test: add run_inference tests for OpenAIResponsesLLMService

Uses real LLMContext and adapter (only HTTP client is mocked) to test
basic inference, client exception propagation, system_instruction
override, empty context fallback, and max_tokens override.

tests/test_run_inference.py

@@ -15,7 +15,6 @@ from pipecat.adapters.services.anthropic_adapter import AnthropicLLMInvocationPa
 from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMInvocationParams
 from pipecat.adapters.services.gemini_adapter import GeminiLLMInvocationParams
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
-from pipecat.adapters.services.open_ai_responses_adapter import OpenAIResponsesLLMInvocationParams
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.services.anthropic.llm import AnthropicLLMService
 from pipecat.services.aws.llm import AWSBedrockLLMService
@@ -786,42 +785,26 @@ async def test_openai_responses_run_inference_with_llm_context():
         )
         service._client = AsyncMock()
 
-        # Setup mocks
-        mock_context = MagicMock(spec=LLMContext)
-        mock_adapter = MagicMock()
-        test_input = [
-            {"role": "developer", "content": "You are a helpful assistant"},
-            {"role": "user", "content": "Hello, world!"},
-        ]
-        mock_adapter.get_llm_invocation_params.return_value = OpenAIResponsesLLMInvocationParams(
-            input=test_input,
-            tools=OPENAI_NOT_GIVEN,
-            instructions="You are a helpful assistant",
+        context = LLMContext(
+            messages=[
+                {"role": "user", "content": "Hello, world!"},
+            ]
         )
-        service.get_llm_adapter = MagicMock(return_value=mock_adapter)
 
-        # Mock response
         mock_response = MagicMock()
         mock_response.output_text = "Hello! How can I help you today?"
         service._client.responses.create = AsyncMock(return_value=mock_response)
 
-        # Execute
-        result = await service.run_inference(mock_context)
+        result = await service.run_inference(context)
 
-        # Verify
         assert result == "Hello! How can I help you today?"
-        service.get_llm_adapter.assert_called_once()
-        mock_adapter.get_llm_invocation_params.assert_called_once_with(
-            mock_context, system_instruction="You are a helpful assistant"
-        )
-        service._client.responses.create.assert_called_once_with(
-            model="gpt-4.1",
-            stream=False,
-            input=test_input,
-            instructions="You are a helpful assistant",
-            temperature=0.7,
-            max_output_tokens=100,
-        )
+        call_kwargs = service._client.responses.create.call_args.kwargs
+        assert call_kwargs["model"] == "gpt-4.1"
+        assert call_kwargs["stream"] is False
+        assert call_kwargs["input"] == [{"role": "user", "content": "Hello, world!"}]
+        assert call_kwargs["instructions"] == "You are a helpful assistant"
+        assert call_kwargs["temperature"] == 0.7
+        assert call_kwargs["max_output_tokens"] == 100
 
 
 @pytest.mark.asyncio
@@ -831,16 +814,11 @@ async def test_openai_responses_run_inference_client_exception():
         service = OpenAIResponsesLLMService()
         service._client = AsyncMock()
 
-        mock_context = MagicMock(spec=LLMContext)
-        mock_adapter = MagicMock()
-        mock_adapter.get_llm_invocation_params.return_value = OpenAIResponsesLLMInvocationParams(
-            input=[], tools=OPENAI_NOT_GIVEN
-        )
-        service.get_llm_adapter = MagicMock(return_value=mock_adapter)
+        context = LLMContext(messages=[{"role": "user", "content": "Hello"}])
         service._client.responses.create = AsyncMock(side_effect=Exception("API Error"))
 
         with pytest.raises(Exception, match="API Error"):
-            await service.run_inference(mock_context)
+            await service.run_inference(context)
 
 
 @pytest.mark.asyncio
@@ -855,32 +833,47 @@ async def test_openai_responses_run_inference_system_instruction_overrides():
         )
         service._client = AsyncMock()
 
-        mock_context = MagicMock(spec=LLMContext)
-        mock_adapter = MagicMock()
-        test_input = [{"role": "user", "content": "Hello"}]
-        mock_adapter.get_llm_invocation_params.return_value = OpenAIResponsesLLMInvocationParams(
-            input=test_input,
-            tools=OPENAI_NOT_GIVEN,
-            instructions="New system instruction",
+        context = LLMContext(
+            messages=[{"role": "user", "content": "Hello"}],
         )
-        service.get_llm_adapter = MagicMock(return_value=mock_adapter)
 
         mock_response = MagicMock()
         mock_response.output_text = "Response"
         service._client.responses.create = AsyncMock(return_value=mock_response)
 
-        result = await service.run_inference(
-            mock_context, system_instruction="New system instruction"
-        )
+        result = await service.run_inference(context, system_instruction="New system instruction")
 
         assert result == "Response"
-        # The adapter should have been called with the override instruction
-        mock_adapter.get_llm_invocation_params.assert_called_once_with(
-            mock_context, system_instruction="New system instruction"
-        )
-        # The final API call should have the override instruction
         call_kwargs = service._client.responses.create.call_args.kwargs
         assert call_kwargs["instructions"] == "New system instruction"
+        assert call_kwargs["input"] == [{"role": "user", "content": "Hello"}]
+
+
+@pytest.mark.asyncio
+async def test_openai_responses_run_inference_empty_context_with_instruction():
+    """Test that system_instruction becomes a developer message when context is empty."""
+    with patch.object(OpenAIResponsesLLMService, "_create_client"):
+        service = OpenAIResponsesLLMService(
+            settings=OpenAIResponsesLLMService.Settings(
+                model="gpt-4.1",
+                system_instruction="You are helpful",
+            ),
+        )
+        service._client = AsyncMock()
+
+        context = LLMContext(messages=[])
+
+        mock_response = MagicMock()
+        mock_response.output_text = "Response"
+        service._client.responses.create = AsyncMock(return_value=mock_response)
+
+        result = await service.run_inference(context)
+
+        assert result == "Response"
+        call_kwargs = service._client.responses.create.call_args.kwargs
+        # With empty context, instruction should become a developer message
+        assert call_kwargs["input"] == [{"role": "developer", "content": "You are helpful"}]
+        assert "instructions" not in call_kwargs
 
 
 @pytest.mark.asyncio
@@ -895,21 +888,16 @@ async def test_openai_responses_run_inference_max_tokens_override():
         )
         service._client = AsyncMock()
 
-        mock_context = MagicMock(spec=LLMContext)
-        mock_adapter = MagicMock()
-        test_input = [{"role": "user", "content": "Summarize this"}]
-        mock_adapter.get_llm_invocation_params.return_value = OpenAIResponsesLLMInvocationParams(
-            input=test_input, tools=OPENAI_NOT_GIVEN
+        context = LLMContext(
+            messages=[{"role": "user", "content": "Summarize this"}],
         )
-        service.get_llm_adapter = MagicMock(return_value=mock_adapter)
 
         mock_response = MagicMock()
         mock_response.output_text = "Summary"
         service._client.responses.create = AsyncMock(return_value=mock_response)
 
-        result = await service.run_inference(mock_context, max_tokens=200)
+        result = await service.run_inference(context, max_tokens=200)
 
         assert result == "Summary"
         call_kwargs = service._client.responses.create.call_args.kwargs
-        # max_tokens override should take precedence
         assert call_kwargs["max_output_tokens"] == 200