Remove deprecated OpenAILLMContext as well as everything (code paths or whole types) dependent on it (all of which were also deprecated)

tests/test_run_inference.py

@@ -84,61 +84,6 @@ async def test_openai_run_inference_with_llm_context():
         )
 
 
-@pytest.mark.asyncio
-async def test_openai_run_inference_with_openai_llm_context():
-    """Test run_inference with OpenAILLMContext returns expected response."""
-    # Create service with mocked client and specific parameters
-    with patch.object(OpenAILLMService, "create_client"):
-        from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-        from pipecat.services.openai.base_llm import BaseOpenAILLMService
-
-        params = BaseOpenAILLMService.InputParams(
-            temperature=0.8, max_completion_tokens=150, presence_penalty=0.3, top_p=0.9
-        )
-        service = OpenAILLMService(model="gpt-4", params=params)
-        service._client = AsyncMock()
-
-        # Create OpenAILLMContext
-        context = OpenAILLMContext(
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant"},
-                {"role": "user", "content": "Hello, world!"},
-            ],
-            tools=OPENAI_NOT_GIVEN,
-            tool_choice=OPENAI_NOT_GIVEN,
-        )
-
-        # Mock response
-        mock_response = MagicMock()
-        mock_response.choices = [MagicMock()]
-        mock_response.choices[0].message.content = "Hello! How can I help you today?"
-        service._client.chat.completions.create.return_value = mock_response
-
-        # Execute
-        result = await service.run_inference(context)
-
-        # Verify
-        assert result == "Hello! How can I help you today?"
-        service._client.chat.completions.create.assert_called_once_with(
-            model="gpt-4",
-            stream=False,
-            frequency_penalty=OPENAI_NOT_GIVEN,
-            presence_penalty=0.3,
-            seed=OPENAI_NOT_GIVEN,
-            temperature=0.8,
-            top_p=0.9,
-            max_tokens=OPENAI_NOT_GIVEN,
-            max_completion_tokens=150,
-            service_tier=OPENAI_NOT_GIVEN,
-            messages=[
-                {"role": "system", "content": "You are a helpful assistant"},
-                {"role": "user", "content": "Hello, world!"},
-            ],
-            tools=OPENAI_NOT_GIVEN,
-            tool_choice=OPENAI_NOT_GIVEN,
-        )
-
-
 @pytest.mark.asyncio
 async def test_openai_run_inference_client_exception():
     """Test that exceptions from the client are propagated."""
@@ -209,54 +154,6 @@ async def test_anthropic_run_inference_with_llm_context():
     )
 
 
-@pytest.mark.asyncio
-async def test_anthropic_run_inference_with_openai_llm_context():
-    """Test run_inference with OpenAILLMContext returns expected response for Anthropic."""
-    # Create service with mocked client and specific parameters
-    from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-    from pipecat.services.anthropic.llm import AnthropicLLMService
-
-    params = AnthropicLLMService.InputParams(max_tokens=1024, temperature=0.7, top_k=40, top_p=0.9)
-    service = AnthropicLLMService(
-        api_key="test-key", model="claude-3-sonnet-20240229", params=params
-    )
-    service._client = AsyncMock()
-
-    # Create OpenAILLMContext
-    context = OpenAILLMContext(
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant"},
-            {"role": "user", "content": "Hello, world!"},
-        ],
-        tools=NOT_GIVEN,
-        tool_choice=NOT_GIVEN,
-    )
-
-    # Mock response
-    mock_response = MagicMock()
-    mock_response.content = [MagicMock()]
-    mock_response.content[0].text = "Hello! How can I help you today?"
-    service._client.beta.messages.create.return_value = mock_response
-
-    # Execute
-    result = await service.run_inference(context)
-
-    # Verify
-    assert result == "Hello! How can I help you today?"
-    service._client.beta.messages.create.assert_called_once_with(
-        model="claude-3-sonnet-20240229",
-        max_tokens=1024,
-        stream=False,
-        temperature=0.7,
-        top_k=40,
-        top_p=0.9,
-        messages=[{"role": "user", "content": "Hello, world!"}],
-        system="You are a helpful assistant",
-        tools=[],
-        betas=["interleaved-thinking-2025-05-14"],
-    )
-
-
 @pytest.mark.asyncio
 async def test_anthropic_run_inference_client_exception():
     """Test that exceptions from the Anthropic client are propagated."""
@@ -336,61 +233,6 @@ async def test_google_run_inference_client_exception():
         await service.run_inference(mock_context)
 
 
-@pytest.mark.asyncio
-async def test_google_run_inference_with_openai_llm_context():
-    """Test run_inference with OpenAILLMContext returns expected response for Google."""
-    # Create service with mocked client and specific parameters
-    from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-
-    params = GoogleLLMService.InputParams(max_tokens=256, temperature=0.4, top_k=30, top_p=0.75)
-    service = GoogleLLMService(api_key="test-key", model="gemini-2.0-flash", params=params)
-    service._client = AsyncMock()
-
-    # Create OpenAILLMContext
-    context = OpenAILLMContext(
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant"},
-            {"role": "user", "content": "Hello, world!"},
-        ],
-        tools=NOT_GIVEN,
-        tool_choice=NOT_GIVEN,
-    )
-
-    # Mock response
-    mock_response = MagicMock()
-    mock_response.candidates = [MagicMock()]
-    mock_response.candidates[0].content = MagicMock()
-    mock_response.candidates[0].content.parts = [MagicMock()]
-    mock_response.candidates[0].content.parts[0].text = "Hello! How can I help you today?"
-    service._client.aio = AsyncMock()
-    service._client.aio.models = AsyncMock()
-    service._client.aio.models.generate_content = AsyncMock(return_value=mock_response)
-
-    # Execute
-    result = await service.run_inference(context)
-
-    # Verify
-    assert result == "Hello! How can I help you today?"
-
-    # Verify the call includes configured parameters
-    call_kwargs = service._client.aio.models.generate_content.call_args.kwargs
-    assert call_kwargs["model"] == "gemini-2.0-flash"
-    # Contents is a Google Content object, so check its structure
-    contents = call_kwargs["contents"]
-    assert len(contents) == 1
-    assert contents[0].role == "user"
-    assert len(contents[0].parts) == 1
-    assert contents[0].parts[0].text == "Hello, world!"
-    assert "config" in call_kwargs
-    config = call_kwargs["config"]
-    # Config is a GenerateContentConfig object, so access attributes
-    assert config.system_instruction == "You are a helpful assistant"
-    assert config.temperature == 0.4
-    assert config.top_k == 30
-    assert config.top_p == 0.75
-    assert config.max_output_tokens == 256
-
-
 @pytest.mark.asyncio
 async def test_aws_bedrock_run_inference_with_llm_context():
     """Test run_inference with LLMContext returns expected response for AWS Bedrock."""
@@ -445,57 +287,6 @@ async def test_aws_bedrock_run_inference_with_llm_context():
         assert call_kwargs["inferenceConfig"]["topP"] == 0.85
 
 
-@pytest.mark.asyncio
-async def test_aws_bedrock_run_inference_with_openai_llm_context():
-    """Test run_inference with OpenAILLMContext returns expected response for AWS Bedrock."""
-    # Create service with specific parameters
-    from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-    from pipecat.services.aws.llm import AWSBedrockLLMService
-
-    params = AWSBedrockLLMService.InputParams(max_tokens=512, temperature=0.8, top_p=0.95)
-    service = AWSBedrockLLMService(model="anthropic.claude-3-sonnet-20240229-v1:0", params=params)
-
-    # Create OpenAILLMContext
-    context = OpenAILLMContext(
-        messages=[
-            {"role": "system", "content": "You are a helpful assistant"},
-            {"role": "user", "content": "Hello, world!"},
-        ],
-        tools=NOT_GIVEN,
-        tool_choice=NOT_GIVEN,
-    )
-
-    # Mock the client and response
-    mock_client = AsyncMock()
-    mock_response = {
-        "output": {"message": {"content": [{"text": "Hello! How can I help you today?"}]}}
-    }
-    mock_client.converse.return_value = mock_response
-
-    # Patch the _aws_session.client method to be an async context manager
-    mock_context_manager = AsyncMock()
-    mock_context_manager.__aenter__ = AsyncMock(return_value=mock_client)
-    mock_context_manager.__aexit__ = AsyncMock(return_value=None)
-
-    with patch.object(service._aws_session, "client", return_value=mock_context_manager):
-        # Execute
-        result = await service.run_inference(context)
-
-        # Verify
-        assert result == "Hello! How can I help you today?"
-
-        # Verify the call includes configured parameters
-        call_kwargs = mock_client.converse.call_args.kwargs
-        assert call_kwargs["modelId"] == "anthropic.claude-3-sonnet-20240229-v1:0"
-        assert call_kwargs["messages"] == [{"role": "user", "content": [{"text": "Hello, world!"}]}]
-        assert call_kwargs["system"] == [{"text": "You are a helpful assistant"}]
-        assert call_kwargs["additionalModelRequestFields"] == {}
-        assert "inferenceConfig" in call_kwargs
-        assert call_kwargs["inferenceConfig"]["maxTokens"] == 512
-        assert call_kwargs["inferenceConfig"]["temperature"] == 0.8
-        assert call_kwargs["inferenceConfig"]["topP"] == 0.95
-
-
 @pytest.mark.asyncio
 async def test_aws_bedrock_run_inference_client_exception():
     """Test that exceptions from the AWS Bedrock client are propagated."""