From 75a7b397002aea3b9cd6e05915b8392d52f91ca3 Mon Sep 17 00:00:00 2001 From: frankzye1 Date: Sat, 30 Aug 2025 09:27:39 +0800 Subject: [PATCH 01/73] pass function tool description for databricks provider Signed-off-by: frankzye1 --- .../llms/databricks/chat/transformation.py | 13 ++++++--- litellm/types/llms/databricks.py | 2 +- .../test_databricks_chat_transformation.py | 28 +++++++++++++++++++ 3 files changed, 38 insertions(+), 5 deletions(-) diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 908419f7193..3963dd4505c 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -170,12 +170,17 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if tool is None: return None + kwags = { + "name":tool["name"], + "parameters":cast(dict, tool.get("input_schema") or {}) + } + + if tool.get("description"): + kwags["description"] = tool.get("description") + return DatabricksTool( type="function", - function=DatabricksFunction( - name=tool["name"], - parameters=cast(dict, tool.get("input_schema") or {}), - ), + function=DatabricksFunction(**kwags), ) def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]: diff --git a/litellm/types/llms/databricks.py b/litellm/types/llms/databricks.py index bb59b692ef7..e820cede83d 100644 --- a/litellm/types/llms/databricks.py +++ b/litellm/types/llms/databricks.py @@ -49,7 +49,7 @@ AllDatabricksContentValues = Union[str, List[AllDatabricksContentListValues]] class DatabricksFunction(TypedDict, total=False): name: Required[str] - description: dict + description: dict | str parameters: dict strict: bool diff --git a/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py b/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py index fc44d44aba9..55ab4428617 100644 --- a/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py +++ b/tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py @@ -90,3 +90,31 @@ def test_transform_choices_without_signature(): thinking_block = choices[0].message.thinking_blocks[0] assert thinking_block["type"] == "thinking" assert thinking_block["thinking"] == "i'm thinking without signature." + +def test_convert_anthropic_tool_to_databricks_tool_with_description(): + config = DatabricksConfig() + anthropic_tool = { + "name": "test_tool", + "description": "test description", + "input_schema": {"type": "object", "properties": {"test": {"type": "string"}}} + } + + databricks_tool = config.convert_anthropic_tool_to_databricks_tool(anthropic_tool) + + assert databricks_tool is not None + assert databricks_tool["type"] == "function" + assert databricks_tool["function"]["description"] == "test description" + + +def test_convert_anthropic_tool_to_databricks_tool_without_description(): + config = DatabricksConfig() + anthropic_tool = { + "name": "test_tool", + "input_schema": {"type": "object", "properties": {"test": {"type": "string"}}} + } + + databricks_tool = config.convert_anthropic_tool_to_databricks_tool(anthropic_tool) + + assert databricks_tool is not None + assert databricks_tool["type"] == "function" + assert databricks_tool["function"].get("description") is None \ No newline at end of file From a27604b67a98576d105facc21553a0f2e69b729f Mon Sep 17 00:00:00 2001 From: gotsysdba Date: Sat, 30 Aug 2025 20:57:57 +0100 Subject: [PATCH 02/73] Fixes #14090 --- litellm/types/llms/oci.py | 6 +- .../oci/chat/test_oci_chat_transformation.py | 76 +++++++++++++++++++ 2 files changed, 79 insertions(+), 3 deletions(-) diff --git a/litellm/types/llms/oci.py b/litellm/types/llms/oci.py index 52c1b2943f7..e3ea07463e6 100644 --- a/litellm/types/llms/oci.py +++ b/litellm/types/llms/oci.py @@ -121,7 +121,7 @@ class OCICompletionTokenDetails(BaseModel): reasoningTokens: int -class OCIPropmtTokensDetails(BaseModel): +class OCIPromptTokensDetails(BaseModel): """Prompt token details in the OCI response.""" cachedTokens: int @@ -133,8 +133,8 @@ class OCIResponseUsage(BaseModel): promptTokens: int completionTokens: int totalTokens: int - completionTokensDetails: OCICompletionTokenDetails - promptTokensDetails: OCIPropmtTokensDetails + completionTokensDetails: Optional[OCICompletionTokenDetails] = None + promptTokensDetails: Optional[OCIPromptTokensDetails] = None class OCIResponseChoice(BaseModel): diff --git a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py index 547d4bf807e..cbf2ca356e4 100644 --- a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py +++ b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py @@ -305,3 +305,79 @@ class TestOCIChatConfig: assert usage.prompt_tokens == 10 # type: ignore assert usage.completion_tokens == 20 # type: ignore assert usage.total_tokens == 30 # type: ignore + +def test_transform_response_with_missing_usage_details(self): + """ + Tests that usage details default to None if they are missing or null in the API response. + """ + config = OCIChatConfig() + created_time = datetime.datetime.now(datetime.timezone.utc).isoformat().replace("+00:00", "Z") + + # Case 1: usage fields completely missing + mock_oci_response_missing = { + "modelId": TEST_MODEL_NAME, + "modelVersion": "1.0", + "chatResponse": { + "apiFormat": "GENERIC", + "choices": [], + "timeCreated": created_time, + "usage": { + "promptTokens": 5, + "completionTokens": 8, + "totalTokens": 13, + # missing completionTokensDetails and promptTokensDetails + }, + }, + } + + response_missing = httpx.Response(status_code=200, json=mock_oci_response_missing) + result_missing = config.transform_response( + model=TEST_MODEL_NAME, + raw_response=response_missing, + model_response=ModelResponse(), + logging_obj={}, + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding={}, + ) + + usage_missing = result_missing.usage # type: ignore + assert usage_missing.completionTokensDetails is None + assert usage_missing.promptTokensDetails is None + + # Case 2: usage fields explicitly null + mock_oci_response_null = { + "modelId": TEST_MODEL_NAME, + "modelVersion": "1.0", + "chatResponse": { + "apiFormat": "GENERIC", + "choices": [], + "timeCreated": created_time, + "usage": { + "promptTokens": 5, + "completionTokens": 8, + "totalTokens": 13, + "completionTokensDetails": None, + "promptTokensDetails": None, + }, + }, + } + + response_null = httpx.Response(status_code=200, json=mock_oci_response_null) + result_null = config.transform_response( + model=TEST_MODEL_NAME, + raw_response=response_null, + model_response=ModelResponse(), + logging_obj={}, + request_data={}, + messages=[], + optional_params={}, + litellm_params={}, + encoding={}, + ) + + usage_null = result_null.usage # type: ignore + assert usage_null.completionTokensDetails is None + assert usage_null.promptTokensDetails is None \ No newline at end of file From 1ae5466daf021883c8e6cd8ed17820363b7fac51 Mon Sep 17 00:00:00 2001 From: gotsysdba Date: Sat, 30 Aug 2025 21:15:54 +0100 Subject: [PATCH 03/73] Test Case --- .../oci/chat/test_oci_chat_transformation.py | 108 +++++++----------- 1 file changed, 42 insertions(+), 66 deletions(-) diff --git a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py index cbf2ca356e4..3151bed1090 100644 --- a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py +++ b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py @@ -306,78 +306,54 @@ class TestOCIChatConfig: assert usage.completion_tokens == 20 # type: ignore assert usage.total_tokens == 30 # type: ignore -def test_transform_response_with_missing_usage_details(self): +def test_oci_response_usage_handles_missing_and_null_fields(self): """ - Tests that usage details default to None if they are missing or null in the API response. + Test that OCIResponseUsage parses correctly when + completionTokensDetails and promptTokensDetails are missing or null. """ - config = OCIChatConfig() - created_time = datetime.datetime.now(datetime.timezone.utc).isoformat().replace("+00:00", "Z") + from litellm.llms.oci.chat.transformation import OCIResponseUsage - # Case 1: usage fields completely missing - mock_oci_response_missing = { - "modelId": TEST_MODEL_NAME, - "modelVersion": "1.0", - "chatResponse": { - "apiFormat": "GENERIC", - "choices": [], - "timeCreated": created_time, - "usage": { - "promptTokens": 5, - "completionTokens": 8, - "totalTokens": 13, - # missing completionTokensDetails and promptTokensDetails - }, - }, + # Case 1: fields completely missing + data_missing = { + "promptTokens": 10, + "completionTokens": 5, + "totalTokens": 15, + # no completionTokensDetails or promptTokensDetails } - - response_missing = httpx.Response(status_code=200, json=mock_oci_response_missing) - result_missing = config.transform_response( - model=TEST_MODEL_NAME, - raw_response=response_missing, - model_response=ModelResponse(), - logging_obj={}, - request_data={}, - messages=[], - optional_params={}, - litellm_params={}, - encoding={}, - ) - - usage_missing = result_missing.usage # type: ignore + usage_missing = OCIResponseUsage(**data_missing) assert usage_missing.completionTokensDetails is None assert usage_missing.promptTokensDetails is None + assert usage_missing.promptTokens == 10 + assert usage_missing.completionTokens == 5 + assert usage_missing.totalTokens == 15 - # Case 2: usage fields explicitly null - mock_oci_response_null = { - "modelId": TEST_MODEL_NAME, - "modelVersion": "1.0", - "chatResponse": { - "apiFormat": "GENERIC", - "choices": [], - "timeCreated": created_time, - "usage": { - "promptTokens": 5, - "completionTokens": 8, - "totalTokens": 13, - "completionTokensDetails": None, - "promptTokensDetails": None, - }, - }, + # Case 2: fields explicitly null + data_null = { + "promptTokens": 12, + "completionTokens": 8, + "totalTokens": 20, + "completionTokensDetails": None, + "promptTokensDetails": None, } - - response_null = httpx.Response(status_code=200, json=mock_oci_response_null) - result_null = config.transform_response( - model=TEST_MODEL_NAME, - raw_response=response_null, - model_response=ModelResponse(), - logging_obj={}, - request_data={}, - messages=[], - optional_params={}, - litellm_params={}, - encoding={}, - ) - - usage_null = result_null.usage # type: ignore + usage_null = OCIResponseUsage(**data_null) assert usage_null.completionTokensDetails is None - assert usage_null.promptTokensDetails is None \ No newline at end of file + assert usage_null.promptTokensDetails is None + assert usage_null.promptTokens == 12 + assert usage_null.completionTokens == 8 + assert usage_null.totalTokens == 20 + + # Case 3: fields present with values + data_present = { + "promptTokens": 7, + "completionTokens": 3, + "totalTokens": 10, + "completionTokensDetails": {"acceptedPredictionTokens": 2, "reasoningTokens": 1}, + "promptTokensDetails": {"cachedTokens": 4}, + } + usage_present = OCIResponseUsage(**data_present) + assert usage_present.completionTokensDetails.acceptedPredictionTokens == 2 + assert usage_present.completionTokensDetails.reasoningTokens == 1 + assert usage_present.promptTokensDetails.cachedTokens == 4 + assert usage_present.promptTokens == 7 + assert usage_present.completionTokens == 3 + assert usage_present.totalTokens == 10 From 07d9d14b49183c334e3d6ba720a92a904d72a701 Mon Sep 17 00:00:00 2001 From: gotsysdba Date: Sat, 30 Aug 2025 22:02:00 +0100 Subject: [PATCH 04/73] Update OCIResposeClass --- litellm/types/llms/oci.py | 2 +- .../oci/chat/test_oci_chat_transformation.py | 56 ++----------------- 2 files changed, 5 insertions(+), 53 deletions(-) diff --git a/litellm/types/llms/oci.py b/litellm/types/llms/oci.py index e3ea07463e6..75d13192c50 100644 --- a/litellm/types/llms/oci.py +++ b/litellm/types/llms/oci.py @@ -129,7 +129,7 @@ class OCIPromptTokensDetails(BaseModel): class OCIResponseUsage(BaseModel): """Token usage in the OCI response.""" - + promptTokens: int completionTokens: int totalTokens: int diff --git a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py index 3151bed1090..9ff93dfc1a2 100644 --- a/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py +++ b/tests/test_litellm/llms/oci/chat/test_oci_chat_transformation.py @@ -213,6 +213,10 @@ class TestOCIChatConfig: assert result.usage.prompt_tokens == 10 # type: ignore assert result.usage.completion_tokens == 20 # type: ignore assert result.usage.total_tokens == 30 # type: ignore + # These are not handled in the transformer, TBH no idea why they are here + # but, for now, they seem to be always None + assert result.usage.completion_tokens_details is None + assert result.usage.prompt_tokens_details is None def test_transform_response_with_tool_calls(self): """ @@ -305,55 +309,3 @@ class TestOCIChatConfig: assert usage.prompt_tokens == 10 # type: ignore assert usage.completion_tokens == 20 # type: ignore assert usage.total_tokens == 30 # type: ignore - -def test_oci_response_usage_handles_missing_and_null_fields(self): - """ - Test that OCIResponseUsage parses correctly when - completionTokensDetails and promptTokensDetails are missing or null. - """ - from litellm.llms.oci.chat.transformation import OCIResponseUsage - - # Case 1: fields completely missing - data_missing = { - "promptTokens": 10, - "completionTokens": 5, - "totalTokens": 15, - # no completionTokensDetails or promptTokensDetails - } - usage_missing = OCIResponseUsage(**data_missing) - assert usage_missing.completionTokensDetails is None - assert usage_missing.promptTokensDetails is None - assert usage_missing.promptTokens == 10 - assert usage_missing.completionTokens == 5 - assert usage_missing.totalTokens == 15 - - # Case 2: fields explicitly null - data_null = { - "promptTokens": 12, - "completionTokens": 8, - "totalTokens": 20, - "completionTokensDetails": None, - "promptTokensDetails": None, - } - usage_null = OCIResponseUsage(**data_null) - assert usage_null.completionTokensDetails is None - assert usage_null.promptTokensDetails is None - assert usage_null.promptTokens == 12 - assert usage_null.completionTokens == 8 - assert usage_null.totalTokens == 20 - - # Case 3: fields present with values - data_present = { - "promptTokens": 7, - "completionTokens": 3, - "totalTokens": 10, - "completionTokensDetails": {"acceptedPredictionTokens": 2, "reasoningTokens": 1}, - "promptTokensDetails": {"cachedTokens": 4}, - } - usage_present = OCIResponseUsage(**data_present) - assert usage_present.completionTokensDetails.acceptedPredictionTokens == 2 - assert usage_present.completionTokensDetails.reasoningTokens == 1 - assert usage_present.promptTokensDetails.cachedTokens == 4 - assert usage_present.promptTokens == 7 - assert usage_present.completionTokens == 3 - assert usage_present.totalTokens == 10 From 4278c3b596496e055ec0d89197d8930e242d9825 Mon Sep 17 00:00:00 2001 From: Dharamendra Kumar Date: Sun, 31 Aug 2025 11:14:19 -0700 Subject: [PATCH 05/73] feat: Add dependency injection support to BaseLLMAIOHTTPHandler - Add optional client_session, transport, and connector parameters to constructor - Implement session ownership tracking to prevent closing user-provided sessions - Add comprehensive session resolution hierarchy (dynamic > instance > create new) - Include transport control for advanced HTTP stack management - Add 29 comprehensive tests covering all injection scenarios - Maintain backward compatibility with existing code This enhancement allows users to inject their own configured aiohttp sessions, transports, and connectors for fine-grained control over connection pooling, SSL settings, proxy configurations, and other HTTP stack parameters. --- litellm/llms/custom_httpx/aiohttp_handler.py | 94 +++- .../llms/custom_httpx/test_aiohttp_handler.py | 426 ++++++++++++++++++ 2 files changed, 514 insertions(+), 6 deletions(-) create mode 100644 tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py diff --git a/litellm/llms/custom_httpx/aiohttp_handler.py b/litellm/llms/custom_httpx/aiohttp_handler.py index d9fc85877c3..c7a04a49fc2 100644 --- a/litellm/llms/custom_httpx/aiohttp_handler.py +++ b/litellm/llms/custom_httpx/aiohttp_handler.py @@ -17,6 +17,7 @@ from litellm.llms.custom_httpx.http_handler import ( HTTPHandler, _get_httpx_client, ) +from litellm.llms.custom_httpx.aiohttp_transport import LiteLLMAiohttpTransport from litellm.types.llms.openai import FileTypes from litellm.types.utils import HttpHandlerRequestFields, ImageResponse, LlmProviders from litellm.utils import CustomStreamWrapper, ModelResponse, ProviderConfigManager @@ -32,8 +33,71 @@ DEFAULT_TIMEOUT = 600 class BaseLLMAIOHTTPHandler: - def __init__(self): - self.client_session: Optional[aiohttp.ClientSession] = None + def __init__( + self, + client_session: Optional[aiohttp.ClientSession] = None, + transport: Optional[LiteLLMAiohttpTransport] = None, + connector: Optional[aiohttp.BaseConnector] = None, + ): + self.client_session = client_session + self._owns_session = ( + client_session is None + ) # Track if we own the session for cleanup + + self.transport = transport + self._owns_transport = ( + transport is None + ) # Track if we own the transport for cleanup + + self.connector = connector + self._owns_connector = ( + connector is None + ) # Track if we own the connector for cleanup + + def _get_or_create_transport(self) -> Optional[LiteLLMAiohttpTransport]: + """Get existing transport or create a new one if needed.""" + if self.transport: + return self.transport + + # Create a transport using AsyncHTTPHandler's logic + try: + self.transport = AsyncHTTPHandler._create_aiohttp_transport() + self._owns_transport = True + return self.transport + except Exception: + # If transport creation fails, return None (will use direct session) + return None + + def _get_connector(self) -> Optional[aiohttp.BaseConnector]: + """Get or create a connector for the client session.""" + if self.connector: + return self.connector + elif self.transport and hasattr(self.transport, "client"): + # Extract connector from transport if available + client = self.transport.client + if callable(client): + # If client is a factory, we can't extract connector directly + return None + elif hasattr(client, "connector"): + return client.connector + return None + + def _create_client_session_with_transport(self) -> ClientSession: + """Create a new client session using transport or connector configuration.""" + connector = self._get_connector() + + if self.transport and hasattr(self.transport, "_get_valid_client_session"): + # Use transport's session creation if available + session = self.transport._get_valid_client_session() + return session + elif connector: + # Use provided connector + session = aiohttp.ClientSession(connector=connector) + return session + else: + # Default session creation + session = aiohttp.ClientSession() + return session def _get_async_client_session( self, dynamic_client_session: Optional[ClientSession] = None @@ -43,15 +107,33 @@ class BaseLLMAIOHTTPHandler: elif self.client_session: return self.client_session else: - # init client session, and then return new session - self.client_session = aiohttp.ClientSession() + # Create client session using transport/connector if available + self.client_session = self._create_client_session_with_transport() + self._owns_session = True # We created this session, so we own it return self.client_session async def close(self): - """Close the aiohttp client session if it exists.""" - if self.client_session and not self.client_session.closed: + """Close the aiohttp client session and transport if we own them.""" + # Close client session if we own it + if ( + self.client_session + and not self.client_session.closed + and self._owns_session + ): await self.client_session.close() + # Close transport if we own it + if ( + self.transport + and self._owns_transport + and hasattr(self.transport, "aclose") + ): + try: + await self.transport.aclose() + except Exception: + # Ignore errors during transport cleanup + pass + async def _make_common_async_call( self, async_client_session: Optional[ClientSession], diff --git a/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py b/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py new file mode 100644 index 00000000000..21df2aecad3 --- /dev/null +++ b/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py @@ -0,0 +1,426 @@ +import asyncio +import os +import sys +from unittest.mock import AsyncMock, MagicMock, Mock, patch + +import aiohttp +import pytest + +sys.path.insert( + 0, os.path.abspath("../../../..") +) # Adds the parent directory to the system path + +from litellm.llms.custom_httpx.aiohttp_handler import BaseLLMAIOHTTPHandler +from litellm.llms.custom_httpx.aiohttp_transport import LiteLLMAiohttpTransport + + +class TestBaseLLMAIOHTTPHandler: + """Test cases for BaseLLMAIOHTTPHandler dependency injection functionality""" + + def test_init_with_no_client_session(self): + """Test handler initialization with no client session""" + handler = BaseLLMAIOHTTPHandler() + + assert handler.client_session is None + assert handler._owns_session is True + + def test_init_with_provided_client_session(self): + """Test handler initialization with provided client session""" + # Create a mock client session + mock_session = Mock() + + handler = BaseLLMAIOHTTPHandler(client_session=mock_session) + + assert handler.client_session is mock_session + assert handler._owns_session is False + + def test_get_async_client_session_with_dynamic_session(self): + """Test _get_async_client_session with dynamic session parameter""" + handler = BaseLLMAIOHTTPHandler() + + dynamic_session = Mock() + + result = handler._get_async_client_session( + dynamic_client_session=dynamic_session + ) + + assert result is dynamic_session + + def test_get_async_client_session_with_instance_session(self): + """Test _get_async_client_session with instance session""" + instance_session = Mock() + handler = BaseLLMAIOHTTPHandler(client_session=instance_session) + + result = handler._get_async_client_session() + + assert result is instance_session + + @patch("aiohttp.ClientSession") + def test_get_async_client_session_create_new(self, mock_client_session): + """Test _get_async_client_session creates new session when none provided""" + handler = BaseLLMAIOHTTPHandler() + mock_session_instance = Mock() + mock_client_session.return_value = mock_session_instance + + result = handler._get_async_client_session() + + # Verify new session was created + mock_client_session.assert_called_once() + assert handler.client_session is mock_session_instance + assert handler._owns_session is True + assert result is mock_session_instance + + @pytest.mark.asyncio + async def test_close_with_owned_session(self): + """Test close() method with owned session""" + # Create a mock session that we own + mock_session = Mock() + mock_session.closed = False + mock_session.close = AsyncMock() + + # Create handler that owns the session + handler = BaseLLMAIOHTTPHandler() + handler.client_session = mock_session + handler._owns_session = True + + await handler.close() + + # Verify close was called + mock_session.close.assert_called_once() + + @pytest.mark.asyncio + async def test_close_with_non_owned_session(self): + """Test close() method with non-owned session (should not close)""" + # Create a mock session that we don't own + mock_session = Mock() + mock_session.closed = False + mock_session.close = AsyncMock() + + handler = BaseLLMAIOHTTPHandler(client_session=mock_session) + + await handler.close() + + # Verify close was NOT called since we don't own this session + mock_session.close.assert_not_called() + + @pytest.mark.asyncio + async def test_close_with_already_closed_session(self): + """Test close() method with already closed session""" + mock_session = Mock() + mock_session.closed = True + mock_session.close = AsyncMock() + + handler = BaseLLMAIOHTTPHandler() + handler.client_session = mock_session + handler._owns_session = True + + await handler.close() + + # Verify close was NOT called since session is already closed + mock_session.close.assert_not_called() + + @pytest.mark.asyncio + async def test_close_with_no_session(self): + """Test close() method with no session""" + handler = BaseLLMAIOHTTPHandler() + + # Should not raise any exceptions + await handler.close() + + def test_session_priority_dynamic_over_instance(self): + """Test that dynamic session takes priority over instance session""" + instance_session = Mock() + dynamic_session = Mock() + + handler = BaseLLMAIOHTTPHandler(client_session=instance_session) + + result = handler._get_async_client_session( + dynamic_client_session=dynamic_session + ) + + assert result is dynamic_session + assert result is not instance_session + + def test_session_ownership_tracking(self): + """Test proper session ownership tracking in various scenarios""" + # Scenario 1: Provided session - not owned + provided_session = Mock() + handler1 = BaseLLMAIOHTTPHandler(client_session=provided_session) + assert not handler1._owns_session + + # Scenario 2: No session initially - becomes owned when created + handler2 = BaseLLMAIOHTTPHandler() + assert handler2._owns_session + + with patch("aiohttp.ClientSession") as mock_client_session: + mock_session_instance = Mock() + mock_client_session.return_value = mock_session_instance + + handler2._get_async_client_session() + assert handler2._owns_session + + @pytest.mark.asyncio + async def test_context_manager_pattern_compatibility(self): + """Test that the handler works well with context manager pattern""" + mock_session = Mock() + mock_session.closed = False + mock_session.close = AsyncMock() + + # Test as context manager style usage + handler = BaseLLMAIOHTTPHandler() + handler.client_session = mock_session + handler._owns_session = True + + try: + # Simulate some work + session = handler._get_async_client_session() + assert session is mock_session + finally: + await handler.close() + + # Verify cleanup happened + mock_session.close.assert_called_once() + + @patch("litellm.llms.custom_httpx.aiohttp_handler.aiohttp.ClientSession") + def test_lazy_session_creation(self, mock_client_session): + """Test that session is created lazily only when needed""" + handler = BaseLLMAIOHTTPHandler() + + # Session should not be created on init + mock_client_session.assert_not_called() + assert handler.client_session is None + + # Session should be created when requested + mock_session_instance = Mock() + mock_client_session.return_value = mock_session_instance + + session = handler._get_async_client_session() + + mock_client_session.assert_called_once() + assert session is mock_session_instance + assert handler.client_session is mock_session_instance + + def test_session_reuse(self): + """Test that the same session is reused across multiple calls""" + instance_session = Mock() + handler = BaseLLMAIOHTTPHandler(client_session=instance_session) + + # Multiple calls should return the same session + session1 = handler._get_async_client_session() + session2 = handler._get_async_client_session() + session3 = handler._get_async_client_session() + + assert session1 is session2 is session3 is instance_session + + # =============================== + # TRANSPORT INJECTION TESTS + # =============================== + + def test_init_with_transport(self): + """Test handler initialization with provided transport""" + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + + handler = BaseLLMAIOHTTPHandler(transport=mock_transport) + + assert handler.transport is mock_transport + assert handler._owns_transport is False + + def test_init_with_connector(self): + """Test handler initialization with provided connector""" + mock_connector = Mock(spec=aiohttp.BaseConnector) + + handler = BaseLLMAIOHTTPHandler(connector=mock_connector) + + assert handler.connector is mock_connector + assert handler._owns_connector is False + + def test_init_with_transport_and_session(self): + """Test handler initialization with both transport and session""" + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_session = Mock() + + handler = BaseLLMAIOHTTPHandler( + client_session=mock_session, transport=mock_transport + ) + + assert handler.transport is mock_transport + assert handler._owns_transport is False + assert handler.client_session is mock_session + assert handler._owns_session is False + + def test_get_connector_from_provided_connector(self): + """Test _get_connector returns provided connector""" + mock_connector = Mock(spec=aiohttp.BaseConnector) + handler = BaseLLMAIOHTTPHandler(connector=mock_connector) + + result = handler._get_connector() + + assert result is mock_connector + + def test_get_connector_from_transport(self): + """Test _get_connector extracts connector from transport""" + mock_connector = Mock(spec=aiohttp.BaseConnector) + + # Use a simple object instead of Mock to avoid callable issues + class MockSession: + def __init__(self): + self.connector = mock_connector + + mock_session = MockSession() + + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_transport.client = mock_session + + handler = BaseLLMAIOHTTPHandler(transport=mock_transport) + + result = handler._get_connector() + + assert result is mock_connector + + def test_get_connector_from_transport_with_callable_client(self): + """Test _get_connector with transport that has callable client""" + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_transport.client = lambda: Mock() # Callable client + + handler = BaseLLMAIOHTTPHandler(transport=mock_transport) + + result = handler._get_connector() + + assert result is None + + @patch("aiohttp.ClientSession") + def test_create_client_session_with_transport(self, mock_client_session): + """Test session creation using transport""" + mock_session_from_transport = Mock() + + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_transport._get_valid_client_session = Mock( + return_value=mock_session_from_transport + ) + + handler = BaseLLMAIOHTTPHandler(transport=mock_transport) + + result = handler._create_client_session_with_transport() + + # Should use transport's session creation method + mock_transport._get_valid_client_session.assert_called_once() + assert result is mock_session_from_transport + + # Should not call aiohttp.ClientSession directly + mock_client_session.assert_not_called() + + @patch("aiohttp.ClientSession") + def test_create_client_session_with_connector(self, mock_client_session): + """Test session creation using connector""" + mock_connector = Mock(spec=aiohttp.BaseConnector) + mock_session_instance = Mock() + mock_client_session.return_value = mock_session_instance + + handler = BaseLLMAIOHTTPHandler(connector=mock_connector) + + result = handler._create_client_session_with_transport() + + # Should create session with connector + mock_client_session.assert_called_once_with(connector=mock_connector) + assert result is mock_session_instance + + @patch("aiohttp.ClientSession") + def test_create_client_session_default(self, mock_client_session): + """Test default session creation when no transport/connector provided""" + mock_session_instance = Mock() + mock_client_session.return_value = mock_session_instance + + handler = BaseLLMAIOHTTPHandler() + + result = handler._create_client_session_with_transport() + + # Should create default session + mock_client_session.assert_called_once_with() + assert result is mock_session_instance + + @patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler._create_aiohttp_transport" + ) + def test_get_or_create_transport(self, mock_create_transport): + """Test transport creation when none provided""" + mock_transport_instance = Mock(spec=LiteLLMAiohttpTransport) + mock_create_transport.return_value = mock_transport_instance + + handler = BaseLLMAIOHTTPHandler() + + result = handler._get_or_create_transport() + + mock_create_transport.assert_called_once() + assert result is mock_transport_instance + assert handler.transport is mock_transport_instance + assert handler._owns_transport is True + + def test_get_or_create_transport_with_existing(self): + """Test _get_or_create_transport returns existing transport""" + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + handler = BaseLLMAIOHTTPHandler(transport=mock_transport) + + result = handler._get_or_create_transport() + + assert result is mock_transport + + @pytest.mark.asyncio + async def test_close_with_owned_transport(self): + """Test close() method with owned transport""" + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_transport.aclose = AsyncMock() + + handler = BaseLLMAIOHTTPHandler() + handler.transport = mock_transport + handler._owns_transport = True + + await handler.close() + + # Verify transport close was called + mock_transport.aclose.assert_called_once() + + @pytest.mark.asyncio + async def test_close_with_non_owned_transport(self): + """Test close() method with non-owned transport (should not close)""" + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_transport.aclose = AsyncMock() + + handler = BaseLLMAIOHTTPHandler(transport=mock_transport) + + await handler.close() + + # Verify transport close was NOT called since we don't own this transport + mock_transport.aclose.assert_not_called() + + @pytest.mark.asyncio + async def test_close_transport_without_aclose_method(self): + """Test close() handles transport without aclose method gracefully""" + mock_transport = Mock() # No aclose method + + handler = BaseLLMAIOHTTPHandler() + handler.transport = mock_transport + handler._owns_transport = True + + # Should not raise any exceptions + await handler.close() + + def test_transport_priority_hierarchy(self): + """Test that session creation follows the right priority: transport > connector > default""" + # Test with transport having _get_valid_client_session + mock_transport = Mock(spec=LiteLLMAiohttpTransport) + mock_session_from_transport = Mock() + mock_transport._get_valid_client_session = Mock( + return_value=mock_session_from_transport + ) + + mock_connector = Mock(spec=aiohttp.BaseConnector) + + handler = BaseLLMAIOHTTPHandler( + transport=mock_transport, connector=mock_connector + ) + + result = handler._create_client_session_with_transport() + + # Should use transport, not connector + mock_transport._get_valid_client_session.assert_called_once() + assert result is mock_session_from_transport From 009106def0ea9abdd069a42eb1add816ad91c1c2 Mon Sep 17 00:00:00 2001 From: Dharamendra Kumar Date: Sun, 7 Sep 2025 17:42:26 -0700 Subject: [PATCH 06/73] docs for custom aiohttp session --- .../docs/completion/http_handler_config.md | 145 ++++++++++++++++++ docs/my-website/sidebars.js | 1 + 2 files changed, 146 insertions(+) create mode 100644 docs/my-website/docs/completion/http_handler_config.md diff --git a/docs/my-website/docs/completion/http_handler_config.md b/docs/my-website/docs/completion/http_handler_config.md new file mode 100644 index 00000000000..d4a25ce2043 --- /dev/null +++ b/docs/my-website/docs/completion/http_handler_config.md @@ -0,0 +1,145 @@ +# Custom HTTP Handler + +Configure custom aiohttp sessions for better performance and control in LiteLLM completions. + +## Overview + +You can now inject custom `aiohttp.ClientSession` instances into LiteLLM for: +- Custom connection pooling and timeouts +- Corporate proxy and SSL configurations +- Performance optimization +- Request monitoring + +## Basic Usage + +### Default (No Changes Required) +```python +import litellm + +# Works exactly as before +response = await litellm.acompletion( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +### Custom Session +```python +import aiohttp +import litellm +from litellm.llms.custom_httpx.aiohttp_handler import BaseLLMAIOHTTPHandler + +# Create optimized session +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300, limit_per_host=75) +) + +# Replace global handler +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) + +# All completions now use your session +response = await litellm.acompletion(model="gpt-3.5-turbo", messages=[...]) +``` + +## Common Patterns + +### FastAPI Integration +```python +from contextlib import asynccontextmanager +from fastapi import FastAPI +import aiohttp +import litellm + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Startup + session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=180), + connector=aiohttp.TCPConnector(limit=300) + ) + litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler( + client_session=session + ) + yield + # Shutdown + await session.close() + +app = FastAPI(lifespan=lifespan) + +@app.post("/chat") +async def chat(messages: list[dict]): + return await litellm.acompletion(model="gpt-3.5-turbo", messages=messages) +``` + +### Corporate Proxy +```python +import ssl + +# Custom SSL context +ssl_context = ssl.create_default_context() +ssl_context.load_cert_chain('cert.pem', 'key.pem') + +# Proxy session +session = aiohttp.ClientSession( + connector=aiohttp.TCPConnector(ssl=ssl_context), + trust_env=True # Use environment proxy settings +) + +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) +``` + +### High Performance +```python +# Optimized for high throughput +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=300), + connector=aiohttp.TCPConnector( + limit=1000, # High connection limit + limit_per_host=200, # Per host limit + ttl_dns_cache=600, # DNS cache + keepalive_timeout=60, # Keep connections alive + enable_cleanup_closed=True + ) +) + +litellm.base_llm_aiohttp_handler = BaseLLMAIOHTTPHandler(client_session=session) +``` + +## Constructor Options + +```python +BaseLLMAIOHTTPHandler( + client_session=None, # Custom aiohttp.ClientSession + transport=None, # Advanced transport control + connector=None, # Custom aiohttp.BaseConnector +) +``` + +## Resource Management + +- **User sessions**: You manage the lifecycle (call `await session.close()`) +- **Auto-created sessions**: Automatically cleaned up by the handler +- **100% backward compatible**: Existing code works unchanged + +## Configuration Tips + +### Development +```python +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=60), + connector=aiohttp.TCPConnector(limit=50) +) +``` + +### Production +```python +session = aiohttp.ClientSession( + timeout=aiohttp.ClientTimeout(total=300), + connector=aiohttp.TCPConnector( + limit=1000, + limit_per_host=200, + keepalive_timeout=60 + ) +) +``` \ No newline at end of file diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index f81ecda3916..0cf1663a342 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -259,6 +259,7 @@ const sidebars = { "completion/input", "completion/output", "completion/usage", + "completion/http_handler_config", ], }, "response_api", From fa2d3a89718c1e9b751f953344f45c50753d57b5 Mon Sep 17 00:00:00 2001 From: Dharamendra Kumar Date: Sun, 7 Sep 2025 17:51:46 -0700 Subject: [PATCH 07/73] Update poetry.lock --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index da9e29d8caa..2368e6f064a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -1914,7 +1914,7 @@ description = "WSGI HTTP Server for UNIX" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "(python_version >= \"3.10\" or extra == \"proxy\") and (platform_system != \"Windows\" or extra == \"proxy\") and (python_version < \"3.12\" or extra == \"proxy\" or extra == \"mlflow\") and (extra == \"mlflow\" or extra == \"proxy\")" +markers = "platform_system != \"Windows\" and (extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\"" files = [ {file = "gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d"}, {file = "gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec"}, @@ -2850,8 +2850,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.23.3", markers = "python_version >= \"3.11\""}, + {version = ">1.20"}, {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, - {version = ">1.20", markers = "python_version < \"3.10\""}, {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, ] From 5b26e78ad6c576427b1f5a418bcf3422d849ab62 Mon Sep 17 00:00:00 2001 From: Dharamendra Kumar Date: Sun, 7 Sep 2025 18:04:00 -0700 Subject: [PATCH 08/73] Fix linting --- tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py b/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py index 21df2aecad3..6e19b2341a7 100644 --- a/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py +++ b/tests/test_litellm/llms/custom_httpx/test_aiohttp_handler.py @@ -1,7 +1,6 @@ -import asyncio import os import sys -from unittest.mock import AsyncMock, MagicMock, Mock, patch +from unittest.mock import AsyncMock, Mock, patch import aiohttp import pytest From 890ee1abfaf6a127b752deb7053fd22b34561d1e Mon Sep 17 00:00:00 2001 From: Toy-97 Date: Mon, 8 Sep 2025 19:17:58 +0800 Subject: [PATCH 09/73] update: DeepInfra model data refresh [2025-09-08] MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Removed models: deepinfra/Qwen/Qwen2.5-Coder-32B-Instruct deepinfra/deepseek-ai/DeepSeek-V3-0324-Turbo deepinfra/meta-llama/Llama-3.2-90B-Vision-Instruct deepinfra/meta-llama/Llama-4-Maverick-17B-128E-Instruct-Turbo deepinfra/meta-llama/Meta-Llama-3-70B-Instruct deepinfra/mistralai/Devstral-Small-2507 deepinfra/mistralai/Mistral-7B-Instruct-v0.3 deepinfra/mistralai/Mistral-Small-3.1-24B-Instruct-2503 Modified models: deepinfra/deepseek-ai/DeepSeek-R1-0528: - cache_read_input_token_cost: None → 4e-07 deepinfra/google/gemma-3-4b-it: - input_cost_per_token: 2e-08 → 4e-08 - output_cost_per_token: 4e-08 → 8e-08 deepinfra/Qwen/QwQ-32B: - input_cost_per_token: 7.5e-08 → 1.5e-07 - output_cost_per_token: 1.5e-07 → 4e-07 deepinfra/deepseek-ai/DeepSeek-V3-0324: - cache_read_input_token_cost: None → 2.24e-07 deepinfra/deepseek-ai/DeepSeek-V3.1: - input_cost_per_token: 3e-07 → 2.7e-07 - cache_read_input_token_cost: None → 2.16e-07 deepinfra/Qwen/Qwen3-Coder-480B-A35B-Instruct-Turbo: - cache_read_input_token_cost: 1.5e-07 → 2.4e-07 deepinfra/NousResearch/Hermes-3-Llama-3.1-70B: - supports_tool_choice: True → False deepinfra/deepseek-ai/DeepSeek-R1-Turbo: - max_output_tokens: 163840 → 40960 - max_input_tokens: 163840 → 40960 - max_tokens: 163840 → 40960 --- model_prices_and_context_window.json | 542 ++------------------------- 1 file changed, 23 insertions(+), 519 deletions(-) diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index c8b4cc4b791..e9a2b492f93 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -15517,16 +15517,6 @@ "litellm_provider": "ollama", "mode": "completion" }, - "deepinfra/Austism/chronos-hermes-13b-v2": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/Gryphe/MythoMax-L2-13b": { "max_tokens": 4096, "max_input_tokens": 4096, @@ -15537,26 +15527,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Gryphe/MythoMax-L2-13b-turbo": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/KoboldAI/LLaMA2-13B-Tiefighter": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 1e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -15575,78 +15545,18 @@ "output_cost_per_token": 2.8e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/NovaSky-AI/Sky-T1-32B-Preview": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 1.2e-07, - "output_cost_per_token": 1.8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Phind/Phind-CodeLlama-34B-v2": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 6e-07, - "output_cost_per_token": 6e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/Qwen/QVQ-72B-Preview": { - "max_tokens": 32000, - "max_input_tokens": 32000, - "max_output_tokens": 32000, - "input_cost_per_token": 2.5e-07, - "output_cost_per_token": 5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", "supports_tool_choice": false }, "deepinfra/Qwen/QwQ-32B": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 7.5e-08, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/Qwen/QwQ-32B-Preview": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 1.2e-07, - "output_cost_per_token": 1.8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Qwen/Qwen2-72B-Instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 3.5e-07, + "input_cost_per_token": 1.5e-07, "output_cost_per_token": 4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Qwen/Qwen2-7B-Instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/Qwen/Qwen2.5-72B-Instruct": { "max_tokens": 32768, "max_input_tokens": 32768, @@ -15667,26 +15577,6 @@ "mode": "chat", "supports_tool_choice": false }, - "deepinfra/Qwen/Qwen2.5-Coder-32B-Instruct": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 6e-08, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Qwen/Qwen2.5-Coder-7B": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 2.5e-08, - "output_cost_per_token": 5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/Qwen/Qwen2.5-VL-32B-Instruct": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -15773,30 +15663,11 @@ "max_output_tokens": 262144, "input_cost_per_token": 3e-07, "output_cost_per_token": 1.2e-06, + "cache_read_input_token_cost": 2.4e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Sao10K/L3-70B-Euryale-v2.1": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 7e-07, - "output_cost_per_token": 8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Sao10K/L3-8B-Lunaris-v1": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3e-08, - "output_cost_per_token": 6e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/Sao10K/L3-8B-Lunaris-v1-Turbo": { "max_tokens": 8192, "max_input_tokens": 8192, @@ -15843,6 +15714,7 @@ "max_output_tokens": 200000, "input_cost_per_token": 3.3e-06, "output_cost_per_token": 1.65e-05, + "cache_read_input_token_cost": 3.3e-07, "litellm_provider": "deepinfra", "mode": "chat", "supports_tool_choice": true @@ -15867,67 +15739,15 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/bigcode/starcoder2-15b-instruct-v0.1": { - 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"output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/microsoft/WizardLM-2-8x22B": { "max_tokens": 65536, "max_input_tokens": 65536, @@ -16418,66 +16042,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/microsoft/phi-4-reasoning-plus": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 7e-08, - "output_cost_per_token": 3.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/mistralai/Devstral-Small-2505": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 128000, - "input_cost_per_token": 6e-08, - "output_cost_per_token": 1.2e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/mistralai/Devstral-Small-2507": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 128000, - 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"supports_tool_choice": true - }, "deepinfra/mistralai/Mistral-Nemo-Instruct-2407": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -16498,16 +16062,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/mistralai/Mistral-Small-3.1-24B-Instruct-2503": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 128000, - "input_cost_per_token": 5e-08, - "output_cost_per_token": 1e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/mistralai/Mistral-Small-3.2-24B-Instruct-2506": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -16518,16 +16072,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/mistralai/Mixtral-8x22B-Instruct-v0.1": { - "max_tokens": 65536, - "max_input_tokens": 65536, - "max_output_tokens": 65536, - "input_cost_per_token": 6.5e-07, - "output_cost_per_token": 6.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/mistralai/Mixtral-8x7B-Instruct-v0.1": { "max_tokens": 32768, "max_input_tokens": 32768, @@ -16558,16 +16102,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/nvidia/Nemotron-4-340B-Instruct": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 4.2e-06, - "output_cost_per_token": 4.2e-06, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/openai/gpt-oss-120b": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -16588,36 +16122,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/openbmb/MiniCPM-Llama3-V-2_5": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3.4e-07, - "output_cost_per_token": 3.4e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/openchat/openchat-3.6-8b": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/openchat/openchat_3.5": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/zai-org/GLM-4.5": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -21126,4 +20630,4 @@ "notes": "Volcengine Doubao embedding model - text-240715 version with 2560 dimensions" } } -} \ No newline at end of file +} From d89af0abb92beb1fc0908cda1d2ccd732c143e40 Mon Sep 17 00:00:00 2001 From: Dharamendra Kumar Date: Mon, 8 Sep 2025 09:02:34 -0700 Subject: [PATCH 10/73] Revert "Update poetry.lock" This reverts commit fa2d3a89718c1e9b751f953344f45c50753d57b5. --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index 29d1a877087..e03f800bc96 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -1949,7 +1949,7 @@ description = "WSGI HTTP Server for UNIX" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "platform_system != \"Windows\" and (extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\"" +markers = "(python_version >= \"3.10\" or extra == \"proxy\") and (platform_system != \"Windows\" or extra == \"proxy\") and (python_version < \"3.12\" or extra == \"proxy\" or extra == \"mlflow\") and (extra == \"mlflow\" or extra == \"proxy\")" files = [ {file = "gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d"}, {file = "gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec"}, @@ -2885,8 +2885,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.23.3", markers = "python_version >= \"3.11\""}, - {version = ">1.20"}, {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, + {version = ">1.20", markers = "python_version < \"3.10\""}, {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, ] From 6c1c647338d382325c077148c2519b0485d08600 Mon Sep 17 00:00:00 2001 From: Dharamendra Kumar Date: Mon, 8 Sep 2025 11:04:34 -0700 Subject: [PATCH 11/73] Fix conflicts --- poetry.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/poetry.lock b/poetry.lock index e03f800bc96..09fd508eb5d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -1949,7 +1949,7 @@ description = "WSGI HTTP Server for UNIX" optional = true python-versions = ">=3.7" groups = ["main"] -markers = "(python_version >= \"3.10\" or extra == \"proxy\") and (platform_system != \"Windows\" or extra == \"proxy\") and (python_version < \"3.12\" or extra == \"proxy\" or extra == \"mlflow\") and (extra == \"mlflow\" or extra == \"proxy\")" +markers = "platform_system != \"Windows\" and (extra == \"mlflow\" or extra == \"proxy\") and python_version >= \"3.10\" or extra == \"proxy\"" files = [ {file = "gunicorn-23.0.0-py3-none-any.whl", hash = "sha256:ec400d38950de4dfd418cff8328b2c8faed0edb0d517d3394e457c317908ca4d"}, {file = "gunicorn-23.0.0.tar.gz", hash = "sha256:f014447a0101dc57e294f6c18ca6b40227a4c90e9bdb586042628030cba004ec"}, @@ -2885,8 +2885,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.23.3", markers = "python_version >= \"3.11\""}, + {version = ">1.20"}, {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, - {version = ">1.20", markers = "python_version < \"3.10\""}, {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, ] From 7c5ec380c17558a121bb52cbbbc1bd6f5f4baa56 Mon Sep 17 00:00:00 2001 From: swarnabhasinha Date: Tue, 9 Sep 2025 15:57:53 +0530 Subject: [PATCH 12/73] fix: remove anthropic-beta header for Vertex AI requests with prompt caching --- litellm/llms/anthropic/common_utils.py | 11 +- .../test_vertex_ai_prompt_caching_fix.py | 134 ++++++++++++++++++ 2 files changed, 143 insertions(+), 2 deletions(-) create mode 100644 tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 68b5341e954..06ebb5079d9 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -107,8 +107,10 @@ class AnthropicModelInfo(BaseLLMModelInfo): user_anthropic_beta_headers: Optional[List[str]] = None, ) -> dict: betas = set() - if prompt_caching_set: - betas.add("prompt-caching-2024-07-31") + # Note: prompt-caching-2024-07-31 header is no longer required for prompt caching + # as per current Anthropic documentation. It's now generally available. + # if prompt_caching_set: + # betas.add("prompt-caching-2024-07-31") if computer_tool_used: betas.add("computer-use-2024-10-22") # if pdf_used: @@ -176,6 +178,11 @@ class AnthropicModelInfo(BaseLLMModelInfo): mcp_server_used=mcp_server_used, ) + # For Vertex AI requests, remove any user-provided anthropic-beta headers + # since Vertex AI rejects them and they're no longer required for prompt caching + if optional_params.get("is_vertex_request", False): + headers = {k: v for k, v in headers.items() if k != "anthropic-beta"} + headers = {**headers, **anthropic_headers} return headers diff --git a/tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py b/tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py new file mode 100644 index 00000000000..67c37bc6f26 --- /dev/null +++ b/tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py @@ -0,0 +1,134 @@ +""" +Test file for Vertex AI prompt caching fix. + +This test verifies that: +1. The anthropic-beta header is removed for Vertex AI requests +2. Regular Anthropic requests still work correctly +3. Prompt caching detection logic is preserved +""" + +import unittest +from unittest.mock import patch, MagicMock +from litellm.llms.anthropic.common_utils import AnthropicModelInfo + + +class TestVertexAIPromptCachingFix(unittest.TestCase): + """Test cases for the Vertex AI prompt caching fix.""" + + def setUp(self): + """Set up test fixtures.""" + self.model_info = AnthropicModelInfo() + + def _is_cache_control_set(self, messages): + """Helper method to test cache control detection.""" + for message in messages: + if message.get("cache_control", None) is not None: + return True + _message_content = message.get("content") + if _message_content is not None and isinstance(_message_content, list): + for content in _message_content: + if "cache_control" in content: + return True + return False + + def test_vertex_ai_removes_anthropic_beta_header(self): + """Test that anthropic-beta header is removed for Vertex AI requests.""" + # Mock the get_anthropic_headers method + with patch.object(self.model_info, 'get_anthropic_headers') as mock_get_headers: + # Set up the mock to return headers with anthropic-beta + mock_get_headers.return_value = { + 'anthropic-beta': 'prompt-caching-2024-07-31', + 'content-type': 'application/json' + } + + # Test with Vertex AI request + optional_params = {'is_vertex_request': True} + headers = self.model_info.get_anthropic_headers( + model='vertex/claude-3-5-sonnet-20240620', + messages=[], + optional_params=optional_params + ) + + # Verify anthropic-beta header is removed + self.assertNotIn('anthropic-beta', headers) + + def test_regular_anthropic_preserves_anthropic_beta_header(self): + """Test that regular Anthropic requests preserve anthropic-beta header.""" + # Mock the get_anthropic_headers method + with patch.object(self.model_info, 'get_anthropic_headers') as mock_get_headers: + # Set up the mock to return headers with anthropic-beta + mock_get_headers.return_value = { + 'anthropic-beta': 'prompt-caching-2024-07-31', + 'content-type': 'application/json' + } + + # Test with regular Anthropic request (not Vertex AI) + optional_params = {'is_vertex_request': False} + headers = self.model_info.get_anthropic_headers( + model='claude-3-5-sonnet-20240620', + messages=[], + optional_params=optional_params + ) + + # Verify anthropic-beta header is preserved + self.assertIn('anthropic-beta', headers) + + def test_prompt_caching_detection_still_works(self): + """Test that prompt caching detection logic still works.""" + # Test messages with cache_control + messages_with_cache = [ + { + 'role': 'user', + 'content': [ + { + 'type': 'text', + 'text': 'Test prompt', + 'cache_control': {'type': 'ephemeral'} + } + ] + } + ] + + # Test messages without cache_control + messages_without_cache = [ + { + 'role': 'user', + 'content': [ + { + 'type': 'text', + 'text': 'Regular prompt' + } + ] + } + ] + + # Test cache detection + cache_detected_with = self._is_cache_control_set(messages_with_cache) + cache_detected_without = self._is_cache_control_set(messages_without_cache) + + self.assertTrue(cache_detected_with) + self.assertFalse(cache_detected_without) + + def test_vertex_ai_without_user_beta_header(self): + """Test Vertex AI request when no user-provided beta header exists.""" + # Mock the get_anthropic_headers method + with patch.object(self.model_info, 'get_anthropic_headers') as mock_get_headers: + # Set up the mock to return headers without anthropic-beta + mock_get_headers.return_value = { + 'content-type': 'application/json' + } + + # Test with Vertex AI request + optional_params = {'is_vertex_request': True} + headers = self.model_info.get_anthropic_headers( + model='vertex/claude-3-5-sonnet-20240620', + messages=[], + optional_params=optional_params + ) + + # Verify no anthropic-beta header is present + self.assertNotIn('anthropic-beta', headers) + + +if __name__ == '__main__': + unittest.main() \ No newline at end of file From 4076e20b35652615a21bb457ce093e7b520332c3 Mon Sep 17 00:00:00 2001 From: swarnabhasinha Date: Tue, 9 Sep 2025 16:05:04 +0530 Subject: [PATCH 13/73] fix: lint --- litellm/llms/vertex_ai/gemini/transformation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/llms/vertex_ai/gemini/transformation.py b/litellm/llms/vertex_ai/gemini/transformation.py index 6de2931356d..91d04e84a73 100644 --- a/litellm/llms/vertex_ai/gemini/transformation.py +++ b/litellm/llms/vertex_ai/gemini/transformation.py @@ -470,7 +470,7 @@ def _transform_request_body( metadata = litellm_params["metadata"] if "requester_metadata" in metadata: rm = metadata["requester_metadata"] - labels = {k: v for k, v in rm.items() if type(v) is str} + labels = {k: v for k, v in rm.items() if isinstance(v, str)} filtered_params = { k: v for k, v in optional_params.items() if k in config_fields From f8ddd123d10ae308f4c70a599907c147279542f7 Mon Sep 17 00:00:00 2001 From: swarnabhasinha Date: Tue, 9 Sep 2025 16:16:59 +0530 Subject: [PATCH 14/73] fix: add missing patch import in vertex_ai test --- tests/test_litellm/llms/vertex_ai/test_vertex.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex.py b/tests/test_litellm/llms/vertex_ai/test_vertex.py index 7e683d1f54e..02bd622016b 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex.py @@ -10,7 +10,7 @@ import litellm.litellm_core_utils.prompt_templates import litellm.litellm_core_utils.prompt_templates.factory load_dotenv() -from unittest.mock import MagicMock +from unittest.mock import MagicMock, patch sys.path.insert( 0, os.path.abspath("../..") From 59360e64f9cca60312a399cbe1d5e22a31d62292 Mon Sep 17 00:00:00 2001 From: Derek Worthen Date: Tue, 9 Sep 2025 07:01:05 -0700 Subject: [PATCH 15/73] Fix embeddings using azure_ad_token_provider. --- litellm/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/main.py b/litellm/main.py index 1bb84cccdee..d7395eb1457 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -3854,7 +3854,7 @@ def embedding( # noqa: PLR0915 max_retries = kwargs.get("max_retries", None) litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore mock_response: Optional[List[float]] = kwargs.get("mock_response", None) # type: ignore - azure_ad_token_provider = kwargs.pop("azure_ad_token_provider", None) + azure_ad_token_provider = kwargs.get("azure_ad_token_provider", None) aembedding = kwargs.get("aembedding", None) extra_headers = kwargs.get("extra_headers", None) headers = kwargs.get("headers", None) From 4d7e918b6367e49cdbc6f078d49740b742c7e800 Mon Sep 17 00:00:00 2001 From: Oleksandr Tereshchenko Date: Tue, 9 Sep 2025 17:55:03 +0300 Subject: [PATCH 16/73] restore forgotten `raise` keyword in front of an `exception_type()` call --- litellm/litellm_core_utils/streaming_handler.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 2203cac11d0..83b4985b239 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -1937,7 +1937,7 @@ class CustomStreamWrapper: ) ## Map to OpenAI Exception try: - exception_type( + raise exception_type( model=self.model, custom_llm_provider=self.custom_llm_provider, original_exception=e, From f51036e092683a7d83a86d08b7d293203f7c7267 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Wed, 10 Sep 2025 00:32:26 -0400 Subject: [PATCH 17/73] added tags to langchain --- docs/my-website/docs/langchain/langchain.md | 318 ++++++++++++++++++++ docs/my-website/docs/proxy/user_keys.md | 100 ++++++ 2 files changed, 418 insertions(+) diff --git a/docs/my-website/docs/langchain/langchain.md b/docs/my-website/docs/langchain/langchain.md index 78425a73b99..c67375ce1be 100644 --- a/docs/my-website/docs/langchain/langchain.md +++ b/docs/my-website/docs/langchain/langchain.md @@ -162,3 +162,321 @@ Get more details [here](../observability/lunary_integration.md) ## Use LangChain ChatLiteLLM + Langfuse Checkout this section [here](../observability/langfuse_integration#use-langchain-chatlitellm--langfuse) for more details on how to integrate Langfuse with ChatLiteLLM. + +## Using Tags with LangChain and LiteLLM + +Tags are a powerful feature in LiteLLM that allow you to categorize, filter, and track your LLM requests. When using LangChain with LiteLLM, you can pass tags through the `extra_body` parameter in the metadata. + +### Basic Tag Usage + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +os.environ['OPENAI_API_KEY'] = "sk-your-key-here" + +chat = ChatOpenAI( + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["production", "customer-support", "high-priority"] + } + } +) + +messages = [ + SystemMessage(content="You are a helpful customer support assistant."), + HumanMessage(content="How do I reset my password?") +] + +response = chat.invoke(messages) +print(response) +``` + + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +os.environ['ANTHROPIC_API_KEY'] = "sk-ant-your-key-here" + +chat = ChatOpenAI( + model="claude-3-sonnet-20240229", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["research", "analysis", "claude-model"] + } + } +) + +messages = [ + SystemMessage(content="You are a research analyst."), + HumanMessage(content="Analyze this market trend...") +] + +response = chat.invoke(messages) +print(response) +``` + + + + + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +# No API key needed when using proxy +chat = ChatOpenAI( + openai_api_base="http://localhost:4000", # Your proxy URL + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": ["proxy", "team-alpha", "feature-flagged"], + "generation_name": "customer-onboarding", + "trace_user_id": "user-12345" + } + } +) + +messages = [ + SystemMessage(content="You are an onboarding assistant."), + HumanMessage(content="Welcome our new customer!") +] + +response = chat.invoke(messages) +print(response) +``` + + + + +### Advanced Tag Patterns + +#### Dynamic Tags Based on Context + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +def create_chat_with_tags(user_type: str, feature: str): + """Create a chat instance with dynamic tags based on context""" + + # Build tags dynamically + tags = ["langchain-integration"] + + if user_type == "premium": + tags.extend(["premium-user", "high-priority"]) + elif user_type == "enterprise": + tags.extend(["enterprise", "custom-sla"]) + else: + tags.append("standard-user") + + # Add feature-specific tags + if feature == "code-review": + tags.extend(["development", "code-analysis"]) + elif feature == "content-gen": + tags.extend(["marketing", "content-creation"]) + + return ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": tags, + "user_type": user_type, + "feature": feature, + "trace_user_id": f"user-{user_type}-{feature}" + } + } + ) + +# Usage examples +premium_chat = create_chat_with_tags("premium", "code-review") +enterprise_chat = create_chat_with_tags("enterprise", "content-gen") + +messages = [HumanMessage(content="Help me with this task")] +response = premium_chat.invoke(messages) +``` + +#### Tags for Cost Tracking and Analytics + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage + +# Tags for cost tracking +cost_tracking_chat = ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7, + extra_body={ + "metadata": { + "tags": [ + "cost-center-marketing", + "budget-q4-2024", + "project-launch-campaign", + "high-cost-model" # Flag for expensive models + ], + "department": "marketing", + "project_id": "campaign-2024-q4", + "cost_threshold": "high" + } + } +) + +messages = [ + SystemMessage(content="You are a marketing copywriter."), + HumanMessage(content="Create compelling ad copy for our new product launch.") +] + +response = cost_tracking_chat.invoke(messages) +``` + +#### Tags for A/B Testing + +```python +import os +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage, SystemMessage +import random + +def create_ab_test_chat(test_variant: str = None): + """Create chat instance for A/B testing with appropriate tags""" + + if test_variant is None: + test_variant = random.choice(["variant-a", "variant-b"]) + + return ChatOpenAI( + openai_api_base="http://localhost:4000", + model="gpt-4o", + temperature=0.7 if test_variant == "variant-a" else 0.9, # Different temp for variants + extra_body={ + "metadata": { + "tags": [ + "ab-test-experiment-1", + f"variant-{test_variant}", + "temperature-test", + "user-experience" + ], + "experiment_id": "ab-test-001", + "variant": test_variant, + "test_group": "temperature-optimization" + } + } + ) + +# Run A/B test +variant_a_chat = create_ab_test_chat("variant-a") +variant_b_chat = create_ab_test_chat("variant-b") + +test_message = [HumanMessage(content="Explain quantum computing in simple terms")] + +response_a = variant_a_chat.invoke(test_message) +response_b = variant_b_chat.invoke(test_message) +``` + +### Tag Best Practices + +#### 1. **Consistent Naming Convention** +```python +# ✅ Good: Consistent, descriptive tags +tags = ["production", "api-v2", "customer-support", "urgent"] + +# ❌ Avoid: Inconsistent or unclear tags +tags = ["prod", "v2", "support", "urgent123"] +``` + +#### 2. **Hierarchical Tags** +```python +# ✅ Good: Hierarchical structure +tags = ["env:production", "team:backend", "service:api", "priority:high"] + +# This allows for easy filtering and grouping +``` + +#### 3. **Include Context Information** +```python +extra_body={ + "metadata": { + "tags": ["production", "user-onboarding"], + "user_id": "user-12345", + "session_id": "session-abc123", + "feature_flag": "new-onboarding-flow", + "environment": "production" + } +} +``` + +#### 4. **Tag Categories** +Consider organizing tags into categories: +- **Environment**: `production`, `staging`, `development` +- **Team/Service**: `backend`, `frontend`, `api`, `worker` +- **Feature**: `authentication`, `payment`, `notification` +- **Priority**: `critical`, `high`, `medium`, `low` +- **User Type**: `premium`, `enterprise`, `free` + +### Using Tags with LiteLLM Proxy + +When using tags with LiteLLM Proxy, you can: + +1. **Filter requests** based on tags +2. **Track costs** by tags in spend reports +3. **Apply routing rules** based on tags +4. **Monitor usage** with tag-based analytics + +#### Example Proxy Configuration with Tags + +```yaml +# config.yaml +model_list: + - model_name: gpt-4o + litellm_params: + model: gpt-4o + api_key: your-key + +# Tag-based routing rules +tag_routing: + - tags: ["premium", "high-priority"] + models: ["gpt-4o", "claude-3-opus"] + - tags: ["standard"] + models: ["gpt-3.5-turbo", "claude-3-haiku"] +``` + +### Monitoring and Analytics + +Tags enable powerful analytics capabilities: + +```python +# Example: Get spend reports by tags +import requests + +response = requests.get( + "http://localhost:4000/global/spend/report", + headers={"Authorization": "Bearer sk-your-key"}, + params={ + "start_date": "2024-01-01", + "end_date": "2024-12-31", + "group_by": "tags" + } +) + +spend_by_tags = response.json() +``` + +This documentation covers the essential patterns for using tags effectively with LangChain and LiteLLM, enabling better organization, tracking, and analytics of your LLM requests. diff --git a/docs/my-website/docs/proxy/user_keys.md b/docs/my-website/docs/proxy/user_keys.md index ecf6f2d0532..21e1d3dbf40 100644 --- a/docs/my-website/docs/proxy/user_keys.md +++ b/docs/my-website/docs/proxy/user_keys.md @@ -357,6 +357,106 @@ assert user.age == 25 +## Using Tags for Categorization and Tracking + +Tags allow you to categorize, filter, and track your LLM requests. Add tags to your metadata for better organization and analytics. + + + + +```python +import openai +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[{"role": "user", "content": "Hello!"}], + extra_body={ + "metadata": { + "tags": ["production", "customer-support", "urgent"], + "generation_name": "support-bot", + "trace_user_id": "user-123" + } + } +) +``` + + + + + +```python +from langchain_openai import ChatOpenAI +from langchain_core.messages import HumanMessage + +chat = ChatOpenAI( + openai_api_base="http://0.0.0.0:4000", + model="gpt-4o", + extra_body={ + "metadata": { + "tags": ["langchain-integration", "content-gen"], + "trace_user_id": "user-456" + } + } +) + +response = chat.invoke([HumanMessage(content="Generate a blog post")]) +``` + + + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello!"}], + "metadata": { + "tags": ["api-test", "development"], + "trace_user_id": "test-user" + } +}' +``` + + + + + +```js +const { OpenAI } = require('openai'); + +const openai = new OpenAI({ + apiKey: "sk-1234", + baseURL: "http://0.0.0.0:4000" +}); + +async function main() { + const response = await openai.chat.completions.create({ + messages: [{ role: 'user', content: 'Hello!' }], + model: 'gpt-3.5-turbo', + metadata: { + tags: ["javascript-client", "api-test"], + trace_user_id: "js-user-789" + } + }); +} +``` + + + + +### Tag Benefits + +- **Cost Tracking**: Monitor spending by project/team/feature +- **Analytics**: Filter requests by tags in logs and dashboards +- **Routing**: Use tags for conditional model routing +- **Debugging**: Easier troubleshooting with categorized requests + ### Response Format ```json From c20a5b0bbd764d381ad3729a519f28b844878c49 Mon Sep 17 00:00:00 2001 From: mubashir1osmani Date: Wed, 10 Sep 2025 00:51:37 -0400 Subject: [PATCH 18/73] more info for turn_off_logging --- docs/my-website/docs/proxy/config_settings.md | 4 ++-- docs/my-website/docs/proxy/logging.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 7eb355d39f2..e33301bcd2e 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -21,7 +21,7 @@ litellm_settings: failure_callback: ["sentry"] # list of failure callbacks callbacks: ["otel"] # list of callbacks - runs on success and failure service_callbacks: ["datadog", "prometheus"] # logs redis, postgres failures on datadog, prometheus - turn_off_message_logging: boolean # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged. + turn_off_message_logging: boolean # prevent the messages and responses from being logged to on your callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data. redact_user_api_key_info: boolean # Redact information about the user api key (hashed token, user_id, team id, etc.), from logs. Currently supported for Langfuse, OpenTelemetry, Logfire, ArizeAI logging. langfuse_default_tags: ["cache_hit", "cache_key", "proxy_base_url", "user_api_key_alias", "user_api_key_user_id", "user_api_key_user_email", "user_api_key_team_alias", "semantic-similarity", "proxy_base_url"] # default tags for Langfuse Logging @@ -131,7 +131,7 @@ general_settings: | failure_callback | array of strings | List of failure callbacks [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) | | callbacks | array of strings | List of callbacks - runs on success and failure [Doc Proxy logging callbacks](logging), [Doc Metrics](prometheus) | | service_callbacks | array of strings | System health monitoring - Logs redis, postgres failures on specified services (e.g. datadog, prometheus) [Doc Metrics](prometheus) | -| turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged [Proxy Logging](logging) | +| turn_off_message_logging | boolean | If true, prevents messages and responses from being logged to callbacks, but request metadata will still be logged. Useful for privacy/compliance when handling sensitive data [Proxy Logging](logging) | | modify_params | boolean | If true, allows modifying the parameters of the request before it is sent to the LLM provider | | enable_preview_features | boolean | If true, enables preview features - e.g. Azure O1 Models with streaming support.| | redact_user_api_key_info | boolean | If true, redacts information about the user api key from logs [Proxy Logging](logging#redacting-userapikeyinfo) | diff --git a/docs/my-website/docs/proxy/logging.md b/docs/my-website/docs/proxy/logging.md index 5d3f8417222..ff2591daad2 100644 --- a/docs/my-website/docs/proxy/logging.md +++ b/docs/my-website/docs/proxy/logging.md @@ -60,7 +60,7 @@ components in your system, including in logging tools. ### Redact Messages, Response Content -Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked. +Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to your logging provider, but request metadata - e.g. spend, will still be tracked. Useful for privacy/compliance when handling sensitive data. From f6bc4d0bf949da3240382807e74f904ebc164e67 Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Wed, 10 Sep 2025 11:58:15 +0300 Subject: [PATCH 19/73] Noma non blocking on monitor mode --- .../guardrails/guardrail_hooks/noma/noma.py | 228 +++++++++--- .../guardrails/guardrail_hooks/test_noma.py | 325 +++++++++++++++++- 2 files changed, 502 insertions(+), 51 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index ed5929f0564..3bbd183e49b 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -5,6 +5,7 @@ # # +-------------------------------------------------------------+ +import asyncio import copy import os from typing import Any, Dict, Literal, Optional, Union @@ -24,6 +25,10 @@ from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import EmbeddingResponse, ImageResponse +# Type aliases +MessageRole = Literal["user", "assistant"] +LLMResponse = Union[Any, ModelResponse, EmbeddingResponse, ImageResponse] + class NomaBlockedMessage(HTTPException): """Exception raised when Noma guardrail blocks a message""" @@ -164,6 +169,138 @@ class NomaGuardrail(CustomGuardrail): super().__init__(**kwargs) + def _create_background_noma_check( + self, + coro, + ) -> None: + """Create a background task for Noma API calls without blocking the main flow""" + try: + asyncio.create_task(coro) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to create background Noma task: {str(e)}" + ) + + async def _process_user_message_check( + self, + request_data: dict, + user_auth: UserAPIKeyAuth, + ) -> Optional[str]: + """Shared logic for processing user message checks""" + extra_data = self.get_guardrail_dynamic_request_body_params(request_data) + + user_message = await self._extract_user_message(request_data) + if not user_message: + return None + + payload = {"request": {"text": user_message}} + response_json = await self._call_noma_api( + payload=payload, + llm_request_id=None, + request_data=request_data, + user_auth=user_auth, + extra_data=extra_data, + ) + + if self.monitor_mode: + await self._handle_verdict_background("user", user_message, response_json) + else: + await self._check_verdict("user", user_message, response_json) + + return user_message + + async def _process_llm_response_check( + self, + request_data: dict, + response: LLMResponse, + user_auth: UserAPIKeyAuth, + ) -> Optional[str]: + """Shared logic for processing LLM response checks""" + extra_data = self.get_guardrail_dynamic_request_body_params(request_data) + + if not isinstance(response, litellm.ModelResponse): + return None + + content = None + for choice in response.choices: + if isinstance(choice, litellm.Choices) and choice.message.content: + content = choice.message.content + break + + if not content or not isinstance(content, str): + return None + + payload = {"response": {"text": content}} + + response_json = await self._call_noma_api( + payload=payload, + llm_request_id=response.id, + request_data=request_data, + user_auth=user_auth, + extra_data=extra_data, + ) + + if self.monitor_mode: + await self._handle_verdict_background("assistant", content, response_json) + else: + await self._check_verdict("assistant", content, response_json) + + return content + + async def _check_user_message_background( + self, + request_data: dict, + user_auth: UserAPIKeyAuth, + ) -> None: + """Check user message in background for monitor mode - non-blocking""" + try: + await self._process_user_message_check(request_data, user_auth) + except Exception as e: + verbose_proxy_logger.error( + f"Noma background user message check failed: {str(e)}" + ) + + async def _check_llm_response_background( + self, + request_data: dict, + response: LLMResponse, + user_auth: UserAPIKeyAuth, + ) -> None: + """Check LLM response in background for monitor mode - non-blocking""" + try: + await self._process_llm_response_check(request_data, response, user_auth) + except Exception as e: + verbose_proxy_logger.error( + f"Noma background response check failed: {str(e)}" + ) + + async def _handle_verdict_background( + self, + type: MessageRole, + message: str, + response_json: dict, + ) -> None: + """Handle verdict from Noma API in background - logging only, never blocks""" + try: + if not response_json.get("verdict", True): + msg = str.format( + "Noma guardrail blocked {type} message: {message}", + type=type, + message=message, + ) + verbose_proxy_logger.warning(msg) + else: + msg = str.format( + "Noma guardrail allowed {type} message: {message}", + type=type, + message=message, + ) + verbose_proxy_logger.info(msg) + except Exception as e: + verbose_proxy_logger.error( + f"Noma background verdict handling failed: {str(e)}" + ) + async def async_pre_call_hook( self, user_api_key_dict: UserAPIKeyAuth, @@ -191,6 +328,18 @@ class NomaGuardrail(CustomGuardrail): ): return data + # In monitor mode, run Noma check in background and return immediately + if self.monitor_mode: + try: + self._create_background_noma_check( + self._check_user_message_background(data, user_api_key_dict) + ) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to start background Noma pre-call check: {str(e)}" + ) + return data + try: return await self._check_user_message(data, user_api_key_dict) except NomaBlockedMessage: @@ -198,7 +347,7 @@ class NomaGuardrail(CustomGuardrail): except Exception as e: verbose_proxy_logger.error(f"Noma pre-call hook failed: {str(e)}") - if self.block_failures and not self.monitor_mode: + if self.block_failures: raise return data @@ -220,6 +369,18 @@ class NomaGuardrail(CustomGuardrail): if self.should_run_guardrail(data=data, event_type=event_type) is not True: return data + # In monitor mode, run Noma check in background and return immediately + if self.monitor_mode: + try: + self._create_background_noma_check( + self._check_user_message_background(data, user_api_key_dict) + ) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to start background Noma moderation check: {str(e)}" + ) + return data + try: return await self._check_user_message(data, user_api_key_dict) except NomaBlockedMessage: @@ -227,7 +388,7 @@ class NomaGuardrail(CustomGuardrail): except Exception as e: verbose_proxy_logger.error(f"Noma moderation hook failed: {str(e)}") - if self.block_failures and not self.monitor_mode: + if self.block_failures: raise return data @@ -235,19 +396,33 @@ class NomaGuardrail(CustomGuardrail): self, data: dict, user_api_key_dict: UserAPIKeyAuth, - response: Union[Any, ModelResponse, EmbeddingResponse, ImageResponse], + response: LLMResponse, ): event_type: GuardrailEventHooks = GuardrailEventHooks.post_call if self.should_run_guardrail(data=data, event_type=event_type) is not True: return response + # In monitor mode, run Noma check in background and return immediately + if self.monitor_mode: + try: + self._create_background_noma_check( + self._check_llm_response_background( + data, response, user_api_key_dict + ) + ) + except Exception as e: + verbose_proxy_logger.error( + f"Failed to start background Noma post-call check: {str(e)}" + ) + return response + try: return await self._check_llm_response(data, response, user_api_key_dict) except NomaBlockedMessage: raise except Exception as e: verbose_proxy_logger.error(f"Noma post-call hook failed: {str(e)}") - if self.block_failures and not self.monitor_mode: + if self.block_failures: raise return response @@ -257,55 +432,24 @@ class NomaGuardrail(CustomGuardrail): user_auth: UserAPIKeyAuth, ) -> Union[Exception, str, dict, None]: """Check user message for policy violations""" - extra_data = self.get_guardrail_dynamic_request_body_params(request_data) - - user_message = await self._extract_user_message(request_data) + user_message = await self._process_user_message_check(request_data, user_auth) if not user_message: return request_data - payload = {"request": {"text": user_message}} - response_json = await self._call_noma_api( - payload=payload, - llm_request_id=None, - request_data=request_data, - user_auth=user_auth, - extra_data=extra_data, - ) - await self._check_verdict("user", user_message, response_json) - return request_data async def _check_llm_response( self, request_data: dict, - response: Union[Any, ModelResponse, EmbeddingResponse, ImageResponse], + response: LLMResponse, user_auth: UserAPIKeyAuth, ) -> Union[Exception, ModelResponse, Any]: """Check LLM response for policy violations""" - extra_data = self.get_guardrail_dynamic_request_body_params(request_data) - - if not isinstance(response, litellm.ModelResponse): - return response - - content = None - for choice in response.choices: - if isinstance(choice, litellm.Choices) and choice.message.content: - content = choice.message.content - break - - if not content or not isinstance(content, str): - return response - - payload = {"response": {"text": content}} - - response_json = await self._call_noma_api( - payload=payload, - llm_request_id=response.id, - request_data=request_data, - user_auth=user_auth, - extra_data=extra_data, + content = await self._process_llm_response_check( + request_data, response, user_auth ) - await self._check_verdict("assistant", content, response_json) + if not content: + return response return response @@ -371,7 +515,7 @@ class NomaGuardrail(CustomGuardrail): async def _check_verdict( self, - type: Literal["user", "assistant"], + type: MessageRole, message: str, response_json: dict, ) -> None: 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 aeea5f81b10..f1e91db7d59 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -50,7 +50,6 @@ def mock_user_api_key_dict(): soft_budget=None, tpm_limit=None, rpm_limit=None, - parallel_request_limit=None, metadata={}, max_parallel_requests=None, allowed_cache_controls=[], @@ -291,15 +290,38 @@ class TestNomaGuardrailHooks: default_on=True, ) - mock_response = MagicMock() - mock_response.json.return_value = { - "verdict": False, - "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, - } - mock_response.raise_for_status = MagicMock() + with patch.object( + guardrail, "_create_background_noma_check" + ) as mock_create_background: + # Should return immediately without waiting for API call + result = await guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=mock_request_data, + call_type="completion", + ) - with patch.object(guardrail.async_handler, "post", return_value=mock_response): - # Should not raise exception in monitor mode + assert result == mock_request_data + # Verify background task was created + mock_create_background.assert_called_once() + + @pytest.mark.asyncio + async def test_pre_call_hook_monitor_mode_background_task_failure( + self, mock_user_api_key_dict, mock_request_data + ): + """Test pre-call hook in monitor mode when background task creation fails""" + guardrail = NomaGuardrail( + api_key="test-key", + monitor_mode=True, + guardrail_name="test-guardrail", + event_hook="pre_call", + default_on=True, + ) + + with patch.object( + guardrail, "_create_background_noma_check", side_effect=Exception("Task creation failed") + ): + # Should still return successfully even if background task creation fails result = await guardrail.async_pre_call_hook( user_api_key_dict=mock_user_api_key_dict, cache=MagicMock(), @@ -457,6 +479,291 @@ class TestNomaGuardrailHooks: assert message is None +class TestBackgroundProcessing: + """Test the new background processing functionality""" + + @pytest.fixture + def monitor_mode_guardrail(self): + """Create a guardrail with monitor mode enabled""" + return NomaGuardrail( + api_key="test-api-key", + api_base="https://api.test.noma.security/", + application_id="test-app", + monitor_mode=True, # Enable monitor mode + block_failures=True, + guardrail_name="test-noma-guardrail", + event_hook="pre_call", + default_on=True, + ) + + @pytest.mark.asyncio + async def test_process_user_message_check_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test shared helper method in monitor mode""" + mock_response = MagicMock() + mock_response.json.return_value = { + "verdict": False, + "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + } + mock_response.raise_for_status = MagicMock() + + with patch.object( + monitor_mode_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + with patch.object( + monitor_mode_guardrail, "_handle_verdict_background" + ) as mock_handle_verdict: + result = await monitor_mode_guardrail._process_user_message_check( + mock_request_data, mock_user_api_key_dict + ) + + assert result == "Hello, how are you?" + mock_post.assert_called_once() + mock_handle_verdict.assert_called_once_with( + "user", "Hello, how are you?", mock_response.json.return_value + ) + + @pytest.mark.asyncio + async def test_process_user_message_check_non_monitor_mode( + self, noma_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test shared helper method in non-monitor mode""" + mock_response = MagicMock() + mock_response.json.return_value = {"verdict": True} + mock_response.raise_for_status = MagicMock() + + with patch.object( + noma_guardrail.async_handler, "post", return_value=mock_response + ) as mock_post: + with patch.object( + noma_guardrail, "_check_verdict" + ) as mock_check_verdict: + result = await noma_guardrail._process_user_message_check( + mock_request_data, mock_user_api_key_dict + ) + + assert result == "Hello, how are you?" + mock_post.assert_called_once() + mock_check_verdict.assert_called_once_with( + "user", "Hello, how are you?", mock_response.json.return_value + ) + + @pytest.mark.asyncio + async def test_process_llm_response_check_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test LLM response processing in monitor mode""" + from litellm.types.utils import Choices, Message + + response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="I'm doing well, thank you!", 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}, + ) + + mock_api_response = MagicMock() + mock_api_response.json.return_value = {"verdict": True} + mock_api_response.raise_for_status = MagicMock() + + with patch.object( + monitor_mode_guardrail.async_handler, "post", return_value=mock_api_response + ) as mock_post: + with patch.object( + monitor_mode_guardrail, "_handle_verdict_background" + ) as mock_handle_verdict: + result = await monitor_mode_guardrail._process_llm_response_check( + mock_request_data, response, mock_user_api_key_dict + ) + + assert result == "I'm doing well, thank you!" + mock_post.assert_called_once() + mock_handle_verdict.assert_called_once_with( + "assistant", "I'm doing well, thank you!", mock_api_response.json.return_value + ) + + @pytest.mark.asyncio + async def test_check_user_message_background( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test background user message check method""" + with patch.object( + monitor_mode_guardrail, "_process_user_message_check" + ) as mock_process: + await monitor_mode_guardrail._check_user_message_background( + mock_request_data, mock_user_api_key_dict + ) + + mock_process.assert_called_once_with(mock_request_data, mock_user_api_key_dict) + + @pytest.mark.asyncio + async def test_check_user_message_background_exception_handling( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test background user message check handles exceptions gracefully""" + with patch.object( + monitor_mode_guardrail, "_process_user_message_check", + side_effect=Exception("API failed") + ): + # Should not raise exception, just log error + await monitor_mode_guardrail._check_user_message_background( + mock_request_data, mock_user_api_key_dict + ) + + @pytest.mark.asyncio + async def test_check_llm_response_background( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test background LLM response check method""" + from litellm.types.utils import Choices, Message + + response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="Test response", 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}, + ) + + with patch.object( + monitor_mode_guardrail, "_process_llm_response_check" + ) as mock_process: + await monitor_mode_guardrail._check_llm_response_background( + mock_request_data, response, mock_user_api_key_dict + ) + + mock_process.assert_called_once_with( + mock_request_data, response, mock_user_api_key_dict + ) + + @pytest.mark.asyncio + async def test_handle_verdict_background_blocked(self, monitor_mode_guardrail): + """Test background verdict handling for blocked content""" + response_json = { + "verdict": False, + "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + } + + with patch("litellm._logging.verbose_proxy_logger.warning") as mock_warning: + await monitor_mode_guardrail._handle_verdict_background( + "user", "test message", response_json + ) + + mock_warning.assert_called_once() + assert "blocked user message" in mock_warning.call_args[0][0] + + @pytest.mark.asyncio + async def test_handle_verdict_background_allowed(self, monitor_mode_guardrail): + """Test background verdict handling for allowed content""" + response_json = {"verdict": True} + + with patch("litellm._logging.verbose_proxy_logger.info") as mock_info: + await monitor_mode_guardrail._handle_verdict_background( + "assistant", "test response", response_json + ) + + mock_info.assert_called_once() + assert "allowed assistant message" in mock_info.call_args[0][0] + + @pytest.mark.asyncio + async def test_create_background_noma_check(self, monitor_mode_guardrail): + """Test background task creation""" + async def dummy_coroutine(): + return "completed" + + with patch("asyncio.create_task") as mock_create_task: + monitor_mode_guardrail._create_background_noma_check(dummy_coroutine()) + mock_create_task.assert_called_once() + + @pytest.mark.asyncio + async def test_create_background_noma_check_exception(self, monitor_mode_guardrail): + """Test background task creation with exception handling""" + async def dummy_coroutine(): + return "completed" + + with patch("asyncio.create_task", side_effect=Exception("Task creation failed")): + # Should not raise exception, just log error + monitor_mode_guardrail._create_background_noma_check(dummy_coroutine()) + + @pytest.mark.asyncio + async def test_moderation_hook_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test moderation hook in monitor mode""" + # Update event hook to during_call + monitor_mode_guardrail.event_hook = "during_call" + + with patch.object( + monitor_mode_guardrail, "_create_background_noma_check" + ) as mock_create_background: + result = await monitor_mode_guardrail.async_moderation_hook( + data=mock_request_data, + user_api_key_dict=mock_user_api_key_dict, + call_type="completion", + ) + + assert result == mock_request_data + mock_create_background.assert_called_once() + + @pytest.mark.asyncio + async def test_post_call_success_hook_monitor_mode( + self, monitor_mode_guardrail, mock_user_api_key_dict, mock_request_data + ): + """Test post-call success hook in monitor mode""" + from litellm.types.utils import Choices, Message + + # Update event hook to post_call + monitor_mode_guardrail.event_hook = "post_call" + + response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message(content="Test response", 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}, + ) + + with patch.object( + monitor_mode_guardrail, "_create_background_noma_check" + ) as mock_create_background: + result = await monitor_mode_guardrail.async_post_call_success_hook( + data=mock_request_data, + user_api_key_dict=mock_user_api_key_dict, + response=response, + ) + + assert result == response + mock_create_background.assert_called_once() + + class TestIntegration: @pytest.mark.asyncio async def test_full_guardrail_flow(self): From fc2e83587a37673c048a77bc3a1f301504fdc036 Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Wed, 10 Sep 2025 14:11:38 +0300 Subject: [PATCH 20/73] PR fixes --- .../guardrails/guardrail_hooks/noma/noma.py | 40 +++++++------------ 1 file changed, 14 insertions(+), 26 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 3bbd183e49b..1a1ed2acb1f 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -8,7 +8,7 @@ import asyncio import copy import os -from typing import Any, Dict, Literal, Optional, Union +from typing import Any, Dict, Final, Literal, Optional, Union from urllib.parse import urljoin from fastapi import HTTPException @@ -25,6 +25,10 @@ from litellm.proxy._types import UserAPIKeyAuth from litellm.types.guardrails import GuardrailEventHooks from litellm.types.utils import EmbeddingResponse, ImageResponse +# Constants +USER_ROLE: Final[Literal["user"]] = "user" +ASSISTANT_ROLE: Final[Literal["assistant"]] = "assistant" + # Type aliases MessageRole = Literal["user", "assistant"] LLMResponse = Union[Any, ModelResponse, EmbeddingResponse, ImageResponse] @@ -203,9 +207,9 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background("user", user_message, response_json) + await self._handle_verdict_background(USER_ROLE, user_message, response_json) else: - await self._check_verdict("user", user_message, response_json) + await self._check_verdict(USER_ROLE, user_message, response_json) return user_message @@ -241,9 +245,9 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background("assistant", content, response_json) + await self._handle_verdict_background(ASSISTANT_ROLE, content, response_json) else: - await self._check_verdict("assistant", content, response_json) + await self._check_verdict(ASSISTANT_ROLE, content, response_json) return content @@ -283,18 +287,10 @@ class NomaGuardrail(CustomGuardrail): """Handle verdict from Noma API in background - logging only, never blocks""" try: if not response_json.get("verdict", True): - msg = str.format( - "Noma guardrail blocked {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail blocked {type} message: {message}" verbose_proxy_logger.warning(msg) else: - msg = str.format( - "Noma guardrail allowed {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail allowed {type} message: {message}" verbose_proxy_logger.info(msg) except Exception as e: verbose_proxy_logger.error( @@ -460,7 +456,7 @@ class NomaGuardrail(CustomGuardrail): return None # Get the last user message - user_messages = [msg for msg in messages if msg.get("role") == "user"] + user_messages = [msg for msg in messages if msg.get("role") == USER_ROLE] if not user_messages: return None @@ -523,11 +519,7 @@ class NomaGuardrail(CustomGuardrail): Check the verdict from the Noma API and raise an exception if needed """ if not response_json.get("verdict", True): - msg = str.format( - "Noma guardrail blocked {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail blocked {type} message: {message}" if self.monitor_mode: verbose_proxy_logger.warning(msg) @@ -536,11 +528,7 @@ class NomaGuardrail(CustomGuardrail): original_response = response_json.get("originalResponse", {}) raise NomaBlockedMessage(original_response) else: - msg = str.format( - "Noma guardrail allowed {type} message: {message}", - type=type, - message=message, - ) + msg = f"Noma guardrail allowed {type} message: {message}" if self.monitor_mode: verbose_proxy_logger.info(msg) else: From 2668112a18071b8813d2575e27539e0775de58bd Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Wed, 10 Sep 2025 08:48:37 -0700 Subject: [PATCH 21/73] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c8a073432c9..df2350b6c9e 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ Discord - + Slack @@ -408,7 +408,7 @@ All these checks must pass before your PR can be merged. - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) - [Community Discord 💭](https://discord.gg/wuPM9dRgDw) -- [Community Slack 💭](https://join.slack.com/share/enQtOTE0ODczMzk2Nzk4NC01YjUxNjY2YjBlYTFmNDRiZTM3NDFiYTM3MzVkODFiMDVjOGRjMmNmZTZkZTMzOWQzZGQyZWIwYjQ0MWExYmE3) +- [Community Slack 💭](https://www.litellm.ai/support) - Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬ - Our emails ✉️ ishaan@berri.ai / krrish@berri.ai From f3c5a59e5700e6db01b6aaf4642aa7ca6594ff97 Mon Sep 17 00:00:00 2001 From: Arseny Boykov <36469655+Bobronium@users.noreply.github.com> Date: Thu, 11 Sep 2025 00:46:27 +0200 Subject: [PATCH 22/73] Revert "Use _PROXY_MaxParallelRequestsHandler_v3 by default (#14352)" (#14420) This reverts commit 5b680bb4a350b8261b24df7ce806fdcda9c88807. --- litellm/proxy/hooks/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/hooks/__init__.py b/litellm/proxy/hooks/__init__.py index 467565d748a..83d7e173431 100644 --- a/litellm/proxy/hooks/__init__.py +++ b/litellm/proxy/hooks/__init__.py @@ -17,13 +17,13 @@ except ImportError: # List of all available hooks that can be enabled PROXY_HOOKS = { "max_budget_limiter": _PROXY_MaxBudgetLimiter, - "parallel_request_limiter": _PROXY_MaxParallelRequestsHandler_v3, + "parallel_request_limiter": _PROXY_MaxParallelRequestsHandler, "cache_control_check": _PROXY_CacheControlCheck, } ## FEATURE FLAG HOOKS ## -if os.getenv("LEGACY_MULTI_INSTANCE_RATE_LIMITING", "false").lower() == "true": - PROXY_HOOKS["parallel_request_limiter"] = _PROXY_MaxParallelRequestsHandler +if os.getenv("EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING", "false").lower() == "true": + PROXY_HOOKS["parallel_request_limiter"] = _PROXY_MaxParallelRequestsHandler_v3 ### update PROXY_HOOKS with ENTERPRISE_PROXY_HOOKS ### From dc5650eedaafe2e215cb9b9ddf67fc937399cb57 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 15:47:18 -0700 Subject: [PATCH 23/73] Revert "fix: remove anthropic-beta header for Vertex AI requests with prompt caching" (#14421) --- litellm/llms/anthropic/common_utils.py | 11 +- .../test_vertex_ai_prompt_caching_fix.py | 134 ------------------ .../llms/vertex_ai/test_vertex.py | 2 +- 3 files changed, 3 insertions(+), 144 deletions(-) delete mode 100644 tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 06ebb5079d9..68b5341e954 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -107,10 +107,8 @@ class AnthropicModelInfo(BaseLLMModelInfo): user_anthropic_beta_headers: Optional[List[str]] = None, ) -> dict: betas = set() - # Note: prompt-caching-2024-07-31 header is no longer required for prompt caching - # as per current Anthropic documentation. It's now generally available. - # if prompt_caching_set: - # betas.add("prompt-caching-2024-07-31") + if prompt_caching_set: + betas.add("prompt-caching-2024-07-31") if computer_tool_used: betas.add("computer-use-2024-10-22") # if pdf_used: @@ -178,11 +176,6 @@ class AnthropicModelInfo(BaseLLMModelInfo): mcp_server_used=mcp_server_used, ) - # For Vertex AI requests, remove any user-provided anthropic-beta headers - # since Vertex AI rejects them and they're no longer required for prompt caching - if optional_params.get("is_vertex_request", False): - headers = {k: v for k, v in headers.items() if k != "anthropic-beta"} - headers = {**headers, **anthropic_headers} return headers diff --git a/tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py b/tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py deleted file mode 100644 index 67c37bc6f26..00000000000 --- a/tests/litellm/llms/anthropic/test_vertex_ai_prompt_caching_fix.py +++ /dev/null @@ -1,134 +0,0 @@ -""" -Test file for Vertex AI prompt caching fix. - -This test verifies that: -1. The anthropic-beta header is removed for Vertex AI requests -2. Regular Anthropic requests still work correctly -3. Prompt caching detection logic is preserved -""" - -import unittest -from unittest.mock import patch, MagicMock -from litellm.llms.anthropic.common_utils import AnthropicModelInfo - - -class TestVertexAIPromptCachingFix(unittest.TestCase): - """Test cases for the Vertex AI prompt caching fix.""" - - def setUp(self): - """Set up test fixtures.""" - self.model_info = AnthropicModelInfo() - - def _is_cache_control_set(self, messages): - """Helper method to test cache control detection.""" - for message in messages: - if message.get("cache_control", None) is not None: - return True - _message_content = message.get("content") - if _message_content is not None and isinstance(_message_content, list): - for content in _message_content: - if "cache_control" in content: - return True - return False - - def test_vertex_ai_removes_anthropic_beta_header(self): - """Test that anthropic-beta header is removed for Vertex AI requests.""" - # Mock the get_anthropic_headers method - with patch.object(self.model_info, 'get_anthropic_headers') as mock_get_headers: - # Set up the mock to return headers with anthropic-beta - mock_get_headers.return_value = { - 'anthropic-beta': 'prompt-caching-2024-07-31', - 'content-type': 'application/json' - } - - # Test with Vertex AI request - optional_params = {'is_vertex_request': True} - headers = self.model_info.get_anthropic_headers( - model='vertex/claude-3-5-sonnet-20240620', - messages=[], - optional_params=optional_params - ) - - # Verify anthropic-beta header is removed - self.assertNotIn('anthropic-beta', headers) - - def test_regular_anthropic_preserves_anthropic_beta_header(self): - """Test that regular Anthropic requests preserve anthropic-beta header.""" - # Mock the get_anthropic_headers method - with patch.object(self.model_info, 'get_anthropic_headers') as mock_get_headers: - # Set up the mock to return headers with anthropic-beta - mock_get_headers.return_value = { - 'anthropic-beta': 'prompt-caching-2024-07-31', - 'content-type': 'application/json' - } - - # Test with regular Anthropic request (not Vertex AI) - optional_params = {'is_vertex_request': False} - headers = self.model_info.get_anthropic_headers( - model='claude-3-5-sonnet-20240620', - messages=[], - optional_params=optional_params - ) - - # Verify anthropic-beta header is preserved - self.assertIn('anthropic-beta', headers) - - def test_prompt_caching_detection_still_works(self): - """Test that prompt caching detection logic still works.""" - # Test messages with cache_control - messages_with_cache = [ - { - 'role': 'user', - 'content': [ - { - 'type': 'text', - 'text': 'Test prompt', - 'cache_control': {'type': 'ephemeral'} - } - ] - } - ] - - # Test messages without cache_control - messages_without_cache = [ - { - 'role': 'user', - 'content': [ - { - 'type': 'text', - 'text': 'Regular prompt' - } - ] - } - ] - - # Test cache detection - cache_detected_with = self._is_cache_control_set(messages_with_cache) - cache_detected_without = self._is_cache_control_set(messages_without_cache) - - self.assertTrue(cache_detected_with) - self.assertFalse(cache_detected_without) - - def test_vertex_ai_without_user_beta_header(self): - """Test Vertex AI request when no user-provided beta header exists.""" - # Mock the get_anthropic_headers method - with patch.object(self.model_info, 'get_anthropic_headers') as mock_get_headers: - # Set up the mock to return headers without anthropic-beta - mock_get_headers.return_value = { - 'content-type': 'application/json' - } - - # Test with Vertex AI request - optional_params = {'is_vertex_request': True} - headers = self.model_info.get_anthropic_headers( - model='vertex/claude-3-5-sonnet-20240620', - messages=[], - optional_params=optional_params - ) - - # Verify no anthropic-beta header is present - self.assertNotIn('anthropic-beta', headers) - - -if __name__ == '__main__': - unittest.main() \ No newline at end of file diff --git a/tests/test_litellm/llms/vertex_ai/test_vertex.py b/tests/test_litellm/llms/vertex_ai/test_vertex.py index 02bd622016b..7e683d1f54e 100644 --- a/tests/test_litellm/llms/vertex_ai/test_vertex.py +++ b/tests/test_litellm/llms/vertex_ai/test_vertex.py @@ -10,7 +10,7 @@ import litellm.litellm_core_utils.prompt_templates import litellm.litellm_core_utils.prompt_templates.factory load_dotenv() -from unittest.mock import MagicMock, patch +from unittest.mock import MagicMock sys.path.insert( 0, os.path.abspath("../..") From d544c6e595ec0254b0d35e4d99ce23cae1524138 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 15:52:29 -0700 Subject: [PATCH 24/73] fix DatabricksFunction --- ...odel_prices_and_context_window_backup.json | 542 +----------------- litellm/types/llms/databricks.py | 2 +- 2 files changed, 24 insertions(+), 520 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 168cbeeade0..ff7b6b36dc8 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -15389,16 +15389,6 @@ "litellm_provider": "ollama", "mode": "completion" }, - "deepinfra/Austism/chronos-hermes-13b-v2": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/Gryphe/MythoMax-L2-13b": { "max_tokens": 4096, "max_input_tokens": 4096, @@ -15409,26 +15399,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/Gryphe/MythoMax-L2-13b-turbo": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1.3e-07, - "output_cost_per_token": 1.3e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/KoboldAI/LLaMA2-13B-Tiefighter": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 1e-07, - "output_cost_per_token": 1e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/NousResearch/Hermes-3-Llama-3.1-405B": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -15447,78 +15417,18 @@ "output_cost_per_token": 2.8e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/NovaSky-AI/Sky-T1-32B-Preview": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 1.2e-07, - "output_cost_per_token": 1.8e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/Phind/Phind-CodeLlama-34B-v2": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 6e-07, - "output_cost_per_token": 6e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/Qwen/QVQ-72B-Preview": { - "max_tokens": 32000, - "max_input_tokens": 32000, - "max_output_tokens": 32000, - "input_cost_per_token": 2.5e-07, - "output_cost_per_token": 5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", "supports_tool_choice": false }, "deepinfra/Qwen/QwQ-32B": { "max_tokens": 131072, "max_input_tokens": 131072, "max_output_tokens": 131072, - "input_cost_per_token": 7.5e-08, - "output_cost_per_token": 1.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, - "deepinfra/Qwen/QwQ-32B-Preview": { - "max_tokens": 32768, - "max_input_tokens": 32768, - 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"input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/mistralai/Mistral-7B-Instruct-v0.3": { - "max_tokens": 32768, - "max_input_tokens": 32768, - "max_output_tokens": 32768, - "input_cost_per_token": 2.8e-08, - "output_cost_per_token": 5.4e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/mistralai/Mistral-Nemo-Instruct-2407": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -16370,16 +15934,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/mistralai/Mistral-Small-3.1-24B-Instruct-2503": { - "max_tokens": 128000, - "max_input_tokens": 128000, - "max_output_tokens": 128000, - "input_cost_per_token": 5e-08, - "output_cost_per_token": 1e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, "deepinfra/mistralai/Mistral-Small-3.2-24B-Instruct-2506": { "max_tokens": 128000, "max_input_tokens": 128000, @@ -16390,16 +15944,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/mistralai/Mixtral-8x22B-Instruct-v0.1": { - "max_tokens": 65536, - "max_input_tokens": 65536, - "max_output_tokens": 65536, - "input_cost_per_token": 6.5e-07, - "output_cost_per_token": 6.5e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/mistralai/Mixtral-8x7B-Instruct-v0.1": { "max_tokens": 32768, "max_input_tokens": 32768, @@ -16430,16 +15974,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/nvidia/Nemotron-4-340B-Instruct": { - "max_tokens": 4096, - "max_input_tokens": 4096, - "max_output_tokens": 4096, - "input_cost_per_token": 4.2e-06, - "output_cost_per_token": 4.2e-06, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/openai/gpt-oss-120b": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -16460,36 +15994,6 @@ "mode": "chat", "supports_tool_choice": true }, - "deepinfra/openbmb/MiniCPM-Llama3-V-2_5": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 3.4e-07, - "output_cost_per_token": 3.4e-07, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/openchat/openchat-3.6-8b": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": false - }, - "deepinfra/openchat/openchat_3.5": { - "max_tokens": 8192, - "max_input_tokens": 8192, - "max_output_tokens": 8192, - "input_cost_per_token": 5.5e-08, - "output_cost_per_token": 5.5e-08, - "litellm_provider": "deepinfra", - "mode": "chat", - "supports_tool_choice": true - }, "deepinfra/zai-org/GLM-4.5": { "max_tokens": 131072, "max_input_tokens": 131072, @@ -20998,4 +20502,4 @@ "notes": "Volcengine Doubao embedding model - text-240715 version with 2560 dimensions" } } -} \ No newline at end of file +} diff --git a/litellm/types/llms/databricks.py b/litellm/types/llms/databricks.py index c362d065694..c484e80ada3 100644 --- a/litellm/types/llms/databricks.py +++ b/litellm/types/llms/databricks.py @@ -51,7 +51,7 @@ AllDatabricksContentValues = Union[str, List[AllDatabricksContentListValues]] class DatabricksFunction(TypedDict, total=False): name: Required[str] - description: dict | str + description: Union[dict, str] parameters: dict strict: bool From 199c262e33d0ff335cb1b113cc891d46cd5e3293 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 15:55:27 -0700 Subject: [PATCH 25/73] EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING --- docs/my-website/docs/proxy/config_settings.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 82669b10cd5..ee857f700a3 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -473,6 +473,7 @@ router_settings: | EMAIL_SIGNATURE | Custom HTML footer/signature for all emails. Can include HTML tags for formatting and links. | EMAIL_SUBJECT_INVITATION | Custom subject template for invitation emails. | EMAIL_SUBJECT_KEY_CREATED | Custom subject template for key creation emails. +| EXPERIMENTAL_MULTI_INSTANCE_RATE_LIMITING | Flag to enable new multi-instance rate limiting. **Default is False** | FIREWORKS_AI_4_B | Size parameter for Fireworks AI 4B model. Default is 4 | FIREWORKS_AI_16_B | Size parameter for Fireworks AI 16B model. Default is 16 | FIREWORKS_AI_56_B_MOE | Size parameter for Fireworks AI 56B MOE model. Default is 56 From d78ed53cbbeabdfa945abcfd9f4cb78105a177f9 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 16:17:22 -0700 Subject: [PATCH 26/73] fix mypy linting --- .../llms/databricks/chat/transformation.py | 20 ++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 0f3530f85da..cda372470a4 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -169,13 +169,14 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if tool is None: return None - kwags = { - "name":tool["name"], - "parameters":cast(dict, tool.get("input_schema") or {}) + kwags: dict = { + "name": tool["name"], + "parameters": cast(dict, tool.get("input_schema") or {}) } - if tool.get("description"): - kwags["description"] = tool.get("description") + description = tool.get("description") + if description is not None: + kwags["description"] = cast(Union[dict, str], description) return DatabricksTool( type="function", @@ -336,8 +337,8 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): elif isinstance(content, list): content_str = "" for item in content: - if item["type"] == "text": - content_str += item["text"] + if item.get("type") == "text": + content_str += item.get("text", "") return content_str else: raise Exception(f"Unsupported content type: {type(content)}") @@ -366,8 +367,9 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): reasoning_content: Optional[str] = None if isinstance(content, list): for item in content: - if item["type"] == "reasoning": - for sum in item["summary"]: + if item.get("type") == "reasoning": + summary_list = item.get("summary", []) + for sum in summary_list: if reasoning_content is None: reasoning_content = "" reasoning_content += sum["text"] From 1f42e41c8d46bc2d60caf0e3627b034509c6e391 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 16:41:08 -0700 Subject: [PATCH 27/73] [Bug]: Fix Authorization header not being sent to configured MCP servers (#14422) * test: test_mcp_server_config_auth_value_header_used * fix: authentication_token * docs: fix instructions on using responses api with MCPs * mcp fixes --- docs/my-website/docs/mcp.md | 150 ++++++++++++++---- docs/my-website/img/mcp_tools.png | Bin 0 -> 221493 bytes .../mcp_server/mcp_server_manager.py | 5 +- tests/mcp_tests/test_mcp_auth_priority.py | 23 +++ 4 files changed, 143 insertions(+), 35 deletions(-) create mode 100644 docs/my-website/img/mcp_tools.png diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 45cec48cd7e..1e523600a86 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -195,70 +195,155 @@ litellm_settings: ## Using your MCP +### Use on LiteLLM UI + +### Use with Responses API + +Replace `http://localhost:4000` with your LiteLLM Proxy base URL. + - - -#### Connect via OpenAI Responses API - -Use the OpenAI Responses API to connect to your LiteLLM MCP server: + ```bash title="cURL Example" showLineNumbers -curl --location 'https://api.openai.com/v1/responses' \ +curl --location 'http://localhost:4000/v1/responses' \ --header 'Content-Type: application/json' \ ---header "Authorization: Bearer $OPENAI_API_KEY" \ +--header "Authorization: Bearer sk-1234" \ --data '{ - "model": "gpt-4o", + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], "tools": [ { "type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } + "require_approval": "never" } ], - "input": "Run available tools", + "stream": true, "tool_choice": "required" }' ``` + - +```python title="Python SDK Example" showLineNumbers +import openai -#### Connect via LiteLLM Proxy Responses API +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) -Use this when calling LiteLLM Proxy for LLM API requests to `/v1/responses` endpoint. - -```bash title="cURL Example" showLineNumbers -curl --location '/v1/responses' \ ---header 'Content-Type: application/json' \ ---header "Authorization: Bearer $LITELLM_API_KEY" \ ---data '{ - "model": "gpt-4o", - "tools": [ +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ { "type": "mcp", "server_label": "litellm", "server_url": "litellm_proxy", - "require_approval": "never", - "headers": { - "x-litellm-api-key": "Bearer YOUR_LITELLM_API_KEY" - } + "require_approval": "never" } ], - "input": "Run available tools", + stream=True, + tool_choice="required" +) + +print(response) +``` + + + + +#### Specifying MCP Tools + +You can specify which MCP tools are available by using the `allowed_tools` parameter. This allows you to restrict access to specific tools within an MCP server. + +To get the list of allowed tools when using LiteLLM MCP Gateway, you can naigate to the LiteLLM UI on MCP Servers > MCP Tools > Click the Tool > Copy Tool Name. + + + + +```bash title="cURL Example with allowed_tools" showLineNumbers +curl --location 'http://localhost:4000/v1/responses' \ +--header 'Content-Type: application/json' \ +--header "Authorization: Bearer sk-1234" \ +--data '{ + "model": "gpt-5", + "input": [ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + "tools": [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + "stream": true, "tool_choice": "required" }' ``` + - +```python title="Python SDK Example with allowed_tools" showLineNumbers +import openai -#### Connect via Cursor IDE +client = openai.OpenAI( + api_key="sk-1234", + base_url="http://localhost:4000" +) + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ], + stream=True, + tool_choice="required" +) + +print(response) +``` + + + + +### Use with Cursor IDE Use tools directly from Cursor IDE with LiteLLM MCP: @@ -281,9 +366,6 @@ Use tools directly from Cursor IDE with LiteLLM MCP: } ``` - - - #### How it works when server_url="litellm_proxy" When server_url="litellm_proxy", LiteLLM bridges non-MCP providers to your MCP tools. diff --git a/docs/my-website/img/mcp_tools.png b/docs/my-website/img/mcp_tools.png new file mode 100644 index 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zuv2J7TnM27S+EA8#HKQ1gubTZ_kbP{3efbIV^lbuAGqXpz+

+ +
+
+ `mcp-call-${index}`)} + > + {/* List Tools Panel */} + {toolsEvent && ( + +
+ {toolsEvent.item?.tools?.map((tool, index) => ( +
+ {tool.name} +
+ ))} +
+ + )} + + {/* MCP Call Panels */} + {mcpCallEvents.map((callEvent, index) => ( + +
+ {/* Request section */} +
+
Request
+
+ {callEvent.item?.arguments && ( + 
+                        {(() => {
+                          try {
+                            return JSON.stringify(JSON.parse(callEvent.item.arguments), null, 2);
+                          } catch (e) {
+                            return callEvent.item.arguments;
+                          }
+                        })()}
+
+ )} +
+
+ + {/* Approved section */} +
+
+ ✓ Approved +
+
+ + {/* Response section */} + {callEvent.item?.output && ( +
+
Response
+
+ {callEvent.item.output} +
+
+ )} +
+ + ))} + +
+
+ ); +}; + +export default MCPEventsDisplay; diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/anthropic_messages.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/anthropic_messages.tsx index 573c1cc2917..38b7f2eb345 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/anthropic_messages.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/anthropic_messages.tsx @@ -18,7 +18,7 @@ export async function makeAnthropicMessagesRequest( traceId?: string, vector_store_ids?: string[], guardrails?: string[], - selectedMCPTool?: string + selectedMCPTools?: string[] ) { if (!accessToken) { throw new Error("API key is required"); @@ -48,12 +48,13 @@ export async function makeAnthropicMessagesRequest( const startTime = Date.now(); let firstTokenReceived = false; - // Format MCP tool if selected - const tools = selectedMCPTool ? [{ + // Format MCP tools if selected + const tools = selectedMCPTools && selectedMCPTools.length > 0 ? [{ type: "mcp", server_label: "litellm", server_url: `${proxyBaseUrl}/mcp`, require_approval: "never", + allowed_tools: selectedMCPTools, headers: { "x-litellm-api-key": `Bearer ${accessToken}` } diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx index 70f5e36f863..be40bfcff77 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/chat_completion.tsx @@ -17,7 +17,7 @@ export async function makeOpenAIChatCompletionRequest( traceId?: string, vector_store_ids?: string[], guardrails?: string[], - selectedMCPTool?: string, + selectedMCPTools?: string[], onImageGenerated?: (imageUrl: string, model?: string) => void ) { // base url should be the current base_url @@ -49,12 +49,13 @@ export async function makeOpenAIChatCompletionRequest( let fullResponseContent = ""; let fullReasoningContent = ""; - // Format MCP tool if selected - const tools = selectedMCPTool ? [{ + // Format MCP tools if selected + const tools = selectedMCPTools && selectedMCPTools.length > 0 ? [{ type: "mcp", server_label: "litellm", server_url: `${proxyBaseUrl}/mcp`, require_approval: "never", + allowed_tools: selectedMCPTools, headers: { "x-litellm-api-key": `Bearer ${accessToken}` } diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/responses_api.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/responses_api.tsx index b6d656bc708..a7f66c28723 100644 --- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/responses_api.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/responses_api.tsx @@ -5,6 +5,7 @@ import { TokenUsage } from "../ResponseMetrics"; import { getProxyBaseUrl } from "@/components/networking"; import { MCPTool } from "@/components/chat_ui/llm_calls/fetch_mcp_tools"; import NotificationManager from "@/components/molecules/notifications_manager"; +import { MCPEvent } from "../MCPEventsDisplay"; export async function makeOpenAIResponsesRequest( messages: MessageType[], @@ -19,9 +20,10 @@ export async function makeOpenAIResponsesRequest( traceId?: string, vector_store_ids?: string[], guardrails?: string[], - selectedMCPTool?: string, + selectedMCPTools?: string[], previousResponseId?: string | null, - onResponseId?: (responseId: string) => void + onResponseId?: (responseId: string) => void, + onMCPEvent?: (event: MCPEvent) => void ) { if (!accessToken) { throw new Error("API key is required"); @@ -69,15 +71,13 @@ export async function makeOpenAIResponsesRequest( }; }); - // Format MCP tool if selected - const tools = selectedMCPTool ? [{ + // Format MCP tools if selected + const tools = selectedMCPTools && selectedMCPTools.length > 0 ? [{ type: "mcp", server_label: "litellm", - server_url: `${proxyBaseUrl}/mcp`, + server_url: `litellm_proxy/mcp`, require_approval: "never", - headers: { - "x-litellm-api-key": `Bearer ${accessToken}` - } + allowed_tools: selectedMCPTools, }] : undefined; // Create request to OpenAI responses API @@ -100,6 +100,29 @@ export async function makeOpenAIResponsesRequest( // Use a type-safe approach to handle events if (typeof event === 'object' && event !== null) { + // Handle MCP events first + if (event.type?.startsWith('response.mcp_') || + (event.type === "response.output_item.done" && + (event.item?.type === "mcp_list_tools" || event.item?.type === "mcp_call"))) { + console.log("MCP event received:", event); + + if (onMCPEvent) { + const mcpEvent: MCPEvent = { + type: event.type, + sequence_number: event.sequence_number, + output_index: event.output_index, + item_id: event.item_id || event.item?.id, // Handle both structures + item: event.item, + delta: event.delta, + arguments: event.arguments, + timestamp: Date.now() + }; + onMCPEvent(mcpEvent); + } + + // Continue processing other aspects of the event + } + // Check for MCP tool usage if (event.type === "response.output_item.done" && event.item?.type === "mcp_call" && diff --git a/ui/litellm-dashboard/src/components/public_model_hub.tsx b/ui/litellm-dashboard/src/components/public_model_hub.tsx index 615ea67fb1a..cbd894aac68 100644 --- a/ui/litellm-dashboard/src/components/public_model_hub.tsx +++ b/ui/litellm-dashboard/src/components/public_model_hub.tsx @@ -822,6 +822,7 @@ const PublicModelHub: React.FC = ({ accessToken }) => { selectedTags: [], selectedVectorStores: [], selectedGuardrails: [], + selectedMCPTools: [], endpointType: getEndpointType(selectedModel.mode || 'chat'), selectedModel: selectedModel.model_group, selectedSdk: 'openai' @@ -844,6 +845,7 @@ const PublicModelHub: React.FC = ({ accessToken }) => { selectedTags: [], selectedVectorStores: [], selectedGuardrails: [], + selectedMCPTools: [], endpointType: getEndpointType(selectedModel.mode || 'chat'), selectedModel: selectedModel.model_group, selectedSdk: 'openai' From ea377de5a5ba774cf75ecbeb2b810523c7c2f6f0 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 19:34:35 -0700 Subject: [PATCH 30/73] fixes mypy linting --- .../llms/databricks/chat/transformation.py | 30 ++++++++++--------- .../mcp/litellm_proxy_mcp_handler.py | 25 +++++++++------- .../responses/mcp/mcp_streaming_iterator.py | 17 ++++++----- 3 files changed, 39 insertions(+), 33 deletions(-) diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index cda372470a4..4852f2e7106 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -180,7 +180,7 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): return DatabricksTool( type="function", - function=DatabricksFunction(**kwags), + function=DatabricksFunction(name=tool["name"], **kwags), ) def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]: @@ -338,7 +338,8 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): content_str = "" for item in content: if item.get("type") == "text": - content_str += item.get("text", "") + text_value = item.get("text", "") + content_str += str(text_value) if text_value is not None else "" return content_str else: raise Exception(f"Unsupported content type: {type(content)}") @@ -369,18 +370,19 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): for item in content: if item.get("type") == "reasoning": summary_list = item.get("summary", []) - for sum in summary_list: - if reasoning_content is None: - reasoning_content = "" - reasoning_content += sum["text"] - thinking_block = ChatCompletionThinkingBlock( - type="thinking", - thinking=sum.get("text", ""), - signature=sum.get("signature", ""), - ) - if thinking_blocks is None: - thinking_blocks = [] - thinking_blocks.append(thinking_block) + if isinstance(summary_list, list): + for sum in summary_list: + if reasoning_content is None: + reasoning_content = "" + reasoning_content += sum["text"] + thinking_block = ChatCompletionThinkingBlock( + type="thinking", + thinking=sum.get("text", ""), + signature=sum.get("signature", ""), + ) + if thinking_blocks is None: + thinking_blocks = [] + thinking_blocks.append(thinking_block) return reasoning_content, thinking_blocks @staticmethod diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py index 7a9a21a9690..8470003b325 100644 --- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py +++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py @@ -27,10 +27,10 @@ class LiteLLM_Proxy_MCP_Handler: """ if tools: for tool in tools: - if (isinstance(tool, dict) and - tool.get("type") == "mcp" and - tool.get("server_url", "").startswith(LITELLM_PROXY_MCP_SERVER_URL)): - return True + if isinstance(tool, dict) and tool.get("type") == "mcp": + server_url = tool.get("server_url", "") + if isinstance(server_url, str) and server_url.startswith(LITELLM_PROXY_MCP_SERVER_URL): + return True return False @staticmethod @@ -46,10 +46,12 @@ class LiteLLM_Proxy_MCP_Handler: if tools: for tool in tools: - if (isinstance(tool, dict) and - tool.get("type") == "mcp" and - tool.get("server_url", "").startswith(LITELLM_PROXY_MCP_SERVER_URL)): - mcp_tools_with_litellm_proxy.append(tool) + if isinstance(tool, dict) and tool.get("type") == "mcp": + server_url = tool.get("server_url", "") + if isinstance(server_url, str) and server_url.startswith(LITELLM_PROXY_MCP_SERVER_URL): + mcp_tools_with_litellm_proxy.append(tool) + else: + other_tools.append(tool) else: other_tools.append(tool) @@ -74,8 +76,9 @@ class LiteLLM_Proxy_MCP_Handler: if mcp_tools_with_litellm_proxy: for _tool in mcp_tools_with_litellm_proxy: # if user specifies servers as server_url: litellm_proxy/mcp/zapier,github then return zapier,github - if _tool.get("server_url", "").startswith(LITELLM_PROXY_MCP_SERVER_URL_PREFIX): - mcp_servers.append(_tool.get("server_url", "").split("/")[-1]) + server_url = _tool.get("server_url", "") if isinstance(_tool, dict) else "" + if isinstance(server_url, str) and server_url.startswith(LITELLM_PROXY_MCP_SERVER_URL_PREFIX): + mcp_servers.append(server_url.split("/")[-1]) return await _get_tools_from_mcp_servers( user_api_key_auth=user_api_key_auth, @@ -588,7 +591,7 @@ class LiteLLM_Proxy_MCP_Handler: from litellm.responses.mcp.mcp_streaming_iterator import create_mcp_call_events - tool_execution_events = [] + tool_execution_events: List[Any] = [] # Create events for each tool execution for tool_result in tool_results: diff --git a/litellm/responses/mcp/mcp_streaming_iterator.py b/litellm/responses/mcp/mcp_streaming_iterator.py index bf6a9182522..e43426253de 100644 --- a/litellm/responses/mcp/mcp_streaming_iterator.py +++ b/litellm/responses/mcp/mcp_streaming_iterator.py @@ -45,15 +45,15 @@ async def create_mcp_list_tools_events( LiteLLM_Proxy_MCP_Handler, ) - events = [] + events: List[ResponsesAPIStreamingResponse] = [] try: # Extract MCP server names mcp_servers = [] for tool in mcp_tools_with_litellm_proxy: if isinstance(tool, dict) and "server_url" in tool: - server_url = tool["server_url"] - if server_url.startswith("litellm_proxy/mcp/"): + server_url = tool.get("server_url") + if isinstance(server_url, str) and server_url.startswith("litellm_proxy/mcp/"): server_name = server_url.split("/")[-1] mcp_servers.append(server_name) @@ -72,7 +72,7 @@ async def create_mcp_list_tools_events( # Convert tools to dict format for the event mcp_tools_dict = [] for tool in filtered_mcp_tools: - if hasattr(tool, 'model_dump'): + if hasattr(tool, 'model_dump') and callable(getattr(tool, 'model_dump')): mcp_tools_dict.append(tool.model_dump()) elif hasattr(tool, '__dict__'): mcp_tools_dict.append(tool.__dict__) @@ -96,7 +96,8 @@ async def create_mcp_list_tools_events( if mcp_tools_with_litellm_proxy: first_tool = mcp_tools_with_litellm_proxy[0] if isinstance(first_tool, dict): - server_label = first_tool.get("server_label", "") + server_label_value = first_tool.get("server_label", "") + server_label = str(server_label_value) if server_label_value is not None else "" # Format tools for OpenAI output_item.done format formatted_tools = [] @@ -109,9 +110,9 @@ async def create_mcp_list_tools_events( # Add input_schema if available if hasattr(tool, 'inputSchema'): - tool_dict["input_schema"] = tool.inputSchema + tool_dict["input_schema"] = getattr(tool, 'inputSchema') elif hasattr(tool, 'input_schema'): - tool_dict["input_schema"] = tool.input_schema + tool_dict["input_schema"] = getattr(tool, 'input_schema') formatted_tools.append(tool_dict) @@ -171,7 +172,7 @@ def create_mcp_call_events( sequence_start: int = 1 ) -> List[ResponsesAPIStreamingResponse]: """Create MCP call events following OpenAI's specification""" - events = [] + events: List[ResponsesAPIStreamingResponse] = [] item_id = base_item_id or f"mcp_{uuid.uuid4().hex[:8]}" # MCP call in progress event From 385206c4bc614ef464de4f8492ebd63c2cc3977a Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 19:35:29 -0700 Subject: [PATCH 31/73] fix code QA --- litellm/responses/mcp/mcp_streaming_iterator.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/litellm/responses/mcp/mcp_streaming_iterator.py b/litellm/responses/mcp/mcp_streaming_iterator.py index e43426253de..b0673880cec 100644 --- a/litellm/responses/mcp/mcp_streaming_iterator.py +++ b/litellm/responses/mcp/mcp_streaming_iterator.py @@ -41,9 +41,6 @@ async def create_mcp_list_tools_events( pre_processed_mcp_tools: List[Any] ) -> List[ResponsesAPIStreamingResponse]: """Create MCP discovery events using pre-processed tools from the parent""" - from litellm.responses.mcp.litellm_proxy_mcp_handler import ( - LiteLLM_Proxy_MCP_Handler, - ) events: List[ResponsesAPIStreamingResponse] = [] @@ -462,8 +459,6 @@ class MCPEnhancedStreamingIterator(BaseResponsesAPIStreamingIterator): if not tool_calls: return - # Create tool execution events - base_item_id = f"mcp_{uuid.uuid4().hex[:8]}" for tool_call in tool_calls: tool_name, tool_arguments, tool_call_id = LiteLLM_Proxy_MCP_Handler._extract_tool_call_details(tool_call) if tool_name and tool_call_id: From 258b674dbbe51256512cbc297a2ed2c49299d353 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 19:39:23 -0700 Subject: [PATCH 32/73] fix deepinfra test --- litellm/model_prices_and_context_window_backup.json | 3 ++- model_prices_and_context_window.json | 3 ++- .../llms/deepinfra/test_deepinfra_chat_transformation.py | 4 ++++ 3 files changed, 8 insertions(+), 2 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index ff7b6b36dc8..c7fabb0ed9f 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -15702,7 +15702,8 @@ "cache_read_input_token_cost": 2.16e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_reasoning": true }, "deepinfra/google/gemini-2.0-flash-001": { "max_tokens": 1000000, diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index ff7b6b36dc8..c7fabb0ed9f 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -15702,7 +15702,8 @@ "cache_read_input_token_cost": 2.16e-07, "litellm_provider": "deepinfra", "mode": "chat", - "supports_tool_choice": true + "supports_tool_choice": true, + "supports_reasoning": true }, "deepinfra/google/gemini-2.0-flash-001": { "max_tokens": 1000000, diff --git a/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py b/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py index b2e9afb0c19..fc8cf6dc60f 100644 --- a/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py +++ b/tests/test_litellm/llms/deepinfra/test_deepinfra_chat_transformation.py @@ -17,6 +17,10 @@ def test_deepseek_supported_openai_params(): """ from litellm.llms.deepinfra.chat.transformation import DeepInfraConfig + # Ensure we're using the local model cost map + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + litellm.model_cost = litellm.get_model_cost_map(url="") + supported_openai_params = DeepInfraConfig().get_supported_openai_params(model="deepinfra/deepseek-ai/DeepSeek-V3.1") print(supported_openai_params) assert "reasoning_effort" in supported_openai_params From b473344f70b809201522186d6c6490889df00bf3 Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Thu, 11 Sep 2025 11:33:50 +0300 Subject: [PATCH 33/73] Implement anonymization logic --- .../docs/proxy/guardrails/noma_security.md | 17 + .../guardrail_hooks/noma/__init__.py | 1 + .../guardrails/guardrail_hooks/noma/noma.py | 220 +++++- litellm/types/guardrails.py | 11 +- .../guardrails/guardrail_hooks/test_noma.py | 679 +++++++++++++++++- 5 files changed, 900 insertions(+), 28 deletions(-) diff --git a/docs/my-website/docs/proxy/guardrails/noma_security.md b/docs/my-website/docs/proxy/guardrails/noma_security.md index 3a50841d65e..4aebb29eb57 100644 --- a/docs/my-website/docs/proxy/guardrails/noma_security.md +++ b/docs/my-website/docs/proxy/guardrails/noma_security.md @@ -135,6 +135,7 @@ guardrails: # application_id: "my-app" # monitor_mode: false # block_failures: true + # anonymize_input: false ``` ### Required Parameters @@ -147,6 +148,7 @@ guardrails: - **`application_id`**: Your application identifier (defaults to `"litellm"`) - **`monitor_mode`**: If `true`, logs violations without blocking (defaults to `false`) - **`block_failures`**: If `true`, blocks requests when guardrail API failures occur (defaults to `true`) +- **`anonymize_input`**: If `true`, replaces sensitive content with anonymized version (defaults to `false`) ## Environment Variables @@ -158,6 +160,7 @@ export NOMA_API_BASE="https://api.noma.security/" # Optional export NOMA_APPLICATION_ID="my-app" # Optional export NOMA_MONITOR_MODE="false" # Optional export NOMA_BLOCK_FAILURES="true" # Optional +export NOMA_ANONYMIZE_INPUT="false" # Optional ``` ## Advanced Configuration @@ -190,6 +193,20 @@ guardrails: block_failures: false # Allow requests to proceed if guardrail API fails ``` +### Content Anonymization + +Enable anonymization to replace sensitive content instead of blocking: + +```yaml +guardrails: + - guardrail_name: "noma-anonymize" + litellm_params: + guardrail: noma + mode: "pre_call" + api_key: os.environ/NOMA_API_KEY + anonymize_input: true # Replace sensitive data with anonymized version +``` + ### Multiple Guardrails Apply different configurations for input and output: diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py b/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py index dc3e4d9768e..4a96219b0d6 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/__init__.py @@ -18,6 +18,7 @@ def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail" application_id=litellm_params.application_id, monitor_mode=litellm_params.monitor_mode, block_failures=litellm_params.block_failures, + anonymize_input=litellm_params.anonymize_input, event_hook=litellm_params.mode, default_on=litellm_params.default_on, ) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 1a1ed2acb1f..2601a73927d 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -86,6 +86,7 @@ class NomaBlockedMessage(HTTPException): "allowedTopics", "bannedTopics", "topicGuardrails", + "topicDetector", # Mock name for tests ] and isinstance(value, dict): filtered_topics = {} for topic, topic_result in value.items(): @@ -95,7 +96,7 @@ class NomaBlockedMessage(HTTPException): if filtered_topics: result[key] = filtered_topics - elif key == "sensitiveData" and isinstance(value, dict): + elif key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): filtered_sensitive = {} for data_type, data_result in value.items(): if self._is_result_true(data_result): @@ -144,6 +145,7 @@ class NomaGuardrail(CustomGuardrail): application_id: Optional[str] = None, monitor_mode: Optional[bool] = None, block_failures: Optional[bool] = None, + anonymize_input: Optional[bool] = None, **kwargs, ): self.async_handler = get_async_httpx_client( @@ -171,6 +173,13 @@ class NomaGuardrail(CustomGuardrail): else: self.block_failures = block_failures + if anonymize_input is None: + self.anonymize_input = ( + os.environ.get("NOMA_ANONYMIZE_INPUT", "false").lower() == "true" + ) + else: + self.anonymize_input = anonymize_input + super().__init__(**kwargs) def _create_background_noma_check( @@ -207,10 +216,25 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background(USER_ROLE, user_message, response_json) - else: - await self._check_verdict(USER_ROLE, user_message, response_json) + await self._handle_verdict_background( + USER_ROLE, user_message, response_json + ) + return user_message + # Check if we should anonymize content + if self._should_anonymize(response_json, USER_ROLE): + anonymized_content = self._extract_anonymized_content( + response_json, USER_ROLE + ) + if anonymized_content: + # Replace the user message content with anonymized version + self._replace_user_message_content(request_data, anonymized_content) + verbose_proxy_logger.debug( + f"Noma guardrail anonymized user message: {anonymized_content}" + ) + return anonymized_content + + await self._check_verdict(USER_ROLE, user_message, response_json) return user_message async def _process_llm_response_check( @@ -245,12 +269,194 @@ class NomaGuardrail(CustomGuardrail): ) if self.monitor_mode: - await self._handle_verdict_background(ASSISTANT_ROLE, content, response_json) - else: - await self._check_verdict(ASSISTANT_ROLE, content, response_json) + await self._handle_verdict_background( + ASSISTANT_ROLE, content, response_json + ) + return content + # Check if we should anonymize content + if self._should_anonymize(response_json, ASSISTANT_ROLE): + anonymized_content = self._extract_anonymized_content( + response_json, ASSISTANT_ROLE + ) + if anonymized_content: + # Replace the LLM response content with anonymized version + self._replace_llm_response_content(response, anonymized_content) + verbose_proxy_logger.debug( + f"Noma guardrail anonymized LLM response: {anonymized_content}" + ) + return anonymized_content + + await self._check_verdict(ASSISTANT_ROLE, content, response_json) return content + def _should_only_sensitive_data_failed(self, classification_obj: dict) -> bool: + """ + Check if only sensitive data detectors (PII, PCI, secrets) have result=true in the classification. + + Args: + classification_obj: The prompt or response classification object from Noma API + + Returns: + True if only sensitiveData detectors have result=true, False otherwise + """ + if not classification_obj: + return False + + # Track which detectors have result=true (detected violations) + failed_detectors = [] + sensitive_data_detected = False + + for key, value in classification_obj.items(): + if key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): + # Check if any sensitive data detector has result=true + for data_type, data_result in value.items(): + if self._is_result_true(data_result): + sensitive_data_detected = True + # Don't add to failed_detectors as we want to allow these + + elif isinstance(value, dict) and "result" in value: + # Check other detectors - these should NOT have result=true + if self._is_result_true(value): + failed_detectors.append(key) + + elif isinstance(value, dict): + # Handle nested detectors + for nested_key, nested_value in value.items(): + if self._is_result_true(nested_value): + failed_detectors.append(f"{key}.{nested_key}") + + # Return True only if sensitive data was detected AND no other detectors have result=true + return sensitive_data_detected and len(failed_detectors) == 0 + + def _extract_anonymized_content( + self, response_json: dict, message_type: MessageRole + ) -> Optional[str]: + """ + Extract anonymized content from Noma API response. + + Args: + response_json: The full response from Noma API + message_type: Either 'user' or 'assistant' to determine which content to extract + + Returns: + The anonymized content string if available, None otherwise + """ + original_response = response_json.get("originalResponse", {}) + + if message_type == USER_ROLE: + prompt_data = original_response.get("prompt", {}) + anonymized_data = prompt_data.get("anonymizedContent", {}) + return anonymized_data.get("anonymized") + elif message_type == ASSISTANT_ROLE: + response_data = original_response.get("response", {}) + anonymized_data = response_data.get("anonymizedContent", {}) + return anonymized_data.get("anonymized") + + return None + + def _should_anonymize(self, response_json: dict, message_type: MessageRole) -> bool: + """ + Determine if content should be anonymized based on Noma API response. + + Logic: + - If verdict=True: Content is safe, anonymize if anonymized version exists + - If verdict=False: Check if only sensitiveData detectors have result=True + - If yes: Anonymize + - If no: Block (other violations detected) + + Args: + response_json: The full response from Noma API + message_type: Either 'user' or 'assistant' to determine which classification to check + + Returns: + True if content should be anonymized, False if it should be blocked + """ + # Only anonymize in blocking mode when anonymize_input is enabled + if self.monitor_mode or not self.anonymize_input: + return False + + verdict = response_json.get("verdict", True) + # If verdict is True, anonymize (content is considered safe) + if verdict: + return True + + # If verdict is False, check if only sensitive data detectors have result=True + original_response = response_json.get("originalResponse", {}) + + if message_type == USER_ROLE: + classification_obj = original_response.get("prompt", {}) + elif message_type == ASSISTANT_ROLE: + classification_obj = original_response.get("response", {}) + else: + return False + + # Anonymize only if solely sensitive data (PII/PCI/secrets) was detected + return self._should_only_sensitive_data_failed(classification_obj) + + def _is_result_true(self, result_obj: Optional[Dict[str, Any]]) -> bool: + """ + Check if a result object has a "result" field that is True. + + Args: + result_obj: A dictionary that may contain a "result" field + + Returns: + True if the "result" field exists and is True, False otherwise + """ + if not result_obj or not isinstance(result_obj, dict): + return False + + return result_obj.get("result") is True + + def _replace_user_message_content( + self, request_data: dict, anonymized_content: str + ) -> dict: + """ + Replace the user message content in request data with anonymized version. + + Args: + request_data: The original request data + anonymized_content: The anonymized content to replace with + + Returns: + Modified request data with anonymized content + """ + messages = request_data.get("messages", []) + if not messages: + return request_data + + # Find and replace the last user message + for i in range(len(messages) - 1, -1, -1): + if messages[i].get("role") == USER_ROLE: + messages[i]["content"] = anonymized_content + break + + return request_data + + def _replace_llm_response_content( + self, response: LLMResponse, anonymized_content: str + ) -> LLMResponse: + """ + Replace the LLM response content with anonymized version. + + Args: + response: The original LLM response + anonymized_content: The anonymized content to replace with + + Returns: + Modified response with anonymized content + """ + if not isinstance(response, litellm.ModelResponse): + return response + + # Replace content in all choices + for choice in response.choices: + if isinstance(choice, litellm.Choices) and choice.message.content: + choice.message.content = anonymized_content + + return response + async def _check_user_message_background( self, request_data: dict, diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index f31f304bda9..c35b0ff55a0 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -42,6 +42,7 @@ class SupportedGuardrailIntegrations(Enum): OPENAI_MODERATION = "openai_moderation" NOMA = "noma" + class Role(Enum): SYSTEM = "system" ASSISTANT = "assistant" @@ -312,7 +313,6 @@ class BedrockGuardrailConfigModel(BaseModel): ) - class LakeraV2GuardrailConfigModel(BaseModel): """Configuration parameters for the Lakera AI v2 guardrail""" @@ -375,6 +375,10 @@ class NomaGuardrailConfigModel(BaseModel): default=None, description="If True, blocks requests on API failures. Defaults to True if not provided", ) + anonymize_input: Optional[bool] = Field( + default=None, + description="If True, replaces sensitive content with anonymized version when only PII/PCI/secrets are detected. Only applies in blocking mode. Defaults to False if not provided", + ) class BaseLitellmParams(BaseModel): # works for new and patch update guardrails @@ -425,7 +429,8 @@ class BaseLitellmParams(BaseModel): # works for new and patch update guardrails ) model: Optional[str] = Field( - default=None, description="Optional field if guardrail requires a 'model' parameter" + default=None, + description="Optional field if guardrail requires a 'model' parameter", ) # Model Armor params @@ -446,7 +451,7 @@ class BaseLitellmParams(BaseModel): # works for new and patch update guardrails default=True, description="Whether to fail the request if Model Armor encounters an error", ) - + model_config = ConfigDict(extra="allow", protected_namespaces=()) 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 f1e91db7d59..895ad40faa2 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -173,7 +173,7 @@ class TestNomaBlockedMessage: response = { "verdict": False, "prompt": { - "harmfulContent": {"result": True, "confidence": 0.9}, + "contentDetector": {"result": True, "confidence": 0.9}, "code": {"result": False, "confidence": 0.1}, }, } @@ -181,40 +181,40 @@ class TestNomaBlockedMessage: exception = NomaBlockedMessage(response) assert exception.status_code == 400 assert exception.detail["error"] == "Request blocked by Noma guardrail" - assert "harmfulContent" in exception.detail["details"]["prompt"] + assert "contentDetector" in exception.detail["details"]["prompt"] assert "code" not in exception.detail["details"]["prompt"] - def test_blocked_message_with_sensitive_data(self): - """Test blocked message with sensitive data detection""" + def test_blocked_message_with_data_detection(self): + """Test blocked message with data detection""" response = { "verdict": False, "prompt": { - "sensitiveData": { - "email": {"result": True, "entities": ["test@example.com"]}, - "phone": {"result": False}, + "dataDetector": { + "field1": {"result": True, "entities": ["test@example.com"]}, + "field2": {"result": False}, }, }, } exception = NomaBlockedMessage(response) - assert "email" in exception.detail["details"]["prompt"]["sensitiveData"] - assert "phone" not in exception.detail["details"]["prompt"]["sensitiveData"] + assert "field1" in exception.detail["details"]["prompt"]["dataDetector"] + assert "field2" not in exception.detail["details"]["prompt"]["dataDetector"] def test_blocked_message_with_topics(self): """Test blocked message with topic guardrails""" response = { "verdict": False, "prompt": { - "bannedTopics": { - "violence": {"result": True, "confidence": 0.95}, - "politics": {"result": False, "confidence": 0.2}, + "topicDetector": { + "topic1": {"result": True, "confidence": 0.95}, + "topic2": {"result": False, "confidence": 0.2}, }, }, } exception = NomaBlockedMessage(response) - assert "violence" in exception.detail["details"]["prompt"]["bannedTopics"] - assert "politics" not in exception.detail["details"]["prompt"]["bannedTopics"] + assert "topic1" in exception.detail["details"]["prompt"]["topicDetector"] + assert "topic2" not in exception.detail["details"]["prompt"]["topicDetector"] class TestNomaGuardrailHooks: @@ -258,7 +258,7 @@ class TestNomaGuardrailHooks: mock_response.json.return_value = { "verdict": False, "originalResponse": { - "prompt": {"harmfulContent": {"result": True, "confidence": 0.9}} + "prompt": {"contentDetector": {"result": True, "confidence": 0.9}} }, } mock_response.raise_for_status = MagicMock() @@ -275,7 +275,7 @@ class TestNomaGuardrailHooks: ) assert exc_info.value.status_code == 400 - assert "harmfulContent" in exc_info.value.detail["details"]["prompt"] + assert "contentDetector" in exc_info.value.detail["details"]["prompt"] @pytest.mark.asyncio async def test_pre_call_hook_monitor_mode( @@ -504,7 +504,7 @@ class TestBackgroundProcessing: mock_response = MagicMock() mock_response.json.return_value = { "verdict": False, - "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + "originalResponse": {"prompt": {"contentDetector": {"result": True}}}, } mock_response.raise_for_status = MagicMock() @@ -661,7 +661,7 @@ class TestBackgroundProcessing: """Test background verdict handling for blocked content""" response_json = { "verdict": False, - "originalResponse": {"prompt": {"harmfulContent": {"result": True}}}, + "originalResponse": {"prompt": {"contentDetector": {"result": True}}}, } with patch("litellm._logging.verbose_proxy_logger.warning") as mock_warning: @@ -803,3 +803,646 @@ class TestIntegration: ) ) assert len(custom_loggers) >= 2 + + +class TestNomaAnonymizationConfiguration: + """Test anonymize_input configuration parameter""" + + def test_init_with_anonymize_input_env_var(self): + """Test initialization with NOMA_ANONYMIZE_INPUT environment variable""" + with patch.dict( + os.environ, + { + "NOMA_ANONYMIZE_INPUT": "true", + }, + ): + guardrail = NomaGuardrail() + assert guardrail.anonymize_input is True + + with patch.dict( + os.environ, + { + "NOMA_ANONYMIZE_INPUT": "false", + }, + ): + guardrail = NomaGuardrail() + assert guardrail.anonymize_input is False + + def test_init_with_anonymize_input_default(self): + """Test default value for anonymize_input""" + guardrail = NomaGuardrail() + assert guardrail.anonymize_input is False + + def test_init_with_anonymize_input_param_override_env(self): + """Test that constructor param overrides environment variable""" + with patch.dict( + os.environ, + { + "NOMA_ANONYMIZE_INPUT": "true", + }, + ): + guardrail = NomaGuardrail(anonymize_input=False) + assert guardrail.anonymize_input is False + + def test_initialize_guardrail_with_anonymize_input(self): + """Test the initialize_guardrail function with anonymize_input""" + from litellm.types.guardrails import Guardrail, LitellmParams + + litellm_params = LitellmParams( + guardrail="noma", + mode="pre_call", + api_key="test-key", + anonymize_input=True, + ) + + guardrail = Guardrail( + guardrail_name="test-guardrail", + litellm_params=litellm_params, + ) + + with patch("litellm.logging_callback_manager.add_litellm_callback"): + result = initialize_guardrail(litellm_params, guardrail) + assert result.anonymize_input is True + + +class TestNomaAnonymizationLogic: + """Test the anonymization logic helper methods""" + + @pytest.fixture + def anonymize_guardrail(self): + """Create a guardrail with anonymize_input enabled""" + return NomaGuardrail( + api_key="test-api-key", + anonymize_input=True, + monitor_mode=False, + block_failures=True, + ) + + def test_is_result_true(self, anonymize_guardrail): + """Test _is_result_true helper method""" + assert anonymize_guardrail._is_result_true({"result": True}) is True + assert anonymize_guardrail._is_result_true({"result": False}) is False + assert anonymize_guardrail._is_result_true({"other": True}) is False + assert anonymize_guardrail._is_result_true(None) is False + assert anonymize_guardrail._is_result_true({}) is False + assert anonymize_guardrail._is_result_true("not a dict") is False + + def test_should_only_data_detector_failed_true(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed when only data detector triggered""" + classification = { + "dataDetector": { + "dataType1": {"result": True, "status": "SUCCESS"}, + "dataType2": {"result": True, "status": "SUCCESS"}, + "dataType3": {"result": False, "status": "SUCCESS"}, + }, + "contentDetector": {"result": False, "status": "SUCCESS"}, + "intentDetector": {"result": False, "status": "SUCCESS"}, + "code": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is True + + def test_should_only_data_detector_failed_false_other_detectors(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed when other detectors also triggered""" + classification = { + "dataDetector": { + "dataType1": {"result": True, "status": "SUCCESS"}, + }, + "contentDetector": {"result": True, "status": "SUCCESS"}, # This should cause False + "intentDetector": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is False + + def test_should_only_data_detector_failed_false_no_data_detected(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed when no data detected""" + classification = { + "dataDetector": { + "dataType1": {"result": False, "status": "SUCCESS"}, + "dataType2": {"result": False, "status": "SUCCESS"}, + }, + "contentDetector": {"result": False, "status": "SUCCESS"}, + "intentDetector": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is False + + def test_should_only_data_detector_failed_with_nested_detectors(self, anonymize_guardrail): + """Test _should_only_sensitive_data_failed with nested detectors like topicDetector""" + classification = { + "dataDetector": { + "dataType1": {"result": True, "status": "SUCCESS"}, + }, + "topicDetector": { + "topic1": {"result": True, "status": "SUCCESS"}, # This should cause False + }, + "contentDetector": {"result": False, "status": "SUCCESS"}, + } + + result = anonymize_guardrail._should_only_sensitive_data_failed(classification) + assert result is False + + def test_extract_anonymized_content_user(self, anonymize_guardrail): + """Test _extract_anonymized_content for user messages""" + response_json = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is ******* and phone is *******" + } + } + } + } + + result = anonymize_guardrail._extract_anonymized_content(response_json, "user") + assert result == "My email is ******* and phone is *******" + + def test_extract_anonymized_content_assistant(self, anonymize_guardrail): + """Test _extract_anonymized_content for assistant messages""" + response_json = { + "originalResponse": { + "response": { + "anonymizedContent": { + "anonymized": "I can't help with that request." + } + } + } + } + + result = anonymize_guardrail._extract_anonymized_content(response_json, "assistant") + assert result == "I can't help with that request." + + def test_extract_anonymized_content_missing(self, anonymize_guardrail): + """Test _extract_anonymized_content when anonymized content is missing""" + response_json = {"originalResponse": {"prompt": {}}} + + result = anonymize_guardrail._extract_anonymized_content(response_json, "user") + assert result is None + + def test_should_anonymize_verdict_true(self, anonymize_guardrail): + """Test _should_anonymize when verdict is True""" + response_json = {"verdict": True} + + result = anonymize_guardrail._should_anonymize(response_json, "user") + assert result is True + + def test_should_anonymize_verdict_false_only_sensitive(self, anonymize_guardrail): + """Test _should_anonymize when verdict is False but only data detector triggered""" + response_json = { + "verdict": False, + "originalResponse": { + "prompt": { + "dataDetector": {"dataType1": {"result": True}}, + "contentDetector": {"result": False}, + } + } + } + + result = anonymize_guardrail._should_anonymize(response_json, "user") + assert result is True + + def test_should_anonymize_verdict_false_other_detectors(self, anonymize_guardrail): + """Test _should_anonymize when verdict is False and other detectors triggered""" + response_json = { + "verdict": False, + "originalResponse": { + "prompt": { + "dataDetector": {"dataType1": {"result": True}}, + "contentDetector": {"result": True}, + } + } + } + + result = anonymize_guardrail._should_anonymize(response_json, "user") + assert result is False + + def test_should_anonymize_monitor_mode(self): + """Test _should_anonymize in monitor mode (should never anonymize)""" + guardrail = NomaGuardrail( + anonymize_input=True, + monitor_mode=True, + ) + + response_json = {"verdict": True} + result = guardrail._should_anonymize(response_json, "user") + assert result is False + + def test_should_anonymize_disabled(self): + """Test _should_anonymize when anonymize_input is disabled""" + guardrail = NomaGuardrail( + anonymize_input=False, + monitor_mode=False, + ) + + response_json = {"verdict": True} + result = guardrail._should_anonymize(response_json, "user") + assert result is False + + def test_replace_user_message_content(self, anonymize_guardrail): + """Test _replace_user_message_content""" + request_data = { + "messages": [ + {"role": "system", "content": "System prompt"}, + {"role": "user", "content": "My email is test@example.com"}, + {"role": "assistant", "content": "I can help you"}, + {"role": "user", "content": "My phone is 123-456-7890"}, + ] + } + + result = anonymize_guardrail._replace_user_message_content( + request_data, "My phone is *******" + ) + + # Should replace the last user message + assert result["messages"][-1]["content"] == "My phone is *******" + assert result["messages"][1]["content"] == "My email is test@example.com" # Unchanged + + def test_replace_llm_response_content(self, anonymize_guardrail): + """Test _replace_llm_response_content""" + response = ModelResponse( + id="test-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="Your email is test@example.com", 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}, + ) + + result = anonymize_guardrail._replace_llm_response_content( + response, "Your email is *******" + ) + + assert result.choices[0].message.content == "Your email is *******" + + +class TestNomaAnonymizationFlow: + """Test full anonymization flow with real Noma response objects""" + + @pytest.fixture + def anonymize_guardrail(self): + """Create a guardrail with anonymize_input enabled""" + return NomaGuardrail( + api_key="test-api-key", + api_base="https://api.test.noma.security/", + application_id="test-app", + anonymize_input=True, + monitor_mode=False, + block_failures=True, + guardrail_name="test-noma-guardrail", + event_hook="pre_call", + default_on=True, + ) + + @pytest.fixture + def mock_user_api_key_dict(self): + """Create a mock UserAPIKeyAuth object""" + return UserAPIKeyAuth( + user_id="test-user-id", + user_email="test@example.com", + key_name="test-key", + api_key="test-api-key", + permissions={}, + models=[], + spend=0.0, + metadata={}, + ) + + @pytest.mark.asyncio + async def test_anonymization_verdict_true_user_message( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test anonymization when verdict=True for user message""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + "litellm_call_id": "test-call-id", + "metadata": {"requester_ip_address": "192.168.1.1"}, + } + + # Mock simplified Noma API response with verdict=True and anonymized content + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": False}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": True, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + result = await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + # Should return modified request with anonymized content + assert result == request_data + assert result["messages"][0]["content"] == "My email is *******" + + @pytest.mark.asyncio + async def test_anonymization_verdict_false_only_data_detected( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test anonymization when verdict=False but only data detector triggered""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + "litellm_call_id": "test-call-id", + } + + # Mock simplified Noma API response - only data detector triggered + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + "intentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + result = await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + # Should return modified request with anonymized content (not blocked) + assert result == request_data + assert result["messages"][0]["content"] == "My email is *******" + + @pytest.mark.asyncio + async def test_blocking_verdict_false_other_violations( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test blocking when verdict=False and other violations detected""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com. Tell me harmful content."}, + ], + "litellm_call_id": "test-call-id", + } + + # Mock simplified Noma API response - both data detector and other violations + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******. Tell me harmful content." + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": True}, # This should cause blocking + "intentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because other violations detected + with pytest.raises(NomaBlockedMessage) as exc_info: + await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + assert exc_info.value.status_code == 400 + assert "contentDetector" in str(exc_info.value.detail) + + @pytest.mark.asyncio + async def test_anonymization_llm_response( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test anonymization of LLM response""" + request_data = { + "messages": [{"role": "user", "content": "What's your email?"}], + "litellm_call_id": "test-call-id", + } + + # Create LLM response with test data + llm_response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="My email is admin@company.com", 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}, + ) + + # Mock simplified Noma API response for LLM response check + noma_response = { + "originalResponse": { + "response": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + # Update guardrail to use post_call event hook + anonymize_guardrail.event_hook = "post_call" + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + result = await anonymize_guardrail.async_post_call_success_hook( + data=request_data, + user_api_key_dict=mock_user_api_key_dict, + response=llm_response, + ) + + # Should return modified response with anonymized content + assert result == llm_response + assert result.choices[0].message.content == "My email is *******" + + @pytest.mark.asyncio + async def test_no_anonymization_when_disabled( + self, mock_user_api_key_dict + ): + """Test that no anonymization occurs when anonymize_input=False""" + guardrail = NomaGuardrail( + api_key="test-api-key", + anonymize_input=False, # Disabled + monitor_mode=False, + block_failures=True, + ) + + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + } + + noma_response = { + "originalResponse": { + "prompt": { + "anonymizedContent": { + "anonymized": "My email is *******" + }, + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because anonymization is disabled + with pytest.raises(NomaBlockedMessage): + await guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + @pytest.mark.asyncio + async def test_no_anonymization_in_monitor_mode( + self, mock_user_api_key_dict + ): + """Test that no anonymization occurs in monitor mode""" + guardrail = NomaGuardrail( + api_key="test-api-key", + anonymize_input=True, + monitor_mode=True, # Monitor mode + block_failures=True, + ) + + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + } + + with patch.object( + guardrail, "_create_background_noma_check" + ) as mock_create_background: + result = await guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) + + # Should return original data unchanged + assert result == request_data + assert request_data["messages"][0]["content"] == "My email is test@example.com" + mock_create_background.assert_called_once() + + @pytest.mark.asyncio + async def test_anonymization_no_anonymized_content_available( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test behavior when anonymized content is not available""" + request_data = { + "messages": [ + {"role": "user", "content": "My email is test@example.com"}, + ], + } + + noma_response = { + "originalResponse": { + "prompt": { + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because no anonymized content available + with pytest.raises(NomaBlockedMessage): + await anonymize_guardrail.async_pre_call_hook( + user_api_key_dict=mock_user_api_key_dict, + cache=MagicMock(), + data=request_data, + call_type="completion", + ) From b83b497d387b29c3e82ade9a822e8ec0f2a5586a Mon Sep 17 00:00:00 2001 From: Tom Alon Date: Thu, 11 Sep 2025 13:38:06 +0300 Subject: [PATCH 34/73] PR fixes --- .../guardrails/guardrail_hooks/noma/noma.py | 23 ++---- .../guardrails/guardrail_hooks/test_noma.py | 70 +++++++++++++++++-- 2 files changed, 72 insertions(+), 21 deletions(-) diff --git a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py index 2601a73927d..22dffe8b069 100644 --- a/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py +++ b/litellm/proxy/guardrails/guardrail_hooks/noma/noma.py @@ -28,6 +28,7 @@ from litellm.types.utils import EmbeddingResponse, ImageResponse # Constants USER_ROLE: Final[Literal["user"]] = "user" ASSISTANT_ROLE: Final[Literal["assistant"]] = "assistant" +SENSITIVE_DATA_DETECTOR_KEYS: Final[list[str]] = ["sensitiveData", "dataDetector"] # Type aliases MessageRole = Literal["user", "assistant"] @@ -96,7 +97,7 @@ class NomaBlockedMessage(HTTPException): if filtered_topics: result[key] = filtered_topics - elif key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): + elif key in SENSITIVE_DATA_DETECTOR_KEYS and isinstance(value, dict): filtered_sensitive = {} for data_type, data_result in value.items(): if self._is_result_true(data_result): @@ -308,7 +309,7 @@ class NomaGuardrail(CustomGuardrail): sensitive_data_detected = False for key, value in classification_obj.items(): - if key in ["sensitiveData", "dataDetector"] and isinstance(value, dict): + if key in SENSITIVE_DATA_DETECTOR_KEYS and isinstance(value, dict): # Check if any sensitive data detector has result=true for data_type, data_result in value.items(): if self._is_result_true(data_result): @@ -411,20 +412,17 @@ class NomaGuardrail(CustomGuardrail): def _replace_user_message_content( self, request_data: dict, anonymized_content: str - ) -> dict: + ): """ Replace the user message content in request data with anonymized version. Args: request_data: The original request data anonymized_content: The anonymized content to replace with - - Returns: - Modified request data with anonymized content """ messages = request_data.get("messages", []) if not messages: - return request_data + return # Find and replace the last user message for i in range(len(messages) - 1, -1, -1): @@ -432,31 +430,24 @@ class NomaGuardrail(CustomGuardrail): messages[i]["content"] = anonymized_content break - return request_data - def _replace_llm_response_content( self, response: LLMResponse, anonymized_content: str - ) -> LLMResponse: + ): """ Replace the LLM response content with anonymized version. Args: response: The original LLM response anonymized_content: The anonymized content to replace with - - Returns: - Modified response with anonymized content """ if not isinstance(response, litellm.ModelResponse): - return response + return # Replace content in all choices for choice in response.choices: if isinstance(choice, litellm.Choices) and choice.message.content: choice.message.content = anonymized_content - return response - async def _check_user_message_background( self, request_data: 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 895ad40faa2..d98473a1280 100644 --- a/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py +++ b/tests/test_litellm/proxy/guardrails/guardrail_hooks/test_noma.py @@ -1052,13 +1052,13 @@ class TestNomaAnonymizationLogic: ] } - result = anonymize_guardrail._replace_user_message_content( + anonymize_guardrail._replace_user_message_content( request_data, "My phone is *******" ) # Should replace the last user message - assert result["messages"][-1]["content"] == "My phone is *******" - assert result["messages"][1]["content"] == "My email is test@example.com" # Unchanged + assert request_data["messages"][-1]["content"] == "My phone is *******" + assert request_data["messages"][1]["content"] == "My email is test@example.com" # Unchanged def test_replace_llm_response_content(self, anonymize_guardrail): """Test _replace_llm_response_content""" @@ -1080,11 +1080,11 @@ class TestNomaAnonymizationLogic: usage={"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30}, ) - result = anonymize_guardrail._replace_llm_response_content( + anonymize_guardrail._replace_llm_response_content( response, "Your email is *******" ) - assert result.choices[0].message.content == "Your email is *******" + assert response.choices[0].message.content == "Your email is *******" class TestNomaAnonymizationFlow: @@ -1446,3 +1446,63 @@ class TestNomaAnonymizationFlow: data=request_data, call_type="completion", ) + + @pytest.mark.asyncio + async def test_anonymization_llm_response_no_anonymized_content_available( + self, anonymize_guardrail, mock_user_api_key_dict + ): + """Test behavior when LLM response has no anonymized content available""" + request_data = { + "messages": [{"role": "user", "content": "What's your email?"}], + "litellm_call_id": "test-call-id", + } + + # Create LLM response with test data + llm_response = ModelResponse( + id="test-response-id", + choices=[ + Choices( + finish_reason="stop", + index=0, + message=Message( + content="My email is admin@company.com", 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}, + ) + + # Mock Noma API response with no anonymized content available + noma_response = { + "originalResponse": { + "response": { + "dataDetector": { + "dataType1": {"result": True}, + }, + "contentDetector": {"result": False}, + }, + }, + "verdict": False, + } + + mock_response = MagicMock() + mock_response.json.return_value = noma_response + mock_response.raise_for_status = MagicMock() + + # Update guardrail to use post_call event hook + anonymize_guardrail.event_hook = "post_call" + + with patch.object( + anonymize_guardrail.async_handler, "post", return_value=mock_response + ): + # Should raise NomaBlockedMessage because no anonymized content available for LLM response + with pytest.raises(NomaBlockedMessage): + await anonymize_guardrail.async_post_call_success_hook( + data=request_data, + user_api_key_dict=mock_user_api_key_dict, + response=llm_response, + ) From ee5a9d0aa0588821bce3bb8575ab1989bec24238 Mon Sep 17 00:00:00 2001 From: Din Date: Thu, 11 Sep 2025 14:53:37 +0100 Subject: [PATCH 35/73] propagate execution context into logging tasks --- litellm/litellm_core_utils/logging_worker.py | 81 ++++++++++++------- .../test_litellm_logging.py | 70 ++++++++++++++++ .../litellm_core_utils/test_logging_worker.py | 79 ++++++++++++++++++ 3 files changed, 202 insertions(+), 28 deletions(-) diff --git a/litellm/litellm_core_utils/logging_worker.py b/litellm/litellm_core_utils/logging_worker.py index 3f83719dd32..16860ad6852 100644 --- a/litellm/litellm_core_utils/logging_worker.py +++ b/litellm/litellm_core_utils/logging_worker.py @@ -1,10 +1,21 @@ import asyncio import contextlib -from typing import Coroutine, Optional +import contextvars +from typing import Coroutine, Optional, TypedDict from litellm._logging import verbose_logger +class LoggingTask(TypedDict): + """ + A logging task with its associated context to ensure logging is executed in + the original task's context. + """ + + coroutine: Coroutine + context: contextvars.Context + + class LoggingWorker: """ A simple, async logging worker that processes log coroutines in the background. @@ -13,77 +24,84 @@ class LoggingWorker: This leads to a +200 RPS performance improvement when using LiteLLM Python SDK or Proxy Server. - Use this to queue coroutine tasks that are not critical to the main flow of the application. e.g Success/Error callbacks, logging, etc. """ + LOGGING_WORKER_MAX_QUEUE_SIZE = 50_000 LOGGING_WORKER_MAX_TIME_PER_COROUTINE = 20.0 MAX_ITERATIONS_TO_CLEAR_QUEUE = 200 MAX_TIME_TO_CLEAR_QUEUE = 5.0 - + def __init__( - self, - timeout: float = LOGGING_WORKER_MAX_TIME_PER_COROUTINE, + self, + timeout: float = LOGGING_WORKER_MAX_TIME_PER_COROUTINE, max_queue_size: int = LOGGING_WORKER_MAX_QUEUE_SIZE, ): self.timeout = timeout self.max_queue_size = max_queue_size - self._queue: Optional[asyncio.Queue] = None + self._queue: Optional[asyncio.Queue[LoggingTask]] = None self._worker_task: Optional[asyncio.Task] = None - + def _ensure_queue(self) -> None: """Initialize the queue if it doesn't exist.""" if self._queue is None: self._queue = asyncio.Queue(maxsize=self.max_queue_size) - + def start(self) -> None: """Start the logging worker. Idempotent - safe to call multiple times.""" self._ensure_queue() if self._worker_task is None or self._worker_task.done(): self._worker_task = asyncio.create_task(self._worker_loop()) - + async def _worker_loop(self) -> None: """Main worker loop that processes log coroutines sequentially.""" try: if self._queue is None: return - + while True: # Process one coroutine at a time to keep event loop load predictable - coroutine = await self._queue.get() + task = await self._queue.get() try: - await asyncio.wait_for(coroutine, timeout=self.timeout) + # Run the coroutine in its original context + await asyncio.wait_for( + task["context"].run(asyncio.create_task, task["coroutine"]), + timeout=self.timeout, + ) except Exception as e: verbose_logger.exception(f"LoggingWorker error: {e}") pass finally: self._queue.task_done() - + except asyncio.CancelledError: verbose_logger.debug("LoggingWorker cancelled during shutdown") # Attempt to clear remaining items to prevent "never awaited" warnings await self.clear_queue() - + def enqueue(self, coroutine: Coroutine) -> None: """ - Add a coroutine to the logging queue. + Add a coroutine to the logging queue. Hot path: never blocks, drops logs if queue is full. """ if self._queue is None: return - + try: - self._queue.put_nowait(coroutine) + # Capture the current context when enqueueing + task = LoggingTask(coroutine=coroutine, context=contextvars.copy_context()) + self._queue.put_nowait(task) except asyncio.QueueFull as e: verbose_logger.exception(f"LoggingWorker queue is full: {e}") # Drop logs on overload to protect request throughput pass - + def ensure_initialized_and_enqueue(self, async_coroutine: Coroutine): """ Ensure the logging worker is initialized and enqueue the coroutine. """ self.start() self.enqueue(async_coroutine) - + async def stop(self) -> None: """Stop the logging worker and clean up resources.""" if self._worker_task: @@ -91,34 +109,42 @@ class LoggingWorker: with contextlib.suppress(Exception): await self._worker_task self._worker_task = None - + async def flush(self) -> None: """Flush the logging queue.""" if self._queue is None: return while not self._queue.empty(): await self._queue.join() - + async def clear_queue(self): """ Clear the queue with a maximum time limit. """ if self._queue is None: return - + start_time = asyncio.get_event_loop().time() - + for _ in range(self.MAX_ITERATIONS_TO_CLEAR_QUEUE): # Check if we've exceeded the maximum time - if asyncio.get_event_loop().time() - start_time >= self.MAX_TIME_TO_CLEAR_QUEUE: - verbose_logger.warning(f"clear_queue exceeded max_time of {self.MAX_TIME_TO_CLEAR_QUEUE}s, stopping early") + if ( + asyncio.get_event_loop().time() - start_time + >= self.MAX_TIME_TO_CLEAR_QUEUE + ): + verbose_logger.warning( + f"clear_queue exceeded max_time of {self.MAX_TIME_TO_CLEAR_QUEUE}s, stopping early" + ) break - + try: - coroutine = self._queue.get_nowait() + task = self._queue.get_nowait() # Await the coroutine to properly execute and avoid "never awaited" warnings try: - await asyncio.wait_for(coroutine, timeout=self.timeout) + await asyncio.wait_for( + task["context"].run(asyncio.create_task, task["coroutine"]), + timeout=self.timeout, + ) except Exception: # Suppress errors during cleanup pass @@ -129,4 +155,3 @@ class LoggingWorker: # Global instance for backward compatibility GLOBAL_LOGGING_WORKER = LoggingWorker() - 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 48a22dcc8af..fa5164b9c16 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -467,3 +467,73 @@ async def test_e2e_generate_cold_storage_object_key_not_configured(): assert result is None +@pytest.mark.asyncio +async def test_logging_opentelemetry_context_propagation(): + """ + Test that OpenTelemtry context propagation works with async completion. + """ + import asyncio + import litellm + + from litellm.integrations.custom_logger import CustomLogger + from opentelemetry import trace + from opentelemetry.sdk.trace import TracerProvider + from opentelemetry.sdk.trace.export import SimpleSpanProcessor + from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter + + provider = TracerProvider() + exporter = InMemorySpanExporter() + provider.add_span_processor(SimpleSpanProcessor(exporter)) + trace.set_tracer_provider(provider) + tracer = trace.get_tracer(__name__) + + class MockOpenTelemetryLogger(CustomLogger): + async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): + span = tracer.start_span(start_time=start_time.timestamp() * 1e9, name="async_log_success_event") + span.end(end_time=end_time) + + + mock_logging_obj = MockOpenTelemetryLogger() + + litellm.callbacks = [mock_logging_obj] + + with tracer.start_as_current_span("span_1") as span: + span_1_id = span.get_span_context().span_id + await litellm.acompletion( + max_tokens=100, + messages=[{"role": "user", "content": "Hey"}], + model="openai/codex-mini-latest", + mock_response="Hello, world!", + ) + + + with tracer.start_as_current_span("span_2") as span: + span_2_id = span.get_span_context().span_id + await litellm.acompletion( + max_tokens=100, + messages=[{"role": "user", "content": "Hey"}], + model="openai/codex-mini-latest", + mock_response="Hello, world!", + ) + + await asyncio.sleep(1) + spans = exporter.get_finished_spans() + assert len(spans) == 4 + assert span_1_id != span_2_id + sorted_spans = sorted(list(spans), key=lambda x: x.start_time or 0) + + assert sorted_spans[0].name == "span_1" + assert sorted_spans[1].name == "async_log_success_event" + assert sorted_spans[2].name == "span_2" + assert sorted_spans[3].name == "async_log_success_event" + + first_span_context = sorted_spans[0].get_span_context() + assert first_span_context is not None and first_span_context.span_id == span_1_id + second_span_context = sorted_spans[2].get_span_context() + assert second_span_context is not None and second_span_context.span_id == span_2_id + first_completion_span_parent = sorted_spans[1].parent + assert first_completion_span_parent is not None and first_completion_span_parent.span_id == span_1_id + + # This check would fail without the proper context propagation, and span[3] would end up with span_1_id as the parent + second_completion_span_parent = sorted_spans[3].parent + assert second_completion_span_parent is not None and second_completion_span_parent.span_id == span_2_id diff --git a/tests/test_litellm/litellm_core_utils/test_logging_worker.py b/tests/test_litellm/litellm_core_utils/test_logging_worker.py index 24c77339025..47e626e04af 100644 --- a/tests/test_litellm/litellm_core_utils/test_logging_worker.py +++ b/tests/test_litellm/litellm_core_utils/test_logging_worker.py @@ -2,6 +2,7 @@ Tests for the LoggingWorker class to ensure graceful shutdown handling. """ import asyncio +import contextvars import pytest from unittest.mock import AsyncMock, patch @@ -139,3 +140,81 @@ class TestLoggingWorker: # Should have logged queue full exceptions exception_calls = [call for call in mock_logger.exception.call_args_list if "queue is full" in str(call)] assert len(exception_calls) > 0 + + @pytest.mark.asyncio + async def test_context_propagation(self, logging_worker): + """Test that enqueued tasks execute in their original context.""" + # Create a context variable for testing + test_context_var: contextvars.ContextVar[str] = contextvars.ContextVar('test_context_var') + + # Track results from multiple tasks + task_results = [] + + async def test_task(task_id: str): + """A test coroutine that checks if it can access the context variable.""" + # Sleep a bit to simulate real work and ensure context persists + await asyncio.sleep(0.1) + + try: + # Try to get the context variable value + value = test_context_var.get() + task_results.append({ + 'task_id': task_id, + 'context_value': value, + 'context_accessible': True + }) + except LookupError: + # Context variable not found + task_results.append({ + 'task_id': task_id, + 'context_accessible': False, + 'context_value': None + }) + + # Start the logging worker + logging_worker.start() + + # Create two separate contexts and enqueue tasks from each + + # Context 1: Set context var to "context_1" + ctx1 = contextvars.copy_context() + ctx1.run(test_context_var.set, "context_1") + ctx1.run(logging_worker.enqueue, test_task("task_1")) + + # Context 2: Set context var to "context_2" + ctx2 = contextvars.copy_context() + ctx2.run(test_context_var.set, "context_2") + ctx2.run(logging_worker.enqueue, test_task("task_2")) + + # Context 3: No context variable set (should get LookupError) + ctx3 = contextvars.copy_context() + ctx3.run(logging_worker.enqueue, test_task("task_3")) + + # Wait for all tasks to be processed + await asyncio.sleep(0.5) + + # Stop the worker + await logging_worker.stop() + + # Sort results by task_id for consistent testing + task_results.sort(key=lambda x: x['task_id']) + + # Verify that each task saw its own context + assert len(task_results) == 3, f"Expected 3 results, got {len(task_results)}" + + # Task 1 should see "context_1" + task1_result = next((r for r in task_results if r['task_id'] == 'task_1'), None) + assert task1_result is not None, "Task 1 result not found" + assert task1_result['context_accessible'] is True, "Task 1 should have access to context variable" + assert task1_result['context_value'] == "context_1", f"Task 1 should see 'context_1', got: {task1_result['context_value']}" + + # Task 2 should see "context_2" + task2_result = next((r for r in task_results if r['task_id'] == 'task_2'), None) + assert task2_result is not None, "Task 2 result not found" + assert task2_result['context_accessible'] is True, "Task 2 should have access to context variable" + assert task2_result['context_value'] == "context_2", f"Task 2 should see 'context_2', got: {task2_result['context_value']}" + + # Task 3 should not have access to the context variable + task3_result = next((r for r in task_results if r['task_id'] == 'task_3'), None) + assert task3_result is not None, "Task 3 result not found" + assert task3_result['context_accessible'] is False, "Task 3 should not have access to context variable" From 090e0fddf4d1b51cdd19afb839393eb41ed25105 Mon Sep 17 00:00:00 2001 From: Sameer Kankute Date: Thu, 11 Sep 2025 22:20:54 +0530 Subject: [PATCH 36/73] fix vertex ai file upload --- .../openai_files_endpoints/files_endpoints.py | 23 ++ .../test_files_endpoint.py | 173 +++++++++++ .../test_vertex_ai_files_regression.py | 271 ++++++++++++++++++ 3 files changed, 467 insertions(+) create mode 100644 tests/test_litellm/proxy/openai_files_endpoint/test_vertex_ai_files_regression.py diff --git a/litellm/proxy/openai_files_endpoints/files_endpoints.py b/litellm/proxy/openai_files_endpoints/files_endpoints.py index f2a7ccccdf9..143e208a94b 100644 --- a/litellm/proxy/openai_files_endpoints/files_endpoints.py +++ b/litellm/proxy/openai_files_endpoints/files_endpoints.py @@ -69,6 +69,29 @@ def get_files_provider_config( ): global files_config if custom_llm_provider == "vertex_ai": + # For Vertex AI, extract config from model_list instead of files_config + from litellm.proxy.proxy_server import proxy_config + + if hasattr(proxy_config, "config") and "model_list" in proxy_config.config: + for model in proxy_config.config["model_list"]: + if isinstance(model, dict) and "litellm_params" in model: + litellm_params = model["litellm_params"] + if litellm_params.get("model", "").startswith("vertex_ai/"): + # Extract vertex_ai specific parameters + vertex_config = {} + if "vertex_project" in litellm_params: + vertex_config["vertex_project"] = litellm_params[ + "vertex_project" + ] + if "vertex_location" in litellm_params: + vertex_config["vertex_location"] = litellm_params[ + "vertex_location" + ] + if "vertex_credentials" in litellm_params: + vertex_config["vertex_credentials"] = litellm_params[ + "vertex_credentials" + ] + return vertex_config return None if files_config is None: raise ValueError("files_settings is not set, set it on your config.yaml file.") diff --git a/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py b/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py index 710e4265013..73c5c51ab91 100644 --- a/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py +++ b/tests/test_litellm/proxy/openai_files_endpoint/test_files_endpoint.py @@ -476,3 +476,176 @@ def test_create_file_for_each_model( openai_call_found = True break assert openai_call_found, "OpenAI call not found with expected parameters" + + +def test_get_files_provider_config_vertex_ai_with_model_list(): + """ + Test that get_files_provider_config correctly extracts Vertex AI config from model_list + This test verifies the fix for the proxy file upload issue + """ + from litellm.proxy.openai_files_endpoints.files_endpoints import get_files_provider_config, files_config + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with a model_list containing Vertex AI configuration + mock_config = { + 'model_list': [ + { + 'model_name': 'gemini-2.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.5-flash', + 'vertex_project': 'test-project-123', + 'vertex_location': 'us-central1', + 'vertex_credentials': '/path/to/service_account.json' + } + }, + { + 'model_name': 'gpt-3.5-turbo', + 'litellm_params': { + 'model': 'openai/gpt-3.5-turbo', + 'api_key': 'test-key' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + # Mock files_config to avoid ValueError for non-vertex_ai providers + original_files_config = files_config + import litellm.proxy.openai_files_endpoints.files_endpoints + litellm.proxy.openai_files_endpoints.files_endpoints.files_config = [] + + try: + # Test that vertex_ai provider returns the correct config + result = get_files_provider_config('vertex_ai') + + assert result is not None, "get_files_provider_config should return config for vertex_ai" + assert result['vertex_project'] == 'test-project-123' + assert result['vertex_location'] == 'us-central1' + assert result['vertex_credentials'] == '/path/to/service_account.json' + + # Test that non-vertex_ai providers still work as before + result_openai = get_files_provider_config('openai') + assert result_openai is None # Should return None when files_config is empty + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + # Restore original files_config + litellm.proxy.openai_files_endpoints.files_endpoints.files_config = original_files_config + + +def test_get_files_provider_config_vertex_ai_no_model_list(): + """ + Test that get_files_provider_config returns None when no model_list is available + This ensures graceful handling when proxy_config is not properly initialized + """ + from litellm.proxy.openai_files_endpoints.files_endpoints import get_files_provider_config + from litellm.proxy.proxy_server import proxy_config + + # Mock proxy_config without model_list + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = {} + + try: + result = get_files_provider_config('vertex_ai') + assert result is None, "get_files_provider_config should return None when no model_list" + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + +def test_get_files_provider_config_vertex_ai_no_vertex_models(): + """ + Test that get_files_provider_config returns None when no vertex_ai models are in model_list + This ensures the function handles cases where only non-vertex models are configured + """ + from litellm.proxy.openai_files_endpoints.files_endpoints import get_files_provider_config + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with a model_list containing only non-Vertex AI models + mock_config = { + 'model_list': [ + { + 'model_name': 'gpt-3.5-turbo', + 'litellm_params': { + 'model': 'openai/gpt-3.5-turbo', + 'api_key': 'test-key' + } + }, + { + 'model_name': 'claude-3', + 'litellm_params': { + 'model': 'anthropic/claude-3', + 'api_key': 'test-key' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + assert result is None, "get_files_provider_config should return None when no vertex_ai models in model_list" + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + +def test_get_files_provider_config_vertex_ai_partial_config(): + """ + Test that get_files_provider_config handles partial Vertex AI configuration gracefully + This ensures the function works even when some vertex_ai parameters are missing + """ + from litellm.proxy.openai_files_endpoints.files_endpoints import get_files_provider_config + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with partial Vertex AI configuration + mock_config = { + 'model_list': [ + { + 'model_name': 'gemini-2.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.5-flash', + 'vertex_project': 'test-project-123', + # Missing vertex_location and vertex_credentials + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + + assert result is not None, "get_files_provider_config should return config even with partial vertex_ai params" + assert result['vertex_project'] == 'test-project-123' + assert 'vertex_location' not in result + assert 'vertex_credentials' not in result + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') diff --git a/tests/test_litellm/proxy/openai_files_endpoint/test_vertex_ai_files_regression.py b/tests/test_litellm/proxy/openai_files_endpoint/test_vertex_ai_files_regression.py new file mode 100644 index 00000000000..e67d70d7602 --- /dev/null +++ b/tests/test_litellm/proxy/openai_files_endpoint/test_vertex_ai_files_regression.py @@ -0,0 +1,271 @@ +""" +Regression tests for Vertex AI file upload functionality in the proxy. + +This module contains tests to ensure that the fix for Vertex AI file uploads +in the proxy server continues to work and prevents regression of the issue +where get_files_provider_config returned None for vertex_ai provider. +""" + +import pytest +from unittest.mock import Mock, patch +from litellm.proxy.openai_files_endpoints.files_endpoints import get_files_provider_config + + +def test_vertex_ai_files_provider_config_never_returns_none_when_configured(): + """ + Regression test: Ensure that get_files_provider_config never returns None + for vertex_ai when properly configured in model_list. + + This test prevents regression of the bug where vertex_ai provider + always returned None, causing "Could not resolve project_id" errors. + """ + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with a proper Vertex AI configuration + mock_config = { + 'model_list': [ + { + 'model_name': 'gemini-2.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.5-flash', + 'vertex_project': 'test-project-123', + 'vertex_location': 'us-central1', + 'vertex_credentials': '/path/to/service_account.json' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + + # This should NEVER be None when vertex_ai is properly configured + assert result is not None, ( + "CRITICAL REGRESSION: get_files_provider_config returned None for vertex_ai " + "when it should return configuration. This would cause 'Could not resolve project_id' errors." + ) + + # Verify all expected parameters are present + assert 'vertex_project' in result + assert 'vertex_location' in result + assert 'vertex_credentials' in result + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + +def test_vertex_ai_files_provider_config_handles_multiple_vertex_models(): + """ + Test that get_files_provider_config correctly handles multiple Vertex AI models + in the model_list and returns configuration from the first one found. + """ + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with multiple Vertex AI models + mock_config = { + 'model_list': [ + { + 'model_name': 'gemini-1.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-1.5-flash', + 'vertex_project': 'project-1', + 'vertex_location': 'us-east1', + 'vertex_credentials': '/path/to/creds1.json' + } + }, + { + 'model_name': 'gemini-2.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.5-flash', + 'vertex_project': 'project-2', + 'vertex_location': 'us-central1', + 'vertex_credentials': '/path/to/creds2.json' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + + assert result is not None, "Should return config when multiple vertex_ai models are present" + + # Should return config from the first vertex_ai model found + assert result['vertex_project'] == 'project-1' + assert result['vertex_location'] == 'us-east1' + assert result['vertex_credentials'] == '/path/to/creds1.json' + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + +def test_vertex_ai_files_provider_config_ignores_non_vertex_models(): + """ + Test that get_files_provider_config correctly identifies vertex_ai models + and ignores other model types when searching for configuration. + """ + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with mixed model types + mock_config = { + 'model_list': [ + { + 'model_name': 'gpt-3.5-turbo', + 'litellm_params': { + 'model': 'openai/gpt-3.5-turbo', + 'api_key': 'test-key' + } + }, + { + 'model_name': 'gemini-2.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.5-flash', + 'vertex_project': 'test-project', + 'vertex_location': 'us-central1', + 'vertex_credentials': '/path/to/creds.json' + } + }, + { + 'model_name': 'claude-3', + 'litellm_params': { + 'model': 'anthropic/claude-3', + 'api_key': 'test-key' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + + assert result is not None, "Should find vertex_ai config even with mixed model types" + assert result['vertex_project'] == 'test-project' + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + +def test_vertex_ai_files_provider_config_handles_malformed_model_list(): + """ + Test that get_files_provider_config gracefully handles malformed model_list entries + without crashing. + """ + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with malformed entries + mock_config = { + 'model_list': [ + # Missing litellm_params + { + 'model_name': 'gemini-2.5-flash' + }, + # Missing model field + { + 'model_name': 'gemini-1.5-flash', + 'litellm_params': { + 'vertex_project': 'test-project' + } + }, + # Valid vertex_ai model + { + 'model_name': 'gemini-2.0-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.0-flash', + 'vertex_project': 'test-project', + 'vertex_location': 'us-central1', + 'vertex_credentials': '/path/to/creds.json' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + + # Should still work and find the valid vertex_ai model + assert result is not None, "Should handle malformed entries and find valid vertex_ai model" + assert result['vertex_project'] == 'test-project' + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') + + +def test_vertex_ai_files_provider_config_old_behavior_regression(): + """ + Regression test: Ensure that the old behavior of always returning None + for vertex_ai provider is completely eliminated. + + This test specifically checks that the function no longer has the old + hardcoded return None for vertex_ai. + """ + from litellm.proxy.proxy_server import proxy_config + + # Mock the proxy_config with a minimal but valid Vertex AI configuration + mock_config = { + 'model_list': [ + { + 'model_name': 'gemini-2.5-flash', + 'litellm_params': { + 'model': 'vertex_ai/gemini-2.5-flash', + 'vertex_project': 'minimal-project' + } + } + ] + } + + # Mock proxy_config.config + original_config = getattr(proxy_config, 'config', None) + proxy_config.config = mock_config + + try: + result = get_files_provider_config('vertex_ai') + + # The old behavior would always return None here + # The new behavior should return the configuration + assert result is not None, ( + "REGRESSION DETECTED: The old behavior of returning None for vertex_ai " + "has returned. This indicates the fix has been reverted." + ) + + # Verify we get the expected configuration + assert isinstance(result, dict), "Result should be a dictionary" + assert 'vertex_project' in result, "Should contain vertex_project" + + finally: + # Restore original config + if original_config is not None: + proxy_config.config = original_config + else: + delattr(proxy_config, 'config') From e9e548d797ed857e9ebbb9e0f6cde82ee0e98d6d Mon Sep 17 00:00:00 2001 From: Burt Holzman Date: Fri, 5 Sep 2025 16:47:12 -0500 Subject: [PATCH 37/73] Fix provider budgets --- .../spend_management_endpoints.py | 20 ++-- .../test_spend_management_endpoints.py | 94 +++++++++++++++++++ 2 files changed, 108 insertions(+), 6 deletions(-) diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py index 7376c3a402b..affffaeea09 100644 --- a/litellm/proxy/spend_tracking/spend_management_endpoints.py +++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py @@ -10,6 +10,7 @@ from fastapi import APIRouter, Depends, HTTPException, status import litellm from litellm._logging import verbose_proxy_logger +from litellm.router_strategy.budget_limiter import RouterBudgetLimiting from litellm.proxy._types import * from litellm.proxy._types import ProviderBudgetResponse, ProviderBudgetResponseObject from litellm.proxy.auth.user_api_key_auth import user_api_key_auth @@ -2765,16 +2766,23 @@ async def provider_budgets() -> ProviderBudgetResponse: provider_budget_response_dict: Dict[str, ProviderBudgetResponseObject] = {} for _provider, _budget_info in provider_budget_config.items(): - if llm_router.router_budget_logger is None: + router_budget_logger = next( + ( + cb + for cb in (llm_router.optional_callbacks or []) + if isinstance(cb, RouterBudgetLimiting) + ), + None, + ) + if router_budget_logger is None: raise ValueError("No router budget logger found") _provider_spend = ( - await llm_router.router_budget_logger._get_current_provider_spend( + await router_budget_logger._get_current_provider_spend(_provider) or 0.0 + ) + _provider_budget_ttl = ( + await router_budget_logger._get_current_provider_budget_reset_at( _provider ) - or 0.0 - ) - _provider_budget_ttl = await llm_router.router_budget_logger._get_current_provider_budget_reset_at( - _provider ) provider_budget_response_object = ProviderBudgetResponseObject( budget_limit=_budget_info.max_budget, 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 d9ae0665ead..069896e9fb4 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 @@ -18,7 +18,9 @@ import litellm from litellm.proxy._types import SpendLogsPayload from litellm.proxy.hooks.proxy_track_cost_callback import _ProxyDBLogger from litellm.proxy.proxy_server import app, prisma_client +from litellm.proxy.spend_tracking import spend_management_endpoints from litellm.router import Router +from litellm.types.utils import BudgetConfig ignored_keys = [ "request_id", @@ -32,6 +34,18 @@ ignored_keys = [ "metadata.cold_storage_object_key", ] +MODEL_LIST = [ + { + "model_name": "azure-gpt-4o", + "litellm_params": { + "model": "azure/gpt-4o-mini", + "mock_response": "Hello, world!", + "tags": ["default"], + "base_model": "gpt-4o-mini", + }, + }, +] + @pytest.fixture def client(): @@ -43,6 +57,19 @@ def add_anthropic_api_key_to_env(monkeypatch): monkeypatch.setenv("ANTHROPIC_API_KEY", "sk-ant-api03-1234567890") +@pytest.fixture +def disable_budget_sync(monkeypatch): + """Disable periodic sync during tests""" + + async def noop(*a, **k): + return None + + monkeypatch.setattr( + "litellm.router_strategy.budget_limiter.RouterBudgetLimiting.periodic_sync_in_memory_spend_with_redis", + noop, + ) + + @pytest.mark.asyncio async def test_ui_view_spend_logs_with_user_id(client, monkeypatch): # Mock data for the test @@ -1318,3 +1345,70 @@ async def test_view_spend_tags_no_database(client, monkeypatch): # Check the actual error message structure assert "error" in data assert "Database not connected" in data["error"]["message"] + + +@pytest.mark.asyncio +async def test_provider_budget_under(disable_budget_sync): + """Test that router allows completion when under budget""" + provider_budget_config = { + "azure": BudgetConfig(max_budget=0.01, budget_duration="10d") + } + + router = Router( + enable_pre_call_checks=True, + provider_budget_config=provider_budget_config, + model_list=MODEL_LIST, + ) + + response = await router.acompletion( + model="azure-gpt-4o", + messages=[{"role": "user", "content": "Hello, world!"}], + ) + + assert response is not None + + +@pytest.mark.asyncio +async def test_provider_budget_over(disable_budget_sync): + """Test that router allows completion when over budget""" + provider_budget_config = { + "azure": BudgetConfig(max_budget=-0.01, budget_duration="10d") + } + + router = Router( + num_retries=0, + enable_pre_call_checks=True, + provider_budget_config=provider_budget_config, + model_list=MODEL_LIST, + ) + + with pytest.raises(Exception) as e: + response = await router.acompletion( + model="azure-gpt-4o", + messages=[{"role": "user", "content": "Hello, world!"}], + ) + assert "Exceeded budget for provider" in str(e.value) + + +@pytest.mark.asyncio +async def test_provider_budget_provider_budgets(disable_budget_sync): + """Test that provider_budgets() returns correct values""" + provider = "azure" + max_budget = -0.01 + budget_duration = "10d" + provider_budget_config = { + provider: BudgetConfig(max_budget=max_budget, budget_duration=budget_duration) + } + + router = Router( + num_retries=0, + enable_pre_call_checks=True, + provider_budget_config=provider_budget_config, + model_list=MODEL_LIST, + ) + + with patch("litellm.proxy.proxy_server.llm_router", router): + response = await spend_management_endpoints.provider_budgets() + provider_budget_response = response.providers[provider] + assert provider_budget_response.budget_limit == max_budget + assert provider_budget_response.time_period == budget_duration From c6626559a24a972754ac8dfa7c5d1d534a293965 Mon Sep 17 00:00:00 2001 From: Sashanken Date: Thu, 11 Sep 2025 13:05:48 -0700 Subject: [PATCH 38/73] Fixed Log Tab Key Alias filtering inaccurately for failed logs --- .../spend_management_endpoints.py | 9 + package-lock.json | 7979 ++++++++++++++++- package.json | 5 +- .../ui_unit_tests/log_filter_logic.test.tsx | 87 + .../ui_unit_tests/package-lock.json | 103 +- .../src/components/networking.tsx | 4 +- .../components/view_logs/log_filter_logic.tsx | 37 +- 7 files changed, 8186 insertions(+), 38 deletions(-) create mode 100644 tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx diff --git a/litellm/proxy/spend_tracking/spend_management_endpoints.py b/litellm/proxy/spend_tracking/spend_management_endpoints.py index 7376c3a402b..897341ad383 100644 --- a/litellm/proxy/spend_tracking/spend_management_endpoints.py +++ b/litellm/proxy/spend_tracking/spend_management_endpoints.py @@ -1659,6 +1659,9 @@ async def ui_view_spend_logs( # noqa: PLR0915 model: Optional[str] = fastapi.Query( default=None, description="Filter logs by model" ), + key_alias: Optional[str] = fastapi.Query( + default=None, description="Filter logs by key alias" + ), ): """ View spend logs for UI with pagination support @@ -1726,6 +1729,12 @@ async def ui_view_spend_logs( # noqa: PLR0915 if model is not None: where_conditions["model"] = model + + if key_alias is not None: + where_conditions["metadata"] = { + "path": ["user_api_key_alias"], + "string_contains": key_alias + } if min_spend is not None or max_spend is not None: where_conditions["spend"] = {} diff --git a/package-lock.json b/package-lock.json 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"react-copy-to-clipboard": "^5.1.0" }, "devDependencies": { - "@types/react-copy-to-clipboard": "^5.0.7" + "@testing-library/jest-dom": "^6.8.0", + "@testing-library/react": "^14.3.1", + "@types/react-copy-to-clipboard": "^5.0.7", + "jest": "^29.7.0" } } diff --git a/tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx b/tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx new file mode 100644 index 00000000000..81a627c40d2 --- /dev/null +++ b/tests/proxy_admin_ui_tests/ui_unit_tests/log_filter_logic.test.tsx @@ -0,0 +1,87 @@ +import { uiSpendLogsCall } from '../../../ui/litellm-dashboard/src/components/networking'; + +// Mock the networking module +jest.mock('../../../ui/litellm-dashboard/src/components/networking', () => ({ + uiSpendLogsCall: jest.fn(), +})); + +const mockUiSpendLogsCall = uiSpendLogsCall as jest.MockedFunction; + +describe('Key Alias Filtering Integration Test', () => { + beforeEach(() => { + jest.clearAllMocks(); + }); + + it('should call API with correct key_alias parameter', async () => { + // Mock API response with both success and failure logs + const mockResponse = { + data: [ + { request_id: 'req-1', status: 'success', metadata: { user_api_key_alias: 'test-key' } }, + { request_id: 'req-2', status: 'failure', metadata: { user_api_key_alias: 'test-key' } } + ], + total: 2, + page: 1, + page_size: 50, + total_pages: 1 + }; + + mockUiSpendLogsCall.mockResolvedValueOnce(mockResponse); + + // Simulate the API call that would happen when filtering by key alias + const result = await uiSpendLogsCall( + 'test-token', + undefined, + undefined, + undefined, + '2024-01-15 09:00:00', + '2024-01-15 11:00:00', + 1, + 50, + undefined, + undefined, + undefined, + undefined, + 'test-key-alias' // key_alias - this is the fix + ); + + // Verify the API was called correctly + expect(mockUiSpendLogsCall).toHaveBeenCalledWith( + 'test-token', + undefined, + undefined, + undefined, + '2024-01-15 09:00:00', + '2024-01-15 11:00:00', + 1, + 50, + undefined, + undefined, + undefined, + undefined, + 'test-key-alias' // The key assertion - this parameter should be passed through + ); + + // Verify response contains both success and failure logs + expect(result.data).toHaveLength(2); + expect(result.data[0].status).toBe('success'); + expect(result.data[1].status).toBe('failure'); + }); + + it('should pass undefined for empty key alias', async () => { + mockUiSpendLogsCall.mockResolvedValueOnce({ data: [], total: 0, page: 1, page_size: 50, total_pages: 0 }); + + await uiSpendLogsCall( + 'test-token', undefined, undefined, undefined, + '2024-01-15 09:00:00', '2024-01-15 11:00:00', + 1, 50, undefined, undefined, undefined, undefined, + undefined // Empty string should become undefined + ); + + expect(mockUiSpendLogsCall).toHaveBeenCalledWith( + 'test-token', undefined, undefined, undefined, + '2024-01-15 09:00:00', '2024-01-15 11:00:00', + 1, 50, undefined, undefined, undefined, undefined, + undefined // Should be undefined for empty key alias + ); + }); +}); \ No newline at end of file diff --git a/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json b/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json index 6ced636f23d..b8d706fcf84 100644 --- a/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json +++ b/tests/proxy_admin_ui_tests/ui_unit_tests/package-lock.json @@ -8,13 +8,14 @@ "name": "ui-unit-tests", "version": "1.0.0", "dependencies": { - "antd": "^5.0.0", + "@ant-design/icons": "^5.0.0", + "antd": "^5.12.5", "react": "^18.2.0", "react-dom": "^18.2.0" }, "devDependencies": { + "@testing-library/jest-dom": "^6.0.0", "@testing-library/react": "^14.0.0", - "@types/antd": "^1.0.0", "@types/jest": "^29.5.0", "@types/react": "^18.2.0", "@types/react-dom": "^18.2.0", @@ -25,6 +26,13 @@ "typescript": "^5.0.0" } }, + "node_modules/@adobe/css-tools": { + "version": "4.4.4", + "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.4.tgz", + "integrity": "sha512-Elp+iwUx5rN5+Y8xLt5/GRoG20WGoDCQ/1Fb+1LiGtvwbDavuSk0jhD/eZdckHAuzcDzccnkv+rEjyWfRx18gg==", + "dev": true, + "license": "MIT" + }, "node_modules/@ampproject/remapping": { "version": "2.3.0", "resolved": "https://registry.npmjs.org/@ampproject/remapping/-/remapping-2.3.0.tgz", @@ -1167,6 +1175,33 @@ "node": ">=14" } }, + "node_modules/@testing-library/jest-dom": { + "version": "6.8.0", + "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.8.0.tgz", + "integrity": "sha512-WgXcWzVM6idy5JaftTVC8Vs83NKRmGJz4Hqs4oyOuO2J4r/y79vvKZsb+CaGyCSEbUPI6OsewfPd0G1A0/TUZQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "@adobe/css-tools": "^4.4.0", + "aria-query": "^5.0.0", + "css.escape": "^1.5.1", + "dom-accessibility-api": "^0.6.3", + "picocolors": "^1.1.1", + "redent": "^3.0.0" + }, + "engines": { + "node": ">=14", + "npm": ">=6", + "yarn": ">=1" + } + }, + "node_modules/@testing-library/jest-dom/node_modules/dom-accessibility-api": { + "version": "0.6.3", + "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.6.3.tgz", + "integrity": "sha512-7ZgogeTnjuHbo+ct10G9Ffp0mif17idi0IyWNVA/wcwcm7NPOD/WEHVP3n7n3MhXqxoIYm8d6MuZohYWIZ4T3w==", + "dev": true, + "license": "MIT" + }, "node_modules/@testing-library/react": { "version": "14.3.1", "resolved": "https://registry.npmjs.org/@testing-library/react/-/react-14.3.1.tgz", @@ -1194,16 +1229,6 @@ "node": ">= 10" } }, - "node_modules/@types/antd": { - "version": "1.0.4", - "resolved": "https://registry.npmjs.org/@types/antd/-/antd-1.0.4.tgz", - "integrity": "sha512-gp4PGQckP1kNjj2H6juhjKIVwkpXwCIyIvOlwp2DC6geuhVpDHEEB5gwH4hJabVgBAFtrjBPJ58VIRV9VV9W2g==", - "deprecated": "This is a stub types definition. antd provides its own type definitions, so you do not need this installed.", - "dev": true, - "dependencies": { - "antd": "*" - } - }, "node_modules/@types/aria-query": { "version": "5.0.4", "resolved": "https://registry.npmjs.org/@types/aria-query/-/aria-query-5.0.4.tgz", @@ -2081,6 +2106,13 @@ "node": ">= 8" } }, + "node_modules/css.escape": { + "version": "1.5.1", + "resolved": "https://registry.npmjs.org/css.escape/-/css.escape-1.5.1.tgz", + "integrity": "sha512-YUifsXXuknHlUsmlgyY0PKzgPOr7/FjCePfHNt0jxm83wHZi44VDMQ7/fGNkjY3/jV1MC+1CmZbaHzugyeRtpg==", + "dev": true, + "license": "MIT" + }, "node_modules/cssom": { "version": "0.5.0", "resolved": "https://registry.npmjs.org/cssom/-/cssom-0.5.0.tgz", @@ -2974,6 +3006,16 @@ "node": ">=0.8.19" } }, + "node_modules/indent-string": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/indent-string/-/indent-string-4.0.0.tgz", + "integrity": "sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/inflight": { "version": "1.0.6", "resolved": "https://registry.npmjs.org/inflight/-/inflight-1.0.6.tgz", @@ -4559,6 +4601,16 @@ "node": ">=6" } }, + "node_modules/min-indent": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/min-indent/-/min-indent-1.0.1.tgz", + "integrity": "sha512-I9jwMn07Sy/IwOj3zVkVik2JTvgpaykDZEigL6Rx6N9LbMywwUSMtxET+7lVoDLLd3O3IXwJwvuuns8UB/HeAg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=4" + } + }, "node_modules/minimatch": { "version": "3.1.2", "resolved": "https://registry.npmjs.org/minimatch/-/minimatch-3.1.2.tgz", @@ -5552,6 +5604,20 @@ "integrity": "sha512-w2GsyukL62IJnlaff/nRegPQR94C/XXamvMWmSHRJ4y7Ts/4ocGRmTHvOs8PSE6pB3dWOrD/nueuU5sduBsQ4w==", "dev": true }, + "node_modules/redent": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/redent/-/redent-3.0.0.tgz", + "integrity": "sha512-6tDA8g98We0zd0GvVeMT9arEOnTw9qM03L9cJXaCjrip1OO764RDBLBfrB4cwzNGDj5OA5ioymC9GkizgWJDUg==", + "dev": true, + "license": "MIT", + "dependencies": { + "indent-string": "^4.0.0", + "strip-indent": "^3.0.0" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/regenerator-runtime": { "version": "0.14.1", "resolved": "https://registry.npmjs.org/regenerator-runtime/-/regenerator-runtime-0.14.1.tgz", @@ -5965,6 +6031,19 @@ "node": ">=6" } }, + "node_modules/strip-indent": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/strip-indent/-/strip-indent-3.0.0.tgz", + "integrity": "sha512-laJTa3Jb+VQpaC6DseHhF7dXVqHTfJPCRDaEbid/drOhgitgYku/letMUqOXFoWV0zIIUbjpdH2t+tYj4bQMRQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "min-indent": "^1.0.0" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/strip-json-comments": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-3.1.1.tgz", diff --git a/ui/litellm-dashboard/src/components/networking.tsx b/ui/litellm-dashboard/src/components/networking.tsx index c204ca8f893..8855b1b8435 100644 --- a/ui/litellm-dashboard/src/components/networking.tsx +++ b/ui/litellm-dashboard/src/components/networking.tsx @@ -2633,7 +2633,8 @@ export const uiSpendLogsCall = async ( user_id?: string, end_user?: string, status_filter?: string, - model?: string + model?: string, + keyAlias?: string ) => { try { // Construct base URL @@ -2652,6 +2653,7 @@ export const uiSpendLogsCall = async ( if (end_user) queryParams.append("end_user", end_user); if (status_filter) queryParams.append("status_filter", status_filter); if (model) queryParams.append("model", model); + if (keyAlias) queryParams.append("key_alias", keyAlias); // Append query parameters to URL if any exist const queryString = queryParams.toString(); if (queryString) { diff --git a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx index 73b9d944083..e46e78bf35d 100644 --- a/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx +++ b/ui/litellm-dashboard/src/components/view_logs/log_filter_logic.tsx @@ -60,6 +60,7 @@ export function useLogFilterLogic({ const performSearch = useCallback(async (filters: LogFilterState, page = 1) => { if (!accessToken) return; + console.log("Filters being sent to API:", filters); const currentTimestamp = Date.now(); lastSearchTimestamp.current = currentTimestamp; @@ -81,7 +82,8 @@ export function useLogFilterLogic({ filters[FILTER_KEYS.USER_ID] || undefined, filters[FILTER_KEYS.END_USER] || undefined, filters[FILTER_KEYS.STATUS] || undefined, - filters[FILTER_KEYS.MODEL] || undefined + filters[FILTER_KEYS.MODEL] || undefined, + filters[FILTER_KEYS.KEY_ALIAS] || undefined ); if (currentTimestamp === lastSearchTimestamp.current && response.data) { @@ -123,6 +125,19 @@ export function useLogFilterLogic({ }); return; } + + // Only do client-side filtering if no backend filters are active + const hasBackendFilters = + filters[FILTER_KEYS.KEY_ALIAS] || + filters[FILTER_KEYS.KEY_HASH] || + filters[FILTER_KEYS.REQUEST_ID] || + filters[FILTER_KEYS.USER_ID] || + filters[FILTER_KEYS.END_USER]; + + if (hasBackendFilters) { + // Backend is handling filtering, don't override the results + return; + } let filteredData = [...logs.data]; @@ -148,7 +163,7 @@ export function useLogFilterLogic({ log => log.model === filters[FILTER_KEYS.MODEL] ); } - + if (filters[FILTER_KEYS.KEY_HASH]) { filteredData = filteredData.filter( log => log.api_key === filters[FILTER_KEYS.KEY_HASH] @@ -161,24 +176,6 @@ export function useLogFilterLogic({ ); } - // Add key alias filtering - if (filters[FILTER_KEYS.KEY_ALIAS]) { - // We need to fetch the key info to get the key hash for the selected alias - try { - // Get the key hash for the selected alias - const selectedKey = filters[FILTER_KEYS.KEY_ALIAS] - - if (selectedKey) { - // Filter logs by the key hash - filteredData = filteredData.filter( - log => log.metadata?.user_api_key_alias === selectedKey - ); - } - } catch (error) { - console.error("Error fetching key info for alias:", error); - } - } - const newFilteredLogs: PaginatedResponse = { data: filteredData, total: logs.total, From 5c3d407b7a7764c85bd5304a8830cb30f1941858 Mon Sep 17 00:00:00 2001 From: Krish Dholakia Date: Thu, 11 Sep 2025 14:38:25 -0700 Subject: [PATCH 39/73] Revert "Add additionalProperties to vertex ai Schema definition" --- litellm/types/llms/vertex_ai.py | 1 - 1 file changed, 1 deletion(-) diff --git a/litellm/types/llms/vertex_ai.py b/litellm/types/llms/vertex_ai.py index 2687b79f727..f17a284ddfc 100644 --- a/litellm/types/llms/vertex_ai.py +++ b/litellm/types/llms/vertex_ai.py @@ -113,7 +113,6 @@ class Schema(TypedDict, total=False): pattern: str example: Any anyOf: List["Schema"] - additionalProperties: Any class FunctionDeclaration(TypedDict, total=False): From dda115cc6dc4298b0520f936f0bee1c1aa248550 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 18:14:39 -0700 Subject: [PATCH 40/73] [Feat] Cost Tracking - Add support for Tiered Cost Tracking for Qwen API (Dashscope) (#14471) * add dashscope logo * docs fix * docs fix * fix supports_batch_calling * fix naming * fix input_cost_per_audio_token * use output_cost_per_reasoning_token * add tiered_pricing in get_model_info * test fixes * fix cost calc * ruff fix --- docs/my-website/docs/providers/dashscope.md | 2 +- litellm/llms/dashscope/cost_calculator.py | 157 ++++++++- ...odel_prices_and_context_window_backup.json | 298 +++++++++++++++++- litellm/types/utils.py | 1 + litellm/utils.py | 1 + model_prices_and_context_window.json | 298 +++++++++++++++++- .../test_dashscope_cost_calculator.py | 159 ++++++++++ .../src/components/provider_info_helpers.tsx | 9 +- 8 files changed, 893 insertions(+), 32 deletions(-) create mode 100644 tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py diff --git a/docs/my-website/docs/providers/dashscope.md b/docs/my-website/docs/providers/dashscope.md index eb18fa32a47..565776d6c4c 100644 --- a/docs/my-website/docs/providers/dashscope.md +++ b/docs/my-website/docs/providers/dashscope.md @@ -1,4 +1,4 @@ -# Dashscope +# Dashscope (Qwen API) https://dashscope.console.aliyun.com/ **We support ALL Qwen models, just set `dashscope/` as a prefix when sending completion requests** diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 0f4490cb3df..6f9ebe302f4 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -1,21 +1,158 @@ """ -Cost calculator for DeepSeek Chat models. +Cost calculator for Dashscope Chat models. -Handles prompt caching scenario. +Handles tiered pricing and prompt caching scenarios. """ -from typing import Tuple +from dataclasses import dataclass +from typing import List, Optional, Tuple -from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token -from litellm.types.utils import Usage +from litellm.types.utils import ModelInfo, Usage +from litellm.utils import get_model_info + + +@dataclass +class TokenBreakdown: + """Token breakdown for cost calculation.""" + text_tokens: int + cached_tokens: int + completion_tokens: int + reasoning_tokens: int + + +def _extract_token_breakdown(usage: Usage) -> TokenBreakdown: + """Extract token counts from usage, handling cached and reasoning tokens.""" + cached_tokens = 0 + if usage.prompt_tokens_details and hasattr(usage.prompt_tokens_details, "cached_tokens"): + cached_tokens = usage.prompt_tokens_details.cached_tokens or 0 + + text_tokens = usage.prompt_tokens - cached_tokens + + reasoning_tokens = 0 + if (hasattr(usage, "completion_tokens_details") and + usage.completion_tokens_details and + hasattr(usage.completion_tokens_details, "reasoning_tokens")): + reasoning_tokens = usage.completion_tokens_details.reasoning_tokens or 0 + + completion_tokens = (usage.completion_tokens or 0) - reasoning_tokens + + return TokenBreakdown(text_tokens, cached_tokens, completion_tokens, reasoning_tokens) + + +def _calculate_tiered_cost( + tokens: int, + tiered_pricing: List[dict], + cost_key: str, + fallback_cost_key: Optional[str] = None +) -> float: + """Calculate cost using tiered pricing structure.""" + if not tiered_pricing or tokens <= 0: + return 0.0 + + total_cost = 0.0 + tokens_processed = 0 + + for tier in tiered_pricing: + if tokens_processed >= tokens: + break + + tier_range = tier.get("range", []) + if len(tier_range) != 2: + continue + + range_start, range_end = tier_range + + if tokens <= range_start: + break + + tier_start = max(range_start, tokens_processed) + tier_end = min(range_end, tokens) + + if tier_end > tier_start: + tokens_in_tier = tier_end - tier_start + cost_per_token = tier.get(cost_key) or tier.get(fallback_cost_key, 0) + total_cost += tokens_in_tier * cost_per_token + tokens_processed = tier_end + + return total_cost + + +def _calculate_flat_cost(tokens: int, cost_per_token: float) -> float: + """Calculate cost using flat pricing.""" + return tokens * cost_per_token + + +def _calculate_prompt_cost(breakdown: TokenBreakdown, model_info: ModelInfo, tiered_pricing: Optional[List[dict]]) -> float: + """Calculate total prompt cost including cached tokens.""" + if tiered_pricing: + text_cost = _calculate_tiered_cost( + tokens=breakdown.text_tokens, + tiered_pricing=tiered_pricing, + cost_key="input_cost_per_token" + ) + cache_cost = _calculate_tiered_cost( + tokens=breakdown.cached_tokens, + tiered_pricing=tiered_pricing, + cost_key="cache_read_input_token_cost" + ) + return text_cost + cache_cost + + input_cost = model_info.get("input_cost_per_token", 0.0) + cache_cost = model_info.get("cache_read_input_token_cost", input_cost) or input_cost + + return (_calculate_flat_cost(tokens=breakdown.text_tokens, cost_per_token=input_cost) + + _calculate_flat_cost(tokens=breakdown.cached_tokens, cost_per_token=cache_cost)) + + +def _calculate_completion_cost(breakdown: TokenBreakdown, model_info: ModelInfo, tiered_pricing: Optional[List[dict]]) -> float: + """Calculate total completion cost including reasoning tokens.""" + if tiered_pricing: + completion_cost = _calculate_tiered_cost( + tokens=breakdown.completion_tokens, + tiered_pricing=tiered_pricing, + cost_key="output_cost_per_token" + ) + reasoning_cost = _calculate_tiered_cost( + tokens=breakdown.reasoning_tokens, + tiered_pricing=tiered_pricing, + cost_key="output_cost_per_reasoning_token", + fallback_cost_key="output_cost_per_token" + ) + return completion_cost + reasoning_cost + + output_cost = model_info.get("output_cost_per_token", 0.0) + reasoning_cost = model_info.get("output_cost_per_reasoning_token", output_cost) or output_cost + + return (_calculate_flat_cost(tokens=breakdown.completion_tokens, cost_per_token=output_cost) + + _calculate_flat_cost(tokens=breakdown.reasoning_tokens, cost_per_token=reasoning_cost)) def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]: """ - Calculates the cost per token for a given model, prompt tokens, and completion tokens. - - Follows the same logic as Anthropic's cost per token calculation. + Calculate cost per token for Dashscope models. + + Supports both tiered and flat pricing with cached and reasoning tokens. + + Args: + model: Model name without provider prefix + usage: LiteLLM Usage block + + Returns: + Tuple[float, float] - (prompt_cost_in_usd, completion_cost_in_usd) """ - return generic_cost_per_token( - model=model, usage=usage, custom_llm_provider="deepseek" + model_info = get_model_info(model=model, custom_llm_provider="dashscope") + breakdown = _extract_token_breakdown(usage) + tiered_pricing = model_info.get("tiered_pricing") if isinstance(model_info.get("tiered_pricing"), list) else None + + prompt_cost = _calculate_prompt_cost( + breakdown=breakdown, + model_info=model_info, + tiered_pricing=tiered_pricing ) + completion_cost = _calculate_completion_cost( + breakdown=breakdown, + model_info=model_info, + tiered_pricing=tiered_pricing + ) + + return prompt_cost, completion_cost diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index c7fabb0ed9f..972e212e693 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -6,6 +6,7 @@ "input_cost_per_token": 0.0, "output_cost_per_token": 0.0, "output_cost_per_reasoning_token": 0.0, + "input_cost_per_audio_token": 0.0, "litellm_provider": "one of https://docs.litellm.ai/docs/providers", "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", "supports_function_calling": true, @@ -19045,34 +19046,43 @@ "max_tokens": 32768, "max_input_tokens": 30720, "max_output_tokens": 8192, + "input_cost_per_token": 1.6e-06, + "output_cost_per_token": 6.4e-06, "litellm_provider": "dashscope", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "dashscope/qwen-plus-latest": { - "max_tokens": 131072, - "max_input_tokens": 129024, - "max_output_tokens": 16384, + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, "litellm_provider": "dashscope", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 4e-07, "output_cost_per_token": 1.2e-06, "output_cost_per_reasoning_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 3.6e-06, "output_cost_per_reasoning_token": 1.2e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "dashscope/qwen-turbo-latest": { - "max_tokens": 131072, - "max_input_tokens": 129024, + "max_tokens": 1000000, + "max_input_tokens": 1000000, "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, "litellm_provider": "dashscope", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "dashscope/qwen3-30b-a3b": { "max_tokens": 131072, @@ -19083,7 +19093,277 @@ "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-max-preview": { + "max_tokens": 262144, + "max_input_tokens": 258048, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 6e-06}, + {"range": [32e3, 128e3], "input_cost_per_token": 2.4e-06, "output_cost_per_token": 1.2e-05}, + {"range": [128e3, 252e3], "input_cost_per_token": 3.0e-06, "output_cost_per_token": 1.5e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-flash": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-coder": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.5e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-plus": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 1e-06, "output_cost_per_token": 5e-06, "cache_read_input_token_cost": 1e-07}, + {"range": [32e3, 128e3], "input_cost_per_token": 1.8e-06, "output_cost_per_token": 9e-06, "cache_read_input_token_cost": 1.8e-07}, + {"range": [128e3, 256e3], "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05, "cache_read_input_token_cost": 3e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 6e-06, "output_cost_per_token": 6e-05, "cache_read_input_token_cost": 6e-07} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-plus-2025-07-22": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 1e-06, "output_cost_per_token": 5e-06}, + {"range": [32e3, 128e3], "input_cost_per_token": 1.8e-06, "output_cost_per_token": 9e-06}, + {"range": [128e3, 256e3], "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05}, + {"range": [256e3, 1e6], "input_cost_per_token": 6e-06, "output_cost_per_token": 6e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-flash": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 3e-07, "output_cost_per_token": 1.5e-06, "cache_read_input_token_cost": 8e-08}, + {"range": [32e3, 128e3], "input_cost_per_token": 5e-07, "output_cost_per_token": 2.5e-06, "cache_read_input_token_cost": 1.2e-07}, + {"range": [128e3, 256e3], "input_cost_per_token": 8e-07, "output_cost_per_token": 4e-06, "cache_read_input_token_cost": 2e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.6e-06, "output_cost_per_token": 9.6e-06, "cache_read_input_token_cost": 4e-07} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-flash-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 3e-07, "output_cost_per_token": 1.5e-06}, + {"range": [32e3, 128e3], "input_cost_per_token": 5e-07, "output_cost_per_token": 2.5e-06}, + {"range": [128e3, 256e3], "input_cost_per_token": 8e-07, "output_cost_per_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.6e-06, "output_cost_per_token": 9.6e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-09-11": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 4e-07, "output_cost_per_token": 1.2e-06, "output_cost_per_reasoning_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 3.6e-06, "output_cost_per_reasoning_token": 1.2e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 4e-07, "output_cost_per_token": 1.2e-06, "output_cost_per_reasoning_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 3.6e-06, "output_cost_per_reasoning_token": 1.2e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-07-14": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "output_cost_per_reasoning_token": 4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-04-28": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "output_cost_per_reasoning_token": 4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-01-25": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 8192, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-flash-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo-2025-04-28": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo-2024-11-01": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwq-plus": { + "max_tokens": 131072, + "max_input_tokens": 98304, + "max_output_tokens": 8192, + "max_chain_of_thought_tokens": 32768, + "input_cost_per_token": 8e-07, + "output_cost_per_token": 2.4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "moonshot/moonshot-v1-8k": { "max_tokens": 8192, diff --git a/litellm/types/utils.py b/litellm/types/utils.py index c6f7098a1a7..e54516371e5 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -162,6 +162,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False): SearchContextCostPerQuery ] # Cost for using web search tool citation_cost_per_token: Optional[float] # Cost per citation token for Perplexity + tiered_pricing: Optional[List[Dict[str, Any]]] # Tiered pricing structure for models like Dashscope litellm_provider: Required[str] mode: Required[ Literal[ diff --git a/litellm/utils.py b/litellm/utils.py index be26405b43b..84d86bea647 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -4902,6 +4902,7 @@ def _get_model_info_helper( # noqa: PLR0915 citation_cost_per_token=_model_info.get( "citation_cost_per_token", None ), + tiered_pricing=_model_info.get("tiered_pricing", None), litellm_provider=_model_info.get( "litellm_provider", custom_llm_provider ), diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index c7fabb0ed9f..972e212e693 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -6,6 +6,7 @@ "input_cost_per_token": 0.0, "output_cost_per_token": 0.0, "output_cost_per_reasoning_token": 0.0, + "input_cost_per_audio_token": 0.0, "litellm_provider": "one of https://docs.litellm.ai/docs/providers", "mode": "one of: chat, embedding, completion, image_generation, audio_transcription, audio_speech, image_generation, moderation, rerank", "supports_function_calling": true, @@ -19045,34 +19046,43 @@ "max_tokens": 32768, "max_input_tokens": 30720, "max_output_tokens": 8192, + "input_cost_per_token": 1.6e-06, + "output_cost_per_token": 6.4e-06, "litellm_provider": "dashscope", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "dashscope/qwen-plus-latest": { - "max_tokens": 131072, - "max_input_tokens": 129024, - "max_output_tokens": 16384, + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, "litellm_provider": "dashscope", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 4e-07, "output_cost_per_token": 1.2e-06, "output_cost_per_reasoning_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 3.6e-06, "output_cost_per_reasoning_token": 1.2e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "dashscope/qwen-turbo-latest": { - "max_tokens": 131072, - "max_input_tokens": 129024, + "max_tokens": 1000000, + "max_input_tokens": 1000000, "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, "litellm_provider": "dashscope", "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "dashscope/qwen3-30b-a3b": { "max_tokens": 131072, @@ -19083,7 +19093,277 @@ "supports_tool_choice": true, "supports_reasoning": true, "mode": "chat", - "source": "https://bailian.console.alibabacloud.com/?spm=a2c63.p38356.0.0.4a615d7bjSUCb4&tab=doc#/doc/?type=model&url=https%3A%2F%2Fwww.alibabacloud.com%2Fhelp%2Fen%2Fdoc-detail%2F2840914.html" + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-max-preview": { + "max_tokens": 262144, + "max_input_tokens": 258048, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 6e-06}, + {"range": [32e3, 128e3], "input_cost_per_token": 2.4e-06, "output_cost_per_token": 1.2e-05}, + {"range": [128e3, 252e3], "input_cost_per_token": 3.0e-06, "output_cost_per_token": 1.5e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-flash": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-coder": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "input_cost_per_token": 3e-07, + "output_cost_per_token": 1.5e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-plus": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 1e-06, "output_cost_per_token": 5e-06, "cache_read_input_token_cost": 1e-07}, + {"range": [32e3, 128e3], "input_cost_per_token": 1.8e-06, "output_cost_per_token": 9e-06, "cache_read_input_token_cost": 1.8e-07}, + {"range": [128e3, 256e3], "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05, "cache_read_input_token_cost": 3e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 6e-06, "output_cost_per_token": 6e-05, "cache_read_input_token_cost": 6e-07} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-plus-2025-07-22": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 1e-06, "output_cost_per_token": 5e-06}, + {"range": [32e3, 128e3], "input_cost_per_token": 1.8e-06, "output_cost_per_token": 9e-06}, + {"range": [128e3, 256e3], "input_cost_per_token": 3e-06, "output_cost_per_token": 1.5e-05}, + {"range": [256e3, 1e6], "input_cost_per_token": 6e-06, "output_cost_per_token": 6e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-flash": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 3e-07, "output_cost_per_token": 1.5e-06, "cache_read_input_token_cost": 8e-08}, + {"range": [32e3, 128e3], "input_cost_per_token": 5e-07, "output_cost_per_token": 2.5e-06, "cache_read_input_token_cost": 1.2e-07}, + {"range": [128e3, 256e3], "input_cost_per_token": 8e-07, "output_cost_per_token": 4e-06, "cache_read_input_token_cost": 2e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.6e-06, "output_cost_per_token": 9.6e-06, "cache_read_input_token_cost": 4e-07} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen3-coder-flash-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 65536, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 32e3], "input_cost_per_token": 3e-07, "output_cost_per_token": 1.5e-06}, + {"range": [32e3, 128e3], "input_cost_per_token": 5e-07, "output_cost_per_token": 2.5e-06}, + {"range": [128e3, 256e3], "input_cost_per_token": 8e-07, "output_cost_per_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.6e-06, "output_cost_per_token": 9.6e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-09-11": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 4e-07, "output_cost_per_token": 1.2e-06, "output_cost_per_reasoning_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 3.6e-06, "output_cost_per_reasoning_token": 1.2e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 4e-07, "output_cost_per_token": 1.2e-06, "output_cost_per_reasoning_token": 4e-06}, + {"range": [256e3, 1e6], "input_cost_per_token": 1.2e-06, "output_cost_per_token": 3.6e-06, "output_cost_per_reasoning_token": 1.2e-05} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-07-14": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "output_cost_per_reasoning_token": 4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-04-28": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "output_cost_per_reasoning_token": 4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-plus-2025-01-25": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 8192, + "input_cost_per_token": 4e-07, + "output_cost_per_token": 1.2e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-flash-2025-07-28": { + "max_tokens": 1000000, + "max_input_tokens": 997952, + "max_output_tokens": 32768, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "supports_caching": true, + "mode": "chat", + "tiered_pricing": [ + {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, + {"range": [256e3, 1e6], "input_cost_per_token": 2.5e-07, "output_cost_per_token": 2e-06} + ], + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo": { + "max_tokens": 131072, + "max_input_tokens": 129024, + "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo-2025-04-28": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 16384, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "output_cost_per_reasoning_token": 5e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwen-turbo-2024-11-01": { + "max_tokens": 1000000, + "max_input_tokens": 1000000, + "max_output_tokens": 8192, + "input_cost_per_token": 5e-08, + "output_cost_per_token": 2e-07, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" + }, + "dashscope/qwq-plus": { + "max_tokens": 131072, + "max_input_tokens": 98304, + "max_output_tokens": 8192, + "max_chain_of_thought_tokens": 32768, + "input_cost_per_token": 8e-07, + "output_cost_per_token": 2.4e-06, + "litellm_provider": "dashscope", + "supports_function_calling": true, + "supports_tool_choice": true, + "supports_reasoning": true, + "mode": "chat", + "source": "https://www.alibabacloud.com/help/en/model-studio/models" }, "moonshot/moonshot-v1-8k": { "max_tokens": 8192, diff --git a/tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py b/tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py new file mode 100644 index 00000000000..73e992e22d1 --- /dev/null +++ b/tests/test_litellm/llms/dashscope/test_dashscope_cost_calculator.py @@ -0,0 +1,159 @@ +""" +Test suite for Dashscope cost calculation functionality. + +Tests the cost calculation for Dashscope models including: +- Tiered pricing based on input token ranges +- Caching discounts +- Reasoning tokens +- Standard flat pricing fallback +""" + +import json +import math +import os +import sys + +import pytest + +# Add the project root to Python path +sys.path.insert(0, os.path.abspath("../../../..")) + +import litellm +from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, +) +from litellm.types.utils import ( + CompletionTokensDetailsWrapper, + PromptTokensDetailsWrapper, + Usage, +) + + +class TestDashscopeCostCalculator: + """Test suite for Dashscope cost calculation functionality.""" + + @pytest.fixture(autouse=True) + def setup_model_cost_map(self): + """Set up the model cost map for testing.""" + # Ensure we use local model cost map for consistent testing + os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True" + + # Find the project root directory and load model cost map + current_dir = os.path.dirname(os.path.abspath(__file__)) + project_root = current_dir + while not os.path.exists(os.path.join(project_root, "model_prices_and_context_window.json")): + parent = os.path.dirname(project_root) + if parent == project_root: # Reached filesystem root + break + project_root = parent + + model_cost_path = os.path.join(project_root, "model_prices_and_context_window.json") + with open(model_cost_path, "r") as f: + model_cost_map = json.load(f) + litellm.model_cost = model_cost_map + + def test_flat_pricing_basic_cost_calculation(self): + """Test basic cost calculation for flat pricing models (qwen-max).""" + usage = Usage( + prompt_tokens=1000, + completion_tokens=500, + total_tokens=1500 + ) + + prompt_cost, completion_cost = dashscope_cost_per_token( + model="qwen-max", + usage=usage + ) + + # Expected costs for qwen-max: + # Input: 1000 tokens * $1.6e-6 = $0.0016 + # Output: 500 tokens * $6.4e-6 = $0.0032 + expected_prompt_cost = 1000 * 1.6e-6 + expected_completion_cost = 500 * 6.4e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_single_tier(self): + """Test tiered pricing when all tokens fall within first tier.""" + usage = Usage( + prompt_tokens=20000, # Within first tier (0-32K) + completion_tokens=1000, + total_tokens=21000 + ) + + prompt_cost, completion_cost = dashscope_cost_per_token( + model="qwen3-coder-plus", + usage=usage + ) + + # Expected costs for qwen3-coder-plus (tier 1): + # Input: 20,000 tokens * $1e-6 = $0.02 + # Output: 1,000 tokens * $5e-6 = $0.005 + expected_prompt_cost = 20000 * 1e-6 + expected_completion_cost = 1000 * 5e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_multiple_tiers(self): + """Test tiered pricing when tokens span multiple tiers.""" + usage = Usage( + prompt_tokens=150000, # Spans tiers 1 (0-32K), 2 (32K-128K), 3 (128K-256K) + completion_tokens=2000, + total_tokens=152000 + ) + + prompt_cost, completion_cost = dashscope_cost_per_token( + model="qwen3-coder-plus", + usage=usage + ) + + # Expected input cost calculation: + # Tier 1 (0-32K): 32,000 tokens * $1e-6 = $0.032 + # Tier 2 (32K-128K): 96,000 tokens * $1.8e-6 = $0.1728 + # Tier 3 (128K-256K): 22,000 tokens * $3e-6 = $0.066 + # Total input cost = $0.032 + $0.1728 + $0.066 = $0.2708 + + expected_prompt_cost = (32000 * 1e-6) + (96000 * 1.8e-6) + (22000 * 3e-6) + expected_completion_cost = 2000 * 5e-6 # All in tier 1 for output + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_with_caching(self): + """Test tiered pricing with cached tokens.""" + prompt_tokens_details = PromptTokensDetailsWrapper( + cached_tokens=10000 # 10K cached tokens + ) + + usage = Usage( + prompt_tokens=50000, # 40K regular + 10K cached = 50K total + completion_tokens=1000, + total_tokens=51000, + prompt_tokens_details=prompt_tokens_details + ) + + prompt_cost, completion_cost = dashscope_cost_per_token( + model="qwen3-coder-plus", + usage=usage + ) + + # Expected cost calculation: + # Regular tokens: 40,000 (32K in tier 1 + 8K in tier 2) + # - Tier 1: 32,000 * $1e-6 = $0.032 + # - Tier 2: 8,000 * $1.8e-6 = $0.0144 + # Cached tokens: 10,000 in tier 1 at discounted rate + # - Tier 1 cached: 10,000 * $1e-7 = $0.001 + # Total input cost = $0.032 + $0.0144 + $0.001 = $0.0474 + + regular_tokens = 40000 + cached_tokens = 10000 + + expected_regular_cost = (32000 * 1e-6) + (8000 * 1.8e-6) + expected_cached_cost = cached_tokens * 1e-7 # Tier 1 cached rate + expected_prompt_cost = expected_regular_cost + expected_cached_cost + expected_completion_cost = 1000 * 5e-6 + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) \ No newline at end of file diff --git a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx index 31f5b10043b..17aa637af89 100644 --- a/ui/litellm-dashboard/src/components/provider_info_helpers.tsx +++ b/ui/litellm-dashboard/src/components/provider_info_helpers.tsx @@ -11,8 +11,9 @@ export enum Providers { Azure = "Azure", Azure_AI_Studio = "Azure AI Foundry (Studio)", Cerebras = "Cerebras", - Cohere = "Cohere", - Databricks = "Databricks", + Cohere = "Cohere", + Dashscope = "Dashscope", + Databricks = "Databricks (Qwen API)", DeepInfra = "DeepInfra", Deepgram = "Deepgram", Deepseek = "Deepseek", @@ -37,7 +38,7 @@ export enum Providers { Vertex_AI = "Vertex AI (Anthropic, Gemini, etc.)", VolcEngine = "VolcEngine", Voyage = "Voyage AI", - xAI = "xAI", + xAI = "xAI", } export const provider_map: Record = { @@ -56,6 +57,7 @@ export const provider_map: Record = { OpenAI_Text_Compatible: "text-completion-openai", Vertex_AI: "vertex_ai", Databricks: "databricks", + Dashscope: "dashscope", xAI: "xai", Deepseek: "deepseek", Ollama: "ollama", @@ -91,6 +93,7 @@ export const providerLogoMap: Record = { [Providers.Cerebras]: `${asset_logos_folder}cerebras.svg`, [Providers.Cohere]: `${asset_logos_folder}cohere.svg`, [Providers.Databricks]: `${asset_logos_folder}databricks.svg`, + [Providers.Dashscope]: `${asset_logos_folder}dashscope.svg`, [Providers.Deepseek]: `${asset_logos_folder}deepseek.svg`, [Providers.FireworksAI]: `${asset_logos_folder}fireworks.svg`, [Providers.Groq]: `${asset_logos_folder}groq.svg`, From 805069c287376cbc9ff82d08affb461bcbff36de Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 18:52:02 -0700 Subject: [PATCH 41/73] fix(adapters/streaming_iterator.py): Don't send content block after message delta block is sent Fixes https://github.com/BerriAI/litellm/issues/14315 --- litellm/constants.py | 10 +- .../adapters/streaming_iterator.py | 133 +++---- litellm/proxy/_new_secret_config.yaml | 3 + .../hooks/parallel_request_limiter_v3.py | 42 ++- .../test_content_after_stop_reason.py | 343 ++++++++++++++++++ 5 files changed, 448 insertions(+), 83 deletions(-) create mode 100644 tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9e..c0ce0f265b5 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( - os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" @@ -60,7 +60,9 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) ) DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( - os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512) + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) ) # Generic fallback for unknown models @@ -949,7 +951,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" -CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)) +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( + os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) +) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py index aa95183bb6c..e4191a945f3 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/streaming_iterator.py @@ -28,10 +28,6 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): TextBlock, ) - def __init__(self, completion_stream: Any, model: str): - super().__init__(completion_stream) - self.model = model - sent_first_chunk: bool = False sent_content_block_start: bool = False sent_content_block_finish: bool = False @@ -39,6 +35,7 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): sent_last_message: bool = False holding_chunk: Optional[Any] = None holding_stop_reason_chunk: Optional[Any] = None + queued_usage_chunk: bool = False current_content_block_index: int = 0 current_content_block_start: ContentBlockContentBlockDict = TextBlock( type="text", @@ -47,6 +44,10 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): pending_new_content_block: bool = False chunk_queue: deque = deque() # Queue for buffering multiple chunks + def __init__(self, completion_stream: Any, model: str): + super().__init__(completion_stream) + self.model = model + def __next__(self): from .transformation import LiteLLMAnthropicMessagesAdapter @@ -217,77 +218,83 @@ class AnthropicStreamWrapper(AdapterCompletionStreamWrapper): # Queue the merged chunk and reset self.chunk_queue.append(merged_chunk) + self.queued_usage_chunk = True self.holding_stop_reason_chunk = None return self.chunk_queue.popleft() # Check if this processed chunk has a stop_reason - hold it for next chunk - if should_start_new_block and not self.sent_content_block_finish: - # Queue the sequence: content_block_stop -> content_block_start -> current_chunk + if not self.queued_usage_chunk: + if should_start_new_block and not self.sent_content_block_finish: + # Queue the sequence: content_block_stop -> content_block_start -> current_chunk - # 1. Stop current content block - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": max(self.current_content_block_index - 1, 0), - } - ) + # 1. Stop current content block + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": max(self.current_content_block_index - 1, 0), + } + ) - # 2. Start new content block - self.chunk_queue.append( - { - "type": "content_block_start", - "index": self.current_content_block_index, - "content_block": self.current_content_block_start, - } - ) + # 2. Start new content block + self.chunk_queue.append( + { + "type": "content_block_start", + "index": self.current_content_block_index, + "content_block": self.current_content_block_start, + } + ) - # 3. Queue the current chunk (don't lose it!) - self.chunk_queue.append(processed_chunk) - - # Reset state for new block - self.sent_content_block_finish = False - - # Return the first queued item - return self.chunk_queue.popleft() - - if ( - processed_chunk["type"] == "message_delta" - and self.sent_content_block_finish is False - ): - # Queue both the content_block_stop and the holding chunk - self.chunk_queue.append( - { - "type": "content_block_stop", - "index": self.current_content_block_index, - } - ) - self.sent_content_block_finish = True - if processed_chunk.get("delta", {}).get("stop_reason") is not None: - - self.holding_stop_reason_chunk = processed_chunk - else: + # 3. Queue the current chunk (don't lose it!) self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() - elif self.holding_chunk is not None: - # Queue both chunks - self.chunk_queue.append(self.holding_chunk) - self.chunk_queue.append(processed_chunk) - self.holding_chunk = None - return self.chunk_queue.popleft() - else: - # Queue the current chunk - self.chunk_queue.append(processed_chunk) - return self.chunk_queue.popleft() + + # Reset state for new block + self.sent_content_block_finish = False + + # Return the first queued item + return self.chunk_queue.popleft() + + if ( + processed_chunk["type"] == "message_delta" + and self.sent_content_block_finish is False + ): + # Queue both the content_block_stop and the holding chunk + self.chunk_queue.append( + { + "type": "content_block_stop", + "index": self.current_content_block_index, + } + ) + self.sent_content_block_finish = True + if ( + processed_chunk.get("delta", {}).get("stop_reason") + is not None + ): + + self.holding_stop_reason_chunk = processed_chunk + else: + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() + elif self.holding_chunk is not None: + # Queue both chunks + self.chunk_queue.append(self.holding_chunk) + self.chunk_queue.append(processed_chunk) + self.holding_chunk = None + return self.chunk_queue.popleft() + else: + # Queue the current chunk + self.chunk_queue.append(processed_chunk) + return self.chunk_queue.popleft() # Handle any remaining held chunks after stream ends - if self.holding_stop_reason_chunk is not None: - self.chunk_queue.append(self.holding_stop_reason_chunk) - self.holding_stop_reason_chunk = None + if not self.queued_usage_chunk: + if self.holding_stop_reason_chunk is not None: + self.chunk_queue.append(self.holding_stop_reason_chunk) + self.holding_stop_reason_chunk = None - if self.holding_chunk is not None: - self.chunk_queue.append(self.holding_chunk) - self.holding_chunk = None + if self.holding_chunk is not None: + self.chunk_queue.append(self.holding_chunk) + self.holding_chunk = None if not self.sent_last_message: self.sent_last_message = True diff --git a/litellm/proxy/_new_secret_config.yaml b/litellm/proxy/_new_secret_config.yaml index c785dd05c40..691f67d5778 100644 --- a/litellm/proxy/_new_secret_config.yaml +++ b/litellm/proxy/_new_secret_config.yaml @@ -7,3 +7,6 @@ model_list: - model_name: wildcard_models/* litellm_params: model: openai/* + - model_name: xai-grok-3 + litellm_params: + model: xai/grok-3 diff --git a/litellm/proxy/hooks/parallel_request_limiter_v3.py b/litellm/proxy/hooks/parallel_request_limiter_v3.py index b3840761d2a..b3fa71a1c15 100644 --- a/litellm/proxy/hooks/parallel_request_limiter_v3.py +++ b/litellm/proxy/hooks/parallel_request_limiter_v3.py @@ -6,6 +6,7 @@ This is currently in development and not yet ready for production. import os from datetime import datetime +from math import floor from typing import ( TYPE_CHECKING, Any, @@ -17,7 +18,7 @@ from typing import ( Union, cast, ) -from math import floor + from fastapi import HTTPException from litellm import DualCache @@ -95,6 +96,7 @@ end return results """ + class RateLimitDescriptorRateLimitObject(TypedDict, total=False): requests_per_unit: Optional[int] tokens_per_unit: Optional[int] @@ -480,10 +482,15 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): }, ) ) - + # Team Member rate limits - if user_api_key_dict.user_id and (user_api_key_dict.team_member_rpm_limit is not None or user_api_key_dict.team_member_tpm_limit is not None): - team_member_value = f"{user_api_key_dict.team_id}:{user_api_key_dict.user_id}" + if user_api_key_dict.user_id and ( + user_api_key_dict.team_member_rpm_limit is not None + or user_api_key_dict.team_member_tpm_limit is not None + ): + team_member_value = ( + f"{user_api_key_dict.team_id}:{user_api_key_dict.user_id}" + ) descriptors.append( RateLimitDescriptor( key="team_member", @@ -557,13 +564,13 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): # Find which descriptor hit the limit for i, status in enumerate(response["statuses"]): if status["code"] == "OVER_LIMIT": - descriptor = descriptors[floor(i/2)] + descriptor = descriptors[floor(i / 2)] raise HTTPException( status_code=429, detail=f"Rate limit exceeded for {descriptor['key']}: {descriptor['value']}. Remaining: {status['limit_remaining']}", headers={ "retry-after": str(self.window_size), - "rate_limit_type": str(status["rate_limit_type"]) + "rate_limit_type": str(status["rate_limit_type"]), }, # Retry after 1 minute ) @@ -613,7 +620,9 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): # Check if script is available if self.token_increment_script is None: - verbose_proxy_logger.debug("TTL preservation script not available, using regular pipeline") + verbose_proxy_logger.debug( + "TTL preservation script not available, using regular pipeline" + ) await self.internal_usage_cache.dual_cache.async_increment_cache_pipeline( increment_list=pipeline_operations, litellm_parent_otel_span=parent_otel_span, @@ -628,7 +637,7 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): for op in pipeline_operations: # Convert None TTL to 0 for Lua script ttl_value = op["ttl"] if op["ttl"] is not None else 0 - + verbose_proxy_logger.debug( f"Executing TTL-preserving increment for key={op['key']}, " f"increment={op['increment_value']}, ttl={ttl_value}" @@ -693,16 +702,15 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger): ) # Get metadata from kwargs - user_api_key = kwargs["litellm_params"]["metadata"].get("user_api_key") - user_api_key_user_id = kwargs["litellm_params"]["metadata"].get( - "user_api_key_user_id" + litellm_metadata = kwargs["litellm_params"]["metadata"] + if litellm_metadata is None: + return + user_api_key = litellm_metadata.get("user_api_key") + user_api_key_user_id = litellm_metadata.get("user_api_key_user_id") + user_api_key_team_id = litellm_metadata.get("user_api_key_team_id") + user_api_key_end_user_id = kwargs.get("user") or litellm_metadata.get( + "user_api_key_end_user_id" ) - user_api_key_team_id = kwargs["litellm_params"]["metadata"].get( - "user_api_key_team_id" - ) - user_api_key_end_user_id = kwargs.get("user") or kwargs["litellm_params"][ - "metadata" - ].get("user_api_key_end_user_id") model_group = get_model_group_from_litellm_kwargs(kwargs) # Get total tokens from response diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py new file mode 100644 index 00000000000..4a170d666f5 --- /dev/null +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_content_after_stop_reason.py @@ -0,0 +1,343 @@ +""" +Test for AnthropicStreamWrapper handling content blocks that exist after message_delta with stop_reason and usage. + +This tests the scenario where a streaming response includes: +1. Initial content blocks +2. A message_delta chunk with stop_reason and usage +3. Additional content blocks after the stop_reason + +The wrapper should properly handle this by: +- Holding the stop_reason chunk until usage is available +- Merging usage into the stop_reason chunk +- Properly managing content_block_stop/start events for subsequent content +""" + +import os +import sys +from typing import List + +import pytest + +sys.path.insert(0, os.path.abspath("../../../../..")) + +from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import ( + AnthropicStreamWrapper, +) +from litellm.types.utils import Delta, ModelResponse, StreamingChoices, Usage + + +class MockCompletionStreamWithContentAfterStopReason: + """Mock stream that simulates content blocks existing after message_delta with stop_reason and usage.""" + + def __init__(self): + self.responses = [ + # Initial text content + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content="Hello"), index=0, finish_reason=None + ) + ], + ), + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content=" world"), index=0, finish_reason=None + ) + ], + ), + # Message delta with stop_reason AND usage (this is how it actually comes from the API) + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content=""), index=0, finish_reason="stop" + ) + ], + usage=Usage(prompt_tokens=230, completion_tokens=65, total_tokens=295), + ), + # Additional content after the stop_reason - this simulates the scenario + # where there might be additional content blocks after the main response + ModelResponse( + stream=True, + choices=[ + StreamingChoices( + delta=Delta(content=" Additional content"), + index=0, + finish_reason=None, + ) + ], + ), + ] + self.index = 0 + + def __iter__(self): + return self + + def __next__(self): + if self.index >= len(self.responses): + raise StopIteration + response = self.responses[self.index] + self.index += 1 + return response + + def __aiter__(self): + return self + + async def __anext__(self): + if self.index >= len(self.responses): + raise StopAsyncIteration + response = self.responses[self.index] + self.index += 1 + return response + + +def test_anthropic_stream_wrapper_content_after_stop_reason(): + """Test that AnthropicStreamWrapper properly handles content blocks after message_delta with stop_reason.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + chunks = [] + chunk_types = [] + + # Collect all chunks + for chunk in wrapper: + chunks.append(chunk) + chunk_types.append(chunk.get("type")) + + # Verify the expected sequence of chunk types + expected_types = [ + "message_start", # Initial message start + "content_block_start", # Start of first content block + "content_block_delta", # "Hello" + "content_block_delta", # " world" + "content_block_stop", # End of first content block due to stop_reason + "message_delta", # Stop reason with merged usage + "message_stop", # Final message stop + ] + + print(f"Actual chunk types: {chunk_types}") + print(f"Expected chunk types: {expected_types}") + + # Verify we have the expected number of chunks + assert len(chunk_types) >= len( + expected_types + ), f"Expected at least {len(expected_types)} chunks, got {len(chunk_types)}" + + # Verify key chunk types are present + assert "message_start" in chunk_types + assert "content_block_start" in chunk_types + assert "content_block_delta" in chunk_types + assert "content_block_stop" in chunk_types + assert "message_delta" in chunk_types + assert "message_stop" in chunk_types + + # Find the message_delta chunk with stop_reason + message_delta_chunk = None + for chunk in chunks: + if chunk.get("type") == "message_delta": + message_delta_chunk = chunk + break + + assert message_delta_chunk is not None, "message_delta chunk not found" + + # Verify that the message_delta chunk has both stop_reason and usage + delta = message_delta_chunk.get("delta", {}) + usage = message_delta_chunk.get("usage", {}) + + assert ( + delta.get("stop_reason") == "end_turn" + ), f"Expected stop_reason 'end_turn', got {delta.get('stop_reason')}" + assert ( + usage.get("input_tokens") == 230 + ), f"Expected input_tokens 230, got {usage.get('input_tokens')}" + assert ( + usage.get("output_tokens") == 65 + ), f"Expected output_tokens 65, got {usage.get('output_tokens')}" + + # Verify content_block_stop comes before message_delta + content_block_stop_index = None + message_delta_index = None + + for i, chunk_type in enumerate(chunk_types): + if chunk_type == "content_block_stop" and content_block_stop_index is None: + content_block_stop_index = i + elif chunk_type == "message_delta": + message_delta_index = i + + assert content_block_stop_index is not None, "content_block_stop not found" + assert message_delta_index is not None, "message_delta not found" + assert ( + content_block_stop_index < message_delta_index + ), "content_block_stop should come before message_delta" + + +@pytest.mark.asyncio +async def test_async_anthropic_stream_wrapper_content_after_stop_reason(): + """Test async version of AnthropicStreamWrapper handling content blocks after message_delta with stop_reason.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + chunks = [] + chunk_types = [] + + # Collect all chunks asynchronously + async for chunk in wrapper: + chunks.append(chunk) + chunk_types.append(chunk.get("type")) + + print(f"Async - Actual chunk types: {chunk_types}") + + # Verify key chunk types are present + assert "message_start" in chunk_types + assert "content_block_start" in chunk_types + assert "content_block_delta" in chunk_types + assert "content_block_stop" in chunk_types + assert "message_delta" in chunk_types + assert "message_stop" in chunk_types + + # Find the message_delta chunk with stop_reason + message_delta_chunk = None + for chunk in chunks: + if chunk.get("type") == "message_delta": + message_delta_chunk = chunk + break + + assert message_delta_chunk is not None, "message_delta chunk not found" + + # Verify that the message_delta chunk has both stop_reason and usage + delta = message_delta_chunk.get("delta", {}) + usage = message_delta_chunk.get("usage", {}) + + assert ( + delta.get("stop_reason") == "end_turn" + ), f"Expected stop_reason 'end_turn', got {delta.get('stop_reason')}" + assert ( + usage.get("input_tokens") == 230 + ), f"Expected input_tokens 230, got {usage.get('input_tokens')}" + assert ( + usage.get("output_tokens") == 65 + ), f"Expected output_tokens 65, got {usage.get('output_tokens')}" + + +def test_usage_merging_behavior(): + """Test that usage information is properly merged with stop_reason chunk.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + # Process chunks and look specifically for the usage merging behavior + chunks = [] + for chunk in wrapper: + chunks.append(chunk) + # If this is a message_delta with stop_reason, verify it has usage + if ( + chunk.get("type") == "message_delta" + and chunk.get("delta", {}).get("stop_reason") is not None + ): + + usage = chunk.get("usage", {}) + assert ( + usage.get("input_tokens") is not None + ), "Usage should be merged with stop_reason chunk" + assert ( + usage.get("output_tokens") is not None + ), "Usage should be merged with stop_reason chunk" + break + + +def test_sse_wrapper_with_content_after_stop_reason(): + """Test SSE wrapper formatting for the content after stop_reason scenario.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + # Get SSE formatted chunks + sse_chunks = [] + for chunk in wrapper.anthropic_sse_wrapper(): + sse_chunks.append(chunk) + if len(sse_chunks) >= 10: # Limit to avoid infinite loops in tests + break + + # Verify all chunks are properly formatted as bytes + for chunk in sse_chunks: + assert isinstance(chunk, bytes), "SSE chunks should be bytes" + + # Decode and verify SSE format + chunk_str = chunk.decode("utf-8") + lines = chunk_str.split("\n") + + # Should have event and data lines + assert any( + line.startswith("event: ") for line in lines + ), f"Missing event line in: {chunk_str}" + assert any( + line.startswith("data: ") for line in lines + ), f"Missing data line in: {chunk_str}" + + +@pytest.mark.asyncio +async def test_async_sse_wrapper_with_content_after_stop_reason(): + """Test async SSE wrapper formatting for the content after stop_reason scenario.""" + + wrapper = AnthropicStreamWrapper( + completion_stream=MockCompletionStreamWithContentAfterStopReason(), + model="claude-3", + ) + + # Get SSE formatted chunks asynchronously + sse_chunks = [] + async for chunk in wrapper.async_anthropic_sse_wrapper(): + sse_chunks.append(chunk) + if len(sse_chunks) >= 10: # Limit to avoid infinite loops in tests + break + + # Verify all chunks are properly formatted as bytes + for chunk in sse_chunks: + assert isinstance(chunk, bytes), "Async SSE chunks should be bytes" + + # Decode and verify SSE format + chunk_str = chunk.decode("utf-8") + lines = chunk_str.split("\n") + + # Should have event and data lines + assert any( + line.startswith("event: ") for line in lines + ), f"Missing event line in: {chunk_str}" + assert any( + line.startswith("data: ") for line in lines + ), f"Missing data line in: {chunk_str}" + + +if __name__ == "__main__": + # Run a quick test + test_anthropic_stream_wrapper_content_after_stop_reason() + print("✅ Sync test passed") + + import asyncio + + asyncio.run(test_async_anthropic_stream_wrapper_content_after_stop_reason()) + print("✅ Async test passed") + + test_usage_merging_behavior() + print("✅ Usage merging test passed") + + test_sse_wrapper_with_content_after_stop_reason() + print("✅ SSE wrapper test passed") + + asyncio.run(test_async_sse_wrapper_with_content_after_stop_reason()) + print("✅ Async SSE wrapper test passed") + + print("🎉 All tests passed!") From 0f6898ad0a9d8769bb26038f80107b309c8fb91e Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 19:10:00 -0700 Subject: [PATCH 42/73] fix(key_management_endpoints.py): check if key is a hashed token or sk key before lookup Fixes https://github.com/BerriAI/litellm/issues/13887 --- .../key_management_endpoints.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index bd8faf34be8..a7f15cc6c19 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -346,6 +346,7 @@ def handle_key_type(data: GenerateKeyRequest, data_json: dict) -> dict: data_json["allowed_routes"] = ["info_routes"] return data_json + async def validate_team_id_used_in_service_account_request( team_id: Optional[str], prisma_client: Optional[PrismaClient], @@ -358,13 +359,13 @@ async def validate_team_id_used_in_service_account_request( status_code=400, detail="team_id is required for service account keys. Please specify `team_id` in the request body.", ) - + if prisma_client is None: raise HTTPException( status_code=400, detail="prisma_client is required for service account keys. Please specify `prisma_client` in the request body.", ) - + # check if team_id exists in the database team = await prisma_client.db.litellm_teamtable.find_unique( where={"team_id": team_id}, @@ -376,6 +377,7 @@ async def validate_team_id_used_in_service_account_request( ) return True + async def _common_key_generation_helper( # noqa: PLR0915 data: GenerateKeyRequest, user_api_key_dict: UserAPIKeyAuth, @@ -557,7 +559,7 @@ async def _common_key_generation_helper( # noqa: PLR0915 status_code=400, detail={ "error": f"Invalid key format. LiteLLM Virtual Key must start with 'sk-'. Received: {data.key}" - } + }, ) response = await generate_key_helper_fn( @@ -2885,7 +2887,10 @@ async def unblock_key( param="key", code=status.HTTP_400_BAD_REQUEST, ) - hashed_token = hash_token(token=data.key) + if data.key.startswith("sk-"): + hashed_token = hash_token(token=data.key) + else: + hashed_token = data.key if litellm.store_audit_logs is True: # make an audit log for key update From 0c8b311155f01f5075ca4deb09ac3a5b390ad2c6 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 19:15:15 -0700 Subject: [PATCH 43/73] test: add unit testing for both flows on key unblock --- .../test_key_management_endpoints.py | 245 +++++++++++++++--- 1 file changed, 202 insertions(+), 43 deletions(-) diff --git a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py index 3a597adef06..2747fab77fa 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_key_management_endpoints.py @@ -183,7 +183,9 @@ async def test_budget_reset_and_expires_at_first_of_month(monkeypatch): assert ( response_date.month == expected_month ), f"Expected month {expected_month}, got {response_date.month} for {key}" - assert response_date.day == 1, f"Expected day 1, got {response_date.day} for {key}" + assert ( + response_date.day == 1 + ), f"Expected day 1, got {response_date.day} for {key}" @pytest.mark.asyncio @@ -507,7 +509,6 @@ def test_get_new_token_with_invalid_key(): assert "New key must start with 'sk-'" in str(exc_info.value.detail) - @pytest.mark.asyncio async def test_generate_service_account_requires_team_id(): with pytest.raises(HTTPException): @@ -529,11 +530,12 @@ async def test_generate_service_account_works_with_team_id(): from unittest.mock import patch # Mock the database and router dependencies from proxy_server - with patch('litellm.proxy.proxy_server.prisma_client') as mock_prisma, \ - patch('litellm.proxy.proxy_server.llm_router') as mock_router, \ - patch('litellm.proxy.proxy_server.premium_user', False), \ - patch('litellm.proxy.management_endpoints.key_management_endpoints.generate_key_helper_fn') as mock_generate_key: - + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma, patch( + "litellm.proxy.proxy_server.llm_router" + ) as mock_router, patch("litellm.proxy.proxy_server.premium_user", False), patch( + "litellm.proxy.management_endpoints.key_management_endpoints.generate_key_helper_fn" + ) as mock_generate_key: + # Configure mocks mock_prisma.return_value = AsyncMock() mock_router.return_value = None @@ -542,9 +544,9 @@ async def test_generate_service_account_works_with_team_id(): "key": "sk-test-key", "expires": None, "user_id": "test-user", - "team_id": "IJ" + "team_id": "IJ", } - + # This should not raise an exception since team_id is provided await _common_key_generation_helper( data=GenerateKeyRequest( @@ -559,7 +561,6 @@ async def test_generate_service_account_works_with_team_id(): ) - @pytest.mark.asyncio async def test_update_service_account_requires_team_id(): data = UpdateKeyRequest(key="sk-1", metadata={"service_account_id": "sa"}) @@ -571,7 +572,9 @@ async def test_update_service_account_requires_team_id(): @pytest.mark.asyncio async def test_update_service_account_works_with_team_id(): - data = UpdateKeyRequest(key="sk-1", metadata={"service_account_id": "sa"}, team_id="IJ") + data = UpdateKeyRequest( + key="sk-1", metadata={"service_account_id": "sa"}, team_id="IJ" + ) existing_key = LiteLLM_VerificationToken(token="hashed") await prepare_key_update_data(data=data, existing_key_row=existing_key) @@ -580,22 +583,22 @@ async def test_update_service_account_works_with_team_id(): @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_requires_team_id(): """ - Test that validate_team_id_used_in_service_account_request raises HTTPException + Test that validate_team_id_used_in_service_account_request raises HTTPException when team_id is None for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( validate_team_id_used_in_service_account_request, ) - + mock_prisma_client = AsyncMock() - + # Test that HTTPException is raised when team_id is None with pytest.raises(HTTPException) as exc_info: await validate_team_id_used_in_service_account_request( team_id=None, prisma_client=mock_prisma_client, ) - + assert exc_info.value.status_code == 400 assert "team_id is required for service account keys" in str(exc_info.value.detail) @@ -603,7 +606,7 @@ async def test_validate_team_id_used_in_service_account_request_requires_team_id @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_requires_prisma_client(): """ - Test that validate_team_id_used_in_service_account_request raises HTTPException + Test that validate_team_id_used_in_service_account_request raises HTTPException when prisma_client is None for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( @@ -616,78 +619,76 @@ async def test_validate_team_id_used_in_service_account_request_requires_prisma_ team_id="test-team-id", prisma_client=None, ) - + assert exc_info.value.status_code == 400 - assert "prisma_client is required for service account keys" in str(exc_info.value.detail) + assert "prisma_client is required for service account keys" in str( + exc_info.value.detail + ) @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_checks_team_exists(): """ - Test that validate_team_id_used_in_service_account_request validates that + Test that validate_team_id_used_in_service_account_request validates that the team_id exists in the database for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( validate_team_id_used_in_service_account_request, ) - + mock_prisma_client = AsyncMock() - + # Mock the database query to return None (team doesn't exist) mock_find_unique = AsyncMock(return_value=None) mock_prisma_client.db.litellm_teamtable.find_unique = mock_find_unique - + # Test that HTTPException is raised when team doesn't exist in DB with pytest.raises(HTTPException) as exc_info: await validate_team_id_used_in_service_account_request( team_id="non-existent-team-id", prisma_client=mock_prisma_client, ) - + assert exc_info.value.status_code == 400 assert "team_id does not exist in the database" in str(exc_info.value.detail) - + # Verify the database was queried with the correct parameters - mock_find_unique.assert_called_once_with( - where={"team_id": "non-existent-team-id"} - ) + mock_find_unique.assert_called_once_with(where={"team_id": "non-existent-team-id"}) @pytest.mark.asyncio async def test_validate_team_id_used_in_service_account_request_success(): """ - Test that validate_team_id_used_in_service_account_request returns True + Test that validate_team_id_used_in_service_account_request returns True when team_id exists in the database for service account key generation. """ from litellm.proxy.management_endpoints.key_management_endpoints import ( validate_team_id_used_in_service_account_request, ) - + mock_prisma_client = AsyncMock() - + # Mock the database query to return a team object (team exists) mock_team = {"team_id": "existing-team-id", "team_name": "Test Team"} mock_find_unique = AsyncMock(return_value=mock_team) mock_prisma_client.db.litellm_teamtable.find_unique = mock_find_unique - + # Test that function returns True when team exists result = await validate_team_id_used_in_service_account_request( team_id="existing-team-id", prisma_client=mock_prisma_client, ) - + assert result is True - + # Verify the database was queried with the correct parameters - mock_find_unique.assert_called_once_with( - where={"team_id": "existing-team-id"} - ) + mock_find_unique.assert_called_once_with(where={"team_id": "existing-team-id"}) @pytest.mark.asyncio async def test_generate_service_account_key_endpoint_validation(): """ - Test that the /key/service-account/generate endpoint properly validates + Test that the /key/service-account/generate endpoint properly validates team_id requirement and team existence in database. """ from unittest.mock import patch @@ -705,16 +706,16 @@ async def test_generate_service_account_key_endpoint_validation(): ), litellm_changed_by=None, ) - + assert exc_info.value.status_code == 400 assert "team_id is required for service account keys" in str(exc_info.value.detail) - - # Test case 2: Team doesn't exist in database - with patch('litellm.proxy.proxy_server.prisma_client') as mock_prisma: + + # Test case 2: Team doesn't exist in database + with patch("litellm.proxy.proxy_server.prisma_client") as mock_prisma: # Mock team not found mock_find_unique = AsyncMock(return_value=None) mock_prisma.db.litellm_teamtable.find_unique = mock_find_unique - + with pytest.raises(HTTPException) as exc_info: await generate_service_account_key_fn( data=GenerateKeyRequest(team_id="non-existent-team"), @@ -723,7 +724,165 @@ async def test_generate_service_account_key_endpoint_validation(): ), litellm_changed_by=None, ) - + assert exc_info.value.status_code == 400 assert "team_id does not exist in the database" in str(exc_info.value.detail) + +@pytest.mark.asyncio +async def test_unblock_key_supports_both_sk_and_hashed_tokens(monkeypatch): + """ + Test that the unblock_key endpoint correctly handles both sk- prefixed tokens + and hashed tokens by properly converting sk- tokens to hashed format before + database operations. + """ + from unittest.mock import AsyncMock, MagicMock + + from litellm.proxy._types import BlockKeyRequest + from litellm.proxy.management_endpoints.key_management_endpoints import unblock_key + + # Mock dependencies + mock_prisma_client = AsyncMock() + mock_user_api_key_cache = MagicMock() + mock_proxy_logging_obj = MagicMock() + + # Use a proper 64-character hex hash for testing + test_hashed_token = ( + "a1b2c3d4e5f6789012345678901234567890123456789012345678901234abcd" + ) + + # Mock the key record that will be returned from database + mock_key_record = MagicMock() + mock_key_record.token = test_hashed_token + mock_key_record.blocked = False + mock_key_record.model_dump_json.return_value = ( + f'{{"token": "{test_hashed_token}", "blocked": false}}' + ) + + # Mock database operations + mock_prisma_client.db.litellm_verificationtoken.find_unique = AsyncMock( + return_value=mock_key_record + ) + mock_prisma_client.db.litellm_verificationtoken.update = AsyncMock( + return_value=mock_key_record + ) + + # Mock get_key_object and _cache_key_object functions + mock_key_object = MagicMock() + mock_key_object.blocked = True # Initially blocked + + # Mock hash_token function + def mock_hash_token(token): + if token == "sk-test123456789": + return test_hashed_token + return token + + # Apply monkeypatch + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + monkeypatch.setattr( + "litellm.proxy.proxy_server.user_api_key_cache", mock_user_api_key_cache + ) + monkeypatch.setattr( + "litellm.proxy.proxy_server.proxy_logging_obj", mock_proxy_logging_obj + ) + monkeypatch.setattr("litellm.proxy.proxy_server.hash_token", mock_hash_token) + monkeypatch.setattr( + "litellm.store_audit_logs", False + ) # Disable audit logs for simpler test + + # Mock get_key_object and _cache_key_object + async def mock_get_key_object(**kwargs): + return mock_key_object + + async def mock_cache_key_object(**kwargs): + pass + + monkeypatch.setattr( + "litellm.proxy.management_endpoints.key_management_endpoints.get_key_object", + mock_get_key_object, + ) + monkeypatch.setattr( + "litellm.proxy.management_endpoints.key_management_endpoints._cache_key_object", + mock_cache_key_object, + ) + + # Create mock request and user auth + mock_request = MagicMock() + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-admin", user_id="admin_user" + ) + + # Test Case 1: Using sk- prefixed token + sk_token_request = BlockKeyRequest(key="sk-test123456789") + + result = await unblock_key( + data=sk_token_request, + http_request=mock_request, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=None, + ) + + # Verify that the database update was called with hashed token + mock_prisma_client.db.litellm_verificationtoken.update.assert_called_with( + where={"token": test_hashed_token}, data={"blocked": False} + ) + + assert result == mock_key_record + assert mock_key_object.blocked == False # Should be updated to unblocked + + # Reset mocks for second test + mock_prisma_client.db.litellm_verificationtoken.update.reset_mock() + mock_key_object.blocked = True # Reset to blocked state + + # Test Case 2: Using already hashed token + hashed_token_request = BlockKeyRequest(key=test_hashed_token) + + result = await unblock_key( + data=hashed_token_request, + http_request=mock_request, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=None, + ) + + # Verify that the database update was called with the same hashed token + mock_prisma_client.db.litellm_verificationtoken.update.assert_called_with( + where={"token": test_hashed_token}, data={"blocked": False} + ) + + assert result == mock_key_record + assert mock_key_object.blocked == False # Should be updated to unblocked + + +@pytest.mark.asyncio +async def test_unblock_key_invalid_key_format(monkeypatch): + """ + Test that unblock_key properly validates key format and raises appropriate errors + for invalid keys. + """ + from litellm.proxy._types import BlockKeyRequest + from litellm.proxy.management_endpoints.key_management_endpoints import unblock_key + from litellm.proxy.utils import ProxyException + + # Mock prisma_client to avoid DB connection error + mock_prisma_client = AsyncMock() + monkeypatch.setattr("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + # Mock request and user auth + mock_request = MagicMock() + user_api_key_dict = UserAPIKeyAuth( + user_role=LitellmUserRoles.PROXY_ADMIN, api_key="sk-admin", user_id="admin_user" + ) + + # Test with invalid key format + invalid_key_request = BlockKeyRequest(key="invalid-key-format") + + with pytest.raises(ProxyException) as exc_info: + await unblock_key( + data=invalid_key_request, + http_request=mock_request, + user_api_key_dict=user_api_key_dict, + litellm_changed_by=None, + ) + + assert exc_info.value.code == "400" + assert "Invalid key format" in str(exc_info.value.message) From 51d5255452f7c49f01521b57189ea9ec8fbfd490 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 19:39:06 -0700 Subject: [PATCH 44/73] [Bug]: Azure OpenAI & AI Foundry Reject Image Generation Payload Due to extra_body Injection in LiteLLM v1.76.3 (#14475) * add request body azure img gen * fix test_get_optional_params_image_gen_filters_empty_values * test_azure_image_generation_request_body * test_azure_image_generation_request_body --- litellm/utils.py | 37 ++++++++++++------- .../request_payloads/azure_gpt_image_1.json | 4 ++ .../image_gen_tests/test_image_generation.py | 31 ++++++++++++++++ tests/test_litellm/test_utils.py | 9 +++++ 4 files changed, 68 insertions(+), 13 deletions(-) create mode 100644 tests/image_gen_tests/request_payloads/azure_gpt_image_1.json diff --git a/litellm/utils.py b/litellm/utils.py index 84d86bea647..0d2fe5d4d64 100644 --- a/litellm/utils.py +++ b/litellm/utils.py @@ -2501,6 +2501,23 @@ def get_optional_params_transcription( return optional_params +def _map_openai_size_to_vertex_ai_aspect_ratio(size: Optional[str]) -> str: + """Map OpenAI size parameter to Vertex AI aspectRatio.""" + if size is None: + return "1:1" + + # Map OpenAI size strings to Vertex AI aspect ratio strings + # Vertex AI accepts: "1:1", "9:16", "16:9", "4:3", "3:4" + size_to_aspect_ratio = { + "256x256": "1:1", # Square + "512x512": "1:1", # Square + "1024x1024": "1:1", # Square (default) + "1792x1024": "16:9", # Landscape + "1024x1792": "9:16", # Portrait + } + return size_to_aspect_ratio.get(size, "1:1") # Default to square if size not recognized + + def get_optional_params_image_gen( model: Optional[str] = None, n: Optional[int] = None, @@ -2614,19 +2631,7 @@ def get_optional_params_image_gen( # Map OpenAI size parameter to Vertex AI aspectRatio if size is not None: - # Map OpenAI size strings to Vertex AI aspect ratio strings - # Vertex AI accepts: "1:1", "9:16", "16:9", "4:3", "3:4" - size_to_aspect_ratio = { - "256x256": "1:1", # Square - "512x512": "1:1", # Square - "1024x1024": "1:1", # Square (default) - "1792x1024": "16:9", # Landscape - "1024x1792": "9:16", # Portrait - } - aspect_ratio = size_to_aspect_ratio.get( - size, "1:1" - ) # Default to square if size not recognized - optional_params["aspectRatio"] = aspect_ratio + optional_params["aspectRatio"] = _map_openai_size_to_vertex_ai_aspect_ratio(size) openai_params: list[str] = list(default_params.keys()) if provider_config is not None: @@ -2642,6 +2647,12 @@ def get_optional_params_image_gen( openai_params=openai_params, additional_drop_params=additional_drop_params, ) + # remove keys with None or empty dict/list values to avoid sending empty payloads + optional_params = { + k: v + for k, v in optional_params.items() + if v is not None and (not isinstance(v, (dict, list)) or len(v) > 0) + } return optional_params diff --git a/tests/image_gen_tests/request_payloads/azure_gpt_image_1.json b/tests/image_gen_tests/request_payloads/azure_gpt_image_1.json new file mode 100644 index 00000000000..1ad57bc0d4c --- /dev/null +++ b/tests/image_gen_tests/request_payloads/azure_gpt_image_1.json @@ -0,0 +1,4 @@ +{ + "model": "gpt-image-1", + "prompt": "test prompt" +} diff --git a/tests/image_gen_tests/test_image_generation.py b/tests/image_gen_tests/test_image_generation.py index 61843d3151e..c3900342954 100644 --- a/tests/image_gen_tests/test_image_generation.py +++ b/tests/image_gen_tests/test_image_generation.py @@ -5,6 +5,7 @@ import logging import os import sys import traceback +from unittest.mock import AsyncMock, patch sys.path.insert( @@ -329,3 +330,33 @@ async def test_aiml_image_generation_with_dynamic_api_key(): assert captured_json_data is not None assert captured_json_data["prompt"] == "A cute baby sea otter" assert captured_json_data["model"] == "flux-pro/v1.1" + +@pytest.mark.asyncio +async def test_azure_image_generation_request_body(): + from litellm import aimage_generation + test_dir = os.path.dirname(__file__) + expected_path = os.path.join( + test_dir, "request_payloads", "azure_gpt_image_1.json" + ) + with open(expected_path, "r") as f: + expected_body = json.load(f) + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + new_callable=AsyncMock, + ) as mock_post: + mock_post.side_effect = Exception("test") + + with pytest.raises(Exception): + await aimage_generation( + model="azure/gpt-image-1", + prompt="test prompt", + api_base="https://example.azure.com", + api_key="test-key", + api_version="2025-04-01-preview", + ) + + mock_post.assert_called_once() + call_args = mock_post.call_args + request_json = call_args.kwargs.get("json", {}) + assert request_json == expected_body diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 2bf2935901e..f9f150b6234 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -84,6 +84,15 @@ def test_get_optional_params_image_gen_vertex_ai_size(): assert optional_params["sampleCount"] == 1 +def test_get_optional_params_image_gen_filters_empty_values(): + optional_params = get_optional_params_image_gen( + model="gpt-image-1", + custom_llm_provider="openai", + extra_body={}, + ) + assert optional_params == {} + + def test_all_model_configs(): from litellm.llms.vertex_ai.vertex_ai_partner_models.ai21.transformation import ( VertexAIAi21Config, From 69ef062f5565f9411e8648d758a52da878cc52c6 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 19:56:44 -0700 Subject: [PATCH 45/73] fix tiered_pricing test --- ...odel_prices_and_context_window_backup.json | 5 ----- model_prices_and_context_window.json | 5 ----- tests/test_litellm/test_utils.py | 20 +++++++++++++++++++ 3 files changed, 20 insertions(+), 10 deletions(-) diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 972e212e693..46331c7cff7 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -19132,7 +19132,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, @@ -19161,7 +19160,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 32e3], "input_cost_per_token": 1e-06, "output_cost_per_token": 5e-06, "cache_read_input_token_cost": 1e-07}, @@ -19196,7 +19194,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 32e3], "input_cost_per_token": 3e-07, "output_cost_per_token": 1.5e-06, "cache_read_input_token_cost": 8e-08}, @@ -19302,7 +19299,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, @@ -19355,7 +19351,6 @@ "max_tokens": 131072, "max_input_tokens": 98304, "max_output_tokens": 8192, - "max_chain_of_thought_tokens": 32768, "input_cost_per_token": 8e-07, "output_cost_per_token": 2.4e-06, "litellm_provider": "dashscope", diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 972e212e693..46331c7cff7 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -19132,7 +19132,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, @@ -19161,7 +19160,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 32e3], "input_cost_per_token": 1e-06, "output_cost_per_token": 5e-06, "cache_read_input_token_cost": 1e-07}, @@ -19196,7 +19194,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 32e3], "input_cost_per_token": 3e-07, "output_cost_per_token": 1.5e-06, "cache_read_input_token_cost": 8e-08}, @@ -19302,7 +19299,6 @@ "supports_function_calling": true, "supports_tool_choice": true, "supports_reasoning": true, - "supports_caching": true, "mode": "chat", "tiered_pricing": [ {"range": [0, 256e3], "input_cost_per_token": 5e-08, "output_cost_per_token": 4e-07}, @@ -19355,7 +19351,6 @@ "max_tokens": 131072, "max_input_tokens": 98304, "max_output_tokens": 8192, - "max_chain_of_thought_tokens": 32768, "input_cost_per_token": 8e-07, "output_cost_per_token": 2.4e-06, "litellm_provider": "dashscope", diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index f9f150b6234..0c7aa527240 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -652,6 +652,26 @@ def test_aaamodel_prices_and_context_window_json_is_valid(): }, }, "supports_native_streaming": {"type": "boolean"}, + "tiered_pricing": { + "type": "array", + "items": { + "type": "object", + "properties": { + "range": { + "type": "array", + "items": {"type": "number"}, + "minItems": 2, + "maxItems": 2 + }, + "input_cost_per_token": {"type": "number"}, + "output_cost_per_token": {"type": "number"}, + "cache_read_input_token_cost": {"type": "number"}, + "output_cost_per_reasoning_token": {"type": "number"} + }, + "required": ["range"], + "additionalProperties": False + } + }, }, "additionalProperties": False, }, From 94038108ad75dc25efc2bafbd1d9a7b0165aaa1e Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 20:00:54 -0700 Subject: [PATCH 46/73] fix dashscope api base --- .../add_model/provider_specific_fields.tsx | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) 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 ce19a248f83..a76d7b64ffe 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 @@ -206,6 +206,22 @@ const PROVIDER_CREDENTIAL_FIELDS: Record = required: true } ], + [Providers.Dashscope]: [ + { + key: "api_key", + label: "Dashscope API Key", + type: "password", + required: true + }, + { + key: "api_base", + label: "API Base", + placeholder: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + defaultValue: "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", + required: true, + tooltip: "The base URL for your Dashscope server. Defaults to https://dashscope-intl.aliyuncs.com/compatible-mode/v1 if not specified." + } + ], [Providers.OpenAI_Text_Compatible]: [ { key: "api_base", From d89152bb2ccce4ab7713091715dd0abc11dd9ce3 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 20:04:24 -0700 Subject: [PATCH 47/73] feat(litellm_logging.py): support new litellm debug parameter - `litellm_request_debug` on requests enables printing raw request when flag is set to true on requests --- litellm/constants.py | 10 +++-- .../litellm_core_utils/get_litellm_params.py | 2 + litellm/litellm_core_utils/litellm_logging.py | 45 +++++++++++++------ litellm/main.py | 44 ++++++++++++------ litellm/types/utils.py | 3 +- 5 files changed, 73 insertions(+), 31 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 75c25d9ea9e..c0ce0f265b5 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -15,7 +15,7 @@ DEFAULT_SQS_FLUSH_INTERVAL_SECONDS = int( os.getenv("DEFAULT_SQS_FLUSH_INTERVAL_SECONDS", 10) ) DEFAULT_NUM_WORKERS_LITELLM_PROXY = int( - os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", os.cpu_count() or 4) + os.getenv("DEFAULT_NUM_WORKERS_LITELLM_PROXY", 1) ) DEFAULT_SQS_BATCH_SIZE = int(os.getenv("DEFAULT_SQS_BATCH_SIZE", 512)) SQS_SEND_MESSAGE_ACTION = "SendMessage" @@ -60,7 +60,9 @@ DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO = int( os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_PRO", 128) ) DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE = int( - os.getenv("DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512) + os.getenv( + "DEFAULT_REASONING_EFFORT_MINIMAL_THINKING_BUDGET_GEMINI_2_5_FLASH_LITE", 512 + ) ) # Generic fallback for unknown models @@ -949,7 +951,9 @@ LITELLM_CLI_SESSION_TOKEN_PREFIX = "litellm-session-token" DB_SPEND_UPDATE_JOB_NAME = "db_spend_update_job" PROMETHEUS_EMIT_BUDGET_METRICS_JOB_NAME = "prometheus_emit_budget_metrics" CLOUDZERO_EXPORT_USAGE_DATA_JOB_NAME = "cloudzero_export_usage_data" -CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int(os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000)) +CLOUDZERO_MAX_FETCHED_DATA_RECORDS = int( + os.getenv("CLOUDZERO_MAX_FETCHED_DATA_RECORDS", 50000) +) SPEND_LOG_CLEANUP_JOB_NAME = "spend_log_cleanup" SPEND_LOG_RUN_LOOPS = int(os.getenv("SPEND_LOG_RUN_LOOPS", 500)) SPEND_LOG_CLEANUP_BATCH_SIZE = int(os.getenv("SPEND_LOG_CLEANUP_BATCH_SIZE", 1000)) diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index c354dea0241..c167c202e5d 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -62,6 +62,7 @@ def get_litellm_params( use_litellm_proxy: Optional[bool] = None, api_version: Optional[str] = None, max_retries: Optional[int] = None, + litellm_request_debug: Optional[bool] = None, **kwargs, ) -> dict: litellm_params = { @@ -118,5 +119,6 @@ def get_litellm_params( "vertex_credentials": kwargs.get("vertex_credentials"), "vertex_project": kwargs.get("vertex_project"), "use_litellm_proxy": use_litellm_proxy, + "litellm_request_debug": litellm_request_debug, } return litellm_params diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 19d7c5512ba..59dd09f2728 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -245,6 +245,7 @@ class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app custom_pricing: bool = False stream_options = None + litellm_request_debug: bool = False def __init__( self, @@ -470,6 +471,7 @@ class Logging(LiteLLMLoggingBaseClass): **self.litellm_params, **scrub_sensitive_keys_in_metadata(litellm_params), } + self.litellm_request_debug = litellm_params.get("litellm_request_debug", False) self.logger_fn = litellm_params.get("logger_fn", None) verbose_logger.debug(f"self.optional_params: {self.optional_params}") @@ -907,13 +909,19 @@ class Logging(LiteLLMLoggingBaseClass): Prints the RAW curl command sent from LiteLLM """ - if _is_debugging_on(): + if _is_debugging_on() or self.litellm_request_debug: if json_logs: masked_headers = self._get_masked_headers(headers) - verbose_logger.debug( - "POST Request Sent from LiteLLM", - extra={"api_base": {api_base}, **masked_headers}, - ) + if self.litellm_request_debug: + verbose_logger.warning( # .warning ensures this shows up in all environments + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) + else: + verbose_logger.debug( + "POST Request Sent from LiteLLM", + extra={"api_base": {api_base}, **masked_headers}, + ) else: headers = additional_args.get("headers", {}) if headers is None: @@ -926,7 +934,12 @@ class Logging(LiteLLMLoggingBaseClass): additional_args=additional_args, data=data, ) - verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") + if self.litellm_request_debug: + verbose_logger.warning( + f"\033[92m{curl_command}\033[0m\n" + ) # .warning ensures this shows up in all environments + else: + verbose_logger.debug(f"\033[92m{curl_command}\033[0m\n") def _get_request_body(self, data: dict) -> str: return str(data) @@ -1714,12 +1727,16 @@ class Logging(LiteLLMLoggingBaseClass): response_obj=result, start_time=start_time, end_time=end_time, - litellm_call_id=current_call_id - if ( - current_call_id := litellm_params.get("litellm_call_id") - ) - is not None - else str(uuid.uuid4()), + litellm_call_id=( + current_call_id + if ( + current_call_id := litellm_params.get( + "litellm_call_id" + ) + ) + is not None + else str(uuid.uuid4()) + ), print_verbose=print_verbose, ) if callback == "wandb" and weightsBiasesLogger is not None: @@ -3367,6 +3384,7 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 return galileo_logger # type: ignore elif logging_integration == "cloudzero": from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: if isinstance(callback, CloudZeroLogger): return callback # type: ignore @@ -3594,6 +3612,7 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 return callback elif logging_integration == "cloudzero": from litellm.integrations.cloudzero.cloudzero import CloudZeroLogger + for callback in _in_memory_loggers: if isinstance(callback, CloudZeroLogger): return callback @@ -4504,7 +4523,7 @@ def get_standard_logging_object_payload( def emit_standard_logging_payload(payload: StandardLoggingPayload): if os.getenv("LITELLM_PRINT_STANDARD_LOGGING_PAYLOAD"): - print(json.dumps(payload, indent=4)) # noqa + print(json.dumps(payload, indent=4)) # noqa def get_standard_logging_metadata( diff --git a/litellm/main.py b/litellm/main.py index d7395eb1457..6c81d3eded9 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -150,9 +150,9 @@ from .llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from .llms.custom_llm import CustomLLM, custom_chat_llm_router from .llms.databricks.embed.handler import DatabricksEmbeddingHandler from .llms.deprecated_providers import aleph_alpha, palm +from .llms.gemini.common_utils import get_api_key_from_env from .llms.groq.chat.handler import GroqChatCompletion from .llms.heroku.chat.transformation import HerokuChatConfig -from .llms.gemini.common_utils import get_api_key_from_env from .llms.huggingface.embedding.handler import HuggingFaceEmbedding from .llms.nlp_cloud.chat.handler import completion as nlp_cloud_chat_completion from .llms.oci.chat.transformation import OCIChatConfig @@ -358,7 +358,9 @@ async def acompletion( logprobs: Optional[bool] = None, top_logprobs: Optional[int] = None, deployment_id=None, - reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None, + reasoning_effort: Optional[ + Literal["none", "minimal", "low", "medium", "high", "default"] + ] = None, safety_identifier: Optional[str] = None, # set api_base, api_version, api_key base_url: Optional[str] = None, @@ -504,7 +506,9 @@ async def acompletion( } if custom_llm_provider is None: _, custom_llm_provider, _, _ = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=completion_kwargs.get("base_url", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=completion_kwargs.get("base_url", None), ) fallbacks = fallbacks or litellm.model_fallbacks @@ -899,7 +903,9 @@ def completion( # type: ignore # noqa: PLR0915 logit_bias: Optional[dict] = None, user: Optional[str] = None, # openai v1.0+ new params - reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high", "default"]] = None, + reasoning_effort: Optional[ + Literal["none", "minimal", "low", "medium", "high", "default"] + ] = None, response_format: Optional[Union[dict, Type[BaseModel]]] = None, seed: Optional[int] = None, tools: Optional[List] = None, @@ -1116,10 +1122,12 @@ def completion( # type: ignore # noqa: PLR0915 ) if provider_specific_header is not None: - headers.update(ProviderSpecificHeaderUtils.get_provider_specific_headers( - provider_specific_header=provider_specific_header, - custom_llm_provider=custom_llm_provider, - )) + headers.update( + ProviderSpecificHeaderUtils.get_provider_specific_headers( + provider_specific_header=provider_specific_header, + custom_llm_provider=custom_llm_provider, + ) + ) if model_response is not None and hasattr(model_response, "_hidden_params"): model_response._hidden_params["custom_llm_provider"] = custom_llm_provider @@ -1325,6 +1333,7 @@ def completion( # type: ignore # noqa: PLR0915 azure_scope=kwargs.get("azure_scope"), max_retries=max_retries, timeout=timeout, + litellm_request_debug=kwargs.get("litellm_request_debug", False), ) cast(LiteLLMLoggingObj, logging).update_environment_variables( model=model, @@ -2712,9 +2721,7 @@ def completion( # type: ignore # noqa: PLR0915 ) api_key = ( - api_key - or litellm.api_key - or get_secret("VERCEL_AI_GATEWAY_API_KEY") + api_key or litellm.api_key or get_secret("VERCEL_AI_GATEWAY_API_KEY") ) vercel_site_url = get_secret("VERCEL_SITE_URL") or "https://litellm.ai" @@ -2730,7 +2737,7 @@ def completion( # type: ignore # noqa: PLR0915 vercel_headers.update(_headers) headers = vercel_headers - + ## Load Config config = litellm.VercelAIGatewayConfig.get_config() for k, v in config.items(): @@ -3712,7 +3719,9 @@ async def aembedding(*args, **kwargs) -> EmbeddingResponse: func_with_context = partial(ctx.run, func) _, custom_llm_provider, _, _ = get_llm_provider( - model=model, custom_llm_provider=custom_llm_provider, api_base=kwargs.get("api_base", None) + model=model, + custom_llm_provider=custom_llm_provider, + api_base=kwargs.get("api_base", None), ) # Await normally @@ -5780,7 +5789,14 @@ async def ahealth_check( input=input or ["test"], ), "audio_speech": lambda: litellm.aspeech( - **{**_filter_model_params(model_params), **({"voice": "alloy"} if "voice" not in _filter_model_params(model_params) else {})}, + **{ + **_filter_model_params(model_params), + **( + {"voice": "alloy"} + if "voice" not in _filter_model_params(model_params) + else {} + ), + }, input=prompt or "test", ), "audio_transcription": lambda: litellm.atranscription( diff --git a/litellm/types/utils.py b/litellm/types/utils.py index c6f7098a1a7..6452254d11f 100644 --- a/litellm/types/utils.py +++ b/litellm/types/utils.py @@ -1995,7 +1995,7 @@ class StandardLoggingGuardrailInformation(TypedDict, total=False): ] guardrail_request: Optional[dict] guardrail_response: Optional[Union[dict, str, List[dict]]] - guardrail_status: Literal["success", "failure","blocked"] + guardrail_status: Literal["success", "failure", "blocked"] start_time: Optional[float] end_time: Optional[float] duration: Optional[float] @@ -2123,6 +2123,7 @@ all_litellm_params = [ "metadata", "litellm_metadata", "litellm_trace_id", + "litellm_request_debug", "guardrails", "tags", "acompletion", From 1e230e87e3d60fbe5bc7b51504244d68a36c1f3b Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 20:05:29 -0700 Subject: [PATCH 48/73] fixes for mypy --- litellm/responses/mcp/mcp_streaming_iterator.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/litellm/responses/mcp/mcp_streaming_iterator.py b/litellm/responses/mcp/mcp_streaming_iterator.py index b0673880cec..8045d1c1dca 100644 --- a/litellm/responses/mcp/mcp_streaming_iterator.py +++ b/litellm/responses/mcp/mcp_streaming_iterator.py @@ -70,7 +70,8 @@ async def create_mcp_list_tools_events( mcp_tools_dict = [] for tool in filtered_mcp_tools: if hasattr(tool, 'model_dump') and callable(getattr(tool, 'model_dump')): - mcp_tools_dict.append(tool.model_dump()) + # Type cast to help mypy understand this is safe after hasattr check + mcp_tools_dict.append(cast(Any, tool).model_dump()) elif hasattr(tool, '__dict__'): mcp_tools_dict.append(tool.__dict__) else: From 48619f2b7bd6a71a86c4af57d151a21d1538d1ed Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 20:07:26 -0700 Subject: [PATCH 49/73] fix(litellm_logging.py): support emitting raw response on self.litellm_request_debug is true Addresses https://github.com/BerriAI/litellm/issues/13814 --- litellm/litellm_core_utils/litellm_logging.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 59dd09f2728..2f21d280899 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -996,8 +996,14 @@ class Logging(LiteLLMLoggingBaseClass): self.model_call_details["additional_args"] = additional_args self.model_call_details["log_event_type"] = "post_api_call" + if self.litellm_request_debug: + attr = "warning" + else: + attr = "debug" + if json_logs: - verbose_logger.debug( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details @@ -1005,7 +1011,8 @@ class Logging(LiteLLMLoggingBaseClass): ), ) else: - print_verbose( + callattr = getattr(verbose_logger, attr) + callattr( "RAW RESPONSE:\n{}\n\n".format( self.model_call_details.get( "original_response", self.model_call_details From 32d87c242bf6942de3ac738f90ebcae4e8ffab08 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 20:07:41 -0700 Subject: [PATCH 50/73] [Fixes] Using Qwen API Tiered Pricing (#14479) * fix: use dashscope cost calc * add qwen logo --- litellm/cost_calculator.py | 5 ++ litellm/llms/dashscope/cost_calculator.py | 33 +++++----- .../_experimental/out/assets/logos/qwen.png | Bin 0 -> 49453 bytes litellm/proxy/proxy_config.yaml | 4 +- .../test_dashscope_cost_calculator.py | 58 +++++++++++++----- .../out/assets/logos/qwen.png | Bin 0 -> 49453 bytes .../public/assets/logos/qwen.png | Bin 0 -> 49453 bytes 7 files changed, 63 insertions(+), 37 deletions(-) create mode 100644 litellm/proxy/_experimental/out/assets/logos/qwen.png create mode 100644 ui/litellm-dashboard/out/assets/logos/qwen.png create mode 100644 ui/litellm-dashboard/public/assets/logos/qwen.png diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 01f3e2472f8..5d8f5faadf1 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -344,6 +344,11 @@ def cost_per_token( # noqa: PLR0915 return perplexity_cost_per_token(model=model, usage=usage_block) elif custom_llm_provider == "xai": return xai_cost_per_token(model=model, usage=usage_block) + elif custom_llm_provider == "dashscope": + from litellm.llms.dashscope.cost_calculator import ( + cost_per_token as dashscope_cost_per_token, + ) + return dashscope_cost_per_token(model=model, usage=usage_block) else: model_info = _cached_get_model_info_helper( model=model, custom_llm_provider=custom_llm_provider diff --git a/litellm/llms/dashscope/cost_calculator.py b/litellm/llms/dashscope/cost_calculator.py index 6f9ebe302f4..107eb7f5adf 100644 --- a/litellm/llms/dashscope/cost_calculator.py +++ b/litellm/llms/dashscope/cost_calculator.py @@ -45,36 +45,33 @@ def _calculate_tiered_cost( cost_key: str, fallback_cost_key: Optional[str] = None ) -> float: - """Calculate cost using tiered pricing structure.""" + """Calculate cost using tiered pricing structure. + + Finds the appropriate tier based on token count and applies that tier's rate to all tokens. + """ if not tiered_pricing or tokens <= 0: return 0.0 - total_cost = 0.0 - tokens_processed = 0 - + # Find the appropriate tier for the token count for tier in tiered_pricing: - if tokens_processed >= tokens: - break - tier_range = tier.get("range", []) if len(tier_range) != 2: continue range_start, range_end = tier_range - if tokens <= range_start: - break - - tier_start = max(range_start, tokens_processed) - tier_end = min(range_end, tokens) - - if tier_end > tier_start: - tokens_in_tier = tier_end - tier_start + # Check if tokens fall within this tier's range + if range_start <= tokens <= range_end: cost_per_token = tier.get(cost_key) or tier.get(fallback_cost_key, 0) - total_cost += tokens_in_tier * cost_per_token - tokens_processed = tier_end + return tokens * cost_per_token - return total_cost + # If no tier matches, use the last tier (highest tier) + if tiered_pricing: + last_tier = tiered_pricing[-1] + cost_per_token = last_tier.get(cost_key) or last_tier.get(fallback_cost_key, 0) + return tokens * cost_per_token + + return 0.0 def _calculate_flat_cost(tokens: int, cost_per_token: float) -> float: diff --git a/litellm/proxy/_experimental/out/assets/logos/qwen.png b/litellm/proxy/_experimental/out/assets/logos/qwen.png new file mode 100644 index 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TestDashscopeCostCalculator: assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) - def test_tiered_pricing_multiple_tiers(self): - """Test tiered pricing when tokens span multiple tiers.""" + def test_tiered_pricing_higher_tier(self): + """Test tiered pricing when tokens fall in higher tier (tier 3).""" usage = Usage( - prompt_tokens=150000, # Spans tiers 1 (0-32K), 2 (32K-128K), 3 (128K-256K) + prompt_tokens=150000, # Falls in tier 3 (128K-256K) completion_tokens=2000, total_tokens=152000 ) @@ -110,13 +110,13 @@ class TestDashscopeCostCalculator: ) # Expected input cost calculation: - # Tier 1 (0-32K): 32,000 tokens * $1e-6 = $0.032 - # Tier 2 (32K-128K): 96,000 tokens * $1.8e-6 = $0.1728 - # Tier 3 (128K-256K): 22,000 tokens * $3e-6 = $0.066 - # Total input cost = $0.032 + $0.1728 + $0.066 = $0.2708 + # 150,000 tokens falls in tier 3 (128K-256K), so all tokens are charged at tier 3 rate + # Input: 150,000 tokens * $3e-6 = $0.45 + # Output: 2,000 tokens falls in tier 1 (0-32K), so charged at tier 1 rate + # Output: 2,000 tokens * $5e-6 = $0.01 - expected_prompt_cost = (32000 * 1e-6) + (96000 * 1.8e-6) + (22000 * 3e-6) - expected_completion_cost = 2000 * 5e-6 # All in tier 1 for output + expected_prompt_cost = 150000 * 3e-6 # All tokens at tier 3 rate + expected_completion_cost = 2000 * 5e-6 # All tokens at tier 1 rate assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) @@ -140,20 +140,44 @@ class TestDashscopeCostCalculator: ) # Expected cost calculation: - # Regular tokens: 40,000 (32K in tier 1 + 8K in tier 2) - # - Tier 1: 32,000 * $1e-6 = $0.032 - # - Tier 2: 8,000 * $1.8e-6 = $0.0144 - # Cached tokens: 10,000 in tier 1 at discounted rate - # - Tier 1 cached: 10,000 * $1e-7 = $0.001 - # Total input cost = $0.032 + $0.0144 + $0.001 = $0.0474 + # Regular tokens: 40,000 falls in tier 2 (32K-128K), so all charged at tier 2 rate + # - Regular: 40,000 * $1.8e-6 = $0.072 + # Cached tokens: 10,000 falls in tier 1 (0-32K), so charged at tier 1 cached rate + # - Cached: 10,000 * $1e-7 = $0.001 + # Total input cost = $0.072 + $0.001 = $0.073 regular_tokens = 40000 cached_tokens = 10000 - expected_regular_cost = (32000 * 1e-6) + (8000 * 1.8e-6) + expected_regular_cost = regular_tokens * 1.8e-6 # Tier 2 rate expected_cached_cost = cached_tokens * 1e-7 # Tier 1 cached rate expected_prompt_cost = expected_regular_cost + expected_cached_cost - expected_completion_cost = 1000 * 5e-6 + expected_completion_cost = 1000 * 5e-6 # Tier 1 rate + + assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) + assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) + + def test_tiered_pricing_highest_tier(self): + """Test tiered pricing when tokens exceed highest tier range.""" + usage = Usage( + prompt_tokens=2000000, # Exceeds tier 4 max (1M), should use tier 4 rate + completion_tokens=5000, + total_tokens=2005000 + ) + + prompt_cost, completion_cost = dashscope_cost_per_token( + model="qwen3-coder-plus", + usage=usage + ) + + # Expected cost calculation: + # 2,000,000 tokens exceeds tier 4 (256K-1M), so use tier 4 rate for all tokens + # Input: 2,000,000 tokens * $6e-6 = $12.0 + # Output: 5,000 tokens falls in tier 1 (0-32K), so charged at tier 1 rate + # Output: 5,000 tokens * $5e-6 = $0.025 + + expected_prompt_cost = 2000000 * 6e-6 # Tier 4 rate (highest tier) + expected_completion_cost = 5000 * 5e-6 # Tier 1 rate assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-10) assert math.isclose(completion_cost, expected_completion_cost, rel_tol=1e-10) \ No newline at end of file diff --git a/ui/litellm-dashboard/out/assets/logos/qwen.png b/ui/litellm-dashboard/out/assets/logos/qwen.png new file mode 100644 index 0000000000000000000000000000000000000000..d9feba46a28e64110d309b027a932328c62e5d95 GIT binary patch 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zad{bBHnKa`H3&{*K5{8eM}XVgu!qyYiI4o62lMLOwC!AmrW3Fu(H^i@o&bW?#!-I(cdYh9WP6=E`6mg%=&Pl$mr^H$FOgnZM)L@5+_V6jb|$t zo4prPxZ@{=X#)?Yq_GsL(&HNi;0^+-(SZVX|M-tZ4I70|uH$zB{&>Xy>%WEueG=(~ WSdSYrJr;`iChYdxTa{UOT=)&NoEbF$ literal 0 HcmV?d00001 From 698a2dd382f59face841ba387ae9d40ed41f47bc Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 20:12:01 -0700 Subject: [PATCH 51/73] fix: convert_anthropic_tool_to_databricks_tool --- litellm/llms/databricks/chat/transformation.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/litellm/llms/databricks/chat/transformation.py b/litellm/llms/databricks/chat/transformation.py index 4852f2e7106..a1370074238 100644 --- a/litellm/llms/databricks/chat/transformation.py +++ b/litellm/llms/databricks/chat/transformation.py @@ -169,18 +169,20 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig): if tool is None: return None - kwags: dict = { + # Build DatabricksFunction explicitly to avoid parameter conflicts + function_params: DatabricksFunction = { "name": tool["name"], "parameters": cast(dict, tool.get("input_schema") or {}) } - + + # Only add description if it exists description = tool.get("description") if description is not None: - kwags["description"] = cast(Union[dict, str], description) + function_params["description"] = cast(Union[dict, str], description) return DatabricksTool( type="function", - function=DatabricksFunction(name=tool["name"], **kwags), + function=function_params, ) def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]: From c802c472b5598de67126d3c93b75ac08ca498c86 Mon Sep 17 00:00:00 2001 From: Krrish Dholakia Date: Thu, 11 Sep 2025 20:17:39 -0700 Subject: [PATCH 52/73] docs(debugging.md): document new feature Closes https://github.com/BerriAI/litellm/issues/13814 --- docs/my-website/docs/proxy/debugging.md | 68 +++++++++++++++++++------ 1 file changed, 53 insertions(+), 15 deletions(-) diff --git a/docs/my-website/docs/proxy/debugging.md b/docs/my-website/docs/proxy/debugging.md index 5cca6541763..fbcac24a4d6 100644 --- a/docs/my-website/docs/proxy/debugging.md +++ b/docs/my-website/docs/proxy/debugging.md @@ -11,13 +11,13 @@ The proxy also supports json logs. [See here](#json-logs) **via cli** -```bash +```bash showLineNumbers $ litellm --debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "INFO" ``` @@ -25,25 +25,25 @@ os.environ["LITELLM_LOG"] = "INFO" **via cli** -```bash +```bash showLineNumbers $ litellm --detailed_debug ``` **via env** -```python +```python showLineNumbers os.environ["LITELLM_LOG"] = "DEBUG" ``` ### Debug Logs Run the proxy with `--detailed_debug` to view detailed debug logs -```shell +```shell showLineNumbers litellm --config /path/to/config.yaml --detailed_debug ``` When making requests you should see the POST request sent by LiteLLM to the LLM on the Terminal output -```shell +```shell showLineNumbers POST Request Sent from LiteLLM: curl -X POST \ https://api.openai.com/v1/chat/completions \ @@ -51,25 +51,63 @@ https://api.openai.com/v1/chat/completions \ -d '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "this is a test request, write a short poem"}]}' ``` +## Debug single request + +Pass in `litellm_request_debug=True` in the request body + +```bash showLineNumbers +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model":"fake-openai-endpoint", + "messages": [{"role": "user","content": "How many r in the word strawberry?"}], + "litellm_request_debug": true +}' +``` + +This will emit the raw request sent by LiteLLM to the API Provider and raw response received from the API Provider for **just** this request in the logs. + + +```bash showLineNumbers +INFO: Uvicorn running on http://0.0.0.0:4000 (Press CTRL+C to quit) +20:14:06 - LiteLLM:WARNING: litellm_logging.py:938 - + +POST Request Sent from LiteLLM: +curl -X POST \ +https://exampleopenaiendpoint-production.up.railway.app/chat/completions \ +-H 'Authorization: Be****ey' -H 'Content-Type: application/json' \ +-d '{'model': 'fake', 'messages': [{'role': 'user', 'content': 'How many r in the word strawberry?'}], 'stream': False}' + + +20:14:06 - LiteLLM:WARNING: litellm_logging.py:1015 - RAW RESPONSE: +{"id":"chatcmpl-817fc08f0d6c451485d571dab39b26a1","object":"chat.completion","created":1677652288,"model":"gpt-3.5-turbo-0301","system_fingerprint":"fp_44709d6fcb","choices":[{"index":0,"message":{"role":"assistant","content":"\n\nHello there, how may I assist you today?"},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":9,"completion_tokens":12,"total_tokens":21}} + + +INFO: 127.0.0.1:56155 - "POST /chat/completions HTTP/1.1" 200 OK + +``` + + ## JSON LOGS Set `JSON_LOGS="True"` in your env: -```bash +```bash showLineNumbers export JSON_LOGS="True" ``` **OR** Set `json_logs: true` in your yaml: -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` Start proxy -```bash +```bash showLineNumbers $ litellm ``` @@ -80,7 +118,7 @@ The proxy will now all logs in json format. Turn off fastapi's default 'INFO' logs 1. Turn on 'json logs' -```yaml +```yaml showLineNumbers litellm_settings: json_logs: true ``` @@ -89,20 +127,20 @@ litellm_settings: Only get logs if an error occurs. -```bash +```bash showLineNumbers LITELLM_LOG="ERROR" ``` 3. Start proxy -```bash +```bash showLineNumbers $ litellm ``` Expected Output: -```bash +```bash showLineNumbers # no info statements ``` @@ -119,14 +157,14 @@ This can be caused due to all your models hitting rate limit errors, causing the How to control this? - Adjust the cooldown time -```yaml +```yaml showLineNumbers router_settings: cooldown_time: 0 # 👈 KEY CHANGE ``` - Disable Cooldowns [NOT RECOMMENDED] -```yaml +```yaml showLineNumbers router_settings: disable_cooldowns: True ``` From f374103c46ad2c4d8f24b427ed956b8f37cc6935 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Thu, 11 Sep 2025 20:20:52 -0700 Subject: [PATCH 53/73] UI new build --- .../_buildManifest.js | 0 .../_ssgManifest.js | 0 .../out/_next/static/chunks/162-4e7640b4d68e1ae4.js | 1 + .../out/_next/static/chunks/162-714ca0ed10a07f66.js | 1 - .../out/_next/static/chunks/220-5061c4cea850d728.js | 8 ++++---- .../out/_next/static/chunks/866-9e1803a09e9ae8da.js | 2 +- .../out/_next/static/chunks/app/page-127adcf8da2b5294.js | 1 + .../out/_next/static/chunks/app/page-8dc8d9524a1f3965.js | 1 - .../out/_next/static/css/060d5ddee53e45ce.css | 3 --- .../out/_next/static/css/c528590c6415a94c.css | 3 +++ litellm/proxy/_experimental/out/index.html | 2 +- litellm/proxy/_experimental/out/index.txt | 4 ++-- litellm/proxy/_experimental/out/model_hub.txt | 4 ++-- .../{model_hub_table/index.html => model_hub_table.html} | 2 +- litellm/proxy/_experimental/out/model_hub_table.txt | 4 ++-- litellm/proxy/_experimental/out/onboarding.html | 1 + litellm/proxy/_experimental/out/onboarding.txt | 2 +- ui/litellm-dashboard/out/404.html | 2 +- .../_buildManifest.js | 0 .../_ssgManifest.js | 0 .../out/_next/static/chunks/162-4e7640b4d68e1ae4.js | 1 + .../out/_next/static/chunks/162-714ca0ed10a07f66.js | 1 - .../out/_next/static/chunks/220-5061c4cea850d728.js | 8 ++++---- .../out/_next/static/chunks/866-9e1803a09e9ae8da.js | 2 +- .../out/_next/static/chunks/app/page-127adcf8da2b5294.js | 1 + .../out/_next/static/chunks/app/page-8dc8d9524a1f3965.js | 1 - .../out/_next/static/css/060d5ddee53e45ce.css | 3 --- .../out/_next/static/css/c528590c6415a94c.css | 3 +++ ui/litellm-dashboard/out/index.html | 2 +- ui/litellm-dashboard/out/index.txt | 4 ++-- ui/litellm-dashboard/out/model_hub.html | 2 +- ui/litellm-dashboard/out/model_hub.txt | 4 ++-- ui/litellm-dashboard/out/model_hub_table.html | 2 +- ui/litellm-dashboard/out/model_hub_table.txt | 4 ++-- ui/litellm-dashboard/out/onboarding.html | 2 +- ui/litellm-dashboard/out/onboarding.txt | 2 +- 36 files changed, 42 insertions(+), 41 deletions(-) rename litellm/proxy/_experimental/out/_next/static/{0GF-OyXnYlAPMWfyPAZSs => FMlZjJYLUentCU02Wj6R_}/_buildManifest.js (100%) rename litellm/proxy/_experimental/out/_next/static/{0GF-OyXnYlAPMWfyPAZSs => FMlZjJYLUentCU02Wj6R_}/_ssgManifest.js (100%) create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/162-714ca0ed10a07f66.js rename ui/litellm-dashboard/out/_next/static/chunks/220-1c8d82f7ce7658c4.js => litellm/proxy/_experimental/out/_next/static/chunks/220-5061c4cea850d728.js (71%) rename ui/litellm-dashboard/out/_next/static/chunks/866-3523e0e07cf314f6.js => litellm/proxy/_experimental/out/_next/static/chunks/866-9e1803a09e9ae8da.js (99%) create mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/page-127adcf8da2b5294.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/chunks/app/page-8dc8d9524a1f3965.js delete mode 100644 litellm/proxy/_experimental/out/_next/static/css/060d5ddee53e45ce.css create mode 100644 litellm/proxy/_experimental/out/_next/static/css/c528590c6415a94c.css rename litellm/proxy/_experimental/out/{model_hub_table/index.html => model_hub_table.html} (92%) create mode 100644 litellm/proxy/_experimental/out/onboarding.html rename ui/litellm-dashboard/out/_next/static/{0GF-OyXnYlAPMWfyPAZSs => FMlZjJYLUentCU02Wj6R_}/_buildManifest.js (100%) rename ui/litellm-dashboard/out/_next/static/{0GF-OyXnYlAPMWfyPAZSs => FMlZjJYLUentCU02Wj6R_}/_ssgManifest.js (100%) create mode 100644 ui/litellm-dashboard/out/_next/static/chunks/162-4e7640b4d68e1ae4.js delete mode 100644 ui/litellm-dashboard/out/_next/static/chunks/162-714ca0ed10a07f66.js rename litellm/proxy/_experimental/out/_next/static/chunks/220-1c8d82f7ce7658c4.js => ui/litellm-dashboard/out/_next/static/chunks/220-5061c4cea850d728.js (71%) rename litellm/proxy/_experimental/out/_next/static/chunks/866-3523e0e07cf314f6.js => ui/litellm-dashboard/out/_next/static/chunks/866-9e1803a09e9ae8da.js (99%) create mode 100644 ui/litellm-dashboard/out/_next/static/chunks/app/page-127adcf8da2b5294.js delete mode 100644 ui/litellm-dashboard/out/_next/static/chunks/app/page-8dc8d9524a1f3965.js delete mode 100644 ui/litellm-dashboard/out/_next/static/css/060d5ddee53e45ce.css create mode 100644 ui/litellm-dashboard/out/_next/static/css/c528590c6415a94c.css diff --git a/litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_buildManifest.js b/litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_buildManifest.js similarity index 100% rename from litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_buildManifest.js rename to litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_buildManifest.js diff --git a/litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_ssgManifest.js b/litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_ssgManifest.js similarity index 100% rename from litellm/proxy/_experimental/out/_next/static/0GF-OyXnYlAPMWfyPAZSs/_ssgManifest.js rename to litellm/proxy/_experimental/out/_next/static/FMlZjJYLUentCU02Wj6R_/_ssgManifest.js diff --git a/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js b/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js new file mode 100644 index 00000000000..e9bbcb3d1f6 --- /dev/null +++ b/litellm/proxy/_experimental/out/_next/static/chunks/162-4e7640b4d68e1ae4.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[162],{36724:function(e,t,n){n.d(t,{Dx:function(){return i.Z},Zb:function(){return s.Z},xv:function(){return r.Z},zx:function(){return a.Z}});var a=n(20831),s=n(12514),r=n(84264),i=n(96761)},19130:function(e,t,n){n.d(t,{RM:function(){return s.Z},SC:function(){return l.Z},iA:function(){return a.Z},pj:function(){return r.Z},ss:function(){return i.Z},xs:function(){return o.Z}});var a=n(21626),s=n(97214),r=n(28241),i=n(58834),o=n(69552),l=n(71876)},88658:function(e,t,n){n.d(t,{L:function(){return s}});var a=n(49817);let s=e=>{let t;let{apiKeySource:n,accessToken:s,apiKey:r,inputMessage:i,chatHistory:o,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedMCPTools:m,endpointType:p,selectedModel:u,selectedSdk:g}=e,x="session"===n?s:r,h=window.location.origin,f=i||"Your prompt here",_=f.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),b=o.filter(e=>!e.isImage).map(e=>{let{role:t,content:n}=e;return{role:t,content:n}}),v={};l.length>0&&(v.tags=l),c.length>0&&(v.vector_stores=c),d.length>0&&(v.guardrails=d);let j=u||"your-model-name",y="azure"===g?'import openai\n\nclient = openai.AzureOpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n azure_endpoint="').concat(h,'",\n api_version="2024-02-01"\n)'):'import openai\n\nclient = openai.OpenAI(\n api_key="'.concat(x||"YOUR_LITELLM_API_KEY",'",\n base_url="').concat(h,'"\n)');switch(p){case a.KP.CHAT:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.chat.completions.create(\n model="'.concat(j,'",\n messages=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.chat.completions.create(\n# model="').concat(j,'",\n# messages=[\n# {\n# "role": "user",\n# "content": [\n# {\n# "type": "text",\n# "text": "').concat(_,'"\n# },\n# {\n# "type": "image_url",\n# "image_url": {\n# "url": f"data:image/jpeg;base64,{base64_file}" # or data:application/pdf;base64,{base64_file}\n# }\n# }\n# ]\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(v).length>0,n="";if(e){let e=JSON.stringify({metadata:v},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=b.length>0?b:[{role:"user",content:f}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(j,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(j,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(_,'"},\n# {\n# "type": "input_image",\n# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file}\n# },\n# ],\n# }\n# ]').concat(n,"\n# )\n# print(response_with_file.output_text)\n");break}case a.KP.IMAGE:t="azure"===g?"\n# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI.\n# This snippet uses 'client.images.generate' and will create a new image based on your prompt.\n# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context.\nimport os\nimport requests\nimport json\nimport time\nfrom PIL import Image\n\nresult = client.images.generate(\n model=\"".concat(j,'",\n prompt="').concat(i,'",\n n=1\n)\n\njson_response = json.loads(result.model_dump_json())\n\n# Set the directory for the stored image\nimage_dir = os.path.join(os.curdir, \'images\')\n\n# If the directory doesn\'t exist, create it\nif not os.path.isdir(image_dir):\n os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===g?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n'):"\nimport base64\nimport os\nimport time\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(_,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(j,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;default:t="\n# Code generation for this endpoint is not implemented yet."}return"".concat(y,"\n").concat(t)}},49817:function(e,t,n){var a,s,r,i;n.d(t,{KP:function(){return s},vf:function(){return l}}),(r=a||(a={})).IMAGE_GENERATION="image_generation",r.CHAT="chat",r.RESPONSES="responses",r.IMAGE_EDITS="image_edits",r.ANTHROPIC_MESSAGES="anthropic_messages",(i=s||(s={})).IMAGE="image",i.CHAT="chat",i.RESPONSES="responses",i.IMAGE_EDITS="image_edits",i.ANTHROPIC_MESSAGES="anthropic_messages";let o={image_generation:"image",chat:"chat",responses:"responses",image_edits:"image_edits",anthropic_messages:"anthropic_messages"},l=e=>{if(console.log("getEndpointType:",e),Object.values(a).includes(e)){let t=o[e];return console.log("endpointType:",t),t}return"chat"}},29488:function(e,t,n){n.d(t,{Hc:function(){return 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print(response_with_file)\n");break}case a.KP.RESPONSES:{let e=Object.keys(b).length>0,n="";if(e){let e=JSON.stringify({metadata:b},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();n=",\n extra_body=".concat(e)}let a=_.length>0?_:[{role:"user",content:h}];t='\nimport base64\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# Example with text only\nresponse = client.responses.create(\n model="'.concat(v,'",\n input=').concat(JSON.stringify(a,null,4)).concat(n,'\n)\n\nprint(response.output_text)\n\n# Example with image or PDF (uncomment and provide file path to use)\n# base64_file = encode_image("path/to/your/file.jpg") # or .pdf\n# response_with_file = client.responses.create(\n# model="').concat(v,'",\n# input=[\n# {\n# "role": "user",\n# "content": [\n# {"type": "input_text", "text": "').concat(f,'"},\n# {\n# "type": 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os.mkdir(image_dir)\n\n# Initialize the image path\nimage_filename = f"generated_image_{int(time.time())}.png"\nimage_path = os.path.join(image_dir, image_filename)\n\ntry:\n # Retrieve the generated image\n if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"):\n image_url = json_response["data"][0]["url"]\n generated_image = requests.get(image_url).content\n with open(image_path, "wb") as image_file:\n image_file.write(generated_image)\n\n print(f"Image saved to {image_path}")\n # Display the image\n image = Image.open(image_path)\n image.show()\n else:\n print("Could not find image URL in response.")\n print("Full response:", json_response)\nexcept Exception as e:\n print(f"An error occurred: {e}")\n print("Full response:", json_response)\n'):"\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, \"rb\") as image_file:\n return base64.b64encode(image_file.read()).decode('utf-8')\n\n# Helper function to create a file (simplified for this example)\ndef create_file(image_path):\n # In a real implementation, this would upload the file to OpenAI\n # For this example, we'll just return a placeholder ID\n return f\"file_{os.path.basename(image_path).replace('.', '_')}\"\n\n# The prompt entered by the user\nprompt = \"".concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. Model response:")\n print("\\n".join(text_response))\n else:\n print("No image data found in response.")\n print("Full response for debugging:")\n print(response)\n');break;case a.KP.IMAGE_EDITS:t="azure"===u?'\nimport base64\nimport os\nimport time\nimport json\nfrom PIL import Image\nimport requests\n\n# Helper function to encode images to base64\ndef encode_image(image_path):\n with open(image_path, "rb") as image_file:\n return base64.b64encode(image_file.read()).decode(\'utf-8\')\n\n# The prompt entered by the user\nprompt = "'.concat(f,'"\n\n# Encode images to base64\nbase64_image1 = encode_image("body-lotion.png")\nbase64_image2 = encode_image("soap.png")\n\n# Create file IDs\nfile_id1 = create_file("body-lotion.png")\nfile_id2 = create_file("incense-kit.png")\n\nresponse = client.responses.create(\n model="').concat(v,'",\n input=[\n {\n "role": "user",\n "content": [\n {"type": "input_text", "text": prompt},\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image1}",\n },\n {\n "type": "input_image",\n "image_url": f"data:image/jpeg;base64,{base64_image2}",\n },\n {\n "type": "input_image",\n "file_id": file_id1,\n },\n {\n "type": "input_image",\n "file_id": file_id2,\n }\n ],\n }\n ],\n tools=[{"type": "image_generation"}],\n)\n\n# Process the response\nimage_generation_calls = [\n output\n for output in response.output\n if output.type == "image_generation_call"\n]\n\nimage_data = [output.result for output in image_generation_calls]\n\nif image_data:\n image_base64 = image_data[0]\n image_filename = f"edited_image_{int(time.time())}.png"\n with open(image_filename, "wb") as f:\n f.write(base64.b64decode(image_base64))\n print(f"Image saved to {image_filename}")\nelse:\n # If no image is generated, there might be a text response with an explanation\n text_response = [output.text for output in response.output if hasattr(output, \'text\')]\n if text_response:\n print("No image generated. 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Key format (#14428) Co-authored-by: Cursor Agent Co-authored-by: ishaan --- .../management_endpoints/key_management_endpoints.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/litellm/proxy/management_endpoints/key_management_endpoints.py b/litellm/proxy/management_endpoints/key_management_endpoints.py index 0bf957f2656..bd8faf34be8 100644 --- a/litellm/proxy/management_endpoints/key_management_endpoints.py +++ b/litellm/proxy/management_endpoints/key_management_endpoints.py @@ -551,6 +551,15 @@ async def _common_key_generation_helper( # noqa: PLR0915 prisma_client=prisma_client, ) + # Validate user-provided key format + if data.key is not None and not data.key.startswith("sk-"): + raise HTTPException( + status_code=400, + detail={ + "error": f"Invalid key format. LiteLLM Virtual Key must start with 'sk-'. Received: {data.key}" + } + ) + response = await generate_key_helper_fn( request_type="key", **data_json, table_name="key" ) From a13aa4740ab450207599491b5ba710fccf9cff10 Mon Sep 17 00:00:00 2001 From: Ishaan Jaff Date: Wed, 10 Sep 2025 19:12:11 -0700 Subject: [PATCH 29/73] [Fixes] Bug fixes to using LiteLLM MCP Gateway (#14392) * fix: use _get_mcp_servers_in_path * fix checks for using litellm_proxy as MCP tool provider * fix: fix mcp_tools_with_litellm_proxy * fix: fix aresponses_api_with_mcp * aresponses_api_with_mcp * test_mcp_allowed_tools_filtering * fix: _filter_mcp_tools_by_allowed_tools * fix: _filter_mcp_tools_by_allowed_tools * test_streaming_responses_api_with_mcp_tools * fixes: test tools transfrom MCP->OpenaI spec * test_streaming_responses_api_with_mcp_tools * fix: chat ui allow multi select with allowed tools * fix: use correct MCP events with litellm proxy response API * fix get_event_model_class * fix litellm proxy MCP handler * fix MCPEnhancedStreamingIterator * chat ui show list tools result * UI: show MCP events * fix stream iterator * fixes: litellm proxy mcp handler * test responses + mcp * fix: update responses api with mcp handling * ruff check fix * central: _process_mcp_tools_to_openai_format * fix: refactor code * test_mcp_allowed_tools_filtering * test mcp with litellm proxy * fix mcp call * demo: video using MCP ui * fixes for using stream iterator * test_no_duplicate_mcp_tools_in_streaming_e2e * docs fix * fix code snippet --- .../mcp/mcp_with_litellm_proxy.py | 36 + docs/my-website/docs/mcp.md | 20 +- litellm/experimental_mcp_client/tools.py | 42 +- .../llms/openai/responses/transformation.py | 8 + .../proxy/_experimental/mcp_server/server.py | 81 +- .../mcp_management_endpoints.py | 45 +- litellm/proxy/proxy_config.yaml | 11 + litellm/responses/main.py | 177 +++- .../mcp/litellm_proxy_mcp_handler.py | 346 ++++++- .../responses/mcp/mcp_streaming_iterator.py | 604 ++++++++++++ litellm/types/llms/openai.py | 78 ++ .../mcp_tests/test_aresponses_api_with_mcp.py | 887 +++++++++++++++++- .../experimental_mcp_client/test_tools.py | 95 ++ .../src/components/chat_ui.tsx | 301 +++--- .../src/components/chat_ui/CodeSnippets.tsx | 2 + .../components/chat_ui/MCPEventsDisplay.tsx | 263 ++++++ .../chat_ui/llm_calls/anthropic_messages.tsx | 7 +- .../chat_ui/llm_calls/chat_completion.tsx | 7 +- .../chat_ui/llm_calls/responses_api.tsx | 39 +- .../src/components/public_model_hub.tsx | 2 + 20 files changed, 2814 insertions(+), 237 deletions(-) create mode 100644 cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py create mode 100644 litellm/responses/mcp/mcp_streaming_iterator.py create mode 100644 ui/litellm-dashboard/src/components/chat_ui/MCPEventsDisplay.tsx diff --git a/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py b/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py new file mode 100644 index 00000000000..351b0920eb8 --- /dev/null +++ b/cookbook/litellm_proxy_server/mcp/mcp_with_litellm_proxy.py @@ -0,0 +1,36 @@ +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. + +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" +import openai + +client = openai.OpenAI( + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here +) +print("Making API request to Responses API with MCP tools") + +response = client.responses.create( + model="gpt-5", + input=[ + { + "role": "user", + "content": "give me TLDR of what BerriAI/litellm repo is about", + "type": "message" + } + ], + tools=[ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy", + "require_approval": "never" + } + ], + stream=True, + tool_choice="required" +) + +for chunk in response: + print("response chunk: ", chunk) diff --git a/docs/my-website/docs/mcp.md b/docs/my-website/docs/mcp.md index 1e523600a86..18c99051709 100644 --- a/docs/my-website/docs/mcp.md +++ b/docs/my-website/docs/mcp.md @@ -197,10 +197,17 @@ litellm_settings: ### Use on LiteLLM UI +Follow this walkthrough to use your MCP on LiteLLM UI + + + ### Use with Responses API Replace `http://localhost:4000` with your LiteLLM Proxy base URL. +Demo Video Using Responses API with LiteLLM Proxy: [Demo video here](https://www.loom.com/share/34587e618c5c47c0b0d67b4e4d02718f?sid=2caf3d45-ead4-4490-bcc1-8d6dd6041c02) + + @@ -234,12 +241,18 @@ curl --location 'http://localhost:4000/v1/responses' \ ```python title="Python SDK Example" showLineNumbers +""" +Use LiteLLM Proxy MCP Gateway to call MCP tools. + +When using LiteLLM Proxy, you can use the same MCP tools across all your LLM providers. +""" import openai client = openai.OpenAI( - api_key="sk-1234", - base_url="http://localhost:4000" + api_key="sk-1234", # paste your litellm proxy api key here + base_url="http://localhost:4000" # paste your litellm proxy base url here ) +print("Making API request to Responses API with MCP tools") response = client.responses.create( model="gpt-5", @@ -262,7 +275,8 @@ response = client.responses.create( tool_choice="required" ) -print(response) +for chunk in response: + print("response chunk: ", chunk) ``` diff --git a/litellm/experimental_mcp_client/tools.py b/litellm/experimental_mcp_client/tools.py index bfbd3f96a5c..b716e3171e7 100644 --- a/litellm/experimental_mcp_client/tools.py +++ b/litellm/experimental_mcp_client/tools.py @@ -17,22 +17,60 @@ from litellm.types.utils import ChatCompletionMessageToolCall ######################################################## def transform_mcp_tool_to_openai_tool(mcp_tool: MCPTool) -> ChatCompletionToolParam: """Convert an MCP tool to an OpenAI tool.""" + normalized_parameters = _normalize_mcp_input_schema(mcp_tool.inputSchema) + return ChatCompletionToolParam( type="function", function=FunctionDefinition( name=mcp_tool.name, description=mcp_tool.description or "", - parameters=mcp_tool.inputSchema, + parameters=normalized_parameters, strict=False, ), ) +def _normalize_mcp_input_schema(input_schema: dict) -> dict: + """ + Normalize MCP input schema to ensure it's valid for OpenAI function calling. + + OpenAI requires that function parameters have: + - type: 'object' + - properties: dict (can be empty) + - additionalProperties: false (recommended) + """ + if not input_schema: + return { + "type": "object", + "properties": {}, + "additionalProperties": False + } + + # Make a copy to avoid modifying the original + normalized_schema = dict(input_schema) + + # Ensure type is 'object' + if "type" not in normalized_schema: + normalized_schema["type"] = "object" + + # Ensure properties exists (can be empty) + if "properties" not in normalized_schema: + normalized_schema["properties"] = {} + + # Add additionalProperties if not present (recommended by OpenAI) + if "additionalProperties" not in normalized_schema: + normalized_schema["additionalProperties"] = False + + return normalized_schema + + def transform_mcp_tool_to_openai_responses_api_tool(mcp_tool: MCPTool) -> FunctionToolParam: """Convert an MCP tool to an OpenAI Responses API tool.""" + normalized_parameters = _normalize_mcp_input_schema(mcp_tool.inputSchema) + return FunctionToolParam( name=mcp_tool.name, - parameters=mcp_tool.inputSchema, + parameters=normalized_parameters, strict=False, type="function", description=mcp_tool.description or "", diff --git a/litellm/llms/openai/responses/transformation.py b/litellm/llms/openai/responses/transformation.py index 392d47f9822..1d52f74b7b9 100644 --- a/litellm/llms/openai/responses/transformation.py +++ b/litellm/llms/openai/responses/transformation.py @@ -272,6 +272,14 @@ class OpenAIResponsesAPIConfig(BaseResponsesAPIConfig): ResponsesAPIStreamEvents.WEB_SEARCH_CALL_IN_PROGRESS: WebSearchCallInProgressEvent, ResponsesAPIStreamEvents.WEB_SEARCH_CALL_SEARCHING: WebSearchCallSearchingEvent, ResponsesAPIStreamEvents.WEB_SEARCH_CALL_COMPLETED: WebSearchCallCompletedEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_IN_PROGRESS: MCPListToolsInProgressEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_COMPLETED: MCPListToolsCompletedEvent, + ResponsesAPIStreamEvents.MCP_LIST_TOOLS_FAILED: MCPListToolsFailedEvent, + ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS: MCPCallInProgressEvent, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DELTA: MCPCallArgumentsDeltaEvent, + ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DONE: MCPCallArgumentsDoneEvent, + ResponsesAPIStreamEvents.MCP_CALL_COMPLETED: MCPCallCompletedEvent, + ResponsesAPIStreamEvents.MCP_CALL_FAILED: MCPCallFailedEvent, ResponsesAPIStreamEvents.ERROR: ErrorEvent, } diff --git a/litellm/proxy/_experimental/mcp_server/server.py b/litellm/proxy/_experimental/mcp_server/server.py index 38619112ccc..d0461f91e9e 100644 --- a/litellm/proxy/_experimental/mcp_server/server.py +++ b/litellm/proxy/_experimental/mcp_server/server.py @@ -215,9 +215,9 @@ if MCP_AVAILABLE: """ from fastapi import Request + from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException from litellm.proxy.litellm_pre_call_utils import add_litellm_data_to_request from litellm.proxy.proxy_server import proxy_config - from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException # Validate arguments user_api_key_auth, mcp_auth_header, _, mcp_server_auth_headers, mcp_protocol_version = get_auth_context() @@ -279,33 +279,15 @@ if MCP_AVAILABLE: ############ Helper Functions ########################## ######################################################## - async def _get_tools_from_mcp_servers( - user_api_key_auth: Optional[UserAPIKeyAuth], - mcp_auth_header: Optional[str], + async def _get_allowed_mcp_servers_from_mcp_server_names( mcp_servers: Optional[List[str]], - mcp_server_auth_headers: Optional[Dict[str, str]] = None, - mcp_protocol_version: Optional[str] = None, - ) -> List[MCPTool]: + allowed_mcp_servers: List[str], + ) -> List[str]: """ - Helper method to fetch tools from MCP servers based on server filtering criteria. - - Args: - user_api_key_auth: User authentication info for access control - mcp_auth_header: Optional auth header for MCP server (deprecated) - mcp_servers: Optional list of server names/aliases to filter by - mcp_server_auth_headers: Optional dict of server-specific auth headers {server_alias: auth_value} - - Returns: - List[MCPTool]: Combined list of tools from filtered servers + Get the filtered MCP servers from the MCP server names """ - if not MCP_AVAILABLE: - return [] - - # Get allowed MCP servers based on user permissions - allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) - - filtered_server_ids = set() - + from typing import Set + filtered_server_ids: Set[str] = set() # Filter servers based on mcp_servers parameter if provided if mcp_servers is not None: for server_or_group in mcp_servers: @@ -336,6 +318,40 @@ if MCP_AVAILABLE: if filtered_server_ids: allowed_mcp_servers = list(filtered_server_ids) + + return allowed_mcp_servers + + async def _get_tools_from_mcp_servers( + user_api_key_auth: Optional[UserAPIKeyAuth], + mcp_auth_header: Optional[str], + mcp_servers: Optional[List[str]], + mcp_server_auth_headers: Optional[Dict[str, str]] = None, + mcp_protocol_version: Optional[str] = None, + ) -> List[MCPTool]: + """ + Helper method to fetch tools from MCP servers based on server filtering criteria. + + Args: + user_api_key_auth: User authentication info for access control + mcp_auth_header: Optional auth header for MCP server (deprecated) + mcp_servers: Optional list of server names/aliases to filter by + mcp_server_auth_headers: Optional dict of server-specific auth headers {server_alias: auth_value} + + Returns: + List[MCPTool]: Combined list of tools from filtered servers + """ + if not MCP_AVAILABLE: + return [] + + # Get allowed MCP servers based on user permissions + allowed_mcp_servers = await global_mcp_server_manager.get_allowed_mcp_servers(user_api_key_auth) + + if mcp_servers is not None: + allowed_mcp_servers = await _get_allowed_mcp_servers_from_mcp_server_names( + mcp_servers=mcp_servers, + allowed_mcp_servers=allowed_mcp_servers, + ) + # Get tools from each allowed server all_tools = [] @@ -556,20 +572,25 @@ if MCP_AVAILABLE: except Exception as e: return [TextContent(text=f"Error: {str(e)}", type="text")] - async def extract_mcp_auth_context(scope, path): + def _get_mcp_servers_in_path(path: str) -> Optional[List[str]]: """ - Extracts mcp_servers from the path and processes the MCP request for auth context. - Returns: (user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers) + Get the MCP servers from the path """ import re - - mcp_servers_from_path = None + mcp_servers_from_path: Optional[List[str]] = None mcp_path_match = re.match(r"^/mcp/([^/]+)(/.*)?$", path) if mcp_path_match: mcp_servers_str = mcp_path_match.group(1) if mcp_servers_str: mcp_servers_from_path = [s.strip() for s in mcp_servers_str.split(",") if s.strip()] + return mcp_servers_from_path + async def extract_mcp_auth_context(scope, path): + """ + Extracts mcp_servers from the path and processes the MCP request for auth context. + Returns: (user_api_key_auth, mcp_auth_header, mcp_servers, mcp_server_auth_headers) + """ + mcp_servers_from_path = _get_mcp_servers_in_path(path) if mcp_servers_from_path is not None: ( user_api_key_auth, diff --git a/litellm/proxy/management_endpoints/mcp_management_endpoints.py b/litellm/proxy/management_endpoints/mcp_management_endpoints.py index be265c73bfc..a26ae32229a 100644 --- a/litellm/proxy/management_endpoints/mcp_management_endpoints.py +++ b/litellm/proxy/management_endpoints/mcp_management_endpoints.py @@ -17,8 +17,8 @@ Endpoints here: """ import importlib -from typing import Iterable, List, Optional from datetime import datetime +from typing import Iterable, List, Optional from fastapi import APIRouter, Depends, Header, HTTPException, Response, status from fastapi.responses import JSONResponse @@ -26,7 +26,9 @@ from fastapi.responses import JSONResponse import litellm from litellm._logging import verbose_logger, verbose_proxy_logger from litellm.constants import LITELLM_PROXY_ADMIN_NAME -from litellm.proxy._experimental.mcp_server.utils import validate_and_normalize_mcp_server_payload +from litellm.proxy._experimental.mcp_server.utils import ( + validate_and_normalize_mcp_server_payload, +) router = APIRouter(prefix="/v1/mcp", tags=["mcp"]) MCP_AVAILABLE: bool = True @@ -94,34 +96,17 @@ if MCP_AVAILABLE: """ Get all MCP tools available for the current key, including those from access groups """ - from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import ( - MCPRequestHandler, - ) - from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( - global_mcp_server_manager, + from litellm.proxy._experimental.mcp_server.server import _list_mcp_tools + tools = await _list_mcp_tools( + user_api_key_auth=user_api_key_dict, + mcp_auth_header=None, + mcp_servers=None, + mcp_server_auth_headers=None, + mcp_protocol_version=None, ) + dumped_tools = [dict(tool) for tool in tools] - # This now includes both direct and access group servers - server_ids = await MCPRequestHandler._get_allowed_mcp_servers_for_key(user_api_key_dict) - - tools = [] - errors = [] - for server_id in server_ids: - try: - server_tools = await global_mcp_server_manager.get_tools_for_server(server_id) - tools.extend(server_tools) - verbose_proxy_logger.debug(f"Successfully fetched {len(server_tools)} tools from server {server_id}") - except Exception as e: - error_msg = f"Failed to get tools from server {server_id}: {str(e)}" - verbose_proxy_logger.warning(error_msg) - errors.append(error_msg) - # Continue with other servers instead of failing completely - - verbose_proxy_logger.debug(f"Available tools: {tools}") - if errors: - verbose_proxy_logger.warning(f"Some servers failed to respond: {errors}") - - return {"tools": tools} + return {"tools": dumped_tools} @router.get( "/access_groups", @@ -134,8 +119,10 @@ if MCP_AVAILABLE: """ Get all available MCP access groups from the database AND config """ + from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( + global_mcp_server_manager, + ) from litellm.proxy.proxy_server import prisma_client - from litellm.proxy._experimental.mcp_server.mcp_server_manager import global_mcp_server_manager access_groups = set() diff --git a/litellm/proxy/proxy_config.yaml b/litellm/proxy/proxy_config.yaml index 7ee09105254..54bfdac55f9 100644 --- a/litellm/proxy/proxy_config.yaml +++ b/litellm/proxy/proxy_config.yaml @@ -3,5 +3,16 @@ model_list: litellm_params: model: openai/* api_base: https://exampleopenaiendpoint-production-0ee2.up.railway.app/ + - model_name: bedrock/* + litellm_params: + model: bedrock/* + - model_name: openai/* + litellm_params: + model: openai/* + - model_name: gemini/* + litellm_params: + model: gemini/* + + litellm_settings: callbacks: ["cloudzero"] \ No newline at end of file diff --git a/litellm/responses/main.py b/litellm/responses/main.py index 47ecbcf02c0..04ee2b343f5 100644 --- a/litellm/responses/main.py +++ b/litellm/responses/main.py @@ -1,12 +1,24 @@ import asyncio import contextvars from functools import partial -from typing import Any, Coroutine, Dict, Iterable, List, Literal, Optional, Type, Union +from typing import ( + TYPE_CHECKING, + Any, + Coroutine, + Dict, + Iterable, + List, + Literal, + Optional, + Type, + Union, +) import httpx from pydantic import BaseModel 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.llms.base_llm.responses.transformation import BaseResponsesAPIConfig @@ -22,15 +34,27 @@ from litellm.types.llms.openai import ( ResponseInputParam, ResponsesAPIOptionalRequestParams, ResponsesAPIResponse, - ResponseText, ToolChoice, ToolParam, ) + +# Handle ResponseText import with fallback +if TYPE_CHECKING: + from litellm.types.llms.openai import ResponseText +else: + ResponseText = str # Fallback for ResponseText import from litellm.types.responses.main import * from litellm.types.router import GenericLiteLLMParams from litellm.utils import ProviderConfigManager, client -from .streaming_iterator import BaseResponsesAPIStreamingIterator +if TYPE_CHECKING: + from mcp.types import Tool as MCPTool +else: + MCPTool = Any + +from .streaming_iterator import ( + BaseResponsesAPIStreamingIterator, +) ####### ENVIRONMENT VARIABLES ################### # Initialize any necessary instances or variables here @@ -141,17 +165,15 @@ async def aresponses_api_with_mcp( other_tools, ) = LiteLLM_Proxy_MCP_Handler._parse_mcp_tools(tools) - # Get available tools from MCP manager if we have MCP tools - openai_tools = [] - mcp_tools_fetched = [] - if mcp_tools_with_litellm_proxy: - user_api_key_auth = kwargs.get("user_api_key_auth") - mcp_tools_fetched = await LiteLLM_Proxy_MCP_Handler._get_mcp_tools_from_manager( - user_api_key_auth - ) - openai_tools = LiteLLM_Proxy_MCP_Handler._transform_mcp_tools_to_openai( - mcp_tools_fetched - ) + # Process MCP tools through the complete pipeline (fetch + filter + deduplicate + transform) + user_api_key_auth = kwargs.get("user_api_key_auth") + + # Get original MCP tools (for events) and OpenAI tools (for LLM) by reusing existing methods + original_mcp_tools = await LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform( + user_api_key_auth=user_api_key_auth, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy + ) + openai_tools = LiteLLM_Proxy_MCP_Handler._transform_mcp_tools_to_openai(original_mcp_tools) # Combine with other tools all_tools = openai_tools + other_tools if (openai_tools or other_tools) else None @@ -182,23 +204,68 @@ async def aresponses_api_with_mcp( **kwargs, } + # Handle MCP streaming if requested + if stream and mcp_tools_with_litellm_proxy: + # Generate MCP discovery events using the already processed tools + import uuid + + from litellm.responses.mcp.mcp_streaming_iterator import ( + create_mcp_list_tools_events, + ) + + base_item_id = f"mcp_{uuid.uuid4().hex[:8]}" + mcp_discovery_events = await create_mcp_list_tools_events( + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, + user_api_key_auth=user_api_key_auth, + base_item_id=base_item_id, + pre_processed_mcp_tools=original_mcp_tools + ) + + return LiteLLM_Proxy_MCP_Handler._create_mcp_streaming_response( + input=input, + model=model, + all_tools=all_tools, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, + mcp_discovery_events=mcp_discovery_events, + call_params=call_params, + previous_response_id=previous_response_id, + **kwargs + ) + + # Determine if we should auto-execute tools + should_auto_execute = ( + bool(mcp_tools_with_litellm_proxy) + and LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools( + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy + ) + ) + + # Prepare parameters for the initial call + initial_call_params = LiteLLM_Proxy_MCP_Handler._prepare_initial_call_params( + call_params=call_params, + should_auto_execute=should_auto_execute + ) + + ######################################################### # Make initial response API call - # TODO: if should auto-execute is True, then this first response should not be streamed + ######################################################### response = await aresponses( input=input, model=model, tools=all_tools, previous_response_id=previous_response_id, - **call_params, + **initial_call_params, ) - # Check if we need to auto-execute tool calls (only for non-streaming responses) + verbose_logger.debug("Initial response %s", response) + + ######################################################### + # Auto-Execute Tools Handling + # If auto-execute tools is True, then we need to execute the tool calls + ######################################################### if ( - mcp_tools_with_litellm_proxy + should_auto_execute and isinstance(response, ResponsesAPIResponse) - and LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools( - mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy - ) ): # type: ignore tool_calls = LiteLLM_Proxy_MCP_Handler._extract_tool_calls_from_response( response=response @@ -217,20 +284,49 @@ async def aresponses_api_with_mcp( response=response, tool_results=tool_results, original_input=input ) + # Prepare parameters for follow-up call (restores original stream setting) + follow_up_call_params = LiteLLM_Proxy_MCP_Handler._prepare_follow_up_call_params( + call_params=call_params, + original_stream_setting=stream or False + ) + + # Create tool execution events for streaming if needed + tool_execution_events = [] + if stream: + tool_execution_events = LiteLLM_Proxy_MCP_Handler._create_tool_execution_events( + tool_calls=tool_calls, + tool_results=tool_results + ) + final_response = await LiteLLM_Proxy_MCP_Handler._make_follow_up_call( follow_up_input=follow_up_input, model=model, all_tools=all_tools, response_id=response.id, - **call_params, + **follow_up_call_params, ) - # Add custom output elements to the final response - if isinstance(final_response, ResponsesAPIResponse): + # If streaming and we have tool execution events, wrap the response + if stream and tool_execution_events and (hasattr(final_response, '__aiter__') or hasattr(final_response, '__iter__')): + from litellm.responses.mcp.mcp_streaming_iterator import ( + MCPEnhancedStreamingIterator, + ) + final_response = MCPEnhancedStreamingIterator( + base_iterator=final_response, + mcp_events=tool_execution_events + ) + + # Add custom output elements to the final response (for non-streaming) + elif isinstance(final_response, ResponsesAPIResponse): + # Fetch MCP tools again for output elements (without OpenAI transformation) + mcp_tools_for_output = await LiteLLM_Proxy_MCP_Handler._process_mcp_tools_without_openai_transform( + user_api_key_auth=user_api_key_auth, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy + ) final_response = ( LiteLLM_Proxy_MCP_Handler._add_mcp_output_elements_to_response( response=final_response, - mcp_tools_fetched=mcp_tools_fetched, + mcp_tools_fetched=mcp_tools_for_output, tool_results=tool_results, ) ) @@ -401,13 +497,13 @@ def responses( Synchronous version of the Responses API. Uses the synchronous HTTP handler to make requests. """ + local_vars = locals() from litellm.responses.mcp.litellm_proxy_mcp_handler import ( LiteLLM_Proxy_MCP_Handler, ) - local_vars = locals() try: - litellm_logging_obj: LiteLLMLoggingObj = kwargs.get("litellm_logging_obj") # type: ignore + litellm_logging_obj: LiteLLMLoggingObj = kwargs.pop("litellm_logging_obj") # type: ignore litellm_call_id: Optional[str] = kwargs.get("litellm_call_id", None) _is_async = kwargs.pop("aresponses", False) is True @@ -448,7 +544,32 @@ def responses( ######################################################### if LiteLLM_Proxy_MCP_Handler._should_use_litellm_mcp_gateway(tools=tools): return aresponses_api_with_mcp( - **local_vars, + input=input, + model=model, + include=include, + instructions=instructions, + max_output_tokens=max_output_tokens, + prompt=prompt, + metadata=metadata, + parallel_tool_calls=parallel_tool_calls, + previous_response_id=previous_response_id, + reasoning=reasoning, + store=store, + background=background, + stream=stream, + temperature=temperature, + text=text, + tool_choice=tool_choice, + tools=tools, + top_p=top_p, + truncation=truncation, + user=user, + extra_headers=extra_headers, + extra_query=extra_query, + extra_body=extra_body, + timeout=timeout, + custom_llm_provider=custom_llm_provider, + **kwargs, ) # get provider config diff --git a/litellm/responses/mcp/litellm_proxy_mcp_handler.py b/litellm/responses/mcp/litellm_proxy_mcp_handler.py index 5c72b9b6521..7a9a21a9690 100644 --- a/litellm/responses/mcp/litellm_proxy_mcp_handler.py +++ b/litellm/responses/mcp/litellm_proxy_mcp_handler.py @@ -1,10 +1,17 @@ -from typing import Any, Dict, Iterable, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Union from litellm._logging import verbose_logger from litellm.responses.main import aresponses from litellm.responses.streaming_iterator import BaseResponsesAPIStreamingIterator from litellm.types.llms.openai import ResponsesAPIResponse, ToolParam +if TYPE_CHECKING: + from mcp.types import Tool as MCPTool +else: + MCPTool = Any + +LITELLM_PROXY_MCP_SERVER_URL = "litellm_proxy" +LITELLM_PROXY_MCP_SERVER_URL_PREFIX = f"{LITELLM_PROXY_MCP_SERVER_URL}/mcp/" class LiteLLM_Proxy_MCP_Handler: """ @@ -22,7 +29,7 @@ class LiteLLM_Proxy_MCP_Handler: for tool in tools: if (isinstance(tool, dict) and tool.get("type") == "mcp" and - tool.get("server_url") == "litellm_proxy"): + tool.get("server_url", "").startswith(LITELLM_PROXY_MCP_SERVER_URL)): return True return False @@ -41,7 +48,7 @@ class LiteLLM_Proxy_MCP_Handler: for tool in tools: if (isinstance(tool, dict) and tool.get("type") == "mcp" and - tool.get("server_url") == "litellm_proxy"): + tool.get("server_url", "").startswith(LITELLM_PROXY_MCP_SERVER_URL)): mcp_tools_with_litellm_proxy.append(tool) else: other_tools.append(tool) @@ -49,13 +56,167 @@ class LiteLLM_Proxy_MCP_Handler: return mcp_tools_with_litellm_proxy, other_tools @staticmethod - async def _get_mcp_tools_from_manager(user_api_key_auth: Any) -> List[Any]: - """Get available tools from the MCP server manager.""" - from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( - global_mcp_server_manager, + async def _get_mcp_tools_from_manager( + user_api_key_auth: Any, + mcp_tools_with_litellm_proxy: Optional[Iterable[ToolParam]], + ) -> List[MCPTool]: + """ + Get available tools from the MCP server manager. + + Args: + user_api_key_auth: User authentication info for access control + mcp_tools_with_litellm_proxy: ToolParam objects with server_url starting with "litellm_proxy" + """ + from litellm.proxy._experimental.mcp_server.server import ( + _get_tools_from_mcp_servers, + ) + mcp_servers: List[str] = [] + if mcp_tools_with_litellm_proxy: + for _tool in mcp_tools_with_litellm_proxy: + # if user specifies servers as server_url: litellm_proxy/mcp/zapier,github then return zapier,github + if _tool.get("server_url", "").startswith(LITELLM_PROXY_MCP_SERVER_URL_PREFIX): + mcp_servers.append(_tool.get("server_url", "").split("/")[-1]) + + return await _get_tools_from_mcp_servers( + user_api_key_auth=user_api_key_auth, + mcp_auth_header=None, + mcp_servers=mcp_servers, + mcp_server_auth_headers=None, + mcp_protocol_version=None, + ) + + @staticmethod + def _deduplicate_mcp_tools(mcp_tools: List[Any]) -> List[Any]: + """ + Deduplicate MCP tools by name, keeping the first occurrence of each tool. + + Args: + mcp_tools: List of MCP tools that may contain duplicates + + Returns: + List of deduplicated MCP tools + """ + seen_names = set() + deduplicated_tools = [] + + for tool in mcp_tools: + tool_name = getattr(tool, 'name', None) if hasattr(tool, 'name') else tool.get('name') if isinstance(tool, dict) else None + if tool_name and tool_name not in seen_names: + seen_names.add(tool_name) + deduplicated_tools.append(tool) + + return deduplicated_tools + + @staticmethod + def _filter_mcp_tools_by_allowed_tools( + mcp_tools: List[Any], + mcp_tools_with_litellm_proxy: List[ToolParam] + ) -> List[Any]: + """Filter MCP tools based on allowed_tools parameter from the original tool configs.""" + # Collect all allowed tool names from all MCP tool configs + allowed_tool_names = set() + for tool_config in mcp_tools_with_litellm_proxy: + if isinstance(tool_config, dict) and "allowed_tools" in tool_config: + allowed_tools = tool_config.get("allowed_tools", []) + if isinstance(allowed_tools, list): + allowed_tool_names.update(allowed_tools) + + # If no allowed_tools specified, return all tools + if not allowed_tool_names: + return mcp_tools + + # Filter tools based on allowed names + filtered_tools = [] + for mcp_tool in mcp_tools: + tool_name = getattr(mcp_tool, 'name', None) if hasattr(mcp_tool, 'name') else mcp_tool.get('name') if isinstance(mcp_tool, dict) else None + if tool_name and tool_name in allowed_tool_names: + filtered_tools.append(mcp_tool) + + return filtered_tools + + @staticmethod + async def _process_mcp_tools_to_openai_format( + user_api_key_auth: Any, + mcp_tools_with_litellm_proxy: List[ToolParam] + ) -> List[Any]: + """ + Centralized method to process MCP tools through the complete pipeline: + 1. Fetch tools from MCP manager + 2. Filter based on allowed_tools parameter + 3. Deduplicate tools by name + 4. Transform to OpenAI format + + Args: + user_api_key_auth: User authentication info for access control + mcp_tools_with_litellm_proxy: ToolParam objects with server_url starting with "litellm_proxy" + + Returns: + List of tools in OpenAI format ready to be sent to the LLM + """ + if not mcp_tools_with_litellm_proxy: + return [] + + # Step 1: Fetch MCP tools from manager + mcp_tools_fetched = await LiteLLM_Proxy_MCP_Handler._get_mcp_tools_from_manager( + user_api_key_auth=user_api_key_auth, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, ) - return await global_mcp_server_manager.list_tools(user_api_key_auth=user_api_key_auth) + # Step 2: Filter tools based on allowed_tools parameter + filtered_mcp_tools = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mcp_tools_fetched, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, + ) + + # Step 3: Deduplicate tools after filtering + deduplicated_mcp_tools = LiteLLM_Proxy_MCP_Handler._deduplicate_mcp_tools( + filtered_mcp_tools + ) + + # Step 4: Transform to OpenAI format + openai_tools = LiteLLM_Proxy_MCP_Handler._transform_mcp_tools_to_openai( + deduplicated_mcp_tools + ) + + return openai_tools + + @staticmethod + async def _process_mcp_tools_without_openai_transform( + user_api_key_auth: Any, + mcp_tools_with_litellm_proxy: List[ToolParam] + ) -> List[Any]: + """ + Process MCP tools through filtering and deduplication pipeline without OpenAI transformation. + This is useful for cases where we need the original MCP tool objects (e.g., for events). + + Args: + user_api_key_auth: User authentication info for access control + mcp_tools_with_litellm_proxy: ToolParam objects with server_url starting with "litellm_proxy" + + Returns: + List of filtered and deduplicated MCP tools in their original format + """ + if not mcp_tools_with_litellm_proxy: + return [] + + # Step 1: Fetch MCP tools from manager + mcp_tools_fetched = await LiteLLM_Proxy_MCP_Handler._get_mcp_tools_from_manager( + user_api_key_auth=user_api_key_auth, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, + ) + + # Step 2: Filter tools based on allowed_tools parameter + filtered_mcp_tools = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mcp_tools_fetched, + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, + ) + + # Step 3: Deduplicate tools after filtering + deduplicated_mcp_tools = LiteLLM_Proxy_MCP_Handler._deduplicate_mcp_tools( + filtered_mcp_tools + ) + + return deduplicated_mcp_tools @staticmethod def _transform_mcp_tools_to_openai(mcp_tools: List[Any]) -> List[Any]: @@ -178,11 +339,12 @@ class LiteLLM_Proxy_MCP_Handler: user_api_key_auth: Any ) -> List[Dict[str, Any]]: """Execute tool calls and return results.""" + from fastapi import HTTPException + + from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException from litellm.proxy._experimental.mcp_server.mcp_server_manager import ( global_mcp_server_manager, ) - from litellm.exceptions import BlockedPiiEntityError, GuardrailRaisedException - from fastapi import HTTPException tool_results = [] tool_call_id: Optional[str] = None @@ -331,6 +493,170 @@ class LiteLLM_Proxy_MCP_Handler: **call_params ) + @staticmethod + def _create_mcp_streaming_response( + input: Union[str, Any], + model: str, + all_tools: Optional[List[Any]], + mcp_tools_with_litellm_proxy: List[Any], + mcp_discovery_events: List[Any], + call_params: Dict[str, Any], + previous_response_id: Optional[str], + **kwargs + ) -> Any: + """ + Create MCP enhanced streaming response that handles the full MCP workflow. + + This creates a streaming iterator that: + 1. Immediately emits MCP discovery events + 2. Makes the LLM call and streams the response + 3. Handles tool execution and follow-up calls + """ + from litellm.responses.mcp.mcp_streaming_iterator import ( + MCPEnhancedStreamingIterator, + ) + + # Build the complete request parameters by merging all sources + request_params = LiteLLM_Proxy_MCP_Handler._build_request_params( + input=input, + model=model, + all_tools=all_tools, + call_params=call_params, + previous_response_id=previous_response_id, + **kwargs + ) + + # Create the enhanced streaming iterator that will handle everything + return MCPEnhancedStreamingIterator( + base_iterator=None, # Will be created internally + mcp_events=mcp_discovery_events, # Pre-generated MCP discovery events + mcp_tools_with_litellm_proxy=mcp_tools_with_litellm_proxy, + user_api_key_auth=kwargs.get("user_api_key_auth"), + original_request_params=request_params + ) + + @staticmethod + def _build_request_params( + input: Union[str, Any], + model: str, + all_tools: Optional[List[Any]], + call_params: Dict[str, Any], + previous_response_id: Optional[str], + **kwargs + ) -> Dict[str, Any]: + """ + Build a clean request parameters dictionary for MCP streaming. + + Combines input, model, tools with call_params and additional kwargs + in a clean, maintainable way. + """ + # Start with the core required parameters + request_params = { + 'input': input, + 'model': model, + 'tools': all_tools, + } + + # Add previous_response_id if provided + if previous_response_id is not None: + request_params['previous_response_id'] = previous_response_id + + # Merge in all call_params (which contains most of the API parameters) + request_params.update(call_params) + + # Merge in any additional kwargs + request_params.update(kwargs) + + return request_params + + @staticmethod + def _create_tool_execution_events( + tool_calls: List[Any], + tool_results: List[Dict[str, Any]] + ) -> List[Any]: + """ + Create MCP tool execution events for streaming. + + Args: + tool_calls: List of tool calls from the LLM response + tool_results: List of tool execution results + + Returns: + List of MCP tool execution events for streaming + """ + import uuid + + from litellm.responses.mcp.mcp_streaming_iterator import create_mcp_call_events + + tool_execution_events = [] + + # Create events for each tool execution + for tool_result in tool_results: + tool_call_id = tool_result.get("tool_call_id", "unknown") + result_text = tool_result.get("result", "") + + # Extract tool name and arguments from tool calls + tool_name = "unknown" + tool_arguments = "{}" + for tool_call in tool_calls: + name, args, call_id = LiteLLM_Proxy_MCP_Handler._extract_tool_call_details(tool_call) + if call_id == tool_call_id: + tool_name = name or "unknown" + tool_arguments = args or "{}" + break + + execution_events = create_mcp_call_events( + tool_name=tool_name, + tool_call_id=tool_call_id, + arguments=tool_arguments, # Use actual arguments + result=result_text, + base_item_id=f"mcp_{uuid.uuid4().hex[:8]}", # Unique ID for each tool call + sequence_start=len(tool_execution_events) + 1 + ) + tool_execution_events.extend(execution_events) + + return tool_execution_events + + @staticmethod + def _prepare_initial_call_params( + call_params: Dict[str, Any], + should_auto_execute: bool + ) -> Dict[str, Any]: + """ + Prepare call parameters for the initial LLM call. + + For auto-execute scenarios, we need to disable streaming for the initial call + so we can process the tool calls before streaming the final response. + """ + initial_params = call_params.copy() + + if should_auto_execute: + # Disable streaming for initial call when auto-executing tools + initial_params["stream"] = False + + return initial_params + + @staticmethod + def _prepare_follow_up_call_params( + call_params: Dict[str, Any], + original_stream_setting: bool + ) -> Dict[str, Any]: + """ + Prepare call parameters for the follow-up LLM call after tool execution. + + Restores the original streaming setting and removes tool_choice since + we're now providing tool results, not requesting tool calls. + """ + follow_up_params = call_params.copy() + + # Restore original streaming setting for follow-up call + follow_up_params["stream"] = original_stream_setting + + # Remove tool_choice since we're providing results, not requesting tool calls + follow_up_params.pop("tool_choice", None) + + return follow_up_params + @staticmethod def _add_mcp_output_elements_to_response( response: ResponsesAPIResponse, diff --git a/litellm/responses/mcp/mcp_streaming_iterator.py b/litellm/responses/mcp/mcp_streaming_iterator.py new file mode 100644 index 00000000000..bf6a9182522 --- /dev/null +++ b/litellm/responses/mcp/mcp_streaming_iterator.py @@ -0,0 +1,604 @@ +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + Optional, + Union, + cast, +) + +from litellm._logging import verbose_logger +from litellm.responses.streaming_iterator import ( + BaseResponsesAPIStreamingIterator, +) +from litellm.types.llms.openai import ( + MCPCallArgumentsDeltaEvent, + MCPCallArgumentsDoneEvent, + MCPCallCompletedEvent, + MCPCallFailedEvent, + MCPCallInProgressEvent, + MCPListToolsCompletedEvent, + MCPListToolsFailedEvent, + MCPListToolsInProgressEvent, + ResponsesAPIResponse, + ResponsesAPIStreamEvents, + ResponsesAPIStreamingResponse, + ToolParam, +) + +if TYPE_CHECKING: + from mcp.types import Tool as MCPTool +else: + MCPTool = Any + + +async def create_mcp_list_tools_events( + mcp_tools_with_litellm_proxy: List[ToolParam], + user_api_key_auth: Any, + base_item_id: str, + pre_processed_mcp_tools: List[Any] +) -> List[ResponsesAPIStreamingResponse]: + """Create MCP discovery events using pre-processed tools from the parent""" + from litellm.responses.mcp.litellm_proxy_mcp_handler import ( + LiteLLM_Proxy_MCP_Handler, + ) + + events = [] + + try: + # Extract MCP server names + mcp_servers = [] + for tool in mcp_tools_with_litellm_proxy: + if isinstance(tool, dict) and "server_url" in tool: + server_url = tool["server_url"] + if server_url.startswith("litellm_proxy/mcp/"): + server_name = server_url.split("/")[-1] + mcp_servers.append(server_name) + + # Emit list tools in progress event + in_progress_event = MCPListToolsInProgressEvent( + type=ResponsesAPIStreamEvents.MCP_LIST_TOOLS_IN_PROGRESS, + sequence_number=1, + output_index=0, + item_id=base_item_id, + ) + events.append(in_progress_event) + + # Use the pre-processed MCP tools that were already fetched, filtered, and deduplicated by the parent + filtered_mcp_tools = pre_processed_mcp_tools + + # Convert tools to dict format for the event + mcp_tools_dict = [] + for tool in filtered_mcp_tools: + if hasattr(tool, 'model_dump'): + mcp_tools_dict.append(tool.model_dump()) + elif hasattr(tool, '__dict__'): + mcp_tools_dict.append(tool.__dict__) + else: + mcp_tools_dict.append({"name": getattr(tool, 'name', str(tool))}) + + # Emit list tools completed event + completed_event = MCPListToolsCompletedEvent( + type=ResponsesAPIStreamEvents.MCP_LIST_TOOLS_COMPLETED, + sequence_number=2, + output_index=0, + item_id=base_item_id, + ) + events.append(completed_event) + + # Add output_item.done event with the actual tools list (matching OpenAI format) + from litellm.types.llms.openai import OutputItemDoneEvent + + # Extract server label from the first MCP tool config + server_label = "" + if mcp_tools_with_litellm_proxy: + first_tool = mcp_tools_with_litellm_proxy[0] + if isinstance(first_tool, dict): + server_label = first_tool.get("server_label", "") + + # Format tools for OpenAI output_item.done format + formatted_tools = [] + for tool in filtered_mcp_tools: + tool_dict = { + "name": getattr(tool, 'name', 'unknown'), + "description": getattr(tool, 'description', ''), + "annotations": {"read_only": False}, + } + + # Add input_schema if available + if hasattr(tool, 'inputSchema'): + tool_dict["input_schema"] = tool.inputSchema + elif hasattr(tool, 'input_schema'): + tool_dict["input_schema"] = tool.input_schema + + formatted_tools.append(tool_dict) + + # Create the output_item.done event with MCP tools list + output_item_done_event = OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=0, + item={ + "id": base_item_id, + "type": "mcp_list_tools", + "server_label": server_label, + "tools": formatted_tools + } + ) + events.append(output_item_done_event) + + verbose_logger.debug(f"Created {len(events)} MCP discovery events") + + except Exception as e: + verbose_logger.error(f"Error creating MCP list tools events: {e}") + import traceback + traceback.print_exc() + + # Emit failed event on error + failed_event = MCPListToolsFailedEvent( + type=ResponsesAPIStreamEvents.MCP_LIST_TOOLS_FAILED, + sequence_number=2, + output_index=0, + item_id=base_item_id, + ) + events.append(failed_event) + + # Still emit output_item.done event even on failure (with empty tools list) + from litellm.types.llms.openai import OutputItemDoneEvent + + output_item_done_event = OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=0, + item={ + "id": base_item_id, + "type": "mcp_list_tools", + "server_label": "", + "tools": [] + } + ) + events.append(output_item_done_event) + + return events + + +def create_mcp_call_events( + tool_name: str, + tool_call_id: str, + arguments: str, + result: Optional[str] = None, + base_item_id: Optional[str] = None, + sequence_start: int = 1 +) -> List[ResponsesAPIStreamingResponse]: + """Create MCP call events following OpenAI's specification""" + events = [] + item_id = base_item_id or f"mcp_{uuid.uuid4().hex[:8]}" + + # MCP call in progress event + in_progress_event = MCPCallInProgressEvent( + type=ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS, + sequence_number=sequence_start, + output_index=0, + item_id=item_id, + ) + events.append(in_progress_event) + + # MCP call arguments delta event (streaming the arguments) + arguments_delta_event = MCPCallArgumentsDeltaEvent( + type=ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DELTA, + output_index=0, + item_id=item_id, + delta=arguments, # JSON string with arguments + sequence_number=sequence_start + 1, + ) + events.append(arguments_delta_event) + + # MCP call arguments done event + arguments_done_event = MCPCallArgumentsDoneEvent( + type=ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DONE, + output_index=0, + item_id=item_id, + arguments=arguments, # Complete JSON string with finalized arguments + sequence_number=sequence_start + 2, + ) + events.append(arguments_done_event) + + # MCP call completed event (or failed if result indicates failure) + if result is not None: + completed_event = MCPCallCompletedEvent( + type=ResponsesAPIStreamEvents.MCP_CALL_COMPLETED, + sequence_number=sequence_start + 3, + item_id=item_id, + output_index=0, + ) + events.append(completed_event) + + # Add output_item.done event with the tool call result + from litellm.types.llms.openai import OutputItemDoneEvent + + output_item_done_event = OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=0, + item={ + "id": item_id, + "type": "mcp_call", + "approval_request_id": f"mcpr_{uuid.uuid4().hex[:8]}", + "arguments": arguments, + "error": None, + "name": tool_name, + "output": result, + "server_label": "litellm" + }, + ) + events.append(output_item_done_event) + else: + failed_event = MCPCallFailedEvent( + type=ResponsesAPIStreamEvents.MCP_CALL_FAILED, + sequence_number=sequence_start + 3, + item_id=item_id, + output_index=0, + ) + events.append(failed_event) + + return events + + +class MCPEnhancedStreamingIterator(BaseResponsesAPIStreamingIterator): + """ + A complete MCP streaming iterator that handles the entire flow: + 1. Immediately emits MCP discovery events + 2. Makes the first LLM call and streams its response + 3. Handles tool execution and follow-up calls for auto-execute tools + 4. Emits tool execution events in the stream + """ + + def __init__( + self, + base_iterator: Any, # Can be None - will be created internally + mcp_events: List[ResponsesAPIStreamingResponse], + mcp_tools_with_litellm_proxy: Optional[List[Any]] = None, + user_api_key_auth: Any = None, + original_request_params: Optional[Dict[str, Any]] = None + ): + # MCP setup + self.mcp_tools_with_litellm_proxy = mcp_tools_with_litellm_proxy or [] + self.user_api_key_auth = user_api_key_auth + self.original_request_params = original_request_params or {} + self.should_auto_execute = self._should_auto_execute_tools() + + # Streaming state management + self.phase = "mcp_discovery" # mcp_discovery -> initial_response -> tool_execution -> follow_up_response -> finished + self.finished = False + + # Event queues and generation flags + self.mcp_discovery_events: List[ResponsesAPIStreamingResponse] = mcp_events # Pre-generated MCP discovery events + self.tool_execution_events: List[ResponsesAPIStreamingResponse] = [] + self.mcp_discovery_generated = True # Events are already generated + self.mcp_events = mcp_events # Store the initial MCP events for backward compatibility + + # Iterator references + self.base_iterator: Optional[Union[Any, ResponsesAPIResponse]] = base_iterator # Will be created when needed + self.follow_up_iterator: Optional[Any] = None + + # Response collection for tool execution + self.collected_response: Optional[ResponsesAPIResponse] = None + + # Set up model metadata (will be updated when we get the real iterator) + self.model = self.original_request_params.get('model', 'unknown') + self.litellm_metadata = {} + self.custom_llm_provider = self.original_request_params.get('custom_llm_provider', None) + + # Mark as async iterator + self.is_async = True + + def _should_auto_execute_tools(self) -> bool: + """Check if tools should be auto-executed""" + from litellm.responses.mcp.litellm_proxy_mcp_handler import ( + LiteLLM_Proxy_MCP_Handler, + ) + return LiteLLM_Proxy_MCP_Handler._should_auto_execute_tools( + self.mcp_tools_with_litellm_proxy + ) + + def __aiter__(self): + return self + + async def __anext__(self) -> ResponsesAPIStreamingResponse: + """ + Phase-based streaming: + 1. mcp_discovery - Emit MCP discovery events + 2. initial_response - Stream the first LLM response + 3. tool_execution - Emit tool execution events + 4. follow_up_response - Stream the follow-up response + 5. finished - End iteration + """ + + # Phase 1: MCP Discovery Events + if self.phase == "mcp_discovery": + # Generate MCP discovery events if not already done + # MCP discovery events are already generated and available + + # Emit MCP discovery events + if self.mcp_discovery_events: + return self.mcp_discovery_events.pop(0) + + # All MCP discovery events emitted, move to next phase + verbose_logger.debug("MCP discovery phase complete, transitioning to initial_response") + self.phase = "initial_response" + await self._create_initial_response_iterator() + # Fall through to process the initial response immediately + + # Phase 2: Initial Response Stream + if self.phase == "initial_response": + if self.base_iterator: + # Check if base_iterator is actually iterable + if hasattr(self.base_iterator, '__anext__'): + try: + chunk = await cast(Any, self.base_iterator).__anext__() # type: ignore[attr-defined] + + # If auto-execution is enabled, check for completed responses + if self.should_auto_execute and self._is_response_completed(chunk): + # Collect the response for tool execution + response_obj = getattr(chunk, 'response', None) + if isinstance(response_obj, ResponsesAPIResponse): + self.collected_response = response_obj + # Move to tool execution phase after emitting this chunk + self.phase = "tool_execution" + await self._generate_tool_execution_events() + + return chunk + except StopAsyncIteration: + # Initial response ended, move to next phase + if self.should_auto_execute and self.collected_response: + self.phase = "tool_execution" + await self._generate_tool_execution_events() + else: + self.phase = "finished" + raise + else: + # base_iterator is not async iterable (likely a ResponsesAPIResponse) + # Collect it for tool execution if needed + if self.should_auto_execute and isinstance(self.base_iterator, ResponsesAPIResponse): + self.collected_response = self.base_iterator + self.phase = "tool_execution" + await self._generate_tool_execution_events() + else: + self.phase = "finished" + raise StopAsyncIteration + + # Phase 3: Tool Execution Events + if self.phase == "tool_execution": + # Emit any queued tool execution events + if self.tool_execution_events: + return self.tool_execution_events.pop(0) + + # Move to follow-up response phase + self.phase = "follow_up_response" + await self._create_follow_up_iterator() + + # Phase 4: Follow-up Response Stream + if self.phase == "follow_up_response": + if self.follow_up_iterator: + try: + return await cast(Any, self.follow_up_iterator).__anext__() # type: ignore[attr-defined] + except StopAsyncIteration: + self.phase = "finished" + raise + else: + self.phase = "finished" + raise StopAsyncIteration + + # Phase 5: Finished + if self.phase == "finished": + raise StopAsyncIteration + + # Should not reach here + raise StopAsyncIteration + + def _is_response_completed(self, chunk: ResponsesAPIStreamingResponse) -> bool: + """Check if this chunk indicates the response is completed""" + from litellm.types.llms.openai import ResponsesAPIStreamEvents + return getattr(chunk, 'type', None) == ResponsesAPIStreamEvents.RESPONSE_COMPLETED + + + async def _create_initial_response_iterator(self) -> None: + """Create the initial response iterator by making the first LLM call""" + try: + # Import the core aresponses function that doesn't have MCP logic + from litellm.responses.main import aresponses + + # Make the initial response API call - but avoid the MCP wrapper + params = self.original_request_params.copy() + params['stream'] = True # Ensure streaming + + # Use the pre-fetched all_tools from original_request_params (no re-processing needed) + params_for_llm = {} + for key, value in params.items(): + params_for_llm[key] = value # Copy all params as-is since tools are already processed + + tools_count = len(params_for_llm.get('tools', [])) + verbose_logger.debug(f"Making LLM call with {tools_count} tools") + response = await aresponses(**params_for_llm) + + # Set the base iterator + if hasattr(response, '__aiter__') or hasattr(response, '__iter__'): + self.base_iterator = response + # Copy metadata from the real iterator + self.model = getattr(response, 'model', self.model) + self.litellm_metadata = getattr(response, 'litellm_metadata', {}) + self.custom_llm_provider = getattr(response, 'custom_llm_provider', self.custom_llm_provider) + verbose_logger.debug(f"Created base iterator: {type(self.base_iterator)}") + else: + # Non-streaming response - this shouldn't happen but handle it + verbose_logger.warning(f"Got non-streaming response: {type(response)}") + self.base_iterator = None + self.phase = "finished" + + except Exception as e: + verbose_logger.error(f"Error creating initial response iterator: {e}") + import traceback + traceback.print_exc() + self.base_iterator = None + self.phase = "finished" + + async def _generate_tool_execution_events(self) -> None: + """Generate tool execution events and execute tools""" + if not self.collected_response: + return + + import uuid + + from litellm.responses.mcp.litellm_proxy_mcp_handler import ( + LiteLLM_Proxy_MCP_Handler, + ) + + try: + # Extract tool calls from the response + if self.collected_response is not None: + tool_calls = LiteLLM_Proxy_MCP_Handler._extract_tool_calls_from_response(self.collected_response) # type: ignore[arg-type] + else: + tool_calls = [] + if not tool_calls: + return + + # Create tool execution events + base_item_id = f"mcp_{uuid.uuid4().hex[:8]}" + for tool_call in tool_calls: + tool_name, tool_arguments, tool_call_id = LiteLLM_Proxy_MCP_Handler._extract_tool_call_details(tool_call) + if tool_name and tool_call_id: + # Create MCP call events for this tool execution + call_events = create_mcp_call_events( + tool_name=tool_name, + tool_call_id=tool_call_id, + arguments=tool_arguments or "{}", # JSON string with arguments + result=None, # Will be set after execution + base_item_id=f"mcp_{uuid.uuid4().hex[:8]}", + sequence_start=len(self.tool_execution_events) + 1 + ) + # Add the in_progress and arguments events (not the completed event yet) + self.tool_execution_events.extend(call_events[:-1]) + + # Execute the tools + tool_results = await LiteLLM_Proxy_MCP_Handler._execute_tool_calls( + tool_calls=tool_calls, + user_api_key_auth=self.user_api_key_auth + ) + + # Create completion events and output_item.done events for tool execution + for tool_result in tool_results: + tool_call_id = tool_result.get("tool_call_id", "unknown") + result_text = tool_result.get("result", "") + + # Find matching tool name and arguments + tool_name = "unknown" + tool_arguments = "{}" + for tool_call in tool_calls: + name, args, call_id = LiteLLM_Proxy_MCP_Handler._extract_tool_call_details(tool_call) + if call_id == tool_call_id: + tool_name = name or "unknown" + tool_arguments = args or "{}" + break + + item_id = f"mcp_{uuid.uuid4().hex[:8]}" + + # Create the completion event + completed_event = MCPCallCompletedEvent( + type=ResponsesAPIStreamEvents.MCP_CALL_COMPLETED, + sequence_number=len(self.tool_execution_events) + 1, + item_id=item_id, + output_index=0, + ) + self.tool_execution_events.append(completed_event) + + # Create output_item.done event with the tool call result + from litellm.types.llms.openai import OutputItemDoneEvent + + output_item_done_event = OutputItemDoneEvent( + type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, + output_index=0, + item={ + "id": item_id, + "type": "mcp_call", + "approval_request_id": f"mcpr_{uuid.uuid4().hex[:8]}", + "arguments": tool_arguments, + "error": None, + "name": tool_name, + "output": result_text, + "server_label": "litellm" # or extract from tool config + }, + ) + self.tool_execution_events.append(output_item_done_event) + + # Store tool results for follow-up call + self.tool_results = tool_results + + except Exception as e: + verbose_logger.error(f"Error in tool execution: {e}") + import traceback + traceback.print_exc() + self.tool_results = [] + + async def _create_follow_up_iterator(self) -> None: + """Create the follow-up response iterator with tool results""" + if not self.collected_response or not hasattr(self, 'tool_results'): + return + + from litellm.responses.main import aresponses + from litellm.responses.mcp.litellm_proxy_mcp_handler import ( + LiteLLM_Proxy_MCP_Handler, + ) + + try: + # Create follow-up input + if self.collected_response is not None: + follow_up_input = LiteLLM_Proxy_MCP_Handler._create_follow_up_input( + response=self.collected_response, # type: ignore[arg-type] + tool_results=self.tool_results, + original_input=self.original_request_params.get('input') + ) + + # Make follow-up call with streaming + follow_up_params = self.original_request_params.copy() + follow_up_params.update({ + 'input': follow_up_input, + 'previous_response_id': self.collected_response.id, # type: ignore[attr-defined] + 'stream': True + }) + else: + return + # Remove tool_choice to avoid forcing more tool calls + follow_up_params.pop('tool_choice', None) + + follow_up_response = await aresponses(**follow_up_params) + + # Set up the follow-up iterator + if hasattr(follow_up_response, '__aiter__'): + self.follow_up_iterator = follow_up_response + + except Exception as e: + verbose_logger.error(f"Error creating follow-up iterator: {e}") + import traceback + traceback.print_exc() + self.follow_up_iterator = None + + + def __iter__(self): + return self + + def __next__(self) -> ResponsesAPIStreamingResponse: + # First, emit any queued MCP events + if self.mcp_events: # type: ignore[attr-defined] + return self.mcp_events.pop(0) # type: ignore[attr-defined] + + # Then delegate to the base iterator + if not self.is_async: + try: + if self.base_iterator and hasattr(self.base_iterator, '__next__'): + return next(cast(Any, self.base_iterator)) # type: ignore[arg-type] + else: + raise StopIteration + except StopIteration: + self.finished = True + raise + else: + raise RuntimeError("Cannot use sync iteration on async iterator") diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py index c6d126be681..79e3c73dbc1 100644 --- a/litellm/types/llms/openai.py +++ b/litellm/types/llms/openai.py @@ -1112,6 +1112,16 @@ class ResponsesAPIStreamEvents(str, Enum): WEB_SEARCH_CALL_SEARCHING = "response.web_search_call.searching" WEB_SEARCH_CALL_COMPLETED = "response.web_search_call.completed" + # MCP events - matching OpenAI's official specification + MCP_LIST_TOOLS_IN_PROGRESS = "response.mcp_list_tools.in_progress" + MCP_LIST_TOOLS_COMPLETED = "response.mcp_list_tools.completed" + MCP_LIST_TOOLS_FAILED = "response.mcp_list_tools.failed" + MCP_CALL_IN_PROGRESS = "response.mcp_call.in_progress" + MCP_CALL_ARGUMENTS_DELTA = "response.mcp_call_arguments.delta" + MCP_CALL_ARGUMENTS_DONE = "response.mcp_call_arguments.done" + MCP_CALL_COMPLETED = "response.mcp_call.completed" + MCP_CALL_FAILED = "response.mcp_call.failed" + # Error event ERROR = "error" @@ -1275,6 +1285,66 @@ class WebSearchCallCompletedEvent(BaseLiteLLMOpenAIResponseObject): item_id: str +# MCP List Tools Events +class MCPListToolsInProgressEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_LIST_TOOLS_IN_PROGRESS] + sequence_number: int + output_index: int + item_id: str + + +class MCPListToolsCompletedEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_LIST_TOOLS_COMPLETED] + sequence_number: int + output_index: int + item_id: str + + +class MCPListToolsFailedEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_LIST_TOOLS_FAILED] + sequence_number: int + output_index: int + item_id: str + + +# MCP Call Events +class MCPCallInProgressEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_CALL_IN_PROGRESS] + sequence_number: int + output_index: int + item_id: str + + +class MCPCallArgumentsDeltaEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DELTA] + output_index: int + item_id: str + delta: str # JSON string containing partial update to arguments + sequence_number: int + + +class MCPCallArgumentsDoneEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_CALL_ARGUMENTS_DONE] + output_index: int + item_id: str + arguments: str # JSON string containing finalized arguments + sequence_number: int + + +class MCPCallCompletedEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_CALL_COMPLETED] + sequence_number: int + item_id: str + output_index: int + + +class MCPCallFailedEvent(BaseLiteLLMOpenAIResponseObject): + type: Literal[ResponsesAPIStreamEvents.MCP_CALL_FAILED] + sequence_number: int + item_id: str + output_index: int + + class ErrorEvent(BaseLiteLLMOpenAIResponseObject): type: Literal[ResponsesAPIStreamEvents.ERROR] code: Optional[str] @@ -1315,6 +1385,14 @@ ResponsesAPIStreamingResponse = Annotated[ WebSearchCallInProgressEvent, WebSearchCallSearchingEvent, WebSearchCallCompletedEvent, + MCPListToolsInProgressEvent, + MCPListToolsCompletedEvent, + MCPListToolsFailedEvent, + MCPCallInProgressEvent, + MCPCallArgumentsDeltaEvent, + MCPCallArgumentsDoneEvent, + MCPCallCompletedEvent, + MCPCallFailedEvent, ErrorEvent, GenericEvent, ], diff --git a/tests/mcp_tests/test_aresponses_api_with_mcp.py b/tests/mcp_tests/test_aresponses_api_with_mcp.py index 500bd8bcc06..64bc58eb40c 100644 --- a/tests/mcp_tests/test_aresponses_api_with_mcp.py +++ b/tests/mcp_tests/test_aresponses_api_with_mcp.py @@ -6,8 +6,9 @@ from typing import List, Any, cast sys.path.insert(0, os.path.abspath("../../..")) # Import required modules +import litellm from litellm.responses.mcp.litellm_proxy_mcp_handler import LiteLLM_Proxy_MCP_Handler -from litellm.types.llms.openai import ResponsesAPIResponse, OpenAIMcpServerTool +from litellm.types.llms.openai import ResponsesAPIResponse, OpenAIMcpServerTool, ToolParam class MockUserAPIKeyAuth: @@ -251,3 +252,887 @@ async def test_aresponses_api_with_mcp_mock_integration(): print(f"MCP tools parsed: {len(mcp_parsed)}") print(f"Other tools parsed: {len(other_parsed)}") + +@pytest.mark.asyncio +async def test_mcp_allowed_tools_filtering(): + """ + Test the allowed_tools filtering functionality for MCP tools. + This test verifies that when allowed_tools is specified in MCP tool config, + only the allowed tools are passed to the LLM. + """ + from litellm.responses.mcp.litellm_proxy_mcp_handler import LiteLLM_Proxy_MCP_Handler + + # Mock MCP tools returned from the server (simulating all available tools) + mock_mcp_tools_from_server = [ + # Mock MCP tool object with name attribute + type('MCPTool', (), { + 'name': 'search_tiktoken_documentation', + 'description': 'Search tiktoken documentation', + 'inputSchema': {'type': 'object', 'properties': {'query': {'type': 'string'}}} + })(), + type('MCPTool', (), { + 'name': 'fetch_tiktoken_documentation', + 'description': 'Fetch tiktoken documentation', + 'inputSchema': {'type': 'object', 'properties': {'path': {'type': 'string'}}} + })(), + type('MCPTool', (), { + 'name': 'list_tiktoken_functions', + 'description': 'List tiktoken functions', + 'inputSchema': {'type': 'object', 'properties': {}} + })(), + type('MCPTool', (), { + 'name': 'get_tiktoken_examples', + 'description': 'Get tiktoken examples', + 'inputSchema': {'type': 'object', 'properties': {}} + })() + ] + + # Test Case 1: MCP tool config with allowed_tools specified + mcp_tool_config_with_allowed_tools = [ + { + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/tiktoken", + "allowed_tools": ["search_tiktoken_documentation", "fetch_tiktoken_documentation"], + "require_approval": "never" + } + ] + + # Filter tools using the helper function + filtered_tools = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_from_server, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], mcp_tool_config_with_allowed_tools) + ) + + # Should only return the 2 allowed tools + assert len(filtered_tools) == 2, f"Expected 2 filtered tools, got {len(filtered_tools)}" + + # Check that only allowed tools are included + filtered_tool_names = [tool.name for tool in filtered_tools] + expected_allowed_tools = ["search_tiktoken_documentation", "fetch_tiktoken_documentation"] + + assert set(filtered_tool_names) == set(expected_allowed_tools), \ + f"Expected tools {expected_allowed_tools}, got {filtered_tool_names}" + + # Verify excluded tools are not present + excluded_tools = ["list_tiktoken_functions", "get_tiktoken_examples"] + for excluded_tool in excluded_tools: + assert excluded_tool not in filtered_tool_names, \ + f"Tool {excluded_tool} should have been filtered out" + + print("✓ Test Case 1: allowed_tools filtering works correctly") + + # Test Case 2: MCP tool config without allowed_tools (should return all tools) + mcp_tool_config_without_allowed_tools = [ + { + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/tiktoken", + "require_approval": "never" + } + ] + + filtered_tools_all = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_from_server, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], mcp_tool_config_without_allowed_tools) + ) + + # Should return all 4 tools when no allowed_tools specified + assert len(filtered_tools_all) == 4, f"Expected 4 tools when no allowed_tools specified, got {len(filtered_tools_all)}" + + print("✓ Test Case 2: no allowed_tools returns all tools") + + # Test Case 3: Test deduplication of duplicate tools + mock_mcp_tools_with_duplicates = [ + # First instance of duplicate tool + type('MCPTool', (), { + 'name': 'GitMCP-fetch_litellm_documentation', + 'description': 'Fetch entire documentation file from GitHub repository: BerriAI/litellm. Useful for general questions. Always call this tool first if asked about BerriAI/litellm.', + 'inputSchema': {'type': 'object', 'properties': {}, 'additionalProperties': False} + })(), + # Second instance of duplicate tool (should be filtered out) + type('MCPTool', (), { + 'name': 'GitMCP-fetch_litellm_documentation', + 'description': 'Fetch entire documentation file from GitHub repository: BerriAI/litellm. Useful for general questions. Always call this tool first if asked about BerriAI/litellm.', + 'inputSchema': {'type': 'object', 'properties': {}, 'additionalProperties': False} + })(), + # Other unique tools + type('MCPTool', (), { + 'name': 'GitMCP-search_litellm_documentation', + 'description': 'Semantically search within the fetched documentation from GitHub repository: BerriAI/litellm. Useful for specific queries.', + 'inputSchema': {'type': 'object', 'properties': {'query': {'type': 'string'}}, 'required': ['query'], 'additionalProperties': False} + })(), + ] + + mcp_tool_config_with_duplicates = [ + { + "type": "mcp", + "server_label": "litellm", + "server_url": "litellm_proxy/mcp", + "require_approval": "never", + "allowed_tools": ["GitMCP-fetch_litellm_documentation"] + } + ] + + # First filter by allowed tools + filtered_tools_with_duplicates = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_with_duplicates, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], mcp_tool_config_with_duplicates) + ) + + # Then deduplicate the filtered tools + filtered_tools_deduplicated = LiteLLM_Proxy_MCP_Handler._deduplicate_mcp_tools( + filtered_tools_with_duplicates + ) + + # Should only return 1 tool (the duplicate should be removed) + assert len(filtered_tools_deduplicated) == 1, f"Expected 1 tool after deduplication, got {len(filtered_tools_deduplicated)}" + + # Check that the correct tool is present + assert filtered_tools_deduplicated[0].name == "GitMCP-fetch_litellm_documentation", \ + f"Expected GitMCP-fetch_litellm_documentation, got {filtered_tools_deduplicated[0].name}" + + print("✓ Test Case 3: duplicate tools are properly deduplicated") + + # Test Case 3b: Test standalone deduplication method + standalone_deduplicated = LiteLLM_Proxy_MCP_Handler._deduplicate_mcp_tools(mock_mcp_tools_with_duplicates) + + # Should return 2 unique tools (GitMCP-fetch_litellm_documentation and GitMCP-search_litellm_documentation) + assert len(standalone_deduplicated) == 2, f"Expected 2 unique tools after standalone deduplication, got {len(standalone_deduplicated)}" + + unique_tool_names = [tool.name for tool in standalone_deduplicated] + expected_unique_names = ["GitMCP-fetch_litellm_documentation", "GitMCP-search_litellm_documentation"] + assert set(unique_tool_names) == set(expected_unique_names), \ + f"Expected {expected_unique_names}, got {unique_tool_names}" + + print("✓ Test Case 3b: standalone deduplication method works correctly") + + # Test Case 4: Multiple MCP tool configs with different allowed_tools + multiple_mcp_configs = [ + { + "type": "mcp", + "server_label": "gitmcp1", + "server_url": "https://gitmcp.io/openai/tiktoken", + "allowed_tools": ["search_tiktoken_documentation"], + "require_approval": "never" + }, + { + "type": "mcp", + "server_label": "gitmcp2", + "server_url": "https://gitmcp.io/openai/tiktoken", + "allowed_tools": ["fetch_tiktoken_documentation", "get_tiktoken_examples"], + "require_approval": "never" + } + ] + + filtered_tools_multiple = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_from_server, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], multiple_mcp_configs) + ) + + # Should return union of all allowed tools (3 unique tools) + assert len(filtered_tools_multiple) == 3, f"Expected 3 tools from multiple configs, got {len(filtered_tools_multiple)}" + + filtered_multiple_names = [tool.name for tool in filtered_tools_multiple] + expected_multiple_tools = ["search_tiktoken_documentation", "fetch_tiktoken_documentation", "get_tiktoken_examples"] + + assert set(filtered_multiple_names) == set(expected_multiple_tools), \ + f"Expected tools {expected_multiple_tools}, got {filtered_multiple_names}" + + print("✓ Test Case 3: multiple MCP configs with different allowed_tools works correctly") + + # Test Case 4: Empty allowed_tools list (should return no tools) + mcp_config_empty_allowed = [ + { + "type": "mcp", + "server_label": "gitmcp", + "server_url": "https://gitmcp.io/openai/tiktoken", + "allowed_tools": [], + "require_approval": "never" + } + ] + + filtered_tools_empty = LiteLLM_Proxy_MCP_Handler._filter_mcp_tools_by_allowed_tools( + mcp_tools=mock_mcp_tools_from_server, + mcp_tools_with_litellm_proxy=cast(List[ToolParam], mcp_config_empty_allowed) + ) + + # Should return all tools when allowed_tools is empty list (no filtering) + assert len(filtered_tools_empty) == 4, f"Expected 4 tools when allowed_tools is empty list, got {len(filtered_tools_empty)}" + + print("✓ Test Case 4: empty allowed_tools list returns all tools") + + print("✓ MCP allowed_tools filtering test completed successfully!") + +@pytest.mark.asyncio +async def test_streaming_mcp_events_validation(): + """ + Test that MCP streaming events are properly emitted when using streaming with MCP tools. + + This test validates: + 1. MCP discovery events are emitted first + 2. Regular streaming response events follow + 3. Tool execution events are emitted when tools are auto-executed + """ + from unittest.mock import AsyncMock, patch + from litellm.types.llms.openai import ResponsesAPIStreamEvents + + print("🧪 Testing MCP streaming events...") + + # Mock MCP tools that would be returned from the manager + mock_mcp_tools = [ + type('MCPTool', (), { + 'name': 'search_repo', + 'description': 'Search BerriAI/litellm repository for information', + 'inputSchema': { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + })(), + type('MCPTool', (), { + 'name': 'get_repo_info', + 'description': 'Get repository information', + 'inputSchema': { + "type": "object", + "properties": { + "repo_name": {"type": "string", "description": "Repository name"} + }, + "required": ["repo_name"] + } + })() + ] + + # Mock the MCP operations + with patch.object(LiteLLM_Proxy_MCP_Handler, '_get_mcp_tools_from_manager', new_callable=AsyncMock) as mock_get_tools, \ + patch.object(LiteLLM_Proxy_MCP_Handler, '_execute_tool_calls', new_callable=AsyncMock) as mock_execute_tools: + + # Setup MCP mocks + mock_get_tools.return_value = mock_mcp_tools + + def mock_execute_tool_calls_side_effect(tool_calls, user_api_key_auth): + """Mock tool execution with realistic results""" + results = [] + for tool_call in tool_calls: + call_id = None + if isinstance(tool_call, dict): + call_id = tool_call.get("call_id") or tool_call.get("id") + elif hasattr(tool_call, 'call_id'): + call_id = tool_call.call_id + elif hasattr(tool_call, 'id'): + call_id = tool_call.id + + if call_id: + results.append({ + "tool_call_id": call_id, + "result": "LiteLLM is a unified interface for 100+ LLMs that provides consistent OpenAI-format output and includes proxy server capabilities." + }) + return results + + mock_execute_tools.side_effect = mock_execute_tool_calls_side_effect + + # Configure MCP tool with streaming and auto-execution + mcp_tool_config = { + "type": "mcp", + "server_url": "litellm_proxy/mcp/test_server", + "require_approval": "never" # This enables auto-execution + } + + print("📞 Making streaming request with MCP tools...") + + # Make streaming request with MCP tools + response = await litellm.aresponses( + model="gpt-4o-mini", # Use cheaper model for testing + tools=[mcp_tool_config], + tool_choice="required", + input=[{ + "role": "user", + "type": "message", + "content": "What is LiteLLM? Give me a brief overview." + }], + stream=True + ) + + print(f"📋 Response type: {type(response)}") + assert hasattr(response, '__aiter__'), "Response should be async iterable for streaming" + + # Collect all streaming events + events = [] + event_types = [] + mcp_discovery_events = [] + mcp_execution_events = [] + regular_events = [] + + print("🔄 Collecting streaming events...") + + try: + async for chunk in response: + events.append(chunk) + event_type = getattr(chunk, 'type', 'unknown') + event_types.append(event_type) + + # Categorize events + if event_type in [ + ResponsesAPIStreamEvents.MCP_TOOLS_DISCOVERY_STARTED, + ResponsesAPIStreamEvents.MCP_TOOLS_DISCOVERY_COMPLETED + ]: + mcp_discovery_events.append(chunk) + elif event_type in [ + ResponsesAPIStreamEvents.MCP_TOOL_EXECUTION_STARTED, + ResponsesAPIStreamEvents.MCP_TOOL_EXECUTION_COMPLETED + ]: + mcp_execution_events.append(chunk) + else: + regular_events.append(chunk) + + print(f"📦 Event: {event_type}") + + # Print MCP-specific event details + if hasattr(chunk, 'mcp_servers'): + print(f" 🔧 MCP Servers: {chunk.mcp_servers}") + elif hasattr(chunk, 'mcp_tools'): + print(f" 🛠️ MCP Tools: {len(chunk.mcp_tools)} tools discovered") + elif hasattr(chunk, 'tool_name'): + print(f" ⚙️ Tool: {chunk.tool_name}") + if hasattr(chunk, 'result'): + print(f" ✅ Result: {chunk.result[:100]}...") + + except Exception as e: + print(f"❌ Error during streaming: {e}") + # Continue with validation of events collected so far + + print(f"\n📊 Event Summary:") + print(f" Total events: {len(events)}") + print(f" MCP discovery events: {len(mcp_discovery_events)}") + print(f" MCP execution events: {len(mcp_execution_events)}") + print(f" Regular streaming events: {len(regular_events)}") + print(f" Event types: {set(event_types)}") + + # Validate MCP discovery events + if mcp_discovery_events: + print("✅ MCP discovery events found!") + + # Check for discovery started event + started_events = [e for e in mcp_discovery_events if e.type == ResponsesAPIStreamEvents.MCP_TOOLS_DISCOVERY_STARTED] + if started_events: + print(f" 🚀 Discovery started events: {len(started_events)}") + started_event = started_events[0] + if hasattr(started_event, 'mcp_servers'): + print(f" 📡 MCP servers: {started_event.mcp_servers}") + + # Check for discovery completed event + completed_events = [e for e in mcp_discovery_events if e.type == ResponsesAPIStreamEvents.MCP_TOOLS_DISCOVERY_COMPLETED] + if completed_events: + print(f" 🏁 Discovery completed events: {len(completed_events)}") + completed_event = completed_events[0] + if hasattr(completed_event, 'mcp_tools'): + print(f" 🔧 Tools discovered: {len(completed_event.mcp_tools)}") + else: + print("⚠️ No MCP discovery events found") + + # Validate MCP execution events (if auto-execution occurred) + if mcp_execution_events: + print("✅ MCP tool execution events found!") + execution_started = [e for e in mcp_execution_events if e.type == ResponsesAPIStreamEvents.MCP_TOOL_EXECUTION_STARTED] + execution_completed = [e for e in mcp_execution_events if e.type == ResponsesAPIStreamEvents.MCP_TOOL_EXECUTION_COMPLETED] + print(f" 🚀 Execution started events: {len(execution_started)}") + print(f" 🏁 Execution completed events: {len(execution_completed)}") + + # Validate that we got some form of streaming response + assert len(events) > 0, "Should have received at least some streaming events" + + # Verify MCP mocks were called + assert mock_get_tools.called, "MCP tools should have been fetched" + print("✅ MCP tool fetching was called") + + print("🎉 MCP streaming events validation completed!") + return { + 'total_events': len(events), + 'mcp_discovery_events': len(mcp_discovery_events), + 'mcp_execution_events': len(mcp_execution_events), + 'regular_events': len(regular_events), + 'event_types': list(set(event_types)) + } + + +@pytest.mark.asyncio +async def test_streaming_responses_api_with_mcp_tools(): + """ + Test the streaming responses API with MCP tools when using server_url="litellm_proxy" + + Under the hood the follow occurs + + - MCP: responses called litellm MCP manager.list_tools (MOCKED) + - Request 1: Made to gpt-4o with fetched tools (REAL LLM CALL) + - MCP: Execute tool call from request 1 and returns result (MOCKED) + - Request 2: Made to gpt-4o with fetched tools and tool results (REAL LLM CALL) + + Return the user the result of request 2 + """ + from unittest.mock import AsyncMock, patch + + print("🧪 Testing basic streaming with MCP tools...") + + # Mock MCP tools that would be returned from the manager + mock_mcp_tools = [ + type('MCPTool', (), { + 'name': 'search_repo', + 'description': 'Search BerriAI/litellm repository for information', + 'inputSchema': { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + })() + ] + + # Only mock the MCP-specific operations, let LLM responses be real + with patch.object(LiteLLM_Proxy_MCP_Handler, '_get_mcp_tools_from_manager', new_callable=AsyncMock) as mock_get_tools, \ + patch.object(LiteLLM_Proxy_MCP_Handler, '_execute_tool_calls', new_callable=AsyncMock) as mock_execute_tools: + + # Setup MCP mocks only + mock_get_tools.return_value = mock_mcp_tools + + # Create a dynamic mock that will match the actual tool call ID from the LLM response + def mock_execute_tool_calls_side_effect(tool_calls, user_api_key_auth): + """Mock function that returns results matching the actual tool call IDs from the LLM""" + results = [] + for tool_call in tool_calls: + # Extract call_id from the tool call + call_id = None + if isinstance(tool_call, dict): + call_id = tool_call.get("call_id") or tool_call.get("id") + elif hasattr(tool_call, 'call_id'): + call_id = tool_call.call_id + elif hasattr(tool_call, 'id'): + call_id = tool_call.id + + if call_id: + results.append({ + "tool_call_id": call_id, + "result": "LiteLLM is a unified interface for 100+ LLMs that translates inputs to provider-specific completion endpoints and provides consistent OpenAI-format output." + }) + return results + + mock_execute_tools.side_effect = mock_execute_tool_calls_side_effect + + # Make the actual call - LLM responses will be real + mcp_tool_config = cast(Any, { + "type": "mcp", + "server_url": "litellm_proxy", + "require_approval": "never" + }) + response = await litellm.aresponses( + model="gpt-4o-mini", + tools=[mcp_tool_config], + tool_choice="required", + input=[ + { + "role": "user", + "type": "message", + "content": "give me a TLDR of what BerriAI/litellm is about" + } + ], + stream=True + ) + + print(f"📋 Response type: {type(response)}") + assert hasattr(response, '__aiter__'), "Response should be an async streaming response" + + # Collect streaming chunks + chunks = [] + async for chunk in response: + chunks.append(chunk) + print(f"📦 Chunk type: {getattr(chunk, 'type', 'unknown')}") + + print(f"📊 Total chunks received: {len(chunks)}") + + # Verify MCP mocks were called (may be called multiple times in streaming) + assert mock_get_tools.call_count >= 1, f"Expected MCP tools to be fetched at least once, got {mock_get_tools.call_count}" + print(f"MCP tools fetched: {len(mock_mcp_tools)}") + + # Verify we got a response + assert response is not None + assert len(chunks) > 0, "Should have received streaming chunks" + + print("Basic streaming responses API with MCP tools test passed!") + + +@pytest.mark.asyncio +async def test_mcp_parameter_preparation_helpers(): + """ + Test the new parameter preparation helper methods for clean MCP handling. + + Tests: + 1. _prepare_initial_call_params - handles stream disabling for auto-execute + 2. _prepare_follow_up_call_params - restores stream and removes tool_choice + 3. _build_request_params - clean parameter merging + """ + from litellm.responses.mcp.litellm_proxy_mcp_handler import LiteLLM_Proxy_MCP_Handler + + print("🧪 Testing MCP parameter preparation helpers...") + + # Test _prepare_initial_call_params + base_call_params = { + "stream": True, + "temperature": 0.7, + "tool_choice": "required", + "max_output_tokens": 1000 + } + + # Test Case 1: Auto-execute scenario (should disable streaming) + initial_params_auto = LiteLLM_Proxy_MCP_Handler._prepare_initial_call_params( + call_params=base_call_params, + should_auto_execute=True + ) + + assert initial_params_auto["stream"] == False, "Stream should be disabled for auto-execute" + assert initial_params_auto["temperature"] == 0.7, "Other params should be preserved" + assert initial_params_auto["tool_choice"] == "required", "tool_choice should be preserved for initial call" + assert base_call_params["stream"] == True, "Original params should not be mutated" + + print("✅ _prepare_initial_call_params (auto-execute) works correctly") + + # Test Case 2: No auto-execute scenario (should preserve streaming) + initial_params_no_auto = LiteLLM_Proxy_MCP_Handler._prepare_initial_call_params( + call_params=base_call_params, + should_auto_execute=False + ) + + assert initial_params_no_auto["stream"] == True, "Stream should be preserved when not auto-executing" + assert initial_params_no_auto["temperature"] == 0.7, "Other params should be preserved" + + print("✅ _prepare_initial_call_params (no auto-execute) works correctly") + + # Test _prepare_follow_up_call_params + follow_up_params = LiteLLM_Proxy_MCP_Handler._prepare_follow_up_call_params( + call_params=base_call_params, + original_stream_setting=True + ) + + assert follow_up_params["stream"] == True, "Stream should be restored to original setting" + assert "tool_choice" not in follow_up_params, "tool_choice should be removed for follow-up call" + assert follow_up_params["temperature"] == 0.7, "Other params should be preserved" + assert base_call_params["tool_choice"] == "required", "Original params should not be mutated" + + print("✅ _prepare_follow_up_call_params works correctly") + + # Test _build_request_params + input_data = [{"role": "user", "content": "test", "type": "message"}] + model = "gpt-4o-mini" + tools = [{"type": "function", "name": "test_tool"}] + call_params = {"stream": True, "temperature": 0.8} + previous_response_id = "resp_123" + extra_kwargs = {"custom_param": "test_value"} + + request_params = LiteLLM_Proxy_MCP_Handler._build_request_params( + input=input_data, + model=model, + all_tools=tools, + call_params=call_params, + previous_response_id=previous_response_id, + **extra_kwargs + ) + + # Verify core parameters + assert request_params["input"] == input_data, "Input should be included" + assert request_params["model"] == model, "Model should be included" + assert request_params["tools"] == tools, "Tools should be included" + assert request_params["previous_response_id"] == previous_response_id, "Previous response ID should be included" + + # Verify call_params are merged + assert request_params["stream"] == True, "call_params should be merged" + assert request_params["temperature"] == 0.8, "call_params should be merged" + + # Verify extra kwargs are merged + assert request_params["custom_param"] == "test_value", "Extra kwargs should be merged" + + print("✅ _build_request_params works correctly") + + # Test _build_request_params with None previous_response_id + request_params_no_prev = LiteLLM_Proxy_MCP_Handler._build_request_params( + input=input_data, + model=model, + all_tools=tools, + call_params=call_params, + previous_response_id=None + ) + + assert "previous_response_id" not in request_params_no_prev, "None previous_response_id should not be included" + + print("✅ _build_request_params handles None previous_response_id correctly") + + print("🎉 All MCP parameter preparation helper tests passed!") + + +@pytest.mark.asyncio +async def test_mcp_tool_execution_events_creation(): + """ + Test the _create_tool_execution_events helper method for generating streaming events. + """ + from litellm.responses.mcp.litellm_proxy_mcp_handler import LiteLLM_Proxy_MCP_Handler + + print("Testing MCP tool execution events creation...") + + # Mock tool calls (simulating what comes from LLM response in function_call format) + mock_tool_calls = [ + { + "id": "call_abc123", + "name": "search_repo", + "arguments": '{"query": "LiteLLM overview"}', + "type": "function_call" + }, + { + "id": "call_def456", + "name": "get_repo_info", + "arguments": '{"repo_name": "BerriAI/litellm"}', + "type": "function_call" + } + ] + + # Mock tool results (simulating what comes from tool execution) + mock_tool_results = [ + { + "tool_call_id": "call_abc123", + "result": "LiteLLM is a unified interface for 100+ LLMs" + }, + { + "tool_call_id": "call_def456", + "result": "Repository: BerriAI/litellm - Python library for LLM integration" + } + ] + + # Create tool execution events + execution_events = LiteLLM_Proxy_MCP_Handler._create_tool_execution_events( + tool_calls=mock_tool_calls, + tool_results=mock_tool_results + ) + + # Verify events were created + assert len(execution_events) > 0, "Should create tool execution events" + print(f"Created {len(execution_events)} tool execution events") + + # Verify events have proper structure + for event in execution_events: + assert hasattr(event, 'type'), "Event should have type attribute" + event_type = str(event.type) + assert 'mcp_call' in event_type.lower() or 'output_item' in event_type.lower(), f"Event should be MCP-related: {event_type}" + + # Check for sequence numbers + if hasattr(event, 'sequence_number'): + assert isinstance(event.sequence_number, int), "Sequence number should be integer" + assert event.sequence_number > 0, "Sequence number should be positive" + + print("Tool execution events have proper structure") + + # Test with empty inputs + empty_events = LiteLLM_Proxy_MCP_Handler._create_tool_execution_events( + tool_calls=[], + tool_results=[] + ) + + assert len(empty_events) == 0, "Should create no events for empty inputs" + print("Handles empty inputs correctly") + + print("MCP tool execution events creation test passed!") + + +@pytest.mark.asyncio +async def test_no_duplicate_mcp_tools_in_streaming_e2e(): + """ + End-to-end test to validate that MCP tools are not duplicated when using streaming. + + This test protects against the bug where: + 1. Parent function (aresponses_api_with_mcp) processed MCP tools once + 2. Streaming iterator processed MCP tools again, causing duplicates + + The test mocks the MCP manager response but validates the actual tools + sent to the LLM to ensure no duplication occurs. + """ + from unittest.mock import AsyncMock, patch, call + from litellm.responses.mcp.litellm_proxy_mcp_handler import LiteLLM_Proxy_MCP_Handler + + print("Testing no duplicate MCP tools in streaming E2E...") + + # Mock MCP tools that would be returned from the manager + mock_mcp_tools = [ + type('MCPTool', (), { + 'name': 'search_docs', + 'description': 'Search documentation for information', + 'inputSchema': { + "type": "object", + "properties": { + "query": {"type": "string", "description": "Search query"} + }, + "required": ["query"] + } + })(), + type('MCPTool', (), { + 'name': 'get_file_content', + 'description': 'Get content of a specific file', + 'inputSchema': { + "type": "object", + "properties": { + "file_path": {"type": "string", "description": "Path to file"} + }, + "required": ["file_path"] + } + })() + ] + + # Track all calls to the underlying LLM to detect duplicates + llm_call_tools = [] + + async def capture_llm_tools(**kwargs): + """Capture the tools parameter from LLM calls""" + tools = kwargs.get('tools', []) + llm_call_tools.append(tools) + + # Return a minimal mock async streaming response + class MockStreamingResponse: + async def __aiter__(self): + yield type('MockChunk', (), { + 'type': 'response.completed', + 'output': [] + })() + + return MockStreamingResponse() + + # Mock both the MCP manager and the underlying LLM call + with patch.object(LiteLLM_Proxy_MCP_Handler, '_get_mcp_tools_from_manager', new_callable=AsyncMock) as mock_get_tools, \ + patch('litellm.aresponses', side_effect=capture_llm_tools) as mock_aresponses: + + # Setup MCP mock to return our test tools + mock_get_tools.return_value = mock_mcp_tools + + # Configure MCP tool for streaming + mcp_tool_config = { + "type": "mcp", + "server_url": "litellm_proxy/mcp/test_server", + "require_approval": "always" # Disable auto-execution to focus on tool duplication + } + + print("Making streaming request with MCP tools...") + + # Make streaming request with MCP tools + try: + response = await litellm.aresponses( + model="gpt-4o-mini", + tools=[mcp_tool_config], + input=[{ + "role": "user", + "type": "message", + "content": "Search the documentation for information about authentication." + }], + stream=True + ) + + # Consume the streaming response + chunks = [] + async for chunk in response: + chunks.append(chunk) + + except Exception as e: + print(f"Request failed (expected for test): {e}") + # Continue with validation even if request fails + + # Validate underlying LLM was called (this proves our mocking works) + assert len(llm_call_tools) > 0, "LLM should have been called at least once" + print(f"LLM called {len(llm_call_tools)} time(s)") + + # If MCP tools were processed, validate they were fetched exactly once + # (This protects against duplicate fetching) + if mock_get_tools.call_count > 0: + assert mock_get_tools.call_count == 1, f"MCP tools should be fetched exactly once, got {mock_get_tools.call_count} calls" + print(f"MCP tools fetched exactly once: {mock_get_tools.call_count}") + else: + print("MCP tools not fetched (likely due to test mocking - this is OK for validation)") + + # Analyze tools sent to LLM for duplicates + for call_idx, tools_in_call in enumerate(llm_call_tools): + print(f"LLM Call {call_idx + 1}: {len(tools_in_call)} tools") + + if tools_in_call: + # Extract tool names to check for duplicates + tool_names = [] + for tool in tools_in_call: + if isinstance(tool, dict): + tool_name = tool.get('function', {}).get('name') or tool.get('name') + else: + tool_name = getattr(tool, 'name', str(tool)) + + if tool_name: + tool_names.append(tool_name) + + print(f" Tool names: {tool_names}") + + # Check for duplicate tool names + unique_tool_names = set(tool_names) + duplicates = [name for name in tool_names if tool_names.count(name) > 1] + + assert len(duplicates) == 0, f"Found duplicate tools in LLM call {call_idx + 1}: {duplicates}" + assert len(tool_names) == len(unique_tool_names), f"Tool names should be unique in call {call_idx + 1}" + + print(f" No duplicate tools found in call {call_idx + 1}") + + # Validate that MCP tools were properly transformed to OpenAI format + openai_format_tools = [tool for tool in tools_in_call if isinstance(tool, dict) and 'function' in tool] + if openai_format_tools: + print(f" Found {len(openai_format_tools)} OpenAI-format tools") + + # Verify tools have proper OpenAI structure + for tool in openai_format_tools: + assert 'type' in tool, "Tool should have 'type' field" + assert tool['type'] == 'function', "Tool type should be 'function'" + assert 'function' in tool, "Tool should have 'function' field" + assert 'name' in tool['function'], "Function should have 'name'" + assert 'description' in tool['function'], "Function should have 'description'" + assert 'parameters' in tool['function'], "Function should have 'parameters'" + + print(f" All tools have proper OpenAI format") + + # The key validation: ensure no duplicate fetching occurred + # This is the main protection against the bug we fixed + if mock_get_tools.call_count > 1: + print(f"ERROR: Duplicate MCP fetching detected! Called {mock_get_tools.call_count} times") + assert False, f"MCP tools should be fetched exactly once, but were fetched {mock_get_tools.call_count} times" + + # Additional validation: ensure no duplicate tools in any LLM call + total_duplicates_found = 0 + for call_idx, tools_in_call in enumerate(llm_call_tools): + if tools_in_call: + tool_names = [] + for tool in tools_in_call: + if isinstance(tool, dict): + tool_name = tool.get('function', {}).get('name') or tool.get('name') + if tool_name: + tool_names.append(tool_name) + + duplicates = [name for name in tool_names if tool_names.count(name) > 1] + if duplicates: + total_duplicates_found += len(set(duplicates)) + print(f"ERROR: Duplicate tools in call {call_idx + 1}: {set(duplicates)}") + + if total_duplicates_found > 0: + assert False, f"Found {total_duplicates_found} duplicate tools across all LLM calls" + + print("No duplicate MCP tools E2E test passed!") + print(f"Summary:") + print(f" - MCP manager called: {mock_get_tools.call_count} time(s)") + print(f" - LLM called: {len(llm_call_tools)} time(s)") + print(f" - Unique tools per call: {[len(set(getattr(t.get('function', {}), 'name', 'unknown') if isinstance(t, dict) else str(t) for t in tools)) for tools in llm_call_tools]}") + print(f" - No duplicate tools detected") + + return { + 'mcp_manager_calls': mock_get_tools.call_count, + 'llm_calls': len(llm_call_tools), + 'tools_per_call': [len(tools) for tools in llm_call_tools], + 'duplicate_tools_found': False + } + + + \ No newline at end of file diff --git a/tests/test_litellm/experimental_mcp_client/test_tools.py b/tests/test_litellm/experimental_mcp_client/test_tools.py index ec430ecc9bf..254d8e517c2 100644 --- a/tests/test_litellm/experimental_mcp_client/test_tools.py +++ b/tests/test_litellm/experimental_mcp_client/test_tools.py @@ -19,9 +19,11 @@ from mcp.types import Tool as MCPTool from litellm.experimental_mcp_client.tools import ( _get_function_arguments, + _normalize_mcp_input_schema, call_mcp_tool, call_openai_tool, load_mcp_tools, + transform_mcp_tool_to_openai_responses_api_tool, transform_mcp_tool_to_openai_tool, transform_openai_tool_call_request_to_mcp_tool_call_request, ) @@ -73,6 +75,7 @@ def test_transform_mcp_tool_to_openai_tool(mock_mcp_tool): assert openai_tool["function"]["parameters"] == { "type": "object", "properties": {"test": {"type": "string"}}, + "additionalProperties": False, } @@ -155,3 +158,95 @@ async def test_call_mcp_tool(mock_session, mock_mcp_tool_call_result): mock_session.call_tool.assert_called_once_with( name="test_tool", arguments={"test": "value"} ) + + +def test_normalize_mcp_input_schema(): + """Test MCP input schema normalization for OpenAI compatibility.""" + # Test case 1: Empty/None schema should get default structure + assert _normalize_mcp_input_schema(None) == { + "type": "object", + "properties": {}, + "additionalProperties": False + } + + assert _normalize_mcp_input_schema({}) == { + "type": "object", + "properties": {}, + "additionalProperties": False + } + + # Test case 2: Schema with only type should get properties added + schema_with_type_only = {"type": "object"} + normalized = _normalize_mcp_input_schema(schema_with_type_only) + assert normalized == { + "type": "object", + "properties": {}, + "additionalProperties": False + } + + # Test case 3: Schema missing type should get type added + schema_missing_type = {"properties": {"param": {"type": "string"}}} + normalized = _normalize_mcp_input_schema(schema_missing_type) + assert normalized == { + "type": "object", + "properties": {"param": {"type": "string"}}, + "additionalProperties": False + } + + # Test case 4: Complete schema should be preserved with additionalProperties added + complete_schema = { + "type": "object", + "properties": {"param": {"type": "string"}}, + "required": ["param"] + } + normalized = _normalize_mcp_input_schema(complete_schema) + assert normalized == { + "type": "object", + "properties": {"param": {"type": "string"}}, + "required": ["param"], + "additionalProperties": False + } + + # Test case 5: Schema with existing additionalProperties should be preserved + schema_with_additional = { + "type": "object", + "properties": {"param": {"type": "string"}}, + "additionalProperties": True + } + normalized = _normalize_mcp_input_schema(schema_with_additional) + assert normalized["additionalProperties"] == True + + +def test_transform_mcp_tool_to_openai_responses_api_tool(): + """Test transformation to OpenAI Responses API tool format with schema normalization.""" + # Test case 1: Tool with minimal schema (the problematic case from the error) + minimal_tool = MCPTool( + name="GitMCP-fetch_litellm_documentation", + description="Fetch entire documentation file from GitHub repository", + inputSchema={"type": "object"} # This was causing the error + ) + + openai_tool = transform_mcp_tool_to_openai_responses_api_tool(minimal_tool) + assert openai_tool["name"] == "GitMCP-fetch_litellm_documentation" + assert openai_tool["type"] == "function" + assert openai_tool["strict"] == False + assert openai_tool["parameters"]["type"] == "object" + assert openai_tool["parameters"]["properties"] == {} + assert openai_tool["parameters"]["additionalProperties"] == False + + # Test case 2: Tool with complete schema + complete_tool = MCPTool( + name="test_tool_complete", + description="A test tool with complete schema", + inputSchema={ + "type": "object", + "properties": {"query": {"type": "string", "description": "Search query"}}, + "required": ["query"] + } + ) + + openai_tool = transform_mcp_tool_to_openai_responses_api_tool(complete_tool) + assert openai_tool["parameters"]["type"] == "object" + assert "query" in openai_tool["parameters"]["properties"] + assert openai_tool["parameters"]["required"] == ["query"] + assert openai_tool["parameters"]["additionalProperties"] == False diff --git a/ui/litellm-dashboard/src/components/chat_ui.tsx b/ui/litellm-dashboard/src/components/chat_ui.tsx index beccef260f9..6504576578e 100644 --- a/ui/litellm-dashboard/src/components/chat_ui.tsx +++ b/ui/litellm-dashboard/src/components/chat_ui.tsx @@ -52,6 +52,7 @@ import ChatImageUpload from "./chat_ui/ChatImageUpload"; import ChatImageRenderer from "./chat_ui/ChatImageRenderer"; import { createChatMultimodalMessage, createChatDisplayMessage } from "./chat_ui/ChatImageUtils"; import SessionManagement from "./chat_ui/SessionManagement"; +import MCPEventsDisplay, { MCPEvent } from "./chat_ui/MCPEventsDisplay"; import { SendOutlined, ApiOutlined, @@ -94,15 +95,15 @@ const ChatUI: React.FC = ({ }) => { const [isMCPToolsModalVisible, setIsMCPToolsModalVisible] = useState(false); const [mcpTools, setMCPTools] = useState([]); - const [selectedMCPTools, setSelectedMCPTools] = useState(() => { + const [selectedMCPTools, setSelectedMCPTools] = useState(() => { const saved = sessionStorage.getItem('selectedMCPTools'); try { const parsed = saved ? JSON.parse(saved) : []; - // Convert from array to single string if needed - return Array.isArray(parsed) ? (parsed[0] || '') : parsed; + // Convert from single string to array if needed for backward compatibility + return Array.isArray(parsed) ? parsed : (parsed ? [parsed] : []); } catch (error) { console.error("Error parsing selectedMCPTools from sessionStorage", error); - return ''; + return []; } }); const [isLoadingMCPTools, setIsLoadingMCPTools] = useState(false); @@ -179,6 +180,7 @@ const ChatUI: React.FC = ({ const [isGetCodeModalVisible, setIsGetCodeModalVisible] = useState(false); const [generatedCode, setGeneratedCode] = useState(""); const [selectedSdk, setSelectedSdk] = useState<'openai' | 'azure'>('openai'); + const [mcpEvents, setMCPEvents] = useState([]); const chatEndRef = useRef(null); @@ -215,13 +217,14 @@ const ChatUI: React.FC = ({ selectedTags, selectedVectorStores, selectedGuardrails, + selectedMCPTools, endpointType, selectedModel, selectedSdk, }); setGeneratedCode(code); } - }, [isGetCodeModalVisible, selectedSdk, apiKeySource, accessToken, apiKey, inputMessage, chatHistory, selectedTags, selectedVectorStores, selectedGuardrails, endpointType, selectedModel]); + }, [isGetCodeModalVisible, selectedSdk, apiKeySource, accessToken, apiKey, inputMessage, chatHistory, selectedTags, selectedVectorStores, selectedGuardrails, selectedMCPTools, endpointType, selectedModel]); useEffect(() => { const handler = setTimeout(() => { @@ -443,6 +446,27 @@ const ChatUI: React.FC = ({ } }; + const handleMCPEvent = (event: MCPEvent) => { + console.log("ChatUI: Received MCP event:", event); + setMCPEvents(prev => { + // Check if this is a duplicate event (same item_id and type) + const isDuplicate = prev.some(existingEvent => + existingEvent.item_id === event.item_id && + existingEvent.type === event.type && + existingEvent.sequence_number === event.sequence_number + ); + + if (isDuplicate) { + console.log("ChatUI: Duplicate MCP event, skipping"); + return prev; + } + + const newEvents = [...prev, event]; + console.log("ChatUI: Updated MCP events:", newEvents); + return newEvents; + }); + }; + const updateImageUI = (imageUrl: string, model: string) => { setChatHistory((prevHistory) => [ ...prevHistory, @@ -617,6 +641,7 @@ const ChatUI: React.FC = ({ } setChatHistory([...chatHistory, displayMessage]); + setMCPEvents([]); // Clear previous MCP events for new conversation turn setIsLoading(true); try { @@ -643,7 +668,7 @@ const ChatUI: React.FC = ({ traceId, selectedVectorStores.length > 0 ? selectedVectorStores : undefined, selectedGuardrails.length > 0 ? selectedGuardrails : undefined, - selectedMCPTools, // Pass the selected tool directly + selectedMCPTools, // Pass the selected tools array updateChatImageUI // Pass the image callback ); } else if (endpointType === EndpointType.IMAGE) { @@ -694,9 +719,10 @@ const ChatUI: React.FC = ({ traceId, selectedVectorStores.length > 0 ? selectedVectorStores : undefined, selectedGuardrails.length > 0 ? selectedGuardrails : undefined, - selectedMCPTools, // Pass the selected tool directly + selectedMCPTools, // Pass the selected tools array useApiSessionManagement ? responsesSessionId : null, // Only pass session ID if API mode is enabled - handleResponseId // Pass callback to capture new response ID + handleResponseId, // Pass callback to capture new response ID + handleMCPEvent // Pass MCP event handler ); } else if (endpointType === EndpointType.ANTHROPIC_MESSAGES) { const apiChatHistory = [...chatHistory.filter(msg => !msg.isImage).map(({ role, content }) => ({ role, content })), newUserMessage]; @@ -714,7 +740,7 @@ const ChatUI: React.FC = ({ traceId, selectedVectorStores.length > 0 ? selectedVectorStores : undefined, selectedGuardrails.length > 0 ? selectedGuardrails : undefined, - selectedMCPTools // Pass the selected tool directly + selectedMCPTools // Pass the selected tools array ); } } @@ -749,6 +775,7 @@ const ChatUI: React.FC = ({ setChatHistory([]); setMessageTraceId(null); setResponsesSessionId(null); // Clear responses session ID + setMCPEvents([]); // Clear MCP events handleRemoveAllImages(); // Clear any uploaded images for image edits handleRemoveResponsesImage(); // Clear any uploaded images for responses handleRemoveChatImage(); // Clear any uploaded images for chat completions @@ -797,8 +824,6 @@ const ChatUI: React.FC = ({ style={{ width: "100%" }} onChange={(value) => { setApiKeySource(value as "session" | "custom"); - // Clear MCP tool selection when switching API key source - setSelectedMCPTools(''); }} options={[ { value: 'session', label: 'Current UI Session' }, @@ -865,10 +890,6 @@ const ChatUI: React.FC = ({ endpointType={endpointType} onEndpointChange={(value) => { setEndpointType(value); - // Clear MCP tools if switching away from responses endpoint - if (value !== EndpointType.RESPONSES) { - setSelectedMCPTools(''); - } }} className="mb-4" /> @@ -900,20 +921,22 @@ const ChatUI: React.FC = ({ MCP Tool + title="Select MCP tools to use in your conversation, only available for /v1/responses endpoint"> setSelectedMCPTools(value)} optionLabelProp="label" allowClear + maxTagCount="responsive" > {mcpTools.map((tool) => ( { selectedTags, selectedVectorStores, selectedGuardrails, + selectedMCPTools, endpointType, selectedModel, selectedSdk, diff --git a/ui/litellm-dashboard/src/components/chat_ui/MCPEventsDisplay.tsx b/ui/litellm-dashboard/src/components/chat_ui/MCPEventsDisplay.tsx new file mode 100644 index 00000000000..e931b80e531 --- /dev/null +++ b/ui/litellm-dashboard/src/components/chat_ui/MCPEventsDisplay.tsx @@ -0,0 +1,263 @@ +import React from 'react'; +import { Typography, Collapse } from 'antd'; + +const { Text } = Typography; +const { Panel } = Collapse; + +export interface MCPEvent { + type: string; + sequence_number?: number; + output_index?: number; + item_id?: string; + item?: { + id?: string; + type?: string; + server_label?: string; + tools?: Array<{ + name: string; + description: string; + annotations?: { + read_only?: boolean; + }; + input_schema?: any; + }>; + name?: string; + arguments?: string; + output?: string; + }; + delta?: string; + arguments?: string; + timestamp?: number; +} + +interface MCPEventsDisplayProps { + events: MCPEvent[]; + className?: string; +} + +const MCPEventsDisplay: React.FC = ({ events, className }) => { + console.log("MCPEventsDisplay: Received events:", events); + + if (!events || events.length === 0) { + console.log("MCPEventsDisplay: No events, returning null"); + return null; + } + + // Find the list tools event + const toolsEvent = events.find(event => + event.type === 'response.output_item.done' && + event.item?.type === 'mcp_list_tools' && + event.item.tools && + event.item.tools.length > 0 + ); + + // Find MCP call events + const mcpCallEvents = events.filter(event => + event.type === 'response.output_item.done' && + event.item?.type === 'mcp_call' + ); + + console.log("MCPEventsDisplay: toolsEvent:", toolsEvent); + console.log("MCPEventsDisplay: mcpCallEvents:", mcpCallEvents); + + if (!toolsEvent && mcpCallEvents.length === 0) { + console.log("MCPEventsDisplay: No valid events found, returning null"); + return null; + } + + + return ( +