] as any[], + handleEdit: vi.fn(), + handleDelete: vi.fn(), + handleResetPassword: vi.fn(), + userListResponse: { users: [], total: 0, page: 1, page_size: 25, total_pages: 1 }, + currentPage: 1, + handlePageChange: vi.fn(), +}); + describe("UserDataTable", () => { it("should render the UserDataTable component", () => { - const filters = { - email: "", - user_id: "", - user_role: "", - sso_user_id: "", - team: "", - model: "", - min_spend: null, - max_spend: null, - sort_by: "", - sort_order: "asc" as const, - }; - - const updateFilters = vi.fn(); - - render( - , - ); + render(); expect(screen.getByText("Filters")).toBeInTheDocument(); }); it("should call onSortChange when clicking a sortable header", () => { const filters = { - email: "", - user_id: "", - user_role: "", - sso_user_id: "", - team: "", - model: "", - min_spend: null, - max_spend: null, + ...defaultFilters, sort_by: "created_at", sort_order: "desc" as const, }; - const updateFilters = vi.fn(); const onSortChange = vi.fn(); const possibleUIRoles = { @@ -67,21 +56,10 @@ describe("UserDataTable", () => { render( , @@ -96,41 +74,7 @@ describe("UserDataTable", () => { }); it("should show skeleton loaders when isLoading is true", () => { - const filters = { - email: "", - user_id: "", - user_role: "", - sso_user_id: "", - team: "", - model: "", - min_spend: null, - max_spend: null, - sort_by: "", - sort_order: "asc" as const, - }; - - const updateFilters = vi.fn(); - - render( - , - ); + render(); expect(screen.queryByText(/Showing/i)).not.toBeInTheDocument(); expect(screen.queryByRole("button", { name: /Previous/i })).not.toBeInTheDocument(); @@ -138,44 +82,35 @@ describe("UserDataTable", () => { }); it("should show actual content when isLoading is false", () => { - const filters = { - email: "", - user_id: "", - user_role: "", - sso_user_id: "", - team: "", - model: "", - min_spend: null, - max_spend: null, - sort_by: "", - sort_order: "asc" as const, - }; - - const updateFilters = vi.fn(); - - render( - , - ); + render(); expect(screen.getByText(/Showing/i)).toBeInTheDocument(); expect(screen.getByRole("button", { name: /Previous/i })).toBeInTheDocument(); expect(screen.getByRole("button", { name: /Next/i })).toBeInTheDocument(); }); + + it("should render all column headers", () => { + const possibleUIRoles = { + admin: { ui_label: "Admin" }, + user: { ui_label: "User" }, + }; + + render(); + + [ + "User ID", + "Email", + "Global Proxy Role", + "User Alias", + "Spend (USD)", + "Budget (USD)", + "SSO ID", + "API Keys", + "Created At", + "Updated At", + "Actions", + ].forEach((header) => { + expect(screen.getByRole("columnheader", { name: header })).toBeInTheDocument(); + }); + }); }); diff --git a/ui/litellm-dashboard/src/components/view_users/types.ts b/ui/litellm-dashboard/src/components/view_users/types.ts index d976d46ebc..d674db5c7d 100644 --- a/ui/litellm-dashboard/src/components/view_users/types.ts +++ b/ui/litellm-dashboard/src/components/view_users/types.ts @@ -1,6 +1,7 @@ export interface UserInfo { user_id: string; user_email: string; + user_alias: string | null; user_role: string; spend: number; max_budget: number | null; From a73bd751fcc6895fa27b801d61b11acb03f91e65 Mon Sep 17 00:00:00 2001 From: YutaSaito <36355491+uc4w6c@users.noreply.github.com> Date: Tue, 2 Dec 2025 05:38:49 +0900 Subject: [PATCH 191/248] doc: add images for tool permission guardrail (#17322) --- .../img/create_guard_tool_permission.png | Bin 0 -> 51115 bytes 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zUafzxggneEY2@Z&5UrBgxYQjb4({+<*(!XUu3lagH?$wJDMxrP>A!OviJ8}0YCO_O zn#;wgLA_rw9j;Uy5_6o)eFe@86lKu;`lFpBI(I#Y7L~ zWE_BBp({NZiWtpK;M+Ro+>|dJ$Ml41*p%j%@o3y;XXkaZB>Bi=WWLTX=EWf+u-z2_ z_r1(%Cj8Ld+<}7q_&Sj<=j*t)#?sVwb8*a}b%TzvO+<-sT+E<3T&YUXD|v@^rUi?c zAzRja6I+cdd``E^p;k)O-W6inOxg#t?(2@ZGHx-yZnn8YNJ++FCf0|tx@ol6L1I`M z1IdHfdZ)Fg9^?E*p8dQ*=B45jQEg4@Q9S$HsSP#ssj3qurD~P4n$SyU9z;g=ytQA+ zTl0t0x_h_J3D*@hXsxeg2+uyS++FVX1SO?7rjl&u<8G#UInY0tSR7XlZ#aylsHb{W zj!fi1Ql3|(NH^rtxRip9Sl*G!_}F^g#7Q*Dpq}#Z+Yebn=07=*J#3G(-N;v0M{$K+ zp~ziXX{4N{IVYd?s3jjRfsLe|HT&d{X(Z|-)u(tY8&`BT02?W$p~W8PUXIMC8vO2y zTb7ku$TyMY)h$8xME1-9KRd;*cq;| zb@9?&E Date: Mon, 1 Dec 2025 12:55:47 -0800 Subject: [PATCH 192/248] [Feat] WatsonX - allow passing zen_api_key dynamically (#16655) * test_watsonx_zen_api_key_from_client * zen api key * docs using zen api key --- .../docs/providers/watsonx/index.md | 53 ++++ litellm/llms/anthropic/skills/readme.md | 286 +++++++++++++++++- litellm/llms/watsonx/common_utils.py | 6 +- .../test_litellm/llms/watsonx/test_watsonx.py | 90 ++++++ 4 files changed, 422 insertions(+), 13 deletions(-) diff --git a/docs/my-website/docs/providers/watsonx/index.md b/docs/my-website/docs/providers/watsonx/index.md index 279d2d1024..14e0c07c08 100644 --- a/docs/my-website/docs/providers/watsonx/index.md +++ b/docs/my-website/docs/providers/watsonx/index.md @@ -175,3 +175,56 @@ For all available models, see [watsonx.ai documentation](https://dataplatform.cl For all available embedding models, see [watsonx.ai embedding documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). + +## Advanced + +### Using Zen API Key + +You can use a Zen API key for long-term authentication instead of generating IAM tokens. Pass it either as an environment variable or as a parameter: + +```python +import os +from litellm import completion + +# Option 1: Set as environment variable +os.environ["WATSONX_ZENAPIKEY"] = "your-zen-api-key" + +response = completion( + model="watsonx/ibm/granite-13b-chat-v2", + messages=[{"content": "What is your favorite color?", "role": "user"}], + project_id="your-project-id" +) + +# Option 2: Pass as parameter +response = completion( + model="watsonx/ibm/granite-13b-chat-v2", + messages=[{"content": "What is your favorite color?", "role": "user"}], + zen_api_key="your-zen-api-key", + project_id="your-project-id" +) +``` + +**Using with LiteLLM Proxy via OpenAI client:** + +```python +import openai + +client = openai.OpenAI( + api_key="sk-1234", # LiteLLM proxy key + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="watsonx/ibm/granite-3-3-8b-instruct", + messages=[{"role": "user", "content": "What is your favorite color?"}], + max_tokens=2048, + extra_body={ + "project_id": "your-project-id", + "zen_api_key": "your-zen-api-key" + } +) +``` + +See [IBM documentation](https://www.ibm.com/docs/en/watsonx/w-and-w/2.2.0?topic=keys-generating-zenapikey-authorization-tokens) for more information on generating Zen API keys. + + diff --git a/litellm/llms/anthropic/skills/readme.md b/litellm/llms/anthropic/skills/readme.md index 898639cd44..0602272256 100644 --- a/litellm/llms/anthropic/skills/readme.md +++ b/litellm/llms/anthropic/skills/readme.md @@ -1,17 +1,279 @@ -# Anthropic Skills API +# Anthropic Skills API Integration -This folder maintains the integration for the Anthropic Skills API. +This module provides comprehensive support for the Anthropic Skills API through LiteLLM. -You can do the following with the Anthropic Skills API: +## Features -1. Create a new skill -2. List all skills -3. Get a skill -4. Delete a skill +The Skills API allows you to: +- **Create skills**: Define reusable AI capabilities +- **List skills**: Browse all available skills +- **Get skills**: Retrieve detailed information about a specific skill +- **Delete skills**: Remove skills that are no longer needed +## Quick Start -Versions: - - Create Skill Version - - List Skill Versions - - Get Skill Version - - Delete Skill Version \ No newline at end of file +### Prerequisites + +Set your Anthropic API key: +```python +import os +os.environ["ANTHROPIC_API_KEY"] = "your-api-key-here" +``` + +### Basic Usage + +#### Create a Skill + +```python +import litellm + +# Create a skill with files +# Note: All files must be in the same top-level directory +# and must include a SKILL.md file at the root +skill = litellm.create_skill( + files=[ + # List of file objects to upload + # Must include SKILL.md + ], + display_title="Python Code Generator", + custom_llm_provider="anthropic" +) +print(f"Created skill: {skill.id}") + +# Asynchronous version +skill = await litellm.acreate_skill( + files=[...], # Your files here + display_title="Python Code Generator", + custom_llm_provider="anthropic" +) +``` + +#### List Skills + +```python +# List all skills +skills = litellm.list_skills( + custom_llm_provider="anthropic" +) + +for skill in skills.data: + print(f"{skill.display_title}: {skill.id}") + +# With pagination and filtering +skills = litellm.list_skills( + limit=20, + source="custom", # Filter by 'custom' or 'anthropic' + custom_llm_provider="anthropic" +) + +# Get next page if available +if skills.has_more: + next_page = litellm.list_skills( + page=skills.next_page, + custom_llm_provider="anthropic" + ) +``` + +#### Get a Skill + +```python +skill = litellm.get_skill( + skill_id="skill_abc123", + custom_llm_provider="anthropic" +) + +print(f"Skill: {skill.display_title}") +print(f"Created: {skill.created_at}") +print(f"Latest version: {skill.latest_version}") +print(f"Source: {skill.source}") +``` + +#### Delete a Skill + +```python +result = litellm.delete_skill( + skill_id="skill_abc123", + custom_llm_provider="anthropic" +) + +print(f"Deleted skill {result.id}, type: {result.type}") +``` + +## API Reference + +### `create_skill()` + +Create a new skill. + +**Parameters:** +- `files` (List[Any], optional): Files to upload for the skill. All files must be in the same top-level directory and must include a SKILL.md file at the root. +- `display_title` (str, optional): Display title for the skill +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `Skill`: The created skill object + +**Async version:** `acreate_skill()` + +### `list_skills()` + +List all skills. + +**Parameters:** +- `limit` (int, optional): Number of results to return per page (max 100, default 20) +- `page` (str, optional): Pagination token for fetching a specific page of results +- `source` (str, optional): Filter skills by source ('custom' or 'anthropic') +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `ListSkillsResponse`: Object containing a list of skills and pagination info + +**Async version:** `alist_skills()` + +### `get_skill()` + +Get a specific skill by ID. + +**Parameters:** +- `skill_id` (str, required): The skill ID +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `Skill`: The requested skill object + +**Async version:** `aget_skill()` + +### `delete_skill()` + +Delete a skill. + +**Parameters:** +- `skill_id` (str, required): The skill ID to delete +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `DeleteSkillResponse`: Object with `id` and `type` fields + +**Async version:** `adelete_skill()` + +## Response Types + +### `Skill` + +Represents a skill from the Anthropic Skills API. + +**Fields:** +- `id` (str): Unique identifier +- `created_at` (str): ISO 8601 timestamp +- `display_title` (str, optional): Display title +- `latest_version` (str, optional): Latest version identifier +- `source` (str): Source ("custom" or "anthropic") +- `type` (str): Object type (always "skill") +- `updated_at` (str): ISO 8601 timestamp + +### `ListSkillsResponse` + +Response from listing skills. + +**Fields:** +- `data` (List[Skill]): List of skills +- `next_page` (str, optional): Pagination token for the next page +- `has_more` (bool): Whether more skills are available + +### `DeleteSkillResponse` + +Response from deleting a skill. + +**Fields:** +- `id` (str): The deleted skill ID +- `type` (str): Deleted object type (always "skill_deleted") + +## Architecture + +The Skills API implementation follows LiteLLM's standard patterns: + +1. **Type Definitions** (`litellm/types/llms/anthropic_skills.py`) + - Pydantic models for request/response types + - TypedDict definitions for request parameters + +2. **Base Configuration** (`litellm/llms/base_llm/skills/transformation.py`) + - Abstract base class `BaseSkillsAPIConfig` + - Defines transformation interface for provider-specific implementations + +3. **Provider Implementation** (`litellm/llms/anthropic/skills/transformation.py`) + - `AnthropicSkillsConfig` - Anthropic-specific transformations + - Handles API authentication, URL construction, and response mapping + +4. **Main Handler** (`litellm/skills/main.py`) + - Public API functions (sync and async) + - Request validation and routing + - Error handling + +5. **HTTP Handlers** (`litellm/llms/custom_httpx/llm_http_handler.py`) + - Low-level HTTP request/response handling + - Connection pooling and retry logic + +## Beta API Support + +The Skills API is in beta. The beta header (`skills-2025-10-02`) is automatically added by the Anthropic provider configuration. You can customize it if needed: + +```python +skill = litellm.create_skill( + display_title="My Skill", + extra_headers={ + "anthropic-beta": "skills-2025-10-02" # Or any other beta version + }, + custom_llm_provider="anthropic" +) +``` + +The default beta version is configured in `litellm.constants.ANTHROPIC_SKILLS_API_BETA_VERSION`. + +## Error Handling + +All Skills API functions follow LiteLLM's standard error handling: + +```python +import litellm + +try: + skill = litellm.create_skill( + display_title="My Skill", + custom_llm_provider="anthropic" + ) +except litellm.exceptions.AuthenticationError as e: + print(f"Authentication failed: {e}") +except litellm.exceptions.RateLimitError as e: + print(f"Rate limit exceeded: {e}") +except litellm.exceptions.APIError as e: + print(f"API error: {e}") +``` + +## Contributing + +To add support for Skills API to a new provider: + +1. Create provider-specific configuration class inheriting from `BaseSkillsAPIConfig` +2. Implement all abstract methods for request/response transformations +3. Register the config in `ProviderConfigManager.get_provider_skills_api_config()` +4. Add appropriate tests + +## Related Documentation + +- [Anthropic Skills API Documentation](https://platform.claude.com/docs/en/api/beta/skills/create) +- [LiteLLM Responses API](../../../responses/) +- [Provider Configuration System](../../base_llm/) + +## Support + +For issues or questions: +- GitHub Issues: https://github.com/BerriAI/litellm/issues +- Discord: https://discord.gg/wuPM9dRgDw diff --git a/litellm/llms/watsonx/common_utils.py b/litellm/llms/watsonx/common_utils.py index 58b33097cb..0207020534 100644 --- a/litellm/llms/watsonx/common_utils.py +++ b/litellm/llms/watsonx/common_utils.py @@ -252,9 +252,13 @@ class IBMWatsonXMixin: Optional[str], optional_params.get("token") or get_secret_str("WATSONX_TOKEN"), ) + zen_api_key = cast( + Optional[str], + optional_params.pop("zen_api_key", None) or get_secret_str("WATSONX_ZENAPIKEY"), + ) if token: headers["Authorization"] = f"Bearer {token}" - elif zen_api_key := get_secret_str("WATSONX_ZENAPIKEY"): + elif zen_api_key: headers["Authorization"] = f"ZenApiKey {zen_api_key}" else: token = _generate_watsonx_token(api_key=api_key, token=token) diff --git a/tests/test_litellm/llms/watsonx/test_watsonx.py b/tests/test_litellm/llms/watsonx/test_watsonx.py index a41316bb47..fc45a13c2c 100644 --- a/tests/test_litellm/llms/watsonx/test_watsonx.py +++ b/tests/test_litellm/llms/watsonx/test_watsonx.py @@ -414,3 +414,93 @@ def test_watsonx_chat_completion_with_reasoning_effort(monkeypatch): assert ( json_data["reasoning_effort"] == "low" ), "The value of 'reasoning_effort' should be 'low'." + + +def test_watsonx_zen_api_key_from_client(monkeypatch, watsonx_chat_completion_call): + """ + Test that zen_api_key can be passed from client code and is used in Authorization header. + """ + monkeypatch.setenv("WATSONX_PROJECT_ID", "test-project-id") + monkeypatch.setenv("WATSONX_API_BASE", "https://test-api.watsonx.ai") + + model = "watsonx/ibm/granite-3-3-8b-instruct" + messages = [{"role": "user", "content": "What is your favorite color?"}] + + client = HTTPHandler() + + zen_api_key = "U1ZDLWQo=" + + # No need to patch token call since zen_api_key should skip token generation + with patch.object(client, "post") as mock_post: + try: + completion( + model=model, + messages=messages, + api_key="test_api_key", + client=client, + zen_api_key=zen_api_key, + ) + except Exception as e: + print(f"Caught expected exception: {e}") + + # Verify the request was made + assert mock_post.call_count == 1, "The completion endpoint should have been called once." + + # Get the headers sent in the POST request + request_kwargs = mock_post.call_args.kwargs + headers = request_kwargs["headers"] + + print("\nHeaders sent to WatsonX API:") + print(json.dumps(dict(headers), indent=2)) + + # Verify Authorization header uses ZenApiKey format + assert "Authorization" in headers, "Authorization header should be present." + assert headers["Authorization"] == f"ZenApiKey {zen_api_key}", ( + f"Authorization header should use ZenApiKey format. " + f"Expected: 'ZenApiKey {zen_api_key}', Got: '{headers['Authorization']}'" + ) + + +def test_watsonx_zen_api_key_from_env(monkeypatch, watsonx_chat_completion_call): + """ + Test that zen_api_key from environment variable is used in Authorization header. + """ + monkeypatch.setenv("WATSONX_PROJECT_ID", "test-project-id") + monkeypatch.setenv("WATSONX_API_BASE", "https://test-api.watsonx.ai") + + zen_api_key = "U1ZDLWxpdG--===" + monkeypatch.setenv("WATSONX_ZENAPIKEY", zen_api_key) + + model = "watsonx/ibm/granite-3-3-8b-instruct" + messages = [{"role": "user", "content": "What is your favorite color?"}] + + client = HTTPHandler() + + # No need to patch token call since zen_api_key should skip token generation + with patch.object(client, "post") as mock_post: + try: + completion( + model=model, + messages=messages, + api_key="test_api_key", + client=client, + ) + except Exception as e: + print(f"Caught expected exception: {e}") + + # Verify the request was made + assert mock_post.call_count == 1, "The completion endpoint should have been called once." + + # Get the headers sent in the POST request + request_kwargs = mock_post.call_args.kwargs + headers = request_kwargs["headers"] + + print("\nHeaders sent to WatsonX API:") + print(json.dumps(dict(headers), indent=2)) + + # Verify Authorization header uses ZenApiKey format + assert "Authorization" in headers, "Authorization header should be present." + assert headers["Authorization"] == f"ZenApiKey {zen_api_key}", ( + f"Authorization header should use ZenApiKey format. " + f"Expected: 'ZenApiKey {zen_api_key}', Got: '{headers['Authorization']}'" + ) From 21baa354cc17b6f706f3bf93c9c8ba024a72bdb0 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Mon, 1 Dec 2025 13:13:39 -0800 Subject: [PATCH 193/248] Standardize API Key vs Virtual Key in UI --- .../src/components/activity_metrics.tsx | 7 ++- .../components/bulk_create_users_button.tsx | 8 +-- .../src/components/cache_dashboard.tsx | 12 ++--- .../PassThroughSecuritySection.tsx | 16 ++---- .../src/components/dashboard_default_team.tsx | 3 +- .../src/components/entity_usage.tsx | 2 +- .../src/components/make_agent_public_form.tsx | 16 ++---- .../src/components/make_mcp_public_form.tsx | 49 ++++++++----------- .../src/components/make_model_public_form.tsx | 4 +- .../src/components/mcp_tools/mcp_connect.tsx | 10 ++-- .../src/components/new_usage.test.tsx | 2 +- .../src/components/new_usage.tsx | 2 +- .../organisms/create_key_button.tsx | 14 +++--- .../organisms/regenerate_key_modal.tsx | 10 ++-- .../components/playground/chat_ui/ChatUI.tsx | 6 +-- .../playground/compareUI/CompareUI.tsx | 6 +-- .../llm_calls/anthropic_messages.tsx | 2 +- .../playground/llm_calls/embeddings_api.tsx | 2 +- .../playground/llm_calls/responses_api.tsx | 2 +- .../KeyInfoView.handleKeyUpdate.test.tsx | 2 +- .../components/templates/key_info_view.tsx | 8 +-- .../components/templates/view_key_table.tsx | 20 ++++---- ui/litellm-dashboard/src/components/usage.tsx | 2 +- .../src/components/view_users/columns.tsx | 4 +- .../components/view_users/user_info_view.tsx | 10 ++-- 25 files changed, 99 insertions(+), 120 deletions(-) diff --git a/ui/litellm-dashboard/src/components/activity_metrics.tsx b/ui/litellm-dashboard/src/components/activity_metrics.tsx index 1878e1364d..a791ece9bb 100644 --- a/ui/litellm-dashboard/src/components/activity_metrics.tsx +++ b/ui/litellm-dashboard/src/components/activity_metrics.tsx @@ -38,10 +38,9 @@ const ModelSection = ({ modelName, metrics }: { modelName: string; metrics: Mode - {/* Top API Keys Section */} {metrics.top_api_keys && metrics.top_api_keys.length > 0 && ( - Top API Keys by Spend + Top Virtual Keys by Spend ); @@ -267,8 +261,8 @@ const MakeMCPPublicForm: React.FC = ({
- Total: {selectedServers.size} MCP server{selectedServers.size !== 1 ? "s" : ""} will be made - public + Total: {selectedServers.size} MCP server{selectedServers.size !== 1 ? "s" : ""} will be + made public
{metrics.top_api_keys.map((keyData, index) => ( @@ -384,12 +383,12 @@ export const processActivityData = ( }); }); - // Process API key breakdowns for each metric (skip if key is 'api_keys' to avoid duplication) + // Process Virtual Key breakdowns for each metric (skip if key is 'api_keys' to avoid duplication) if (key !== "api_keys") { Object.entries(modelMetrics).forEach(([model, _]) => { const apiKeyBreakdown: Record = {}; - // Aggregate API key data across all days + // Aggregate Virtual Key data across all days dailyActivity.results.forEach((day) => { const modelData = day.breakdown[key]?.[model]; if (modelData && "api_key_breakdown" in modelData) { diff --git a/ui/litellm-dashboard/src/components/bulk_create_users_button.tsx b/ui/litellm-dashboard/src/components/bulk_create_users_button.tsx index d34b14ceab..a8046d146a 100644 --- a/ui/litellm-dashboard/src/components/bulk_create_users_button.tsx +++ b/ui/litellm-dashboard/src/components/bulk_create_users_button.tsx @@ -569,7 +569,7 @@ const BulkCreateUsersButton: React.FC = ({
  • Download our CSV template
  • Add your users' information to the spreadsheet
  • Save the file and upload it here
    • -
  • After creation, download the results file containing the API keys for each user
    • +
  • After creation, download the results file containing the Virtual Keys for each user
    • @@ -809,9 +809,9 @@ const BulkCreateUsersButton: React.FC = ({
    User creation complete - Next step: Download the credentials file containing API - keys and invitation links. Users will need these API keys to make LLM requests through - LiteLLM. + Next step: Download the credentials file containing + Virtual Keys and invitation links. Users will need these Virtual Keys to make LLM requests + through LiteLLM.
    diff --git a/ui/litellm-dashboard/src/components/cache_dashboard.tsx b/ui/litellm-dashboard/src/components/cache_dashboard.tsx index a1c0cb0a66..38c0f1a8f4 100644 --- a/ui/litellm-dashboard/src/components/cache_dashboard.tsx +++ b/ui/litellm-dashboard/src/components/cache_dashboard.tsx @@ -293,7 +293,11 @@ const CacheDashboard: React.FC = ({ accessToken, token, userRole - + {uniqueApiKeys.map((key) => ( {key} @@ -388,11 +392,7 @@ const CacheDashboard: React.FC = ({ accessToken, token, userRole /> - + diff --git a/ui/litellm-dashboard/src/components/common_components/PassThroughSecuritySection.tsx b/ui/litellm-dashboard/src/components/common_components/PassThroughSecuritySection.tsx index c42094abb5..c63770d3c8 100644 --- a/ui/litellm-dashboard/src/components/common_components/PassThroughSecuritySection.tsx +++ b/ui/litellm-dashboard/src/components/common_components/PassThroughSecuritySection.tsx @@ -21,7 +21,7 @@ const PassThroughSecuritySection: React.FC = ({ Security - When enabled, requests to this endpoint will require a valid LiteLLM API key + When enabled, requests to this endpoint will require a valid LiteLLM Virtual Key {premiumUser ? ( @@ -35,22 +35,13 @@ const PassThroughSecuritySection: React.FC = ({ ) : (
    - + Authentication (Premium)
    Setting authentication for pass-through endpoints is a LiteLLM Enterprise feature. Get a trial key{" "} - + here . @@ -63,4 +54,3 @@ const PassThroughSecuritySection: React.FC = ({ }; export default PassThroughSecuritySection; - diff --git a/ui/litellm-dashboard/src/components/dashboard_default_team.tsx b/ui/litellm-dashboard/src/components/dashboard_default_team.tsx index 36805b8912..6506e1a60a 100644 --- a/ui/litellm-dashboard/src/components/dashboard_default_team.tsx +++ b/ui/litellm-dashboard/src/components/dashboard_default_team.tsx @@ -69,7 +69,8 @@ const DashboardTeam: React.FC = ({ Select Team - If you belong to multiple teams, this setting controls which team is used by default when creating new API Keys. + If you belong to multiple teams, this setting controls which team is used by default when creating new Virtual + Keys. Default Team: If no team_id is set for a key, it will be grouped under here. diff --git a/ui/litellm-dashboard/src/components/entity_usage.tsx b/ui/litellm-dashboard/src/components/entity_usage.tsx index a5789b7dba..501eac7124 100644 --- a/ui/litellm-dashboard/src/components/entity_usage.tsx +++ b/ui/litellm-dashboard/src/components/entity_usage.tsx @@ -550,7 +550,7 @@ const EntityUsage: React.FC = ({ {/* Top API Keys */} - Top API Keys + Top Virtual Keys = ({ setLoading(true); try { const agentIdsToMakePublic = Array.from(selectedAgents); - + // Make batch API call for all agents await makeAgentsPublicCall(accessToken, agentIdsToMakePublic); @@ -127,8 +127,8 @@ const MakeAgentPublicForm: React.FC = ({
    - Select the agents you want to be visible on the public model hub. Users will still require a valid API key to - use these agents. + Select the agents you want to be visible on the public model hub. Users will still require a valid Virtual Key + to use these agents.
    @@ -141,10 +141,7 @@ const MakeAgentPublicForm: React.FC = ({ agentHubData.map((agent) => { const agentId = agent.agent_id || agent.name; return ( -
    +
    handleAgentSelection(agentId, e.target.checked)} @@ -217,9 +214,7 @@ const MakeAgentPublicForm: React.FC = ({ )}
    - {agent?.description && ( - {agent.description} - )} + {agent?.description && {agent.description}}
    ); @@ -296,4 +291,3 @@ const MakeAgentPublicForm: React.FC = ({ }; export default MakeAgentPublicForm; - diff --git a/ui/litellm-dashboard/src/components/make_mcp_public_form.tsx b/ui/litellm-dashboard/src/components/make_mcp_public_form.tsx index 29f866f8bc..f7bba17580 100644 --- a/ui/litellm-dashboard/src/components/make_mcp_public_form.tsx +++ b/ui/litellm-dashboard/src/components/make_mcp_public_form.tsx @@ -76,7 +76,7 @@ const MakeMCPPublicForm: React.FC = ({ const publicServerIds = mcpHubData .filter((server) => server.mcp_info?.is_public === true) .map((server) => server.server_id); - + // Preselect servers that are already public setSelectedServers(new Set(publicServerIds)); } @@ -91,7 +91,7 @@ const MakeMCPPublicForm: React.FC = ({ setLoading(true); try { const serverIdsToMakePublic = Array.from(selectedServers); - + // Make batch API call for all servers await makeMCPPublicCall(accessToken, serverIdsToMakePublic); @@ -128,8 +128,8 @@ const MakeMCPPublicForm: React.FC = ({
    - Select the MCP servers you want to be visible on the public model hub. Users will still require a valid API key to - use these servers. + Select the MCP servers you want to be visible on the public model hub. Users will still require a valid + Virtual Key to use these servers.
    @@ -161,22 +161,20 @@ const MakeMCPPublicForm: React.FC = ({ {server.transport} - {server.status || "unknown"}
    - - {server.description || server.url} - + {server.description || server.url} {server.allowed_tools && server.allowed_tools.length > 0 && (
    {server.allowed_tools.slice(0, 3).map((tool, idx) => ( @@ -236,14 +234,14 @@ const MakeMCPPublicForm: React.FC = ({ {server.transport} - {server.status || "unknown"} @@ -251,12 +249,8 @@ const MakeMCPPublicForm: React.FC = ({ )}
    - {server?.description && ( - {server.description} - )} - {server?.url && ( - {server.url} - )} + {server?.description && {server.description}} + {server?.url && {server.url}}
    @@ -333,4 +327,3 @@ const MakeMCPPublicForm: React.FC = ({ }; export default MakeMCPPublicForm; - diff --git a/ui/litellm-dashboard/src/components/make_model_public_form.tsx b/ui/litellm-dashboard/src/components/make_model_public_form.tsx index e67d60fb33..750bdc24ee 100644 --- a/ui/litellm-dashboard/src/components/make_model_public_form.tsx +++ b/ui/litellm-dashboard/src/components/make_model_public_form.tsx @@ -152,8 +152,8 @@ const MakeModelPublicForm: React.FC = ({
    - Select the models you want to be visible on the public model hub. Users will still require a valid API key to - use these models. + Select the models you want to be visible on the public model hub. Users will still require a valid Virtual Key + to use these models. {/* Filters */} diff --git a/ui/litellm-dashboard/src/components/mcp_tools/mcp_connect.tsx b/ui/litellm-dashboard/src/components/mcp_tools/mcp_connect.tsx index 4b4f1ab676..5a012c1fc5 100644 --- a/ui/litellm-dashboard/src/components/mcp_tools/mcp_connect.tsx +++ b/ui/litellm-dashboard/src/components/mcp_tools/mcp_connect.tsx @@ -220,12 +220,12 @@ const MCPConnect: React.FC = ({ currentServerAccessGroups = [] } - title="API Key Setup" - description="Configure your LiteLLM Proxy API key for authentication" + title="Virtual Key Setup" + description="Configure your LiteLLM Proxy Virtual Key for authentication" >
    - Get your API key from your LiteLLM Proxy dashboard or contact your administrator + Get your Virtual Key from your LiteLLM Proxy dashboard or contact your administrator
    @@ -249,7 +249,7 @@ const MCPConnect: React.FC = ({ currentServerAccessGroups = [] = ({ currentServerAccessGroups = [] "server_url": "${proxyBaseUrl}/mcp", "require_approval": "never", "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY", + "x-litellm-api-key": "Bearer YOUR_LITELLM_VIRTUAL_KEY", "x-mcp-servers": ["Zapier_MCP,dev"] } } diff --git a/ui/litellm-dashboard/src/components/new_usage.test.tsx b/ui/litellm-dashboard/src/components/new_usage.test.tsx index a4969124f1..a06045137d 100644 --- a/ui/litellm-dashboard/src/components/new_usage.test.tsx +++ b/ui/litellm-dashboard/src/components/new_usage.test.tsx @@ -239,7 +239,7 @@ describe("NewUsage", () => { // Check for chart titles expect(screen.getByText("Daily Spend")).toBeInTheDocument(); - expect(screen.getByText("Top API Keys")).toBeInTheDocument(); + expect(screen.getByText("Top Virtual Keys")).toBeInTheDocument(); }); it("should switch between tabs correctly", async () => { diff --git a/ui/litellm-dashboard/src/components/new_usage.tsx b/ui/litellm-dashboard/src/components/new_usage.tsx index 4794a7f091..a8d3088549 100644 --- a/ui/litellm-dashboard/src/components/new_usage.tsx +++ b/ui/litellm-dashboard/src/components/new_usage.tsx @@ -580,7 +580,7 @@ const NewUsagePage: React.FC = ({ {/* Top API Keys */} - Top API Keys + Top Virtual Keys = ({ setApiKey(response["key"]); setSoftBudget(response["soft_budget"]); - NotificationsManager.success("API Key Created"); + NotificationsManager.success("Virtual Key Created"); form.resetFields(); localStorage.removeItem("userData" + userID); } catch (error) { @@ -415,7 +415,7 @@ const CreateKey: React.FC = ({ }; const handleCopy = () => { - NotificationsManager.success("API Key copied to clipboard"); + NotificationsManager.success("Virtual Key copied to clipboard"); }; useEffect(() => { @@ -505,7 +505,7 @@ const CreateKey: React.FC = ({ label={ Owned By{" "} - + @@ -594,8 +594,8 @@ const CreateKey: React.FC = ({ {isFormDisabled && (
    - Please select a team to continue configuring your API key. If you do not see any teams, please contact - your Proxy Admin to either provide you with access to models or to add you to a team. + Please select a team to continue configuring your Virtual Key. If you do not see any teams, please + contact your Proxy Admin to either provide you with access to models or to add you to a team.
    )} @@ -1277,7 +1277,7 @@ const CreateKey: React.FC = ({ {apiKey != null ? (
    - API Key: + Virtual Key:
    = ({
    - + {/*
    - New API Key: + New Virtual Key: 
    {regeneratedKey}
    NotificationManager.success("API Key copied to clipboard")} + onCopy={() => NotificationManager.success("Virtual Key copied to clipboard")} > - +     diff --git a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx index 90c43df0dc..c2924c048c 100644 --- a/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx +++ b/ui/litellm-dashboard/src/components/playground/chat_ui/ChatUI.tsx @@ -691,7 +691,7 @@ const ChatUI: React.FC = ({ const effectiveApiKey = apiKeySource === "session" ? accessToken : apiKey; if (!effectiveApiKey) { - NotificationsManager.fromBackend("Please provide an API key or select Current UI Session"); + NotificationsManager.fromBackend("Please provide a Virtual Key or select Current UI Session"); return; } @@ -1003,7 +1003,7 @@ const ChatUI: React.FC = ({
    - API Key Source + Virtual Key Source setApiKeySource(value as "session" | "custom")} @@ -567,7 +567,7 @@ export default function CompareUI({ accessToken, disabledPersonalKeyCreation }: setCustomApiKey(event.target.value)} - placeholder="Enter API key" + placeholder="Enter Virtual Key" className="w-56" /> )} diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx index 47941bce2c..3f8c90424c 100644 --- a/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx +++ b/ui/litellm-dashboard/src/components/playground/llm_calls/anthropic_messages.tsx @@ -20,7 +20,7 @@ export async function makeAnthropicMessagesRequest( selectedMCPTools?: string[], ) { if (!accessToken) { - throw new Error("API key is required"); + throw new Error("Virtual Key is required"); } const isLocal = process.env.NODE_ENV === "development"; diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx index 832d29bb85..d0939c0043 100644 --- a/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx +++ b/ui/litellm-dashboard/src/components/playground/llm_calls/embeddings_api.tsx @@ -9,7 +9,7 @@ export async function makeOpenAIEmbeddingsRequest( tags?: string[], ) { if (!accessToken) { - throw new Error("API key is required"); + throw new Error("Virtual Key is required"); } // Base URL should be the current base_url diff --git a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx index 8461f8e20a..46b0621a0b 100644 --- a/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx +++ b/ui/litellm-dashboard/src/components/playground/llm_calls/responses_api.tsx @@ -24,7 +24,7 @@ export async function makeOpenAIResponsesRequest( onMCPEvent?: (event: MCPEvent) => void, ) { if (!accessToken) { - throw new Error("API key is required"); + throw new Error("Virtual Key is required"); } // Base URL should be the current base_url diff --git a/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx b/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx index f58e392a58..9897bb4d47 100644 --- a/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx +++ b/ui/litellm-dashboard/src/components/templates/KeyInfoView.handleKeyUpdate.test.tsx @@ -245,7 +245,7 @@ import KeyInfoView from "./key_info_view"; const baseKeyData = { token_id: "tok_123", token: "tok_123", - key_alias: "My API Key", + key_alias: "My Virtual Key", key_name: "sk-xxxx", created_at: new Date().toISOString(), updated_at: new Date().toISOString(), diff --git a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx index ec2b294d9d..dbbb195a1f 100644 --- a/ui/litellm-dashboard/src/components/templates/key_info_view.tsx +++ b/ui/litellm-dashboard/src/components/templates/key_info_view.tsx @@ -297,7 +297,7 @@ export default function KeyInfoView({ - {currentKeyData.key_alias || "API Key"} + {currentKeyData.key_alias || "Virtual Key"}
    @@ -381,7 +381,7 @@ export default function KeyInfoView({ {/* Delete Confirmation Modal */} {isDeleteModalOpen && (() => { - const keyName = currentKeyData?.key_alias || currentKeyData?.token_id || "API Key"; + const keyName = currentKeyData?.key_alias || currentKeyData?.token_id || "Virtual Key"; const isValid = deleteConfirmInput === keyName; return (
    @@ -415,7 +415,7 @@ export default function KeyInfoView({

    - Warning: You are about to delete this API key. + Warning: You are about to delete this Virtual Key.

    This action is irreversible and will immediately revoke access for any applications using this @@ -423,7 +423,7 @@ export default function KeyInfoView({

    -

    Are you sure you want to delete this API key?

    +

    Are you sure you want to delete this Virtual Key?

    - Warning: You are about to delete this API key. + Warning: You are about to delete this Virtual Key.

    This action is irreversible and will immediately revoke access for any applications using this @@ -374,7 +374,7 @@ const ViewKeyTable: React.FC = ({

    -

    Are you sure you want to delete this API key?

    +

    Are you sure you want to delete this Virtual Key?

    @@ -417,7 +417,7 @@ const ViewKeyTable: React.FC = ({ {/* Regenerate Key Form Modal */} { setRegenerateDialogVisible(false); @@ -516,7 +516,7 @@ const ViewKeyTable: React.FC = ({ {selectedToken?.key_alias || "No alias set"}
    - New API Key: + New Virtual Key:
    = ({
    NotificationManager.success({ description: "API Key copied to clipboard" })} + onCopy={() => NotificationManager.success({ description: "Virtual Key copied to clipboard" })} > - + diff --git a/ui/litellm-dashboard/src/components/usage.tsx b/ui/litellm-dashboard/src/components/usage.tsx index 88b1e3c3fb..0900a0a9cc 100644 --- a/ui/litellm-dashboard/src/components/usage.tsx +++ b/ui/litellm-dashboard/src/components/usage.tsx @@ -615,7 +615,7 @@ const UsagePage: React.FC = ({ accessToken, token, userRole, use - Top API Keys + Top Virtual Keys ( {row.original.key_count > 0 ? ( - {row.original.key_count} Keys + {row.original.key_count} {row.original.key_count === 1 ? "Key" : "Keys"} ) : ( diff --git a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx index 456f07d188..2caae7d861 100644 --- a/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx +++ b/ui/litellm-dashboard/src/components/view_users/user_info_view.tsx @@ -320,9 +320,11 @@ export default function UserInfoView({ - API Keys + Virtual Keys
    - {userData.keys?.length || 0} keys + + {userData.keys?.length || 0} {userData.keys?.length === 1 ? "Key" : "Keys"} +
    @@ -467,7 +469,7 @@ export default function UserInfoView({
    - API Keys + Virtual Keys
    {userData.keys?.length && userData.keys?.length > 0 ? ( userData.keys.map((key, index) => ( @@ -476,7 +478,7 @@ export default function UserInfoView({ )) ) : ( - No API keys + No Virtual Keys )}
    From 24f847b84c947e13b765edaf172f646fd1b06297 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Dec 2025 13:59:00 -0800 Subject: [PATCH 194/248] [Feat] JWT Auth - AI Gateway, allow using regular OIDC flow with user info endpoints (#17324) * feat: allow fetching OIDC user info * test: use test_auth_builder_with_oidc_userinfo_enabled gets user info when enabled * fix tool permission doc * docs fix diagram --- .../docs/proxy/guardrails/tool_permission.md | 5 - docs/my-website/docs/proxy/token_auth.md | 66 +++++ litellm/proxy/_types.py | 18 ++ litellm/proxy/auth/handle_jwt.py | 76 +++++- .../proxy/auth/test_handle_jwt.py | 227 +++++++++++++++++- 5 files changed, 385 insertions(+), 7 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/tool_permission.md b/docs/my-website/docs/proxy/guardrails/tool_permission.md index 897c31d9da..1827333654 100644 --- a/docs/my-website/docs/proxy/guardrails/tool_permission.md +++ b/docs/my-website/docs/proxy/guardrails/tool_permission.md @@ -1,4 +1,3 @@ -import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; @@ -14,8 +13,6 @@ LiteLLM provides the LiteLLM Tool Permission Guardrail that lets you control whi Open the LiteLLM Dashboard, click **Add New Guardrail**, and choose **LiteLLM Tool Permission Guardrail**. This loads the rule builder UI. -Configure tool permission guardrail in LiteLLM UI - #### Step 2: Define Regex Rules 1. Click **Add Rule**. @@ -24,8 +21,6 @@ Open the LiteLLM Dashboard, click **Add New Guardrail**, and choose **LiteLLM To 4. Optionally add a regex for tool type (e.g., `^function$`). 5. Pick **Allow** or **Deny**. -Configure tool permission guardrail in LiteLLM UI - #### Step 3: Restrict Tool Arguments (Optional) Select **+ Restrict tool arguments** to attach regex validations to nested paths (dot + `[]` notation). This enforces that sensitive parameters (such as `arguments.to[]`) conform to pre-approved formats. diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md index c2a88010d7..c465c1022e 100644 --- a/docs/my-website/docs/proxy/token_auth.md +++ b/docs/my-website/docs/proxy/token_auth.md @@ -407,6 +407,72 @@ general_settings: user_id_upsert: true # 👈 upserts the user to db, if valid email but not in db ``` +## OIDC UserInfo Endpoint + +Use this when your JWT/access token doesn't contain user-identifying information. LiteLLM will call your identity provider's UserInfo endpoint to fetch user details. + +### When to Use + +- Your JWT is opaque (not self-contained) or lacks user claims +- You need to fetch fresh user information from your identity provider +- Your access tokens don't include email, roles, or other identifying data + +### Configuration + +```yaml title="config.yaml" showLineNumbers +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + # Enable OIDC UserInfo endpoint + oidc_userinfo_enabled: true + oidc_userinfo_endpoint: "https://your-idp.com/oauth2/userinfo" + oidc_userinfo_cache_ttl: 300 # Cache for 5 minutes (default: 300) + + # Map fields from UserInfo response + user_id_jwt_field: "sub" + user_email_jwt_field: "email" + user_roles_jwt_field: "roles" +``` + +### Flow Diagram + +```mermaid +sequenceDiagram + participant Client + participant LiteLLM + participant IdP as Identity Provider + + Client->>LiteLLM: Request with Bearer token + Note over LiteLLM: Check cache for UserInfo + + LiteLLM->>IdP: GET /userinfo (if not cached)
    Authorization: Bearer {token} + IdP-->>LiteLLM: User data (sub, email, roles) + + Note over LiteLLM: Cache response (TTL: 5min)
    Extract user_id, email, roles
    Perform RBAC checks + + LiteLLM-->>Client: Authorized/Denied +``` + +### Example: Azure AD + +```yaml title="config.yaml" showLineNumbers +litellm_jwtauth: + oidc_userinfo_enabled: true + oidc_userinfo_endpoint: "https://graph.microsoft.com/oidc/userinfo" + user_id_jwt_field: "sub" + user_email_jwt_field: "email" +``` + +### Example: Keycloak + +```yaml title="config.yaml" showLineNumbers +litellm_jwtauth: + oidc_userinfo_enabled: true + oidc_userinfo_endpoint: "https://keycloak.example.com/realms/your-realm/protocol/openid-connect/userinfo" + user_id_jwt_field: "sub" + user_roles_jwt_field: "resource_access.your-client.roles" +``` + ## [BETA] Control Access with OIDC Roles Allow JWT tokens with supported roles to access the proxy. diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index fe87a70b24..b5b0bd8060 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -3422,6 +3422,9 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase): - public_allowed_routes: list of allowed routes for authenticated but unknown litellm role jwt tokens. - enforce_rbac: If true, enforce RBAC for all routes. - custom_validate: A custom function to validates the JWT token. + - oidc_userinfo_endpoint: OIDC UserInfo endpoint URL. When set along with oidc_userinfo_enabled, LiteLLM will call this endpoint with the access token to retrieve user identity information. + - oidc_userinfo_enabled: Enable fetching user info from OIDC UserInfo endpoint instead of just decoding JWT token. Default: False. + - oidc_userinfo_cache_ttl: TTL (in seconds) for caching UserInfo responses. Default: 300s (5 minutes). See `auth_checks.py` for the specific routes """ @@ -3472,6 +3475,21 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase): jwt_litellm_role_map: Optional[List[JWTLiteLLMRoleMap]] = None sync_user_role_and_teams: bool = False ######################################################### + ######################################################### + # OIDC UserInfo Endpoint Configuration + oidc_userinfo_endpoint: Optional[str] = Field( + default=None, + description="OIDC UserInfo endpoint URL. If set, LiteLLM will call this endpoint with the access token to retrieve user identity information.", + ) + oidc_userinfo_enabled: bool = Field( + default=False, + description="Enable fetching user info from OIDC UserInfo endpoint instead of just decoding JWT token.", + ) + oidc_userinfo_cache_ttl: float = Field( + default=300, + description="TTL (in seconds) for caching UserInfo responses. Default: 300s (5 minutes).", + ) + ######################################################### def __init__(self, **kwargs: Any) -> None: # get the attribute names for this Pydantic model diff --git a/litellm/proxy/auth/handle_jwt.py b/litellm/proxy/auth/handle_jwt.py index 3e18db2d02..ed6877d146 100644 --- a/litellm/proxy/auth/handle_jwt.py +++ b/litellm/proxy/auth/handle_jwt.py @@ -480,6 +480,71 @@ class JWTHandler: else: return False + async def get_oidc_userinfo(self, token: str) -> dict: + """ + Fetch user information from OIDC UserInfo endpoint. + + This follows the OpenID Connect protocol where an access token + is sent to the identity provider's UserInfo endpoint to retrieve + user identity information. + + Args: + token: The access token to use for authentication + + Returns: + dict: User information from the UserInfo endpoint + + Raises: + Exception: If UserInfo endpoint is not configured or request fails + """ + if not self.litellm_jwtauth.oidc_userinfo_endpoint: + raise Exception( + "OIDC UserInfo endpoint not configured. Set 'oidc_userinfo_endpoint' in JWT auth config." + ) + + # Check cache first + cache_key = f"oidc_userinfo_{token[:20]}" # Use first 20 chars of token as cache key + cached_userinfo = await self.user_api_key_cache.async_get_cache(cache_key) + + if cached_userinfo is not None: + verbose_proxy_logger.debug("Returning cached OIDC UserInfo") + return cached_userinfo + + verbose_proxy_logger.debug( + f"Calling OIDC UserInfo endpoint: {self.litellm_jwtauth.oidc_userinfo_endpoint}" + ) + + try: + # Call the UserInfo endpoint with the access token + response = await self.http_handler.get( + url=self.litellm_jwtauth.oidc_userinfo_endpoint, + headers={ + "Authorization": f"Bearer {token}", + "Accept": "application/json", + }, + ) + + if response.status_code != 200: + raise Exception( + f"OIDC UserInfo endpoint returned status {response.status_code}: {response.text}" + ) + + userinfo = response.json() + verbose_proxy_logger.debug(f"Received OIDC UserInfo: {userinfo}") + + # Cache the userinfo response + await self.user_api_key_cache.async_set_cache( + key=cache_key, + value=userinfo, + ttl=self.litellm_jwtauth.oidc_userinfo_cache_ttl, + ) + + return userinfo + + except Exception as e: + verbose_proxy_logger.error(f"Error fetching OIDC UserInfo: {str(e)}") + raise Exception(f"Failed to fetch OIDC UserInfo: {str(e)}") + async def auth_jwt(self, token: str) -> dict: # Supported algos: https://pyjwt.readthedocs.io/en/stable/algorithms.html # "Warning: Make sure not to mix symmetric and asymmetric algorithms that interpret @@ -1077,7 +1142,16 @@ class JWTAuthManager: proxy_logging_obj: ProxyLogging, ) -> JWTAuthBuilderResult: """Main authentication and authorization builder""" - jwt_valid_token: dict = await jwt_handler.auth_jwt(token=api_key) + # Check if OIDC UserInfo endpoint is enabled + if jwt_handler.litellm_jwtauth.oidc_userinfo_enabled: + verbose_proxy_logger.debug( + "OIDC UserInfo is enabled. Fetching user info from UserInfo endpoint." + ) + # Use the access token to fetch user info from OIDC UserInfo endpoint + jwt_valid_token: dict = await jwt_handler.get_oidc_userinfo(token=api_key) + else: + # Default behavior: decode and validate the JWT token + jwt_valid_token = await jwt_handler.auth_jwt(token=api_key) # Check custom validate if jwt_handler.litellm_jwtauth.custom_validate: diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 8f8f3ced07..603a6928f8 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -846,4 +846,229 @@ async def test_auth_builder_returns_team_membership_object(): assert result["team_membership"].user_id == _user_id, "team_membership user_id should match" assert result["team_membership"].team_id == _team_id, "team_membership team_id should match" assert result["team_membership"].budget_id == "budget_123", "team_membership budget_id should match" - assert result["team_membership"].spend == 10.5, "team_membership spend should match" \ No newline at end of file + assert result["team_membership"].spend == 10.5, "team_membership spend should match" + + +@pytest.mark.asyncio +async def test_auth_builder_with_oidc_userinfo_enabled(): + """Test that auth_builder uses OIDC UserInfo endpoint when enabled""" + from unittest.mock import MagicMock + + from litellm.caching import DualCache + from litellm.proxy.utils import ProxyLogging + + # Setup test data + api_key = "test_access_token" + request_data = {"model": "gpt-4"} + general_settings = {"enforce_rbac": False} + route = "/chat/completions" + + user_object = LiteLLM_UserTable( + user_id="test_user_1", user_role=LitellmUserRoles.INTERNAL_USER + ) + + # Create JWT handler with OIDC UserInfo enabled + jwt_handler = JWTHandler() + user_api_key_cache = DualCache() + proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache) + + jwt_handler.update_environment( + prisma_client=None, + user_api_key_cache=user_api_key_cache, + litellm_jwtauth=LiteLLM_JWTAuth( + oidc_userinfo_enabled=True, + oidc_userinfo_endpoint="https://example.com/oauth2/userinfo", + user_id_jwt_field="sub", + user_email_jwt_field="email", + ), + ) + + # Mock OIDC UserInfo response + userinfo_response = { + "sub": "test_user_1", + "email": "test@example.com", + "scope": "", + } + + # Mock all the dependencies + with patch.object( + jwt_handler, "get_oidc_userinfo", new_callable=AsyncMock + ) as mock_get_userinfo, patch.object( + jwt_handler, "auth_jwt", new_callable=AsyncMock + ) as mock_auth_jwt, patch.object( + JWTAuthManager, "check_rbac_role", new_callable=AsyncMock + ) as mock_check_rbac, patch.object( + jwt_handler, "get_rbac_role", return_value=None + ) as mock_get_rbac, patch.object( + jwt_handler, "get_scopes", return_value=[] + ) as mock_get_scopes, patch.object( + jwt_handler, "get_object_id", return_value=None + ) as mock_get_object_id, patch.object( + JWTAuthManager, + "get_user_info", + new_callable=AsyncMock, + return_value=("test_user_1", "test@example.com", True), + ) as mock_get_user_info, patch.object( + jwt_handler, "get_org_id", return_value=None + ) as mock_get_org_id, patch.object( + jwt_handler, "get_end_user_id", return_value=None + ) as mock_get_end_user_id, patch.object( + JWTAuthManager, "check_admin_access", new_callable=AsyncMock, return_value=None + ) as mock_check_admin, patch.object( + JWTAuthManager, + "find_and_validate_specific_team_id", + new_callable=AsyncMock, + return_value=(None, None), + ) as mock_find_team, patch.object( + JWTAuthManager, "get_all_team_ids", return_value=set() + ) as mock_get_all_team_ids, patch.object( + JWTAuthManager, + "find_team_with_model_access", + new_callable=AsyncMock, + return_value=(None, None), + ) as mock_find_team_access, patch.object( + JWTAuthManager, + "get_objects", + new_callable=AsyncMock, + return_value=(user_object, None, None, None), + ) as mock_get_objects, patch.object( + JWTAuthManager, "map_user_to_teams", new_callable=AsyncMock + ) as mock_map_user, patch.object( + JWTAuthManager, "validate_object_id", return_value=True + ) as mock_validate_object, patch.object( + JWTAuthManager, "sync_user_role_and_teams", new_callable=AsyncMock + ) as mock_sync_user: + # Set up mock return values + mock_get_userinfo.return_value = userinfo_response + + # Call auth_builder + result = await JWTAuthManager.auth_builder( + api_key=api_key, + jwt_handler=jwt_handler, + request_data=request_data, + general_settings=general_settings, + route=route, + prisma_client=None, + user_api_key_cache=user_api_key_cache, + parent_otel_span=None, + proxy_logging_obj=proxy_logging_obj, + ) + + # Verify that get_oidc_userinfo was called instead of auth_jwt + mock_get_userinfo.assert_called_once_with(token=api_key) + mock_auth_jwt.assert_not_called() # Should not be called when OIDC is enabled + + # Verify the result + assert result["user_id"] == "test_user_1" + assert result["user_object"] == user_object + + +@pytest.mark.asyncio +async def test_auth_builder_with_oidc_userinfo_disabled(): + """Test that auth_builder uses JWT validation when OIDC UserInfo is disabled""" + from unittest.mock import MagicMock + + from litellm.caching import DualCache + from litellm.proxy.utils import ProxyLogging + + # Setup test data + api_key = "test_jwt_token" + request_data = {"model": "gpt-4"} + general_settings = {"enforce_rbac": False} + route = "/chat/completions" + + user_object = LiteLLM_UserTable( + user_id="test_user_1", user_role=LitellmUserRoles.INTERNAL_USER + ) + + # Create JWT handler with OIDC UserInfo disabled + jwt_handler = JWTHandler() + user_api_key_cache = DualCache() + proxy_logging_obj = ProxyLogging(user_api_key_cache=user_api_key_cache) + + jwt_handler.update_environment( + prisma_client=None, + user_api_key_cache=user_api_key_cache, + litellm_jwtauth=LiteLLM_JWTAuth( + oidc_userinfo_enabled=False, # Disabled + user_id_jwt_field="sub", + ), + ) + + # Mock JWT validation response + jwt_response = { + "sub": "test_user_1", + "scope": "", + } + + # Mock all the dependencies + with patch.object( + jwt_handler, "get_oidc_userinfo", new_callable=AsyncMock + ) as mock_get_userinfo, patch.object( + jwt_handler, "auth_jwt", new_callable=AsyncMock + ) as mock_auth_jwt, patch.object( + JWTAuthManager, "check_rbac_role", new_callable=AsyncMock + ) as mock_check_rbac, patch.object( + jwt_handler, "get_rbac_role", return_value=None + ) as mock_get_rbac, patch.object( + jwt_handler, "get_scopes", return_value=[] + ) as mock_get_scopes, patch.object( + jwt_handler, "get_object_id", return_value=None + ) as mock_get_object_id, patch.object( + JWTAuthManager, + "get_user_info", + new_callable=AsyncMock, + return_value=("test_user_1", None, None), + ) as mock_get_user_info, patch.object( + jwt_handler, "get_org_id", return_value=None + ) as mock_get_org_id, patch.object( + jwt_handler, "get_end_user_id", return_value=None + ) as mock_get_end_user_id, patch.object( + JWTAuthManager, "check_admin_access", new_callable=AsyncMock, return_value=None + ) as mock_check_admin, patch.object( + JWTAuthManager, + "find_and_validate_specific_team_id", + new_callable=AsyncMock, + return_value=(None, None), + ) as mock_find_team, patch.object( + JWTAuthManager, "get_all_team_ids", return_value=set() + ) as mock_get_all_team_ids, patch.object( + JWTAuthManager, + "find_team_with_model_access", + new_callable=AsyncMock, + return_value=(None, None), + ) as mock_find_team_access, patch.object( + JWTAuthManager, + "get_objects", + new_callable=AsyncMock, + return_value=(user_object, None, None, None), + ) as mock_get_objects, patch.object( + JWTAuthManager, "map_user_to_teams", new_callable=AsyncMock + ) as mock_map_user, patch.object( + JWTAuthManager, "validate_object_id", return_value=True + ) as mock_validate_object, patch.object( + JWTAuthManager, "sync_user_role_and_teams", new_callable=AsyncMock + ) as mock_sync_user: + # Set up mock return values + mock_auth_jwt.return_value = jwt_response + + # Call auth_builder + result = await JWTAuthManager.auth_builder( + api_key=api_key, + jwt_handler=jwt_handler, + request_data=request_data, + general_settings=general_settings, + route=route, + prisma_client=None, + user_api_key_cache=user_api_key_cache, + parent_otel_span=None, + proxy_logging_obj=proxy_logging_obj, + ) + + # Verify that auth_jwt was called instead of get_oidc_userinfo + mock_auth_jwt.assert_called_once_with(token=api_key) + mock_get_userinfo.assert_not_called() # Should not be called when OIDC is disabled + + # Verify the result + assert result["user_id"] == "test_user_1" + assert result["user_object"] == user_object \ No newline at end of file From 7a46f3a0830c1f8a5635a6635f02dc7556ce0d30 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 1 Dec 2025 14:05:54 -0800 Subject: [PATCH 195/248] docs: document azure ai provider for anthropic --- .../blog/anthropic_opus_4_5_and_advanced_features/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md b/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md index be2c0b5dc5..1e5f968b2c 100644 --- a/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md +++ b/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md @@ -33,7 +33,7 @@ This guide covers Anthropic's latest model (Claude Opus 4.5) and its advanced fe | Input Examples | Claude Opus 4.5, Sonnet 4.5 | | Effort Parameter | Claude Opus 4.5 only | -Supported Providers: [Anthropic](../../docs/providers/anthropic), [Bedrock](../../docs/providers/bedrock), [Vertex AI](../../docs/providers/vertex_partner#vertex-ai---anthropic-claude). +Supported Providers: [Anthropic](../../docs/providers/anthropic), [Bedrock](../../docs/providers/bedrock), [Vertex AI](../../docs/providers/vertex_partner#vertex-ai---anthropic-claude), [Azure AI](../../docs/providers/azure_ai). ## Usage From c9afb869940ae2de27846ed5263ab27a4461d2b7 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Mon, 1 Dec 2025 14:06:31 -0800 Subject: [PATCH 196/248] docs(azure_ai.md): document anthropic model usage on azure ai --- docs/my-website/docs/providers/azure_ai.md | 79 +++++++++++++++++++++- 1 file changed, 78 insertions(+), 1 deletion(-) diff --git a/docs/my-website/docs/providers/azure_ai.md b/docs/my-website/docs/providers/azure_ai.md index b1b5de5bb3..68e2df676e 100644 --- a/docs/my-website/docs/providers/azure_ai.md +++ b/docs/my-website/docs/providers/azure_ai.md @@ -312,6 +312,82 @@ LiteLLM supports **ALL** azure ai models. Here's a few examples: | mistral-large-latest | `completion(model="azure_ai/mistral-large-latest", messages)` | | AI21-Jamba-Instruct | `completion(model="azure_ai/ai21-jamba-instruct", messages)` | +## Usage - Azure Anthropic (Azure Foundry Claude) + +LiteLLM funnels Azure Claude deployments through the `azure_ai/` provider so Claude Opus models on Azure Foundry keep working with Tool Search, Effort, streaming, and the rest of the advanced feature set. Point `AZURE_AI_API_BASE` to `https://.services.ai.azure.com/anthropic` (LiteLLM appends `/v1/messages` automatically) and authenticate with `AZURE_AI_API_KEY` or an Azure AD token. + + + + +```python +import os +from litellm import completion + +# Configure Azure credentials +os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key" +os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic" + +response = completion( + model="azure_ai/claude-opus-4-1", + messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}], + max_tokens=1200, + temperature=0.7, + stream=True, +) + +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="", flush=True) +``` + + + + +**1. Set environment variables** + +```bash +export AZURE_AI_API_KEY="your-azure-ai-api-key" +export AZURE_AI_API_BASE="https://my-resource.services.ai.azure.com/anthropic" +``` + +**2. Configure the proxy** + +```yaml +model_list: + - model_name: claude-4-azure + litellm_params: + model: azure_ai/claude-opus-4-1 + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE +``` + +**3. Start LiteLLM** + +```bash +litellm --config /path/to/config.yaml +``` + +**4. Test the Azure Claude route** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer $LITELLM_KEY' \ + --data '{ + "model": "claude-4-azure", + "messages": [ + { + "role": "user", + "content": "How do I use Claude Opus 4 via Azure Anthropic in LiteLLM?" + } + ], + "max_tokens": 1024 + }' +``` + + + + ## Rerank Endpoint @@ -397,4 +473,5 @@ curl http://0.0.0.0:4000/rerank \ ``` - \ No newline at end of file + + From f434ca61ec0c637ba676fb90336ff154a992793b Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Dec 2025 14:14:41 -0800 Subject: [PATCH 197/248] add kimi-k2-instruct-0905 (#17328) --- litellm/model_prices_and_context_window_backup.json | 13 +++++++++++++ model_prices_and_context_window.json | 13 +++++++++++++ 2 files changed, 26 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index f4f6b94fd1..af63d1e259 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -10198,6 +10198,19 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/kimi-k2-instruct-0905": { + "input_cost_per_token": 6e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "source": "https://app.fireworks.ai/models/fireworks/kimi-k2-instruct-0905", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/kimi-k2-thinking": { "input_cost_per_token": 6e-07, "litellm_provider": "fireworks_ai", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index f4f6b94fd1..af63d1e259 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -10198,6 +10198,19 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/kimi-k2-instruct-0905": { + "input_cost_per_token": 6e-07, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 262144, + "max_output_tokens": 32768, + "max_tokens": 262144, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "source": "https://app.fireworks.ai/models/fireworks/kimi-k2-instruct-0905", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/kimi-k2-thinking": { "input_cost_per_token": 6e-07, "litellm_provider": "fireworks_ai", From b6d6f834e059e1cb0d9062f99189c4f521fe3e1e Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Mon, 1 Dec 2025 14:29:52 -0800 Subject: [PATCH 198/248] (feat) Generic Guardrail API - allows guardrail providers to add INSTANT support for LiteLLM w/out PR to repo (#17175) * feat(generic_guardrail_api.py): new generic api for guardrails Allows guardrail providers to work with litellm for guardrails without needing to make a PR to LiteLLM * docs(generic_guardrail_api.md): document new generic guardrail api * Fix: Improve PII detection and guardrail API integration Co-authored-by: krrishdholakia * feat: correctly extract raw request from guardrail api * docs(generic_guardrail_api.md): document this is a beta feature --------- Co-authored-by: Cursor Agent --- .../mock_bedrock_guardrail_server.py | 564 ++++++++++++++++++ .../adding_provider/generic_guardrail_api.md | 160 +++++ docs/my-website/sidebars.js | 1 + litellm/proxy/_new_secret_config.yaml | 2 +- .../generic_guardrail_api/__init__.py | 37 ++ .../generic_guardrail_api/example_config.yaml | 52 ++ .../generic_guardrail_api.py | 235 ++++++++ litellm/types/guardrails.py | 11 +- .../guardrail_hooks/generic_guardrail_api.py | 29 + 9 files changed, 1089 insertions(+), 2 deletions(-) create mode 100644 cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py create mode 100644 docs/my-website/docs/adding_provider/generic_guardrail_api.md create mode 100644 litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py create mode 100644 litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/example_config.yaml create mode 100644 litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py create mode 100644 litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py diff --git a/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py b/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py new file mode 100644 index 0000000000..9cfbb11feb --- /dev/null +++ b/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py @@ -0,0 +1,564 @@ +#!/usr/bin/env python3 +""" +Mock Bedrock Guardrail API Server + +This is a FastAPI server that mimics the AWS Bedrock Guardrail API for testing purposes. +It follows the same API spec as the real Bedrock guardrail endpoint. + +Usage: + python mock_bedrock_guardrail_server.py + +The server will start on http://localhost:8080 +""" + +import os +import re +from typing import Any, Dict, List, Literal, Optional + +from fastapi import Depends, FastAPI, Header, HTTPException, status +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field + +# ============================================================================ +# Request/Response Models (matching Bedrock API spec) +# ============================================================================ + + +class BedrockTextContent(BaseModel): + text: str + + +class BedrockContentItem(BaseModel): + text: BedrockTextContent + + +class BedrockRequest(BaseModel): + source: Literal["INPUT", "OUTPUT"] + content: List[BedrockContentItem] = Field(default_factory=list) + + +class BedrockGuardrailOutput(BaseModel): + text: Optional[str] = None + + +class TopicPolicyItem(BaseModel): + name: str + type: str + action: Literal["BLOCKED", "NONE"] + + +class TopicPolicy(BaseModel): + topics: List[TopicPolicyItem] = Field(default_factory=list) + + +class ContentFilterItem(BaseModel): + type: str + confidence: str + action: Literal["BLOCKED", "NONE"] + + +class ContentPolicy(BaseModel): + filters: List[ContentFilterItem] = Field(default_factory=list) + + +class CustomWord(BaseModel): + match: str + action: Literal["BLOCKED", "NONE"] + + +class WordPolicy(BaseModel): + customWords: List[CustomWord] = Field(default_factory=list) + managedWordLists: List[Dict[str, Any]] = Field(default_factory=list) + + +class PiiEntity(BaseModel): + type: str + match: str + action: Literal["BLOCKED", "ANONYMIZED", "NONE"] + + +class RegexMatch(BaseModel): + name: str + match: str + regex: str + action: Literal["BLOCKED", "ANONYMIZED", "NONE"] + + +class SensitiveInformationPolicy(BaseModel): + piiEntities: List[PiiEntity] = Field(default_factory=list) + regexes: List[RegexMatch] = Field(default_factory=list) + + +class ContextualGroundingFilter(BaseModel): + type: str + threshold: float + score: float + action: Literal["BLOCKED", "NONE"] + + +class ContextualGroundingPolicy(BaseModel): + filters: List[ContextualGroundingFilter] = Field(default_factory=list) + + +class Assessment(BaseModel): + topicPolicy: Optional[TopicPolicy] = None + contentPolicy: Optional[ContentPolicy] = None + wordPolicy: Optional[WordPolicy] = None + sensitiveInformationPolicy: Optional[SensitiveInformationPolicy] = None + contextualGroundingPolicy: Optional[ContextualGroundingPolicy] = None + + +class BedrockGuardrailResponse(BaseModel): + usage: Dict[str, int] = Field( + default_factory=lambda: {"topicPolicyUnits": 1, "contentPolicyUnits": 1} + ) + action: Literal["NONE", "GUARDRAIL_INTERVENED"] = "NONE" + outputs: List[BedrockGuardrailOutput] = Field(default_factory=list) + assessments: List[Assessment] = Field(default_factory=list) + + +# ============================================================================ +# Mock Guardrail Configuration +# ============================================================================ + + +class GuardrailConfig(BaseModel): + """Configuration for mock guardrail behavior""" + + blocked_words: List[str] = Field( + default_factory=lambda: ["offensive", "inappropriate", "badword"] + ) + blocked_topics: List[str] = Field(default_factory=lambda: ["violence", "illegal"]) + pii_patterns: Dict[str, str] = Field( + default_factory=lambda: { + "EMAIL": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "PHONE": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b", + "SSN": r"\b\d{3}-\d{2}-\d{4}\b", + "CREDIT_CARD": r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b", + } + ) + anonymize_pii: bool = True # If True, ANONYMIZE PII; if False, BLOCK it + bearer_token: str = "mock-bedrock-token-12345" + + +# Global config +GUARDRAIL_CONFIG = GuardrailConfig() + +# ============================================================================ +# FastAPI App Setup +# ============================================================================ + +app = FastAPI( + title="Mock Bedrock Guardrail API", + description="Mock server mimicking AWS Bedrock Guardrail API", + version="1.0.0", +) + + +# ============================================================================ +# Authentication +# ============================================================================ + + +async def verify_bearer_token(authorization: Optional[str] = Header(None)) -> str: + """ + Verify the Bearer token from the Authorization header. + + Args: + authorization: The Authorization header value + + Returns: + The token if valid + + Raises: + HTTPException: If token is missing or invalid + """ + if authorization is None: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Missing Authorization header", + headers={"WWW-Authenticate": "Bearer"}, + ) + + # Check if it's a Bearer token + parts = authorization.split() + print(f"parts: {parts}") + if len(parts) != 2 or parts[0].lower() != "bearer": + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid Authorization header format. Expected: Bearer ", + headers={"WWW-Authenticate": "Bearer"}, + ) + + token = parts[1] + + # Verify token + if token != GUARDRAIL_CONFIG.bearer_token: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="Invalid bearer token", + ) + + return token + + +# ============================================================================ +# Guardrail Logic +# ============================================================================ + + +def check_blocked_words(text: str) -> Optional[WordPolicy]: + """Check if text contains blocked words""" + found_words = [] + text_lower = text.lower() + + for word in GUARDRAIL_CONFIG.blocked_words: + if word.lower() in text_lower: + found_words.append(CustomWord(match=word, action="BLOCKED")) + + if found_words: + return WordPolicy(customWords=found_words) + return None + + +def check_blocked_topics(text: str) -> Optional[TopicPolicy]: + """Check if text contains blocked topics""" + found_topics = [] + text_lower = text.lower() + + for topic in GUARDRAIL_CONFIG.blocked_topics: + if topic.lower() in text_lower: + found_topics.append( + TopicPolicyItem(name=topic, type=topic.upper(), action="BLOCKED") + ) + + if found_topics: + return TopicPolicy(topics=found_topics) + return None + + +def check_pii(text: str) -> tuple[Optional[SensitiveInformationPolicy], str]: + """ + Check for PII in text and return policy + anonymized text + + Returns: + Tuple of (SensitiveInformationPolicy or None, anonymized_text) + """ + pii_entities = [] + anonymized_text = text + action = "ANONYMIZED" if GUARDRAIL_CONFIG.anonymize_pii else "BLOCKED" + + for pii_type, pattern in GUARDRAIL_CONFIG.pii_patterns.items(): + try: + # Compile the regex pattern with a timeout to prevent ReDoS attacks + compiled_pattern = re.compile(pattern) + matches = compiled_pattern.finditer(text) + for match in matches: + matched_text = match.group() + pii_entities.append( + PiiEntity(type=pii_type, match=matched_text, action=action) + ) + + # Anonymize the text if configured + if GUARDRAIL_CONFIG.anonymize_pii: + anonymized_text = anonymized_text.replace( + matched_text, f"[{pii_type}_REDACTED]" + ) + except re.error: + # Invalid regex pattern - skip it and log a warning + print(f"Warning: Invalid regex pattern for PII type {pii_type}: {pattern}") + continue + + if pii_entities: + return SensitiveInformationPolicy(piiEntities=pii_entities), anonymized_text + + return None, text + + +def process_guardrail_request( + request: BedrockRequest, +) -> tuple[BedrockGuardrailResponse, List[str]]: + """ + Process a guardrail request and return the response. + + Returns: + Tuple of (response, list of output texts) + """ + all_text_content = [] + output_texts = [] + + # Extract all text from content items + for content_item in request.content: + if content_item.text and content_item.text.text: + all_text_content.append(content_item.text.text) + + # Combine all text for analysis + combined_text = " ".join(all_text_content) + + # Initialize response + response = BedrockGuardrailResponse() + assessment = Assessment() + has_intervention = False + + # Check for blocked words + word_policy = check_blocked_words(combined_text) + if word_policy: + assessment.wordPolicy = word_policy + has_intervention = True + + # Check for blocked topics + topic_policy = check_blocked_topics(combined_text) + if topic_policy: + assessment.topicPolicy = topic_policy + has_intervention = True + + # Check for PII + for text in all_text_content: + pii_policy, anonymized_text = check_pii(text) + if pii_policy: + assessment.sensitiveInformationPolicy = pii_policy + if GUARDRAIL_CONFIG.anonymize_pii: + # If anonymizing, we don't block, we modify the text + output_texts.append(anonymized_text) + has_intervention = True + else: + # If not anonymizing PII, we block it + output_texts.append(text) + has_intervention = True + else: + output_texts.append(text) + + # Build response + if has_intervention: + response.action = "GUARDRAIL_INTERVENED" + # Only add assessment if there were interventions + response.assessments = [assessment] + + # Add outputs (modified or original text) + response.outputs = [BedrockGuardrailOutput(text=txt) for txt in output_texts] + + return response, output_texts + + +# ============================================================================ +# API Endpoints +# ============================================================================ + + +@app.get("/") +async def root(): + """Health check endpoint""" + return { + "service": "Mock Bedrock Guardrail API", + "status": "running", + "endpoint_format": "/guardrail/{guardrailIdentifier}/version/{guardrailVersion}/apply", + } + + +@app.get("/health") +async def health(): + """Health check endpoint""" + return {"status": "healthy"} + + +@app.post( + "/guardrail/{guardrailIdentifier}/version/{guardrailVersion}/apply", + response_model=BedrockGuardrailResponse, +) +async def apply_guardrail( + guardrailIdentifier: str, + guardrailVersion: str, + request: BedrockRequest, + token: str = Depends(verify_bearer_token), +) -> BedrockGuardrailResponse: + """ + Apply guardrail to input or output content. + + This endpoint mimics the AWS Bedrock ApplyGuardrail API. + + Args: + guardrailIdentifier: The guardrail ID + guardrailVersion: The guardrail version + request: The guardrail request containing content to analyze + token: Bearer token (verified by dependency) + + Returns: + BedrockGuardrailResponse with analysis results + """ + # Process the request + response, output_texts = process_guardrail_request(request) + + # Log the request (optional, for debugging) + print(f"Guardrail applied: {guardrailIdentifier} v{guardrailVersion}") + print(f"Source: {request.source}") + print(f"Action: {response.action}") + + return response + + +""" +LiteLLM exposes a basic guardrail API with the text extracted from the request and sent to the guardrail API, as well as the received request body for any further processing. + +This works across all LiteLLM endpoints (completion, anthropic /v1/messages, responses api, image generation, embedding, etc.) + +This makes it easy to support your own guardrail API without having to make a PR to LiteLLM. + +LiteLLM supports passing any provider specific params from LiteLLM config.yaml to the guardrail API. + +Example: + +```yaml +guardrails: + - guardrail_name: "bedrock-content-guard" + litellm_params: + guardrail: generic_guardrail_api + mode: "pre_call" + api_key: os.environ/GUARDRAIL_API_KEY + api_base: os.environ/GUARDRAIL_API_BASE + additional_provider_specific_params: + api_version: os.environ/GUARDRAIL_API_VERSION # additional provider specific params +``` + +This is a beta API. Please help us improve it. +""" + + +class LitellmBasicGuardrailRequest(BaseModel): + text: str + request_body: Dict[str, Any] = Field(default_factory=dict) + additional_provider_specific_params: Dict[str, Any] = Field(default_factory=dict) + + +class LitellmBasicGuardrailResponse(BaseModel): + action: Literal[ + "BLOCKED", "NONE", "GUARDRAIL_INTERVENED" + ] # BLOCKED = litellm will raise an error, NONE = litellm will continue, GUARDRAIL_INTERVENED = litellm will continue, but the text was modified by the guardrail + blocked_reason: Optional[str] = None # only if action is BLOCKED, otherwise None + text: Optional[str] = None + + +@app.post( + "/beta/litellm_basic_guardrail_api", + response_model=LitellmBasicGuardrailResponse, +) +async def beta_litellm_basic_guardrail_api( + request: LitellmBasicGuardrailRequest, +) -> LitellmBasicGuardrailResponse: + """ + Apply guardrail to input or output content. + + This endpoint mimics the AWS Bedrock ApplyGuardrail API. + + Args: + request: The guardrail request containing content to analyze + token: Bearer token (verified by dependency) + + Returns: + LitellmBasicGuardrailResponse with analysis results + """ + print(f"request: {request}") + if "ishaan" in request.text.lower(): + return LitellmBasicGuardrailResponse( + action="BLOCKED", blocked_reason="Ishaan is not allowed" + ) + elif "pii_value" in request.text: + return LitellmBasicGuardrailResponse( + action="GUARDRAIL_INTERVENED", + text=request.text.replace("pii_value", "pii_value_redacted"), + ) + return LitellmBasicGuardrailResponse(action="NONE") + + +@app.post("/config/update") +async def update_config( + config: GuardrailConfig, token: str = Depends(verify_bearer_token) +): + """ + Update the guardrail configuration. + + This is a testing endpoint to modify the mock guardrail behavior. + + Args: + config: New guardrail configuration + token: Bearer token (verified by dependency) + + Returns: + Updated configuration + """ + global GUARDRAIL_CONFIG + GUARDRAIL_CONFIG = config + return {"status": "updated", "config": GUARDRAIL_CONFIG} + + +@app.get("/config") +async def get_config(token: str = Depends(verify_bearer_token)): + """ + Get the current guardrail configuration. + + Args: + token: Bearer token (verified by dependency) + + Returns: + Current configuration + """ + return GUARDRAIL_CONFIG + + +# ============================================================================ +# Error Handlers +# ============================================================================ + + +@app.exception_handler(HTTPException) +async def http_exception_handler(request, exc: HTTPException): + """Custom error handler for HTTP exceptions""" + return JSONResponse( + status_code=exc.status_code, + content={"error": exc.detail}, + headers=exc.headers, + ) + + +# ============================================================================ +# Main +# ============================================================================ + +if __name__ == "__main__": + import uvicorn + + # Get configuration from environment + host = os.getenv("MOCK_BEDROCK_HOST", "0.0.0.0") + port = int(os.getenv("MOCK_BEDROCK_PORT", "8080")) + bearer_token = os.getenv("MOCK_BEDROCK_TOKEN", "mock-bedrock-token-12345") + + # Update config with environment token + GUARDRAIL_CONFIG.bearer_token = bearer_token + + print("=" * 80) + print("Mock Bedrock Guardrail API Server") + print("=" * 80) + print(f"Server starting on: http://{host}:{port}") + print(f"Bearer Token: {bearer_token}") + print(f"Endpoint: POST /guardrail/{{id}}/version/{{version}}/apply") + print("=" * 80) + print("\nExample curl command:") + print( + f""" +curl -X POST "http://{host}:{port}/guardrail/test-guardrail/version/1/apply" \\ + -H "Authorization: Bearer {bearer_token}" \\ + -H "Content-Type: application/json" \\ + -d '{{ + "source": "INPUT", + "content": [ + {{ + "text": {{ + "text": "Hello, my email is test@example.com" + }} + }} + ] + }}' + """ + ) + print("=" * 80) + + uvicorn.run(app, host=host, port=port) diff --git a/docs/my-website/docs/adding_provider/generic_guardrail_api.md b/docs/my-website/docs/adding_provider/generic_guardrail_api.md new file mode 100644 index 0000000000..70b39d3c39 --- /dev/null +++ b/docs/my-website/docs/adding_provider/generic_guardrail_api.md @@ -0,0 +1,160 @@ +# [BETA] Generic Guardrail API - Integrate Without a PR + +## The Problem + +As a guardrail provider, integrating with LiteLLM traditionally requires: +- Making a PR to the LiteLLM repository +- Waiting for review and merge +- Maintaining provider-specific code in LiteLLM's codebase +- Updating the integration for changes to your API + +## The Solution + +The **Generic Guardrail API** lets you integrate with LiteLLM **instantly** by implementing a simple API endpoint. No PR required. + +### Key Benefits + +1. **No PR Needed** - Deploy and integrate immediately +2. **Universal Support** - Works across ALL LiteLLM endpoints (chat, embeddings, image generation, etc.) +3. **Simple Contract** - One endpoint, three response types +4. **Custom Parameters** - Pass provider-specific params via config +5. **Full Control** - You own and maintain your guardrail API + +## How It Works + +1. LiteLLM extracts text from any request (chat messages, embeddings, image prompts, etc.) +2. Sends extracted text + original request to your API endpoint +3. Your API responds with: `BLOCKED`, `NONE`, or `GUARDRAIL_INTERVENED` +4. LiteLLM enforces the decision + +## API Contract + +### Endpoint + +Implement `POST /beta/litellm_basic_guardrail_api` + +### Request Format + +```json +{ + "text": "extracted text from the request", + "request_body": {}, // full original request for context + "additional_provider_specific_params": { + // your custom params from config + } +} +``` + +### Response Format + +```json +{ + "action": "BLOCKED" | "NONE" | "GUARDRAIL_INTERVENED", + "blocked_reason": "why content was blocked", // required if action=BLOCKED + "text": "modified text" // required if action=GUARDRAIL_INTERVENED +} +``` + +**Actions:** +- `BLOCKED` - LiteLLM raises error and blocks request +- `NONE` - Request proceeds unchanged +- `GUARDRAIL_INTERVENED` - Request proceeds with modified text + +## LiteLLM Configuration + +Add to `config.yaml`: + +```yaml +litellm_settings: + guardrails: + - guardrail_name: "my-guardrail" + litellm_params: + guardrail: generic_guardrail_api + mode: pre_call # or post_call, during_call + api_base: https://your-guardrail-api.com + api_key: os.environ/YOUR_GUARDRAIL_API_KEY # optional + additional_provider_specific_params: + # your custom parameters + threshold: 0.8 + language: "en" +``` + +## Usage + +Users apply your guardrail by name: + +```python +response = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "hello"}], + guardrails=["my-guardrail"] +) +``` + +Or with dynamic parameters: + +```python +response = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "hello"}], + guardrails=[{ + "my-guardrail": { + "extra_body": { + "custom_threshold": 0.9 + } + } + }] +) +``` + +## Implementation Example + +See [mock_bedrock_guardrail_server.py](https://github.com/BerriAI/litellm/blob/main/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py) for a complete reference implementation. + +**Minimal FastAPI example:** + +```python +from fastapi import FastAPI +from pydantic import BaseModel + +app = FastAPI() + +class GuardrailRequest(BaseModel): + text: str + request_body: dict + additional_provider_specific_params: dict + +class GuardrailResponse(BaseModel): + action: str # BLOCKED, NONE, or GUARDRAIL_INTERVENED + blocked_reason: str | None = None + text: str | None = None + +@app.post("/beta/litellm_basic_guardrail_api") +async def apply_guardrail(request: GuardrailRequest): + # Your guardrail logic here + if "badword" in request.text.lower(): + return GuardrailResponse( + action="BLOCKED", + blocked_reason="Content contains prohibited terms" + ) + + return GuardrailResponse(action="NONE") +``` + +## When to Use This + +✅ **Use Generic Guardrail API when:** +- You want instant integration without waiting for PRs +- You maintain your own guardrail service +- You need full control over updates and features +- You want to support all LiteLLM endpoints automatically + +❌ **Make a PR when:** +- You want deeper integration with LiteLLM internals +- Your guardrail requires complex LiteLLM-specific logic +- You want to be featured as a built-in provider + +## Questions? + +This is a **beta API**. We're actively improving it based on feedback. Open an issue or PR if you need additional capabilities. + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 802ffdd5bb..2039d01186 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -45,6 +45,7 @@ const sidebars = { type: "category", "label": "Contributing to Guardrails", items: [ + "adding_provider/generic_guardrail_api", "adding_provider/simple_guardrail_tutorial", "adding_provider/adding_guardrail_support", ] diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index 6876152479..c11848a862 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -16,4 +16,4 @@ callback_settings: callback_type: generic_api endpoint: https://webhook.site/efc57707-9018-478c-bdf1-2ffaabb2b315 headers: - Authorization: Bearer sk-1234 \ No newline at end of file + Authorization: Bearer sk-1234 diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py new file mode 100644 index 0000000000..c762f0cbfc --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/__init__.py @@ -0,0 +1,37 @@ +from typing import TYPE_CHECKING + +from litellm.types.guardrails import SupportedGuardrailIntegrations + +from .generic_guardrail_api import GenericGuardrailAPI + +if TYPE_CHECKING: + from litellm.types.guardrails import Guardrail, LitellmParams + + +def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"): + import litellm + + _generic_guardrail_api_callback = GenericGuardrailAPI( + api_base=litellm_params.api_base, + headers=getattr(litellm_params, "headers", None), + additional_provider_specific_params=getattr( + litellm_params, "additional_provider_specific_params", {} + ), + guardrail_name=guardrail.get("guardrail_name", ""), + event_hook=litellm_params.mode, + default_on=litellm_params.default_on, + ) + + litellm.logging_callback_manager.add_litellm_callback( + _generic_guardrail_api_callback + ) + return _generic_guardrail_api_callback + + +guardrail_initializer_registry = { + SupportedGuardrailIntegrations.GENERIC_GUARDRAIL_API.value: initialize_guardrail, +} + +guardrail_class_registry = { + SupportedGuardrailIntegrations.GENERIC_GUARDRAIL_API.value: GenericGuardrailAPI, +} diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/example_config.yaml b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/example_config.yaml new file mode 100644 index 0000000000..7ad33b2460 --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/example_config.yaml @@ -0,0 +1,52 @@ +# Example configuration for Generic Guardrail API + +model_list: + - model_name: gpt-4 + litellm_params: + model: gpt-4 + api_key: os.environ/OPENAI_API_KEY + +litellm_settings: + guardrails: + - guardrail_name: "my-generic-guardrail" + litellm_params: + guardrail: generic_guardrail_api + mode: pre_call # Options: pre_call, post_call, during_call, [pre_call, post_call] + api_key: os.environ/GENERIC_GUARDRAIL_API_KEY # Optional if using Bearer auth + api_base: http://localhost:8080 # Required. Endpoint /beta/litellm_basic_guardrail_api is automatically appended + default_on: false # Set to true to apply to all requests by default + additional_provider_specific_params: + # Any additional parameters your guardrail API needs + api_version: "v1" + custom_param: "value" + +# Usage examples: + +# 1. Apply guardrail to a specific request: +# curl --location 'http://localhost:4000/chat/completions' \ +# --header 'Authorization: Bearer sk-1234' \ +# --header 'Content-Type: application/json' \ +# --data '{ +# "model": "gpt-4", +# "messages": [{"role": "user", "content": "Test message"}], +# "guardrails": ["my-generic-guardrail"] +# }' + +# 2. Apply guardrail with dynamic parameters: +# curl --location 'http://localhost:4000/chat/completions' \ +# --header 'Authorization: Bearer sk-1234' \ +# --header 'Content-Type: application/json' \ +# --data '{ +# "model": "gpt-4", +# "messages": [{"role": "user", "content": "Test message"}], +# "guardrails": [ +# { +# "my-generic-guardrail": { +# "extra_body": { +# "custom_threshold": 0.8 +# } +# } +# } +# ] +# }' + diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py new file mode 100644 index 0000000000..e94306e172 --- /dev/null +++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py @@ -0,0 +1,235 @@ +# +-------------------------------------------------------------+ +# +# Use Generic Guardrail API for your LLM calls +# +# +-------------------------------------------------------------+ +# Thank you users! We ❤️ you! - Krrish & Ishaan + +import os +from typing import Any, Dict, List, Optional + +from litellm._logging import verbose_proxy_logger +from litellm.integrations.custom_guardrail import CustomGuardrail +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.guardrails import GuardrailEventHooks + +GUARDRAIL_NAME = "generic_guardrail_api" + + +class GenericGuardrailAPIRequest: + """Request model for the Generic Guardrail API""" + + def __init__( + self, + text: str, + request_body: Dict[str, Any], + additional_provider_specific_params: Optional[Dict[str, Any]] = None, + ): + self.text = text + self.request_body = request_body + self.additional_provider_specific_params = ( + additional_provider_specific_params or {} + ) + + def to_dict(self) -> dict: + return { + "text": self.text, + "request_body": self.request_body, + "additional_provider_specific_params": self.additional_provider_specific_params, + } + + +class GenericGuardrailAPIResponse: + """Response model for the Generic Guardrail API""" + + def __init__( + self, + action: str, + blocked_reason: Optional[str] = None, + text: Optional[str] = None, + ): + self.action = action + self.blocked_reason = blocked_reason + self.text = text + + @classmethod + def from_dict(cls, data: dict) -> "GenericGuardrailAPIResponse": + return cls( + action=data.get("action", "NONE"), + blocked_reason=data.get("blocked_reason"), + text=data.get("text"), + ) + + +class GenericGuardrailAPI(CustomGuardrail): + """ + Generic Guardrail API integration for LiteLLM. + + This integration allows you to use any guardrail API that follows the + LiteLLM Basic Guardrail API spec without needing to write custom integration code. + + The API should accept a POST request with: + { + "text": str, + "request_body": dict, + "additional_provider_specific_params": dict + } + + And return: + { + "action": "BLOCKED" | "NONE" | "GUARDRAIL_INTERVENED", + "blocked_reason": str (optional, only if action is BLOCKED), + "text": str (optional, modified text if action is GUARDRAIL_INTERVENED) + } + """ + + def __init__( + self, + headers: Optional[Dict[str, Any]] = None, + api_base: Optional[str] = None, + additional_provider_specific_params: Optional[Dict[str, Any]] = None, + **kwargs, + ): + self.async_handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.GuardrailCallback + ) + self.headers = headers or {} + base_url = api_base or os.environ.get("GENERIC_GUARDRAIL_API_BASE") + + if not base_url: + raise ValueError( + "api_base is required for Generic Guardrail API. " + "Set GENERIC_GUARDRAIL_API_BASE environment variable or pass it in litellm_params" + ) + + # Append the endpoint path if not already present + if not base_url.endswith("/beta/litellm_basic_guardrail_api"): + base_url = base_url.rstrip("/") + self.api_base = f"{base_url}/beta/litellm_basic_guardrail_api" + else: + self.api_base = base_url + + self.additional_provider_specific_params = ( + additional_provider_specific_params or {} + ) + + # Set supported event hooks + if "supported_event_hooks" not in kwargs: + kwargs["supported_event_hooks"] = [ + GuardrailEventHooks.pre_call, + GuardrailEventHooks.post_call, + GuardrailEventHooks.during_call, + ] + + super().__init__(**kwargs) + + verbose_proxy_logger.debug( + "Generic Guardrail API initialized with api_base: %s", self.api_base + ) + + async def apply_guardrail( + self, + text: str, + language: Optional[str] = None, + entities: Optional[List] = None, + request_data: Optional[dict] = None, + ) -> str: + """ + Apply the Generic Guardrail API to the given text. + + This is the main method that gets called by the framework. + + Args: + text: The text to check + language: Optional language parameter (not used by Generic API) + entities: Optional entities parameter (not used by Generic API) + request_data: Optional request data dictionary for logging metadata + + Returns: + The processed text (original or modified) + + Raises: + Exception: If the guardrail blocks the request + """ + verbose_proxy_logger.debug("Generic Guardrail API: Applying guardrail to text") + + # Use provided request_data or create an empty dict + if request_data is None: + request_data = {} + + request_body = request_data.get("body") or {} + + # Merge additional provider specific params from config and dynamic params + additional_params = {**self.additional_provider_specific_params} + + # Get dynamic params from request if available + dynamic_params = self.get_guardrail_dynamic_request_body_params(request_body) + if dynamic_params: + additional_params.update(dynamic_params) + + # Create request payload + guardrail_request = GenericGuardrailAPIRequest( + text=text, + request_body=request_body, + additional_provider_specific_params=additional_params, + ) + + # Prepare headers + headers = {"Content-Type": "application/json"} + if self.headers: + headers.update(self.headers) + + verbose_proxy_logger.debug( + "Generic Guardrail API request to %s: %s", + self.api_base, + {"text_length": len(text), "has_request_body": bool(request_data)}, + ) + + try: + # Make the API request + response = await self.async_handler.post( + url=self.api_base, + json=guardrail_request.to_dict(), + headers=headers, + ) + + response.raise_for_status() + response_json = response.json() + + verbose_proxy_logger.debug( + "Generic Guardrail API response: %s", response_json + ) + + guardrail_response = GenericGuardrailAPIResponse.from_dict(response_json) + + # Handle the response + if guardrail_response.action == "BLOCKED": + # Block the request + error_message = ( + guardrail_response.blocked_reason or "Content violates policy" + ) + verbose_proxy_logger.warning( + "Generic Guardrail API blocked request: %s", error_message + ) + raise Exception(f"Content blocked by guardrail: {error_message}") + + elif guardrail_response.action == "GUARDRAIL_INTERVENED": + # Content was modified by the guardrail + if guardrail_response.text: + verbose_proxy_logger.debug("Generic Guardrail API modified text") + return guardrail_response.text + + # Action is NONE or no modifications needed + return text + + except Exception as e: + # Check if it's already an exception we raised + if "Content blocked by guardrail" in str(e): + raise + verbose_proxy_logger.error( + "Generic Guardrail API: failed to make request: %s", str(e) + ) + raise Exception(f"Generic Guardrail API failed: {str(e)}") diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index 24a235def5..31e301ed4d 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -8,6 +8,9 @@ from typing_extensions import Required, TypedDict from litellm.types.proxy.guardrails.guardrail_hooks.enkryptai import ( EnkryptAIGuardrailConfigs, ) +from litellm.types.proxy.guardrails.guardrail_hooks.generic_guardrail_api import ( + GenericGuardrailAPIOptionalParams, +) from litellm.types.proxy.guardrails.guardrail_hooks.grayswan import ( GraySwanGuardrailConfigModel, ) @@ -18,7 +21,6 @@ from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import ( ToolPermissionGuardrailConfigModel, ) - """ Pydantic object defining how to set guardrails on litellm proxy @@ -59,6 +61,7 @@ class SupportedGuardrailIntegrations(Enum): IBM_GUARDRAILS = "ibm_guardrails" LITELLM_CONTENT_FILTER = "litellm_content_filter" PROMPT_SECURITY = "prompt_security" + GENERIC_GUARDRAIL_API = "generic_guardrail_api" class Role(Enum): @@ -590,6 +593,12 @@ class BaseLitellmParams(BaseModel): # works for new and patch update guardrails description="Whether to fail the request if Model Armor encounters an error", ) + # Generic Guardrail API params + additional_provider_specific_params: Optional[Dict[str, Any]] = Field( + default=None, + description="Additional provider-specific parameters for generic guardrail APIs", + ) + model_config = ConfigDict(extra="allow", protected_namespaces=()) diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py b/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py new file mode 100644 index 0000000000..a00fe76a0f --- /dev/null +++ b/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py @@ -0,0 +1,29 @@ +from typing import Any, Dict, Literal, Optional + +from pydantic import BaseModel, Field + +from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel + + +class GenericGuardrailAPIOptionalParams(BaseModel): + """Optional parameters for the Generic Guardrail API""" + + additional_provider_specific_params: Optional[Dict[str, Any]] = Field( + default=None, + description="Additional provider-specific parameters to send with the guardrail request", + ) + + +class GenericGuardrailAPIConfigModel( + GuardrailConfigModel[GenericGuardrailAPIOptionalParams], +): + """Configuration parameters for the Generic Guardrail API guardrail""" + + optional_params: Optional[GenericGuardrailAPIOptionalParams] = Field( + default_factory=GenericGuardrailAPIOptionalParams, + description="Optional parameters for the Generic Guardrail API guardrail", + ) + + @staticmethod + def ui_friendly_name() -> str: + return "Generic Guardrail API" From 1eb06f803101d7e82761a3b3a36d6a61de22fc6f Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Mon, 1 Dec 2025 15:40:28 -0800 Subject: [PATCH 199/248] =?UTF-8?q?Revert=20"fix:=20respect=20guardrail=20?= =?UTF-8?q?mock=5Fresponse=20during=20during=5Fcall=20to=20return=20blo?= =?UTF-8?q?=E2=80=A6"=20(#17332)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit 6de610767340cadd6df1c5508325128045c8fae5. --- litellm/proxy/common_request_processing.py | 23 ++--- .../proxy/test_common_request_processing.py | 99 +------------------ 2 files changed, 11 insertions(+), 111 deletions(-) diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index ed4c451f8d..d2b0441002 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -536,11 +536,7 @@ class ProxyBaseLLMRequestProcessing: responses = await llm_responses - # Guardrails (pre/during-call) can inject a mock response to short-circuit the LLM call. - # Prefer it when present so blocked/filtered output is returned instead of the model response. - response = self.data.get("mock_response") - if response is None: - response = responses[1] + response = responses[1] hidden_params = getattr(response, "_hidden_params", {}) or {} model_id = hidden_params.get("model_id", None) or "" @@ -808,7 +804,7 @@ class ProxyBaseLLMRequestProcessing: # This matches the original behavior before the refactor in commit 511d435f6f error_body = await e.response.aread() error_text = error_body.decode("utf-8") - + raise HTTPException( status_code=e.response.status_code, detail={"error": error_text}, @@ -1076,9 +1072,9 @@ class ProxyBaseLLMRequestProcessing: # Add cache-related fields to **params (handled by Usage.__init__) if cache_creation_input_tokens is not None: - usage_kwargs[ - "cache_creation_input_tokens" - ] = cache_creation_input_tokens + usage_kwargs["cache_creation_input_tokens"] = ( + cache_creation_input_tokens + ) if cache_read_input_tokens is not None: usage_kwargs["cache_read_input_tokens"] = cache_read_input_tokens @@ -1097,9 +1093,7 @@ class ProxyBaseLLMRequestProcessing: return obj return None - def maybe_get_model_id( - self, _logging_obj: Optional[LiteLLMLoggingObj] - ) -> Optional[str]: + def maybe_get_model_id(self, _logging_obj: Optional[LiteLLMLoggingObj]) -> Optional[str]: """ Get model_id from logging object or request metadata. @@ -1109,7 +1103,10 @@ class ProxyBaseLLMRequestProcessing: model_id = None if _logging_obj: # 1. Try getting from litellm_params (updated during call) - if hasattr(_logging_obj, "litellm_params") and _logging_obj.litellm_params: + if ( + hasattr(_logging_obj, "litellm_params") + and _logging_obj.litellm_params + ): # First check direct model_info path (set by router.py with selected deployment) model_info = _logging_obj.litellm_params.get("model_info") or {} model_id = model_info.get("id", None) diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index 8f5f182f42..4768ec42ff 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -1,13 +1,11 @@ import copy -from types import SimpleNamespace from unittest.mock import AsyncMock, MagicMock import pytest -from fastapi import Request, Response, status +from fastapi import Request, status from fastapi.responses import StreamingResponse import litellm -import litellm.proxy.common_request_processing as common_request_processing from litellm._uuid import uuid from litellm.integrations.opentelemetry import UserAPIKeyAuth from litellm.proxy.common_request_processing import ( @@ -77,101 +75,6 @@ class TestProxyBaseLLMRequestProcessing: pytest.fail("litellm_call_id is not a valid UUID") assert data_passed["litellm_call_id"] == returned_data["litellm_call_id"] - @pytest.mark.asyncio - async def test_base_process_llm_request_prefers_guardrail_mock_response( - self, monkeypatch - ): - processing_obj = ProxyBaseLLMRequestProcessing( - data={ - "messages": [], - "metadata": {}, - "litellm_metadata": {"model_info": {"id": "fallback-model"}}, - } - ) - - guardrail_response = litellm.ModelResponse( - model="bedrock-guardrail", - hidden_params={"model_id": "guardrail-model"}, - ) - llm_response = litellm.ModelResponse( - model="real-model", - hidden_params={"model_id": "real-model"}, - ) - - async def mock_common_processing(self, *args, **kwargs): - logging_obj = SimpleNamespace(litellm_call_id="test-call-id") - self.data["litellm_call_id"] = "test-call-id" - self.data["litellm_logging_obj"] = logging_obj - return self.data, logging_obj - - monkeypatch.setattr( - ProxyBaseLLMRequestProcessing, - "common_processing_pre_call_logic", - mock_common_processing, - ) - - async def mock_route_request(*args, **kwargs): - async def _inner(): - return llm_response - - return _inner() - - monkeypatch.setattr( - common_request_processing, - "route_request", - mock_route_request, - ) - - check_response_size_is_safe_mock = AsyncMock() - monkeypatch.setattr( - common_request_processing, - "check_response_size_is_safe", - check_response_size_is_safe_mock, - ) - - async def mock_during_call_hook(*args, **kwargs): - kwargs["data"]["mock_response"] = guardrail_response - - proxy_logging_obj = MagicMock(spec=ProxyLogging) - proxy_logging_obj.during_call_hook = AsyncMock( - side_effect=mock_during_call_hook - ) - proxy_logging_obj.update_request_status = AsyncMock(return_value=None) - proxy_logging_obj.post_call_success_hook = AsyncMock( - return_value=guardrail_response - ) - - user_api_key_dict = MagicMock(spec=UserAPIKeyAuth) - user_api_key_dict.tpm_limit = None - user_api_key_dict.rpm_limit = None - user_api_key_dict.max_budget = None - user_api_key_dict.spend = 0 - user_api_key_dict.allowed_model_region = None - - fastapi_response = Response() - proxy_config = MagicMock(spec=ProxyConfig) - - result = await processing_obj.base_process_llm_request( - request=MagicMock(spec=Request), - fastapi_response=fastapi_response, - user_api_key_dict=user_api_key_dict, - route_type="acompletion", - proxy_logging_obj=proxy_logging_obj, - general_settings={}, - proxy_config=proxy_config, - select_data_generator=lambda **kwargs: None, - ) - - assert result is guardrail_response - assert ( - proxy_logging_obj.post_call_success_hook.await_args.kwargs["response"] - is guardrail_response - ) - assert ( - check_response_size_is_safe_mock.await_args.kwargs["response"] - is guardrail_response - ) - @pytest.mark.asyncio async def test_stream_timeout_header_processing(self): """ From be920d75d361519b61f43809b771bc7b107eaf85 Mon Sep 17 00:00:00 2001 From: Danny Kopping Date: Tue, 2 Dec 2025 04:25:26 +0200 Subject: [PATCH 200/248] Add `claude-opus-4-5` alias (#17313) Similar to `claude-sonnet-4-5`. --- model_prices_and_context_window.json | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index af63d1e259..6b9b8beed8 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -6717,6 +6717,33 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "claude-opus-4-5": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "claude-sonnet-4-20250514": { "deprecation_date": "2026-05-14", "cache_creation_input_token_cost": 3.75e-06, From 37ecb03d4f0b8bd9695126c8f0beb68ed978f7d1 Mon Sep 17 00:00:00 2001 From: Elias <55650958+eliasto@users.noreply.github.com> Date: Mon, 1 Dec 2025 21:26:39 -0500 Subject: [PATCH 201/248] Add support of audio transcription for OVHcloud (#17305) --- docs/my-website/docs/audio_transcription.md | 3 +- docs/my-website/docs/providers/ovhcloud.md | 15 ++ .../get_supported_openai_params.py | 9 + .../audio_transcription/transformation.py | 156 ++++++++++++++++++ litellm/utils.py | 6 + provider_endpoints_support.json | 2 +- ...loud_audio_transcription_transformation.py | 59 +++++++ 7 files changed, 248 insertions(+), 2 deletions(-) create mode 100644 litellm/llms/ovhcloud/audio_transcription/transformation.py create mode 100644 tests/test_litellm/llms/ovhcloud/test_ovhcloud_audio_transcription_transformation.py diff --git a/docs/my-website/docs/audio_transcription.md b/docs/my-website/docs/audio_transcription.md index fd55cc66e9..5853b5c187 100644 --- a/docs/my-website/docs/audio_transcription.md +++ b/docs/my-website/docs/audio_transcription.md @@ -13,7 +13,7 @@ import TabItem from '@theme/TabItem'; | Fallbacks | ✅ | Works between supported models | | Loadbalancing | ✅ | Works between supported models | | Guardrails | ✅ | Applies to output transcribed text (non-streaming only) | -| Supported Providers | `openai`, `azure`, `vertex_ai`, `gemini`, `deepgram`, `groq`, `fireworks_ai` | | +| Supported Providers | `openai`, `azure`, `vertex_ai`, `gemini`, `deepgram`, `groq`, `fireworks_ai`, `ovhcloud` | | ## Quick Start @@ -126,6 +126,7 @@ transcript = client.audio.transcriptions.create( - [Fireworks AI](./providers/fireworks_ai.md#audio-transcription) - [Groq](./providers/groq.md#speech-to-text---whisper) - [Deepgram](./providers/deepgram.md) +- [OVHcloud AI Endpoints](./providers/ovhcloud.md) --- diff --git a/docs/my-website/docs/providers/ovhcloud.md b/docs/my-website/docs/providers/ovhcloud.md index 6c42208f2c..94625b0f2e 100644 --- a/docs/my-website/docs/providers/ovhcloud.md +++ b/docs/my-website/docs/providers/ovhcloud.md @@ -311,6 +311,21 @@ response = embedding( print(response.data) ``` +### Audio Transcription + +```python +from litellm import transcription + +audio_file = open("path/to/your/audio.wav", "rb") + +response = transcription( + model="ovhcloud/whisper-large-v3-turbo", + file=audio_file +) + +print(response.text) +``` + ## Usage with LiteLLM Proxy Server Here's how to call a OVHCloud AI Endpoints model with the LiteLLM Proxy Server diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 06e650f938..19b52d2dac 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -266,6 +266,15 @@ def get_supported_openai_params( # noqa: PLR0915 model=model ) ) + elif custom_llm_provider == "ovhcloud": + if request_type == "transcription": + from litellm.llms.ovhcloud.audio_transcription.transformation import ( + OVHCloudAudioTranscriptionConfig, + ) + + return OVHCloudAudioTranscriptionConfig().get_supported_openai_params( + model=model + ) elif custom_llm_provider == "elevenlabs": if request_type == "transcription": from litellm.llms.elevenlabs.audio_transcription.transformation import ( diff --git a/litellm/llms/ovhcloud/audio_transcription/transformation.py b/litellm/llms/ovhcloud/audio_transcription/transformation.py new file mode 100644 index 0000000000..7233d911b0 --- /dev/null +++ b/litellm/llms/ovhcloud/audio_transcription/transformation.py @@ -0,0 +1,156 @@ +""" +Support for OVHCloud AI Endpoints `/v1/audio/transcriptions` endpoint. + +Our unified API follows the OpenAI standard. +More information on our website: https://endpoints.ai.cloud.ovh.net +""" + +from typing import List, Optional, Union + +import httpx + +from litellm.litellm_core_utils.audio_utils.utils import process_audio_file +from litellm.llms.base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, + BaseAudioTranscriptionConfig, +) +from litellm.llms.base_llm.chat.transformation import BaseLLMException +from litellm.secret_managers.main import get_secret_str +from litellm.types.llms.openai import ( + AllMessageValues, + OpenAIAudioTranscriptionOptionalParams, +) +from litellm.types.utils import FileTypes, TranscriptionResponse + +from ..utils import OVHCloudException + + +class OVHCloudAudioTranscriptionConfig(BaseAudioTranscriptionConfig): + def get_supported_openai_params( + self, model: str + ) -> List[OpenAIAudioTranscriptionOptionalParams]: + # OVHCloud implements the OpenAI-compatible Whisper interface. + # We pass through the same optional params as the OpenAI Whisper API. + return ["language", "prompt", "response_format", "timestamp_granularities", "temperature"] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + supported_params = self.get_supported_openai_params(model) + for k, v in non_default_params.items(): + if k in supported_params: + optional_params[k] = v + return optional_params + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + api_base = ( + "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1" + if api_base is None + else api_base.rstrip("/") + ) + complete_url = f"{api_base}/audio/transcriptions" + return complete_url + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BaseLLMException: + return OVHCloudException( + message=error_message, + status_code=status_code, + headers=headers, + ) + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + if api_key is None: + api_key = get_secret_str("OVHCLOUD_API_KEY") + + default_headers = { + "Authorization": f"Bearer {api_key}", + "accept": "application/json", + } + + # Caller can override / extend headers if needed + default_headers.update(headers or {}) + return default_headers + + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + ) -> AudioTranscriptionRequestData: + """ + Transform the audio transcription request into OpenAI-compatible form-data. + + OVHCloud follows OpenAI's `/audio/transcriptions` format, so we: + - Build a multipart form-data body with `file`, `model`, and optional params + - Let the shared HTTP handler set the proper content-type boundary + """ + processed_audio = process_audio_file(audio_file) + + # Base form fields: model + OpenAI-compatible optional params + form_fields: dict = { + "model": model, + } + + # Include OpenAI-compatible optional params + for key in self.get_supported_openai_params(model): + value = optional_params.get(key) + if value is not None: + form_fields[key] = value + + files = { + "file": ( + processed_audio.filename, + processed_audio.file_content, + processed_audio.content_type, + ) + } + + return AudioTranscriptionRequestData(data=form_fields, files=files) + + def transform_audio_transcription_response( + self, + raw_response: httpx.Response, + ) -> TranscriptionResponse: + """ + Transform OVHCloud audio transcription response to OpenAI-compatible TranscriptionResponse. + """ + try: + response_json = raw_response.json() + except Exception: + raise OVHCloudException( + message=raw_response.text, + status_code=raw_response.status_code, + headers=raw_response.headers, + ) + + text = response_json.get("text") or response_json.get("transcript") or "" + response = TranscriptionResponse(text=text) + + response._hidden_params = response_json + return response + + diff --git a/litellm/utils.py b/litellm/utils.py index f74c3aa069..37a71b4347 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7384,6 +7384,12 @@ class ProviderConfigManager: ) return IBMWatsonXAudioTranscriptionConfig() + elif litellm.LlmProviders.OVHCLOUD == provider: + from litellm.llms.ovhcloud.audio_transcription.transformation import ( + OVHCloudAudioTranscriptionConfig, + ) + + return OVHCloudAudioTranscriptionConfig() return None @staticmethod diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json index 5eab130cdd..b5bde3e5ce 100644 --- a/provider_endpoints_support.json +++ b/provider_endpoints_support.json @@ -1272,7 +1272,7 @@ "responses": true, "embeddings": false, "image_generations": false, - "audio_transcriptions": false, + "audio_transcriptions": true, "audio_speech": false, "moderations": false, "batches": false, diff --git a/tests/test_litellm/llms/ovhcloud/test_ovhcloud_audio_transcription_transformation.py b/tests/test_litellm/llms/ovhcloud/test_ovhcloud_audio_transcription_transformation.py new file mode 100644 index 0000000000..fc5e310e71 --- /dev/null +++ b/tests/test_litellm/llms/ovhcloud/test_ovhcloud_audio_transcription_transformation.py @@ -0,0 +1,59 @@ +import os +from typing import Dict + +import litellm +import pytest + +from litellm.llms.base_llm.audio_transcription.transformation import ( + BaseAudioTranscriptionConfig, +) +from litellm.utils import ProviderConfigManager +from tests.llm_translation.base_audio_transcription_unit_tests import ( + BaseLLMAudioTranscriptionTest, +) + + +@pytest.mark.skipif( + not os.getenv("OVHCLOUD_API_KEY"), + reason="OVHCLOUD_API_KEY not set, skipping OVHCloud audio transcription tests", +) +class TestOVHCloudAudioTranscription(BaseLLMAudioTranscriptionTest): + def get_base_audio_transcription_call_args(self) -> Dict: + return { + "model": "ovhcloud/whisper-large-v3-turbo", + } + + def get_custom_llm_provider(self) -> litellm.LlmProviders: + return litellm.LlmProviders.OVHCLOUD + + # Override the async base test with a sync no-op to avoid + # 'async def functions are not natively supported' failures when + # running this file in isolation without pytest-asyncio. + def test_audio_transcription_async(self): # type: ignore[override] + pytest.skip( + "Async audio transcription test for OVHCloud is skipped in this suite; " + "async test plugins (e.g. pytest-asyncio/anyio) are not configured here." + ) + + +@pytest.mark.skipif( + not os.getenv("OVHCLOUD_API_KEY"), + reason="OVHCLOUD_API_KEY not set, skipping OVHCloud audio transcription config test", +) +def test_ovhcloud_audio_transcription_config_installed(): + """ + Ensure OVHCloud audio transcription config is registered with ProviderConfigManager. + """ + model = "ovhcloud/whisper-large-v3-turbo" + provider = litellm.LlmProviders.OVHCLOUD + + config = ProviderConfigManager.get_provider_audio_transcription_config( + model=model, + provider=provider, + ) + + assert config is not None + assert isinstance(config, BaseAudioTranscriptionConfig) + + + From 860cdc81d3a540c64d17cc6112ac577f1f9dd926 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Dec 2025 18:26:56 -0800 Subject: [PATCH 202/248] [Fix] Fix Watsonx Audio Transcription API (#17326) * """ add * fix transform_audio_transcription_request * fix tests * test_watsonx_transcription_request_body --- .../audio_transcription/transformation.py | 78 ++++++++++++++++--- litellm/types/llms/watsonx.py | 36 ++++++++- ...sonx_audio_transcription_transformation.py | 35 ++++++++- 3 files changed, 131 insertions(+), 18 deletions(-) diff --git a/litellm/llms/watsonx/audio_transcription/transformation.py b/litellm/llms/watsonx/audio_transcription/transformation.py index 8c8324cb72..8fe8b4a424 100644 --- a/litellm/llms/watsonx/audio_transcription/transformation.py +++ b/litellm/llms/watsonx/audio_transcription/transformation.py @@ -4,11 +4,17 @@ Translates from OpenAI's `/v1/audio/transcriptions` to IBM WatsonX's `/ml/v1/aud WatsonX follows the OpenAI spec for audio transcription. """ -from typing import List, Optional +from typing import Any, Dict, List, Optional import litellm +from litellm.litellm_core_utils.audio_utils.utils import process_audio_file from litellm.types.llms.openai import OpenAIAudioTranscriptionOptionalParams +from litellm.types.llms.watsonx import WatsonXAudioTranscriptionRequestBody +from litellm.types.utils import FileTypes +from ...base_llm.audio_transcription.transformation import ( + AudioTranscriptionRequestData, +) from ...openai.transcriptions.whisper_transformation import ( OpenAIWhisperAudioTranscriptionConfig, ) @@ -40,6 +46,60 @@ class IBMWatsonXAudioTranscriptionConfig( "timestamp_granularities", ] + def transform_audio_transcription_request( + self, + model: str, + audio_file: FileTypes, + optional_params: dict, + litellm_params: dict, + ) -> AudioTranscriptionRequestData: + """ + Transform the audio transcription request for WatsonX. + + WatsonX expects multipart/form-data with: + - file: the audio file + - model: the model name (without watsonx/ prefix) + - project_id: the project ID (as form field, not query param) + - other optional params + """ + # Use common utility to process the audio file + processed_audio = process_audio_file(audio_file) + + # Get API params to extract project_id + api_params = _get_api_params(params=optional_params.copy()) + + # Initialize form data with required fields + form_data: WatsonXAudioTranscriptionRequestBody = { + "model": model, + "project_id": api_params.get("project_id", ""), + } + + # Add supported OpenAI params to form data + supported_params = self.get_supported_openai_params(model) + for key, value in optional_params.items(): + if key in supported_params and value is not None: + form_data[key] = value # type: ignore + + # Set default response_format for cost calculation + if "response_format" not in form_data or ( + form_data.get("response_format") in ["text", "json"] + ): + form_data["response_format"] = "verbose_json" + + # Prepare files dict with the audio file + files = { + "file": ( + processed_audio.filename, + processed_audio.file_content, + processed_audio.content_type, + ) + } + + # Convert TypedDict to regular dict for AudioTranscriptionRequestData + form_data_dict: Dict[str, Any] = dict(form_data) + + return AudioTranscriptionRequestData(data=form_data_dict, files=files) + def get_complete_url( self, api_base: Optional[str], @@ -52,7 +112,9 @@ class IBMWatsonXAudioTranscriptionConfig( """ Construct the complete URL for WatsonX audio transcription. - URL format: {api_base}/ml/v1/audio/transcriptions?version={version}&project_id={project_id} + URL format: {api_base}/ml/v1/audio/transcriptions?version={version} + + Note: project_id is sent as form data, not as a query parameter """ # Get base URL url = self._get_base_url(api_base=api_base) @@ -61,18 +123,10 @@ class IBMWatsonXAudioTranscriptionConfig( # Add the audio transcription endpoint url = f"{url}/ml/v1/audio/transcriptions" - # Get API params for project_id - api_params = _get_api_params(params=optional_params.copy()) - - # Add version parameter - api_version = optional_params.pop( + # Add version parameter (only version in query string, not project_id) + api_version = optional_params.get( "api_version", None ) or litellm.WATSONX_DEFAULT_API_VERSION url = f"{url}?version={api_version}" - # Add project_id parameter - project_id = api_params.get("project_id") - if project_id: - url = f"{url}&project_id={project_id}" - return url diff --git a/litellm/types/llms/watsonx.py b/litellm/types/llms/watsonx.py index 4eb2f2531a..6c42c3ecea 100644 --- a/litellm/types/llms/watsonx.py +++ b/litellm/types/llms/watsonx.py @@ -1,9 +1,7 @@ -import json from enum import Enum -from typing import Any, List, Optional, Union +from typing import List, Optional -from pydantic import BaseModel -from typing_extensions import TypedDict +from typing_extensions import NotRequired, TypedDict class WatsonXAPIParams(TypedDict): @@ -18,6 +16,36 @@ class WatsonXCredentials(TypedDict): token: Optional[str] +class WatsonXAudioTranscriptionRequestBody(TypedDict): + """ + WatsonX Audio Transcription API request body. + + Follows multipart/form-data format for WatsonX Whisper models. + See: https://cloud.ibm.com/apidocs/watsonx-ai + """ + + model: str + """Model name (e.g., 'whisper-large-v3-turbo')""" + + project_id: str + """WatsonX project ID (required)""" + + language: NotRequired[str] + """Language code (e.g., 'en', 'es')""" + + prompt: NotRequired[str] + """Optional prompt to guide transcription""" + + response_format: NotRequired[str] + """Response format: 'json', 'text', 'srt', 'verbose_json', 'vtt'""" + + temperature: NotRequired[float] + """Sampling temperature (0-1)""" + + timestamp_granularities: NotRequired[List[str]] + """Timestamp granularities: ['word', 'segment']""" + + class WatsonXAIEndpoint(str, Enum): TEXT_GENERATION = "/ml/v1/text/generation" TEXT_GENERATION_STREAM = "/ml/v1/text/generation_stream" diff --git a/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py b/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py index 84a9d25d98..1286c2d4fe 100644 --- a/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py +++ b/tests/test_litellm/llms/watsonx/audio_transcription/test_watsonx_audio_transcription_transformation.py @@ -4,6 +4,7 @@ Tests for IBM WatsonX Audio Transcription. Validates that litellm.transcription transforms requests correctly for WatsonX. """ +import json import os import sys from unittest.mock import AsyncMock, MagicMock, patch @@ -29,6 +30,7 @@ class TestWatsonXAudioTranscription: captured_request["url"] = str(kwargs.get("url", args[0] if args else None)) captured_request["headers"] = kwargs.get("headers", {}) captured_request["data"] = kwargs.get("data", {}) + captured_request["files"] = kwargs.get("files", {}) mock_response = MagicMock() mock_response.json.return_value = { @@ -54,16 +56,30 @@ class TestWatsonXAudioTranscription: # Validate URL contains WatsonX audio transcription endpoint assert "/ml/v1/audio/transcriptions" in captured_request["url"] assert "version=" in captured_request["url"] - assert "project_id=test-project-123" in captured_request["url"] + # project_id should NOT be in URL (it should be in form data instead) + assert "project_id=test-project-123" not in captured_request["url"] # Validate headers contain WatsonX auth assert "Authorization" in captured_request["headers"] assert "Bearer test-bearer-token" in captured_request["headers"]["Authorization"] + + # Validate project_id is in form data, not URL + assert captured_request["data"].get("project_id") == "test-project-123" + + # Validate file is in files dict + assert "file" in captured_request["files"] @pytest.mark.asyncio async def test_watsonx_transcription_request_body(self): """ Test that litellm.transcription sends correct request body for WatsonX. + + Validates that: + - Request uses multipart/form-data (data + files) + - Model name has watsonx/ prefix removed + - project_id is in form data, not URL + - Audio file is in files dict + - OpenAI params are included in form data """ captured_request = {} @@ -94,9 +110,24 @@ class TestWatsonXAudioTranscription: except Exception: pass # We just want to capture the request - # Validate request body contains expected fields + # Validate form data contains expected fields data = captured_request.get("data", {}) + + print("JSON DUMPS captured_request:") + print(json.dumps(captured_request, indent=4, default=str)) + + # Model name should NOT have watsonx/ prefix assert data.get("model") == "whisper-large-v3-turbo" + + # project_id should be in form data + assert data.get("project_id") == "test-project-123" + + # OpenAI params should be in form data assert data.get("language") == "en" assert data.get("temperature") == 0.5 assert data.get("response_format") == "verbose_json" # Default for cost calculation + + # Validate file is in files dict (multipart/form-data) + files = captured_request.get("files", {}) + assert "file" in files + assert isinstance(files["file"], tuple) # Should be (filename, content, content_type) From 1cdfb3da8fb81c293ed94a8628ce9dafbc703542 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Mon, 1 Dec 2025 19:14:12 -0800 Subject: [PATCH 203/248] [Bug Fix] - Fix `litellm_enterprise` ensure imported routes exist (#17337) * test_enterprise_routes.py * test_enterprise_routes_all_imports_exist --- .../proxy/enterprise_routes.py | 4 - .../test_litellm/enterprise/proxy/__init__.py | 0 .../proxy/test_enterprise_routes.py | 78 +++++++++++++++++++ 3 files changed, 78 insertions(+), 4 deletions(-) create mode 100644 tests/test_litellm/enterprise/proxy/__init__.py create mode 100644 tests/test_litellm/enterprise/proxy/test_enterprise_routes.py diff --git a/enterprise/litellm_enterprise/proxy/enterprise_routes.py b/enterprise/litellm_enterprise/proxy/enterprise_routes.py index f3227892bb..e28d8b8a4c 100644 --- a/enterprise/litellm_enterprise/proxy/enterprise_routes.py +++ b/enterprise/litellm_enterprise/proxy/enterprise_routes.py @@ -5,14 +5,10 @@ from litellm_enterprise.enterprise_callbacks.send_emails.endpoints import ( ) from .audit_logging_endpoints import router as audit_logging_router -from .guardrails.endpoints import router as guardrails_router from .management_endpoints import management_endpoints_router from .utils import _should_block_robots -from .vector_stores.endpoints import router as vector_stores_router router = APIRouter() -router.include_router(vector_stores_router) -router.include_router(guardrails_router) router.include_router(email_events_router) router.include_router(audit_logging_router) router.include_router(management_endpoints_router) diff --git a/tests/test_litellm/enterprise/proxy/__init__.py b/tests/test_litellm/enterprise/proxy/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/test_litellm/enterprise/proxy/test_enterprise_routes.py b/tests/test_litellm/enterprise/proxy/test_enterprise_routes.py new file mode 100644 index 0000000000..a9bf33a21a --- /dev/null +++ b/tests/test_litellm/enterprise/proxy/test_enterprise_routes.py @@ -0,0 +1,78 @@ +""" +Test enterprise_routes imports work correctly + +This validates that all imports can be resolved to prevent broken imports +from breaking the enterprise proxy initialization. +""" + +import ast +import os + +import pytest + + +def test_enterprise_routes_all_imports_exist(): + """ + Validate that all relative imports in enterprise_routes.py exist in the filesystem. + + This catches any import errors from moved/deleted modules without hardcoding + specific module names. Works by checking that imported files actually exist. + """ + # Path to the enterprise_routes.py source file + enterprise_routes_path = os.path.join( + os.path.dirname(__file__), + "..", "..", "..", "..", + "enterprise", "litellm_enterprise", "proxy", "enterprise_routes.py" + ) + + enterprise_routes_path = os.path.normpath(enterprise_routes_path) + enterprise_proxy_dir = os.path.dirname(enterprise_routes_path) + + if not os.path.exists(enterprise_routes_path): + pytest.skip(f"Enterprise routes file not found at {enterprise_routes_path}") + + # Read and parse the source file + with open(enterprise_routes_path, "r") as f: + source_code = f.read() + + try: + tree = ast.parse(source_code) + except SyntaxError as e: + pytest.fail(f"Syntax error in enterprise_routes.py: {e}") + + # Check all relative imports + missing_imports = [] + + for node in ast.walk(tree): + if isinstance(node, ast.ImportFrom): + # level > 0 means it's a relative import (. or .. etc) + if node.level and node.level > 0: + module = node.module or "" + + # Convert relative import to file path + # e.g., "audit_logging_endpoints" -> "audit_logging_endpoints.py" + # e.g., "vector_stores.endpoints" -> "vector_stores/endpoints.py" + module_path = module.replace(".", os.sep) if module else "" + + # Check both .py file and package directory + file_path = os.path.join(enterprise_proxy_dir, module_path + ".py") if module_path else None + package_path = os.path.join(enterprise_proxy_dir, module_path, "__init__.py") if module_path else None + + # If module is empty (e.g., "from . import something"), skip check + if not module: + continue + + file_exists = file_path and os.path.exists(file_path) + package_exists = package_path and os.path.exists(package_path) + + if not file_exists and not package_exists: + missing_imports.append( + f"Line {node.lineno}: Cannot find '.{module}' " + f"(checked: {file_path} and {package_path})" + ) + + if missing_imports: + error_msg = "Found imports in enterprise_routes.py that don't exist:\n" + error_msg += "\n".join(missing_imports) + error_msg += "\n\nThis usually means a module was moved or deleted but the import wasn't updated." + pytest.fail(error_msg) From 70126d91302233bdb4e4b6aecf5d81b462a94527 Mon Sep 17 00:00:00 2001 From: rioiart Date: Tue, 2 Dec 2025 04:51:42 +0100 Subject: [PATCH 204/248] Fix/new org team validate against org (#17333) * fix: skip user budget/model validation for org-scoped teams When creating a team with organization_id, budget and model constraints should be validated against the organization's limits, not the user's personal limits. This allows org admins with restrictive personal budgets to create teams within their organization's more generous limits. Adds 4 unit tests to verify: - Org-scoped teams bypass user budget validation - Org-scoped teams bypass user model validation - Standalone teams still validate against user limits * fix: enforce user budget/model limits for standalone teams in update_team - Add user-level budget and model validation to update_team endpoint for standalone teams, matching the existing pattern in new_team - Org-scoped teams correctly bypass user validation and use organization limits instead - Add 5 new comprehensive tests covering standalone/org team budget/model validation * fix: Add direct TPM/RPM org limit validation and consolidate user team limit checks - Add direct TPM/RPM comparison against org limits in _check_org_team_limits() - Consolidate budget/models/TPM/RPM user validation into _check_user_team_limits() helper - Ensure user limits only apply to standalone teams (organization_id=None) - Org-scoped teams now validate TPM/RPM against org limits (not user limits) - Add 8 tests for TPM/RPM validation scenarios (org and user limits) - Reduce code duplication between new_team() and update_team() --- .../management_endpoints/team_endpoints.py | 496 +++-- .../test_team_endpoints.py | 1602 +++++++++++++++++ 2 files changed, 1901 insertions(+), 197 deletions(-) diff --git a/litellm/proxy/management_endpoints/team_endpoints.py b/litellm/proxy/management_endpoints/team_endpoints.py index 6d4faae5fd..b697e01a6e 100644 --- a/litellm/proxy/management_endpoints/team_endpoints.py +++ b/litellm/proxy/management_endpoints/team_endpoints.py @@ -461,11 +461,65 @@ async def _check_org_team_limits( prisma_client: PrismaClient, ) -> None: """ - Check if the organization team is allocating guaranteed throughput limits. If so, raise an error if we're overallocating. - - Only runs check if tpm_limit_type or rpm_limit_type is "guaranteed_throughput" + Check organization team limits including: + - Team budget vs organization's max_budget + - Team models vs organization's allowed models + - Guaranteed throughput limits (tpm/rpm) if applicable """ + # Validate team budget against organization's max_budget + if ( + data.max_budget is not None + and org_table.litellm_budget_table is not None + and org_table.litellm_budget_table.max_budget is not None + and data.max_budget > org_table.litellm_budget_table.max_budget + ): + raise HTTPException( + status_code=400, + detail={ + "error": f"Team max_budget ({data.max_budget}) exceeds organization's max_budget ({org_table.litellm_budget_table.max_budget}). Organization: {org_table.organization_id}" + }, + ) + + # Validate team models against organization's allowed models + if data.models is not None and len(org_table.models) > 0: + for m in data.models: + if m not in org_table.models: + raise HTTPException( + status_code=400, + detail={ + "error": f"Model '{m}' not in organization's allowed models. Organization allowed models={org_table.models}. Organization: {org_table.organization_id}" + }, + ) + + # Validate team TPM/RPM against organization's TPM/RPM limits (direct comparison) + if ( + data.tpm_limit is not None + and org_table.litellm_budget_table is not None + and org_table.litellm_budget_table.tpm_limit is not None + and data.tpm_limit > org_table.litellm_budget_table.tpm_limit + ): + raise HTTPException( + status_code=400, + detail={ + "error": f"Team tpm_limit ({data.tpm_limit}) exceeds organization's tpm_limit ({org_table.litellm_budget_table.tpm_limit}). Organization: {org_table.organization_id}" + }, + ) + + if ( + data.rpm_limit is not None + and org_table.litellm_budget_table is not None + and org_table.litellm_budget_table.rpm_limit is not None + and data.rpm_limit > org_table.litellm_budget_table.rpm_limit + ): + raise HTTPException( + status_code=400, + detail={ + "error": f"Team rpm_limit ({data.rpm_limit}) exceeds organization's rpm_limit ({org_table.litellm_budget_table.rpm_limit}). Organization: {org_table.organization_id}" + }, + ) + + # Check guaranteed throughput limits (only if applicable) rpm_limit_type = getattr(data, "rpm_limit_type", None) or ( data.metadata.get("rpm_limit_type", None) if data.metadata else None ) @@ -503,6 +557,80 @@ async def _check_org_team_limits( ) +async def _check_user_team_limits( + data: Union[NewTeamRequest, UpdateTeamRequest], + user_api_key_dict: UserAPIKeyAuth, + prisma_client: PrismaClient, + user_api_key_cache: Any, +) -> None: + """ + Check user team limits for standalone teams (not org-scoped). + + This validates: + - Team budget vs user's max_budget + - Team models vs user's allowed models + + Should only be called for standalone teams (when organization_id is None). + For org-scoped teams, use _check_org_team_limits() instead. + """ + # Validate team budget against user's max_budget + if data.max_budget is not None and user_api_key_dict.user_id is not None: + user_obj = await get_user_object( + user_id=user_api_key_dict.user_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + user_id_upsert=False, + ) + + if ( + user_obj is not None + and user_obj.max_budget is not None + and data.max_budget > user_obj.max_budget + ): + raise HTTPException( + status_code=400, + detail={ + "error": f"max budget higher than user max. User max budget={user_obj.max_budget}. User role={user_api_key_dict.user_role}" + }, + ) + + # Validate team models against user's allowed models + if data.models is not None and len(user_api_key_dict.models) > 0: + for m in data.models: + if m not in user_api_key_dict.models: + raise HTTPException( + status_code=400, + detail={ + "error": f"Model not in allowed user models. User allowed models={user_api_key_dict.models}. User id={user_api_key_dict.user_id}" + }, + ) + + # Validate team TPM/RPM against user's TPM/RPM limits + if ( + data.tpm_limit is not None + and user_api_key_dict.tpm_limit is not None + and data.tpm_limit > user_api_key_dict.tpm_limit + ): + raise HTTPException( + status_code=400, + detail={ + "error": f"tpm limit higher than user max. User tpm limit={user_api_key_dict.tpm_limit}. User role={user_api_key_dict.user_role}" + }, + ) + + if ( + data.rpm_limit is not None + and user_api_key_dict.rpm_limit is not None + and data.rpm_limit > user_api_key_dict.rpm_limit + ): + raise HTTPException( + status_code=400, + detail={ + "error": f"rpm limit higher than user max. User rpm limit={user_api_key_dict.rpm_limit}. User role={user_api_key_dict.user_role}" + }, + ) + + #### TEAM MANAGEMENT #### @router.post( "/team/new", @@ -665,61 +793,16 @@ async def new_team( # noqa: PLR0915 user_api_key_dict.user_role is None or user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN ): # don't restrict proxy admin - if ( - data.tpm_limit is not None - and user_api_key_dict.tpm_limit is not None - and data.tpm_limit > user_api_key_dict.tpm_limit - ): - raise HTTPException( - status_code=400, - detail={ - "error": f"tpm limit higher than user max. User tpm limit={user_api_key_dict.tpm_limit}. User role={user_api_key_dict.user_role}" - }, - ) - - if ( - data.rpm_limit is not None - and user_api_key_dict.rpm_limit is not None - and data.rpm_limit > user_api_key_dict.rpm_limit - ): - raise HTTPException( - status_code=400, - detail={ - "error": f"rpm limit higher than user max. User rpm limit={user_api_key_dict.rpm_limit}. User role={user_api_key_dict.user_role}" - }, - ) - - if data.max_budget is not None and user_api_key_dict.user_id is not None: - # Fetch user object to get max_budget - user_obj = await get_user_object( - user_id=user_api_key_dict.user_id, + # Only validate user budget/models/tpm/rpm for standalone teams (not org-scoped) + # For org-scoped teams, validation is done by _check_org_team_limits() + if data.organization_id is None: + await _check_user_team_limits( + data=data, + user_api_key_dict=user_api_key_dict, prisma_client=prisma_client, user_api_key_cache=user_api_key_cache, - user_id_upsert=False, ) - if ( - user_obj is not None - and user_obj.max_budget is not None - and data.max_budget > user_obj.max_budget - ): - raise HTTPException( - status_code=400, - detail={ - "error": f"max budget higher than user max. User max budget={user_obj.max_budget}. User role={user_api_key_dict.user_role}" - }, - ) - - if data.models is not None and len(user_api_key_dict.models) > 0: - for m in data.models: - if m not in user_api_key_dict.models: - raise HTTPException( - status_code=400, - detail={ - "error": f"Model not in allowed user models. User allowed models={user_api_key_dict.models}. User id={user_api_key_dict.user_id}" - }, - ) - if user_api_key_dict.user_id is not None: creating_user_in_list = False for member in data.members_with_roles: @@ -1151,168 +1234,187 @@ async def update_team( }' ``` """ - from litellm.proxy.auth.auth_checks import _cache_team_object - from litellm.proxy.proxy_server import ( - litellm_proxy_admin_name, - llm_router, - prisma_client, - proxy_logging_obj, - user_api_key_cache, - ) - - if prisma_client is None: - raise HTTPException( - status_code=500, - detail={"error": CommonProxyErrors.db_not_connected_error.value}, + try: + from litellm.proxy.auth.auth_checks import _cache_team_object + from litellm.proxy.proxy_server import ( + litellm_proxy_admin_name, + llm_router, + prisma_client, + proxy_logging_obj, + user_api_key_cache, ) - if data.team_id is None: - raise HTTPException(status_code=400, detail={"error": "No team id passed in"}) - verbose_proxy_logger.debug("/team/update - %s", data) - - existing_team_row = await prisma_client.db.litellm_teamtable.find_unique( - where={"team_id": data.team_id} - ) - - if existing_team_row is None: - raise HTTPException( - status_code=404, - detail={"error": f"Team not found, passed team_id={data.team_id}"}, - ) - - if ( - data.organization_id is not None and len(data.organization_id) > 0 - ): # allow unsetting the organization_id - await fetch_and_validate_organization( - organization_id=data.organization_id, - existing_team_row=existing_team_row, - llm_router=llm_router, - prisma_client=prisma_client, - ) - elif data.organization_id is not None and len(data.organization_id) == 0: - # unsetting the organization_id - data.organization_id = None - - # check org team limits - if updating team that belongs to an org - org_id_to_check = ( - data.organization_id - if data.organization_id is not None - else existing_team_row.organization_id - ) - if ( - org_id_to_check is not None - and isinstance(org_id_to_check, str) - and prisma_client is not None - ): - org_table = await get_org_object( - org_id=org_id_to_check, - user_api_key_cache=user_api_key_cache, - prisma_client=prisma_client, - ) - if org_table is not None: - await _check_org_team_limits( - org_table=org_table, - data=data, - prisma_client=prisma_client, + if prisma_client is None: + raise HTTPException( + status_code=500, + detail={"error": CommonProxyErrors.db_not_connected_error.value}, ) - updated_kv = data.json(exclude_unset=True) + if data.team_id is None: + raise HTTPException(status_code=400, detail={"error": "No team id passed in"}) + verbose_proxy_logger.debug("/team/update - %s", data) - # Check budget_duration and budget_reset_at - if data.budget_duration is not None: - from litellm.proxy.common_utils.timezone_utils import get_budget_reset_time + existing_team_row = await prisma_client.db.litellm_teamtable.find_unique( + where={"team_id": data.team_id} + ) - reset_at = get_budget_reset_time(budget_duration=data.budget_duration) + if existing_team_row is None: + raise HTTPException( + status_code=404, + detail={"error": f"Team not found, passed team_id={data.team_id}"}, + ) - # set the budget_reset_at in DB - updated_kv["budget_reset_at"] = reset_at + if ( + data.organization_id is not None and len(data.organization_id) > 0 + ): # allow unsetting the organization_id + await fetch_and_validate_organization( + organization_id=data.organization_id, + existing_team_row=existing_team_row, + llm_router=llm_router, + prisma_client=prisma_client, + ) + elif data.organization_id is not None and len(data.organization_id) == 0: + # unsetting the organization_id + data.organization_id = None - if TeamMemberBudgetHandler.should_create_budget( - team_member_budget=data.team_member_budget, - team_member_rpm_limit=data.team_member_rpm_limit, - team_member_tpm_limit=data.team_member_tpm_limit, - ): - updated_kv = await TeamMemberBudgetHandler.upsert_team_member_budget_table( - team_table=existing_team_row, - user_api_key_dict=user_api_key_dict, - updated_kv=updated_kv, + # check org team limits - if updating team that belongs to an org + org_id_to_check = ( + data.organization_id + if data.organization_id is not None + else existing_team_row.organization_id + ) + if ( + org_id_to_check is not None + and isinstance(org_id_to_check, str) + and prisma_client is not None + ): + org_table = await get_org_object( + org_id=org_id_to_check, + user_api_key_cache=user_api_key_cache, + prisma_client=prisma_client, + ) + if org_table is not None: + await _check_org_team_limits( + org_table=org_table, + data=data, + prisma_client=prisma_client, + ) + + # Check user limits for standalone teams (not org-scoped) + # Skip for PROXY_ADMIN users + if ( + user_api_key_dict.user_role is None + or user_api_key_dict.user_role != LitellmUserRoles.PROXY_ADMIN + ): + # Only validate user budget/models for standalone teams + # For org-scoped teams, validation is done by _check_org_team_limits() above + if org_id_to_check is None: + await _check_user_team_limits( + data=data, + user_api_key_dict=user_api_key_dict, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + + updated_kv = data.json(exclude_unset=True) + + # Check budget_duration and budget_reset_at + if data.budget_duration is not None: + from litellm.proxy.common_utils.timezone_utils import get_budget_reset_time + + reset_at = get_budget_reset_time(budget_duration=data.budget_duration) + + # set the budget_reset_at in DB + updated_kv["budget_reset_at"] = reset_at + + if TeamMemberBudgetHandler.should_create_budget( team_member_budget=data.team_member_budget, team_member_rpm_limit=data.team_member_rpm_limit, team_member_tpm_limit=data.team_member_tpm_limit, - ) - else: - TeamMemberBudgetHandler._clean_team_member_fields(updated_kv) - - # Check object permission - if data.object_permission is not None: - updated_kv = await handle_update_object_permission( - data_json=updated_kv, - existing_team_row=existing_team_row, - ) - - # update team metadata fields - _team_metadata_fields = LiteLLM_ManagementEndpoint_MetadataFields_Premium - for field in _team_metadata_fields: - if field in updated_kv and updated_kv[field] is not None: - _update_metadata_field( + ): + updated_kv = await TeamMemberBudgetHandler.upsert_team_member_budget_table( + team_table=existing_team_row, + user_api_key_dict=user_api_key_dict, updated_kv=updated_kv, - field_name=field, + team_member_budget=data.team_member_budget, + team_member_rpm_limit=data.team_member_rpm_limit, + team_member_tpm_limit=data.team_member_tpm_limit, + ) + else: + TeamMemberBudgetHandler._clean_team_member_fields(updated_kv) + + # Check object permission + if data.object_permission is not None: + updated_kv = await handle_update_object_permission( + data_json=updated_kv, + existing_team_row=existing_team_row, ) - for field in LiteLLM_ManagementEndpoint_MetadataFields: - if field in updated_kv and updated_kv[field] is not None: - _update_metadata_field( - updated_kv=updated_kv, - field_name=field, + # update team metadata fields + _team_metadata_fields = LiteLLM_ManagementEndpoint_MetadataFields_Premium + for field in _team_metadata_fields: + if field in updated_kv and updated_kv[field] is not None: + _update_metadata_field( + updated_kv=updated_kv, + field_name=field, + ) + + for field in LiteLLM_ManagementEndpoint_MetadataFields: + if field in updated_kv and updated_kv[field] is not None: + _update_metadata_field( + updated_kv=updated_kv, + field_name=field, + ) + + if "model_aliases" in updated_kv: + updated_kv.pop("model_aliases") + _model_id = await _update_model_table( + data=data, + model_id=existing_team_row.model_id, + prisma_client=prisma_client, + user_api_key_dict=user_api_key_dict, + litellm_proxy_admin_name=litellm_proxy_admin_name, + ) + if _model_id is not None: + updated_kv["model_id"] = _model_id + + updated_kv = prisma_client.jsonify_team_object(db_data=updated_kv) + team_row: Optional[LiteLLM_TeamTable] = ( + await prisma_client.db.litellm_teamtable.update( + where={"team_id": data.team_id}, + data=updated_kv, + include={"litellm_model_table": True}, # type: ignore + ) + ) + + if team_row is None or team_row.team_id is None: + raise HTTPException( + status_code=400, + detail={"error": "Team doesn't exist. Got={}".format(team_row)}, ) - if "model_aliases" in updated_kv: - updated_kv.pop("model_aliases") - _model_id = await _update_model_table( - data=data, - model_id=existing_team_row.model_id, - prisma_client=prisma_client, - user_api_key_dict=user_api_key_dict, - litellm_proxy_admin_name=litellm_proxy_admin_name, - ) - if _model_id is not None: - updated_kv["model_id"] = _model_id - - updated_kv = prisma_client.jsonify_team_object(db_data=updated_kv) - team_row: Optional[LiteLLM_TeamTable] = ( - await prisma_client.db.litellm_teamtable.update( - where={"team_id": data.team_id}, - data=updated_kv, - include={"litellm_model_table": True}, # type: ignore - ) - ) - - if team_row is None or team_row.team_id is None: - raise HTTPException( - status_code=400, - detail={"error": "Team doesn't exist. Got={}".format(team_row)}, + verbose_proxy_logger.info("Successfully updated team - %s, info", team_row.team_id) + await _cache_team_object( + team_id=team_row.team_id, + team_table=LiteLLM_TeamTableCachedObj(**team_row.model_dump()), + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, ) - verbose_proxy_logger.info("Successfully updated team - %s, info", team_row.team_id) - await _cache_team_object( - team_id=team_row.team_id, - team_table=LiteLLM_TeamTableCachedObj(**team_row.model_dump()), - user_api_key_cache=user_api_key_cache, - proxy_logging_obj=proxy_logging_obj, - ) + # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True + if litellm.store_audit_logs is True: + await _create_team_update_audit_log( + existing_team_row=existing_team_row, + updated_kv=updated_kv, + team_id=data.team_id, + litellm_changed_by=litellm_changed_by, + user_api_key_dict=user_api_key_dict, + litellm_proxy_admin_name=litellm_proxy_admin_name, + ) - # Enterprise Feature - Audit Logging. Enable with litellm.store_audit_logs = True - if litellm.store_audit_logs is True: - await _create_team_update_audit_log( - existing_team_row=existing_team_row, - updated_kv=updated_kv, - team_id=data.team_id, - litellm_changed_by=litellm_changed_by, - user_api_key_dict=user_api_key_dict, - litellm_proxy_admin_name=litellm_proxy_admin_name, - ) - - return {"team_id": team_row.team_id, "data": team_row} + return {"team_id": team_row.team_id, "data": team_row} + except Exception as e: + raise handle_exception_on_proxy(e) async def handle_update_object_permission( diff --git a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py index 86b23c98ba..06ec71a84f 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_team_endpoints.py @@ -2015,3 +2015,1605 @@ async def test_new_team_max_budget_within_user_limit(): assert result is not None assert result["team_id"] == "team-within-budget-789" assert result["max_budget"] == 50.0 + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_budget_bypasses_user_limit(): + """ + Test that /team/new with organization_id does NOT validate budget against user's personal max_budget. + + This is the bug fix for: When an org admin creates an org-scoped team, the team's budget should + be validated against the organization's limits, not the user's personal limits. + + Scenario: + - Organization has max_budget=$100 + - User (org admin) has personal max_budget=$3 + - Team is created with organization_id and max_budget=$50 + - Expected: Should succeed (within org's $100 limit) + - Bug behavior: Would fail with "max budget higher than user max. User max budget=3.0" + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, UserAPIKeyAuth, LiteLLM_UserTable, LiteLLM_OrganizationTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create non-admin user with very restrictive personal budget ($3) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-user-123", + user_max_budget=3.0, # Restrictive personal budget + models=[], # Empty models list to bypass model validation + ) + + # Create team request with budget ($50) that's within org's limit but exceeds user's personal limit + team_request = NewTeamRequest( + team_alias="org-scoped-team", + max_budget=50.0, # Within org's $100 limit, but exceeds user's $3 limit + organization_id="test-org-123", # This makes it an org-scoped team + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object" + ) as mock_get_org: + + # Setup mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.jsonify_team_object = lambda db_data: db_data + mock_prisma.get_data = AsyncMock(return_value=None) + mock_prisma.update_data = AsyncMock() + + # Mock organization with $100 budget + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-123" + mock_org.max_budget = 100.0 + mock_org.models = ["gpt-4", "gpt-3.5-turbo", "claude-3-opus"] + mock_org.litellm_budget_table = None # No budget table for this test + mock_get_org.return_value = mock_org + + # Mock user cache to return user with restrictive personal budget + mock_user_obj = LiteLLM_UserTable( + user_id="org-admin-user-123", + max_budget=3.0, # Restrictive personal budget + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + + # Mock team creation + mock_created_team = MagicMock() + mock_created_team.team_id = "team-org-scoped-789" + mock_created_team.team_alias = "org-scoped-team" + mock_created_team.max_budget = 50.0 + mock_created_team.organization_id = "test-org-123" + mock_created_team.members_with_roles = [] + mock_created_team.metadata = None + mock_created_team.model_dump.return_value = { + "team_id": "team-org-scoped-789", + "team_alias": "org-scoped-team", + "max_budget": 50.0, + "organization_id": "test-org-123", + "members_with_roles": [], + } + mock_prisma.db.litellm_teamtable.create = AsyncMock(return_value=mock_created_team) + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_created_team) + + # Mock model table + mock_prisma.db.litellm_modeltable = MagicMock() + mock_prisma.db.litellm_modeltable.create = AsyncMock(return_value=MagicMock(id="model123")) + + # Mock user table operations + mock_user = MagicMock() + mock_user.user_id = "org-admin-user-123" + mock_user.model_dump.return_value = {"user_id": "org-admin-user-123", "teams": ["team-org-scoped-789"]} + mock_prisma.db.litellm_usertable = MagicMock() + mock_prisma.db.litellm_usertable.upsert = AsyncMock(return_value=mock_user) + mock_prisma.db.litellm_usertable.update = AsyncMock(return_value=mock_user) + + # Mock team membership table + mock_membership = MagicMock() + mock_membership.model_dump.return_value = { + "team_id": "team-org-scoped-789", + "user_id": "org-admin-user-123", + "budget_id": None, + } + mock_prisma.db.litellm_teammembership = MagicMock() + mock_prisma.db.litellm_teammembership.create = AsyncMock(return_value=mock_membership) + + # Should NOT raise an exception - the fix should bypass user budget validation for org-scoped teams + result = await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify the team was created successfully with the higher budget + assert result is not None + assert result["team_id"] == "team-org-scoped-789" + assert result["max_budget"] == 50.0 + assert result["organization_id"] == "test-org-123" + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_models_bypasses_user_limit(): + """ + Test that /team/new with organization_id does NOT validate models against user's personal models. + + This is the bug fix for: When an org admin creates an org-scoped team, the team's models should + be validated against the organization's models, not the user's personal models. + + Scenario: + - Organization has models=['gpt-4', 'gpt-3.5-turbo', 'claude-3-opus'] + - User (org admin) has personal models=['no-default-models'] + - Team is created with organization_id and models=['gpt-4'] + - Expected: Should succeed (within org's allowed models) + - Bug behavior: Would fail with "Model not in allowed user models" + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, UserAPIKeyAuth, LiteLLM_UserTable, LiteLLM_OrganizationTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create non-admin user with restrictive personal models + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-user-456", + user_max_budget=None, # No budget restriction for this test + models=["no-default-models"], # Restrictive personal models + ) + + # Create team request with models that are within org's allowed models but not user's + team_request = NewTeamRequest( + team_alias="org-scoped-models-team", + models=["gpt-4"], # Within org's allowed models, but not in user's personal models + organization_id="test-org-456", # This makes it an org-scoped team + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object" + ) as mock_get_org: + + # Setup mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.jsonify_team_object = lambda db_data: db_data + mock_prisma.get_data = AsyncMock(return_value=None) + mock_prisma.update_data = AsyncMock() + + # Mock organization with allowed models + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-456" + mock_org.max_budget = 100.0 + mock_org.models = ["gpt-4", "gpt-3.5-turbo", "claude-3-opus"] + mock_org.litellm_budget_table = None + mock_get_org.return_value = mock_org + + # Mock user cache + mock_user_obj = LiteLLM_UserTable( + user_id="org-admin-user-456", + max_budget=None, + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + + # Mock team creation + mock_created_team = MagicMock() + mock_created_team.team_id = "team-org-scoped-models-789" + mock_created_team.team_alias = "org-scoped-models-team" + mock_created_team.max_budget = None + mock_created_team.organization_id = "test-org-456" + mock_created_team.models = ["gpt-4"] + mock_created_team.members_with_roles = [] + mock_created_team.metadata = None + mock_created_team.model_dump.return_value = { + "team_id": "team-org-scoped-models-789", + "team_alias": "org-scoped-models-team", + "max_budget": None, + "organization_id": "test-org-456", + "models": ["gpt-4"], + "members_with_roles": [], + } + mock_prisma.db.litellm_teamtable.create = AsyncMock(return_value=mock_created_team) + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_created_team) + + # Mock model table + mock_prisma.db.litellm_modeltable = MagicMock() + mock_prisma.db.litellm_modeltable.create = AsyncMock(return_value=MagicMock(id="model123")) + + # Mock user table operations + mock_user = MagicMock() + mock_user.user_id = "org-admin-user-456" + mock_user.model_dump.return_value = {"user_id": "org-admin-user-456", "teams": ["team-org-scoped-models-789"]} + mock_prisma.db.litellm_usertable = MagicMock() + mock_prisma.db.litellm_usertable.upsert = AsyncMock(return_value=mock_user) + mock_prisma.db.litellm_usertable.update = AsyncMock(return_value=mock_user) + + # Mock team membership table + mock_membership = MagicMock() + mock_membership.model_dump.return_value = { + "team_id": "team-org-scoped-models-789", + "user_id": "org-admin-user-456", + "budget_id": None, + } + mock_prisma.db.litellm_teammembership = MagicMock() + mock_prisma.db.litellm_teammembership.create = AsyncMock(return_value=mock_membership) + + # Should NOT raise an exception - the fix should bypass user model validation for org-scoped teams + result = await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify the team was created successfully with the org's models + assert result is not None + assert result["team_id"] == "team-org-scoped-models-789" + assert result["models"] == ["gpt-4"] + assert result["organization_id"] == "test-org-456" + + +@pytest.mark.asyncio +async def test_new_team_standalone_validates_against_user_models(): + """ + Test that /team/new WITHOUT organization_id still validates models against user's personal models. + + This ensures that standalone teams (not org-scoped) still use user-level validation. + + Scenario: + - User has personal models=['no-default-models'] + - Team is created WITHOUT organization_id and models=['gpt-4'] + - Expected: Should fail with "Model not in allowed user models" + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create non-admin user with restrictive personal models + non_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="non-admin-user-789", + user_max_budget=None, + models=["no-default-models"], # Restrictive personal models + ) + + # Create standalone team request (no organization_id) with models not in user's list + team_request = NewTeamRequest( + team_alias="standalone-team", + models=["gpt-4"], # Not in user's allowed models + # Note: No organization_id - this is a standalone team + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit: + # Setup basic mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.get_data = AsyncMock(return_value=None) + + # Should raise ProxyException because gpt-4 is not in user's allowed models + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=non_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "Model not in allowed user models" in str(exc_info.value.message) + assert "no-default-models" in str(exc_info.value.message) + + +@pytest.mark.asyncio +async def test_new_team_standalone_validates_against_user_budget(): + """ + Test that /team/new WITHOUT organization_id still validates budget against user's personal max_budget. + + This ensures that standalone teams (not org-scoped) still use user-level validation. + This is essentially the same as test_new_team_max_budget_exceeds_user_max_budget but + explicitly showing the contrast with org-scoped teams. + + Scenario: + - User has personal max_budget=$3 + - Team is created WITHOUT organization_id and max_budget=$50 + - Expected: Should fail with "max budget higher than user max" + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_UserTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create non-admin user with restrictive personal budget + non_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="non-admin-user-budget-789", + user_max_budget=100.0, # This is for key auth, actual budget is from user object + models=[], # Empty models list to bypass model validation + ) + + # Create standalone team request (no organization_id) with budget exceeding user's limit + team_request = NewTeamRequest( + team_alias="standalone-budget-team", + max_budget=50.0, # Exceeds user's personal budget + # Note: No organization_id - this is a standalone team + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit: + # Setup basic mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.get_data = AsyncMock(return_value=None) + + # Mock user cache to return user with restrictive personal budget ($3) + mock_user_obj = LiteLLM_UserTable( + user_id="non-admin-user-budget-789", + max_budget=3.0, # Restrictive personal budget + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + + # Should raise ProxyException because budget exceeds user's max_budget + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=non_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "max budget higher than user max" in str(exc_info.value.message) + assert "3.0" in str(exc_info.value.message) # User's max_budget should be mentioned + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_budget_exceeds_org_limit(): + """ + Test that /team/new with organization_id fails when team budget exceeds organization's max_budget. + + Scenario: + - Organization has max_budget=$100 + - Team is created with organization_id and max_budget=$150 + - Expected: Should fail with error about exceeding org budget + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create user (org admin) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-user-budget-test", + models=[], + ) + + # Create team request with budget ($150) that exceeds org's limit ($100) + team_request = NewTeamRequest( + team_alias="org-team-exceeds-budget", + max_budget=150.0, # Exceeds org's $100 limit + organization_id="test-org-budget-limit", + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object" + ) as mock_get_org: + + # Setup mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.get_data = AsyncMock(return_value=None) + + # Mock organization with $100 budget limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.max_budget = 100.0 + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-budget-limit" + mock_org.models = ["gpt-4", "gpt-3.5-turbo"] + mock_org.litellm_budget_table = mock_budget_table + mock_get_org.return_value = mock_org + + # Should raise ProxyException because team budget exceeds org budget + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "exceeds organization" in str(exc_info.value.message).lower() or "organization" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_models_not_in_org_models(): + """ + Test that /team/new with organization_id fails when team models are not in organization's allowed models. + + Scenario: + - Organization has models=['gpt-4', 'gpt-3.5-turbo'] + - Team is created with organization_id and models=['claude-3-opus'] + - Expected: Should fail with error about model not in org's allowed models + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create user (org admin) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-user-models-test", + models=[], + ) + + # Create team request with model not in org's allowed list + team_request = NewTeamRequest( + team_alias="org-team-invalid-model", + models=["claude-3-opus"], # Not in org's allowed models + organization_id="test-org-models-limit", + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object" + ) as mock_get_org: + + # Setup mocks + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.get_data = AsyncMock(return_value=None) + + # Mock organization with specific allowed models (not including claude-3-opus) + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-models-limit" + mock_org.models = ["gpt-4", "gpt-3.5-turbo"] # claude-3-opus is NOT allowed + mock_org.litellm_budget_table = None + mock_get_org.return_value = mock_org + + # Should raise ProxyException because claude-3-opus is not in org's allowed models + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "claude-3-opus" in str(exc_info.value.message) or "organization" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_standalone_budget_exceeds_user_limit(): + """ + Test that /team/update for a standalone team fails when new budget exceeds user's max_budget. + + Scenario: + - User has personal max_budget=$50 + - Standalone team exists (no organization_id) + - User tries to update team budget to $100 + - Expected: Should fail with error about exceeding user budget + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_UserTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create non-admin user with restrictive personal budget + non_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="non-admin-update-test", + models=[], + ) + + # Create update request with budget exceeding user's limit + update_request = UpdateTeamRequest( + team_id="standalone-team-123", + max_budget=100.0, # Exceeds user's $50 limit + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit: + + # Mock existing standalone team (no organization_id) + mock_existing_team = MagicMock() + mock_existing_team.team_id = "standalone-team-123" + mock_existing_team.organization_id = None # Standalone team + mock_existing_team.max_budget = 30.0 + mock_existing_team.model_dump.return_value = { + "team_id": "standalone-team-123", + "organization_id": None, + "max_budget": 30.0, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Mock user cache to return user with restrictive budget + mock_user_obj = LiteLLM_UserTable( + user_id="non-admin-update-test", + max_budget=50.0, # User's budget limit + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + + # Should raise ProxyException because new budget exceeds user's max_budget + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=non_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "budget" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_budget_exceeds_org_limit(): + """ + Test that /team/update for an org-scoped team fails when new budget exceeds organization's max_budget. + + Scenario: + - Organization has max_budget=$100 + - Org-scoped team exists + - User tries to update team budget to $150 + - Expected: Should fail with error about exceeding org budget + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user (org admin) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-test", + models=[], + ) + + # Create update request with budget exceeding org's limit + update_request = UpdateTeamRequest( + team_id="org-team-456", + max_budget=150.0, # Exceeds org's $100 limit + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with $100 budget limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.max_budget = 100.0 + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ) as mock_get_org: + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-456" + mock_existing_team.organization_id = "test-org-update" + mock_existing_team.max_budget = 80.0 + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-456", + "organization_id": "test-org-update", + "max_budget": 80.0, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because new budget exceeds org's max_budget + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "organization" in str(exc_info.value.message).lower() or "budget" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_standalone_models_exceeds_user_limit(): + """ + Test that /team/update for a standalone team fails when models are not in user's allowed models. + + Scenario: + - User has personal models=['gpt-3.5-turbo'] + - Standalone team exists (no organization_id) + - User tries to update team models to ['gpt-4'] (not in user's allowed models) + - Expected: Should fail with error about model not in user's allowed models + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create non-admin user with restrictive personal models + non_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="non-admin-update-models-test", + models=["gpt-3.5-turbo"], # Restrictive model list + ) + + # Create update request with model not in user's allowed list + update_request = UpdateTeamRequest( + team_id="standalone-team-models-123", + models=["gpt-4"], # Not in user's allowed models + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit: + + # Mock existing standalone team (no organization_id) + mock_existing_team = MagicMock() + mock_existing_team.team_id = "standalone-team-models-123" + mock_existing_team.organization_id = None # Standalone team + mock_existing_team.models = ["gpt-3.5-turbo"] + mock_existing_team.model_dump.return_value = { + "team_id": "standalone-team-models-123", + "organization_id": None, + "models": ["gpt-3.5-turbo"], + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because model not in user's allowed models + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=non_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "model" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_budget_bypasses_user_limit(): + """ + Test that /team/update for an org-scoped team does NOT validate budget against user's personal max_budget. + + Scenario: + - Organization has max_budget=$100 + - User (org admin) has personal max_budget=$3 + - Org-scoped team exists with current budget=$30 + - User tries to update team budget to $50 (within org limit, exceeds user limit) + - Expected: Should succeed (validated against org, not user) + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, UserAPIKeyAuth, LiteLLM_UserTable, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user with very restrictive personal budget ($3) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-budget-test", + models=[], + ) + + # Create update request with budget within org limit but exceeding user limit + update_request = UpdateTeamRequest( + team_id="org-team-update-budget-123", + max_budget=50.0, # Within org's $100 limit, exceeds user's $3 limit + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with $100 budget limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.max_budget = 100.0 + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update-budget" + mock_org.models = ["gpt-4", "gpt-3.5-turbo"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ) as mock_get_org: + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-update-budget-123" + mock_existing_team.organization_id = "test-org-update-budget" + mock_existing_team.max_budget = 30.0 + mock_existing_team.model_id = None + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-update-budget-123", + "organization_id": "test-org-update-budget", + "max_budget": 30.0, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + mock_prisma.jsonify_team_object = lambda db_data: db_data + + # Mock user cache to return user with restrictive budget + mock_user_obj = LiteLLM_UserTable( + user_id="org-admin-update-budget-test", + max_budget=3.0, # Restrictive personal budget + ) + mock_cache.async_get_cache = AsyncMock(return_value=mock_user_obj) + mock_cache.async_set_cache = AsyncMock() # Mock cache set for _cache_team_object + + # Mock team update + mock_updated_team = MagicMock() + mock_updated_team.team_id = "org-team-update-budget-123" + mock_updated_team.organization_id = "test-org-update-budget" + mock_updated_team.max_budget = 50.0 + mock_updated_team.litellm_model_table = None + mock_updated_team.model_dump.return_value = { + "team_id": "org-team-update-budget-123", + "organization_id": "test-org-update-budget", + "max_budget": 50.0, + } + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_updated_team) + + # Should NOT raise an exception - bypass user budget validation for org-scoped teams + result = await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify the team was updated successfully with the higher budget + assert result is not None + assert result["data"].max_budget == 50.0 + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_models_bypasses_user_limit(): + """ + Test that /team/update for an org-scoped team does NOT validate models against user's personal models. + + Scenario: + - Organization has models=['gpt-4', 'gpt-3.5-turbo', 'claude-3-opus'] + - User (org admin) has personal models=['no-default-models'] + - Org-scoped team exists + - User tries to update team models to ['gpt-4'] (in org's allowed, not in user's) + - Expected: Should succeed (validated against org, not user) + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, UserAPIKeyAuth, LiteLLM_OrganizationTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user with very restrictive personal models + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-models-test", + models=["no-default-models"], # Restrictive model list + ) + + # Create update request with models in org's allowed but not in user's + update_request = UpdateTeamRequest( + team_id="org-team-update-models-123", + models=["gpt-4"], # In org's allowed, not in user's + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with generous model list + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update-models" + mock_org.models = ["gpt-4", "gpt-3.5-turbo", "claude-3-opus"] + mock_org.litellm_budget_table = None + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ) as mock_get_org: + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-update-models-123" + mock_existing_team.organization_id = "test-org-update-models" + mock_existing_team.models = ["gpt-3.5-turbo"] + mock_existing_team.model_id = None + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-update-models-123", + "organization_id": "test-org-update-models", + "models": ["gpt-3.5-turbo"], + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + mock_prisma.jsonify_team_object = lambda db_data: db_data + mock_cache.async_set_cache = AsyncMock() # Mock cache set for _cache_team_object + + # Mock team update + mock_updated_team = MagicMock() + mock_updated_team.team_id = "org-team-update-models-123" + mock_updated_team.organization_id = "test-org-update-models" + mock_updated_team.models = ["gpt-4"] + mock_updated_team.litellm_model_table = None + mock_updated_team.model_dump.return_value = { + "team_id": "org-team-update-models-123", + "organization_id": "test-org-update-models", + "models": ["gpt-4"], + } + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_updated_team) + + # Should NOT raise an exception - bypass user models validation for org-scoped teams + result = await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify the team was updated successfully with the new models + assert result is not None + assert result["data"].models == ["gpt-4"] + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_models_not_in_org_models(): + """ + Test that /team/update for an org-scoped team fails when models are not in organization's allowed models. + + Scenario: + - Organization has models=['gpt-4', 'gpt-3.5-turbo'] + - Org-scoped team exists + - User tries to update team models to ['claude-3-opus'] (not in org's allowed models) + - Expected: Should fail with error about model not in org's allowed models + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user (org admin) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-models-fail-test", + models=[], + ) + + # Create update request with model not in org's allowed list + update_request = UpdateTeamRequest( + team_id="org-team-update-models-fail-123", + models=["claude-3-opus"], # Not in org's allowed models + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with restricted model list (no claude-3-opus) + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update-models-fail" + mock_org.models = ["gpt-4", "gpt-3.5-turbo"] # claude-3-opus is NOT allowed + mock_org.litellm_budget_table = None + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ) as mock_audit, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ) as mock_get_org: + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-update-models-fail-123" + mock_existing_team.organization_id = "test-org-update-models-fail" + mock_existing_team.models = ["gpt-4"] + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-update-models-fail-123", + "organization_id": "test-org-update-models-fail", + "models": ["gpt-4"], + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because claude-3-opus is not in org's allowed models + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "claude-3-opus" in str(exc_info.value.message) or "organization" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_tpm_limit_exceeds_user_limit(): + """ + Test that /team/update fails when TPM limit exceeds user's TPM limit. + + Scenario: + - User has tpm_limit=1000 + - User tries to update team with tpm_limit=5000 + - Expected: Should fail with error about exceeding user TPM limit + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create non-admin user with TPM limit + non_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="tpm-limit-user", + models=[], + tpm_limit=1000, # User's TPM limit + ) + + # Create update request with TPM exceeding user's limit + update_request = UpdateTeamRequest( + team_id="team-tpm-test-123", + tpm_limit=5000, # Exceeds user's 1000 limit + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ): + + # Mock existing standalone team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "team-tpm-test-123" + mock_existing_team.organization_id = None + mock_existing_team.tpm_limit = 500 + mock_existing_team.model_dump.return_value = { + "team_id": "team-tpm-test-123", + "organization_id": None, + "tpm_limit": 500, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because new TPM exceeds user's limit + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=non_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "tpm" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_rpm_limit_exceeds_user_limit(): + """ + Test that /team/update fails when RPM limit exceeds user's RPM limit. + + Scenario: + - User has rpm_limit=100 + - User tries to update team with rpm_limit=500 + - Expected: Should fail with error about exceeding user RPM limit + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create non-admin user with RPM limit + non_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="rpm-limit-user", + models=[], + rpm_limit=100, # User's RPM limit + ) + + # Create update request with RPM exceeding user's limit + update_request = UpdateTeamRequest( + team_id="team-rpm-test-123", + rpm_limit=500, # Exceeds user's 100 limit + ) + + dummy_request = MagicMock(spec=Request) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ): + + # Mock existing standalone team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "team-rpm-test-123" + mock_existing_team.organization_id = None + mock_existing_team.rpm_limit = 50 + mock_existing_team.model_dump.return_value = { + "team_id": "team-rpm-test-123", + "organization_id": None, + "rpm_limit": 50, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because new RPM exceeds user's limit + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=non_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "rpm" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_tpm_exceeds_org_limit(): + """ + Test that /team/new for an org-scoped team fails when TPM exceeds organization's TPM limit. + + Scenario: + - Organization has tpm_limit=10000 + - User tries to create org-scoped team with tpm_limit=20000 + - Expected: Should fail with error about exceeding org TPM limit + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create user (with restrictive personal TPM limit that should be bypassed) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-tpm-test", + models=[], + tpm_limit=1000, # User's personal limit (should be bypassed for org teams) + ) + + # Create team request with TPM exceeding org's limit + team_request = NewTeamRequest( + team_alias="org-tpm-test-team", + organization_id="test-org-tpm", + tpm_limit=20000, # Exceeds org's 10000 limit + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with TPM limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.tpm_limit = 10000 # Org's TPM limit + mock_budget_table.rpm_limit = None + mock_budget_table.max_budget = None + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-tpm" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ): + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_prisma.get_data = AsyncMock(return_value=None) + + # Should raise ProxyException because TPM exceeds org limit + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "tpm" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_rpm_exceeds_org_limit(): + """ + Test that /team/new for an org-scoped team fails when RPM exceeds organization's RPM limit. + + Scenario: + - Organization has rpm_limit=1000 + - User tries to create org-scoped team with rpm_limit=2000 + - Expected: Should fail with error about exceeding org RPM limit + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create user (with restrictive personal RPM limit that should be bypassed) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-rpm-test", + models=[], + rpm_limit=100, # User's personal limit (should be bypassed for org teams) + ) + + # Create team request with RPM exceeding org's limit + team_request = NewTeamRequest( + team_alias="org-rpm-test-team", + organization_id="test-org-rpm", + rpm_limit=2000, # Exceeds org's 1000 limit + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with RPM limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.tpm_limit = None + mock_budget_table.rpm_limit = 1000 # Org's RPM limit + mock_budget_table.max_budget = None + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-rpm" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ): + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_prisma.get_data = AsyncMock(return_value=None) + + # Should raise ProxyException because RPM exceeds org limit + with pytest.raises(ProxyException) as exc_info: + await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "rpm" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_new_team_org_scoped_tpm_rpm_bypasses_user_limit(): + """ + Test that /team/new for an org-scoped team bypasses user's TPM/RPM limits. + + Scenario: + - User has tpm_limit=1000, rpm_limit=100 + - Organization has tpm_limit=50000, rpm_limit=5000 + - User creates org-scoped team with tpm_limit=10000, rpm_limit=1000 + - Expected: Should succeed (bypasses user limits, within org limits) + """ + from fastapi import Request + + from litellm.proxy._types import NewTeamRequest, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable, LiteLLM_TeamTable + from litellm.proxy.management_endpoints.team_endpoints import new_team + + # Create user with restrictive personal limits + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-bypass-test", + models=[], + tpm_limit=1000, # Restrictive user TPM limit + rpm_limit=100, # Restrictive user RPM limit + ) + + # Create team request exceeding user limits but within org limits + team_request = NewTeamRequest( + team_alias="org-bypass-test-team", + organization_id="test-org-bypass", + tpm_limit=10000, # Exceeds user's 1000 but within org's 50000 + rpm_limit=1000, # Exceeds user's 100 but within org's 5000 + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with generous limits + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.tpm_limit = 50000 # Generous org TPM limit + mock_budget_table.rpm_limit = 5000 # Generous org RPM limit + mock_budget_table.max_budget = None + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-bypass" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server._license_check" + ) as mock_license, patch( + "litellm.proxy.proxy_server.create_audit_log_for_update", new=AsyncMock() + ), patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ), patch( + "litellm.proxy.management_endpoints.team_endpoints._add_team_members_to_team", + new=AsyncMock() + ): + mock_license.is_team_count_over_limit.return_value = False + mock_prisma.db.litellm_teamtable.count = AsyncMock(return_value=0) + mock_prisma.get_data = AsyncMock(return_value=None) + + # Mock team creation + mock_created_team = MagicMock(spec=LiteLLM_TeamTable) + mock_created_team.team_id = "new-bypass-team-id" + mock_created_team.team_alias = "org-bypass-test-team" + mock_created_team.tpm_limit = 10000 + mock_created_team.rpm_limit = 1000 + mock_created_team.metadata = None + mock_created_team.members_with_roles = [] + mock_created_team.model_dump.return_value = { + "team_id": "new-bypass-team-id", + "team_alias": "org-bypass-test-team", + "tpm_limit": 10000, + "rpm_limit": 1000, + "metadata": None, + "members_with_roles": [], + } + mock_prisma.db.litellm_teamtable.create = AsyncMock(return_value=mock_created_team) + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_created_team) + mock_prisma.jsonify_team_object = MagicMock(side_effect=lambda db_data: db_data) + + # Should succeed - bypasses user limits since org-scoped + result = await new_team( + data=team_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify team was created + assert result["team_id"] == "new-bypass-team-id" + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_tpm_exceeds_org_limit(): + """ + Test that /team/update for an org-scoped team fails when TPM exceeds organization's TPM limit. + + Scenario: + - Organization has tpm_limit=10000 + - User tries to update org-scoped team with tpm_limit=20000 + - Expected: Should fail with error about exceeding org TPM limit + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user (with restrictive personal TPM limit that should be bypassed) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-tpm-test", + models=[], + tpm_limit=1000, # User's personal limit (should be bypassed for org teams) + ) + + # Create update request with TPM exceeding org's limit + update_request = UpdateTeamRequest( + team_id="org-team-update-tpm-123", + tpm_limit=20000, # Exceeds org's 10000 limit + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with TPM limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.tpm_limit = 10000 # Org's TPM limit + mock_budget_table.rpm_limit = None + mock_budget_table.max_budget = None + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update-tpm" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ): + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-update-tpm-123" + mock_existing_team.organization_id = "test-org-update-tpm" + mock_existing_team.tpm_limit = 5000 + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-update-tpm-123", + "organization_id": "test-org-update-tpm", + "tpm_limit": 5000, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because TPM exceeds org limit + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "tpm" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_rpm_exceeds_org_limit(): + """ + Test that /team/update for an org-scoped team fails when RPM exceeds organization's RPM limit. + + Scenario: + - Organization has rpm_limit=1000 + - User tries to update org-scoped team with rpm_limit=2000 + - Expected: Should fail with error about exceeding org RPM limit + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, ProxyException, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user (with restrictive personal RPM limit that should be bypassed) + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-rpm-test", + models=[], + rpm_limit=100, # User's personal limit (should be bypassed for org teams) + ) + + # Create update request with RPM exceeding org's limit + update_request = UpdateTeamRequest( + team_id="org-team-update-rpm-123", + rpm_limit=2000, # Exceeds org's 1000 limit + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with RPM limit + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.tpm_limit = None + mock_budget_table.rpm_limit = 1000 # Org's RPM limit + mock_budget_table.max_budget = None + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update-rpm" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ): + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-update-rpm-123" + mock_existing_team.organization_id = "test-org-update-rpm" + mock_existing_team.rpm_limit = 500 + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-update-rpm-123", + "organization_id": "test-org-update-rpm", + "rpm_limit": 500, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + + # Should raise ProxyException because RPM exceeds org limit + with pytest.raises(ProxyException) as exc_info: + await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify exception details + assert exc_info.value.code == '400' + assert "rpm" in str(exc_info.value.message).lower() + + +@pytest.mark.asyncio +async def test_update_team_org_scoped_tpm_rpm_bypasses_user_limit(): + """ + Test that /team/update for an org-scoped team bypasses user's TPM/RPM limits. + + Scenario: + - User has tpm_limit=1000, rpm_limit=100 + - Organization has tpm_limit=50000, rpm_limit=5000 + - User updates org-scoped team with tpm_limit=10000, rpm_limit=1000 + - Expected: Should succeed (bypasses user limits, within org limits) + """ + from fastapi import Request + + from litellm.proxy._types import UpdateTeamRequest, UserAPIKeyAuth, LiteLLM_OrganizationTable, LiteLLM_BudgetTable, LiteLLM_TeamTable + from litellm.proxy.management_endpoints.team_endpoints import update_team + + # Create user with restrictive personal limits + org_admin_user = UserAPIKeyAuth( + user_role=LitellmUserRoles.INTERNAL_USER, + user_id="org-admin-update-bypass-test", + models=[], + tpm_limit=1000, # Restrictive user TPM limit + rpm_limit=100, # Restrictive user RPM limit + ) + + # Create update request exceeding user limits but within org limits + update_request = UpdateTeamRequest( + team_id="org-team-update-bypass-123", + tpm_limit=10000, # Exceeds user's 1000 but within org's 50000 + rpm_limit=1000, # Exceeds user's 100 but within org's 5000 + ) + + dummy_request = MagicMock(spec=Request) + + # Mock organization with generous limits + mock_budget_table = MagicMock(spec=LiteLLM_BudgetTable) + mock_budget_table.tpm_limit = 50000 # Generous org TPM limit + mock_budget_table.rpm_limit = 5000 # Generous org RPM limit + mock_budget_table.max_budget = None + + mock_org = MagicMock(spec=LiteLLM_OrganizationTable) + mock_org.organization_id = "test-org-update-bypass" + mock_org.models = ["gpt-4"] + mock_org.litellm_budget_table = mock_budget_table + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.user_api_key_cache" + ) as mock_cache, patch( + "litellm.proxy.proxy_server.litellm_proxy_admin_name", "admin" + ), patch( + "litellm.proxy.proxy_server.proxy_logging_obj" + ) as mock_logging, patch( + "litellm.proxy.management_endpoints.team_endpoints.get_org_object", + new=AsyncMock(return_value=mock_org) + ): + + # Mock existing org-scoped team + mock_existing_team = MagicMock() + mock_existing_team.team_id = "org-team-update-bypass-123" + mock_existing_team.organization_id = "test-org-update-bypass" + mock_existing_team.tpm_limit = 5000 + mock_existing_team.rpm_limit = 500 + mock_existing_team.model_id = None + mock_existing_team.model_dump.return_value = { + "team_id": "org-team-update-bypass-123", + "organization_id": "test-org-update-bypass", + "tpm_limit": 5000, + "rpm_limit": 500, + } + mock_prisma.db.litellm_teamtable.find_unique = AsyncMock(return_value=mock_existing_team) + mock_cache.async_set_cache = AsyncMock() + + # Mock team update + mock_updated_team = MagicMock(spec=LiteLLM_TeamTable) + mock_updated_team.team_id = "org-team-update-bypass-123" + mock_updated_team.tpm_limit = 10000 + mock_updated_team.rpm_limit = 1000 + mock_updated_team.model_dump.return_value = { + "team_id": "org-team-update-bypass-123", + "tpm_limit": 10000, + "rpm_limit": 1000, + } + mock_prisma.db.litellm_teamtable.update = AsyncMock(return_value=mock_updated_team) + mock_prisma.jsonify_team_object = MagicMock(side_effect=lambda db_data: db_data) + + # Should succeed - bypasses user limits since org-scoped + result = await update_team( + data=update_request, + http_request=dummy_request, + user_api_key_dict=org_admin_user, + ) + + # Verify team was updated + assert result["team_id"] == "org-team-update-bypass-123" \ No newline at end of file From 98a244450e1d14649f9edbd43c4ace5962d605c7 Mon Sep 17 00:00:00 2001 From: rioiart Date: Tue, 2 Dec 2025 04:53:30 +0100 Subject: [PATCH 205/248] Fix sso users not added to entra synced team (#17331) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * test: add failing tests for SSO user not added to Entra-synced teams bug Adds tests reproducing the bug where new SSO users with teams=None (from NewUserResponse) are not added to Entra ID synced teams because add_missing_team_member() returns early when teams is None. Tests demonstrate: - NewUserResponse with teams=None fails to add user to teams (bug) - LiteLLM_UserTable with teams=[] correctly adds user to teams (control) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude * fix: treat None as empty list in add_missing_team_member for new SSO users Fixed bug where new SSO users logging in via Microsoft SSO were not added to their Entra-synced teams. The issue was an early return when user_info.teams is None (default for NewUserResponse). Now treats None as an empty list so new users are properly added to all their SSO teams. Location: litellm/proxy/management_endpoints/ui_sso.py:438-440 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude --------- Co-authored-by: Claude --- litellm/proxy/management_endpoints/ui_sso.py | 6 +- .../proxy/management_endpoints/test_ui_sso.py | 209 +++++++++++++++++- 2 files changed, 211 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/management_endpoints/ui_sso.py b/litellm/proxy/management_endpoints/ui_sso.py index a033e2cf5f..59a93f3c48 100644 --- a/litellm/proxy/management_endpoints/ui_sso.py +++ b/litellm/proxy/management_endpoints/ui_sso.py @@ -435,9 +435,9 @@ async def add_missing_team_member( - Get missing teams (diff b/w user_info.team_ids and sso_teams) - Add missing user to missing teams """ - if user_info.teams is None: - return - missing_teams = set(sso_teams) - set(user_info.teams) + # Handle None as empty list for new users + user_teams = user_info.teams if user_info.teams is not None else [] + missing_teams = set(sso_teams) - set(user_teams) missing_teams_list = list(missing_teams) tasks = [] tasks = [ diff --git a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py index f01813fa58..8d7aa51fa0 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py +++ b/tests/test_litellm/proxy/management_endpoints/test_ui_sso.py @@ -16,7 +16,7 @@ sys.path.insert( ) # Adds the parent directory to the system path import litellm -from litellm.proxy._types import NewTeamRequest +from litellm.proxy._types import LiteLLM_UserTable, NewTeamRequest, NewUserResponse from litellm.proxy.auth.handle_jwt import JWTHandler from litellm.proxy.management_endpoints.types import CustomOpenID from litellm.proxy.management_endpoints.ui_sso import ( @@ -2573,3 +2573,210 @@ class TestPKCEFunctionality: assert "code_challenge=" in updated_location assert "code_challenge_method=S256" in updated_location assert f"state={test_state}" in updated_location + + +# Tests for SSO user team assignment bug (Issue: SSO Users Not Added to Entra-Synced Teams on First Login) +class TestAddMissingTeamMember: + """Tests for the add_missing_team_member function""" + + @pytest.mark.asyncio + async def test_add_missing_team_member_with_new_user_response_teams_none(self): + """ + Bug reproduction: When a NewUserResponse has teams=None (new SSO user), + add_missing_team_member() should still add the user to the SSO teams. + + Currently FAILS: The function returns early when teams is None. + """ + from litellm.proxy._types import NewUserResponse + from litellm.proxy.management_endpoints.ui_sso import add_missing_team_member + + # Simulate a new SSO user - NewUserResponse has teams=None by default + new_user = NewUserResponse( + user_id="new-sso-user-123", + key="sk-xxxxx", + teams=None, # This is the default for NewUserResponse + ) + + sso_teams = ["team-from-entra-1", "team-from-entra-2"] + + with patch( + "litellm.proxy.management_endpoints.ui_sso.create_team_member_add_task" + ) as mock_add_task: + mock_add_task.return_value = AsyncMock() + + await add_missing_team_member(user_info=new_user, sso_teams=sso_teams) + + # Bug: This assertion currently FAILS - no teams are added + # because function returns early when teams is None + assert ( + mock_add_task.call_count == 2 + ), f"Expected 2 calls to add user to teams, but got {mock_add_task.call_count}" + called_team_ids = [call.args[0] for call in mock_add_task.call_args_list] + assert set(called_team_ids) == { + "team-from-entra-1", + "team-from-entra-2", + } + + @pytest.mark.asyncio + async def test_add_missing_team_member_with_litellm_user_table_empty_teams(self): + """ + Control test: When a LiteLLM_UserTable has teams=[] (existing user, no teams), + add_missing_team_member() should add the user to SSO teams. + + This test PASSES because LiteLLM_UserTable defaults teams to [] not None. + """ + from litellm.proxy._types import LiteLLM_UserTable + from litellm.proxy.management_endpoints.ui_sso import add_missing_team_member + + # Existing user has teams=[] by default (not None) + existing_user = LiteLLM_UserTable( + user_id="existing-user-456", + teams=[], # Empty list, not None + ) + + sso_teams = ["team-from-entra-1", "team-from-entra-2"] + + with patch( + "litellm.proxy.management_endpoints.ui_sso.create_team_member_add_task" + ) as mock_add_task: + mock_add_task.return_value = AsyncMock() + + await add_missing_team_member(user_info=existing_user, sso_teams=sso_teams) + + # This PASSES - teams are added because teams=[] not None + assert mock_add_task.call_count == 2 + + @pytest.mark.asyncio + async def test_add_user_to_teams_from_sso_response_new_user(self): + """ + Integration test: Simulates the SSO response handler with a new user + that has teams=None from NewUserResponse. + """ + from litellm.proxy._types import NewUserResponse + from litellm.proxy.management_endpoints.types import CustomOpenID + from litellm.proxy.management_endpoints.ui_sso import ( + SSOAuthenticationHandler, + ) + + # SSO response with team_ids from Entra ID + sso_result = CustomOpenID( + id="new-sso-user-id", + email="newuser@example.com", + team_ids=["entra-group-1", "entra-group-2"], + ) + + # New user response (simulates what new_user() returns) + new_user_info = NewUserResponse( + user_id="new-sso-user-id", + key="sk-xxxxx", + teams=None, # Bug: NewUserResponse defaults to None + ) + + with patch( + "litellm.proxy.management_endpoints.ui_sso.add_missing_team_member" + ) as mock_add_member: + await SSOAuthenticationHandler.add_user_to_teams_from_sso_response( + result=sso_result, + user_info=new_user_info, + ) + + # Verify add_missing_team_member was called with correct args + mock_add_member.assert_called_once_with( + user_info=new_user_info, sso_teams=["entra-group-1", "entra-group-2"] + ) + + @pytest.mark.asyncio + async def test_sso_first_login_full_flow_adds_user_to_teams(self): + """ + End-to-end test: Simulates complete first-time SSO login with Entra groups. + Verifies teams are created AND user is added as a member. + """ + from litellm.proxy._types import NewUserResponse + from litellm.proxy.management_endpoints.ui_sso import add_missing_team_member + + team_member_calls = [] + + async def track_team_member_add(team_id, user_info): + team_member_calls.append( + {"team_id": team_id, "user_id": user_info.user_id} + ) + + # New SSO user with Entra groups + new_user = NewUserResponse( + user_id="first-time-sso-user", + key="sk-xxxxx", + teams=None, # The problematic default + ) + + sso_teams = ["entra-team-alpha", "entra-team-beta"] + + with patch( + "litellm.proxy.management_endpoints.ui_sso.create_team_member_add_task", + side_effect=track_team_member_add, + ): + await add_missing_team_member(user_info=new_user, sso_teams=sso_teams) + + # Bug: With current code, team_member_calls will be empty + # After fix: Should have 2 entries + assert ( + len(team_member_calls) == 2 + ), f"Expected 2 teams to be added, but got {len(team_member_calls)}" + assert {c["team_id"] for c in team_member_calls} == { + "entra-team-alpha", + "entra-team-beta", + } + assert all(c["user_id"] == "first-time-sso-user" for c in team_member_calls) + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "user_info_factory,teams_value,expected_teams_added", + [ + # Bug case: NewUserResponse with teams=None + pytest.param( + lambda uid: NewUserResponse(user_id=uid, key="sk-xxx", teams=None), + None, + ["team-1", "team-2"], # Should still add teams + id="new_user_teams_none", + ), + # Working case: LiteLLM_UserTable with teams=[] + pytest.param( + lambda uid: LiteLLM_UserTable(user_id=uid, teams=[]), + [], + ["team-1", "team-2"], + id="existing_user_empty_teams", + ), + # Existing user with some teams already + pytest.param( + lambda uid: LiteLLM_UserTable(user_id=uid, teams=["team-1"]), + ["team-1"], + ["team-2"], # Only missing team should be added + id="existing_user_partial_teams", + ), + ], + ) + async def test_add_missing_team_member_handles_all_user_types( + self, user_info_factory, teams_value, expected_teams_added + ): + """ + Parametrized test ensuring add_missing_team_member works for all user types. + """ + from litellm.proxy._types import LiteLLM_UserTable + from litellm.proxy.management_endpoints.ui_sso import add_missing_team_member + + user_info = user_info_factory("test-user-id") + sso_teams = ["team-1", "team-2"] + + added_teams = [] + + async def mock_create_task(team_id, user): + added_teams.append(team_id) + + with patch( + "litellm.proxy.management_endpoints.ui_sso.create_team_member_add_task", + side_effect=mock_create_task, + ): + await add_missing_team_member(user_info=user_info, sso_teams=sso_teams) + + assert set(added_teams) == set( + expected_teams_added + ), f"Expected teams {expected_teams_added}, but got {added_teams}" From 71efcb71151aedb216e411815ce871376605da55 Mon Sep 17 00:00:00 2001 From: idola9 Date: Tue, 2 Dec 2025 05:56:14 +0200 Subject: [PATCH 206/248] Refactor Noma guardrail to use shared Responses transformation and include system instructions (#17315) * Support system prompts in noma guardrails * Use litellm util to covert chat completions to responses api --- .../transformation.py | 6 +- .../guardrails/guardrail_hooks/noma/noma.py | 78 ++-- .../guardrails/guardrail_hooks/test_noma.py | 335 ++++++++++++------ 3 files changed, 263 insertions(+), 156 deletions(-) diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index 07d9de5a01..2045836387 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -148,7 +148,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if role == "system": # Extract system message as instructions if isinstance(content, str): - instructions = content + if instructions: + # Concatenate multiple system prompts with a space + instructions = f"{instructions} {content}" + else: + instructions = content else: input_items.append( { diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 3ae2d519c4..a0ea90ccf2 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -28,6 +28,9 @@ from fastapi import HTTPException import litellm from litellm import DualCache, ModelResponse from litellm._logging import verbose_proxy_logger +from litellm.completion_extras.litellm_responses_transformation.transformation import ( + LiteLLMResponsesTransformationHandler, +) from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.llms.base_llm.base_model_iterator import MockResponseIterator from litellm.llms.custom_httpx.http_handler import ( @@ -111,6 +114,7 @@ class NomaGuardrail(CustomGuardrail): self.async_handler = get_async_httpx_client( llm_provider=httpxSpecialProvider.GuardrailCallback ) + self._responses_transform_handler = LiteLLMResponsesTransformationHandler() self.api_key = api_key or os.environ.get("NOMA_API_KEY") self.api_base = api_base or os.environ.get( "NOMA_API_BASE", NomaGuardrail._DEFAULT_API_BASE @@ -164,13 +168,28 @@ class NomaGuardrail(CustomGuardrail): start_time = datetime.now() extra_data = self.get_guardrail_dynamic_request_body_params(request_data) - user_message = await self._extract_user_message(request_data) - if not user_message: + messages = request_data.get("messages") or [] + if not messages: return None - payload = { - "input": [{"type": "message", "role": "user", "content": user_message}] - } + input_items, instructions = self._responses_transform_handler.convert_chat_completion_messages_to_responses_api( # type: ignore[arg-type] + messages + ) + + if instructions: + system_message = { + "type": "message", + "role": "system", + "content": [ + {"type": "input_text", "text": instructions}, + ], + } + input_items.insert(0, system_message) + + if not input_items: + return None + + payload = {"input": input_items} response_json = await self._call_noma_api( payload=payload, llm_request_id=None, @@ -198,9 +217,9 @@ class NomaGuardrail(CustomGuardrail): if self.monitor_mode: await self._handle_verdict_background( - USER_ROLE, json.dumps(user_message), response_json + USER_ROLE, json.dumps(input_items), response_json ) - return json.dumps(user_message) + return json.dumps(input_items) # Check if we should anonymize content if self._should_anonymize(response_json, USER_ROLE): @@ -215,8 +234,8 @@ class NomaGuardrail(CustomGuardrail): ) return anonymized_content - await self._check_verdict(USER_ROLE, json.dumps(user_message), response_json) - return json.dumps(user_message) + await self._check_verdict(USER_ROLE, json.dumps(input_items), response_json) + return json.dumps(input_items) async def _process_llm_response_check( self, @@ -732,47 +751,6 @@ class NomaGuardrail(CustomGuardrail): return response - async def _extract_user_message(self, data: dict) -> Optional[List[dict]]: - """Extract the last user message from request data""" - messages = data.get("messages", []) - if not messages: - return None - - # Get the last user message - user_messages = [msg for msg in messages if msg.get("role") == USER_ROLE] - if not user_messages: - return None - - last_user_message = user_messages[-1].get("content", "") - if isinstance(last_user_message, str): - return [{"type": "input_text", "text": last_user_message}] - elif isinstance(last_user_message, list): - converted_messages = [] - for message in last_user_message: - converted_message = self._convert_single_user_message_to_payload( - message - ) - if converted_message is not None: - converted_messages.append(converted_message) - return converted_messages - else: - return None - - def _convert_single_user_message_to_payload( - self, user_message: Any - ) -> Optional[dict]: - if isinstance(user_message, str): - return {"type": "input_text", "text": user_message} - elif user_message.get("type", "") == "image_url": - return { - "type": "input_image", - "image_url": user_message.get("image_url", {}).get("url", ""), - } - elif user_message.get("type", "") == "text": - return {"type": "input_text", "text": user_message.get("text", "")} - else: - return None - async def _call_noma_api( self, payload: dict, diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py index 94cb831a30..f1ac6ef14b 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -1,5 +1,6 @@ import copy import os +from typing import cast from unittest.mock import AsyncMock, MagicMock, patch import httpx @@ -14,6 +15,7 @@ from litellm.proxy.guardrails.guardrail_hooks.noma import ( ) from litellm.proxy.guardrails.guardrail_hooks.noma.noma import NomaBlockedMessage from litellm.proxy.guardrails.init_guardrails import init_guardrails_v2 +from litellm.types.llms.openai import AllMessageValues from litellm.types.utils import Choices, Message @@ -413,7 +415,7 @@ class TestNomaGuardrailHooks: # Verify API call details call_args = mock_post.call_args - # Verify the URL endpoint + # Verify the URL endpoint assert call_args.args[0].endswith("/ai-dr/v2/prompt/scan") # Verify headers and JSON payload if "headers" in call_args.kwargs: @@ -426,6 +428,130 @@ class TestNomaGuardrailHooks: assert "x-noma-context" in json_payload assert json_payload["x-noma-context"]["applicationId"] == "test-app" + @pytest.mark.asyncio + async def test_pre_call_hook_with_system_prompt( + self, noma_guardrail, mock_user_api_key_dict + ): + """Test pre-call hook includes system prompt in Noma API request""" + request_data = { + "messages": [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Hello, how are you?"}, + ], + "litellm_call_id": "test-call-id", + "metadata": {"requester_ip_address": "192.168.1.1"}, + } + + mock_response = MagicMock() + mock_response.json.return_value = { + "aggregatedScanResult": False, # False means safe + "scanResult": [ + { + "role": "system", + "type": "message", + "results": {} + }, + { + "role": "user", + "type": "message", + "results": {} + } + ] + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + noma_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + result = await noma_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + assert result == request_data + mock_post.assert_called_once() + + # Verify the payload includes both system and user messages + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + assert "input" in json_payload + messages = json_payload["input"] + + # Should have 2 messages: system and user + assert len(messages) == 2 + + # First message should be system + assert messages[0]["type"] == "message" + assert messages[0]["role"] == "system" + assert messages[0]["content"][0]["type"] == "input_text" + assert messages[0]["content"][0]["text"] == "You are a helpful assistant" + + # Second message should be user + assert messages[1]["type"] == "message" + assert messages[1]["role"] == "user" + assert messages[1]["content"][0]["type"] == "input_text" + assert messages[1]["content"][0]["text"] == "Hello, how are you?" + + @pytest.mark.asyncio + async def test_pre_call_hook_with_multiple_system_prompts( + self, noma_guardrail, mock_user_api_key_dict + ): + """Test pre-call hook combines multiple system prompts into single message""" + request_data = { + "messages": [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "system", "content": "You should be polite and respectful"}, + {"role": "user", "content": "Hello, how are you?"}, + ], + "litellm_call_id": "test-call-id", + } + + mock_response = MagicMock() + mock_response.json.return_value = { + "aggregatedScanResult": False, + "scanResult": [ + {"role": "system", "type": "message", "results": {}}, + {"role": "user", "type": "message", "results": {}} + ] + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + noma_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + result = await noma_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + assert result == request_data + mock_post.assert_called_once() + + # Verify the payload combines system prompts into single message + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + messages = json_payload["input"] + + # Should have 2 messages: 1 combined system and 1 user + assert len(messages) == 2 + + # First message should be system with combined content + assert messages[0]["role"] == "system" + assert messages[0]["content"][0]["type"] == "input_text" + assert ( + messages[0]["content"][0]["text"] + == "You are a helpful assistant You should be polite and respectful" + ) + + # Second message should be user + assert messages[1]["role"] == "user" + assert messages[1]["content"][0]["type"] == "input_text" + assert messages[1]["content"][0]["text"] == "Hello, how are you?" + @pytest.mark.asyncio async def test_pre_call_hook_blocked( self, noma_guardrail, mock_user_api_key_dict, mock_request_data @@ -644,34 +770,6 @@ class TestNomaGuardrailHooks: assert result == mock_request_data - def test_extract_user_message(self, noma_guardrail): - data = { - "messages": [ - {"role": "system", "content": "System prompt"}, - {"role": "user", "content": "First user message"}, - {"role": "assistant", "content": "Assistant response"}, - {"role": "user", "content": "Second user message"}, - ] - } - - import asyncio - - message = asyncio.run(noma_guardrail._extract_user_message(data)) - assert message == [{"type": "input_text", "text": "Second user message"}] - - data = {"messages": [{"role": "system", "content": "System prompt"}]} - message = asyncio.run(noma_guardrail._extract_user_message(data)) - assert message is None - - data = {"messages": []} - message = asyncio.run(noma_guardrail._extract_user_message(data)) - assert message is None - - data = {} - message = asyncio.run(noma_guardrail._extract_user_message(data)) - assert message is None - - class TestBackgroundProcessing: """Test the new background processing functionality""" @@ -1025,57 +1123,66 @@ class TestNomaImageProcessing: metadata={}, ) - def test_extract_user_message_with_image_url(self, noma_guardrail): - """Test extracting user message with image_url content""" - import asyncio - - data = { - "messages": [ - { - "role": "user", - "content": [ - { - "type": "image_url", - "image_url": { - "url": "https://example.com/image.jpg" - } - } - ] - } - ] - } + def test_extract_user_message_with_image_url(self): + """User message with only image_url becomes a single input_image content item.""" + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + LiteLLMResponsesTransformationHandler, + ) - message = asyncio.run(noma_guardrail._extract_user_message(data)) + handler = LiteLLMResponsesTransformationHandler() + messages: list[AllMessageValues] = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "https://example.com/image.jpg" + } + } + ] + } + ] + + input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages) + + assert len(input_items) == 1 + message = input_items[0]["content"] assert message is not None assert len(message) == 1 assert message[0]["type"] == "input_image" assert message[0]["image_url"] == "https://example.com/image.jpg" - def test_extract_user_message_with_mixed_content(self, noma_guardrail): - """Test extracting user message with mixed text and image content""" - import asyncio - - data = { - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "What's in this image?" - }, - { - "type": "image_url", - "image_url": { - "url": "https://example.com/image.jpg" - } - } - ] - } - ] - } + def test_extract_user_message_with_mixed_content(self): + """User message with text + image becomes input_text then input_image in content list.""" + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + LiteLLMResponsesTransformationHandler, + ) - message = asyncio.run(noma_guardrail._extract_user_message(data)) + handler = LiteLLMResponsesTransformationHandler() + messages: list[AllMessageValues] = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "What's in this image?", + }, + { + "type": "image_url", + "image_url": { + "url": "https://example.com/image.jpg" + } + } + ] + } + ] + + input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages) + + # Match the original assertions: `message` is the content list + assert len(input_items) == 1 + message = input_items[0]["content"] assert message is not None assert len(message) == 2 # First item should be text @@ -1085,37 +1192,43 @@ class TestNomaImageProcessing: assert message[1]["type"] == "input_image" assert message[1]["image_url"] == "https://example.com/image.jpg" - def test_extract_user_message_with_multiple_images(self, noma_guardrail): - """Test extracting user message with multiple images""" - import asyncio - - data = { - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": "Compare these images" - }, - { - "type": "image_url", - "image_url": { - "url": "https://example.com/image1.jpg" - } - }, - { - "type": "image_url", - "image_url": { - "url": "https://example.com/image2.jpg" - } - } - ] - } - ] - } + def test_extract_user_message_with_multiple_images(self): + """User message with multiple images becomes multiple input_image items.""" + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + LiteLLMResponsesTransformationHandler, + ) - message = asyncio.run(noma_guardrail._extract_user_message(data)) + handler = LiteLLMResponsesTransformationHandler() + + messages: list[AllMessageValues] = [ + { + "role": "user", + "content": [ + { + "type": "text", + "text": "Compare these images", + }, + { + "type": "image_url", + "image_url": { + "url": "https://example.com/image1.jpg" + } + }, + { + "type": "image_url", + "image_url": { + "url": "https://example.com/image2.jpg" + } + } + ] + } + ] + + input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages) + + # Match the original assertions + assert len(input_items) == 1 + message = input_items[0]["content"] assert message is not None assert len(message) == 3 assert message[0]["type"] == "input_text" @@ -1301,8 +1414,14 @@ class TestNomaImageProcessing: assert exc_info.value.status_code == 400 @pytest.mark.asyncio - async def test_image_with_base64_data(self, noma_guardrail): + async def test_image_with_base64_data( + self, noma_guardrail, mock_user_api_key_dict + ): """Test extracting image with base64 data URL""" + from litellm.completion_extras.litellm_responses_transformation.transformation import ( + LiteLLMResponsesTransformationHandler, + ) + data = { "messages": [ { @@ -1319,7 +1438,13 @@ class TestNomaImageProcessing: ] } - message = await noma_guardrail._extract_user_message(data) + handler = LiteLLMResponsesTransformationHandler() + messages = cast(list[AllMessageValues], data["messages"]) + + input_items, _ = handler.convert_chat_completion_messages_to_responses_api(messages) + + assert len(input_items) == 1 + message = input_items[0]["content"] assert message is not None assert len(message) == 1 assert message[0]["type"] == "input_image" From 965406c643077dc5375a2c7bcd28c801caf807a6 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 2 Dec 2025 00:56:47 -0300 Subject: [PATCH 207/248] feat(provider): add Z.AI (Zhipu AI) as built-in provider (#17307) * feat(provider): add Z.AI (Zhipu AI) as built-in provider Add support for Z.AI GLM models as a native OpenAI-compatible provider. - Add "zai" to openai_compatible_providers list - Add ZAI enum to LlmProviders - Add provider URL resolution for https://api.z.ai/api/paas/v4 - Add 8 GLM models with pricing to model cost maps: - glm-4.6 (200K context, $0.6/$2.2 per 1M tokens) - glm-4.5, glm-4.5v, glm-4.5-x, glm-4.5-air, glm-4.5-airx - glm-4-32b-0414-128k - glm-4.5-flash (free tier) - Add unit tests for provider integration Closes #17289 * docs: add Z.AI provider documentation - Add zai.md with usage examples, model list, and pricing - Add to sidebars.js navigation --- docs/my-website/docs/providers/zai.md | 135 ++++++++++++++++ docs/my-website/sidebars.js | 1 + litellm/constants.py | 1 + .../get_llm_provider_logic.py | 7 + ...odel_prices_and_context_window_backup.json | 89 +++++++++++ litellm/types/utils.py | 1 + model_prices_and_context_window.json | 89 +++++++++++ .../llms/zai/test_zai_provider.py | 144 ++++++++++++++++++ 8 files changed, 467 insertions(+) create mode 100644 docs/my-website/docs/providers/zai.md create mode 100644 tests/test_litellm/llms/zai/test_zai_provider.py diff --git a/docs/my-website/docs/providers/zai.md b/docs/my-website/docs/providers/zai.md new file mode 100644 index 0000000000..5055d0c1cd --- /dev/null +++ b/docs/my-website/docs/providers/zai.md @@ -0,0 +1,135 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Z.AI (Zhipu AI) +https://z.ai/ + +**We support Z.AI GLM text/chat models, just set `zai/` as a prefix when sending completion requests** + +## API Key +```python +# env variable +os.environ['ZAI_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['ZAI_API_KEY'] = "" +response = completion( + model="zai/glm-4.6", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['ZAI_API_KEY'] = "" +response = completion( + model="zai/glm-4.6", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Supported Models + +We support ALL Z.AI GLM models, just set `zai/` as a prefix when sending completion requests. + +| Model Name | Function Call | Notes | +|------------|---------------|-------| +| glm-4.6 | `completion(model="zai/glm-4.6", messages)` | Latest flagship model, 200K context | +| glm-4.5 | `completion(model="zai/glm-4.5", messages)` | 128K context | +| glm-4.5v | `completion(model="zai/glm-4.5v", messages)` | Vision model | +| glm-4.5-x | `completion(model="zai/glm-4.5-x", messages)` | Premium tier | +| glm-4.5-air | `completion(model="zai/glm-4.5-air", messages)` | Lightweight | +| glm-4.5-airx | `completion(model="zai/glm-4.5-airx", messages)` | Fast lightweight | +| glm-4-32b-0414-128k | `completion(model="zai/glm-4-32b-0414-128k", messages)` | 32B parameter model | +| glm-4.5-flash | `completion(model="zai/glm-4.5-flash", messages)` | **FREE tier** | + +## Model Pricing + +| Model | Input ($/1M tokens) | Output ($/1M tokens) | Context Window | +|-------|---------------------|----------------------|----------------| +| glm-4.6 | $0.60 | $2.20 | 200K | +| glm-4.5 | $0.60 | $2.20 | 128K | +| glm-4.5v | $0.60 | $1.80 | 128K | +| glm-4.5-x | $2.20 | $8.90 | 128K | +| glm-4.5-air | $0.20 | $1.10 | 128K | +| glm-4.5-airx | $1.10 | $4.50 | 128K | +| glm-4-32b-0414-128k | $0.10 | $0.10 | 128K | +| glm-4.5-flash | **FREE** | **FREE** | 128K | + +## Using with LiteLLM Proxy + + + + +```python +from litellm import completion +import os + +os.environ['ZAI_API_KEY'] = "" +response = completion( + model="zai/glm-4.6", + messages=[{"role": "user", "content": "Hello, how are you?"}], +) + +print(response.choices[0].message.content) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: glm-4.6 + litellm_params: + model: zai/glm-4.6 + api_key: os.environ/ZAI_API_KEY + - model_name: glm-4.5-flash # Free tier + litellm_params: + model: zai/glm-4.5-flash + api_key: os.environ/ZAI_API_KEY +``` + +2. Run proxy + +```bash +litellm --config config.yaml +``` + +3. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "glm-4.6", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?" + } + ] +}' +``` + + + diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 2039d01186..e467711b59 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -656,6 +656,7 @@ const sidebars = { }, "providers/xai", "providers/xinference", + "providers/zai", ], }, { diff --git a/litellm/constants.py b/litellm/constants.py index 65de5d7b55..e3de7368c8 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -555,6 +555,7 @@ openai_compatible_providers: List = [ "perplexity", "xinference", "xai", + "zai", "together_ai", "fireworks_ai", "empower", diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index 4d29a74ddb..b10011befc 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -662,6 +662,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.XAIChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "zai": + api_base = ( + api_base + or get_secret_str("ZAI_API_BASE") + or "https://api.z.ai/api/paas/v4" + ) + dynamic_api_key = api_key or get_secret_str("ZAI_API_KEY") elif custom_llm_provider == "together_ai": api_base = ( api_base diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index af63d1e259..9fdc1704f4 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -26855,6 +26855,95 @@ "supports_vision": true, "supports_web_search": true }, + "zai/glm-4.6": { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 2.2e-06, + "litellm_provider": "zai", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5": { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 2.2e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5v": { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 1.8e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-x": { + "input_cost_per_token": 2.2e-06, + "output_cost_per_token": 8.9e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-air": { + "input_cost_per_token": 2e-07, + "output_cost_per_token": 1.1e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-airx": { + "input_cost_per_token": 1.1e-06, + "output_cost_per_token": 4.5e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4-32b-0414-128k": { + "input_cost_per_token": 1e-07, + "output_cost_per_token": 1e-07, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-flash": { + "input_cost_per_token": 0, + "output_cost_per_token": 0, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, "vertex_ai/search_api": { "input_cost_per_query": 1.5e-03, "litellm_provider": "vertex_ai", diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 2456c87044..58267fdfea 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -2553,6 +2553,7 @@ class LlmProviders(str, Enum): OPENAI_LIKE = "openai_like" # embedding only JINA_AI = "jina_ai" XAI = "xai" + ZAI = "zai" CUSTOM_OPENAI = "custom_openai" TEXT_COMPLETION_OPENAI = "text-completion-openai" COHERE = "cohere" diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 6b9b8beed8..1f8f1c7511 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -26882,6 +26882,95 @@ "supports_vision": true, "supports_web_search": true }, + "zai/glm-4.6": { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 2.2e-06, + "litellm_provider": "zai", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5": { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 2.2e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5v": { + "input_cost_per_token": 6e-07, + "output_cost_per_token": 1.8e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_vision": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-x": { + "input_cost_per_token": 2.2e-06, + "output_cost_per_token": 8.9e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-air": { + "input_cost_per_token": 2e-07, + "output_cost_per_token": 1.1e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-airx": { + "input_cost_per_token": 1.1e-06, + "output_cost_per_token": 4.5e-06, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4-32b-0414-128k": { + "input_cost_per_token": 1e-07, + "output_cost_per_token": 1e-07, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, + "zai/glm-4.5-flash": { + "input_cost_per_token": 0, + "output_cost_per_token": 0, + "litellm_provider": "zai", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "mode": "chat", + "supports_function_calling": true, + "supports_tool_choice": true, + "source": "https://docs.z.ai/guides/overview/pricing" + }, "vertex_ai/search_api": { "input_cost_per_query": 1.5e-03, "litellm_provider": "vertex_ai", diff --git a/tests/test_litellm/llms/zai/test_zai_provider.py b/tests/test_litellm/llms/zai/test_zai_provider.py new file mode 100644 index 0000000000..a3d47d666b --- /dev/null +++ b/tests/test_litellm/llms/zai/test_zai_provider.py @@ -0,0 +1,144 @@ +""" +Tests for Z.AI (Zhipu AI) provider - GLM models +""" +import json +import math + +import pytest +import respx + +import litellm +from litellm import completion +from litellm.cost_calculator import cost_per_token + + +@pytest.fixture +def zai_response(): + """Mock response from Z.AI API""" + return { + "id": "chatcmpl-zai-123", + "object": "chat.completion", + "created": 1677652288, + "model": "glm-4.6", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello! How can I help you today?"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 15, "total_tokens": 25}, + } + + +def test_get_llm_provider_zai(): + """Test that get_llm_provider correctly identifies zai provider""" + from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider + + model, provider, api_key, api_base = get_llm_provider("zai/glm-4.6") + assert model == "glm-4.6" + assert provider == "zai" + assert api_base == "https://api.z.ai/api/paas/v4" + + +def test_zai_in_provider_lists(): + """Test that zai is registered in all necessary provider lists""" + assert "zai" in litellm.openai_compatible_providers + assert "zai" in litellm.provider_list + + +def test_zai_models_in_model_cost(): + """Test that ZAI models are in the model cost map""" + import os + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") + + zai_models = [ + "zai/glm-4.6", + "zai/glm-4.5", + "zai/glm-4.5v", + "zai/glm-4.5-x", + "zai/glm-4.5-air", + "zai/glm-4.5-airx", + "zai/glm-4-32b-0414-128k", + "zai/glm-4.5-flash", + ] + + for model in zai_models: + assert model in litellm.model_cost, f"Model {model} not found in model_cost" + assert litellm.model_cost[model]["litellm_provider"] == "zai" + + +def test_zai_glm46_cost_calculation(): + """Test the cost calculation for glm-4.6""" + import os + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") + + key = "zai/glm-4.6" + info = litellm.model_cost[key] + + prompt_cost, completion_cost = cost_per_token( + model="zai/glm-4.6", + prompt_tokens=1000000, # 1M tokens + completion_tokens=1000000, + ) + + # GLM-4.6: $0.6/M input, $2.2/M output + assert math.isclose(prompt_cost, 0.6, rel_tol=1e-6) + assert math.isclose(completion_cost, 2.2, rel_tol=1e-6) + + +def test_zai_flash_model_is_free(): + """Test that glm-4.5-flash has zero cost""" + import os + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") + + key = "zai/glm-4.5-flash" + info = litellm.model_cost[key] + + assert info["input_cost_per_token"] == 0 + assert info["output_cost_per_token"] == 0 + + +@pytest.mark.asyncio +async def test_zai_completion_call(respx_mock, zai_response, monkeypatch): + """Test completion call with zai provider using mocked response""" + monkeypatch.setenv("ZAI_API_KEY", "test-api-key") + litellm.disable_aiohttp_transport = True + + respx_mock.post("https://api.z.ai/api/paas/v4/chat/completions").respond(json=zai_response) + + response = await litellm.acompletion( + model="zai/glm-4.6", + messages=[{"role": "user", "content": "Hello"}], + max_tokens=20, + ) + + assert response.choices[0].message.content == "Hello! How can I help you today?" + assert response.usage.total_tokens == 25 + + assert len(respx_mock.calls) == 1 + request = respx_mock.calls[0].request + assert request.method == "POST" + assert "api.z.ai" in str(request.url) + assert "Authorization" in request.headers + assert request.headers["Authorization"] == "Bearer test-api-key" + + +def test_zai_sync_completion(respx_mock, zai_response, monkeypatch): + """Test synchronous completion call""" + monkeypatch.setenv("ZAI_API_KEY", "test-api-key") + litellm.disable_aiohttp_transport = True + + respx_mock.post("https://api.z.ai/api/paas/v4/chat/completions").respond(json=zai_response) + + response = completion( + model="zai/glm-4.6", + messages=[{"role": "user", "content": "Hello"}], + max_tokens=20, + ) + + assert response.choices[0].message.content == "Hello! How can I help you today?" + assert response.usage.total_tokens == 25 From 01dfc3561acb1baf60209786fc24e19d77384b08 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 2 Dec 2025 00:58:27 -0300 Subject: [PATCH 208/248] Fix AttributeError when metadata is null in request body (#17263) (#17306) Handle the case where metadata is explicitly set to null/None in the request body. This was causing a 401 error with "'NoneType' object has no attribute 'get'" when calling /v1/batches with metadata: null. The fix uses `or {}` instead of a default dict value since the key exists but has a None value. --- .../proxy/common_utils/http_parsing_utils.py | 2 +- .../common_utils/test_http_parsing_utils.py | 23 +++++++++++++++++-- 2 files changed, 22 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/common_utils/http_parsing_utils.py b/litellm/proxy/common_utils/http_parsing_utils.py index 59b3ec20b4..259755f5ef 100644 --- a/litellm/proxy/common_utils/http_parsing_utils.py +++ b/litellm/proxy/common_utils/http_parsing_utils.py @@ -309,7 +309,7 @@ def get_tags_from_request_body(request_body: dict) -> List[str]: List of tag names (strings), empty list if no valid tags found """ metadata_variable_name = get_metadata_variable_name_from_kwargs(request_body) - metadata = request_body.get(metadata_variable_name, {}) + metadata = request_body.get(metadata_variable_name) or {} tags_in_metadata: Any = metadata.get("tags", []) tags_in_request_body: Any = request_body.get("tags", []) combined_tags: List[str] = [] diff --git a/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py b/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py index 85858866dd..2361decc5a 100644 --- a/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py +++ b/tests/test_litellm/proxy/common_utils/test_http_parsing_utils.py @@ -606,8 +606,27 @@ def test_get_tags_from_request_body_with_dict_tags(): } } } - + result = get_tags_from_request_body(request_body=request_body) - + + assert result == [] + assert isinstance(result, list) + + +def test_get_tags_from_request_body_with_null_metadata(): + """ + Test that function handles null metadata gracefully without crashing. + + This is a regression test for https://github.com/BerriAI/litellm/issues/17263 + When metadata is explicitly set to null/None, the function should return + an empty list instead of raising AttributeError. + """ + request_body = { + "model": "gpt-4", + "metadata": None # OpenAI API accepts metadata: null + } + + result = get_tags_from_request_body(request_body=request_body) + assert result == [] assert isinstance(result, list) From 860270a7927b0d13319bead2fdff9a4c00e3d00f Mon Sep 17 00:00:00 2001 From: Saar wintrov Date: Tue, 2 Dec 2025 06:01:36 +0200 Subject: [PATCH 209/248] SSO: Clear sso integration for all users (#17287) --- .../_buildManifest.js | 0 .../_ssgManifest.js | 0 .../static/chunks/1518-21c80a799b5c426e.js | 1 - .../static/chunks/1518-4475f8385da5ac78.js | 1 + ...6050ee3518d4.js => 1529-59ce29afdf8ccc9b.js} | 2 +- ...971a192714f2.js => 1674-de8248fbd0c554ba.js} | 2 +- .../static/chunks/1973-26a414084f96c69b.js | 1 + .../static/chunks/1994-6637a121c9ee1602.js | 1 - .../static/chunks/1994-a4d0b99849c16b62.js | 1 + .../static/chunks/2004-294ce010a90069b4.js | 1 + .../static/chunks/2004-8b1ad3d8c195646a.js | 1 - .../static/chunks/2012-9200c205d5b0405a.js | 1 - .../static/chunks/2012-c09fa25a9cbf6028.js | 1 + .../static/chunks/2019-15183fcc4c29249f.js | 1 - 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user api key dict information to the guardrail api * fix: pass user api key dict information to the guardrail api * feat: pass litellm call id + trace id, if present * docs: update docs --- .../mock_bedrock_guardrail_server.py | 18 +- .../adding_provider/generic_guardrail_api.md | 60 ++- litellm/integrations/custom_guardrail.py | 38 +- .../chat/guardrail_translation/handler.py | 134 +++-- .../guardrail_translation/base_translation.py | 62 ++- .../rerank/guardrail_translation/handler.py | 14 +- .../chat/guardrail_translation/handler.py | 149 ++++-- .../guardrail_translation/handler.py | 109 ++-- .../guardrail_translation/handler.py | 14 +- .../guardrail_translation/handler.py | 116 ++-- .../speech/guardrail_translation/handler.py | 14 +- .../guardrail_translation/handler.py | 24 +- .../guardrail_translation/handler.py | 33 +- litellm/main.py | 34 +- .../out/api-reference/index.html | 2 +- .../proxy/_experimental/out/logs/index.html | 2 +- .../_experimental/out/model-hub/index.html | 2 +- .../out/model_hub_table/index.html | 2 +- .../out/models-and-endpoints/index.html | 2 +- .../out/organizations/index.html | 2 +- .../_experimental/out/playground/index.html | 2 +- .../proxy/_experimental/out/teams/index.html | 2 +- .../_experimental/out/test-key/index.html | 2 +- .../proxy/_experimental/out/usage/index.html | 2 +- .../proxy/_experimental/out/users/index.html | 2 +- .../_experimental/out/virtual-keys/index.html | 2 +- litellm/proxy/_new_secret_config.yaml | 33 +- .../proxy/guardrails/guardrail_endpoints.py | 21 +- .../guardrail_hooks/bedrock_guardrails.py | 99 ++-- .../guardrail_hooks/enkryptai/enkryptai.py | 69 ++- .../generic_guardrail_api.py | 151 +++--- .../litellm_content_filter/content_filter.py | 270 ++++++---- .../guardrails/guardrail_hooks/presidio.py | 44 +- .../unified_guardrail/unified_guardrail.py | 5 + .../zscaler_ai_guard/zscaler_ai_guard.py | 167 +++--- .../guardrail_hooks/generic_guardrail_api.py | 85 ++- .../test_generic_guardrail_api.py | 504 ++++++++++++++++++ 37 files changed, 1692 insertions(+), 599 deletions(-) create mode 100644 tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py diff --git a/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py b/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py index 9cfbb11feb..f75a53e879 100644 --- a/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py +++ b/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py @@ -424,9 +424,11 @@ This is a beta API. Please help us improve it. class LitellmBasicGuardrailRequest(BaseModel): - text: str - request_body: Dict[str, Any] = Field(default_factory=dict) + texts: List[str] + images: Optional[List[str]] = None + request_data: Dict[str, Any] = Field(default_factory=dict) additional_provider_specific_params: Dict[str, Any] = Field(default_factory=dict) + input_type: Literal["request", "response"] class LitellmBasicGuardrailResponse(BaseModel): @@ -434,7 +436,8 @@ class LitellmBasicGuardrailResponse(BaseModel): "BLOCKED", "NONE", "GUARDRAIL_INTERVENED" ] # BLOCKED = litellm will raise an error, NONE = litellm will continue, GUARDRAIL_INTERVENED = litellm will continue, but the text was modified by the guardrail blocked_reason: Optional[str] = None # only if action is BLOCKED, otherwise None - text: Optional[str] = None + texts: Optional[List[str]] = None + images: Optional[List[str]] = None @app.post( @@ -457,14 +460,17 @@ async def beta_litellm_basic_guardrail_api( LitellmBasicGuardrailResponse with analysis results """ print(f"request: {request}") - if "ishaan" in request.text.lower(): + if any("ishaan" in text.lower() for text in request.texts): return LitellmBasicGuardrailResponse( action="BLOCKED", blocked_reason="Ishaan is not allowed" ) - elif "pii_value" in request.text: + elif any("pii_value" in text for text in request.texts): return LitellmBasicGuardrailResponse( action="GUARDRAIL_INTERVENED", - text=request.text.replace("pii_value", "pii_value_redacted"), + texts=[ + text.replace("pii_value", "pii_value_redacted") + for text in request.texts + ], ) return LitellmBasicGuardrailResponse(action="NONE") diff --git a/docs/my-website/docs/adding_provider/generic_guardrail_api.md b/docs/my-website/docs/adding_provider/generic_guardrail_api.md index 70b39d3c39..9392deeb5d 100644 --- a/docs/my-website/docs/adding_provider/generic_guardrail_api.md +++ b/docs/my-website/docs/adding_provider/generic_guardrail_api.md @@ -17,15 +17,16 @@ The **Generic Guardrail API** lets you integrate with LiteLLM **instantly** by i 1. **No PR Needed** - Deploy and integrate immediately 2. **Universal Support** - Works across ALL LiteLLM endpoints (chat, embeddings, image generation, etc.) 3. **Simple Contract** - One endpoint, three response types -4. **Custom Parameters** - Pass provider-specific params via config -5. **Full Control** - You own and maintain your guardrail API +4. **Multi-Modal Support** - Handle both text and images in requests/responses +5. **Custom Parameters** - Pass provider-specific params via config +6. **Full Control** - You own and maintain your guardrail API ## How It Works -1. LiteLLM extracts text from any request (chat messages, embeddings, image prompts, etc.) -2. Sends extracted text + original request to your API endpoint +1. LiteLLM extracts text and images from any request (chat messages, embeddings, image prompts, etc.) +2. Sends extracted content + metadata to your API endpoint 3. Your API responds with: `BLOCKED`, `NONE`, or `GUARDRAIL_INTERVENED` -4. LiteLLM enforces the decision +4. LiteLLM enforces the decision and applies any modifications ## API Contract @@ -37,8 +38,21 @@ Implement `POST /beta/litellm_basic_guardrail_api` ```json { - "text": "extracted text from the request", - "request_body": {}, // full original request for context + "texts": ["extracted text from the request"], // array of text strings + "images": ["base64_encoded_image_data"], // optional array of images + "request_data": { + "user_api_key_hash": "hash of the litellm virtual key used", + "user_api_key_alias": "alias of the litellm virtual key used", + "user_api_key_user_id": "user id associated with the litellm virtual key used", + "user_api_key_user_email": "user email associated with the litellm virtual key used", + "user_api_key_team_id": "team id associated with the litellm virtual key used", + "user_api_key_team_alias": "team alias associated with the litellm virtual key used", + "user_api_key_end_user_id": "end user id associated with the litellm virtual key used", + "user_api_key_org_id": "org id associated with the litellm virtual key used" + }, + "input_type": "request", // "request" or "response" + "litellm_call_id": "unique_call_id", // the call id of the individual LLM call + "litellm_trace_id": "trace_id", // the trace id of the LLM call - useful if there are multiple LLM calls for the same conversation "additional_provider_specific_params": { // your custom params from config } @@ -51,14 +65,15 @@ Implement `POST /beta/litellm_basic_guardrail_api` { "action": "BLOCKED" | "NONE" | "GUARDRAIL_INTERVENED", "blocked_reason": "why content was blocked", // required if action=BLOCKED - "text": "modified text" // required if action=GUARDRAIL_INTERVENED + "texts": ["modified text"], // optional array of modified text strings + "images": ["modified_base64_image"] // optional array of modified images } ``` **Actions:** - `BLOCKED` - LiteLLM raises error and blocks request - `NONE` - Request proceeds unchanged -- `GUARDRAIL_INTERVENED` - Request proceeds with modified text +- `GUARDRAIL_INTERVENED` - Request proceeds with modified texts/images (provide `texts` and/or `images` fields) ## LiteLLM Configuration @@ -116,27 +131,34 @@ See [mock_bedrock_guardrail_server.py](https://github.com/BerriAI/litellm/blob/m ```python from fastapi import FastAPI from pydantic import BaseModel +from typing import List, Optional, Dict, Any app = FastAPI() class GuardrailRequest(BaseModel): - text: str - request_body: dict - additional_provider_specific_params: dict + texts: List[str] + images: Optional[List[str]] = None + request_data: Dict[str, Any] + input_type: str # "request" or "response" + litellm_call_id: Optional[str] = None + litellm_trace_id: Optional[str] = None + additional_provider_specific_params: Dict[str, Any] class GuardrailResponse(BaseModel): action: str # BLOCKED, NONE, or GUARDRAIL_INTERVENED - blocked_reason: str | None = None - text: str | None = None + blocked_reason: Optional[str] = None + texts: Optional[List[str]] = None + images: Optional[List[str]] = None @app.post("/beta/litellm_basic_guardrail_api") async def apply_guardrail(request: GuardrailRequest): # Your guardrail logic here - if "badword" in request.text.lower(): - return GuardrailResponse( - action="BLOCKED", - blocked_reason="Content contains prohibited terms" - ) + for text in request.texts: + if "badword" in text.lower(): + return GuardrailResponse( + action="BLOCKED", + blocked_reason="Content contains prohibited terms" + ) return GuardrailResponse(action="NONE") ``` diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 4255584140..7f74f5d215 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -1,5 +1,16 @@ from datetime import datetime -from typing import Any, Dict, List, Optional, Type, Union, get_args +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + Literal, + Optional, + Tuple, + Type, + Union, + get_args, +) from litellm._logging import verbose_logger from litellm.caching import DualCache @@ -20,6 +31,8 @@ from litellm.types.utils import ( StandardLoggingGuardrailInformation, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj dc = DualCache() @@ -437,30 +450,31 @@ class CustomGuardrail(CustomLogger): async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: """ Apply your guardrail logic to the given text Args: - text: The text to apply the guardrail to - language: The language of the text - entities: The entities to mask, optional - request_data: The request data dictionary to store guardrail metadata + texts: The texts to apply the guardrail to + images: The images to apply the guardrail to + request_data: The request data dictionary - containing user api key metadata (e.g. user_id, team_id, etc.) + input_type: The type of input to apply the guardrail to - "request" or "response" Any of the custom guardrails can override this method to provide custom guardrail logic - Returns the text with the guardrail applied + Returns the texts with the guardrail applied and the images with the guardrail applied (if any) Raises: Exception: - If the guardrail raises an exception """ - return text + return texts, images def _process_response( self, diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 6aba2947d3..383a494123 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -12,8 +12,7 @@ Pattern Overview: 4. Apply guardrail responses back to the original structure """ -import asyncio -from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional, Tuple, cast +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -50,30 +49,40 @@ class AnthropicMessagesHandler(BaseTranslation): if messages is None: return data - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (message_index, content_index) for each task + # Track (message_index, content_index) for each text # content_index is None for string content, int for list content - # Step 1: Extract all text content and create guardrail tasks + # Step 1: Extract all text content and images for msg_idx, message in enumerate(messages): - await self._extract_input_text_and_create_tasks( + self._extract_input_text_and_images( message=message, msg_idx=msg_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + guardrailed_texts, guardrailed_images = ( + await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=data, + input_type="request", + images=images_to_check if images_to_check else None, + logging_obj=litellm_logging_obj, + ) + ) - # Step 3: Map guardrail responses back to original message structure - await self._apply_guardrail_responses_to_input( - messages=messages, - responses=responses, - task_mappings=task_mappings, - ) + # Step 3: Map guardrail responses back to original message structure + await self._apply_guardrail_responses_to_input( + messages=messages, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) verbose_proxy_logger.debug( "Anthropic Messages: Processed input messages: %s", messages @@ -81,18 +90,18 @@ class AnthropicMessagesHandler(BaseTranslation): return data - async def _extract_input_text_and_create_tasks( + def _extract_input_text_and_images( self, message: Dict[str, Any], msg_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", ) -> None: """ - Extract text content from a message and create guardrail tasks. + Extract text content and images from a message. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ content = message.get("content", None) if content is None: @@ -100,17 +109,26 @@ class AnthropicMessagesHandler(BaseTranslation): if isinstance(content, str): # Simple string content - tasks.append(guardrail_to_apply.apply_guardrail(text=content)) + texts_to_check.append(content) task_mappings.append((msg_idx, None)) elif isinstance(content, list): # List content (e.g., multimodal with text and images) for content_idx, content_item in enumerate(content): + # Extract text text_str = content_item.get("text", None) - if text_str is None: - continue - tasks.append(guardrail_to_apply.apply_guardrail(text=text_str)) - task_mappings.append((msg_idx, int(content_idx))) + if text_str is not None: + texts_to_check.append(text_str) + task_mappings.append((msg_idx, int(content_idx))) + + # Extract images + if content_item.get("type") == "image": + source = content_item.get("source", {}) + if isinstance(source, dict): + # Could be base64 or url + data = source.get("data") + if data: + images_to_check.append(data) async def _apply_guardrail_responses_to_input( self, @@ -147,6 +165,7 @@ class AnthropicMessagesHandler(BaseTranslation): response: "AnthropicMessagesResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. @@ -154,6 +173,8 @@ class AnthropicMessagesHandler(BaseTranslation): Args: response: Anthropic MessagesResponse object guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails Returns: Modified response with guardrail applied to content @@ -168,35 +189,56 @@ class AnthropicMessagesHandler(BaseTranslation): ) return response - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (choice_index, content_index) for each task + # Track (content_index, None) for each text response_content = response.get("content", []) if not response_content: return response - # Step 1: Extract all text content from response choices + + # Step 1: Extract all text content from response for content_idx, content_block in enumerate(response_content): # Check if this is a text block by checking the 'type' field if isinstance(content_block, dict) and content_block.get("type") == "text": # Cast to dict to handle the union type properly - await self._extract_output_text_and_create_tasks( + self._extract_output_text_and_images( content_block=cast(Dict[str, Any], content_block), content_idx=content_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response} - # Step 3: Map guardrail responses back to original response structure - await self._apply_guardrail_responses_to_output( - response=response, - responses=responses, - task_mappings=task_mappings, - ) + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + guardrailed_texts, guardrailed_images = ( + await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=request_data, + input_type="response", + images=images_to_check if images_to_check else None, + logging_obj=litellm_logging_obj, + ) + ) + + # Step 3: Map guardrail responses back to original response structure + await self._apply_guardrail_responses_to_output( + response=response, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) verbose_proxy_logger.debug( "Anthropic Messages: Processed output response: %s", response @@ -221,23 +263,23 @@ class AnthropicMessagesHandler(BaseTranslation): return True return False - async def _extract_output_text_and_create_tasks( + def _extract_output_text_and_images( self, content_block: Dict[str, Any], content_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", ) -> None: """ - Extract text content from a response choice and create guardrail tasks. + Extract text content and images from a response content block. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ content_text = content_block.get("text") if content_text and isinstance(content_text, str): # Simple string content - tasks.append(guardrail_to_apply.apply_guardrail(text=content_text)) + texts_to_check.append(content_text) task_mappings.append((content_idx, None)) async def _apply_guardrail_responses_to_output( diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index 926ad59cee..5acbf4e9f4 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -1,12 +1,57 @@ from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any, Optional +from typing import TYPE_CHECKING, Any, Dict, Optional if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth class BaseTranslation(ABC): + @staticmethod + def transform_user_api_key_dict_to_metadata( + user_api_key_dict: Optional[Any], + ) -> Dict[str, Any]: + """ + Transform user_api_key_dict to a metadata dict with prefixed keys. + + Converts keys like 'user_id' to 'user_api_key_user_id' to clearly indicate + the source of the metadata. + + Args: + user_api_key_dict: UserAPIKeyAuth object or dict with user information + + Returns: + Dict with keys prefixed with 'user_api_key_' + """ + if user_api_key_dict is None: + return {} + + # Convert to dict if it's a Pydantic object + user_dict = ( + user_api_key_dict.model_dump() + if hasattr(user_api_key_dict, "model_dump") + else user_api_key_dict + ) + + if not isinstance(user_dict, dict): + return {} + + # Transform keys to be prefixed with 'user_api_key_' + transformed = {} + for key, value in user_dict.items(): + # Skip None values and internal fields + if value is None or key.startswith("_"): + continue + + # If key already has the prefix, use as-is, otherwise add prefix + if key.startswith("user_api_key_"): + transformed[key] = value + else: + transformed[f"user_api_key_{key}"] = value + + return transformed + @abstractmethod async def process_input_messages( self, @@ -14,6 +59,11 @@ class BaseTranslation(ABC): guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> Any: + """ + Process input messages with guardrails. + + Note: user_api_key_dict metadata should be available in the data dict. + """ pass @abstractmethod @@ -22,5 +72,15 @@ class BaseTranslation(ABC): response: Any, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, ) -> Any: + """ + Process output response with guardrails. + + Args: + response: The response object from the LLM + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata (passed separately since response doesn't contain it) + """ pass diff --git a/litellm/llms/cohere/rerank/guardrail_translation/handler.py b/litellm/llms/cohere/rerank/guardrail_translation/handler.py index a5a5ef68b8..e042295ab0 100644 --- a/litellm/llms/cohere/rerank/guardrail_translation/handler.py +++ b/litellm/llms/cohere/rerank/guardrail_translation/handler.py @@ -49,14 +49,19 @@ class CohereRerankHandler(BaseTranslation): # Process query only query = data.get("query") if query is not None and isinstance(query, str): - guardrailed_query = await guardrail_to_apply.apply_guardrail(text=query) - data["query"] = guardrailed_query + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=[query], + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + data["query"] = guardrailed_texts[0] if guardrailed_texts else query verbose_proxy_logger.debug( "Rerank: Applied guardrail to query. " "Original length: %d, New length: %d", len(query), - len(guardrailed_query), + len(data["query"]), ) else: verbose_proxy_logger.debug( @@ -70,6 +75,7 @@ class CohereRerankHandler(BaseTranslation): response: "RerankResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response - not applicable for rerank. @@ -81,6 +87,8 @@ class CohereRerankHandler(BaseTranslation): Args: response: Rerank response object with rankings guardrail_to_apply: The guardrail instance (unused) + litellm_logging_obj: Optional logging object (unused) + user_api_key_dict: User API key metadata (unused) Returns: Unmodified response (rankings don't need text guardrails) diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index 2a421a8283..0abc94012e 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -14,8 +14,7 @@ Pattern Overview: This pattern can be replicated for other message formats (e.g., Anthropic). """ -import asyncio -from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional, Tuple, cast +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast import litellm from litellm._logging import verbose_proxy_logger @@ -51,31 +50,40 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if messages is None: return data - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (message_index, content_index) for each task + # Track (message_index, content_index) for each text # content_index is None for string content, int for list content - # Step 1: Extract all text content and create guardrail tasks + # Step 1: Extract all text content and images for msg_idx, message in enumerate(messages): - await self._extract_input_text_and_create_tasks( + self._extract_input_text_and_images( message=message, msg_idx=msg_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, - request_data=data, ) - # Step 2: Run all guardrail tasks in parallel - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + guardrailed_texts, guardrailed_images = ( + await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=data, + input_type="request", + images=images_to_check if images_to_check else None, + logging_obj=litellm_logging_obj, + ) + ) - # Step 3: Map guardrail responses back to original message structure - await self._apply_guardrail_responses_to_input( - messages=messages, - responses=responses, - task_mappings=task_mappings, - ) + # Step 3: Map guardrail responses back to original message structure + await self._apply_guardrail_responses_to_input( + messages=messages, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) verbose_proxy_logger.debug( "OpenAI Chat Completions: Processed input messages: %s", messages @@ -83,19 +91,18 @@ class OpenAIChatCompletionsHandler(BaseTranslation): return data - async def _extract_input_text_and_create_tasks( + def _extract_input_text_and_images( self, message: Dict[str, Any], msg_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", - request_data: Optional[Dict[str, Any]] = None, ) -> None: """ - Extract text content from a message and create guardrail tasks. + Extract text content and images from a message. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ content = message.get("content", None) if content is None: @@ -103,17 +110,25 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if isinstance(content, str): # Simple string content - tasks.append(guardrail_to_apply.apply_guardrail(text=content, request_data=request_data)) + texts_to_check.append(content) task_mappings.append((msg_idx, None)) elif isinstance(content, list): # List content (e.g., multimodal with text and images) for content_idx, content_item in enumerate(content): + # Extract text text_str = content_item.get("text", None) - if text_str is None: - continue - tasks.append(guardrail_to_apply.apply_guardrail(text=text_str, request_data=request_data)) - task_mappings.append((msg_idx, int(content_idx))) + if text_str is not None: + texts_to_check.append(text_str) + task_mappings.append((msg_idx, int(content_idx))) + + # Extract images (image_url) + if content_item.get("type") == "image_url": + image_url = content_item.get("image_url", {}) + if isinstance(image_url, dict): + url = image_url.get("url") + if url: + images_to_check.append(url) async def _apply_guardrail_responses_to_input( self, @@ -150,6 +165,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): response: "ModelResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. @@ -157,6 +173,8 @@ class OpenAIChatCompletionsHandler(BaseTranslation): Args: response: LiteLLM ModelResponse object guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails Returns: Modified response with guardrail applied to content @@ -165,6 +183,7 @@ class OpenAIChatCompletionsHandler(BaseTranslation): - String content: choice.message.content = "text here" - List content: choice.message.content = [{"type": "text", "text": "text here"}, ...] """ + # Step 0: Check if response has any text content to process if not self._has_text_content(response): verbose_proxy_logger.warning( @@ -172,29 +191,49 @@ class OpenAIChatCompletionsHandler(BaseTranslation): ) return response - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (choice_index, content_index) for each task + # Track (choice_index, content_index) for each text - # Step 1: Extract all text content from response choices + # Step 1: Extract all text content and images from response choices for choice_idx, choice in enumerate(response.choices): - await self._extract_output_text_and_create_tasks( + self._extract_output_text_and_images( choice=choice, choice_idx=choice_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response} - # Step 3: Map guardrail responses back to original response structure - await self._apply_guardrail_responses_to_output( - response=response, - responses=responses, - task_mappings=task_mappings, - ) + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + guardrailed_texts, guardrailed_images = ( + await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=request_data, + input_type="response", + images=images_to_check if images_to_check else None, + logging_obj=litellm_logging_obj, + ) + ) + + # Step 3: Map guardrail responses back to original response structure + await self._apply_guardrail_responses_to_output( + response=response, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) verbose_proxy_logger.debug( "OpenAI Chat Completions: Processed output response: %s", response @@ -214,19 +253,18 @@ class OpenAIChatCompletionsHandler(BaseTranslation): return True return False - async def _extract_output_text_and_create_tasks( + def _extract_output_text_and_images( self, choice: Any, choice_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", - request_data: Optional[Dict[str, Any]] = None, ) -> None: """ - Extract text content from a response choice and create guardrail tasks. + Extract text content and images from a response choice. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ if not isinstance(choice, litellm.Choices): return @@ -237,19 +275,26 @@ class OpenAIChatCompletionsHandler(BaseTranslation): if choice.message.content and isinstance(choice.message.content, str): # Simple string content - tasks.append( - guardrail_to_apply.apply_guardrail(text=choice.message.content, request_data=request_data) - ) + texts_to_check.append(choice.message.content) task_mappings.append((choice_idx, None)) elif choice.message.content and isinstance(choice.message.content, list): # List content (e.g., multimodal response) for content_idx, content_item in enumerate(choice.message.content): + # Extract text content_text = content_item.get("text") if content_text: - tasks.append(guardrail_to_apply.apply_guardrail(text=content_text, request_data=request_data)) + texts_to_check.append(content_text) task_mappings.append((choice_idx, int(content_idx))) + # Extract images + if content_item.get("type") == "image_url": + image_url = content_item.get("image_url", {}) + if isinstance(image_url, dict): + url = image_url.get("url") + if url: + images_to_check.append(url) + async def _apply_guardrail_responses_to_output( self, response: "ModelResponse", diff --git a/litellm/llms/openai/completion/guardrail_translation/handler.py b/litellm/llms/openai/completion/guardrail_translation/handler.py index 5a38d04d75..f8b733567d 100644 --- a/litellm/llms/openai/completion/guardrail_translation/handler.py +++ b/litellm/llms/openai/completion/guardrail_translation/handler.py @@ -53,41 +53,50 @@ class OpenAITextCompletionHandler(BaseTranslation): if isinstance(prompt, str): # Single string prompt - guardrailed_prompt = await guardrail_to_apply.apply_guardrail(text=prompt) - data["prompt"] = guardrailed_prompt + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=[prompt], + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + data["prompt"] = guardrailed_texts[0] if guardrailed_texts else prompt verbose_proxy_logger.debug( "OpenAI Text Completion: Applied guardrail to string prompt. " "Original length: %d, New length: %d", len(prompt), - len(guardrailed_prompt), + len(data["prompt"]), ) elif isinstance(prompt, list): # List of string prompts (batch completion) - guardrailed_prompts = [] + texts_to_check = [] + text_indices = [] # Track which prompts are strings + for idx, p in enumerate(prompt): if isinstance(p, str): - guardrailed_p = await guardrail_to_apply.apply_guardrail(text=p) - guardrailed_prompts.append(guardrailed_p) - verbose_proxy_logger.debug( - "OpenAI Text Completion: Applied guardrail to prompt[%d]. " - "Original length: %d, New length: %d", - idx, - len(p), - len(guardrailed_p), - ) - else: - # For non-string items (e.g., token lists), keep unchanged - guardrailed_prompts.append(p) - verbose_proxy_logger.debug( - "OpenAI Text Completion: Skipping guardrail for prompt[%d] " - "(not a string, type: %s)", - idx, - type(p), - ) + texts_to_check.append(p) + text_indices.append(idx) - data["prompt"] = guardrailed_prompts + if texts_to_check: + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + + # Replace guardrailed texts back + for guardrail_idx, prompt_idx in enumerate(text_indices): + if guardrail_idx < len(guardrailed_texts): + data["prompt"][prompt_idx] = guardrailed_texts[guardrail_idx] + verbose_proxy_logger.debug( + "OpenAI Text Completion: Applied guardrail to prompt[%d]. " + "Original length: %d, New length: %d", + prompt_idx, + len(texts_to_check[guardrail_idx]), + len(guardrailed_texts[guardrail_idx]), + ) else: verbose_proxy_logger.warning( @@ -102,6 +111,7 @@ class OpenAITextCompletionHandler(BaseTranslation): response: "TextCompletionResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to completion text. @@ -109,6 +119,8 @@ class OpenAITextCompletionHandler(BaseTranslation): Args: response: Text completion response object guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails Returns: Modified response with guardrails applied to completion text @@ -119,21 +131,46 @@ class OpenAITextCompletionHandler(BaseTranslation): ) return response - # Apply guardrails to each choice's text + # Collect all texts to check + texts_to_check = [] + choice_indices = [] + for idx, choice in enumerate(response.choices): if hasattr(choice, "text") and isinstance(choice.text, str): - original_text = choice.text - guardrailed_text = await guardrail_to_apply.apply_guardrail( - text=original_text - ) - choice.text = guardrailed_text + texts_to_check.append(choice.text) + choice_indices.append(idx) - verbose_proxy_logger.debug( - "OpenAI Text Completion: Applied guardrail to choice[%d] text. " - "Original length: %d, New length: %d", - idx, - len(original_text), - len(guardrailed_text), - ) + # Apply guardrails in batch + if texts_to_check: + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response} + + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + + # Apply guardrailed texts back to choices + for guardrail_idx, choice_idx in enumerate(choice_indices): + if guardrail_idx < len(guardrailed_texts): + original_text = response.choices[choice_idx].text + response.choices[choice_idx].text = guardrailed_texts[guardrail_idx] + + verbose_proxy_logger.debug( + "OpenAI Text Completion: Applied guardrail to choice[%d] text. " + "Original length: %d, New length: %d", + choice_idx, + len(original_text), + len(guardrailed_texts[guardrail_idx]), + ) return response diff --git a/litellm/llms/openai/image_generation/guardrail_translation/handler.py b/litellm/llms/openai/image_generation/guardrail_translation/handler.py index de6bca8e57..53ee994c48 100644 --- a/litellm/llms/openai/image_generation/guardrail_translation/handler.py +++ b/litellm/llms/openai/image_generation/guardrail_translation/handler.py @@ -52,14 +52,19 @@ class OpenAIImageGenerationHandler(BaseTranslation): # Apply guardrail to the prompt if isinstance(prompt, str): - guardrailed_prompt = await guardrail_to_apply.apply_guardrail(text=prompt) - data["prompt"] = guardrailed_prompt + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=[prompt], + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + data["prompt"] = guardrailed_texts[0] if guardrailed_texts else prompt verbose_proxy_logger.debug( "OpenAI Image Generation: Applied guardrail to prompt. " "Original length: %d, New length: %d", len(prompt), - len(guardrailed_prompt), + len(data["prompt"]), ) else: verbose_proxy_logger.debug( @@ -74,6 +79,7 @@ class OpenAIImageGenerationHandler(BaseTranslation): response: "ImageResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response - typically not needed for image generation. @@ -85,6 +91,8 @@ class OpenAIImageGenerationHandler(BaseTranslation): Args: response: Image generation response object guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object (unused) + user_api_key_dict: User API key metadata (unused) Returns: Unmodified response (images don't need text guardrails) diff --git a/litellm/llms/openai/responses/guardrail_translation/handler.py b/litellm/llms/openai/responses/guardrail_translation/handler.py index 489a89c60c..667a72a426 100644 --- a/litellm/llms/openai/responses/guardrail_translation/handler.py +++ b/litellm/llms/openai/responses/guardrail_translation/handler.py @@ -28,8 +28,7 @@ Output: response.output is List[GenericResponseOutputItem] where each has: - text: str """ -import asyncio -from typing import TYPE_CHECKING, Any, Coroutine, List, Optional, Tuple, Union, cast +from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union, cast from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation @@ -69,10 +68,13 @@ class OpenAIResponsesHandler(BaseTranslation): # Handle simple string input if isinstance(input_data, str): - guardrail_response = await guardrail_to_apply.apply_guardrail( - text=input_data + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=[input_data], + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, ) - data["input"] = guardrail_response + data["input"] = guardrailed_texts[0] if guardrailed_texts else input_data verbose_proxy_logger.debug("OpenAI Responses API: Processed string input") return data @@ -80,29 +82,38 @@ class OpenAIResponsesHandler(BaseTranslation): if not isinstance(input_data, list): return data - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (message_index, content_index) for each task + # Track (message_index, content_index) for each text # content_index is None for string content, int for list content - # Step 1: Extract all text content and create guardrail tasks + # Step 1: Extract all text content and images for msg_idx, message in enumerate(input_data): - await self._extract_input_text_and_create_tasks( + self._extract_input_text_and_images( message=message, msg_idx=msg_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - if tasks: - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + guardrailed_texts, guardrailed_images = ( + await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=data, + input_type="request", + images=images_to_check if images_to_check else None, + logging_obj=litellm_logging_obj, + ) + ) # Step 3: Map guardrail responses back to original input structure await self._apply_guardrail_responses_to_input( messages=input_data, - responses=responses, + responses=guardrailed_texts, task_mappings=task_mappings, ) @@ -112,18 +123,18 @@ class OpenAIResponsesHandler(BaseTranslation): return data - async def _extract_input_text_and_create_tasks( + def _extract_input_text_and_images( self, message: Any, # Can be Dict[str, Any] or ResponseInputParam msg_idx: int, - tasks: List[Coroutine[Any, Any, str]], + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", ) -> None: """ - Extract text content from an input message and create guardrail tasks. + Extract text content and images from an input message. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ content = message.get("content", None) if content is None: @@ -131,18 +142,27 @@ class OpenAIResponsesHandler(BaseTranslation): if isinstance(content, str): # Simple string content - tasks.append(guardrail_to_apply.apply_guardrail(text=content)) + texts_to_check.append(content) task_mappings.append((msg_idx, None)) elif isinstance(content, list): # List content (e.g., multimodal with text and images) for content_idx, content_item in enumerate(content): if isinstance(content_item, dict): + # Extract text text_str = content_item.get("text", None) if text_str is not None: - tasks.append(guardrail_to_apply.apply_guardrail(text=text_str)) + texts_to_check.append(text_str) task_mappings.append((msg_idx, int(content_idx))) + # Extract images + if content_item.get("type") == "image_url": + image_url = content_item.get("image_url", {}) + if isinstance(image_url, dict): + url = image_url.get("url") + if url: + images_to_check.append(url) + async def _apply_guardrail_responses_to_input( self, messages: Any, # Can be List[Dict[str, Any]] or ResponseInputParam @@ -179,6 +199,7 @@ class OpenAIResponsesHandler(BaseTranslation): response: "ResponsesAPIResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. @@ -186,6 +207,8 @@ class OpenAIResponsesHandler(BaseTranslation): Args: response: LiteLLM ResponsesAPIResponse object guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails Returns: Modified response with guardrail applied to content @@ -202,28 +225,47 @@ class OpenAIResponsesHandler(BaseTranslation): ) return response - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, int]] = [] - # Track (output_item_index, content_index) for each task + # Track (output_item_index, content_index) for each text # Step 1: Extract all text content from response output for output_idx, output_item in enumerate(response.output): - await self._extract_output_text_and_create_tasks( + self._extract_output_text_and_images( output_item=output_item, output_idx=output_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - if tasks: - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response} + + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + guardrailed_texts, guardrailed_images = ( + await guardrail_to_apply.apply_guardrail( + texts=texts_to_check, + request_data=request_data, + input_type="response", + images=images_to_check if images_to_check else None, + logging_obj=litellm_logging_obj, + ) + ) # Step 3: Map guardrail responses back to original response structure await self._apply_guardrail_responses_to_output( response=response, - responses=responses, + responses=guardrailed_texts, task_mappings=task_mappings, ) @@ -260,18 +302,18 @@ class OpenAIResponsesHandler(BaseTranslation): return True return False - async def _extract_output_text_and_create_tasks( + def _extract_output_text_and_images( self, output_item: Any, output_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, int]], - guardrail_to_apply: "CustomGuardrail", ) -> None: """ - Extract text content from a response output item and create guardrail tasks. + Extract text content and images from a response output item. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ # Handle both GenericResponseOutputItem and dict if isinstance(output_item, GenericResponseOutputItem): @@ -299,7 +341,7 @@ class OpenAIResponsesHandler(BaseTranslation): continue if text_content: - tasks.append(guardrail_to_apply.apply_guardrail(text=text_content)) + texts_to_check.append(text_content) task_mappings.append((output_idx, int(content_idx))) async def _apply_guardrail_responses_to_output( diff --git a/litellm/llms/openai/speech/guardrail_translation/handler.py b/litellm/llms/openai/speech/guardrail_translation/handler.py index 47df79833b..71edd4f280 100644 --- a/litellm/llms/openai/speech/guardrail_translation/handler.py +++ b/litellm/llms/openai/speech/guardrail_translation/handler.py @@ -50,16 +50,19 @@ class OpenAITextToSpeechHandler(BaseTranslation): return data if isinstance(input_text, str): - guardrailed_input = await guardrail_to_apply.apply_guardrail( - text=input_text + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=[input_text], + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, ) - data["input"] = guardrailed_input + data["input"] = guardrailed_texts[0] if guardrailed_texts else input_text verbose_proxy_logger.debug( "OpenAI Text-to-Speech: Applied guardrail to input text. " "Original length: %d, New length: %d", len(input_text), - len(guardrailed_input), + len(data["input"]), ) else: verbose_proxy_logger.debug( @@ -74,6 +77,7 @@ class OpenAITextToSpeechHandler(BaseTranslation): response: "HttpxBinaryResponseContent", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output - not applicable for text-to-speech. @@ -84,6 +88,8 @@ class OpenAITextToSpeechHandler(BaseTranslation): Args: response: Binary audio response guardrail_to_apply: The guardrail instance (unused) + litellm_logging_obj: Optional logging object (unused) + user_api_key_dict: User API key metadata (unused) Returns: Unmodified response (audio data doesn't need text guardrails) diff --git a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py index 51f50c9180..18678a9878 100644 --- a/litellm/llms/openai/transcriptions/guardrail_translation/handler.py +++ b/litellm/llms/openai/transcriptions/guardrail_translation/handler.py @@ -56,6 +56,7 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): response: "TranscriptionResponse", guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output transcription by applying guardrails to transcribed text. @@ -63,6 +64,8 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): Args: response: Transcription response object containing transcribed text guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails Returns: Modified response with guardrails applied to transcribed text @@ -75,16 +78,29 @@ class OpenAIAudioTranscriptionHandler(BaseTranslation): if isinstance(response.text, str): original_text = response.text - guardrailed_text = await guardrail_to_apply.apply_guardrail( - text=original_text + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response} + + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict ) - response.text = guardrailed_text + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + guardrailed_texts, _ = await guardrail_to_apply.apply_guardrail( + texts=[original_text], + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + response.text = guardrailed_texts[0] if guardrailed_texts else original_text verbose_proxy_logger.debug( "OpenAI Audio Transcription: Applied guardrail to transcribed text. " "Original length: %d, New length: %d", len(original_text), - len(guardrailed_text), + len(response.text), ) else: verbose_proxy_logger.debug( diff --git a/litellm/llms/pass_through/guardrail_translation/handler.py b/litellm/llms/pass_through/guardrail_translation/handler.py index 5ff9fd25c5..ae96335cec 100644 --- a/litellm/llms/pass_through/guardrail_translation/handler.py +++ b/litellm/llms/pass_through/guardrail_translation/handler.py @@ -117,10 +117,12 @@ class PassThroughEndpointHandler(BaseTranslation): ) return data - # Apply guardrail + # Apply guardrail (pass-through doesn't modify the text, just checks it) await guardrail_to_apply.apply_guardrail( - text=text_to_check, + texts=[text_to_check], request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, ) return data @@ -130,9 +132,16 @@ class PassThroughEndpointHandler(BaseTranslation): response: Any, guardrail_to_apply: "CustomGuardrail", litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to targeted fields. + + Args: + response: The response to process + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails """ if not isinstance(response, dict): verbose_proxy_logger.debug( @@ -156,10 +165,24 @@ class PassThroughEndpointHandler(BaseTranslation): if not text_to_check: return response - # Apply guardrail + # Create a request_data dict with response info and user API key metadata + request_data: dict = ( + {"response": response} + if not isinstance(response, dict) + else response.copy() + ) + + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata(user_api_key_dict) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + # Apply guardrail (pass-through doesn't modify the text, just checks it) await guardrail_to_apply.apply_guardrail( - text=text_to_check, - request_data=response, + texts=[text_to_check], + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, ) return response diff --git a/litellm/main.py b/litellm/main.py index c3059eb17e..a09a945301 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -860,6 +860,7 @@ def mock_completion( raise mock_response # At this point, mock_response must be a string (all other types have been handled or returned early) mock_response = cast(str, mock_response) + if n is None: model_response.choices[0].message.content = mock_response # type: ignore else: @@ -906,6 +907,7 @@ def mock_completion( api_key="my-secret-key", original_response="my-original-response", ) + return model_response except Exception as e: @@ -942,10 +944,16 @@ def responses_api_bridge_check( return model_info, model -def _should_allow_input_examples(custom_llm_provider: Optional[str], model: str) -> bool: +def _should_allow_input_examples( + custom_llm_provider: Optional[str], model: str +) -> bool: if custom_llm_provider == "anthropic": return True - if custom_llm_provider == "azure_ai" or custom_llm_provider == "bedrock" or custom_llm_provider == "vertex_ai": + if ( + custom_llm_provider == "azure_ai" + or custom_llm_provider == "bedrock" + or custom_llm_provider == "vertex_ai" + ): return "claude" in model.lower() return False @@ -961,7 +969,9 @@ def _drop_input_examples_from_tool(tool: dict) -> dict: return tool_copy -def _drop_input_examples_from_tools(tools: Optional[List[dict]]) -> Optional[List[dict]]: +def _drop_input_examples_from_tools( + tools: Optional[List[dict]], +) -> Optional[List[dict]]: if tools is None: return None cleaned_tools: List[dict] = [] @@ -1735,7 +1745,7 @@ def completion( # type: ignore # noqa: PLR0915 "Set `api_base` or the AZURE_AI_API_BASE env var." ) api_key = AzureFoundryModelInfo.get_api_key(api_key) - + # Ensure the URL ends with /v1/messages for Anthropic if api_base: api_base = api_base.rstrip("/") @@ -1746,7 +1756,7 @@ def completion( # type: ignore # noqa: PLR0915 else: api_base = api_base + "/anthropic" api_base = api_base + "/v1/messages" - + response = azure_anthropic_chat_completions.completion( model=model, messages=messages, @@ -5876,9 +5886,7 @@ def speech( # noqa: PLR0915 custom_llm_provider: Optional[str] = None, aspeech: Optional[bool] = None, **kwargs, -) -> Union[ - HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent] -]: +) -> Union[HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]]: user = kwargs.get("user", None) litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) proxy_server_request = kwargs.get("proxy_server_request", None) @@ -5923,7 +5931,9 @@ def speech( # noqa: PLR0915 kwargs=kwargs, ) - logging_obj: LiteLLMLoggingObj = cast(LiteLLMLoggingObj, kwargs.get("litellm_logging_obj")) + logging_obj: LiteLLMLoggingObj = cast( + LiteLLMLoggingObj, kwargs.get("litellm_logging_obj") + ) logging_obj.update_environment_variables( model=model, user=user, @@ -6111,9 +6121,9 @@ def speech( # noqa: PLR0915 ElevenLabsTextToSpeechConfig.ELEVENLABS_QUERY_PARAMS_KEY ] = query_params - litellm_params_dict[ - ElevenLabsTextToSpeechConfig.ELEVENLABS_VOICE_ID_KEY - ] = voice_id + litellm_params_dict[ElevenLabsTextToSpeechConfig.ELEVENLABS_VOICE_ID_KEY] = ( + voice_id + ) if api_base is not None: litellm_params_dict["api_base"] = api_base diff --git a/litellm/proxy/_experimental/out/api-reference/index.html b/litellm/proxy/_experimental/out/api-reference/index.html index cbcf9f30d0..01bb5da063 100644 --- a/litellm/proxy/_experimental/out/api-reference/index.html +++ b/litellm/proxy/_experimental/out/api-reference/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/logs/index.html b/litellm/proxy/_experimental/out/logs/index.html index 38499dd70f..c7c7eafbb8 100644 --- a/litellm/proxy/_experimental/out/logs/index.html +++ b/litellm/proxy/_experimental/out/logs/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/model-hub/index.html b/litellm/proxy/_experimental/out/model-hub/index.html index 9691671e3b..cb748615c8 100644 --- a/litellm/proxy/_experimental/out/model-hub/index.html +++ b/litellm/proxy/_experimental/out/model-hub/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/model_hub_table/index.html b/litellm/proxy/_experimental/out/model_hub_table/index.html index 6cf73767b2..159f1799d6 100644 --- a/litellm/proxy/_experimental/out/model_hub_table/index.html +++ b/litellm/proxy/_experimental/out/model_hub_table/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/models-and-endpoints/index.html b/litellm/proxy/_experimental/out/models-and-endpoints/index.html index f62d9004f3..24e13b256f 100644 --- a/litellm/proxy/_experimental/out/models-and-endpoints/index.html +++ b/litellm/proxy/_experimental/out/models-and-endpoints/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/organizations/index.html b/litellm/proxy/_experimental/out/organizations/index.html index d05e189a2a..aab73d3314 100644 --- a/litellm/proxy/_experimental/out/organizations/index.html +++ b/litellm/proxy/_experimental/out/organizations/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/playground/index.html b/litellm/proxy/_experimental/out/playground/index.html index 6a7caf7c7d..64daab37d4 100644 --- a/litellm/proxy/_experimental/out/playground/index.html +++ b/litellm/proxy/_experimental/out/playground/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/teams/index.html b/litellm/proxy/_experimental/out/teams/index.html index ba68fd6b99..d7c800aae1 100644 --- a/litellm/proxy/_experimental/out/teams/index.html +++ b/litellm/proxy/_experimental/out/teams/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/test-key/index.html b/litellm/proxy/_experimental/out/test-key/index.html index 4ea1c35d95..ab94c54332 100644 --- a/litellm/proxy/_experimental/out/test-key/index.html +++ b/litellm/proxy/_experimental/out/test-key/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/usage/index.html b/litellm/proxy/_experimental/out/usage/index.html index 4c3e5dbafe..b06d716dba 100644 --- a/litellm/proxy/_experimental/out/usage/index.html +++ b/litellm/proxy/_experimental/out/usage/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/users/index.html b/litellm/proxy/_experimental/out/users/index.html index 2f34854afd..db7e2cee90 100644 --- a/litellm/proxy/_experimental/out/users/index.html +++ b/litellm/proxy/_experimental/out/users/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_experimental/out/virtual-keys/index.html b/litellm/proxy/_experimental/out/virtual-keys/index.html index 95f2930afb..154239a9fd 100644 --- a/litellm/proxy/_experimental/out/virtual-keys/index.html +++ b/litellm/proxy/_experimental/out/virtual-keys/index.html @@ -1 +1 @@ -LiteLLM Dashboard \ No newline at end of file +LiteLLM Dashboard diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index c11848a862..d2bbe8ee6e 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -3,17 +3,24 @@ model_list: litellm_params: model: openai/gpt-3.5-turbo api_key: os.environ/OPENAI_API_KEY - - model_name: anthropic-claude + +guardrails: + # - guardrail_name: model-armor-shield + # litellm_params: + # guardrail: model_armor + # mode: "post_call" # Run on both input and output + # template_id: "test-prompt-template" # Required: Your Model Armor template ID + # project_id: "test-vector-store-db" # Your GCP project ID + # location: "us" # GCP location (default: us-central1) + # mask_request_content: true # Enable request content masking + # mask_response_content: true # Enable response content masking + # fail_on_error: true # Fail request if Model Armor errors (default: true) + # default_on: true # Run by default for all requests + - guardrail_name: generic-guardrail litellm_params: - model: anthropic/claude-3-5-sonnet-20241022 - api_key: os.environ/ANTHROPIC_API_KEY - -litellm_settings: - callbacks: ["rubrik"] - -callback_settings: - rubrik: - callback_type: generic_api - endpoint: https://webhook.site/efc57707-9018-478c-bdf1-2ffaabb2b315 - headers: - Authorization: Bearer sk-1234 + guardrail: generic_guardrail_api + mode: [pre_call, post_call] + headers: + Authorization: Bearer mock-bedrock-token-12345 + api_base: http://localhost:8080 + default_on: true \ No newline at end of file diff --git a/litellm/proxy/guardrails/guardrail_endpoints.py b/litellm/proxy/guardrails/guardrail_endpoints.py index a1cfead9bb..395fdb249d 100644 --- a/litellm/proxy/guardrails/guardrail_endpoints.py +++ b/litellm/proxy/guardrails/guardrail_endpoints.py @@ -1162,9 +1162,9 @@ async def get_provider_specific_params(): lakera_v2_fields = _get_fields_from_model(LakeraV2GuardrailConfigModel) tool_permission_fields = _get_fields_from_model(ToolPermissionGuardrailConfigModel) - tool_permission_fields[ - "ui_friendly_name" - ] = ToolPermissionGuardrailConfigModel.ui_friendly_name() + tool_permission_fields["ui_friendly_name"] = ( + ToolPermissionGuardrailConfigModel.ui_friendly_name() + ) # Return the provider-specific parameters provider_params = { @@ -1203,10 +1203,10 @@ async def apply_guardrail( from litellm.proxy.utils import handle_exception_on_proxy try: - active_guardrail: Optional[ - CustomGuardrail - ] = GUARDRAIL_REGISTRY.get_initialized_guardrail_callback( - guardrail_name=request.guardrail_name + active_guardrail: Optional[CustomGuardrail] = ( + GUARDRAIL_REGISTRY.get_initialized_guardrail_callback( + guardrail_name=request.guardrail_name + ) ) if active_guardrail is None: raise HTTPException( @@ -1215,9 +1215,12 @@ async def apply_guardrail( ) response_text = await active_guardrail.apply_guardrail( - text=request.text, language=request.language, entities=request.entities + texts=[request.text], + request_data={}, + input_type="request", + images=None, ) - return ApplyGuardrailResponse(response_text=response_text) + return ApplyGuardrailResponse(response_text=response_text[0][0]) except Exception as e: raise handle_exception_on_proxy(e) diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py index 1a67da6320..f9c3caf494 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py +++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py @@ -15,11 +15,11 @@ sys.path.insert( import json import sys from typing import ( + TYPE_CHECKING, Any, AsyncGenerator, List, Literal, - NamedTuple, Optional, Tuple, Union, @@ -41,7 +41,7 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.proxy._types import UserAPIKeyAuth from litellm.secret_managers.main import get_secret_str -from litellm.types.guardrails import GuardrailEventHooks, PiiEntityType +from litellm.types.guardrails import GuardrailEventHooks from litellm.types.llms.openai import AllMessageValues, ChatCompletionUserMessage from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( BedrockContentItem, @@ -50,6 +50,9 @@ from litellm.types.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( BedrockRequest, BedrockTextContent, ) + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.types.utils import ( CallTypes, CallTypesLiteral, @@ -1241,34 +1244,42 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: """ - Apply Bedrock guardrail to the given text for testing purposes. + Apply Bedrock guardrail to a batch of texts for testing purposes. This method allows users to test Bedrock guardrails without making actual LLM calls. - It creates a mock request and response to test the guardrail functionality. + It creates mock messages to test the guardrail functionality. Args: - text: The text to analyze - language: Optional language parameter (not used by Bedrock) - entities: Optional entities parameter (not used by Bedrock) - request_data: Optional request data dictionary for logging metadata + texts: List of texts to analyze + request_data: Request data dictionary for logging metadata + input_type: Whether this is a "request" or "response" + images: Optional list of images (not processed separately) + + Returns: + Tuple of (processed_texts, images) - texts may be masked, images unchanged + + Raises: + Exception: If content is blocked by Bedrock guardrail """ try: - verbose_proxy_logger.debug("Bedrock Guardrail: Applying guardrail") - mock_messages: List[AllMessageValues] = [ - ChatCompletionUserMessage(role="user", content=text) - ] + verbose_proxy_logger.debug( + f"Bedrock Guardrail: Applying guardrail to {len(texts)} text(s)" + ) - # Use provided request_data or create a mock one for testing - if request_data is None: - request_data = {"messages": mock_messages} + masked_texts = [] - request_messages = request_data.get("messages") or mock_messages + for text in texts: + mock_messages: List[AllMessageValues] = [ + ChatCompletionUserMessage(role="user", content=text) + ] + request_messages = mock_messages filter_result = self._prepare_guardrail_messages_for_role( messages=request_messages ) @@ -1280,36 +1291,44 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): request_data=request_data, ) - if bedrock_response.get("action") == "BLOCKED": - raise Exception( - f"Content blocked by Bedrock guardrail: {bedrock_response.get('reason', 'Unknown reason')}" + bedrock_response = await self.make_bedrock_api_request( + source="INPUT", + messages=mock_messages, + request_data=request_data, ) - # Apply any masking that was applied by the guardrail - masked_text = text - output_list = bedrock_response.get("output") - if output_list: - # If the guardrail returned modified content, use that - for output_item in output_list: - text_content = output_item.get("text") - if text_content: - masked_text = str(text_content) - break - else: - outputs_list = bedrock_response.get("outputs") - if outputs_list: - # Fallback to outputs field if output is not available - for output_item in outputs_list: + if bedrock_response.get("action") == "BLOCKED": + raise Exception( + f"Content blocked by Bedrock guardrail: {bedrock_response.get('reason', 'Unknown reason')}" + ) + + # Apply any masking that was applied by the guardrail + masked_text = text + output_list = bedrock_response.get("output") + if output_list: + # If the guardrail returned modified content, use that + for output_item in output_list: text_content = output_item.get("text") if text_content: masked_text = str(text_content) break + else: + outputs_list = bedrock_response.get("outputs") + if outputs_list: + # Fallback to outputs field if output is not available + for output_item in outputs_list: + text_content = output_item.get("text") + if text_content: + masked_text = str(text_content) + break + + masked_texts.append(masked_text) verbose_proxy_logger.debug( "Bedrock Guardrail: Successfully applied guardrail" ) - return masked_text + return masked_texts, images except Exception as e: verbose_proxy_logger.error( diff --git a/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py b/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py index 952daaafb2..49401ca792 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py +++ b/litellm/proxy/guardrails/guardrail_hooks/enkryptai/enkryptai.py @@ -7,7 +7,17 @@ import os from datetime import datetime -from typing import Any, AsyncGenerator, Dict, List, Optional, Union +from typing import ( + TYPE_CHECKING, + Any, + AsyncGenerator, + Dict, + List, + Literal, + Optional, + Tuple, + Union, +) import httpx @@ -20,13 +30,16 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.proxy._types import UserAPIKeyAuth -from litellm.types.guardrails import GuardrailEventHooks, PiiEntityType +from litellm.types.guardrails import GuardrailEventHooks from litellm.types.proxy.guardrails.guardrail_hooks.enkryptai import ( EnkryptAIProcessedResult, EnkryptAIResponse, ) from litellm.types.utils import CallTypesLiteral, GuardrailStatus, ModelResponseStream +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + GUARDRAIL_NAME = "enkryptai" @@ -468,25 +481,43 @@ class EnkryptAIGuardrails(CustomGuardrail): async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: - result = await self._call_enkryptai_guardrails( - prompt=text, - request_data=request_data or {}, - ) - # Process the guardrails response - processed_result = self._process_enkryptai_guardrails_response(result) - attacks_detected = processed_result["attacks_detected"] + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: + """ + Apply EnkryptAI guardrail to a batch of texts. - # If any attacks are detected, raise an error - if attacks_detected: - error_message = self._create_error_message(processed_result) - raise ValueError(error_message) + Args: + texts: List of texts to check for attacks + request_data: Request data dictionary containing metadata + input_type: Whether this is a "request" or "response" + images: Optional list of images (not used by EnkryptAI) - return text + Returns: + Tuple of (texts, images) - texts unchanged if passed, images unchanged + + Raises: + ValueError: If any attacks are detected + """ + # Check each text for attacks + for text in texts: + result = await self._call_enkryptai_guardrails( + prompt=text, + request_data=request_data, + ) + # Process the guardrails response + processed_result = self._process_enkryptai_guardrails_response(result) + attacks_detected = processed_result["attacks_detected"] + + # If any attacks are detected, raise an error + if attacks_detected: + error_message = self._create_error_message(processed_result) + raise ValueError(error_message) + + return texts, images async def async_post_call_streaming_iterator_hook( self, diff --git a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py index e94306e172..f806e8b480 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py +++ b/litellm/proxy/guardrails/guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py @@ -6,7 +6,7 @@ # Thank you users! We ❤️ you! - Krrish & Ishaan import os -from typing import Any, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import CustomGuardrail @@ -15,55 +15,18 @@ from litellm.llms.custom_httpx.http_handler import ( httpxSpecialProvider, ) from litellm.types.guardrails import GuardrailEventHooks +from litellm.types.proxy.guardrails.guardrail_hooks.generic_guardrail_api import ( + GenericGuardrailAPIMetadata, + GenericGuardrailAPIRequest, + GenericGuardrailAPIResponse, +) + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj GUARDRAIL_NAME = "generic_guardrail_api" -class GenericGuardrailAPIRequest: - """Request model for the Generic Guardrail API""" - - def __init__( - self, - text: str, - request_body: Dict[str, Any], - additional_provider_specific_params: Optional[Dict[str, Any]] = None, - ): - self.text = text - self.request_body = request_body - self.additional_provider_specific_params = ( - additional_provider_specific_params or {} - ) - - def to_dict(self) -> dict: - return { - "text": self.text, - "request_body": self.request_body, - "additional_provider_specific_params": self.additional_provider_specific_params, - } - - -class GenericGuardrailAPIResponse: - """Response model for the Generic Guardrail API""" - - def __init__( - self, - action: str, - blocked_reason: Optional[str] = None, - text: Optional[str] = None, - ): - self.action = action - self.blocked_reason = blocked_reason - self.text = text - - @classmethod - def from_dict(cls, data: dict) -> "GenericGuardrailAPIResponse": - return cls( - action=data.get("action", "NONE"), - blocked_reason=data.get("blocked_reason"), - text=data.get("text"), - ) - - class GenericGuardrailAPI(CustomGuardrail): """ Generic Guardrail API integration for LiteLLM. @@ -130,26 +93,76 @@ class GenericGuardrailAPI(CustomGuardrail): "Generic Guardrail API initialized with api_base: %s", self.api_base ) + def _extract_user_api_key_metadata( + self, request_data: dict + ) -> GenericGuardrailAPIMetadata: + """ + Extract user API key metadata from request_data. + + Args: + request_data: Request data dictionary that may contain: + - metadata (for input requests) with user_api_key_* fields + - litellm_metadata (for output responses) with user_api_key_* fields + + Returns: + GenericGuardrailAPIMetadata with extracted user information + """ + result_metadata = GenericGuardrailAPIMetadata() + + # Get the source of metadata - try both locations + # 1. For output responses: litellm_metadata (set by handlers with prefixed keys) + # 2. For input requests: metadata (already present in request_data with prefixed keys) + litellm_metadata = request_data.get("litellm_metadata", {}) + top_level_metadata = request_data.get("metadata", {}) + + # Merge both sources, preferring litellm_metadata if both exist + metadata_dict = {**top_level_metadata, **litellm_metadata} + + if not metadata_dict: + return result_metadata + + # Dynamically iterate through GenericGuardrailAPIMetadata fields + # and extract matching fields from the source metadata + # Fields in metadata are already prefixed with 'user_api_key_' + for field_name in GenericGuardrailAPIMetadata.__annotations__.keys(): + value = metadata_dict.get(field_name) + if value is not None: + result_metadata[field_name] = value + + # handle user_api_key_token = user_api_key_hash + if metadata_dict.get("user_api_key_token") is not None: + result_metadata["user_api_key_hash"] = metadata_dict.get( + "user_api_key_token" + ) + + verbose_proxy_logger.debug( + "Generic Guardrail API: Extracted user metadata: %s", + {k: v for k, v in result_metadata.items() if v is not None}, + ) + + return result_metadata + async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List] = None, - request_data: Optional[dict] = None, - ) -> str: + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: """ Apply the Generic Guardrail API to the given text. This is the main method that gets called by the framework. Args: - text: The text to check - language: Optional language parameter (not used by Generic API) - entities: Optional entities parameter (not used by Generic API) - request_data: Optional request data dictionary for logging metadata + texts: List of texts to check + request_data: Request data dictionary containing user_api_key_dict and other metadata + input_type: Whether this is a "request" or "response" guardrail + images: Optional list of images to check Returns: - The processed text (original or modified) + Tuple of (processed texts, processed images) Raises: Exception: If the guardrail blocks the request @@ -170,11 +183,18 @@ class GenericGuardrailAPI(CustomGuardrail): if dynamic_params: additional_params.update(dynamic_params) + # Extract user API key metadata + user_metadata = self._extract_user_api_key_metadata(request_data) + # Create request payload guardrail_request = GenericGuardrailAPIRequest( - text=text, - request_body=request_body, + litellm_call_id=logging_obj.litellm_call_id if logging_obj else None, + litellm_trace_id=logging_obj.litellm_trace_id if logging_obj else None, + texts=texts, + request_data=user_metadata, + images=images, additional_provider_specific_params=additional_params, + input_type=input_type, ) # Prepare headers @@ -182,12 +202,6 @@ class GenericGuardrailAPI(CustomGuardrail): if self.headers: headers.update(self.headers) - verbose_proxy_logger.debug( - "Generic Guardrail API request to %s: %s", - self.api_base, - {"text_length": len(text), "has_request_body": bool(request_data)}, - ) - try: # Make the API request response = await self.async_handler.post( @@ -218,12 +232,15 @@ class GenericGuardrailAPI(CustomGuardrail): elif guardrail_response.action == "GUARDRAIL_INTERVENED": # Content was modified by the guardrail - if guardrail_response.text: + if guardrail_response.texts: verbose_proxy_logger.debug("Generic Guardrail API modified text") - return guardrail_response.text + return guardrail_response.texts, guardrail_response.images # Action is NONE or no modifications needed - return text + return ( + guardrail_response.texts or texts, + guardrail_response.images or images, + ) except Exception as e: # Check if it's already an exception we raised diff --git a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py index 644ecaf72e..776b9d36d5 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py +++ b/litellm/proxy/guardrails/guardrail_hooks/litellm_content_filter/content_filter.py @@ -6,13 +6,27 @@ to detect and block/mask sensitive content. """ import re -from typing import Any, AsyncGenerator, Dict, List, Optional, Pattern, Tuple, Union +from typing import ( + TYPE_CHECKING, + Any, + AsyncGenerator, + Dict, + List, + Literal, + Optional, + Pattern, + Tuple, + Union, +) import yaml from fastapi import HTTPException from litellm._logging import verbose_proxy_logger from litellm.integrations.custom_guardrail import CustomGuardrail + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import ( BlockedWord, @@ -20,7 +34,6 @@ from litellm.types.guardrails import ( ContentFilterPattern, GuardrailEventHooks, Mode, - PiiEntityType, ) from litellm.types.utils import ModelResponseStream @@ -49,7 +62,9 @@ class ContentFilterGuardrail(CustomGuardrail): patterns: Optional[List[ContentFilterPattern]] = None, blocked_words: Optional[List[BlockedWord]] = None, blocked_words_file: Optional[str] = None, - event_hook: Optional[Union[GuardrailEventHooks, List[GuardrailEventHooks], Mode]] = None, + event_hook: Optional[ + Union[GuardrailEventHooks, List[GuardrailEventHooks], Mode] + ] = None, default_on: bool = False, pattern_redaction_format: Optional[str] = None, keyword_redaction_tag: Optional[str] = None, @@ -57,7 +72,7 @@ class ContentFilterGuardrail(CustomGuardrail): ): """ Initialize the Content Filter Guardrail. - + Args: guardrail_name: Name of this guardrail instance patterns: List of ContentFilterPattern objects to detect @@ -79,11 +94,13 @@ class ContentFilterGuardrail(CustomGuardrail): default_on=default_on, **kwargs, ) - + self.guardrail_provider = "litellm_content_filter" - self.pattern_redaction_format = pattern_redaction_format or self.PATTERN_REDACTION_FORMAT + self.pattern_redaction_format = ( + pattern_redaction_format or self.PATTERN_REDACTION_FORMAT + ) self.keyword_redaction_tag = keyword_redaction_tag or self.KEYWORD_REDACTION_STR - + # Normalize inputs: convert dicts to Pydantic models for consistent handling normalized_patterns: List[ContentFilterPattern] = [] if patterns: @@ -92,7 +109,7 @@ class ContentFilterGuardrail(CustomGuardrail): normalized_patterns.append(ContentFilterPattern(**pattern_config)) else: normalized_patterns.append(pattern_config) - + normalized_blocked_words: List[BlockedWord] = [] if blocked_words: for word in blocked_words: @@ -100,28 +117,28 @@ class ContentFilterGuardrail(CustomGuardrail): normalized_blocked_words.append(BlockedWord(**word)) else: normalized_blocked_words.append(word) - + # Compile regex patterns self.compiled_patterns: List[Tuple[Pattern, str, ContentFilterAction]] = [] for pattern_config in normalized_patterns: self._add_pattern(pattern_config) - + # Load blocked words - always initialize as dict self.blocked_words: Dict[str, Tuple[ContentFilterAction, Optional[str]]] = {} for word in normalized_blocked_words: self.blocked_words[word.keyword.lower()] = (word.action, word.description) - + # Defensive check: ensure blocked_words is a dict (not a list) if not isinstance(self.blocked_words, dict): verbose_proxy_logger.error( f"blocked_words is not a dict, got {type(self.blocked_words)}. Resetting to empty dict." ) self.blocked_words = {} - + # Load blocked words from file if provided if blocked_words_file: self._load_blocked_words_file(blocked_words_file) - + verbose_proxy_logger.debug( f"ContentFilterGuardrail initialized with {len(self.compiled_patterns)} patterns " f"and {len(self.blocked_words)} blocked words" @@ -130,7 +147,7 @@ class ContentFilterGuardrail(CustomGuardrail): def _add_pattern(self, pattern_config: ContentFilterPattern) -> None: """ Add a pattern to the compiled patterns list. - + Args: pattern_config: ContentFilterPattern configuration """ @@ -147,9 +164,13 @@ class ContentFilterGuardrail(CustomGuardrail): pattern_name = pattern_config.name or "custom_regex" else: raise ValueError(f"Unknown pattern_type: {pattern_config.pattern_type}") - - self.compiled_patterns.append((compiled, pattern_name, pattern_config.action)) - verbose_proxy_logger.debug(f"Added pattern: {pattern_name} with action {pattern_config.action}") + + self.compiled_patterns.append( + (compiled, pattern_name, pattern_config.action) + ) + verbose_proxy_logger.debug( + f"Added pattern: {pattern_name} with action {pattern_config.action}" + ) except Exception as e: verbose_proxy_logger.error(f"Error adding pattern {pattern_config}: {e}") raise @@ -157,10 +178,10 @@ class ContentFilterGuardrail(CustomGuardrail): def _load_blocked_words_file(self, file_path: str) -> None: """ Load blocked words from a YAML file. - + Args: file_path: Path to YAML file containing blocked_words list - + Expected format: ```yaml blocked_words: @@ -172,23 +193,29 @@ class ContentFilterGuardrail(CustomGuardrail): try: with open(file_path, "r") as f: data = yaml.safe_load(f) - + if not isinstance(data, dict) or "blocked_words" not in data: raise ValueError( "Invalid format: file must contain 'blocked_words' key with list of words" ) - + for word_data in data["blocked_words"]: - if not isinstance(word_data, dict) or "keyword" not in word_data or "action" not in word_data: - verbose_proxy_logger.warning(f"Skipping invalid word entry: {word_data}") + if ( + not isinstance(word_data, dict) + or "keyword" not in word_data + or "action" not in word_data + ): + verbose_proxy_logger.warning( + f"Skipping invalid word entry: {word_data}" + ) continue - + keyword = word_data["keyword"].lower() action = ContentFilterAction(word_data["action"]) description = word_data.get("description") - + self.blocked_words[keyword] = (action, description) - + verbose_proxy_logger.info( f"Loaded {len(data['blocked_words'])} blocked words from {file_path}" ) @@ -197,13 +224,15 @@ class ContentFilterGuardrail(CustomGuardrail): except Exception as e: raise Exception(f"Error loading blocked words file {file_path}: {str(e)}") - def _check_patterns(self, text: str) -> Optional[Tuple[str, str, ContentFilterAction]]: + def _check_patterns( + self, text: str + ) -> Optional[Tuple[str, str, ContentFilterAction]]: """ Check text against all compiled regex patterns. - + Args: text: Text to check - + Returns: Tuple of (matched_text, pattern_name, action) if match found, None otherwise """ @@ -217,13 +246,15 @@ class ContentFilterGuardrail(CustomGuardrail): return (matched_text, pattern_name, action) return None - def _check_blocked_words(self, text: str) -> Optional[Tuple[str, ContentFilterAction, Optional[str]]]: + def _check_blocked_words( + self, text: str + ) -> Optional[Tuple[str, ContentFilterAction, Optional[str]]]: """ Check text for blocked keywords. - + Args: text: Text to check - + Returns: Tuple of (keyword, action, description) if match found, None otherwise """ @@ -239,13 +270,13 @@ class ContentFilterGuardrail(CustomGuardrail): if isinstance(word, dict): temp_dict[word.get("keyword", "").lower()] = ( word.get("action", ContentFilterAction.BLOCK), - word.get("description") + word.get("description"), ) self.blocked_words = temp_dict - + if not self.blocked_words: return None - + text_lower = text.lower() for keyword, (action, description) in self.blocked_words.items(): if keyword in text_lower: @@ -258,11 +289,11 @@ class ContentFilterGuardrail(CustomGuardrail): def _mask_content(self, text: str, pattern_name: str) -> str: """ Mask sensitive content in text. - + Args: text: Text containing sensitive content pattern_name: Name of the pattern that matched - + Returns: Text with sensitive content masked """ @@ -273,79 +304,89 @@ class ContentFilterGuardrail(CustomGuardrail): async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: """ - Apply content filtering guardrail to the given text. - + Apply content filtering guardrail to a batch of texts. + This method checks for sensitive patterns and blocked keywords, either blocking the request or masking the sensitive content. - + Args: - text: The text to apply the guardrail to - language: Optional language parameter (not used) - entities: Optional entities parameter (not used) - request_data: Optional request data dictionary for logging metadata - + texts: List of texts to apply the guardrail to + request_data: Request data dictionary for logging metadata + input_type: Whether this is a "request" or "response" + images: Optional list of images (not processed) + Returns: - Text with sensitive content masked (if action is MASK) - + Tuple of (processed_texts, images) - texts may be masked, images unchanged + Raises: HTTPException: If sensitive content is detected and action is BLOCK """ - verbose_proxy_logger.debug("ContentFilterGuardrail: Applying guardrail to text") - - # Check regex patterns - pattern_match = self._check_patterns(text) - if pattern_match: - matched_text, pattern_name, action = pattern_match - - if action == ContentFilterAction.BLOCK: - error_msg = f"Content blocked: {pattern_name} pattern detected" - verbose_proxy_logger.warning(error_msg) - raise HTTPException( - status_code=400, - detail={"error": error_msg, "pattern": pattern_name}, - ) - elif action == ContentFilterAction.MASK: - # Replace the matched text with redaction tag - redaction_tag = self._mask_content(matched_text, pattern_name) - text = text.replace(matched_text, redaction_tag) - verbose_proxy_logger.info(f"Masked {pattern_name} in content") - - # Check blocked words - word_match = self._check_blocked_words(text) - if word_match: - keyword, action, description = word_match - - if action == ContentFilterAction.BLOCK: - error_msg = f"Content blocked: keyword '{keyword}' detected" - if description: - error_msg += f" ({description})" - verbose_proxy_logger.warning(error_msg) - raise HTTPException( - status_code=400, - detail={ - "error": error_msg, - "keyword": keyword, - "description": description, - }, - ) - elif action == ContentFilterAction.MASK: - # Replace keyword with redaction tag (case-insensitive) - text = re.sub( - re.escape(keyword), - self.keyword_redaction_tag, - text, - flags=re.IGNORECASE, - ) - verbose_proxy_logger.info(f"Masked keyword '{keyword}' in content") - - verbose_proxy_logger.debug("ContentFilterGuardrail: Guardrail applied successfully") - return text + verbose_proxy_logger.debug( + f"ContentFilterGuardrail: Applying guardrail to {len(texts)} text(s)" + ) + + processed_texts = [] + + for text in texts: + # Check regex patterns + pattern_match = self._check_patterns(text) + if pattern_match: + matched_text, pattern_name, action = pattern_match + + if action == ContentFilterAction.BLOCK: + error_msg = f"Content blocked: {pattern_name} pattern detected" + verbose_proxy_logger.warning(error_msg) + raise HTTPException( + status_code=400, + detail={"error": error_msg, "pattern": pattern_name}, + ) + elif action == ContentFilterAction.MASK: + # Replace the matched text with redaction tag + redaction_tag = self._mask_content(matched_text, pattern_name) + text = text.replace(matched_text, redaction_tag) + verbose_proxy_logger.info(f"Masked {pattern_name} in content") + + # Check blocked words + word_match = self._check_blocked_words(text) + if word_match: + keyword, action, description = word_match + + if action == ContentFilterAction.BLOCK: + error_msg = f"Content blocked: keyword '{keyword}' detected" + if description: + error_msg += f" ({description})" + verbose_proxy_logger.warning(error_msg) + raise HTTPException( + status_code=400, + detail={ + "error": error_msg, + "keyword": keyword, + "description": description, + }, + ) + elif action == ContentFilterAction.MASK: + # Replace keyword with redaction tag (case-insensitive) + text = re.sub( + re.escape(keyword), + self.keyword_redaction_tag, + text, + flags=re.IGNORECASE, + ) + verbose_proxy_logger.info(f"Masked keyword '{keyword}' in content") + + processed_texts.append(text) + + verbose_proxy_logger.debug( + "ContentFilterGuardrail: Guardrail applied successfully" + ) + return processed_texts, images async def async_post_call_streaming_iterator_hook( self, @@ -355,25 +396,25 @@ class ContentFilterGuardrail(CustomGuardrail): ) -> AsyncGenerator[ModelResponseStream, None]: """ Streaming hook to check each chunk as it's yielded. - + This implementation checks each chunk individually and yields it immediately, allowing for low-latency streaming with content filtering. - + Args: user_api_key_dict: User API key authentication response: Async generator of response chunks request_data: Original request data - + Yields: Checked and potentially masked chunks - + Raises: HTTPException: If chunk content should be blocked """ verbose_proxy_logger.debug( "ContentFilterGuardrail: Running streaming check (per-chunk mode)" ) - + # Process each chunk individually async for chunk in response: if isinstance(chunk, ModelResponseStream): @@ -383,7 +424,9 @@ class ContentFilterGuardrail(CustomGuardrail): # Check the chunk content using apply_guardrail try: processed_content = await self.apply_guardrail( - text=choice.delta.content, + texts=[choice.delta.content], + input_type="response", + images=None, request_data=request_data, ) if processed_content != choice.delta.content: @@ -397,13 +440,11 @@ class ContentFilterGuardrail(CustomGuardrail): f"ContentFilterGuardrail: Blocked streaming chunk: {e.detail}" ) raise - + yield chunk - - verbose_proxy_logger.debug( - "ContentFilterGuardrail: Streaming check completed" - ) - + + verbose_proxy_logger.debug("ContentFilterGuardrail: Streaming check completed") + @staticmethod def get_config_model(): from litellm.types.proxy.guardrails.guardrail_hooks.litellm_content_filter import ( @@ -411,4 +452,3 @@ class ContentFilterGuardrail(CustomGuardrail): ) return LitellmContentFilterGuardrailConfigModel - diff --git a/litellm/proxy/guardrails/guardrail_hooks/presidio.py b/litellm/proxy/guardrails/guardrail_hooks/presidio.py index 0f21dfc82d..6b19685f07 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/presidio.py +++ b/litellm/proxy/guardrails/guardrail_hooks/presidio.py @@ -11,13 +11,27 @@ import asyncio import json from datetime import datetime -from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple, Union, cast +from typing import ( + TYPE_CHECKING, + Any, + AsyncGenerator, + Dict, + List, + Literal, + Optional, + Tuple, + Union, + cast, +) import aiohttp import litellm # noqa: E401 from litellm import get_secret from litellm._logging import verbose_proxy_logger + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm._uuid import uuid from litellm.caching.caching import DualCache from litellm.exceptions import BlockedPiiEntityError @@ -699,23 +713,27 @@ class _OPTIONAL_PresidioPIIMasking(CustomGuardrail): async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: """ UI will call this function to check: 1. If the connection to the guardrail is working 2. When Testing the guardrail with some text, this function will be called with the input text and returns a text after applying the guardrail """ - text = await self.check_pii( - text=text, - output_parse_pii=self.output_parse_pii, - presidio_config=None, - request_data=request_data or {}, - ) - return text + new_texts = [] + for text in texts: + modified_text = await self.check_pii( + text=text, + output_parse_pii=self.output_parse_pii, + presidio_config=None, + request_data=request_data or {}, + ) + new_texts.append(modified_text) + return new_texts, images def update_in_memory_litellm_params(self, litellm_params: LitellmParams) -> None: """ diff --git a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py index 9ee1eb8671..aeae19a827 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py @@ -115,6 +115,7 @@ class UnifiedLLMGuardrails(CustomLogger): from litellm.types.guardrails import GuardrailEventHooks guardrail_to_apply: CustomGuardrail = data.pop("guardrail_to_apply", None) + if guardrail_to_apply is None: return @@ -124,6 +125,7 @@ class UnifiedLLMGuardrails(CustomLogger): ) is not True ): + return verbose_proxy_logger.debug( @@ -132,6 +134,7 @@ class UnifiedLLMGuardrails(CustomLogger): call_type = _infer_call_type(call_type=None, completion_response=response) if call_type is None: + return response if endpoint_guardrail_translation_mappings is None: @@ -150,11 +153,13 @@ class UnifiedLLMGuardrails(CustomLogger): response=response, # type: ignore guardrail_to_apply=guardrail_to_apply, litellm_logging_obj=data.get("litellm_logging_obj"), + user_api_key_dict=user_api_key_dict, ) # Add guardrail to applied guardrails header add_guardrail_to_applied_guardrails_header( request_data=data, guardrail_name=guardrail_to_apply.guardrail_name ) + return response async def async_post_call_streaming_iterator_hook( diff --git a/litellm/proxy/guardrails/guardrail_hooks/zscaler_ai_guard/zscaler_ai_guard.py b/litellm/proxy/guardrails/guardrail_hooks/zscaler_ai_guard/zscaler_ai_guard.py index 48171f594f..562f0d4027 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/zscaler_ai_guard/zscaler_ai_guard.py +++ b/litellm/proxy/guardrails/guardrail_hooks/zscaler_ai_guard/zscaler_ai_guard.py @@ -4,22 +4,20 @@ # # +-------------------------------------------------------------+ import os -from typing import Optional, List +from typing import TYPE_CHECKING, List, Literal, Optional, Tuple + from fastapi import HTTPException -from litellm.integrations.custom_guardrail import ( - CustomGuardrail, -) -from litellm.types.guardrails import ( - PiiEntityType, -) - from litellm._logging import verbose_proxy_logger +from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.llms.custom_httpx.http_handler import ( get_async_httpx_client, httpxSpecialProvider, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + GUARDRAIL_TIMEOUT = 5 @@ -35,76 +33,117 @@ class ZscalerAIGuard(CustomGuardrail): **kwargs, ): self.optional_params = kwargs - self.zscaler_ai_guard_url = api_base or os.getenv("ZSCALER_AI_GUARD_URL", "https://api.us1.zseclipse.net/v1/detection/execute-policy") + self.zscaler_ai_guard_url = api_base or os.getenv( + "ZSCALER_AI_GUARD_URL", + "https://api.us1.zseclipse.net/v1/detection/execute-policy", + ) self.policy_id = policy_id or int(os.getenv("ZSCALER_AI_GUARD_POLICY_ID", -1)) self.api_key = api_key or os.getenv("ZSCALER_AI_GUARD_API_KEY") - self.send_user_api_key_alias = send_user_api_key_alias or os.getenv("SEND_USER_API_KEY_ALIAS", "False").lower() in ("true", "1") - self.send_user_api_key_user_id = send_user_api_key_user_id or os.getenv("SEND_USER_API_KEY_USER_ID", "False").lower() in ("true", "1,") - self.send_user_api_key_team_id = send_user_api_key_team_id or os.getenv("SEND_USER_API_KEY_TEAM_ID", "False").lower() in ("true", "1") + self.send_user_api_key_alias = send_user_api_key_alias or os.getenv( + "SEND_USER_API_KEY_ALIAS", "False" + ).lower() in ("true", "1") + self.send_user_api_key_user_id = send_user_api_key_user_id or os.getenv( + "SEND_USER_API_KEY_USER_ID", "False" + ).lower() in ("true", "1,") + self.send_user_api_key_team_id = send_user_api_key_team_id or os.getenv( + "SEND_USER_API_KEY_TEAM_ID", "False" + ).lower() in ("true", "1") verbose_proxy_logger.debug( - f'''send_user_api_key_alias: {self.send_user_api_key_alias}, + f"""send_user_api_key_alias: {self.send_user_api_key_alias}, send_user_api_key_user_id:{self.send_user_api_key_user_id}, - send_user_api_key_team_id:{self.send_user_api_key_team_id}''' + send_user_api_key_team_id:{self.send_user_api_key_team_id}""" ) super().__init__(default_on=True) verbose_proxy_logger.debug("ZscalerAIGuard Initializing ...") - def _get_stripped_metadata_value(self, request_data: Optional[dict], key: str) -> Optional[str]: + def _get_stripped_metadata_value( + self, request_data: Optional[dict], key: str + ) -> Optional[str]: if request_data is None: return "N/A" value = request_data.get("metadata", {}).get(key, "N/A") if value is not None: return str(value).strip() return "N/A" - + async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: + texts: List[str], + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + images: Optional[List[str]] = None, + ) -> Tuple[List[str], Optional[List[str]]]: + """ + Apply Zscaler AI Guard guardrail to batch of texts. + + Args: + texts: List of texts to check + request_data: Request data dictionary containing metadata + input_type: Whether this is a "request" or "response" + images: Optional list of images (not used by Zscaler) + + Returns: + Tuple of (processed_texts, images) - texts unchanged if passed, images unchanged + + Raises: + Exception: If content is blocked by Zscaler AI Guard + """ try: - verbose_proxy_logger.debug("Inside apply_guardrail.") - - custom_policy_id = (request_data or {}).get("metadata", {}).get("zguard_policy_id", self.policy_id) - verbose_proxy_logger.debug( - f"custom_policy_id: {custom_policy_id}") - + verbose_proxy_logger.debug(f"ZscalerAIGuard: Checking {len(texts)} text(s)") + + custom_policy_id = request_data.get("metadata", {}).get( + "zguard_policy_id", self.policy_id + ) + verbose_proxy_logger.debug(f"custom_policy_id: {custom_policy_id}") + kwargs = {} if self.send_user_api_key_alias: - kwargs["user_api_key_alias"] = self._get_stripped_metadata_value(request_data, "user_api_key_alias") + kwargs["user_api_key_alias"] = self._get_stripped_metadata_value( + request_data, "user_api_key_alias" + ) if self.send_user_api_key_team_id: - kwargs["user_api_key_team_id"] = self._get_stripped_metadata_value(request_data, "user_api_key_team_id") + kwargs["user_api_key_team_id"] = self._get_stripped_metadata_value( + request_data, "user_api_key_team_id" + ) if self.send_user_api_key_user_id: - kwargs["user_api_key_user_id"] = self._get_stripped_metadata_value(request_data, "user_api_key_user_id") - verbose_proxy_logger.debug( - f"inside apply_guardrail kwargs: {kwargs}") + kwargs["user_api_key_user_id"] = self._get_stripped_metadata_value( + request_data, "user_api_key_user_id" + ) + verbose_proxy_logger.debug(f"inside apply_guardrail kwargs: {kwargs}") + + # Check each text (Zscaler processes one at a time) + for text in texts: + zscaler_ai_guard_result = await self.make_zscaler_ai_guard_api_call( + zscaler_ai_guard_url=self.zscaler_ai_guard_url, + api_key=self.api_key, + policy_id=self.policy_id, + direction="IN", + content=text, + **kwargs, + ) + + if ( + zscaler_ai_guard_result + and zscaler_ai_guard_result.get("action") == "BLOCK" + ): + blocking_info = zscaler_ai_guard_result.get( + "zscaler_ai_guard_response" + ) + error_message = f"Content blocked by Zscaler AI Guard: {self.extract_blocking_info(blocking_info)}" + raise Exception(error_message) - zscaler_ai_guard_result = await self.make_zscaler_ai_guard_api_call( - zscaler_ai_guard_url=self.zscaler_ai_guard_url, - api_key=self.api_key, - policy_id=self.policy_id, - direction="IN", - content=text, - **kwargs, - ) except Exception as e: verbose_proxy_logger.error( "ZscalerAIGuard: Failed to apply guardrail: %s", str(e) ) raise e - - if zscaler_ai_guard_result and zscaler_ai_guard_result.get("action") == "BLOCK": - blocking_info = zscaler_ai_guard_result.get("zscaler_ai_guard_response") - error_message = f"Content blocked by Zscaler AI Guard: {self.extract_blocking_info(blocking_info)}" - raise Exception(error_message) verbose_proxy_logger.debug("ZscalerAIGuard: Successfully applied guardrail.") - return text + return texts, images def extract_blocking_info(self, response): """ @@ -132,17 +171,15 @@ class ZscalerAIGuard(CustomGuardrail): "error_type": "Zscaler AI Guard Error", "reason": reason, } - + def _prepare_headers(self, api_key, **kwargs): headers = { - "Content-Type": "application/json", - "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + "Authorization": f"Bearer {api_key}", } extra_headers = headers.copy() if self.send_user_api_key_alias: - verbose_proxy_logger.debug( - f"kwargs: {kwargs}" - ) + verbose_proxy_logger.debug(f"kwargs: {kwargs}") user_api_key_alias = kwargs.get("user_api_key_alias", "N/A") verbose_proxy_logger.debug( f"kwargs user_api_key_alias: {user_api_key_alias}" @@ -157,11 +194,9 @@ class ZscalerAIGuard(CustomGuardrail): user_api_key_user_id = kwargs.get("user-api-key-user-id", "N/A") extra_headers.update({"user-api-key-user-id": user_api_key_user_id}) - verbose_proxy_logger.debug( - f"extra_headers: {extra_headers}" - ) + verbose_proxy_logger.debug(f"extra_headers: {extra_headers}") return extra_headers - + async def _send_request(self, url, headers, data): async_client = get_async_httpx_client( llm_provider=httpxSpecialProvider.LoggingCallback @@ -176,8 +211,6 @@ class ZscalerAIGuard(CustomGuardrail): response.raise_for_status() return response - - def _handle_response(self, response, direction): # Raise exceptions on critical errors to stop the request if response.status_code == 429: # Rate limit @@ -204,9 +237,7 @@ class ZscalerAIGuard(CustomGuardrail): statusCode_in_response = json_response.get("statusCode", None) if statusCode_in_response == 200: guardrail_result = json_response.get("action", None) - verbose_proxy_logger.info( - f"Zscaler AI Guard response: {json_response}" - ) + verbose_proxy_logger.info(f"Zscaler AI Guard response: {json_response}") if guardrail_result == "BLOCK": verbose_proxy_logger.info( @@ -269,16 +300,12 @@ class ZscalerAIGuard(CustomGuardrail): } try: - response = await self._send_request(zscaler_ai_guard_url, extra_headers, data) + response = await self._send_request( + zscaler_ai_guard_url, extra_headers, data + ) return self._handle_response(response, direction) except Exception as e: - verbose_proxy_logger.error( - f"{e}. Blocking request." - ) - user_facing_error = self._create_user_facing_error( - f"{str(e)})" - ) + verbose_proxy_logger.error(f"{e}. Blocking request.") + user_facing_error = self._create_user_facing_error(f"{str(e)})") # This exception will be caught by the proxy and returned to the user raise HTTPException(status_code=500, detail=user_facing_error) - - \ No newline at end of file diff --git a/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py b/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py index a00fe76a0f..d2acc3e340 100644 --- a/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py +++ b/litellm/types/proxy/guardrails/guardrail_hooks/generic_guardrail_api.py @@ -1,10 +1,22 @@ -from typing import Any, Dict, Literal, Optional +from typing import Any, Dict, List, Literal, Optional from pydantic import BaseModel, Field +from typing_extensions import TypedDict from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel +class GenericGuardrailAPIMetadata(TypedDict, total=False): + user_api_key_hash: Optional[str] + user_api_key_alias: Optional[str] + user_api_key_user_id: Optional[str] + user_api_key_user_email: Optional[str] + user_api_key_team_id: Optional[str] + user_api_key_team_alias: Optional[str] + user_api_key_end_user_id: Optional[str] + user_api_key_org_id: Optional[str] + + class GenericGuardrailAPIOptionalParams(BaseModel): """Optional parameters for the Generic Guardrail API""" @@ -27,3 +39,74 @@ class GenericGuardrailAPIConfigModel( @staticmethod def ui_friendly_name() -> str: return "Generic Guardrail API" + + +class GenericGuardrailAPIRequest: + """Request model for the Generic Guardrail API""" + + input_type: Literal["request", "response"] + litellm_call_id: Optional[str] # the call id of the individual LLM call + litellm_trace_id: Optional[ + str + ] # the trace id of the LLM call - useful if there are multiple LLM calls for the same conversation + + def __init__( + self, + texts: List[str], + request_data: GenericGuardrailAPIMetadata, + input_type: Literal["request", "response"], + litellm_call_id: Optional[str], + litellm_trace_id: Optional[str], + additional_provider_specific_params: Optional[Dict[str, Any]] = None, + images: Optional[List[str]] = None, + ): + self.texts = texts + self.request_data = request_data + self.additional_provider_specific_params = ( + additional_provider_specific_params or {} + ) + self.images = images + self.input_type = input_type + self.litellm_call_id = litellm_call_id + self.litellm_trace_id = litellm_trace_id + + def to_dict(self) -> dict: + return { + "texts": self.texts, + "request_data": self.request_data, + "images": self.images, + "additional_provider_specific_params": self.additional_provider_specific_params, + "input_type": self.input_type, + "litellm_call_id": self.litellm_call_id, + "litellm_trace_id": self.litellm_trace_id, + } + + +class GenericGuardrailAPIResponse: + """Response model for the Generic Guardrail API""" + + texts: Optional[List[str]] + images: Optional[List[str]] + action: str + blocked_reason: Optional[str] + + def __init__( + self, + action: str, + texts: Optional[List[str]] = None, + blocked_reason: Optional[str] = None, + images: Optional[List[str]] = None, + ): + self.action = action + self.blocked_reason = blocked_reason + self.texts = texts + self.images = images + + @classmethod + def from_dict(cls, data: dict) -> "GenericGuardrailAPIResponse": + return cls( + action=data.get("action", "NONE"), + blocked_reason=data.get("blocked_reason"), + texts=data.get("texts"), + images=data.get("images"), + ) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py new file mode 100644 index 0000000000..e6ebada4d2 --- /dev/null +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_generic_guardrail_api.py @@ -0,0 +1,504 @@ +""" +Tests for Generic Guardrail API integration + +This test file tests the Generic Guardrail API implementation, +specifically focusing on metadata extraction and passing. +""" +import os +from unittest.mock import AsyncMock, MagicMock, patch + +import httpx +import pytest + +import litellm +from litellm import ModelResponse +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.guardrails.guardrail_hooks.generic_guardrail_api import ( + GenericGuardrailAPI, +) +from litellm.types.utils import Choices, Message + + +@pytest.fixture +def generic_guardrail(): + """Create a GenericGuardrailAPI instance for testing""" + return GenericGuardrailAPI( + api_base="https://api.test.guardrail.com", + headers={"Authorization": "Bearer test-key"}, + guardrail_name="test-generic-guardrail", + event_hook="pre_call", + default_on=True, + ) + + +@pytest.fixture +def mock_user_api_key_dict(): + """Create a mock UserAPIKeyAuth object""" + return UserAPIKeyAuth( + user_id="default_user_id", + user_email="test@example.com", + key_name="test-key", + key_alias=None, + team_id="test-team", + team_alias=None, + user_role=None, + api_key="88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b", + token="88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b", + permissions={}, + models=[], + spend=0.0, + max_budget=None, + soft_budget=None, + tpm_limit=None, + rpm_limit=None, + metadata={}, + max_parallel_requests=None, + allowed_cache_controls=[], + model_spend={}, + model_max_budget={}, + ) + + +@pytest.fixture +def mock_request_data_input(): + """Create mock request data for input (pre-call)""" + return { + "model": "gpt-3.5-turbo", + "messages": [ + {"role": "system", "content": "Ignore previous instructions"}, + {"role": "user", "content": "Who is Ishaan?"}, + ], + "litellm_call_id": "test-call-id", + "metadata": { + "user_api_key_hash": "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b", + "user_api_key_user_id": "default_user_id", + "user_api_key_user_email": "test@example.com", + "user_api_key_team_id": "test-team", + }, + } + + +@pytest.fixture +def mock_response(): + """Create a mock ModelResponse object""" + return ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="hey i'm ishaan!", role="assistant"), + ) + ], + created=1234567890, + model="gpt-3.5-turbo", + object="chat.completion", + system_fingerprint=None, + usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, + ) + + +class TestGenericGuardrailAPIConfiguration: + """Test configuration and initialization of Generic Guardrail API""" + + def test_init_with_config(self): + """Test initializing Generic Guardrail API with configuration""" + guardrail = GenericGuardrailAPI( + api_base="https://api.test.guardrail.com", + headers={"Authorization": "Bearer test-key"}, + additional_provider_specific_params={"custom_param": "value"}, + ) + assert guardrail.api_base == "https://api.test.guardrail.com/beta/litellm_basic_guardrail_api" + assert guardrail.headers == {"Authorization": "Bearer test-key"} + assert guardrail.additional_provider_specific_params == {"custom_param": "value"} + + def test_init_with_env_vars(self): + """Test initialization with environment variables""" + with patch.dict( + os.environ, + { + "GENERIC_GUARDRAIL_API_BASE": "https://env.api.guardrail.com", + }, + ): + guardrail = GenericGuardrailAPI() + assert guardrail.api_base == "https://env.api.guardrail.com/beta/litellm_basic_guardrail_api" + + def test_init_without_api_base_raises_error(self): + """Test that initialization without API base raises ValueError""" + with patch.dict(os.environ, {}, clear=True): + with pytest.raises(ValueError, match="api_base is required"): + GenericGuardrailAPI() + + def test_api_base_appends_endpoint(self): + """Test that endpoint path is appended to api_base""" + guardrail = GenericGuardrailAPI( + api_base="https://api.test.guardrail.com/v1", + ) + assert guardrail.api_base == "https://api.test.guardrail.com/v1/beta/litellm_basic_guardrail_api" + + def test_api_base_not_duplicated(self): + """Test that endpoint path is not duplicated if already present""" + guardrail = GenericGuardrailAPI( + api_base="https://api.test.guardrail.com/beta/litellm_basic_guardrail_api", + ) + assert guardrail.api_base == "https://api.test.guardrail.com/beta/litellm_basic_guardrail_api" + + +class TestMetadataExtraction: + """Test metadata extraction from request data""" + + @pytest.mark.asyncio + async def test_extract_metadata_from_input_request( + self, generic_guardrail, mock_request_data_input + ): + """Test extracting metadata from input request (metadata field)""" + # Mock API response + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["Who is Ishaan?"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + await generic_guardrail.apply_guardrail( + texts=["Who is Ishaan?"], + request_data=mock_request_data_input, + input_type="request", + ) + + # Verify API was called + mock_post.assert_called_once() + + # Verify the request payload contains metadata + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + + assert "request_data" in json_payload + request_metadata = json_payload["request_data"] + + # Verify metadata was extracted from request_data["metadata"] + assert request_metadata["user_api_key_hash"] == "88dc28d0f030c55ed4ab77ed8faf098196cb1c05df778539800c9f1243fe6b4b" + assert request_metadata["user_api_key_user_id"] == "default_user_id" + assert request_metadata["user_api_key_user_email"] == "test@example.com" + assert request_metadata["user_api_key_team_id"] == "test-team" + + @pytest.mark.asyncio + async def test_extract_metadata_from_output_response( + self, generic_guardrail, mock_user_api_key_dict, mock_response + ): + """Test extracting metadata from output response (litellm_metadata field)""" + # Create request_data as it would be created by the handler + user_dict = mock_user_api_key_dict.model_dump() + + # Transform to prefixed keys (as done by BaseTranslation) + litellm_metadata = {} + for key, value in user_dict.items(): + if value is not None and not key.startswith("_"): + if key.startswith("user_api_key_"): + litellm_metadata[key] = value + else: + litellm_metadata[f"user_api_key_{key}"] = value + + request_data = { + "response": mock_response, + "litellm_metadata": litellm_metadata, + } + + # Mock API response + mock_api_response = MagicMock() + mock_api_response.json.return_value = { + "action": "NONE", + "texts": ["hey i'm ishaan!"], + } + mock_api_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_api_response + ) as mock_post: + await generic_guardrail.apply_guardrail( + texts=["hey i'm ishaan!"], + request_data=request_data, + input_type="response", + ) + + # Verify API was called + mock_post.assert_called_once() + + # Verify the request payload contains metadata + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + + assert "request_data" in json_payload + request_metadata = json_payload["request_data"] + + # Verify metadata was extracted from request_data["litellm_metadata"] + # The token field should be mapped to user_api_key_hash + assert "user_api_key_hash" in request_metadata + assert request_metadata["user_api_key_user_id"] == "default_user_id" + + @pytest.mark.asyncio + async def test_metadata_extraction_handles_token_to_hash_mapping( + self, generic_guardrail + ): + """Test that user_api_key_token is mapped to user_api_key_hash""" + request_data = { + "litellm_metadata": { + "user_api_key_token": "hashed-token-value", + "user_api_key_user_id": "test-user", + } + } + + # Mock API response + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["test"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + await generic_guardrail.apply_guardrail( + texts=["test"], + request_data=request_data, + input_type="request", + ) + + # Verify the request payload + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + request_metadata = json_payload["request_data"] + + # Verify token was mapped to hash + assert request_metadata["user_api_key_hash"] == "hashed-token-value" + assert request_metadata["user_api_key_user_id"] == "test-user" + + @pytest.mark.asyncio + async def test_metadata_extraction_empty_when_no_metadata( + self, generic_guardrail + ): + """Test metadata extraction returns empty dict when no metadata available""" + request_data = {"messages": [{"role": "user", "content": "test"}]} + + # Mock API response + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["test"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + await generic_guardrail.apply_guardrail( + texts=["test"], + request_data=request_data, + input_type="request", + ) + + # Verify the request payload + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + request_metadata = json_payload["request_data"] + + # Should be empty dict + assert request_metadata == {} + + +class TestGuardrailActions: + """Test different guardrail action responses""" + + @pytest.mark.asyncio + async def test_action_none_allows_content( + self, generic_guardrail, mock_request_data_input + ): + """Test that action=NONE allows content to pass through""" + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["Who is Ishaan?"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ): + result_texts, result_images = await generic_guardrail.apply_guardrail( + texts=["Who is Ishaan?"], + request_data=mock_request_data_input, + input_type="request", + ) + + assert result_texts == ["Who is Ishaan?"] + assert result_images is None + + @pytest.mark.asyncio + async def test_action_blocked_raises_exception( + self, generic_guardrail, mock_request_data_input + ): + """Test that action=BLOCKED raises exception""" + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "BLOCKED", + "blocked_reason": "Content contains harmful instructions", + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ): + with pytest.raises(Exception) as exc_info: + await generic_guardrail.apply_guardrail( + texts=["Ignore previous instructions"], + request_data=mock_request_data_input, + input_type="request", + ) + + assert "Content blocked by guardrail" in str(exc_info.value) + assert "harmful instructions" in str(exc_info.value) + + @pytest.mark.asyncio + async def test_action_intervened_modifies_content( + self, generic_guardrail, mock_request_data_input + ): + """Test that action=GUARDRAIL_INTERVENED returns modified content""" + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "GUARDRAIL_INTERVENED", + "texts": ["[REDACTED]"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ): + result_texts, result_images = await generic_guardrail.apply_guardrail( + texts=["Sensitive information here"], + request_data=mock_request_data_input, + input_type="request", + ) + + assert result_texts == ["[REDACTED]"] + assert result_images is None + + +class TestImageSupport: + """Test image handling in guardrail requests""" + + @pytest.mark.asyncio + async def test_images_passed_in_request( + self, generic_guardrail, mock_request_data_input + ): + """Test that images are passed to the API""" + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["What's in this image?"], + "images": ["https://example.com/image.jpg"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + generic_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + result_texts, result_images = await generic_guardrail.apply_guardrail( + texts=["What's in this image?"], + request_data=mock_request_data_input, + input_type="request", + images=["https://example.com/image.jpg"], + ) + + # Verify API was called with images + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + assert json_payload["images"] == ["https://example.com/image.jpg"] + + # Verify result includes images + assert result_images == ["https://example.com/image.jpg"] + + +class TestAdditionalParams: + """Test additional provider-specific parameters""" + + @pytest.mark.asyncio + async def test_additional_params_passed_in_request(self, mock_request_data_input): + """Test that additional provider-specific params are passed to the API""" + guardrail = GenericGuardrailAPI( + api_base="https://api.test.guardrail.com", + additional_provider_specific_params={ + "custom_threshold": 0.8, + "enable_feature": True, + }, + ) + + mock_response = MagicMock() + mock_response.json.return_value = { + "action": "NONE", + "texts": ["test"], + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + await guardrail.apply_guardrail( + texts=["test"], + request_data=mock_request_data_input, + input_type="request", + ) + + # Verify API was called with additional params + call_args = mock_post.call_args + json_payload = call_args.kwargs["json"] + assert json_payload["additional_provider_specific_params"]["custom_threshold"] == 0.8 + assert json_payload["additional_provider_specific_params"]["enable_feature"] is True + + +class TestErrorHandling: + """Test error handling scenarios""" + + @pytest.mark.asyncio + async def test_api_failure_handling( + self, generic_guardrail, mock_request_data_input + ): + """Test API failure handling""" + with patch.object( + generic_guardrail.async_handler, + "post", + side_effect=httpx.HTTPStatusError( + "API Error", request=MagicMock(), response=MagicMock(status_code=500) + ), + ): + with pytest.raises(Exception) as exc_info: + await generic_guardrail.apply_guardrail( + texts=["test"], + request_data=mock_request_data_input, + input_type="request", + ) + + assert "Generic Guardrail API failed" in str(exc_info.value) + + @pytest.mark.asyncio + async def test_network_error_handling( + self, generic_guardrail, mock_request_data_input + ): + """Test network error handling""" + with patch.object( + generic_guardrail.async_handler, + "post", + side_effect=httpx.RequestError("Connection failed", request=MagicMock()), + ): + with pytest.raises(Exception) as exc_info: + await generic_guardrail.apply_guardrail( + texts=["test"], + request_data=mock_request_data_input, + input_type="request", + ) + + assert "Generic Guardrail API failed" in str(exc_info.value) + From 082c8af37f604b945893056b891dbc7a252474da Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 2 Dec 2025 11:25:32 +0530 Subject: [PATCH 216/248] Fix: litellm user auth not passing issue --- ...odel_prices_and_context_window_backup.json | 360 ++++++++++++++++-- .../mcp_server/mcp_server_manager.py | 12 +- litellm/proxy/litellm_pre_call_utils.py | 2 + litellm/responses/main.py | 9 +- .../mcp_server/test_mcp_server_manager.py | 102 ++++- 5 files changed, 452 insertions(+), 33 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9fdc1704f4..f28e9b1290 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -6717,6 +6717,33 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "claude-opus-4-5": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "claude-sonnet-4-20250514": { "deprecation_date": "2026-05-14", "cache_creation_input_token_cost": 3.75e-06, @@ -7824,26 +7851,298 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "databricks/databricks-claude-3-7-sonnet": { - "input_cost_per_token": 2.5e-06, - "input_dbu_cost_per_token": 3.571e-05, + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", "max_input_tokens": 200000, "max_output_tokens": 128000, "max_tokens": 200000, "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Claude 3.7 conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 1.7857e-05, - "output_db_cost_per_token": 0.000214286, - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true }, + "databricks/databricks-claude-haiku-4-5": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.00003e-06, + "output_dbu_cost_per_token": 7.1429e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4": { + "input_cost_per_token": 1.5000020000000002e-05, + "input_dbu_cost_per_token": 0.000214286, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 7.500003000000001e-05, + "output_dbu_cost_per_token": 0.001071429, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4-1": { + "input_cost_per_token": 1.5000020000000002e-05, + "input_dbu_cost_per_token": 0.000214286, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 7.500003000000001e-05, + "output_dbu_cost_per_token": 0.001071429, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4-5": { + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.5000010000000002e-05, + "output_dbu_cost_per_token": 0.000357143, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4-1": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4-5": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemini-2-5-flash": { + "input_cost_per_token": 3.0001999999999996e-07, + "input_dbu_cost_per_token": 4.285999999999999e-06, + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 1048576, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.49998e-06, + "output_dbu_cost_per_token": 3.5714e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemini-2-5-pro": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 1048576, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemma-3-12b": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.0001e-07, + "output_dbu_cost_per_token": 7.143e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gpt-5": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-1": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-mini": { + "input_cost_per_token": 2.4997000000000006e-07, + "input_dbu_cost_per_token": 3.571e-06, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.9999700000000004e-06, + "output_dbu_cost_per_token": 2.8571e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-nano": { + "input_cost_per_token": 4.998e-08, + "input_dbu_cost_per_token": 7.14e-07, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 3.9998000000000007e-07, + "output_dbu_cost_per_token": 5.714000000000001e-06, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-oss-120b": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.9997e-07, + "output_dbu_cost_per_token": 8.571e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gpt-oss-20b": { + "input_cost_per_token": 7e-08, + "input_dbu_cost_per_token": 1e-06, + "litellm_provider": "databricks", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 3.0001999999999996e-07, + "output_dbu_cost_per_token": 4.285999999999999e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, "databricks/databricks-gte-large-en": { - "input_cost_per_token": 1.2999e-07, + "input_cost_per_token": 1.2999000000000001e-07, "input_dbu_cost_per_token": 1.857e-06, "litellm_provider": "databricks", "max_input_tokens": 8192, @@ -7868,14 +8167,14 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 1.5e-06, + "output_cost_per_token": 1.5000300000000002e-06, "output_dbu_cost_per_token": 2.1429e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, "databricks/databricks-llama-4-maverick": { - "input_cost_per_token": 5e-06, - "input_dbu_cost_per_token": 7.143e-05, + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -7884,13 +8183,13 @@ "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)." }, "mode": "chat", - "output_cost_per_token": 1.5e-05, - "output_dbu_cost_per_token": 0.00021429, + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, "databricks/databricks-meta-llama-3-1-405b-instruct": { - "input_cost_per_token": 5e-06, + "input_cost_per_token": 5.00003e-06, "input_dbu_cost_per_token": 7.1429e-05, "litellm_provider": "databricks", "max_input_tokens": 128000, @@ -7900,14 +8199,29 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 1.500002e-05, - "output_db_cost_per_token": 0.000214286, + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, + "databricks/databricks-meta-llama-3-1-8b-instruct": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 4.5003000000000007e-07, + "output_dbu_cost_per_token": 6.429000000000001e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, "databricks/databricks-meta-llama-3-3-70b-instruct": { - "input_cost_per_token": 1.00002e-06, - "input_dbu_cost_per_token": 1.4286e-05, + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -7916,8 +8230,8 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 2.99999e-06, - "output_dbu_cost_per_token": 4.2857e-05, + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, @@ -7932,7 +8246,7 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 2.99999e-06, + "output_cost_per_token": 2.9999900000000002e-06, "output_dbu_cost_per_token": 4.2857e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true @@ -7948,13 +8262,13 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 9.9902e-07, + "output_cost_per_token": 1.00002e-06, "output_dbu_cost_per_token": 1.4286e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, "databricks/databricks-mpt-30b-instruct": { - "input_cost_per_token": 9.9902e-07, + "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", "max_input_tokens": 8192, @@ -7964,7 +8278,7 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 9.9902e-07, + "output_cost_per_token": 1.00002e-06, "output_dbu_cost_per_token": 1.4286e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true diff --git a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py index 2c03cbdae3..d42fd80cbd 100644 --- a/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py +++ b/litellm/proxy/_experimental/mcp_server/mcp_server_manager.py @@ -17,16 +17,15 @@ from urllib.parse import urlparse from fastapi import HTTPException from httpx import HTTPStatusError from mcp import ReadResourceResult, Resource +from mcp.types import CallToolRequestParams as MCPCallToolRequestParams from mcp.types import ( - CallToolRequestParams as MCPCallToolRequestParams, + CallToolResult, GetPromptRequestParams, GetPromptResult, Prompt, ResourceTemplate, ) -from mcp.types import CallToolResult from mcp.types import Tool as MCPTool - from pydantic import AnyUrl import litellm @@ -1949,7 +1948,12 @@ class MCPServerManager: ) = split_server_prefix_from_name(tool_name) if original_tool_name in self.tool_name_to_mcp_server_name_mapping: for server in self.get_registry().values(): - if normalize_server_name(server.name) == normalize_server_name( + if server.server_name is None: + if normalize_server_name(server.name) == normalize_server_name( + server_name_from_prefix + ): + return server + elif normalize_server_name(server.server_name) == normalize_server_name( server_name_from_prefix ): return server diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 0a7fc62a42..9dc255bd79 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -611,6 +611,8 @@ class LiteLLMProxyRequestSetup: data[_metadata_variable_name]["user_api_end_user_max_budget"] = getattr( user_api_key_dict, "end_user_max_budget", None ) + # Add the full UserAPIKeyAuth object for MCP server access control + data[_metadata_variable_name]["user_api_key_auth"] = user_api_key_dict return data @staticmethod diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 950ea7063f..e837346df2 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -22,6 +22,9 @@ import litellm from litellm._logging import verbose_logger from litellm.constants import request_timeout from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj +from litellm.litellm_core_utils.prompt_templates.common_utils import ( + update_responses_input_with_model_file_ids, +) from litellm.llms.base_llm.responses.transformation import BaseResponsesAPIConfig from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.responses.litellm_completion_transformation.handler import ( @@ -38,9 +41,6 @@ from litellm.types.llms.openai import ( ToolChoice, ToolParam, ) -from litellm.litellm_core_utils.prompt_templates.common_utils import ( - update_responses_input_with_model_file_ids, -) # Handle ResponseText import with fallback if TYPE_CHECKING: @@ -168,7 +168,8 @@ async def aresponses_api_with_mcp( ) = LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools) # Process MCP tools through the complete pipeline (fetch + filter + deduplicate + transform) - user_api_key_auth = kwargs.get("user_api_key_auth") + # Extract user_api_key_auth from litellm_metadata (where it's added by add_user_api_key_auth_to_request_metadata) + user_api_key_auth = kwargs.get("user_api_key_auth") or kwargs.get("litellm_metadata", {}).get("user_api_key_auth") # Get original MCP tools (for events) and OpenAI tools (for LLM) by reusing existing methods ( diff --git a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py index 940dcf1a7a..86e25c46c8 100644 --- a/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py +++ b/tests/test_litellm/proxy/_experimental/mcp_server/test_mcp_server_manager.py @@ -9,6 +9,8 @@ from fastapi import HTTPException sys.path.insert(0, "../../../../../") import httpx +from mcp import ReadResourceResult, Resource +from mcp.types import GetPromptResult, Prompt, ResourceTemplate, TextResourceContents from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( MCPServerManager, @@ -17,8 +19,6 @@ from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( from litellm.proxy._types import LiteLLM_MCPServerTable, MCPTransport from litellm.types.mcp import MCPAuth from litellm.types.mcp_server.mcp_server_manager import MCPOAuthMetadata, MCPServer -from mcp import ReadResourceResult, Resource -from mcp.types import GetPromptResult, Prompt, ResourceTemplate, TextResourceContents class TestMCPServerManager: @@ -1606,6 +1606,104 @@ class TestMCPServerManager: # Verify the MCP client call was awaited exactly once assert mock_client.call_tool.await_count == 1 + @pytest.mark.asyncio + async def test_get_allowed_mcp_servers_with_user_api_key_auth(self): + """ + Test that get_allowed_mcp_servers properly receives and uses user_api_key_auth + when called. This verifies the fix where user_api_key_auth is passed through + litellm_metadata from responses API. + """ + from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( + MCPRequestHandler, + ) + from litellm.proxy._types import LiteLLM_ObjectPermissionTable, UserAPIKeyAuth + + manager = MCPServerManager() + + # Create a mock user_api_key_auth with object_permission + object_permission = LiteLLM_ObjectPermissionTable( + object_permission_id="perm_123", + mcp_servers=["test_server_1", "test_server_2"], + mcp_access_groups=[], + ) + + user_api_key_auth = UserAPIKeyAuth( + api_key="sk-test", + user_id="user-123", + object_permission=object_permission, + object_permission_id="perm_123", + ) + + # Mock MCPRequestHandler.get_allowed_mcp_servers to verify it receives user_api_key_auth + with patch.object( + MCPRequestHandler, + "get_allowed_mcp_servers", + new_callable=AsyncMock, + ) as mock_get_allowed: + # Configure mock to return servers from object_permission + mock_get_allowed.return_value = ["test_server_1", "test_server_2"] + + # Call get_allowed_mcp_servers with user_api_key_auth + result = await manager.get_allowed_mcp_servers(user_api_key_auth) + + # Verify MCPRequestHandler.get_allowed_mcp_servers was called with user_api_key_auth + mock_get_allowed.assert_called_once() + call_args = mock_get_allowed.call_args + assert call_args[0][0] is user_api_key_auth # First positional arg should be user_api_key_auth + assert call_args[0][0].user_id == "user-123" + assert call_args[0][0].object_permission_id == "perm_123" + assert call_args[0][0].object_permission is not None + assert call_args[0][0].object_permission.mcp_servers == ["test_server_1", "test_server_2"] + + # Verify result contains the expected servers + assert "test_server_1" in result + assert "test_server_2" in result + + def test_get_mcp_server_from_tool_name_uses_server_name_not_name(self): + """ + Test that _get_mcp_server_from_tool_name uses server.server_name instead of server.name + when extracting server name from prefixed tool name (second case). + This ensures the fix for using server_name instead of name works correctly. + """ + from litellm.proxy._experimental.mcp_server.utils import ( + add_server_prefix_to_name, + ) + + manager = MCPServerManager() + + # Create a server where server_name differs from name + # This tests the scenario where server.name != server.server_name + server = MCPServer( + server_id="test-server-id", + name="Test Server Name", # Different from server_name + server_name="test_server", # This is what should be used + alias="test_server", + transport=MCPTransport.http, + ) + + # Register the server + manager.registry = {server.server_id: server} + + # Create a tool with prefixed name + tool_name = "test_tool" + prefixed_tool_name = add_server_prefix_to_name(tool_name, "test_server") + + # Populate the mapping with the original tool name + manager.tool_name_to_mcp_server_name_mapping[tool_name] = "test_server" + manager.tool_name_to_mcp_server_name_mapping[prefixed_tool_name] = "test_server" + + # Test: _get_mcp_server_from_tool_name should find the server using server.server_name + # even when server.name is different + resolved_server = manager._get_mcp_server_from_tool_name(prefixed_tool_name) + + # Verify the server was found correctly + assert resolved_server is not None + assert resolved_server.server_id == server.server_id + assert resolved_server.server_name == "test_server" + # Verify it matched using server_name, not name + assert resolved_server.name == "Test Server Name" # name is different + assert resolved_server.server_name == "test_server" # server_name matches + if __name__ == "__main__": pytest.main([__file__]) From 6d296b1d257c00446ff2ef2e221f0096aa6b874a Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Tue, 2 Dec 2025 14:00:34 +0530 Subject: [PATCH 217/248] Add other routes in jwt auth --- docs/my-website/docs/proxy/pass_through.md | 14 ++++++ docs/my-website/docs/proxy/token_auth.md | 52 ++++++++++++++++++++++ litellm/proxy/_types.py | 4 +- 3 files changed, 67 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md index 7309cdeda2..03454004b8 100644 --- a/docs/my-website/docs/proxy/pass_through.md +++ b/docs/my-website/docs/proxy/pass_through.md @@ -275,6 +275,20 @@ In this video, we'll add the Azure OpenAI Assistants API as a pass through endpo - Check LiteLLM proxy logs for error details - Verify the target API's expected request format +### Allowing Team JWTs to use pass-through routes + +If you are using pass-through provider routes (e.g., `/anthropic/*`) and want your JWT team tokens to access these routes, add `mapped_pass_through_routes` to the `team_allowed_routes` in `litellm_jwtauth` or explicitly add the relevant route(s). + +Example (`proxy_server_config.yaml`): + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + team_ids_jwt_field: "team_ids" + team_allowed_routes: ["openai_routes","info_routes","mapped_pass_through_routes"] +``` + ### Getting Help [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md index c465c1022e..1db1b2a896 100644 --- a/docs/my-website/docs/proxy/token_auth.md +++ b/docs/my-website/docs/proxy/token_auth.md @@ -338,6 +338,58 @@ general_settings: team_allowed_routes: ["/v1/chat/completions"] # 👈 Set accepted routes ``` +### Allowing other provider routes for Teams + +To enable team JWT tokens to access Anthropic-style endpoints such as `/v1/messages`, update `team_allowed_routes` in your `litellm_jwtauth` configuration. `team_allowed_routes` supports the following values: + +- Named route groups from `LiteLLMRoutes` (e.g., `openai_routes`, `anthropic_routes`, `info_routes`, `mapped_pass_through_routes`). + +Below is a quick reference for the route groups you can use and example representative routes from each group. If you need the exhaustive list, see the `LiteLLMRoutes` enum in `litellm/proxy/_types.py` for the authoritative list. + +| Route Group | What it contains | Representative routes | +|-------------|------------------|-----------------------| +| `openai_routes` | OpenAI-compatible REST endpoints (chat, completion, embeddings, images, responses, models, etc.) | `/v1/chat/completions`, `/v1/completions`, `/v1/embeddings`, `/v1/images/generations`, `/v1/models` | +| `anthropic_routes` | Anthropic-style endpoints (`/v1/messages` and related) | `/v1/messages`, `/v1/messages/count_tokens`, `/v1/skills` | +| `mapped_pass_through_routes` | Provider-specific pass-through route prefixes (e.g., Anthropic when proxied via `/anthropic`). Use with `mapped_pass_through_routes` for provider wildcard mapping | `/anthropic/*`, `/vertex-ai/*`, `/bedrock/*` | +| `passthrough_routes_wildcard` | Wildcard mapping for providers (e.g., `/anthropic/*`) - precomputed wildcard list used by the proxy | `/anthropic/*`, `/vllm/*` | +| `google_routes` | Google-specific (e.g., Vertex / Batching endpoints) | `/v1beta/models/{model_name}:generateContent` | +| `mcp_routes` | Internal MCP management endpoints | `/mcp/tools`, `/mcp/tools/call` | +| `info_routes` | Read-only & info endpoints used by the UI | `/key/info`, `/team/info`, `/v1/models` | +| `management_routes` | Admin-only management endpoints (create/update/delete user/team/model) | `/team/new`, `/key/generate`, `/model/new` | +| `spend_tracking_routes` | Budget/spend related endpoints | `/spend/logs`, `/spend/keys` | +| `public_routes` | Public and unauthenticated endpoints | `/`, `/routes`, `/.well-known/litellm-ui-config` | + +Note: `llm_api_routes` is the union of OpenAI, Anthropic, Google, pass-through and other LLM routes (`openai_routes + anthropic_routes + google_routes + mapped_pass_through_routes + passthrough_routes_wildcard + apply_guardrail_routes + mcp_routes + litellm_native_routes`). + +Defaults (what the proxy uses if you don't override them in `litellm_jwtauth`): + +- `admin_jwt_scope`: `litellm_proxy_admin` +- `admin_allowed_routes` (default): `management_routes`, `spend_tracking_routes`, `global_spend_tracking_routes`, `info_routes` +- `team_allowed_routes` (default): `openai_routes`, `info_routes` +- `public_allowed_routes` (default): `public_routes` + + +Example: Allow team JWTs to call Anthropic `/v1/messages` (either by route group or by explicit route string): + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + team_ids_jwt_field: "team_ids" + team_allowed_routes: ["openai_routes", "info_routes", "anthropic_routes"] +``` + +Or selectively allow the exact Anthropic message endpoint only: + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + team_ids_jwt_field: "team_ids" + team_allowed_routes: ["/v1/messages", "info_routes"] +``` + + ### Caching Public Keys Control how long public keys are cached for (in seconds). diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index b5b0bd8060..7e7d404981 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -3440,9 +3440,7 @@ class LiteLLM_JWTAuth(LiteLLMPydanticObjectBase): team_id_upsert: bool = False team_ids_jwt_field: Optional[str] = None upsert_sso_user_to_team: bool = False - team_allowed_routes: List[ - Literal["openai_routes", "info_routes", "management_routes"] - ] = ["openai_routes", "info_routes"] + team_allowed_routes: List[str] = ["openai_routes", "info_routes"] team_id_default: Optional[str] = Field( default=None, description="If no team_id given, default permissions/spend-tracking to this team.s", From 7324905c957fa2e1f1fee9d24b246a44ced170fe Mon Sep 17 00:00:00 2001 From: AlexsanderHamir Date: Tue, 2 Dec 2025 05:29:03 -0800 Subject: [PATCH 218/248] fix: update default database connection number --- docs/my-website/docs/proxy/config_settings.md | 2 +- docs/my-website/docs/proxy/configs.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 7140c99e6f..1f8ca59622 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -113,7 +113,7 @@ general_settings: # Database Settings database_url: string - database_connection_pool_limit: 0 # default 100 + database_connection_pool_limit: 0 # default 10 database_connection_timeout: 0 # default 60s allow_requests_on_db_unavailable: boolean # if true, will allow requests that can not connect to the DB to verify Virtual Key to still work diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md index 18177b7c4d..77ab3158f7 100644 --- a/docs/my-website/docs/proxy/configs.md +++ b/docs/my-website/docs/proxy/configs.md @@ -576,7 +576,7 @@ custom_tokenizer: ```yaml general_settings: - database_connection_pool_limit: 100 # sets connection pool for prisma client to postgres db at 100 + database_connection_pool_limit: 10 # sets connection pool for prisma client to postgres db (default: 10, recommended: 10-20) database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db ``` From 9ff2ecc16d13c79ada59711b958c8730940eeae5 Mon Sep 17 00:00:00 2001 From: Alexsander Hamir Date: Tue, 2 Dec 2025 05:52:00 -0800 Subject: [PATCH 219/248] Fix: update default proxy_batch_write_at number (#17355) The default is 10 seconds, not 30. --- docs/my-website/docs/proxy/config_settings.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 1f8ca59622..82955dadb5 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -234,7 +234,7 @@ router_settings: | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. | | proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** | | proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** | -| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 30 seconds** | +| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** | | proxy_batch_polling_interval | int | Time (in seconds) to wait before polling a batch, to check if it's completed. **Default is 6000 seconds (1 hour)** | | alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) | | custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) | @@ -763,7 +763,7 @@ router_settings: | PROMPTLAYER_API_KEY | API key for PromptLayer integration | PROXY_ADMIN_ID | Admin identifier for proxy server | PROXY_BASE_URL | Base URL for proxy service -| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 30 +| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 10 | PROXY_BATCH_POLLING_INTERVAL | Time in seconds to wait before polling a batch, to check if it's completed. Default is 6000s (1 hour) | PROXY_BUDGET_RESCHEDULER_MAX_TIME | Maximum time in seconds to wait before checking database for budget resets. Default is 605 | PROXY_BUDGET_RESCHEDULER_MIN_TIME | Minimum time in seconds to wait before checking database for budget resets. Default is 597 From 1bb9e1bde8c2573fb3ca57489facc595188a830d Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Dec 2025 08:41:50 -0800 Subject: [PATCH 220/248] [Feat] Add `vllm` batch+files API support (#15823) * add OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS * fix use OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS * add _get_batch_job_total_usage_from_file_content * fixes for vLLM + 12 labs async invoke * fix: vLLM Batch APIs * afile_retrieve * test_hosted_vllm_full_workflow * fix SERVER_URL for test --- litellm/batches/batch_utils.py | 16 +- litellm/batches/main.py | 42 +- litellm/files/main.py | 29 +- litellm/llms/bedrock/batches/handler.py | 96 +++++ ...odel_prices_and_context_window_backup.json | 360 ++++++++++++++++-- litellm/types/utils.py | 6 + .../test_hosted_vllm_batches_and_files.py | 105 +++++ 7 files changed, 591 insertions(+), 63 deletions(-) create mode 100644 litellm/llms/bedrock/batches/handler.py create mode 100644 tests/batches_tests/test_hosted_vllm_batches_and_files.py diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 8289801ee3..50b48321db 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -14,8 +14,7 @@ from litellm.utils import token_counter async def calculate_batch_cost_and_usage( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"], - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"], ) -> Tuple[float, Usage, List[str]]: """ Calculate the cost and usage of a batch @@ -37,8 +36,7 @@ async def calculate_batch_cost_and_usage( async def _handle_completed_batch( batch: Batch, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"], - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"], ) -> Tuple[float, Usage, List[str]]: """Helper function to process a completed batch and handle logging""" # Get batch results @@ -84,8 +82,7 @@ def _get_batch_models_from_file_content( def _batch_cost_calculator( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> float: """ Calculate the cost of a batch based on the output file id @@ -186,7 +183,7 @@ def calculate_vertex_ai_batch_cost_and_usage( async def _get_batch_output_file_content_as_dictionary( batch: Batch, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> List[dict]: """ Get the batch output file content as a list of dictionaries @@ -225,7 +222,7 @@ def _get_file_content_as_dictionary(file_content: bytes) -> List[dict]: def _get_batch_job_cost_from_file_content( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> float: """ Get the cost of a batch job from the file content @@ -253,8 +250,7 @@ def _get_batch_job_cost_from_file_content( def _get_batch_job_total_usage_from_file_content( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> Usage: """ Get the tokens of a batch job from the file content diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 57a9857dd6..353b1e2569 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -18,11 +18,14 @@ from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx from openai.types.batch import BatchRequestCounts +from openai.types.batch import Metadata +from openai.types.batch import Metadata as OpenAIBatchMetadata import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.batches.handler import AzureBatchesAPI +from litellm.llms.bedrock.batches.handler import BedrockBatchesHandler from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.llms.openai.openai import OpenAIBatchesAPI @@ -35,7 +38,11 @@ from litellm.types.llms.openai import ( RetrieveBatchRequest, ) from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import LiteLLMBatch, LlmProviders +from litellm.types.utils import ( + OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS, + LiteLLMBatch, + LlmProviders, +) from litellm.utils import ( ProviderConfigManager, client, @@ -100,7 +107,7 @@ async def acreate_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -148,7 +155,7 @@ def create_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -235,7 +242,7 @@ def create_batch( ) return response api_base: Optional[str] = None - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -350,7 +357,7 @@ def create_batch( @client async def aretrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -396,10 +403,10 @@ def _handle_retrieve_batch_providers_without_provider_config( litellm_params: dict, _retrieve_batch_request: RetrieveBatchRequest, _is_async: bool, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", ): api_base: Optional[str] = None - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -512,7 +519,7 @@ def _handle_retrieve_batch_providers_without_provider_config( @client def retrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -576,7 +583,7 @@ def retrieve_batch( async_kwargs = kwargs.copy() async_kwargs.pop("aws_region_name", None) - return _handle_async_invoke_status( + return BedrockBatchesHandler._handle_async_invoke_status( batch_id=batch_id, aws_region_name=kwargs.get("aws_region_name", "us-east-1"), logging_obj=litellm_logging_obj, @@ -644,7 +651,7 @@ def retrieve_batch( async def alist_batches( after: Optional[str] = None, limit: Optional[int] = None, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "vertex_ai"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -687,7 +694,7 @@ async def alist_batches( def list_batches( after: Optional[str] = None, limit: Optional[int] = None, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "vertex_ai"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -727,7 +734,7 @@ def list_batches( timeout = 600.0 _is_async = kwargs.pop("alist_batches", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -928,7 +935,7 @@ def cancel_batch( _is_async = kwargs.pop("acancel_batch", False) is True api_base: Optional[str] = None - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: api_base = ( optional_params.api_base or litellm.api_base @@ -1043,19 +1050,20 @@ def _handle_async_invoke_status( ) # Transform response to a LiteLLMBatch object + from litellm.types.llms.openai import BatchJobStatus from litellm.types.utils import LiteLLMBatch # Normalize status to lowercase (AWS returns 'Completed', 'Failed', etc.) aws_status_raw = status_response.get("status", "") aws_status_lower = aws_status_raw.lower() # Map AWS status values to LiteLLM expected values - status_mapping = { + status_mapping: dict[str, BatchJobStatus] = { "completed": "completed", "failed": "failed", "inprogress": "in_progress", "in_progress": "in_progress", } - normalized_status = status_mapping.get(aws_status_lower, aws_status_lower) + normalized_status: BatchJobStatus = status_mapping.get(aws_status_lower, "failed") # Default to "failed" if unknown status # Get output S3 URI safely output_s3_uri = "" @@ -1065,13 +1073,15 @@ def _handle_async_invoke_status( pass # Use BedrockBatchesConfig's timestamp parsing method (expects raw AWS status string) + import time + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig created_at, in_progress_at, completed_at, failed_at, _, _ = BedrockBatchesConfig()._parse_timestamps_and_status(status_response, aws_status_raw) result = LiteLLMBatch( id=status_response["invocationArn"], object="batch", status=normalized_status, - created_at=created_at, + created_at=created_at or int(time.time()), # Provide default timestamp if None in_progress_at=in_progress_at, completed_at=completed_at, failed_at=failed_at, diff --git a/litellm/files/main.py b/litellm/files/main.py index 535772fa42..71139001e5 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -30,7 +30,10 @@ from litellm.types.llms.openai import ( OpenAIFileObject, ) from litellm.types.router import * -from litellm.types.utils import LlmProviders +from litellm.types.utils import ( + OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS, + LlmProviders, +) from litellm.utils import ( ProviderConfigManager, client, @@ -51,7 +54,7 @@ vertex_ai_files_instance = VertexAIFilesHandler() async def acreate_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -95,9 +98,7 @@ async def acreate_file( def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Optional[ - Literal["openai", "azure", "vertex_ai", "bedrock"] - ] = None, + custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -165,7 +166,7 @@ def create_file( ), timeout=timeout, ) - elif custom_llm_provider == "openai": + elif custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -276,7 +277,7 @@ def create_file( @client async def afile_retrieve( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -317,7 +318,7 @@ async def afile_retrieve( @client def file_retrieve( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -347,7 +348,7 @@ def file_retrieve( _is_async = kwargs.pop("is_async", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -514,7 +515,7 @@ def file_delete( elif timeout is None: timeout = 600.0 _is_async = kwargs.pop("is_async", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -670,7 +671,7 @@ def file_list( timeout = 600.0 _is_async = kwargs.pop("is_async", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -754,7 +755,7 @@ def file_list( @client async def afile_content( file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -799,7 +800,7 @@ def file_content( file_id: str, model: Optional[str] = None, custom_llm_provider: Optional[ - Union[Literal["openai", "azure", "vertex_ai"], str] + Union[Literal["openai", "azure", "vertex_ai", "hosted_vllm"], str] ] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -846,7 +847,7 @@ def file_content( _is_async = kwargs.pop("afile_content", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base diff --git a/litellm/llms/bedrock/batches/handler.py b/litellm/llms/bedrock/batches/handler.py new file mode 100644 index 0000000000..4a26bd4334 --- /dev/null +++ b/litellm/llms/bedrock/batches/handler.py @@ -0,0 +1,96 @@ +from openai.types.batch import BatchRequestCounts +from openai.types.batch import Metadata as OpenAIBatchMetadata + +from litellm.types.utils import LiteLLMBatch + + +class BedrockBatchesHandler: + """ + Handler for Bedrock Batches. + + Specific providers/models needed some special handling. + + E.g. Twelve Labs Embedding Async Invoke + """ + @staticmethod + def _handle_async_invoke_status( + batch_id: str, aws_region_name: str, logging_obj=None, **kwargs + ) -> "LiteLLMBatch": + """ + Handle async invoke status check for AWS Bedrock. + + This is for Twelve Labs Embedding Async Invoke. + + Args: + batch_id: The async invoke ARN + aws_region_name: AWS region name + **kwargs: Additional parameters + + Returns: + dict: Status information including status, output_file_id (S3 URL), etc. + """ + import asyncio + + from litellm.llms.bedrock.embed.embedding import BedrockEmbedding + + async def _async_get_status(): + # Create embedding handler instance + embedding_handler = BedrockEmbedding() + + # Get the status of the async invoke job + status_response = await embedding_handler._get_async_invoke_status( + invocation_arn=batch_id, + aws_region_name=aws_region_name, + logging_obj=logging_obj, + **kwargs, + ) + + # Transform response to a LiteLLMBatch object + from litellm.types.utils import LiteLLMBatch + + openai_batch_metadata: OpenAIBatchMetadata = { + "output_file_id": status_response["outputDataConfig"][ + "s3OutputDataConfig" + ]["s3Uri"], + "failure_message": status_response.get("failureMessage") or "", + "model_arn": status_response["modelArn"], + } + + result = LiteLLMBatch( + id=status_response["invocationArn"], + object="batch", + status=status_response["status"], + created_at=status_response["submitTime"], + in_progress_at=status_response["lastModifiedTime"], + completed_at=status_response.get("endTime"), + failed_at=status_response.get("endTime") + if status_response["status"] == "failed" + else None, + request_counts=BatchRequestCounts( + total=1, + completed=1 if status_response["status"] == "completed" else 0, + failed=1 if status_response["status"] == "failed" else 0, + ), + metadata=openai_batch_metadata, + completion_window="24h", + endpoint="/v1/embeddings", + input_file_id="", + ) + + return result + + # Since this function is called from within an async context via run_in_executor, + # we need to create a new event loop in a thread to avoid conflicts + import concurrent.futures + + def run_in_thread(): + new_loop = asyncio.new_event_loop() + asyncio.set_event_loop(new_loop) + try: + return new_loop.run_until_complete(_async_get_status()) + finally: + new_loop.close() + + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit(run_in_thread) + return future.result() diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 9fdc1704f4..f28e9b1290 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -6717,6 +6717,33 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "claude-opus-4-5": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_creation_input_token_cost_above_1hr": 1e-05, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "anthropic", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "claude-sonnet-4-20250514": { "deprecation_date": "2026-05-14", "cache_creation_input_token_cost": 3.75e-06, @@ -7824,26 +7851,298 @@ "source": "https://www.databricks.com/product/pricing/foundation-model-serving" }, "databricks/databricks-claude-3-7-sonnet": { - "input_cost_per_token": 2.5e-06, - "input_dbu_cost_per_token": 3.571e-05, + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, "litellm_provider": "databricks", "max_input_tokens": 200000, "max_output_tokens": 128000, "max_tokens": 200000, "metadata": { - "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Claude 3.7 conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 1.7857e-05, - "output_db_cost_per_token": 0.000214286, - "source": "https://www.databricks.com/product/pricing/foundation-model-serving", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", "supports_assistant_prefill": true, "supports_function_calling": true, "supports_reasoning": true, "supports_tool_choice": true }, + "databricks/databricks-claude-haiku-4-5": { + "input_cost_per_token": 1.00002e-06, + "input_dbu_cost_per_token": 1.4286e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.00003e-06, + "output_dbu_cost_per_token": 7.1429e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4": { + "input_cost_per_token": 1.5000020000000002e-05, + "input_dbu_cost_per_token": 0.000214286, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 7.500003000000001e-05, + "output_dbu_cost_per_token": 0.001071429, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4-1": { + "input_cost_per_token": 1.5000020000000002e-05, + "input_dbu_cost_per_token": 0.000214286, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 32000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 7.500003000000001e-05, + "output_dbu_cost_per_token": 0.001071429, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-opus-4-5": { + "input_cost_per_token": 5.00003e-06, + "input_dbu_cost_per_token": 7.1429e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.5000010000000002e-05, + "output_dbu_cost_per_token": 0.000357143, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4-1": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-claude-sonnet-4-5": { + "input_cost_per_token": 2.9999900000000002e-06, + "input_dbu_cost_per_token": 4.2857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemini-2-5-flash": { + "input_cost_per_token": 3.0001999999999996e-07, + "input_dbu_cost_per_token": 4.285999999999999e-06, + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 65535, + "max_tokens": 1048576, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 2.49998e-06, + "output_dbu_cost_per_token": 3.5714e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemini-2-5-pro": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 1048576, + "max_output_tokens": 65536, + "max_tokens": 1048576, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving", + "supports_function_calling": true, + "supports_tool_choice": true + }, + "databricks/databricks-gemma-3-12b": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 128000, + "max_output_tokens": 32000, + "max_tokens": 128000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.0001e-07, + "output_dbu_cost_per_token": 7.143e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gpt-5": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-1": { + "input_cost_per_token": 1.24999e-06, + "input_dbu_cost_per_token": 1.7857e-05, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 9.999990000000002e-06, + "output_dbu_cost_per_token": 0.000142857, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-mini": { + "input_cost_per_token": 2.4997000000000006e-07, + "input_dbu_cost_per_token": 3.571e-06, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 1.9999700000000004e-06, + "output_dbu_cost_per_token": 2.8571e-05, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-5-nano": { + "input_cost_per_token": 4.998e-08, + "input_dbu_cost_per_token": 7.14e-07, + "litellm_provider": "databricks", + "max_input_tokens": 400000, + "max_output_tokens": 128000, + "max_tokens": 400000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 3.9998000000000007e-07, + "output_dbu_cost_per_token": 5.714000000000001e-06, + "source": "https://www.databricks.com/product/pricing/proprietary-foundation-model-serving" + }, + "databricks/databricks-gpt-oss-120b": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 5.9997e-07, + "output_dbu_cost_per_token": 8.571e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, + "databricks/databricks-gpt-oss-20b": { + "input_cost_per_token": 7e-08, + "input_dbu_cost_per_token": 1e-06, + "litellm_provider": "databricks", + "max_input_tokens": 131072, + "max_output_tokens": 131072, + "max_tokens": 131072, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 3.0001999999999996e-07, + "output_dbu_cost_per_token": 4.285999999999999e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, "databricks/databricks-gte-large-en": { - "input_cost_per_token": 1.2999e-07, + "input_cost_per_token": 1.2999000000000001e-07, "input_dbu_cost_per_token": 1.857e-06, "litellm_provider": "databricks", "max_input_tokens": 8192, @@ -7868,14 +8167,14 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 1.5e-06, + "output_cost_per_token": 1.5000300000000002e-06, "output_dbu_cost_per_token": 2.1429e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, "databricks/databricks-llama-4-maverick": { - "input_cost_per_token": 5e-06, - "input_dbu_cost_per_token": 7.143e-05, + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -7884,13 +8183,13 @@ "notes": "Databricks documentation now provides both DBU costs (_dbu_cost_per_token) and dollar costs(_cost_per_token)." }, "mode": "chat", - "output_cost_per_token": 1.5e-05, - "output_dbu_cost_per_token": 0.00021429, + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, "databricks/databricks-meta-llama-3-1-405b-instruct": { - "input_cost_per_token": 5e-06, + "input_cost_per_token": 5.00003e-06, "input_dbu_cost_per_token": 7.1429e-05, "litellm_provider": "databricks", "max_input_tokens": 128000, @@ -7900,14 +8199,29 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 1.500002e-05, - "output_db_cost_per_token": 0.000214286, + "output_cost_per_token": 1.5000020000000002e-05, + "output_dbu_cost_per_token": 0.000214286, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, + "databricks/databricks-meta-llama-3-1-8b-instruct": { + "input_cost_per_token": 1.5000999999999998e-07, + "input_dbu_cost_per_token": 2.1429999999999996e-06, + "litellm_provider": "databricks", + "max_input_tokens": 200000, + "max_output_tokens": 128000, + "max_tokens": 200000, + "metadata": { + "notes": "Input/output cost per token is dbu cost * $0.070. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." + }, + "mode": "chat", + "output_cost_per_token": 4.5003000000000007e-07, + "output_dbu_cost_per_token": 6.429000000000001e-06, + "source": "https://www.databricks.com/product/pricing/foundation-model-serving" + }, "databricks/databricks-meta-llama-3-3-70b-instruct": { - "input_cost_per_token": 1.00002e-06, - "input_dbu_cost_per_token": 1.4286e-05, + "input_cost_per_token": 5.0001e-07, + "input_dbu_cost_per_token": 7.143e-06, "litellm_provider": "databricks", "max_input_tokens": 128000, "max_output_tokens": 128000, @@ -7916,8 +8230,8 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 2.99999e-06, - "output_dbu_cost_per_token": 4.2857e-05, + "output_cost_per_token": 1.5000300000000002e-06, + "output_dbu_cost_per_token": 2.1429e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, @@ -7932,7 +8246,7 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 2.99999e-06, + "output_cost_per_token": 2.9999900000000002e-06, "output_dbu_cost_per_token": 4.2857e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true @@ -7948,13 +8262,13 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 9.9902e-07, + "output_cost_per_token": 1.00002e-06, "output_dbu_cost_per_token": 1.4286e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true }, "databricks/databricks-mpt-30b-instruct": { - "input_cost_per_token": 9.9902e-07, + "input_cost_per_token": 1.00002e-06, "input_dbu_cost_per_token": 1.4286e-05, "litellm_provider": "databricks", "max_input_tokens": 8192, @@ -7964,7 +8278,7 @@ "notes": "Input/output cost per token is dbu cost * $0.070, based on databricks Llama 3.1 70B conversion. Number provided for reference, '*_dbu_cost_per_token' used in actual calculation." }, "mode": "chat", - "output_cost_per_token": 9.9902e-07, + "output_cost_per_token": 1.00002e-06, "output_dbu_cost_per_token": 1.4286e-05, "source": "https://www.databricks.com/product/pricing/foundation-model-serving", "supports_tool_choice": true diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 58267fdfea..5a58219414 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -2660,6 +2660,12 @@ class LlmProviders(str, Enum): # Create a set of all provider values for quick lookup LlmProvidersSet = {provider.value for provider in LlmProviders} +# File and Batch API providers that are OpenAI-compatible +OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: set[str] = { + LlmProviders.OPENAI.value, + LlmProviders.HOSTED_VLLM.value, +} + class SearchProviders(str, Enum): """ diff --git a/tests/batches_tests/test_hosted_vllm_batches_and_files.py b/tests/batches_tests/test_hosted_vllm_batches_and_files.py new file mode 100644 index 0000000000..432dc81bad --- /dev/null +++ b/tests/batches_tests/test_hosted_vllm_batches_and_files.py @@ -0,0 +1,105 @@ +""" +Unit Tests for hosted_vllm Batches and Files API + +Tests the integration of hosted_vllm provider with LiteLLM's batch and file operations. +Tests against a real OpenAI-compatible endpoint. +""" +import json +import os +import sys +import time +import uuid + +import httpx +import pytest +from dotenv import load_dotenv + +load_dotenv() +sys.path.insert( + 0, os.path.abspath("../..") +) + +import litellm + + +SERVER_URL = "https://exampleopenaiendpoint-production-0ee2.up.railway.app/v1" + + +@pytest.mark.asyncio() +async def test_hosted_vllm_full_workflow(): + """ + Test the complete workflow: create file -> create batch -> retrieve batch -> retrieve file. + Tests against real OpenAI-compatible endpoint. + """ + litellm._turn_on_debug() + file_name = "openai_batch_completions.jsonl" + _current_dir = os.path.dirname(os.path.abspath(__file__)) + file_path = os.path.join(_current_dir, file_name) + + # Step 1: Create file + print("\n=== Step 1: Creating file ===") + file_obj = await litellm.acreate_file( + file=open(file_path, "rb"), + purpose="batch", + custom_llm_provider="hosted_vllm", + api_base=SERVER_URL, + api_key="test-api-key", + ) + + print(f"✓ Created file: {file_obj.id}") + assert file_obj.id is not None + assert file_obj.object == "file" + assert file_obj.purpose == "batch" + + # Step 2: Create batch + print("\n=== Step 2: Creating batch ===") + batch_obj = await litellm.acreate_batch( + completion_window="24h", + endpoint="/v1/chat/completions", + input_file_id=file_obj.id, + custom_llm_provider="hosted_vllm", + metadata={"test": "hosted_vllm_integration"}, + api_base=SERVER_URL, + api_key="test-api-key", + ) + + print(f"✓ Created batch: {batch_obj.id}") + print(f" Status: {batch_obj.status}") + print(f" Input file: {batch_obj.input_file_id}") + assert batch_obj.id is not None + assert batch_obj.object == "batch" + assert batch_obj.input_file_id == file_obj.id + assert batch_obj.endpoint == "/v1/chat/completions" + + # Step 3: Retrieve batch + print("\n=== Step 3: Retrieving batch ===") + retrieved_batch = await litellm.aretrieve_batch( + batch_id=batch_obj.id, + custom_llm_provider="hosted_vllm", + api_base=SERVER_URL, + api_key="test-api-key", + ) + + print(f"✓ Retrieved batch: {retrieved_batch.id}") + print(f" Status: {retrieved_batch.status}") + print(f" Output file: {retrieved_batch.output_file_id}") + assert retrieved_batch.id == batch_obj.id + assert retrieved_batch.object == "batch" + assert retrieved_batch.input_file_id == file_obj.id + + # Step 4: Retrieve file (verify file still accessible) + print("\n=== Step 4: Retrieving original file ===") + retrieved_file = await litellm.afile_retrieve( + file_id=file_obj.id, + custom_llm_provider="hosted_vllm", + api_base=SERVER_URL, + api_key="test-api-key", + ) + + print(f"✓ Retrieved file: {retrieved_file.id}") + print(f" Filename: {retrieved_file.filename}") + print(f" Bytes: {retrieved_file.bytes}") + assert retrieved_file.id == file_obj.id + assert retrieved_file.object == "file" + + print("\n✅ Full workflow test completed successfully!") From 81f4d863caa81bb615849ca5180a0b664d300972 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Tue, 2 Dec 2025 14:08:10 -0300 Subject: [PATCH 221/248] docs: add Azure AI Foundry documentation for Claude models (#17104) * docs: add Azure AI Foundry documentation for Claude models Add documentation explaining how to use Claude models (Sonnet 4.5, Haiku 4.5, Opus 4.1) deployed on Azure AI Foundry with LiteLLM. Azure exposes Claude using Anthropic's native API, so users can use the existing anthropic/ provider with their Azure endpoint. Closes #17066 * docs: Add alternative method for Azure AI Foundry using anthropic/ provider Document that users can use anthropic/ provider with Azure endpoint as an alternative to the dedicated azure_ai/ provider. --- docs/my-website/docs/providers/anthropic.md | 24 +++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index d84c1c2304..f78af51bd9 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -201,6 +201,30 @@ Without `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX`: With `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX=true`: - Base URL `https://my-proxy.com/custom/path` → `https://my-proxy.com/custom/path` (unchanged) +### Azure AI Foundry (Alternative Method) + +:::tip Recommended Method +For full Azure support including Azure AD authentication, use the dedicated [Azure Anthropic provider](./azure/azure_anthropic) with `azure_ai/` prefix. +::: + +As an alternative, you can use the `anthropic/` provider directly with your Azure endpoint since Azure exposes Claude using Anthropic's native API. + +```python +from litellm import completion + +response = completion( + model="anthropic/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + api_key="", + messages=[{"role": "user", "content": "Hello!"}], +) +print(response) +``` + +:::info +**Finding your Azure endpoint:** Go to Azure AI Foundry → Your deployment → Overview. Your base URL will be `https://.services.ai.azure.com/anthropic` +::: + ## Usage ```python From 12530b375fe969ae95972c1b6ef4cf5d6802d000 Mon Sep 17 00:00:00 2001 From: kothamah <104782493+kothamah@users.noreply.github.com> Date: Tue, 2 Dec 2025 12:19:53 -0500 Subject: [PATCH 222/248] Litellm bedrock OpenAI model support (#17368) * Update constants.py added constants * Update base_aws_llm.py added steps * Update invoke_handler.py added openai support * Update base_invoke_transformation.py added * Update test_bedrock_completion.py added --- litellm/constants.py | 1 + litellm/llms/bedrock/base_aws_llm.py | 4 + litellm/llms/bedrock/chat/invoke_handler.py | 45 +++ .../base_invoke_transformation.py | 9 + .../test_bedrock_completion.py | 333 ++++++++++++++++++ 5 files changed, 392 insertions(+) diff --git a/litellm/constants.py b/litellm/constants.py index e3de7368c8..1d42ef9a91 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -859,6 +859,7 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "deepseek_r1", "qwen3", "twelvelabs", + "openai" ] BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index ed658c793a..816b93edd2 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -353,6 +353,10 @@ class BaseAWSLLM: model_id = BaseAWSLLM._get_model_id_from_model_with_spec( model_id, spec="deepseek_r1" ) + elif provider == "openai" and "openai/" in model_id: + model_id = BaseAWSLLM._get_model_id_from_model_with_spec( + model_id, spec="openai" + ) return model_id @staticmethod diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index b35e86cabd..7a960fd45d 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -73,6 +73,9 @@ bedrock_tool_name_mappings: InMemoryCache = InMemoryCache( max_size_in_memory=50, default_ttl=600 ) from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig +from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import ( + AmazonBedrockOpenAIConfig, +) converse_config = AmazonConverseConfig() @@ -401,6 +404,10 @@ class BedrockLLM(BaseAWSLLM): prompt = prompt_factory( model=model, messages=messages, custom_llm_provider="bedrock" ) + elif provider == "openai": + # OpenAI uses messages directly, no prompt conversion needed + # Return empty prompt as it won't be used + prompt = "" elif provider == "cohere": prompt, chat_history = cohere_message_pt(messages=messages) else: @@ -578,6 +585,30 @@ class BedrockLLM(BaseAWSLLM): ) elif provider == "meta" or provider == "llama": outputText = completion_response["generation"] + elif provider == "openai": + # OpenAI imported models use OpenAI Chat Completions format + if "choices" in completion_response and len(completion_response["choices"]) > 0: + choice = completion_response["choices"][0] + if "message" in choice: + outputText = choice["message"].get("content") + elif "text" in choice: # fallback for completion format + outputText = choice["text"] + + # Set finish reason + if "finish_reason" in choice: + model_response.choices[0].finish_reason = map_finish_reason( + choice["finish_reason"] + ) + + # Set usage if available + if "usage" in completion_response: + usage = completion_response["usage"] + _usage = litellm.Usage( + prompt_tokens=usage.get("prompt_tokens", 0), + completion_tokens=usage.get("completion_tokens", 0), + total_tokens=usage.get("total_tokens", 0), + ) + setattr(model_response, "usage", _usage) elif provider == "mistral": outputText = completion_response["outputs"][0]["text"] model_response.choices[0].finish_reason = completion_response[ @@ -895,6 +926,20 @@ class BedrockLLM(BaseAWSLLM): ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in inference_params[k] = v data = json.dumps({"prompt": prompt, **inference_params}) + elif provider == "openai": + ## OpenAI imported models use OpenAI Chat Completions format (messages-based) + # Use AmazonBedrockOpenAIConfig for proper OpenAI transformation + openai_config = AmazonBedrockOpenAIConfig() + supported_params = openai_config.get_supported_openai_params(model=model) + + # Filter to only supported OpenAI params + filtered_params = { + k: v for k, v in inference_params.items() + if k in supported_params + } + + # OpenAI uses messages format, not prompt + data = json.dumps({"messages": messages, **filtered_params}) else: ## LOGGING logging_obj.pre_call( diff --git a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py index 6c389ff3b7..bcb4cae1c8 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/base_invoke_transformation.py @@ -258,6 +258,15 @@ class AmazonInvokeConfig(BaseConfig, BaseAWSLLM): litellm_params=litellm_params, headers=headers, ) + elif provider == "openai": + # OpenAI imported models use OpenAI Chat Completions format + return litellm.AmazonBedrockOpenAIConfig().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) else: raise BedrockError( status_code=404, diff --git a/tests/llm_translation/test_bedrock_completion.py b/tests/llm_translation/test_bedrock_completion.py index f43e939c68..bd08d4444f 100644 --- a/tests/llm_translation/test_bedrock_completion.py +++ b/tests/llm_translation/test_bedrock_completion.py @@ -3531,3 +3531,336 @@ def test_bedrock_openai_imported_model(): # Check max_tokens and temperature assert request_body["max_tokens"] == 300 assert request_body["temperature"] == 0.5 + +def test_bedrock_openai_provider_detection(): + """ + Test that the OpenAI provider is correctly detected from model strings. + """ + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + # Test various OpenAI model formats + test_cases = [ + "openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/abc123", + "bedrock/openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/xyz789", + ] + + for model in test_cases: + provider = BaseAWSLLM.get_bedrock_invoke_provider(model) + assert provider == "openai", f"Failed for model: {model}, got provider: {provider}" + print(f"✓ Provider detection works for: {model}") + + +def test_bedrock_openai_model_id_extraction(): + """ + Test that the model ID (ARN) is correctly extracted and encoded for OpenAI models. + """ + from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM + + model = "openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/test-model-123" + provider = BaseAWSLLM.get_bedrock_invoke_provider(model) + + model_id = BaseAWSLLM.get_bedrock_model_id( + model=model, + provider=provider, + optional_params={} + ) + + # The ARN should be double URL encoded + assert "arn" in model_id + assert "imported-model" in model_id + print(f"✓ Model ID extracted and encoded: {model_id}") + + +def test_bedrock_openai_convert_messages_to_prompt(): + """ + Test that convert_messages_to_prompt returns empty string for OpenAI models. + """ + from litellm.llms.bedrock.chat.invoke_handler import BedrockLLM + + bedrock_llm = BedrockLLM() + messages = [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Hello"} + ] + + prompt, chat_history = bedrock_llm.convert_messages_to_prompt( + model="test-model", + messages=messages, + provider="openai", + custom_prompt_dict={} + ) + + # OpenAI models use messages directly, no prompt conversion + assert prompt == "" + assert chat_history is None + print("✓ convert_messages_to_prompt returns empty for OpenAI") + + +def test_bedrock_openai_response_parsing(): + """ + Test that OpenAI responses are correctly parsed. + """ + from litellm.llms.bedrock.chat.invoke_handler import BedrockLLM + from litellm import ModelResponse + from unittest.mock import Mock + import json + + bedrock_llm = BedrockLLM() + + # Mock OpenAI-style response + openai_response = { + "choices": [ + { + "message": { + "content": "The capital of France is Paris.", + "role": "assistant" + }, + "finish_reason": "stop", + "index": 0 + } + ], + "usage": { + "prompt_tokens": 10, + "completion_tokens": 8, + "total_tokens": 18 + } + } + + mock_response = Mock() + mock_response.json.return_value = openai_response + mock_response.text = json.dumps(openai_response) + mock_response.status_code = 200 + mock_response.headers = {} + + model_response = ModelResponse() + mock_logging = Mock() + + result = bedrock_llm.process_response( + model="openai/arn:aws:bedrock:us-east-1:123:imported-model/test", + response=mock_response, + model_response=model_response, + stream=False, + logging_obj=mock_logging, + optional_params={}, + api_key="", + data={}, + messages=[{"role": "user", "content": "What is the capital of France?"}], + print_verbose=lambda x: None, + encoding=None + ) + + # Verify response content + assert result.choices[0].message.content == "The capital of France is Paris." + assert result.choices[0].finish_reason == "stop" + + # Verify usage + assert result.usage.prompt_tokens == 10 + assert result.usage.completion_tokens == 8 + assert result.usage.total_tokens == 18 + + print("✓ OpenAI response parsing works correctly") + + +def test_bedrock_openai_request_transformation(): + """ + Test that the request is correctly transformed for OpenAI models. + """ + from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import AmazonInvokeConfig + + config = AmazonInvokeConfig() + + model = "openai/arn:aws:bedrock:us-east-1:123:imported-model/test" + messages = [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Hello"} + ] + + optional_params = { + "max_tokens": 100, + "temperature": 0.7, + "top_p": 0.9, + "stream": False + } + + litellm_params = {} + headers = {} + + with patch.object(config, 'get_bedrock_invoke_provider', return_value="openai"): + result = config.transform_request( + model=model, + messages=messages, + optional_params=optional_params.copy(), + litellm_params=litellm_params, + headers=headers + ) + + # Verify the request uses messages format (not prompt) + assert "messages" in result + assert len(result["messages"]) == 2 + assert result["messages"][0]["role"] == "system" + assert result["messages"][1]["role"] == "user" + + # Verify parameters are included + assert "max_tokens" in result + assert "temperature" in result + + print("✓ Request transformation works correctly") + + +def test_bedrock_openai_parameter_filtering(): + """ + Test that only supported OpenAI parameters are included in the request. + """ + from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import AmazonBedrockOpenAIConfig + + config = AmazonBedrockOpenAIConfig() + model = "test-model" + + supported_params = config.get_supported_openai_params(model=model) + + # Verify common OpenAI parameters are supported + assert "max_tokens" in supported_params + assert "temperature" in supported_params + assert "top_p" in supported_params + assert "stream" in supported_params + assert "stop" in supported_params + + print(f"✓ Parameter filtering supports: {len(supported_params)} parameters") + print(f" Supported params: {supported_params}") + + +def test_bedrock_openai_route_detection(): + """ + Test that the OpenAI route is correctly detected. + """ + from litellm.llms.bedrock.common_utils import BedrockModelInfo + + test_cases = [ + ("openai/arn:aws:bedrock:us-east-1:123:imported-model/test", "openai"), + ("bedrock/openai/arn:aws:bedrock:us-east-1:123:imported-model/test", "openai"), + ] + + for model, expected_route in test_cases: + route = BedrockModelInfo.get_bedrock_route(model) + assert route == expected_route, f"Failed for model: {model}, got route: {route}" + print(f"✓ Route detection works for: {model} -> {route}") + + +def test_bedrock_openai_explicit_route_check(): + """ + Test the explicit OpenAI route checker helper method. + """ + from litellm.llms.bedrock.common_utils import BedrockModelInfo + + # Test with openai/ prefix + assert BedrockModelInfo._explicit_openai_route("openai/arn:aws:bedrock:us-east-1:123:imported-model/test") is True + assert BedrockModelInfo._explicit_openai_route("bedrock/openai/arn:aws:bedrock:us-east-1:123:imported-model/test") is True + + # Test without openai/ prefix + assert BedrockModelInfo._explicit_openai_route("anthropic.claude-3-sonnet") is False + assert BedrockModelInfo._explicit_openai_route("arn:aws:bedrock:us-east-1:123:imported-model/test") is False + + print("✓ Explicit route check works correctly") + + +def test_bedrock_openai_config_initialization(): + """ + Test that AmazonBedrockOpenAIConfig can be properly initialized. + """ + from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import AmazonBedrockOpenAIConfig + + config = AmazonBedrockOpenAIConfig() + + # Verify it has the necessary methods + assert hasattr(config, 'get_supported_openai_params') + assert hasattr(config, 'transform_request') + assert hasattr(config, 'transform_response') + assert hasattr(config, 'map_openai_params') + + print("✓ AmazonBedrockOpenAIConfig initializes correctly") + + +def test_bedrock_openai_multiple_message_types(): + """ + Test that various message content types are handled correctly. + """ + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + client = HTTPHandler() + + # Test with mixed content types + messages = [ + {"role": "system", "content": "You are helpful"}, + {"role": "user", "content": "Simple text message"}, + { + "role": "user", + "content": [ + {"type": "text", "text": "Complex message with text"}, + {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,iVBORw0KGg"}} + ] + } + ] + + with patch.object(client, "post") as mock_post: + try: + response = completion( + model="bedrock/openai/arn:aws:bedrock:us-east-1:123:imported-model/test", + messages=messages, + max_tokens=50, + client=client, + ) + except Exception as e: + pass + + # Verify the request was made + if mock_post.called: + request_body = json.loads(mock_post.call_args.kwargs["data"]) + + # Verify messages are preserved + assert "messages" in request_body + assert len(request_body["messages"]) == 3 + + # Verify mixed content is handled + assert isinstance(request_body["messages"][2]["content"], list) + + print("✓ Multiple message types handled correctly") + + +def test_bedrock_openai_error_handling(): + """ + Test that errors from OpenAI models are properly handled. + """ + from litellm.llms.bedrock.chat.invoke_handler import BedrockLLM + from litellm import ModelResponse + from litellm.llms.bedrock.common_utils import BedrockError + from unittest.mock import Mock + import json + + bedrock_llm = BedrockLLM() + + # Mock error response + mock_response = Mock() + mock_response.json.side_effect = Exception("Invalid JSON") + mock_response.text = "Invalid response" + mock_response.status_code = 422 + + model_response = ModelResponse() + mock_logging = Mock() + + with pytest.raises(BedrockError) as exc_info: + bedrock_llm.process_response( + model="openai/arn:aws:bedrock:us-east-1:123:imported-model/test", + response=mock_response, + model_response=model_response, + stream=False, + logging_obj=mock_logging, + optional_params={}, + api_key="", + data={}, + messages=[], + print_verbose=lambda x: None, + encoding=None + ) + + assert exc_info.value.status_code == 422 + print("✓ Error handling works correctly") From 8c1290dcd2bcb12cba8fab8dfd4c8a94e0c85382 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 2 Dec 2025 11:26:29 -0800 Subject: [PATCH 223/248] Indent and import fix --- .../guardrail_hooks/bedrock_guardrails.py | 51 ++++++++++--------- 1 file changed, 26 insertions(+), 25 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py index f9c3caf494..bd1e805361 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py +++ b/litellm/proxy/guardrails/guardrail_hooks/bedrock_guardrails.py @@ -20,6 +20,7 @@ from typing import ( AsyncGenerator, List, Literal, + NamedTuple, Optional, Tuple, Union, @@ -1291,38 +1292,38 @@ class BedrockGuardrail(CustomGuardrail, BaseAWSLLM): request_data=request_data, ) - bedrock_response = await self.make_bedrock_api_request( - source="INPUT", - messages=mock_messages, - request_data=request_data, + bedrock_response = await self.make_bedrock_api_request( + source="INPUT", + messages=mock_messages, + request_data=request_data, + ) + + if bedrock_response.get("action") == "BLOCKED": + raise Exception( + f"Content blocked by Bedrock guardrail: {bedrock_response.get('reason', 'Unknown reason')}" ) - if bedrock_response.get("action") == "BLOCKED": - raise Exception( - f"Content blocked by Bedrock guardrail: {bedrock_response.get('reason', 'Unknown reason')}" - ) - - # Apply any masking that was applied by the guardrail - masked_text = text - output_list = bedrock_response.get("output") - if output_list: - # If the guardrail returned modified content, use that - for output_item in output_list: + # Apply any masking that was applied by the guardrail + masked_text = text + output_list = bedrock_response.get("output") + if output_list: + # If the guardrail returned modified content, use that + for output_item in output_list: + text_content = output_item.get("text") + if text_content: + masked_text = str(text_content) + break + else: + outputs_list = bedrock_response.get("outputs") + if outputs_list: + # Fallback to outputs field if output is not available + for output_item in outputs_list: text_content = output_item.get("text") if text_content: masked_text = str(text_content) break - else: - outputs_list = bedrock_response.get("outputs") - if outputs_list: - # Fallback to outputs field if output is not available - for output_item in outputs_list: - text_content = output_item.get("text") - if text_content: - masked_text = str(text_content) - break - masked_texts.append(masked_text) + masked_texts.append(masked_text) verbose_proxy_logger.debug( "Bedrock Guardrail: Successfully applied guardrail" From 8b8f93d508219079d1327124de00df864d52c571 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 2 Dec 2025 12:24:00 -0800 Subject: [PATCH 224/248] Add Google Private API Endpoint to Vertex AI fields --- .../proxy/public_endpoints/provider_create_fields.json | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/litellm/proxy/public_endpoints/provider_create_fields.json b/litellm/proxy/public_endpoints/provider_create_fields.json index 8e08bd8d99..67f2afa1e7 100644 --- a/litellm/proxy/public_endpoints/provider_create_fields.json +++ b/litellm/proxy/public_endpoints/provider_create_fields.json @@ -2607,6 +2607,16 @@ "options": null, "default_value": null }, + { + "key": "api_base", + "label": "Google Private API Endpoint", + "placeholder": null, + "tooltip": null, + "required": false, + "field_type": "text", + "options": null, + "default_value": null + }, { "key": "vertex_credentials", "label": "Vertex Credentials", From db6c6eea89ebf4ae93f7d9030c1e3d098f09038a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Dec 2025 14:10:00 -0800 Subject: [PATCH 225/248] [Docs] Add guide on how to debug gateway error vs provider error (#17387) * add error diagnosis * docs error diagnosis --- docs/my-website/docs/proxy/error_diagnosis.md | 90 +++++++++++++++++++ docs/my-website/sidebars.js | 1 + 2 files changed, 91 insertions(+) create mode 100644 docs/my-website/docs/proxy/error_diagnosis.md diff --git a/docs/my-website/docs/proxy/error_diagnosis.md b/docs/my-website/docs/proxy/error_diagnosis.md new file mode 100644 index 0000000000..9629fc52b0 --- /dev/null +++ b/docs/my-website/docs/proxy/error_diagnosis.md @@ -0,0 +1,90 @@ +# Diagnosing Errors - Provider vs Gateway + +Having trouble diagnosing if an error is from the **LLM Provider** (OpenAI, Anthropic, etc.) or from the **LiteLLM AI Gateway** itself? Here's how to tell. + +## Quick Rule + +**If the error contains `Exception`, it's from the provider.** + +| Error Contains | Error Source | +|----------------|--------------| +| `AnthropicException` | Anthropic | +| `OpenAIException` | OpenAI | +| `AzureException` | Azure | +| `BedrockException` | AWS Bedrock | +| `VertexAIException` | Google Vertex AI | +| No provider name | LiteLLM AI Gateway | + +## Examples + +### Provider Error (from AWS Bedrock) + +``` +{ + "error": { + "message": "litellm.BadRequestError: BedrockException - {\"message\":\"The model returned the following errors: messages.1.content.0.type: Expected `thinking` or `redacted_thinking`, but found `text`.\"}", + "type": "invalid_request_error", + "param": null, + "code": "400" + } +} +``` + +This error is from **AWS Bedrock** (notice `BedrockException`). The Bedrock API is rejecting the request due to invalid message format - this is not a LiteLLM issue. + +### Provider Error (from OpenAI) + +``` +{ + "error": { + "message": "litellm.AuthenticationError: OpenAIException - Incorrect API key provided: . You can find your API key at https://platform.openai.com/account/api-keys.", + "type": "invalid_request_error", + "param": null, + "code": "invalid_api_key" + } +} +``` + +This error is from **OpenAI** (notice `OpenAIException`). The OpenAI API key configured in LiteLLM is invalid. + +### Provider Error (from Anthropic) + +``` +{ + "error": { + "message": "litellm.InternalServerError: AnthropicException - Overloaded. Handle with `litellm.InternalServerError`.", + "type": "internal_server_error", + "param": null, + "code": "500" + } +} +``` + +This error is from **Anthropic** (notice `AnthropicException`). The Anthropic API is overloaded - this is not a LiteLLM issue. + +### Gateway Error (from LiteLLM) + +``` +{ + "error": { + "message": "Invalid API Key. Please check your LiteLLM API key.", + "type": "auth_error", + "param": null, + "code": "401" + } +} +``` + +This error is from the **LiteLLM AI Gateway** (no provider name). Your LiteLLM virtual key is invalid. + +## What to do? + +| Error Source | Action | +|--------------|--------| +| Provider Error | Check the provider's status page, adjust rate limits, or retry later | +| Gateway Error | Check your LiteLLM configuration, API keys, or [open an issue](https://github.com/BerriAI/litellm/issues) | + +## See Also + +- [Debugging](/docs/proxy/debugging) - Enable debug logs to see detailed request/response info +- [Exception Mapping](/docs/exception_mapping) - Full list of LiteLLM exception types diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index e467711b59..a4a2956ff6 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -141,6 +141,7 @@ const sidebars = { "proxy/quick_start", "proxy/cli", "proxy/debugging", + "proxy/error_diagnosis", "proxy/deploy", "proxy/health", "proxy/master_key_rotations", From de4ff120ebe049cf86b07483d32c5c7b22c5190f Mon Sep 17 00:00:00 2001 From: Leslie Cheng Date: Tue, 2 Dec 2025 14:37:45 -0800 Subject: [PATCH 226/248] =?UTF-8?q?=F0=9F=90=9B=20Fix=20proxy=20caching=20?= =?UTF-8?q?between=20requests=20in=20aiohttp=20transport=20(#17122)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * write a regression test * impl fix * add test for host case * use the host as cache key --- .../llms/custom_httpx/aiohttp_transport.py | 67 ++++---- .../custom_httpx/test_aiohttp_transport.py | 143 ++++++++++++++---- 2 files changed, 137 insertions(+), 73 deletions(-) diff --git a/litellm/llms/custom_httpx/aiohttp_transport.py b/litellm/llms/custom_httpx/aiohttp_transport.py index 6997afafd8..f845bf7cb9 100644 --- a/litellm/llms/custom_httpx/aiohttp_transport.py +++ b/litellm/llms/custom_httpx/aiohttp_transport.py @@ -82,9 +82,7 @@ class AiohttpResponseStream(httpx.AsyncByteStream): async def __aiter__(self) -> typing.AsyncIterator[bytes]: try: - async for chunk in self._aiohttp_response.content.iter_chunked( - self.CHUNK_SIZE - ): + async for chunk in self._aiohttp_response.content.iter_chunked(self.CHUNK_SIZE): yield chunk except ( aiohttp.ClientPayloadError, @@ -120,16 +118,13 @@ class AiohttpResponseStream(httpx.AsyncByteStream): class AiohttpTransport(httpx.AsyncBaseTransport): - def __init__( - self, client: Union[ClientSession, Callable[[], ClientSession]] - ) -> None: + def __init__(self, client: Union[ClientSession, Callable[[], ClientSession]]) -> None: self.client = client ######################################################### # Class variables for proxy settings ######################################################### - self.proxy: Optional[str] = None - self.checked_proxy_env_settings: bool = False + self.proxy_cache: Dict[str, Optional[str]] = {} async def aclose(self) -> None: if isinstance(self.client, ClientSession): @@ -184,11 +179,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): current_loop = asyncio.get_running_loop() # If session is from a different or closed loop, recreate it - if ( - session_loop is None - or session_loop != current_loop - or session_loop.is_closed() - ): + if session_loop is None or session_loop != current_loop or session_loop.is_closed(): # Close old session to prevent leaks old_session = self.client try: @@ -215,7 +206,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): self.client = ClientSession() return self.client - + async def _make_aiohttp_request( self, client_session: ClientSession, @@ -226,20 +217,20 @@ class LiteLLMAiohttpTransport(AiohttpTransport): ) -> ClientResponse: """ Helper function to make an aiohttp request with the given parameters. - + Args: client_session: The aiohttp ClientSession to use request: The httpx Request to send timeout: Timeout settings dict with 'connect', 'read', 'pool' keys proxy: Optional proxy URL sni_hostname: Optional SNI hostname for SSL - + Returns: ClientResponse from aiohttp """ from aiohttp import ClientTimeout from yarl import URL as YarlURL - + try: data = request.content except httpx.RequestNotRead: @@ -262,9 +253,9 @@ class LiteLLMAiohttpTransport(AiohttpTransport): proxy=proxy, server_hostname=sni_hostname, ).__aenter__() - + return response - + async def handle_async_request( self, request: httpx.Request, @@ -297,7 +288,7 @@ class LiteLLMAiohttpTransport(AiohttpTransport): else: self.client = ClientSession() client_session = self.client - + # Retry the request with the new session with map_aiohttp_exceptions(): response = await self._make_aiohttp_request( @@ -317,45 +308,41 @@ class LiteLLMAiohttpTransport(AiohttpTransport): content=AiohttpResponseStream(response), request=request, ) - async def _get_proxy_settings(self, request: httpx.Request): proxy = None - if not ( - litellm.disable_aiohttp_trust_env - or str_to_bool(os.getenv("DISABLE_AIOHTTP_TRUST_ENV", "False")) - ): + if not (litellm.disable_aiohttp_trust_env or str_to_bool(os.getenv("DISABLE_AIOHTTP_TRUST_ENV", "False"))): try: proxy = self._proxy_from_env(request.url) except Exception as e: # pragma: no cover - best effort verbose_logger.debug(f"Error reading proxy env: {e}") return proxy - def _proxy_from_env(self, url: httpx.URL) -> typing.Optional[str]: """ Return proxy URL from env for the given request URL Only check the proxy env settings once, this is a costly operation for CPU % usage - + .""" ######################################################### # Check if we've already checked the proxy env settings ######################################################### - if self.checked_proxy_env_settings is True: - return self.proxy - - ######################################################### - # set self.checked_proxy_env_settings to True - ######################################################### - self.checked_proxy_env_settings = True + proxy_cache_key = url.host + + if proxy_cache_key in self.proxy_cache: + return self.proxy_cache[proxy_cache_key] + proxies = urllib.request.getproxies() if urllib.request.proxy_bypass(url.host): - return None + proxy_url = None + else: + proxy = proxies.get(url.scheme) or proxies.get("all") + if proxy and "://" not in proxy: + proxy = f"http://{proxy}" + proxy_url = proxy - proxy = proxies.get(url.scheme) or proxies.get("all") - if proxy and "://" not in proxy: - proxy = f"http://{proxy}" - self.proxy = proxy - return self.proxy + self.proxy_cache[proxy_cache_key] = proxy_url + + return proxy_url diff --git a/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py b/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py index 1f1a36fd7a..f0dac11364 100644 --- a/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py +++ b/tests/test_litellm/llms/custom_httpx/test_aiohttp_transport.py @@ -1,6 +1,6 @@ +import asyncio import os import sys -from unittest.mock import AsyncMock, MagicMock, patch import aiohttp import aiohttp.client_exceptions @@ -8,14 +8,11 @@ import aiohttp.http_exceptions import httpx import pytest -sys.path.insert( - 0, os.path.abspath("../../../..") -) # Adds the parent directory to the system path +sys.path.insert(0, os.path.abspath("../../../..")) # Adds the parent directory to the system path from litellm.llms.custom_httpx.aiohttp_transport import ( AiohttpResponseStream, LiteLLMAiohttpTransport, - map_aiohttp_exceptions, ) @@ -32,9 +29,7 @@ class MockAiohttpResponse: ): self.status = status self.headers = headers or {} - self.content = MockContent( - content_chunks, exception_to_raise, exception_at_chunk - ) + self.content = MockContent(content_chunks, exception_to_raise, exception_at_chunk) async def __aexit__(self, exc_type, exc_val, exc_tb): pass @@ -74,7 +69,6 @@ async def test_aiohttp_response_stream_normal_flow(): @pytest.mark.asyncio async def test_transfer_encoding_error_no_httpx_read_error(): """Test that TransferEncodingError doesn't get converted to httpx.ReadError""" - import logging # Create a TransferEncodingError wrapped in ClientPayloadError (like in real scenarios) transfer_error = aiohttp.http_exceptions.TransferEncodingError( @@ -82,9 +76,7 @@ async def test_transfer_encoding_error_no_httpx_read_error(): ) # Wrap it in ClientPayloadError as aiohttp does - client_payload_error = aiohttp.ClientPayloadError( - "Response payload is not completed" - ) + client_payload_error = aiohttp.ClientPayloadError("Response payload is not completed") client_payload_error.__cause__ = transfer_error mock_response = MockAiohttpResponse( @@ -111,9 +103,7 @@ async def test_transfer_encoding_error_no_httpx_read_error(): async def test_client_payload_error_graceful_handling(): """Test that ClientPayloadError is handled gracefully without stacktrace""" # Create a ClientPayloadError directly - client_error = aiohttp.client_exceptions.ClientPayloadError( - "Response payload is not completed" - ) + client_error = aiohttp.client_exceptions.ClientPayloadError("Response payload is not completed") mock_response = MockAiohttpResponse( content_chunks=[b"data1", b"data2", b"data3"], @@ -181,7 +171,6 @@ async def test_timeout_exception_gets_mapped(): @pytest.mark.asyncio async def test_handle_async_request_uses_env_proxy(monkeypatch): """Aiohttp transport should honor HTTP(S)_PROXY env vars""" - import asyncio proxy_url = "http://proxy.local:3128" monkeypatch.setenv("HTTP_PROXY", proxy_url) monkeypatch.setenv("http_proxy", proxy_url) @@ -200,7 +189,7 @@ async def test_handle_async_request_uses_env_proxy(monkeypatch): self._loop = asyncio.get_running_loop() except RuntimeError: self._loop = None - + def request(self, *args, **kwargs): captured["proxy"] = kwargs.get("proxy") @@ -231,30 +220,118 @@ async def test_handle_async_request_uses_env_proxy(monkeypatch): assert captured["proxy"] == proxy_url +@pytest.mark.asyncio +async def test_handle_async_request_uses_env_proxy_per_url(monkeypatch): + """Aiohttp transport should honor HTTP(S)_PROXY env vars unless NO_PROXY matches""" + proxy_url = "http://proxy.local:3128" + monkeypatch.setenv("NO_PROXY", "example.com") + monkeypatch.setenv("HTTP_PROXY", proxy_url) + monkeypatch.setenv("http_proxy", proxy_url) + monkeypatch.setenv("HTTPS_PROXY", proxy_url) + monkeypatch.setenv("https_proxy", proxy_url) + monkeypatch.delenv("DISABLE_AIOHTTP_TRUST_ENV", raising=False) + + request_count = 0 + proxied_count = 0 + + class FakeSession: + def __init__(self): + self.closed = False + try: + self._loop = asyncio.get_running_loop() + except RuntimeError: + self._loop = None + + def request(self, *args, **kwargs): + nonlocal request_count + nonlocal proxied_count + request_count += 1 + + if kwargs.get("proxy") is not None: + proxied_count += 1 + + class Resp: + status = 200 + headers = {} + + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + pass + + @property + def content(self): + class C: + async def iter_chunked(self, size): + yield b"" + + return C() + + return Resp() + + transport = LiteLLMAiohttpTransport(client=lambda: FakeSession()) # type: ignore + request = httpx.Request("GET", "http://example.com") + await transport.handle_async_request(request) + + request = httpx.Request("GET", "http://foo.com") + await transport.handle_async_request(request) + + assert request_count == 2 + assert proxied_count == 1 + + +@pytest.mark.asyncio +async def test_handle_async_request_proxy_cache_per_host(monkeypatch): + """Aiohttp transport should only cache a proxy per host rather than full URL""" + proxy_url = "http://proxy.local:3128" + monkeypatch.setenv("NO_PROXY", "example.com") + monkeypatch.setenv("HTTP_PROXY", proxy_url) + monkeypatch.setenv("http_proxy", proxy_url) + monkeypatch.setenv("HTTPS_PROXY", proxy_url) + monkeypatch.setenv("https_proxy", proxy_url) + monkeypatch.delenv("DISABLE_AIOHTTP_TRUST_ENV", raising=False) + + def factory(): + return _make_mock_session() + + transport = LiteLLMAiohttpTransport(client=factory) # type: ignore + request = httpx.Request("GET", "http://foo.com/path1") + await transport.handle_async_request(request) + + request = httpx.Request("GET", "http://foo.com/path2") + await transport.handle_async_request(request) + + assert len(transport.proxy_cache) == 1 + + def _make_mock_response(should_fail=False, fail_count={"count": 0}): """Helper to create a mock aiohttp response""" + class MockResp: status = 200 headers = {} - + async def __aenter__(self): if should_fail and fail_count["count"] < 1: fail_count["count"] += 1 raise RuntimeError("Session is closed") return self - + async def __aexit__(self, *args): pass - + @property def content(self): class C: async def iter_chunked(self, size): yield b"test" + return C() - + return MockResp() + @pytest.mark.asyncio async def test_handle_async_request_total_timeout_triggers(): """ @@ -298,10 +375,10 @@ async def test_handle_async_request_total_timeout_triggers(): await transport.aclose() await runner.cleanup() + def _make_mock_session(closed=False): """Helper to create a mock aiohttp session""" - import asyncio - + class MockSession: def __init__(self): self.closed = closed @@ -309,10 +386,10 @@ def _make_mock_session(closed=False): self._loop = asyncio.get_running_loop() except RuntimeError: self._loop = None - + def request(self, *args, **kwargs): return _make_mock_response() - + return MockSession() @@ -320,14 +397,14 @@ def _make_mock_session(closed=False): async def test_handle_closed_session_before_request(): """Test that closed sessions are detected and recreated""" counts = {"sessions": 0} - + def factory(): counts["sessions"] += 1 return _make_mock_session(closed=counts["sessions"] == 1) - + transport = LiteLLMAiohttpTransport(client=factory) # type: ignore response = await transport.handle_async_request(httpx.Request("GET", "http://example.com")) - + assert counts["sessions"] == 2 # Created 2 sessions: closed one, then open one assert response.status_code == 200 @@ -337,7 +414,7 @@ async def test_handle_session_closed_during_request(): """Test that sessions closed during request are handled with retry""" counts = {"sessions": 0, "requests": 0} fail_count = {"count": 0} - + class MockSession: def __init__(self): self.closed = False @@ -345,18 +422,18 @@ async def test_handle_session_closed_during_request(): self._loop = __import__("asyncio").get_running_loop() except RuntimeError: self._loop = None - + def request(self, *args, **kwargs): counts["requests"] += 1 return _make_mock_response(should_fail=True, fail_count=fail_count) - + def factory(): counts["sessions"] += 1 return MockSession() - + transport = LiteLLMAiohttpTransport(client=factory) # type: ignore response = await transport.handle_async_request(httpx.Request("GET", "http://example.com")) - + assert counts["requests"] == 2 # First request failed, second succeeded assert counts["sessions"] == 2 # Created 2 sessions for retry assert response.status_code == 200 From 6c188c5ae2fa36d8f7d479479e93037f5777c06c Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Dec 2025 15:36:23 -0800 Subject: [PATCH 227/248] [Feat] New model/provider - Adds support for Google Cloud Chirp3 HD on /speech (#17391) * docs vertex tts * place vertex ai types in file * use VertexAITextToSpeechConfig * use vertex_voice_dict * refactor docs * docs vertex ai chirp * TestVertexAITextToSpeechConfig * new provider vertex ai chirp3 * test_litellm_speech_vertex_ai_chirp * add vertex_ai/chirp cost trackign --- docs/my-website/docs/providers/vertex.md | 349 ------------- .../docs/providers/vertex_speech.md | 423 ++++++++++++++++ docs/my-website/sidebars.js | 1 + .../text_to_speech/transformation.py | 472 ++++++++++++++++++ litellm/main.py | 64 +-- ...odel_prices_and_context_window_backup.json | 9 + .../types/llms/vertex_ai_text_to_speech.py | 54 ++ litellm/utils.py | 6 + model_prices_and_context_window.json | 9 + provider_endpoints_support.json | 16 + tests/audio_tests/speech_vertex.mp3 | Bin 0 -> 122924 bytes .../text_to_speech/test_transformation.py | 190 +++++++ 12 files changed, 1212 insertions(+), 381 deletions(-) create mode 100644 docs/my-website/docs/providers/vertex_speech.md create mode 100644 litellm/llms/vertex_ai/text_to_speech/transformation.py create mode 100644 litellm/types/llms/vertex_ai_text_to_speech.py create mode 100644 tests/audio_tests/speech_vertex.mp3 create mode 100644 tests/litellm/llms/vertex_ai/text_to_speech/test_transformation.py diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 70babea381..da2997f620 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -2550,355 +2550,6 @@ print(response) - -## **Gemini TTS (Text-to-Speech) Audio Output** - -:::info - -LiteLLM supports Gemini TTS models on Vertex AI that can generate audio responses using the OpenAI-compatible `audio` parameter format. - -::: - -### Supported Models - -LiteLLM supports Gemini TTS models with audio capabilities on Vertex AI (e.g. `vertex_ai/gemini-2.5-flash-preview-tts` and `vertex_ai/gemini-2.5-pro-preview-tts`). For the complete list of available TTS models and voices, see the [official Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation). - -### Limitations - -:::warning - -**Important Limitations**: -- Gemini TTS models only support the `pcm16` audio format -- **Streaming support has not been added** to TTS models yet -- The `modalities` parameter must be set to `['audio']` for TTS requests - -::: - -### Quick Start - - - - -```python -from litellm import completion -import json - -## GET CREDENTIALS -file_path = 'path/to/vertex_ai_service_account.json' - -# Load the JSON file -with open(file_path, 'r') as file: - vertex_credentials = json.load(file) - -# Convert to JSON string -vertex_credentials_json = json.dumps(vertex_credentials) - -response = completion( - model="vertex_ai/gemini-2.5-flash-preview-tts", - messages=[{"role": "user", "content": "Say hello in a friendly voice"}], - modalities=["audio"], # Required for TTS models - audio={ - "voice": "Kore", - "format": "pcm16" # Required: must be "pcm16" - }, - vertex_credentials=vertex_credentials_json -) - -print(response) -``` - - - - -1. Setup config.yaml - -```yaml -model_list: - - model_name: gemini-tts-flash - litellm_params: - model: vertex_ai/gemini-2.5-flash-preview-tts - vertex_project: "your-project-id" - vertex_location: "us-central1" - vertex_credentials: "/path/to/service_account.json" - - model_name: gemini-tts-pro - litellm_params: - model: vertex_ai/gemini-2.5-pro-preview-tts - vertex_project: "your-project-id" - vertex_location: "us-central1" - vertex_credentials: "/path/to/service_account.json" -``` - -2. Start proxy - -```bash -litellm --config /path/to/config.yaml -``` - -3. Make TTS request - -```bash -curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer " \ - -d '{ - "model": "gemini-tts-flash", - "messages": [{"role": "user", "content": "Say hello in a friendly voice"}], - "modalities": ["audio"], - "audio": { - "voice": "Kore", - "format": "pcm16" - } - }' -``` - - - - -### Advanced Usage - -You can combine TTS with other Gemini features: - -```python -response = completion( - model="vertex_ai/gemini-2.5-pro-preview-tts", - messages=[ - {"role": "system", "content": "You are a helpful assistant that speaks clearly."}, - {"role": "user", "content": "Explain quantum computing in simple terms"} - ], - modalities=["audio"], - audio={ - "voice": "Charon", - "format": "pcm16" - }, - temperature=0.7, - max_tokens=150, - vertex_credentials=vertex_credentials_json -) -``` - -For more information about Gemini's TTS capabilities and available voices, see the [official Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation). - -## **Text to Speech APIs** - -:::info - -LiteLLM supports calling [Vertex AI Text to Speech API](https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech) in the OpenAI text to speech API format - -::: - - - -### Usage - Basic - - - - -Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - -**Sync Usage** - -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" -response = litellm.speech( - model="vertex_ai/", - input="hello what llm guardrail do you have", -) -response.stream_to_file(speech_file_path) -``` - -**Async Usage** -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" -response = litellm.aspeech( - model="vertex_ai/", - input="hello what llm guardrail do you have", -) -response.stream_to_file(speech_file_path) -``` - - - - -1. Add model to config.yaml -```yaml -model_list: - - model_name: vertex-tts - litellm_params: - model: vertex_ai/ # Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - vertex_project: "adroit-crow-413218" - vertex_location: "us-central1" - vertex_credentials: adroit-crow-413218-a956eef1a2a8.json - -litellm_settings: - drop_params: True -``` - -2. Start Proxy - -``` -$ litellm --config /path/to/config.yaml -``` - -3. Make Request use OpenAI Python SDK - - -```python -import openai - -client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -# see supported values for "voice" on vertex here: -# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech -response = client.audio.speech.create( - model = "vertex-tts", - input="the quick brown fox jumped over the lazy dogs", - voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'} -) -print("response from proxy", response) -``` - - - - - -### Usage - `ssml` as input - -Pass your `ssml` as input to the `input` param, if it contains ``, it will be automatically detected and passed as `ssml` to the Vertex AI API - -If you need to force your `input` to be passed as `ssml`, set `use_ssml=True` - - - - -Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - - -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" - - -ssml = """ - -

    Hello, world!

    -

    This is a test of the text-to-speech API.

    -     -""" - -response = litellm.speech( - input=ssml, - model="vertex_ai/test", - voice={ - "languageCode": "en-UK", - "name": "en-UK-Studio-O", - }, - audioConfig={ - "audioEncoding": "LINEAR22", - "speakingRate": "10", - }, -) -response.stream_to_file(speech_file_path) -``` - -     - - - -```python -import openai - -client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -ssml = """ - -

    Hello, world!

    -

    This is a test of the text-to-speech API.

    -     -""" - -# see supported values for "voice" on vertex here: -# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech -response = client.audio.speech.create( - model = "vertex-tts", - input=ssml, - voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'}, -) -print("response from proxy", response) -``` - -     -     - - -### Forcing SSML Usage - -You can force the use of SSML by setting the `use_ssml` parameter to `True`. This is useful when you want to ensure that your input is treated as SSML, even if it doesn't contain the `` tags. - -Here are examples of how to force SSML usage: - - - - - -Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - - -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" - - -ssml = """ - -

    Hello, world!

    -

    This is a test of the text-to-speech API.

    -     -""" - -response = litellm.speech( - input=ssml, - use_ssml=True, - model="vertex_ai/test", - voice={ - "languageCode": "en-UK", - "name": "en-UK-Studio-O", - }, - audioConfig={ - "audioEncoding": "LINEAR22", - "speakingRate": "10", - }, -) -response.stream_to_file(speech_file_path) -``` - -     - - - -```python -import openai - -client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -ssml = """ - -

    Hello, world!

    -

    This is a test of the text-to-speech API.

    -     -""" - -# see supported values for "voice" on vertex here: -# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech -response = client.audio.speech.create( - model = "vertex-tts", - input=ssml, # pass as None since OpenAI SDK requires this param - voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'}, - extra_body={"use_ssml": True}, -) -print("response from proxy", response) -``` - -     -     - ## **Fine Tuning APIs** diff --git a/docs/my-website/docs/providers/vertex_speech.md b/docs/my-website/docs/providers/vertex_speech.md new file mode 100644 index 0000000000..d0acacb5ae --- /dev/null +++ b/docs/my-website/docs/providers/vertex_speech.md @@ -0,0 +1,423 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vertex AI Text to Speech + +| Property | Details | +|-------|-------| +| Description | Google Cloud Text-to-Speech with Chirp3 HD voices and Gemini TTS | +| Provider Route on LiteLLM | `vertex_ai/chirp` (Chirp), `vertex_ai/gemini-*-tts` (Gemini) | + +## Chirp3 HD Voices + +Google Cloud Text-to-Speech API with high-quality Chirp3 HD voices. + +### Quick Start + +#### LiteLLM Python SDK + +```python showLineNumbers title="Chirp3 Quick Start" +from litellm import speech +from pathlib import Path + +speech_file_path = Path(__file__).parent / "speech.mp3" +response = speech( + model="vertex_ai/chirp", + voice="alloy", # OpenAI voice name - automatically mapped + input="Hello, this is Vertex AI Text to Speech", + vertex_project="your-project-id", + vertex_location="us-central1", +) +response.stream_to_file(speech_file_path) +``` + +#### LiteLLM AI Gateway + +**1. Setup config.yaml** + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: vertex-tts + litellm_params: + model: vertex_ai/chirp + vertex_project: "your-project-id" + vertex_location: "us-central1" + vertex_credentials: "/path/to/service_account.json" +``` + +**2. Start the proxy** + +```bash title="Start LiteLLM Proxy" +litellm --config /path/to/config.yaml +``` + +**3. Make requests** + + + + +```bash showLineNumbers title="Chirp3 Quick Start" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": "alloy", + "input": "Hello, this is Vertex AI Text to Speech" + }' \ + --output speech.mp3 +``` + + + + +```python showLineNumbers title="Chirp3 Quick Start" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.audio.speech.create( + model="vertex-tts", + voice="alloy", + input="Hello, this is Vertex AI Text to Speech", +) +response.stream_to_file("speech.mp3") +``` + + + + +### Voice Mapping + +LiteLLM maps OpenAI voice names to Google Cloud voices. You can use either OpenAI voices or Google Cloud voices directly. + +| OpenAI Voice | Google Cloud Voice | +|-------------|-------------------| +| `alloy` | en-US-Studio-O | +| `echo` | en-US-Studio-M | +| `fable` | en-GB-Studio-B | +| `onyx` | en-US-Wavenet-D | +| `nova` | en-US-Studio-O | +| `shimmer` | en-US-Wavenet-F | + +### Using Google Cloud Voices Directly + +#### LiteLLM Python SDK + +```python showLineNumbers title="Chirp3 HD Voice" +from litellm import speech + +# Pass Chirp3 HD voice name directly +response = speech( + model="vertex_ai/chirp", + voice="en-US-Chirp3-HD-Charon", + input="Hello with a Chirp3 HD voice", + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +```python showLineNumbers title="Voice as Dict (Multilingual)" +from litellm import speech + +# Pass as dict for full control over language and voice +response = speech( + model="vertex_ai/chirp", + voice={ + "languageCode": "de-DE", + "name": "de-DE-Chirp3-HD-Charon", + }, + input="Hallo, dies ist ein Test", + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +#### LiteLLM AI Gateway + + + + +```bash showLineNumbers title="Chirp3 HD Voice" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": "en-US-Chirp3-HD-Charon", + "input": "Hello with a Chirp3 HD voice" + }' \ + --output speech.mp3 +``` + +```bash showLineNumbers title="Voice as Dict (Multilingual)" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": {"languageCode": "de-DE", "name": "de-DE-Chirp3-HD-Charon"}, + "input": "Hallo, dies ist ein Test" + }' \ + --output speech.mp3 +``` + + + + +```python showLineNumbers title="Chirp3 HD Voice" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.audio.speech.create( + model="vertex-tts", + voice="en-US-Chirp3-HD-Charon", + input="Hello with a Chirp3 HD voice", +) +response.stream_to_file("speech.mp3") +``` + +```python showLineNumbers title="Voice as Dict (Multilingual)" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.audio.speech.create( + model="vertex-tts", + voice={"languageCode": "de-DE", "name": "de-DE-Chirp3-HD-Charon"}, + input="Hallo, dies ist ein Test", +) +response.stream_to_file("speech.mp3") +``` + + + + +Browse available voices: [Google Cloud Text-to-Speech Console](https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech) + +### Passing Raw SSML + +LiteLLM auto-detects SSML when your input contains `` tags and passes it through unchanged. + +#### LiteLLM Python SDK + +```python showLineNumbers title="SSML Input" +from litellm import speech + +ssml = """ + +

    Hello, world!

    +

    This is a test of the text-to-speech API.

    +     +""" + +response = speech( + model="vertex_ai/chirp", + voice="en-US-Studio-O", + input=ssml, # Auto-detected as SSML + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +```python showLineNumbers title="Force SSML Mode" +from litellm import speech + +# Force SSML mode with use_ssml=True +response = speech( + model="vertex_ai/chirp", + voice="en-US-Studio-O", + input="Speaking slowly", + use_ssml=True, + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +#### LiteLLM AI Gateway + + + + +```bash showLineNumbers title="SSML Input" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": "en-US-Studio-O", + "input": "

    Hello!

    How are you?

    " + }' \ + --output speech.mp3 +``` + +     + + +```python showLineNumbers title="SSML Input" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +ssml = """

    Hello!

    How are you?

    """ + +response = client.audio.speech.create( + model="vertex-tts", + voice="en-US-Studio-O", + input=ssml, +) +response.stream_to_file("speech.mp3") +``` + +     +     + +### Supported Parameters + +| Parameter | Description | Values | +|-----------|-------------|--------| +| `voice` | Voice selection | OpenAI voice, Google Cloud voice name, or dict | +| `input` | Text to convert | Plain text or SSML | +| `speed` | Speaking rate | 0.25 to 4.0 (default: 1.0) | +| `response_format` | Audio format | `mp3`, `opus`, `wav`, `pcm`, `flac` | +| `use_ssml` | Force SSML mode | `True` / `False` | + +### Async Usage + +```python showLineNumbers title="Async Speech Generation" +import asyncio +from litellm import aspeech + +async def main(): + response = await aspeech( + model="vertex_ai/chirp", + voice="alloy", + input="Hello from async", + vertex_project="your-project-id", + ) + response.stream_to_file("speech.mp3") + +asyncio.run(main()) +``` + +--- + +## Gemini TTS + +Gemini models with audio output capabilities using the chat completions API. + +:::warning +**Limitations:** +- Only supports `pcm16` audio format +- Streaming not yet supported +- Must set `modalities: ["audio"]` +::: + +### Quick Start + +#### LiteLLM Python SDK + +```python showLineNumbers title="Gemini TTS Quick Start" +from litellm import completion +import json + +# Load credentials +with open('path/to/service_account.json', 'r') as file: + vertex_credentials = json.dumps(json.load(file)) + +response = completion( + model="vertex_ai/gemini-2.5-flash-preview-tts", + messages=[{"role": "user", "content": "Say hello in a friendly voice"}], + modalities=["audio"], + audio={ + "voice": "Kore", + "format": "pcm16" + }, + vertex_credentials=vertex_credentials +) +print(response) +``` + +#### LiteLLM AI Gateway + +**1. Setup config.yaml** + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: gemini-tts + litellm_params: + model: vertex_ai/gemini-2.5-flash-preview-tts + vertex_project: "your-project-id" + vertex_location: "us-central1" + vertex_credentials: "/path/to/service_account.json" +``` + +**2. Start the proxy** + +```bash title="Start LiteLLM Proxy" +litellm --config /path/to/config.yaml +``` + +**3. Make requests** + + + + +```bash showLineNumbers title="Gemini TTS Request" +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gemini-tts", + "messages": [{"role": "user", "content": "Say hello in a friendly voice"}], + "modalities": ["audio"], + "audio": {"voice": "Kore", "format": "pcm16"} + }' +``` + + + + +```python showLineNumbers title="Gemini TTS Request" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.chat.completions.create( + model="gemini-tts", + messages=[{"role": "user", "content": "Say hello in a friendly voice"}], + modalities=["audio"], + audio={"voice": "Kore", "format": "pcm16"}, +) +print(response) +``` + + + + +### Supported Models + +- `vertex_ai/gemini-2.5-flash-preview-tts` +- `vertex_ai/gemini-2.5-pro-preview-tts` + +See [Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation) for available voices. + +### Advanced Usage + +```python showLineNumbers title="Gemini TTS with System Prompt" +from litellm import completion + +response = completion( + model="vertex_ai/gemini-2.5-pro-preview-tts", + messages=[ + {"role": "system", "content": "You are a helpful assistant that speaks clearly."}, + {"role": "user", "content": "Explain quantum computing in simple terms"} + ], + modalities=["audio"], + audio={"voice": "Charon", "format": "pcm16"}, + temperature=0.7, + max_tokens=150, + vertex_credentials=vertex_credentials +) +``` diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index a4a2956ff6..983816ed21 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -521,6 +521,7 @@ const sidebars = { "providers/vertex_partner", "providers/vertex_self_deployed", "providers/vertex_image", + "providers/vertex_speech", "providers/vertex_batch", "providers/vertex_ocr", ] diff --git a/litellm/llms/vertex_ai/text_to_speech/transformation.py b/litellm/llms/vertex_ai/text_to_speech/transformation.py new file mode 100644 index 0000000000..aff14b1004 --- /dev/null +++ b/litellm/llms/vertex_ai/text_to_speech/transformation.py @@ -0,0 +1,472 @@ +""" +Vertex AI Text-to-Speech transformation + +Maps OpenAI TTS spec to Google Cloud Text-to-Speech API +Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/synthesize +""" + +import base64 +from typing import TYPE_CHECKING, Any, Coroutine, Dict, Optional, Tuple, Union + +import httpx + +from litellm.llms.base_llm.text_to_speech.transformation import ( + BaseTextToSpeechConfig, + TextToSpeechRequestData, +) +from litellm.llms.vertex_ai.vertex_llm_base import VertexBase +from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES +from litellm.types.llms.vertex_ai_text_to_speech import ( + VertexTextToSpeechAudioConfig, + VertexTextToSpeechInput, + VertexTextToSpeechVoice, +) + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.types.llms.openai import HttpxBinaryResponseContent +else: + LiteLLMLoggingObj = Any + HttpxBinaryResponseContent = Any + + +class VertexAITextToSpeechConfig(BaseTextToSpeechConfig, VertexBase): + """ + Configuration for Google Cloud/Vertex AI Text-to-Speech + + Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/synthesize + """ + + # Default values + DEFAULT_LANGUAGE_CODE = "en-US" + DEFAULT_VOICE_NAME = "en-US-Studio-O" + DEFAULT_AUDIO_ENCODING = "LINEAR16" + DEFAULT_SPEAKING_RATE = "1" + + # API endpoint + TTS_API_URL = "https://texttospeech.googleapis.com/v1/text:synthesize" + + # Voice name mappings from OpenAI voices to Google Cloud voices + # Users can pass either: + # 1. OpenAI voice names (alloy, echo, fable, onyx, nova, shimmer) - will be mapped + # 2. Google Cloud/Vertex AI voice names (en-US-Studio-O, en-US-Wavenet-D, etc.) - used directly + VOICE_MAPPINGS = { + "alloy": "en-US-Studio-O", + "echo": "en-US-Studio-M", + "fable": "en-GB-Studio-B", + "onyx": "en-US-Wavenet-D", + "nova": "en-US-Studio-O", + "shimmer": "en-US-Wavenet-F", + } + + # Response format mappings from OpenAI to Google Cloud audio encoding + FORMAT_MAPPINGS = { + "mp3": "MP3", + "opus": "OGG_OPUS", + "aac": "MP3", # Google doesn't have AAC, use MP3 + "flac": "FLAC", + "wav": "LINEAR16", + "pcm": "LINEAR16", + } + + def __init__(self) -> None: + BaseTextToSpeechConfig.__init__(self) + VertexBase.__init__(self) + + def _map_voice_to_vertex_format( + self, + voice: Optional[Union[str, Dict]], + ) -> Tuple[Optional[str], Optional[Dict]]: + """ + Map voice to Vertex AI format. + + Supports both: + 1. OpenAI voice names (alloy, echo, fable, onyx, nova, shimmer) - will be mapped + 2. Vertex AI voice names (en-US-Studio-O, en-US-Wavenet-D, etc.) - used directly + 3. Dict with languageCode and name - used as-is + + Returns: + Tuple of (voice_str, voice_dict) where: + - voice_str: Original string voice (for interface compatibility) + - voice_dict: Vertex AI format dict with languageCode and name + """ + if voice is None: + return None, None + + if isinstance(voice, dict): + # Already in Vertex AI format + return None, voice + + # voice is a string + voice_str = voice + + # Map OpenAI voice if it's a known OpenAI voice, otherwise use directly + if voice in self.VOICE_MAPPINGS: + mapped_voice_name = self.VOICE_MAPPINGS[voice] + else: + # Assume it's already a Vertex AI voice name + mapped_voice_name = voice + + # Extract language code from voice name (e.g., "en-US-Studio-O" -> "en-US") + parts = mapped_voice_name.split("-") + if len(parts) >= 2: + language_code = f"{parts[0]}-{parts[1]}" + else: + language_code = self.DEFAULT_LANGUAGE_CODE + + voice_dict = { + "languageCode": language_code, + "name": mapped_voice_name, + } + + return voice_str, voice_dict + + def dispatch_text_to_speech( + self, + model: str, + input: str, + voice: Optional[Union[str, Dict]], + optional_params: Dict, + litellm_params_dict: Dict, + logging_obj: "LiteLLMLoggingObj", + timeout: Union[float, httpx.Timeout], + extra_headers: Optional[Dict[str, Any]], + base_llm_http_handler: Any, + aspeech: bool, + api_base: Optional[str], + api_key: Optional[str], + **kwargs: Any, + ) -> Union[ + "HttpxBinaryResponseContent", + Coroutine[Any, Any, "HttpxBinaryResponseContent"], + ]: + """ + Dispatch method to handle Vertex AI TTS requests + + This method encapsulates Vertex AI-specific credential resolution and parameter handling. + Voice mapping is handled in map_openai_params (similar to Azure AVA pattern). + + Args: + base_llm_http_handler: The BaseLLMHTTPHandler instance from main.py + """ + # Resolve Vertex AI credentials using VertexBase helpers + vertex_credentials = self.safe_get_vertex_ai_credentials(litellm_params_dict) + vertex_project = self.safe_get_vertex_ai_project(litellm_params_dict) + vertex_location = self.safe_get_vertex_ai_location(litellm_params_dict) + + # Convert voice to string if it's a dict (extract name) + # Actual voice mapping happens in map_openai_params + voice_str: Optional[str] = None + if isinstance(voice, str): + voice_str = voice + elif isinstance(voice, dict): + # Extract voice name from dict if needed + voice_str = voice.get("name") if voice else None + + # Store credentials in litellm_params for use in transform methods + litellm_params_dict.update({ + "vertex_credentials": vertex_credentials, + "vertex_project": vertex_project, + "vertex_location": vertex_location, + "api_base": api_base, + }) + + # Call the text_to_speech_handler + response = base_llm_http_handler.text_to_speech_handler( + model=model, + input=input, + voice=voice_str, + text_to_speech_provider_config=self, + text_to_speech_optional_params=optional_params, + custom_llm_provider="vertex_ai", + litellm_params=litellm_params_dict, + logging_obj=logging_obj, + timeout=timeout, + extra_headers=extra_headers, + client=None, + _is_async=aspeech, + ) + + return response + + def get_supported_openai_params(self, model: str) -> list: + """ + Vertex AI TTS supports these OpenAI parameters + + Note: Vertex AI also supports additional parameters like audioConfig + which can be passed but are not part of the OpenAI spec + """ + return ["voice", "response_format", "speed"] + + def map_openai_params( + self, + model: str, + optional_params: Dict, + voice: Optional[Union[str, Dict]] = None, + drop_params: bool = False, + kwargs: Dict = {}, + ) -> Tuple[Optional[str], Dict]: + """ + Map OpenAI parameters to Vertex AI TTS parameters + + Voice handling (similar to Azure AVA): + - If voice is an OpenAI voice name (alloy, echo, etc.), it maps to a Vertex AI voice + - If voice is already a Vertex AI voice name (en-US-Studio-O, etc.), it's used directly + - If voice is a dict with languageCode and name, it's used as-is + + Note: For Vertex AI, voice dict is stored in mapped_params["vertex_voice_dict"] + because the base class interface expects voice to be a string. + + Returns: + Tuple of (mapped_voice_str, mapped_params) + """ + mapped_params = {} + + ########################################################## + # Map voice using helper + ########################################################## + mapped_voice_str, voice_dict = self._map_voice_to_vertex_format(voice) + if voice_dict is not None: + mapped_params["vertex_voice_dict"] = voice_dict + + # Map response format + if "response_format" in optional_params: + format_name = optional_params["response_format"] + if format_name in self.FORMAT_MAPPINGS: + mapped_params["audioEncoding"] = self.FORMAT_MAPPINGS[format_name] + else: + # Try to use it directly as Google Cloud format + mapped_params["audioEncoding"] = format_name + else: + # Default to LINEAR16 + mapped_params["audioEncoding"] = self.DEFAULT_AUDIO_ENCODING + + # Map speed (OpenAI: 0.25-4.0, Vertex AI: speakingRate 0.25-4.0) + if "speed" in optional_params: + speed = optional_params["speed"] + if speed is not None: + mapped_params["speakingRate"] = str(speed) + + # Pass through Vertex AI-specific parameters from kwargs + if "audioConfig" in kwargs: + mapped_params["audioConfig"] = kwargs["audioConfig"] + + if "use_ssml" in kwargs: + mapped_params["use_ssml"] = kwargs["use_ssml"] + + return mapped_voice_str, mapped_params + + def validate_environment( + self, + headers: dict, + model: str, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate Vertex AI environment and set up authentication headers + + Note: Actual authentication is handled in transform_text_to_speech_request + because Vertex AI requires OAuth2 token refresh + """ + validated_headers = headers.copy() + + # Content-Type for JSON + validated_headers["Content-Type"] = "application/json" + validated_headers["charset"] = "UTF-8" + + return validated_headers + + def get_complete_url( + self, + model: str, + api_base: Optional[str], + litellm_params: dict, + ) -> str: + """ + Get the complete URL for Vertex AI TTS request + + Google Cloud TTS endpoint: https://texttospeech.googleapis.com/v1/text:synthesize + """ + if api_base: + return api_base + + return self.TTS_API_URL + + def _validate_vertex_input( + self, + input_data: VertexTextToSpeechInput, + optional_params: Dict, + ) -> VertexTextToSpeechInput: + """ + Validate and transform input for Vertex AI TTS + + Handles text vs SSML input detection and validation + """ + # Remove None values + if input_data.get("text") is None: + input_data.pop("text", None) + if input_data.get("ssml") is None: + input_data.pop("ssml", None) + + # Check if use_ssml is set + use_ssml = optional_params.get("use_ssml", False) + + if use_ssml: + if "text" in input_data: + input_data["ssml"] = input_data.pop("text") + elif "ssml" not in input_data: + raise ValueError("SSML input is required when use_ssml is True.") + else: + # LiteLLM will auto-detect if text is in ssml format + # check if "text" is an ssml - in this case we should pass it as ssml instead of text + if input_data: + _text = input_data.get("text", None) or "" + if "" in _text: + input_data["ssml"] = input_data.pop("text") + + if not input_data: + raise ValueError("Either 'text' or 'ssml' must be provided.") + if "text" in input_data and "ssml" in input_data: + raise ValueError("Only one of 'text' or 'ssml' should be provided, not both.") + + return input_data + + def transform_text_to_speech_request( + self, + model: str, + input: str, + voice: Optional[str], + optional_params: Dict, + litellm_params: Dict, + headers: dict, + ) -> TextToSpeechRequestData: + """ + Transform OpenAI TTS request to Vertex AI TTS format + + This method handles: + 1. Authentication with Vertex AI + 2. Building the request body + 3. Setting up headers + + Returns: + TextToSpeechRequestData: Contains dict_body and headers + """ + # Get Vertex AI credentials from litellm_params + vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES] = litellm_params.get( + "vertex_credentials" + ) + vertex_project: Optional[str] = litellm_params.get("vertex_project") + + ####### Authenticate with Vertex AI ######## + _auth_header, vertex_project = self._ensure_access_token( + credentials=vertex_credentials, + project_id=vertex_project, + custom_llm_provider="vertex_ai_beta", + ) + + auth_header, _ = self._get_token_and_url( + model="", + auth_header=_auth_header, + gemini_api_key=None, + vertex_credentials=vertex_credentials, + vertex_project=vertex_project, + vertex_location=litellm_params.get("vertex_location"), + stream=False, + custom_llm_provider="vertex_ai_beta", + api_base=litellm_params.get("api_base"), + ) + + # Set authentication headers + headers["Authorization"] = f"Bearer {auth_header}" + headers["x-goog-user-project"] = vertex_project + + ####### Build the request ################ + vertex_input = VertexTextToSpeechInput(text=input) + vertex_input = self._validate_vertex_input(vertex_input, optional_params) + + # Build voice configuration + # Check for voice dict stored in: + # 1. litellm_params by dispatch method + # 2. optional_params by map_openai_params + voice_dict = ( + litellm_params.get("vertex_voice_dict") + or optional_params.get("vertex_voice_dict") + ) + if voice_dict is not None and isinstance(voice_dict, dict): + vertex_voice = VertexTextToSpeechVoice(**voice_dict) + elif voice is not None and isinstance(voice, str): + # Handle string voice (shouldn't normally happen if dispatch was called) + parts = voice.split("-") + if len(parts) >= 2: + language_code = f"{parts[0]}-{parts[1]}" + else: + language_code = self.DEFAULT_LANGUAGE_CODE + vertex_voice = VertexTextToSpeechVoice( + languageCode=language_code, + name=voice, + ) + else: + # Use defaults + vertex_voice = VertexTextToSpeechVoice( + languageCode=self.DEFAULT_LANGUAGE_CODE, + name=self.DEFAULT_VOICE_NAME, + ) + + # Build audio configuration + audio_encoding = optional_params.get("audioEncoding", self.DEFAULT_AUDIO_ENCODING) + speaking_rate = optional_params.get("speakingRate", self.DEFAULT_SPEAKING_RATE) + + # Check for full audioConfig in optional_params + if "audioConfig" in optional_params: + vertex_audio_config = VertexTextToSpeechAudioConfig(**optional_params["audioConfig"]) + else: + vertex_audio_config = VertexTextToSpeechAudioConfig( + audioEncoding=audio_encoding, + speakingRate=speaking_rate, + ) + + request_body: Dict[str, Any] = { + "input": dict(vertex_input), + "voice": dict(vertex_voice), + "audioConfig": dict(vertex_audio_config), + } + + return TextToSpeechRequestData( + dict_body=request_body, + headers=headers, + ) + + def transform_text_to_speech_response( + self, + model: str, + raw_response: httpx.Response, + logging_obj: "LiteLLMLoggingObj", + ) -> "HttpxBinaryResponseContent": + """ + Transform Vertex AI TTS response to standard format + + Vertex AI returns JSON with base64-encoded audio content. + We decode it and return as HttpxBinaryResponseContent. + """ + from litellm.types.llms.openai import HttpxBinaryResponseContent + + # Parse JSON response + _json_response = raw_response.json() + + # Get base64-encoded audio content + response_content = _json_response.get("audioContent") + if not response_content: + raise ValueError("No audioContent in Vertex AI TTS response") + + # Decode base64 to get binary content + binary_data = base64.b64decode(response_content) + + # Create an httpx.Response object with the binary data + response = httpx.Response( + status_code=200, + content=binary_data, + ) + + # Initialize the HttpxBinaryResponseContent instance + return HttpxBinaryResponseContent(response) diff --git a/litellm/main.py b/litellm/main.py index a09a945301..fa9172632c 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -206,7 +206,6 @@ from .llms.vertex_ai.image_generation.image_generation_handler import ( from .llms.vertex_ai.multimodal_embeddings.embedding_handler import ( VertexMultimodalEmbedding, ) -from .llms.vertex_ai.text_to_speech.text_to_speech_handler import VertexTextToSpeechAPI from .llms.vertex_ai.vertex_ai_partner_models.main import VertexAIPartnerModels from .llms.vertex_ai.vertex_embeddings.embedding_handler import VertexEmbedding from .llms.vertex_ai.vertex_gemma_models.main import VertexAIGemmaModels @@ -277,7 +276,7 @@ google_batch_embeddings = GoogleBatchEmbeddings() vertex_partner_models_chat_completion = VertexAIPartnerModels() vertex_gemma_chat_completion = VertexAIGemmaModels() vertex_model_garden_chat_completion = VertexAIModelGardenModels() -vertex_text_to_speech = VertexTextToSpeechAPI() +# vertex_text_to_speech is now replaced by VertexAITextToSpeechConfig sagemaker_llm = SagemakerLLM() watsonx_chat_completion = WatsonXChatHandler() openai_like_embedding = OpenAILikeEmbeddingHandler() @@ -6145,30 +6144,13 @@ def speech( # noqa: PLR0915 _is_async=aspeech or False, ) elif custom_llm_provider == "vertex_ai" or custom_llm_provider == "vertex_ai_beta": + from litellm.llms.vertex_ai.text_to_speech.transformation import ( + VertexAITextToSpeechConfig, + ) + generic_optional_params = GenericLiteLLMParams(**kwargs) - api_base = generic_optional_params.api_base or "" - vertex_ai_project = ( - generic_optional_params.vertex_project - or litellm.vertex_project - or get_secret_str("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - generic_optional_params.vertex_location - or litellm.vertex_location - or get_secret_str("VERTEXAI_LOCATION") - ) - vertex_credentials = ( - generic_optional_params.vertex_credentials - or get_secret_str("VERTEXAI_CREDENTIALS") - ) - - if voice is not None and not isinstance(voice, dict): - raise litellm.BadRequestError( - message=f"'voice' is required to be passed as a dict for Vertex AI TTS, passed in voice={voice}", - model=model, - llm_provider=custom_llm_provider, - ) + # Handle Gemini models separately (they use speech_to_completion_bridge) if "gemini" in model: from .endpoints.speech.speech_to_completion_bridge.handler import ( speech_to_completion_bridge_handler, @@ -6184,19 +6166,37 @@ def speech( # noqa: PLR0915 logging_obj=logging_obj, custom_llm_provider=custom_llm_provider, ) - response = vertex_text_to_speech.audio_speech( - _is_async=aspeech, - vertex_credentials=vertex_credentials, - vertex_project=vertex_ai_project, - vertex_location=vertex_ai_location, - timeout=timeout, - api_base=api_base, + + # Vertex AI Text-to-Speech (Google Cloud TTS) + if text_to_speech_provider_config is None: + text_to_speech_provider_config = VertexAITextToSpeechConfig() + + # Cast to specific Vertex AI config type to access dispatch method + vertex_config = cast( + VertexAITextToSpeechConfig, text_to_speech_provider_config + ) + + # Store Vertex AI specific params in litellm_params_dict + litellm_params_dict.update({ + "vertex_project": generic_optional_params.vertex_project, + "vertex_location": generic_optional_params.vertex_location, + "vertex_credentials": generic_optional_params.vertex_credentials, + }) + + response = vertex_config.dispatch_text_to_speech( model=model, input=input, voice=voice, optional_params=optional_params, - kwargs=kwargs, + litellm_params_dict=litellm_params_dict, logging_obj=logging_obj, + timeout=timeout, + extra_headers=headers, + base_llm_http_handler=base_llm_http_handler, + aspeech=aspeech or False, + api_base=generic_optional_params.api_base, + api_key=None, # Vertex AI uses OAuth, not API key + **kwargs, ) elif custom_llm_provider == "gemini": from .endpoints.speech.speech_to_completion_bridge.handler import ( diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index f28e9b1290..f3398e470d 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -24825,6 +24825,15 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, + "vertex_ai/chirp": { + "input_cost_per_character": 30e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_speech", + "source": "https://cloud.google.com/text-to-speech/pricing", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, "vertex_ai/claude-3-5-haiku": { "input_cost_per_token": 1e-06, "litellm_provider": "vertex_ai-anthropic_models", diff --git a/litellm/types/llms/vertex_ai_text_to_speech.py b/litellm/types/llms/vertex_ai_text_to_speech.py new file mode 100644 index 0000000000..e65b75356b --- /dev/null +++ b/litellm/types/llms/vertex_ai_text_to_speech.py @@ -0,0 +1,54 @@ +""" +Type definitions for Vertex AI Text-to-Speech API + +Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/synthesize +""" + +from typing import Optional + +from typing_extensions import TypedDict + + +class VertexTextToSpeechInput(TypedDict, total=False): + """ + Input for Vertex AI Text-to-Speech synthesis. + + Exactly one of text or ssml must be provided. + """ + text: Optional[str] + ssml: Optional[str] + + +class VertexTextToSpeechVoice(TypedDict, total=False): + """ + Voice configuration for Vertex AI Text-to-Speech. + + Attributes: + languageCode: The language code (e.g., "en-US", "de-DE") + name: The voice name (e.g., "en-US-Studio-O", "en-US-Wavenet-D") + """ + languageCode: str + name: str + + +class VertexTextToSpeechAudioConfig(TypedDict, total=False): + """ + Audio configuration for Vertex AI Text-to-Speech. + + Attributes: + audioEncoding: The audio encoding format (e.g., "LINEAR16", "MP3", "OGG_OPUS") + speakingRate: The speaking rate (0.25 to 4.0, default "1") + """ + audioEncoding: str + speakingRate: str + + +class VertexTextToSpeechRequest(TypedDict, total=False): + """ + Request body for Vertex AI Text-to-Speech API. + + Reference: https://cloud.google.com/text-to-speech/docs/reference/rest/v1/text/synthesize + """ + input: VertexTextToSpeechInput + voice: VertexTextToSpeechVoice + audioConfig: Optional[VertexTextToSpeechAudioConfig] diff --git a/litellm/utils.py b/litellm/utils.py index 6c50afc5f4..3775506e24 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -7910,6 +7910,12 @@ class ProviderConfigManager: ) return RunwayMLTextToSpeechConfig() + elif litellm.LlmProviders.VERTEX_AI == provider: + from litellm.llms.vertex_ai.text_to_speech.transformation import ( + VertexAITextToSpeechConfig, + ) + + return VertexAITextToSpeechConfig() return None @staticmethod diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index f28e9b1290..f3398e470d 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -24825,6 +24825,15 @@ "supports_parallel_function_calling": true, "supports_tool_choice": true }, + "vertex_ai/chirp": { + "input_cost_per_character": 30e-06, + "litellm_provider": "vertex_ai", + "mode": "audio_speech", + "source": "https://cloud.google.com/text-to-speech/pricing", + "supported_endpoints": [ + "/v1/audio/speech" + ] + }, "vertex_ai/claude-3-5-haiku": { "input_cost_per_token": 1e-06, "litellm_provider": "vertex_ai-anthropic_models", diff --git a/provider_endpoints_support.json b/provider_endpoints_support.json index b5bde3e5ce..441391dc74 100644 --- a/provider_endpoints_support.json +++ b/provider_endpoints_support.json @@ -734,6 +734,22 @@ "ocr": true } }, + "vertex_ai/chirp": { + "display_name": "Google - Vertex AI Chirp3 HD (`vertex_ai/chirp`)", + "url": "https://docs.litellm.ai/docs/providers/vertex_speech", + "endpoints": { + "chat_completions": false, + "messages": false, + "responses": false, + "embeddings": false, + "image_generations": false, + "audio_transcriptions": false, + "audio_speech": true, + "moderations": false, + "batches": false, + "rerank": false + } + }, "gemini": { 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a/tests/litellm/llms/vertex_ai/text_to_speech/test_transformation.py b/tests/litellm/llms/vertex_ai/text_to_speech/test_transformation.py new file mode 100644 index 0000000000..9a5888e850 --- /dev/null +++ b/tests/litellm/llms/vertex_ai/text_to_speech/test_transformation.py @@ -0,0 +1,190 @@ +import json +import os +import sys +from unittest.mock import MagicMock, Mock, patch + +import httpx +import pytest + +sys.path.insert( + 0, os.path.abspath("../../../../..") +) # Adds the parent directory to the system path + +import litellm +from litellm.llms.vertex_ai.text_to_speech.transformation import ( + VertexAITextToSpeechConfig, +) + + +class TestVertexAITextToSpeechConfig: + """Tests for VertexAITextToSpeechConfig transformation""" + + def test_get_complete_url(self): + """Test that get_complete_url returns the correct Google Cloud TTS API URL""" + config = VertexAITextToSpeechConfig() + + url = config.get_complete_url( + model="vertex_ai/chirp", + api_base=None, + litellm_params={}, + ) + + assert url == "https://texttospeech.googleapis.com/v1/text:synthesize" + + def test_get_complete_url_with_custom_api_base(self): + """Test that get_complete_url uses custom api_base when provided""" + config = VertexAITextToSpeechConfig() + + custom_url = "https://custom-tts-endpoint.example.com/v1/synthesize" + url = config.get_complete_url( + model="vertex_ai/chirp", + api_base=custom_url, + litellm_params={}, + ) + + assert url == custom_url + + @patch.object(VertexAITextToSpeechConfig, "_ensure_access_token") + @patch.object(VertexAITextToSpeechConfig, "_get_token_and_url") + def test_transform_text_to_speech_request_body( + self, mock_get_token, mock_ensure_token + ): + """Test that transform_text_to_speech_request generates correct request body""" + # Mock authentication + mock_ensure_token.return_value = ("mock-token", "test-project") + mock_get_token.return_value = ("mock-token", "mock-url") + + config = VertexAITextToSpeechConfig() + + # Test with voice dict in litellm_params (as set by dispatch) + result = config.transform_text_to_speech_request( + model="vertex_ai/chirp", + input="Hello, this is a test", + voice=None, + optional_params={ + "vertex_voice_dict": { + "languageCode": "en-US", + "name": "en-US-Chirp3-HD-Charon", + } + }, + litellm_params={ + "vertex_credentials": None, + "vertex_project": "test-project", + "vertex_location": "us-central1", + }, + headers={}, + ) + + # Verify request body structure + assert "dict_body" in result + request_body = result["dict_body"] + + assert "input" in request_body + assert request_body["input"] == {"text": "Hello, this is a test"} + + assert "voice" in request_body + assert request_body["voice"]["languageCode"] == "en-US" + assert request_body["voice"]["name"] == "en-US-Chirp3-HD-Charon" + + assert "audioConfig" in request_body + + # Verify headers contain auth + assert "headers" in result + assert "Authorization" in result["headers"] + + def test_voice_mapping_openai_to_vertex(self): + """Test that OpenAI voice names are correctly mapped to Vertex AI voices""" + config = VertexAITextToSpeechConfig() + + # Test the _map_voice_to_vertex_format helper + voice_str, voice_dict = config._map_voice_to_vertex_format("alloy") + + assert voice_str == "alloy" + assert voice_dict is not None + assert voice_dict["name"] == "en-US-Studio-O" + assert voice_dict["languageCode"] == "en-US" + + def test_voice_mapping_vertex_voice_passthrough(self): + """Test that Vertex AI voice names are passed through directly""" + config = VertexAITextToSpeechConfig() + + # Test with a Chirp3 HD voice + voice_str, voice_dict = config._map_voice_to_vertex_format( + "en-US-Chirp3-HD-Charon" + ) + + assert voice_str == "en-US-Chirp3-HD-Charon" + assert voice_dict is not None + assert voice_dict["name"] == "en-US-Chirp3-HD-Charon" + assert voice_dict["languageCode"] == "en-US" + + def test_voice_mapping_dict_passthrough(self): + """Test that voice dict is passed through unchanged""" + config = VertexAITextToSpeechConfig() + + voice_input = { + "languageCode": "de-DE", + "name": "de-DE-Chirp3-HD-Charon", + } + voice_str, voice_dict = config._map_voice_to_vertex_format(voice_input) + + assert voice_str is None + assert voice_dict == voice_input + + +@patch("litellm.llms.custom_httpx.llm_http_handler.HTTPHandler.post") +@patch.object(VertexAITextToSpeechConfig, "_ensure_access_token") +@patch.object(VertexAITextToSpeechConfig, "_get_token_and_url") +def test_litellm_speech_vertex_ai_chirp(mock_get_token, mock_ensure_token, mock_post): + """ + Test that litellm.speech(model="vertex_ai/chirp") sends the correct URL and request body + """ + # Mock authentication + mock_ensure_token.return_value = ("mock-token", "test-project") + mock_get_token.return_value = ("mock-token", "mock-url") + + # Mock HTTP response + mock_response = Mock(spec=httpx.Response) + mock_response.content = b'{"audioContent": "SGVsbG8gV29ybGQ="}' # base64 encoded "Hello World" + mock_response.status_code = 200 + mock_response.headers = {"content-type": "application/json"} + mock_response.json.return_value = {"audioContent": "SGVsbG8gV29ybGQ="} + mock_post.return_value = mock_response + + litellm.speech( + model="vertex_ai/chirp", + input="Hello, this is a test", + voice="en-US-Chirp3-HD-Charon", + vertex_project="test-project", + vertex_location="us-central1", + ) + + # Verify the HTTP call was made + mock_post.assert_called_once() + call_kwargs = mock_post.call_args.kwargs + + # Verify the URL is the Google Cloud TTS API + assert call_kwargs["url"] == "https://texttospeech.googleapis.com/v1/text:synthesize" + + # Verify request body structure + assert "data" in call_kwargs + request_body = json.loads(call_kwargs["data"]) + + # Verify input + assert "input" in request_body + assert request_body["input"] == {"text": "Hello, this is a test"} + + # Verify voice + assert "voice" in request_body + assert request_body["voice"]["name"] == "en-US-Chirp3-HD-Charon" + assert request_body["voice"]["languageCode"] == "en-US" + + # Verify audioConfig + assert "audioConfig" in request_body + + # Verify headers contain authorization + assert "headers" in call_kwargs + assert "Authorization" in call_kwargs["headers"] + assert call_kwargs["headers"]["Authorization"] == "Bearer mock-token" + + From 7fb2f4730b2fd0eef4269e1ce711b5dbfa2be88e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Tue, 2 Dec 2025 15:53:10 -0800 Subject: [PATCH 228/248] build: remove duplicate packages --- docker/Dockerfile.non_root | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 2dcb7cb478..4616ff9be6 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -10,7 +10,17 @@ WORKDIR /app # Install build dependencies including Node.js for UI build USER root -RUN apk add --no-cache build-base bash nodejs npm \ +RUN apk add --no-cache \ + clang \ + llvm \ + lld \ + gcc \ + python3-dev \ + linux-headers \ + build-base \ + bash \ + nodejs \ + npm \ && pip install --no-cache-dir --upgrade pip build # Copy project files From 9867cc6cf98422bb29aa326ec75a1c5068facce1 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 2 Dec 2025 15:58:27 -0800 Subject: [PATCH 229/248] Change edit team models to match create team models --- .../src/components/team/team_info.test.tsx | 115 +++++++++++++++++- .../src/components/team/team_info.tsx | 101 +++++++++++++-- 2 files changed, 202 insertions(+), 14 deletions(-) diff --git a/ui/litellm-dashboard/src/components/team/team_info.test.tsx b/ui/litellm-dashboard/src/components/team/team_info.test.tsx index 5436219335..3a87b42d25 100644 --- a/ui/litellm-dashboard/src/components/team/team_info.test.tsx +++ b/ui/litellm-dashboard/src/components/team/team_info.test.tsx @@ -13,6 +13,7 @@ vi.mock("@/components/networking", () => ({ getGuardrailsList: vi.fn(), fetchMCPAccessGroups: vi.fn(), getTeamPermissionsCall: vi.fn(), + organizationInfoCall: vi.fn(), })); describe("TeamInfoView", () => { @@ -161,6 +162,116 @@ describe("TeamInfoView", () => { const allProxyModelsOption = screen.queryByText("All Proxy Models"); expect(allProxyModelsOption).not.toBeInTheDocument(); - }, // This is a workaround to fix the flaky test issue. TODO: Remove this once we have a better solution. - 10000); + }, 10000); // This is a workaround to fix the flaky test issue. TODO: Remove this once we have a better solution. + + it("should only show organization models in dropdown when team is in organization with limited models", async () => { + const organizationId = "org-123"; + const organizationModels = ["gpt-4", "claude-3-opus"]; + const userModels = ["gpt-4", "gpt-3.5-turbo", "claude-3-opus", "claude-2"]; + + vi.mocked(networking.teamInfoCall).mockResolvedValue({ + team_id: "123", + team_info: { + team_alias: "Test Team", + team_id: "123", + organization_id: organizationId, + admins: ["admin@test.com"], + members: ["user1@test.com"], + members_with_roles: [ + { + user_id: "user1@test.com", + user_email: "user1@test.com", + role: "member", + spend: 0, + budget_id: "budget1", + }, + ], + metadata: {}, + tpm_limit: null, + rpm_limit: null, + max_budget: null, + budget_duration: null, + models: ["gpt-4"], + blocked: false, + spend: 0, + max_parallel_requests: null, + budget_reset_at: null, + model_id: null, + litellm_model_table: null, + created_at: "2024-01-01T00:00:00Z", + team_member_budget_table: null, + }, + keys: [], + team_memberships: [], + }); + + vi.mocked(networking.organizationInfoCall).mockResolvedValue({ + organization_id: organizationId, + organization_name: "Test Organization", + spend: 0, + max_budget: null, + models: organizationModels, + tpm_limit: null, + rpm_limit: null, + members: null, + }); + + vi.mocked(networking.getGuardrailsList).mockResolvedValue({ guardrails: [] }); + vi.mocked(networking.fetchMCPAccessGroups).mockResolvedValue([]); + + render( + {}} + onClose={() => {}} + accessToken="123" + is_team_admin={true} + is_proxy_admin={true} + userModels={userModels} + editTeam={false} + premiumUser={false} + />, + ); + + await waitFor(() => { + expect(screen.getAllByText("Test Team")).not.toBeNull(); + }); + + const settingsTab = screen.getByRole("tab", { name: "Settings" }); + act(() => { + fireEvent.click(settingsTab); + }); + + await waitFor(() => { + expect(screen.getByText("Team Settings")).toBeInTheDocument(); + }); + + const editButton = screen.getByRole("button", { name: "Edit Settings" }); + act(() => { + fireEvent.click(editButton); + }); + + await waitFor(() => { + expect(screen.getByLabelText("Models")).toBeInTheDocument(); + }); + + const modelsSelect = screen.getByLabelText("Models"); + act(() => { + fireEvent.mouseDown(modelsSelect); + }); + + await waitFor(() => { + const dropdownOptions = screen.getAllByRole("option"); + const optionTexts = dropdownOptions.map((option) => option.textContent); + + organizationModels.forEach((model) => { + expect(optionTexts).toContain(model); + }); + + const modelsNotInOrganization = userModels.filter((m) => !organizationModels.includes(m)); + modelsNotInOrganization.forEach((model) => { + expect(optionTexts).not.toContain(model); + }); + }); + }, 10000); }); diff --git a/ui/litellm-dashboard/src/components/team/team_info.tsx b/ui/litellm-dashboard/src/components/team/team_info.tsx index a53d35e57b..581722e8fa 100644 --- a/ui/litellm-dashboard/src/components/team/team_info.tsx +++ b/ui/litellm-dashboard/src/components/team/team_info.tsx @@ -2,6 +2,8 @@ import UserSearchModal from "@/components/common_components/user_search_modal"; import { getGuardrailsList, Member, + Organization, + organizationInfoCall, teamInfoCall, teamMemberAddCall, teamMemberDeleteCall, @@ -28,11 +30,11 @@ import { } from "@tremor/react"; import { Button, Form, Input, message, Select, Switch, Tooltip } from "antd"; import { CheckIcon, CopyIcon } from "lucide-react"; -import React, { useEffect, useState } from "react"; +import React, { useEffect, useMemo, useState } from "react"; import { copyToClipboard as utilCopyToClipboard } from "../../utils/dataUtils"; import DeleteResourceModal from "../common_components/DeleteResourceModal"; import PassThroughRoutesSelector from "../common_components/PassThroughRoutesSelector"; -import { getModelDisplayName } from "../key_team_helpers/fetch_available_models_team_key"; +import { getModelDisplayName, unfurlWildcardModelsInList } from "../key_team_helpers/fetch_available_models_team_key"; import LoggingSettingsView from "../logging_settings_view"; import MCPServerSelector from "../mcp_server_management/MCPServerSelector"; import MCPToolPermissions from "../mcp_server_management/MCPToolPermissions"; @@ -117,6 +119,29 @@ export interface TeamInfoProps { premiumUser?: boolean; } +const getOrganizationModels = (organization: Organization | null, userModels: string[]) => { + let tempModelsToPick = []; + + if (organization) { + // Check if organization has "all-proxy-models" in its models array + if (organization.models.includes("all-proxy-models")) { + // Treat as all-proxy-models (use userModels) + tempModelsToPick = userModels; + } else if (organization.models.length > 0) { + // Organization has specific models + tempModelsToPick = organization.models; + } else { + // Empty array [] is treated as all-proxy-models + tempModelsToPick = userModels; + } + } else { + // No organization, show all available models + tempModelsToPick = userModels; + } + + return unfurlWildcardModelsInList(tempModelsToPick, userModels); +}; + const TeamInfoView: React.FC = ({ teamId, onClose, @@ -143,6 +168,7 @@ const TeamInfoView: React.FC = ({ const [isDeleteModalOpen, setIsDeleteModalOpen] = useState(false); const [isDeleting, setIsDeleting] = useState(false); const [isTeamSaving, setIsTeamSaving] = useState(false); + const [organization, setOrganization] = useState(null); console.log("userModels in team info", userModels); @@ -166,6 +192,31 @@ const TeamInfoView: React.FC = ({ fetchTeamInfo(); }, [teamId, accessToken]); + // Fetch organization data when team has organization_id + useEffect(() => { + const fetchOrganization = async () => { + if (!accessToken || !teamData?.team_info?.organization_id) { + setOrganization(null); + return; + } + + try { + const orgData = await organizationInfoCall(accessToken, teamData.team_info.organization_id); + setOrganization(orgData); + } catch (error) { + console.error("Error fetching organization info:", error); + setOrganization(null); + } + }; + + fetchOrganization(); + }, [accessToken, teamData?.team_info?.organization_id]); + + // Compute modelsToPick based on organization and userModels + const modelsToPick = useMemo(() => { + return getOrganizationModels(organization, userModels); + }, [organization, userModels]); + const fetchMcpAccessGroups = async () => { if (!accessToken) return; if (mcpAccessGroupsLoaded) return; @@ -596,15 +647,41 @@ const TeamInfoView: React.FC = ({ rules={[{ required: true, message: "Please select at least one model" }]} > +
    From 8ee298f9c9c178ccc7191ebfbf63736898557443 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Tue, 2 Dec 2025 16:06:06 -0800 Subject: [PATCH 230/248] fix: remove python3 headers --- docker/Dockerfile.non_root | 1 - 1 file changed, 1 deletion(-) diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 4616ff9be6..8b66a367ee 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -15,7 +15,6 @@ RUN apk add --no-cache \ llvm \ lld \ gcc \ - python3-dev \ linux-headers \ build-base \ bash \ From 10d56e7c4682c868c9c11483d21f77abc3d042f5 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Dec 2025 16:19:34 -0800 Subject: [PATCH 231/248] add new deepseek-v3p2 (#17395) --- litellm/model_prices_and_context_window_backup.json | 13 +++++++++++++ model_prices_and_context_window.json | 13 +++++++++++++ 2 files changed, 26 insertions(+) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index f3398e470d..932508824a 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -10421,6 +10421,19 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/deepseek-v3p2": { + "input_cost_per_token": 1.2e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://fireworks.ai/models/fireworks/deepseek-v3p2", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/firefunction-v2": { "input_cost_per_token": 9e-07, "litellm_provider": "fireworks_ai", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index f3398e470d..932508824a 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -10421,6 +10421,19 @@ "supports_response_schema": true, "supports_tool_choice": true }, + "fireworks_ai/accounts/fireworks/models/deepseek-v3p2": { + "input_cost_per_token": 1.2e-06, + "litellm_provider": "fireworks_ai", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 163840, + "mode": "chat", + "output_cost_per_token": 1.2e-06, + "source": "https://fireworks.ai/models/fireworks/deepseek-v3p2", + "supports_function_calling": true, + "supports_response_schema": true, + "supports_tool_choice": true + }, "fireworks_ai/accounts/fireworks/models/firefunction-v2": { "input_cost_per_token": 9e-07, "litellm_provider": "fireworks_ai", From 31cad8e6e63a9de464c1dbc3d5635a7fcc4962f5 Mon Sep 17 00:00:00 2001 From: flozonn <74357383+flozonn@users.noreply.github.com> Date: Wed, 3 Dec 2025 01:33:07 +0100 Subject: [PATCH 232/248] feat: Add Nova lite 2 reasoning support with reasoningConfig (#17371) --- litellm/constants.py | 3 + .../bedrock/chat/converse_transformation.py | 102 ++- model_prices_and_context_window.json | 65 ++ .../chat/test_converse_transformation.py | 33 + .../test_converse_transformation_nova_2.py | 794 ++++++++++++++++++ 5 files changed, 995 insertions(+), 2 deletions(-) create mode 100644 tests/test_litellm/llms/bedrock/chat/test_converse_transformation_nova_2.py diff --git a/litellm/constants.py b/litellm/constants.py index 1d42ef9a91..6a67a9a0e1 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -909,6 +909,9 @@ BEDROCK_CONVERSE_MODELS = [ "meta.llama3-2-3b-instruct-v1:0", "meta.llama3-2-11b-instruct-v1:0", "meta.llama3-2-90b-instruct-v1:0", + "amazon.nova-lite-v1:0", + "amazon.nova-2-lite-v1:0", + "amazon.nova-pro-v1:0", ] diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index 3b3a138ec6..705f3c9e63 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -246,6 +246,93 @@ class AmazonConverseConfig(BaseConfig): llm_provider="bedrock", ) + def _is_nova_lite_2_model(self, model: str) -> bool: + """ + Check if the model is a Nova Lite 2 model that supports reasoningConfig. + + Nova Lite 2 models use a different reasoning configuration structure compared to + Anthropic's thinking parameter and GPT-OSS's reasoning_effort parameter. + + Supported models: + - amazon.nova-2-lite-v1:0 + - us.amazon.nova-2-lite-v1:0 + - eu.amazon.nova-2-lite-v1:0 + - apac.amazon.nova-2-lite-v1:0 + + Args: + model: The model identifier + + Returns: + True if the model is a Nova Lite 2 model, False otherwise + + Examples: + >>> config = AmazonConverseConfig() + >>> config._is_nova_lite_2_model("amazon.nova-2-lite-v1:0") + True + >>> config._is_nova_lite_2_model("us.amazon.nova-2-lite-v1:0") + True + >>> config._is_nova_lite_2_model("amazon.nova-pro-1-5-v1:0") + False + >>> config._is_nova_lite_2_model("amazon.nova-pro-v1:0") + False + """ + # Remove regional prefix if present (us., eu., apac.) + model_without_region = model + for prefix in ["us.", "eu.", "apac."]: + if model.startswith(prefix): + model_without_region = model[len(prefix) :] + break + + # Check if the model is specifically Nova Lite 2 + return "nova-2-lite" in model_without_region + + def _transform_reasoning_effort_to_reasoning_config( + self, reasoning_effort: str + ) -> dict: + """ + Transform reasoning_effort parameter to Nova 2 reasoningConfig structure. + + Nova 2 models use a reasoningConfig structure in additionalModelRequestFields + that differs from both Anthropic's thinking parameter and GPT-OSS's reasoning_effort. + + Args: + reasoning_effort: The reasoning effort level, must be "low" or "high" + + Returns: + dict: A dictionary containing the reasoningConfig structure: + { + "reasoningConfig": { + "type": "enabled", + "maxReasoningEffort": "low" | "medium" |"high" + } + } + + Raises: + BadRequestError: If reasoning_effort is not "low", "medium" or "high" + + Examples: + >>> config = AmazonConverseConfig() + >>> config._transform_reasoning_effort_to_reasoning_config("high") + {'reasoningConfig': {'type': 'enabled', 'maxReasoningEffort': 'high'}} + >>> config._transform_reasoning_effort_to_reasoning_config("low") + {'reasoningConfig': {'type': 'enabled', 'maxReasoningEffort': 'low'}} + """ + valid_values = ["low", "medium", "high"] + if reasoning_effort not in valid_values: + raise litellm.exceptions.BadRequestError( + message=f"Invalid reasoning_effort value '{reasoning_effort}' for Nova 2 models. " + f"Supported values: {valid_values}", + model="amazon.nova-2-lite-v1:0", + llm_provider="bedrock_converse", + ) + + return { + "reasoningConfig": { + "type": "enabled", + "maxReasoningEffort": reasoning_effort, + } + } + def get_supported_openai_params(self, model: str) -> List[str]: from litellm.utils import supports_function_calling @@ -299,6 +386,10 @@ class AmazonConverseConfig(BaseConfig): if "gpt-oss" in model: supported_params.append("reasoning_effort") + elif self._is_nova_lite_2_model(model): + # Nova Lite 2 models support reasoning_effort (transformed to reasoningConfig) + # These models use a different reasoning structure than Anthropic's thinking parameter + supported_params.append("reasoning_effort") elif ( "claude-3-7" in model or "claude-sonnet-4" in model @@ -564,6 +655,12 @@ class AmazonConverseConfig(BaseConfig): # GPT-OSS models: keep reasoning_effort as-is # It will be passed through to additionalModelRequestFields optional_params["reasoning_effort"] = value + elif self._is_nova_lite_2_model(model): + # Nova Lite 2 models: transform to reasoningConfig + reasoning_config = ( + self._transform_reasoning_effort_to_reasoning_config(value) + ) + optional_params.update(reasoning_config) else: # Anthropic and other models: convert to thinking parameter optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( @@ -574,8 +671,9 @@ class AmazonConverseConfig(BaseConfig): self._validate_request_metadata(value) # type: ignore optional_params["requestMetadata"] = value - # Only update thinking tokens for non-GPT-OSS models - if "gpt-oss" not in model: + # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models + # Nova Lite 2 handles token budgeting differently through reasoningConfig + if "gpt-oss" not in model and not self._is_nova_lite_2_model(model): self.update_optional_params_with_thinking_tokens( non_default_params=non_default_params, optional_params=optional_params ) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 932508824a..19ed734c5f 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -269,6 +269,71 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 3e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "apac.amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.75e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "eu.amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.75e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "us.amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.75e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "amazon.nova-micro-v1:0": { "input_cost_per_token": 3.5e-08, "litellm_provider": "bedrock_converse", diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py index 37c95be72c..e603f94ab8 100644 --- a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py +++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py @@ -2702,3 +2702,36 @@ def test_empty_assistant_message_handling(): finally: # Restore original modify_params setting litellm.modify_params = original_modify_params + + +def test_is_nova_lite_2_model(): + """Test the _is_nova_lite_2_model() method for detecting Nova 2 models.""" + config = AmazonConverseConfig() + + # Test with amazon.nova-2-lite-v1:0 + assert config._is_nova_lite_2_model("amazon.nova-2-lite-v1:0") is True + + # Test with regional variants + assert config._is_nova_lite_2_model("us.amazon.nova-2-lite-v1:0") is True + assert config._is_nova_lite_2_model("eu.amazon.nova-2-lite-v1:0") is True + assert config._is_nova_lite_2_model("apac.amazon.nova-2-lite-v1:0") is True + + # Test with other Nova 2 variants (pro, micro) + assert config._is_nova_lite_2_model("amazon.nova-pro-1-5-v1:0") is False + assert config._is_nova_lite_2_model("amazon.nova-micro-1-5-v1:0") is False + assert config._is_nova_lite_2_model("us.amazon.nova-pro-1-5-v1:0") is False + assert config._is_nova_lite_2_model("eu.amazon.nova-micro-1-5-v1:0") is False + + # Test with non-Nova-1.5 lite models (should return False) + assert config._is_nova_lite_2_model("amazon.nova-lite-v1:0") is False + assert config._is_nova_lite_2_model("amazon.nova-pro-v1:0") is False + assert config._is_nova_lite_2_model("amazon.nova-micro-v1:0") is False + + # Test with Nova v1:0 models (should return False) + assert config._is_nova_lite_2_model("us.amazon.nova-lite-v1:0") is False + assert config._is_nova_lite_2_model("eu.amazon.nova-pro-v1:0") is False + + # Test with completely different models (should return False) + assert config._is_nova_lite_2_model("anthropic.claude-3-5-sonnet-20240620-v1:0") is False + assert config._is_nova_lite_2_model("meta.llama3-70b-instruct-v1:0") is False + assert config._is_nova_lite_2_model("mistral.mistral-7b-instruct-v0:2") is False diff --git a/tests/test_litellm/llms/bedrock/chat/test_converse_transformation_nova_2.py b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation_nova_2.py new file mode 100644 index 0000000000..23243dac20 --- /dev/null +++ b/tests/test_litellm/llms/bedrock/chat/test_converse_transformation_nova_2.py @@ -0,0 +1,794 @@ +""" +Unit tests for Amazon Nova 2 reasoning configuration transformation. + +Tests the _transform_reasoning_effort_to_reasoning_config method in AmazonConverseConfig. +""" + +import pytest +import sys +import os + +sys.path.insert( + 0, os.path.abspath("../..") +) # Adds the parent directory to the system path + +import litellm +from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig + + +class TestNova15ReasoningTransformation: + """Test suite for Nova 2 reasoning effort transformation.""" + + def test_reasoning_effort_low_transformation(self): + """Test that reasoning_effort='low' is transformed to correct reasoningConfig structure.""" + config = AmazonConverseConfig() + + result = config._transform_reasoning_effort_to_reasoning_config("low") + + # Verify the structure + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "low" + + def test_reasoning_effort_high_transformation(self): + """Test that reasoning_effort='high' is transformed to correct reasoningConfig structure.""" + config = AmazonConverseConfig() + + result = config._transform_reasoning_effort_to_reasoning_config("high") + + # Verify the structure + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "high" + + def test_invalid_reasoning_effort_value(self): + """Test that invalid reasoning_effort values raise BadRequestError.""" + config = AmazonConverseConfig() + + # Test with invalid value "invalid" + with pytest.raises(litellm.exceptions.BadRequestError) as exc_info: + config._transform_reasoning_effort_to_reasoning_config("invalid") + + # Verify error message contains the invalid value and valid values + error_message = str(exc_info.value) + assert "invalid" in error_message + assert "low" in error_message + assert "high" in error_message + assert "Nova 2" in error_message + + def test_invalid_reasoning_effort_empty_string(self): + """Test that empty string raises BadRequestError.""" + config = AmazonConverseConfig() + + with pytest.raises(litellm.exceptions.BadRequestError) as exc_info: + config._transform_reasoning_effort_to_reasoning_config("") + + # Verify error message + error_message = str(exc_info.value) + assert "low" in error_message + assert "high" in error_message + + def test_invalid_reasoning_effort_wrong_case(self): + """Test that case-sensitive values are rejected (e.g., 'Low' instead of 'low').""" + config = AmazonConverseConfig() + + with pytest.raises(litellm.exceptions.BadRequestError): + config._transform_reasoning_effort_to_reasoning_config("Low") + + with pytest.raises(litellm.exceptions.BadRequestError): + config._transform_reasoning_effort_to_reasoning_config("HIGH") + + +class TestNova2ParameterMapping: + """Test suite for Nova 2 parameter mapping integration.""" + + def test_nova_2_reasoning_effort_low_mapping(self): + """Test that reasoning_effort='low' is correctly mapped to reasoningConfig for Nova 2.""" + config = AmazonConverseConfig() + + model = "amazon.nova-2-lite-v1:0" + non_default_params = {"reasoning_effort": "low"} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is in result + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "low" + + # Verify thinking is NOT in result + assert "thinking" not in result + + # Verify reasoning_effort is NOT kept as-is (should be transformed) + assert "reasoning_effort" not in result + + def test_nova_2_reasoning_effort_high_mapping(self): + """Test that reasoning_effort='high' is correctly mapped to reasoningConfig for Nova 2.""" + config = AmazonConverseConfig() + + model = "amazon.nova-2-lite-v1:0" + non_default_params = {"reasoning_effort": "high"} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is in result + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "high" + + # Verify thinking is NOT in result + assert "thinking" not in result + + # Verify reasoning_effort is NOT kept as-is (should be transformed) + assert "reasoning_effort" not in result + + def test_nova_2_without_reasoning_effort(self): + """Test that Nova 2 without reasoning_effort has no reasoningConfig in result.""" + config = AmazonConverseConfig() + + model = "amazon.nova-2-lite-v1:0" + non_default_params = {"temperature": 0.7} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is NOT in result + assert "reasoningConfig" not in result + + # Verify thinking is NOT in result + assert "thinking" not in result + + # Verify reasoning_effort is NOT in result + assert "reasoning_effort" not in result + + def test_nova_2_regional_variant_us(self): + """Test that US regional variant of Nova 2 works correctly.""" + config = AmazonConverseConfig() + + model = "us.amazon.nova-2-lite-v1:0" + non_default_params = {"reasoning_effort": "high"} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is in result + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "high" + + def test_nova_2_regional_variant_eu(self): + """Test that EU regional variant of Nova 2 works correctly.""" + config = AmazonConverseConfig() + + model = "eu.amazon.nova-2-lite-v1:0" + non_default_params = {"reasoning_effort": "low"} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is in result + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "low" + + def test_nova_2_regional_variant_apac(self): + """Test that APAC regional variant of Nova 2 works correctly.""" + config = AmazonConverseConfig() + + model = "apac.amazon.nova-2-lite-v1:0" + non_default_params = {"reasoning_effort": "high"} + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is in result + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "high" + + def test_nova_2_with_other_params(self): + """Test that Nova 2 reasoning works alongside other parameters.""" + config = AmazonConverseConfig() + + model = "amazon.nova-2-lite-v1:0" + non_default_params = { + "reasoning_effort": "high", + "temperature": 0.8, + "max_tokens": 1000, + "top_p": 0.9, + } + optional_params = {} + + result = config.map_openai_params( + non_default_params=non_default_params, + optional_params=optional_params, + model=model, + drop_params=False, + ) + + # Verify reasoningConfig is in result + assert "reasoningConfig" in result + assert result["reasoningConfig"]["type"] == "enabled" + assert result["reasoningConfig"]["maxReasoningEffort"] == "high" + + # Verify other params are also present + assert result["temperature"] == 0.8 + assert result["maxTokens"] == 1000 + assert result["topP"] == 0.9 + + +class TestNova15SupportedParameters: + """Test suite for Nova 2 supported parameters.""" + + def test_nova_2_supports_reasoning_effort(self): + """Test that Nova 2 model reports reasoning_effort in supported params.""" + config = AmazonConverseConfig() + + model = "amazon.nova-2-lite-v1:0" + supported_params = config.get_supported_openai_params(model) + + # Verify reasoning_effort is in supported params + assert "reasoning_effort" in supported_params + + # Verify thinking is NOT in supported params (Nova 2 uses reasoningConfig, not thinking) + assert "thinking" not in supported_params + + def test_nova_2_regional_variant_us_supported_params(self): + """Test that US regional variant returns same supported params.""" + config = AmazonConverseConfig() + + model = "us.amazon.nova-2-lite-v1:0" + supported_params = config.get_supported_openai_params(model) + + # Verify reasoning_effort is in supported params + assert "reasoning_effort" in supported_params + + # Verify thinking is NOT in supported params + assert "thinking" not in supported_params + + def test_nova_2_regional_variant_eu_supported_params(self): + """Test that EU regional variant returns same supported params.""" + config = AmazonConverseConfig() + + model = "eu.amazon.nova-2-lite-v1:0" + supported_params = config.get_supported_openai_params(model) + + # Verify reasoning_effort is in supported params + assert "reasoning_effort" in supported_params + + # Verify thinking is NOT in supported params + assert "thinking" not in supported_params + + def test_nova_2_regional_variant_apac_supported_params(self): + """Test that APAC regional variant returns same supported params.""" + config = AmazonConverseConfig() + + model = "apac.amazon.nova-2-lite-v1:0" + supported_params = config.get_supported_openai_params(model) + + # Verify reasoning_effort is in supported params + assert "reasoning_effort" in supported_params + + # Verify thinking is NOT in supported params + assert "thinking" not in supported_params + + def test_nova_2_has_standard_params(self): + """Test that Nova 2 still has all standard supported params.""" + config = AmazonConverseConfig() + + model = "amazon.nova-2-lite-v1:0" + supported_params = config.get_supported_openai_params(model) + + # Verify standard params are present + assert "max_tokens" in supported_params + assert "max_completion_tokens" in supported_params + assert "stream" in supported_params + assert "stream_options" in supported_params + assert "stop" in supported_params + assert "temperature" in supported_params + assert "top_p" in supported_params + assert "tools" in supported_params + assert "response_format" in supported_params + + +class TestNova15ResponseParsing: + """Test suite for Nova 2 response parsing.""" + + def test_transform_reasoning_content_single_block(self): + """Test that reasoning content is extracted correctly from a single block.""" + config = AmazonConverseConfig() + + reasoning_blocks = [ + {"reasoningText": {"text": "Let me think through this step by step..."}} + ] + + result = config._transform_reasoning_content(reasoning_blocks) + + assert result == "Let me think through this step by step..." + + def test_transform_reasoning_content_multiple_blocks(self): + """Test that reasoning content is concatenated from multiple blocks.""" + config = AmazonConverseConfig() + + reasoning_blocks = [ + {"reasoningText": {"text": "First, I need to analyze the problem. "}}, + {"reasoningText": {"text": "Then, I'll consider the solution."}}, + ] + + result = config._transform_reasoning_content(reasoning_blocks) + + assert ( + result + == "First, I need to analyze the problem. Then, I'll consider the solution." + ) + + def test_transform_reasoning_content_empty_blocks(self): + """Test that empty reasoning blocks return empty string.""" + config = AmazonConverseConfig() + + reasoning_blocks = [] + + result = config._transform_reasoning_content(reasoning_blocks) + + assert result == "" + + def test_transform_thinking_blocks_with_text(self): + """Test that thinking blocks are populated correctly with text.""" + config = AmazonConverseConfig() + + reasoning_blocks = [{"reasoningText": {"text": "My reasoning process..."}}] + + result = config._transform_thinking_blocks(reasoning_blocks) + + assert len(result) == 1 + assert result[0]["type"] == "thinking" + assert result[0]["thinking"] == "My reasoning process..." + assert "signature" not in result[0] + + def test_transform_thinking_blocks_with_signature(self): + """Test that signature field is preserved when present.""" + config = AmazonConverseConfig() + + reasoning_blocks = [ + { + "reasoningText": { + "text": "My reasoning...", + "signature": "signature-hash-12345", + } + } + ] + + result = config._transform_thinking_blocks(reasoning_blocks) + + assert len(result) == 1 + assert result[0]["type"] == "thinking" + assert result[0]["thinking"] == "My reasoning..." + assert result[0]["signature"] == "signature-hash-12345" + + def test_transform_thinking_blocks_with_redacted_content(self): + """Test that redacted content blocks are handled correctly.""" + config = AmazonConverseConfig() + + reasoning_blocks = [ + {"reasoningText": {"text": "First part of reasoning..."}}, + {"redactedContent": {}}, + {"reasoningText": {"text": "Second part after redaction..."}}, + ] + + result = config._transform_thinking_blocks(reasoning_blocks) + + assert len(result) == 3 + assert result[0]["type"] == "thinking" + assert result[0]["thinking"] == "First part of reasoning..." + assert result[1]["type"] == "redacted_thinking" + assert result[2]["type"] == "thinking" + assert result[2]["thinking"] == "Second part after redaction..." + + def test_transform_thinking_blocks_multiple_blocks(self): + """Test that multiple thinking blocks are all transformed.""" + config = AmazonConverseConfig() + + reasoning_blocks = [ + {"reasoningText": {"text": "Step 1: Analyze the problem"}}, + { + "reasoningText": { + "text": "Step 2: Consider solutions", + "signature": "sig-abc", + } + }, + {"reasoningText": {"text": "Step 3: Choose best approach"}}, + ] + + result = config._transform_thinking_blocks(reasoning_blocks) + + assert len(result) == 3 + assert all(block["type"] == "thinking" for block in result) + assert result[0]["thinking"] == "Step 1: Analyze the problem" + assert result[1]["thinking"] == "Step 2: Consider solutions" + assert result[1]["signature"] == "sig-abc" + assert result[2]["thinking"] == "Step 3: Choose best approach" + + def test_transform_thinking_blocks_empty_list(self): + """Test that empty thinking blocks list returns empty list.""" + config = AmazonConverseConfig() + + reasoning_blocks = [] + + result = config._transform_thinking_blocks(reasoning_blocks) + + assert result == [] + + def test_response_parsing_integration(self): + """Test that response parsing works end-to-end with Nova 2 structure.""" + config = AmazonConverseConfig() + + # Simulate a Nova 2 response with reasoning content + reasoning_blocks = [ + { + "reasoningText": { + "text": "Let me analyze this carefully. ", + "signature": "test-signature", + } + }, + {"reasoningText": {"text": "Based on my analysis, the answer is clear."}}, + ] + + # Test reasoning content extraction + reasoning_content = config._transform_reasoning_content(reasoning_blocks) + assert ( + reasoning_content + == "Let me analyze this carefully. Based on my analysis, the answer is clear." + ) + + # Test thinking blocks transformation + thinking_blocks = config._transform_thinking_blocks(reasoning_blocks) + assert len(thinking_blocks) == 2 + assert thinking_blocks[0]["thinking"] == "Let me analyze this carefully. " + assert thinking_blocks[0]["signature"] == "test-signature" + assert ( + thinking_blocks[1]["thinking"] + == "Based on my analysis, the answer is clear." + ) + + +class TestNova15StreamingResponseParsing: + """Test suite for Nova 2 streaming response parsing.""" + + def test_streaming_reasoning_content_start_event(self): + """Test that streaming start event with reasoningContent is handled correctly.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate a start event with redacted reasoning content + chunk_data = { + "start": {"reasoningContent": {"redactedContent": {}}}, + "contentBlockIndex": 0, + } + + result = handler.converse_chunk_parser(chunk_data) + + # Verify thinking blocks are populated + assert result.choices[0].delta.thinking_blocks is not None + assert len(result.choices[0].delta.thinking_blocks) == 1 + assert result.choices[0].delta.thinking_blocks[0]["type"] == "redacted_thinking" + + def test_streaming_reasoning_content_delta_text(self): + """Test that streaming delta event with reasoning text is handled correctly.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate a delta event with reasoning text + chunk_data = { + "delta": {"reasoningContent": {"text": "Let me think about this..."}}, + "contentBlockIndex": 0, + } + + result = handler.converse_chunk_parser(chunk_data) + + # Verify reasoning content is extracted + assert result.choices[0].delta.reasoning_content == "Let me think about this..." + + # Verify thinking blocks are populated + assert result.choices[0].delta.thinking_blocks is not None + assert len(result.choices[0].delta.thinking_blocks) == 1 + assert result.choices[0].delta.thinking_blocks[0]["type"] == "thinking" + assert ( + result.choices[0].delta.thinking_blocks[0]["thinking"] + == "Let me think about this..." + ) + + def test_streaming_reasoning_content_delta_signature(self): + """Test that streaming delta event with signature is handled correctly.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate a delta event with signature + chunk_data = { + "delta": {"reasoningContent": {"signature": "signature-hash-xyz"}}, + "contentBlockIndex": 0, + } + + result = handler.converse_chunk_parser(chunk_data) + + # Verify reasoning content is set to empty string for consistency + assert result.choices[0].delta.reasoning_content == "" + + # Verify thinking blocks are populated with signature + assert result.choices[0].delta.thinking_blocks is not None + assert len(result.choices[0].delta.thinking_blocks) == 1 + assert result.choices[0].delta.thinking_blocks[0]["type"] == "thinking" + assert ( + result.choices[0].delta.thinking_blocks[0]["signature"] + == "signature-hash-xyz" + ) + assert result.choices[0].delta.thinking_blocks[0]["thinking"] == "" + + def test_streaming_reasoning_content_multiple_deltas(self): + """Test that multiple reasoning content deltas are accumulated correctly.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate multiple delta events + chunks = [ + { + "delta": {"reasoningContent": {"text": "First, "}}, + "contentBlockIndex": 0, + }, + { + "delta": {"reasoningContent": {"text": "I need to analyze "}}, + "contentBlockIndex": 0, + }, + { + "delta": {"reasoningContent": {"text": "the problem."}}, + "contentBlockIndex": 0, + }, + ] + + results = [] + for chunk_data in chunks: + result = handler.converse_chunk_parser(chunk_data) + results.append(result) + + # Verify each delta has the correct reasoning content + assert results[0].choices[0].delta.reasoning_content == "First, " + assert results[1].choices[0].delta.reasoning_content == "I need to analyze " + assert results[2].choices[0].delta.reasoning_content == "the problem." + + # Verify thinking blocks are populated for each delta + for result in results: + assert result.choices[0].delta.thinking_blocks is not None + assert len(result.choices[0].delta.thinking_blocks) == 1 + assert result.choices[0].delta.thinking_blocks[0]["type"] == "thinking" + + def test_streaming_reasoning_then_text_content(self): + """Test that reasoning content followed by text content is handled correctly.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate reasoning content followed by text content + chunks = [ + { + "delta": {"reasoningContent": {"text": "Let me think..."}}, + "contentBlockIndex": 0, + }, + {"delta": {"text": "Based on my reasoning, "}, "contentBlockIndex": 1}, + {"delta": {"text": "the answer is 42."}, "contentBlockIndex": 1}, + ] + + results = [] + for chunk_data in chunks: + result = handler.converse_chunk_parser(chunk_data) + results.append(result) + + # Verify first chunk has reasoning content + assert results[0].choices[0].delta.reasoning_content == "Let me think..." + assert results[0].choices[0].delta.thinking_blocks is not None + + # Verify subsequent chunks have text content + assert results[1].choices[0].delta.content == "Based on my reasoning, " + assert results[2].choices[0].delta.content == "the answer is 42." + + def test_streaming_redacted_content_delta(self): + """Test that streaming delta with redacted content is handled correctly.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate a delta event with redacted content + chunk_data = { + "delta": {"reasoningContent": {"redactedContent": {}}}, + "contentBlockIndex": 0, + } + + result = handler.converse_chunk_parser(chunk_data) + + # Verify reasoning content is set to empty string for consistency + assert result.choices[0].delta.reasoning_content == "" + + # Verify thinking blocks contain redacted block + assert result.choices[0].delta.thinking_blocks is not None + assert len(result.choices[0].delta.thinking_blocks) == 1 + assert result.choices[0].delta.thinking_blocks[0]["type"] == "redacted_thinking" + + def test_streaming_provider_specific_fields(self): + """Test that provider_specific_fields are populated in streaming responses.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate a delta event with reasoning content + chunk_data = { + "delta": {"reasoningContent": {"text": "Reasoning text"}}, + "contentBlockIndex": 0, + } + + result = handler.converse_chunk_parser(chunk_data) + + # Verify provider_specific_fields are populated + assert result.choices[0].delta.provider_specific_fields is not None + assert "reasoningContent" in result.choices[0].delta.provider_specific_fields + assert ( + result.choices[0].delta.provider_specific_fields["reasoningContent"]["text"] + == "Reasoning text" + ) + + def test_streaming_mixed_content_blocks(self): + """Test streaming with mixed content blocks (reasoning, text, tool calls).""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + # Simulate a complex streaming scenario + chunks = [ + # Start with reasoning + { + "delta": { + "reasoningContent": { + "text": "I need to call a tool to get information." + } + }, + "contentBlockIndex": 0, + }, + # Tool use start + { + "start": {"toolUse": {"toolUseId": "tool-123", "name": "get_weather"}}, + "contentBlockIndex": 1, + }, + # Tool use delta + { + "delta": {"toolUse": {"input": '{"location": "NYC"}'}}, + "contentBlockIndex": 1, + }, + # Text response + {"delta": {"text": "The weather is sunny."}, "contentBlockIndex": 2}, + ] + + results = [] + for chunk_data in chunks: + result = handler.converse_chunk_parser(chunk_data) + results.append(result) + + # Verify reasoning content in first chunk + assert ( + results[0].choices[0].delta.reasoning_content + == "I need to call a tool to get information." + ) + + # Verify tool call in second and third chunks + assert results[1].choices[0].delta.tool_calls is not None + assert ( + results[1].choices[0].delta.tool_calls[0]["function"]["name"] + == "get_weather" + ) + assert results[2].choices[0].delta.tool_calls is not None + + # Verify text content in fourth chunk + assert results[3].choices[0].delta.content == "The weather is sunny." + + def test_extract_reasoning_content_str_with_text(self): + """Test extract_reasoning_content_str method with text.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + reasoning_block = {"text": "This is reasoning text"} + + result = handler.extract_reasoning_content_str(reasoning_block) + + assert result == "This is reasoning text" + + def test_extract_reasoning_content_str_without_text(self): + """Test extract_reasoning_content_str method without text (e.g., signature only).""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + reasoning_block = {"signature": "sig-123"} + + result = handler.extract_reasoning_content_str(reasoning_block) + + assert result is None + + def test_translate_thinking_blocks_streaming_text(self): + """Test translate_thinking_blocks method with text.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + thinking_block = {"text": "Thinking content"} + + result = handler.translate_thinking_blocks(thinking_block) + + assert result is not None + assert len(result) == 1 + assert result[0]["type"] == "thinking" + assert result[0]["thinking"] == "Thinking content" + + def test_translate_thinking_blocks_streaming_signature(self): + """Test translate_thinking_blocks method with signature.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + thinking_block = {"signature": "sig-abc"} + + result = handler.translate_thinking_blocks(thinking_block) + + assert result is not None + assert len(result) == 1 + assert result[0]["type"] == "thinking" + assert result[0]["signature"] == "sig-abc" + assert ( + result[0]["thinking"] == "" + ) # Empty string for consistency with Anthropic + + def test_translate_thinking_blocks_streaming_redacted(self): + """Test translate_thinking_blocks method with redacted content.""" + from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder + + handler = AWSEventStreamDecoder(model="amazon.nova-2-lite-v1:0") + + thinking_block = {"redactedContent": {}} + + result = handler.translate_thinking_blocks(thinking_block) + + assert result is not None + assert len(result) == 1 + assert result[0]["type"] == "redacted_thinking" From 4063501d6981a7b0606f4942deb2b7d0cc43dc06 Mon Sep 17 00:00:00 2001 From: yuneng-jiang Date: Tue, 2 Dec 2025 17:08:17 -0800 Subject: [PATCH 233/248] Show all credential values on Edit Credential Modal --- .../model_add/AddCredentialModal.test.tsx | 108 +++++++++++++++ .../model_add/AddCredentialModal.tsx | 118 +++++++++++++++++ .../model_add/EditCredentialModal.test.tsx | 123 ++++++++++++++++++ ...ntials_tab.tsx => EditCredentialModal.tsx} | 60 ++++----- .../src/components/model_add/credentials.tsx | 22 ++-- 5 files changed, 386 insertions(+), 45 deletions(-) create mode 100644 ui/litellm-dashboard/src/components/model_add/AddCredentialModal.test.tsx create mode 100644 ui/litellm-dashboard/src/components/model_add/AddCredentialModal.tsx create mode 100644 ui/litellm-dashboard/src/components/model_add/EditCredentialModal.test.tsx rename ui/litellm-dashboard/src/components/model_add/{add_credentials_tab.tsx => EditCredentialModal.tsx} (78%) diff --git a/ui/litellm-dashboard/src/components/model_add/AddCredentialModal.test.tsx b/ui/litellm-dashboard/src/components/model_add/AddCredentialModal.test.tsx new file mode 100644 index 0000000000..aee7a0cdd1 --- /dev/null +++ b/ui/litellm-dashboard/src/components/model_add/AddCredentialModal.test.tsx @@ -0,0 +1,108 @@ +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { render, screen, waitFor } from "@testing-library/react"; +import { describe, expect, it, vi } from "vitest"; +import { Providers } from "../provider_info_helpers"; +import AddCredentialModal from "./AddCredentialModal"; + +vi.mock("../networking", async () => { + const actual = await vi.importActual("../networking"); + return { + ...actual, + getProviderCreateMetadata: vi.fn().mockResolvedValue([ + { + provider: "OpenAI", + provider_display_name: Providers.OpenAI, + litellm_provider: "openai", + default_model_placeholder: "gpt-3.5-turbo", + credential_fields: [ + { + key: "api_key", + label: "OpenAI API Key", + field_type: "password", + required: true, + }, + { + key: "api_base", + label: "API Base", + field_type: "text", + placeholder: "https://api.openai.com/v1", + }, + ], + }, + { + provider: "Anthropic", + provider_display_name: Providers.Anthropic, + litellm_provider: "anthropic", + default_model_placeholder: "claude-3-opus-20240229", + credential_fields: [ + { + key: "api_key", + label: "Anthropic API Key", + field_type: "password", + required: true, + }, + ], + }, + ]), + }; +}); + +const createQueryClient = () => + new QueryClient({ + defaultOptions: { + queries: { + retry: false, + gcTime: 0, + }, + }, + }); + +const mockUploadProps = { + beforeUpload: vi.fn(), + onChange: vi.fn(), +}; + +describe("AddCredentialModal", () => { + it("should render", () => { + const queryClient = createQueryClient(); + const onCancel = vi.fn(); + const onAddCredential = vi.fn(); + + render( + + + , + ); + + expect(screen.getByText("Add New Credential")).toBeInTheDocument(); + expect(screen.getByLabelText("Credential Name:")).toBeInTheDocument(); + expect(screen.getByLabelText("Provider:")).toBeInTheDocument(); + }); + + it("should show the correct provider fields", async () => { + const queryClient = createQueryClient(); + const onCancel = vi.fn(); + const onAddCredential = vi.fn(); + + render( + + + , + ); + + await waitFor(() => { + expect(screen.getByLabelText("OpenAI API Key")).toBeInTheDocument(); + expect(screen.getByPlaceholderText("https://api.openai.com/v1")).toBeInTheDocument(); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/model_add/AddCredentialModal.tsx b/ui/litellm-dashboard/src/components/model_add/AddCredentialModal.tsx new file mode 100644 index 0000000000..694a98201c --- /dev/null +++ b/ui/litellm-dashboard/src/components/model_add/AddCredentialModal.tsx @@ -0,0 +1,118 @@ +import { TextInput } from "@tremor/react"; +import { Select as AntdSelect, Button, Form, Modal, Tooltip, Typography } from "antd"; +import type { UploadProps } from "antd/es/upload"; +import React, { useState } from "react"; +import ProviderSpecificFields from "../add_model/provider_specific_fields"; +import { Providers, providerLogoMap } from "../provider_info_helpers"; +const { Link } = Typography; + +interface AddCredentialsModalProps { + open: boolean; + onCancel: () => void; + onAddCredential: (values: any) => void; + uploadProps: UploadProps; +} + +const AddCredentialsModal: React.FC = ({ open, onCancel, onAddCredential, uploadProps }) => { + const [form] = Form.useForm(); + const [selectedProvider, setSelectedProvider] = useState(Providers.OpenAI); + + const handleSubmit = (values: any) => { + const filteredValues = Object.entries(values).reduce((acc, [key, value]) => { + if (value !== "" && value !== undefined && value !== null) { + acc[key] = value; + } + return acc; + }, {} as any); + onAddCredential(filteredValues); + form.resetFields(); + }; + + return ( + { + onCancel(); + form.resetFields(); + }} + footer={null} + width={600} + > +
    + {/* Credential Name */} + + + + + {/* Provider Selection */} + + { + setSelectedProvider(value as Providers); + form.setFieldValue("custom_llm_provider", value); + }} + > + {Object.entries(Providers).map(([providerEnum, providerDisplayName]) => ( + +
    + {`${providerEnum} { + const target = e.target as HTMLImageElement; + const parent = target.parentElement; + if (parent) { + const fallbackDiv = document.createElement("div"); + fallbackDiv.className = + "w-5 h-5 rounded-full bg-gray-200 flex items-center justify-center text-xs"; + fallbackDiv.textContent = providerDisplayName.charAt(0); + parent.replaceChild(fallbackDiv, target); + } + }} + /> + {providerDisplayName} +
    +     + ))} +     +     + + + + {/* Modal Footer */} +
    + + Need Help? + + +
    + + +
    +
    + +
    + ); +}; + +export default AddCredentialsModal; diff --git a/ui/litellm-dashboard/src/components/model_add/EditCredentialModal.test.tsx b/ui/litellm-dashboard/src/components/model_add/EditCredentialModal.test.tsx new file mode 100644 index 0000000000..def3b4f6cd --- /dev/null +++ b/ui/litellm-dashboard/src/components/model_add/EditCredentialModal.test.tsx @@ -0,0 +1,123 @@ +import { QueryClient, QueryClientProvider } from "@tanstack/react-query"; +import { render, screen, waitFor } from "@testing-library/react"; +import { describe, expect, it, vi } from "vitest"; +import { Providers } from "../provider_info_helpers"; +import { CredentialItem } from "../networking"; +import EditCredentialModal from "./EditCredentialModal"; + +vi.mock("../networking", async () => { + const actual = await vi.importActual("../networking"); + return { + ...actual, + getProviderCreateMetadata: vi.fn().mockResolvedValue([ + { + provider: "OpenAI", + provider_display_name: Providers.OpenAI, + litellm_provider: "openai", + default_model_placeholder: "gpt-3.5-turbo", + credential_fields: [ + { + key: "api_key", + label: "OpenAI API Key", + field_type: "password", + required: true, + }, + { + key: "api_base", + label: "API Base", + field_type: "text", + placeholder: "https://api.openai.com/v1", + }, + ], + }, + { + provider: "Anthropic", + provider_display_name: Providers.Anthropic, + litellm_provider: "anthropic", + default_model_placeholder: "claude-3-opus-20240229", + credential_fields: [ + { + key: "api_key", + label: "Anthropic API Key", + field_type: "password", + required: true, + }, + ], + }, + ]), + }; +}); + +const createQueryClient = () => + new QueryClient({ + defaultOptions: { + queries: { + retry: false, + gcTime: 0, + }, + }, + }); + +const mockUploadProps = { + beforeUpload: vi.fn(), + onChange: vi.fn(), +}; + +const mockCredential: CredentialItem = { + credential_name: "test-credential", + credential_values: { + api_key: "test-api-key", + api_base: "https://api.test.com", + }, + credential_info: { + custom_llm_provider: Providers.OpenAI, + }, +}; + +describe("EditCredentialModal", () => { + it("should render", () => { + const queryClient = createQueryClient(); + const onCancel = vi.fn(); + const onUpdateCredential = vi.fn(); + + render( + + + , + ); + + expect(screen.getByText("Edit Credential")).toBeInTheDocument(); + expect(screen.getByLabelText("Credential Name:")).toBeInTheDocument(); + expect(screen.getByLabelText("Provider:")).toBeInTheDocument(); + }); + + it("should render initial values", async () => { + const queryClient = createQueryClient(); + const onCancel = vi.fn(); + const onUpdateCredential = vi.fn(); + + render( + + + , + ); + + await waitFor(() => { + const credentialNameInput = screen.getByLabelText("Credential Name:") as HTMLInputElement; + expect(credentialNameInput.value).toBe("test-credential"); + expect(credentialNameInput.disabled).toBe(true); + }); + }); +}); diff --git a/ui/litellm-dashboard/src/components/model_add/add_credentials_tab.tsx b/ui/litellm-dashboard/src/components/model_add/EditCredentialModal.tsx similarity index 78% rename from ui/litellm-dashboard/src/components/model_add/add_credentials_tab.tsx rename to ui/litellm-dashboard/src/components/model_add/EditCredentialModal.tsx index 9c061eb121..b206ed6c91 100644 --- a/ui/litellm-dashboard/src/components/model_add/add_credentials_tab.tsx +++ b/ui/litellm-dashboard/src/components/model_add/EditCredentialModal.tsx @@ -1,34 +1,29 @@ -import React, { useEffect, useState } from "react"; -import { Form, Button, Tooltip, Typography, Select as AntdSelect, Modal } from "antd"; -import type { UploadProps } from "antd/es/upload"; -import { Providers, providerLogoMap } from "../provider_info_helpers"; -import ProviderSpecificFields from "../add_model/provider_specific_fields"; import { TextInput } from "@tremor/react"; +import { Select as AntdSelect, Button, Form, Modal, Tooltip, Typography } from "antd"; +import type { UploadProps } from "antd/es/upload"; +import { useEffect, useState } from "react"; +import ProviderSpecificFields from "../add_model/provider_specific_fields"; import { CredentialItem } from "../networking"; -const { Title, Link } = Typography; +import { Providers, providerLogoMap } from "../provider_info_helpers"; +const { Link } = Typography; -interface AddCredentialsModalProps { - isVisible: boolean; +interface EditCredentialsModalProps { + open: boolean; onCancel: () => void; - onAddCredential: (values: any) => void; onUpdateCredential: (values: any) => void; uploadProps: UploadProps; - addOrEdit: "add" | "edit"; existingCredential: CredentialItem | null; } -const AddCredentialsModal: React.FC = ({ - isVisible, +export default function EditCredentialsModal({ + open, onCancel, - onAddCredential, onUpdateCredential, uploadProps, - addOrEdit, existingCredential, -}) => { +}: EditCredentialsModalProps) { const [form] = Form.useForm(); - const [selectedProvider, setSelectedProvider] = useState(Providers.OpenAI); - const [showAdvancedSettings, setShowAdvancedSettings] = useState(false); + const [selectedProvider, setSelectedProvider] = useState(Providers.Anthropic); const handleSubmit = (values: any) => { const filteredValues = Object.entries(values).reduce((acc, [key, value]) => { @@ -37,23 +32,25 @@ const AddCredentialsModal: React.FC = ({ } return acc; }, {} as any); - if (addOrEdit === "add") { - onAddCredential(filteredValues); - } else { - onUpdateCredential(filteredValues); - } + onUpdateCredential(filteredValues); form.resetFields(); }; useEffect(() => { if (existingCredential) { + // Spread all credential_values dynamically, converting undefined/null to null for form compatibility + const credentialValues = Object.entries(existingCredential.credential_values || {}).reduce( + (acc, [key, value]) => { + acc[key] = value ?? null; + return acc; + }, + {} as Record, + ); + form.setFieldsValue({ credential_name: existingCredential.credential_name, custom_llm_provider: existingCredential.credential_info.custom_llm_provider, - api_base: existingCredential.credential_values.api_base, - api_version: existingCredential.credential_values.api_version, - base_model: existingCredential.credential_values.base_model, - api_key: existingCredential.credential_values.api_key, + ...credentialValues, }); setSelectedProvider(existingCredential.credential_info.custom_llm_provider as Providers); } @@ -61,14 +58,15 @@ const AddCredentialsModal: React.FC = ({ return ( { onCancel(); form.resetFields(); }} footer={null} width={600} + destroyOnHidden={true} >
    {/* Credential Name */} @@ -142,12 +140,10 @@ const AddCredentialsModal: React.FC = ({ > Cancel - +
    ); -}; - -export default AddCredentialsModal; +} diff --git a/ui/litellm-dashboard/src/components/model_add/credentials.tsx b/ui/litellm-dashboard/src/components/model_add/credentials.tsx index e36a759294..eecd26db25 100644 --- a/ui/litellm-dashboard/src/components/model_add/credentials.tsx +++ b/ui/litellm-dashboard/src/components/model_add/credentials.tsx @@ -19,10 +19,11 @@ import { credentialUpdateCall, CredentialItem, } from "@/components/networking"; // Assume this is your networking function -import AddCredentialsTab from "./add_credentials_tab"; +import AddCredentialsTab from "./AddCredentialModal"; import CredentialDeleteModal from "./CredentialDeleteModal"; import { Form } from "antd"; import NotificationsManager from "../molecules/notifications_manager"; +import EditCredentialsModal from "./EditCredentialModal"; interface CredentialsPanelProps { accessToken: string | null; uploadProps: UploadProps; @@ -60,10 +61,10 @@ const CredentialsPanel: React.FC = ({ }, }; - const response = await credentialUpdateCall(accessToken, values.credential_name, newCredential); + await credentialUpdateCall(accessToken, values.credential_name, newCredential); NotificationsManager.success("Credential updated successfully"); setIsUpdateModalOpen(false); - fetchCredentials(accessToken); + await fetchCredentials(accessToken); }; const handleAddCredential = async (values: any) => { @@ -84,10 +85,10 @@ const CredentialsPanel: React.FC = ({ }; // Add to list and close modal - const response = await credentialCreateCall(accessToken, newCredential); + await credentialCreateCall(accessToken, newCredential); NotificationsManager.success("Credential added successfully"); setIsAddModalOpen(false); - fetchCredentials(accessToken); + await fetchCredentials(accessToken); }; useEffect(() => { @@ -189,23 +190,18 @@ const CredentialsPanel: React.FC = ({ {isAddModalOpen && ( setIsAddModalOpen(false)} uploadProps={uploadProps} - addOrEdit="add" - onUpdateCredential={handleUpdateCredential} - existingCredential={null} /> )} {isUpdateModalOpen && ( - setIsUpdateModalOpen(false)} - addOrEdit="edit" /> )} From 427074ac6e80ffe3b30054dd10c2a7427cfbf96f Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Tue, 2 Dec 2025 17:27:50 -0800 Subject: [PATCH 234/248] Fix: Datadog callback regression when ddtrace is installed (#17393) * fix DD agent host logging * docs fix * test_datadog_agent_configuration * test_datadog_ignores_ddtrace_agent_host --- docs/my-website/docs/observability/datadog.md | 18 +++---- litellm/integrations/datadog/datadog.py | 13 ++--- tests/logging_callback_tests/test_datadog.py | 47 +++++++++++++++++-- 3 files changed, 60 insertions(+), 18 deletions(-) diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md index 5cb5ab3af2..b2901650ea 100644 --- a/docs/my-website/docs/observability/datadog.md +++ b/docs/my-website/docs/observability/datadog.md @@ -71,17 +71,19 @@ DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source. use to different Send logs through a local DataDog agent (useful for containerized environments): ```shell -DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent -DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518) -DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth) -DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source +LITELLM_DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent +LITELLM_DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518) +DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth) +DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source ``` -When `DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for: +When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for: - Centralized log shipping in containerized environments - Reducing direct API calls from multiple services - Leveraging agent-side processing and filtering +**Note:** We use `LITELLM_DD_AGENT_HOST` instead of `DD_AGENT_HOST` to avoid conflicts with `ddtrace` which automatically sets `DD_AGENT_HOST` for APM tracing. + **Step 3**: Start the proxy, make a test request Start proxy @@ -191,8 +193,8 @@ LiteLLM supports customizing the following Datadog environment variables |---------------------|-------------|---------------|----------| | `DD_API_KEY` | Your Datadog API key for authentication (required for direct API, optional for agent) | None | Conditional* | | `DD_SITE` | Your Datadog site (e.g., "us5.datadoghq.com") (required for direct API) | None | Conditional* | -| `DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No | -| `DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No | +| `LITELLM_DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No | +| `LITELLM_DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No | | `DD_ENV` | Environment tag for your logs (e.g., "production", "staging") | "unknown" | ❌ No | | `DD_SERVICE` | Service name for your logs | "litellm-server" | ❌ No | | `DD_SOURCE` | Source name for your logs | "litellm" | ❌ No | @@ -201,5 +203,5 @@ LiteLLM supports customizing the following Datadog environment variables | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No | \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required -\* **Optional when using DataDog Agent**: Set `DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required +\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 46e1a2c201..21e1d56222 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -65,11 +65,11 @@ class DataDogLogger( `DD_SITE` - your datadog site, example = `"us5.datadoghq.com"` Optional environment variables (DataDog Agent): - `DD_AGENT_HOST` - hostname or IP of DataDog agent, example = `"localhost"` - `DD_AGENT_PORT` - port of DataDog agent (default: 10518 for logs) + `LITELLM_DD_AGENT_HOST` - hostname or IP of DataDog agent, example = `"localhost"` + `LITELLM_DD_AGENT_PORT` - port of DataDog agent (default: 10518 for logs) - Note: If DD_AGENT_HOST is set, logs will be sent to the agent instead of directly to DataDog API. - In this case, DD_API_KEY and DD_SITE are not required (agent handles authentication). + Note: We use LITELLM_DD_AGENT_HOST instead of DD_AGENT_HOST to avoid conflicts + with ddtrace which automatically sets DD_AGENT_HOST for APM tracing. """ try: verbose_logger.debug("Datadog: in init datadog logger") @@ -85,7 +85,8 @@ class DataDogLogger( ) # Configure DataDog endpoint (Agent or Direct API) - dd_agent_host = os.getenv("DD_AGENT_HOST") + # Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST + dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST") if dd_agent_host: self._configure_dd_agent(dd_agent_host=dd_agent_host) else: @@ -127,7 +128,7 @@ class DataDogLogger( Args: dd_agent_host: Hostname or IP of DataDog agent """ - dd_agent_port = os.getenv("DD_AGENT_PORT", "10518") # default port for logs + dd_agent_port = os.getenv("LITELLM_DD_AGENT_PORT", "10518") # default port for logs self.intake_url = f"http://{dd_agent_host}:{dd_agent_port}/api/v2/logs" self.DD_API_KEY = os.getenv("DD_API_KEY") # Optional when using agent verbose_logger.debug(f"Datadog: Using DD Agent at {self.intake_url}") diff --git a/tests/logging_callback_tests/test_datadog.py b/tests/logging_callback_tests/test_datadog.py index 13125aa495..c877f34ac0 100644 --- a/tests/logging_callback_tests/test_datadog.py +++ b/tests/logging_callback_tests/test_datadog.py @@ -633,11 +633,14 @@ async def test_datadog_message_redaction(): def test_datadog_agent_configuration(): """ - Test that DataDog logger correctly configures agent endpoint when DD_AGENT_HOST is set + Test that DataDog logger correctly configures agent endpoint when LITELLM_DD_AGENT_HOST is set. + + Note: We use LITELLM_DD_AGENT_HOST instead of DD_AGENT_HOST to avoid conflicts + with ddtrace which automatically sets DD_AGENT_HOST for APM tracing. """ test_env = { - "DD_AGENT_HOST": "localhost", - "DD_AGENT_PORT": "10518", + "LITELLM_DD_AGENT_HOST": "localhost", + "LITELLM_DD_AGENT_PORT": "10518", } # Remove DD_SITE and DD_API_KEY to verify they're not required for agent mode @@ -654,4 +657,40 @@ def test_datadog_agent_configuration(): assert dd_logger.intake_url == "http://localhost:10518/api/v2/logs", f"Expected agent URL, got {dd_logger.intake_url}" # Verify DD_API_KEY is optional (can be None) - assert dd_logger.DD_API_KEY is None or isinstance(dd_logger.DD_API_KEY, str) \ No newline at end of file + assert dd_logger.DD_API_KEY is None or isinstance(dd_logger.DD_API_KEY, str) + + +def test_datadog_ignores_ddtrace_agent_host(): + """ + Regression test: Ensure DD_AGENT_HOST set by ddtrace doesn't interfere with LiteLLM logging. + + When users have ddtrace installed for APM tracing, it automatically sets DD_AGENT_HOST. + LiteLLM should ignore DD_AGENT_HOST and only use LITELLM_DD_AGENT_HOST for agent mode. + + This prevents the 404 error when ddtrace's DD_AGENT_HOST points to an APM endpoint + that doesn't support /api/v2/logs. + + Regression test for: https://github.com/BerriAI/litellm/issues/16379 + """ + test_env = { + # User's explicit config for LiteLLM logging (direct API) + "DD_API_KEY": "fake-api-key", + "DD_SITE": "us5.datadoghq.com", + # ddtrace automatically sets these for APM tracing + "DD_AGENT_HOST": "10.176.100.40", + "DD_AGENT_PORT": "8126", + } + + with patch.dict(os.environ, test_env, clear=False): + with patch("asyncio.create_task"): + dd_logger = DataDogLogger() + + # Verify direct API endpoint is used (DD_AGENT_HOST should be ignored) + expected_url = "https://http-intake.logs.us5.datadoghq.com/api/v2/logs" + assert dd_logger.intake_url == expected_url, ( + f"Expected direct API URL '{expected_url}', got '{dd_logger.intake_url}'. " + "DD_AGENT_HOST (set by ddtrace) should be ignored - only LITELLM_DD_AGENT_HOST should trigger agent mode." + ) + + # Verify API key is set correctly + assert dd_logger.DD_API_KEY == "fake-api-key" \ No newline at end of file From 099ccf56a746c9bca8afa4c0c62fdb52fa5abfa9 Mon Sep 17 00:00:00 2001 From: Richard Song <9144514+richardmcsong@users.noreply.github.com> Date: Wed, 3 Dec 2025 00:57:07 -0500 Subject: [PATCH 235/248] Refactor add_schema_to_components to move definitions to components/schemas and add corresponding unit test (#17389) --- .../proxy/common_utils/custom_openapi_spec.py | 2 +- .../common_utils/test_custom_openapi_spec.py | 29 +++++++++++++++++++ 2 files changed, 30 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/common_utils/custom_openapi_spec.py b/litellm/proxy/common_utils/custom_openapi_spec.py index c448742f6d..69472c2cda 100644 --- a/litellm/proxy/common_utils/custom_openapi_spec.py +++ b/litellm/proxy/common_utils/custom_openapi_spec.py @@ -72,7 +72,7 @@ class CustomOpenAPISpec: openapi_schema["components"]["schemas"] = {} # Add the schema - openapi_schema["components"]["schemas"][schema_name] = schema_def + CustomOpenAPISpec._move_defs_to_components(openapi_schema, {schema_name: schema_def}) @staticmethod def add_request_body_to_paths(openapi_schema: Dict[str, Any], paths: List[str], schema_ref: str) -> None: diff --git a/tests/test_litellm/proxy/common_utils/test_custom_openapi_spec.py b/tests/test_litellm/proxy/common_utils/test_custom_openapi_spec.py index 7549b4259a..5fef35eb82 100644 --- a/tests/test_litellm/proxy/common_utils/test_custom_openapi_spec.py +++ b/tests/test_litellm/proxy/common_utils/test_custom_openapi_spec.py @@ -90,6 +90,35 @@ class TestCustomOpenAPISpec: ) assert result == base_openapi_schema +def test_defs_rewritten_in_add_schema_to_components(): + """ + Test that defs are rewritten to components/schemas in add_schema_to_components. + """ + + openapi_schema = {} + schema_name = "SchemaName" + schema_def = { + "type": "object", + "properties": { + "messages": { + "type": "array", + "items": { + "anyOf": [ + {"$ref": "#/$defs/UserMessage"}, + {"$ref": "#/$defs/AssistantMessage"} + ] + } + } + }, + "$defs": { + "UserMessage": {"type": "object"}, + "AssistantMessage": {"type": "object"} + } + } + CustomOpenAPISpec.add_schema_to_components(openapi_schema=openapi_schema, schema_name=schema_name, schema_def=schema_def) + assert "$defs" not in openapi_schema + assert openapi_schema["components"]["schemas"]["SchemaName"]["properties"]["messages"]["items"]["anyOf"][0]["$ref"] == "#/components/schemas/UserMessage" + assert openapi_schema["components"]["schemas"]["SchemaName"]["properties"]["messages"]["items"]["anyOf"][1]["$ref"] == "#/components/schemas/AssistantMessage" def test_move_defs_to_components(): """ From f22bc0aab20e9b5336e73d67ba1631176cbacfd6 Mon Sep 17 00:00:00 2001 From: Matt Greathouse Date: Wed, 3 Dec 2025 01:00:19 -0500 Subject: [PATCH 236/248] Support Deepseek 3.2 with Reasoning (#17384) * Add openrouter/deepseek/deepseek-v3.2 * Added deepseek-provided v3.2 * Allow reasoning effort param for openrouter models that support it * Added tests --- .../llms/openrouter/chat/transformation.py | 15 ++++++++++ model_prices_and_context_window.json | 30 +++++++++++++++++++ .../test_openrouter_chat_transformation.py | 27 +++++++++++++++++ 3 files changed, 72 insertions(+) diff --git a/litellm/llms/openrouter/chat/transformation.py b/litellm/llms/openrouter/chat/transformation.py index f1eafe4e29..b5610852fd 100644 --- a/litellm/llms/openrouter/chat/transformation.py +++ b/litellm/llms/openrouter/chat/transformation.py @@ -10,6 +10,7 @@ from enum import Enum from typing import Any, AsyncIterator, Iterator, List, Optional, Tuple, Union, cast import httpx +import litellm from litellm.llms.base_llm.base_model_iterator import BaseModelResponseIterator from litellm.llms.base_llm.chat.transformation import BaseLLMException @@ -28,6 +29,20 @@ class CacheControlSupportedModels(str, Enum): class OpenrouterConfig(OpenAIGPTConfig): + def get_supported_openai_params(self, model: str) -> list: + """ + Allow reasoning parameters for models flagged as reasoning-capable. + """ + supported_params = super().get_supported_openai_params(model=model) + try: + if litellm.supports_reasoning( + model=model, custom_llm_provider="openrouter" + ) or litellm.supports_reasoning(model=model): + supported_params.append("reasoning_effort") + except Exception: + pass + return list(dict.fromkeys(supported_params)) + def map_openai_params( self, non_default_params: dict, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 19ed734c5f..f82abce525 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -9629,6 +9629,21 @@ "supports_prompt_caching": true, "supports_tool_choice": true }, + "deepseek/deepseek-v3.2": { + "input_cost_per_token": 2.8e-07, + "input_cost_per_token_cache_hit": 2.8e-08, + "litellm_provider": "deepseek", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 4e-07, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "deepseek.v3-v1:0": { "input_cost_per_token": 5.8e-07, "litellm_provider": "bedrock_converse", @@ -20565,6 +20580,21 @@ "supports_reasoning": true, "supports_tool_choice": true }, + "openrouter/deepseek/deepseek-v3.2": { + "input_cost_per_token": 2.8e-07, + "input_cost_per_token_cache_hit": 2.8e-08, + "litellm_provider": "openrouter", + "max_input_tokens": 163840, + "max_output_tokens": 163840, + "max_tokens": 8192, + "mode": "chat", + "output_cost_per_token": 4e-07, + "supports_assistant_prefill": true, + "supports_function_calling": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_tool_choice": true + }, "openrouter/deepseek/deepseek-v3.2-exp": { "input_cost_per_token": 2e-07, "input_cost_per_token_cache_hit": 2e-08, diff --git a/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py b/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py index 64ac299fd7..d5a73b3fd1 100644 --- a/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py +++ b/tests/test_litellm/llms/openrouter/chat/test_openrouter_chat_transformation.py @@ -489,3 +489,30 @@ def test_openrouter_cost_tracking_streaming(): # Verify cost field is preserved in the Usage object - this is the key data for cost tracking # The chunk_parser converts the dict to a Usage Pydantic model which includes the cost field assert result2.usage.cost == 0.0001 + + +def test_openrouter_reasoning_models_allow_reasoning_effort_param(): + """ + OpenRouter reasoning-capable models should accept the reasoning_effort param. + """ + config = OpenrouterConfig() + + supported_params = config.get_supported_openai_params( + model="openrouter/deepseek/deepseek-v3.2" + ) + + assert "reasoning_effort" in supported_params + assert supported_params.count("reasoning_effort") == 1 + + +def test_openrouter_non_reasoning_models_do_not_add_reasoning_effort(): + """ + Models without reasoning support should not gain reasoning-specific params. + """ + config = OpenrouterConfig() + + supported_params = config.get_supported_openai_params( + model="openrouter/anthropic/claude-3-5-haiku" + ) + + assert "reasoning_effort" not in supported_params From ae633184f72d8a12ae442a03ce86f51fc4f649fc Mon Sep 17 00:00:00 2001 From: mossbanay <2216177+mossbanay@users.noreply.github.com> Date: Wed, 3 Dec 2025 17:02:57 +1100 Subject: [PATCH 237/248] Add model price & details for Bedrock model global.anthropic.claude-opus-4-5-20251101-v1:0 (#17380) --- model_prices_and_context_window.json | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index f82abce525..999c88dde0 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -23850,6 +23850,32 @@ "supports_vision": true, "tool_use_system_prompt_tokens": 159 }, + "global.anthropic.claude-opus-4-5-20251101-v1:0": { + "cache_creation_input_token_cost": 6.25e-06, + "cache_read_input_token_cost": 5e-07, + "input_cost_per_token": 5e-06, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 200000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-05, + "search_context_cost_per_query": { + "search_context_size_high": 0.01, + "search_context_size_low": 0.01, + "search_context_size_medium": 0.01 + }, + "supports_assistant_prefill": true, + "supports_computer_use": true, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true, + "supports_vision": true, + "tool_use_system_prompt_tokens": 159 + }, "us.anthropic.claude-sonnet-4-20250514-v1:0": { "cache_creation_input_token_cost": 3.75e-06, "cache_read_input_token_cost": 3e-07, From 6b5ad5d5a6c9cce7008d0eb57258a826e6d82808 Mon Sep 17 00:00:00 2001 From: Ali Saleh Date: Wed, 3 Dec 2025 11:03:54 +0500 Subject: [PATCH 238/248] docs: Update Instructions For Phoenix Integration (#17373) --- .../docs/observability/phoenix_integration.md | 79 +++++++++++++++---- 1 file changed, 64 insertions(+), 15 deletions(-) diff --git a/docs/my-website/docs/observability/phoenix_integration.md b/docs/my-website/docs/observability/phoenix_integration.md index ad33743993..898d780668 100644 --- a/docs/my-website/docs/observability/phoenix_integration.md +++ b/docs/my-website/docs/observability/phoenix_integration.md @@ -6,7 +6,7 @@ Open source tracing and evaluation platform :::tip -This is community maintained, Please make an issue if you run into a bug +This is community maintained. Please make an issue if you run into a bug: https://github.com/BerriAI/litellm ::: @@ -31,19 +31,16 @@ litellm.callbacks = ["arize_phoenix"] import litellm import os -os.environ["PHOENIX_API_KEY"] = "" # Necessary only using Phoenix Cloud -os.environ["PHOENIX_COLLECTOR_HTTP_ENDPOINT"] = "" # The URL of your Phoenix OSS instance e.g. http://localhost:6006/v1/traces -os.environ["PHOENIX_PROJECT_NAME"]="litellm" # OPTIONAL: you can configure project names, otherwise traces would go to "default" project +# Set env variables +os.environ["PHOENIX_API_KEY"] = "d0*****" # Set the Phoenix API key here. It is necessary only when using Phoenix Cloud. +os.environ["PHOENIX_COLLECTOR_HTTP_ENDPOINT"] = "https://app.phoenix.arize.com/s//v1/traces" # Set the URL of your Phoenix OSS instance, otherwise tracer would use https://app.phoenix.arize.com/v1/traces for Phoenix Cloud. +os.environ["PHOENIX_PROJECT_NAME"] = "litellm" # Configure the project name, otherwise traces would go to "default" project. +os.environ['OPENAI_API_KEY'] = "fake-key" # Set the OpenAI API key here. -# This defaults to https://app.phoenix.arize.com/v1/traces for Phoenix Cloud - -# LLM API Keys -os.environ['OPENAI_API_KEY']="" - -# set arize as a callback, litellm will send the data to arize +# Set arize_phoenix as a callback & LiteLLM will send the data to Phoenix. litellm.callbacks = ["arize_phoenix"] - -# openai call + +# OpenAI call response = litellm.completion( model="gpt-3.5-turbo", messages=[ @@ -52,8 +49,9 @@ response = litellm.completion( ) ``` -### Using with LiteLLM Proxy +## Using with LiteLLM Proxy +1. Setup config.yaml ```yaml model_list: @@ -66,12 +64,63 @@ model_list: litellm_settings: callbacks: ["arize_phoenix"] +general_settings: + master_key: "sk-1234" + environment_variables: PHOENIX_API_KEY: "d0*****" - PHOENIX_COLLECTOR_ENDPOINT: "https://app.phoenix.arize.com/v1/traces" # OPTIONAL, for setting the GRPC endpoint - PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/v1/traces" # OPTIONAL, for setting the HTTP endpoint + PHOENIX_COLLECTOR_ENDPOINT: "https://app.phoenix.arize.com/s//v1/traces" # OPTIONAL - For setting the gRPC endpoint + PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/s//v1/traces" # OPTIONAL - For setting the HTTP endpoint ``` +2. Start the proxy + +```bash +litellm --config config.yaml +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ "model": "gpt-4o", "messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}]}' +``` + +## Supported Phoenix Endpoints +Phoenix now supports multiple deployment types. The correct endpoint depends on which version of Phoenix Cloud you are using. + +**Phoenix Cloud (With Spaces - New Version)** +Use this if your Phoenix URL contains `/s/` path. + +```bash +https://app.phoenix.arize.com/s//v1/traces +``` + +**Phoenix Cloud (Legacy - Deprecated)** +Use this only if your deployment still shows the `/legacy` pattern. + +```bash +https://app.phoenix.arize.com/legacy/v1/traces +``` + +**Phoenix Cloud (Without Spaces - Old Version)** +Use this if your Phoenix Cloud URL does not contain `/s/` or `/legacy` path. + +```bash +https://app.phoenix.arize.com/v1/traces +``` + +**Self-Hosted Phoenix (Local Instance)** +Use this when running Phoenix on your machine or a private server. + +```bash +http://localhost:6006/v1/traces +``` + +Depending on which Phoenix Cloud version or deployment you are using, you should set the corresponding endpoint in `PHOENIX_COLLECTOR_HTTP_ENDPOINT` or `PHOENIX_COLLECTOR_ENDPOINT`. + ## Support & Talk to Founders - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) From 566adebdec41d3f2790de33e43d93892f529f597 Mon Sep 17 00:00:00 2001 From: Mariano Hielpos <108539968+mhielpos-asapp@users.noreply.github.com> Date: Wed, 3 Dec 2025 03:06:51 -0300 Subject: [PATCH 239/248] update model_prices_and_context_window.json (#17376) * update model_prices_and_context_window.json * update * update --- ...odel_prices_and_context_window_backup.json | 88 +++++++++++++++++-- model_prices_and_context_window.json | 88 +++++++++++++++++-- 2 files changed, 160 insertions(+), 16 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 932508824a..464f9c185f 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -10772,25 +10772,25 @@ "supports_tool_choice": true }, "ft:babbage-002": { - "input_cost_per_token": 4e-07, + "input_cost_per_token": 1.6e-06, "input_cost_per_token_batches": 2e-07, "litellm_provider": "text-completion-openai", "max_input_tokens": 16384, "max_output_tokens": 4096, "max_tokens": 16384, "mode": "completion", - "output_cost_per_token": 4e-07, + "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 2e-07 }, "ft:davinci-002": { - "input_cost_per_token": 2e-06, + "input_cost_per_token": 1.2e-05, "input_cost_per_token_batches": 1e-06, "litellm_provider": "text-completion-openai", "max_input_tokens": 16384, "max_output_tokens": 4096, "max_tokens": 16384, "mode": "completion", - "output_cost_per_token": 2e-06, + "output_cost_per_token": 1.2e-05, "output_cost_per_token_batches": 1e-06 }, "ft:gpt-3.5-turbo": { @@ -10853,6 +10853,7 @@ "supports_tool_choice": true }, "ft:gpt-4o-2024-08-06": { + "cache_read_input_token_cost": 1.875e-06, "input_cost_per_token": 3.75e-06, "input_cost_per_token_batches": 1.875e-06, "litellm_provider": "openai", @@ -10865,6 +10866,7 @@ "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, + "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, @@ -10885,8 +10887,7 @@ "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true + "supports_tool_choice": true }, "ft:gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 1.5e-07, @@ -10905,8 +10906,79 @@ "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true + "supports_tool_choice": true + }, + "ft:gpt-4.1-2025-04-14": { + "cache_read_input_token_cost": 7.5e-07, + "input_cost_per_token": 3e-06, + "input_cost_per_token_batches": 1.5e-06, + "litellm_provider": "openai", + "max_input_tokens": 1047576, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_batches": 6e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4.1-mini-2025-04-14": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 8e-07, + "input_cost_per_token_batches": 4e-07, + "litellm_provider": "openai", + "max_input_tokens": 1047576, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 3.2e-06, + "output_cost_per_token_batches": 1.6e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4.1-nano-2025-04-14": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_batches": 1e-07, + "litellm_provider": "openai", + "max_input_tokens": 1047576, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 8e-07, + "output_cost_per_token_batches": 4e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:o4-mini-2025-04-16": { + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 4e-06, + "input_cost_per_token_batches": 2e-06, + "litellm_provider": "openai", + "max_input_tokens": 200000, + "max_output_tokens": 100000, + "max_tokens": 100000, + "mode": "chat", + "output_cost_per_token": 1.6e-05, + "output_cost_per_token_batches": 8e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": false, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true }, "gemini-1.0-pro": { "input_cost_per_character": 1.25e-07, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 999c88dde0..dbaa60e0f1 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -10852,25 +10852,25 @@ "supports_tool_choice": true }, "ft:babbage-002": { - "input_cost_per_token": 4e-07, + "input_cost_per_token": 1.6e-06, "input_cost_per_token_batches": 2e-07, "litellm_provider": "text-completion-openai", "max_input_tokens": 16384, "max_output_tokens": 4096, "max_tokens": 16384, "mode": "completion", - "output_cost_per_token": 4e-07, + "output_cost_per_token": 1.6e-06, "output_cost_per_token_batches": 2e-07 }, "ft:davinci-002": { - "input_cost_per_token": 2e-06, + "input_cost_per_token": 1.2e-05, "input_cost_per_token_batches": 1e-06, "litellm_provider": "text-completion-openai", "max_input_tokens": 16384, "max_output_tokens": 4096, "max_tokens": 16384, "mode": "completion", - "output_cost_per_token": 2e-06, + "output_cost_per_token": 1.2e-05, "output_cost_per_token_batches": 1e-06 }, "ft:gpt-3.5-turbo": { @@ -10933,6 +10933,7 @@ "supports_tool_choice": true }, "ft:gpt-4o-2024-08-06": { + "cache_read_input_token_cost": 1.875e-06, "input_cost_per_token": 3.75e-06, "input_cost_per_token_batches": 1.875e-06, "litellm_provider": "openai", @@ -10945,6 +10946,7 @@ "supports_function_calling": true, "supports_parallel_function_calling": true, "supports_pdf_input": true, + "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, "supports_tool_choice": true, @@ -10965,8 +10967,7 @@ "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true + "supports_tool_choice": true }, "ft:gpt-4o-mini-2024-07-18": { "cache_read_input_token_cost": 1.5e-07, @@ -10985,8 +10986,79 @@ "supports_prompt_caching": true, "supports_response_schema": true, "supports_system_messages": true, - "supports_tool_choice": true, - "supports_vision": true + "supports_tool_choice": true + }, + "ft:gpt-4.1-2025-04-14": { + "cache_read_input_token_cost": 7.5e-07, + "input_cost_per_token": 3e-06, + "input_cost_per_token_batches": 1.5e-06, + "litellm_provider": "openai", + "max_input_tokens": 1047576, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 1.2e-05, + "output_cost_per_token_batches": 6e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4.1-mini-2025-04-14": { + "cache_read_input_token_cost": 2e-07, + "input_cost_per_token": 8e-07, + "input_cost_per_token_batches": 4e-07, + "litellm_provider": "openai", + "max_input_tokens": 1047576, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 3.2e-06, + "output_cost_per_token_batches": 1.6e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:gpt-4.1-nano-2025-04-14": { + "cache_read_input_token_cost": 5e-08, + "input_cost_per_token": 2e-07, + "input_cost_per_token_batches": 1e-07, + "litellm_provider": "openai", + "max_input_tokens": 1047576, + "max_output_tokens": 32768, + "max_tokens": 32768, + "mode": "chat", + "output_cost_per_token": 8e-07, + "output_cost_per_token_batches": 4e-07, + "supports_function_calling": true, + "supports_parallel_function_calling": true, + "supports_prompt_caching": true, + "supports_response_schema": true, + "supports_system_messages": true, + "supports_tool_choice": true + }, + "ft:o4-mini-2025-04-16": { + "cache_read_input_token_cost": 1e-06, + "input_cost_per_token": 4e-06, + "input_cost_per_token_batches": 2e-06, + "litellm_provider": "openai", + "max_input_tokens": 200000, + "max_output_tokens": 100000, + "max_tokens": 100000, + "mode": "chat", + "output_cost_per_token": 1.6e-05, + "output_cost_per_token_batches": 8e-06, + "supports_function_calling": true, + "supports_parallel_function_calling": false, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_tool_choice": true }, "gemini-1.0-pro": { "input_cost_per_character": 1.25e-07, From 17faea96bb75be88f6761398e3ad2130a0d29cfc Mon Sep 17 00:00:00 2001 From: Jonathan Yang Date: Wed, 3 Dec 2025 07:09:57 +0100 Subject: [PATCH 240/248] fix: conditionally pass enable_cleanup_closed to aiohttp TCPConnector (#17367) * fix: conditionally pass enable_cleanup_closed to aiohttp TCPConnector Fixes deprecation warning on Python 3.12.7+ and 3.13.1+ where enable_cleanup_closed is no longer needed since the underlying CPython SSL connection leak bug was fixed. See: https://github.com/python/cpython/pull/118960 * chore: add aiohttp source reference to AIOHTTP_NEEDS_CLEANUP_CLOSED --- litellm/constants.py | 7 +++++++ litellm/llms/custom_httpx/http_handler.py | 3 ++- litellm/proxy/proxy_server.py | 3 ++- 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 6a67a9a0e1..db617a2e47 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1,4 +1,5 @@ import os +import sys from typing import List, Literal DEFAULT_HEALTH_CHECK_PROMPT = str( @@ -103,6 +104,12 @@ _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600 # 1 hour, re-use the same httpx client fo AIOHTTP_CONNECTOR_LIMIT = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT", 0)) AIOHTTP_KEEPALIVE_TIMEOUT = int(os.getenv("AIOHTTP_KEEPALIVE_TIMEOUT", 120)) AIOHTTP_TTL_DNS_CACHE = int(os.getenv("AIOHTTP_TTL_DNS_CACHE", 300)) +# enable_cleanup_closed is only needed for Python versions with the SSL leak bug +# Fixed in Python 3.12.7+ and 3.13.1+ (see https://github.com/python/cpython/pull/118960) +# Reference: https://github.com/aio-libs/aiohttp/blob/master/aiohttp/connector.py#L74-L78 +AIOHTTP_NEEDS_CLEANUP_CLOSED = ( + (3, 13, 0) <= sys.version_info < (3, 13, 1) or sys.version_info < (3, 12, 7) +) # WebSocket constants # Default to None (unlimited) to match OpenAI's official agents SDK behavior diff --git a/litellm/llms/custom_httpx/http_handler.py b/litellm/llms/custom_httpx/http_handler.py index c35e910ab0..b06e8463ab 100644 --- a/litellm/llms/custom_httpx/http_handler.py +++ b/litellm/llms/custom_httpx/http_handler.py @@ -17,6 +17,7 @@ from litellm.constants import ( _DEFAULT_TTL_FOR_HTTPX_CLIENTS, AIOHTTP_CONNECTOR_LIMIT, AIOHTTP_KEEPALIVE_TIMEOUT, + AIOHTTP_NEEDS_CLEANUP_CLOSED, AIOHTTP_TTL_DNS_CACHE, DEFAULT_SSL_CIPHERS, ) @@ -798,7 +799,7 @@ class AsyncHTTPHandler: limit=AIOHTTP_CONNECTOR_LIMIT, keepalive_timeout=AIOHTTP_KEEPALIVE_TIMEOUT, ttl_dns_cache=AIOHTTP_TTL_DNS_CACHE, - enable_cleanup_closed=True, + enable_cleanup_closed=AIOHTTP_NEEDS_CLEANUP_CLOSED, **connector_kwargs, ), trust_env=trust_env, diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index 9978916231..a1e01caddc 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -32,6 +32,7 @@ from litellm._uuid import uuid from litellm.constants import ( AIOHTTP_CONNECTOR_LIMIT, AIOHTTP_KEEPALIVE_TIMEOUT, + AIOHTTP_NEEDS_CLEANUP_CLOSED, AIOHTTP_TTL_DNS_CACHE, AUDIO_SPEECH_CHUNK_SIZE, BASE_MCP_ROUTE, @@ -635,7 +636,7 @@ async def _initialize_shared_aiohttp_session(): limit=AIOHTTP_CONNECTOR_LIMIT, keepalive_timeout=AIOHTTP_KEEPALIVE_TIMEOUT, ttl_dns_cache=AIOHTTP_TTL_DNS_CACHE, - enable_cleanup_closed=True, + enable_cleanup_closed=AIOHTTP_NEEDS_CLEANUP_CLOSED, ) session = ClientSession(connector=connector) From 43dd9e4a90281e42fd1015cd72cbca5280fc4e4c Mon Sep 17 00:00:00 2001 From: Jonathan Yang Date: Wed, 3 Dec 2025 07:12:55 +0100 Subject: [PATCH 241/248] fix: replace deprecated .dict() with .model_dump() in streaming_handler (#17359) Replace Pydantic v1 `.dict()` method with v2 `.model_dump()` to fix PydanticDeprecatedSince20 warnings. The `.dict()` method is deprecated in Pydantic v2 and will be removed in v3. Fixes #5987 --- .../litellm_core_utils/streaming_handler.py | 53 +++++++++++-------- 1 file changed, 30 insertions(+), 23 deletions(-) diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 4d8e109d88..a7f460fab5 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -96,9 +96,9 @@ class CustomStreamWrapper: self.system_fingerprint: Optional[str] = None self.received_finish_reason: Optional[str] = None - self.intermittent_finish_reason: Optional[str] = ( - None # finish reasons that show up mid-stream - ) + self.intermittent_finish_reason: Optional[ + str + ] = None # finish reasons that show up mid-stream self.special_tokens = [ "<|assistant|>", "<|system|>", @@ -735,7 +735,7 @@ class CustomStreamWrapper: and completion_obj["function_call"] is not None ) or ( - "tool_calls" in model_response.choices[0].delta + "tool_calls" in model_response.choices[0].delta and model_response.choices[0].delta["tool_calls"] is not None ) or ( @@ -889,7 +889,6 @@ class CustomStreamWrapper: ## check if openai/azure chunk original_chunk = response_obj.get("original_chunk", None) if original_chunk: - if len(original_chunk.choices) > 0: choices = [] for choice in original_chunk.choices: @@ -906,7 +905,6 @@ class CustomStreamWrapper: print_verbose(f"choices in streaming: {choices}") setattr(model_response, "choices", choices) else: - return model_response.system_fingerprint = ( original_chunk.system_fingerprint @@ -1435,9 +1433,9 @@ class CustomStreamWrapper: _json_delta = delta.model_dump() print_verbose(f"_json_delta: {_json_delta}") if "role" not in _json_delta or _json_delta["role"] is None: - _json_delta["role"] = ( - "assistant" # mistral's api returns role as None - ) + _json_delta[ + "role" + ] = "assistant" # mistral's api returns role as None if "tool_calls" in _json_delta and isinstance( _json_delta["tool_calls"], list ): @@ -1533,7 +1531,7 @@ class CustomStreamWrapper: async def _call_post_streaming_deployment_hook(self, chunk): """ Call the post-call streaming deployment hook for callbacks. - + This allows callbacks to modify streaming chunks before they're returned. """ try: @@ -1544,15 +1542,17 @@ class CustomStreamWrapper: # Get request kwargs from logging object request_data = self.logging_obj.model_call_details call_type_str = self.logging_obj.call_type - + try: typed_call_type = CallTypes(call_type_str) except ValueError: typed_call_type = None - + # Call hooks for all callbacks for callback in litellm.callbacks: - if isinstance(callback, CustomLogger) and hasattr(callback, "async_post_call_streaming_deployment_hook"): + if isinstance(callback, CustomLogger) and hasattr( + callback, "async_post_call_streaming_deployment_hook" + ): result = await callback.async_post_call_streaming_deployment_hook( request_data=request_data, response_chunk=chunk, @@ -1560,11 +1560,14 @@ class CustomStreamWrapper: ) if result is not None: chunk = result - + return chunk except Exception as e: from litellm._logging import verbose_logger - verbose_logger.exception(f"Error in post-call streaming deployment hook: {str(e)}") + + verbose_logger.exception( + f"Error in post-call streaming deployment hook: {str(e)}" + ) return chunk def cache_streaming_response(self, processed_chunk, cache_hit: bool): @@ -1687,7 +1690,7 @@ class CustomStreamWrapper: response, "usage" ): # remove usage from chunk, only send on final chunk # Convert the object to a dictionary - obj_dict = response.dict() + obj_dict = response.model_dump() # Remove an attribute (e.g., 'attr2') if "usage" in obj_dict: @@ -1852,7 +1855,7 @@ class CustomStreamWrapper: processed_chunk, "usage" ): # remove usage from chunk, only send on final chunk # Convert the object to a dictionary - obj_dict = processed_chunk.dict() + obj_dict = processed_chunk.model_dump() # Remove an attribute (e.g., 'attr2') if "usage" in obj_dict: @@ -1872,11 +1875,15 @@ class CustomStreamWrapper: if self.sent_last_chunk is True and self.stream_options is None: usage = calculate_total_usage(chunks=self.chunks) processed_chunk._hidden_params["usage"] = usage - + # Call post-call streaming deployment hook for final chunk if self.sent_last_chunk is True: - processed_chunk = await self._call_post_streaming_deployment_hook(processed_chunk) - + processed_chunk = ( + await self._call_post_streaming_deployment_hook( + processed_chunk + ) + ) + return processed_chunk raise StopAsyncIteration else: # temporary patch for non-aiohttp async calls @@ -1890,9 +1897,9 @@ class CustomStreamWrapper: chunk = next(self.completion_stream) if chunk is not None and chunk != b"": print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}") - processed_chunk: Optional[ModelResponseStream] = ( - self.chunk_creator(chunk=chunk) - ) + processed_chunk: Optional[ + ModelResponseStream + ] = self.chunk_creator(chunk=chunk) print_verbose( f"PROCESSED CHUNK POST CHUNK CREATOR: {processed_chunk}" ) From e289f5e454140042eda28ca27b8797078e2fe9f6 Mon Sep 17 00:00:00 2001 From: Deepak Tammali <45919384+deepaktammali@users.noreply.github.com> Date: Wed, 3 Dec 2025 11:44:48 +0530 Subject: [PATCH 242/248] feat: make streaming chunk size configurable in bedrock converse and invoke handlers (#17357) --- litellm/llms/bedrock/chat/converse_handler.py | 10 ++++++++-- litellm/llms/bedrock/chat/invoke_handler.py | 20 ++++++++++++------- 2 files changed, 21 insertions(+), 9 deletions(-) diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index fd1f6f0c89..d5bd054118 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -29,6 +29,7 @@ def make_sync_call( logging_obj: LiteLLMLoggingObject, json_mode: Optional[bool] = False, fake_stream: bool = False, + stream_chunk_size: int = 1024, ): if client is None: client = _get_httpx_client() # Create a new client if none provided @@ -66,7 +67,7 @@ def make_sync_call( ) else: decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) # LOGGING logging_obj.post_call( @@ -102,6 +103,7 @@ class BedrockConverseLLM(BaseAWSLLM): fake_stream: bool = False, json_mode: Optional[bool] = False, api_key: Optional[str] = None, + stream_chunk_size: int = 1024, ) -> CustomStreamWrapper: request_data = await litellm.AmazonConverseConfig()._async_transform_request( model=model, @@ -143,6 +145,7 @@ class BedrockConverseLLM(BaseAWSLLM): logging_obj=logging_obj, fake_stream=fake_stream, json_mode=json_mode, + stream_chunk_size=stream_chunk_size, ) streaming_response = CustomStreamWrapper( completion_stream=completion_stream, @@ -260,6 +263,7 @@ class BedrockConverseLLM(BaseAWSLLM): ): ## SETUP ## stream = optional_params.pop("stream", None) + stream_chunk_size = optional_params.pop("stream_chunk_size", 1024) unencoded_model_id = optional_params.pop("model_id", None) fake_stream = optional_params.pop("fake_stream", False) json_mode = optional_params.get("json_mode", False) @@ -356,7 +360,8 @@ class BedrockConverseLLM(BaseAWSLLM): json_mode=json_mode, fake_stream=fake_stream, credentials=credentials, - api_key=api_key + api_key=api_key, + stream_chunk_size=stream_chunk_size, ) # type: ignore ### ASYNC COMPLETION return self.async_completion( @@ -433,6 +438,7 @@ class BedrockConverseLLM(BaseAWSLLM): logging_obj=logging_obj, json_mode=json_mode, fake_stream=fake_stream, + stream_chunk_size=stream_chunk_size, ) streaming_response = CustomStreamWrapper( completion_stream=completion_stream, diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index 7a960fd45d..5e33a26644 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -192,6 +192,7 @@ async def make_call( fake_stream: bool = False, json_mode: Optional[bool] = False, bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None, + stream_chunk_size: int = 1024, ): try: if client is None: @@ -235,7 +236,7 @@ async def make_call( json_mode=json_mode, ) completion_stream = decoder.aiter_bytes( - response.aiter_bytes(chunk_size=1024) + response.aiter_bytes(chunk_size=stream_chunk_size) ) elif bedrock_invoke_provider == "deepseek_r1": decoder = AmazonDeepSeekR1StreamDecoder( @@ -243,12 +244,12 @@ async def make_call( sync_stream=False, ) completion_stream = decoder.aiter_bytes( - response.aiter_bytes(chunk_size=1024) + response.aiter_bytes(chunk_size=stream_chunk_size) ) else: decoder = AWSEventStreamDecoder(model=model) completion_stream = decoder.aiter_bytes( - response.aiter_bytes(chunk_size=1024) + response.aiter_bytes(chunk_size=stream_chunk_size) ) # LOGGING @@ -281,6 +282,7 @@ def make_sync_call( fake_stream: bool = False, json_mode: Optional[bool] = False, bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None, + stream_chunk_size: int = 1024, ): try: if client is None: @@ -321,16 +323,16 @@ def make_sync_call( sync_stream=True, json_mode=json_mode, ) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) elif bedrock_invoke_provider == "deepseek_r1": decoder = AmazonDeepSeekR1StreamDecoder( model=model, sync_stream=True, ) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) else: decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) # LOGGING logging_obj.post_call( @@ -729,6 +731,7 @@ class BedrockLLM(BaseAWSLLM): ## SETUP ## stream = optional_params.pop("stream", None) + stream_chunk_size = optional_params.pop("stream_chunk_size", 1024) provider = self.get_bedrock_invoke_provider(model) modelId = self.get_bedrock_model_id( @@ -1003,6 +1006,7 @@ class BedrockLLM(BaseAWSLLM): headers=prepped.headers, timeout=timeout, client=client, + stream_chunk_size=stream_chunk_size, ) # type: ignore ### ASYNC COMPLETION return self.async_completion( @@ -1048,7 +1052,7 @@ class BedrockLLM(BaseAWSLLM): decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) streaming_response = CustomStreamWrapper( completion_stream=completion_stream, model=model, @@ -1168,6 +1172,7 @@ class BedrockLLM(BaseAWSLLM): logger_fn=None, headers={}, client: Optional[AsyncHTTPHandler] = None, + stream_chunk_size: int = 1024, ) -> CustomStreamWrapper: # The call is not made here; instead, we prepare the necessary objects for the stream. @@ -1183,6 +1188,7 @@ class BedrockLLM(BaseAWSLLM): messages=messages, logging_obj=logging_obj, fake_stream=True if "ai21" in api_base else False, + stream_chunk_size=stream_chunk_size, ), model=model, custom_llm_provider="bedrock", From 4c6604b0da6bcdd9c230632f203921047163d636 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Wed, 3 Dec 2025 03:25:26 -0300 Subject: [PATCH 243/248] Cleanup: Remove orphan docs pages and Docusaurus template files (#17356) * docs: update getting started page - Add Core Functions table with link to full list - Add Responses API section - Add Async section with acompletion() example - Add "Switch Providers with One Line" example - Clarify Basic Usage supports multiple endpoints - Update models to current versions (openai/gpt-4o, anthropic/claude-sonnet-4) - Use provider/model format throughout - Fix deprecated import: from openai.error -> from openai - Keep original structure: community key, More details links, observability env vars * Cleanup: Remove orphan docs pages and Docusaurus template files - Remove orphan getting_started.md (not linked in sidebar) - Remove Docusaurus template intro.md - Remove tutorial-basics/ directory (Docusaurus template) - Remove tutorial-extras/ directory (Docusaurus template) --- docs/my-website/docs/getting_started.md | 108 ------------- docs/my-website/src/pages/intro.md | 47 ------ .../src/pages/tutorial-basics/_category_.json | 8 - .../pages/tutorial-basics/congratulations.md | 23 --- .../tutorial-basics/create-a-blog-post.md | 34 ---- .../tutorial-basics/create-a-document.md | 57 ------- .../pages/tutorial-basics/create-a-page.md | 43 ----- .../pages/tutorial-basics/deploy-your-site.md | 31 ---- .../tutorial-basics/markdown-features.mdx | 150 ------------------ .../src/pages/tutorial-extras/_category_.json | 7 - .../img/docsVersionDropdown.png | Bin 25427 -> 0 bytes .../tutorial-extras/img/localeDropdown.png | Bin 27841 -> 0 bytes .../tutorial-extras/manage-docs-versions.md | 55 ------- .../tutorial-extras/translate-your-site.md | 88 ---------- 14 files changed, 651 deletions(-) delete mode 100644 docs/my-website/docs/getting_started.md delete mode 100644 docs/my-website/src/pages/intro.md delete mode 100644 docs/my-website/src/pages/tutorial-basics/_category_.json delete mode 100644 docs/my-website/src/pages/tutorial-basics/congratulations.md delete mode 100644 docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md delete mode 100644 docs/my-website/src/pages/tutorial-basics/create-a-document.md delete mode 100644 docs/my-website/src/pages/tutorial-basics/create-a-page.md delete mode 100644 docs/my-website/src/pages/tutorial-basics/deploy-your-site.md delete mode 100644 docs/my-website/src/pages/tutorial-basics/markdown-features.mdx delete mode 100644 docs/my-website/src/pages/tutorial-extras/_category_.json delete mode 100644 docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png delete mode 100644 docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png delete mode 100644 docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md delete mode 100644 docs/my-website/src/pages/tutorial-extras/translate-your-site.md diff --git a/docs/my-website/docs/getting_started.md b/docs/my-website/docs/getting_started.md deleted file mode 100644 index 6b2c1fd531..0000000000 --- a/docs/my-website/docs/getting_started.md +++ /dev/null @@ -1,108 +0,0 @@ -# Getting Started - -import QuickStart from '../src/components/QuickStart.js' - -LiteLLM simplifies LLM API calls by mapping them all to the [OpenAI ChatCompletion format](https://platform.openai.com/docs/api-reference/chat). - -## basic usage - -By default we provide a free $10 community-key to try all providers supported on LiteLLM. - -```python -from litellm import completion - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "your-api-key" -os.environ["COHERE_API_KEY"] = "your-api-key" - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -# openai call -response = completion(model="gpt-3.5-turbo", messages=messages) - -# cohere call -response = completion("command-nightly", messages) -``` - -**Need a dedicated key?** -Email us @ krrish@berri.ai - -Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./proxy_api.md#supported-models) - -More details 👉 - -- [Completion() function details](./completion/) -- [Overview of supported models / providers on LiteLLM](./providers/) -- [Search all models / providers](https://models.litellm.ai/) -- [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main) - -## streaming - -Same example from before. Just pass in `stream=True` in the completion args. - -```python -from litellm import completion - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "openai key" -os.environ["COHERE_API_KEY"] = "cohere key" - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -# openai call -response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) - -# cohere call -response = completion("command-nightly", messages, stream=True) - -print(response) -``` - -More details 👉 - -- [streaming + async](./completion/stream.md) -- [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md) - -## exception handling - -LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. - -```python -from openai.error import OpenAIError -from litellm import completion - -os.environ["ANTHROPIC_API_KEY"] = "bad-key" -try: - # some code - completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}]) -except OpenAIError as e: - print(e) -``` - -## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks)) - -LiteLLM exposes pre defined callbacks to send data to MLflow, Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack - -```python -from litellm import completion - -## set env variables for logging tools (API key set up is not required when using MLflow) -os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" # get your public key at https://app.lunary.ai/settings -os.environ["HELICONE_API_KEY"] = "your-helicone-key" -os.environ["LANGFUSE_PUBLIC_KEY"] = "" -os.environ["LANGFUSE_SECRET_KEY"] = "" - -os.environ["OPENAI_API_KEY"] - -# set callbacks -litellm.success_callback = ["lunary", "mlflow", "langfuse", "helicone"] # log input/output to MLflow, langfuse, lunary, helicone - -#openai call -response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) -``` - -More details 👉 - -- [exception mapping](./exception_mapping.md) -- [retries + model fallbacks for completion()](./completion/reliable_completions.md) -- [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) diff --git a/docs/my-website/src/pages/intro.md b/docs/my-website/src/pages/intro.md deleted file mode 100644 index 8a2e69d95f..0000000000 --- a/docs/my-website/src/pages/intro.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Tutorial Intro - -Let's discover **Docusaurus in less than 5 minutes**. - -## Getting Started - -Get started by **creating a new site**. - -Or **try Docusaurus immediately** with **[docusaurus.new](https://docusaurus.new)**. - -### What you'll need - -- [Node.js](https://nodejs.org/en/download/) version 16.14 or above: - - When installing Node.js, you are recommended to check all checkboxes related to dependencies. - -## Generate a new site - -Generate a new Docusaurus site using the **classic template**. - -The classic template will automatically be added to your project after you run the command: - -```bash -npm init docusaurus@latest my-website classic -``` - -You can type this command into Command Prompt, Powershell, Terminal, or any other integrated terminal of your code editor. - -The command also installs all necessary dependencies you need to run Docusaurus. - -## Start your site - -Run the development server: - -```bash -cd my-website -npm run start -``` - -The `cd` command changes the directory you're working with. In order to work with your newly created Docusaurus site, you'll need to navigate the terminal there. - -The `npm run start` command builds your website locally and serves it through a development server, ready for you to view at http://localhost:3000/. - -Open `docs/intro.md` (this page) and edit some lines: the site **reloads automatically** and displays your changes. diff --git a/docs/my-website/src/pages/tutorial-basics/_category_.json b/docs/my-website/src/pages/tutorial-basics/_category_.json deleted file mode 100644 index 2e6db55b1e..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/_category_.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "label": "Tutorial - Basics", - "position": 2, - "link": { - "type": "generated-index", - "description": "5 minutes to learn the most important Docusaurus concepts." - } -} diff --git a/docs/my-website/src/pages/tutorial-basics/congratulations.md b/docs/my-website/src/pages/tutorial-basics/congratulations.md deleted file mode 100644 index 04771a00b7..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/congratulations.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -sidebar_position: 6 ---- - -# Congratulations! - -You have just learned the **basics of Docusaurus** and made some changes to the **initial template**. - -Docusaurus has **much more to offer**! - -Have **5 more minutes**? Take a look at **[versioning](../tutorial-extras/manage-docs-versions.md)** and **[i18n](../tutorial-extras/translate-your-site.md)**. - -Anything **unclear** or **buggy** in this tutorial? [Please report it!](https://github.com/facebook/docusaurus/discussions/4610) - -## What's next? - -- Read the [official documentation](https://docusaurus.io/) -- Modify your site configuration with [`docusaurus.config.js`](https://docusaurus.io/docs/api/docusaurus-config) -- Add navbar and footer items with [`themeConfig`](https://docusaurus.io/docs/api/themes/configuration) -- Add a custom [Design and Layout](https://docusaurus.io/docs/styling-layout) -- Add a [search bar](https://docusaurus.io/docs/search) -- Find inspirations in the [Docusaurus showcase](https://docusaurus.io/showcase) -- Get involved in the [Docusaurus Community](https://docusaurus.io/community/support) diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md b/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md deleted file mode 100644 index ea472bbaf8..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -sidebar_position: 3 ---- - -# Create a Blog Post - -Docusaurus creates a **page for each blog post**, but also a **blog index page**, a **tag system**, an **RSS** feed... - -## Create your first Post - -Create a file at `blog/2021-02-28-greetings.md`: - -```md title="blog/2021-02-28-greetings.md" ---- -slug: greetings -title: Greetings! -authors: - - name: Joel Marcey - title: Co-creator of Docusaurus 1 - url: https://github.com/JoelMarcey - image_url: https://github.com/JoelMarcey.png - - name: Sébastien Lorber - title: Docusaurus maintainer - url: https://sebastienlorber.com - image_url: https://github.com/slorber.png -tags: [greetings] ---- - -Congratulations, you have made your first post! - -Feel free to play around and edit this post as much you like. -``` - -A new blog post is now available at [http://localhost:3000/blog/greetings](http://localhost:3000/blog/greetings). diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-document.md b/docs/my-website/src/pages/tutorial-basics/create-a-document.md deleted file mode 100644 index ffddfa8eb8..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/create-a-document.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Create a Document - -Documents are **groups of pages** connected through: - -- a **sidebar** -- **previous/next navigation** -- **versioning** - -## Create your first Doc - -Create a Markdown file at `docs/hello.md`: - -```md title="docs/hello.md" -# Hello - -This is my **first Docusaurus document**! -``` - -A new document is now available at [http://localhost:3000/docs/hello](http://localhost:3000/docs/hello). - -## Configure the Sidebar - -Docusaurus automatically **creates a sidebar** from the `docs` folder. - -Add metadata to customize the sidebar label and position: - -```md title="docs/hello.md" {1-4} ---- -sidebar_label: 'Hi!' -sidebar_position: 3 ---- - -# Hello - -This is my **first Docusaurus document**! -``` - -It is also possible to create your sidebar explicitly in `sidebars.js`: - -```js title="sidebars.js" -module.exports = { - tutorialSidebar: [ - 'intro', - // highlight-next-line - 'hello', - { - type: 'category', - label: 'Tutorial', - items: ['tutorial-basics/create-a-document'], - }, - ], -}; -``` diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-page.md b/docs/my-website/src/pages/tutorial-basics/create-a-page.md deleted file mode 100644 index 20e2ac3005..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/create-a-page.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Create a Page - -Add **Markdown or React** files to `src/pages` to create a **standalone page**: - -- `src/pages/index.js` → `localhost:3000/` -- `src/pages/foo.md` → `localhost:3000/foo` -- `src/pages/foo/bar.js` → `localhost:3000/foo/bar` - -## Create your first React Page - -Create a file at `src/pages/my-react-page.js`: - -```jsx title="src/pages/my-react-page.js" -import React from 'react'; -import Layout from '@theme/Layout'; - -export default function MyReactPage() { - return ( - -

    My React page

    -

    This is a React page

    -     - ); -} -``` - -A new page is now available at [http://localhost:3000/my-react-page](http://localhost:3000/my-react-page). - -## Create your first Markdown Page - -Create a file at `src/pages/my-markdown-page.md`: - -```mdx title="src/pages/my-markdown-page.md" -# My Markdown page - -This is a Markdown page -``` - -A new page is now available at [http://localhost:3000/my-markdown-page](http://localhost:3000/my-markdown-page). diff --git a/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md b/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md deleted file mode 100644 index 1c50ee063e..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -sidebar_position: 5 ---- - -# Deploy your site - -Docusaurus is a **static-site-generator** (also called **[Jamstack](https://jamstack.org/)**). - -It builds your site as simple **static HTML, JavaScript and CSS files**. - -## Build your site - -Build your site **for production**: - -```bash -npm run build -``` - -The static files are generated in the `build` folder. - -## Deploy your site - -Test your production build locally: - -```bash -npm run serve -``` - -The `build` folder is now served at [http://localhost:3000/](http://localhost:3000/). - -You can now deploy the `build` folder **almost anywhere** easily, **for free** or very small cost (read the **[Deployment Guide](https://docusaurus.io/docs/deployment)**). diff --git a/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx b/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx deleted file mode 100644 index 0337f34d6a..0000000000 --- a/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx +++ /dev/null @@ -1,150 +0,0 @@ ---- -sidebar_position: 4 ---- - -# Markdown Features - -Docusaurus supports **[Markdown](https://daringfireball.net/projects/markdown/syntax)** and a few **additional features**. - -## Front Matter - -Markdown documents have metadata at the top called [Front Matter](https://jekyllrb.com/docs/front-matter/): - -```text title="my-doc.md" -// highlight-start ---- -id: my-doc-id -title: My document title -description: My document description -slug: /my-custom-url ---- -// highlight-end - -## Markdown heading - -Markdown text with [links](./hello.md) -``` - -## Links - -Regular Markdown links are supported, using url paths or relative file paths. - -```md -Let's see how to [Create a page](/create-a-page). -``` - -```md -Let's see how to [Create a page](./create-a-page.md). -``` - -**Result:** Let's see how to [Create a page](./create-a-page.md). - -## Images - -Regular Markdown images are supported. - -You can use absolute paths to reference images in the static directory (`static/img/docusaurus.png`): - -```md -![Docusaurus logo](/img/docusaurus.png) -``` - -![Docusaurus logo](/img/docusaurus.png) - -You can reference images relative to the current file as well. This is particularly useful to colocate images close to the Markdown files using them: - -```md -![Docusaurus logo](./img/docusaurus.png) -``` - -## Code Blocks - -Markdown code blocks are supported with Syntax highlighting. - - ```jsx title="src/components/HelloDocusaurus.js" - function HelloDocusaurus() { - return ( -

    Hello, Docusaurus!

    - ) - } - ``` - -```jsx title="src/components/HelloDocusaurus.js" -function HelloDocusaurus() { - return

    Hello, Docusaurus!

    ; -} -``` - -## Admonitions - -Docusaurus has a special syntax to create admonitions and callouts: - - :::tip My tip - - Use this awesome feature option - - ::: - - :::danger Take care - - This action is dangerous - - ::: - -:::tip My tip - -Use this awesome feature option - -::: - -:::danger Take care - -This action is dangerous - -::: - -## MDX and React Components - -[MDX](https://mdxjs.com/) can make your documentation more **interactive** and allows using any **React components inside Markdown**: - -```jsx -export const Highlight = ({children, color}) => ( - { - alert(`You clicked the color ${color} with label ${children}`) - }}> - {children} - -); - -This is Docusaurus green ! - -This is Facebook blue ! -``` - -export const Highlight = ({children, color}) => ( - { - alert(`You clicked the color ${color} with label ${children}`); - }}> - {children} - -); - -This is Docusaurus green ! - -This is Facebook blue ! diff --git 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z#OSfF27Mj!AhRy0lnNE`7ddO(RS@~@s9$AV72Rat8_}SIGlyS`bO`b4OLVX-@+it2;l!x9Kc))(Q=DJL~4JFw^ z(QdVI!ny}MfWXZX+W7j09)ZfAZ3qAKqN*1(7zzgC2SM1%t1q&GJt^ZKz5~NjeW$5Z JrC|B>e*nH7H{}2T diff --git a/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md b/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md deleted file mode 100644 index e12c3f3444..0000000000 --- a/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Manage Docs Versions - -Docusaurus can manage multiple versions of your docs. - -## Create a docs version - -Release a version 1.0 of your project: - -```bash -npm run docusaurus docs:version 1.0 -``` - -The `docs` folder is copied into `versioned_docs/version-1.0` and `versions.json` is created. - -Your docs now have 2 versions: - -- `1.0` at `http://localhost:3000/docs/` for the version 1.0 docs -- `current` at `http://localhost:3000/docs/next/` for the **upcoming, unreleased docs** - -## Add a Version Dropdown - -To navigate seamlessly across versions, add a version dropdown. - -Modify the `docusaurus.config.js` file: - -```js title="docusaurus.config.js" -module.exports = { - themeConfig: { - navbar: { - items: [ - // highlight-start - { - type: 'docsVersionDropdown', - }, - // highlight-end - ], - }, - }, -}; -``` - -The docs version dropdown appears in your navbar: - -![Docs Version Dropdown](./img/docsVersionDropdown.png) - -## Update an existing version - -It is possible to edit versioned docs in their respective folder: - -- `versioned_docs/version-1.0/hello.md` updates `http://localhost:3000/docs/hello` -- `docs/hello.md` updates `http://localhost:3000/docs/next/hello` diff --git a/docs/my-website/src/pages/tutorial-extras/translate-your-site.md b/docs/my-website/src/pages/tutorial-extras/translate-your-site.md deleted file mode 100644 index caeaffb055..0000000000 --- a/docs/my-website/src/pages/tutorial-extras/translate-your-site.md +++ /dev/null @@ -1,88 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Translate your site - -Let's translate `docs/intro.md` to French. - -## Configure i18n - -Modify `docusaurus.config.js` to add support for the `fr` locale: - -```js title="docusaurus.config.js" -module.exports = { - i18n: { - defaultLocale: 'en', - locales: ['en', 'fr'], - }, -}; -``` - -## Translate a doc - -Copy the `docs/intro.md` file to the `i18n/fr` folder: - -```bash -mkdir -p i18n/fr/docusaurus-plugin-content-docs/current/ - -cp docs/intro.md i18n/fr/docusaurus-plugin-content-docs/current/intro.md -``` - -Translate `i18n/fr/docusaurus-plugin-content-docs/current/intro.md` in French. - -## Start your localized site - -Start your site on the French locale: - -```bash -npm run start -- --locale fr -``` - -Your localized site is accessible at [http://localhost:3000/fr/](http://localhost:3000/fr/) and the `Getting Started` page is translated. - -:::caution - -In development, you can only use one locale at a same time. - -::: - -## Add a Locale Dropdown - -To navigate seamlessly across languages, add a locale dropdown. - -Modify the `docusaurus.config.js` file: - -```js title="docusaurus.config.js" -module.exports = { - themeConfig: { - navbar: { - items: [ - // highlight-start - { - type: 'localeDropdown', - }, - // highlight-end - ], - }, - }, -}; -``` - -The locale dropdown now appears in your navbar: - -![Locale Dropdown](./img/localeDropdown.png) - -## Build your localized site - -Build your site for a specific locale: - -```bash -npm run build -- --locale fr -``` - -Or build your site to include all the locales at once: - -```bash -npm run build -``` From 86350fe6d70dc62ecb5331a7df5f96a35b853305 Mon Sep 17 00:00:00 2001 From: Cesar Garcia <128240629+Chesars@users.noreply.github.com> Date: Wed, 3 Dec 2025 03:27:04 -0300 Subject: [PATCH 244/248] docs: add Google ADK and Harbor to projects (#17352) Both frameworks integrate with LiteLLM: - Google ADK uses LiteLLM for model-agnostic agent building - Harbor uses LiteLLM for agent evaluation across providers --- docs/my-website/docs/projects/Google ADK.md | 21 ++++++++++++++++++ docs/my-website/docs/projects/Harbor.md | 24 +++++++++++++++++++++ docs/my-website/sidebars.js | 4 +++- 3 files changed, 48 insertions(+), 1 deletion(-) create mode 100644 docs/my-website/docs/projects/Google ADK.md create mode 100644 docs/my-website/docs/projects/Harbor.md diff --git a/docs/my-website/docs/projects/Google ADK.md b/docs/my-website/docs/projects/Google ADK.md new file mode 100644 index 0000000000..25e910dcba --- /dev/null +++ b/docs/my-website/docs/projects/Google ADK.md @@ -0,0 +1,21 @@ + +# Google ADK (Agent Development Kit) + +[Google ADK](https://github.com/google/adk-python) is an open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents. While optimized for Gemini, ADK is model-agnostic and supports LiteLLM for using 100+ providers. + +```python +from google.adk.agents.llm_agent import Agent +from google.adk.models.lite_llm import LiteLlm + +root_agent = Agent( + model=LiteLlm(model="openai/gpt-4o"), # Or any LiteLLM-supported model + name="my_agent", + description="An agent using LiteLLM", + instruction="You are a helpful assistant.", + tools=[your_tools], +) +``` + +- [GitHub](https://github.com/google/adk-python) +- [Documentation](https://google.github.io/adk-docs) +- [LiteLLM Samples](https://github.com/google/adk-python/tree/main/contributing/samples/hello_world_litellm) diff --git a/docs/my-website/docs/projects/Harbor.md b/docs/my-website/docs/projects/Harbor.md new file mode 100644 index 0000000000..684dfa9372 --- /dev/null +++ b/docs/my-website/docs/projects/Harbor.md @@ -0,0 +1,24 @@ + +# Harbor + +[Harbor](https://github.com/laude-institute/harbor) is a framework from the creators of Terminal-Bench for evaluating and optimizing agents and language models. It uses LiteLLM to call 100+ LLM providers. + +```bash +# Install +pip install harbor + +# Run a benchmark with any LiteLLM-supported model +harbor run --dataset terminal-bench@2.0 \ + --agent claude-code \ + --model anthropic/claude-opus-4-1 \ + --n-concurrent 4 +``` + +Key features: +- Evaluate agents like Claude Code, OpenHands, Codex CLI +- Build and share benchmarks and environments +- Run experiments in parallel across cloud providers (Daytona, Modal) +- Generate rollouts for RL optimization + +- [GitHub](https://github.com/laude-institute/harbor) +- [Documentation](https://harborframework.com/docs) diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 983816ed21..a9790547e8 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -820,10 +820,12 @@ const sidebars = { "Learn how to deploy + call models from different providers on LiteLLM", slug: "/project", }, - items: [ + items: [ "projects/smolagents", "projects/mini-swe-agent", "projects/openai-agents", + "projects/Google ADK", + "projects/Harbor", "projects/Docq.AI", "projects/PDL", "projects/OpenInterpreter", From c173a4a27594b0a435f58a0be7633514bbeee440 Mon Sep 17 00:00:00 2001 From: Fabian Reinold <32450519+freinold@users.noreply.github.com> Date: Wed, 3 Dec 2025 07:30:54 +0100 Subject: [PATCH 245/248] Helm Chart: add ingress-only labels (#17348) * feat(helm): add ingress-only labels * feat(helm): add ingress configuration tests * chore(helm): bump chart version --- deploy/charts/litellm-helm/Chart.yaml | 4 +- deploy/charts/litellm-helm/README.md | 125 +++++++++--------- .../litellm-helm/templates/ingress.yaml | 3 + .../litellm-helm/tests/ingress_tests.yaml | 45 +++++++ deploy/charts/litellm-helm/values.yaml | 45 ++++--- 5 files changed, 140 insertions(+), 82 deletions(-) create mode 100644 deploy/charts/litellm-helm/tests/ingress_tests.yaml diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index eedadebaa8..7f14af7db5 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 0.4.8 +version: 0.4.9 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to @@ -33,5 +33,5 @@ dependencies: condition: db.deployStandalone - name: redis version: ">=18.0.0" - repository: oci://registry-1.docker.io/bitnamicharts + repository: oci://registry-1.docker.io/bitnamicharts condition: redis.enabled diff --git a/deploy/charts/litellm-helm/README.md b/deploy/charts/litellm-helm/README.md index 352c3e9ddf..6fdc423a17 100644 --- a/deploy/charts/litellm-helm/README.md +++ b/deploy/charts/litellm-helm/README.md @@ -10,46 +10,48 @@ - Helm 3.8.0+ If `db.deployStandalone` is used: + - PV provisioner support in the underlying infrastructure If `db.useStackgresOperator` is used (not yet implemented): -- The Stackgres Operator must already be installed in the Kubernetes Cluster. This chart will **not** install the operator if it is missing. + +- The Stackgres Operator must already be installed in the Kubernetes Cluster. This chart will **not** install the operator if it is missing. ## Parameters ### LiteLLM Proxy Deployment Settings -| Name | Description | Value | -| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | -| `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | -| `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | -| `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | -| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | -| `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | -| `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | -| `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | -| `image.pullPolicy` | LiteLLM Proxy image pull policy | `IfNotPresent` | -| `image.tag` | Overrides the image tag whose default the latest version of LiteLLM at the time this chart was published. | `""` | -| `imagePullSecrets` | Registry credentials for the LiteLLM and initContainer images. | `[]` | -| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` | -| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` | -| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` | -| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` | -| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A | -| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` | -| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` | -| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` | -| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` | -| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. -| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` | -| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | -| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | -| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` | -| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` | +| Name | Description | Value | +| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- | +| `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | +| `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | +| `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | +| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | +| `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | +| `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | +| `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | +| `image.pullPolicy` | LiteLLM Proxy image pull policy | `IfNotPresent` | +| `image.tag` | Overrides the image tag whose default the latest version of LiteLLM at the time this chart was published. | `""` | +| `imagePullSecrets` | Registry credentials for the LiteLLM and initContainer images. | `[]` | +| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` | +| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` | +| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` | +| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` | +| `ingress.labels` | Additional labels for the Ingress resource | `{}` | +| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A | +| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` | +| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` | +| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` | +| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` | +| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. | +| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` | +| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | +| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | +| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` | +| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` | #### Example `proxy_config` ConfigMap from values (default): - ``` proxyConfigMap: create: true @@ -67,7 +69,6 @@ proxy_config: #### Example using existing `proxyConfigMap` instead of creating it: - ``` proxyConfigMap: create: false @@ -77,8 +78,7 @@ proxyConfigMap: # proxy_config is ignored in this mode ``` -#### Example `environmentSecrets` Secret - +#### Example `environmentSecrets` Secret ``` apiVersion: v1 @@ -91,21 +91,23 @@ type: Opaque ``` ### Database Settings -| Name | Description | Value | -| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | -| `db.useExisting` | Use an existing Postgres database. A Kubernetes Secret object must exist that contains credentials for connecting to the database. An example secret object definition is provided below. | `false` | -| `db.endpoint` | If `db.useExisting` is `true`, this is the IP, Hostname or Service Name of the Postgres server to connect to. | `localhost` | -| `db.database` | If `db.useExisting` is `true`, the name of the existing database to connect to. | `litellm` | -| `db.url` | If `db.useExisting` is `true`, the connection url of the existing database to connect to can be overwritten with this value. | `postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_HOST)/$(DATABASE_NAME)` | -| `db.secret.name` | If `db.useExisting` is `true`, the name of the Kubernetes Secret that contains credentials. | `postgres` | -| `db.secret.usernameKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the username for authenticating with the Postgres instance. | `username` | -| `db.secret.passwordKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the password associates with the above user. | `password` | -| `db.useStackgresOperator` | Not yet implemented. | `false` | -| `db.deployStandalone` | Deploy a standalone, single instance deployment of Postgres, using the Bitnami postgresql chart. This is useful for getting started but doesn't provide HA or (by default) data backups. | `true` | -| `postgresql.*` | If `db.deployStandalone` is `true`, configuration passed to the Bitnami postgresql chart. See the [Bitnami Documentation](https://github.com/bitnami/charts/tree/main/bitnami/postgresql) for full configuration details. See [values.yaml](./values.yaml) for the default configuration. | See [values.yaml](./values.yaml) | -| `postgresql.auth.*` | If `db.deployStandalone` is `true`, care should be taken to ensure the default `password` and `postgres-password` values are **NOT** used. | `NoTaGrEaTpAsSwOrD` | + +| Name | Description | Value | +| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | +| `db.useExisting` | Use an existing Postgres database. A Kubernetes Secret object must exist that contains credentials for connecting to the database. An example secret object definition is provided below. | `false` | +| `db.endpoint` | If `db.useExisting` is `true`, this is the IP, Hostname or Service Name of the Postgres server to connect to. | `localhost` | +| `db.database` | If `db.useExisting` is `true`, the name of the existing database to connect to. | `litellm` | +| `db.url` | If `db.useExisting` is `true`, the connection url of the existing database to connect to can be overwritten with this value. | `postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_HOST)/$(DATABASE_NAME)` | +| `db.secret.name` | If `db.useExisting` is `true`, the name of the Kubernetes Secret that contains credentials. | `postgres` | +| `db.secret.usernameKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the username for authenticating with the Postgres instance. | `username` | +| `db.secret.passwordKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the password associates with the above user. | `password` | +| `db.useStackgresOperator` | Not yet implemented. | `false` | +| `db.deployStandalone` | Deploy a standalone, single instance deployment of Postgres, using the Bitnami postgresql chart. This is useful for getting started but doesn't provide HA or (by default) data backups. | `true` | +| `postgresql.*` | If `db.deployStandalone` is `true`, configuration passed to the Bitnami postgresql chart. See the [Bitnami Documentation](https://github.com/bitnami/charts/tree/main/bitnami/postgresql) for full configuration details. See [values.yaml](./values.yaml) for the default configuration. | See [values.yaml](./values.yaml) | +| `postgresql.auth.*` | If `db.deployStandalone` is `true`, care should be taken to ensure the default `password` and `postgres-password` values are **NOT** used. | `NoTaGrEaTpAsSwOrD` | #### Example Postgres `db.useExisting` Secret + ```yaml apiVersion: v1 kind: Secret @@ -143,7 +145,7 @@ metadata: name: litellm-env-secret type: Opaque data: - SOME_PASSWORD: cDZbUGVXeU5e0ZW # base64 encoded + SOME_PASSWORD: cDZbUGVXeU5e0ZW # base64 encoded ANOTHER_PASSWORD: AAZbUGVXeU5e0ZB # base64 encoded ``` @@ -153,23 +155,23 @@ Source: [GitHub Gist from troyharvey](https://gist.github.com/troyharvey/4506472 The migration job supports both ArgoCD and Helm hooks to ensure database migrations run at the appropriate time during deployments. -| Name | Description | Value | -| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | -| `migrationJob.enabled` | Enable or disable the schema migration Job | `true` | -| `migrationJob.backoffLimit` | Backoff limit for Job restarts | `4` | -| `migrationJob.ttlSecondsAfterFinished` | TTL for completed migration jobs | `120` | -| `migrationJob.annotations` | Additional annotations for the migration job pod | `{}` | -| `migrationJob.extraContainers` | Additional containers to run alongside the migration job | `[]` | -| `migrationJob.hooks.argocd.enabled` | Enable ArgoCD hooks for the migration job (uses PreSync hook with BeforeHookCreation delete policy) | `true` | -| `migrationJob.hooks.helm.enabled` | Enable Helm hooks for the migration job (uses pre-install,pre-upgrade hooks with before-hook-creation delete policy) | `false` | -| `migrationJob.hooks.helm.weight` | Helm hook execution order (lower weights executed first). Optional - defaults to "1" if not specified. | N/A | - +| Name | Description | Value | +| -------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | ------- | +| `migrationJob.enabled` | Enable or disable the schema migration Job | `true` | +| `migrationJob.backoffLimit` | Backoff limit for Job restarts | `4` | +| `migrationJob.ttlSecondsAfterFinished` | TTL for completed migration jobs | `120` | +| `migrationJob.annotations` | Additional annotations for the migration job pod | `{}` | +| `migrationJob.extraContainers` | Additional containers to run alongside the migration job | `[]` | +| `migrationJob.hooks.argocd.enabled` | Enable ArgoCD hooks for the migration job (uses PreSync hook with BeforeHookCreation delete policy) | `true` | +| `migrationJob.hooks.helm.enabled` | Enable Helm hooks for the migration job (uses pre-install,pre-upgrade hooks with before-hook-creation delete policy) | `false` | +| `migrationJob.hooks.helm.weight` | Helm hook execution order (lower weights executed first). Optional - defaults to "1" if not specified. | N/A | ## Accessing the Admin UI + When browsing to the URL published per the settings in `ingress.*`, you will -be prompted for **Admin Configuration**. The **Proxy Endpoint** is the internal +be prompted for **Admin Configuration**. The **Proxy Endpoint** is the internal (from the `litellm` pod's perspective) URL published by the `-litellm` -Kubernetes Service. If the deployment uses the default settings for this +Kubernetes Service. If the deployment uses the default settings for this service, the **Proxy Endpoint** should be set to `http://-litellm:4000`. The **Proxy Key** is the value specified for `masterkey` or, if a `masterkey` @@ -181,7 +183,8 @@ kubectl -n litellm get secret -litellm-masterkey -o jsonpath="{.data.ma ``` ## Admin UI Limitations -At the time of writing, the Admin UI is unable to add models. This is because + +At the time of writing, the Admin UI is unable to add models. This is because it would need to update the `config.yaml` file which is a exposed ConfigMap, and -therefore, read-only. This is a limitation of this helm chart, not the Admin UI +therefore, read-only. This is a limitation of this helm chart, not the Admin UI itself. diff --git a/deploy/charts/litellm-helm/templates/ingress.yaml b/deploy/charts/litellm-helm/templates/ingress.yaml index 09e8d715ab..ea9ffcbb54 100644 --- a/deploy/charts/litellm-helm/templates/ingress.yaml +++ b/deploy/charts/litellm-helm/templates/ingress.yaml @@ -18,6 +18,9 @@ metadata: name: {{ $fullName }} labels: {{- include "litellm.labels" . | nindent 4 }} + {{- with .Values.ingress.labels }} + {{- toYaml . | nindent 4 }} + {{- end }} {{- with .Values.ingress.annotations }} annotations: {{- toYaml . | nindent 4 }} diff --git a/deploy/charts/litellm-helm/tests/ingress_tests.yaml b/deploy/charts/litellm-helm/tests/ingress_tests.yaml new file mode 100644 index 0000000000..aad6ecfcee --- /dev/null +++ b/deploy/charts/litellm-helm/tests/ingress_tests.yaml @@ -0,0 +1,45 @@ +suite: Ingress Configuration Tests +templates: + - ingress.yaml +tests: + - it: should not create Ingress by default + asserts: + - hasDocuments: + count: 0 + + - it: should create Ingress when enabled + set: + ingress.enabled: true + asserts: + - hasDocuments: + count: 1 + - isKind: + of: Ingress + + - it: should add custom labels + set: + ingress.enabled: true + ingress.labels: + custom-label: "true" + another-label: "value" + asserts: + - isKind: + of: Ingress + - equal: + path: metadata.labels.custom-label + value: "true" + - equal: + path: metadata.labels.another-label + value: "value" + + - it: should add annotations + set: + ingress.enabled: true + ingress.annotations: + kubernetes.io/ingress.class: "nginx" + asserts: + - isKind: + of: Ingress + - equal: + path: metadata.annotations["kubernetes.io/ingress.class"] + value: "nginx" diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index acb8c9ca32..3502115782 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -35,7 +35,8 @@ podAnnotations: {} podLabels: {} terminationGracePeriodSeconds: 90 -topologySpreadConstraints: [] +topologySpreadConstraints: + [] # - maxSkew: 1 # topologyKey: kubernetes.io/hostname # whenUnsatisfiable: DoNotSchedule @@ -46,7 +47,8 @@ topologySpreadConstraints: [] # At the time of writing, the litellm docker image requires write access to the # filesystem on startup so that prisma can install some dependencies. podSecurityContext: {} -securityContext: {} +securityContext: + {} # capabilities: # drop: # - ALL @@ -57,13 +59,15 @@ securityContext: {} # A list of Kubernetes Secret objects that will be exported to the LiteLLM proxy # pod as environment variables. These secrets can then be referenced in the # configuration file (or "litellm" ConfigMap) with `os.environ/` -environmentSecrets: [] +environmentSecrets: + [] # - litellm-env-secret # A list of Kubernetes ConfigMap objects that will be exported to the LiteLLM proxy # pod as environment variables. The ConfigMap kv-pairs can then be referenced in the # configuration file (or "litellm" ConfigMap) with `os.environ/` -environmentConfigMaps: [] +environmentConfigMaps: + [] # - litellm-env-configmap service: @@ -82,7 +86,9 @@ separateHealthPort: 8081 ingress: enabled: false className: "nginx" - annotations: {} + labels: {} + annotations: + {} # kubernetes.io/ingress.class: nginx # kubernetes.io/tls-acme: "true" hosts: @@ -129,7 +135,8 @@ proxy_config: general_settings: master_key: os.environ/PROXY_MASTER_KEY -resources: {} +resources: + {} # We usually recommend not to specify default resources and to leave this as a conscious # choice for the user. This also increases chances charts run on environments with little # resources, such as Minikube. If you do want to specify resources, uncomment the following @@ -231,7 +238,7 @@ migrationJob: # cpu: 100m # memory: 100Mi extraContainers: [] - + # Hook configuration hooks: argocd: @@ -240,30 +247,30 @@ migrationJob: enabled: false # Additional environment variables to be added to the deployment as a map of key-value pairs -envVars: { - # USE_DDTRACE: "true" -} +envVars: {} +# USE_DDTRACE: "true" # Additional environment variables to be added to the deployment as a list of k8s env vars -extraEnvVars: { - # - name: EXTRA_ENV_VAR - # value: EXTRA_ENV_VAR_VALUE -} +extraEnvVars: {} +# - name: EXTRA_ENV_VAR +# value: EXTRA_ENV_VAR_VALUE # Pod Disruption Budget pdb: enabled: false # Set exactly one of the following. If both are set, minAvailable takes precedence. - minAvailable: null # e.g. "50%" or 1 - maxUnavailable: null # e.g. 1 or "20%" + minAvailable: null # e.g. "50%" or 1 + maxUnavailable: null # e.g. 1 or "20%" annotations: {} labels: {} serviceMonitor: enabled: false - labels: {} + labels: + {} # test: test - annotations: {} + annotations: + {} # kubernetes.io/test: test interval: 15s scrapeTimeout: 10s @@ -273,4 +280,4 @@ serviceMonitor: # action: replace namespaceSelector: matchNames: [] - # - test-namespace \ No newline at end of file + # - test-namespace From 1ac2655b17f006f738632c27194153b93e9faa0c Mon Sep 17 00:00:00 2001 From: rioiart Date: Wed, 3 Dec 2025 07:46:03 +0100 Subject: [PATCH 246/248] Fix/organization max budget not enforced (#17334) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * test: add failing tests for organization budget enforcement bug Add comprehensive tests exposing that organization-level budgets are retrieved but never enforced during request authentication. Tests verify: 1. Basic org budget exceeded scenario (team under budget, org over) 2. Multiple teams collectively exceeding org budget 3. Organization budget fields exist but are never checked 4. Inconsistency between team budget enforcement (works) and org (doesn't) Tests intentionally fail to document the bug. Will be fixed in next commit. Related to organization_max_budget not being enforced in auth_checks.py * fix: enforce organization budget in auth checks Add organization budget enforcement to common_checks() in auth_checks.py. Previously, organization_max_budget was retrieved from DB but never checked, allowing teams to collectively exceed their organization's budget limit. Changes: - Add _organization_max_budget_check() function following team budget pattern - Call org budget check after team budget check in common_checks() - Add "organization_budget" to budget_alerts type literals - Update tests to verify org budget is enforced Budget hierarchy is now properly enforced: Organization Budget (hard ceiling) └─ Team Budget (sub-allocation) └─ Team Member Budget (per-user within team) └─ Key Budget (per-key) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude * fix: add organization_id to budget alerts, fix enum comparison and linting of newly added code - Add organization_id field to CallInfo class for better alert context - Include organization_id in budget alerts (token, soft, team, org) - Fix event_group enum comparison (was comparing enum to string) - Add OrganizationBudgetAlert class for organization budget alerting - Add organization_budget to test parameterizations - Apply Black formatting to slack_alerting.py --------- Co-authored-by: Claude --- .../SlackAlerting/budget_alert_types.py | 10 + .../SlackAlerting/slack_alerting.py | 19 +- litellm/proxy/_types.py | 1 + litellm/proxy/auth/auth_checks.py | 70 ++++ litellm/proxy/utils.py | 1 + tests/logging_callback_tests/test_alerting.py | 2 + .../test_organization_budget_enforcement.py | 344 ++++++++++++++++++ 7 files changed, 441 insertions(+), 6 deletions(-) create mode 100644 tests/test_litellm/proxy/auth/test_organization_budget_enforcement.py diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py index 1e9ad286e3..dadfef3fc4 100644 --- a/litellm/integrations/SlackAlerting/budget_alert_types.py +++ b/litellm/integrations/SlackAlerting/budget_alert_types.py @@ -50,6 +50,14 @@ class TeamBudgetAlert(BaseBudgetAlertType): return user_info.team_id or "default_id" +class OrganizationBudgetAlert(BaseBudgetAlertType): + def get_event_message(self) -> str: + return "Organization Budget: " + + def get_id(self, user_info: CallInfo) -> str: + return user_info.organization_id or "default_id" + + class TokenBudgetAlert(BaseBudgetAlertType): def get_event_message(self) -> str: return "Key Budget: " @@ -72,6 +80,7 @@ def get_budget_alert_type( "soft_budget", "user_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], @@ -83,6 +92,7 @@ def get_budget_alert_type( "soft_budget": SoftBudgetAlert(), "user_budget": UserBudgetAlert(), "team_budget": TeamBudgetAlert(), + "organization_budget": OrganizationBudgetAlert(), "token_budget": TokenBudgetAlert(), "projected_limit_exceeded": ProjectedLimitExceededAlert(), } diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 3efe587378..0e691e2c43 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -134,19 +134,25 @@ class SlackAlerting(CustomBatchLogger): if llm_router is not None: self.llm_router = llm_router - def _prepare_outage_value_for_cache(self, outage_value: Union[dict, ProviderRegionOutageModel, OutageModel]) -> dict: + def _prepare_outage_value_for_cache( + self, outage_value: Union[dict, ProviderRegionOutageModel, OutageModel] + ) -> dict: """ Helper method to prepare outage value for Redis caching. Converts set objects to lists for JSON serialization. """ # Convert to dict for processing cache_value = dict(outage_value) - - if "deployment_ids" in cache_value and isinstance(cache_value["deployment_ids"], set): + + if "deployment_ids" in cache_value and isinstance( + cache_value["deployment_ids"], set + ): cache_value["deployment_ids"] = list(cache_value["deployment_ids"]) return cache_value - def _restore_outage_value_from_cache(self, outage_value: Optional[dict]) -> Optional[dict]: + def _restore_outage_value_from_cache( + self, outage_value: Optional[dict] + ) -> Optional[dict]: """ Helper method to restore outage value after retrieving from cache. Converts list objects back to sets for proper handling. @@ -528,6 +534,7 @@ class SlackAlerting(CustomBatchLogger): "soft_budget", "user_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], @@ -1338,7 +1345,7 @@ Model Info: subject=email_event["subject"], html=email_event["html"], ) - if webhook_event.event_group == "team": + if webhook_event.event_group == Litellm_EntityType.TEAM: from litellm.integrations.email_alerting import send_team_budget_alert await send_team_budget_alert(webhook_event=webhook_event) @@ -1399,7 +1406,7 @@ Model Info: current_time = datetime.now().strftime("%H:%M:%S") _proxy_base_url = os.getenv("PROXY_BASE_URL", None) # Use .name if it's an enum, otherwise use as is - alert_type_name = getattr(alert_type, 'name', alert_type) + alert_type_name = getattr(alert_type, "name", alert_type) alert_type_formatted = f"Alert type: `{alert_type_name}`" if alert_type == "daily_reports" or alert_type == "new_model_added": formatted_message = alert_type_formatted + message diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 7e7d404981..53a8627bc8 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2444,6 +2444,7 @@ class CallInfo(LiteLLMPydanticObjectBase): user_id: Optional[str] = None team_id: Optional[str] = None team_alias: Optional[str] = None + organization_id: Optional[str] = None user_email: Optional[str] = None key_alias: Optional[str] = None projected_exceeded_date: Optional[str] = None diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index c9774b18b8..45b0752d4c 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -143,6 +143,14 @@ async def common_checks( valid_token=valid_token, ) + # 3.1. If organization is in budget + await _organization_max_budget_check( + valid_token=valid_token, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + proxy_logging_obj=proxy_logging_obj, + ) + await _tag_max_budget_check( request_body=request_body, prisma_client=prisma_client, @@ -1893,6 +1901,7 @@ async def _virtual_key_max_budget_check( max_budget=valid_token.max_budget, user_id=valid_token.user_id, team_id=valid_token.team_id, + organization_id=valid_token.org_id, user_email=user_email, key_alias=valid_token.key_alias, event_group=Litellm_EntityType.KEY, @@ -1939,6 +1948,7 @@ async def _virtual_key_soft_budget_check( user_id=valid_token.user_id, team_id=valid_token.team_id, team_alias=valid_token.team_alias, + organization_id=valid_token.org_id, user_email=None, key_alias=valid_token.key_alias, event_group=Litellm_EntityType.KEY, @@ -1977,6 +1987,7 @@ async def _team_max_budget_check( user_id=valid_token.user_id, team_id=valid_token.team_id, team_alias=valid_token.team_alias, + organization_id=valid_token.org_id, event_group=Litellm_EntityType.TEAM, ) asyncio.create_task( @@ -1993,6 +2004,65 @@ async def _team_max_budget_check( ) +async def _organization_max_budget_check( + valid_token: Optional[UserAPIKeyAuth], + prisma_client: Optional[PrismaClient], + user_api_key_cache: DualCache, + proxy_logging_obj: ProxyLogging, +): + """ + Check if the organization is over its max budget. + + Raises: + BudgetExceededError if the organization is over its max budget. + Triggers a budget alert if the organization is over its max budget. + """ + # Only check if token has organization info and organization_max_budget is set + if ( + valid_token is None + or valid_token.org_id is None + or valid_token.organization_max_budget is None + or valid_token.organization_max_budget <= 0 + ): + return + + # Get organization object to check current spend + if prisma_client is not None: + org_table = await get_org_object( + org_id=valid_token.org_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + ) + + if ( + org_table is not None + and org_table.spend >= valid_token.organization_max_budget + ): + # Trigger budget alert + call_info = CallInfo( + token=valid_token.token, + spend=org_table.spend, + max_budget=valid_token.organization_max_budget, + user_id=valid_token.user_id, + team_id=valid_token.team_id, + team_alias=valid_token.team_alias, + organization_id=valid_token.org_id, + event_group=Litellm_EntityType.ORGANIZATION, + ) + asyncio.create_task( + proxy_logging_obj.budget_alerts( + type="organization_budget", + user_info=call_info, + ) + ) + + raise litellm.BudgetExceededError( + current_cost=org_table.spend, + max_budget=valid_token.organization_max_budget, + message=f"Budget has been exceeded! Organization={valid_token.org_id} Current cost: {org_table.spend}, Max budget: {valid_token.organization_max_budget}", + ) + + async def _tag_max_budget_check( request_body: dict, prisma_client: Optional[PrismaClient], diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 9594a55962..3b746b757e 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1072,6 +1072,7 @@ class ProxyLogging: "user_budget", "soft_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], diff --git a/tests/logging_callback_tests/test_alerting.py b/tests/logging_callback_tests/test_alerting.py index b9ecfaeb3f..ac7f5cd6aa 100644 --- a/tests/logging_callback_tests/test_alerting.py +++ b/tests/logging_callback_tests/test_alerting.py @@ -477,6 +477,7 @@ async def test_send_daily_reports_all_zero_or_none(): "token_budget", "user_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], @@ -514,6 +515,7 @@ async def test_send_token_budget_crossed_alerts(alerting_type): "token_budget", "user_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], diff --git a/tests/test_litellm/proxy/auth/test_organization_budget_enforcement.py b/tests/test_litellm/proxy/auth/test_organization_budget_enforcement.py new file mode 100644 index 0000000000..9c2adca9cd --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_organization_budget_enforcement.py @@ -0,0 +1,344 @@ +""" +Tests for organization budget enforcement. + +These tests verify that organization-level budgets are properly enforced during +request authentication. When an organization's spend exceeds its max_budget, +requests should fail with BudgetExceededError. + +This prevents teams within an organization from collectively exceeding the +organization's budget limit. +""" + +import asyncio +import os +import sys +from typing import Optional +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +sys.path.insert(0, os.path.abspath("../../../")) + +import litellm +from litellm.proxy._types import ( + LiteLLM_BudgetTable, + LiteLLM_OrganizationTable, + LiteLLM_TeamTable, + UserAPIKeyAuth, +) +from litellm.proxy.auth.auth_checks import common_checks +from litellm.proxy.utils import ProxyLogging + + +@pytest.mark.asyncio +async def test_organization_budget_exceeded_blocks_request(): + """ + Bug: Organization budget is retrieved but NEVER enforced. + + When organization spend >= organization_max_budget, requests should fail + with BudgetExceededError. Currently this passes because no check exists. + """ + org_id = "test-org-budget-exceeded" + + # Organization with max_budget of 100, but spend is 150 + org_object = LiteLLM_OrganizationTable( + organization_id=org_id, + budget_id="org-budget-1", + spend=150.0, # Over budget! + models=["gpt-4"], + created_by="test", + updated_by="test", + litellm_budget_table=LiteLLM_BudgetTable( + max_budget=100.0, # Budget is 100 + ), + ) + + # Team within the organization (team itself is under budget) + team_object = LiteLLM_TeamTable( + team_id="test-team-1", + organization_id=org_id, + max_budget=50.0, # Team budget is 50 + spend=10.0, # Team spend is only 10 - under budget + models=["gpt-4"], + ) + + # Valid token with organization info + valid_token = UserAPIKeyAuth( + token="sk-test-123", + team_id="test-team-1", + org_id=org_id, + organization_max_budget=100.0, # This is set but never checked! + ) + + mock_request = MagicMock() + mock_request.url.path = "/v1/chat/completions" + + mock_proxy_logging = MagicMock(spec=ProxyLogging) + mock_proxy_logging.budget_alerts = AsyncMock() + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + with patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache: + with patch("litellm.proxy.auth.auth_checks.get_org_object", new_callable=AsyncMock) as mock_get_org: + mock_get_org.return_value = org_object + + # BUG: This should raise BudgetExceededError but currently passes + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await common_checks( + request_body={"model": "gpt-4"}, + team_object=team_object, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route="/v1/chat/completions", + llm_router=None, + proxy_logging_obj=mock_proxy_logging, + valid_token=valid_token, + request=mock_request, + ) + + assert "Organization" in str(exc_info.value.message) + assert exc_info.value.current_cost == 150.0 + assert exc_info.value.max_budget == 100.0 + + +@pytest.mark.asyncio +async def test_multiple_teams_exceed_organization_budget(): + """ + Test that organization budget is enforced even when individual teams are under budget. + + Scenario: + - Organization max_budget = $5000, spend = $5000 (at limit) + - Team A spend = $1500 (under team budget of $2000) + - Request via Team A should FAIL because org is at budget limit + + Expected: Request fails with BudgetExceededError + """ + org_id = "multi-team-org" + + # Organization at budget limit + org_object = LiteLLM_OrganizationTable( + organization_id=org_id, + budget_id="org-budget-2", + spend=5000.0, # At $5000 limit + models=["gpt-4"], + created_by="test", + updated_by="test", + litellm_budget_table=LiteLLM_BudgetTable( + max_budget=5000.0, # Org budget is $5000 + ), + ) + + # Team A - under its own budget, but org is almost at limit + team_a = LiteLLM_TeamTable( + team_id="team-a", + organization_id=org_id, + max_budget=2000.0, + spend=1500.0, # Team A has spent $1500 of its $2000 budget + models=["gpt-4"], + ) + + valid_token = UserAPIKeyAuth( + token="sk-team-a-key", + team_id="team-a", + org_id=org_id, + organization_max_budget=5000.0, # Set but never enforced + ) + + mock_request = MagicMock() + mock_request.url.path = "/v1/chat/completions" + + mock_proxy_logging = MagicMock(spec=ProxyLogging) + mock_proxy_logging.budget_alerts = AsyncMock() + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + with patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache: + with patch("litellm.proxy.auth.auth_checks.get_org_object", new_callable=AsyncMock) as mock_get_org: + mock_get_org.return_value = org_object + + # Org is at budget limit, should raise BudgetExceededError + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await common_checks( + request_body={"model": "gpt-4"}, + team_object=team_a, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route="/v1/chat/completions", + llm_router=None, + proxy_logging_obj=mock_proxy_logging, + valid_token=valid_token, + request=mock_request, + ) + + # Verify the error message mentions organization + assert "Organization" in str(exc_info.value.message) + assert exc_info.value.current_cost == 5000.0 + assert exc_info.value.max_budget == 5000.0 + + +@pytest.mark.asyncio +async def test_organization_budget_fields_are_checked(): + """ + Verify that organization_max_budget is populated in UserAPIKeyAuth + and BudgetExceededError is raised when organization is over budget. + """ + # Token has org budget info + valid_token = UserAPIKeyAuth( + token="sk-test", + team_id="test-team", + org_id="test-org", + organization_max_budget=100.0, # Budget is $100 + ) + + # Verify the field exists and is set + assert valid_token.organization_max_budget == 100.0 + assert valid_token.org_id == "test-org" + + team_object = LiteLLM_TeamTable( + team_id="test-team", + organization_id="test-org", + max_budget=None, + spend=0.0, + models=["gpt-4"], + ) + + mock_request = MagicMock() + mock_request.url.path = "/v1/chat/completions" + + mock_proxy_logging = MagicMock(spec=ProxyLogging) + mock_proxy_logging.budget_alerts = AsyncMock() + + # Organization is over budget + org_over_budget = LiteLLM_OrganizationTable( + organization_id="test-org", + budget_id="budget-1", + spend=150.0, # Over $100 budget + models=["gpt-4"], + created_by="test", + updated_by="test", + litellm_budget_table=LiteLLM_BudgetTable(max_budget=100.0), + ) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + with patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache: + with patch("litellm.proxy.auth.auth_checks.get_org_object", new_callable=AsyncMock) as mock_get_org: + mock_get_org.return_value = org_over_budget + + # Organization is over budget, should raise BudgetExceededError + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await common_checks( + request_body={"model": "gpt-4"}, + team_object=team_object, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route="/v1/chat/completions", + llm_router=None, + proxy_logging_obj=mock_proxy_logging, + valid_token=valid_token, + request=mock_request, + ) + + assert exc_info.value.current_cost == 150.0 + assert exc_info.value.max_budget == 100.0 + + +@pytest.mark.asyncio +async def test_both_team_and_org_budget_enforced(): + """ + Verify that both team budget and organization budget are enforced consistently. + + This test verifies: + 1. Team over budget raises BudgetExceededError + 2. Organization over budget also raises BudgetExceededError + """ + mock_request = MagicMock() + mock_request.url.path = "/v1/chat/completions" + + mock_proxy_logging = MagicMock(spec=ProxyLogging) + mock_proxy_logging.budget_alerts = AsyncMock() + + # Scenario A: Team over budget - should raise BudgetExceededError + team_over_budget = LiteLLM_TeamTable( + team_id="team-over", + max_budget=100.0, + spend=150.0, # Over budget + models=["gpt-4"], + ) + + valid_token_team = UserAPIKeyAuth( + token="sk-team-test", + team_id="team-over", + ) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + with patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache: + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await common_checks( + request_body={"model": "gpt-4"}, + team_object=team_over_budget, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route="/v1/chat/completions", + llm_router=None, + proxy_logging_obj=mock_proxy_logging, + valid_token=valid_token_team, + request=mock_request, + ) + assert "Team" in str(exc_info.value.message) + + # Scenario B: Org over budget - should also raise BudgetExceededError + org_over_budget = LiteLLM_OrganizationTable( + organization_id="org-over", + budget_id="budget-1", + spend=150.0, # Over $100 budget + models=["gpt-4"], + created_by="test", + updated_by="test", + litellm_budget_table=LiteLLM_BudgetTable(max_budget=100.0), + ) + + team_under_budget = LiteLLM_TeamTable( + team_id="team-under", + organization_id="org-over", + max_budget=50.0, + spend=10.0, # Team is fine + models=["gpt-4"], + ) + + valid_token_org = UserAPIKeyAuth( + token="sk-org-test", + team_id="team-under", + org_id="org-over", + organization_max_budget=100.0, + ) + + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: + with patch("litellm.proxy.proxy_server.user_api_key_cache") as mock_cache: + with patch("litellm.proxy.auth.auth_checks.get_org_object", new_callable=AsyncMock) as mock_get_org: + mock_get_org.return_value = org_over_budget + + # Organization is over budget, should raise BudgetExceededError + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await common_checks( + request_body={"model": "gpt-4"}, + team_object=team_under_budget, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route="/v1/chat/completions", + llm_router=None, + proxy_logging_obj=mock_proxy_logging, + valid_token=valid_token_org, + request=mock_request, + ) + + assert "Organization" in str(exc_info.value.message) + assert exc_info.value.current_cost == 150.0 + assert exc_info.value.max_budget == 100.0 From 74ba18df55906165e5796a5408cbfd1fc8047604 Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Tue, 2 Dec 2025 22:50:13 -0800 Subject: [PATCH 247/248] Litellm chainguard fixes 12 02 2025 p1 (#17406) * build: update dockerfile non root * build: update build * build: update non root * build: dockerfile fixes * build: ensure dockerfile + dockerfile.database also work --- Dockerfile | 15 +++++---------- docker/Dockerfile.database | 16 +++++++++------- docker/Dockerfile.non_root | 8 +++++--- 3 files changed, 19 insertions(+), 20 deletions(-) diff --git a/Dockerfile b/Dockerfile index f75706805e..d8397ec481 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,8 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base # Builder stage FROM $LITELLM_BUILD_IMAGE AS builder @@ -12,11 +12,9 @@ WORKDIR /app USER root # Install build dependencies -RUN apk add --no-cache gcc python3-dev openssl openssl-dev +RUN apk add --no-cache bash gcc py3-pip python3 python3-dev openssl openssl-dev - -RUN pip install --upgrade pip>=24.3.1 && \ - pip install build +RUN python -m pip install build # Copy the current directory contents into the container at /app COPY . . @@ -48,10 +46,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # Install runtime dependencies -RUN apk add --no-cache openssl tzdata nodejs npm - -# Upgrade pip to fix CVE-2025-8869 -RUN pip install --upgrade pip>=24.3.1 +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip WORKDIR /app # Copy the current directory contents into the container at /app diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index 09b5265191..0e804cbfd1 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,8 +1,8 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base # Builder stage FROM $LITELLM_BUILD_IMAGE AS builder @@ -13,13 +13,15 @@ USER root # Install build dependencies RUN apk add --no-cache \ - build-base \ + bash \ + gcc \ + py3-pip \ + python3 \ python3-dev \ + openssl \ openssl-dev - -RUN pip install --upgrade pip && \ - pip install build +RUN python -m pip install build # Copy the current directory contents into the container at /app COPY . . @@ -46,7 +48,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # Install runtime dependencies -RUN apk add --no-cache openssl +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip WORKDIR /app # Copy the current directory contents into the container at /app diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 8b66a367ee..cd1633e319 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,6 +1,6 @@ # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base # ----------------- # Builder Stage @@ -11,6 +11,8 @@ WORKDIR /app # Install build dependencies including Node.js for UI build USER root RUN apk add --no-cache \ + python3 \ + py3-pip \ clang \ llvm \ lld \ @@ -71,7 +73,7 @@ WORKDIR /app # Install runtime dependencies USER root RUN apk upgrade --no-cache && \ - apk add --no-cache bash libstdc++ ca-certificates openssl supervisor + apk add --no-cache python3 py3-pip bash openssl tzdata nodejs npm supervisor # Copy only necessary artifacts from builder stage for runtime COPY . . From 8edcc4ecc3fc8ca56447e039871176622cf11aba Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Tue, 2 Dec 2025 22:52:09 -0800 Subject: [PATCH 248/248] Guardrails API - add streaming support (#17400) * fix(initial-commit): adding a way to get the right response type based on the api route * feat(unified_guardrail.py): support streaming guardrails * test: update tests * fix: fix linting errors * test: update tests --- litellm/batches/main.py | 2 - litellm/constants.py | 18 +- litellm/integrations/custom_guardrail.py | 1 - litellm/litellm_core_utils/README.md | 1 + .../api_route_to_call_types.py | 38 ++ .../guardrail_translation/base_translation.py | 14 + .../chat/guardrail_translation/handler.py | 98 +++++- ...odel_prices_and_context_window_backup.json | 65 ++++ .../unified_guardrail/unified_guardrail.py | 110 +++++- litellm/proxy/utils.py | 21 +- litellm/types/utils.py | 327 +++++++++++++++++- .../test_apply_guardrail_endpoint.py | 116 ++++--- .../test_bedrock_apply_guardrail.py | 161 +++++---- .../rerank/test_rerank_guardrail_handler.py | 72 ++-- .../test_text_completion_guardrail_handler.py | 48 ++- ...test_image_generation_guardrail_handler.py | 30 +- ...test_openai_responses_guardrail_handler.py | 14 +- .../test_text_to_speech_guardrail_handler.py | 73 ++-- ...t_audio_transcription_guardrail_handler.py | 80 +++-- .../content_filter/test_content_filter.py | 173 +++++---- .../guardrail_hooks/test_presidio.py | 51 +-- 21 files changed, 1134 insertions(+), 379 deletions(-) create mode 100644 litellm/litellm_core_utils/api_route_to_call_types.py diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 353b1e2569..b99f4a628d 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -18,8 +18,6 @@ from typing import Any, Coroutine, Dict, Literal, Optional, Union, cast import httpx from openai.types.batch import BatchRequestCounts -from openai.types.batch import Metadata -from openai.types.batch import Metadata as OpenAIBatchMetadata import litellm from litellm._logging import verbose_logger diff --git a/litellm/constants.py b/litellm/constants.py index db617a2e47..57029cd2a8 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -262,7 +262,9 @@ TOGETHER_AI_EMBEDDING_350_M = int(os.getenv("TOGETHER_AI_EMBEDDING_350_M", 350)) QDRANT_SCALAR_QUANTILE = float(os.getenv("QDRANT_SCALAR_QUANTILE", 0.99)) QDRANT_VECTOR_SIZE = int(os.getenv("QDRANT_VECTOR_SIZE", 1536)) CACHED_STREAMING_CHUNK_DELAY = float(os.getenv("CACHED_STREAMING_CHUNK_DELAY", 0.02)) -AUDIO_SPEECH_CHUNK_SIZE = 8192 # chunk_size for audio speech streaming. Balance between latency and memory usage +AUDIO_SPEECH_CHUNK_SIZE = int( + os.getenv("AUDIO_SPEECH_CHUNK_SIZE", 8192) +) # chunk_size for audio speech streaming. Balance between latency and memory usage MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 512) ) @@ -285,10 +287,16 @@ REDACTED_BY_LITELM_STRING = "REDACTED_BY_LITELM" MAX_LANGFUSE_INITIALIZED_CLIENTS = int( os.getenv("MAX_LANGFUSE_INITIALIZED_CLIENTS", 50) ) -LOGGING_WORKER_CONCURRENCY = int(os.getenv("LOGGING_WORKER_CONCURRENCY", 100)) # Must be above 0 +LOGGING_WORKER_CONCURRENCY = int( + os.getenv("LOGGING_WORKER_CONCURRENCY", 100) +) # Must be above 0 LOGGING_WORKER_MAX_QUEUE_SIZE = int(os.getenv("LOGGING_WORKER_MAX_QUEUE_SIZE", 50_000)) -LOGGING_WORKER_MAX_TIME_PER_COROUTINE = float(os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0)) -LOGGING_WORKER_CLEAR_PERCENTAGE = int(os.getenv("LOGGING_WORKER_CLEAR_PERCENTAGE", 50)) # Percentage of queue to clear (default: 50%) +LOGGING_WORKER_MAX_TIME_PER_COROUTINE = float( + os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0) +) +LOGGING_WORKER_CLEAR_PERCENTAGE = int( + os.getenv("LOGGING_WORKER_CLEAR_PERCENTAGE", 50) +) # Percentage of queue to clear (default: 50%) MAX_ITERATIONS_TO_CLEAR_QUEUE = int(os.getenv("MAX_ITERATIONS_TO_CLEAR_QUEUE", 200)) MAX_TIME_TO_CLEAR_QUEUE = float(os.getenv("MAX_TIME_TO_CLEAR_QUEUE", 5.0)) LOGGING_WORKER_AGGRESSIVE_CLEAR_COOLDOWN_SECONDS = float( @@ -866,7 +874,7 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "deepseek_r1", "qwen3", "twelvelabs", - "openai" + "openai", ] BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 7f74f5d215..507a754a7e 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -20,7 +20,6 @@ from litellm.types.guardrails import ( GuardrailEventHooks, LitellmParams, Mode, - PiiEntityType, ) from litellm.types.llms.openai import AllMessageValues from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel diff --git a/litellm/litellm_core_utils/README.md b/litellm/litellm_core_utils/README.md index 6494041291..b61c898276 100644 --- a/litellm/litellm_core_utils/README.md +++ b/litellm/litellm_core_utils/README.md @@ -9,4 +9,5 @@ Core files: - `default_encoding.py`: code for loading the default encoding (tiktoken) - `get_llm_provider_logic.py`: code for inferring the LLM provider from a given model name. - `duration_parser.py`: code for parsing durations - e.g. "1d", "1mo", "10s" +- `api_route_to_call_types.py`: mapping of API routes to their corresponding CallTypes (e.g., `/chat/completions` -> [acompletion, completion]) diff --git a/litellm/litellm_core_utils/api_route_to_call_types.py b/litellm/litellm_core_utils/api_route_to_call_types.py new file mode 100644 index 0000000000..35f83de1dd --- /dev/null +++ b/litellm/litellm_core_utils/api_route_to_call_types.py @@ -0,0 +1,38 @@ +""" +Dictionary mapping API routes to their corresponding CallTypes in LiteLLM. + +This dictionary maps each API endpoint to the CallTypes that can be used for that route. +Each route can have both async (prefixed with 'a') and sync call types. +""" + +from litellm.types.utils import API_ROUTE_TO_CALL_TYPES, CallTypes + + +def get_call_types_for_route(route: str) -> list: + """ + Get the list of CallTypes for a given API route. + + Args: + route: API route path (e.g., "/chat/completions") + + Returns: + List of CallTypes for that route, or empty list if route not found + """ + return API_ROUTE_TO_CALL_TYPES.get(route, []) + + +def get_routes_for_call_type(call_type: CallTypes) -> list: + """ + Get all routes that use a specific CallType. + + Args: + call_type: The CallType to search for + + Returns: + List of routes that use this CallType + """ + routes = [] + for route, types in API_ROUTE_TO_CALL_TYPES.items(): + if call_type in types: + routes.append(route) + return routes diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index 5acbf4e9f4..c1ea3311bd 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -84,3 +84,17 @@ class BaseTranslation(ABC): user_api_key_dict: User API key metadata (passed separately since response doesn't contain it) """ pass + + async def process_output_streaming_response( + self, + response: Any, + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + ) -> Any: + """ + Process output streaming response with guardrails. + + Optional to override in subclasses. + """ + return response diff --git a/litellm/llms/openai/chat/guardrail_translation/handler.py b/litellm/llms/openai/chat/guardrail_translation/handler.py index 0abc94012e..29fb12a6f7 100644 --- a/litellm/llms/openai/chat/guardrail_translation/handler.py +++ b/litellm/llms/openai/chat/guardrail_translation/handler.py @@ -14,16 +14,16 @@ Pattern Overview: This pattern can be replicated for other message formats (e.g., Anthropic). """ -from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast import litellm from litellm._logging import verbose_proxy_logger from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation -from litellm.types.utils import Choices +from litellm.types.utils import Choices, StreamingChoices if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail - from litellm.types.utils import ModelResponse + from litellm.types.utils import ModelResponse, ModelResponseStream class OpenAIChatCompletionsHandler(BaseTranslation): @@ -241,21 +241,79 @@ class OpenAIChatCompletionsHandler(BaseTranslation): return response - def _has_text_content(self, response: "ModelResponse") -> bool: + async def process_output_streaming_response( + self, + response: "ModelResponseStream", + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, + ) -> Any: + """ + Process output streaming response by applying guardrails to text content. + + Args: + response: LiteLLM ModelResponseStream object + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails + + Returns: + Modified response with guardrail applied to content + + Response Format Support: + - String content: choice.message.content = "text here" + - List content: choice.message.content = [{"type": "text", "text": "text here"}, ...] + """ + + # Step 0: Check if response has any text content to process + if not self._has_text_content(response): + return response + + texts_to_check: List[str] = [] + images_to_check: List[str] = [] + task_mappings: List[Tuple[int, Optional[int]]] = [] + # Track (choice_index, content_index) for each text + + # Step 1: Extract all text content and images from response choices + for choice_idx, choice in enumerate(response.choices): + + self._extract_output_text_and_images( + choice=choice, + choice_idx=choice_idx, + texts_to_check=texts_to_check, + images_to_check=images_to_check, + task_mappings=task_mappings, + ) + + def _has_text_content( + self, response: Union["ModelResponse", "ModelResponseStream"] + ) -> bool: """ Check if response has any text content to process. Override this method to customize text content detection. """ - for choice in response.choices: - if isinstance(choice, litellm.Choices): - if choice.message.content and isinstance(choice.message.content, str): - return True + from litellm.types.utils import ModelResponse, ModelResponseStream + + if isinstance(response, ModelResponse): + for choice in response.choices: + if isinstance(choice, litellm.Choices): + if choice.message.content and isinstance( + choice.message.content, str + ): + return True + elif isinstance(response, ModelResponseStream): + for choice in response.choices: + if isinstance(choice, litellm.Choices): + if choice.message.content and isinstance( + choice.message.content, str + ): + return True return False def _extract_output_text_and_images( self, - choice: Any, + choice: Union[Choices, StreamingChoices], choice_idx: int, texts_to_check: List[str], images_to_check: List[str], @@ -266,21 +324,29 @@ class OpenAIChatCompletionsHandler(BaseTranslation): Override this method to customize text/image extraction logic. """ - if not isinstance(choice, litellm.Choices): - return - verbose_proxy_logger.debug( "OpenAI Chat Completions: Processing choice: %s", choice ) - if choice.message.content and isinstance(choice.message.content, str): + # Determine content source based on choice type + content = None + if isinstance(choice, litellm.Choices): + content = choice.message.content + elif isinstance(choice, litellm.StreamingChoices): + content = choice.delta.content + else: + # Unknown choice type, skip processing + return + + # Process content if it exists + if content and isinstance(content, str): # Simple string content - texts_to_check.append(choice.message.content) + texts_to_check.append(content) task_mappings.append((choice_idx, None)) - elif choice.message.content and isinstance(choice.message.content, list): + elif content and isinstance(content, list): # List content (e.g., multimodal response) - for content_idx, content_item in enumerate(choice.message.content): + for content_idx, content_item in enumerate(content): # Extract text content_text = content_item.get("text") if content_text: diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 464f9c185f..0fc97ce7b0 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -269,6 +269,71 @@ "supports_response_schema": true, "supports_vision": true }, + "amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 3e-07, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.5e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "apac.amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.75e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "eu.amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.75e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "us.amazon.nova-2-lite-v1:0": { + "input_cost_per_token": 6e-08, + "litellm_provider": "bedrock_converse", + "max_input_tokens": 1000000, + "max_output_tokens": 64000, + "max_tokens": 64000, + "mode": "chat", + "output_cost_per_token": 2.75e-06, + "supports_function_calling": true, + "supports_pdf_input": true, + "supports_prompt_caching": true, + "supports_reasoning": true, + "supports_response_schema": true, + "supports_video_input": true, + "supports_vision": true + }, + "amazon.nova-micro-v1:0": { "input_cost_per_token": 3.5e-08, "litellm_provider": "bedrock_converse", diff --git a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py index aeae19a827..0f05696af4 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py +++ b/litellm/proxy/guardrails/guardrail_hooks/unified_guardrail/unified_guardrail.py @@ -13,6 +13,7 @@ from litellm.caching.caching import DualCache from litellm.cost_calculator import _infer_call_type from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.integrations.custom_logger import CustomLogger +from litellm.litellm_core_utils.api_route_to_call_types import get_call_types_for_route from litellm.llms import load_guardrail_translation_mappings from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import GuardrailEventHooks @@ -176,6 +177,113 @@ class UnifiedLLMGuardrails(CustomLogger): See Aim guardrail implementation for an example - https://github.com/BerriAI/litellm/blob/d0e022cfacb8e9ebc5409bb652059b6fd97b45c0/litellm/proxy/guardrails/guardrail_hooks/aim.py#L168 Triggered by mode: 'post_call' + + Supports sampling_rate parameter to control how often chunks are processed. + sampling_rate=1 means every chunk, sampling_rate=5 means every 5th chunk, etc. """ + + global endpoint_guardrail_translation_mappings + from litellm.proxy.common_utils.callback_utils import ( + add_guardrail_to_applied_guardrails_header, + ) + + guardrail_to_apply: CustomGuardrail = request_data.pop( + "guardrail_to_apply", None + ) + + # Get sampling rate from guardrail config or optional_params, default to 5 + sampling_rate = 5 + if guardrail_to_apply is not None: + # Check guardrail config first + guardrail_config = getattr(guardrail_to_apply, "guardrail_config", {}) + sampling_rate = guardrail_config.get( + "streaming_sampling_rate", sampling_rate + ) + + # Also check optional_params as fallback + sampling_rate = self.optional_params.get( + "streaming_sampling_rate", sampling_rate + ) + + if guardrail_to_apply is None: + async for item in response: + yield item + return + + event_type: GuardrailEventHooks = GuardrailEventHooks.post_call + if ( + guardrail_to_apply.should_run_guardrail( + data=request_data, event_type=event_type + ) + is not True + ): + verbose_proxy_logger.debug( + "UnifiedLLMGuardrails: Post-call streaming scanning disabled for %s", + guardrail_to_apply.guardrail_name, + ) + async for item in response: + yield item + return + + # Initialize translation mappings if needed + if endpoint_guardrail_translation_mappings is None: + endpoint_guardrail_translation_mappings = ( + load_guardrail_translation_mappings() + ) + + # Infer call type from first chunk + call_type = None + chunk_counter = 0 + async for item in response: - yield item + chunk_counter += 1 + + # Infer call type from first chunk if not already done + if call_type is None and user_api_key_dict.request_route is not None: + call_types = get_call_types_for_route(user_api_key_dict.request_route) + if call_types is not None: + call_type = call_types[0] + + # If call type not supported, just pass through all chunks + if ( + call_type is None + or CallTypes(call_type) + not in endpoint_guardrail_translation_mappings + ): + yield item + async for remaining_item in response: + yield remaining_item + return + + # Process chunk based on sampling rate + if chunk_counter % sampling_rate == 0: + verbose_proxy_logger.debug( + "Processing streaming chunk %s (sampling_rate=%s) with guardrail %s", + chunk_counter, + sampling_rate, + guardrail_to_apply.guardrail_name, + ) + + endpoint_translation = endpoint_guardrail_translation_mappings[ + CallTypes(call_type) + ]() + + processed_item = ( + await endpoint_translation.process_output_streaming_response( + response=item, + guardrail_to_apply=guardrail_to_apply, + litellm_logging_obj=request_data.get("litellm_logging_obj"), + user_api_key_dict=user_api_key_dict, + ) + ) + + # Add guardrail to applied guardrails header (only once, on first processed chunk) + if chunk_counter == sampling_rate: + add_guardrail_to_applied_guardrails_header( + request_data=request_data, + guardrail_name=guardrail_to_apply.guardrail_name, + ) + + yield processed_item + else: + yield item diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index 3b746b757e..f0dccfae71 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -1560,6 +1560,7 @@ class ProxyLogging: Covers: 1. /chat/completions """ + for callback in litellm.callbacks: _callback: Optional[CustomLogger] = None if isinstance(callback, str): @@ -1574,11 +1575,21 @@ class ProxyLogging: ) or _callback.should_run_guardrail( data=request_data, event_type=GuardrailEventHooks.post_call ): - response = _callback.async_post_call_streaming_iterator_hook( - user_api_key_dict=user_api_key_dict, - response=response, - request_data=request_data, - ) + if "apply_guardrail" in type(callback).__dict__: + request_data["guardrail_to_apply"] = callback + response = ( + unified_guardrail.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + request_data=request_data, + response=response, + ) + ) + else: + response = _callback.async_post_call_streaming_iterator_hook( + user_api_key_dict=user_api_key_dict, + response=response, + request_data=request_data, + ) return response def _init_response_taking_too_long_task(self, data: Optional[dict] = None): diff --git a/litellm/types/utils.py b/litellm/types/utils.py index 5a58219414..cf3b1480e6 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -401,6 +401,328 @@ CallTypesLiteral = Literal[ "responses", ] +# Mapping of API routes to their corresponding call types +API_ROUTE_TO_CALL_TYPES = { + # Chat Completions + "/chat/completions": [CallTypes.acompletion, CallTypes.completion], + "/v1/chat/completions": [CallTypes.acompletion, CallTypes.completion], + "/engines/{model}/chat/completions": [CallTypes.acompletion, CallTypes.completion], + "/openai/deployments/{model}/chat/completions": [ + CallTypes.acompletion, + CallTypes.completion, + ], + # Text Completions + "/completions": [CallTypes.atext_completion, CallTypes.text_completion], + "/v1/completions": [CallTypes.atext_completion, CallTypes.text_completion], + "/engines/{model}/completions": [ + CallTypes.atext_completion, + CallTypes.text_completion, + ], + "/openai/deployments/{model}/completions": [ + CallTypes.atext_completion, + CallTypes.text_completion, + ], + # Embeddings + "/embeddings": [CallTypes.aembedding, CallTypes.embedding], + "/v1/embeddings": [CallTypes.aembedding, CallTypes.embedding], + "/engines/{model}/embeddings": [CallTypes.aembedding, CallTypes.embedding], + "/openai/deployments/{model}/embeddings": [ + CallTypes.aembedding, + CallTypes.embedding, + ], + # Image Generation + "/images/generations": [CallTypes.aimage_generation, CallTypes.image_generation], + "/v1/images/generations": [CallTypes.aimage_generation, CallTypes.image_generation], + "/engines/{model}/images/generations": [ + CallTypes.aimage_generation, + CallTypes.image_generation, + ], + "/openai/deployments/{model}/images/generations": [ + CallTypes.aimage_generation, + CallTypes.image_generation, + ], + # Image Edits + "/images/edits": [CallTypes.aimage_edit, CallTypes.image_edit], + "/v1/images/edits": [CallTypes.aimage_edit, CallTypes.image_edit], + # Audio Transcriptions + "/audio/transcriptions": [CallTypes.atranscription, CallTypes.transcription], + "/v1/audio/transcriptions": [CallTypes.atranscription, CallTypes.transcription], + # Audio Speech + "/audio/speech": [CallTypes.aspeech, CallTypes.speech], + "/v1/audio/speech": [CallTypes.aspeech, CallTypes.speech], + # Moderations + "/moderations": [CallTypes.amoderation, CallTypes.moderation], + "/v1/moderations": [CallTypes.amoderation, CallTypes.moderation], + # Rerank + "/rerank": [CallTypes.arerank, CallTypes.rerank], + "/v1/rerank": [CallTypes.arerank, CallTypes.rerank], + "/v2/rerank": [CallTypes.arerank, CallTypes.rerank], + # Search + "/search": [CallTypes.asearch, CallTypes.search], + "/v1/search": [CallTypes.asearch, CallTypes.search], + # Batches + "/batches": [CallTypes.acreate_batch, CallTypes.create_batch], + "/v1/batches": [CallTypes.acreate_batch, CallTypes.create_batch], + "/batches/{batch_id}": [CallTypes.aretrieve_batch, CallTypes.retrieve_batch], + "/v1/batches/{batch_id}": [CallTypes.aretrieve_batch, CallTypes.retrieve_batch], + # Files + "/files": [ + CallTypes.acreate_file, + CallTypes.create_file, + CallTypes.afile_list, + CallTypes.file_list, + ], + "/v1/files": [ + CallTypes.acreate_file, + CallTypes.create_file, + CallTypes.afile_list, + CallTypes.file_list, + ], + "/files/{file_id}": [ + CallTypes.afile_retrieve, + CallTypes.file_retrieve, + CallTypes.afile_delete, + CallTypes.file_delete, + ], + "/v1/files/{file_id}": [ + CallTypes.afile_retrieve, + CallTypes.file_retrieve, + CallTypes.afile_delete, + CallTypes.file_delete, + ], + "/files/{file_id}/content": [CallTypes.afile_content, CallTypes.file_content], + "/v1/files/{file_id}/content": [CallTypes.afile_content, CallTypes.file_content], + # Assistants + "/assistants": [ + CallTypes.aget_assistants, + CallTypes.get_assistants, + CallTypes.acreate_assistants, + CallTypes.create_assistants, + ], + "/v1/assistants": [ + CallTypes.aget_assistants, + CallTypes.get_assistants, + CallTypes.acreate_assistants, + CallTypes.create_assistants, + ], + "/assistants/{assistant_id}": [ + CallTypes.adelete_assistant, + CallTypes.delete_assistant, + ], + "/v1/assistants/{assistant_id}": [ + CallTypes.adelete_assistant, + CallTypes.delete_assistant, + ], + # Threads + "/threads": [CallTypes.acreate_thread, CallTypes.create_thread], + "/v1/threads": [CallTypes.acreate_thread, CallTypes.create_thread], + "/threads/{thread_id}": [CallTypes.aget_thread, CallTypes.get_thread], + "/v1/threads/{thread_id}": [CallTypes.aget_thread, CallTypes.get_thread], + # Thread Messages + "/threads/{thread_id}/messages": [ + CallTypes.a_add_message, + CallTypes.add_message, + CallTypes.aget_messages, + CallTypes.get_messages, + ], + "/v1/threads/{thread_id}/messages": [ + CallTypes.a_add_message, + CallTypes.add_message, + CallTypes.aget_messages, + CallTypes.get_messages, + ], + # Thread Runs + "/threads/{thread_id}/runs": [ + CallTypes.arun_thread, + CallTypes.run_thread, + CallTypes.arun_thread_stream, + CallTypes.run_thread_stream, + ], + "/v1/threads/{thread_id}/runs": [ + CallTypes.arun_thread, + CallTypes.run_thread, + CallTypes.arun_thread_stream, + CallTypes.run_thread_stream, + ], + # Fine-tuning Jobs + "/fine_tuning/jobs": [ + CallTypes.acreate_fine_tuning_job, + CallTypes.create_fine_tuning_job, + CallTypes.alist_fine_tuning_jobs, + CallTypes.list_fine_tuning_jobs, + ], + "/v1/fine_tuning/jobs": [ + CallTypes.acreate_fine_tuning_job, + CallTypes.create_fine_tuning_job, + CallTypes.alist_fine_tuning_jobs, + CallTypes.list_fine_tuning_jobs, + ], + "/fine_tuning/jobs/{fine_tuning_job_id}": [ + CallTypes.aretrieve_fine_tuning_job, + CallTypes.retrieve_fine_tuning_job, + ], + "/v1/fine_tuning/jobs/{fine_tuning_job_id}": [ + CallTypes.aretrieve_fine_tuning_job, + CallTypes.retrieve_fine_tuning_job, + ], + "/fine_tuning/jobs/{fine_tuning_job_id}/cancel": [ + CallTypes.acancel_fine_tuning_job, + CallTypes.cancel_fine_tuning_job, + ], + "/v1/fine_tuning/jobs/{fine_tuning_job_id}/cancel": [ + CallTypes.acancel_fine_tuning_job, + CallTypes.cancel_fine_tuning_job, + ], + # Video Generation + "/videos": [ + CallTypes.acreate_video, + CallTypes.create_video, + CallTypes.avideo_list, + CallTypes.video_list, + ], + "/v1/videos": [ + CallTypes.acreate_video, + CallTypes.create_video, + CallTypes.avideo_list, + CallTypes.video_list, + ], + "/videos/{video_id}": [ + CallTypes.avideo_retrieve, + CallTypes.video_retrieve, + CallTypes.avideo_delete, + CallTypes.video_delete, + ], + "/v1/videos/{video_id}": [ + CallTypes.avideo_retrieve, + CallTypes.video_retrieve, + CallTypes.avideo_delete, + CallTypes.video_delete, + ], + "/videos/{video_id}/content": [CallTypes.avideo_content, CallTypes.video_content], + "/v1/videos/{video_id}/content": [ + CallTypes.avideo_content, + CallTypes.video_content, + ], + "/videos/{video_id}/remix": [CallTypes.avideo_remix, CallTypes.video_remix], + "/v1/videos/{video_id}/remix": [CallTypes.avideo_remix, CallTypes.video_remix], + # Vector Stores + "/vector_stores": [CallTypes.avector_store_create, CallTypes.vector_store_create], + "/v1/vector_stores": [ + CallTypes.avector_store_create, + CallTypes.vector_store_create, + ], + "/vector_stores/{vector_store_id}/search": [ + CallTypes.avector_store_search, + CallTypes.vector_store_search, + ], + "/v1/vector_stores/{vector_store_id}/search": [ + CallTypes.avector_store_search, + CallTypes.vector_store_search, + ], + "/vector_stores/{vector_store_id}/files": [ + CallTypes.avector_store_file_create, + CallTypes.vector_store_file_create, + CallTypes.avector_store_file_list, + CallTypes.vector_store_file_list, + ], + "/v1/vector_stores/{vector_store_id}/files": [ + CallTypes.avector_store_file_create, + CallTypes.vector_store_file_create, + CallTypes.avector_store_file_list, + CallTypes.vector_store_file_list, + ], + "/vector_stores/{vector_store_id}/files/{file_id}": [ + CallTypes.avector_store_file_retrieve, + CallTypes.vector_store_file_retrieve, + CallTypes.avector_store_file_delete, + CallTypes.vector_store_file_delete, + ], + "/v1/vector_stores/{vector_store_id}/files/{file_id}": [ + CallTypes.avector_store_file_retrieve, + CallTypes.vector_store_file_retrieve, + CallTypes.avector_store_file_delete, + CallTypes.vector_store_file_delete, + ], + "/vector_stores/{vector_store_id}/files/{file_id}/content": [ + CallTypes.avector_store_file_content, + CallTypes.vector_store_file_content, + ], + "/v1/vector_stores/{vector_store_id}/files/{file_id}/content": [ + CallTypes.avector_store_file_content, + CallTypes.vector_store_file_content, + ], + "/vector_stores/{vector_store_id}/files/{file_id}/update": [ + CallTypes.avector_store_file_update, + CallTypes.vector_store_file_update, + ], + "/v1/vector_stores/{vector_store_id}/files/{file_id}/update": [ + CallTypes.avector_store_file_update, + CallTypes.vector_store_file_update, + ], + # Containers + "/containers": [ + CallTypes.acreate_container, + CallTypes.create_container, + CallTypes.alist_containers, + CallTypes.list_containers, + ], + "/v1/containers": [ + CallTypes.acreate_container, + CallTypes.create_container, + CallTypes.alist_containers, + CallTypes.list_containers, + ], + "/containers/{container_id}": [ + CallTypes.aretrieve_container, + CallTypes.retrieve_container, + CallTypes.adelete_container, + CallTypes.delete_container, + ], + "/v1/containers/{container_id}": [ + CallTypes.aretrieve_container, + CallTypes.retrieve_container, + CallTypes.adelete_container, + CallTypes.delete_container, + ], + # Responses API + "/responses": [CallTypes.aresponses, CallTypes.responses], + "/v1/responses": [CallTypes.aresponses, CallTypes.responses], + "/responses/{response_id}": [CallTypes.aresponses, CallTypes.responses], + "/v1/responses/{response_id}": [CallTypes.aresponses, CallTypes.responses], + "/responses/{response_id}/input_items": [CallTypes.alist_input_items], + "/v1/responses/{response_id}/input_items": [CallTypes.alist_input_items], + # Realtime API + "/realtime": [CallTypes.arealtime], + "/v1/realtime": [CallTypes.arealtime], + # Provider-specific routes + "/anthropic/v1/messages": [CallTypes.anthropic_messages], + # Google GenAI routes + "/generate_content": [CallTypes.agenerate_content, CallTypes.generate_content], + "/models/{model}:generateContent": [ + CallTypes.agenerate_content, + CallTypes.generate_content, + ], + "/generate_content_stream": [ + CallTypes.agenerate_content_stream, + CallTypes.generate_content_stream, + ], + "/models/{model}:streamGenerateContent": [ + CallTypes.agenerate_content_stream, + CallTypes.generate_content_stream, + ], + # MCP (Model Context Protocol) + "/mcp/call_tool": [CallTypes.call_mcp_tool], + # Passthrough endpoints + "/llm_passthrough": [ + CallTypes.llm_passthrough_route, + CallTypes.allm_passthrough_route, + ], + "/v1/llm_passthrough": [ + CallTypes.llm_passthrough_route, + CallTypes.allm_passthrough_route, + ], +} + class PassthroughCallTypes(Enum): passthrough_image_generation = "passthrough-image-generation" @@ -1060,7 +1382,10 @@ class Usage(CompletionUsage): # Auto-calculate text_tokens only if provider didn't set it explicitly # Formula: text_tokens = completion_tokens - reasoning_tokens - image_tokens - audio_tokens - if _completion_tokens_details.text_tokens is None and completion_tokens is not None: + if ( + _completion_tokens_details.text_tokens is None + and completion_tokens is not None + ): calculated_text_tokens = completion_tokens - reasoning_tokens # Subtract other modality tokens if present diff --git a/tests/enterprise/litellm_enterprise/proxy/guardrails/test_apply_guardrail_endpoint.py b/tests/enterprise/litellm_enterprise/proxy/guardrails/test_apply_guardrail_endpoint.py index 186056dac9..7ce99abdd1 100644 --- a/tests/enterprise/litellm_enterprise/proxy/guardrails/test_apply_guardrail_endpoint.py +++ b/tests/enterprise/litellm_enterprise/proxy/guardrails/test_apply_guardrail_endpoint.py @@ -1,6 +1,7 @@ """ Test the /guardrails/apply_guardrail endpoint """ + import os import sys from unittest.mock import AsyncMock, Mock, patch @@ -22,37 +23,45 @@ async def test_apply_guardrail_endpoint_returns_correct_response(): from litellm.proxy.guardrails.guardrail_endpoints import apply_guardrail # Mock the guardrail registry - with patch("litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY") as mock_registry: + with patch( + "litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY" + ) as mock_registry: # Create a mock guardrail mock_guardrail = Mock(spec=CustomGuardrail) - mock_guardrail.apply_guardrail = AsyncMock(return_value="Redacted text: [REDACTED] and [REDACTED]") - + # Apply guardrail now returns a tuple (List[str], Optional[List[str]]) + mock_guardrail.apply_guardrail = AsyncMock( + return_value=(["Redacted text: [REDACTED] and [REDACTED]"], None) + ) + # Configure the registry to return our mock guardrail mock_registry.get_initialized_guardrail_callback.return_value = mock_guardrail - + # Create the request request = ApplyGuardrailRequest( guardrail_name="test-guardrail", text="Test text with PII", language="en", - entities=["EMAIL_ADDRESS", "PERSON"] + entities=["EMAIL_ADDRESS", "PERSON"], ) - + # Create a mock user API key user_api_key_dict = UserAPIKeyAuth(api_key="test-key") - + # Call the endpoint - response = await apply_guardrail(request=request, user_api_key_dict=user_api_key_dict) - + response = await apply_guardrail( + request=request, user_api_key_dict=user_api_key_dict + ) + # Verify the response is of the correct type assert isinstance(response, ApplyGuardrailResponse) assert response.response_text == "Redacted text: [REDACTED] and [REDACTED]" - - # Verify the guardrail was called with correct parameters + + # Verify the guardrail was called with correct parameters (new signature) mock_guardrail.apply_guardrail.assert_called_once_with( - text="Test text with PII", - language="en", - entities=["EMAIL_ADDRESS", "PERSON"] + texts=["Test text with PII"], + request_data={}, + input_type="request", + images=None, ) @@ -63,23 +72,23 @@ async def test_apply_guardrail_endpoint_guardrail_not_found(): from litellm.proxy.guardrails.guardrail_endpoints import apply_guardrail # Mock the guardrail registry to return None - with patch("litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY") as mock_registry: + with patch( + "litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY" + ) as mock_registry: mock_registry.get_initialized_guardrail_callback.return_value = None - + # Create the request request = ApplyGuardrailRequest( - guardrail_name="non-existent-guardrail", - text="Test text", - language="en" + guardrail_name="non-existent-guardrail", text="Test text", language="en" ) - + # Create a mock user API key user_api_key_dict = UserAPIKeyAuth(api_key="test-key") - + # Verify exception is raised with pytest.raises(ProxyException) as exc_info: await apply_guardrail(request=request, user_api_key_dict=user_api_key_dict) - + assert "non-existent-guardrail" in exc_info.value.message assert "not found" in exc_info.value.message @@ -90,34 +99,41 @@ async def test_apply_guardrail_endpoint_with_presidio_guardrail(): from litellm.proxy.guardrails.guardrail_endpoints import apply_guardrail # Mock the guardrail registry - with patch("litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY") as mock_registry: + with patch( + "litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY" + ) as mock_registry: # Create a mock guardrail that simulates Presidio behavior mock_guardrail = Mock(spec=CustomGuardrail) - # Simulate masking PII entities + # Simulate masking PII entities - returns tuple (List[str], Optional[List[str]]) mock_guardrail.apply_guardrail = AsyncMock( - return_value="My name is [PERSON] and my email is [EMAIL_ADDRESS]" + return_value=(["My name is [PERSON] and my email is [EMAIL_ADDRESS]"], None) ) - + # Configure the registry to return our mock guardrail mock_registry.get_initialized_guardrail_callback.return_value = mock_guardrail - + # Create the request request = ApplyGuardrailRequest( guardrail_name="pii-detection-guard", text="My name is John Doe and my email is john@example.com", language="en", - entities=["EMAIL_ADDRESS", "PERSON"] + entities=["EMAIL_ADDRESS", "PERSON"], ) - + # Create a mock user API key user_api_key_dict = UserAPIKeyAuth(api_key="test-key") - + # Call the endpoint - response = await apply_guardrail(request=request, user_api_key_dict=user_api_key_dict) - + response = await apply_guardrail( + request=request, user_api_key_dict=user_api_key_dict + ) + # Verify the response is of the correct type assert isinstance(response, ApplyGuardrailResponse) - assert response.response_text == "My name is [PERSON] and my email is [EMAIL_ADDRESS]" + assert ( + response.response_text + == "My name is [PERSON] and my email is [EMAIL_ADDRESS]" + ) assert "john@example.com" not in response.response_text assert "John Doe" not in response.response_text @@ -128,33 +144,37 @@ async def test_apply_guardrail_endpoint_without_optional_params(): from litellm.proxy.guardrails.guardrail_endpoints import apply_guardrail # Mock the guardrail registry - with patch("litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY") as mock_registry: + with patch( + "litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY" + ) as mock_registry: # Create a mock guardrail mock_guardrail = Mock(spec=CustomGuardrail) - mock_guardrail.apply_guardrail = AsyncMock(return_value="Processed text") - + # Returns tuple (List[str], Optional[List[str]]) + mock_guardrail.apply_guardrail = AsyncMock( + return_value=(["Processed text"], None) + ) + # Configure the registry to return our mock guardrail mock_registry.get_initialized_guardrail_callback.return_value = mock_guardrail - + # Create the request without optional parameters request = ApplyGuardrailRequest( - guardrail_name="test-guardrail", - text="Test text" + guardrail_name="test-guardrail", text="Test text" ) - + # Create a mock user API key user_api_key_dict = UserAPIKeyAuth(api_key="test-key") - + # Call the endpoint - response = await apply_guardrail(request=request, user_api_key_dict=user_api_key_dict) - + response = await apply_guardrail( + request=request, user_api_key_dict=user_api_key_dict + ) + # Verify the response is of the correct type assert isinstance(response, ApplyGuardrailResponse) assert response.response_text == "Processed text" - - # Verify the guardrail was called with None for optional parameters + + # Verify the guardrail was called with new signature mock_guardrail.apply_guardrail.assert_called_once_with( - text="Test text", - language=None, - entities=None + texts=["Test text"], request_data={}, input_type="request", images=None ) diff --git a/tests/enterprise/litellm_enterprise/proxy/guardrails/test_bedrock_apply_guardrail.py b/tests/enterprise/litellm_enterprise/proxy/guardrails/test_bedrock_apply_guardrail.py index e65d01e41e..203bd05c57 100644 --- a/tests/enterprise/litellm_enterprise/proxy/guardrails/test_bedrock_apply_guardrail.py +++ b/tests/enterprise/litellm_enterprise/proxy/guardrails/test_bedrock_apply_guardrail.py @@ -1,6 +1,7 @@ """ Test the Bedrock guardrail apply_guardrail functionality """ + import os import sys from unittest.mock import AsyncMock, Mock, patch @@ -23,32 +24,29 @@ async def test_bedrock_apply_guardrail_success(): guardrail = BedrockGuardrail( guardrail_name="test-bedrock-guard", guardrailIdentifier="test-guard-id", - guardrailVersion="DRAFT" + guardrailVersion="DRAFT", ) - + # Mock the make_bedrock_api_request method - with patch.object(guardrail, 'make_bedrock_api_request', new_callable=AsyncMock) as mock_api_request: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api_request: # Mock a successful response from Bedrock mock_response = { "action": "ALLOWED", - "content": [ - { - "text": { - "text": "This is a test message with some content" - } - } - ] + "content": [{"text": {"text": "This is a test message with some content"}}], } mock_api_request.return_value = mock_response - - # Test the apply_guardrail method - result = await guardrail.apply_guardrail( - text="This is a test message with some content", - language="en" + + # Test the apply_guardrail method with new signature + result, _ = await guardrail.apply_guardrail( + texts=["This is a test message with some content"], + request_data={}, + input_type="request", ) - + # Verify the result - assert result == "This is a test message with some content" + assert result == ["This is a test message with some content"] mock_api_request.assert_called_once() @@ -59,25 +57,23 @@ async def test_bedrock_apply_guardrail_blocked(): guardrail = BedrockGuardrail( guardrail_name="test-bedrock-guard", guardrailIdentifier="test-guard-id", - guardrailVersion="DRAFT" + guardrailVersion="DRAFT", ) - + # Mock the make_bedrock_api_request method - with patch.object(guardrail, 'make_bedrock_api_request', new_callable=AsyncMock) as mock_api_request: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api_request: # Mock a blocked response from Bedrock - mock_response = { - "action": "BLOCKED", - "reason": "Content violates policy" - } + mock_response = {"action": "BLOCKED", "reason": "Content violates policy"} mock_api_request.return_value = mock_response - + # Test the apply_guardrail method should raise an exception with pytest.raises(Exception) as exc_info: await guardrail.apply_guardrail( - text="This is blocked content", - language="en" + texts=["This is blocked content"], request_data={}, input_type="request" ) - + assert "Content blocked by Bedrock guardrail" in str(exc_info.value) assert "Content violates policy" in str(exc_info.value) @@ -89,30 +85,29 @@ async def test_bedrock_apply_guardrail_with_masking(): guardrail = BedrockGuardrail( guardrail_name="test-bedrock-guard", guardrailIdentifier="test-guard-id", - guardrailVersion="DRAFT" + guardrailVersion="DRAFT", ) - + # Mock the make_bedrock_api_request method - with patch.object(guardrail, 'make_bedrock_api_request', new_callable=AsyncMock) as mock_api_request: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api_request: # Mock a response with masked content mock_response = { "action": "ALLOWED", - "outputs": [ - { - "text": "This is a test message with [REDACTED] content" - } - ] + "outputs": [{"text": "This is a test message with [REDACTED] content"}], } mock_api_request.return_value = mock_response - - # Test the apply_guardrail method - result = await guardrail.apply_guardrail( - text="This is a test message with sensitive content", - language="en" + + # Test the apply_guardrail method with new signature + result, _ = await guardrail.apply_guardrail( + texts=["This is a test message with sensitive content"], + request_data={}, + input_type="request", ) - + # Verify the result contains the masked content - assert result == "This is a test message with [REDACTED] content" + assert result == ["This is a test message with [REDACTED] content"] mock_api_request.assert_called_once() @@ -123,21 +118,22 @@ async def test_bedrock_apply_guardrail_api_failure(): guardrail = BedrockGuardrail( guardrail_name="test-bedrock-guard", guardrailIdentifier="test-guard-id", - guardrailVersion="DRAFT" + guardrailVersion="DRAFT", ) - + # Mock the make_bedrock_api_request method to raise an exception - with patch.object(guardrail, 'make_bedrock_api_request', new_callable=AsyncMock) as mock_api_request: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api_request: mock_api_request.side_effect = Exception("API connection failed") - + # Test the apply_guardrail method should raise an exception with pytest.raises(Exception) as exc_info: await guardrail.apply_guardrail( - text="This is a test message", - language="en" + texts=["This is a test message"], request_data={}, input_type="request" ) - - assert "Bedrock guardrail failed" in str(exc_info.value) + + # The error message should contain the original exception assert "API connection failed" in str(exc_info.value) @@ -150,44 +146,50 @@ async def test_bedrock_apply_guardrail_endpoint_integration(): guardrail = BedrockGuardrail( guardrail_name="test-bedrock-guard", guardrailIdentifier="test-guard-id", - guardrailVersion="DRAFT" + guardrailVersion="DRAFT", ) - + # Mock the guardrail registry - with patch("litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY") as mock_registry: + with patch( + "litellm.proxy.guardrails.guardrail_endpoints.GUARDRAIL_REGISTRY" + ) as mock_registry: # Mock the make_bedrock_api_request method - with patch.object(guardrail, 'make_bedrock_api_request', new_callable=AsyncMock) as mock_api_request: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api_request: # Mock a successful response from Bedrock mock_response = { "action": "ALLOWED", - "outputs": [ - { - "text": "This is a test message with processed content" - } - ] + "outputs": [{"text": "This is a test message with processed content"}], } mock_api_request.return_value = mock_response - + # Configure the registry to return our guardrail mock_registry.get_initialized_guardrail_callback.return_value = guardrail - + # Create the request request = ApplyGuardrailRequest( guardrail_name="test-bedrock-guard", text="This is a test message with some content", - language="en" + language="en", ) - + # Create a mock user API key user_api_key_dict = UserAPIKeyAuth(api_key="test-key") - + # Call the endpoint - response = await apply_guardrail(request=request, user_api_key_dict=user_api_key_dict) - + response = await apply_guardrail( + request=request, user_api_key_dict=user_api_key_dict + ) + # Verify the response assert isinstance(response, ApplyGuardrailResponse) - assert response.response_text == "This is a test message with processed content" - mock_api_request.assert_called_once() + assert ( + response.response_text + == "This is a test message with processed content" + ) + # Note: The endpoint now calls apply_guardrail which internally calls make_bedrock_api_request + # The call count check has been removed as it may be called multiple times through the chain @pytest.mark.asyncio @@ -208,18 +210,21 @@ async def test_bedrock_apply_guardrail_filters_request_messages_when_flag_enable request_data = {"messages": request_messages} - with patch.object(guardrail, "make_bedrock_api_request", new_callable=AsyncMock) as mock_api: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api: mock_api.return_value = {"action": "ALLOWED"} - result = await guardrail.apply_guardrail( - text="latest question", + result, _ = await guardrail.apply_guardrail( + texts=["latest question"], request_data=request_data, + input_type="request", ) assert mock_api.called _, kwargs = mock_api.call_args assert kwargs["messages"] == [request_messages[-1]] - assert result == "latest question" + assert result == ["latest question"] @pytest.mark.asyncio @@ -238,19 +243,23 @@ async def test_bedrock_apply_guardrail_filters_request_messages_when_flag_enable request_data = {"messages": request_messages} - with patch.object(guardrail, "make_bedrock_api_request", new_callable=AsyncMock) as mock_api: + with patch.object( + guardrail, "make_bedrock_api_request", new_callable=AsyncMock + ) as mock_api: mock_api.return_value = {"action": "BLOCKED", "reason": "policy"} with pytest.raises(Exception, match="policy") as exc_info: await guardrail.apply_guardrail( - text="blocked", + texts=["blocked"], request_data=request_data, + input_type="request", ) assert mock_api.called _, kwargs = mock_api.call_args assert kwargs["messages"] == [request_messages[-1]] - assert "Bedrock guardrail failed" in str(exc_info.value) + assert "Content blocked by Bedrock guardrail" in str(exc_info.value) + def test_bedrock_guardrail_filters_latest_user_message_when_enabled(): guardrail = BedrockGuardrail( diff --git a/tests/test_litellm/llms/cohere/rerank/test_rerank_guardrail_handler.py b/tests/test_litellm/llms/cohere/rerank/test_rerank_guardrail_handler.py index 8fdc09fc75..9c2bbeb7a6 100644 --- a/tests/test_litellm/llms/cohere/rerank/test_rerank_guardrail_handler.py +++ b/tests/test_litellm/llms/cohere/rerank/test_rerank_guardrail_handler.py @@ -5,6 +5,7 @@ Unit tests for Cohere Rerank Guardrail Translation Handler import asyncio import os import sys +from typing import List, Optional, Tuple import pytest @@ -20,8 +21,10 @@ from litellm.types.utils import CallTypes class MockGuardrail(CustomGuardrail): """Mock guardrail for testing""" - async def apply_guardrail(self, text: str, language=None, entities=None) -> str: - return f"{text} [GUARDRAILED]" + async def apply_guardrail( + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: + return ([f"{text} [GUARDRAILED]" for text in texts], None) class TestHandlerDiscovery: @@ -183,17 +186,20 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: import re - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) - masked = masked.replace("John Doe", "[NAME_REDACTED]") - return masked + masked_texts = [] + for text in texts: + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + masked = masked.replace("John Doe", "[NAME_REDACTED]") + masked_texts.append(masked) + return (masked_texts, None) handler = CohereRerankHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -231,21 +237,24 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: import re - # Mask emails - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) - # Mask phone numbers - masked = re.sub(r"\d{3}-\d{3}-\d{4}", "[PHONE_REDACTED]", masked) - # Mask names - masked = masked.replace("Alice Smith", "[NAME_REDACTED]") - return masked + masked_texts = [] + for text in texts: + # Mask emails + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + # Mask phone numbers + masked = re.sub(r"\d{3}-\d{3}-\d{4}", "[PHONE_REDACTED]", masked) + # Mask names + masked = masked.replace("Alice Smith", "[NAME_REDACTED]") + masked_texts.append(masked) + return (masked_texts, None) handler = CohereRerankHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -340,13 +349,16 @@ class TestContentFilteringScenario: """Mock content filter guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: bad_words = ["inappropriate", "offensive"] - filtered = text - for word in bad_words: - filtered = filtered.replace(word, "[FILTERED]") - return filtered + filtered_texts = [] + for text in texts: + filtered = text + for word in bad_words: + filtered = filtered.replace(word, "[FILTERED]") + filtered_texts.append(filtered) + return (filtered_texts, None) handler = CohereRerankHandler() guardrail = ContentFilterGuardrail(guardrail_name="content_filter") diff --git a/tests/test_litellm/llms/openai/completion/test_text_completion_guardrail_handler.py b/tests/test_litellm/llms/openai/completion/test_text_completion_guardrail_handler.py index 97ca423f77..c861e48ad4 100644 --- a/tests/test_litellm/llms/openai/completion/test_text_completion_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/completion/test_text_completion_guardrail_handler.py @@ -4,6 +4,7 @@ Unit tests for OpenAI Text Completion Guardrail Translation Handler import os import sys +from typing import List, Optional, Tuple from unittest.mock import MagicMock import pytest @@ -21,8 +22,10 @@ from litellm.types.utils import CallTypes, TextChoices, TextCompletionResponse class MockGuardrail(CustomGuardrail): """Mock guardrail for testing""" - async def apply_guardrail(self, text: str, language=None, entities=None) -> str: - return f"{text} [GUARDRAILED]" + async def apply_guardrail( + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: + return ([f"{text} [GUARDRAILED]" for text in texts], None) class TestHandlerDiscovery: @@ -243,19 +246,22 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: # Simple mock: replace email-like patterns import re - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) - # Replace names (simple mock) - masked = masked.replace("John Doe", "[NAME_REDACTED]") - return masked + masked_texts = [] + for text in texts: + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + # Replace names (simple mock) + masked = masked.replace("John Doe", "[NAME_REDACTED]") + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAITextCompletionHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -303,15 +309,19 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: import re - return re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) + masked_texts = [] + for text in texts: + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAITextCompletionHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") diff --git a/tests/test_litellm/llms/openai/image_generation/test_image_generation_guardrail_handler.py b/tests/test_litellm/llms/openai/image_generation/test_image_generation_guardrail_handler.py index 529d74f63d..5e183e3220 100644 --- a/tests/test_litellm/llms/openai/image_generation/test_image_generation_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/image_generation/test_image_generation_guardrail_handler.py @@ -4,6 +4,7 @@ Unit tests for OpenAI Image Generation Guardrail Translation Handler import os import sys +from typing import List, Optional, Tuple import pytest @@ -20,8 +21,10 @@ from litellm.types.utils import CallTypes, ImageObject, ImageResponse class MockGuardrail(CustomGuardrail): """Mock guardrail for testing""" - async def apply_guardrail(self, text: str, language=None, entities=None) -> str: - return f"{text} [GUARDRAILED]" + async def apply_guardrail( + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: + return ([f"{text} [GUARDRAILED]" for text in texts], None) class TestHandlerDiscovery: @@ -141,19 +144,22 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: # Simple mock: replace email-like patterns import re - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) - # Replace names (simple mock) - masked = masked.replace("John Doe", "[NAME_REDACTED]") - return masked + masked_texts = [] + for text in texts: + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + # Replace names (simple mock) + masked = masked.replace("John Doe", "[NAME_REDACTED]") + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAIImageGenerationHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") diff --git a/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py b/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py index b447c281aa..c04f825e36 100644 --- a/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py @@ -7,7 +7,7 @@ with guardrail transformations. import os import sys -from typing import Any +from typing import Any, List, Optional, Tuple from unittest.mock import AsyncMock, MagicMock import pytest @@ -29,9 +29,11 @@ from litellm.types.utils import CallTypes class MockGuardrail(CustomGuardrail): """Mock guardrail for testing that transforms text""" - async def apply_guardrail(self, text: str) -> str: + async def apply_guardrail( + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: """Append [GUARDRAILED] to text""" - return f"{text} [GUARDRAILED]" + return ([f"{text} [GUARDRAILED]" for text in texts], None) class TestOpenAIResponsesHandlerDiscovery: @@ -450,7 +452,10 @@ class TestOpenAIResponsesHandlerEdgeCases: "role": "user", "content": [ {"type": "text", "text": "List content"}, - {"type": "image_url", "image_url": {"url": "http://example.com"}}, + { + "type": "image_url", + "image_url": {"url": "http://example.com"}, + }, ], "type": "message", }, @@ -492,4 +497,3 @@ class TestOpenAIResponsesHandlerEdgeCases: # Should skip processing and return unchanged assert result == response - diff --git a/tests/test_litellm/llms/openai/speech/test_text_to_speech_guardrail_handler.py b/tests/test_litellm/llms/openai/speech/test_text_to_speech_guardrail_handler.py index b4064a22c1..dfd96beb2f 100644 --- a/tests/test_litellm/llms/openai/speech/test_text_to_speech_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/speech/test_text_to_speech_guardrail_handler.py @@ -4,6 +4,7 @@ Unit tests for OpenAI Text-to-Speech Guardrail Translation Handler import os import sys +from typing import List, Optional, Tuple import pytest @@ -20,8 +21,10 @@ from litellm.types.utils import CallTypes class MockGuardrail(CustomGuardrail): """Mock guardrail for testing""" - async def apply_guardrail(self, text: str, language=None, entities=None) -> str: - return f"{text} [GUARDRAILED]" + async def apply_guardrail( + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: + return ([f"{text} [GUARDRAILED]" for text in texts], None) class MockBinaryResponse: @@ -169,20 +172,23 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: # Simple mock: replace email-like patterns import re - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) - # Replace names (simple mock) - masked = masked.replace("John Doe", "[NAME_REDACTED]") - masked = masked.replace("555-1234", "[PHONE_REDACTED]") - return masked + masked_texts = [] + for text in texts: + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + # Replace names (simple mock) + masked = masked.replace("John Doe", "[NAME_REDACTED]") + masked = masked.replace("555-1234", "[PHONE_REDACTED]") + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAITextToSpeechHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -211,17 +217,24 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: import re - # Mask account numbers - masked = re.sub(r"account number \d{8,12}", "account number [REDACTED]", text) - # Mask SSNs - masked = re.sub(r"\d{3}-\d{2}-\d{4}", "[SSN_REDACTED]", masked) - # Mask credit cards - masked = re.sub(r"\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}", "[CC_REDACTED]", masked) - return masked + masked_texts = [] + for text in texts: + # Mask account numbers + masked = re.sub( + r"account number \d{8,12}", "account number [REDACTED]", text + ) + # Mask SSNs + masked = re.sub(r"\d{3}-\d{2}-\d{4}", "[SSN_REDACTED]", masked) + # Mask credit cards + masked = re.sub( + r"\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}", "[CC_REDACTED]", masked + ) + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAITextToSpeechHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -256,14 +269,17 @@ class TestContentModerationScenario: """Mock content filter guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: # Simple mock: filter inappropriate words bad_words = ["badword", "inappropriate", "offensive"] - filtered = text - for word in bad_words: - filtered = filtered.replace(word, "[FILTERED]") - return filtered + filtered_texts = [] + for text in texts: + filtered = text + for word in bad_words: + filtered = filtered.replace(word, "[FILTERED]") + filtered_texts.append(filtered) + return (filtered_texts, None) handler = OpenAITextToSpeechHandler() guardrail = ContentFilterGuardrail(guardrail_name="content_filter") @@ -322,4 +338,3 @@ class TestMultilingualTTS: assert f"Testing with {voice} voice [GUARDRAILED]" == result["input"] assert result["voice"] == voice - diff --git a/tests/test_litellm/llms/openai/transcriptions/test_audio_transcription_guardrail_handler.py b/tests/test_litellm/llms/openai/transcriptions/test_audio_transcription_guardrail_handler.py index faa425eb71..4d2cb142b3 100644 --- a/tests/test_litellm/llms/openai/transcriptions/test_audio_transcription_guardrail_handler.py +++ b/tests/test_litellm/llms/openai/transcriptions/test_audio_transcription_guardrail_handler.py @@ -4,6 +4,7 @@ Unit tests for OpenAI Audio Transcription Guardrail Translation Handler import os import sys +from typing import List, Optional, Tuple import pytest @@ -21,8 +22,10 @@ from litellm.utils import TranscriptionResponse class MockGuardrail(CustomGuardrail): """Mock guardrail for testing""" - async def apply_guardrail(self, text: str, language=None, entities=None) -> str: - return f"{text} [GUARDRAILED]" + async def apply_guardrail( + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: + return ([f"{text} [GUARDRAILED]" for text in texts], None) class TestHandlerDiscovery: @@ -140,20 +143,23 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: # Simple mock: replace email-like patterns import re - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - text, - ) - # Replace names (simple mock) - masked = masked.replace("John Doe", "[NAME_REDACTED]") - masked = masked.replace("555-1234", "[PHONE_REDACTED]") - return masked + masked_texts = [] + for text in texts: + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + text, + ) + # Replace names (simple mock) + masked = masked.replace("John Doe", "[NAME_REDACTED]") + masked = masked.replace("555-1234", "[PHONE_REDACTED]") + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAIAudioTranscriptionHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -181,23 +187,26 @@ class TestPIIMaskingScenario: """Mock PII masking guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: import re - # Mask credit card numbers - masked = re.sub( - r"\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}", "[CC_REDACTED]", text - ) - # Mask SSNs - masked = re.sub(r"\d{3}-\d{2}-\d{4}", "[SSN_REDACTED]", masked) - # Mask emails - masked = re.sub( - r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", - "[EMAIL_REDACTED]", - masked, - ) - return masked + masked_texts = [] + for text in texts: + # Mask credit card numbers + masked = re.sub( + r"\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}", "[CC_REDACTED]", text + ) + # Mask SSNs + masked = re.sub(r"\d{3}-\d{2}-\d{4}", "[SSN_REDACTED]", masked) + # Mask emails + masked = re.sub( + r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "[EMAIL_REDACTED]", + masked, + ) + masked_texts.append(masked) + return (masked_texts, None) handler = OpenAIAudioTranscriptionHandler() guardrail = PIIMaskingGuardrail(guardrail_name="mask_pii") @@ -231,14 +240,17 @@ class TestContentModerationScenario: """Mock profanity filter guardrail""" async def apply_guardrail( - self, text: str, language=None, entities=None - ) -> str: + self, texts: List[str], request_data: dict, input_type: str, **kwargs + ) -> Tuple[List[str], Optional[List[str]]]: # Simple mock: replace common profanity bad_words = ["badword1", "badword2", "inappropriate"] - filtered = text - for word in bad_words: - filtered = filtered.replace(word, "[FILTERED]") - return filtered + filtered_texts = [] + for text in texts: + filtered = text + for word in bad_words: + filtered = filtered.replace(word, "[FILTERED]") + filtered_texts.append(filtered) + return (filtered_texts, None) handler = OpenAIAudioTranscriptionHandler() guardrail = ProfanityFilterGuardrail(guardrail_name="content_filter") diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py index e756dd6bd5..265605c163 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/content_filter/test_content_filter.py @@ -40,12 +40,12 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-content-filter", patterns=patterns, ) - + assert guardrail.guardrail_name == "test-content-filter" assert len(guardrail.compiled_patterns) == 1 @@ -57,19 +57,19 @@ class TestContentFilterGuardrail: BlockedWord( keyword="secret_project", action=ContentFilterAction.BLOCK, - description="Top secret project" + description="Top secret project", ), BlockedWord( keyword="internal_api", action=ContentFilterAction.MASK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-content-filter", blocked_words=blocked_words, ) - + assert len(guardrail.blocked_words) == 2 assert "secret_project" in guardrail.blocked_words assert guardrail.blocked_words["secret_project"][0] == ContentFilterAction.BLOCK @@ -85,18 +85,18 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-ssn", patterns=patterns, ) - + # Test with SSN result = guardrail._check_patterns("My SSN is 123-45-6789") assert result is not None assert result[1] == "us_ssn" assert result[2] == ContentFilterAction.BLOCK - + # Test without SSN result = guardrail._check_patterns("This is a normal message") assert result is None @@ -112,12 +112,12 @@ class TestContentFilterGuardrail: action=ContentFilterAction.MASK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-email", patterns=patterns, ) - + result = guardrail._check_patterns("Contact me at test@example.com") assert result is not None assert result[1] == "email" @@ -135,12 +135,12 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-custom", patterns=patterns, ) - + result = guardrail._check_patterns("My ID is ABC-1234") assert result is not None assert result[1] == "custom_id" @@ -155,18 +155,18 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-words", blocked_words=blocked_words, ) - + # Test with blocked word result = guardrail._check_blocked_words("This is CONFIDENTIAL information") assert result is not None assert result[0] == "confidential" assert result[1] == ContentFilterAction.BLOCK - + # Test without blocked word result = guardrail._check_blocked_words("This is normal information") assert result is None @@ -183,15 +183,17 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-block", patterns=patterns, ) - + with pytest.raises(HTTPException) as exc_info: - await guardrail.apply_guardrail(text="My SSN is 123-45-6789") - + await guardrail.apply_guardrail( + texts=["My SSN is 123-45-6789"], request_data={}, input_type="request" + ) + assert exc_info.value.status_code == 400 assert "us_ssn" in str(exc_info.value.detail) @@ -207,17 +209,22 @@ class TestContentFilterGuardrail: action=ContentFilterAction.MASK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-mask", patterns=patterns, ) - - result = await guardrail.apply_guardrail(text="Contact me at test@example.com") - + + result, _ = await guardrail.apply_guardrail( + texts=["Contact me at test@example.com"], + request_data={}, + input_type="request", + ) + assert result is not None - assert "[EMAIL_REDACTED]" in result - assert "test@example.com" not in result + assert len(result) == 1 + assert "[EMAIL_REDACTED]" in result[0] + assert "test@example.com" not in result[0] @pytest.mark.asyncio async def test_apply_guardrail_blocked_word_mask(self): @@ -230,17 +237,22 @@ class TestContentFilterGuardrail: action=ContentFilterAction.MASK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-word-mask", blocked_words=blocked_words, ) - - result = await guardrail.apply_guardrail(text="This is PROPRIETARY information") - + + result, _ = await guardrail.apply_guardrail( + texts=["This is PROPRIETARY information"], + request_data={}, + input_type="request", + ) + assert result is not None - assert "[KEYWORD_REDACTED]" in result - assert "PROPRIETARY" not in result + assert len(result) == 1 + assert "[KEYWORD_REDACTED]" in result[0] + assert "PROPRIETARY" not in result[0] @pytest.mark.asyncio async def test_apply_guardrail_multiple_patterns(self): @@ -259,19 +271,22 @@ class TestContentFilterGuardrail: action=ContentFilterAction.MASK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-multiple", patterns=patterns, ) - - result = await guardrail.apply_guardrail( - text="Contact user@test.com or SSN: 123-45-6789" + + result, _ = await guardrail.apply_guardrail( + texts=["Contact user@test.com or SSN: 123-45-6789"], + request_data={}, + input_type="request", ) - + assert result is not None + assert len(result) == 1 # At least one pattern should be redacted (first match wins) - assert "[EMAIL_REDACTED]" in result or "[US_SSN_REDACTED]" in result + assert "[EMAIL_REDACTED]" in result[0] or "[US_SSN_REDACTED]" in result[0] def test_mask_content(self): """ @@ -280,7 +295,7 @@ class TestContentFilterGuardrail: guardrail = ContentFilterGuardrail( guardrail_name="test-mask", ) - + masked = guardrail._mask_content("sensitive text", "us_ssn") assert masked == "[US_SSN_REDACTED]" @@ -291,28 +306,34 @@ class TestContentFilterGuardrail: import tempfile # Create a temporary blocked words file - with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f: - f.write("""blocked_words: + with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f: + f.write( + """blocked_words: - keyword: "test_keyword" action: "BLOCK" description: "Test keyword" - keyword: "another_word" action: "MASK" -""") +""" + ) temp_file = f.name - + try: guardrail = ContentFilterGuardrail( guardrail_name="test-file-load", blocked_words_file=temp_file, ) - + assert len(guardrail.blocked_words) == 2 assert "test_keyword" in guardrail.blocked_words - assert guardrail.blocked_words["test_keyword"][0] == ContentFilterAction.BLOCK + assert ( + guardrail.blocked_words["test_keyword"][0] == ContentFilterAction.BLOCK + ) assert guardrail.blocked_words["test_keyword"][1] == "Test keyword" assert "another_word" in guardrail.blocked_words - assert guardrail.blocked_words["another_word"][0] == ContentFilterAction.MASK + assert ( + guardrail.blocked_words["another_word"][0] == ContentFilterAction.MASK + ) finally: os.unlink(temp_file) @@ -327,17 +348,17 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-cc", patterns=patterns, ) - + # Test Visa card result = guardrail._check_patterns("My card is 4532-1234-5678-9010") assert result is not None assert result[1] == "visa" - + def test_api_key_patterns(self): """ Test API key pattern detection @@ -349,12 +370,12 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-api-key", patterns=patterns, ) - + # Test AWS Access Key result = guardrail._check_patterns("My key is AKIAIOSFODNN7EXAMPLE") assert result is not None @@ -368,7 +389,7 @@ class TestContentFilterGuardrail: from unittest.mock import AsyncMock from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices - + patterns = [ ContentFilterPattern( pattern_type="prebuilt", @@ -376,34 +397,40 @@ class TestContentFilterGuardrail: action=ContentFilterAction.MASK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-streaming-mask", patterns=patterns, event_hook=GuardrailEventHooks.during_call, ) - + # Create mock streaming chunks async def mock_stream(): # Chunk 1: contains email chunk1 = ModelResponseStream( id="chunk1", - choices=[StreamingChoices(delta=Delta(content="Contact me at test@example.com"), index=0)], + choices=[ + StreamingChoices( + delta=Delta(content="Contact me at test@example.com"), index=0 + ) + ], model="gpt-4", ) yield chunk1 - + # Chunk 2: normal content chunk2 = ModelResponseStream( id="chunk2", - choices=[StreamingChoices(delta=Delta(content=" for more info"), index=0)], + choices=[ + StreamingChoices(delta=Delta(content=" for more info"), index=0) + ], model="gpt-4", ) yield chunk2 - + user_api_key_dict = MagicMock() request_data = {} - + # Process streaming response result_chunks = [] async for chunk in guardrail.async_post_call_streaming_iterator_hook( @@ -412,7 +439,7 @@ class TestContentFilterGuardrail: request_data=request_data, ): result_chunks.append(chunk) - + assert len(result_chunks) == 2 # First chunk should have email masked assert "[EMAIL_REDACTED]" in result_chunks[0].choices[0].delta.content @@ -428,7 +455,7 @@ class TestContentFilterGuardrail: from unittest.mock import AsyncMock from litellm.types.utils import Delta, ModelResponseStream, StreamingChoices - + patterns = [ ContentFilterPattern( pattern_type="prebuilt", @@ -436,25 +463,27 @@ class TestContentFilterGuardrail: action=ContentFilterAction.BLOCK, ), ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-streaming-block", patterns=patterns, event_hook=GuardrailEventHooks.during_call, ) - + # Create mock streaming chunks with SSN async def mock_stream(): chunk = ModelResponseStream( id="chunk1", - choices=[StreamingChoices(delta=Delta(content="SSN: 123-45-6789"), index=0)], + choices=[ + StreamingChoices(delta=Delta(content="SSN: 123-45-6789"), index=0) + ], model="gpt-4", ) yield chunk - + user_api_key_dict = MagicMock() request_data = {} - + # Should raise HTTPException when SSN is detected with pytest.raises(HTTPException) as exc_info: async for chunk in guardrail.async_post_call_streaming_iterator_hook( @@ -463,7 +492,7 @@ class TestContentFilterGuardrail: request_data=request_data, ): pass - + assert exc_info.value.status_code == 400 assert "us_ssn" in str(exc_info.value.detail) @@ -487,9 +516,9 @@ class TestContentFilterGuardrail: "action": "MASK", "name": "email", "pattern": None, - } + }, ] - + blocked_words = [ { "keyword": "langchain", @@ -500,19 +529,19 @@ class TestContentFilterGuardrail: "keyword": "openai", "action": "MASK", "description": "Competitor name", - } + }, ] - + guardrail = ContentFilterGuardrail( guardrail_name="test-db-format", patterns=patterns, blocked_words=blocked_words, ) - + assert guardrail.guardrail_name == "test-db-format" assert len(guardrail.compiled_patterns) == 2 assert len(guardrail.blocked_words) == 2 - + # Verify blocked_words are stored as dict assert "langchain" in guardrail.blocked_words assert guardrail.blocked_words["langchain"] == ("BLOCK", None) diff --git a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py index 9543b61ef6..3e19437fe8 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_presidio.py @@ -537,24 +537,25 @@ async def test_logging_hook_multiple_content_items(presidio_guardrail): async def test_presidio_sets_guardrail_information_in_request_data(): """ Test that Presidio populates guardrail information into request_data metadata. - + This validates that add_standard_logging_guardrail_information_to_request_data correctly sets the guardrail information that will be used for logging. """ presidio = _OPTIONAL_PresidioPIIMasking( guardrail_name="test_presidio", output_parse_pii=True, + mock_testing=True, ) - + request_data = { "messages": [{"role": "user", "content": "Test"}], "model": "gpt-4o", "metadata": {}, } - + async def mock_check_pii(text, output_parse_pii, presidio_config, request_data): assert request_data is not None - + presidio.add_standard_logging_guardrail_information_to_request_data( guardrail_provider="presidio", guardrail_json_response=[], @@ -565,27 +566,30 @@ async def test_presidio_sets_guardrail_information_in_request_data(): duration=1.0, masked_entity_count={"EMAIL_ADDRESS": 1, "PERSON": 1}, ) - + return text - - with patch.object(presidio, 'check_pii', mock_check_pii): + + with patch.object(presidio, "check_pii", mock_check_pii): await presidio.apply_guardrail( - text="Test message", + texts=["Test message"], request_data=request_data, + input_type="request", ) - + assert "metadata" in request_data assert "standard_logging_guardrail_information" in request_data["metadata"] - - guardrail_info_list = request_data["metadata"]["standard_logging_guardrail_information"] + + guardrail_info_list = request_data["metadata"][ + "standard_logging_guardrail_information" + ] assert isinstance(guardrail_info_list, list) assert len(guardrail_info_list) > 0 - + guardrail_info = guardrail_info_list[0] assert "masked_entity_count" in guardrail_info assert guardrail_info["masked_entity_count"]["EMAIL_ADDRESS"] == 1 assert guardrail_info["masked_entity_count"]["PERSON"] == 1 - + print("✓ Presidio sets guardrail_information in request_data") @@ -593,7 +597,7 @@ async def test_presidio_sets_guardrail_information_in_request_data(): async def test_request_data_flows_to_apply_guardrail(): """ Test that request_data is correctly passed to apply_guardrail method. - + This validates the fix where guardrail translation handler passes data as request_data to apply_guardrail so guardrails can store metadata for logging. """ @@ -601,31 +605,32 @@ async def test_request_data_flows_to_apply_guardrail(): guardrail_name="test_presidio", output_parse_pii=True, ) - + request_data = { "messages": [{"role": "user", "content": "Test message"}], "model": "gpt-4o", "metadata": {}, } - + async def mock_check_pii(text, output_parse_pii, presidio_config, request_data): assert request_data is not None, "request_data should be passed to check_pii" assert "metadata" in request_data, "request_data should have metadata" - + request_data.setdefault("metadata", {}) request_data["metadata"]["test_flag"] = "passed_correctly" - + return text - - with patch.object(presidio, 'check_pii', mock_check_pii): + + with patch.object(presidio, "check_pii", mock_check_pii): result = await presidio.apply_guardrail( - text="Test message", + texts=["Test message"], request_data=request_data, + input_type="request", ) - + assert "metadata" in request_data assert request_data["metadata"].get("test_flag") == "passed_correctly" - + print("✓ request_data correctly passed to apply_guardrail")