Add OpenAI Responses API core

tests/test_responses_api.py

@@ -0,0 +1,232 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Tests for the OpenAI-compatible Responses API."""
+
+import platform
+import sys
+from types import SimpleNamespace
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+from fastapi.testclient import TestClient
+
+pytestmark = pytest.mark.skipif(
+    sys.platform != "darwin" or platform.machine() != "arm64",
+    reason="Requires Apple Silicon",
+)
+
+
+@pytest.fixture()
+def client():
+    from vllm_mlx.server import app
+
+    return TestClient(app)
+
+
+@pytest.fixture(autouse=True)
+def server_state():
+    import vllm_mlx.server as srv
+
+    original_engine = srv._engine
+    original_model_name = srv._model_name
+    original_store = srv._responses_store
+    original_api_key = srv._api_key
+
+    srv._engine = None
+    srv._model_name = "test-model"
+    srv._responses_store = {}
+    srv._api_key = None
+
+    try:
+        yield
+    finally:
+        srv._engine = original_engine
+        srv._model_name = original_model_name
+        srv._responses_store = original_store
+        srv._api_key = original_api_key
+
+
+def _mock_engine(*outputs):
+    engine = MagicMock()
+    engine.model_name = "test-model"
+    engine.preserve_native_tool_format = False
+    engine.chat = AsyncMock(side_effect=list(outputs))
+    return engine
+
+
+def _output(text: str, prompt_tokens: int = 7, completion_tokens: int = 3):
+    return SimpleNamespace(
+        text=text,
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        finish_reason="stop",
+    )
+
+
+class TestResponsesEndpoint:
+    def test_basic_response(self, client):
+        import vllm_mlx.server as srv
+
+        srv._engine = _mock_engine(_output("Hello there"))
+
+        resp = client.post(
+            "/v1/responses",
+            json={"model": "test-model", "input": "Say hello"},
+        )
+
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["object"] == "response"
+        assert body["output_text"] == "Hello there"
+        assert body["output"][0]["type"] == "message"
+        assert body["output"][0]["content"][0]["type"] == "output_text"
+        assert body["usage"]["input_tokens"] == 7
+        assert body["usage"]["output_tokens"] == 3
+
+    def test_previous_response_id_reuses_prior_context(self, client):
+        import vllm_mlx.server as srv
+
+        engine = _mock_engine(_output("First answer"), _output("Second answer"))
+        srv._engine = engine
+
+        first = client.post(
+            "/v1/responses",
+            json={"model": "test-model", "input": "First prompt"},
+        )
+        first_id = first.json()["id"]
+
+        second = client.post(
+            "/v1/responses",
+            json={
+                "model": "test-model",
+                "previous_response_id": first_id,
+                "input": "Follow-up prompt",
+            },
+        )
+
+        assert second.status_code == 200
+        second_messages = engine.chat.call_args_list[1].kwargs["messages"]
+        assert second_messages[0]["role"] == "user"
+        assert second_messages[0]["content"] == "First prompt"
+        assert second_messages[1]["role"] == "assistant"
+        assert second_messages[1]["content"] == "First answer"
+        assert second_messages[2]["role"] == "user"
+        assert second_messages[2]["content"] == "Follow-up prompt"
+
+    def test_function_call_output_input_is_mapped_cleanly(self, client):
+        import vllm_mlx.server as srv
+
+        engine = _mock_engine(_output("Done"))
+        srv._engine = engine
+
+        resp = client.post(
+            "/v1/responses",
+            json={
+                "model": "test-model",
+                "input": [
+                    {"type": "message", "role": "user", "content": "Run it"},
+                    {
+                        "type": "function_call",
+                        "call_id": "call_1",
+                        "name": "shell",
+                        "arguments": "{\"cmd\":\"pwd\"}",
+                    },
+                    {
+                        "type": "function_call_output",
+                        "call_id": "call_1",
+                        "output": "/tmp/work",
+                    },
+                ],
+            },
+        )
+
+        assert resp.status_code == 200
+        messages = engine.chat.call_args.kwargs["messages"]
+        assert messages[1]["role"] == "assistant"
+        assert "[Calling tool: shell(" in messages[1]["content"]
+        assert messages[2]["role"] == "user"
+        assert "[Tool Result (call_1)]" in messages[2]["content"]
+        assert "/tmp/work" in messages[2]["content"]
+
+    def test_unsupported_tools_and_items_do_not_fail(self, client):
+        import vllm_mlx.server as srv
+
+        engine = _mock_engine(_output("Fallback answer"))
+        srv._engine = engine
+
+        resp = client.post(
+            "/v1/responses",
+            json={
+                "model": "test-model",
+                "input": [
+                    {"type": "message", "role": "user", "content": "Answer directly"},
+                    {
+                        "type": "web_search_call",
+                        "status": "completed",
+                        "action": {"type": "search", "query": "ignored"},
+                    },
+                ],
+                "tools": [
+                    {"type": "web_search_preview"},
+                    {"type": "file_search", "vector_store_ids": ["vs_123"]},
+                    {
+                        "type": "function",
+                        "name": "shell",
+                        "parameters": {"type": "object", "properties": {}},
+                    },
+                ],
+            },
+        )
+
+        assert resp.status_code == 200
+        messages = engine.chat.call_args.kwargs["messages"]
+        assert messages[0]["role"] == "system"
+        assert "not available on this backend" in messages[0]["content"]
+        assert messages[1]["role"] == "user"
+        assert engine.chat.call_args.kwargs["tools"][0]["type"] == "function"
+
+    def test_function_call_response_item(self, client):
+        import vllm_mlx.server as srv
+
+        srv._engine = _mock_engine(
+            _output('<tool_call>{"name":"shell","arguments":{"cmd":"pwd"}}</tool_call>')
+        )
+
+        resp = client.post(
+            "/v1/responses",
+            json={
+                "model": "test-model",
+                "input": "Use a tool",
+                "tools": [
+                    {
+                        "type": "function",
+                        "name": "shell",
+                        "parameters": {"type": "object", "properties": {}},
+                    }
+                ],
+            },
+        )
+
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["output"][0]["type"] == "function_call"
+        assert body["output"][0]["name"] == "shell"
+        assert body["output_text"] == ""
+
+    def test_streaming_response_events(self, client):
+        import vllm_mlx.server as srv
+
+        srv._engine = _mock_engine(_output("Hello stream"))
+
+        with client.stream(
+            "POST",
+            "/v1/responses",
+            json={"model": "test-model", "input": "Hello", "stream": True},
+        ) as resp:
+            stream_text = "".join(resp.iter_text())
+
+        assert resp.status_code == 200
+        assert "event: response.created" in stream_text
+        assert "event: response.in_progress" in stream_text
+        assert "event: response.output_text.delta" in stream_text
+        assert "event: response.completed" in stream_text
+        assert "Hello stream" in stream_text

vllm_mlx/api/__init__.py

@@ -53,6 +53,24 @@
     EmbeddingUsage,
     EmbeddingResponse,
 )
+from .responses_models import (
+    ResponseTextFormat,
+    ResponseTextConfig,
+    ResponseReasoningConfig,
+    ResponseTextContentPart,
+    ResponseReasoningTextPart,
+    ResponseReasoningSummaryTextPart,
+    ResponseMessageItem,
+    ResponseReasoningItem,
+    ResponseFunctionCallItem,
+    ResponseFunctionCallOutputItem,
+    ResponseFunctionTool,
+    ResponsesUsage,
+    ResponseError,
+    ResponseIncompleteDetails,
+    ResponsesRequest,
+    ResponseObject,
+)
 
 from .utils import (
     clean_output_text,
@@ -111,6 +129,22 @@
     "EmbeddingData",
     "EmbeddingUsage",
     "EmbeddingResponse",
+    "ResponseTextFormat",
+    "ResponseTextConfig",
+    "ResponseReasoningConfig",
+    "ResponseTextContentPart",
+    "ResponseReasoningTextPart",
+    "ResponseReasoningSummaryTextPart",
+    "ResponseMessageItem",
+    "ResponseReasoningItem",
+    "ResponseFunctionCallItem",
+    "ResponseFunctionCallOutputItem",
+    "ResponseFunctionTool",
+    "ResponsesUsage",
+    "ResponseError",
+    "ResponseIncompleteDetails",
+    "ResponsesRequest",
+    "ResponseObject",
     # Utils
     "clean_output_text",
     "is_mllm_model",