diff --git a/litellm/main.py b/litellm/main.py index 34410f9497c..6ab2fcd4b03 100644 --- a/litellm/main.py +++ b/litellm/main.py @@ -100,6 +100,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import ( from litellm.litellm_core_utils.request_timeout_resolver import ( get_configured_request_timeout, ) +from litellm.llms.azure_ai.common_utils import azure_ai_supports_native_responses from litellm.llms.base_llm import BaseConfig, BaseImageGenerationConfig from litellm.llms.base_llm.base_model_iterator import ( convert_model_response_to_streaming, @@ -1118,16 +1119,23 @@ def responses_api_bridge_check( reasoning_active = reasoning_effort != "none" # The reasoning+tools constraint is enforced by the real OpenAI backend behind any api.openai.com # host (the default URL or a PrivateLink hostname such as .privatelink.api.openai.com) and - # by Azure OpenAI. Resolve the effective base arg>global>env>default exactly as the chat handler - # does, so a custom base set via litellm.api_base or OPENAI_BASE_URL/OPENAI_API_BASE isn't misread - # as the default and bridged to a /responses route it lacks. A whitespace-only base collapses to - # the default too. + # by Azure OpenAI, whether reached through the azure provider or as a Foundry OpenAI v1 host through + # the azure_ai provider. Resolve the effective OpenAI base arg>global>env>default exactly as the chat + # handler does, so a custom base set via litellm.api_base or OPENAI_BASE_URL/OPENAI_API_BASE isn't + # misread as the default and bridged to a /responses route it lacks. A whitespace-only base + # collapses to the default too. resolved_api_base: Final = _resolve_openai_api_base(api_base).strip() + on_foundry_openai_endpoint: Final = custom_llm_provider == "azure_ai" and azure_ai_supports_native_responses( + model, api_base + ) on_constraint_enforcing_endpoint: Final = ( - custom_llm_provider == "azure" or resolved_api_base == "" or _is_openai_backed_api_base(resolved_api_base) + custom_llm_provider == "azure" + or on_foundry_openai_endpoint + or resolved_api_base == "" + or _is_openai_backed_api_base(resolved_api_base) ) if ( - custom_llm_provider in ("openai", "azure") + (custom_llm_provider in ("openai", "azure") or on_foundry_openai_endpoint) and model_info.get("mode") != "responses" and OpenAIGPT5Config.is_model_gpt_5_model(model) and not OpenAIGPT5Config.is_model_gpt_5_search_model(model) diff --git a/tests/test_litellm/test_main.py b/tests/test_litellm/test_main.py index 3c90675d04d..e0ca0c1cbf9 100644 --- a/tests/test_litellm/test_main.py +++ b/tests/test_litellm/test_main.py @@ -1308,6 +1308,71 @@ def test_responses_api_bridge_check_azure_with_api_base_and_unset_effort_routes( assert model_info.get("mode") == "responses" +_FOUNDRY_API_BASE: Final = "https://myproject.services.ai.azure.com" +_FOUNDRY_FUNCTION_TOOL: Final = ({"type": "function", "function": {"name": "get_weather"}},) + + +@pytest.mark.parametrize( + "api_base, reasoning_effort", + [ + pytest.param(_FOUNDRY_API_BASE, None, id="foundry-host-unset-effort"), + pytest.param(_FOUNDRY_API_BASE, "low", id="foundry-host-explicit-effort"), + pytest.param("https://myresource.openai.azure.com", None, id="azure-openai-host-unset-effort"), + ], +) +def test_responses_api_bridge_check_azure_ai_foundry_gpt_5_4_plus_tools_routes_to_responses(api_base, reasoning_effort): + """ + An azure_ai deployment of a gpt-5.4+ model on a Foundry OpenAI v1 host is the same Azure OpenAI + backend the azure provider bridges: its chat surface rejects function tools whenever reasoning is + on, and for gpt-6-astra it rejects reasoning_effort "none" too, so the Responses route on the same + endpoint is the only way to serve the request. Regression guard: the gate used to bridge only the + openai and azure providers, so these requests died at Foundry's /models/chat/completions. + """ + from litellm.main import responses_api_bridge_check + + model_info, model = responses_api_bridge_check( + model="gpt-6-astra", + custom_llm_provider="azure_ai", + tools=_FOUNDRY_FUNCTION_TOOL, + reasoning_effort=reasoning_effort, + api_base=api_base, + ) + + assert model == "gpt-6-astra" + assert model_info.get("mode") == "responses" + + +@pytest.mark.parametrize( + "model_name, api_base, reasoning_effort", + [ + pytest.param("gpt-6-astra", _FOUNDRY_API_BASE, "none", id="explicit-none-stays-chat"), + pytest.param("gpt-6-astra", "https://myproject.models.ai.azure.com", None, id="serverless-host-stays-chat"), + pytest.param("Mistral-large-2411", _FOUNDRY_API_BASE, None, id="non-gpt-5-model-stays-chat"), + pytest.param("claude-opus-4-1", _FOUNDRY_API_BASE, None, id="claude-on-foundry-stays-chat"), + ], +) +def test_responses_api_bridge_check_azure_ai_without_foundry_responses_route_stays_chat( + model_name, api_base, reasoning_effort +): + """ + The azure_ai bridge fires only where the Foundry Responses config is selectable: a serverless + host, a non-OpenAI model, and claude-on-Foundry have no Responses route to bridge to, and an + explicit reasoning_effort "none" keeps the request chat-servable on the same terms as azure. + """ + from litellm.main import responses_api_bridge_check + + model_info, model = responses_api_bridge_check( + model=model_name, + custom_llm_provider="azure_ai", + tools=_FOUNDRY_FUNCTION_TOOL, + reasoning_effort=reasoning_effort, + api_base=api_base, + ) + + assert model == model_name + assert model_info.get("mode") != "responses" + + def test_responses_api_bridge_check_older_gpt_5_tools_without_reasoning_stays_chat(): """Pre-5.4 GPT-5 names keep the old boundary: tools alone never bridge.""" from litellm.main import responses_api_bridge_check @@ -1488,6 +1553,86 @@ def test_responses_bridge_preserves_reasoning_effort_with_drop_params( assert request_body["reasoning"] == {"effort": "high"} +_FOUNDRY_RESPONSES_FUNCTION_CALL_BODY: Final = { + "id": "resp_foundry", + "object": "response", + "created_at": 1789852145, + "status": "completed", + "model": "gpt-6-astra", + "output": [ + { + "id": "fc_1", + "type": "function_call", + "status": "completed", + "arguments": '{"city":"Paris"}', + "call_id": "call_1", + "name": "get_weather", + } + ], + "parallel_tool_calls": True, + "usage": { + "input_tokens": 53, + "output_tokens": 18, + "total_tokens": 71, + "output_tokens_details": {"reasoning_tokens": 0}, + }, + "error": None, + "incomplete_details": None, + "instructions": None, + "metadata": {}, + "temperature": 1.0, + "tool_choice": "auto", + "tools": [], + "top_p": 1.0, + "max_output_tokens": 200, + "previous_response_id": None, + "reasoning": {"effort": "medium", "summary": None}, + "truncation": "disabled", + "user": None, +} + + +def test_completion_bridges_azure_ai_foundry_gpt_5_4_plus_function_tools_to_responses( + respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch +): + """ + The bridged azure_ai call is posted to /openai/v1/responses with the tool in Responses + shape and Foundry's api-key header, never to /models/chat/completions, and comes back as a + chat completion carrying the function call. + """ + monkeypatch.setattr(litellm, "disable_aiohttp_transport", True) + responses_route: Final = respx_mock.post(f"{_FOUNDRY_API_BASE}/openai/v1/responses").respond( + json=_FOUNDRY_RESPONSES_FUNCTION_CALL_BODY + ) + + response: Final = litellm.completion( + model="azure_ai/gpt-6-astra", + messages=[{"role": "user", "content": "What is the weather in Paris? Use the tool."}], + tools=[ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather for a city", + "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}, + }, + } + ], + max_tokens=200, + api_base=_FOUNDRY_API_BASE, + api_key="fake-foundry-key", + ) + + assert [str(call.request.url) for call in respx_mock.calls] == [f"{_FOUNDRY_API_BASE}/openai/v1/responses"] + request: Final = responses_route.calls[0].request + request_body: Final = json.loads(request.content) + assert request_body["tools"][0]["type"] == "function" + assert request_body["tools"][0]["name"] == "get_weather" + assert request.headers["api-key"] == "fake-foundry-key" + assert response.choices[0].finish_reason == "tool_calls" + assert response.choices[0].message.tool_calls[0].function.name == "get_weather" + + @pytest.mark.parametrize( "model, model_info, expected_model_param, expected_base_model_param", [