mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-07 08:26:10 +00:00
fix(router.py): fix print statement
This commit is contained in:
Krrish Dholakia
parent
5efe59e6e2
commit
92ebf5b918
1 changed files with 175 additions and 160 deletions
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@ -374,13 +374,25 @@ def test_get_fallback_model_group_from_fallbacks(model_list):
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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async def test_deployment_callback_on_success(model_list, sync_mode):
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async def test_deployment_callback_on_success(sync_mode):
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"""Test if the '_deployment_callback_on_success' function is working correctly"""
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import time
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model_list = [
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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"api_key": os.getenv("OPENAI_API_KEY"),
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"rpm": 100,
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},
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"model_info": {"id": "100"},
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}
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]
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router = Router(model_list=model_list)
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standard_logging_payload = create_standard_logging_payload()
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standard_logging_payload["total_tokens"] = 100
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standard_logging_payload["model_id"] = "100"
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kwargs = {
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"litellm_params": {
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"metadata": {
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@ -1181,6 +1193,7 @@ def test_cached_get_model_group_info(model_list):
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def test_init_responses_api_endpoints(model_list):
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"""Test if the '_init_responses_api_endpoints' function is working correctly"""
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from typing import Callable
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router = Router(model_list=model_list)
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assert router.aget_responses is not None
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@ -1215,29 +1228,29 @@ def test_mock_router_testing_params_str_to_bool_conversion(
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):
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"""Test if MockRouterTestingParams.from_kwargs correctly converts string values to booleans using str_to_bool"""
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from litellm.types.router import MockRouterTestingParams
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kwargs = {
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"mock_testing_fallbacks": mock_testing_fallbacks,
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"mock_testing_context_fallbacks": mock_testing_context_fallbacks,
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"mock_testing_content_policy_fallbacks": mock_testing_content_policy_fallbacks,
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"other_param": "should_remain", # This should not be affected
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}
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# Make a copy to verify kwargs are properly popped
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original_kwargs = kwargs.copy()
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mock_params = MockRouterTestingParams.from_kwargs(kwargs)
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# Verify the converted values
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assert mock_params.mock_testing_fallbacks == expected_fallbacks
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assert mock_params.mock_testing_context_fallbacks == expected_context
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assert mock_params.mock_testing_content_policy_fallbacks == expected_content_policy
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# Verify that the mock testing params were popped from kwargs
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assert "mock_testing_fallbacks" not in kwargs
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assert "mock_testing_context_fallbacks" not in kwargs
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assert "mock_testing_content_policy_fallbacks" not in kwargs
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# Verify other params remain unchanged
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assert kwargs["other_param"] == "should_remain"
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@ -1245,50 +1258,49 @@ def test_mock_router_testing_params_str_to_bool_conversion(
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def test_is_auto_router_deployment(model_list):
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"""Test if the '_is_auto_router_deployment' function correctly identifies auto-router deployments"""
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router = Router(model_list=model_list)
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# Test case 1: Model starts with "auto_router/" - should return True
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litellm_params_auto = LiteLLM_Params(model="auto_router/my-auto-router")
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assert router._is_auto_router_deployment(litellm_params_auto) is True
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# Test case 2: Model doesn't start with "auto_router/" - should return False
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litellm_params_regular = LiteLLM_Params(model="gpt-3.5-turbo")
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assert router._is_auto_router_deployment(litellm_params_regular) is False
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# Test case 3: Model is empty string - should return False
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litellm_params_empty = LiteLLM_Params(model="")
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assert router._is_auto_router_deployment(litellm_params_empty) is False
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# Test case 4: Model contains "auto_router/" but doesn't start with it - should return False
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litellm_params_contains = LiteLLM_Params(model="prefix_auto_router/something")
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assert router._is_auto_router_deployment(litellm_params_contains) is False
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@patch('litellm.router_strategy.auto_router.auto_router.AutoRouter')
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@patch("litellm.router_strategy.auto_router.auto_router.AutoRouter")
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def test_init_auto_router_deployment_success(mock_auto_router, model_list):
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"""Test if the 'init_auto_router_deployment' function successfully initializes auto-router when all params provided"""
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router = Router(model_list=model_list)
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# Create a mock AutoRouter instance
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mock_auto_router_instance = MagicMock()
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mock_auto_router.return_value = mock_auto_router_instance
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# Test case: All required parameters provided
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litellm_params = LiteLLM_Params(
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model="auto_router/test",
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auto_router_config_path="/path/to/config",
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auto_router_default_model="gpt-3.5-turbo",
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auto_router_embedding_model="text-embedding-ada-002"
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auto_router_embedding_model="text-embedding-ada-002",
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)
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deployment = Deployment(
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model_name="test-auto-router",
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model_name="test-auto-router",
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litellm_params=litellm_params,
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model_info={"id": "test-id"}
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model_info={"id": "test-id"},
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)
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# Should not raise any exception
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router.init_auto_router_deployment(deployment)
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# Verify AutoRouter was called with correct parameters
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mock_auto_router.assert_called_once_with(
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model_name="test-auto-router",
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@ -1298,86 +1310,96 @@ def test_init_auto_router_deployment_success(mock_auto_router, model_list):
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embedding_model="text-embedding-ada-002",
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litellm_router_instance=router,
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)
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# Verify the auto-router was added to the router's auto_routers dict
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assert "test-auto-router" in router.auto_routers
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assert router.auto_routers["test-auto-router"] == mock_auto_router_instance
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@patch('litellm.router_strategy.auto_router.auto_router.AutoRouter')
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@patch("litellm.router_strategy.auto_router.auto_router.AutoRouter")
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def test_init_auto_router_deployment_duplicate_model_name(mock_auto_router, model_list):
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"""Test if the 'init_auto_router_deployment' function raises ValueError when model_name already exists"""
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router = Router(model_list=model_list)
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# Create a mock AutoRouter instance
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mock_auto_router_instance = MagicMock()
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mock_auto_router.return_value = mock_auto_router_instance
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# Add an existing auto-router
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router.auto_routers["test-auto-router"] = mock_auto_router_instance
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# Try to add another auto-router with the same name
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litellm_params = LiteLLM_Params(
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model="auto_router/test",
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auto_router_config_path="/path/to/config",
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auto_router_default_model="gpt-3.5-turbo",
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auto_router_embedding_model="text-embedding-ada-002"
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auto_router_embedding_model="text-embedding-ada-002",
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)
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deployment = Deployment(
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model_name="test-auto-router",
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model_name="test-auto-router",
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litellm_params=litellm_params,
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model_info={"id": "test-id"}
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model_info={"id": "test-id"},
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)
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with pytest.raises(ValueError, match="Auto-router deployment test-auto-router already exists"):
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with pytest.raises(
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ValueError, match="Auto-router deployment test-auto-router already exists"
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):
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router.init_auto_router_deployment(deployment)
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def test_generate_model_id_with_deployment_model_name(model_list):
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"""Test that _generate_model_id works correctly with deployment model_name and handles None values properly"""
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router = Router(model_list=model_list)
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# Test case 1: Normal case with valid model_group and litellm_params
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model_group = "gpt-4.1"
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litellm_params = {
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"model": "gpt-4.1",
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"api_key": "test_key",
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"api_base": "https://api.openai.com/v1"
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"api_base": "https://api.openai.com/v1",
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}
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try:
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result = router._generate_model_id(model_group=model_group, litellm_params=litellm_params)
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result = router._generate_model_id(
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model_group=model_group, litellm_params=litellm_params
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)
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assert isinstance(result, str)
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assert len(result) > 0
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print(f"✓ Success with valid model_group: {result}")
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except Exception as e:
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pytest.fail(f"Failed with valid model_group: {e}")
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# Test case 2: Edge case with None model_group (this should fail as expected - our fix prevents this from happening)
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try:
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result = router._generate_model_id(model_group=None, litellm_params=litellm_params)
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pytest.fail("Expected TypeError when model_group is None - this confirms our fix is needed")
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result = router._generate_model_id(
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model_group=None, litellm_params=litellm_params
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)
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pytest.fail(
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"Expected TypeError when model_group is None - this confirms our fix is needed"
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)
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except TypeError as e:
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assert "unsupported operand type(s) for +=" in str(e)
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print(f"✓ Correctly failed with None model_group (as expected): {e}")
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except Exception as e:
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pytest.fail(f"Unexpected error with None model_group: {e}")
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# Test case 3: Edge case with None key in litellm_params
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litellm_params_with_none_key = {
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"model": "gpt-4.1",
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"api_key": "test_key",
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None: "should_be_skipped" # This should be handled gracefully
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None: "should_be_skipped", # This should be handled gracefully
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}
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try:
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result = router._generate_model_id(model_group=model_group, litellm_params=litellm_params_with_none_key)
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result = router._generate_model_id(
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model_group=model_group, litellm_params=litellm_params_with_none_key
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)
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assert isinstance(result, str)
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assert len(result) > 0
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print(f"✓ Success with None key in litellm_params: {result}")
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except Exception as e:
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pytest.fail(f"Failed with None key in litellm_params: {e}")
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# Test case 4: Edge case with empty litellm_params
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try:
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result = router._generate_model_id(model_group=model_group, litellm_params={})
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@ -1386,216 +1408,213 @@ def test_generate_model_id_with_deployment_model_name(model_list):
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print(f"✓ Success with empty litellm_params: {result}")
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except Exception as e:
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pytest.fail(f"Failed with empty litellm_params: {e}")
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# Test case 5: Verify that the same inputs produce the same result (deterministic)
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result1 = router._generate_model_id(model_group=model_group, litellm_params=litellm_params)
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result2 = router._generate_model_id(model_group=model_group, litellm_params=litellm_params)
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result1 = router._generate_model_id(
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model_group=model_group, litellm_params=litellm_params
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)
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result2 = router._generate_model_id(
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model_group=model_group, litellm_params=litellm_params
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)
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assert result1 == result2, "Model ID generation should be deterministic"
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print("✓ All _generate_model_id tests passed!")
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def test_handle_clientside_credential_with_deployment_model_name(model_list):
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"""Test that _handle_clientside_credential uses deployment model_name correctly"""
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router = Router(model_list=model_list)
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# Mock deployment with model_name
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deployment = {
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"model_name": "gpt-4.1",
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"litellm_params": {
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"model": "gpt-4.1",
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"api_key": "test_key"
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}
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"litellm_params": {"model": "gpt-4.1", "api_key": "test_key"},
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}
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# Mock kwargs with empty metadata (simulating the original issue)
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kwargs = {
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"metadata": {}, # Empty metadata, no model_group
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"litellm_params": {
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"api_key": "client_side_key",
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"api_base": "https://api.openai.com/v1"
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}
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"api_base": "https://api.openai.com/v1",
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},
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}
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# Mock dynamic_litellm_params that would be returned by get_dynamic_litellm_params
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dynamic_litellm_params = {
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"api_key": "client_side_key",
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"api_base": "https://api.openai.com/v1"
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"api_base": "https://api.openai.com/v1",
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}
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# Test that the method doesn't fail when metadata is empty
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try:
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# This would normally call _generate_model_id internally
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# We're testing that the fix prevents the TypeError
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model_group = deployment["model_name"] # This is what our fix does
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assert model_group == "gpt-4.1"
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# Verify that _generate_model_id works with this model_group
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result = router._generate_model_id(model_group=model_group, litellm_params=dynamic_litellm_params)
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result = router._generate_model_id(
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model_group=model_group, litellm_params=dynamic_litellm_params
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)
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assert isinstance(result, str)
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assert len(result) > 0
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print(f"✓ Success with deployment model_name: {result}")
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except Exception as e:
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pytest.fail(f"Failed with deployment model_name: {e}")
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print("✓ _handle_clientside_credential test passed!")
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@pytest.mark.parametrize("function_name, expected_metadata_key", [
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("acompletion", "metadata"),
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("_ageneric_api_call_with_fallbacks", "litellm_metadata"),
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("batch", "litellm_metadata"),
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("completion", "metadata"),
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("acreate_file", "litellm_metadata"),
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("aget_file", "litellm_metadata"),
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])
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def test_handle_clientside_credential_metadata_loading(model_list, function_name, expected_metadata_key):
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@pytest.mark.parametrize(
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"function_name, expected_metadata_key",
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[
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("acompletion", "metadata"),
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("_ageneric_api_call_with_fallbacks", "litellm_metadata"),
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("batch", "litellm_metadata"),
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("completion", "metadata"),
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("acreate_file", "litellm_metadata"),
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("aget_file", "litellm_metadata"),
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],
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)
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def test_handle_clientside_credential_metadata_loading(
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model_list, function_name, expected_metadata_key
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):
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"""Test that _handle_clientside_credential correctly loads metadata based on function name"""
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router = Router(model_list=model_list)
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# Mock deployment
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deployment = {
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"model_name": "gpt-4.1",
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"litellm_params": {
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"model": "gpt-4.1",
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"api_key": "test_key"
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},
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"model_info": {
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"id": "original-id-123"
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}
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"litellm_params": {"model": "gpt-4.1", "api_key": "test_key"},
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"model_info": {"id": "original-id-123"},
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}
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# Mock kwargs with clientside credentials and metadata
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kwargs = {
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"api_key": "client_side_key",
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"api_base": "https://api.openai.com/v1",
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expected_metadata_key: {
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"model_group": "gpt-4.1",
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"custom_field": "test_value"
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}
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expected_metadata_key: {"model_group": "gpt-4.1", "custom_field": "test_value"},
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}
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# Call the function
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result_deployment = router._handle_clientside_credential(
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deployment=deployment,
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kwargs=kwargs,
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function_name=function_name
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deployment=deployment, kwargs=kwargs, function_name=function_name
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)
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# Verify the result is a Deployment object
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assert isinstance(result_deployment, Deployment)
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# Verify the deployment has the correct model_name (should be the model_group from metadata)
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assert result_deployment.model_name == "gpt-4.1"
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# Verify the litellm_params contain the clientside credentials
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assert result_deployment.litellm_params.api_key == "client_side_key"
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assert result_deployment.litellm_params.api_base == "https://api.openai.com/v1"
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# Verify the model_info has been updated with a new ID
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assert result_deployment.model_info.id != "original-id-123"
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assert result_deployment.model_info.original_model_id == "original-id-123"
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# Verify the deployment was added to the router
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assert len(router.model_list) == len(model_list) + 1
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# Test that the function correctly uses the right metadata key
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# For acompletion, it should use "metadata"
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# For _ageneric_api_call_with_fallbacks/batch, it should use "litellm_metadata"
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if function_name == "acompletion":
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assert "metadata" in kwargs
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assert "litellm_metadata" not in kwargs
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elif function_name in ["_ageneric_api_call_with_fallbacks", "batch", "acreate_file", "aget_file"]:
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elif function_name in [
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"_ageneric_api_call_with_fallbacks",
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"batch",
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"acreate_file",
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"aget_file",
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]:
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||||
assert "litellm_metadata" in kwargs
|
||||
# Note: acompletion would not have litellm_metadata, but other functions might have both
|
||||
|
||||
print(f"✓ Success with function_name '{function_name}' using '{expected_metadata_key}' metadata key")
|
||||
|
||||
print(
|
||||
f"✓ Success with function_name '{function_name}' using '{expected_metadata_key}' metadata key"
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("function_name, metadata_key", [
|
||||
("acompletion", "metadata"),
|
||||
("_ageneric_api_call_with_fallbacks", "litellm_metadata"),
|
||||
])
|
||||
def test_handle_clientside_credential_metadata_variable_name(model_list, function_name, metadata_key):
|
||||
@pytest.mark.parametrize(
|
||||
"function_name, metadata_key",
|
||||
[
|
||||
("acompletion", "metadata"),
|
||||
("_ageneric_api_call_with_fallbacks", "litellm_metadata"),
|
||||
],
|
||||
)
|
||||
def test_handle_clientside_credential_metadata_variable_name(
|
||||
model_list, function_name, metadata_key
|
||||
):
|
||||
"""Test that _handle_clientside_credential uses the correct metadata variable name based on function name"""
|
||||
from litellm.router_utils.batch_utils import _get_router_metadata_variable_name
|
||||
|
||||
|
||||
router = Router(model_list=model_list)
|
||||
|
||||
|
||||
# Verify the metadata variable name is correct for each function
|
||||
expected_metadata_key = _get_router_metadata_variable_name(function_name=function_name)
|
||||
expected_metadata_key = _get_router_metadata_variable_name(
|
||||
function_name=function_name
|
||||
)
|
||||
assert expected_metadata_key == metadata_key
|
||||
|
||||
|
||||
# Mock deployment
|
||||
deployment = {
|
||||
"model_name": "gpt-4.1",
|
||||
"litellm_params": {
|
||||
"model": "gpt-4.1",
|
||||
"api_key": "test_key"
|
||||
},
|
||||
"model_info": {
|
||||
"id": "original-id-456"
|
||||
}
|
||||
"litellm_params": {"model": "gpt-4.1", "api_key": "test_key"},
|
||||
"model_info": {"id": "original-id-456"},
|
||||
}
|
||||
|
||||
|
||||
# Mock kwargs with clientside credentials and the correct metadata key
|
||||
kwargs = {
|
||||
"api_key": "client_side_key",
|
||||
"api_base": "https://api.openai.com/v1",
|
||||
metadata_key: {
|
||||
"model_group": "gpt-4.1",
|
||||
"test_field": "test_value"
|
||||
}
|
||||
metadata_key: {"model_group": "gpt-4.1", "test_field": "test_value"},
|
||||
}
|
||||
|
||||
|
||||
# Call the function
|
||||
result_deployment = router._handle_clientside_credential(
|
||||
deployment=deployment,
|
||||
kwargs=kwargs,
|
||||
function_name=function_name
|
||||
deployment=deployment, kwargs=kwargs, function_name=function_name
|
||||
)
|
||||
|
||||
|
||||
# Verify the function correctly extracted model_group from the right metadata key
|
||||
assert result_deployment.model_name == "gpt-4.1"
|
||||
|
||||
|
||||
# Verify the deployment was created with the correct metadata
|
||||
assert result_deployment.litellm_params.api_key == "client_side_key"
|
||||
assert result_deployment.litellm_params.api_base == "https://api.openai.com/v1"
|
||||
|
||||
print(f"✓ Success with function_name '{function_name}' correctly using '{metadata_key}' for metadata")
|
||||
|
||||
print(
|
||||
f"✓ Success with function_name '{function_name}' correctly using '{metadata_key}' for metadata"
|
||||
)
|
||||
|
||||
|
||||
def test_handle_clientside_credential_no_metadata(model_list):
|
||||
"""Test that _handle_clientside_credential handles cases where no metadata is provided"""
|
||||
router = Router(model_list=model_list)
|
||||
|
||||
|
||||
# Mock deployment
|
||||
deployment = {
|
||||
"model_name": "gpt-4.1",
|
||||
"litellm_params": {
|
||||
"model": "gpt-4.1",
|
||||
"api_key": "test_key"
|
||||
},
|
||||
"model_info": {
|
||||
"id": "original-id-789"
|
||||
}
|
||||
"litellm_params": {"model": "gpt-4.1", "api_key": "test_key"},
|
||||
"model_info": {"id": "original-id-789"},
|
||||
}
|
||||
|
||||
|
||||
# Mock kwargs with clientside credentials but NO metadata
|
||||
kwargs = {
|
||||
"api_key": "client_side_key",
|
||||
"api_base": "https://api.openai.com/v1"
|
||||
"api_base": "https://api.openai.com/v1",
|
||||
# No metadata key at all
|
||||
}
|
||||
|
||||
|
||||
# This should fail because there's no model_group in metadata
|
||||
# The function expects to find model_group in the metadata
|
||||
try:
|
||||
result_deployment = router._handle_clientside_credential(
|
||||
deployment=deployment,
|
||||
kwargs=kwargs,
|
||||
function_name="acompletion"
|
||||
deployment=deployment, kwargs=kwargs, function_name="acompletion"
|
||||
)
|
||||
# If we get here, the function should have used deployment.model_name as fallback
|
||||
assert result_deployment.model_name == "gpt-4.1"
|
||||
|
|
@ -1603,19 +1622,19 @@ def test_handle_clientside_credential_no_metadata(model_list):
|
|||
except Exception as e:
|
||||
# This is expected behavior - the function needs model_group to generate model_id
|
||||
print(f"✓ Correctly handled no metadata case: {e}")
|
||||
|
||||
|
||||
# Test with empty metadata
|
||||
kwargs_with_empty_metadata = {
|
||||
"api_key": "client_side_key",
|
||||
"api_base": "https://api.openai.com/v1",
|
||||
"metadata": {} # Empty metadata
|
||||
"metadata": {}, # Empty metadata
|
||||
}
|
||||
|
||||
|
||||
try:
|
||||
result_deployment = router._handle_clientside_credential(
|
||||
deployment=deployment,
|
||||
kwargs=kwargs_with_empty_metadata,
|
||||
function_name="acompletion"
|
||||
function_name="acompletion",
|
||||
)
|
||||
# Should fail because empty metadata has no model_group
|
||||
pytest.fail("Expected failure with empty metadata")
|
||||
|
|
@ -1626,36 +1645,31 @@ def test_handle_clientside_credential_no_metadata(model_list):
|
|||
def test_handle_clientside_credential_with_responses_function(model_list):
|
||||
"""Test that _handle_clientside_credential works correctly with responses function name"""
|
||||
router = Router(model_list=model_list)
|
||||
|
||||
|
||||
# Mock deployment
|
||||
deployment = {
|
||||
"model_name": "gpt-4.1",
|
||||
"litellm_params": {
|
||||
"model": "gpt-4.1",
|
||||
"api_key": "test_key"
|
||||
},
|
||||
"model_info": {
|
||||
"id": "original-id-responses"
|
||||
}
|
||||
"litellm_params": {"model": "gpt-4.1", "api_key": "test_key"},
|
||||
"model_info": {"id": "original-id-responses"},
|
||||
}
|
||||
|
||||
|
||||
# Mock kwargs with clientside credentials and litellm_metadata (for responses function)
|
||||
kwargs = {
|
||||
"api_key": "client_side_key",
|
||||
"api_base": "https://api.openai.com/v1",
|
||||
"litellm_metadata": {
|
||||
"model_group": "gpt-4.1",
|
||||
"responses_field": "responses_value"
|
||||
}
|
||||
"responses_field": "responses_value",
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
# Call the function with _ageneric_api_call_with_fallbacks function name (which handles responses)
|
||||
result_deployment = router._handle_clientside_credential(
|
||||
deployment=deployment,
|
||||
kwargs=kwargs,
|
||||
function_name="_ageneric_api_call_with_fallbacks"
|
||||
function_name="_ageneric_api_call_with_fallbacks",
|
||||
)
|
||||
|
||||
|
||||
# Verify the result
|
||||
assert isinstance(result_deployment, Deployment)
|
||||
assert result_deployment.model_name == "gpt-4.1"
|
||||
|
|
@ -1663,9 +1677,10 @@ def test_handle_clientside_credential_with_responses_function(model_list):
|
|||
assert result_deployment.litellm_params.api_base == "https://api.openai.com/v1"
|
||||
assert result_deployment.model_info.id != "original-id-responses"
|
||||
assert result_deployment.model_info.original_model_id == "original-id-responses"
|
||||
|
||||
|
||||
# Verify the deployment was added to the router
|
||||
assert len(router.model_list) == len(model_list) + 1
|
||||
|
||||
print("✓ Success with _ageneric_api_call_with_fallbacks function name and litellm_metadata")
|
||||
|
||||
print(
|
||||
"✓ Success with _ageneric_api_call_with_fallbacks function name and litellm_metadata"
|
||||
)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue