From e4a047526334dc97a93ef364878b14760e85408d Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Sat, 25 Jul 2026 10:57:19 -0700 Subject: [PATCH 001/266] test: remove four mirror test files that exercise none of their module A second mutation batch scored the previously unmapped mirror files on current staging. These four generate mutants for the module they are named after, yet no test in the file executes any of them; their test-context coverage lands on generic shared machinery or, for the guardrail translation handler remainder, on no litellm line at all. Eight sibling findings that do exercise a different real module are kept for retargeting instead of removal. --- .../datadog/test_datadog_llm_observability.py | 1195 ----------------- .../guardrail_translation/test_handler.py | 37 - .../test_reasoning_content_transformation.py | 296 ---- tests/test_litellm/test_azure_video_router.py | 53 - 4 files changed, 1581 deletions(-) delete mode 100644 tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py delete mode 100644 tests/test_litellm/llms/pass_through/guardrail_translation/test_handler.py delete mode 100644 tests/test_litellm/responses/litellm_completion_transformation/test_reasoning_content_transformation.py delete mode 100644 tests/test_litellm/test_azure_video_router.py diff --git a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py b/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py deleted file mode 100644 index 1cc3591392b..00000000000 --- a/tests/test_litellm/integrations/datadog/test_datadog_llm_observability.py +++ /dev/null @@ -1,1195 +0,0 @@ -import asyncio -import os -import sys -from datetime import datetime, timedelta, timezone -from typing import Optional -from unittest.mock import MagicMock, Mock, patch - -import pytest - -# Adds the grandparent directory to sys.path to allow importing project modules -sys.path.insert(0, os.path.abspath("../..")) -import litellm -from litellm.integrations.custom_logger import CustomLogger -from litellm.integrations.datadog.datadog_llm_obs import DataDogLLMObsLogger -from litellm.types.integrations.datadog_llm_obs import ( - DatadogLLMObsInitParams, -) -from litellm.types.utils import ( - StandardLoggingGuardrailInformation, - StandardLoggingHiddenParams, - StandardLoggingMetadata, - StandardLoggingModelInformation, - StandardLoggingPayload, - StandardLoggingPayloadErrorInformation, -) - - -def create_standard_logging_payload_with_cache() -> StandardLoggingPayload: - """Create a real StandardLoggingPayload object for testing""" - return StandardLoggingPayload( - id="test-request-id-456", - call_type="completion", - response_cost=0.05, - response_cost_failure_debug_info=None, - status="success", - total_tokens=30, - prompt_tokens=10, - completion_tokens=20, - startTime=1234567890.0, - endTime=1234567891.0, - completionStartTime=1234567890.5, - model_map_information=StandardLoggingModelInformation( - model_map_key="gpt-4", model_map_value=None - ), - model="gpt-4", - model_id="model-123", - model_group="openai-gpt", - api_base="https://api.openai.com", - metadata=StandardLoggingMetadata( - user_api_key_hash="test_hash", - user_api_key_org_id=None, - user_api_key_alias="test_alias", - user_api_key_team_id="test_team", - user_api_key_user_id="test_user", - user_api_key_team_alias="test_team_alias", - spend_logs_metadata=None, - requester_ip_address="127.0.0.1", - requester_metadata=None, - ), - cache_hit=True, - cache_key="test-cache-key-789", - saved_cache_cost=0.02, - request_tags=[], - end_user=None, - requester_ip_address="127.0.0.1", - messages=[{"role": "user", "content": "Hello, world!"}], - response={"choices": [{"message": {"content": "Hi there!"}}]}, - error_str=None, - model_parameters={"stream": True}, - hidden_params=StandardLoggingHiddenParams( - model_id="model-123", - cache_key="test-cache-key-789", - api_base="https://api.openai.com", - response_cost="0.05", - additional_headers=None, - ), - trace_id="test-trace-id-123", - custom_llm_provider="openai", - ) - - -def create_standard_logging_payload_with_failure() -> StandardLoggingPayload: - """Create a StandardLoggingPayload object for failure testing""" - return StandardLoggingPayload( - id="test-request-id-failure-789", - call_type="completion", - response_cost=0.0, - response_cost_failure_debug_info=None, - status="failure", - total_tokens=0, - prompt_tokens=10, - completion_tokens=0, - startTime=1234567890.0, - endTime=1234567891.0, - completionStartTime=1234567890.5, - model_map_information=StandardLoggingModelInformation( - model_map_key="gpt-4", model_map_value=None - ), - model="gpt-4", - model_id="model-123", - model_group="openai-gpt", - api_base="https://api.openai.com", - metadata=StandardLoggingMetadata( - user_api_key_hash="test_hash", - user_api_key_org_id=None, - user_api_key_alias="test_alias", - user_api_key_team_id="test_team", - user_api_key_user_id="test_user", - user_api_key_team_alias="test_team_alias", - spend_logs_metadata=None, - requester_ip_address="127.0.0.1", - requester_metadata=None, - ), - cache_hit=False, - cache_key=None, - saved_cache_cost=0.0, - request_tags=[], - end_user=None, - requester_ip_address="127.0.0.1", - messages=[{"role": "user", "content": "Hello, world!"}], - response=None, - error_str="RateLimitError: You exceeded your current quota", - error_information=StandardLoggingPayloadErrorInformation( - error_code="rate_limit_exceeded", - error_class="RateLimitError", - llm_provider="openai", - traceback="Traceback (most recent call last):\n File test.py, line 1\n RateLimitError: You exceeded your current quota", - error_message="RateLimitError: You exceeded your current quota", - ), - model_parameters={"stream": False}, - hidden_params=StandardLoggingHiddenParams( - model_id="model-123", - cache_key=None, - api_base="https://api.openai.com", - response_cost="0.0", - additional_headers=None, - ), - trace_id="test-trace-id-failure-456", - custom_llm_provider="openai", - ) - - -class TestDataDogLLMObsLogger: - """Test suite for DataDog LLM Observability Logger""" - - @pytest.fixture - def mock_env_vars(self): - """Mock environment variables for DataDog""" - with patch.dict( - os.environ, {"DD_API_KEY": "test_api_key", "DD_SITE": "us5.datadoghq.com"} - ): - yield - - @pytest.fixture - def mock_response_obj(self): - """Create a mock response object""" - mock_response = Mock() - mock_response.__getitem__ = Mock( - return_value={ - "choices": [ - { - "message": Mock( - json=Mock( - return_value={"role": "assistant", "content": "Hello!"} - ) - ) - } - ] - } - ) - return mock_response - - def test_cost_and_trace_id_integration(self, mock_env_vars, mock_response_obj): - """Test that total_cost is passed and trace_id from standard payload is used""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_cache() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": { - "metadata": {"trace_id": "old-trace-id-should-be-ignored"} - }, - } - - start_time = datetime.now() - end_time = datetime.now() - - payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) - - # Test 1: Verify total_cost is correctly extracted from response_cost - assert payload["metrics"].get("total_cost") == 0.05 - - # Test 2: Verify trace_id comes from standard_logging_payload, not metadata - assert payload["trace_id"] == "test-trace-id-123" - - # Test 3: Verify saved_cache_cost is in metadata - metadata = payload["meta"]["metadata"] - assert metadata["saved_cache_cost"] == 0.02 - assert metadata["cache_hit"] is True - assert metadata["cache_key"] == "test-cache-key-789" - - # Test 4: Verify is_streamed_request is in metadata - assert metadata["is_streamed_request"] is True - - def test_cache_metadata_fields(self, mock_env_vars, mock_response_obj): - """Test that cache-related metadata fields are correctly tracked""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_cache() - - # Test the _get_dd_llm_obs_payload_metadata method directly - metadata = logger._get_dd_llm_obs_payload_metadata(standard_payload) - - # Verify all cache-related fields are present - assert metadata["cache_hit"] is True - assert metadata["cache_key"] == "test-cache-key-789" - assert metadata["saved_cache_cost"] == 0.02 - assert metadata["id"] == "test-request-id-456" - assert metadata["trace_id"] == "test-trace-id-123" - assert metadata["model_name"] == "gpt-4" - assert metadata["model_provider"] == "openai" - - def test_get_time_to_first_token_seconds(self, mock_env_vars): - """Test the _get_time_to_first_token_seconds method for streaming calls""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Test streaming case (completion_start_time available) - streaming_payload = create_standard_logging_payload_with_cache() - # Modify times for testing: start=1000, completion_start=1002, end=1005 - streaming_payload["startTime"] = 1000.0 - streaming_payload["completionStartTime"] = 1002.0 - streaming_payload["endTime"] = 1005.0 - - # Test streaming case: should use completion_start_time - start_time - time_to_first_token = logger._get_time_to_first_token_seconds( - streaming_payload - ) - assert time_to_first_token == 2.0 # 1002.0 - 1000.0 = 2.0 seconds - - def test_datadog_span_kind_mapping(self, mock_env_vars): - """Test that call_type values are correctly mapped to DataDog span kinds""" - from litellm.types.utils import CallTypes - - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Test embedding operations - assert ( - logger._get_datadog_span_kind(CallTypes.embedding.value, "123") - == "embedding" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.aembedding.value, "123") - == "embedding" - ) - - # Test LLM completion operations - assert logger._get_datadog_span_kind(CallTypes.completion.value, None) == "llm" - assert logger._get_datadog_span_kind(CallTypes.acompletion.value, None) == "llm" - assert ( - logger._get_datadog_span_kind(CallTypes.text_completion.value, None) - == "llm" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.generate_content.value, None) - == "llm" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.anthropic_messages.value, None) - == "llm" - ) - assert logger._get_datadog_span_kind(CallTypes.responses.value, None) == "llm" - assert logger._get_datadog_span_kind(CallTypes.aresponses.value, None) == "llm" - - # Test tool operations - assert ( - logger._get_datadog_span_kind(CallTypes.call_mcp_tool.value, "123") - == "tool" - ) - - # Test retrieval operations - assert ( - logger._get_datadog_span_kind(CallTypes.get_assistants.value, "123") - == "retrieval" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.file_retrieve.value, "123") - == "retrieval" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.retrieve_batch.value, "123") - == "retrieval" - ) - - # Test task operations - assert ( - logger._get_datadog_span_kind(CallTypes.create_batch.value, "123") == "task" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.image_generation.value, "123") - == "task" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.moderation.value, "123") == "task" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.transcription.value, "123") - == "task" - ) - - # Test default fallback - assert logger._get_datadog_span_kind("unknown_call_type", None) == "llm" - assert logger._get_datadog_span_kind(None, None) == "llm" - - def test_datadog_span_kind_defaults_without_parent(self, mock_env_vars): - """Test that non-llm kinds fallback to llm when no parent span is provided""" - from litellm.types.utils import CallTypes - - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Tool/task/retrieval span kinds should fallback to llm when parent_id missing - assert ( - logger._get_datadog_span_kind(CallTypes.call_mcp_tool.value, None) == "llm" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.create_batch.value, None) == "llm" - ) - assert ( - logger._get_datadog_span_kind(CallTypes.get_assistants.value, None) == "llm" - ) - - @pytest.mark.asyncio - async def test_async_log_failure_event(self, mock_env_vars): - """Test that async_log_failure_event correctly processes failure payloads according to DD LLM Obs API spec""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Ensure log_queue starts empty - logger.log_queue = [] - - standard_failure_payload = create_standard_logging_payload_with_failure() - - kwargs = { - "standard_logging_object": standard_failure_payload, - "model": "gpt-4", - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() + timedelta(seconds=2) - - # Mock async_send_batch to prevent actual network calls - with patch.object(logger, "async_send_batch") as mock_send_batch: - # Call the method under test - await logger.async_log_failure_event(kwargs, None, start_time, end_time) - - # Verify payload was added to queue - assert len(logger.log_queue) == 1 - - # Verify the payload has correct failure characteristics according to DD LLM Obs API spec - payload = logger.log_queue[0] - assert payload["trace_id"] == "test-trace-id-failure-456" - assert ( - payload["meta"]["metadata"]["id"] == "test-request-id-failure-789" - ) - assert payload["status"] == "error" - - # Verify error information follows DD LLM Obs API spec - assert ( - payload["meta"]["error"]["message"] - == "RateLimitError: You exceeded your current quota" - ) - assert payload["meta"]["error"]["type"] == "RateLimitError" - assert ( - payload["meta"]["error"]["stack"] - == "Traceback (most recent call last):\n File test.py, line 1\n RateLimitError: You exceeded your current quota" - ) - - assert payload["metrics"]["total_cost"] == 0.0 - assert payload["metrics"]["total_tokens"] == 0 - assert payload["metrics"]["output_tokens"] == 0 - - # Verify batch sending not triggered (queue size < batch_size) - mock_send_batch.assert_not_called() - - -class TestDataDogLLMObsLoggerForRedaction(DataDogLLMObsLogger): - """Test suite for DataDog LLM Observability Logger""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.logged_standard_logging_payload: Optional[StandardLoggingPayload] = None - - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - self.logged_standard_logging_payload = kwargs.get("standard_logging_object") - - -class TestS3Logger(CustomLogger): - """Test suite for S3 Logger""" - - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.logged_standard_logging_payload: Optional[StandardLoggingPayload] = None - - async def async_log_success_event(self, kwargs, response_obj, start_time, end_time): - self.logged_standard_logging_payload = kwargs.get("standard_logging_object") - - -@pytest.mark.asyncio -async def test_dd_llms_obs_redaction(mock_env_vars): - # init DD with turn_off_message_logging=True - litellm._turn_on_debug() - from litellm.types.utils import LiteLLMCommonStrings - - litellm.datadog_llm_observability_params = DatadogLLMObsInitParams( - turn_off_message_logging=True - ) - dd_llms_obs_logger = TestDataDogLLMObsLoggerForRedaction() - test_s3_logger = TestS3Logger() - litellm.callbacks = [dd_llms_obs_logger, test_s3_logger] - - # call litellm - await litellm.acompletion( - model="gpt-4o", - mock_response="Hi there!", - messages=[{"role": "user", "content": "Hello, world!"}], - ) - - # sleep 1 second for logging to complete - await asyncio.sleep(1) - - ################# - # test validation - # 1. both loggers logged a standard_logging_payload - # 2. DD LLM Obs standard_logging_payload has messages and response redacted - # 3. S3 standard_logging_payload does not have messages and response redacted - - assert dd_llms_obs_logger.logged_standard_logging_payload is not None - assert test_s3_logger.logged_standard_logging_payload is not None - - assert ( - dd_llms_obs_logger.logged_standard_logging_payload["messages"][0]["content"] - == "redacted-by-litellm" - ) - assert ( - dd_llms_obs_logger.logged_standard_logging_payload["response"]["choices"][0][ - "message" - ]["content"] - == "redacted-by-litellm" - ) - - assert test_s3_logger.logged_standard_logging_payload["messages"] == [ - {"role": "user", "content": "Hello, world!"} - ] - assert ( - test_s3_logger.logged_standard_logging_payload["response"]["choices"][0][ - "message" - ]["content"] - == "Hi there!" - ) - - -@pytest.fixture -def mock_env_vars(): - """Mock environment variables for DataDog""" - with patch.dict( - os.environ, {"DD_API_KEY": "test_api_key", "DD_SITE": "us5.datadoghq.com"} - ): - yield - - -@pytest.mark.asyncio -async def test_create_llm_obs_payload(mock_env_vars): - datadog_llm_obs_logger = DataDogLLMObsLogger() - standard_logging_payload = create_standard_logging_payload_with_cache() - payload = datadog_llm_obs_logger.create_llm_obs_payload( - kwargs={ - "model": "gpt-4", - "messages": [{"role": "user", "content": "Hello"}], - "standard_logging_object": standard_logging_payload, - }, - start_time=datetime.now(), - end_time=datetime.now() + timedelta(seconds=1), - ) - - assert payload["name"] == "litellm_llm_call" - assert payload["meta"]["kind"] == "llm" - assert payload["meta"]["input"]["messages"] == [ - {"role": "user", "content": "Hello, world!"} - ] - assert payload["meta"]["output"]["messages"][0]["content"] == "Hi there!" - assert payload["metrics"]["input_tokens"] == 10 - assert payload["metrics"]["output_tokens"] == 20 - assert payload["metrics"]["total_tokens"] == 30 - - -def create_standard_logging_payload_with_latency_metrics() -> StandardLoggingPayload: - """Create a StandardLoggingPayload object with latency metrics for testing""" - guardrail_info = StandardLoggingGuardrailInformation( - guardrail_name="test_guardrail", - guardrail_status="success", - start_time=1234567890.0, - end_time=1234567890.5, - duration=0.5, # 500ms - guardrail_request={"input": "test input message", "user_id": "test_user"}, - guardrail_response={ - "output": "filtered output", - "flagged": False, - "score": 0.1, - }, - ) - - hidden_params = StandardLoggingHiddenParams( - model_id="model-123", - cache_key="test-cache-key", - api_base="https://api.openai.com", - response_cost="0.05", - litellm_overhead_time_ms=150.0, # 150ms - additional_headers=None, - ) - - return StandardLoggingPayload( - id="test-request-id-latency", - call_type="completion", - response_cost=0.05, - response_cost_failure_debug_info=None, - status="success", - total_tokens=30, - prompt_tokens=10, - completion_tokens=20, - startTime=1234567890.0, - endTime=1234567892.0, - completionStartTime=1234567890.8, # 800ms after start - response_time=2.0, - model_map_information=StandardLoggingModelInformation( - model_map_key="gpt-4", model_map_value=None - ), - model="gpt-4", - model_id="model-123", - model_group="openai-gpt", - api_base="https://api.openai.com", - metadata=StandardLoggingMetadata( - user_api_key_hash="test_hash", - user_api_key_org_id=None, - user_api_key_alias="test_alias", - user_api_key_team_id="test_team", - user_api_key_user_id="test_user", - user_api_key_team_alias="test_team_alias", - spend_logs_metadata=None, - requester_ip_address="127.0.0.1", - requester_metadata=None, - ), - cache_hit=False, - cache_key=None, - saved_cache_cost=0.0, - request_tags=[], - end_user=None, - requester_ip_address="127.0.0.1", - messages=[{"role": "user", "content": "Hello, world!"}], - response={"choices": [{"message": {"content": "Hi there!"}}]}, - error_str=None, - error_information=None, - model_parameters={"stream": True}, - hidden_params=hidden_params, - guardrail_information=[guardrail_info], - trace_id="test-trace-id-latency", - custom_llm_provider="openai", - ) - - -def test_latency_metrics_in_metadata(mock_env_vars): - """Test that time to first token, litellm overhead, and guardrail overhead are included in metadata""" - with ( - patch("litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client"), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_latency_metrics() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() - - # Test the metadata generation directly - metadata = logger._get_dd_llm_obs_payload_metadata(standard_payload) - latency_metadata = metadata.get("latency_metrics", {}) - - # Verify time to first token is included (800ms) - assert "time_to_first_token_ms" in latency_metadata - assert ( - abs(latency_metadata["time_to_first_token_ms"] - 800.0) < 0.001 - ) # 0.8 seconds * 1000 with tolerance for floating-point precision - - # Verify litellm overhead is included (150ms) - assert "litellm_overhead_time_ms" in latency_metadata - assert latency_metadata["litellm_overhead_time_ms"] == 150.0 - - # Verify guardrail overhead is included (500ms) - assert "guardrail_overhead_time_ms" in latency_metadata - assert ( - latency_metadata["guardrail_overhead_time_ms"] == 500.0 - ) # 0.5 seconds * 1000 - - # Verify these metrics are also included in the full payload - payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) - payload_metadata_latency = payload["meta"]["metadata"]["latency_metrics"] - - assert abs(payload_metadata_latency["time_to_first_token_ms"] - 800.0) < 0.001 - assert payload_metadata_latency["litellm_overhead_time_ms"] == 150.0 - assert payload_metadata_latency["guardrail_overhead_time_ms"] == 500.0 - - -def test_latency_metrics_edge_cases(mock_env_vars): - """Test latency metrics with edge cases (missing fields, zero values, etc.)""" - with ( - patch("litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client"), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Test case 1: No latency metrics present - standard_payload = create_standard_logging_payload_with_cache() - metadata = logger._get_dd_llm_obs_payload_metadata(standard_payload) - - # Should not have latency fields if data is missing/zero - assert "time_to_first_token_ms" not in metadata # Will be 0, so not included - assert ( - "litellm_overhead_time_ms" not in metadata - ) # Not present in hidden_params - assert "guardrail_overhead_time_ms" not in metadata # No guardrail_information - - # Test case 2: Zero time to first token should not be included - standard_payload = create_standard_logging_payload_with_cache() - standard_payload["startTime"] = 1000.0 - standard_payload["completionStartTime"] = 1000.0 # Same time = 0 difference - metadata = logger._get_dd_llm_obs_payload_metadata(standard_payload) - assert "time_to_first_token_ms" not in metadata - - # Test case 3: Missing guardrail duration should not crash - standard_payload = create_standard_logging_payload_with_cache() - standard_payload["guardrail_information"] = [ - StandardLoggingGuardrailInformation( - guardrail_name="test", - guardrail_status="success", - # duration is missing - ) - ] - metadata = logger._get_dd_llm_obs_payload_metadata(standard_payload) - assert "guardrail_overhead_time_ms" not in metadata - - -def test_guardrail_information_in_metadata(mock_env_vars): - """Test that guardrail_information is included in metadata with input/output fields""" - with ( - patch("litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client"), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Create a standard payload with guardrail information - standard_payload = create_standard_logging_payload_with_latency_metrics() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() - - # Create the payload and verify guardrail_information is in metadata - payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) - metadata = payload["meta"]["metadata"] - - # Verify guardrail_information is present in metadata - assert "guardrail_information" in metadata - assert metadata["guardrail_information"] is not None - - # Verify the guardrail information structure - guardrail_info = metadata["guardrail_information"] - assert guardrail_info[0]["guardrail_name"] == "test_guardrail" - assert guardrail_info[0]["guardrail_status"] == "success" - assert guardrail_info[0]["duration"] == 0.5 - - # Verify input/output fields are present - assert "guardrail_request" in guardrail_info[0] - assert "guardrail_response" in guardrail_info[0] - - # Validate the input/output content - assert guardrail_info[0]["guardrail_request"]["input"] == "test input message" - assert guardrail_info[0]["guardrail_request"]["user_id"] == "test_user" - assert guardrail_info[0]["guardrail_response"]["output"] == "filtered output" - assert guardrail_info[0]["guardrail_response"]["flagged"] is False - assert guardrail_info[0]["guardrail_response"]["score"] == 0.1 - - -def create_standard_logging_payload_with_tool_calls() -> StandardLoggingPayload: - """Create a StandardLoggingPayload object with tool calls for testing""" - return { - "id": "test-request-id-tool-calls", - "trace_id": "test-trace-id-tool-calls", - "call_type": "completion", - "stream": None, - "response_cost": 0.05, - "response_cost_failure_debug_info": None, - "status": "success", - "custom_llm_provider": "openai", - "total_tokens": 50, - "prompt_tokens": 20, - "completion_tokens": 30, - "startTime": 1234567890.0, - "endTime": 1234567891.0, - "completionStartTime": 1234567890.5, - "response_time": 1.0, - "model_map_information": {"model_map_key": "gpt-4", "model_map_value": None}, - "model": "gpt-4", - "model_id": "model-123", - "model_group": "openai-gpt", - "api_base": "https://api.openai.com", - "metadata": { - "user_api_key_hash": "test_hash", - "user_api_key_org_id": None, - "user_api_key_alias": "test_alias", - "user_api_key_team_id": "test_team", - "user_api_key_user_id": "test_user", - "user_api_key_team_alias": "test_team_alias", - "user_api_key_user_email": None, - "user_api_key_end_user_id": None, - "user_api_key_request_route": None, - "spend_logs_metadata": None, - "requester_ip_address": "127.0.0.1", - "requester_metadata": None, - "requester_custom_headers": None, - "prompt_management_metadata": None, - "mcp_tool_call_metadata": None, - "vector_store_request_metadata": None, - "applied_guardrails": None, - "usage_object": None, - "cold_storage_object_key": None, - }, - "cache_hit": False, - "cache_key": None, - "saved_cache_cost": 0.0, - "request_tags": [], - "end_user": None, - "requester_ip_address": "127.0.0.1", - "messages": [ - {"role": "user", "content": "What's the weather?"}, - { - "role": "assistant", - "content": "I'll check the weather for you.", - "tool_calls": [ - { - "id": "call_123", - "type": "function", - "function": { - "name": "get_weather", - "arguments": '{"location": "NYC"}', - }, - } - ], - }, - { - "role": "tool", - "tool_call_id": "call_123", - "content": '{"temperature": 72, "condition": "sunny"}', - }, - ], - "response": { - "choices": [ - { - "message": { - "role": "assistant", - "content": "It's 72°F and sunny in NYC!", - "tool_calls": [ - { - "id": "call_456", - "type": "function", - "function": { - "name": "format_response", - "arguments": '{"temp": 72, "condition": "sunny"}', - }, - } - ], - } - } - ] - }, - "error_str": None, - "error_information": None, - "model_parameters": {"temperature": 0.7}, - "hidden_params": { - "model_id": "model-123", - "cache_key": None, - "api_base": "https://api.openai.com", - "response_cost": "0.05", - "litellm_overhead_time_ms": None, - "additional_headers": None, - "batch_models": None, - "litellm_model_name": None, - "usage_object": None, - }, - "guardrail_information": None, - "standard_built_in_tools_params": None, - } # type: ignore - - -class TestDataDogLLMObsLoggerToolCalls: - """Simple test suite for DataDog LLM Observability Logger tool call handling""" - - @pytest.fixture - def mock_env_vars(self): - """Mock environment variables for DataDog""" - with patch.dict( - os.environ, {"DD_API_KEY": "test_api_key", "DD_SITE": "us5.datadoghq.com"} - ): - yield - - def test_tool_call_span_kind_mapping(self, mock_env_vars): - """Test that tool call operations are correctly mapped to 'tool' span kind""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - # Test MCP tool call mapping - from litellm.types.utils import CallTypes - - assert ( - logger._get_datadog_span_kind(CallTypes.call_mcp_tool.value, "123") - == "tool" - ) - - def test_tool_call_payload_creation(self, mock_env_vars): - """Test that tool call payloads are created correctly""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_tool_calls() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() - - payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) - - # Verify basic payload structure - assert payload.get("name") == "litellm_llm_call" - assert payload.get("status") == "ok" - assert ( - payload.get("meta", {}).get("kind") == "llm" - ) # Regular completion, not tool call - - # Verify metrics - metrics = payload.get("metrics", {}) - assert metrics.get("input_tokens") == 20 - assert metrics.get("output_tokens") == 30 - assert metrics.get("total_tokens") == 50 - - def test_tool_call_messages_preserved(self, mock_env_vars): - """Test that tool call messages are preserved in the payload""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_tool_calls() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() - - payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) - - # Verify input messages include tool calls - meta = payload.get("meta", {}) - input_meta = meta.get("input", {}) - input_messages = input_meta.get("messages", []) - assert len(input_messages) == 3 - - # Check assistant message has tool calls - assistant_msg = input_messages[1] - assert assistant_msg.get("role") == "assistant" - assert "tool_calls" in assistant_msg - tool_calls = assistant_msg.get("tool_calls", []) - assert len(tool_calls) == 1 - tool_call = tool_calls[0] - function_info = tool_call.get("function", {}) - assert function_info.get("name") == "get_weather" - - # Check tool message - tool_msg = input_messages[2] - assert tool_msg.get("role") == "tool" - assert tool_msg.get("tool_call_id") == "call_123" - - def test_tool_call_response_handling(self, mock_env_vars): - """Test that tool calls in response are handled correctly""" - with ( - patch( - "litellm.integrations.datadog.datadog_llm_obs.get_async_httpx_client" - ), - patch("asyncio.create_task"), - ): - logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_tool_calls() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() - - payload = logger.create_llm_obs_payload(kwargs, start_time, end_time) - - # Verify output messages include tool calls - meta = payload.get("meta", {}) - output_meta = meta.get("output", {}) - output_messages = output_meta.get("messages", []) - assert len(output_messages) == 1 - - output_msg = output_messages[0] - assert output_msg.get("role") == "assistant" - assert "tool_calls" in output_msg - output_tool_calls = output_msg.get("tool_calls", []) - assert len(output_tool_calls) == 1 - output_function_info = output_tool_calls[0].get("function", {}) - assert output_function_info.get("name") == "format_response" - - -def create_standard_logging_payload_with_spend_metrics() -> StandardLoggingPayload: - """Create a StandardLoggingPayload object with spend metrics for testing""" - from datetime import datetime, timezone - - # Create a budget reset time 10 days from now (using "10d" format) - budget_reset_at = datetime.now(timezone.utc) + timedelta(days=10) - - return { - "id": "test-request-id-spend", - "trace_id": "test-trace-id-spend", - "call_type": "completion", - "stream": None, - "response_cost": 0.15, - "response_cost_failure_debug_info": None, - "status": "success", - "custom_llm_provider": "openai", - "total_tokens": 30, - "prompt_tokens": 10, - "completion_tokens": 20, - "startTime": 1234567890.0, - "endTime": 1234567891.0, - "completionStartTime": 1234567890.5, - "response_time": 1.0, - "model_map_information": {"model_map_key": "gpt-4", "model_map_value": None}, - "model": "gpt-4", - "model_id": "model-123", - "model_group": "openai-gpt", - "api_base": "https://api.openai.com", - "metadata": { - "user_api_key_hash": "test_hash", - "user_api_key_org_id": None, - "user_api_key_alias": "test_alias", - "user_api_key_team_id": "test_team", - "user_api_key_user_id": "test_user", - "user_api_key_team_alias": "test_team_alias", - "user_api_key_user_email": None, - "user_api_key_end_user_id": None, - "user_api_key_request_route": None, - "user_api_key_spend": 0.67, - "user_api_key_max_budget": 10.0, # $10 max budget - "user_api_key_budget_reset_at": budget_reset_at.isoformat(), # ISO format: 2025-09-26T... - "spend_logs_metadata": None, - "requester_ip_address": "127.0.0.1", - "requester_metadata": None, - "requester_custom_headers": None, - "prompt_management_metadata": None, - "mcp_tool_call_metadata": None, - "vector_store_request_metadata": None, - "applied_guardrails": None, - "usage_object": None, - "cold_storage_object_key": None, - }, - "cache_hit": False, - "cache_key": None, - "saved_cache_cost": 0.0, - "request_tags": [], - "end_user": None, - "requester_ip_address": "127.0.0.1", - "messages": [{"role": "user", "content": "Hello, world!"}], - "response": {"choices": [{"message": {"content": "Hi there!"}}]}, - "error_str": None, - "error_information": None, - "model_parameters": {"stream": False}, - "hidden_params": { - "model_id": "model-123", - "cache_key": None, - "api_base": "https://api.openai.com", - "response_cost": "0.15", - "litellm_overhead_time_ms": None, - "additional_headers": None, - "batch_models": None, - "litellm_model_name": None, - "usage_object": None, - }, - "guardrail_information": None, - "standard_built_in_tools_params": None, - } # type: ignore - - -@pytest.mark.asyncio -async def test_datadog_llm_obs_spend_metrics(mock_env_vars): - """Test that budget metrics are properly extracted and logged""" - datadog_llm_obs_logger = DataDogLLMObsLogger() - - # Create a standard logging payload with spend metrics - payload = create_standard_logging_payload_with_spend_metrics() - - # Show the budget reset time in ISO format - budget_reset_iso = payload["metadata"]["user_api_key_budget_reset_at"] - print(f"Budget reset time (ISO format): {budget_reset_iso}") - from datetime import datetime, timezone - - print(f"Current time: {datetime.now(timezone.utc).isoformat()}") - - # Test the _get_spend_metrics method - spend_metrics = datadog_llm_obs_logger._get_spend_metrics(payload) - - # Verify budget metrics are present - assert "user_api_key_max_budget" in spend_metrics - assert spend_metrics["user_api_key_max_budget"] == 10.0 - - assert "user_api_key_budget_reset_at" in spend_metrics - # The budget reset should be a datetime string in ISO format - budget_reset = spend_metrics["user_api_key_budget_reset_at"] - assert isinstance(budget_reset, str) - print(f"Budget reset datetime: {budget_reset}") - # Should be close to 10 days from now - budget_reset_dt = datetime.fromisoformat(budget_reset.replace("Z", "+00:00")) - now = datetime.now(timezone.utc) - time_diff = (budget_reset_dt - now).total_seconds() / 86400 # days - assert 9.5 <= time_diff <= 10.5 # Should be close to 10 days - - print(f"Spend metrics: {spend_metrics}") - - -@pytest.mark.asyncio -async def test_datadog_llm_obs_spend_metrics_no_budget(mock_env_vars): - """Test that spend metrics work when no budget is set""" - datadog_llm_obs_logger = DataDogLLMObsLogger() - - # Create a standard logging payload without budget metadata - payload = create_standard_logging_payload_with_spend_metrics() - - # Remove budget-related metadata to test no-budget scenario - payload["metadata"].pop("user_api_key_max_budget", None) - payload["metadata"].pop("user_api_key_budget_reset_at", None) - - # Test the _get_spend_metrics method - spend_metrics = datadog_llm_obs_logger._get_spend_metrics(payload) - - # Verify only response cost is present - assert "response_cost" in spend_metrics - assert spend_metrics["response_cost"] == 0.15 - - # Budget metrics should not be present - assert "user_api_key_max_budget" not in spend_metrics - assert "user_api_key_budget_reset_at" not in spend_metrics - - print(f"Spend metrics (no budget): {spend_metrics}") - - -@pytest.mark.asyncio -async def test_spend_metrics_in_datadog_payload(mock_env_vars): - """Test that spend metrics are correctly included in DataDog LLM Observability payloads""" - from datetime import datetime - - datadog_llm_obs_logger = DataDogLLMObsLogger() - - standard_payload = create_standard_logging_payload_with_spend_metrics() - - kwargs = { - "standard_logging_object": standard_payload, - "litellm_params": {"metadata": {}}, - } - - start_time = datetime.now() - end_time = datetime.now() - - payload = datadog_llm_obs_logger.create_llm_obs_payload( - kwargs, start_time, end_time - ) - - # Verify basic payload structure - assert payload.get("name") == "litellm_llm_call" - assert payload.get("status") == "ok" - - # Verify spend metrics are included in metadata - meta = payload.get("meta", {}) - assert meta is not None, "Meta section should exist in payload" - - metadata = meta.get("metadata", {}) - assert metadata is not None, "Metadata section should exist in meta" - - spend_metrics = metadata.get("spend_metrics", {}) - assert spend_metrics, "Spend metrics should exist in metadata" - - # Check that all metrics are present - assert "response_cost" in spend_metrics - assert "user_api_key_spend" in spend_metrics - assert "user_api_key_max_budget" in spend_metrics - assert "user_api_key_budget_reset_at" in spend_metrics - - # Verify the values are correct - assert spend_metrics["response_cost"] == 0.15 # response_cost - assert spend_metrics["user_api_key_spend"] == 0.67 # lol - assert spend_metrics["user_api_key_max_budget"] == 10.0 # max budget - - # Verify budget reset is a datetime string in ISO format - budget_reset = spend_metrics["user_api_key_budget_reset_at"] - assert isinstance(budget_reset, str) - print( - f"Budget reset in payload: {budget_reset}" - ) # In StandardLoggingUserAPIKeyMetadata - user_api_key_budget_reset_at: Optional[str] = None - - # In DDLLMObsSpendMetrics - user_api_key_budget_reset_at: str - # Should be close to 10 days from now - from datetime import datetime, timezone - - budget_reset_dt = datetime.fromisoformat(budget_reset.replace("Z", "+00:00")) - now = datetime.now(timezone.utc) - time_diff = (budget_reset_dt - now).total_seconds() / 86400 # days - assert 9.5 <= time_diff <= 10.5 # Should be close to 10 days diff --git a/tests/test_litellm/llms/pass_through/guardrail_translation/test_handler.py b/tests/test_litellm/llms/pass_through/guardrail_translation/test_handler.py deleted file mode 100644 index 1043c26c6ec..00000000000 --- a/tests/test_litellm/llms/pass_through/guardrail_translation/test_handler.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -Tests for the guardrail_translation_mappings registry. - -Validates: -- allm_passthrough_route is registered in the mappings (regression: this was the bug) -""" - -from litellm.llms.pass_through.guardrail_translation import ( - guardrail_translation_mappings, -) -from litellm.llms.pass_through.guardrail_translation.handler import ( - LlmPassthroughRouteHandler, -) -from litellm.types.utils import CallTypes - - -class TestRegistry: - def test_allm_passthrough_route_registered(self): - """Regression: missing this mapping was the root cause of the bug.""" - assert CallTypes.allm_passthrough_route in guardrail_translation_mappings - - def test_allm_passthrough_route_maps_to_llm_passthrough_route_handler(self): - assert ( - guardrail_translation_mappings[CallTypes.allm_passthrough_route] - is LlmPassthroughRouteHandler - ) - - def test_pass_through_still_registered(self): - from litellm.llms.pass_through.guardrail_translation.handler import ( - PassThroughEndpointHandler, - ) - - assert ( - guardrail_translation_mappings[CallTypes.pass_through] - is PassThroughEndpointHandler - ) - diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_reasoning_content_transformation.py b/tests/test_litellm/responses/litellm_completion_transformation/test_reasoning_content_transformation.py deleted file mode 100644 index 020b5de0a2a..00000000000 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_reasoning_content_transformation.py +++ /dev/null @@ -1,296 +0,0 @@ -""" -Test reasoning content preservation in Responses API transformation -""" - -from unittest.mock import AsyncMock - -from litellm.responses.litellm_completion_transformation.streaming_iterator import ( - LiteLLMCompletionStreamingIterator, -) -from litellm.responses.litellm_completion_transformation.transformation import ( - LiteLLMCompletionResponsesConfig, -) -from litellm.types.utils import ( - Choices, - Delta, - Message, - ModelResponse, - ModelResponseStream, - StreamingChoices, -) - - -class TestReasoningContentStreaming: - """Test reasoning content preservation during streaming""" - - def test_reasoning_content_in_delta(self): - """Test that reasoning content is preserved in streaming deltas""" - # Setup - chunk = ModelResponseStream( - id="test-id", - created=1234567890, - model="test-model", - object="chat.completion.chunk", - choices=[ - StreamingChoices( - finish_reason=None, - index=0, - delta=Delta( - content="", - role="assistant", - reasoning_content="Let me think about this problem...", - ), - ) - ], - ) - - mock_stream = AsyncMock() - - iterator = LiteLLMCompletionStreamingIterator( - model="test-model", - litellm_custom_stream_wrapper=mock_stream, - request_input="Test input", - responses_api_request={}, - ) - - # Execute - transformed_chunk = ( - iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk) - ) - - # Assert - assert transformed_chunk.delta == "Let me think about this problem..." - assert transformed_chunk.type == "response.reasoning_summary_text.delta" - - def test_mixed_content_and_reasoning(self): - """Test handling of both content and reasoning content""" - # Setup - chunk = ModelResponseStream( - id="test-id", - created=1234567890, - model="test-model", - object="chat.completion.chunk", - choices=[ - StreamingChoices( - finish_reason=None, - index=0, - delta=Delta( - content="Here is the answer", - role="assistant", - reasoning_content="First, let me analyze...", - ), - ) - ], - ) - - mock_stream = AsyncMock() - iterator = LiteLLMCompletionStreamingIterator( - model="test-model", - litellm_custom_stream_wrapper=mock_stream, - request_input="Test input", - responses_api_request={}, - ) - - # Execute - transformed_chunk = ( - iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk) - ) - - # Assert - assert transformed_chunk.delta == "First, let me analyze..." - assert transformed_chunk.type == "response.reasoning_summary_text.delta" - - def test_no_reasoning_content(self): - """Test handling when no reasoning content is present""" - # Setup - chunk = ModelResponseStream( - id="test-id", - created=1234567890, - model="test-model", - object="chat.completion.chunk", - choices=[ - StreamingChoices( - finish_reason=None, - index=0, - delta=Delta( - content="Regular content only", - role="assistant", - ), - ) - ], - ) - - mock_stream = AsyncMock() - iterator = LiteLLMCompletionStreamingIterator( - model="test-model", - litellm_custom_stream_wrapper=mock_stream, - request_input="Test input", - responses_api_request={}, - ) - - # Execute - transformed_chunk = ( - iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk) - ) - - # Assert - assert transformed_chunk.delta == "Regular content only" - assert transformed_chunk.type == "response.output_text.delta" - - -class TestReasoningContentFinalResponse: - """Test reasoning content preservation in final response transformation""" - - def test_reasoning_content_in_final_response(self): - """Test that reasoning content is included in final response""" - # Setup - response = ModelResponse( - id="test-id", - created=1234567890, - model="test-model", - object="chat.completion", - choices=[ - Choices( - finish_reason="stop", - index=0, - message=Message( - content="Here is my answer", - role="assistant", - reasoning_content="Let me think step by step about this problem...", - ), - ) - ], - ) - - # Execute - responses_api_response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( - request_input="Test input", - responses_api_request={}, - chat_completion_response=response, - ) - - # Assert - assert hasattr(responses_api_response, "output") - assert len(responses_api_response.output) > 0 - - reasoning_items = [ - item for item in responses_api_response.output if item.type == "reasoning" - ] - assert len(reasoning_items) > 0, "No reasoning item found in output" - - reasoning_item = reasoning_items[0] - assert ( - reasoning_item.content[0].text - == "Let me think step by step about this problem..." - ) - - def test_no_reasoning_content_in_response(self): - """Test handling when no reasoning content in response""" - # Setup - response = ModelResponse( - id="test-id", - created=1234567890, - model="test-model", - object="chat.completion", - choices=[ - Choices( - finish_reason="stop", - index=0, - message=Message( - content="Simple answer", - role="assistant", - ), - ) - ], - ) - - # Execute - responses_api_response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( - request_input="Test input", - responses_api_request={}, - chat_completion_response=response, - ) - - # Assert - reasoning_items = [ - item for item in responses_api_response.output if item.type == "reasoning" - ] - assert ( - len(reasoning_items) == 0 - ), "Should have no reasoning items when no reasoning content present" - - def test_multiple_choices_with_reasoning(self): - """Test handling multiple choices, first with reasoning content""" - # Setup - response = ModelResponse( - id="test-id", - created=1234567890, - model="test-model", - object="chat.completion", - choices=[ - Choices( - finish_reason="stop", - index=0, - message=Message( - content="First answer", - role="assistant", - reasoning_content="Reasoning for first answer", - ), - ), - Choices( - finish_reason="stop", - index=1, - message=Message( - content="Second answer", - role="assistant", - reasoning_content="Reasoning for second answer", - ), - ), - ], - ) - - # Execute - responses_api_response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( - request_input="Test input", - responses_api_request={}, - chat_completion_response=response, - ) - - # Assert - reasoning_items = [ - item for item in responses_api_response.output if item.type == "reasoning" - ] - assert len(reasoning_items) == 1, "Should have exactly one reasoning item" - assert reasoning_items[0].content[0].text == "Reasoning for first answer" - - -def test_streaming_chunk_id_raw(): - """Test that streaming chunk IDs are raw (not encoded) to match OpenAI format""" - chunk = ModelResponseStream( - id="chunk-123", - created=1234567890, - model="test-model", - object="chat.completion.chunk", - choices=[ - StreamingChoices( - finish_reason=None, - index=0, - delta=Delta(content="Hello", role="assistant"), - ) - ], - ) - - iterator = LiteLLMCompletionStreamingIterator( - model="test-model", - litellm_custom_stream_wrapper=AsyncMock(), - request_input="Test input", - responses_api_request={}, - custom_llm_provider="openai", - litellm_metadata={"model_info": {"id": "gpt-4"}}, - ) - - result = iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk) - - # Streaming chunk IDs should be raw (like OpenAI's msg_xxx format) - assert result.item_id == "chunk-123" # Should be raw, not encoded - assert not result.item_id.startswith("resp_") # Should NOT have resp_ prefix diff --git a/tests/test_litellm/test_azure_video_router.py b/tests/test_litellm/test_azure_video_router.py deleted file mode 100644 index e7e2e0a01ea..00000000000 --- a/tests/test_litellm/test_azure_video_router.py +++ /dev/null @@ -1,53 +0,0 @@ -""" -Test suite for Azure video router functionality. -Tests that the router method gets called correctly for Azure video generation. -""" - -import pytest -from unittest.mock import Mock, patch, MagicMock -import litellm - - -class TestAzureVideoRouter: - """Test suite for Azure video router functionality""" - - def setup_method(self): - """Setup test fixtures""" - self.model = "azure/sora-2" - self.prompt = "A beautiful sunset over mountains" - self.seconds = "5" - self.size = "1280x720" - - @patch("litellm.videos.main.base_llm_http_handler") - def test_azure_video_generation_router_call_mock(self, mock_handler): - """Test that Azure video generation calls the router method with mock response""" - # Setup mock response - mock_response = { - "id": "video_123", - "model": "sora-2", - "object": "video", - "status": "processing", - "created_at": 1234567890, - "progress": 0, - } - - # Configure the mock handler - mock_handler.video_generation_handler.return_value = mock_response - - # Call the video generation function with mock response - result = litellm.video_generation( - prompt=self.prompt, - model=self.model, - seconds=self.seconds, - size=self.size, - custom_llm_provider="azure", - mock_response=mock_response, - ) - - # Verify the result is a VideoObject with the expected data - assert result.id == mock_response["id"] - assert result.model == mock_response["model"] - assert result.object == mock_response["object"] - assert result.status == mock_response["status"] - assert result.created_at == mock_response["created_at"] - assert result.progress == mock_response["progress"] From bae58eb4e0aa015d5085264e8b0d9a342f163ce5 Mon Sep 17 00:00:00 2001 From: eugene-yao-zocdoc Date: Fri, 31 Jul 2026 13:03:13 -0400 Subject: [PATCH 002/266] fix(anthropic): preserve mid-turn system messages Generated with AI Co-Authored-By: Claude Code --- .../chat/guardrail_translation/handler.py | 209 ++++-- .../adapters/transformation.py | 48 +- .../responses_adapters/transformation.py | 49 +- litellm/types/guardrails.py | 47 +- litellm/types/llms/anthropic.py | 12 + .../test_anthropic_guardrail_handler.py | 699 ++++++++++++++++++ ...al_pass_through_adapters_transformation.py | 218 ++++++ .../context_management/test_compact.py | 54 ++ .../test_responses_adapters_transformation.py | 138 ++++ 9 files changed, 1380 insertions(+), 94 deletions(-) diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index a549db94224..82707e741c0 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -13,6 +13,7 @@ Pattern Overview: """ import json +from copy import deepcopy from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast from litellm._logging import verbose_proxy_logger @@ -24,7 +25,6 @@ from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTra from litellm.llms.base_llm.guardrail_translation.utils import ( effective_skip_system_message_for_guardrail, effective_skip_tool_message_for_guardrail, - openai_messages_without_system, openai_messages_without_tool, ) from litellm.proxy.pass_through_endpoints.llm_provider_handlers.anthropic_passthrough_logging_handler import ( @@ -59,14 +59,10 @@ if TYPE_CHECKING: class AnthropicMessagesHandler(BaseTranslation): - """ - Handler for processing Anthropic messages with guardrails. + """Process Anthropic messages with guardrails. - This class provides methods to: - 1. Process input messages (pre-call hook) - 2. Process output responses (post-call hook) - - Methods can be overridden to customize behavior for different message formats. + In-sequence system entries are untrusted client input. This handler scans and preserves + them through guardrail rewrites; downstream provider handling is out of scope. """ def __init__(self): @@ -279,14 +275,26 @@ class AnthropicMessagesHandler(BaseTranslation): skip_system = effective_skip_system_message_for_guardrail(guardrail_to_apply) skip_tool = effective_skip_tool_message_for_guardrail(guardrail_to_apply) - chat_completion_compatible_request = self._translate_to_openai(data) + # Exclude only the trusted top-level prompt. In-sequence system entries are untrusted + # and must stay aligned with texts_to_check for positional masking. When the top-level + # prompt is included, the pre-existing count mismatch disables positional masking. + translation_source = { # mutable-ok: API message payload + key: value for key, value in data.items() if key != "system" + } # mutable-ok: API message payload + chat_completion_compatible_request = self._translate_to_openai(translation_source) structured_messages = cast( List[AllMessageValues], chat_completion_compatible_request.get("messages", []), ) - if skip_system: - structured_messages = openai_messages_without_system(structured_messages) + has_midturn_system_message = any( + str(message.get("role") or "").lower() == "system" for message in structured_messages + ) + hoisted_system_message: AllMessageValues | None = None + if not skip_system: + hoisted_system_message = self._hoisted_top_level_system_message(data) + if hoisted_system_message is not None: + structured_messages.insert(0, hoisted_system_message) if skip_tool: structured_messages = openai_messages_without_tool(structured_messages) @@ -346,7 +354,12 @@ class AnthropicMessagesHandler(BaseTranslation): guardrailed_structured_messages is not None and guardrailed_structured_messages is not original_structured_messages ): - self._write_back_structured_messages(data, guardrailed_structured_messages) + self._write_back_structured_messages( + data, + guardrailed_structured_messages, + hoisted_system_message=hoisted_system_message, + preserve_system_messages=has_midturn_system_message, + ) else: # Step 3: Map guardrail responses back to original message structure await self._apply_guardrail_responses_to_input( @@ -359,36 +372,120 @@ class AnthropicMessagesHandler(BaseTranslation): return data - @staticmethod - def _write_back_structured_messages(data: dict, structured_messages: list) -> None: - """Convert compressed structured_messages back to Anthropic format and write to data. + def _hoisted_top_level_system_message( + self, data: dict + ) -> AllMessageValues | None: # mutable-ok: API message payload + """Return the system message produced by translating the top-level prompt.""" + system = data.get("system") + if not system: + return None + probe = self._translate_to_openai( + { # mutable-ok: API message payload + "model": data.get("model") or "", + "messages": [], # mutable-ok: API message payload + "system": system, + } + ) + hoisted = probe.get("messages") or [] # mutable-ok: API message payload + return hoisted[0] if hoisted else None - ``anthropic_messages_pt`` merges every run of consecutive user/tool rows - into a single message, so a turn carrying only tool results and the user - turn that follows it come back fused, and the request the model sees no - longer has the boundaries the client sent. Converting a row at a time - would keep them apart but breaks tool pairing: an assistant row whose - tool results sit outside its own call reads as an orphaned tool call, - and under ``modify_params`` the sanitizer answers it with a synthetic - "tool execution skipped" result and drops the real one. Converting each - assistant row together with the tool rows that answer it, and every - other row on its own, satisfies both. - """ + @staticmethod + def _openai_system_message_to_anthropic( + message: dict[str, Any], + ) -> dict[str, Any] | None: # mutable-ok: API message payload + """Convert an OpenAI system message to the client's Anthropic-shaped entry.""" + content = message.get("content") + if isinstance(content, str): + return ( + {"role": "system", "content": content} if content else None # mutable-ok: API message payload + ) # mutable-ok: API message payload + if not isinstance(content, list): + return None + blocks: list[dict[str, Any]] = [] # mutable-ok: API message payload + for block in content: + if not isinstance(block, dict) or block.get("type") != "text": + continue + text = block.get("text") + if not isinstance(text, str) or not text: + continue + anthropic_block: dict[str, Any] = { # mutable-ok: API message payload + "type": "text", + "text": text, + } # mutable-ok: API message payload + cache_control = block.get("cache_control") + if cache_control: + anthropic_block["cache_control"] = deepcopy(cache_control) + blocks.append(anthropic_block) + return ( + {"role": "system", "content": blocks} if blocks else None # mutable-ok: API message payload + ) # mutable-ok: API message payload + + @staticmethod + def _is_hoisted_top_level_system(message: Any, hoisted_system_message: Any) -> bool: + """Match the hoisted prompt by identity, or by value after serialization.""" + if hoisted_system_message is None: + return False + if message is hoisted_system_message: + return True + return ( + isinstance(message, dict) and isinstance(hoisted_system_message, dict) and message == hoisted_system_message + ) + + @staticmethod + def _write_back_structured_messages( + data: dict, # mutable-ok: API message payload + structured_messages: list, # mutable-ok: API message payload + hoisted_system_message: Any = None, + preserve_system_messages: bool = False, + ) -> None: + """Write a guardrail's structured-message rewrite back without losing corrections.""" from litellm.litellm_core_utils.prompt_templates.factory import ( anthropic_messages_pt, group_tool_exchanges, ) + def _is_system(message: Any) -> bool: + return isinstance(message, dict) and str(message.get("role") or "").lower() == "system" + model = str(data.get("model") or "") - non_system = [m for m in structured_messages if m.get("role") != "system"] - groups = tuple([non_system[index] for index in group] for group in group_tool_exchanges(non_system)) or ( - non_system, - ) - converted = [ - message - for group in groups - for message in anthropic_messages_pt(messages=group, model=model, llm_provider="anthropic") - ] + converted: list = [] # mutable-ok: API message payload + + def _convert_run(run: list) -> None: # mutable-ok: API message payload + for group in group_tool_exchanges(run): + converted.extend( + anthropic_messages_pt( + messages=[ # mutable-ok: API message payload + run[index] for index in group + ], # mutable-ok: API message payload + model=model, + llm_provider="anthropic", + ) + ) + + run: list = [] # mutable-ok: API message payload + hoisted_dropped = False + for message in structured_messages: + if not _is_system(message): + run.append(message) + continue + _convert_run(run) + run = [] # mutable-ok: API message payload + if not hoisted_dropped and AnthropicMessagesHandler._is_hoisted_top_level_system( + message, hoisted_system_message + ): + hoisted_dropped = True + continue + if preserve_system_messages: + anthropic_system = AnthropicMessagesHandler._openai_system_message_to_anthropic(message) + if anthropic_system is not None: + converted.append(anthropic_system) + _convert_run(run) + if not any(not _is_system(message) for message in converted): + converted.extend( + anthropic_messages_pt( + messages=[], model=model, llm_provider="anthropic" + ) # mutable-ok: API message payload + ) # mutable-ok: API message payload for msg in converted: content = msg.get("content") if isinstance(content, list): @@ -397,6 +494,29 @@ class AnthropicMessagesHandler(BaseTranslation): block.pop("cache_control", None) data["messages"] = converted + @staticmethod + def _extract_midturn_system_text( + message: dict[str, Any], # mutable-ok: API message payload + msg_idx: int, + texts_to_check: list[str], # mutable-ok: API message payload + task_mappings: list[tuple[int, int | None]], # mutable-ok: API message payload + ) -> None: + content = message.get("content") + if isinstance(content, str): + if content: + texts_to_check.append(content) + task_mappings.append((msg_idx, None)) + return + if not isinstance(content, list): + return + for content_idx, content_item in enumerate(content): + if not isinstance(content_item, dict) or content_item.get("type") != "text": + continue + text_str = content_item.get("text") + if isinstance(text_str, str) and text_str: + texts_to_check.append(text_str) + task_mappings.append((msg_idx, content_idx)) + def extract_request_tool_names(self, data: dict) -> List[str]: """Extract tool names from Anthropic messages request (tools[].name).""" names: List[str] = [] @@ -415,15 +535,18 @@ class AnthropicMessagesHandler(BaseTranslation): skip_system_message: bool = False, skip_tool_message: bool = False, ) -> None: - """ - Extract text content and images from a message. - - Override this method to customize text/image extraction logic. - """ - role = str(message.get("role") or "").lower() - if skip_system_message and role == "system": + """Extract text content and images from a message.""" + role = str(message.get("role") or "") + if role == "system": + # Match the adapter's filtering so positional guardrail write-back stays aligned. + self._extract_midturn_system_text( + message=message, + msg_idx=msg_idx, + texts_to_check=texts_to_check, + task_mappings=task_mappings, + ) return - if skip_tool_message and role == "tool": + if skip_tool_message and role.lower() == "tool": return content = message.get("content", None) diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 86c9c1db481..707d53e9006 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -85,12 +85,12 @@ from litellm.llms.anthropic.experimental_pass_through.context_management import ) from litellm.types.llms.anthropic import ( ANTHROPIC_HOSTED_TOOLS, + AllAnthropicPassThroughMessageValues, AllAnthropicToolsValues, - AnthopicMessagesAssistantMessageParam, AnthropicFinishReason, AnthropicMessagesRequest, + AnthropicMessagesSystemMessageParam, AnthropicMessagesToolChoice, - AnthropicMessagesUserMessageParam, AnthropicResponseContentBlockRedactedThinking, AnthropicResponseContentBlockText, AnthropicResponseContentBlockThinking, @@ -354,12 +354,7 @@ class LiteLLMAnthropicMessagesAdapter: def translate_anthropic_messages_to_openai( self, - messages: List[ - Union[ - AnthropicMessagesUserMessageParam, - AnthopicMessagesAssistantMessageParam, - ] - ], + messages: List[AllAnthropicPassThroughMessageValues], # mutable-ok: API message payload model: Optional[str] = None, ) -> List: new_messages: List[AllMessageValues] = [] @@ -367,6 +362,11 @@ class LiteLLMAnthropicMessagesAdapter: user_message: Optional[ChatCompletionUserMessage] = None tool_message_list: List[ChatCompletionToolMessage] = [] new_user_content_list: List[Union[ChatCompletionTextObject, ChatCompletionImageObject]] = [] + if m["role"] == "system": + system_message = self._translate_midturn_system_message_to_openai(m, model) + if system_message is not None: + new_messages.append(system_message) + continue ## USER MESSAGE ## if m["role"] == "user": ## translate user message @@ -867,6 +867,29 @@ class LiteLLMAnthropicMessagesAdapter: for def_schema in schema[key].values(): LiteLLMAnthropicMessagesAdapter._add_additional_properties_false(def_schema) + def _translate_midturn_system_message_to_openai( + self, + message: AnthropicMessagesSystemMessageParam, + model: str | None, + ) -> ChatCompletionSystemMessage | None: + """Translate an in-sequence system entry without changing its role or position.""" + content = message.get("content") + if isinstance(content, str): + return ChatCompletionSystemMessage(role="system", content=content) if content else None + if not isinstance(content, list): + return None + text_parts: list[ChatCompletionTextObject] = [] # mutable-ok: API message payload + for block in content: + if not isinstance(block, dict) or block.get("type") != "text": + continue + text = block.get("text") + if not text: + continue + text_obj = ChatCompletionTextObject(type="text", text=text) + self._add_cache_control_if_applicable(block, text_obj, model) + text_parts.append(text_obj) + return ChatCompletionSystemMessage(role="system", content=text_parts) if text_parts else None + def _add_system_message_to_messages( self, new_messages: List[AllMessageValues], @@ -1068,13 +1091,8 @@ class LiteLLMAnthropicMessagesAdapter: tool_name_mapping: Dict[str, str] = {} ## CONVERT ANTHROPIC MESSAGES TO OPENAI - messages_list: List[Union[AnthropicMessagesUserMessageParam, AnthopicMessagesAssistantMessageParam]] = cast( - List[ - Union[ - AnthropicMessagesUserMessageParam, - AnthopicMessagesAssistantMessageParam, - ] - ], + messages_list = cast( + List[AllAnthropicPassThroughMessageValues], anthropic_message_request["messages"], ) new_messages = self.translate_anthropic_messages_to_openai( diff --git a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py index 172e54de98e..cbe36100eb0 100644 --- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py @@ -6,6 +6,7 @@ path used for OpenAI and Azure models. """ import json +from collections.abc import Iterable from typing import Any, Dict, List, Optional, Union, cast from litellm.litellm_core_utils.reasoning_effort_utils import ( @@ -15,15 +16,15 @@ from litellm.llms.anthropic.experimental_pass_through.utils import ( is_reasoning_auto_summary_enabled, ) from litellm.types.llms.anthropic import ( + AllAnthropicPassThroughMessageValues, AllAnthropicToolsValues, - AnthopicMessagesAssistantMessageParam, AnthropicFinishReason, AnthropicMessagesRequest, AnthropicMessagesToolChoice, - AnthropicMessagesUserMessageParam, AnthropicResponseContentBlockText, AnthropicResponseContentBlockThinking, AnthropicResponseContentBlockToolUse, + AnthropicSystemMessageContent, ) from litellm.types.llms.anthropic_messages.anthropic_response import ( AnthropicMessagesResponse, @@ -54,19 +55,32 @@ class LiteLLMAnthropicToResponsesAPIAdapter: return source.get("url") return None + @staticmethod + def _translate_midturn_system_content_to_responses( + content: Union[str, Iterable[AnthropicSystemMessageContent]], + ) -> list[dict[str, str]]: # mutable-ok: API message payload + """Convert in-sequence system content to Responses input-text parts.""" + if isinstance(content, str): + return ( + [{"type": "input_text", "text": content}] if content else [] # mutable-ok: API message payload + ) # mutable-ok: API message payload + if not isinstance(content, list): + return [] # mutable-ok: API message payload + return [ # mutable-ok: API message payload + {"type": "input_text", "text": text} # mutable-ok: API message payload + for block in content + if isinstance(block, dict) and block.get("type") == "text" and (text := block.get("text")) + ] + def translate_messages_to_responses_input( self, - messages: List[ - Union[ - AnthropicMessagesUserMessageParam, - AnthopicMessagesAssistantMessageParam, - ] - ], + messages: List[AllAnthropicPassThroughMessageValues], # mutable-ok: API message payload ) -> List[Dict[str, Any]]: """ Convert Anthropic messages list to Responses API `input` items. Mapping: + system text -> message(role=system, input_text) user text -> message(role=user, input_text) user image -> message(role=user, input_image) user tool_result -> function_call_output @@ -76,6 +90,18 @@ class LiteLLMAnthropicToResponsesAPIAdapter: input_items: List[Dict[str, Any]] = [] for m in messages: + if m["role"] == "system": + system_parts = self._translate_midturn_system_content_to_responses(m.get("content")) + if system_parts: + input_items.append( + { # mutable-ok: API message payload + "type": "message", + "role": "system", + "content": system_parts, + } + ) + continue + role = m["role"] content = m.get("content") @@ -287,12 +313,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter: """ model: str = anthropic_request["model"] messages_list = cast( - List[ - Union[ - AnthropicMessagesUserMessageParam, - AnthopicMessagesAssistantMessageParam, - ] - ], + List[AllAnthropicPassThroughMessageValues], anthropic_request["messages"], ) diff --git a/litellm/types/guardrails.py b/litellm/types/guardrails.py index af419d8cb6f..2e0da24ccda 100644 --- a/litellm/types/guardrails.py +++ b/litellm/types/guardrails.py @@ -11,12 +11,24 @@ from litellm.types.proxy.guardrails.guardrail_hooks.akto import ( from litellm.types.proxy.guardrails.guardrail_hooks.block_code_execution import ( BlockCodeExecutionGuardrailConfigModel, ) +from litellm.types.proxy.guardrails.guardrail_hooks.cisco_ai_defense import ( + CiscoAIDefenseGuardrailConfigModel, +) +from litellm.types.proxy.guardrails.guardrail_hooks.compresr import ( + CompresrGuardrailConfigModel, +) from litellm.types.proxy.guardrails.guardrail_hooks.enkryptai import ( EnkryptAIGuardrailConfigs, ) from litellm.types.proxy.guardrails.guardrail_hooks.grayswan import ( GraySwanGuardrailConfigModel, ) +from litellm.types.proxy.guardrails.guardrail_hooks.headroom import ( + HeadroomGuardrailConfigModel, +) +from litellm.types.proxy.guardrails.guardrail_hooks.hiddenlayer import ( + HiddenlayerGuardrailConfigModel, +) from litellm.types.proxy.guardrails.guardrail_hooks.ibm import ( IBMGuardrailsBaseConfigModel, ) @@ -29,38 +41,26 @@ from litellm.types.proxy.guardrails.guardrail_hooks.ovalix import ( from litellm.types.proxy.guardrails.guardrail_hooks.promptguard import ( PromptGuardConfigModel, ) -from litellm.types.proxy.guardrails.guardrail_hooks.xecguard import ( - XecGuardConfigModel, +from litellm.types.proxy.guardrails.guardrail_hooks.qohash import ( + QostodianNexusConfigModel, ) from litellm.types.proxy.guardrails.guardrail_hooks.qualifire import ( QualifireGuardrailConfigModel, ) -from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import ( - ToolPermissionGuardrailConfigModel, -) -from litellm.types.proxy.guardrails.guardrail_hooks.hiddenlayer import ( - HiddenlayerGuardrailConfigModel, -) -from litellm.types.proxy.guardrails.guardrail_hooks.qohash import ( - QostodianNexusConfigModel, -) from litellm.types.proxy.guardrails.guardrail_hooks.repelloai import ( RepelloAIGuardrailConfigModel, ) -from litellm.types.proxy.guardrails.guardrail_hooks.vigil_guard import ( - VigilGuardGuardrailConfigModel, -) -from litellm.types.proxy.guardrails.guardrail_hooks.cisco_ai_defense import ( - CiscoAIDefenseGuardrailConfigModel, -) from litellm.types.proxy.guardrails.guardrail_hooks.singulr import ( SingulrGuardrailConfigModel, ) -from litellm.types.proxy.guardrails.guardrail_hooks.headroom import ( - HeadroomGuardrailConfigModel, +from litellm.types.proxy.guardrails.guardrail_hooks.tool_permission import ( + ToolPermissionGuardrailConfigModel, ) -from litellm.types.proxy.guardrails.guardrail_hooks.compresr import ( - CompresrGuardrailConfigModel, +from litellm.types.proxy.guardrails.guardrail_hooks.vigil_guard import ( + VigilGuardGuardrailConfigModel, +) +from litellm.types.proxy.guardrails.guardrail_hooks.xecguard import ( + XecGuardConfigModel, ) """ @@ -743,7 +743,10 @@ class BaseLitellmParams(ContentFilterConfigModel): # works for new and patch up "When True, unified guardrails skip system-role messages when building " "evaluation inputs (texts and structured_messages). When False, system " "messages are included even if litellm_settings sets a global skip. When " - "None, use the global litellm.skip_system_message_in_guardrail setting." + "None, use the global litellm.skip_system_message_in_guardrail setting. " + "For Anthropic /v1/messages, the flag applies only to the trusted top-level " + "system prompt. In-sequence system entries are untrusted client input and remain " + "in texts and structured_messages." ), ) diff --git a/litellm/types/llms/anthropic.py b/litellm/types/llms/anthropic.py index c24d072217a..29faf500b3d 100644 --- a/litellm/types/llms/anthropic.py +++ b/litellm/types/llms/anthropic.py @@ -365,8 +365,20 @@ class AnthropicSystemMessageContent(TypedDict, total=False): cache_control: Optional[Union[dict, ChatCompletionCachedContent]] +class AnthropicMessagesSystemMessageParam(TypedDict, total=False): + role: Required[Literal["system"]] + content: Required[Union[str, Iterable[AnthropicSystemMessageContent]]] + + AllAnthropicMessageValues = Union[AnthropicMessagesUserMessageParam, AnthopicMessagesAssistantMessageParam] +# System is not a native Anthropic message role; only pass-through adapters use this union. +AllAnthropicPassThroughMessageValues = Union[ + AnthropicMessagesUserMessageParam, + AnthopicMessagesAssistantMessageParam, + AnthropicMessagesSystemMessageParam, +] + class AnthropicMessagesRequestOptionalParams(TypedDict, total=False): max_tokens: Optional[int] diff --git a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py index 48acdd348e9..7757b0fa5a4 100644 --- a/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py +++ b/tests/test_litellm/llms/anthropic/chat/guardrail_translation/test_anthropic_guardrail_handler.py @@ -75,6 +75,51 @@ class MockRecordingGuardrail(CustomGuardrail): return inputs +class MockMaskingGuardrail(CustomGuardrail): + """Capture request inputs and mask one known prohibited value.""" + + def __init__(self, skip_system_message_in_guardrail: Optional[bool] = True): + super().__init__(guardrail_name="masking-test") + self.skip_system_message_in_guardrail = skip_system_message_in_guardrail + self.inputs: Optional[GenericGuardrailAPIInputs] = None + + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional[Any] = None, + ) -> GenericGuardrailAPIInputs: + self.inputs = inputs.copy() + masked_inputs = inputs.copy() + masked_inputs["texts"] = [ + "[MASKED]" if text == "prohibited correction" else text for text in inputs.get("texts", []) + ] + return masked_inputs + + +class MockCompactingGuardrail(CustomGuardrail): + """Stand in for a compaction guardrail that rewrites `structured_messages` wholesale.""" + + def __init__(self, replacement_messages: list): + super().__init__(guardrail_name="compacting-test") + self.replacement_messages = replacement_messages + self.inputs: Optional[GenericGuardrailAPIInputs] = None + + async def apply_guardrail( + self, + inputs: GenericGuardrailAPIInputs, + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional[Any] = None, + ) -> GenericGuardrailAPIInputs: + self.inputs = inputs.copy() + rewritten = inputs.copy() + # A new list object -- this is what signals a rewrite to the handler. + rewritten["structured_messages"] = list(self.replacement_messages) + return rewritten + + class TestAnthropicMessagesHandlerStreamingRequestData: """Post-call guardrails on streaming /v1/messages receive the response and identity metadata""" @@ -210,6 +255,660 @@ class TestAnthropicMessagesHandlerInputProcessing: assert data.get("litellm_metadata", {}).get("guardrails") assert guardrail.dynamic_params == {"policy_id": "policy-123"} + @pytest.mark.asyncio + async def test_midturn_system_correction_is_guardrailed_when_top_level_system_is_skipped( + self, + ): + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail() + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "safe text"}, + { + "role": "system", + "content": [ + {"type": "unsupported", "text": "discarded text"}, + {"type": "text", "text": "prohibited correction"}, + ], + }, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + assert guardrail.inputs["texts"] == ["safe text", "prohibited correction"] + assert "trusted top-level system prompt" not in guardrail.inputs["texts"] + assert data["messages"][1]["content"][0]["text"] == "discarded text" + assert data["messages"][1]["content"][1]["text"] == "[MASKED]" + + @pytest.mark.asyncio + async def test_string_midturn_system_correction_is_guardrailed(self): + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail() + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [{"role": "system", "content": "prohibited correction"}], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + assert guardrail.inputs["texts"] == ["prohibited correction"] + assert data["messages"][0]["content"] == "[MASKED]" + + @pytest.mark.asyncio + async def test_unsupported_midturn_system_content_is_not_guardrailed(self): + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail() + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + { + "role": "system", + "content": [{"type": "image", "source": {"type": "url"}}], + } + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is None + + @pytest.mark.asyncio + async def test_skip_system_message_excludes_only_hoisted_top_level_system(self): + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail() + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "safe text"}, + {"role": "system", "content": "prohibited correction"}, + {"role": "user", "content": "continue"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + structured = guardrail.inputs["structured_messages"] + assert [m["role"] for m in structured] == ["user", "system", "user"] + assert structured[1]["content"] == "prohibited correction" + + @pytest.mark.asyncio + async def test_default_skip_false_scans_midturn_system_and_hoists_top_level_system( + self, + ): + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail(skip_system_message_in_guardrail=None) + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "safe text"}, + {"role": "system", "content": "prohibited correction"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + assert guardrail.inputs["texts"] == ["safe text", "prohibited correction"] + structured = guardrail.inputs["structured_messages"] + assert [m["role"] for m in structured] == ["system", "user", "system"] + assert structured[0]["content"] == "trusted top-level system prompt" + assert data["messages"][1]["content"] == "[MASKED]" + + @pytest.mark.asyncio + async def test_bedrock_masking_slice_is_unavailable_when_top_level_system_is_included( + self, + ): + from litellm.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( + BedrockGuardrail, + ) + + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail(skip_system_message_in_guardrail=None) + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "safe text"}, + {"role": "system", "content": "prohibited correction"}, + {"role": "user", "content": "latest question"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + texts = guardrail.inputs["texts"] + structured = guardrail.inputs["structured_messages"] + + bedrock = BedrockGuardrail(guardrailIdentifier="gi", guardrailVersion="1") + assert sum(bedrock._count_message_texts(m) for m in structured) == len(texts) + 1 + latest_user_index = bedrock._find_latest_message_index(structured, target_role="user") + assert ( + bedrock._locate_message_texts_slice( + structured_messages=structured, + target_index=latest_user_index, + texts=texts, + ) + is None + ) + assert ( + bedrock._merge_masked_texts( + masked_texts=["{MASKED}"], + texts=texts, + scanned_slice=None, + scanned_role_subset=True, + ) + == texts + ) + + @pytest.mark.asyncio + @pytest.mark.parametrize("skip_system_message_in_guardrail", [True, None]) + async def test_midturn_system_text_extraction_matches_translation_in_both_skip_modes( + self, + skip_system_message_in_guardrail: Optional[bool], + ): + from litellm.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( + BedrockGuardrail, + ) + + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail(skip_system_message_in_guardrail=skip_system_message_in_guardrail) + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "safe text"}, + { + "role": "system", + "content": [ + {"type": "text", "text": ""}, + {"type": "image", "source": {"type": "url", "url": "https://example.com/a.png"}}, + {"type": "text", "text": "prohibited correction"}, + ], + }, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + texts = guardrail.inputs["texts"] + structured = guardrail.inputs["structured_messages"] + assert texts == ["safe text", "prohibited correction"] + bedrock = BedrockGuardrail(guardrailIdentifier="gi", guardrailVersion="1") + assert sum(bedrock._count_message_texts(m) for m in structured) == len(texts) + assert data["messages"][1]["content"][2]["text"] == "[MASKED]" + + @pytest.mark.asyncio + async def test_bedrock_masking_slice_stays_aligned_with_midturn_system(self): + from litellm.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( + BedrockGuardrail, + ) + + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail() + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "safe text"}, + { + "role": "system", + "content": [ + {"type": "text", "text": "prohibited correction"}, + {"type": "text", "text": "second correction"}, + ], + }, + {"role": "user", "content": "latest question"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + texts = guardrail.inputs["texts"] + structured = guardrail.inputs["structured_messages"] + + bedrock = BedrockGuardrail(guardrailIdentifier="gi", guardrailVersion="1") + total = sum(bedrock._count_message_texts(m) for m in structured) + assert total == len(texts) + + latest_user_index = bedrock._find_latest_message_index(structured, target_role="user") + assert latest_user_index == 2 + scanned_slice = bedrock._locate_message_texts_slice( + structured_messages=structured, + target_index=latest_user_index, + texts=texts, + ) + assert scanned_slice == (3, 1) + + merged = bedrock._merge_masked_texts( + masked_texts=["{MASKED}"], + texts=texts, + scanned_slice=scanned_slice, + scanned_role_subset=True, + ) + assert merged == [ + "safe text", + "prohibited correction", + "second correction", + "{MASKED}", + ] + + @pytest.mark.asyncio + async def test_compaction_rewrite_keeps_midturn_system_messages(self): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "user", "content": "compacted history"}, + { + "role": "system", + "content": [{"type": "text", "text": "use the corrected result"}], + }, + {"role": "user", "content": "continue"}, + ] + ) + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "continue"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["user", "system", "user"] + assert data["messages"][1]["content"] == [{"type": "text", "text": "use the corrected result"}] + assert data["messages"][0]["content"] == [{"type": "text", "text": "compacted history"}] + assert data["messages"][2]["content"] == [{"type": "text", "text": "continue"}] + assert data["system"] == "trusted top-level system prompt" + + @pytest.mark.asyncio + async def test_compaction_rewrite_does_not_duplicate_hoisted_top_level_system(self): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "system", "content": "trusted top-level system prompt"}, + {"role": "user", "content": "compacted history"}, + {"role": "system", "content": "use the corrected result"}, + ] + ) + guardrail.skip_system_message_in_guardrail = None + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["user", "system"] + assert data["messages"][1]["content"] == "use the corrected result" + assert data["system"] == "trusted top-level system prompt" + + @pytest.mark.asyncio + async def test_compaction_rewrite_keeps_leading_midturn_system_when_system_is_skipped( + self, + ): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "compacted history"}, + ] + ) + guardrail.skip_system_message_in_guardrail = True + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "trusted top-level system prompt", + "messages": [ + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "original history"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["system", "user"] + assert data["messages"][0]["content"] == "use the corrected result" + + @pytest.mark.asyncio + async def test_compaction_rewrite_keeps_leading_correction_when_top_level_system_hoists_nothing( + self, + ): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "compacted history"}, + ] + ) + guardrail.skip_system_message_in_guardrail = None + data = { + "model": "claude-3-5-sonnet-20241022", + "system": [{"type": "image", "source": {"type": "url", "url": "https://example.com/a.png"}}], + "messages": [ + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "original history"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["system", "user"] + assert data["messages"][0]["content"] == "use the corrected result" + + @pytest.mark.asyncio + async def test_compaction_rewrite_keeps_leading_correction_when_hoisted_prompt_is_dropped( + self, + ): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "system", "content": "CLIENT CORRECTION"}, + {"role": "user", "content": "compacted history"}, + ] + ) + guardrail.skip_system_message_in_guardrail = None + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "TRUSTED", + "messages": [ + {"role": "system", "content": "CLIENT CORRECTION"}, + {"role": "user", "content": "original history"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + assert guardrail.inputs["structured_messages"][0] == { + "role": "system", + "content": "TRUSTED", + } + assert [m["role"] for m in data["messages"]] == ["system", "user"] + assert data["messages"][0]["content"] == "CLIENT CORRECTION" + assert data["system"] == "TRUSTED" + + @pytest.mark.asyncio + async def test_compaction_rewrite_drops_hoisted_prompt_matched_by_content_copy(self): + import json + + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + json.loads(json.dumps({"role": "system", "content": "TRUSTED"})), + {"role": "user", "content": "compacted history"}, + {"role": "system", "content": "CLIENT CORRECTION"}, + ] + ) + guardrail.skip_system_message_in_guardrail = None + data = { + "model": "claude-3-5-sonnet-20241022", + "system": "TRUSTED", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "CLIENT CORRECTION"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["user", "system"] + assert data["messages"][1]["content"] == "CLIENT CORRECTION" + assert data["system"] == "TRUSTED" + + @pytest.mark.asyncio + async def test_compaction_rewrite_preserves_cache_control_on_system_blocks(self): + """ + `cache_control` on an in-sequence system text block survives the write-back, and is + copied rather than aliased into the guardrail's own returned list. + """ + handler = AnthropicMessagesHandler() + source_cache_control = {"type": "ephemeral"} + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "user", "content": "compacted history"}, + { + "role": "system", + "content": [ + { + "type": "text", + "text": "use the corrected result", + "cache_control": source_cache_control, + } + ], + }, + ] + ) + guardrail.skip_system_message_in_guardrail = True + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert data["messages"][1]["content"] == [ + { + "type": "text", + "text": "use the corrected result", + "cache_control": {"type": "ephemeral"}, + } + ] + assert data["messages"][1]["content"][0]["cache_control"] is not source_cache_control + + @pytest.mark.asyncio + async def test_compaction_rewrite_rstrips_trailing_assistant_in_each_run(self): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "user", "content": "compacted history"}, + {"role": "assistant", "content": "earlier "}, + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "continue"}, + {"role": "assistant", "content": "prefill "}, + ] + ) + guardrail.skip_system_message_in_guardrail = True + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == [ + "user", + "assistant", + "system", + "user", + "assistant", + ] + assert data["messages"][1]["content"] == [{"type": "text", "text": "earlier"}] + assert data["messages"][-1]["content"] == [{"type": "text", "text": "prefill"}] + + @pytest.mark.asyncio + async def test_compaction_rewrite_drops_text_free_system_message(self): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[ + {"role": "user", "content": "compacted history"}, + {"role": "system", "content": [{"type": "text", "text": ""}]}, + {"role": "system", "content": ""}, + {"role": "user", "content": "continue"}, + ] + ) + guardrail.skip_system_message_in_guardrail = True + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "continue"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["user", "user"] + assert data["messages"][0]["content"] == [{"type": "text", "text": "compacted history"}] + assert data["messages"][1]["content"] == [{"type": "text", "text": "continue"}] + + @pytest.mark.asyncio + async def test_noncanonical_system_role_casing_is_still_scanned(self): + handler = AnthropicMessagesHandler() + guardrail = MockMaskingGuardrail() + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "safe text"}, + {"role": "System", "content": "prohibited correction"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert guardrail.inputs is not None + assert "prohibited correction" in guardrail.inputs["texts"] + assert data["messages"][1]["content"] == "[MASKED]" + + @pytest.mark.asyncio + async def test_tool_result_turns_have_a_preexisting_alignment_gap(self): + from litellm.proxy.guardrails.guardrail_hooks.bedrock_guardrails import ( + BedrockGuardrail, + ) + + handler = AnthropicMessagesHandler() + bedrock = BedrockGuardrail(guardrailIdentifier="gi", guardrailVersion="1") + tool_loop = [ + {"role": "user", "content": "call the tool"}, + { + "role": "assistant", + "content": [{"type": "tool_use", "id": "tu_1", "name": "get", "input": {"a": 1}}], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "tu_1", + "content": [{"type": "text", "text": "tool output"}], + } + ], + }, + ] + + async def _slice_for(messages: list): + guardrail = MockMaskingGuardrail() + data = {"model": "claude-3-5-sonnet-20241022", "messages": messages} + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + assert guardrail.inputs is not None + texts = guardrail.inputs["texts"] + structured = guardrail.inputs["structured_messages"] + target_index = bedrock._find_latest_message_index(structured, target_role="user") + return ( + sum(bedrock._count_message_texts(m) for m in structured) - len(texts), + bedrock._locate_message_texts_slice( + structured_messages=structured, + target_index=target_index, + texts=texts, + ), + ) + + with_system = await _slice_for( + tool_loop + + [ + {"role": "system", "content": "use the corrected result"}, + {"role": "user", "content": "latest question"}, + ] + ) + without_system = await _slice_for(tool_loop + [{"role": "user", "content": "latest question"}]) + + assert with_system == without_system == (1, None) + + @pytest.mark.asyncio + async def test_compaction_rewrite_to_only_system_messages_is_rejected(self): + import litellm + + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[{"role": "system", "content": "use the corrected result"}] + ) + guardrail.skip_system_message_in_guardrail = True + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + ], + } + + with patch.object(litellm, "modify_params", False): + with pytest.raises(litellm.BadRequestError, match="at least one non-system message"): + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + @pytest.mark.asyncio + async def test_compaction_rewrite_to_only_system_messages_repaired_with_modify_params( + self, + ): + import litellm + + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail( + replacement_messages=[{"role": "system", "content": "use the corrected result"}] + ) + guardrail.skip_system_message_in_guardrail = True + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "original history"}, + {"role": "system", "content": "use the corrected result"}, + ], + } + + with patch.object(litellm, "modify_params", True): + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert [m["role"] for m in data["messages"]] == ["system", "user"] + assert data["messages"][0]["content"] == "use the corrected result" + + @pytest.mark.asyncio + async def test_compaction_rewrite_without_system_messages_is_unchanged(self): + handler = AnthropicMessagesHandler() + guardrail = MockCompactingGuardrail(replacement_messages=[{"role": "user", "content": "compacted history"}]) + data = { + "model": "claude-3-5-sonnet-20241022", + "messages": [ + {"role": "user", "content": "a"}, + {"role": "assistant", "content": "b"}, + {"role": "user", "content": "c"}, + ], + } + + await handler.process_input_messages(data=data, guardrail_to_apply=guardrail) + + assert data["messages"] == [{"role": "user", "content": [{"type": "text", "text": "compacted history"}]}] + @pytest.mark.asyncio async def test_process_output_streaming_response_empty_choices(self): """Test that streaming response with empty choices doesn't raise IndexError diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py index c0c6e315b5b..b72620f9918 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py @@ -413,6 +413,224 @@ def test_translate_anthropic_messages_to_openai_tool_message_placement(): ), "Tool message should be placed before user message" +@pytest.mark.parametrize( + ("system_content", "expected_content"), + [ + ("Use the corrected result.", "Use the corrected result."), + ( + [{"type": "text", "text": "Use the corrected result."}], + [{"type": "text", "text": "Use the corrected result."}], + ), + ( + [ + { + "type": "image", + "source": {"type": "url", "url": "https://example.com/a.png"}, + }, + {"type": "text", "text": "Use the corrected result."}, + ], + [{"type": "text", "text": "Use the corrected result."}], + ), + ( + [ + {"type": "text", "text": "First correction."}, + {"type": "text", "text": "Second correction."}, + ], + [ + {"type": "text", "text": "First correction."}, + {"type": "text", "text": "Second correction."}, + ], + ), + ], +) +def test_translate_anthropic_messages_to_openai_preserves_midturn_system_correction( + system_content: object, + expected_content: object, +): + messages = [ + { + "role": "assistant", + "content": [ + { + "type": "tool_use", + "id": "toolu_01234", + "name": "get_weather", + "input": {"location": "Boston"}, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_01234", + "content": "Rainy, 55°F", + } + ], + }, + {"role": "system", "content": system_content}, + {"role": "user", "content": "Continue."}, + ] + + result = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai( + messages=messages, + model="claude-3-5-sonnet-20240620", + ) + + assert result == [ + { + "role": "assistant", + "content": None, + "thinking_blocks": None, + "tool_calls": [ + { + "id": "toolu_01234", + "type": "function", + "function": { + "name": "get_weather", + "arguments": '{"location": "Boston"}', + }, + } + ], + }, + { + "role": "tool", + "tool_call_id": "toolu_01234", + "content": "Rainy, 55°F", + }, + {"role": "system", "content": expected_content}, + {"role": "user", "content": "Continue."}, + ] + + +def test_translate_anthropic_messages_to_openai_preserves_midturn_system_cache_control(): + """ + `cache_control` on an in-sequence system text block survives, matching how the + hoisted top-level `system` prompt and user text blocks are already handled. + """ + messages = [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Use the corrected result.", + "cache_control": {"type": "ephemeral"}, + } + ], + } + ] + + result = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai( + messages=messages, + model="claude-3-5-sonnet-20240620", + ) + + assert result == [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Use the corrected result.", + "cache_control": {"type": "ephemeral"}, + } + ], + } + ] + + +def test_translate_anthropic_messages_to_openai_drops_midturn_system_cache_control_for_non_claude(): + """ + `cache_control` goes through the same `_add_cache_control_if_applicable` gate as the + hoisted top-level prompt and user text blocks, so a non-Claude *requested model name* + does not get it. That gate is a best-effort check of the requested name before routing + (behind the proxy it is often a public alias), not a guarantee about the backend that + ultimately serves the request. + """ + messages = [ + { + "role": "system", + "content": [ + { + "type": "text", + "text": "Use the corrected result.", + "cache_control": {"type": "ephemeral"}, + } + ], + } + ] + + result = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai( + messages=messages, + model="gpt-4o", + ) + + assert result == [ + { + "role": "system", + "content": [{"type": "text", "text": "Use the corrected result."}], + } + ] + + +@pytest.mark.parametrize( + "system_content", + [ + "", + [{"type": "text", "text": ""}], + [ + { + "type": "image", + "source": {"type": "url", "url": "https://example.com/a.png"}, + } + ], + None, + ], +) +def test_translate_anthropic_messages_to_openai_drops_empty_midturn_system( + system_content: object, +): + messages = [{"role": "system", "content": system_content}] + + result = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai( + messages=messages, + model="claude-3-5-sonnet-20240620", + ) + + assert result == [] + + +def test_translate_anthropic_to_openai_orders_top_level_and_midturn_system(): + """ + Request level: the trusted top-level prompt is hoisted to index 0 exactly once and the + in-sequence correction keeps its own position and `role: "system"` -- no duplication of + either, and no reordering of the surrounding turns. + """ + openai_request, _ = LiteLLMAnthropicMessagesAdapter().translate_anthropic_to_openai( + anthropic_message_request={ + "model": "claude-3-5-sonnet-20240620", + "max_tokens": 100, + "system": "Trusted top-level prompt.", + "messages": [ + {"role": "user", "content": "First question."}, + {"role": "assistant", "content": "First answer."}, + {"role": "system", "content": "Use the corrected result."}, + {"role": "user", "content": "Continue."}, + ], + } + ) + + assert openai_request["messages"] == [ + {"role": "system", "content": "Trusted top-level prompt."}, + {"role": "user", "content": "First question."}, + {"role": "assistant", "content": "First answer.", "thinking_blocks": None}, + {"role": "system", "content": "Use the corrected result."}, + {"role": "user", "content": "Continue."}, + ] + + def test_translate_openai_content_to_anthropic_empty_function_arguments(): """Test that empty function arguments are handled safely and don't cause JSON parsing errors.""" diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_compact.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_compact.py index 9c8df1c79f9..6cc1d9e5add 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_compact.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/context_management/test_compact.py @@ -2475,3 +2475,57 @@ def test_endpoint_runs_failure_hook_on_500_context_management_error(): body = response.json() assert body["type"] == "error" failure_hook.assert_awaited_once() + + +def test_count_effective_tokens_counts_midturn_system_correction(): + from litellm.llms.anthropic.experimental_pass_through.context_management.editors.compact import ( + _count_effective_tokens, + ) + + base: List[Dict[str, Any]] = [ + {"role": "user", "content": "hello"}, + {"role": "assistant", "content": "hi"}, + ] + correction = { + "role": "system", + "content": [{"type": "text", "text": "use the corrected result " * 20}], + } + + without_correction = _count_effective_tokens( + model=MODEL, effective_messages=base, compaction_block=None, tools=None + ) + with_correction = _count_effective_tokens( + model=MODEL, + effective_messages=base + [correction], + compaction_block=None, + tools=None, + ) + + assert with_correction > without_correction + + +def test_build_summary_messages_keeps_midturn_system_correction_in_place(): + from litellm.llms.anthropic.experimental_pass_through.context_management.editors.compact import ( + _build_summary_messages, + ) + + summary_messages = _build_summary_messages( + effective_messages=[ + {"role": "user", "content": "original question"}, + {"role": "system", "content": "use the corrected result"}, + {"role": "assistant", "content": "acknowledged"}, + ], + prompt="summarize the conversation", + system="caller system prompt", + ) + + assert [m["role"] for m in summary_messages] == [ + "system", + "user", + "system", + "assistant", + "user", + ] + assert summary_messages[0]["content"] == "caller system prompt" + assert summary_messages[2]["content"] == "use the corrected result" + assert summary_messages[-1]["content"] == "summarize the conversation" diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py index 606ff39b35e..8963012ecd5 100644 --- a/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py +++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/responses_adapters/test_responses_adapters_transformation.py @@ -9,6 +9,8 @@ import sys from typing import Any, Dict, List from unittest.mock import MagicMock +import pytest + sys.path.insert(0, os.path.abspath("../../../../../../..")) from litellm.constants import ( @@ -221,6 +223,106 @@ class TestTranslateMessagesToResponsesInput: {"type": "input_text", "text": "Second part."}, ] + @pytest.mark.parametrize( + "system_content", + [ + "Use the corrected result.", + [{"type": "text", "text": "Use the corrected result."}], + [ + {"type": "image", "source": {"type": "url", "url": "https://example.com/a.png"}}, + {"type": "text", "text": "Use the corrected result."}, + ], + ], + ) + def test_midturn_system_correction_stays_system_in_sequence(self, system_content: object): + messages = [ + { + "role": "assistant", + "content": [ + { + "type": "tool_use", + "id": "toolu_01234", + "name": "get_weather", + "input": {"location": "Boston"}, + } + ], + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_01234", + "content": "Rainy, 55°F", + } + ], + }, + {"role": "system", "content": system_content}, + {"role": "user", "content": "Continue."}, + ] + + result = _translate_messages(messages) + + assert result == [ + { + "type": "function_call", + "call_id": "toolu_01234", + "name": "get_weather", + "arguments": '{"location": "Boston"}', + }, + { + "type": "function_call_output", + "call_id": "toolu_01234", + "output": "Rainy, 55°F", + }, + { + "type": "message", + "role": "system", + "content": [{"type": "input_text", "text": "Use the corrected result."}], + }, + { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": "Continue."}], + }, + ] + + def test_midturn_system_correction_keeps_multiple_text_blocks(self): + messages = [ + { + "role": "system", + "content": [ + {"type": "text", "text": "First correction."}, + {"type": "text", "text": "Second correction."}, + ], + } + ] + + assert _translate_messages(messages) == [ + { + "type": "message", + "role": "system", + "content": [ + {"type": "input_text", "text": "First correction."}, + {"type": "input_text", "text": "Second correction."}, + ], + } + ] + + @pytest.mark.parametrize( + "system_content", + [ + "", + [{"type": "text", "text": ""}], + [{"type": "image", "source": {"type": "url", "url": "https://example.com/a.png"}}], + None, + ], + ) + def test_empty_or_unsupported_midturn_system_correction_is_dropped(self, system_content: object): + messages = [{"role": "system", "content": system_content}] + + assert _translate_messages(messages) == [] + def test_user_base64_image(self): """User message with base64 image source becomes input_image with data URL.""" messages = [ @@ -722,6 +824,42 @@ class TestTranslateRequestBroaderCoverage: kwargs = _ADAPTER.translate_request(req) assert kwargs["instructions"] == "You are a helpful assistant." + def test_top_level_system_and_midturn_correction_are_not_duplicated(self): + """ + Request level: the trusted top-level prompt goes to `instructions` only, and the + in-sequence correction stays a `role: "system"` input item in its original position. + Neither appears twice, and the surrounding turns keep their order. + """ + req = _make_request( + system="Trusted top-level prompt.", + messages=[ + {"role": "user", "content": "First question."}, + {"role": "system", "content": "Use the corrected result."}, + {"role": "user", "content": "Continue."}, + ], + ) + + kwargs = _ADAPTER.translate_request(req) + + assert kwargs["instructions"] == "Trusted top-level prompt." + assert kwargs["input"] == [ + { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": "First question."}], + }, + { + "type": "message", + "role": "system", + "content": [{"type": "input_text", "text": "Use the corrected result."}], + }, + { + "type": "message", + "role": "user", + "content": [{"type": "input_text", "text": "Continue."}], + }, + ] + def test_system_list_of_text_blocks_joined(self): req = _make_request( system=[ From 81a80b8c632a9b5d942193bb71eda601630e9906 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Sun, 2 Aug 2026 01:02:27 +0000 Subject: [PATCH 003/266] fix(anthropic): preserve speed=fast in usage for /v1/messages and pass-through Fast mode is priced with a provider-specific multiplier applied off usage.speed, but only chat completions kept that field. The Messages route rebuilt usage with empty optional params, stream reassembly dropped speed and inference_geo, and the pass-through handler never read speed off the request body, so fast-mode spend was logged at the standard rate. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/litellm_core_utils/litellm_logging.py | 2 +- .../streaming_chunk_builder_utils.py | 16 +++ .../anthropic_passthrough_logging_handler.py | 24 +++- .../streaming_chunk_builder_utils.py | 2 + .../test_litellm_logging.py | 35 ++++++ .../test_streaming_chunk_builder_utils.py | 39 ++++++ ...t_anthropic_passthrough_logging_handler.py | 113 ++++++++++++++++++ 7 files changed, 228 insertions(+), 3 deletions(-) diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index c2dc7189934..15330a2a910 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -3278,7 +3278,7 @@ class Logging(LiteLLMLoggingBaseClass): model=self.model, messages=[], logging_obj=self, - optional_params={}, + optional_params=self.optional_params or {}, api_key="", request_data={}, encoding=litellm.encoding, diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index d52d9849310..5ceb64547a0 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -569,6 +569,8 @@ class ChunkProcessor: # lost and 1h cache writes get billed at the 5m rate. cache_creation_token_details: Optional[CacheCreationTokenDetails] = None cost: Optional[float] = None + inference_geo: Optional[str] = None + speed: Optional[str] = None for chunk in chunks: usage_chunk = self._extract_usage_chunk(chunk) @@ -627,6 +629,13 @@ class ChunkProcessor: if usage_chunk_dict["cost"] is not None: cost = usage_chunk_dict["cost"] + chunk_inference_geo = getattr(usage_chunk, "inference_geo", None) + if isinstance(chunk_inference_geo, str): + inference_geo = chunk_inference_geo + chunk_speed = getattr(usage_chunk, "speed", None) + if isinstance(chunk_speed, str): + speed = chunk_speed + prompt_tokens_details = self._attach_cache_creation_token_details( prompt_tokens_details, cache_creation_token_details ) @@ -647,6 +656,8 @@ class ChunkProcessor: completion_tokens_details=completion_tokens_details, prompt_tokens_details=prompt_tokens_details, cost=cost, + inference_geo=inference_geo, + speed=speed, ) @staticmethod @@ -806,6 +817,11 @@ class ChunkProcessor: if cost is not None: setattr(returned_usage, "cost", cost) + if calculated_usage_per_chunk["inference_geo"] is not None: + setattr(returned_usage, "inference_geo", calculated_usage_per_chunk["inference_geo"]) + if calculated_usage_per_chunk["speed"] is not None: + setattr(returned_usage, "speed", calculated_usage_per_chunk["speed"]) + # Return a new usage object with the new values returned_usage = Usage(**returned_usage.model_dump()) diff --git a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py index 50e90699194..112780581de 100644 --- a/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py +++ b/litellm/proxy/pass_through_endpoints/llm_provider_handlers/anthropic_passthrough_logging_handler.py @@ -80,7 +80,9 @@ class AnthropicPassthroughLoggingHandler: model=model, messages=[], logging_obj=logging_obj, - optional_params={}, + optional_params=AnthropicPassthroughLoggingHandler._cost_relevant_request_params( + request_body or kwargs.get("request_body") + ), api_key="", request_data={}, encoding=litellm.encoding, @@ -102,6 +104,15 @@ class AnthropicPassthroughLoggingHandler: "kwargs": kwargs, } + @staticmethod + def _cost_relevant_request_params(request_body: Optional[dict]) -> dict: + """ + Request params that change how the response is priced, and so must reach the + usage-building paths. Anthropic's ``speed=fast`` multiplies non-cache token cost. + """ + speed = (request_body or {}).get("speed") + return {"speed": speed} if isinstance(speed, str) else {} + @staticmethod def _get_user_from_metadata( passthrough_logging_payload: PassthroughStandardLoggingPayload, @@ -315,6 +326,7 @@ class AnthropicPassthroughLoggingHandler: - Logs in litellm callbacks """ + speed = AnthropicPassthroughLoggingHandler._cost_relevant_request_params(request_body).get("speed") model = request_body.get("model", "") # Check if it's available in the logging object if ( @@ -334,6 +346,7 @@ class AnthropicPassthroughLoggingHandler: all_chunks=all_chunks, litellm_logging_obj=litellm_logging_obj, model=model, + speed=speed, ) except Exception as e: # stream_chunk_builder re-raises assembly failures (as litellm.APIError) @@ -355,6 +368,7 @@ class AnthropicPassthroughLoggingHandler: complete_streaming_response = AnthropicPassthroughLoggingHandler._build_usage_only_response_from_chunks( all_chunks=all_chunks, model=model, + speed=speed, ) except Exception as e: verbose_proxy_logger.warning( @@ -419,6 +433,7 @@ class AnthropicPassthroughLoggingHandler: all_chunks: Sequence[Union[str, bytes]], litellm_logging_obj: LiteLLMLoggingObj, model: str, + speed: Optional[str] = None, ) -> Optional[Union[ModelResponse, TextCompletionResponse]]: """ Builds complete response from raw Anthropic chunks. @@ -443,11 +458,13 @@ class AnthropicPassthroughLoggingHandler: all_chunks=collapsed, litellm_logging_obj=litellm_logging_obj, model=model, + speed=speed, ) return AnthropicPassthroughLoggingHandler._build_complete_streaming_response_legacy( all_chunks=all_chunks, litellm_logging_obj=litellm_logging_obj, model=model, + speed=speed, ) # Anthropic SSE block/delta types that the fast path is NOT allowed to @@ -575,6 +592,7 @@ class AnthropicPassthroughLoggingHandler: all_chunks: Sequence[Union[str, bytes]], litellm_logging_obj: LiteLLMLoggingObj, model: str, + speed: Optional[str] = None, ) -> Optional[Union[ModelResponse, TextCompletionResponse]]: """ Original reconstruction: convert every SSE event to a generic chunk @@ -590,6 +608,7 @@ class AnthropicPassthroughLoggingHandler: anthropic_model_response_iterator = AnthropicModelResponseIterator( streaming_response=None, sync_stream=False, + speed=speed, ) all_openai_chunks = [] @@ -649,6 +668,7 @@ class AnthropicPassthroughLoggingHandler: def _build_usage_only_response_from_chunks( all_chunks: Sequence[Union[str, bytes]], model: str, + speed: Optional[str] = None, ) -> Optional[ModelResponse]: """ Build a usage-bearing ModelResponse from Anthropic SSE token-usage events, for @@ -742,7 +762,7 @@ class AnthropicPassthroughLoggingHandler: usage_object["server_tool_use"] = _server_tool_use if inference_geo is not None: usage_object["inference_geo"] = inference_geo - usage_obj = AnthropicConfig().calculate_usage(usage_object=usage_object, reasoning_content=None) + usage_obj = AnthropicConfig().calculate_usage(usage_object=usage_object, reasoning_content=None, speed=speed) return ModelResponse( model=resolved_model, choices=[ diff --git a/litellm/types/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/types/litellm_core_utils/streaming_chunk_builder_utils.py index c9f9d4e6baa..4ff47171e32 100644 --- a/litellm/types/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/types/litellm_core_utils/streaming_chunk_builder_utils.py @@ -15,3 +15,5 @@ class UsagePerChunk(TypedDict): completion_tokens_details: Optional[CompletionTokensDetails] prompt_tokens_details: Optional[PromptTokensDetailsWrapper] cost: Optional[float] + inference_geo: Optional[str] + speed: Optional[str] 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 eaa4bd3e3fc..1cd4174c57c 100644 --- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py +++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py @@ -4153,3 +4153,38 @@ def test_pre_call_does_not_pin_request_in_module_state(logging_obj): logging_obj.post_call(original_response='{"ok": true}', input=big_input, api_key="sk-test") assert litellm.error_logs == {} + + +def test_handle_anthropic_messages_response_logging_preserves_fast_mode_speed(): + """/v1/messages non-streaming rebuilds usage by re-transforming the raw Anthropic + response. Anthropic's fast-mode multiplier is applied off ``usage.speed``, which the + response body never carries, so the request's optional params have to be passed in or + fast-mode spend is logged at the standard rate.""" + import httpx + + logging_obj = LitellmLogging( + model="claude-opus-4-8", + messages=[{"role": "user", "content": "hi"}], + stream=False, + call_type="anthropic_messages", + start_time=time.time(), + litellm_call_id="lit-5115", + function_id="lit-5115", + ) + logging_obj.optional_params = {"speed": "fast"} + logging_obj.model_call_details["httpx_response"] = httpx.Response( + status_code=200, + json={ + "id": "msg_1", + "type": "message", + "role": "assistant", + "model": "claude-opus-4-8", + "content": [{"type": "text", "text": "ok"}], + "stop_reason": "end_turn", + "usage": {"input_tokens": 1000, "cache_read_input_tokens": 200, "output_tokens": 100}, + }, + ) + + result = logging_obj._handle_anthropic_messages_response_logging(result=None) + + assert result.usage.speed == "fast" # type: ignore[attr-defined] diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py index be8c5a05601..72bbad7ae49 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py @@ -992,3 +992,42 @@ def test_cost_field_in_usage_chunks(): assert usage.cost == 0.00025 assert usage.prompt_tokens == 10 assert usage.completion_tokens == 5 + + +def test_anthropic_speed_and_geo_survive_stream_assembly(): + """Anthropic prices fast mode and non-global regions with a multiplier read off + ``usage.speed`` / ``usage.inference_geo``. Dropping them while reassembling a stream + bills streamed fast-mode calls at the standard rate.""" + from litellm.llms.anthropic.cost_calculation import cost_per_token + + def _usage(**extra): + usage = Usage(completion_tokens=100, prompt_tokens=1000, total_tokens=1100) + for key, value in extra.items(): + setattr(usage, key, value) + return usage + + def _chunk(usage): + return ModelResponseStream( + id="chatcmpl-1", + created=1745513206, + model="claude-opus-4-8", + choices=[StreamingChoices(finish_reason="stop", index=0, delta=Delta(content="Hi"))], + usage=usage, + ) + + fast_chunk = _chunk(_usage(speed="fast", inference_geo="global")) + fast_usage = ChunkProcessor(chunks=[fast_chunk]).calculate_usage( + chunks=[fast_chunk], model="claude-opus-4-8", completion_output="Hi" + ) + standard_chunk = _chunk(_usage(inference_geo="global")) + standard_usage = ChunkProcessor(chunks=[standard_chunk]).calculate_usage( + chunks=[standard_chunk], model="claude-opus-4-8", completion_output="Hi" + ) + + assert fast_usage.speed == "fast" + assert fast_usage.inference_geo == "global" + assert getattr(standard_usage, "speed", None) is None + + fast_cost = sum(cost_per_token(model="claude-opus-4-8", usage=fast_usage)) + standard_cost = sum(cost_per_token(model="claude-opus-4-8", usage=standard_usage)) + assert fast_cost == pytest.approx(standard_cost * 2.0) diff --git a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py index 947a7a64beb..ffbdb3485ae 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/llm_provider_handlers/test_anthropic_passthrough_logging_handler.py @@ -2192,3 +2192,116 @@ class TestAnthropicResponseCostRecordedOnModelCallDetails: logging_obj.model_call_details["response_cost"] == kwargs["response_cost"] ) assert logging_obj.model_call_details["response_cost"] > 0 + + +class TestAnthropicPassthroughFastMode: + """Anthropic charges a provider-specific multiplier for ``speed=fast``, and the + multiplier is applied off ``usage.speed``. The pass-through handler only sees the + speed in the request body, so it has to thread it into every usage-building path or + fast-mode pass-through spend is under-reported.""" + + MODEL = "claude-opus-4-8" + STREAM_CHUNKS = [ + 'event: message_start', + 'data: {"type": "message_start", "message": {"id": "msg_1", "type": "message", "role": "assistant",' + ' "model": "claude-opus-4-8", "content": [], "stop_reason": null,' + ' "usage": {"input_tokens": 1000, "cache_read_input_tokens": 200, "output_tokens": 0}}}', + 'event: content_block_start', + 'data: {"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}}', + 'event: content_block_delta', + 'data: {"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "ok"}}', + 'event: content_block_stop', + 'data: {"type": "content_block_stop", "index": 0}', + 'event: message_delta', + 'data: {"type": "message_delta", "delta": {"stop_reason": "end_turn"},' + ' "usage": {"input_tokens": 1000, "cache_read_input_tokens": 200, "output_tokens": 100}}', + 'event: message_stop', + 'data: {"type": "message_stop"}', + ] + + def _logging_obj(self) -> LiteLLMLoggingObj: + return LiteLLMLoggingObj( + model=self.MODEL, + messages=[], + stream=True, + call_type="pass_through_endpoint", + start_time=datetime.now(), + litellm_call_id="fast-mode", + function_id="fast-mode", + ) + + def _cost(self, response) -> float: + import litellm + + return litellm.completion_cost(completion_response=response, model=f"anthropic/{self.MODEL}") + + def _expected_fast_cost(self, standard_cost: float) -> float: + import litellm + + model_info = litellm.get_model_info(model=self.MODEL, custom_llm_provider="anthropic") + cache_read_cost = 200 * (model_info.get("cache_read_input_token_cost") or 0.0) + return (standard_cost - cache_read_cost) * 2.0 + cache_read_cost + + def test_non_streaming_applies_fast_multiplier(self): + import httpx + + response_body = { + "id": "msg_1", + "type": "message", + "role": "assistant", + "model": self.MODEL, + "content": [{"type": "text", "text": "ok"}], + "stop_reason": "end_turn", + "usage": {"input_tokens": 1000, "cache_read_input_tokens": 200, "output_tokens": 100}, + } + + def _handle(request_body): + logging_obj = self._logging_obj() + logging_obj.model_call_details["stream"] = False + return AnthropicPassthroughLoggingHandler.anthropic_passthrough_handler( + httpx_response=httpx.Response(status_code=200, json=response_body), + response_body=response_body, + logging_obj=logging_obj, + url_route="https://api.anthropic.com/v1/messages", + result="", + start_time=datetime.now(), + end_time=datetime.now(), + cache_hit=False, + request_body=request_body, + ) + + fast = _handle({"model": self.MODEL, "speed": "fast"}) + standard = _handle({"model": self.MODEL}) + + assert fast["result"].usage.speed == "fast" + assert self._cost(fast["result"]) == pytest.approx(self._expected_fast_cost(self._cost(standard["result"]))) + + def test_streaming_reconstruction_applies_fast_multiplier(self): + fast = AnthropicPassthroughLoggingHandler._build_complete_streaming_response( + all_chunks=self.STREAM_CHUNKS, + litellm_logging_obj=self._logging_obj(), + model=self.MODEL, + speed="fast", + ) + standard = AnthropicPassthroughLoggingHandler._build_complete_streaming_response( + all_chunks=self.STREAM_CHUNKS, + litellm_logging_obj=self._logging_obj(), + model=self.MODEL, + ) + + assert fast.usage.speed == "fast" + assert self._cost(fast) == pytest.approx(self._expected_fast_cost(self._cost(standard))) + + def test_usage_only_fallback_applies_fast_multiplier(self): + fast = AnthropicPassthroughLoggingHandler._build_usage_only_response_from_chunks( + all_chunks=self.STREAM_CHUNKS, + model=self.MODEL, + speed="fast", + ) + standard = AnthropicPassthroughLoggingHandler._build_usage_only_response_from_chunks( + all_chunks=self.STREAM_CHUNKS, + model=self.MODEL, + ) + + assert fast.usage.speed == "fast" + assert self._cost(fast) == pytest.approx(self._expected_fast_cost(self._cost(standard))) From 5a5bb8c9d844870c25684e169960f1571d08e5ce Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Thu, 6 Aug 2026 15:29:39 +0000 Subject: [PATCH 004/266] fix(proxy): stop /{provider}/v1/files from capturing /openai_passthrough The native files and batches routes declare /{provider}/v1/... and their routers are mounted before the passthrough router, so /openai_passthrough/v1/files and /openai_passthrough/v1/batches matched them with provider="openai_passthrough" and 500'd on the LlmProviders lookup instead of reaching openai_proxy_route. Move the dedicated /openai_passthrough prefix onto its own router mounted ahead of the batches and files routers. /openai/... and every other provider prefix keep their current behavior. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../llm_passthrough_endpoints.py | 3 +- litellm/proxy/proxy_server.py | 2 + .../test_llm_pass_through_endpoints.py | 57 +++++++++++++++++++ 3 files changed, 61 insertions(+), 1 deletion(-) diff --git a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py index 38da00a3bb9..baa74c19182 100644 --- a/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py +++ b/litellm/proxy/pass_through_endpoints/llm_passthrough_endpoints.py @@ -60,6 +60,7 @@ from .passthrough_endpoint_router import PassthroughEndpointRouter vertex_llm_base: Final = VertexBase() router: Final = APIRouter() +openai_passthrough_router: Final = APIRouter() default_vertex_config: Final = None passthrough_endpoint_router: Final = PassthroughEndpointRouter() @@ -1875,7 +1876,7 @@ async def vertex_proxy_route( ) -@router.api_route( +@openai_passthrough_router.api_route( "/openai_passthrough/{endpoint:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"], tags=["OpenAI Pass-through", "pass-through"], diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index fb9c4e67aad..e75277e7f0a 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -522,6 +522,7 @@ from litellm.proxy.openai_files_endpoints.files_endpoints import ( set_files_config, ) from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import ( + openai_passthrough_router, passthrough_endpoint_router, vertex_ai_live_websocket_passthrough, ) @@ -16433,6 +16434,7 @@ app.include_router(search_router) app.include_router(image_router) app.include_router(fine_tuning_router) app.include_router(credential_router) +app.include_router(openai_passthrough_router) app.include_router(batches_router) app.include_router(openai_files_router) app.include_router(llm_passthrough_router) diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py index 181846fe289..27d6e4c8585 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_llm_pass_through_endpoints.py @@ -2814,6 +2814,63 @@ class TestOpenAIPassthroughRoute: assert result == {"id": "asst_123", "object": "assistant"} +def _resolve_route_name(method: str, path: str) -> str | None: + from starlette.routing import Match + + from litellm.proxy.proxy_server import app + + scope = { + "type": "http", + "method": method, + "path": path, + "headers": [], + "query_string": b"", + "root_path": "", + } + for route in app.router.routes: + if route.matches(scope)[0] == Match.FULL: + return getattr(route, "name", None) + return None + + +@pytest.mark.parametrize( + "method, path", + [ + ("POST", "/openai_passthrough/v1/files"), + ("GET", "/openai_passthrough/v1/files"), + ("GET", "/openai_passthrough/v1/files/file-abc123"), + ("DELETE", "/openai_passthrough/v1/files/file-abc123"), + ("GET", "/openai_passthrough/v1/files/file-abc123/content"), + ("POST", "/openai_passthrough/v1/batches"), + ("GET", "/openai_passthrough/v1/batches"), + ("GET", "/openai_passthrough/v1/batches/batch_abc123"), + ("POST", "/openai_passthrough/v1/batches/batch_abc123/cancel"), + ("POST", "/openai_passthrough/v1/responses"), + ], +) +def test_openai_passthrough_prefix_wins_over_native_provider_routes(method, path): + """ + /openai_passthrough exists to guarantee passthrough, so the native + /{provider}/v1/files and /{provider}/v1/batches routes must never capture it + with provider="openai_passthrough" (which 500s on the LlmProviders lookup). + """ + assert _resolve_route_name(method, path) == "openai_proxy_route" + + +@pytest.mark.parametrize( + "method, path, expected_name", + [ + ("POST", "/openai/v1/files", "create_file"), + ("GET", "/azure/v1/files", "list_files"), + ("POST", "/v1/files", "create_file"), + ("POST", "/v1/batches", "create_batch"), + ("POST", "/openai/v1/chat/completions", "openai_proxy_route"), + ], +) +def test_native_provider_routes_are_unchanged(method, path, expected_name): + assert _resolve_route_name(method, path) == expected_name + + class TestCursorProxyRoute: """Tests for the Cursor Cloud Agents pass-through route.""" From 357f90fa39d18c9a158a978ebd1ed0fecac6044d Mon Sep 17 00:00:00 2001 From: "devin-ai-integration[bot]" <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Thu, 6 Aug 2026 15:33:40 +0000 Subject: [PATCH 005/266] fix(proxy): scope file list pagination cursors to the caller GET /v1/files filters data down to the caller's own managed files but left first_id and last_id as the upstream page's, so a non-owner got back file ids belonging to other users even with an empty data array --- .../proxy/hooks/managed_files.py | 15 +++ .../proxy/hooks/test_managed_files.py | 100 ++++++++++++++++++ 2 files changed, 115 insertions(+) diff --git a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py index 0036603bcd1..851e202e2fb 100644 --- a/enterprise/litellm_enterprise/proxy/hooks/managed_files.py +++ b/enterprise/litellm_enterprise/proxy/hooks/managed_files.py @@ -1270,10 +1270,25 @@ class _PROXY_LiteLLMManagedFiles(CustomLogger, BaseFileEndpoints): ) ## Filter the response to only include the files created by the user response.data = user_created_file_ids # type: ignore + self._scope_list_page_cursors(response, user_created_file_ids) return response return response return response + @staticmethod + def _scope_list_page_cursors(response: AsyncCursorPage, data: List[OpenAIFileObject]) -> None: + """Rebuild ``first_id`` / ``last_id`` from the caller-scoped page. + + The upstream cursors point at rows that were just filtered out, so + leaving them in place discloses other callers' file ids. + """ + if hasattr(response, "first_id"): + response.first_id = data[0].id if data else None + if hasattr(response, "last_id"): + response.last_id = data[-1].id if data else None + if not data and hasattr(response, "has_more"): + response.has_more = False + async def afile_retrieve( self, file_id: str, litellm_parent_otel_span: Optional[Span], llm_router=None ) -> OpenAIFileObject: diff --git a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py index 50af6465d06..3384c553740 100644 --- a/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py +++ b/tests/enterprise/litellm_enterprise/proxy/hooks/test_managed_files.py @@ -2861,3 +2861,103 @@ async def test_same_user_different_keys_can_access_batch(): assert "batch_id" in result2 # Both keys should get the same result assert result1["batch_id"] == result2["batch_id"] + + +@pytest.mark.asyncio +async def test_file_list_cursors_are_scoped_to_the_caller(): + """A non-owner must not learn other callers' file ids through the page cursors.""" + from openai.pagination import AsyncCursorPage + from openai.types import FileObject + + from litellm.proxy._types import UserAPIKeyAuth + + owner_file = FileObject( + id="file-owner-1", + bytes=100, + created_at=1, + filename="owner.jsonl", + object="file", + purpose="batch", + status="processed", + ) + upstream_page = AsyncCursorPage[FileObject].construct( + data=[owner_file], + has_more=True, + first_id=owner_file.id, + last_id=owner_file.id, + object="list", + ) + + prisma_client = AsyncMock() + prisma_client.db.litellm_managedfiletable.find_many.return_value = [] + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + response = await proxy_managed_files.async_post_call_success_hook( + data={}, + user_api_key_dict=UserAPIKeyAuth( + user_id="other-user", team_id="other-team", parent_otel_span=MagicMock() + ), + response=upstream_page, + ) + + assert response.data == [] + assert response.first_id is None + assert response.last_id is None + assert response.has_more is False + + +@pytest.mark.asyncio +async def test_file_list_cursors_follow_the_owner_scoped_page(): + from openai.pagination import AsyncCursorPage + from openai.types import FileObject + + from litellm.proxy._types import UserAPIKeyAuth + + def _raw_file(file_id: str) -> FileObject: + return FileObject( + id=file_id, + bytes=100, + created_at=1, + filename=f"{file_id}.jsonl", + object="file", + purpose="batch", + status="processed", + ) + + upstream_page = AsyncCursorPage[FileObject].construct( + data=[_raw_file("file-someone-else"), _raw_file("file-mine")], + has_more=False, + first_id="file-someone-else", + last_id="file-mine", + object="list", + ) + + managed_row = MagicMock() + managed_row.file_object = { + "id": "litellm_proxy:mine", + "bytes": 100, + "created_at": 1, + "filename": "mine.jsonl", + "object": "file", + "purpose": "batch", + "status": "processed", + } + prisma_client = AsyncMock() + prisma_client.db.litellm_managedfiletable.find_many.return_value = [managed_row] + proxy_managed_files = _PROXY_LiteLLMManagedFiles( + DualCache(), prisma_client=prisma_client + ) + + response = await proxy_managed_files.async_post_call_success_hook( + data={}, + user_api_key_dict=UserAPIKeyAuth( + user_id="mine-user", parent_otel_span=MagicMock() + ), + response=upstream_page, + ) + + assert [file_object.id for file_object in response.data] == ["litellm_proxy:mine"] + assert response.first_id == "litellm_proxy:mine" + assert response.last_id == "litellm_proxy:mine" From bd1478e4add9bb4a724e25dc1b5d921cb898b85d Mon Sep 17 00:00:00 2001 From: Daniel Cadenas Date: Wed, 8 Jul 2026 16:08:51 -0300 Subject: [PATCH 006/266] fix(responses): preserve Codex namespace tool calls --- .../streaming_iterator.py | 28 +- .../transformation.py | 96 +++- .../test_litellm_completion_responses.py | 461 +++++++++++++++++- 3 files changed, 571 insertions(+), 14 deletions(-) diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py index ddd05075763..09964d56c81 100644 --- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py +++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py @@ -124,6 +124,15 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): except (TypeError, ValueError): return None + def _responses_namespace_tool_call_fields(self, fn_name: str) -> tuple[str, str | None]: + tools = self.responses_api_request.get("tools") + namespace_map = LiteLLMCompletionResponsesConfig._namespace_tool_name_map(tools) + mapped = namespace_map.get(fn_name) + if mapped: + namespace, tool_name = mapped + return tool_name, namespace + return fn_name, None + def _is_reasoning_end(self, chunk): delta: Final = chunk.choices[0].delta @@ -182,13 +191,17 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): else: fn_name = str(getattr(fn, "name", "") or "") fn_args_delta = str(getattr(fn, "arguments", "") or "") + tool_name, tool_namespace = self._responses_namespace_tool_call_fields(fn_name) output_index = self._get_or_assign_tool_output_index(call_id) if call_id not in self._tool_args_by_call_id: self._tool_args_by_call_id[call_id] = "" self._sequence_number += 1 - item_kwargs = build_tool_call_item_kwargs(call_id, fn_name, "", "in_progress", self._custom_tool_names) + names = self._custom_tool_names + item_kwargs = build_tool_call_item_kwargs(call_id, tool_name, "", "in_progress", names) + if tool_namespace: + item_kwargs["namespace"] = tool_namespace event = OutputItemAddedEvent( type=ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, output_index=output_index, @@ -249,6 +262,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): else: fn_name = str(getattr(fn, "name", "") or "") fn_args = str(getattr(fn, "arguments", "") or "") + tool_name, tool_namespace = self._responses_namespace_tool_call_fields(fn_name) # Track if this is a new tool call that wasn't streamed is_new_tool_call = call_id not in self._tool_args_by_call_id @@ -257,7 +271,10 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): if is_new_tool_call: self._tool_args_by_call_id[call_id] = "" self._sequence_number += 1 - item_kwargs = build_tool_call_item_kwargs(call_id, fn_name, "", "in_progress", self._custom_tool_names) + names = self._custom_tool_names + item_kwargs = build_tool_call_item_kwargs(call_id, tool_name, "", "in_progress", names) + if tool_namespace: + item_kwargs["namespace"] = tool_namespace event = OutputItemAddedEvent( type=ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED, output_index=output_index, @@ -299,9 +316,10 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._pending_tool_events.append(done_event) self._sequence_number += 1 - item_kwargs = build_tool_call_item_kwargs( - call_id, fn_name, final_args, "completed", self._custom_tool_names - ) + names = self._custom_tool_names + item_kwargs = build_tool_call_item_kwargs(call_id, tool_name, final_args, "completed", names) + if tool_namespace: + item_kwargs["namespace"] = tool_namespace item_done_event = OutputItemDoneEvent( type=ResponsesAPIStreamEvents.OUTPUT_ITEM_DONE, output_index=output_index, diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index f54023836e5..4134a65c95a 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -78,6 +78,10 @@ from .custom_tools import ( unwrap_custom_tool_arguments, ) +NsMap = dict[str, tuple[str, str]] +NsTool = dict[str, Any] +RespTools = list[FunctionToolParam | OpenAIMcpServerTool] | None + ########### Initialize Classes used for Responses API ########### TOOL_CALLS_CACHE: Final = InMemoryCache() @@ -1204,6 +1208,10 @@ class LiteLLMCompletionResponsesConfig: if "cache_control" in item: image_block["cache_control"] = item["cache_control"] content_list.append(image_block) + elif item.get("type") == "encrypted_content": + encrypted_content = item.get("encrypted_content") + if encrypted_content is not None: + content_list.append({"type": "text", "text": str(encrypted_content)}) else: # Skip text blocks with None text to avoid downstream errors text_value = item.get("text") @@ -1264,6 +1272,45 @@ class LiteLLMCompletionResponsesConfig: """ return ChatCompletionSystemMessage(role="system", content=instructions or "") + @staticmethod + def _build_ns_chat_tool(ns: str, ns_desc: str, ns_tool: NsTool, nested: bool) -> ChatCompletionToolParam | None: + if nested and ns_tool.get("type") != "function": + return None + + raw_parameters = ns_tool.get("parameters") + parameters = dict(raw_parameters) if isinstance(raw_parameters, dict) else {} + normalized_parameters = parameters if parameters and "type" in parameters else {**parameters, "type": "object"} + tool_name = str(ns_tool.get("name") or "") + raw_description = str(ns_tool.get("description") or "") + description = raw_description + if nested and ns_desc: + description = f"{ns_desc}\n\n{raw_description}" if raw_description else ns_desc + chat_tool_name = f"{ns}__{tool_name}" if nested else tool_name + function: dict[str, Any] = {"name": chat_tool_name} + function["description"] = description + function["parameters"] = normalized_parameters + function["strict"] = bool(ns_tool.get("strict", False)) + return {"type": "function", "function": function} + + @staticmethod + def _namespace_chat_tools(tool: dict[str, Any]) -> list[ChatCompletionToolParam]: + namespace = str(tool.get("name") or "") + namespace_description = str(tool.get("description") or "") + namespace_tools = tool.get("tools") + if isinstance(namespace_tools, list): + chat_completion_tools: list[ChatCompletionToolParam] = [] + ns = namespace + ns_desc = namespace_description + for ns_tool in namespace_tools: + if not isinstance(ns_tool, dict): + continue + chat_tool = LiteLLMCompletionResponsesConfig._build_ns_chat_tool(ns, ns_desc, ns_tool, True) + if chat_tool is not None: + chat_completion_tools.append(chat_tool) + return chat_completion_tools + flat_tool = LiteLLMCompletionResponsesConfig._build_ns_chat_tool(namespace, namespace_description, tool, False) + return [flat_tool] if flat_tool is not None else [] + @staticmethod def transform_responses_api_tools_to_chat_completion_tools( tools: list[FunctionToolParam | OpenAIMcpServerTool] | None, @@ -1317,13 +1364,15 @@ class LiteLLMCompletionResponsesConfig: if tool.get("input_examples"): chat_completion_tool["input_examples"] = tool.get("input_examples") chat_completion_tools.append(cast(ChatCompletionToolParam, chat_completion_tool)) + elif tool.get("type") == "namespace": + chat_completion_tools.extend(LiteLLMCompletionResponsesConfig._namespace_chat_tools(tool)) elif tool.get("type") == "custom": converted = convert_custom_tool_to_function_tool(tool) if converted is not None: chat_completion_tools.append(converted) else: _tool_type = tool.get("type") - if _tool_type in ("computer_use", "image_generation", "namespace", "shell"): + if _tool_type in ("computer_use", "image_generation", "shell"): # Drop unsupported Responses-API-only tool types that have no # Chat Completions equivalent. Passing them through verbatim # causes providers to reject the request with "'function' is a @@ -1379,6 +1428,42 @@ class LiteLLMCompletionResponsesConfig: result.append(dict(tool)) return result + @staticmethod + def _namespace_tool_name_map(tools: RespTools) -> NsMap: + namespace_tool_names: NsMap = {} + ambiguous_unqualified_names: set[str] = set() + for tool in tools or []: + if not isinstance(tool, dict) or tool.get("type") != "namespace": + continue + namespace = str(tool.get("name") or "") + for namespace_tool in tool.get("tools") or []: + if not isinstance(namespace_tool, dict) or namespace_tool.get("type") != "function": + continue + tool_name = str(namespace_tool.get("name") or "") + namespace_tool_names[f"{namespace}__{tool_name}"] = (namespace, tool_name) + if tool_name in ambiguous_unqualified_names: + continue + existing = namespace_tool_names.get(tool_name) + if existing is not None and existing != (namespace, tool_name): + ambiguous_unqualified_names.add(tool_name) + namespace_tool_names.pop(tool_name, None) + continue + namespace_tool_names[tool_name] = (namespace, tool_name) + return namespace_tool_names + + @staticmethod + def _restore_namespace_tool_name(tool_name: str, names: NsMap) -> tuple[str, str | None]: + mapped = names.get(tool_name) + if mapped is None: + return tool_name, None + namespace, restored_tool_name = mapped + return restored_tool_name, namespace + + @staticmethod + def _set_tool_call_namespace(output_tool_call: ResponseFunctionToolCall, namespace: str | None) -> None: + if namespace: + setattr(output_tool_call, "namespace", namespace) + @staticmethod def transform_chat_completion_tools_to_responses_tools( chat_completion_response: ModelResponse, @@ -1404,8 +1489,11 @@ class LiteLLMCompletionResponsesConfig: # Extract custom tool names from the original request custom_tool_names: set[str] = set() + namespace_tool_names: NsMap = {} if responses_api_request and "tools" in responses_api_request: - custom_tool_names = extract_custom_tool_names(responses_api_request["tools"]) + req_tools = responses_api_request["tools"] + custom_tool_names = extract_custom_tool_names(req_tools) + namespace_tool_names = LiteLLMCompletionResponsesConfig._namespace_tool_name_map(req_tools) responses_tools: Final[list[ResponseFunctionToolCall | CustomToolCallOutputItem]] = [] for tool in all_chat_completion_tools: @@ -1430,6 +1518,9 @@ class LiteLLMCompletionResponsesConfig: responses_tools.append(custom_item) else: # Build regular function_call output item + restore_name = LiteLLMCompletionResponsesConfig._restore_namespace_tool_name + tool_name, namespace = restore_name(tool_name, namespace_tool_names) + provider_specific_fields: dict | None = None if hasattr(tool, "provider_specific_fields") and getattr(tool, "provider_specific_fields", None): provider_specific_fields = getattr(tool, "provider_specific_fields") @@ -1454,6 +1545,7 @@ class LiteLLMCompletionResponsesConfig: type="function_call", status=function_definition.get("status") or "completed", ) + LiteLLMCompletionResponsesConfig._set_tool_call_namespace(output_tool_call, namespace) # Pass through provider_specific_fields as-is if present if provider_specific_fields: diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py index 51f757c9eaf..38c50693759 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py @@ -11,10 +11,6 @@ from litellm.responses.litellm_completion_transformation.transformation import ( TOOL_CALLS_CACHE, LiteLLMCompletionResponsesConfig, ) -from litellm.types.llms.openai import ( - ChatCompletionResponseMessage, - ChatCompletionToolMessage, -) from litellm.types.utils import ( ChatCompletionMessageToolCall, Choices, @@ -608,6 +604,71 @@ class TestLiteLLMCompletionResponsesConfig: assert hasattr(responses_api_response, "_hidden_params") assert responses_api_response._hidden_params == {} + def test_transform_chat_completion_response_restores_namespace_tool_call(self): + tool_call_id = "call_namespace_restore" + chat_completion_response = ModelResponse( + id="test-response-id", + created=1234567890, + model="gemini-3.1-pro-preview-customtools", + object="chat.completion", + choices=[ + Choices( + finish_reason="tool_calls", + index=0, + message=Message( + content=None, + role="assistant", + tool_calls=[ + ChatCompletionMessageToolCall( + id=tool_call_id, + type="function", + function=Function( + name="collaboration__spawn_agent", + arguments='{"message":"hello"}', + ), + ) + ], + ), + ) + ], + ) + + try: + responses_api_response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( + request_input="Spawn an agent", + responses_api_request={ + "tools": [ + { + "type": "namespace", + "name": "collaboration", + "tools": [ + { + "type": "function", + "name": "spawn_agent", + "parameters": { + "type": "object", + "properties": {}, + }, + } + ], + } + ] + }, + chat_completion_response=chat_completion_response, + ) + finally: + TOOL_CALLS_CACHE.delete_cache(key=tool_call_id) + + tool_calls = [ + item + for item in responses_api_response.output + if item.type == "function_call" + ] + assert len(tool_calls) == 1 + assert tool_calls[0].name == "spawn_agent" + assert tool_calls[0].namespace == "collaboration" + assert tool_calls[0].arguments == '{"message":"hello"}' + class TestFunctionCallTransformation: """Test cases for function_call input transformation""" @@ -1037,6 +1098,29 @@ class TestContentTypeTransformation: assert result[0]["text"] == "valid text" assert result[1]["text"] == "another valid" + def test_encrypted_content_blocks_preserved_as_text(self): + """ + OpenAI Responses agent messages can include encrypted_content blocks. + Chat-completions providers need the payload as text instead of silently + dropping it. + """ + content = [ + {"type": "input_text", "text": "Payload:\n"}, + { + "type": "encrypted_content", + "encrypted_content": "Reply exactly INPUT_AGENT_OK", + }, + ] + + result = LiteLLMCompletionResponsesConfig._transform_responses_api_content_to_chat_completion_content( + content + ) + + assert result == [ + {"type": "text", "text": "Payload:\n"}, + {"type": "text", "text": "Reply exactly INPUT_AGENT_OK"}, + ] + class TestToolTransformation: """Test cases for tool transformation from Responses API to Chat Completion format""" @@ -1642,6 +1726,181 @@ class TestToolTransformation: assert "web_search_options" not in result + def test_transform_nested_namespace_tools_to_function_tools(self): + """Codex Responses namespace tools contain nested functions that chat + providers need as flattened function names.""" + namespace_tool = { + "type": "namespace", + "name": "collaboration", + "description": "Multi-agent tools", + "tools": [ + { + "type": "function", + "name": "spawn_agent", + "description": "Spawn an agent", + "parameters": { + "type": "object", + "properties": { + "task_name": {"type": "string"}, + "message": {"type": "string"}, + }, + "required": ["task_name", "message"], + }, + } + ], + } + + result_tools, web_search_options = ( + LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools( + tools=[namespace_tool] + ) + ) + + assert web_search_options is None + assert len(result_tools) == 1 + result_tool = result_tools[0] + assert result_tool["type"] == "function" + assert result_tool["function"]["name"] == "collaboration__spawn_agent" + assert result_tool["function"]["parameters"] == namespace_tool["tools"][0]["parameters"] + assert result_tool["function"]["description"] == "Multi-agent tools\n\nSpawn an agent" + + def test_transform_flat_namespace_tools_to_function_tools(self): + namespace_tool = { + "type": "namespace", + "name": "mcp__node_repl", + "description": "Run JavaScript in the node REPL", + "parameters": { + "type": "object", + "properties": { + "code": { + "type": "string", + "description": "JavaScript source to evaluate", + } + }, + "required": ["code"], + }, + } + + result_tools, web_search_options = ( + LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools( + tools=[namespace_tool] + ) + ) + + assert web_search_options is None + assert len(result_tools) == 1 + result_tool = result_tools[0] + assert result_tool["type"] == "function" + assert result_tool["function"]["name"] == "mcp__node_repl" + assert result_tool["function"]["description"] == "Run JavaScript in the node REPL" + assert result_tool["function"]["parameters"] == namespace_tool["parameters"] + + def test_namespace_tool_name_map_accepts_unique_unqualified_tool_names(self): + """Some chat providers return the nested tool name without its namespace.""" + namespace_tool = { + "type": "namespace", + "name": "collaboration", + "tools": [ + { + "type": "function", + "name": "wait_agent", + "parameters": {"type": "object", "properties": {}}, + } + ], + } + + result = LiteLLMCompletionResponsesConfig._namespace_tool_name_map( + [namespace_tool] + ) + + assert result["collaboration__wait_agent"] == ("collaboration", "wait_agent") + assert result["wait_agent"] == ("collaboration", "wait_agent") + + def test_namespace_tool_name_map_drops_ambiguous_unqualified_names(self): + tools = [ + {"type": "function", "name": "ordinary"}, + { + "type": "namespace", + "name": "alpha", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object", "properties": {}}, + } + ], + }, + { + "type": "namespace", + "name": "beta", + "tools": [ + {"type": "namespace", "name": "ignored"}, + { + "type": "function", + "name": "run", + "parameters": {"type": "object", "properties": {}}, + }, + ], + }, + { + "type": "namespace", + "name": "gamma", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object", "properties": {}}, + } + ], + }, + ] + + result = LiteLLMCompletionResponsesConfig._namespace_tool_name_map( + tools + ) + + assert result["alpha__run"] == ("alpha", "run") + assert result["beta__run"] == ("beta", "run") + assert result["gamma__run"] == ("gamma", "run") + assert "run" not in result + + def test_restore_namespace_tool_name_leaves_unknown_tool_unchanged(self): + tool_name, namespace = LiteLLMCompletionResponsesConfig._restore_namespace_tool_name( + "mcp__node_repl", + {}, + ) + + assert tool_name == "mcp__node_repl" + assert namespace is None + + def test_transform_nested_namespace_ignores_non_function_subtools(self): + namespace_tool = { + "type": "namespace", + "name": "collaboration", + "tools": [ + "ignored", + {"type": "namespace", "name": "ignored"}, + { + "type": "function", + "name": "spawn_agent", + "parameters": {"properties": {"task_name": {"type": "string"}}}, + }, + ], + } + + result_tools, _ = ( + LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools( + tools=[namespace_tool] + ) + ) + + assert len(result_tools) == 1 + assert result_tools[0]["function"]["name"] == "collaboration__spawn_agent" + assert result_tools[0]["function"]["parameters"] == { + "properties": {"task_name": {"type": "string"}}, + "type": "object", + } + def test_bedrock_anthropic_responses_tools_yield_only_function_toolspec(self): """ End-to-end (no network) of the LIT-3858 acceptance criterion: the mixed tools array @@ -2185,7 +2444,7 @@ class TestStreamingIDConsistency: # Transform chunks to response API events event1 = iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk1) event2 = iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk2) - event3 = iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk3) + iterator._transform_chat_completion_chunk_to_response_api_chunk(chunk3) # Assert: All events should use the same item_id (from the first chunk) assert event1 is not None, "First event should not be None" @@ -2612,8 +2871,6 @@ class TestEnsureOutputItemContentPartAdded: def _make_iterator(self): """Create a minimal LiteLLMCompletionStreamingIterator for testing.""" - from unittest.mock import MagicMock - from litellm.responses.litellm_completion_transformation.streaming_iterator import ( LiteLLMCompletionStreamingIterator, ) @@ -2628,6 +2885,15 @@ class TestEnsureOutputItemContentPartAdded: iterator._cached_reasoning_item_id = None iterator._reasoning_active = False iterator._pending_response_events = [] + iterator._pending_tool_events = [] + iterator._tool_output_index_by_call_id = {} + iterator._tool_args_by_call_id = {} + iterator._tool_call_id_by_index = {} + iterator._ambiguous_tool_call_indexes = set() + iterator._next_tool_output_index = 1 + iterator._final_tool_events_queued = False + iterator._custom_tool_names = set() + iterator.responses_api_request = {} return iterator def _make_text_chunk(self): @@ -2674,6 +2940,187 @@ class TestEnsureOutputItemContentPartAdded: assert events[1].part.type == "output_text" assert iterator.sent_content_part_added_event is True + def test_streaming_namespace_tool_calls_restore_responses_namespace(self): + """Flattened chat-completion namespace tool calls must stream back as + Responses function calls with name + namespace split.""" + iterator = self._make_iterator() + iterator.responses_api_request = { + "tools": [ + { + "type": "namespace", + "name": "collaboration", + "tools": [ + { + "type": "function", + "name": "spawn_agent", + "parameters": {"type": "object", "properties": {}}, + } + ], + } + ] + } + + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_1", + "function": { + "name": "collaboration__spawn_agent", + "arguments": '{"task_name":"input_test"}', + }, + } + ] + ) + + added = iterator._pending_tool_events[0] + assert added.item.name == "spawn_agent" + assert added.item.namespace == "collaboration" + + def test_streaming_unqualified_namespace_tool_calls_restore_namespace(self): + """A unique nested tool name without the namespace still maps back.""" + iterator = self._make_iterator() + iterator.responses_api_request = { + "tools": [ + { + "type": "namespace", + "name": "collaboration", + "tools": [ + { + "type": "function", + "name": "wait_agent", + "parameters": {"type": "object", "properties": {}}, + } + ], + } + ] + } + + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_1", + "function": { + "name": "wait_agent", + "arguments": "{}", + }, + } + ] + ) + + added = iterator._pending_tool_events[0] + assert added.item.name == "wait_agent" + assert added.item.namespace == "collaboration" + + def test_streaming_flat_namespace_tool_call_keeps_flat_name(self): + iterator = self._make_iterator() + iterator.responses_api_request = { + "tools": [ + { + "type": "namespace", + "name": "mcp__node_repl", + "description": "Run JavaScript", + "parameters": { + "type": "object", + "properties": {"code": {"type": "string"}}, + }, + } + ] + } + + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_1", + "function": { + "name": "mcp__node_repl", + "arguments": '{"code":"1+1"}', + }, + } + ] + ) + + chat_completion_response = ModelResponse( + id="chatcmpl-test", + created=1234567890, + model="gemini-3.1-pro-preview-customtools", + object="chat.completion", + choices=[ + Choices( + finish_reason="tool_calls", + index=0, + message=Message( + content=None, + role="assistant", + tool_calls=[ + ChatCompletionMessageToolCall( + id="call_1", + type="function", + function=Function( + name="mcp__node_repl", + arguments='{"code":"1+1"}', + ), + ) + ], + ), + ) + ], + ) + + iterator._queue_final_tool_call_done_events(chat_completion_response) + + added = iterator._pending_tool_events[0] + done = iterator._pending_tool_events[-1] + assert added.item.name == "mcp__node_repl" + assert getattr(added.item, "namespace", None) is None + assert done.item.name == "mcp__node_repl" + assert getattr(done.item, "namespace", None) is None + + def test_streaming_final_only_namespace_tool_call_restores_namespace(self): + from unittest.mock import MagicMock + + iterator = self._make_iterator() + iterator.responses_api_request = { + "tools": [ + { + "type": "namespace", + "name": "collaboration", + "tools": [ + { + "type": "function", + "name": "spawn_agent", + "parameters": {"type": "object", "properties": {}}, + } + ], + } + ] + } + message = MagicMock() + message.tool_calls = [ + { + "id": "call_1", + "function": { + "name": "collaboration__spawn_agent", + "arguments": '{"message":"hello world"}', + }, + } + ] + complete_response = MagicMock() + complete_response.choices = [MagicMock(message=message)] + + iterator._queue_final_tool_call_done_events(complete_response) + + added = iterator._pending_tool_events[0] + delta_events = iterator._pending_tool_events[1:-2] + done = iterator._pending_tool_events[-1] + assert added.item.name == "spawn_agent" + assert added.item.namespace == "collaboration" + assert "".join(event.delta for event in delta_events) == '{"message":"hello world"}' + assert done.item.name == "spawn_agent" + assert done.item.namespace == "collaboration" + def test_emit_response_completed_uses_stream_finish_reason(self): """ When the assembled model response carries finish_reason="content_filter" From 08d4a39f23eac2108b814f50a1127b390be9114a Mon Sep 17 00:00:00 2001 From: Daniel Cadenas Date: Thu, 6 Aug 2026 21:25:57 -0300 Subject: [PATCH 007/266] fix(responses): harden namespace tool mapping --- .../custom_tools.py | 4 +- .../streaming_iterator.py | 7 +- .../transformation.py | 198 ++++++++------ .../test_litellm_completion_responses.py | 251 +++++++++++++++++- 4 files changed, 379 insertions(+), 81 deletions(-) diff --git a/litellm/responses/litellm_completion_transformation/custom_tools.py b/litellm/responses/litellm_completion_transformation/custom_tools.py index de4df3175e4..fa4ed73a1d6 100644 --- a/litellm/responses/litellm_completion_transformation/custom_tools.py +++ b/litellm/responses/litellm_completion_transformation/custom_tools.py @@ -111,7 +111,7 @@ class _CustomToolFormat(BaseModel): _ALLOWED_CALLERS_ADAPTER: Final = TypeAdapter(list[str] | None) -def _validated_allowed_callers(value: object) -> list[str] | None: +def validated_allowed_callers(value: object) -> list[str] | None: try: return _ALLOWED_CALLERS_ADAPTER.validate_python(value, strict=True) except ValidationError as exc: @@ -143,7 +143,7 @@ def convert_custom_tool_to_function_tool(tool: Mapping[str, object]) -> ChatComp name: Final = raw_name if isinstance(raw_name, str) else "" raw_description: Final = tool.get("description") description = (raw_description if isinstance(raw_description, str) else "") + _grammar_suffix(tool.get("format")) - allowed_callers: Final = _validated_allowed_callers(tool.get("allowed_callers")) + allowed_callers: Final = validated_allowed_callers(tool.get("allowed_callers")) function_chunk: Final = ChatCompletionToolParamFunctionChunk( name=name, description=description, diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py index 09964d56c81..192f55df481 100644 --- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py +++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py @@ -105,6 +105,9 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): self._accumulated_reasoning_content_parts: list[str] = [] self._accumulated_provider_specific_fields: dict[str, Any] = {} self._custom_tool_names: set[str] = extract_custom_tool_names(self.responses_api_request.get("tools")) + self._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( + self.responses_api_request.get("tools") + ) def _get_or_assign_tool_output_index(self, call_id: str) -> int: existing: Final = self._tool_output_index_by_call_id.get(call_id) @@ -125,9 +128,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator): return None def _responses_namespace_tool_call_fields(self, fn_name: str) -> tuple[str, str | None]: - tools = self.responses_api_request.get("tools") - namespace_map = LiteLLMCompletionResponsesConfig._namespace_tool_name_map(tools) - mapped = namespace_map.get(fn_name) + mapped: Final = self._namespace_tool_names.get(fn_name) if mapped: namespace, tool_name = mapped return tool_name, namespace diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py index 4134a65c95a..1854ce6866a 100644 --- a/litellm/responses/litellm_completion_transformation/transformation.py +++ b/litellm/responses/litellm_completion_transformation/transformation.py @@ -4,8 +4,9 @@ Handles transforming from Responses API -> LiteLLM completion (Chat Completion import json import re -from collections.abc import Sequence -from typing import Any, Final, Literal, cast +from collections.abc import Mapping, Sequence +from types import MappingProxyType +from typing import Any, Final, Literal, TypeAlias, cast from openai.types.chat.chat_completion_named_tool_choice_param import ( ChatCompletionNamedToolChoiceParam, @@ -37,9 +38,11 @@ from litellm.types.llms.openai import ( ChatCompletionToolCallFunctionChunk, ChatCompletionToolMessage, ChatCompletionToolParam, + ChatCompletionToolParamFunctionChunk, ChatCompletionUserMessage, GenericChatCompletionMessage, InputTokensDetails, + OpenAIChatCompletionTextObject, OpenAIMcpServerTool, OpenAIWebSearchOptions, OpenAIWebSearchUserLocation, @@ -76,11 +79,12 @@ from .custom_tools import ( extract_custom_tool_names, is_custom_tool_call, unwrap_custom_tool_arguments, + validated_allowed_callers, ) -NsMap = dict[str, tuple[str, str]] -NsTool = dict[str, Any] -RespTools = list[FunctionToolParam | OpenAIMcpServerTool] | None +NamespaceNameMap: TypeAlias = Mapping[str, tuple[str, str]] +NamespaceTool: TypeAlias = Mapping[str, object] +ResponseTools: TypeAlias = Sequence[Mapping[str, object]] | None ########### Initialize Classes used for Responses API ########### TOOL_CALLS_CACHE: Final = InMemoryCache() @@ -1211,7 +1215,9 @@ class LiteLLMCompletionResponsesConfig: elif item.get("type") == "encrypted_content": encrypted_content = item.get("encrypted_content") if encrypted_content is not None: - content_list.append({"type": "text", "text": str(encrypted_content)}) + content_list.append( + OpenAIChatCompletionTextObject(type="text", text=str(encrypted_content)) + ) else: # Skip text blocks with None text to avoid downstream errors text_value = item.get("text") @@ -1273,43 +1279,88 @@ class LiteLLMCompletionResponsesConfig: return ChatCompletionSystemMessage(role="system", content=instructions or "") @staticmethod - def _build_ns_chat_tool(ns: str, ns_desc: str, ns_tool: NsTool, nested: bool) -> ChatCompletionToolParam | None: - if nested and ns_tool.get("type") != "function": + def _build_ns_chat_tool( + namespace: str, + namespace_description: str, + namespace_tool: NamespaceTool, + nested: bool, + ) -> ChatCompletionToolParam | None: + if nested and namespace_tool.get("type") != "function": return None - raw_parameters = ns_tool.get("parameters") - parameters = dict(raw_parameters) if isinstance(raw_parameters, dict) else {} - normalized_parameters = parameters if parameters and "type" in parameters else {**parameters, "type": "object"} - tool_name = str(ns_tool.get("name") or "") - raw_description = str(ns_tool.get("description") or "") - description = raw_description - if nested and ns_desc: - description = f"{ns_desc}\n\n{raw_description}" if raw_description else ns_desc - chat_tool_name = f"{ns}__{tool_name}" if nested else tool_name - function: dict[str, Any] = {"name": chat_tool_name} - function["description"] = description - function["parameters"] = normalized_parameters - function["strict"] = bool(ns_tool.get("strict", False)) - return {"type": "function", "function": function} + raw_parameters: Final = namespace_tool.get("parameters") + parameters: Final = ( + MappingProxyType(raw_parameters) if isinstance(raw_parameters, Mapping) else MappingProxyType({}) + ) + normalized_parameters: Final = ( + parameters if parameters and "type" in parameters else MappingProxyType({**parameters, "type": "object"}) + ) + tool_name: Final = str(namespace_tool.get("name") or "") + raw_description: Final = str(namespace_tool.get("description") or "") + description: Final = ( + f"{namespace_description}\n\n{raw_description}" + if nested and namespace_description and raw_description + else namespace_description + if nested and namespace_description + else raw_description + ) + chat_tool_name: Final = f"{namespace}__{tool_name}" if nested else tool_name + function: Final = ChatCompletionToolParamFunctionChunk( + name=chat_tool_name, + description=description, + parameters=normalized_parameters, + strict=bool(namespace_tool.get("strict", False)), + ) + allowed_callers: Final = validated_allowed_callers(namespace_tool.get("allowed_callers")) + if allowed_callers is None: + return ChatCompletionToolParam(type="function", function=function) + return ChatCompletionToolParam(type="function", function=function, allowed_callers=allowed_callers) @staticmethod - def _namespace_chat_tools(tool: dict[str, Any]) -> list[ChatCompletionToolParam]: - namespace = str(tool.get("name") or "") - namespace_description = str(tool.get("description") or "") - namespace_tools = tool.get("tools") - if isinstance(namespace_tools, list): - chat_completion_tools: list[ChatCompletionToolParam] = [] - ns = namespace - ns_desc = namespace_description - for ns_tool in namespace_tools: - if not isinstance(ns_tool, dict): - continue - chat_tool = LiteLLMCompletionResponsesConfig._build_ns_chat_tool(ns, ns_desc, ns_tool, True) - if chat_tool is not None: - chat_completion_tools.append(chat_tool) - return chat_completion_tools - flat_tool = LiteLLMCompletionResponsesConfig._build_ns_chat_tool(namespace, namespace_description, tool, False) - return [flat_tool] if flat_tool is not None else [] + def _namespace_chat_tools(tool: NamespaceTool) -> tuple[ChatCompletionToolParam, ...]: + namespace: Final = str(tool.get("name") or "") + namespace_description: Final = str(tool.get("description") or "") + namespace_tools: Final = tool.get("tools") + if isinstance(namespace_tools, Sequence) and not isinstance(namespace_tools, (str, bytes)): + return tuple( + chat_tool + for raw_tool in namespace_tools + if isinstance(raw_tool, Mapping) + if ( + chat_tool := LiteLLMCompletionResponsesConfig._build_ns_chat_tool( + namespace, + namespace_description, + raw_tool, + True, + ) + ) + is not None + ) + flat_tool: Final = LiteLLMCompletionResponsesConfig._build_ns_chat_tool( + namespace, namespace_description, tool, False + ) + return (flat_tool,) if flat_tool is not None else () + + @staticmethod + def _validate_namespace_name_collisions(tools: ResponseTools) -> None: + top_level_function_names: Final = frozenset( + str(tool.get("name") or "") for tool in tools or () if tool.get("type") == "function" + ) + flattened_namespace_names: Final = frozenset( + f"{(tool.get('name') or '')!s}__{(namespace_tool.get('name') or '')!s}" + for tool in tools or () + if tool.get("type") == "namespace" + for namespace_tools in (tool.get("tools"),) + if isinstance(namespace_tools, Sequence) and not isinstance(namespace_tools, (str, bytes)) + for namespace_tool in namespace_tools + if isinstance(namespace_tool, Mapping) and namespace_tool.get("type") == "function" + ) + conflicting_tool_names: Final = top_level_function_names & flattened_namespace_names + if conflicting_tool_names: + raise ValueError( + "Top-level function names conflict with flattened namespace tools: " + + ", ".join(sorted(conflicting_tool_names)) + ) @staticmethod def transform_responses_api_tools_to_chat_completion_tools( @@ -1323,6 +1374,7 @@ class LiteLLMCompletionResponsesConfig: """ if tools is None: return [], None + LiteLLMCompletionResponsesConfig._validate_namespace_name_collisions(tools) chat_completion_tools: Final[list[ChatCompletionToolParam | OpenAIMcpServerTool]] = [] web_search_options: OpenAIWebSearchOptions | None = None for tool in tools: @@ -1429,41 +1481,43 @@ class LiteLLMCompletionResponsesConfig: return result @staticmethod - def _namespace_tool_name_map(tools: RespTools) -> NsMap: - namespace_tool_names: NsMap = {} - ambiguous_unqualified_names: set[str] = set() - for tool in tools or []: - if not isinstance(tool, dict) or tool.get("type") != "namespace": - continue - namespace = str(tool.get("name") or "") - for namespace_tool in tool.get("tools") or []: - if not isinstance(namespace_tool, dict) or namespace_tool.get("type") != "function": - continue - tool_name = str(namespace_tool.get("name") or "") - namespace_tool_names[f"{namespace}__{tool_name}"] = (namespace, tool_name) - if tool_name in ambiguous_unqualified_names: - continue - existing = namespace_tool_names.get(tool_name) - if existing is not None and existing != (namespace, tool_name): - ambiguous_unqualified_names.add(tool_name) - namespace_tool_names.pop(tool_name, None) - continue - namespace_tool_names[tool_name] = (namespace, tool_name) - return namespace_tool_names + def namespace_tool_name_map(tools: ResponseTools) -> NamespaceNameMap: + namespace_entries: Final = tuple( + (str(tool.get("name") or ""), str(namespace_tool.get("name") or "")) + for tool in tools or () + if tool.get("type") == "namespace" + for namespace_tools in (tool.get("tools"),) + if isinstance(namespace_tools, Sequence) and not isinstance(namespace_tools, (str, bytes)) + for namespace_tool in namespace_tools + if isinstance(namespace_tool, Mapping) and namespace_tool.get("type") == "function" + ) + top_level_function_names: Final = frozenset( + str(tool.get("name") or "") for tool in tools or () if tool.get("type") == "function" + ) + unqualified_counts: Final = MappingProxyType( + { + tool_name: sum(1 for _, candidate_name in namespace_entries if candidate_name == tool_name) + for tool_name in frozenset(tool_name for _, tool_name in namespace_entries) + } + ) + unambiguous_entries: Final = tuple( + (tool_name, (namespace, tool_name)) + for namespace, tool_name in namespace_entries + if tool_name not in top_level_function_names and unqualified_counts[tool_name] == 1 + ) + qualified_entries: Final = tuple( + (f"{namespace}__{tool_name}", (namespace, tool_name)) for namespace, tool_name in namespace_entries + ) + return MappingProxyType(dict(qualified_entries + unambiguous_entries)) @staticmethod - def _restore_namespace_tool_name(tool_name: str, names: NsMap) -> tuple[str, str | None]: + def _restore_namespace_tool_name(tool_name: str, names: NamespaceNameMap) -> tuple[str, str | None]: mapped = names.get(tool_name) if mapped is None: return tool_name, None namespace, restored_tool_name = mapped return restored_tool_name, namespace - @staticmethod - def _set_tool_call_namespace(output_tool_call: ResponseFunctionToolCall, namespace: str | None) -> None: - if namespace: - setattr(output_tool_call, "namespace", namespace) - @staticmethod def transform_chat_completion_tools_to_responses_tools( chat_completion_response: ModelResponse, @@ -1487,13 +1541,9 @@ class LiteLLMCompletionResponsesConfig: value=tool_call, ) - # Extract custom tool names from the original request - custom_tool_names: set[str] = set() - namespace_tool_names: NsMap = {} - if responses_api_request and "tools" in responses_api_request: - req_tools = responses_api_request["tools"] - custom_tool_names = extract_custom_tool_names(req_tools) - namespace_tool_names = LiteLLMCompletionResponsesConfig._namespace_tool_name_map(req_tools) + request_tools: Final = responses_api_request.get("tools") if responses_api_request is not None else None + custom_tool_names: Final = extract_custom_tool_names(request_tools) + namespace_tool_names: Final = LiteLLMCompletionResponsesConfig.namespace_tool_name_map(request_tools) responses_tools: Final[list[ResponseFunctionToolCall | CustomToolCallOutputItem]] = [] for tool in all_chat_completion_tools: @@ -1545,7 +1595,7 @@ class LiteLLMCompletionResponsesConfig: type="function_call", status=function_definition.get("status") or "completed", ) - LiteLLMCompletionResponsesConfig._set_tool_call_namespace(output_tool_call, namespace) + output_tool_call.namespace = namespace # Pass through provider_specific_fields as-is if present if provider_specific_fields: diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py index 38c50693759..f4c744f726d 100644 --- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py +++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py @@ -670,6 +670,58 @@ class TestLiteLLMCompletionResponsesConfig: assert tool_calls[0].arguments == '{"message":"hello"}' + def test_transform_top_level_function_collision_stays_unnamespaced(self): + tool_call_id = "call_top_level_collision" + chat_completion_response = ModelResponse( + choices=[ + Choices( + finish_reason="tool_calls", + index=0, + message=Message( + content=None, + role="assistant", + tool_calls=[ + ChatCompletionMessageToolCall( + id=tool_call_id, + type="function", + function=Function(name="run", arguments="{}"), + ) + ], + ), + ) + ] + ) + responses_api_request = { + "tools": [ + {"type": "function", "name": "run", "parameters": {"type": "object"}}, + { + "type": "namespace", + "name": "admin", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object"}, + } + ], + }, + ] + } + + try: + response = LiteLLMCompletionResponsesConfig.transform_chat_completion_response_to_responses_api_response( + request_input="Run the tool", + responses_api_request=responses_api_request, + chat_completion_response=chat_completion_response, + ) + finally: + TOOL_CALLS_CACHE.delete_cache(key=tool_call_id) + + tool_call = next(item for item in response.output if item.type == "function_call") + assert tool_call.name == "run" + assert getattr(tool_call, "namespace", None) is None + + class TestFunctionCallTransformation: """Test cases for function_call input transformation""" @@ -1764,6 +1816,55 @@ class TestToolTransformation: assert result_tool["function"]["parameters"] == namespace_tool["tools"][0]["parameters"] assert result_tool["function"]["description"] == "Multi-agent tools\n\nSpawn an agent" + @pytest.mark.parametrize("nested", [True, False]) + def test_transform_namespace_tools_preserves_allowed_callers(self, nested): + function_tool = { + "type": "function", + "name": "spawn_agent", + "parameters": {"type": "object", "properties": {}}, + "allowed_callers": ["code_execution_20250825"], + } + namespace_tool = ( + { + "type": "namespace", + "name": "collaboration", + "tools": [function_tool], + } + if nested + else {**function_tool, "type": "namespace"} + ) + + result_tools, _ = LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools( + tools=[namespace_tool] + ) + + assert result_tools[0]["allowed_callers"] == ["code_execution_20250825"] + + + @pytest.mark.parametrize("nested", [True, False]) + def test_transform_namespace_tools_rejects_invalid_allowed_callers(self, nested): + function_tool = { + "type": "function", + "name": "spawn_agent", + "parameters": {"type": "object", "properties": {}}, + "allowed_callers": "code_execution_20250825", + } + namespace_tool = ( + { + "type": "namespace", + "name": "collaboration", + "tools": [function_tool], + } + if nested + else {**function_tool, "type": "namespace"} + ) + + with pytest.raises(ValueError, match="allowed_callers must be a list of strings"): + LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools( + tools=[namespace_tool] + ) + + def test_transform_flat_namespace_tools_to_function_tools(self): namespace_tool = { "type": "namespace", @@ -1809,7 +1910,7 @@ class TestToolTransformation: ], } - result = LiteLLMCompletionResponsesConfig._namespace_tool_name_map( + result = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( [namespace_tool] ) @@ -1855,7 +1956,7 @@ class TestToolTransformation: }, ] - result = LiteLLMCompletionResponsesConfig._namespace_tool_name_map( + result = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( tools ) @@ -1864,6 +1965,55 @@ class TestToolTransformation: assert result["gamma__run"] == ("gamma", "run") assert "run" not in result + def test_namespace_tool_name_map_drops_top_level_function_collision(self): + tools = [ + {"type": "function", "name": "run", "parameters": {"type": "object"}}, + { + "type": "namespace", + "name": "admin", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object"}, + } + ], + }, + ] + + result = LiteLLMCompletionResponsesConfig.namespace_tool_name_map(tools) + + assert result["admin__run"] == ("admin", "run") + assert "run" not in result + + + def test_transform_tools_rejects_flattened_name_collision(self): + tools = [ + { + "type": "function", + "name": "admin__run", + "parameters": {"type": "object"}, + }, + { + "type": "namespace", + "name": "admin", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object"}, + } + ], + }, + ] + + with pytest.raises( + ValueError, + match="Top-level function names conflict with flattened namespace tools: admin__run", + ): + LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools(tools=tools) + + def test_restore_namespace_tool_name_leaves_unknown_tool_unchanged(self): tool_name, namespace = LiteLLMCompletionResponsesConfig._restore_namespace_tool_name( "mcp__node_repl", @@ -2894,6 +3044,7 @@ class TestEnsureOutputItemContentPartAdded: iterator._final_tool_events_queued = False iterator._custom_tool_names = set() iterator.responses_api_request = {} + iterator._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map(None) return iterator def _make_text_chunk(self): @@ -2960,6 +3111,10 @@ class TestEnsureOutputItemContentPartAdded: ] } + iterator._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( + iterator.responses_api_request.get("tools") + ) + iterator._queue_tool_call_delta_events( [ { @@ -2996,6 +3151,10 @@ class TestEnsureOutputItemContentPartAdded: ] } + iterator._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( + iterator.responses_api_request.get("tools") + ) + iterator._queue_tool_call_delta_events( [ { @@ -3029,6 +3188,10 @@ class TestEnsureOutputItemContentPartAdded: ] } + iterator._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( + iterator.responses_api_request.get("tools") + ) + iterator._queue_tool_call_delta_events( [ { @@ -3097,6 +3260,10 @@ class TestEnsureOutputItemContentPartAdded: } ] } + iterator._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( + iterator.responses_api_request.get("tools") + ) + message = MagicMock() message.tool_calls = [ { @@ -3121,6 +3288,86 @@ class TestEnsureOutputItemContentPartAdded: assert done.item.name == "spawn_agent" assert done.item.namespace == "collaboration" + def test_streaming_top_level_function_collision_stays_unnamespaced(self): + iterator = self._make_iterator() + iterator.responses_api_request = { + "tools": [ + {"type": "function", "name": "run", "parameters": {"type": "object"}}, + { + "type": "namespace", + "name": "admin", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object"}, + } + ], + }, + ] + } + + iterator._namespace_tool_names = LiteLLMCompletionResponsesConfig.namespace_tool_name_map( + iterator.responses_api_request.get("tools") + ) + + iterator._queue_tool_call_delta_events( + [ + { + "index": 0, + "id": "call_top_level", + "function": {"name": "run", "arguments": "{}"}, + } + ] + ) + + added = iterator._pending_tool_events[0] + assert added.item.name == "run" + assert getattr(added.item, "namespace", None) is None + + + def test_streaming_namespace_map_is_built_once(self): + from unittest.mock import MagicMock, patch + + from litellm.responses.litellm_completion_transformation.streaming_iterator import ( + LiteLLMCompletionStreamingIterator, + ) + + mock_stream_wrapper = MagicMock() + mock_stream_wrapper.logging_obj = MagicMock() + request = { + "tools": [ + { + "type": "namespace", + "name": "admin", + "tools": [ + { + "type": "function", + "name": "run", + "parameters": {"type": "object"}, + } + ], + } + ] + } + + with patch.object( + LiteLLMCompletionResponsesConfig, + "namespace_tool_name_map", + wraps=LiteLLMCompletionResponsesConfig.namespace_tool_name_map, + ) as namespace_map: + iterator = LiteLLMCompletionStreamingIterator( + model="test-model", + litellm_custom_stream_wrapper=mock_stream_wrapper, + request_input="test", + responses_api_request=request, + ) + iterator._responses_namespace_tool_call_fields("admin__run") + iterator._responses_namespace_tool_call_fields("admin__run") + + namespace_map.assert_called_once_with(request["tools"]) + + def test_emit_response_completed_uses_stream_finish_reason(self): """ When the assembled model response carries finish_reason="content_filter" From 8466ed0920021b765c0ec6483b0e96d121f2373b Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Mon, 3 Aug 2026 21:05:51 +0000 Subject: [PATCH 008/266] fix(websearch_interception): bill and rate limit intercepted searches against the calling key An intercepted web search called litellm.asearch() with only the search tool's litellm_params, so the search request carried no owner. The proxy's spend hook skips any call with no key, user or team attached, so the search's provider cost never reached SpendLogs; it was missing from the Logs page and never counted against the caller's budget. The same path never ran the rate limiter either, so an intercepted search was free of the key's RPM/TPM limits. The search now carries the originating key's attribution metadata (key hash, alias, user, team, org, plus model_group set to the resolved search tool) and runs the caller's rate limit checks before hitting the provider, matching what a direct /v1/search request gets. SDK calls with no proxy auth context are unchanged. Resolves LIT-5033 Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../websearch_interception/handler.py | 46 ++++++++- .../test_websearch_interception_handler.py | 97 +++++++++++++++++-- 2 files changed, 132 insertions(+), 11 deletions(-) diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 71388134e98..6be64c89828 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -40,7 +40,7 @@ from litellm.types.integrations.websearch_interception import ( WebSearchInterceptionConfig, ) from litellm.types.llms.openai import AllMessageValues -from litellm.types.utils import CallTypes, LlmProviders +from litellm.types.utils import CallTypes, LlmProviders, StandardLoggingUserAPIKeyMetadata from litellm.utils import ProviderConfigManager if TYPE_CHECKING: @@ -1288,10 +1288,14 @@ class WebSearchInterceptionLogger(CustomLogger): search_tool: Final = self._select_search_tool_from_router(llm_router=llm_router) search_provider: str | None = None search_litellm_params: dict[str, Any] = {} + search_tool_name: str | None = None if search_tool is not None: await self._authorize_search_tool(search_tool=search_tool, kwargs=kwargs) search_litellm_params = dict(search_tool.get("litellm_params", {}) or {}) search_provider = search_litellm_params.get("search_provider") + selected_tool_name = search_tool.get("search_tool_name") + if isinstance(selected_tool_name, str) and selected_tool_name: + search_tool_name = selected_tool_name # Fallback to perplexity if no router or no search tools configured if not search_provider: @@ -1304,10 +1308,22 @@ class WebSearchInterceptionLogger(CustomLogger): verbose_logger.debug( "WebSearchInterception: Executing search for '%s' using provider '%s'", query, search_provider ) + user_api_key_auth: Final = self._get_user_api_key_auth_from_kwargs(kwargs) + search_metadata: Final = ( + None + if user_api_key_auth is None + else self._build_search_request_metadata( + user_api_key_auth=user_api_key_auth, + search_tool_name=search_tool_name, + ) + ) search_kwargs: Final = { - key: value - for key, value in search_litellm_params.items() - if key != "search_provider" and value is not None + **{ + key: value + for key, value in search_litellm_params.items() + if key != "search_provider" and value is not None + }, + **({} if search_metadata is None else {"litellm_metadata": search_metadata}), } result: Final = await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) @@ -1366,6 +1382,28 @@ class WebSearchInterceptionLogger(CustomLogger): team_object=team_object, ) + @staticmethod + def _build_search_request_metadata( + user_api_key_auth: "UserAPIKeyAuth", + search_tool_name: str | None, + ) -> dict[str, object]: + """ + Spend-tracking metadata for the intercepted search, so its provider cost is logged + and billed against the key/user/team that made the originating LLM request instead + of being dropped by the proxy's spend hook for lack of an owner. + """ + from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup + + user_api_key_metadata: StandardLoggingUserAPIKeyMetadata = ( + LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(user_api_key_dict=user_api_key_auth) + ) + return { + **user_api_key_metadata, + **({} if search_tool_name is None else {"model_group": search_tool_name}), + "user_api_key": user_api_key_auth.api_key, + "user_api_key_auth": user_api_key_auth, + } + @staticmethod def _get_user_api_key_auth_from_kwargs(kwargs: Mapping[str, object] | None) -> "UserAPIKeyAuth | None": if not kwargs: diff --git a/tests/test_litellm/integrations/websearch_interception/test_websearch_interception_handler.py b/tests/test_litellm/integrations/websearch_interception/test_websearch_interception_handler.py index b6ff3b70a4d..f39f41a6d12 100644 --- a/tests/test_litellm/integrations/websearch_interception/test_websearch_interception_handler.py +++ b/tests/test_litellm/integrations/websearch_interception/test_websearch_interception_handler.py @@ -221,14 +221,97 @@ async def test_execute_search_passes_selected_search_tool_litellm_params(monkeyp kwargs={"litellm_params": {"metadata": {"user_api_key_auth": user_api_key_auth}}}, ) - mock_asearch.assert_awaited_once_with( - query="what is litellm", - search_provider="tavily", - api_key="fake-ui-key", - api_base="https://api.tavily.com", - timeout=10.0, - max_retries=2, + forwarded_kwargs = mock_asearch.await_args.kwargs + assert forwarded_kwargs["query"] == "what is litellm" + assert forwarded_kwargs["search_provider"] == "tavily" + assert forwarded_kwargs["api_key"] == "fake-ui-key" + assert forwarded_kwargs["api_base"] == "https://api.tavily.com" + assert forwarded_kwargs["timeout"] == 10.0 + assert forwarded_kwargs["max_retries"] == 2 + + +@pytest.mark.asyncio +async def test_execute_search_attributes_spend_to_the_calling_key(monkeypatch): + """An intercepted search is billed and logged against the key that made the LLM request. + + Without the forwarded attribution metadata the proxy's spend hook skips the search + entirely, so its provider cost never reaches SpendLogs or any budget. + """ + import litellm + from litellm.proxy import proxy_server + from litellm.proxy.hooks.proxy_track_cost_callback import _should_track_cost_callback + + logger = WebSearchInterceptionLogger( + enabled_providers=["bedrock"], + search_tool_name="perplexity-sonar-pro", ) + router = MagicMock() + router.search_tools = [ + { + "search_tool_name": "perplexity-sonar-pro", + "litellm_params": {"search_provider": "perplexity", "api_key": "fake-key"}, + } + ] + mock_asearch = AsyncMock(return_value=SearchResponse(object="search", results=[])) + user_api_key_auth = UserAPIKeyAuth( + api_key="hashed-sk-1234", + key_alias="alice-key", + user_id="user-alice", + org_id="org-1", + ) + + monkeypatch.setattr(proxy_server, "llm_router", router) + monkeypatch.setattr(litellm, "asearch", mock_asearch) + + await logger._execute_search( + "what is litellm", + kwargs={"litellm_params": {"metadata": {"user_api_key_auth": user_api_key_auth}}}, + ) + + forwarded_metadata = mock_asearch.await_args.kwargs["litellm_metadata"] + assert forwarded_metadata["user_api_key"] == "hashed-sk-1234" + assert forwarded_metadata["user_api_key_hash"] == "hashed-sk-1234" + assert forwarded_metadata["user_api_key_alias"] == "alice-key" + assert forwarded_metadata["user_api_key_user_id"] == "user-alice" + assert forwarded_metadata["user_api_key_org_id"] == "org-1" + assert forwarded_metadata["model_group"] == "perplexity-sonar-pro" + assert ( + _should_track_cost_callback( + user_api_key=forwarded_metadata["user_api_key"], + user_id=forwarded_metadata["user_api_key_user_id"], + team_id=forwarded_metadata["user_api_key_team_id"], + end_user_id=None, + call_type="asearch", + ) + is True + ) + + +@pytest.mark.asyncio +async def test_execute_search_without_proxy_auth_context_stays_sdk_only(monkeypatch): + """SDK callers have no key to attribute the search to, so no proxy metadata is invented.""" + import litellm + from litellm.proxy import proxy_server + + logger = WebSearchInterceptionLogger( + enabled_providers=["bedrock"], + search_tool_name="perplexity-sonar-pro", + ) + router = MagicMock() + router.search_tools = [ + { + "search_tool_name": "perplexity-sonar-pro", + "litellm_params": {"search_provider": "perplexity", "api_key": "fake-key"}, + } + ] + mock_asearch = AsyncMock(return_value=SearchResponse(object="search", results=[])) + + monkeypatch.setattr(proxy_server, "llm_router", router) + monkeypatch.setattr(litellm, "asearch", mock_asearch) + + await logger._execute_search("what is litellm", kwargs={"litellm_params": {}}) + + assert "litellm_metadata" not in mock_asearch.await_args.kwargs @pytest.mark.asyncio From 8d6247c9c12a3e37359c2287a6c2cc3d1bee61e1 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Fri, 7 Aug 2026 03:00:04 +0000 Subject: [PATCH 009/266] fix(proxy): send SSE keepalive pings on OpenAI-shaped streaming routes Streaming /chat/completions and /v1/responses emit nothing, not even response headers, until the upstream yields its first chunk, so an ingress with an idle read timeout (nginx proxy-read-timeout) drops long time-to-first-token streams. Reuses the existing Anthropic keepalive wrapper with a configurable ping payload, emitting an SSE comment on the OpenAI-shaped routes so conformant clients ignore it. Off unless litellm_settings.sse_keepalive_ping_interval_seconds is set. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/__init__.py | 6 ++ litellm/proxy/common_request_processing.py | 13 ++- litellm/proxy/common_utils/sse_keepalive.py | 7 +- .../proxy/common_utils/test_sse_keepalive.py | 54 ++++++++++--- .../proxy/test_common_request_processing.py | 81 +++++++++++++++++++ 5 files changed, 148 insertions(+), 13 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index e0c7d56361c..a29ee73013b 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -245,6 +245,12 @@ use_chat_completions_url_for_anthropic_messages: bool = bool( # config.yaml. strip_anthropic_total_tokens: bool = False anthropic_sse_ping_interval_seconds: float = 15.0 +# Emit an SSE comment (": ping") on OpenAI-shaped streaming routes (/chat/completions, +# /v1/responses, ...) whenever the upstream has sent nothing for this many seconds, so +# intermediaries with an idle read timeout (e.g. nginx `proxy-read-timeout`) don't drop +# long time-to-first-token streams. Disabled unless set, via +# `litellm_settings.sse_keepalive_ping_interval_seconds` in config.yaml. +sse_keepalive_ping_interval_seconds: float | None = None route_all_chat_openai_to_responses: bool = ( os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true" ) # When True, routes all OpenAI /chat/completions requests through the Responses API bridge diff --git a/litellm/proxy/common_request_processing.py b/litellm/proxy/common_request_processing.py index 159d7508f4e..49627a91cf6 100644 --- a/litellm/proxy/common_request_processing.py +++ b/litellm/proxy/common_request_processing.py @@ -46,7 +46,11 @@ from litellm.proxy.common_utils.callback_utils import ( get_logging_caching_headers, get_remaining_tokens_and_requests_from_request_data, ) -from litellm.proxy.common_utils.sse_keepalive import wrap_sse_stream_with_keepalive_pings +from litellm.proxy.common_utils.sse_keepalive import ( + ANTHROPIC_PING_SSE_CHUNK, + SSE_COMMENT_PING_CHUNK, + wrap_sse_stream_with_keepalive_pings, +) from litellm.proxy.dd_span_tagger import DDSpanTagger from litellm.proxy.route_llm_request import route_request from litellm.proxy.utils import ProxyLogging, _check_and_merge_model_level_guardrails @@ -1984,6 +1988,7 @@ class ProxyBaseLLMRequestProcessing: generator=wrap_sse_stream_with_keepalive_pings( stream=selected_data_generator, ping_interval_seconds=litellm.anthropic_sse_ping_interval_seconds, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, ), media_type="text/event-stream", headers=custom_headers, @@ -2015,7 +2020,11 @@ class ProxyBaseLLMRequestProcessing: ) ) return await create_response( - generator=selected_data_generator, + generator=wrap_sse_stream_with_keepalive_pings( + stream=selected_data_generator, + ping_interval_seconds=litellm.sse_keepalive_ping_interval_seconds, + ping_chunk=SSE_COMMENT_PING_CHUNK, + ), media_type="text/event-stream", headers=custom_headers, request=request, diff --git a/litellm/proxy/common_utils/sse_keepalive.py b/litellm/proxy/common_utils/sse_keepalive.py index 6700700ff7c..d3ff4b0ca30 100644 --- a/litellm/proxy/common_utils/sse_keepalive.py +++ b/litellm/proxy/common_utils/sse_keepalive.py @@ -7,6 +7,7 @@ from typing import Final import anyio ANTHROPIC_PING_SSE_CHUNK: Final = 'event: ping\ndata: {"type": "ping"}\n\n' +SSE_COMMENT_PING_CHUNK: Final = ": ping\n\n" def _coerce_interval(ping_interval_seconds: float | str | None) -> float | None: @@ -24,16 +25,18 @@ def _coerce_interval(ping_interval_seconds: float | str | None) -> float | None: def wrap_sse_stream_with_keepalive_pings( stream: AsyncGenerator[str, None], ping_interval_seconds: float | str | None, + ping_chunk: str, ) -> AsyncGenerator[str, None]: interval: Final = _coerce_interval(ping_interval_seconds) if interval is None: return stream - return _keepalive_ping_stream(stream=stream, ping_interval_seconds=interval) + return _keepalive_ping_stream(stream=stream, ping_interval_seconds=interval, ping_chunk=ping_chunk) async def _keepalive_ping_stream( stream: AsyncGenerator[str, None], ping_interval_seconds: float, + ping_chunk: str, ) -> AsyncGenerator[str, None]: pending = asyncio.ensure_future( stream.__anext__() @@ -42,7 +45,7 @@ async def _keepalive_ping_stream( while True: await asyncio.wait({pending}, timeout=ping_interval_seconds) if not pending.done(): - yield ANTHROPIC_PING_SSE_CHUNK + yield ping_chunk continue try: yield pending.result() diff --git a/tests/test_litellm/proxy/common_utils/test_sse_keepalive.py b/tests/test_litellm/proxy/common_utils/test_sse_keepalive.py index 9cca9bbfe12..e343e46e187 100644 --- a/tests/test_litellm/proxy/common_utils/test_sse_keepalive.py +++ b/tests/test_litellm/proxy/common_utils/test_sse_keepalive.py @@ -22,7 +22,11 @@ async def test_pings_fill_mid_stream_silence_and_preserve_chunk_order(): await asyncio.sleep(0.3) yield TEXT_DELTA_CHUNK - wrapped: Final = wrap_sse_stream_with_keepalive_pings(stream=gappy_stream(), ping_interval_seconds=0.05) + wrapped: Final = wrap_sse_stream_with_keepalive_pings( + stream=gappy_stream(), + ping_interval_seconds=0.05, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) collected: Final = [chunk async for chunk in wrapped] assert collected[0] == MESSAGE_START_CHUNK @@ -40,7 +44,11 @@ async def test_ping_emitted_while_waiting_for_first_chunk(): await asyncio.sleep(0.2) yield MESSAGE_START_CHUNK - wrapped: Final = wrap_sse_stream_with_keepalive_pings(stream=slow_start_stream(), ping_interval_seconds=0.05) + wrapped: Final = wrap_sse_stream_with_keepalive_pings( + stream=slow_start_stream(), + ping_interval_seconds=0.05, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) collected: Final = [chunk async for chunk in wrapped] assert collected[0] == ANTHROPIC_PING_SSE_CHUNK @@ -54,7 +62,11 @@ async def test_no_pings_when_chunks_arrive_faster_than_interval(): yield TEXT_DELTA_CHUNK yield TEXT_DELTA_CHUNK - wrapped: Final = wrap_sse_stream_with_keepalive_pings(stream=fast_stream(), ping_interval_seconds=1.0) + wrapped: Final = wrap_sse_stream_with_keepalive_pings( + stream=fast_stream(), + ping_interval_seconds=1.0, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) collected: Final = [chunk async for chunk in wrapped] assert collected == [MESSAGE_START_CHUNK, TEXT_DELTA_CHUNK, TEXT_DELTA_CHUNK] @@ -66,7 +78,11 @@ async def test_upstream_exception_propagates(): yield MESSAGE_START_CHUNK raise ValueError("upstream broke") - wrapped: Final = wrap_sse_stream_with_keepalive_pings(stream=failing_stream(), ping_interval_seconds=5.0) + wrapped: Final = wrap_sse_stream_with_keepalive_pings( + stream=failing_stream(), + ping_interval_seconds=5.0, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) assert await wrapped.__anext__() == MESSAGE_START_CHUNK with pytest.raises(ValueError, match="upstream broke"): @@ -85,7 +101,11 @@ async def test_aclose_mid_silence_cancels_upstream_and_runs_its_cleanup(): finally: upstream_cleaned_up.set() - wrapped: Final = wrap_sse_stream_with_keepalive_pings(stream=hung_stream(), ping_interval_seconds=0.05) + wrapped: Final = wrap_sse_stream_with_keepalive_pings( + stream=hung_stream(), + ping_interval_seconds=0.05, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) assert await wrapped.__anext__() == MESSAGE_START_CHUNK assert await wrapped.__anext__() == ANTHROPIC_PING_SSE_CHUNK @@ -100,7 +120,11 @@ async def test_non_positive_interval_returns_stream_unwrapped(): yield MESSAGE_START_CHUNK stream: Final = any_stream() - assert wrap_sse_stream_with_keepalive_pings(stream=stream, ping_interval_seconds=0) is stream + assert wrap_sse_stream_with_keepalive_pings( + stream=stream, + ping_interval_seconds=0, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) is stream await stream.aclose() @@ -123,7 +147,11 @@ async def test_invalid_config_interval_returns_stream_unwrapped(bad_interval: fl yield MESSAGE_START_CHUNK stream: Final = any_stream() - assert wrap_sse_stream_with_keepalive_pings(stream=stream, ping_interval_seconds=bad_interval) is stream + assert wrap_sse_stream_with_keepalive_pings( + stream=stream, + ping_interval_seconds=bad_interval, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) is stream await stream.aclose() @@ -133,7 +161,11 @@ async def test_numeric_string_interval_from_yaml_config_enables_pings(): await asyncio.sleep(0.2) yield MESSAGE_START_CHUNK - wrapped: Final = wrap_sse_stream_with_keepalive_pings(stream=slow_start_stream(), ping_interval_seconds="0.05") + wrapped: Final = wrap_sse_stream_with_keepalive_pings( + stream=slow_start_stream(), + ping_interval_seconds="0.05", + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ) collected: Final = [chunk async for chunk in wrapped] assert collected[0] == ANTHROPIC_PING_SSE_CHUNK @@ -147,7 +179,11 @@ async def test_create_response_streams_ping_first_for_slow_upstream(): yield MESSAGE_START_CHUNK response: Final = await create_response( - generator=wrap_sse_stream_with_keepalive_pings(stream=slow_start_stream(), ping_interval_seconds=0.05), + generator=wrap_sse_stream_with_keepalive_pings( + stream=slow_start_stream(), + ping_interval_seconds=0.05, + ping_chunk=ANTHROPIC_PING_SSE_CHUNK, + ), media_type="text/event-stream", headers={}, ) diff --git a/tests/test_litellm/proxy/test_common_request_processing.py b/tests/test_litellm/proxy/test_common_request_processing.py index aacc7498ccb..dd19560c166 100644 --- a/tests/test_litellm/proxy/test_common_request_processing.py +++ b/tests/test_litellm/proxy/test_common_request_processing.py @@ -34,10 +34,12 @@ from litellm.proxy.common_request_processing import ( _UpstreamClosingStreamingResponse, create_response, ) +from litellm.proxy.common_utils.sse_keepalive import SSE_COMMENT_PING_CHUNK from litellm.proxy.dd_span_tagger import DDSpanTagger from litellm.proxy._types import ProxyException from litellm.proxy._types import UserAPIKeyAuth as ProxyUserAPIKeyAuth from litellm.proxy.utils import ProxyLogging +from litellm.types.utils import ModelResponseStream class TestProxyBaseLLMRequestProcessing: @@ -5746,3 +5748,82 @@ class TestPerRequestModelGroupAlias: ) assert merged_for == ["group-b"] + + +class TestOpenAISseKeepalivePings: + """ + Regression for a streaming request dying at an ingress idle read timeout + (e.g. nginx `proxy-read-timeout`) when time-to-first-token exceeds it: the + OpenAI-shaped SSE routes must emit a keepalive comment while the upstream is + silent, once `litellm.sse_keepalive_ping_interval_seconds` is configured. + """ + + async def _run(self, monkeypatch, first_chunk_delay: float): + import litellm.proxy.common_request_processing as crp + from litellm.proxy._types import UserAPIKeyAuth as RealUserAPIKeyAuth + + logging_obj = MagicMock() + logging_obj.litellm_call_id = "call-keepalive" + logging_obj.cost_breakdown = None + processing_obj = ProxyBaseLLMRequestProcessing( + data={"model": "gpt-4o", "stream": True, "litellm_logging_obj": logging_obj} + ) + + async def upstream(): + await asyncio.sleep(first_chunk_delay) + yield ModelResponseStream() + + async def fake_route_request(**kwargs): + async def _llm_call(): + return upstream() + + return _llm_call() + + monkeypatch.setattr(crp, "route_request", fake_route_request) + + def select_data_generator(response, user_api_key_dict, request_data, request): + async def _gen(): + async for _ in response: + yield 'data: {"choices": []}\n\n' + + return _gen() + + proxy_logging_obj = MagicMock(spec=ProxyLogging) + proxy_logging_obj.during_call_hook = AsyncMock(return_value=None) + proxy_logging_obj.update_request_status = AsyncMock(return_value=None) + proxy_logging_obj.post_call_response_headers_hook = AsyncMock(return_value=None) + proxy_logging_obj.post_call_success_hook = AsyncMock() + + return await processing_obj.base_process_llm_request( + request=MagicMock(spec=Request, headers={}), + fastapi_response=Response(), + user_api_key_dict=RealUserAPIKeyAuth(api_key="sk-test"), + route_type="acompletion", + proxy_logging_obj=proxy_logging_obj, + general_settings={}, + proxy_config=MagicMock(spec=ProxyConfig), + select_data_generator=select_data_generator, + llm_router=None, + skip_pre_call_logic=True, + ) + + @pytest.mark.asyncio + async def test_ping_precedes_slow_first_chunk_on_chat_completions(self, monkeypatch): + monkeypatch.setattr(litellm, "sse_keepalive_ping_interval_seconds", 0.05) + + result = await self._run(monkeypatch, first_chunk_delay=0.3) + + assert isinstance(result, StreamingResponse) + streamed = [chunk async for chunk in result.body_iterator] + assert streamed[0] == SSE_COMMENT_PING_CHUNK + assert streamed[-1] == 'data: {"choices": []}\n\n' + + @pytest.mark.asyncio + async def test_no_pings_emitted_when_interval_unset(self, monkeypatch): + monkeypatch.setattr(litellm, "sse_keepalive_ping_interval_seconds", None) + + result = await self._run(monkeypatch, first_chunk_delay=0.3) + + assert isinstance(result, StreamingResponse) + streamed = [chunk async for chunk in result.body_iterator] + assert streamed == ['data: {"choices": []}\n\n'] From c597d3fabb969f0ff2eb6b4f9e7df8993f8ef0c4 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Fri, 7 Aug 2026 03:28:10 +0000 Subject: [PATCH 010/266] chore: drop source comment on sse_keepalive_ping_interval_seconds Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/__init__.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/litellm/__init__.py b/litellm/__init__.py index a29ee73013b..7d5051790cc 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -245,11 +245,6 @@ use_chat_completions_url_for_anthropic_messages: bool = bool( # config.yaml. strip_anthropic_total_tokens: bool = False anthropic_sse_ping_interval_seconds: float = 15.0 -# Emit an SSE comment (": ping") on OpenAI-shaped streaming routes (/chat/completions, -# /v1/responses, ...) whenever the upstream has sent nothing for this many seconds, so -# intermediaries with an idle read timeout (e.g. nginx `proxy-read-timeout`) don't drop -# long time-to-first-token streams. Disabled unless set, via -# `litellm_settings.sse_keepalive_ping_interval_seconds` in config.yaml. sse_keepalive_ping_interval_seconds: float | None = None route_all_chat_openai_to_responses: bool = ( os.getenv("LITELLM_ROUTE_ALL_CHAT_OPENAI_TO_RESPONSES", "false").lower() == "true" From 9d69fdac72b860627f627f9802ceb4758a7c372d Mon Sep 17 00:00:00 2001 From: daleselaji-dev <265319989+daleselaji-dev@users.noreply.github.com> Date: Fri, 7 Aug 2026 13:14:56 +0800 Subject: [PATCH 011/266] fix(bedrock): use deployment credentials for AWS requests --- .../llms/bedrock/batches/transformation.py | 11 ++- litellm/llms/bedrock/files/transformation.py | 11 ++- .../test_bedrock_request_credentials.py | 95 +++++++++++++++++++ 3 files changed, 109 insertions(+), 8 deletions(-) create mode 100644 tests/litellm/llms/bedrock/test_bedrock_request_credentials.py diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index 9e3dec26673..8b437af8581 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -130,7 +130,8 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): Get the complete URL for Bedrock batch creation. Bedrock batch jobs are created via the model invocation job API. """ - aws_region_name: Final = self._get_aws_region_name(optional_params, model) + request_params: Final = {**litellm_params, **optional_params} + aws_region_name: Final = self._get_aws_region_name(request_params, model) # Bedrock model invocation job endpoint # Format: https://bedrock.{region}.amazonaws.com/model-invocation-job @@ -232,14 +233,15 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # For Bedrock, we need to return a pre-signed request with AWS auth headers # Use common utility for AWS signing + request_params: Final = {**litellm_params, **optional_params} endpoint_url: Final = ( - f"https://bedrock.{self._get_aws_region_name(optional_params, model)}.amazonaws.com/model-invocation-job" + f"https://bedrock.{self._get_aws_region_name(request_params, model)}.amazonaws.com/model-invocation-job" ) signed_headers, signed_data = self.common_utils.sign_aws_request( service_name="bedrock", data=bedrock_request, endpoint_url=endpoint_url, - optional_params=optional_params, + optional_params=request_params, method="POST", ) @@ -387,11 +389,12 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): endpoint_url: Final = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" # Use common utility for AWS signing + request_params: Final = {**litellm_params, **optional_params} signed_headers, _ = self.common_utils.sign_aws_request( service_name="bedrock", data={}, # GET request has no body endpoint_url=endpoint_url, - optional_params=optional_params, + optional_params=request_params, method="GET", ) diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index 728fd02f001..072343ae936 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -257,6 +257,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ Get the complete S3 URL for the file upload request """ + request_params: Final = {**litellm_params, **optional_params} bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") if not bucket_name: raise ValueError( @@ -265,7 +266,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): bucket_name, object_prefix = split_configured_cloud_bucket_name(bucket_name) s3_region_name: Final = litellm_params.get("s3_region_name") or optional_params.get("s3_region_name") - aws_region_name: Final = s3_region_name or self._get_aws_region_name(optional_params, model) + aws_region_name: Final = s3_region_name or self._get_aws_region_name(request_params, model) file_data: Final = data.get("file") purpose: Final = data.get("purpose") @@ -281,7 +282,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): # S3 endpoint URL format s3_endpoint_url: Final = ( - optional_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com" + request_params.get("s3_endpoint_url") or f"https://s3.{aws_region_name}.amazonaws.com" ).rstrip("/") return f"{s3_endpoint_url}/{bucket_name}/{encoded_object_name}" @@ -728,14 +729,16 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): if s3_region_name: optional_params = {**optional_params, "aws_region_name": s3_region_name} + request_params: Final = {**litellm_params, **optional_params} + # Sign the request and return a pre-signed request object signed_headers, signed_body = self._sign_s3_request( content=file_content, api_base=api_base, - optional_params=optional_params, + optional_params=request_params, s3_encryption_key_id=resolve_s3_encryption_key_id( litellm_params=litellm_params, - optional_params=optional_params, + optional_params=request_params, ), ) diff --git a/tests/litellm/llms/bedrock/test_bedrock_request_credentials.py b/tests/litellm/llms/bedrock/test_bedrock_request_credentials.py new file mode 100644 index 00000000000..72da2daf9b5 --- /dev/null +++ b/tests/litellm/llms/bedrock/test_bedrock_request_credentials.py @@ -0,0 +1,95 @@ +from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig +from litellm.llms.bedrock.files.transformation import BedrockFilesConfig + + +def test_bedrock_file_upload_signing_uses_deployment_credentials(monkeypatch): + config = BedrockFilesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, "" + + monkeypatch.setattr(config, "_sign_s3_request", capture_signing) + + result = config.transform_create_file_request( + model="", + create_file_data={ + "file": ( + "batch.jsonl", + b'{"custom_id":"req-1","body":{"model":"bedrock/model"}}\n', + "application/jsonl", + ), + "purpose": "batch", + }, + optional_params={}, + litellm_params={ + "s3_bucket_name": "deployment-bucket", + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + }, + ) + + assert "eu-west-1" in result["url"] + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" + assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" + assert captured["optional_params"]["aws_region_name"] == "eu-west-1" + + +def test_bedrock_batch_signing_uses_deployment_credentials(monkeypatch): + config = BedrockBatchesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, b"{}" + + monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) + + result = config.transform_create_batch_request( + model="us.anthropic.claude-haiku-4-5-20251001-v1:0", + create_batch_data={ + "input_file_id": "s3://deployment-bucket/input.jsonl", + "completion_window": "24h", + "endpoint": "/v1/chat/completions", + }, + optional_params={}, + litellm_params={ + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + "aws_batch_role_arn": "arn:aws:iam::123456789012:role/bedrock-batch", + }, + ) + + assert result["url"].startswith("https://bedrock.eu-west-1.amazonaws.com/") + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" + assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" + assert captured["optional_params"]["aws_region_name"] == "eu-west-1" + + +def test_bedrock_batch_retrieval_signing_uses_deployment_credentials(monkeypatch): + config = BedrockBatchesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, b"" + + monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) + + result = config.transform_retrieve_batch_request( + batch_id="arn:aws:bedrock:eu-west-1:123456789012:model-invocation-job/job-1", + optional_params={}, + litellm_params={ + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + }, + ) + + assert result["url"].startswith("https://bedrock.eu-west-1.amazonaws.com/") + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" + assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" + assert captured["optional_params"]["aws_region_name"] == "eu-west-1" From f9b86b253a3fb87d003bb5ccc80c7d89aa91dd62 Mon Sep 17 00:00:00 2001 From: Harry Qian Date: Tue, 4 Aug 2026 17:14:26 +0800 Subject: [PATCH 012/266] fix(proxy): restore query-param validation under fastapi>=0.140.7 fastapi 0.140.7 removed get_flat_dependant(), which broke the import in management_v1/common.py and took down every /management/v1 route. Switch to get_flat_params() and filter to ParamTypes.query so unknown-query-param rejection keeps matching the old behavior. --- .../management_endpoints/management_v1/common.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/litellm/proxy/management_endpoints/management_v1/common.py b/litellm/proxy/management_endpoints/management_v1/common.py index 8525d67a041..ec79820465a 100644 --- a/litellm/proxy/management_endpoints/management_v1/common.py +++ b/litellm/proxy/management_endpoints/management_v1/common.py @@ -4,7 +4,8 @@ from typing import Final from urllib.parse import urlencode from fastapi import Request -from fastapi.dependencies.utils import get_flat_dependant +from fastapi.dependencies.utils import get_flat_params +from fastapi.params import ParamTypes from fastapi.responses import JSONResponse from litellm.types.proxy.management_endpoints.management_v1 import ( @@ -42,7 +43,13 @@ def _declared_query_params(request: Request) -> frozenset[str]: dependant: Final = getattr(route, "dependant", None) if dependant is None: return frozenset() - return frozenset(field.alias for field in get_flat_dependant(dependant, skip_repeats=True).query_params) + # fastapi>=0.140.7 removed get_flat_dependant(); get_flat_params() returns the + # flattened (deduped) param list. Filter to query params to match the old behavior. + return frozenset( + field.alias + for field in get_flat_params(dependant) + if getattr(field.field_info, "in_", None) == ParamTypes.query + ) def escape_like(value: str) -> str: From da443d1266615507f52a101b461c80e0265069ae Mon Sep 17 00:00:00 2001 From: Harry Qian Date: Tue, 4 Aug 2026 18:21:22 +0800 Subject: [PATCH 013/266] test(proxy): lock in query-param validation across fastapi param types Guards _declared_query_params against a regression in the get_flat_params migration: the flatten step returns path, query, header and cookie params together, so a dropped ParamTypes.query filter would wrongly treat path or header names as declared query params and accept unknown ones. Removing the filter fails these tests. --- .../management_v1/test_common.py | 95 +++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 tests/test_litellm/proxy/management_endpoints/management_v1/test_common.py diff --git a/tests/test_litellm/proxy/management_endpoints/management_v1/test_common.py b/tests/test_litellm/proxy/management_endpoints/management_v1/test_common.py new file mode 100644 index 00000000000..167a06ed551 --- /dev/null +++ b/tests/test_litellm/proxy/management_endpoints/management_v1/test_common.py @@ -0,0 +1,95 @@ +from typing import Annotated + +from fastapi import Depends, FastAPI, Header, Query, Request +from fastapi.testclient import TestClient + +from litellm.proxy.management_endpoints.management_v1.common import ( + ManagementProblem, + PROBLEM_CONTENT_TYPE, + _declared_query_params, + problem_response, + reject_unknown_query_params, +) + + +def _client() -> TestClient: + app = FastAPI() + + @app.exception_handler(ManagementProblem) + async def _handle(_request: Request, exc: ManagementProblem): + return problem_response(exc.problem) + + @app.get("/things/{thing_id}", dependencies=[Depends(reject_unknown_query_params)]) + def _handler( + thing_id: str, + request: Request, + status: Annotated[str | None, Query(alias="filter[status]")] = None, + page: Annotated[int, Query(ge=1)] = 1, + x_trace: Annotated[str | None, Header()] = None, + ) -> dict[str, bool]: + return {"ok": True} + + return TestClient(app, raise_server_exceptions=False) + + +def test_a_declared_query_param_is_accepted_by_its_alias(): + response = _client().get("/things/abc", params={"filter[status]": "active", "page": "2"}) + assert response.status_code == 200, response.text + + +def test_an_unknown_query_param_is_rejected_as_a_problem(): + response = _client().get("/things/abc", params={"bogus": "x"}) + assert response.status_code == 400 + assert response.headers["content-type"].startswith(PROBLEM_CONTENT_TYPE) + assert "bogus" in response.json()["detail"] + + +def test_a_path_param_name_is_not_a_declared_query_param(): + """The flatten step returns path+query+header together; only query names count as declared. + + If the ParamTypes.query filter were dropped, `thing_id` (a path param) would leak + into the declared set and this request would be wrongly accepted. + """ + response = _client().get("/things/abc", params={"thing_id": "x"}) + assert response.status_code == 400 + assert "thing_id" in response.json()["detail"] + + +def test_a_header_param_name_is_not_a_declared_query_param(): + response = _client().get("/things/abc", params={"x-trace": "x"}) + assert response.status_code == 400 + assert "x-trace" in response.json()["detail"] + + +def test_declared_query_params_isolates_query_aliases_from_other_param_types(): + captured: dict[str, frozenset[str]] = {} + app = FastAPI() + + @app.get("/things/{thing_id}") + def _handler( + thing_id: str, + request: Request, + status: Annotated[str | None, Query(alias="filter[status]")] = None, + page: Annotated[int, Query(ge=1)] = 1, + x_trace: Annotated[str | None, Header()] = None, + ) -> dict[str, bool]: + captured["declared"] = _declared_query_params(request) + return {"ok": True} + + TestClient(app).get("/things/abc") + assert captured["declared"] == frozenset({"filter[status]", "page"}) + + +def test_declared_query_params_is_empty_when_the_route_has_no_dependant(): + request = Request( + { + "type": "http", + "method": "GET", + "scheme": "http", + "root_path": "", + "path": "/things/abc", + "query_string": b"", + "headers": [(b"host", b"testserver")], + } + ) + assert _declared_query_params(request) == frozenset() From b11d342022e1dfe88ad05438ecab49524173028a Mon Sep 17 00:00:00 2001 From: daleselaji-dev <265319989+daleselaji-dev@users.noreply.github.com> Date: Fri, 7 Aug 2026 16:40:17 +0800 Subject: [PATCH 014/266] chore(bedrock): document mutable signing params --- litellm/llms/bedrock/batches/transformation.py | 15 ++++++++++++--- litellm/llms/bedrock/files/transformation.py | 10 ++++++++-- 2 files changed, 20 insertions(+), 5 deletions(-) diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index 8b437af8581..e92a2e7a00a 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -130,7 +130,10 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): Get the complete URL for Bedrock batch creation. Bedrock batch jobs are created via the model invocation job API. """ - request_params: Final = {**litellm_params, **optional_params} + request_params: Final = { + **litellm_params, + **optional_params, + } # mutable-ok: merged params are read by AWS helpers aws_region_name: Final = self._get_aws_region_name(request_params, model) # Bedrock model invocation job endpoint @@ -233,7 +236,10 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # For Bedrock, we need to return a pre-signed request with AWS auth headers # Use common utility for AWS signing - request_params: Final = {**litellm_params, **optional_params} + request_params: Final = { + **litellm_params, + **optional_params, + } # mutable-ok: merged params are read by AWS helpers endpoint_url: Final = ( f"https://bedrock.{self._get_aws_region_name(request_params, model)}.amazonaws.com/model-invocation-job" ) @@ -389,7 +395,10 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): endpoint_url: Final = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" # Use common utility for AWS signing - request_params: Final = {**litellm_params, **optional_params} + request_params: Final = { + **litellm_params, + **optional_params, + } # mutable-ok: merged params are read by AWS helpers signed_headers, _ = self.common_utils.sign_aws_request( service_name="bedrock", data={}, # GET request has no body diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index 072343ae936..cd4f52ddbfe 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -257,7 +257,10 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ Get the complete S3 URL for the file upload request """ - request_params: Final = {**litellm_params, **optional_params} + request_params: Final = { + **litellm_params, + **optional_params, + } # mutable-ok: merged params are read by AWS helpers bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") if not bucket_name: raise ValueError( @@ -729,7 +732,10 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): if s3_region_name: optional_params = {**optional_params, "aws_region_name": s3_region_name} - request_params: Final = {**litellm_params, **optional_params} + request_params: Final = { + **litellm_params, + **optional_params, + } # mutable-ok: merged params are read by AWS helpers # Sign the request and return a pre-signed request object signed_headers, signed_body = self._sign_s3_request( From 8f998a9ca477942815b525656bbc6f52a9fcecef Mon Sep 17 00:00:00 2001 From: daleselaji-dev <265319989+daleselaji-dev@users.noreply.github.com> Date: Fri, 7 Aug 2026 16:45:06 +0800 Subject: [PATCH 015/266] fix(bedrock): prevent caller AWS identity override --- .../llms/bedrock/batches/transformation.py | 21 ++- litellm/llms/bedrock/common_utils.py | 38 +++++ litellm/llms/bedrock/files/transformation.py | 12 +- .../test_bedrock_files_and_batches.py | 130 ++++++++++++++++++ .../test_bedrock_request_credentials.py | 95 ------------- 5 files changed, 179 insertions(+), 117 deletions(-) delete mode 100644 tests/litellm/llms/bedrock/test_bedrock_request_credentials.py diff --git a/litellm/llms/bedrock/batches/transformation.py b/litellm/llms/bedrock/batches/transformation.py index e92a2e7a00a..04f395f2bf1 100644 --- a/litellm/llms/bedrock/batches/transformation.py +++ b/litellm/llms/bedrock/batches/transformation.py @@ -28,7 +28,11 @@ from litellm.types.llms.openai import ( from litellm.types.utils import LiteLLMBatch, LlmProviders from ..base_aws_llm import BaseAWSLLM -from ..common_utils import CommonBatchFilesUtils, resolve_s3_encryption_key_id +from ..common_utils import ( + CommonBatchFilesUtils, + merge_bedrock_aws_request_params, + resolve_s3_encryption_key_id, +) # Bedrock batch input files are uploaded as # s3://bucket/litellm-bedrock-files-{model, ":" -> "-"}-{uuid4}.jsonl (see @@ -130,10 +134,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): Get the complete URL for Bedrock batch creation. Bedrock batch jobs are created via the model invocation job API. """ - request_params: Final = { - **litellm_params, - **optional_params, - } # mutable-ok: merged params are read by AWS helpers + request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) aws_region_name: Final = self._get_aws_region_name(request_params, model) # Bedrock model invocation job endpoint @@ -236,10 +237,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): # For Bedrock, we need to return a pre-signed request with AWS auth headers # Use common utility for AWS signing - request_params: Final = { - **litellm_params, - **optional_params, - } # mutable-ok: merged params are read by AWS helpers + request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) endpoint_url: Final = ( f"https://bedrock.{self._get_aws_region_name(request_params, model)}.amazonaws.com/model-invocation-job" ) @@ -395,10 +393,7 @@ class BedrockBatchesConfig(BaseAWSLLM, BaseBatchesConfig): endpoint_url: Final = f"https://bedrock.{region}.amazonaws.com/model-invocation-job/{encoded_arn}" # Use common utility for AWS signing - request_params: Final = { - **litellm_params, - **optional_params, - } # mutable-ok: merged params are read by AWS helpers + request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) signed_headers, _ = self.common_utils.sign_aws_request( service_name="bedrock", data={}, # GET request has no body diff --git a/litellm/llms/bedrock/common_utils.py b/litellm/llms/bedrock/common_utils.py index d18cb7d8734..57202f2d626 100644 --- a/litellm/llms/bedrock/common_utils.py +++ b/litellm/llms/bedrock/common_utils.py @@ -36,6 +36,44 @@ class BedrockError(BaseLLMException): pass +_BEDROCK_AWS_AUTH_PARAMETER_KEYS: Final[tuple[str, ...]] = ( + "aws_access_key_id", + "aws_secret_access_key", + "aws_session_token", + "aws_region_name", + "aws_session_name", + "aws_profile_name", + "aws_role_name", + "aws_web_identity_token", + "aws_sts_endpoint", + "aws_external_id", +) + + +def merge_bedrock_aws_request_params( + litellm_params: Mapping[str, Any], + optional_params: Mapping[str, Any], +) -> dict[str, Any]: + """Merge deployment and request parameters without allowing auth escalation. + + Deployment configuration is authoritative for AWS authentication. When a + deployment supplies static credentials, caller-supplied profile/role/token + selectors must not redirect signing to another identity available on the + server. Requests may still provide AWS credentials when the deployment has + no static credentials configured. + """ + request_params: Final = {**optional_params, **litellm_params} # mutable-ok: AWS helpers require a plain dict + has_static_deployment_credentials = all( + isinstance(litellm_params.get(key), str) and bool(litellm_params.get(key)) + for key in ("aws_access_key_id", "aws_secret_access_key", "aws_region_name") + ) + if has_static_deployment_credentials: + for key in _BEDROCK_AWS_AUTH_PARAMETER_KEYS: + if key not in litellm_params: + request_params.pop(key, None) + return request_params + + # Lazy import cache to avoid circular imports and performance impact _get_model_info = None diff --git a/litellm/llms/bedrock/files/transformation.py b/litellm/llms/bedrock/files/transformation.py index cd4f52ddbfe..7663105ba4a 100644 --- a/litellm/llms/bedrock/files/transformation.py +++ b/litellm/llms/bedrock/files/transformation.py @@ -46,7 +46,7 @@ from litellm.types.utils import ExtractedFileData, LlmProviders, SpecialEnums from litellm.utils import get_llm_provider from ..base_aws_llm import BaseAWSLLM -from ..common_utils import BedrockError, resolve_s3_encryption_key_id +from ..common_utils import BedrockError, merge_bedrock_aws_request_params, resolve_s3_encryption_key_id # litellm_params key used to hand the SigV4-signed GET headers from # `transform_file_content_request` to `validate_environment` (the only hook @@ -257,10 +257,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): """ Get the complete S3 URL for the file upload request """ - request_params: Final = { - **litellm_params, - **optional_params, - } # mutable-ok: merged params are read by AWS helpers + request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) bucket_name = litellm_params.get("s3_bucket_name") or os.getenv("AWS_S3_BUCKET_NAME") if not bucket_name: raise ValueError( @@ -732,10 +729,7 @@ class BedrockFilesConfig(BaseAWSLLM, BaseFilesConfig): if s3_region_name: optional_params = {**optional_params, "aws_region_name": s3_region_name} - request_params: Final = { - **litellm_params, - **optional_params, - } # mutable-ok: merged params are read by AWS helpers + request_params: Final = merge_bedrock_aws_request_params(litellm_params, optional_params) # Sign the request and return a pre-signed request object signed_headers, signed_body = self._sign_s3_request( diff --git a/tests/batches_tests/test_bedrock_files_and_batches.py b/tests/batches_tests/test_bedrock_files_and_batches.py index 431d5a2a60c..52e88937916 100644 --- a/tests/batches_tests/test_bedrock_files_and_batches.py +++ b/tests/batches_tests/test_bedrock_files_and_batches.py @@ -389,3 +389,133 @@ def test_bedrock_batch_with_encryption_key_in_post_request(): ) print("SUCCESS: s3_encryption_key_id properly included in AWS POST request") + + +def test_bedrock_file_upload_signing_uses_deployment_credentials(monkeypatch): + from litellm.llms.bedrock.files.transformation import BedrockFilesConfig + + config = BedrockFilesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, "" + + monkeypatch.setattr(config, "_sign_s3_request", capture_signing) + + result = config.transform_create_file_request( + model="", + create_file_data={ + "file": ( + "batch.jsonl", + b'{"custom_id":"req-1","body":{"model":"bedrock/model"}}\n', + "application/jsonl", + ), + "purpose": "batch", + }, + optional_params={}, + litellm_params={ + "s3_bucket_name": "deployment-bucket", + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + }, + ) + + assert "eu-west-1" in result["url"] + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" + assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" + assert captured["optional_params"]["aws_region_name"] == "eu-west-1" + + +def test_bedrock_batch_signing_uses_deployment_credentials(monkeypatch): + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + + config = BedrockBatchesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, b"{}" + + monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) + + result = config.transform_create_batch_request( + model="us.anthropic.claude-haiku-4-5-20251001-v1:0", + create_batch_data={ + "input_file_id": "s3://deployment-bucket/input.jsonl", + "completion_window": "24h", + "endpoint": "/v1/chat/completions", + }, + optional_params={}, + litellm_params={ + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + "aws_batch_role_arn": "arn:aws:iam::123456789012:role/bedrock-batch", + }, + ) + + assert result["url"].startswith("https://bedrock.eu-west-1.amazonaws.com/") + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" + assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" + assert captured["optional_params"]["aws_region_name"] == "eu-west-1" + + +def test_bedrock_batch_retrieval_signing_uses_deployment_credentials(monkeypatch): + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + + config = BedrockBatchesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, b"" + + monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) + + result = config.transform_retrieve_batch_request( + batch_id="arn:aws:bedrock:eu-west-1:123456789012:model-invocation-job/job-1", + optional_params={}, + litellm_params={ + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + }, + ) + + assert result["url"].startswith("https://bedrock.eu-west-1.amazonaws.com/") + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" + assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" + assert captured["optional_params"]["aws_region_name"] == "eu-west-1" + + +def test_bedrock_deployment_credentials_block_caller_profile_override(monkeypatch): + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + + config = BedrockBatchesConfig() + captured = {} + + def capture_signing(**kwargs): + captured.update(kwargs) + return {}, b"{}" + + monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) + + config.transform_create_batch_request( + model="us.anthropic.claude-haiku-4-5-20251001-v1:0", + create_batch_data={ + "input_file_id": "s3://deployment-bucket/input.jsonl", + "completion_window": "24h", + }, + optional_params={"aws_profile_name": "caller-controlled-profile"}, + litellm_params={ + "aws_access_key_id": "deployment-access-key", + "aws_secret_access_key": "deployment-secret", + "aws_region_name": "eu-west-1", + "aws_batch_role_arn": "arn:aws:iam::123456789012:role/bedrock-batch", + }, + ) + + assert "aws_profile_name" not in captured["optional_params"] + assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" diff --git a/tests/litellm/llms/bedrock/test_bedrock_request_credentials.py b/tests/litellm/llms/bedrock/test_bedrock_request_credentials.py deleted file mode 100644 index 72da2daf9b5..00000000000 --- a/tests/litellm/llms/bedrock/test_bedrock_request_credentials.py +++ /dev/null @@ -1,95 +0,0 @@ -from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig -from litellm.llms.bedrock.files.transformation import BedrockFilesConfig - - -def test_bedrock_file_upload_signing_uses_deployment_credentials(monkeypatch): - config = BedrockFilesConfig() - captured = {} - - def capture_signing(**kwargs): - captured.update(kwargs) - return {}, "" - - monkeypatch.setattr(config, "_sign_s3_request", capture_signing) - - result = config.transform_create_file_request( - model="", - create_file_data={ - "file": ( - "batch.jsonl", - b'{"custom_id":"req-1","body":{"model":"bedrock/model"}}\n', - "application/jsonl", - ), - "purpose": "batch", - }, - optional_params={}, - litellm_params={ - "s3_bucket_name": "deployment-bucket", - "aws_access_key_id": "deployment-access-key", - "aws_secret_access_key": "deployment-secret", - "aws_region_name": "eu-west-1", - }, - ) - - assert "eu-west-1" in result["url"] - assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" - assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" - assert captured["optional_params"]["aws_region_name"] == "eu-west-1" - - -def test_bedrock_batch_signing_uses_deployment_credentials(monkeypatch): - config = BedrockBatchesConfig() - captured = {} - - def capture_signing(**kwargs): - captured.update(kwargs) - return {}, b"{}" - - monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) - - result = config.transform_create_batch_request( - model="us.anthropic.claude-haiku-4-5-20251001-v1:0", - create_batch_data={ - "input_file_id": "s3://deployment-bucket/input.jsonl", - "completion_window": "24h", - "endpoint": "/v1/chat/completions", - }, - optional_params={}, - litellm_params={ - "aws_access_key_id": "deployment-access-key", - "aws_secret_access_key": "deployment-secret", - "aws_region_name": "eu-west-1", - "aws_batch_role_arn": "arn:aws:iam::123456789012:role/bedrock-batch", - }, - ) - - assert result["url"].startswith("https://bedrock.eu-west-1.amazonaws.com/") - assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" - assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" - assert captured["optional_params"]["aws_region_name"] == "eu-west-1" - - -def test_bedrock_batch_retrieval_signing_uses_deployment_credentials(monkeypatch): - config = BedrockBatchesConfig() - captured = {} - - def capture_signing(**kwargs): - captured.update(kwargs) - return {}, b"" - - monkeypatch.setattr(config.common_utils, "sign_aws_request", capture_signing) - - result = config.transform_retrieve_batch_request( - batch_id="arn:aws:bedrock:eu-west-1:123456789012:model-invocation-job/job-1", - optional_params={}, - litellm_params={ - "aws_access_key_id": "deployment-access-key", - "aws_secret_access_key": "deployment-secret", - "aws_region_name": "eu-west-1", - }, - ) - - assert result["url"].startswith("https://bedrock.eu-west-1.amazonaws.com/") - assert captured["optional_params"]["aws_access_key_id"] == "deployment-access-key" - assert captured["optional_params"]["aws_secret_access_key"] == "deployment-secret" - assert captured["optional_params"]["aws_region_name"] == "eu-west-1" From 5883aa354d42a3225fef485034e86a52a275cdd7 Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Fri, 7 Aug 2026 04:45:39 -0700 Subject: [PATCH 016/266] fix(router): keep batch fallbacks inside the model group that owns the file A batch or fine-tuning job is created from a file the caller already uploaded, and that file only exists under the credentials of the deployment that stored it. When the router fell back to a different model group it handed that file id to a provider that has never seen it, so the caller got the second provider's complaint about the file id instead of the error that explains what was actually wrong with their request. run_async_fallback now skips fallback targets outside the original model group whenever the request carries input_file_id or training_file. Order-based fallbacks stay inside the group, so retrying across deployments still works. The same handler also crashed with "'NoneType' object has no attribute 'update'" whenever a fallback fired on a request with metadata set to None, which /v1/batches always does when the caller sends no metadata, turning the provider's 400 into a 500. Record the model group with a merge instead of setdefault, and write it to litellm_metadata on the endpoints that use it so the router's bookkeeping no longer lands in the metadata stored on the provider's batch. --- .../router_utils/fallback_event_handlers.py | 41 ++++- .../test_fallback_event_handlers.py | 141 ++++++++++++++++++ tests/test_litellm/test_router.py | 62 ++++++++ 3 files changed, 241 insertions(+), 3 deletions(-) diff --git a/litellm/router_utils/fallback_event_handlers.py b/litellm/router_utils/fallback_event_handlers.py index 1c6bb52ccb8..c4a84a1d61e 100644 --- a/litellm/router_utils/fallback_event_handlers.py +++ b/litellm/router_utils/fallback_event_handlers.py @@ -9,6 +9,7 @@ from litellm.router_utils.add_retry_fallback_headers import ( add_fallback_headers_to_response, get_fallback_error_info, ) +from litellm.router_utils.batch_utils import _get_router_metadata_variable_name from litellm.types.router import LiteLLMParamsTypedDict if TYPE_CHECKING: @@ -82,6 +83,28 @@ def get_fallback_model_group(fallbacks: list[Any], model_group: str) -> tuple[li return fallback_model_group, generic_fallback_idx +PROVIDER_SCOPED_RESOURCE_KEYS: Final = ("input_file_id", "training_file") + + +def _get_fallback_target_model_group(fallback_entry: str | dict[str, object]) -> str | None: + if isinstance(fallback_entry, str): + return fallback_entry + target: Final = fallback_entry.get("model") + return target if isinstance(target, str) else None + + +def references_provider_scoped_resource(kwargs: dict[str, object]) -> bool: + """ + True when the request names a file that only exists under one provider's credentials. + + Batch and fine-tuning jobs are created from a file the caller already uploaded, and + that file lives in the account of the deployment that stored it. Handing the id to a + different model group can only fail, and the second provider's error replaces the + error the caller actually needs to see. + """ + return any(kwargs.get(key) for key in PROVIDER_SCOPED_RESOURCE_KEYS) + + async def run_async_fallback( *args: tuple[Any], litellm_router: LitellmRouter, @@ -120,10 +143,21 @@ async def run_async_fallback( error_from_fallbacks = original_exception fallback_errors = (get_fallback_error_info(original_exception),) + metadata_variable_name: Final = _get_router_metadata_variable_name( + function_name=getattr(kwargs.get("original_function"), "__name__", None) + ) + same_model_group_only: Final = references_provider_scoped_resource(kwargs) for mg in fallback_model_group: if mg == original_model_group: continue + if same_model_group_only and _get_fallback_target_model_group(mg) != original_model_group: + verbose_router_logger.info( + "Skipping fallback to model_group = %s: request is pinned to model_group = %s by its uploaded file", + mask_sensitive_structure(mg), + original_model_group, + ) + continue try: # LOGGING kwargs = litellm_router.log_retry(kwargs=kwargs, e=original_exception) @@ -132,9 +166,10 @@ async def run_async_fallback( kwargs["model"] = mg elif isinstance(mg, dict): kwargs.update(mg) - kwargs.setdefault("metadata", {}).update( - {"model_group": kwargs.get("model", None)} - ) # update model_group used, if fallbacks are done + kwargs[metadata_variable_name] = { + **(kwargs.get(metadata_variable_name) or {}), + "model_group": kwargs.get("model", None), + } # update model_group used, if fallbacks are done fallback_depth = fallback_depth + 1 kwargs["fallback_depth"] = fallback_depth kwargs["max_fallbacks"] = max_fallbacks diff --git a/tests/test_litellm/router_utils/test_fallback_event_handlers.py b/tests/test_litellm/router_utils/test_fallback_event_handlers.py index 98a34de295c..d93aa4ab023 100644 --- a/tests/test_litellm/router_utils/test_fallback_event_handlers.py +++ b/tests/test_litellm/router_utils/test_fallback_event_handlers.py @@ -142,6 +142,147 @@ async def test_run_async_fallback_skips_original_model_group(): assert response._hidden_params["additional_headers"]["x-litellm-attempted-fallbacks"] == 1 +class AttemptRecordingRouter: + def __init__(self): + self.attempted_model_groups = [] + self.received_kwargs = None + + def log_retry(self, kwargs, e): + return kwargs + + async def async_function_with_fallbacks(self, *args, **kwargs): + self.attempted_model_groups.append(kwargs.get("model")) + self.received_kwargs = kwargs + return StreamingWrapper() + + +async def _acreate_batch(*args, **kwargs): + raise AssertionError("only used for its __name__") + + +@pytest.mark.asyncio +async def test_run_async_fallback_keeps_uploaded_file_requests_in_their_model_group(): + """An input_file_id only exists under the credentials of the group it was uploaded + to, so a cross-group fallback can only fail with the wrong provider's error.""" + router = AttemptRecordingRouter() + owning_provider_error = RuntimeError("openai connection error") + + with pytest.raises(RuntimeError, match="openai connection error"): + await run_async_fallback( + litellm_router=router, + fallback_model_group=["azure-group"], + original_model_group="openai-group", + original_exception=owning_provider_error, + max_fallbacks=3, + fallback_depth=0, + model="openai-group", + input_file_id="file-owned-by-openai", + original_function=_acreate_batch, + ) + + assert router.attempted_model_groups == [] + + +@pytest.mark.asyncio +async def test_run_async_fallback_keeps_fine_tuning_requests_in_their_model_group(): + router = AttemptRecordingRouter() + + with pytest.raises(RuntimeError, match="openai connection error"): + await run_async_fallback( + litellm_router=router, + fallback_model_group=["azure-group"], + original_model_group="openai-group", + original_exception=RuntimeError("openai connection error"), + max_fallbacks=3, + fallback_depth=0, + model="openai-group", + training_file="file-owned-by-openai", + ) + + assert router.attempted_model_groups == [] + + +@pytest.mark.asyncio +async def test_run_async_fallback_allows_same_model_group_retry_for_uploaded_file_requests(): + """Order-based fallbacks stay inside the owning group, so they must still run.""" + router = AttemptRecordingRouter() + + await run_async_fallback( + litellm_router=router, + fallback_model_group=[{"model": "openai-group", "_target_order": 2}], + original_model_group="openai-group", + original_exception=RuntimeError("first deployment failed"), + max_fallbacks=3, + fallback_depth=0, + model="openai-group", + input_file_id="file-owned-by-openai", + original_function=_acreate_batch, + ) + + assert router.attempted_model_groups == ["openai-group"] + + +@pytest.mark.asyncio +async def test_run_async_fallback_still_crosses_model_groups_without_an_uploaded_file(): + router = AttemptRecordingRouter() + + await run_async_fallback( + litellm_router=router, + fallback_model_group=["azure-group"], + original_model_group="openai-group", + original_exception=RuntimeError("openai connection error"), + max_fallbacks=3, + fallback_depth=0, + model="openai-group", + ) + + assert router.attempted_model_groups == ["azure-group"] + + +@pytest.mark.asyncio +async def test_run_async_fallback_handles_explicitly_none_metadata(): + """/v1/batches always sets `metadata`, and sets it to None when the caller sent + none, so setdefault() on it hands back None instead of a dict.""" + router = AttemptRecordingRouter() + + await run_async_fallback( + litellm_router=router, + fallback_model_group=["azure-group"], + original_model_group="openai-group", + original_exception=RuntimeError("openai connection error"), + max_fallbacks=3, + fallback_depth=0, + model="openai-group", + metadata=None, + ) + + assert router.received_kwargs["metadata"] == {"model_group": "azure-group"} + + +@pytest.mark.asyncio +async def test_run_async_fallback_records_batch_model_group_outside_provider_metadata(): + """`metadata` on a batch request is forwarded to the provider and stored on the + batch, so the router's own model_group belongs in litellm_metadata.""" + router = AttemptRecordingRouter() + + await run_async_fallback( + litellm_router=router, + fallback_model_group=[{"model": "openai-group", "_target_order": 2}], + original_model_group="openai-group", + original_exception=RuntimeError("first deployment failed"), + max_fallbacks=3, + fallback_depth=0, + model="openai-group", + input_file_id="file-owned-by-openai", + metadata={"caller": "nightly-job"}, + litellm_metadata={"model_group": "openai-group"}, + original_function=_acreate_batch, + ) + + assert router.received_kwargs["metadata"] == {"caller": "nightly-job"} + assert router.received_kwargs["litellm_metadata"]["model_group"] == "openai-group" + + def test_get_fallback_model_group_does_not_mutate_fallbacks(): """A string fallback must be resolved without mutating the caller's fallbacks list, which is the live router config shared across requests.""" diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index 4a3395a7d3f..b76e69bc978 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -6755,6 +6755,68 @@ async def test_acreate_batch_disable_fallbacks_surfaces_owning_provider_error(): assert mock_create.call_args.kwargs["model"] == "owning-model" +@pytest.mark.asyncio +async def test_acreate_batch_surfaces_owning_provider_error_without_disable_fallbacks(): + """The router itself has to keep a batch inside the group that owns the input file: + the proxy only sets disable_fallbacks on the managed-files route, so the caller + otherwise gets the fallback provider's error for a file it never received.""" + from litellm.types.utils import LiteLLMBatch + + router = litellm.Router( + model_list=[ + { + "model_name": "owning-model", + "litellm_params": { + "model": "openai/gpt-4o-mini", + "api_key": "sk-owning", + }, + }, + { + "model_name": "fallback-model", + "litellm_params": { + "model": "azure/gpt-4o-mini", + "api_key": "sk-fallback", + "api_base": "https://fallback.openai.azure.com", + "api_version": "2024-08-01-preview", + }, + }, + ], + fallbacks=[{"owning-model": ["fallback-model"]}], + num_retries=0, + ) + attempted_models = [] + + async def _acreate_batch(model, **kwargs): + attempted_models.append(model) + if model == "owning-model": + raise litellm.APIConnectionError( + message="Connection error - openai is unreachable", + model="openai/gpt-4o-mini", + llm_provider="openai", + ) + return LiteLLMBatch( + id="batch-created-on-the-wrong-provider", + completion_window="24h", + created_at=0, + endpoint="/v1/chat/completions", + input_file_id="file-owned-by-openai", + object="batch", + status="validating", + ) + + with patch.object(router, "_acreate_batch", _acreate_batch): + with pytest.raises(litellm.APIConnectionError, match="openai is unreachable"): + await router.acreate_batch( + model="owning-model", + input_file_id="file-owned-by-openai", + endpoint="/v1/chat/completions", + completion_window="24h", + metadata={"team": "batch-jobs"}, + ) + + assert attempted_models == ["owning-model"] + + @pytest.mark.asyncio async def test_acreate_batch_request_bedrock_tags_override_deployment_tags(): import httpx From d7bc63da5c5eb44daeaaa2a876f757257bb5d68a Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Fri, 7 Aug 2026 05:25:48 -0700 Subject: [PATCH 017/266] style(router): drop the inline comment on the fallback metadata merge --- litellm/router_utils/fallback_event_handlers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/litellm/router_utils/fallback_event_handlers.py b/litellm/router_utils/fallback_event_handlers.py index c4a84a1d61e..00df20be845 100644 --- a/litellm/router_utils/fallback_event_handlers.py +++ b/litellm/router_utils/fallback_event_handlers.py @@ -169,7 +169,7 @@ async def run_async_fallback( kwargs[metadata_variable_name] = { **(kwargs.get(metadata_variable_name) or {}), "model_group": kwargs.get("model", None), - } # update model_group used, if fallbacks are done + } fallback_depth = fallback_depth + 1 kwargs["fallback_depth"] = fallback_depth kwargs["max_fallbacks"] = max_fallbacks From c19ab70d960f16374b5ee31a171c23aa7bc99897 Mon Sep 17 00:00:00 2001 From: "devin-ai-integration[bot]" <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Fri, 7 Aug 2026 20:51:17 +0000 Subject: [PATCH 018/266] fix(proxy): forward resolved provider and deployment pricing in /cost/estimate estimate_cost resolved on-prem aliases (e.g. nvidia/zai-org/glm-5.2) to their underlying model and custom_llm_provider via the router, then called completion_cost without either, so provider inference ran on the bare model and raised "LLM Provider NOT provided"; deployment-configured per-token pricing was dropped too, so priced on-prem deployments estimated 0. The resolver now returns a frozen ResolvedCostModel(model, provider, custom_cost_per_token) and estimate_cost forwards both into completion_cost and surfaces the configured per-token pricing in the response, deriving that pricing as single Final values. Resolves LIT-5210 --- .../cost_tracking_settings.py | 102 +++++++----- .../test_cost_tracking_settings.py | 146 +++++++++++++++--- 2 files changed, 189 insertions(+), 59 deletions(-) diff --git a/litellm/proxy/management_endpoints/cost_tracking_settings.py b/litellm/proxy/management_endpoints/cost_tracking_settings.py index b1e071fa359..5d0feecfdf3 100644 --- a/litellm/proxy/management_endpoints/cost_tracking_settings.py +++ b/litellm/proxy/management_endpoints/cost_tracking_settings.py @@ -10,6 +10,8 @@ PATCH /config/cost_margin_config - Update cost margin configuration POST /cost/estimate - Estimate cost for a given model and token counts """ +from collections.abc import Mapping +from dataclasses import dataclass from typing import Final from fastapi import APIRouter, Depends, HTTPException @@ -24,29 +26,57 @@ from litellm.proxy._types import ( UserAPIKeyAuth, ) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth -from litellm.types.utils import LlmProvidersSet +from litellm.types.utils import CostPerToken, LlmProvidersSet, ModelInfo router: Final = APIRouter() -def _resolve_model_for_cost_lookup(model: str) -> tuple[str, str | None]: +@dataclass(frozen=True, slots=True) +class ResolvedCostModel: + model: str + provider: str | None + custom_cost_per_token: CostPerToken | None + + +def _extract_custom_pricing(litellm_params: Mapping[str, object]) -> CostPerToken | None: + """ + Pull per-token pricing configured on a deployment so on-prem / self-hosted + models (absent from the public cost map) still estimate a real cost. + """ + input_cost: Final = litellm_params.get("input_cost_per_token") + output_cost: Final = litellm_params.get("output_cost_per_token") + + input_price: Final = float(input_cost) if isinstance(input_cost, (int, float)) else None + output_price: Final = float(output_cost) if isinstance(output_cost, (int, float)) else None + + if input_price is None and output_price is None: + return None + + return CostPerToken( + input_cost_per_token=input_price or 0.0, + output_cost_per_token=output_price or 0.0, + ) + + +def _lookup_model_info(model: str) -> ModelInfo | None: + try: + return litellm.get_model_info(model=model) + except Exception: + return None + + +def _resolve_model_for_cost_lookup(model: str) -> ResolvedCostModel: """ Resolve a model name (which may be a router alias/model_group) to the - underlying litellm model name for cost lookup. + underlying litellm model name, provider, and any deployment-configured + pricing used for cost lookup. Args: model: The model name from the request (could be a router alias like 'e-model-router' or an actual model name like 'azure_ai/gpt-4') - - Returns: - Tuple of (resolved_model_name, custom_llm_provider) - - resolved_model_name: The actual model name to use for cost lookup - - custom_llm_provider: The provider if resolved from router, None otherwise """ from litellm.proxy.proxy_server import llm_router - custom_llm_provider: str | None = None - # Try to resolve from router if available if llm_router is not None: try: @@ -57,31 +87,25 @@ def _resolve_model_for_cost_lookup(model: str) -> tuple[str, str | None]: first_deployment: Final = deployments[0] litellm_params: Final = first_deployment.get("litellm_params", {}) model_info: Final = first_deployment.get("model_info", {}) + custom_llm_provider: Final = litellm_params.get("custom_llm_provider") + provider: Final = str(custom_llm_provider) if custom_llm_provider is not None else None + custom_cost_per_token: Final = _extract_custom_pricing(litellm_params) # Check base_model first (needed for Azure custom deployment names) base_model: Final = model_info.get("base_model") or litellm_params.get("base_model") if base_model: verbose_proxy_logger.debug("Resolved model '%s' to base_model '%s' from router", model, base_model) - custom_llm_provider = litellm_params.get("custom_llm_provider") - return ( - str(base_model), - (str(custom_llm_provider) if custom_llm_provider is not None else None), - ) + return ResolvedCostModel(str(base_model), provider, custom_cost_per_token) resolved_model: Final = litellm_params.get("model") - if resolved_model: verbose_proxy_logger.debug("Resolved model '%s' to '%s' from router", model, resolved_model) - custom_llm_provider = litellm_params.get("custom_llm_provider") - return ( - str(resolved_model), - (str(custom_llm_provider) if custom_llm_provider is not None else None), - ) + return ResolvedCostModel(str(resolved_model), provider, custom_cost_per_token) except Exception as e: verbose_proxy_logger.debug("Could not resolve model '%s' from router: %s", model, e) # Return original model if not resolved - return model, custom_llm_provider + return ResolvedCostModel(model, None, None) def _calculate_period_costs(num_requests, cost_per_request, input_cost, output_cost, margin_cost): @@ -450,7 +474,9 @@ async def estimate_cost( from litellm.types.utils import ModelResponse, Usage # Resolve model name (handles router aliases like 'e-model-router' -> 'azure_ai/gpt-4') - resolved_model, resolved_provider = _resolve_model_for_cost_lookup(request.model) + resolved: Final = _resolve_model_for_cost_lookup(request.model) + resolved_model: Final = resolved.model + resolved_provider: Final = resolved.provider verbose_proxy_logger.debug("Cost estimate: request.model='%s' resolved to '%s'", request.model, resolved_model) @@ -480,6 +506,8 @@ async def estimate_cost( cost_per_request: Final = completion_cost( completion_response=mock_response, model=resolved_model, + custom_llm_provider=resolved_provider, + custom_cost_per_token=resolved.custom_cost_per_token, litellm_logging_obj=litellm_logging_obj, ) except Exception as e: @@ -497,20 +525,22 @@ async def estimate_cost( output_cost: Final = cost_breakdown.get("output_cost", 0.0) if cost_breakdown else 0.0 margin_cost: Final = cost_breakdown.get("margin_total_amount", 0.0) if cost_breakdown else 0.0 - # Get model info for per-token pricing display - try: - model_info: Final = litellm.get_model_info(model=resolved_model) - input_cost_per_token = model_info.get("input_cost_per_token") - output_cost_per_token = model_info.get("output_cost_per_token") - custom_llm_provider = model_info.get("litellm_provider") - except Exception: - input_cost_per_token = None - output_cost_per_token = None - custom_llm_provider = None + model_info: Final = _lookup_model_info(resolved_model) + mapped_input_price: Final = model_info.get("input_cost_per_token") if model_info is not None else None + mapped_output_price: Final = model_info.get("output_cost_per_token") if model_info is not None else None + mapped_provider: Final = model_info.get("litellm_provider") if model_info is not None else None - # Use provider from router resolution if not found in model_info - if custom_llm_provider is None and resolved_provider is not None: - custom_llm_provider = resolved_provider + input_cost_per_token: Final = ( + resolved.custom_cost_per_token["input_cost_per_token"] + if resolved.custom_cost_per_token is not None + else mapped_input_price + ) + output_cost_per_token: Final = ( + resolved.custom_cost_per_token["output_cost_per_token"] + if resolved.custom_cost_per_token is not None + else mapped_output_price + ) + custom_llm_provider: Final = mapped_provider if mapped_provider is not None else resolved_provider # Calculate daily and monthly costs ( diff --git a/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py b/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py index bc463d5e75d..c62f0370d46 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py +++ b/tests/test_litellm/proxy/management_endpoints/test_cost_tracking_settings.py @@ -322,9 +322,9 @@ class TestResolveModelForCostLookup: "litellm.proxy.proxy_server.llm_router", mock_router, ): - resolved_model, provider = _resolve_model_for_cost_lookup("gpt-5.3-codex") + resolved = _resolve_model_for_cost_lookup("gpt-5.3-codex") - assert resolved_model == "azure/gpt-4o" + assert resolved.model == "azure/gpt-4o" mock_router.get_model_list.assert_called_once_with(model_name="gpt-5.3-codex") def test_falls_back_to_litellm_params_model_when_no_base_model(self): @@ -352,9 +352,9 @@ class TestResolveModelForCostLookup: "litellm.proxy.proxy_server.llm_router", mock_router, ): - resolved_model, provider = _resolve_model_for_cost_lookup("gpt-4") + resolved = _resolve_model_for_cost_lookup("gpt-4") - assert resolved_model == "openai/gpt-4" + assert resolved.model == "openai/gpt-4" def test_resolves_base_model_from_litellm_params(self): """ @@ -383,9 +383,9 @@ class TestResolveModelForCostLookup: "litellm.proxy.proxy_server.llm_router", mock_router, ): - resolved_model, provider = _resolve_model_for_cost_lookup("my-azure-model") + resolved = _resolve_model_for_cost_lookup("my-azure-model") - assert resolved_model == "azure/gpt-4o-mini" + assert resolved.model == "azure/gpt-4o-mini" def test_returns_original_model_when_no_router(self): """ @@ -399,12 +399,10 @@ class TestResolveModelForCostLookup: "litellm.proxy.proxy_server.llm_router", None, ): - resolved_model, provider = _resolve_model_for_cost_lookup( - "azure/openai/gpt-5.3-codex" - ) + resolved = _resolve_model_for_cost_lookup("azure/openai/gpt-5.3-codex") - assert resolved_model == "azure/openai/gpt-5.3-codex" - assert provider is None + assert resolved.model == "azure/openai/gpt-5.3-codex" + assert resolved.provider is None def test_returns_custom_llm_provider_on_base_model_path(self): """base_model path: the custom_llm_provider from litellm_params is @@ -427,10 +425,10 @@ class TestResolveModelForCostLookup: ] with patch("litellm.proxy.proxy_server.llm_router", mock_router): - resolved_model, provider = _resolve_model_for_cost_lookup("my-azure-model") + resolved = _resolve_model_for_cost_lookup("my-azure-model") - assert resolved_model == "azure/gpt-4o" - assert provider == "azure" + assert resolved.model == "azure/gpt-4o" + assert resolved.provider == "azure" def test_returns_custom_llm_provider_on_resolved_model_path(self): """resolved-model path (no base_model): the custom_llm_provider from @@ -452,10 +450,10 @@ class TestResolveModelForCostLookup: ] with patch("litellm.proxy.proxy_server.llm_router", mock_router): - resolved_model, provider = _resolve_model_for_cost_lookup("gpt-4") + resolved = _resolve_model_for_cost_lookup("gpt-4") - assert resolved_model == "openai/gpt-4" - assert provider == "openai" + assert resolved.model == "openai/gpt-4" + assert resolved.provider == "openai" def test_resolves_base_model_when_deployment_has_no_litellm_params(self): """A deployment can omit litellm_params entirely; base_model from @@ -474,10 +472,10 @@ class TestResolveModelForCostLookup: ] with patch("litellm.proxy.proxy_server.llm_router", mock_router): - resolved_model, provider = _resolve_model_for_cost_lookup("my-azure-model") + resolved = _resolve_model_for_cost_lookup("my-azure-model") - assert resolved_model == "azure/gpt-4o" - assert provider is None + assert resolved.model == "azure/gpt-4o" + assert resolved.provider is None def test_resolves_model_when_deployment_has_no_model_info(self): """A deployment can omit model_info entirely; litellm_params.model must @@ -496,7 +494,109 @@ class TestResolveModelForCostLookup: ] with patch("litellm.proxy.proxy_server.llm_router", mock_router): - resolved_model, provider = _resolve_model_for_cost_lookup("gpt-4") + resolved = _resolve_model_for_cost_lookup("gpt-4") - assert resolved_model == "openai/gpt-4" - assert provider is None + assert resolved.model == "openai/gpt-4" + assert resolved.provider is None + + +class TestEstimateCostOnPremProvider: + """Regression tests for LIT-5210: /cost/estimate on on-prem deployment aliases.""" + + @pytest.mark.asyncio + async def test_estimate_cost_onprem_model_without_pricing(self): + """ + On-prem deployments (custom_llm_provider set, model absent from the cost map) + must not 500 with "LLM Provider NOT provided". The resolved provider has to be + forwarded to completion_cost so provider inference doesn't run on the bare model. + + completion_cost is intentionally NOT mocked. + """ + from litellm.proxy._types import CostEstimateRequest + from litellm.proxy.management_endpoints.cost_tracking_settings import ( + estimate_cost, + ) + + request = CostEstimateRequest( + model="nvidia/zai-org/glm-5.2", + input_tokens=1000, + output_tokens=500, + ) + + mock_router = MagicMock() + mock_router.get_model_list.return_value = [ + { + "model_name": "nvidia/zai-org/glm-5.2", + "litellm_params": { + "model": "zai-org/GLM-5.2", + "custom_llm_provider": "openai", + }, + "model_info": {}, + } + ] + + saved_model_cost = dict(litellm.model_cost) + litellm.register_model( + { + "openai/zai-org/GLM-5.2": { + "input_cost_per_token": 0.0, + "output_cost_per_token": 0.0, + "litellm_provider": "openai", + "mode": "chat", + } + } + ) + try: + with patch("litellm.proxy.proxy_server.llm_router", mock_router): + response = await estimate_cost(request=request, user_api_key_dict=MagicMock()) + finally: + litellm.model_cost = saved_model_cost + + assert response.model == "nvidia/zai-org/glm-5.2" + assert response.provider == "openai" + assert response.cost_per_request == 0.0 + + @pytest.mark.asyncio + async def test_estimate_cost_onprem_model_with_configured_pricing(self): + """ + On-prem deployments with input/output_cost_per_token configured must estimate a + real cost using that pricing, not fall back to 0.0. + + completion_cost is intentionally NOT mocked. + """ + from litellm.proxy._types import CostEstimateRequest + from litellm.proxy.management_endpoints.cost_tracking_settings import ( + estimate_cost, + ) + + request = CostEstimateRequest( + model="nvidia/zai-org/glm-5.2", + input_tokens=1000, + output_tokens=500, + num_requests_per_day=100, + ) + + mock_router = MagicMock() + mock_router.get_model_list.return_value = [ + { + "model_name": "nvidia/zai-org/glm-5.2", + "litellm_params": { + "model": "zai-org/GLM-5.2", + "custom_llm_provider": "openai", + "input_cost_per_token": 0.000001, + "output_cost_per_token": 0.000002, + }, + "model_info": {}, + } + ] + + with patch("litellm.proxy.proxy_server.llm_router", mock_router): + response = await estimate_cost(request=request, user_api_key_dict=MagicMock()) + + assert response.provider == "openai" + assert response.cost_per_request == pytest.approx(0.002) + assert response.input_cost_per_request == pytest.approx(0.001) + assert response.output_cost_per_request == pytest.approx(0.001) + assert response.daily_cost == pytest.approx(0.2) + assert response.input_cost_per_token == pytest.approx(0.000001) + assert response.output_cost_per_token == pytest.approx(0.000002) From 108b0f935a259f0d3b9225ff5b15efcfbd0a766e Mon Sep 17 00:00:00 2001 From: atomic <5234009+atomic@users.noreply.github.com> Date: Tue, 21 Jul 2026 15:49:46 -0700 Subject: [PATCH 019/266] fix(nvidia_nim): preserve image passages and stop sending top_k to /v1/ranking The NVIDIA NIM native /v1/ranking endpoint accepts only model, query, passages, and truncate. The rerank transform stringified structured image documents into text passages, so VL rerank models scored serialized JSON instead of the image, and it mapped Cohere top_n to top_k, which /v1/ranking rejects with a 400 validation error. - preserve structured documents (text, image, mixed) as passages - for nvidia_nim/ranking/ models, keep top_n out of the provider request and truncate the converted response client-side - guard the response document echo for image-only passages Fixes #34165 --- .../rerank/ranking_transformation.py | 116 ++++++++++- .../llms/nvidia_nim/rerank/transformation.py | 29 ++- tests/llm_translation/test_nvidia_nim.py | 180 ++++++++++++++++++ 3 files changed, 314 insertions(+), 11 deletions(-) diff --git a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py index 6671ba09a8a..58f26b2eb75 100644 --- a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py +++ b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py @@ -6,15 +6,25 @@ Use this by passing "nvidia_nim/ranking/" to force the /v1/ranking endpoi Reference: https://build.nvidia.com/nvidia/llama-3_2-nv-rerankqa-1b-v2/deploy """ -from typing import Final +from typing import Any, Final +import httpx + +from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig +from litellm.types.rerank import RerankResponse class NvidiaNimRankingConfig(NvidiaNimRerankConfig): """ Configuration for NVIDIA NIM models that use the /v1/ranking endpoint. - + + The native /v1/ranking request schema accepts only 'model', 'query', + 'passages', and 'truncate' -- requests containing 'top_k' are rejected + with a 400 validation error. Cohere-compatible 'top_n' is therefore + applied client-side by truncating the converted response instead of + being forwarded to the endpoint. + Example: curl -X "POST" 'https://ai.api.nvidia.com/v1/ranking' \ -H 'Accept: application/json' \ @@ -27,6 +37,14 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): }' """ + def __init__(self) -> None: + super().__init__() + # top_n captured in transform_rerank_request and applied in + # transform_rerank_response. The provider config is instantiated + # per-request (see ProviderConfigManager.get_provider_rerank_config), + # so this does not leak across requests. + self._client_side_top_n: int | None = None + def _get_clean_model_name(self, model: str) -> str: """Strip 'nvidia_nim/' and 'ranking/' prefixes from model name.""" # First strip nvidia_nim/ prefix if present @@ -58,6 +76,47 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): return f"{api_base}/v1/ranking" + def map_cohere_rerank_params( + self, + non_default_params: dict | None, + model: str, + drop_params: bool, + query: str, + documents: list[str | dict[str, Any]], + custom_llm_provider: str | None = None, + top_n: int | None = None, + rank_fields: list[str] | None = None, + return_documents: bool | None = True, + max_chunks_per_doc: int | None = None, + max_tokens_per_doc: int | None = None, + instruction: str | None = None, + ) -> dict: + """ + Keep Cohere's top_n as-is instead of mapping it to top_k. + + The native /v1/ranking endpoint rejects top_k, so top_n is applied + client-side after the response is converted. + """ + optional_params = super().map_cohere_rerank_params( + non_default_params=non_default_params, + model=model, + drop_params=drop_params, + query=query, + documents=documents, + custom_llm_provider=custom_llm_provider, + top_n=None, # do not map top_n -> top_k for /v1/ranking + rank_fields=rank_fields, + return_documents=return_documents, + max_chunks_per_doc=max_chunks_per_doc, + max_tokens_per_doc=max_tokens_per_doc, + instruction=instruction, + ) + # /v1/ranking rejects top_k even when passed as a provider-specific param + optional_params.pop("top_k", None) + if top_n is not None: + optional_params["top_n"] = top_n + return optional_params + def transform_rerank_request( self, model: str, @@ -67,11 +126,62 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): ) -> dict: """ Transform request, using clean model name without 'ranking/' prefix. + + top_n / top_k are stripped from the outgoing request: the native + /v1/ranking endpoint accepts only model, query, passages, and + truncate. top_n is stashed and applied client-side in + transform_rerank_response. """ + top_n = optional_rerank_params.get("top_n") + if top_n is not None: + if isinstance(top_n, bool) or not isinstance(top_n, int) or top_n < 1: + raise ValueError(f"top_n must be a positive integer, got: {top_n!r}") + self._client_side_top_n = top_n + clean_model: Final = self._get_clean_model_name(model) + filtered_params: Final = { + k: v for k, v in optional_rerank_params.items() if k not in ("top_n", "top_k") + } return super().transform_rerank_request( model=clean_model, - optional_rerank_params=optional_rerank_params, + optional_rerank_params=filtered_params, headers=headers, litellm_params=litellm_params, ) + + def transform_rerank_response( + self, + model: str, + raw_response: httpx.Response, + model_response: RerankResponse, + logging_obj: LiteLLMLoggingObj, + api_key: str | None = None, + request_data: dict = {}, + optional_params: dict = {}, + litellm_params: dict = {}, + ) -> RerankResponse: + """ + Convert the native ranking response, then apply top_n client-side. + + /v1/ranking returns rankings sorted by relevance, but sort before + truncating in case a server returns them unsorted. + """ + response = super().transform_rerank_response( + model=model, + raw_response=raw_response, + model_response=model_response, + logging_obj=logging_obj, + api_key=api_key, + request_data=request_data, + optional_params=optional_params, + litellm_params=litellm_params, + ) + + top_n = optional_params.get("top_n") or self._client_side_top_n + if top_n is not None and response.results is not None and len(response.results) > top_n: + response.results = sorted( + response.results, + key=lambda result: result["relevance_score"], + reverse=True, + )[:top_n] + return response diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py index aeb1190d0a5..7d4ecdb4cdc 100644 --- a/litellm/llms/nvidia_nim/rerank/transformation.py +++ b/litellm/llms/nvidia_nim/rerank/transformation.py @@ -21,8 +21,9 @@ class NvidiaNimQueryObject(TypedDict): text: Required[str] -class NvidiaNimPassageObject(TypedDict): - text: Required[str] +class NvidiaNimPassageObject(TypedDict, total=False): + text: str + image: str class NvidiaNimRerankRequest(TypedDict, total=False): @@ -53,6 +54,11 @@ class NvidiaNimRerankConfig(BaseRerankConfig): DEFAULT_NIM_RERANK_API_BASE = "https://ai.api.nvidia.com" + # Structured document fields forwarded to the ranking API as-is. + # VL rerank models (e.g. nvidia/llama-nemotron-rerank-vl-1b-v2) accept + # image passages alongside text passages. + SUPPORTED_PASSAGE_FIELDS = ("text", "image") + def __init__(self) -> None: pass @@ -206,11 +212,17 @@ class NvidiaNimRerankConfig(BaseRerankConfig): if isinstance(doc, str): passages.append({"text": doc}) elif isinstance(doc, dict): - # If document is already a dict, check if it has 'text' field - if "text" in doc: - passages.append({"text": doc["text"]}) + # Preserve structured passages (text, image, or mixed) so + # VL rerank models receive image passages intact + supported_fields: NvidiaNimPassageObject = { + field: doc[field] # type: ignore[misc] + for field in self.SUPPORTED_PASSAGE_FIELDS + if field in doc + } + if supported_fields: + passages.append(supported_fields) else: - # Otherwise, stringify the dict + # No supported fields - stringify the dict import json passages.append({"text": json.dumps(doc)}) @@ -304,9 +316,10 @@ class NvidiaNimRerankConfig(BaseRerankConfig): "relevance_score": ranking["logit"], } - # Include document if it was in the original request + # Include document if it was in the original request. + # Image-only passages carry no 'text' field, so guard the lookup. index: int = ranking["index"] - if index < len(original_passages): + if index < len(original_passages) and "text" in original_passages[index]: result_item["document"] = {"text": original_passages[index]["text"]} results.append(result_item) diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 80e764147bb..60d2a960d5b 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -303,3 +303,183 @@ class TestNvidiaNim(BaseLLMRerankTest): ), ): await super().test_basic_rerank(sync_mode=sync_mode) + + +# --------------------------------------------------------------------------- +# Regression tests for https://github.com/BerriAI/litellm/issues/34165 +# +# The native /v1/ranking endpoint accepts only model, query, passages, and +# truncate. Two defects are covered here: +# 1. structured image documents were json.dumps-stringified into text passages +# 2. Cohere top_n was mapped to top_k, which /v1/ranking rejects with a 400 +# --------------------------------------------------------------------------- + +from litellm.llms.nvidia_nim.rerank.ranking_transformation import ( + NvidiaNimRankingConfig, +) +from litellm.types.rerank import RerankResponse + +RANKING_MODEL = "ranking/nvidia/llama-nemotron-rerank-vl-1b-v2" +IMAGE_DOC = {"image": "data:image/jpeg;base64,/9j/4AAQSkZJRg=="} +TEXT_DOC = {"text": "a plain text passage"} +MIXED_DOC = {"text": "caption for the image", "image": "data:image/png;base64,iVBORw0KGgo="} + + +def _build_ranking_request(documents, top_n=None, non_default_params=None): + """Run map_cohere_rerank_params + transform_rerank_request for /v1/ranking.""" + config = NvidiaNimRankingConfig() + optional_params = config.map_cohere_rerank_params( + non_default_params=non_default_params, + model=RANKING_MODEL, + drop_params=False, + query="which passage shows a cat?", + documents=documents, + top_n=top_n, + ) + request_data = config.transform_rerank_request( + model=RANKING_MODEL, + optional_rerank_params=optional_params, + headers={}, + ) + return config, request_data + + +def _build_ranking_response(config, request_data, rankings): + """Run transform_rerank_response against a mocked raw ranking response.""" + raw_response = MagicMock() + raw_response.json.return_value = {"rankings": rankings} + return config.transform_rerank_response( + model=RANKING_MODEL, + raw_response=raw_response, + model_response=RerankResponse(), + logging_obj=MagicMock(), + request_data=request_data, + ) + + +class TestNvidiaNimRankingRequestTransform: + def test_string_documents(self): + _, request_data = _build_ranking_request(["passage one", "passage two"]) + assert request_data["passages"] == [ + {"text": "passage one"}, + {"text": "passage two"}, + ] + + def test_text_object_documents(self): + _, request_data = _build_ranking_request([TEXT_DOC]) + assert request_data["passages"] == [TEXT_DOC] + + def test_image_object_documents_are_preserved(self): + _, request_data = _build_ranking_request([IMAGE_DOC, TEXT_DOC]) + assert request_data["passages"] == [IMAGE_DOC, TEXT_DOC] + + def test_mixed_text_image_documents_are_preserved(self): + _, request_data = _build_ranking_request([MIXED_DOC]) + assert request_data["passages"] == [MIXED_DOC] + + def test_unsupported_dict_documents_are_stringified(self): + doc = {"title": "no supported fields here"} + _, request_data = _build_ranking_request([doc]) + assert request_data["passages"] == [{"text": json.dumps(doc)}] + + def test_top_n_is_not_sent_to_the_ranking_endpoint(self): + _, request_data = _build_ranking_request(["a", "b"], top_n=1) + assert "top_k" not in request_data + assert "top_n" not in request_data + + def test_provider_specific_top_k_is_stripped(self): + _, request_data = _build_ranking_request(["a", "b"], non_default_params={"top_k": 2}) + assert "top_k" not in request_data + + @pytest.mark.parametrize("invalid_top_n", [0, -1, 1.5, "2", True]) + def test_invalid_top_n_raises_value_error(self, invalid_top_n): + with pytest.raises(ValueError, match="top_n"): + _build_ranking_request(["a", "b"], top_n=invalid_top_n) + + +class TestNvidiaNimRankingResponseTransform: + RANKINGS = [ + {"index": 0, "logit": 0.95}, + {"index": 1, "logit": 0.75}, + {"index": 2, "logit": 0.55}, + ] + + def test_top_n_one_truncates_to_best_result(self): + config, request_data = _build_ranking_request(["a", "b", "c"], top_n=1) + response = _build_ranking_response(config, request_data, self.RANKINGS) + assert len(response.results) == 1 + assert response.results[0]["index"] == 0 + + def test_top_n_equal_to_document_count_keeps_all_results(self): + config, request_data = _build_ranking_request(["a", "b", "c"], top_n=3) + response = _build_ranking_response(config, request_data, self.RANKINGS) + assert len(response.results) == 3 + + def test_top_n_greater_than_document_count_keeps_all_results(self): + config, request_data = _build_ranking_request(["a", "b", "c"], top_n=10) + response = _build_ranking_response(config, request_data, self.RANKINGS) + assert len(response.results) == 3 + + def test_top_n_truncation_keeps_most_relevant_results(self): + unsorted_rankings = [ + {"index": 0, "logit": 0.10}, + {"index": 1, "logit": 0.90}, + {"index": 2, "logit": 0.50}, + ] + config, request_data = _build_ranking_request(["a", "b", "c"], top_n=2) + response = _build_ranking_response(config, request_data, unsorted_rankings) + assert [result["index"] for result in response.results] == [1, 2] + + def test_image_only_passages_do_not_break_document_echo(self): + config, request_data = _build_ranking_request([IMAGE_DOC, TEXT_DOC]) + response = _build_ranking_response(config, request_data, self.RANKINGS[:2]) + assert len(response.results) == 2 + # Image-only passage has no text to echo back + assert "document" not in response.results[0] + assert response.results[1]["document"] == {"text": TEXT_DOC["text"]} + + +@pytest.mark.asyncio() +async def test_nvidia_nim_ranking_endpoint_image_documents_and_top_n(): + """ + End-to-end (mocked transport): image documents reach /v1/ranking intact + and top_n is applied client-side instead of being sent as top_k. + """ + mock_response = AsyncMock() + + def return_val(): + return { + "rankings": [ + {"index": 0, "logit": 0.95}, + {"index": 1, "logit": 0.75}, + ], + } + + mock_response.json = return_val + mock_response.headers = {"key": "value"} + mock_response.status_code = 200 + + with patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + return_value=mock_response, + ) as mock_post: + response = await litellm.arerank( + model="nvidia_nim/ranking/nvidia/llama-nemotron-rerank-vl-1b-v2", + query="which passage shows a cat?", + documents=[IMAGE_DOC, TEXT_DOC], + top_n=1, + api_key="fake-api-key", + ) + + mock_post.assert_called_once() + request_data = json.loads(mock_post.call_args.kwargs["data"]) + + assert mock_post.call_args.kwargs["url"] == "https://ai.api.nvidia.com/v1/ranking" + # Image passage preserved as-is, not stringified into text + assert request_data["passages"] == [IMAGE_DOC, TEXT_DOC] + # Neither top_k nor top_n is sent to the native endpoint + assert "top_k" not in request_data + assert "top_n" not in request_data + # top_n applied client-side on the converted response + assert len(response.results) == 1 + assert response.results[0]["index"] == 0 From c0aa6527731f96c71507f7d3b36673d7fced933c Mon Sep 17 00:00:00 2001 From: atomic <5234009+atomic@users.noreply.github.com> Date: Tue, 21 Jul 2026 16:01:12 -0700 Subject: [PATCH 020/266] fix(nvidia_nim): modernize ranking transform annotations, cover retrieval route Satisfy the strict ruff budget gate (UP006/UP045) by using builtin generics and PEP 604 unions in ranking_transformation.py, and add request-transform tests for the default /v1/retrieval/{model}/reranking route: top_n still maps to top_k there, and structured text/image/mixed documents pass through the shared passage preservation. --- tests/llm_translation/test_nvidia_nim.py | 54 ++++++++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 60d2a960d5b..5448fba4d0b 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -317,6 +317,7 @@ class TestNvidiaNim(BaseLLMRerankTest): from litellm.llms.nvidia_nim.rerank.ranking_transformation import ( NvidiaNimRankingConfig, ) +from litellm.llms.nvidia_nim.rerank.transformation import NvidiaNimRerankConfig from litellm.types.rerank import RerankResponse RANKING_MODEL = "ranking/nvidia/llama-nemotron-rerank-vl-1b-v2" @@ -483,3 +484,56 @@ async def test_nvidia_nim_ranking_endpoint_image_documents_and_top_n(): # top_n applied client-side on the converted response assert len(response.results) == 1 assert response.results[0]["index"] == 0 + + +class TestNvidiaNimRetrievalRerankRequestTransform: + """ + The default /v1/retrieval/{model}/reranking route keeps its existing + contract: top_n still maps to top_k, and structured documents now pass + through the same passage preservation as the /v1/ranking route. + """ + + def _build_request(self, documents, top_n=None): + config = NvidiaNimRerankConfig() + optional_params = config.map_cohere_rerank_params( + non_default_params=None, + model="nvidia/llama-3_2-nv-rerankqa-1b-v2", + drop_params=False, + query="which passage shows a cat?", + documents=documents, + top_n=top_n, + ) + return config.transform_rerank_request( + model="nvidia/llama-3_2-nv-rerankqa-1b-v2", + optional_rerank_params=optional_params, + headers={}, + ) + + def test_top_n_still_maps_to_top_k(self): + request_data = self._build_request(["a", "b"], top_n=1) + assert request_data["top_k"] == 1 + assert "top_n" not in request_data + + def test_string_documents_unchanged(self): + request_data = self._build_request(["passage one", "passage two"]) + assert request_data["passages"] == [ + {"text": "passage one"}, + {"text": "passage two"}, + ] + + def test_text_object_documents_unchanged(self): + request_data = self._build_request([TEXT_DOC]) + assert request_data["passages"] == [TEXT_DOC] + + def test_image_object_documents_are_preserved(self): + request_data = self._build_request([IMAGE_DOC, TEXT_DOC]) + assert request_data["passages"] == [IMAGE_DOC, TEXT_DOC] + + def test_mixed_text_image_documents_are_preserved(self): + request_data = self._build_request([MIXED_DOC]) + assert request_data["passages"] == [MIXED_DOC] + + def test_unsupported_dict_documents_are_stringified(self): + doc = {"title": "no supported fields here"} + request_data = self._build_request([doc]) + assert request_data["passages"] == [{"text": json.dumps(doc)}] From 1d7db7564a4d408a1d7d464dfb470682376ba24b Mon Sep 17 00:00:00 2001 From: atomic <5234009+atomic@users.noreply.github.com> Date: Fri, 7 Aug 2026 17:07:14 -0700 Subject: [PATCH 021/266] fix(nvidia_nim): satisfy current lint gates --- .../rerank/ranking_transformation.py | 34 +++++++++++-------- .../llms/nvidia_nim/rerank/transformation.py | 10 +++--- 2 files changed, 24 insertions(+), 20 deletions(-) diff --git a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py index 58f26b2eb75..1b211faf8b3 100644 --- a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py +++ b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py @@ -78,26 +78,26 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): def map_cohere_rerank_params( self, - non_default_params: dict | None, + non_default_params: dict | None, # mutable-ok: matches BaseRerankConfig's request contract model: str, drop_params: bool, query: str, - documents: list[str | dict[str, Any]], + documents: list[str | dict[str, Any]], # mutable-ok: matches BaseRerankConfig's document contract custom_llm_provider: str | None = None, top_n: int | None = None, - rank_fields: list[str] | None = None, + rank_fields: list[str] | None = None, # mutable-ok: matches BaseRerankConfig's field contract return_documents: bool | None = True, max_chunks_per_doc: int | None = None, max_tokens_per_doc: int | None = None, instruction: str | None = None, - ) -> dict: + ) -> dict: # mutable-ok: LiteLLM provider transforms return mutable request dictionaries """ Keep Cohere's top_n as-is instead of mapping it to top_k. The native /v1/ranking endpoint rejects top_k, so top_n is applied client-side after the response is converted. """ - optional_params = super().map_cohere_rerank_params( + optional_params: Final = super().map_cohere_rerank_params( non_default_params=non_default_params, model=model, drop_params=drop_params, @@ -132,14 +132,14 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): truncate. top_n is stashed and applied client-side in transform_rerank_response. """ - top_n = optional_rerank_params.get("top_n") + top_n: Final = optional_rerank_params.get("top_n") if top_n is not None: if isinstance(top_n, bool) or not isinstance(top_n, int) or top_n < 1: raise ValueError(f"top_n must be a positive integer, got: {top_n!r}") self._client_side_top_n = top_n clean_model: Final = self._get_clean_model_name(model) - filtered_params: Final = { + filtered_params: Final = { # mutable-ok: the base transformer requires a mutable request dictionary k: v for k, v in optional_rerank_params.items() if k not in ("top_n", "top_k") } return super().transform_rerank_request( @@ -156,9 +156,9 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): model_response: RerankResponse, logging_obj: LiteLLMLoggingObj, api_key: str | None = None, - request_data: dict = {}, - optional_params: dict = {}, - litellm_params: dict = {}, + request_data: dict | None = None, # mutable-ok: matches BaseRerankConfig's response contract + optional_params: dict | None = None, # mutable-ok: matches BaseRerankConfig's response contract + litellm_params: dict | None = None, # mutable-ok: matches BaseRerankConfig's response contract ) -> RerankResponse: """ Convert the native ranking response, then apply top_n client-side. @@ -166,18 +166,22 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): /v1/ranking returns rankings sorted by relevance, but sort before truncating in case a server returns them unsorted. """ - response = super().transform_rerank_response( + resolved_request_data: Final = request_data or {} # mutable-ok: the base transformer requires a dictionary + resolved_optional_params: Final = optional_params or {} # mutable-ok: response options are keyed lookups + resolved_litellm_params: Final = litellm_params or {} # mutable-ok: the base transformer requires a dictionary + + response: Final = super().transform_rerank_response( model=model, raw_response=raw_response, model_response=model_response, logging_obj=logging_obj, api_key=api_key, - request_data=request_data, - optional_params=optional_params, - litellm_params=litellm_params, + request_data=resolved_request_data, + optional_params=resolved_optional_params, + litellm_params=resolved_litellm_params, ) - top_n = optional_params.get("top_n") or self._client_side_top_n + top_n: Final = resolved_optional_params.get("top_n") or self._client_side_top_n if top_n is not None and response.results is not None and len(response.results) > top_n: response.results = sorted( response.results, diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py index 7d4ecdb4cdc..65cb818295a 100644 --- a/litellm/llms/nvidia_nim/rerank/transformation.py +++ b/litellm/llms/nvidia_nim/rerank/transformation.py @@ -214,11 +214,11 @@ class NvidiaNimRerankConfig(BaseRerankConfig): elif isinstance(doc, dict): # Preserve structured passages (text, image, or mixed) so # VL rerank models receive image passages intact - supported_fields: NvidiaNimPassageObject = { - field: doc[field] # type: ignore[misc] - for field in self.SUPPORTED_PASSAGE_FIELDS - if field in doc - } + supported_fields: NvidiaNimPassageObject = {} # mutable-ok: assembling a request TypedDict + if "text" in doc: + supported_fields["text"] = doc["text"] + if "image" in doc: + supported_fields["image"] = doc["image"] if supported_fields: passages.append(supported_fields) else: From f3640447906f07c5bb0dc59ab454b0ac6d98f665 Mon Sep 17 00:00:00 2001 From: atomic <5234009+atomic@users.noreply.github.com> Date: Fri, 7 Aug 2026 17:14:30 -0700 Subject: [PATCH 022/266] fix(nvidia_nim): scope image passages to ranking route --- .../nvidia_nim/rerank/ranking_transformation.py | 2 ++ litellm/llms/nvidia_nim/rerank/transformation.py | 15 +++++++-------- tests/llm_translation/test_nvidia_nim.py | 11 +++++++---- 3 files changed, 16 insertions(+), 12 deletions(-) diff --git a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py index 1b211faf8b3..f3b7ef7d8b0 100644 --- a/litellm/llms/nvidia_nim/rerank/ranking_transformation.py +++ b/litellm/llms/nvidia_nim/rerank/ranking_transformation.py @@ -37,6 +37,8 @@ class NvidiaNimRankingConfig(NvidiaNimRerankConfig): }' """ + SUPPORTED_PASSAGE_FIELDS: tuple[str, ...] = ("text", "image") + def __init__(self) -> None: super().__init__() # top_n captured in transform_rerank_request and applied in diff --git a/litellm/llms/nvidia_nim/rerank/transformation.py b/litellm/llms/nvidia_nim/rerank/transformation.py index 65cb818295a..bb07f9ec74f 100644 --- a/litellm/llms/nvidia_nim/rerank/transformation.py +++ b/litellm/llms/nvidia_nim/rerank/transformation.py @@ -54,10 +54,9 @@ class NvidiaNimRerankConfig(BaseRerankConfig): DEFAULT_NIM_RERANK_API_BASE = "https://ai.api.nvidia.com" - # Structured document fields forwarded to the ranking API as-is. - # VL rerank models (e.g. nvidia/llama-nemotron-rerank-vl-1b-v2) accept - # image passages alongside text passages. - SUPPORTED_PASSAGE_FIELDS = ("text", "image") + # The legacy retrieval rerank route accepts text passages only. The native + # ranking subclass expands this tuple for VL models that accept images. + SUPPORTED_PASSAGE_FIELDS: tuple[str, ...] = ("text",) def __init__(self) -> None: pass @@ -212,12 +211,12 @@ class NvidiaNimRerankConfig(BaseRerankConfig): if isinstance(doc, str): passages.append({"text": doc}) elif isinstance(doc, dict): - # Preserve structured passages (text, image, or mixed) so - # VL rerank models receive image passages intact + # Preserve only the structured passage fields supported by the + # selected rerank route. supported_fields: NvidiaNimPassageObject = {} # mutable-ok: assembling a request TypedDict - if "text" in doc: + if "text" in self.SUPPORTED_PASSAGE_FIELDS and "text" in doc: supported_fields["text"] = doc["text"] - if "image" in doc: + if "image" in self.SUPPORTED_PASSAGE_FIELDS and "image" in doc: supported_fields["image"] = doc["image"] if supported_fields: passages.append(supported_fields) diff --git a/tests/llm_translation/test_nvidia_nim.py b/tests/llm_translation/test_nvidia_nim.py index 5448fba4d0b..4bad94e0834 100644 --- a/tests/llm_translation/test_nvidia_nim.py +++ b/tests/llm_translation/test_nvidia_nim.py @@ -525,13 +525,16 @@ class TestNvidiaNimRetrievalRerankRequestTransform: request_data = self._build_request([TEXT_DOC]) assert request_data["passages"] == [TEXT_DOC] - def test_image_object_documents_are_preserved(self): + def test_image_object_documents_keep_retrieval_behavior(self): request_data = self._build_request([IMAGE_DOC, TEXT_DOC]) - assert request_data["passages"] == [IMAGE_DOC, TEXT_DOC] + assert request_data["passages"] == [ + {"text": json.dumps(IMAGE_DOC)}, + TEXT_DOC, + ] - def test_mixed_text_image_documents_are_preserved(self): + def test_mixed_text_image_documents_keep_text_only(self): request_data = self._build_request([MIXED_DOC]) - assert request_data["passages"] == [MIXED_DOC] + assert request_data["passages"] == [{"text": MIXED_DOC["text"]}] def test_unsupported_dict_documents_are_stringified(self): doc = {"title": "no supported fields here"} From a2bb97fa7c80a04be85f4b26f7ffb70a988c1a84 Mon Sep 17 00:00:00 2001 From: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Sat, 8 Aug 2026 02:13:27 +0000 Subject: [PATCH 023/266] fix(websearch_interception): satisfy type discipline gate for search metadata forwarding Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- .../websearch_interception/handler.py | 40 ++++++++++++------- 1 file changed, 25 insertions(+), 15 deletions(-) diff --git a/litellm/integrations/websearch_interception/handler.py b/litellm/integrations/websearch_interception/handler.py index 6be64c89828..3e24c64ad82 100644 --- a/litellm/integrations/websearch_interception/handler.py +++ b/litellm/integrations/websearch_interception/handler.py @@ -1288,14 +1288,11 @@ class WebSearchInterceptionLogger(CustomLogger): search_tool: Final = self._select_search_tool_from_router(llm_router=llm_router) search_provider: str | None = None search_litellm_params: dict[str, Any] = {} - search_tool_name: str | None = None + search_tool_name: Final = self._selected_search_tool_name(search_tool=search_tool) if search_tool is not None: await self._authorize_search_tool(search_tool=search_tool, kwargs=kwargs) search_litellm_params = dict(search_tool.get("litellm_params", {}) or {}) search_provider = search_litellm_params.get("search_provider") - selected_tool_name = search_tool.get("search_tool_name") - if isinstance(selected_tool_name, str) and selected_tool_name: - search_tool_name = selected_tool_name # Fallback to perplexity if no router or no search tools configured if not search_provider: @@ -1318,14 +1315,20 @@ class WebSearchInterceptionLogger(CustomLogger): ) ) search_kwargs: Final = { - **{ - key: value - for key, value in search_litellm_params.items() - if key != "search_provider" and value is not None - }, - **({} if search_metadata is None else {"litellm_metadata": search_metadata}), + key: value + for key, value in search_litellm_params.items() + if key != "search_provider" and value is not None } - result: Final = await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) + result: Final = ( + await litellm.asearch(query=query, search_provider=search_provider, **search_kwargs) + if search_metadata is None + else await litellm.asearch( + query=query, + search_provider=search_provider, + litellm_metadata=search_metadata, + **search_kwargs, + ) + ) # Format using transformation function search_result_text: Final = WebSearchTransformation.format_search_response(result) @@ -1386,7 +1389,7 @@ class WebSearchInterceptionLogger(CustomLogger): def _build_search_request_metadata( user_api_key_auth: "UserAPIKeyAuth", search_tool_name: str | None, - ) -> dict[str, object]: + ) -> Mapping[str, object]: """ Spend-tracking metadata for the intercepted search, so its provider cost is logged and billed against the key/user/team that made the originating LLM request instead @@ -1394,16 +1397,23 @@ class WebSearchInterceptionLogger(CustomLogger): """ from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup - user_api_key_metadata: StandardLoggingUserAPIKeyMetadata = ( + user_api_key_metadata: Final[StandardLoggingUserAPIKeyMetadata] = ( LiteLLMProxyRequestSetup.get_sanitized_user_information_from_key(user_api_key_dict=user_api_key_auth) ) - return { + return { # mutable-ok: litellm's metadata channel is a plain dict its logging path reads and enriches **user_api_key_metadata, - **({} if search_tool_name is None else {"model_group": search_tool_name}), + "model_group": search_tool_name, "user_api_key": user_api_key_auth.api_key, "user_api_key_auth": user_api_key_auth, } + @staticmethod + def _selected_search_tool_name(search_tool: Mapping[str, object] | None) -> str | None: + if search_tool is None: + return None + search_tool_name: Final = search_tool.get("search_tool_name") + return search_tool_name if isinstance(search_tool_name, str) and search_tool_name else None + @staticmethod def _get_user_api_key_auth_from_kwargs(kwargs: Mapping[str, object] | None) -> "UserAPIKeyAuth | None": if not kwargs: From 855c49d0ef01a09161f8d2ec195be01447669f1a Mon Sep 17 00:00:00 2001 From: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Date: Sat, 8 Aug 2026 01:44:56 -0700 Subject: [PATCH 024/266] fix(proxy): skip prisma-dependent hooks when no database is attached --- .../storage_backend_service.py | 10 ++ litellm/proxy/utils.py | 5 + .../test_storage_backend_service.py | 127 ++++++++++++++++++ .../utils/proxy_logging/test_lifecycle.py | 94 +++++++++++-- 4 files changed, 228 insertions(+), 8 deletions(-) create mode 100644 tests/test_litellm/proxy/openai_files_endpoint/test_storage_backend_service.py diff --git a/litellm/proxy/openai_files_endpoints/storage_backend_service.py b/litellm/proxy/openai_files_endpoints/storage_backend_service.py index 4c301c96f30..e766f335071 100644 --- a/litellm/proxy/openai_files_endpoints/storage_backend_service.py +++ b/litellm/proxy/openai_files_endpoints/storage_backend_service.py @@ -68,6 +68,16 @@ class StorageBackendFileService: code=400, ) + if target_model_names: + managed_files_hook: Final = proxy_logging_obj.get_proxy_hook("managed_files") + if not isinstance(managed_files_hook, BaseFileEndpoints): + raise ProxyException( + message="Uploading with target_model_names requires a database-connected proxy, and this proxy has no database configured", + type="invalid_request_error", + param="target_model_names", + code=400, + ) + # Extract file information file_content: Final = file_data["content"] filename: Final = file_data.get("filename", "file") diff --git a/litellm/proxy/utils.py b/litellm/proxy/utils.py index e59c6adaf22..5f22ca021ac 100644 --- a/litellm/proxy/utils.py +++ b/litellm/proxy/utils.py @@ -544,6 +544,11 @@ class ProxyLogging: for hook in PROXY_HOOKS: proxy_hook = get_proxy_hook(hook) expected_args = inspect.getfullargspec(proxy_hook).args + if "prisma_client" in expected_args and prisma_client is None: + verbose_proxy_logger.debug( + "Skipping proxy hook %s: it requires a database and no prisma client is configured", hook + ) + continue passed_in_args: dict[str, Any] = {} if "internal_usage_cache" in expected_args: passed_in_args["internal_usage_cache"] = self.internal_usage_cache diff --git a/tests/test_litellm/proxy/openai_files_endpoint/test_storage_backend_service.py b/tests/test_litellm/proxy/openai_files_endpoint/test_storage_backend_service.py new file mode 100644 index 00000000000..07a85a70815 --- /dev/null +++ b/tests/test_litellm/proxy/openai_files_endpoint/test_storage_backend_service.py @@ -0,0 +1,127 @@ +import pytest + +from litellm.llms.base_llm.files.transformation import BaseFileEndpoints +from litellm.proxy._types import ProxyException, UserAPIKeyAuth +from litellm.proxy.openai_files_endpoints import storage_backend_service +from litellm.proxy.openai_files_endpoints.storage_backend_service import ( + StorageBackendFileService, +) + + +class _RecordingStorageBackend: + def __init__(self): + self.upload_calls = [] + + async def upload_file(self, **kwargs): + self.upload_calls.append(kwargs) + return "https://storage.example/blob-1" + + +class _FakeManagedFilesHook(BaseFileEndpoints): + def __init__(self): + self.stored = [] + + async def acreate_file( + self, create_file_request, llm_router, target_model_names_list, litellm_parent_otel_span, user_api_key_dict + ): + raise NotImplementedError + + async def afile_retrieve(self, file_id, litellm_parent_otel_span, llm_router=None): + raise NotImplementedError + + async def afile_list(self, purpose, litellm_parent_otel_span, **data): + raise NotImplementedError + + async def afile_delete(self, file_id, litellm_parent_otel_span, llm_router, **data): + raise NotImplementedError + + async def afile_content(self, file_id, litellm_parent_otel_span, llm_router, **data): + raise NotImplementedError + + async def store_unified_file_id(self, **kwargs): + self.stored.append(kwargs) + + +class _FakeProxyLogging: + def __init__(self, hook): + self._hook = hook + + def get_proxy_hook(self, hook_name): + return self._hook if hook_name == "managed_files" else None + + +def _file_data(): + return {"content": b"x", "filename": "input.jsonl", "content_type": "application/jsonl"} + + +@pytest.mark.asyncio +async def test_upload_with_target_model_names_but_no_hook_raises_before_uploading(monkeypatch): + backend = _RecordingStorageBackend() + monkeypatch.setattr(storage_backend_service, "get_storage_backend", lambda name: backend) + + with pytest.raises(ProxyException) as exc_info: + await StorageBackendFileService.upload_file_to_storage_backend( + file_data=_file_data(), + target_storage="azure_storage", + target_model_names=["gpt-x"], + purpose="batch", + proxy_logging_obj=_FakeProxyLogging(hook=None), + user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"), + ) + + snapshot = { + "code": exc_info.value.code, + "message_names_requirement": "requires a database-connected proxy" in exc_info.value.message, + "upload_calls": backend.upload_calls, + } + assert snapshot == {"code": "400", "message_names_requirement": True, "upload_calls": []} + + +@pytest.mark.asyncio +async def test_upload_without_target_model_names_skips_hook_requirement(monkeypatch): + backend = _RecordingStorageBackend() + monkeypatch.setattr(storage_backend_service, "get_storage_backend", lambda name: backend) + + file_object = await StorageBackendFileService.upload_file_to_storage_backend( + file_data=_file_data(), + target_storage="azure_storage", + target_model_names=[], + purpose="batch", + proxy_logging_obj=_FakeProxyLogging(hook=None), + user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"), + ) + + snapshot = { + "upload_count": len(backend.upload_calls), + "id_prefix": file_object.id.split("-")[0], + } + assert snapshot == {"upload_count": 1, "id_prefix": "file"} + + +@pytest.mark.asyncio +async def test_upload_with_target_model_names_and_hook_stores_unified_id(monkeypatch): + backend = _RecordingStorageBackend() + monkeypatch.setattr(storage_backend_service, "get_storage_backend", lambda name: backend) + hook = _FakeManagedFilesHook() + + file_object = await StorageBackendFileService.upload_file_to_storage_backend( + file_data=_file_data(), + target_storage="azure_storage", + target_model_names=["gpt-x"], + purpose="batch", + proxy_logging_obj=_FakeProxyLogging(hook=hook), + user_api_key_dict=UserAPIKeyAuth(api_key="sk-test"), + ) + + snapshot = { + "upload_count": len(backend.upload_calls), + "store_count": len(hook.stored), + "stored_id_matches_response": hook.stored[0]["file_id"] == file_object.id, + "model_mappings": hook.stored[0]["model_mappings"], + } + assert snapshot == { + "upload_count": 1, + "store_count": 1, + "stored_id_matches_response": True, + "model_mappings": {"gpt-x": "https://storage.example/blob-1"}, + } diff --git a/tests/test_litellm/proxy/utils/proxy_logging/test_lifecycle.py b/tests/test_litellm/proxy/utils/proxy_logging/test_lifecycle.py index e33da672599..cf906259246 100644 --- a/tests/test_litellm/proxy/utils/proxy_logging/test_lifecycle.py +++ b/tests/test_litellm/proxy/utils/proxy_logging/test_lifecycle.py @@ -8,7 +8,6 @@ because they are direct dependents on the lifecycle state. from __future__ import annotations -import asyncio from typing import Any, Dict, List from unittest.mock import AsyncMock, MagicMock, patch @@ -17,7 +16,6 @@ import pytest import litellm from litellm.proxy.common_utils.user_api_key_cache import UserApiKeyCache from litellm.proxy.utils import ( - InternalUsageCache, ProxyLogging, ) @@ -102,9 +100,7 @@ def test_update_values_with_no_args_is_noop(proxy_logging): def test_update_values_invalid_type_for_alerting_raises(proxy_logging): - proxy_logging.slack_alerting_instance = MagicMock( - update_values=MagicMock(side_effect=TypeError("bad type")) - ) + proxy_logging.slack_alerting_instance = MagicMock(update_values=MagicMock(side_effect=TypeError("bad type"))) with pytest.raises(TypeError): proxy_logging.update_values(alerting={"not": "a list"}) # type: ignore[arg-type] @@ -190,6 +186,90 @@ def test_add_proxy_hooks_registers_callbacks(proxy_logging, monkeypatch): } +def _stub_hook_classes(): + class _PrismaFreeHook: + def __init__(self, internal_usage_cache): + self.internal_usage_cache = internal_usage_cache + + class _PrismaRequiringHook: + def __init__(self, internal_usage_cache, prisma_client): + self.internal_usage_cache = internal_usage_cache + self.prisma_client = prisma_client + + class _PrismaOnlyHook: + def __init__(self, prisma_client): + self.prisma_client = prisma_client + + return { + "cache_control_check": _PrismaFreeHook, + "needs_db_hook": _PrismaRequiringHook, + "db_only_hook": _PrismaOnlyHook, + } + + +def test_add_proxy_hooks_skips_prisma_requiring_hook_when_no_db(proxy_logging, monkeypatch): + hook_classes = _stub_hook_classes() + registered: List[Any] = [] + + from litellm.proxy import utils as utils_mod + + monkeypatch.setattr(utils_mod, "PROXY_HOOKS", list(hook_classes.keys())) + monkeypatch.setattr(utils_mod, "get_proxy_hook", hook_classes.__getitem__) + monkeypatch.setattr( + litellm.logging_callback_manager, + "add_litellm_callback", + lambda cb: registered.append(cb), + ) + + with patch("litellm.proxy.proxy_server.prisma_client", None): + proxy_logging._add_proxy_hooks(llm_router=None) + + snapshot = { + "mapping_keys": list(proxy_logging.proxy_hook_mapping.keys()), + "registered_types": [type(r).__name__ for r in registered], + "needs_db_hook_lookup": proxy_logging.get_proxy_hook("needs_db_hook"), + "db_only_hook_lookup": proxy_logging.get_proxy_hook("db_only_hook"), + } + assert snapshot == { + "mapping_keys": ["cache_control_check"], + "registered_types": ["_PrismaFreeHook"], + "needs_db_hook_lookup": None, + "db_only_hook_lookup": None, + } + + +def test_add_proxy_hooks_registers_prisma_requiring_hook_with_db(proxy_logging, monkeypatch): + hook_classes = _stub_hook_classes() + registered: List[Any] = [] + fake_prisma = MagicMock() + + from litellm.proxy import utils as utils_mod + + monkeypatch.setattr(utils_mod, "PROXY_HOOKS", list(hook_classes.keys())) + monkeypatch.setattr(utils_mod, "get_proxy_hook", hook_classes.__getitem__) + monkeypatch.setattr( + litellm.logging_callback_manager, + "add_litellm_callback", + lambda cb: registered.append(cb), + ) + + with patch("litellm.proxy.proxy_server.prisma_client", fake_prisma): + proxy_logging._add_proxy_hooks(llm_router=None) + + snapshot = { + "mapping_keys": list(proxy_logging.proxy_hook_mapping.keys()), + "registered_count": len(registered), + "needs_db_hook_got_prisma": proxy_logging.proxy_hook_mapping["needs_db_hook"].prisma_client is fake_prisma, + "db_only_hook_got_prisma": proxy_logging.proxy_hook_mapping["db_only_hook"].prisma_client is fake_prisma, + } + assert snapshot == { + "mapping_keys": ["cache_control_check", "needs_db_hook", "db_only_hook"], + "registered_count": 3, + "needs_db_hook_got_prisma": True, + "db_only_hook_got_prisma": True, + } + + def test_add_proxy_hooks_unknown_hook_raises(proxy_logging, monkeypatch): from litellm.proxy import utils as utils_mod @@ -267,9 +347,7 @@ def test_init_litellm_callbacks_replaces_string_with_instance(proxy_logging, mon snapshot = { "replaced_first_item": litellm.callbacks[0] is sentinel_instance, "callbacks_grew_with_service": len(litellm.callbacks) >= 2, - "service_logging_appended": any( - "ServiceLogging" in type(c).__name__ for c in litellm.callbacks - ), + "service_logging_appended": any("ServiceLogging" in type(c).__name__ for c in litellm.callbacks), } assert snapshot == { "replaced_first_item": True, From 0d7f7c689a58aeea6cd26b8cb9fba90b31be8955 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Sat, 8 Aug 2026 12:19:22 -0700 Subject: [PATCH 025/266] test: repair stale CircleCI contracts --- litellm/proxy/db/autorouter_session_rollup.py | 47 +++++++++++++++++ .../auto_router_endpoints.py | 50 +------------------ tests/agent_tests/test_a2a_agent.py | 2 +- .../test_proxy_budget_reset.py | 24 ++++++--- .../base_responses_api.py | 4 +- .../spend/test_autorouter_session_rollup.py | 16 +++--- .../src/components/team/TeamInfo.test.tsx | 23 +-------- ui/litellm-dashboard/src/lib/http/schema.d.ts | 2 +- 8 files changed, 81 insertions(+), 87 deletions(-) diff --git a/litellm/proxy/db/autorouter_session_rollup.py b/litellm/proxy/db/autorouter_session_rollup.py index b1f074c26b7..9732b1d7402 100644 --- a/litellm/proxy/db/autorouter_session_rollup.py +++ b/litellm/proxy/db/autorouter_session_rollup.py @@ -33,6 +33,53 @@ if TYPE_CHECKING: CACHE_TTL_5M_SECONDS: Final = 300 CACHE_TTL_1H_SECONDS: Final = 3600 +AUTOROUTER_BENCHMARKS_SQL: Final = """ +WITH windowed AS ( + SELECT * FROM "LiteLLM_AutoRouterSession" + WHERE last_turn_at >= $1::timestamp AND first_turn_at < $2::timestamp +), +tier_maps AS ( + SELECT router_name, router_type, jsonb_object_agg(tier, tier_turns) AS tier_turns + FROM ( + SELECT router_name, router_type, kv.key AS tier, SUM((kv.value)::int)::int AS tier_turns + FROM windowed, LATERAL jsonb_each_text(tier_turns) AS kv + GROUP BY router_name, router_type, kv.key + ) per_tier + GROUP BY router_name, router_type +) +SELECT + agg.*, + COALESCE(tier_maps.tier_turns, '{}'::jsonb) AS tier_turns +FROM ( +SELECT + router_name, + router_type, + COUNT(*)::int AS sessions, + COALESCE(SUM(turns), 0)::int AS turns, + COALESCE(SUM(unordered_turns), 0)::int AS unordered_turns, + COALESCE(SUM(covered_turns), 0)::int AS covered_turns, + COALESCE(SUM(cache_hits), 0)::int AS cache_hits, + COALESCE(SUM(same_model_turns), 0)::int AS same_model_turns, + COALESCE(SUM(same_model_hits), 0)::int AS same_model_hits, + COALESCE(SUM(first_visit_turns), 0)::int AS first_visit_turns, + COALESCE(SUM(first_visit_hits), 0)::int AS first_visit_hits, + COALESCE(SUM(return_turns), 0)::int AS return_turns, + COALESCE(SUM(return_hits), 0)::int AS return_hits, + COALESCE(SUM(return_expired_misses), 0)::int AS return_expired_misses, + COALESCE(SUM(return_within_ttl_misses), 0)::int AS return_within_ttl_misses, + COALESCE(SUM(ttl_5m_turns), 0)::int AS ttl_5m_turns, + COALESCE(SUM(ttl_1h_turns), 0)::int AS ttl_1h_turns, + COALESCE(SUM(total_tokens), 0)::bigint AS total_tokens, + COALESCE(SUM(spend), 0)::float8 AS spend, + COALESCE(SUM(saved_spend), 0)::float8 AS saved_spend, + COALESCE(SUM(EXTRACT(EPOCH FROM (last_turn_at - first_turn_at))), 0)::float8 AS session_seconds +FROM windowed +GROUP BY router_name, router_type +) agg +LEFT JOIN tier_maps USING (router_name, router_type) +ORDER BY agg.spend DESC +""" + @dataclass(frozen=True, slots=True) class AutoRouterTurnTransaction: diff --git a/litellm/proxy/management_endpoints/auto_router_endpoints.py b/litellm/proxy/management_endpoints/auto_router_endpoints.py index d4221845b0c..8b6aafea751 100644 --- a/litellm/proxy/management_endpoints/auto_router_endpoints.py +++ b/litellm/proxy/management_endpoints/auto_router_endpoints.py @@ -26,6 +26,7 @@ from litellm.proxy.auth.auth_checks import ( can_key_call_resolved_model, ) from litellm.proxy.auth.user_api_key_auth import user_api_key_auth +from litellm.proxy.db.autorouter_session_rollup import AUTOROUTER_BENCHMARKS_SQL from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup from litellm.repositories.team_repository import TeamRepository from litellm.router_strategy.complexity_router import ComplexityRouter @@ -285,53 +286,6 @@ class _SessionAggRow(BaseModel): _SESSION_AGG_ROWS: Final = TypeAdapter(list[_SessionAggRow]) -_BENCHMARKS_SQL: Final = """ -WITH windowed AS ( - SELECT * FROM "LiteLLM_AutoRouterSession" - WHERE last_turn_at >= $1::timestamp AND first_turn_at < $2::timestamp -), -tier_maps AS ( - SELECT router_name, router_type, jsonb_object_agg(tier, tier_turns) AS tier_turns - FROM ( - SELECT router_name, router_type, kv.key AS tier, SUM((kv.value)::int)::int AS tier_turns - FROM windowed, LATERAL jsonb_each_text(tier_turns) AS kv - GROUP BY router_name, router_type, kv.key - ) per_tier - GROUP BY router_name, router_type -) -SELECT - agg.*, - COALESCE(tier_maps.tier_turns, '{}'::jsonb) AS tier_turns -FROM ( -SELECT - router_name, - router_type, - COUNT(*)::int AS sessions, - COALESCE(SUM(turns), 0)::int AS turns, - COALESCE(SUM(unordered_turns), 0)::int AS unordered_turns, - COALESCE(SUM(covered_turns), 0)::int AS covered_turns, - COALESCE(SUM(cache_hits), 0)::int AS cache_hits, - COALESCE(SUM(same_model_turns), 0)::int AS same_model_turns, - COALESCE(SUM(same_model_hits), 0)::int AS same_model_hits, - COALESCE(SUM(first_visit_turns), 0)::int AS first_visit_turns, - COALESCE(SUM(first_visit_hits), 0)::int AS first_visit_hits, - COALESCE(SUM(return_turns), 0)::int AS return_turns, - COALESCE(SUM(return_hits), 0)::int AS return_hits, - COALESCE(SUM(return_expired_misses), 0)::int AS return_expired_misses, - COALESCE(SUM(return_within_ttl_misses), 0)::int AS return_within_ttl_misses, - COALESCE(SUM(ttl_5m_turns), 0)::int AS ttl_5m_turns, - COALESCE(SUM(ttl_1h_turns), 0)::int AS ttl_1h_turns, - COALESCE(SUM(total_tokens), 0)::bigint AS total_tokens, - COALESCE(SUM(spend), 0)::float8 AS spend, - COALESCE(SUM(saved_spend), 0)::float8 AS saved_spend, - COALESCE(SUM(EXTRACT(EPOCH FROM (last_turn_at - first_turn_at))), 0)::float8 AS session_seconds -FROM windowed -GROUP BY router_name, router_type -) agg -LEFT JOIN tier_maps USING (router_name, router_type) -ORDER BY agg.spend DESC -""" - def _parse_benchmark_day(value: str) -> datetime: try: @@ -455,7 +409,7 @@ async def get_auto_router_benchmarks( raise HTTPException(status_code=400, detail="end_date must not be earlier than start_date") raw_rows: Final = await prisma_client.db.query_raw( - _BENCHMARKS_SQL, + AUTOROUTER_BENCHMARKS_SQL, start_day.isoformat(), (end_day + timedelta(days=1)).isoformat(), ) diff --git a/tests/agent_tests/test_a2a_agent.py b/tests/agent_tests/test_a2a_agent.py index f5ad9601369..1f72ced64f1 100644 --- a/tests/agent_tests/test_a2a_agent.py +++ b/tests/agent_tests/test_a2a_agent.py @@ -40,7 +40,7 @@ class MockA2AClient: name="mock-agent", url="http://mock-agent.local" ) - async def send_message(self, request): + async def send_message(self, request, *, context=None): from a2a.compat.v0_3.conversions import pb2_v10 for text in ("hel", "hello"): diff --git a/tests/litellm_utils_tests/test_proxy_budget_reset.py b/tests/litellm_utils_tests/test_proxy_budget_reset.py index 44da3ea06a0..00d5380b2f4 100644 --- a/tests/litellm_utils_tests/test_proxy_budget_reset.py +++ b/tests/litellm_utils_tests/test_proxy_budget_reset.py @@ -622,8 +622,12 @@ async def test_service_logger_keys_success(): logger success hook is called with the correct event metadata and no exception is logged. """ keys = [ - {"id": "key1", "spend": 10.0, "budget_duration": 60, "token": "key1"}, - {"id": "key2", "spend": 15.0, "budget_duration": 60, "token": "key2"}, + _attrify( + {"id": "key1", "spend": 10.0, "budget_duration": 60, "token": "key1"} + ), + _attrify( + {"id": "key2", "spend": 15.0, "budget_duration": 60, "token": "key2"} + ), ] prisma_client = MagicMock() prisma_client.get_data = AsyncMock(return_value=keys) @@ -740,8 +744,12 @@ async def test_service_logger_users_success(): the correct metadata and no exception is logged. """ users = [ - {"id": "user1", "spend": 20.0, "budget_duration": 120, "user_id": "user1"}, - {"id": "user2", "spend": 25.0, "budget_duration": 120, "user_id": "user2"}, + _attrify( + {"id": "user1", "spend": 20.0, "budget_duration": 120, "user_id": "user1"} + ), + _attrify( + {"id": "user2", "spend": 25.0, "budget_duration": 120, "user_id": "user2"} + ), ] prisma_client = MagicMock() prisma_client.get_data = AsyncMock(return_value=users) @@ -853,8 +861,12 @@ async def test_service_logger_teams_success(): the proper metadata and nothing is logged as an exception. """ teams = [ - {"id": "team1", "spend": 30.0, "budget_duration": 180, "team_id": "team1"}, - {"id": "team2", "spend": 35.0, "budget_duration": 180, "team_id": "team2"}, + _attrify( + {"id": "team1", "spend": 30.0, "budget_duration": 180, "team_id": "team1"} + ), + _attrify( + {"id": "team2", "spend": 35.0, "budget_duration": 180, "team_id": "team2"} + ), ] prisma_client = MagicMock() prisma_client.get_data = AsyncMock(return_value=teams) diff --git a/tests/llm_responses_api_testing/base_responses_api.py b/tests/llm_responses_api_testing/base_responses_api.py index 407091a65b3..f5751aa79e8 100644 --- a/tests/llm_responses_api_testing/base_responses_api.py +++ b/tests/llm_responses_api_testing/base_responses_api.py @@ -338,7 +338,7 @@ class BaseResponsesAPITest(ABC): ) assert result is not None assert result.id == response.id - assert result.output == response.output + assert result.output_text == response.output_text else: raise ValueError("response is not a ResponsesAPIResponse") else: @@ -352,7 +352,7 @@ class BaseResponsesAPITest(ABC): ) assert result is not None assert result.id == response.id - assert result.output == response.output + assert result.output_text == response.output_text else: raise ValueError("response is not a ResponsesAPIResponse") diff --git a/tests/proxy_behavior/spend/test_autorouter_session_rollup.py b/tests/proxy_behavior/spend/test_autorouter_session_rollup.py index 65b70f13a3b..7bc61c40ea0 100644 --- a/tests/proxy_behavior/spend/test_autorouter_session_rollup.py +++ b/tests/proxy_behavior/spend/test_autorouter_session_rollup.py @@ -12,8 +12,10 @@ from typing import Final import pytest -from litellm.proxy.db.autorouter_session_rollup import UPSERT_AUTOROUTER_SESSION_SQL -from litellm.proxy.management_endpoints.auto_router_endpoints import _BENCHMARKS_SQL +from litellm.proxy.db.autorouter_session_rollup import ( + AUTOROUTER_BENCHMARKS_SQL, + UPSERT_AUTOROUTER_SESSION_SQL, +) pytestmark = pytest.mark.asyncio(loop_scope="session") @@ -164,7 +166,7 @@ async def test_the_benchmarks_aggregate_reads_only_overlapping_sessions(db): await _turn(db, key, "A", T0 - timedelta(days=40), session_id=out_of_window, router=router) rows = await db.query_raw( - _BENCHMARKS_SQL, + AUTOROUTER_BENCHMARKS_SQL, (T0 - timedelta(days=1)).isoformat(), (T0 + timedelta(days=1)).isoformat(), ) @@ -186,7 +188,7 @@ async def test_a_reconfigured_alias_reports_each_router_type_as_its_own_group(db await _turn(db, key, "A", T0 + timedelta(seconds=10), session_id=f"s-{uuid.uuid4()}", router=router, router_type="quality") rows = await db.query_raw( - _BENCHMARKS_SQL, + AUTOROUTER_BENCHMARKS_SQL, (T0 - timedelta(days=1)).isoformat(), (T0 + timedelta(days=1)).isoformat(), ) @@ -248,7 +250,7 @@ async def test_the_benchmarks_aggregate_sums_tier_turns_across_sessions(db): await _turn(db, key, "C", T0 + timedelta(seconds=30), session_id=f"s-{uuid.uuid4()}", router=router, tier=None) rows = await db.query_raw( - _BENCHMARKS_SQL, + AUTOROUTER_BENCHMARKS_SQL, (T0 - timedelta(days=1)).isoformat(), (T0 + timedelta(days=1)).isoformat(), ) @@ -275,7 +277,7 @@ async def test_tier_maps_stay_separate_per_router_type_on_a_reconfigured_alias(d ) rows = await db.query_raw( - _BENCHMARKS_SQL, + AUTOROUTER_BENCHMARKS_SQL, (T0 - timedelta(days=1)).isoformat(), (T0 + timedelta(days=1)).isoformat(), ) @@ -289,7 +291,7 @@ async def test_a_window_with_no_tiered_turns_aggregates_to_an_empty_map(db): await _turn(db, key, "A", T0, session_id=f"s-{uuid.uuid4()}", router=router, tier=None) rows = await db.query_raw( - _BENCHMARKS_SQL, + AUTOROUTER_BENCHMARKS_SQL, (T0 - timedelta(days=1)).isoformat(), (T0 + timedelta(days=1)).isoformat(), ) diff --git a/ui/litellm-dashboard/src/components/team/TeamInfo.test.tsx b/ui/litellm-dashboard/src/components/team/TeamInfo.test.tsx index 513719a2ad9..50e10285148 100644 --- a/ui/litellm-dashboard/src/components/team/TeamInfo.test.tsx +++ b/ui/litellm-dashboard/src/components/team/TeamInfo.test.tsx @@ -919,7 +919,7 @@ describe("TeamInfoView", () => { }); }; - it("prefills pairs from team metadata, hides UI-managed keys, and round-trips typed values on save", async () => { + it("should preserve metadata types and hide managed keys", async () => { const user = userEvent.setup({ delay: null }); vi.mocked(networking.teamInfoCall).mockResolvedValue( createMockTeamData({ @@ -964,27 +964,6 @@ describe("TeamInfoView", () => { expect(updateArg.model_tpm_limit).toEqual({ "gpt-4": 100 }); }); - it("includes a newly added pair in the team update", async () => { - const user = userEvent.setup({ delay: null }); - vi.mocked(networking.teamInfoCall).mockResolvedValue(createMockTeamData({ models: ["gpt-4"] })); - vi.mocked(networking.teamUpdateCall).mockResolvedValue({ data: {}, team_id: "123" } as any); - - renderWithProviders(); - await openSettingsEditor(user); - - await user.click(screen.getByRole("button", { name: /add key-value pair/i })); - await user.type(screen.getByPlaceholderText("Key"), "cost_center"); - await user.type(screen.getByPlaceholderText("Value"), "eng-1"); - - await user.click(screen.getByRole("button", { name: /save changes/i })); - - await waitFor(() => { - expect(networking.teamUpdateCall).toHaveBeenCalled(); - }); - - expect(vi.mocked(networking.teamUpdateCall).mock.calls[0][1].metadata).toMatchObject({ cost_center: "eng-1" }); - }); - it("should keep declared keys as ordinary prefilled rows and submit the edited value", async () => { const user = userEvent.setup({ delay: null }); vi.mocked(useTeamMetadataSchema).mockReturnValue({ diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index a75c23da1cf..2fc70883c5e 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -24313,7 +24313,7 @@ export interface components { * @description Default role assigned to new users created * @default internal_user_viewer */ - user_role: ("proxy_admin" | "proxy_admin_viewer" | "internal_user" | "internal_user_viewer") | null; + user_role: ("internal_user" | "internal_user_viewer" | "proxy_admin" | "proxy_admin_viewer") | null; }; /** * DefaultTeamSSOParams From 4150248095bd5a43e44d70c3b3d94eea74069611 Mon Sep 17 00:00:00 2001 From: Mateo Wang <277851410+mateo-berri@users.noreply.github.com> Date: Sat, 8 Aug 2026 12:38:23 -0700 Subject: [PATCH 026/266] chore: remove pre-commit rule some users do not use make pre-commit as it is a multi-minute process. I personally use it but I want users themselves to decide whether to pre-commit before each commit or not, based on what works best for them --- CLAUDE.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index abd5993eb1a..b0da2970fc8 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -41,9 +41,7 @@ Python max line length is 120, not 88 When you fix violations gated by `ruff-strict-budget.json`, `type-discipline-budget.json`, or `basedpyright-code-budget.json`, run `make lint-budget-update` and commit the lowered limits so the ceilings ratchet down instead of leaving stale headroom. It measures the working tree, so it must contain exactly the fixes you're committing -Run `make check` (formerly `make pre-commit`, which still works as an alias) before every commit, merge commits included. It runs the CI-gating lint scoped to your staged files, so stage everything you intend to commit first; it warns about changed files you left unstaged and names the checks that were skipped because of them. With nothing staged it instead checks the working tree's diff against the merge base with origin/litellm_internal_staging, which is how you predict the CI lint on an already-committed branch, e.g. right after a merge commit. Deleted files count toward which checks run (a deletion alone can turn CI red) in both modes - -`make check` saves its complete output to a log file in .git (overwriting previous logs) and prints that path as its first and last output lines. To inspect a run, read or grep that log instead of re-running the multi-minute checks just to see a different slice +`make check` (f.k.a. `make pre-commit`, which still works identically as an alias) saves its complete output to a log file in .git (overwriting previous logs) and prints that path as its first and last output lines. To inspect a run, read or grep that log instead of re-running the multi-minute checks just to see a different slice If you're trying to create a new function that relies on untyped stuff, instead of adding more Any's and pushing `reportAny` / `reportExplicitAny` closer to their basedpyright ceilings, just validate it in the caller with Pydantic (a model or `TypeAdapter` that returns the typed thing or raises will do) and then pass the now typed variable in From 12aeb53aec57b2967a50cb826bf0ef2167bc632c Mon Sep 17 00:00:00 2001 From: "devin-ai-integration[bot]" <158243242+devin-ai-integration[bot]@users.noreply.github.com> Date: Sat, 8 Aug 2026 12:40:00 -0700 Subject: [PATCH 027/266] fix(otel): mark v2 server spans as failed for pre-call errors (#34546) * fix(otel): mark v2 server spans as failed for pre-call errors (LIT-4780) Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(otel): authenticate malformed-body requests before rejecting them (LIT-4780) Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(auth): cover malformed-body rejection when auth error is recovered Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(auth): skip authorization for a request whose body never parsed Deferring the parse failure ran the full auth phase, including budget reservation, whose reserved amount is only released by the endpoint's post call path; the endpoint never runs, so malformed requests leaked reservations and locked a budgeted key out. Authorization now runs only when the body parsed, and a parse failure with a rejected key keeps returning the 400 it returned before. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: shivam Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- litellm/integrations/otel/logger.py | 20 ++- litellm/proxy/auth/user_api_key_auth.py | 170 ++++++++++++------ .../integrations/otel/test_otel_v2_logger.py | 35 +++- .../proxy/auth/test_user_api_key_auth.py | 111 ++++++++++++ 4 files changed, 269 insertions(+), 67 deletions(-) diff --git a/litellm/integrations/otel/logger.py b/litellm/integrations/otel/logger.py index e273289168a..2c83406afed 100644 --- a/litellm/integrations/otel/logger.py +++ b/litellm/integrations/otel/logger.py @@ -33,6 +33,7 @@ from litellm.integrations.otel.model.payloads import ( is_mcp_list_tools, is_mcp_tool_call, ) +from litellm.integrations.otel.model.semconv import Error from litellm.integrations.otel.model.spans import SpanRole, span_role_for_service from litellm.integrations.otel.model.utils import to_ns from litellm.integrations.otel.plumbing.context import ( @@ -634,18 +635,23 @@ class OpenTelemetryV2(CustomLogger): """Stamp the v2 error.* attributes on the FastAPI-owned SERVER span for a failure that dies before any LLM-call span exists (malformed body, auth / validation rejection). Called from the proxy's global exception handler via - ``_close_dangling_otel_server_span``. The instrumentor still owns the span's - status and lifecycle, so this only decorates it — never sets status, never - ends it — and emits no exception event, matching v1's SERVER-span behavior - and avoiding a duplicate of the event ``async_post_call_failure_hook`` or - the ``auth`` phase span already records.""" + ``_close_dangling_otel_server_span``, which swallows the exception into a + ``JSONResponse`` so the instrumentor never sees it and leaves the span + ``UNSET``; the status is set here instead (v1 did the same from the handler) + so a failed request reads as failed and not merely as a span carrying an + error message. The instrumentor still owns the span's lifecycle, so this + never ends it. The exception event is recorded only when nothing stamped + this span already — ``async_post_call_failure_hook`` and the ``auth`` phase + span record their own, and a second event would duplicate it — while the + attributes are always restamped so ``error.code`` stays pinned to the real + response status.""" if span is None or not is_recordable_span(span): return + already_stamped: Final = Error.TYPE in (getattr(span, "attributes", None) or ()) stamp_error( span, _span_error_from_exception(exception, status_code=status_code), - record_event=False, - set_status=False, + record_event=not already_stamped, ) async def async_post_call_failure_hook( diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 9dc450befc2..4baa7b99a4f 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -1044,6 +1044,22 @@ def _ensure_parent_otel_span_on_request_state(request: Request) -> None: request.state.parent_otel_span = parent_otel_span +async def _read_request_body_deferring_parse_failure( + request: Request, +) -> tuple[dict, ProxyException | None]: + """Parse the body, returning a parse failure instead of raising it. + + A body that fails to parse is still a request from a known caller, so auth + must run (resolving identity onto the request's trace) before the 400 goes + out; the caller re-raises the returned exception once identity is seeded. + """ + try: + parsed_body: Final = await _read_request_body(request=request) + except ProxyException as parse_exception: + return {}, parse_exception # mutable-ok: request_data is a plain dict across the whole auth path + return populate_request_with_path_params(request_data=parsed_body, request=request), None + + async def _user_api_key_auth_builder( request: Request, api_key: str, @@ -2516,6 +2532,72 @@ def _resolve_request_principal(request: Request, valid_token: UserAPIKeyAuth) -> ) +async def _authorize_authenticated_request( + user_api_key_auth_obj: UserAPIKeyAuth, + request: Request, + request_data: dict, + route: str, + api_key: str, +) -> UserAPIKeyAuth | None: + """Authorize an already-authenticated request: disabled-route check, the single + ``common_checks`` gate (which also reserves budget), and end-user fallback + resolution. Returns the auth object the exception handler recovered when a check + failed but the request may proceed anyway, else ``None``. + """ + ## ENSURE DISABLE ROUTE WORKS ACROSS ALL USER AUTH FLOWS ## + RouteChecks.should_call_route(route=route, valid_token=user_api_key_auth_obj, request=request) + + # Single authorization point. Builder paths MUST NOT call common_checks. + # Route through the same exception handler the builder uses so + # authorization failures (ProxyException, or plain Exception from + # admin-only-route / model-access / budget checks) surface as + # ProxyException consistently with pre-refactor behavior. + try: + await _run_centralized_common_checks( + user_api_key_auth_obj=user_api_key_auth_obj, + request=request, + request_data=request_data, + route=route, + ) + except Exception as e: + return await UserAPIKeyAuthExceptionHandler._handle_authentication_error( + e=e, + request=request, + request_data=request_data, + route=route, + parent_otel_span=user_api_key_auth_obj.parent_otel_span, + api_key=api_key, + resolved_identity=user_api_key_auth_obj, + ) + + # Defense-in-depth: ``_user_api_key_auth_builder`` has multiple early-return + # paths (no master key, /user/auth route, JWT short-circuits) that bypass + # the end-user resolution block. If those paths produced an auth obj + # without an ``end_user_id`` set, fall back to extracting from the request + # body so spend logs are still attributed correctly. Validation honours + # ``litellm.validate_end_user_id_in_db``. + if user_api_key_auth_obj.end_user_id is None: + from litellm.proxy.proxy_server import ( + prisma_client, + proxy_logging_obj, + user_api_key_cache, + ) + + raw_end_user_id: Final = get_end_user_id_from_request_body(request_data, _safe_get_request_headers(request)) + if raw_end_user_id is not None: + resolved_end_user_id: Final = await resolve_and_validate_end_user_id( + raw_end_user_id=raw_end_user_id, + prisma_client=prisma_client, + user_api_key_cache=user_api_key_cache, + parent_otel_span=user_api_key_auth_obj.parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + route=route, + ) + if resolved_end_user_id is not None: + user_api_key_auth_obj.end_user_id = resolved_end_user_id + return None + + @tracer.wrap() async def user_api_key_auth( request: Request, @@ -2536,8 +2618,7 @@ async def user_api_key_auth( # close, and the trace never reaches the backend. _ensure_parent_otel_span_on_request_state(request) - request_data = await _read_request_body(request=request) - request_data = populate_request_with_path_params(request_data=request_data, request=request) + request_data, body_parse_exception = await _read_request_body_deferring_parse_failure(request=request) route: Final[str] = get_request_route(request=request) ## CHECK IF ROUTE IS ALLOWED @@ -2545,69 +2626,41 @@ async def user_api_key_auth( # triggers (key/user/team object reads) nest under it instead of flattening # onto the server span. No-op when OTel V2 isn't active. with phase_span(f"auth {route}"): - user_api_key_auth_obj: Final = await _user_api_key_auth_builder( - request=request, - api_key=api_key, - azure_api_key_header=azure_api_key_header, - anthropic_api_key_header=anthropic_api_key_header, - google_ai_studio_api_key_header=google_ai_studio_api_key_header, - azure_apim_header=azure_apim_header, - request_data=request_data, - custom_litellm_key_header=custom_litellm_key_header, - ) + try: + user_api_key_auth_obj: Final = await _user_api_key_auth_builder( + request=request, + api_key=api_key, + azure_api_key_header=azure_api_key_header, + anthropic_api_key_header=anthropic_api_key_header, + google_ai_studio_api_key_header=google_ai_studio_api_key_header, + azure_apim_header=azure_apim_header, + request_data=request_data, + custom_litellm_key_header=custom_litellm_key_header, + ) + except Exception: + # The body was read first, so a caller who sent both a malformed body and + # a rejected key used to get the 400; the response is unchanged, and the + # auth failure is still recorded on the trace by the handler that ran. + if body_parse_exception is not None: + raise body_parse_exception + raise user_api_key_auth_obj.budget_reservation = None - ## ENSURE DISABLE ROUTE WORKS ACROSS ALL USER AUTH FLOWS ## - RouteChecks.should_call_route(route=route, valid_token=user_api_key_auth_obj, request=request) - - # Single authorization point. Builder paths MUST NOT call common_checks. - # Route through the same exception handler the builder uses so - # authorization failures (ProxyException, or plain Exception from - # admin-only-route / model-access / budget checks) surface as - # ProxyException consistently with pre-refactor behavior. - try: - await _run_centralized_common_checks( + # A body that never parsed is authenticated (so the trace carries identity + # and this ``auth`` span) but not authorized: there is no model to check it + # against, and budget reservation would increment live spend counters that + # only the endpoint's post-call path releases; the endpoint never runs, since + # the parse failure is raised below. + if body_parse_exception is None: + recovered_auth_obj: Final = await _authorize_authenticated_request( user_api_key_auth_obj=user_api_key_auth_obj, request=request, request_data=request_data, route=route, - ) - except Exception as e: - return await UserAPIKeyAuthExceptionHandler._handle_authentication_error( - e=e, - request=request, - request_data=request_data, - route=route, - parent_otel_span=user_api_key_auth_obj.parent_otel_span, api_key=api_key, - resolved_identity=user_api_key_auth_obj, ) - - # Defense-in-depth: ``_user_api_key_auth_builder`` has multiple early-return - # paths (no master key, /user/auth route, JWT short-circuits) that bypass - # the end-user resolution block. If those paths produced an auth obj - # without an ``end_user_id`` set, fall back to extracting from the request - # body so spend logs are still attributed correctly. Validation honours - # ``litellm.validate_end_user_id_in_db``. - if user_api_key_auth_obj.end_user_id is None: - from litellm.proxy.proxy_server import ( - prisma_client, - proxy_logging_obj, - user_api_key_cache, - ) - - raw_end_user_id: Final = get_end_user_id_from_request_body(request_data, _safe_get_request_headers(request)) - if raw_end_user_id is not None: - resolved_end_user_id: Final = await resolve_and_validate_end_user_id( - raw_end_user_id=raw_end_user_id, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=user_api_key_auth_obj.parent_otel_span, - proxy_logging_obj=proxy_logging_obj, - route=route, - ) - if resolved_end_user_id is not None: - user_api_key_auth_obj.end_user_id = resolved_end_user_id + if recovered_auth_obj is not None: + return recovered_auth_obj # Identity is now resolved. Seed it AFTER the auth span closes so the Baggage # persists on the request task (detaching the span's context token inside the @@ -2619,6 +2672,9 @@ async def user_api_key_auth( ) user_api_key_auth_obj.request_route = normalize_request_route(route) + if body_parse_exception is not None: + raise body_parse_exception + # Resolve caller identity once, here at the seam, into a single per-request # Principal projected off the key object the builder already fetched (no # second lookup). Downstream consumers read identity off this instead of diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_logger.py b/tests/test_litellm/integrations/otel/test_otel_v2_logger.py index 2573ad5a375..82b074220fa 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_logger.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_logger.py @@ -1195,8 +1195,13 @@ def test_async_post_call_failure_hook_skips_a_transport_that_already_answered(): def test_record_error_attributes_on_span_decorates_without_ending(): """PATH A: a failure that dies before any LLM-call span (malformed body, validation) is stamped onto the instrumentor-owned SERVER span. The method must - not end the span or emit a duplicate exception event, and must pin error.code - to the real response status (not the exception's own code).""" + not end the span, and must pin error.code to the real response status (not the + exception's own code). + + LIT-4780: the instrumentor never sees the exception (the proxy handler turns it + into a JSONResponse), so nothing else marks the span as failed; the status and + the exception event have to come from here or the trace shows the error message + on an otherwise successful-looking request.""" logger, exporter = _logger() server = logger._emitter.start_span(SpanRole.PROXY_REQUEST, LITELLM_PROXY_REQUEST_SPAN_NAME) logger.record_error_attributes_on_span(server, _proxy_exc("Invalid JSON body", 400), 422) @@ -1206,7 +1211,31 @@ def test_record_error_attributes_on_span_decorates_without_ending(): assert span.attributes["error.type"] == "ProxyException" assert span.attributes["error.message"] == "Invalid JSON body" assert span.attributes["litellm.provider.error.code"] == "422" - assert all(e.name != "exception" for e in span.events) + assert span.status.status_code is StatusCode.ERROR + assert [e.name for e in span.events] == ["exception"] + + +def test_record_error_attributes_on_span_does_not_duplicate_an_already_stamped_error(): + """A failure that already went through ``async_post_call_failure_hook`` reaches + the exception handler too; the second stamp must keep one exception event while + still repinning error.code to the real response status.""" + from litellm.proxy._types import UserAPIKeyAuth + + logger, exporter = _logger() + server = logger._emitter.start_span(SpanRole.PROXY_REQUEST, LITELLM_PROXY_REQUEST_SPAN_NAME) + set_request_root_span(server) + exc = _proxy_exc("Authentication Error, invalid key", 401) + asyncio.run( + logger.async_post_call_failure_hook( + request_data={}, original_exception=exc, user_api_key_dict=UserAPIKeyAuth() + ) + ) + logger.record_error_attributes_on_span(server, exc, 400) + server.end() + (span,) = exporter.get_finished_spans() + assert [e.name for e in span.events] == ["exception"] + assert span.attributes["litellm.provider.error.code"] == "400" + assert span.status.status_code is StatusCode.ERROR def test_record_error_attributes_on_span_ignores_below_400_and_missing_span(): diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py index 6fc44ef7519..60d9689dc0b 100644 --- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py +++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py @@ -4786,6 +4786,117 @@ async def test_user_api_key_auth_does_not_overwrite_end_user_id_set_by_builder() setattr(_proxy_server_mod, k, v) +@pytest.mark.asyncio +async def test_user_api_key_auth_authenticates_before_raising_malformed_body_error(): + """Regression (LIT-4780): a body that fails to parse must still be authenticated + first, so the rejected request's trace carries the caller's key / team / user + identity instead of an anonymous root span. The parse error is re-raised + unchanged once identity is seeded.""" + from fastapi import Request + from starlette.datastructures import URL + + import litellm.proxy.proxy_server as _proxy_server_mod + + builder_token = UserAPIKeyAuth(api_key="sk-test", user_id="u1", team_id="team-1") + + request = Request( + scope={ + "type": "http", + "headers": [(b"content-type", b"application/json")], + "method": "POST", + } + ) + request._url = URL(url="/chat/completions") + request._body = b'{}{"model": "gpt-4o"}' + + attrs = _proxy_attrs_for_centralized_checks(user_custom_auth=None) + originals = {a: getattr(_proxy_server_mod, a, None) for a in attrs} + try: + for k, v in attrs.items(): + setattr(_proxy_server_mod, k, v) + with ( + patch( + "litellm.proxy.auth.user_api_key_auth._user_api_key_auth_builder", + new_callable=AsyncMock, + return_value=builder_token, + ) as mock_builder, + patch( + "litellm.proxy.auth.user_api_key_auth._run_centralized_common_checks", + new_callable=AsyncMock, + ) as mock_common_checks, + patch( + "litellm.proxy.auth.user_api_key_auth.RouteChecks.should_call_route", + ), + patch( + "litellm.proxy.auth.user_api_key_auth.seed_request_identity", + ) as mock_seed, + ): + with pytest.raises(ProxyException) as exc_info: + await user_api_key_auth(request=request, api_key="Bearer sk-test") + + assert "Invalid JSON payload" in str(exc_info.value.message) + assert exc_info.value.code == str(status.HTTP_400_BAD_REQUEST) + mock_builder.assert_awaited_once() + assert mock_seed.call_args.args[0] is builder_token + # authorization must not run for a request that is about to be rejected: + # ``common_checks`` reserves budget against live spend counters that only the + # endpoint's post-call path releases, and the endpoint never runs here + mock_common_checks.assert_not_awaited() + finally: + for k, v in originals.items(): + setattr(_proxy_server_mod, k, v) + + +@pytest.mark.asyncio +async def test_user_api_key_auth_malformed_body_with_rejected_key_still_returns_the_parse_error(): + """The body is read before the key is authenticated, so a caller who sends both a + malformed body and a key that fails auth gets the 400. Authenticating the request + first (LIT-4780) must not turn that into the auth status code.""" + from fastapi import Request + from starlette.datastructures import URL + + import litellm.proxy.proxy_server as _proxy_server_mod + + request = Request( + scope={ + "type": "http", + "headers": [(b"content-type", b"application/json")], + "method": "POST", + } + ) + request._url = URL(url="/chat/completions") + request._body = b'{}{"model": "gpt-4o"}' + + attrs = _proxy_attrs_for_centralized_checks(user_custom_auth=None) + originals = {a: getattr(_proxy_server_mod, a, None) for a in attrs} + try: + for k, v in attrs.items(): + setattr(_proxy_server_mod, k, v) + with ( + patch( + "litellm.proxy.auth.user_api_key_auth._user_api_key_auth_builder", + new_callable=AsyncMock, + side_effect=ProxyException( + message="Authentication Error, invalid key", + type="auth_error", + param="None", + code=status.HTTP_401_UNAUTHORIZED, + ), + ), + patch( + "litellm.proxy.auth.user_api_key_auth.RouteChecks.should_call_route", + ), + ): + with pytest.raises(ProxyException) as exc_info: + await user_api_key_auth(request=request, api_key="Bearer sk-bad") + + assert "Invalid JSON payload" in str(exc_info.value.message) + assert exc_info.value.code == str(status.HTTP_400_BAD_REQUEST) + finally: + for k, v in originals.items(): + setattr(_proxy_server_mod, k, v) + + def _proxy_attrs_for_db_lookup(): """Minimal proxy_server attributes for driving the real ``_user_api_key_auth_builder`` down to the DB key lookup.""" From 1a40a673942a52830155a8d414c046a4f9376223 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Sat, 8 Aug 2026 12:54:23 -0700 Subject: [PATCH 028/266] fix: stabilize generated user role ordering --- litellm/proxy/_types.py | 4 ++-- ui/litellm-dashboard/src/lib/http/schema.d.ts | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 1fc05ac4653..fa89df39c5f 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -4557,10 +4557,10 @@ class DefaultInternalUserParams(LiteLLMPydanticObjectBase): user_role: ( Literal[ - LitellmUserRoles.INTERNAL_USER, - LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, LitellmUserRoles.PROXY_ADMIN, LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + LitellmUserRoles.INTERNAL_USER, + LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, ] | None ) = Field( diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index 2fc70883c5e..a75c23da1cf 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -24313,7 +24313,7 @@ export interface components { * @description Default role assigned to new users created * @default internal_user_viewer */ - user_role: ("internal_user" | "internal_user_viewer" | "proxy_admin" | "proxy_admin_viewer") | null; + user_role: ("proxy_admin" | "proxy_admin_viewer" | "internal_user" | "internal_user_viewer") | null; }; /** * DefaultTeamSSOParams From ff5f8132d178ee700d23bff3e97d639ea5027cbd Mon Sep 17 00:00:00 2001 From: mateo Date: Sat, 8 Aug 2026 19:55:37 +0000 Subject: [PATCH 029/266] docs: clarify guideline priority ordering in CLAUDE.md Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --- CLAUDE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CLAUDE.md b/CLAUDE.md index b0da2970fc8..f1bb46c1fd3 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -9,7 +9,7 @@ Don't assume that the existing code is correct or the right way of doing things - easy to maintain/change - modern -In that order of importance +In descending order of importance When adding new features, add meaningful tests. Don't add tests that don't check anything substantial and is there just to make the code coverage pass. Yes, code coverage is important, but I'd rather have no signal whether the code is working than tests that don't fail when code is broken. The goal is to have tests that would fail before the feature was added/if the code was mutated in a way that breaks the feature and succeed only when the feature is fully working. I should run mutation testing and see > 90% kill rate From e35ee4e5fa3e25a5f750fa0ee23525a8277a4f62 Mon Sep 17 00:00:00 2001 From: tin-berri Date: Sat, 8 Aug 2026 13:02:29 -0700 Subject: [PATCH 030/266] feat(router): independent, default-on deployment affinity for the auto-router (#36146) --- litellm/constants.py | 1 + litellm/proxy/common_utils/callback_utils.py | 3 +- litellm/proxy/litellm_pre_call_utils.py | 2 + litellm/router.py | 96 ++-- .../complexity_router/complexity_router.py | 50 +- .../complexity_router/config.py | 30 +- .../deployment_affinity_check.py | 266 ++++++++--- litellm/types/router.py | 1 + .../proxy/test_litellm_pre_call_utils.py | 2 + .../router_strategy/test_complexity_router.py | 97 ++++ .../test_deployment_affinity_check.py | 9 +- .../test_session_id_affinity.py | 441 +++++++++++++++++- tests/test_litellm/test_router.py | 65 +++ type-discipline-budget.json | 2 +- ui/litellm-dashboard/src/lib/http/schema.d.ts | 10 +- 15 files changed, 964 insertions(+), 111 deletions(-) diff --git a/litellm/constants.py b/litellm/constants.py index 30d3bb1f26e..3b91f23fe39 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1322,6 +1322,7 @@ X_LITELLM_DISABLE_CALLBACKS: Final = "x-litellm-disable-callbacks" LITELLM_METADATA_FIELD: Final = "litellm_metadata" OLD_LITELLM_METADATA_FIELD: Final = "metadata" RETURN_RAW_MODEL_NAME_METADATA_KEY: Final = "_complexity_router_return_raw_model_name" +SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: Final = "_session_deployment_affinity_ttl" INTERNAL_CALL_ORIGIN_METADATA_KEY: Final = "internal_call_origin" LITELLM_TRUNCATED_PAYLOAD_FIELD: Final = "litellm_truncated" LITELLM_TRUNCATION_DB_SAFEGUARD_NOTE: Final = ( diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index 22200567012..fbf28e223c1 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, Any, Final, Optional import litellm from litellm import get_secret from litellm._logging import verbose_proxy_logger -from litellm.constants import PRE_CALL_EXECUTED_GUARDRAILS_KEY +from litellm.constants import PRE_CALL_EXECUTED_GUARDRAILS_KEY, SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.core_helpers import ( get_metadata_variable_name_from_kwargs, @@ -425,6 +425,7 @@ LITELLM_PROXY_INTERNAL_METADATA_KEYS: Final = frozenset( "_guardrail_pipelines", "_pipeline_managed_guardrails", PRE_CALL_EXECUTED_GUARDRAILS_KEY, + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, "disable_global_guardrails", "disable_global_guardrail", "opted_out_global_guardrails", diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 83ae59ef050..c48fee96646 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -18,6 +18,7 @@ from litellm.constants import ( INTERNAL_CALL_ORIGIN_METADATA_KEY, LITELLM_PROXY_MASTER_KEY_ALIAS, PRE_CALL_EXECUTED_GUARDRAILS_KEY, + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, ) from litellm.litellm_core_utils.credential_accessor import CredentialAccessor from litellm.litellm_core_utils.initialize_dynamic_callback_params import ( @@ -226,6 +227,7 @@ _UNTRUSTED_METADATA_CONTROL_FIELDS: Final = ( "applied_policies", "policy_sources", "routing_decision", + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, INTERNAL_CALL_ORIGIN_METADATA_KEY, "standard_logging_object", "proxy_server_request", diff --git a/litellm/router.py b/litellm/router.py index 1a76eb5d59b..feaf69a44ae 100644 --- a/litellm/router.py +++ b/litellm/router.py @@ -46,6 +46,7 @@ from litellm.constants import ( DEFAULT_HEALTH_CHECK_INTERVAL, DEFAULT_HEALTH_CHECK_STALENESS_MULTIPLIER, DEFAULT_MAX_LRU_CACHE_SIZE, + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, ) from litellm.integrations.custom_logger import CustomLogger from litellm.litellm_core_utils.asyncify import run_async_function @@ -135,6 +136,7 @@ from litellm.router_utils.handle_error import ( from litellm.router_utils.health_state_cache import DeploymentHealthCache from litellm.router_utils.pre_call_checks.deployment_affinity_check import ( DeploymentAffinityCheck, + warn_on_unknown_model_group_affinity_flags, ) from litellm.router_utils.pre_call_checks.io_token_rate_limit_check import ( build_io_token_rate_limit_headers, @@ -603,6 +605,10 @@ class Router: # ``litellm.proxy.auth.auth_checks._is_model_cost_zero``. self._zero_cost_cache: dict[str, bool] = {} + self.deployment_affinity_ttl_seconds = deployment_affinity_ttl_seconds + self.model_group_affinity_config = model_group_affinity_config + warn_on_unknown_model_group_affinity_flags(model_group_affinity_config) + if model_list is not None: # set_model_list will build indices automatically self.set_model_list(model_list) @@ -744,7 +750,6 @@ class Router: litellm.failure_callback = [self.deployment_callback_on_failure] self.routing_strategy_args = routing_strategy_args self.provider_budget_config = provider_budget_config - self.deployment_affinity_ttl_seconds = deployment_affinity_ttl_seconds self.router_budget_logger: RouterBudgetLimiting | None = None if RouterBudgetLimiting.should_init_router_budget_limiter( model_list=model_list, provider_budget_config=self.provider_budget_config @@ -766,7 +771,6 @@ class Router: ) self.model_group_retry_policy: dict[str, RetryPolicy] | None = model_group_retry_policy - self.model_group_affinity_config: dict[str, list[str]] | None = model_group_affinity_config self.allowed_fails_policy: AllowedFailsPolicy | None = None if allowed_fails_policy is not None: @@ -789,21 +793,8 @@ class Router: # If model_group_affinity_config is set but no global affinity checks were # enabled, we still need the DeploymentAffinityCheck callback (with global # flags all False) so per-group config can activate affinity per model group. - if self.model_group_affinity_config and not any( - isinstance(cb, DeploymentAffinityCheck) for cb in (self.optional_callbacks or []) - ): - if self.optional_callbacks is None: - self.optional_callbacks = [] - affinity_callback: Final = DeploymentAffinityCheck( - cache=self.cache, - ttl_seconds=self.deployment_affinity_ttl_seconds, - enable_user_key_affinity=False, - enable_responses_api_affinity=False, - enable_session_id_affinity=False, - model_group_affinity_config=self.model_group_affinity_config, - ) - self.optional_callbacks.append(affinity_callback) - litellm.logging_callback_manager.add_litellm_callback(affinity_callback) + if self.model_group_affinity_config: + self._ensure_deployment_affinity_callback() if self.alerting_config is not None: self._initialize_alerting() @@ -1662,6 +1653,28 @@ class Router: _move_before_deployment_affinity(self.optional_callbacks, ec_callback) _move_before_deployment_affinity(litellm.callbacks, ec_callback) + def _ensure_deployment_affinity_callback(self) -> None: + """Register the DeploymentAffinityCheck callback (global flags all False) if absent. + + Needed when nothing enabled a global affinity flag but affinity can still + activate per request: per-group `model_group_affinity_config` entries, or the + session-affinity marker a complexity router stamps at pre-routing time. + """ + if any(isinstance(cb, DeploymentAffinityCheck) for cb in (self.optional_callbacks or [])): + return + if self.optional_callbacks is None: + self.optional_callbacks = [] + affinity_callback: Final = DeploymentAffinityCheck( + cache=self.cache, + ttl_seconds=self.deployment_affinity_ttl_seconds, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + enable_session_id_affinity=False, + model_group_affinity_config=self.model_group_affinity_config, + ) + self.optional_callbacks.append(affinity_callback) + litellm.logging_callback_manager.add_litellm_callback(affinity_callback) + def add_optional_pre_call_checks(self, optional_pre_call_checks: OptionalPreCallChecks | None): if optional_pre_call_checks is None: return @@ -7683,6 +7696,8 @@ class Router: strategy=complexity_router, strategy_label="Complexity-router", ) + if complexity_router._uses_deployment_pin: + self._ensure_deployment_affinity_callback() def _is_adaptive_router_deployment(self, litellm_params: LiteLLM_Params) -> bool: """True when this deployment opts in via the `auto_router/adaptive_router` model prefix.""" @@ -11190,6 +11205,9 @@ class Router: router_strategy: Final = self._select_pre_routing_strategy(model=model, request_kwargs=request_kwargs) if router_strategy is None: self._record_routing_decision(request_kwargs=request_kwargs, routing_decision=None) + self._stamp_or_clear_metadata_key( + request_kwargs=request_kwargs, key=SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, value=None + ) return None pre_routing_hook_response: Final = await router_strategy.async_pre_routing_hook( @@ -11203,6 +11221,11 @@ class Router: request_kwargs=request_kwargs, routing_decision=(pre_routing_hook_response.routing_decision if pre_routing_hook_response else None), ) + self._stamp_or_clear_metadata_key( + request_kwargs=request_kwargs, + key=SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY, + value=(pre_routing_hook_response.session_affinity_ttl_seconds if pre_routing_hook_response else None), + ) # `model` (the alias, e.g. "smart-router") is never the deployment actually # called - apply the alias's own litellm_params (besides `model` itself, @@ -11234,21 +11257,40 @@ class Router: to the deployment that actually served the request. Every attempt therefore writes or clears, never just writes. """ - if routing_decision is None: + Router._stamp_or_clear_metadata_key( + request_kwargs=request_kwargs, + key="routing_decision", + value=( + None + if routing_decision is None + else Router._redact_prompt_text_if_needed( + request_kwargs=request_kwargs, routing_decision=routing_decision + ) + ), + ) + + @staticmethod + def _stamp_or_clear_metadata_key(request_kwargs: dict, key: str, value: object | None) -> None: + """Write a proxy-internal metadata key for THIS routing attempt, or clear it. + + Fallbacks and retries re-enter the pre-routing hook with the same + `request_kwargs`, so every attempt must write or clear, never just write; + a value left behind by an earlier attempt would be attributed to this one. + `get_or_create_metadata_bucket` is the single owner of "which dict holds + proxy-internal metadata": it picks `litellm_metadata` when present (so the + value never lands in the `metadata` dict that routes like /v1/messages + forward to the provider) and replaces a non-dict value rather than silently + skipping the write. Clearing pops from BOTH buckets so a request whose + bucket resolution changed between attempts cannot resurrect a stale value. + """ + if value is None: for bucket in (request_kwargs.get("metadata"), request_kwargs.get("litellm_metadata")): if isinstance(bucket, dict): - bucket.pop("routing_decision", None) + bucket.pop(key, None) return - # `get_or_create_metadata_bucket` is the single owner of "which dict holds - # proxy-internal metadata": it picks `litellm_metadata` when present (so the - # decision never lands in the `metadata` dict that routes like /v1/messages - # forward to the provider) and replaces a non-dict value rather than silently - # skipping the write. _, metadata_bucket = get_or_create_metadata_bucket(request_kwargs) - metadata_bucket["routing_decision"] = Router._redact_prompt_text_if_needed( - request_kwargs=request_kwargs, routing_decision=routing_decision - ) + metadata_bucket[key] = value @staticmethod def _redact_prompt_text_if_needed( diff --git a/litellm/router_strategy/complexity_router/complexity_router.py b/litellm/router_strategy/complexity_router/complexity_router.py index f6ced0bb9d2..32d252f3f68 100644 --- a/litellm/router_strategy/complexity_router/complexity_router.py +++ b/litellm/router_strategy/complexity_router/complexity_router.py @@ -1651,6 +1651,28 @@ class ComplexityRouter(CustomLogger): caller_scope: Final = self._get_user_api_key_hash_from_request_kwargs(request_kwargs) or "unscoped" return f"complexity_router_session_affinity:v1:{self.model_name}:{caller_scope}:{session_id}" + @property + def _uses_tier_pin(self) -> bool: + return bool(self.config.session_affinity and not self.config.plugins) + + @property + def _uses_deployment_pin(self) -> bool: + """session_affinity implies the deployment pin: a session frozen onto one model + group but load-balanced across its deployments would still go cache-cold, which + is the exact failure both flags exist to prevent.""" + return bool((self.config.deployment_affinity or self.config.session_affinity) and not self.config.plugins) + + def _with_session_deployment_affinity( + self, response: PreRoutingHookResponse | None + ) -> PreRoutingHookResponse | None: + if response is None or not self._uses_deployment_pin: + return response + return response.model_copy( + update={ # mutable-ok: model_copy types update as a plain dict + "session_affinity_ttl_seconds": self.config.session_affinity_ttl_seconds + } + ) + async def async_pre_routing_hook( self, model: str, @@ -1685,7 +1707,7 @@ class ComplexityRouter(CustomLogger): resolved_messages: Final = self._resolve_messages(messages, request_kwargs) conversation_continuing: Final = _conversation_is_continuing(resolved_messages) - use_session_affinity: Final = self.config.session_affinity and not self.config.plugins + use_session_affinity: Final = self._uses_tier_pin session_id: Final = self._get_session_id_from_request_kwargs(request_kwargs) if use_session_affinity else None cache_key = self._get_session_affinity_cache_key(session_id, request_kwargs) if session_id is not None else None @@ -1724,17 +1746,19 @@ class ComplexityRouter(CustomLogger): "ComplexityRouter: routing decision cause=%s, routed_model=%s", cause, routed_model ) has_original_messages: Final = messages is not None and len(messages) > 0 - return PreRoutingHookResponse( - model=routed_model, - messages=messages if has_original_messages else None, - routing_decision=self._build_routing_decision( - routed_model=routed_model, - cause=cause, - tier=self._tier_for_model(routed_model), - escalation_keyword=pin_escalation_keyword, - escalated=escalated, - conversation_continuing=conversation_continuing, - ), + return self._with_session_deployment_affinity( + PreRoutingHookResponse( + model=routed_model, + messages=messages if has_original_messages else None, + routing_decision=self._build_routing_decision( + routed_model=routed_model, + cause=cause, + tier=self._tier_for_model(routed_model), + escalation_keyword=pin_escalation_keyword, + escalated=escalated, + conversation_continuing=conversation_continuing, + ), + ) ) response: Final = await self._classify_and_route( @@ -1752,7 +1776,7 @@ class ComplexityRouter(CustomLogger): value=response.model, ttl=self.config.session_affinity_ttl_seconds, ) - return response + return self._with_session_deployment_affinity(response) async def _classify_and_route( self, diff --git a/litellm/router_strategy/complexity_router/config.py b/litellm/router_strategy/complexity_router/config.py index 69609a973b0..0999af66fd8 100644 --- a/litellm/router_strategy/complexity_router/config.py +++ b/litellm/router_strategy/complexity_router/config.py @@ -508,13 +508,39 @@ class ComplexityRouterConfig(BaseModel): "session's first turn and reuse it for every later turn, skipping re-classification. " "Off by default so every turn is classified on its own merits and routed to the cheapest " "adequate tier. Set True to keep a multi-turn session on one model, which preserves " - "provider prompt caches and avoids cross-model conversation-history errors." + "provider prompt caches and avoids cross-model conversation-history errors. Always " + "implies the deployment pin regardless of deployment_affinity: the session sticks to " + "one deployment of the pinned model, since freezing the model while re-shuffling its " + "deployments would still go cache-cold." + ), + ) + deployment_affinity: bool = Field( + default=True, + description=( + "When True and a session_id is resolvable on the request, pin the deployment chosen " + "inside each routed model group and reuse it whenever the session returns to that " + "group, without pinning which group the session routes to. Independent of " + "session_affinity, which pins the model group instead (and always carries this " + "deployment pin with it): with session_affinity off, " + "every turn is still classified on its own merits while a session that escalates to a " + "stronger tier and comes back still lands on the deployment it used before, which is " + "what keeps a provider prompt cache warm. Pins are held per model group, so switching " + "tiers does not disturb the pin left behind in the previous group. On by default " + "because re-shuffling a conversation across deployments of the same model discards " + "that cache for no benefit; set False to keep every turn load-balanced across the " + "group, which is what a deployment set with tight per-deployment rate limits wants. " + "Inert when no session_id is resolvable, since there is nothing to key a pin on, and " + "suppressed when plugins are configured, for the same reason session_affinity is." ), ) session_affinity_ttl_seconds: int = Field( default=3600, gt=0, - description="TTL for the session affinity pin; refreshed on every cache hit", + description=( + "TTL for the session affinity pin; refreshed on every cache hit. Bounds both the " + "session_affinity model pin and the deployment_affinity deployment pin, so it measures " + "idle time for the session's routing decisions rather than total session length" + ), ) plugins: list[RoutingPlugin] | None = Field( diff --git a/litellm/router_utils/pre_call_checks/deployment_affinity_check.py b/litellm/router_utils/pre_call_checks/deployment_affinity_check.py index b4408d17ffb..7fb90ab89de 100644 --- a/litellm/router_utils/pre_call_checks/deployment_affinity_check.py +++ b/litellm/router_utils/pre_call_checks/deployment_affinity_check.py @@ -13,12 +13,15 @@ where routing to a consistent deployment is still beneficial. """ import hashlib +import json +from collections.abc import Mapping, Sequence from typing import Any, Final, cast from typing_extensions import TypedDict from litellm._logging import verbose_router_logger from litellm.caching.dual_cache import DualCache +from litellm.constants import SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY from litellm.integrations.custom_logger import CustomLogger, Span from litellm.responses.utils import ResponsesAPIRequestUtils from litellm.types.llms.openai import AllMessageValues @@ -29,6 +32,47 @@ class DeploymentAffinityCacheValue(TypedDict): model_id: str +VALID_MODEL_GROUP_AFFINITY_FLAGS: Final = frozenset( + { + "deployment_affinity", + "responses_api_deployment_check", + "session_affinity", + "encrypted_content_affinity", + } +) + + +def warn_on_unknown_model_group_affinity_flags(model_group_affinity_config: Mapping[str, Sequence[str]] | None) -> None: + """`model_group_affinity_config` is one Router-level config consumed by two callbacks: + DeploymentAffinityCheck acts on three of the flags and EncryptedContentAffinityCheck + on the fourth, so typo detection lives here at the schema, not inside either consumer. + """ + if model_group_affinity_config is None: + return + for group, flags in model_group_affinity_config.items(): + unknown = set(flags) - VALID_MODEL_GROUP_AFFINITY_FLAGS + if unknown: + verbose_router_logger.warning( + "model_group_affinity_config: unknown flag(s) %s for model group '%s'; will be ignored. Valid flags: %s", + unknown, + group, + VALID_MODEL_GROUP_AFFINITY_FLAGS, + ) + + +_CLAIM_PIN_SCRIPT: Final = """ +local current = redis.call('GET', KEYS[1]) +if current == false then + redis.call('SET', KEYS[1], ARGV[1], 'EX', ARGV[2]) + return ARGV[1] +end +if current == ARGV[1] then + redis.call('EXPIRE', KEYS[1], ARGV[2]) +end +return current +""" + + class DeploymentAffinityCheck(CustomLogger): """ Router deployment affinity callback. @@ -38,14 +82,6 @@ class DeploymentAffinityCheck(CustomLogger): """ CACHE_KEY_PREFIX = "deployment_affinity:v1" - VALID_FLAGS = frozenset( - { - "deployment_affinity", - "responses_api_deployment_check", - "session_affinity", - "encrypted_content_affinity", - } - ) def __init__( self, @@ -63,15 +99,6 @@ class DeploymentAffinityCheck(CustomLogger): self.enable_responses_api_affinity = enable_responses_api_affinity self.enable_session_id_affinity = enable_session_id_affinity self.model_group_affinity_config: dict[str, list[str]] = model_group_affinity_config or {} - for group, flags in self.model_group_affinity_config.items(): - unknown = set(flags) - self.VALID_FLAGS - if unknown: - verbose_router_logger.warning( - "DeploymentAffinityCheck: unknown flag(s) %s for model group '%s'; will be ignored. Valid flags: %s", - unknown, - group, - self.VALID_FLAGS, - ) def _get_effective_flags(self, model_group: str) -> tuple[bool, bool, bool]: """ @@ -218,8 +245,13 @@ class DeploymentAffinityCheck(CustomLogger): return f"{cls.CACHE_KEY_PREFIX}:{model_group}:{hashed_user_key}" @classmethod - def get_session_affinity_cache_key(cls, model_group: str, session_id: str) -> str: - return f"{cls.CACHE_KEY_PREFIX}:session:{model_group}:{session_id}" + def get_session_affinity_cache_key(cls, model_group: str, session_id: str, user_key: str | None) -> str: + """Session pins are scoped by the caller's hashed API key so two callers reusing + the same client-supplied session_id cannot read or steer each other's pin. + `"unscoped"` covers direct Router usage with no authenticated caller, matching + the complexity router's own session pin key.""" + hashed_user_key: Final = cls._hash_user_key(user_key) if user_key is not None else "unscoped" + return f"{cls.CACHE_KEY_PREFIX}:session:{model_group}:{hashed_user_key}:{session_id}" @staticmethod def _get_user_key_from_metadata_dict(metadata: dict) -> str | None: @@ -278,6 +310,97 @@ class DeploymentAffinityCheck(CustomLogger): return session_id return None + @staticmethod + def _get_marker_session_affinity_ttl(request_kwargs: dict) -> int | None: + """TTL from the session-affinity marker the Router stamps at pre-routing time + when an auto-router routed this request with session_affinity enabled. + Marker presence enables session pinning for this request only; anything that + is not a positive int is treated as absent.""" + for metadata in DeploymentAffinityCheck._iter_metadata_dicts(request_kwargs): + ttl = metadata.get(SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY) + if isinstance(ttl, int) and not isinstance(ttl, bool) and ttl > 0: + return ttl + return None + + @staticmethod + def _pinned_model_id(stored: object) -> str | None: + """Deployment id held by a stored pin, for both the dict shape this writes and the + bare string older writers left behind. None when the value is neither.""" + if isinstance(stored, dict): + model_id: Final = stored.get("model_id") + return str(model_id) if model_id is not None else None + if isinstance(stored, str): + return stored + return None + + def _set_local_pin(self, cache_key: str, value: object, ttl_seconds: int) -> None: + """The one owner of authoritative local pin writes: a plain set keeps a live + key's original expiry (`allow_ttl_override`), so the entry is replaced to make + the TTL real. Every local pin write goes through here so the redis-winner sync + and the pod-local claim can never disagree about expiry again.""" + self.cache.in_memory_cache.delete_cache(cache_key) + self.cache.in_memory_cache.set_cache(cache_key, value, ttl=ttl_seconds) + + async def _claim_pin(self, cache_key: str, pin_value: DeploymentAffinityCacheValue, ttl_seconds: int) -> str | None: + """First-writer-wins pin write: store `pin_value` only when the key is absent and + return the deployment id the key holds afterwards, so a caller learns whether it won + by comparing against its own id, and None when the stored value is one no reader can + interpret. Concurrent claimers converge on the + first write instead of the last. Re-claiming with the stored value refreshes its + TTL, the same keepalive the complexity router's model pin documents: an active + session must not lose its pin mid-conversation just because it outlives the + original write, so `session_affinity_ttl_seconds` bounds idle time, not total + session length. On Redis one Lua script does the get-or-set-or-refresh + atomically (same registration seam the rate limiters use) and the in-memory + tier is synchronized to the winner; without Redis, and whenever Redis is + unreachable, the pod-local check-and-set below stands in and is atomic because it + runs synchronously on the event loop. Degrading to a pod-local claim rather than + propagating the fault is what keeps same-pod stickiness through a Redis blip: the + caller only logs this result, so an escaping error would leave the session with no + pin at all and reshuffle every turn for the outage, which is worse than losing + cross-pod agreement. The redis tier is + resolved per call because the proxy attaches it after Router construction + (`Router._update_redis_cache`); the compiled script is cached per event loop + underneath the registration seam. + """ + redis_cache: Final = self.cache.redis_cache + if redis_cache is not None: + try: + claim_script: Final = redis_cache.async_register_script(_CLAIM_PIN_SCRIPT) + raw: Final = await claim_script(keys=(cache_key,), args=(json.dumps(pin_value), int(ttl_seconds))) + decoded: Final = raw.decode("utf-8") if isinstance(raw, bytes) else raw + if not isinstance(decoded, str): + return pin_value["model_id"] + try: + winner: object = json.loads(decoded) + except json.JSONDecodeError: + winner = decoded + self._set_local_pin(cache_key=cache_key, value=winner, ttl_seconds=ttl_seconds) + return self._pinned_model_id(winner) + except Exception as e: # noqa: BLE001 # any Redis/Lua failure degrades to the pod-local claim, never unpins + verbose_router_logger.debug( + "DeploymentAffinityCheck: redis pin claim failed, falling back to pod-local claim. error=%s", e + ) + + return self._claim_pin_in_memory(cache_key=cache_key, pin_value=pin_value, ttl_seconds=ttl_seconds) + + def _claim_pin_in_memory( + self, cache_key: str, pin_value: DeploymentAffinityCacheValue, ttl_seconds: int + ) -> str | None: + """Pod-local half of the claim, used when no Redis tier is attached and as the + fallback when the Redis claim fails. Mirrors the Lua script exactly, including + the keepalive: re-claiming with the stored value slides the idle window through + `_set_local_pin`. Both branches stay synchronous, hence atomic on the event + loop.""" + existing: Final = self.cache.in_memory_cache.get_cache(cache_key) + if existing is not None: + existing_model_id: Final = self._pinned_model_id(existing) + if existing_model_id == pin_value["model_id"]: + self._set_local_pin(cache_key=cache_key, value=pin_value, ttl_seconds=ttl_seconds) + return existing_model_id + self._set_local_pin(cache_key=cache_key, value=pin_value, ttl_seconds=ttl_seconds) + return pin_value["model_id"] + @staticmethod def _find_deployment_by_model_id(healthy_deployments: list[dict], model_id: str) -> dict | None: for deployment in healthy_deployments: @@ -334,12 +457,21 @@ class DeploymentAffinityCheck(CustomLogger): if stable_model_map_key is None: return typed_healthy_deployments + session_affinity_active: Final = ( + enable_session_id or self._get_marker_session_affinity_ttl(request_kwargs=request_kwargs) is not None + ) + user_key: Final = ( + self._get_user_key_from_request_kwargs(request_kwargs=request_kwargs) + if (session_affinity_active or enable_user_key) + else None + ) + # 2) Session-id -> deployment affinity - if enable_session_id: + if session_affinity_active: session_id: Final = self._get_session_id_from_request_kwargs(request_kwargs=request_kwargs) if session_id is not None: session_cache_key: Final = self.get_session_affinity_cache_key( - model_group=stable_model_map_key, session_id=session_id + model_group=stable_model_map_key, session_id=session_id, user_key=user_key ) session_cache_result: Final = await self.cache.async_get_cache(key=session_cache_key) @@ -371,7 +503,6 @@ class DeploymentAffinityCheck(CustomLogger): if not enable_user_key: return typed_healthy_deployments - user_key: Final = self._get_user_key_from_request_kwargs(request_kwargs=request_kwargs) if user_key is None: return typed_healthy_deployments @@ -438,18 +569,22 @@ class DeploymentAffinityCheck(CustomLogger): enable_session_id, ) = self._get_effective_flags(deployment_model_name) - if not enable_user_key and not enable_session_id: + marker_session_ttl: Final = self._get_marker_session_affinity_ttl(request_kwargs=kwargs) + session_affinity_active: Final = enable_session_id or marker_session_ttl is not None + + if not enable_user_key and not session_affinity_active: return None - user_key = None - if enable_user_key: - user_key = self._get_user_key_from_request_kwargs(request_kwargs=kwargs) + user_key: Final = ( + self._get_user_key_from_request_kwargs(request_kwargs=kwargs) + if (enable_user_key or session_affinity_active) + else None + ) + session_id: Final = ( + self._get_session_id_from_request_kwargs(request_kwargs=kwargs) if session_affinity_active else None + ) - session_id = None - if enable_session_id: - session_id = self._get_session_id_from_request_kwargs(request_kwargs=kwargs) - - if user_key is None and session_id is None: + if not ((enable_user_key and user_key is not None) or session_id is not None): return None model_info = kwargs.get("model_info") @@ -473,22 +608,31 @@ class DeploymentAffinityCheck(CustomLogger): verbose_router_logger.warning("DeploymentAffinityCheck: model_id missing; skipping affinity cache update.") return None - if user_key is not None: + pin_value: Final = DeploymentAffinityCacheValue(model_id=str(model_id)) + + if enable_user_key and user_key is not None: try: cache_key: Final = self.get_affinity_cache_key(model_group=deployment_model_name, user_key=user_key) - await self.cache.async_set_cache( - cache_key, - DeploymentAffinityCacheValue(model_id=str(model_id)), - ttl=self.ttl_seconds, - ) - - verbose_router_logger.debug( - "DeploymentAffinityCheck: set affinity mapping model_map_key=%s deployment=%s ttl=%s user_key=%s", - deployment_model_name, - model_id, - self.ttl_seconds, - self._shorten_for_logs(user_key), + claimed_user_pin: Final = await self._claim_pin( + cache_key=cache_key, + pin_value=pin_value, + ttl_seconds=self.ttl_seconds, ) + if claimed_user_pin == pin_value["model_id"]: + verbose_router_logger.debug( + "DeploymentAffinityCheck: set affinity mapping model_map_key=%s deployment=%s ttl=%s user_key=%s", + deployment_model_name, + model_id, + self.ttl_seconds, + self._shorten_for_logs(user_key), + ) + else: + verbose_router_logger.debug( + "DeploymentAffinityCheck: affinity pin already claimed model_map_key=%s existing=%s ours=%s", + deployment_model_name, + claimed_user_pin, + model_id, + ) except Exception as e: # Non-blocking: affinity is a best-effort optimization. verbose_router_logger.debug( @@ -500,21 +644,31 @@ class DeploymentAffinityCheck(CustomLogger): # Also persist Session-ID affinity if enabled and session-id is provided if session_id is not None: try: + session_affinity_ttl: Final = marker_session_ttl if marker_session_ttl is not None else self.ttl_seconds session_cache_key: Final = self.get_session_affinity_cache_key( - model_group=deployment_model_name, session_id=session_id + model_group=deployment_model_name, session_id=session_id, user_key=user_key ) - await self.cache.async_set_cache( - session_cache_key, - DeploymentAffinityCacheValue(model_id=str(model_id)), - ttl=self.ttl_seconds, - ) - verbose_router_logger.debug( - "DeploymentAffinityCheck: set session affinity mapping model_map_key=%s deployment=%s ttl=%s session_id=%s", - deployment_model_name, - model_id, - self.ttl_seconds, - session_id, + claimed_session_pin: Final = await self._claim_pin( + cache_key=session_cache_key, + pin_value=pin_value, + ttl_seconds=session_affinity_ttl, ) + if claimed_session_pin == pin_value["model_id"]: + verbose_router_logger.debug( + "DeploymentAffinityCheck: set session affinity mapping model_map_key=%s deployment=%s ttl=%s session_id=%s", + deployment_model_name, + model_id, + session_affinity_ttl, + session_id, + ) + else: + verbose_router_logger.debug( + "DeploymentAffinityCheck: session pin already claimed model_map_key=%s existing=%s ours=%s session_id=%s", + deployment_model_name, + claimed_session_pin, + model_id, + session_id, + ) except Exception as e: verbose_router_logger.debug( "DeploymentAffinityCheck: failed to set session affinity cache. model_map_key=%s error=%s", diff --git a/litellm/types/router.py b/litellm/types/router.py index 4280da08cbb..e166d844735 100644 --- a/litellm/types/router.py +++ b/litellm/types/router.py @@ -816,6 +816,7 @@ class PreRoutingHookResponse(BaseModel): model: str messages: list[dict[str, Any]] | None routing_decision: StandardLoggingRoutingDecision | None = None + session_affinity_ttl_seconds: int | None = None _PreRoutingStrategyT_co = TypeVar("_PreRoutingStrategyT_co", covariant=True) diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py index 6d6fd2e5507..22f9e6bb67a 100644 --- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py +++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py @@ -676,6 +676,7 @@ async def test_add_litellm_data_to_request_strips_user_control_fields(): "applied_policies": ["spoofed-policy"], "policy_sources": {"spoofed-policy": "request"}, "routing_decision": {"cause": "forged", "routed_model": "spoofed"}, + "_session_deployment_affinity_ttl": 999999, "internal_call_origin": "autorouter_classifier", "_guardrail_pipelines": [{"name": "spoofed"}], "_pipeline_managed_guardrails": ["evaded"], @@ -719,6 +720,7 @@ async def test_add_litellm_data_to_request_strips_user_control_fields(): "applied_policies", "policy_sources", "routing_decision", + "_session_deployment_affinity_ttl", "internal_call_origin", "_guardrail_pipelines", "_pipeline_managed_guardrails", diff --git a/tests/test_litellm/router_strategy/test_complexity_router.py b/tests/test_litellm/router_strategy/test_complexity_router.py index 356556f3563..94b6b68855b 100644 --- a/tests/test_litellm/router_strategy/test_complexity_router.py +++ b/tests/test_litellm/router_strategy/test_complexity_router.py @@ -3414,6 +3414,103 @@ class TestSessionAffinity: def _request_kwargs(session_id: str) -> Dict: return {"metadata": {"session_id": session_id}} + @pytest.mark.asyncio + async def test_hook_response_carries_session_affinity_ttl_on_classify_and_pin_paths( + self, mock_router_instance, session_affinity_config + ): + """The hook response's session_affinity_ttl_seconds is what the Router stamps as + the deployment-affinity marker, so both the classify path (turn 1) and the + session-pin path (turn 2) must carry the configured TTL.""" + mock_router_instance.cache = DualCache() + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={**session_affinity_config, "session_affinity_ttl_seconds": 321}, + ) + request_kwargs = self._request_kwargs("marker-session") + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=request_kwargs, messages=self.SIMPLE_MESSAGE + ) + second = await router.async_pre_routing_hook( + model="test-model", request_kwargs=request_kwargs, messages=self.SIMPLE_MESSAGE + ) + assert first.session_affinity_ttl_seconds == 321 + assert second.session_affinity_ttl_seconds == 321 + + @pytest.mark.parametrize( + "session_affinity,deployment_affinity,plugins,tier_pinned,deployment_pinned", + [ + (False, False, False, False, False), + (False, True, False, False, True), + (True, False, False, True, True), + (True, True, False, True, True), + (False, True, True, False, False), + (True, True, True, False, False), + ], + ) + @pytest.mark.asyncio + async def test_tier_pin_and_deployment_pin_are_independently_gated( + self, + mock_router_instance, + basic_config, + session_affinity, + deployment_affinity, + plugins, + tier_pinned, + deployment_pinned, + ): + """deployment_affinity pins the deployment inside each routed group without pinning which + group the session routes to, so with session_affinity off the tier must still reclassify + on every turn while the marker the Router stamps is still emitted. Turn 1 classifies + REASONING and turn 2 SIMPLE, so a reclassified turn 2 moves model while a tier-pinned one + does not. plugins suppress both pins, since a stale pin would bypass the plugin pipeline.""" + mock_router_instance.cache = DualCache() + router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={ + **basic_config, + "session_affinity": session_affinity, + "deployment_affinity": deployment_affinity, + **({"plugins": [_DummyPlugin()]} if plugins else {}), + }, + ) + request_kwargs = self._request_kwargs("matrix-session") + first = await router.async_pre_routing_hook( + model="test-model", request_kwargs=request_kwargs, messages=self.REASONING_MESSAGE + ) + second = await router.async_pre_routing_hook( + model="test-model", request_kwargs=request_kwargs, messages=self.SIMPLE_MESSAGE + ) + assert first.model == "o1-preview" + assert second.model == ("o1-preview" if tier_pinned else "gpt-4o-mini") + assert (first.session_affinity_ttl_seconds is not None) is deployment_pinned + assert (second.session_affinity_ttl_seconds is not None) is deployment_pinned + + @pytest.mark.asyncio + async def test_hook_response_has_no_session_affinity_ttl_when_disabled_or_plugins( + self, mock_router_instance, basic_config, session_affinity_config + ): + mock_router_instance.cache = DualCache() + disabled_router = ComplexityRouter( + model_name="test-router", + litellm_router_instance=mock_router_instance, + complexity_router_config={**basic_config, "deployment_affinity": False}, + ) + plugin_router = ComplexityRouter( + model_name="test-router-plugins", + litellm_router_instance=mock_router_instance, + complexity_router_config={**session_affinity_config, "plugins": [_DummyPlugin()]}, + ) + disabled = await disabled_router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-off"), messages=self.SIMPLE_MESSAGE + ) + with_plugins = await plugin_router.async_pre_routing_hook( + model="test-model", request_kwargs=self._request_kwargs("s-plugins"), messages=self.SIMPLE_MESSAGE + ) + assert disabled.session_affinity_ttl_seconds is None + assert with_plugins.session_affinity_ttl_seconds is None + @pytest.mark.asyncio async def test_disabled_by_default_reclassifies_every_turn(self, mock_router_instance, basic_config): """Regression: session_affinity defaults to False, so a shared session_id must NOT diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py b/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py index e29adda3328..428eb0ceafd 100644 --- a/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py +++ b/tests/test_litellm/router_utils/pre_call_checks/test_deployment_affinity_check.py @@ -465,8 +465,7 @@ async def test_async_pre_call_hook_uses_model_map_key_scope(): Deployment affinity caching uses (user_api_key_hash, model_map_key) -> model_id. """ - cache = AsyncMock() - cache.async_set_cache = AsyncMock() + cache = DualCache() callback = DeploymentAffinityCheck( cache=cache, @@ -489,11 +488,7 @@ async def test_async_pre_call_hook_uses_model_map_key_scope(): model_group="claude-sonnet-4-5@20250929", user_key="user-key-abc", ) - cache.async_set_cache.assert_called_once_with( - expected_cache_key, - {"model_id": "model-id-123"}, - ttl=123, - ) + assert await cache.async_get_cache(key=expected_cache_key) == {"model_id": "model-id-123"} @pytest.mark.asyncio diff --git a/tests/test_litellm/router_utils/pre_call_checks/test_session_id_affinity.py b/tests/test_litellm/router_utils/pre_call_checks/test_session_id_affinity.py index 0bcb0247aad..4053e6d118b 100644 --- a/tests/test_litellm/router_utils/pre_call_checks/test_session_id_affinity.py +++ b/tests/test_litellm/router_utils/pre_call_checks/test_session_id_affinity.py @@ -1,6 +1,6 @@ import os import sys -from unittest.mock import AsyncMock, patch +from unittest.mock import AsyncMock, MagicMock, patch import pytest @@ -10,6 +10,7 @@ import json import litellm from litellm.caching.dual_cache import DualCache +from litellm.constants import SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY from litellm.router_utils.pre_call_checks.deployment_affinity_check import ( DeploymentAffinityCheck, ) @@ -163,7 +164,7 @@ async def test_async_session_id_affinity_priority_over_user_key(): await callback.cache.async_set_cache( DeploymentAffinityCheck.get_session_affinity_cache_key( - "model_group", "session1" + "model_group", "session1", user_key="user1" ), {"model_id": "deployment-2"}, ) @@ -180,3 +181,439 @@ async def test_async_session_id_affinity_priority_over_user_key(): assert len(filtered) == 1 assert filtered[0]["model_info"]["id"] == "deployment-2" + + +MOCK_RESPONSES_API_RESPONSE = { + "id": "resp_mock-resp-456", + "object": "response", + "created_at": 1741476542, + "status": "completed", + "model": "azure/computer-use-preview", + "output": [], + "usage": { + "input_tokens": 5, + "output_tokens": 10, + "total_tokens": 15, + "output_tokens_details": {"reasoning_tokens": 0}, + }, +} + + +def _smart_router(session_affinity=True, ttl_seconds=777, deployment_affinity=True): + return litellm.Router( + model_list=[ + { + "model_name": "smart-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_default_model": "target-group", + "complexity_router_config": { + "session_affinity": session_affinity, + "deployment_affinity": deployment_affinity, + "session_affinity_ttl_seconds": ttl_seconds, + "tiers": { + "SIMPLE": "target-group", + "MEDIUM": "target-group", + "COMPLEX": "target-group", + "REASONING": "target-group", + }, + }, + }, + }, + { + "model_name": "target-group", + "litellm_params": { + "model": "azure/computer-use-preview-1", + "api_key": "mock-api-key-1", + "api_version": "mock-api-version", + "api_base": "https://mock-endpoint-1.openai.azure.com", + }, + "model_info": {"id": "deployment-1", "base_model": "computer-use-preview"}, + }, + { + "model_name": "target-group", + "litellm_params": { + "model": "azure/computer-use-preview-2", + "api_key": "mock-api-key-2", + "api_version": "mock-api-version-2", + "api_base": "https://mock-endpoint-2.openai.azure.com", + }, + "model_info": {"id": "deployment-2", "base_model": "computer-use-preview"}, + }, + ], + ) + + +def _session_pin_key(session_id, user_key): + return DeploymentAffinityCheck.get_session_affinity_cache_key( + model_group="target-group", session_id=session_id, user_key=user_key + ) + + +def _cleanup_router_callbacks(router): + for callback in router.optional_callbacks or []: + litellm.logging_callback_manager.remove_callback_from_all_lists(callback) + + +async def _one_turn(router, model, session_id, key_hash): + """One request with the shuffle forced to deployment-1, so any other landing + deployment can only come from a pin read.""" + with ( + patch( + "litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post", + new_callable=AsyncMock, + ) as mock_post, + patch( + "litellm.router_strategy.simple_shuffle.random.choice", + side_effect=lambda seq: seq[0], + ), + ): + mock_post.return_value = MockResponse(MOCK_RESPONSES_API_RESPONSE, 200) + response = await router.aresponses( + model=model, + input=f"turn for {session_id} {key_hash}", + litellm_metadata={"session_id": session_id, "user_api_key_hash": key_hash}, + ) + return response._hidden_params["model_id"] + + +@pytest.mark.asyncio +async def test_auto_router_session_affinity_writes_scoped_pin_and_follows_it(): + """Turn 1 persists a key-scoped deployment pin; a pin seeded to the deployment + the shuffle would never pick is then followed, proving the read path.""" + router = _smart_router() + try: + served = await _one_turn(router, "smart-router", "write-session", "key-1") + assert await router.cache.async_get_cache(key=_session_pin_key("write-session", "key-1")) == { + "model_id": served + } + assert await router.cache.async_get_cache(key=_session_pin_key("write-session", None)) is None + + await router.cache.async_set_cache( + key=_session_pin_key("read-session", "key-1"), value={"model_id": "deployment-2"} + ) + assert await _one_turn(router, "smart-router", "read-session", "key-1") == "deployment-2" + finally: + _cleanup_router_callbacks(router) + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "model,key_hash", + [ + ("target-group", "key-1"), + ("smart-router", "key-2"), + ], + ids=["direct-group-call", "different-api-key"], +) +async def test_seeded_session_pin_is_invisible_outside_its_scope(model, key_hash): + """The pin binds (auto-routed request, api key, session): a direct call to the + group and a different key reusing the session id must both ignore it.""" + router = _smart_router() + try: + await router.cache.async_set_cache( + key=_session_pin_key("scoped-session", "key-1"), value={"model_id": "deployment-2"} + ) + assert await _one_turn(router, model, "scoped-session", key_hash) == "deployment-1" + finally: + _cleanup_router_callbacks(router) + + +@pytest.mark.asyncio +async def test_marker_write_uses_marker_ttl_and_writes_only_the_session_pin(): + """The write hook honors the marker's TTL over the callback default and writes + no user-key entry when only session affinity is active.""" + import time as time_module + + cache = DualCache() + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=3600, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + ) + + await callback.async_pre_call_deployment_hook( + kwargs={ + "model_info": {"id": "deployment-1"}, + "metadata": { + "deployment_model_name": "target-group", + "session_id": "ttl-session", + "user_api_key_hash": "key-1", + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: 777, + }, + }, + call_type=None, + ) + + session_key = _session_pin_key("ttl-session", "key-1") + assert cache.in_memory_cache.cache_dict == {session_key: {"model_id": "deployment-1"}} + assert cache.in_memory_cache.ttl_dict[session_key] == pytest.approx(time_module.time() + 777, abs=5) + + +@pytest.mark.asyncio +@pytest.mark.parametrize("bad_marker", ["777", True, -5, 0, None]) +async def test_malformed_marker_values_do_not_enable_session_affinity(bad_marker): + cache = DualCache() + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=3600, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + ) + + await callback.async_pre_call_deployment_hook( + kwargs={ + "model_info": {"id": "deployment-1"}, + "metadata": { + "deployment_model_name": "target-group", + "session_id": "bad-marker-session", + "user_api_key_hash": "key-1", + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: bad_marker, + }, + }, + call_type=None, + ) + + assert cache.in_memory_cache.cache_dict == {} + + +@pytest.mark.asyncio +@pytest.mark.parametrize("enable_user_key", [False, True], ids=["session-pin", "user-key-pin"]) +async def test_concurrent_first_requests_never_flip_a_claimed_pin(enable_user_key): + """Two overlapping first requests select different deployments before either + write lands. Pins are first-writer-wins claims, so the second write must leave + the stored pin unchanged instead of flipping it.""" + cache = DualCache() + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=3600, + enable_user_key_affinity=enable_user_key, + enable_responses_api_affinity=False, + ) + + def racing_kwargs(deployment_id): + metadata = {"deployment_model_name": "target-group", "user_api_key_hash": "key-1"} + if not enable_user_key: + metadata["session_id"] = "racing-session" + metadata[SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY] = 777 + return {"model_info": {"id": deployment_id}, "metadata": metadata} + + await callback.async_pre_call_deployment_hook(kwargs=racing_kwargs("deployment-1"), call_type=None) + await callback.async_pre_call_deployment_hook(kwargs=racing_kwargs("deployment-2"), call_type=None) + + pinned_key = ( + DeploymentAffinityCheck.get_affinity_cache_key(model_group="target-group", user_key="key-1") + if enable_user_key + else _session_pin_key("racing-session", "key-1") + ) + assert await cache.async_get_cache(key=pinned_key) == {"model_id": "deployment-1"} + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "stored_pin", + [{"model_id": "deployment-1"}, "deployment-1"], + ids=["dict-pin", "legacy-string-pin"], +) +async def test_in_memory_reclaim_slides_idle_window_only_for_the_stored_deployment(stored_pin): + """The pod-local claim mirrors the Lua keepalive: the winning deployment's + re-claim extends the pin's expiry, a losing deployment's claim touches neither + the value nor the expiry, so no-Redis setups keep stickiness across an active + session and ttl bounds idle time there too. Sameness is judged on the pinned + model id, so a legacy string pin written by the Redis branch slides the same.""" + import time as time_module + + cache = DualCache() + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=3600, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + ) + pin_key = _session_pin_key("slide-session", "key-1") + cache.in_memory_cache.set_cache(pin_key, stored_pin, ttl=10) + first_expiry = cache.in_memory_cache.ttl_dict[pin_key] + + reclaimed = await callback._claim_pin(cache_key=pin_key, pin_value={"model_id": "deployment-1"}, ttl_seconds=777) + assert reclaimed == "deployment-1" + assert cache.in_memory_cache.ttl_dict[pin_key] == pytest.approx(time_module.time() + 777, abs=5) + assert cache.in_memory_cache.ttl_dict[pin_key] > first_expiry + + lost = await callback._claim_pin(cache_key=pin_key, pin_value={"model_id": "deployment-2"}, ttl_seconds=10) + assert lost == "deployment-1" + assert cache.in_memory_cache.ttl_dict[pin_key] == pytest.approx(time_module.time() + 777, abs=5) + + +@pytest.mark.asyncio +async def test_claim_pin_uses_redis_attached_after_construction(): + """The proxy attaches Redis via Router._update_redis_cache after the Router (and + this callback) are built. The claim must resolve the redis tier per call, or pins + silently stay pod-local and cross-pod first-writer-wins is lost.""" + cache = DualCache() + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=3600, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + ) + + captured = {} + + async def fake_runner(keys, args, client=None): + captured["keys"] = keys + captured["args"] = args + return b'{"model_id": "other-pod-winner"}' + + late_redis = MagicMock() + late_redis.async_register_script = MagicMock(return_value=fake_runner) + cache.redis_cache = late_redis + + import time as time_module + + pin_key = _session_pin_key("late-redis-session", "key-1") + cache.in_memory_cache.set_cache(pin_key, {"model_id": "other-pod-winner"}, ttl=10) + + claimed = await callback._claim_pin( + cache_key=pin_key, + pin_value={"model_id": "our-deployment"}, + ttl_seconds=777, + ) + + assert claimed == "other-pod-winner" + assert cache.in_memory_cache.ttl_dict[pin_key] == pytest.approx(time_module.time() + 777, abs=5) + assert captured["keys"] == (pin_key,) + assert captured["args"] == ('{"model_id": "our-deployment"}', 777) + assert cache.in_memory_cache.get_cache(_session_pin_key("late-redis-session", "key-1")) == { + "model_id": "other-pod-winner" + } + + +@pytest.mark.asyncio +async def test_claim_pin_falls_back_to_pod_local_when_redis_is_down(): + """A Redis outage must cost cross-pod agreement, never same-pod stickiness. The write + hook only logs this result, so an escaping error would leave the session unpinned and + reshuffle every turn for the whole outage. DualCache's write path, which this claim + replaced, wrote the in-memory tier before ever touching Redis.""" + cache = DualCache() + callback = DeploymentAffinityCheck( + cache=cache, + ttl_seconds=3600, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + ) + + async def exploding_runner(keys, args, client=None): + raise ConnectionError("redis is down") + + down_redis = MagicMock() + down_redis.async_register_script = MagicMock(return_value=exploding_runner) + cache.redis_cache = down_redis + + key = _session_pin_key("outage-session", "key-1") + claimed = await callback._claim_pin(cache_key=key, pin_value={"model_id": "our-deployment"}, ttl_seconds=777) + + assert claimed == "our-deployment" + assert cache.in_memory_cache.get_cache(key) == {"model_id": "our-deployment"} + + second = await callback._claim_pin(cache_key=key, pin_value={"model_id": "another-deployment"}, ttl_seconds=777) + assert second == "our-deployment" + + +@pytest.mark.asyncio +async def test_marker_session_affinity_read_and_write_agree_for_wildcard_groups(): + """Wildcard deployments keep the literal pattern as model_name on both the read + path and the write path, so the marker-gated pin round-trips through one key.""" + callback = DeploymentAffinityCheck( + cache=DualCache(), + ttl_seconds=3600, + enable_user_key_affinity=False, + enable_responses_api_affinity=False, + ) + request_kwargs = { + "model_info": {"id": "wild-deployment-2"}, + "metadata": { + "deployment_model_name": "openai/*", + "session_id": "wild-session", + "user_api_key_hash": "key-1", + SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: 777, + }, + } + + await callback.async_pre_call_deployment_hook(kwargs=request_kwargs, call_type=None) + filtered = await callback.async_filter_deployments( + model="openai/gpt-4o", + healthy_deployments=[ + { + "model_name": "openai/*", + "litellm_params": {"model": "openai/gpt-4o"}, + "model_info": {"id": f"wild-deployment-{i}"}, + } + for i in (1, 2) + ], + messages=[], + request_kwargs=request_kwargs, + ) + + assert [d["model_info"]["id"] for d in filtered] == ["wild-deployment-2"] + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "model,session_affinity,deployment_affinity,expect_marker", + [ + ("smart-router", False, True, True), + ("smart-router", True, False, True), + ("smart-router", False, False, False), + ("target-group", False, True, False), + ], + ids=[ + "deployment-affinity-stamps", + "session-affinity-implies-deployment-pin", + "both-off-no-stamp", + "non-auto-routed-clears", + ], +) +async def test_pre_routing_hook_stamps_or_clears_the_marker_per_attempt( + model, session_affinity, deployment_affinity, expect_marker +): + """Every routing attempt writes or clears the marker, so a fallback from an + auto-routed group to a plain group cannot carry a stale marker. session_affinity + implies the deployment pin: a session frozen onto one group must not re-shuffle + across that group's deployments.""" + router = _smart_router(session_affinity=session_affinity, deployment_affinity=deployment_affinity) + try: + request_kwargs = { + "metadata": {SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: 111}, + "litellm_metadata": {SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY: 111}, + } + await router.async_pre_routing_hook( + model=model, + request_kwargs=request_kwargs, + messages=[{"role": "user", "content": "Hello"}], + ) + if expect_marker: + assert request_kwargs["litellm_metadata"][SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY] == 777 + else: + assert SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY not in request_kwargs["metadata"] + assert SESSION_DEPLOYMENT_AFFINITY_TTL_METADATA_KEY not in request_kwargs["litellm_metadata"] + finally: + _cleanup_router_callbacks(router) + + +def test_complexity_router_with_deployment_affinity_registers_affinity_callback(): + enabled = _smart_router() + session_only = _smart_router(session_affinity=True, deployment_affinity=False) + disabled = _smart_router(session_affinity=False, deployment_affinity=False) + try: + assert [ + (cb.enable_user_key_affinity, cb.enable_responses_api_affinity, cb.enable_session_id_affinity) + for cb in enabled.optional_callbacks or [] + if isinstance(cb, DeploymentAffinityCheck) + ] == [(False, False, False)] + assert any(isinstance(cb, DeploymentAffinityCheck) for cb in session_only.optional_callbacks or []) + assert not any(isinstance(cb, DeploymentAffinityCheck) for cb in disabled.optional_callbacks or []) + finally: + _cleanup_router_callbacks(enabled) + _cleanup_router_callbacks(session_only) + _cleanup_router_callbacks(disabled) diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py index da2d78edb73..67fa827a8e4 100644 --- a/tests/test_litellm/test_router.py +++ b/tests/test_litellm/test_router.py @@ -7550,3 +7550,68 @@ async def test_fallback_failure_detail_from_upstream_is_bounded(): assert capture.messages, "the fallback failure path did not log at ERROR" assert huge_message not in "".join(capture.messages) assert max(len(message) for message in capture.messages) < 5_000 +def test_stamp_or_clear_metadata_key_writes_and_clears_both_buckets(): + request_kwargs = {"metadata": {}} + litellm.Router._stamp_or_clear_metadata_key(request_kwargs=request_kwargs, key="probe", value=7) + assert request_kwargs["metadata"]["probe"] == 7 + + stale_kwargs = {"metadata": {"probe": 7}, "litellm_metadata": {"probe": 7}} + litellm.Router._stamp_or_clear_metadata_key(request_kwargs=stale_kwargs, key="probe", value=None) + assert "probe" not in stale_kwargs["metadata"] + assert "probe" not in stale_kwargs["litellm_metadata"] + + +@pytest.mark.parametrize( + "complexity_router_config,expect_callback", + [ + ({"tiers": {"SIMPLE": "gpt-4o"}}, True), + ({"tiers": {"SIMPLE": "gpt-4o"}, "deployment_affinity": False}, False), + ({"tiers": {"SIMPLE": "gpt-4o"}, "deployment_affinity": False, "session_affinity": True}, True), + ], +) +def test_complexity_router_registers_affinity_callback_for_deployment_pin(complexity_router_config, expect_callback): + """The marker the complexity router stamps is inert unless a DeploymentAffinityCheck is + registered to read it, so deployment_affinity has to pull the callback in, and its default-on + means a bare config registers one. Opting out must skip the callback entirely rather than + register a filter that can never fire, including when session_affinity is on, since the two + pins are independent.""" + from litellm.router_utils.pre_call_checks.deployment_affinity_check import ( + DeploymentAffinityCheck, + ) + + router = litellm.Router( + model_list=[ + {"model_name": "gpt-4o", "litellm_params": {"model": "gpt-4o"}}, + { + "model_name": "my-complexity-router", + "litellm_params": { + "model": "auto_router/complexity_router", + "complexity_router_config": complexity_router_config, + }, + }, + ] + ) + try: + registered = any(isinstance(cb, DeploymentAffinityCheck) for cb in router.optional_callbacks or []) + assert registered is expect_callback + finally: + for cb in router.optional_callbacks or []: + litellm.logging_callback_manager.remove_callback_from_all_lists(cb) + + +def test_ensure_deployment_affinity_callback_is_idempotent(): + from litellm.router_utils.pre_call_checks.deployment_affinity_check import ( + DeploymentAffinityCheck, + ) + + router = litellm.Router(model_list=[]) + try: + router._ensure_deployment_affinity_callback() + router._ensure_deployment_affinity_callback() + affinity_callbacks = [ + cb for cb in router.optional_callbacks or [] if isinstance(cb, DeploymentAffinityCheck) + ] + assert len(affinity_callbacks) == 1 + finally: + for cb in router.optional_callbacks or []: + litellm.logging_callback_manager.remove_callback_from_all_lists(cb) diff --git a/type-discipline-budget.json b/type-discipline-budget.json index 3a670bc7345..d621e85f09b 100644 --- a/type-discipline-budget.json +++ b/type-discipline-budget.json @@ -27,7 +27,7 @@ "limit": 0 }, "LIT010": { - "limit": 16760 + "limit": 16758 }, "LIT011": { "limit": 5598 diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts index a75c23da1cf..b361a16a0e6 100644 --- a/ui/litellm-dashboard/src/lib/http/schema.d.ts +++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts @@ -31695,6 +31695,12 @@ export interface components { * @description Default model to use if tier cannot be determined */ default_model?: string | null; + /** + * Deployment Affinity + * @description When True and a session_id is resolvable on the request, pin the deployment chosen inside each routed model group and reuse it whenever the session returns to that group, without pinning which group the session routes to. Independent of session_affinity, which pins the model group instead (and always carries this deployment pin with it): with session_affinity off, every turn is still classified on its own merits while a session that escalates to a stronger tier and comes back still lands on the deployment it used before, which is what keeps a provider prompt cache warm. Pins are held per model group, so switching tiers does not disturb the pin left behind in the previous group. On by default because re-shuffling a conversation across deployments of the same model discards that cache for no benefit; set False to keep every turn load-balanced across the group, which is what a deployment set with tight per-deployment rate limits wants. Inert when no session_id is resolvable, since there is nothing to key a pin on, and suppressed when plugins are configured, for the same reason session_affinity is. + * @default true + */ + deployment_affinity: boolean; /** * Dimension Weights * @description Weights for each scoring dimension @@ -31752,13 +31758,13 @@ export interface components { semantic_keyword_matching: boolean; /** * Session Affinity - * @description When True and a session_id is resolvable on the request, pin the model chosen on the session's first turn and reuse it for every later turn, skipping re-classification. Off by default so every turn is classified on its own merits and routed to the cheapest adequate tier. Set True to keep a multi-turn session on one model, which preserves provider prompt caches and avoids cross-model conversation-history errors. + * @description When True and a session_id is resolvable on the request, pin the model chosen on the session's first turn and reuse it for every later turn, skipping re-classification. Off by default so every turn is classified on its own merits and routed to the cheapest adequate tier. Set True to keep a multi-turn session on one model, which preserves provider prompt caches and avoids cross-model conversation-history errors. Always implies the deployment pin regardless of deployment_affinity: the session sticks to one deployment of the pinned model, since freezing the model while re-shuffling its deployments would still go cache-cold. * @default false */ session_affinity: boolean; /** * Session Affinity Ttl Seconds - * @description TTL for the session affinity pin; refreshed on every cache hit + * @description TTL for the session affinity pin; refreshed on every cache hit. Bounds both the session_affinity model pin and the deployment_affinity deployment pin, so it measures idle time for the session's routing decisions rather than total session length * @default 3600 */ session_affinity_ttl_seconds: number; From 84d63cbdcff2b3cbf7b2434c43f7405d3b123358 Mon Sep 17 00:00:00 2001 From: Yuneng Jiang Date: Sat, 8 Aug 2026 13:14:45 -0700 Subject: [PATCH 031/266] chore: update Next.js build artifacts (2026-08-08 20:14 UTC, node v24.19.0) --- litellm/proxy/_experimental/out/404.html | 2 +- .../proxy/_experimental/out/404/index.html | 2 +- .../out/__next.!KGRhc2hib2FyZCk.__PAGE__.txt | 8 +- .../out/__next.!KGRhc2hib2FyZCk.txt | 10 +- .../proxy/_experimental/out/__next._full.txt | 60 +-- .../proxy/_experimental/out/__next._head.txt | 8 +- .../proxy/_experimental/out/__next._index.txt | 15 +- .../proxy/_experimental/out/__next._tree.txt | 4 +- .../_buildManifest.js | 0 .../_clientMiddlewareManifest.js | 0 .../_ssgManifest.js | 0 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t,{apiKeySource:r,accessToken:o,apiKey:a,inputMessage:n,chatHistory:s,selectedTags:l,selectedVectorStores:c,selectedGuardrails:d,selectedPolicies:u,selectedMCPServers:p,mcpServers:m,mcpServerToolRestrictions:h,selectedVoice:f,endpointType:g,selectedModel:b,selectedSdk:v,proxySettings:x}=e,y="session"===r?o:a,_=window.location.origin,k=x?.LITELLM_UI_API_DOC_BASE_URL;k&&k.trim()?_=k:x?.PROXY_BASE_URL&&(_=x.PROXY_BASE_URL);let w=n||"Your prompt here",j=w.replace(/\\/g,"\\\\").replace(/"/g,'\\"').replace(/\n/g,"\\n"),S=s.filter(e=>!e.isImage).map(({role:e,content:t})=>({role:e,content:t})),C={};l.length>0&&(C.tags=l),c.length>0&&(C.vector_stores=c),d.length>0&&(C.guardrails=d),u.length>0&&(C.policies=u);let z=b||"your-model-name",R="azure"===v?`import openai - -client = openai.AzureOpenAI( - api_key="${y||"YOUR_LITELLM_API_KEY"}", - azure_endpoint="${_}", - api_version="2024-02-01" -)`:`import openai - -client = openai.OpenAI( - api_key="${y||"YOUR_LITELLM_API_KEY"}", - base_url="${_}" 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or data:application/pdf;base64,{base64_file} -# } -# } -# ] -# } -# ]${r} -# ) -# print(response_with_file) -`;break}case i.RESPONSES:{let e=Object.keys(C).length>0,r="";if(e){let e=JSON.stringify({metadata:C},null,2).split("\n").map(e=>" ".repeat(4)+e).join("\n").trim();r=`, - extra_body=${e}`}let o=S.length>0?S:[{role:"user",content:w}];t=` -import base64 - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Example with text only -response = client.responses.create( - model="${z}", - input=${JSON.stringify(o,null,4)}${r} -) - -print(response.output_text) - -# Example with image or PDF (uncomment and provide file path to use) -# base64_file = encode_image("path/to/your/file.jpg") # or .pdf -# response_with_file = client.responses.create( -# model="${z}", -# input=[ -# { -# "role": "user", -# "content": [ -# {"type": "input_text", "text": "${j}"}, -# { -# "type": "input_image", -# "image_url": f"data:image/jpeg;base64,{base64_file}", # or data:application/pdf;base64,{base64_file} -# }, -# ], -# } -# ]${r} -# ) -# print(response_with_file.output_text) -`;break}case i.IMAGE:t="azure"===v?` -# NOTE: The Azure SDK does not have a direct equivalent to the multi-modal 'responses.create' method shown for OpenAI. -# This snippet uses 'client.images.generate' and will create a new image based on your prompt. -# It does not use the uploaded image, as 'client.images.generate' does not support image inputs in this context. -import os -import requests -import json -import time -from PIL import Image - -result = client.images.generate( - model="${z}", - prompt="${n}", - n=1 -) - -json_response = json.loads(result.model_dump_json()) - -# Set the directory for the stored image -image_dir = os.path.join(os.curdir, 'images') - -# If the directory doesn't exist, create it -if not os.path.isdir(image_dir): - os.mkdir(image_dir) - -# Initialize the image path -image_filename = f"generated_image_{int(time.time())}.png" -image_path = os.path.join(image_dir, image_filename) - -try: - # Retrieve the generated image - if json_response.get("data") && len(json_response["data"]) > 0 && json_response["data"][0].get("url"): - image_url = json_response["data"][0]["url"] - generated_image = requests.get(image_url).content - with open(image_path, "wb") as image_file: - image_file.write(generated_image) - - print(f"Image saved to {image_path}") - # Display the image - image = Image.open(image_path) - image.show() - else: - print("Could not find image URL in response.") - print("Full response:", json_response) -except Exception as e: - print(f"An error occurred: {e}") - print("Full response:", json_response) -`:` -import base64 -import os -import time -import json -from PIL import Image -import requests - -# Helper function to encode images to base64 -def encode_image(image_path): - with open(image_path, "rb") as image_file: - return base64.b64encode(image_file.read()).decode('utf-8') - -# Helper function to create a file (simplified for this example) -def create_file(image_path): - # In a real implementation, this would upload the file to OpenAI - # For this example, we'll just return a placeholder ID - return f"file_{os.path.basename(image_path).replace('.', '_')}" - -# The prompt entered by the user -prompt = "${j}" - -# Encode images to base64 -base64_image1 = encode_image("body-lotion.png") -base64_image2 = encode_image("soap.png") - -# Create file IDs -file_id1 = create_file("body-lotion.png") -file_id2 = create_file("incense-kit.png") - -response = client.responses.create( - model="${z}", - input=[ - { - "role": "user", - "content": [ - {"type": "input_text", "text": prompt}, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image1}", - }, - { - "type": "input_image", - "image_url": f"data:image/jpeg;base64,{base64_image2}", - }, - { - "type": "input_image", - "file_id": file_id1, 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this._client.delete(rt`/v1/messages/batches/${e}?beta=true`,{...s,headers:s7([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}cancel(e,t={},s){let{betas:r}=t??{};return this._client.post(rt`/v1/messages/batches/${e}/cancel?beta=true`,{...s,headers:s7([{"anthropic-beta":[...r??[],"message-batches-2024-09-24"].toString()},s?.headers])})}async results(e,t={},s){let r=await this.retrieve(e);if(!r.results_url)throw new se(`No batch \`results_url\`; Has it finished processing? ${r.processing_status} - ${r.id}`);let{betas:a}=t??{};return this._client.get(r.results_url,{...s,headers:s7([{"anthropic-beta":[...a??[],"message-batches-2024-09-24"].toString(),Accept:"application/binary"},s?.headers]),stream:!0,__binaryResponse:!0})._thenUnwrap((e,t)=>rg.fromResponse(t.response,t.controller))}}let 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r>=0&&s.splice(r,1),this}once(e,t){return(t6(this,Z,"f")[e]||(t6(this,Z,"f")[e]=[])).push({listener:t,once:!0}),this}emitted(e){return new Promise((t,s)=>{t3(this,er,!0,"f"),"error"!==e&&this.once("error",s),this.once(e,t)})}async done(){t3(this,er,!0,"f"),await t6(this,X,"f")}get currentMessage(){return t6(this,H,"f")}async finalMessage(){return await this.done(),t6(this,F,"m",el).call(this)}async finalText(){return await this.done(),t6(this,F,"m",eo).call(this)}_emit(e,...t){if(t6(this,ee,"f"))return;"end"===e&&(t3(this,ee,!0,"f"),t6(this,Y,"f").call(this));let s=t6(this,Z,"f")[e];if(s&&(t6(this,Z,"f")[e]=s.filter(e=>!e.once),s.forEach(({listener:e})=>e(...t))),"abort"===e){let e=t[0];t6(this,er,"f")||s?.length||Promise.reject(e),t6(this,K,"f").call(this,e),t6(this,Q,"f").call(this,e),this._emit("end");return}if("error"===e){let e=t[0];t6(this,er,"f")||s?.length||Promise.reject(e),t6(this,K,"f").call(this,e),t6(this,Q,"f").call(this,e),this._emit("end")}}_emitFinal(){this.receivedMessages.at(-1)&&this._emit("finalMessage",t6(this,F,"m",el).call(this))}async _fromReadableStream(e,t){let s,r=t?.signal;r&&(r.aborted&&this.controller.abort(),s=this.controller.abort.bind(this.controller),r.addEventListener("abort",s));try{t6(this,F,"m",ed).call(this),this._connected(null);let t=sU.fromReadableStream(e,this.controller);for await(let e of t)t6(this,F,"m",eu).call(this,e);if(t.controller.signal?.aborted)throw new ss;t6(this,F,"m",em).call(this)}finally{r&&s&&r.removeEventListener("abort",s)}}[(H=new WeakMap,J=new WeakMap,V=new WeakMap,G=new WeakMap,K=new WeakMap,X=new WeakMap,Y=new WeakMap,Q=new WeakMap,Z=new WeakMap,ee=new WeakMap,et=new WeakMap,es=new WeakMap,er=new WeakMap,ea=new WeakMap,en=new WeakMap,ei=new WeakMap,ec=new WeakMap,F=new WeakSet,el=function(){if(0===this.receivedMessages.length)throw new se("stream ended without producing a Message with role=assistant");return this.receivedMessages.at(-1)},eo=function(){if(0===this.receivedMessages.length)throw new se("stream ended without producing a Message with role=assistant");let e=this.receivedMessages.at(-1).content.filter(e=>"text"===e.type).map(e=>e.text);if(0===e.length)throw new se("stream ended without producing a content block with type=text");return e.join(" ")},ed=function(){this.ended||t3(this,H,void 0,"f")},eu=function(e){if(this.ended)return;let t=t6(this,F,"m",eh).call(this,e);switch(this._emit("streamEvent",e,t),e.type){case"content_block_delta":{let s=t.content.at(-1);switch(e.delta.type){case"text_delta":"text"===s.type&&this._emit("text",e.delta.text,s.text||"");break;case"citations_delta":"text"===s.type&&this._emit("citation",e.delta.citation,s.citations??[]);break;case"input_json_delta":rG(s)&&s.input&&this._emit("inputJson",e.delta.partial_json,s.input);break;case"thinking_delta":"thinking"===s.type&&this._emit("thinking",e.delta.thinking,s.thinking);break;case"signature_delta":"thinking"===s.type&&this._emit("signature",s.signature);break;default:rX(e.delta)}break}case"message_stop":this._addMessageParam(t),this._addMessage(rH(t,t6(this,J,"f"),{logger:t6(this,ei,"f")}),!0);break;case"content_block_stop":this._emit("contentBlock",t.content.at(-1));break;case"message_start":t3(this,H,t,"f")}},em=function(){if(this.ended)throw new se("stream has ended, this shouldn't happen");let e=t6(this,H,"f");if(!e)throw new se("request ended without sending any chunks");return t3(this,H,void 0,"f"),rH(e,t6(this,J,"f"),{logger:t6(this,ei,"f")})},eh=function(e){let t=t6(this,H,"f");if("message_start"===e.type){if(t)throw new se(`Unexpected event order, got ${e.type} before receiving "message_stop"`);return e.message}if(!t)throw new se(`Unexpected event order, got ${e.type} before "message_start"`);switch(e.type){case"message_stop":case"content_block_stop":return t;case"message_delta":return t.stop_reason=e.delta.stop_reason,t.stop_sequence=e.delta.stop_sequence,t.usage.output_tokens=e.usage.output_tokens,null!=e.usage.input_tokens&&(t.usage.input_tokens=e.usage.input_tokens),null!=e.usage.cache_creation_input_tokens&&(t.usage.cache_creation_input_tokens=e.usage.cache_creation_input_tokens),null!=e.usage.cache_read_input_tokens&&(t.usage.cache_read_input_tokens=e.usage.cache_read_input_tokens),null!=e.usage.server_tool_use&&(t.usage.server_tool_use=e.usage.server_tool_use),t;case"content_block_start":return t.content.push({...e.content_block}),t;case"content_block_delta":{let s=t.content.at(e.index);switch(e.delta.type){case"text_delta":s?.type==="text"&&(t.content[e.index]={...s,text:(s.text||"")+e.delta.text});break;case"citations_delta":s?.type==="text"&&(t.content[e.index]={...s,citations:[...s.citations??[],e.delta.citation]});break;case"input_json_delta":if(s&&rG(s)){let r=s[rV]||"";r+=e.delta.partial_json;let a={...s};Object.defineProperty(a,rV,{value:r,enumerable:!1,writable:!0}),r&&(a.input=rj(r)),t.content[e.index]=a}break;case"thinking_delta":s?.type==="thinking"&&(t.content[e.index]={...s,thinking:s.thinking+e.delta.thinking});break;case"signature_delta":s?.type==="thinking"&&(t.content[e.index]={...s,signature:e.delta.signature});break;default:rX(e.delta)}return t}}},Symbol.asyncIterator)](){let e=[],t=[],s=!1;return this.on("streamEvent",s=>{let r=t.shift();r?r.resolve(s):e.push(s)}),this.on("end",()=>{for(let e of(s=!0,t))e.resolve(void 0);t.length=0}),this.on("abort",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),this.on("error",e=>{for(let r of(s=!0,t))r.reject(e);t.length=0}),{next:async()=>e.length?{value:e.shift(),done:!1}:s?{value:void 0,done:!0}:new Promise((e,s)=>t.push({resolve:e,reject:s})).then(e=>e?{value:e,done:!1}:{value:void 0,done:!0}),return:async()=>(this.abort(),{value:void 0,done:!0})}}toReadableStream(){return new sU(this[Symbol.asyncIterator].bind(this),this.controller).toReadableStream()}}function rX(e){}class rY extends s6{create(e,t){return this._client.post("/v1/messages/batches",{body:e,...t})}retrieve(e,t){return this._client.get(rt`/v1/messages/batches/${e}`,t)}list(e={},t){return this._client.getAPIList("/v1/messages/batches",sV,{query:e,...t})}delete(e,t){return this._client.delete(rt`/v1/messages/batches/${e}`,t)}cancel(e,t){return this._client.post(rt`/v1/messages/batches/${e}/cancel`,t)}async results(e,t){let s=await this.retrieve(e);if(!s.results_url)throw new se(`No batch \`results_url\`; Has it finished processing? 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