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fix(llm): inject Gemini safety_settings per-deployment so Azure fallback survives (SKY-11766, incident #646) (#7154)

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Aaron Perez 2026-07-07 09:05:14 -05:00 • committed by GitHub
parent 368d4a5453
commit c14b6cb95f
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2 changed files with 37 additions and 7 deletions
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18
tests/unit/test_api_handler_factory.py
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@ -879,9 +879,23 @@ def test_get_api_parameters_injects_safety_settings_for_gemini_direct_config() -
assert all(setting["threshold"] == "BLOCK_NONE" for setting in params["safety_settings"])
def test_get_api_parameters_injects_safety_settings_for_gemini_router_config() -> None:
def test_get_api_parameters_omits_safety_settings_for_gemini_router_config() -> None:
# Router configs must NOT carry safety_settings at the request level — it would ride along
# to the non-Gemini fallback deployment and 400. Injection happens per-deployment instead.
params = LLMAPIHandlerFactory.get_api_parameters(_gemini_2_5_flash_router())
assert params["safety_settings"] == GEMINI_SAFETY_SETTINGS
assert "safety_settings" not in params
def test_inject_gemini_safety_settings_targets_only_gemini_deployments() -> None:
# Reproduces incident #646: a Gemini primary + Azure fallback in one router. safety_settings
# must land on the Gemini deployment and stay off the Azure one so the fallback hop survives.
model_list = [
{"model_name": "vertex-gemini-2.5-flash-lite", "litellm_params": {"model": "vertex_ai/gemini-2.5-flash-lite"}},
{"model_name": "gpt-5-mini-fallback", "litellm_params": {"model": "azure/gpt-5-mini"}},
]
result = api_handler_factory._inject_gemini_safety_settings(model_list)
assert result[0]["litellm_params"]["safety_settings"] == GEMINI_SAFETY_SETTINGS
assert "safety_settings" not in result[1]["litellm_params"]
def test_get_api_parameters_omits_safety_settings_for_non_gemini_config() -> None: