More tests

tests/integration/test_dynamic_llm_parameters.py

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+"""
+Integration tests for Dynamic LLM Parameters
+Testing end-to-end flow of runtime parameter changes in LLM processors
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from openai.types.chat import ChatCompletion, ChatCompletionMessage
+from openai.types.chat.chat_completion import Choice
+from openai.types.completion_usage import CompletionUsage
+
+from trustgraph.model.text_completion.openai.llm import Processor as OpenAIProcessor
+from trustgraph.base import LlmResult
+
+
+@pytest.mark.integration
+class TestDynamicLlmParameters:
+    """Integration tests for dynamic parameter configuration"""
+
+    @pytest.fixture
+    def mock_openai_client(self):
+        """Mock OpenAI client that returns realistic responses"""
+        client = MagicMock()
+
+        # Default mock response
+        usage = CompletionUsage(prompt_tokens=25, completion_tokens=15, total_tokens=40)
+        message = ChatCompletionMessage(role="assistant", content="Dynamic parameter test response")
+        choice = Choice(index=0, message=message, finish_reason="stop")
+
+        completion = ChatCompletion(
+            id="chatcmpl-test-dynamic",
+            choices=[choice],
+            created=1234567890,
+            model="gpt-4",  # Will be overridden based on test
+            object="chat.completion",
+            usage=usage
+        )
+
+        client.chat.completions.create.return_value = completion
+        return client
+
+    @pytest.fixture
+    def base_processor_config(self):
+        """Base configuration for test processors"""
+        return {
+            "api_key": "test-api-key",
+            "url": "https://api.openai.com/v1",
+            "temperature": 0.0,  # Default temperature
+            "max_output": 1024,
+        }
+
+    @patch('trustgraph.model.text_completion.openai.llm.OpenAI')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_runtime_temperature_override(self, mock_llm_init, mock_async_init,
+                                               mock_openai_class, mock_openai_client, base_processor_config):
+        """Test that temperature can be overridden at runtime"""
+        # Arrange
+        mock_openai_class.return_value = mock_openai_client
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = base_processor_config | {
+            "model": "gpt-3.5-turbo",
+            "concurrency": 1,
+            "taskgroup": AsyncMock(),
+            "id": "test-processor"
+        }
+
+        processor = OpenAIProcessor(**config)
+
+        # Act - Call with different temperature than configured default (0.0)
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model=None,  # Use default model
+            temperature=0.9  # Override temperature
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+        assert result.text == "Dynamic parameter test response"
+
+        # Verify the OpenAI API was called with the overridden temperature
+        mock_openai_client.chat.completions.create.assert_called_once()
+        call_args = mock_openai_client.chat.completions.create.call_args
+
+        assert call_args.kwargs['temperature'] == 0.9  # Should use runtime parameter
+        assert call_args.kwargs['model'] == "gpt-3.5-turbo"  # Should use processor default
+
+    @patch('trustgraph.model.text_completion.openai.llm.OpenAI')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_runtime_model_override(self, mock_llm_init, mock_async_init,
+                                         mock_openai_class, mock_openai_client, base_processor_config):
+        """Test that model can be overridden at runtime"""
+        # Arrange
+        mock_openai_class.return_value = mock_openai_client
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = base_processor_config | {
+            "model": "gpt-3.5-turbo",  # Default model
+            "concurrency": 1,
+            "taskgroup": AsyncMock(),
+            "id": "test-processor"
+        }
+
+        processor = OpenAIProcessor(**config)
+
+        # Act - Call with different model than configured default
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model="gpt-4",        # Override model
+            temperature=None      # Use default temperature
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+
+        # Verify the OpenAI API was called with the overridden model
+        mock_openai_client.chat.completions.create.assert_called_once()
+        call_args = mock_openai_client.chat.completions.create.call_args
+
+        assert call_args.kwargs['model'] == "gpt-4"        # Should use runtime parameter
+        assert call_args.kwargs['temperature'] == 0.0      # Should use processor default
+
+    @patch('trustgraph.model.text_completion.openai.llm.OpenAI')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_both_parameters_override(self, mock_llm_init, mock_async_init,
+                                           mock_openai_class, mock_openai_client, base_processor_config):
+        """Test that both model and temperature can be overridden simultaneously"""
+        # Arrange
+        mock_openai_class.return_value = mock_openai_client
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = base_processor_config | {
+            "model": "gpt-3.5-turbo",  # Default model
+            "concurrency": 1,
+            "taskgroup": AsyncMock(),
+            "id": "test-processor"
+        }
+
+        processor = OpenAIProcessor(**config)
+
+        # Act - Override both parameters
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model="gpt-4",        # Override model
+            temperature=0.5       # Override temperature
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+
+        # Verify both parameters were overridden
+        mock_openai_client.chat.completions.create.assert_called_once()
+        call_args = mock_openai_client.chat.completions.create.call_args
+
+        assert call_args.kwargs['model'] == "gpt-4"        # Should use runtime parameter
+        assert call_args.kwargs['temperature'] == 0.5      # Should use runtime parameter
+
+    @patch('trustgraph.model.text_completion.openai.llm.OpenAI')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_fallback_to_defaults_when_no_override(self, mock_llm_init, mock_async_init,
+                                                        mock_openai_class, mock_openai_client, base_processor_config):
+        """Test that processor falls back to configured defaults when no parameters are provided"""
+        # Arrange
+        mock_openai_class.return_value = mock_openai_client
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = base_processor_config | {
+            "model": "gpt-3.5-turbo",  # Default model
+            "temperature": 0.2,        # Default temperature
+            "concurrency": 1,
+            "taskgroup": AsyncMock(),
+            "id": "test-processor"
+        }
+
+        processor = OpenAIProcessor(**config)
+
+        # Act - Call with no parameter overrides
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model=None,       # Use default
+            temperature=None  # Use default
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+
+        # Verify defaults were used
+        mock_openai_client.chat.completions.create.assert_called_once()
+        call_args = mock_openai_client.chat.completions.create.call_args
+
+        assert call_args.kwargs['model'] == "gpt-3.5-turbo"  # Should use processor default
+        assert call_args.kwargs['temperature'] == 0.2        # Should use processor default
+
+    @patch('trustgraph.model.text_completion.openai.llm.OpenAI')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_multiple_concurrent_calls_different_parameters(self, mock_llm_init, mock_async_init,
+                                                                 mock_openai_class, mock_openai_client, base_processor_config):
+        """Test multiple concurrent calls with different parameters don't interfere"""
+        # Arrange
+        mock_openai_class.return_value = mock_openai_client
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = base_processor_config | {
+            "model": "gpt-3.5-turbo",
+            "concurrency": 1,
+            "taskgroup": AsyncMock(),
+            "id": "test-processor"
+        }
+
+        processor = OpenAIProcessor(**config)
+
+        # Reset the mock to track multiple calls
+        mock_openai_client.reset_mock()
+
+        # Act - Make multiple calls with different parameters concurrently
+        import asyncio
+        tasks = [
+            processor.generate_content("System 1", "Prompt 1", model="gpt-3.5-turbo", temperature=0.1),
+            processor.generate_content("System 2", "Prompt 2", model="gpt-4", temperature=0.8),
+            processor.generate_content("System 3", "Prompt 3", model="gpt-3.5-turbo", temperature=0.5)
+        ]
+
+        results = await asyncio.gather(*tasks)
+
+        # Assert
+        assert len(results) == 3
+        for result in results:
+            assert isinstance(result, LlmResult)
+
+        # Verify all calls were made with correct parameters
+        assert mock_openai_client.chat.completions.create.call_count == 3
+
+        # Get all call arguments
+        call_args_list = mock_openai_client.chat.completions.create.call_args_list
+
+        # Verify each call had the expected parameters
+        expected_params = [
+            ("gpt-3.5-turbo", 0.1),
+            ("gpt-4", 0.8),
+            ("gpt-3.5-turbo", 0.5)
+        ]
+
+        for i, (expected_model, expected_temp) in enumerate(expected_params):
+            call_kwargs = call_args_list[i].kwargs
+            assert call_kwargs['model'] == expected_model
+            assert call_kwargs['temperature'] == expected_temp
+
+    async def test_parameter_boundary_values(self, mock_openai_client, base_processor_config):
+        """Test parameter boundary values (edge cases)"""
+        # This would test extreme values like temperature=0.0, temperature=2.0, etc.
+        # Implementation depends on specific validation requirements
+        pass
+
+    async def test_invalid_parameter_types_handling(self, mock_openai_client, base_processor_config):
+        """Test handling of invalid parameter types"""
+        # This would test what happens with invalid temperature values, non-existent models, etc.
+        # Implementation depends on error handling requirements
+        pass
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])

tests/unit/test_base/test_flow_parameter_specs.py

@@ -0,0 +1,237 @@
+"""
+Unit tests for Flow Parameter Specification functionality
+Testing parameter specification registration and handling in flow processors
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from unittest import IsolatedAsyncioTestCase
+
+from trustgraph.base.flow_processor import FlowProcessor
+from trustgraph.base import ParameterSpec, ConsumerSpec, ProducerSpec
+
+
+class TestFlowParameterSpecs(IsolatedAsyncioTestCase):
+    """Test flow processor parameter specification functionality"""
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_parameter_spec_registration(self, mock_async_init):
+        """Test that parameter specs can be registered with flow processors"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-flow-processor',
+            'concurrency': 1
+        }
+
+        processor = FlowProcessor(**config)
+
+        # Create test parameter specs
+        model_spec = ParameterSpec(name="model")
+        temperature_spec = ParameterSpec(name="temperature")
+
+        # Act
+        processor.register_specification(model_spec)
+        processor.register_specification(temperature_spec)
+
+        # Assert
+        assert len(processor.specifications) >= 2
+
+        param_specs = [spec for spec in processor.specifications
+                      if isinstance(spec, ParameterSpec)]
+        assert len(param_specs) >= 2
+
+        param_names = [spec.name for spec in param_specs]
+        assert "model" in param_names
+        assert "temperature" in param_names
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_mixed_specification_types(self, mock_async_init):
+        """Test registration of mixed specification types (parameters, consumers, producers)"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-flow-processor',
+            'concurrency': 1
+        }
+
+        processor = FlowProcessor(**config)
+
+        # Create different spec types
+        param_spec = ParameterSpec(name="model")
+        consumer_spec = ConsumerSpec(name="input")
+        producer_spec = ProducerSpec(name="output")
+
+        # Act
+        processor.register_specification(param_spec)
+        processor.register_specification(consumer_spec)
+        processor.register_specification(producer_spec)
+
+        # Assert
+        assert len(processor.specifications) == 3
+
+        # Count each type
+        param_specs = [s for s in processor.specifications if isinstance(s, ParameterSpec)]
+        consumer_specs = [s for s in processor.specifications if isinstance(s, ConsumerSpec)]
+        producer_specs = [s for s in processor.specifications if isinstance(s, ProducerSpec)]
+
+        assert len(param_specs) == 1
+        assert len(consumer_specs) == 1
+        assert len(producer_specs) == 1
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_parameter_spec_metadata(self, mock_async_init):
+        """Test parameter specification metadata handling"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-flow-processor',
+            'concurrency': 1
+        }
+
+        processor = FlowProcessor(**config)
+
+        # Create parameter specs with metadata (if supported)
+        model_spec = ParameterSpec(name="model")
+        temperature_spec = ParameterSpec(name="temperature")
+
+        # Act
+        processor.register_specification(model_spec)
+        processor.register_specification(temperature_spec)
+
+        # Assert
+        param_specs = [spec for spec in processor.specifications
+                      if isinstance(spec, ParameterSpec)]
+
+        model_spec_registered = next((s for s in param_specs if s.name == "model"), None)
+        temperature_spec_registered = next((s for s in param_specs if s.name == "temperature"), None)
+
+        assert model_spec_registered is not None
+        assert temperature_spec_registered is not None
+        assert model_spec_registered.name == "model"
+        assert temperature_spec_registered.name == "temperature"
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_duplicate_parameter_spec_handling(self, mock_async_init):
+        """Test handling of duplicate parameter spec registration"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-flow-processor',
+            'concurrency': 1
+        }
+
+        processor = FlowProcessor(**config)
+
+        # Create duplicate parameter specs
+        model_spec1 = ParameterSpec(name="model")
+        model_spec2 = ParameterSpec(name="model")
+
+        # Act
+        processor.register_specification(model_spec1)
+        processor.register_specification(model_spec2)
+
+        # Assert - Should allow duplicates (or handle appropriately)
+        param_specs = [spec for spec in processor.specifications
+                      if isinstance(spec, ParameterSpec) and spec.name == "model"]
+
+        # Either should have 2 duplicates or the system should handle deduplication
+        assert len(param_specs) >= 1  # At least one should be registered
+
+    @patch('trustgraph.base.flow_processor.Flow')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    async def test_parameter_specs_available_to_flows(self, mock_async_init, mock_flow_class):
+        """Test that parameter specs are available when flows are created"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-flow-processor',
+            'concurrency': 1
+        }
+
+        processor = FlowProcessor(**config)
+        processor.id = 'test-processor'
+
+        # Register parameter specs
+        model_spec = ParameterSpec(name="model")
+        temperature_spec = ParameterSpec(name="temperature")
+        processor.register_specification(model_spec)
+        processor.register_specification(temperature_spec)
+
+        mock_flow = AsyncMock()
+        mock_flow_class.return_value = mock_flow
+
+        flow_name = 'test-flow'
+        flow_defn = {'config': 'test-config'}
+
+        # Act
+        await processor.start_flow(flow_name, flow_defn)
+
+        # Assert - Flow should be created with access to processor specifications
+        mock_flow_class.assert_called_once_with('test-processor', flow_name, processor, flow_defn)
+
+        # The flow should have access to the processor's specifications
+        # (The exact mechanism depends on Flow implementation)
+        assert len(processor.specifications) >= 2
+
+
+class TestParameterSpecValidation(IsolatedAsyncioTestCase):
+    """Test parameter specification validation functionality"""
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_parameter_spec_name_validation(self, mock_async_init):
+        """Test parameter spec name validation"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-flow-processor',
+            'concurrency': 1
+        }
+
+        processor = FlowProcessor(**config)
+
+        # Act & Assert - Valid parameter names
+        valid_specs = [
+            ParameterSpec(name="model"),
+            ParameterSpec(name="temperature"),
+            ParameterSpec(name="max_tokens"),
+            ParameterSpec(name="api_key")
+        ]
+
+        for spec in valid_specs:
+            # Should not raise any exceptions
+            processor.register_specification(spec)
+
+        assert len([s for s in processor.specifications if isinstance(s, ParameterSpec)]) >= 4
+
+    def test_parameter_spec_creation_validation(self):
+        """Test parameter spec creation with various inputs"""
+        # Test valid parameter spec creation
+        valid_specs = [
+            ParameterSpec(name="model"),
+            ParameterSpec(name="temperature"),
+            ParameterSpec(name="max_output"),
+        ]
+
+        for spec in valid_specs:
+            assert spec.name is not None
+            assert isinstance(spec.name, str)
+
+        # Test edge cases (if parameter specs have validation)
+        # This depends on the actual ParameterSpec implementation
+        try:
+            empty_name_spec = ParameterSpec(name="")
+            # May or may not be valid depending on implementation
+        except Exception:
+            # If validation exists, it should catch invalid names
+            pass
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])

tests/unit/test_base/test_llm_service_parameters.py

@@ -0,0 +1,248 @@
+"""
+Unit tests for LLM Service Parameter Specifications
+Testing the new parameter-aware functionality added to the LLM base service
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from unittest import IsolatedAsyncioTestCase
+
+from trustgraph.base.llm_service import LlmService, LlmResult
+from trustgraph.base import ParameterSpec
+
+
+class TestLlmServiceParameters(IsolatedAsyncioTestCase):
+    """Test LLM service parameter specification functionality"""
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_parameter_specs_registration(self, mock_async_init):
+        """Test that LLM service registers model and temperature parameter specs"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-llm-service',
+            'concurrency': 1
+        }
+
+        # Act
+        service = LlmService(**config)
+
+        # Assert
+        param_specs = {spec.name: spec for spec in service.specifications
+                      if isinstance(spec, ParameterSpec)}
+
+        assert "model" in param_specs
+        assert "temperature" in param_specs
+        assert len(param_specs) >= 2  # May have other parameter specs
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_model_parameter_spec_properties(self, mock_async_init):
+        """Test that model parameter spec has correct properties"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-llm-service',
+            'concurrency': 1
+        }
+
+        # Act
+        service = LlmService(**config)
+
+        # Assert
+        model_spec = None
+        for spec in service.specifications:
+            if isinstance(spec, ParameterSpec) and spec.name == "model":
+                model_spec = spec
+                break
+
+        assert model_spec is not None
+        assert model_spec.name == "model"
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    def test_temperature_parameter_spec_properties(self, mock_async_init):
+        """Test that temperature parameter spec has correct properties"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-llm-service',
+            'concurrency': 1
+        }
+
+        # Act
+        service = LlmService(**config)
+
+        # Assert
+        temperature_spec = None
+        for spec in service.specifications:
+            if isinstance(spec, ParameterSpec) and spec.name == "temperature":
+                temperature_spec = spec
+                break
+
+        assert temperature_spec is not None
+        assert temperature_spec.name == "temperature"
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    async def test_on_request_extracts_parameters_from_flow(self, mock_async_init):
+        """Test that on_request method extracts model and temperature from flow"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-llm-service',
+            'concurrency': 1
+        }
+
+        service = LlmService(**config)
+
+        # Mock the generate_content method to capture parameters
+        service.generate_content = AsyncMock(return_value=LlmResult(
+            text="test response",
+            in_token=10,
+            out_token=5,
+            model="gpt-4"
+        ))
+
+        # Mock message and flow
+        mock_message = MagicMock()
+        mock_message.value.return_value = MagicMock()
+        mock_message.value.return_value.system = "system prompt"
+        mock_message.value.return_value.prompt = "user prompt"
+        mock_message.properties.return_value = {"id": "test-id"}
+
+        mock_consumer = MagicMock()
+        mock_consumer.name = "request"
+
+        mock_flow = MagicMock()
+        mock_flow.name = "test-flow"
+        mock_flow.return_value = "test-model"  # flow("model") returns this
+        mock_flow.side_effect = lambda param: {
+            "model": "gpt-4",
+            "temperature": 0.7
+        }.get(param, f"mock-{param}")
+
+        mock_producer = AsyncMock()
+        mock_flow.return_value = mock_producer  # flow("response") returns producer
+
+        # Act
+        await service.on_request(mock_message, mock_consumer, mock_flow)
+
+        # Assert
+        # Verify that generate_content was called with parameters from flow
+        service.generate_content.assert_called_once()
+        call_args = service.generate_content.call_args
+
+        assert call_args[0][0] == "system prompt"  # system
+        assert call_args[0][1] == "user prompt"    # prompt
+        assert call_args[0][2] == "gpt-4"          # model
+        assert call_args[0][3] == 0.7              # temperature
+
+        # Verify flow was queried for both parameters
+        mock_flow.assert_any_call("model")
+        mock_flow.assert_any_call("temperature")
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    async def test_on_request_handles_missing_parameters_gracefully(self, mock_async_init):
+        """Test that on_request handles missing parameters gracefully"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-llm-service',
+            'concurrency': 1
+        }
+
+        service = LlmService(**config)
+
+        # Mock the generate_content method
+        service.generate_content = AsyncMock(return_value=LlmResult(
+            text="test response",
+            in_token=10,
+            out_token=5,
+            model="default-model"
+        ))
+
+        # Mock message and flow where flow returns None for parameters
+        mock_message = MagicMock()
+        mock_message.value.return_value = MagicMock()
+        mock_message.value.return_value.system = "system prompt"
+        mock_message.value.return_value.prompt = "user prompt"
+        mock_message.properties.return_value = {"id": "test-id"}
+
+        mock_consumer = MagicMock()
+        mock_consumer.name = "request"
+
+        mock_flow = MagicMock()
+        mock_flow.name = "test-flow"
+        mock_flow.return_value = None  # Both parameters return None
+
+        mock_producer = AsyncMock()
+        mock_flow.return_value = mock_producer
+
+        # Act
+        await service.on_request(mock_message, mock_consumer, mock_flow)
+
+        # Assert
+        # Should still call generate_content, with None values that will use processor defaults
+        service.generate_content.assert_called_once()
+        call_args = service.generate_content.call_args
+
+        assert call_args[0][0] == "system prompt"  # system
+        assert call_args[0][1] == "user prompt"    # prompt
+        assert call_args[0][2] is None             # model (will use processor default)
+        assert call_args[0][3] is None             # temperature (will use processor default)
+
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    async def test_on_request_error_handling_preserves_behavior(self, mock_async_init):
+        """Test that parameter extraction doesn't break existing error handling"""
+        # Arrange
+        mock_async_init.return_value = None
+
+        config = {
+            'id': 'test-llm-service',
+            'concurrency': 1
+        }
+
+        service = LlmService(**config)
+
+        # Mock generate_content to raise an exception
+        service.generate_content = AsyncMock(side_effect=Exception("Test error"))
+
+        # Mock message and flow
+        mock_message = MagicMock()
+        mock_message.value.return_value = MagicMock()
+        mock_message.value.return_value.system = "system prompt"
+        mock_message.value.return_value.prompt = "user prompt"
+        mock_message.properties.return_value = {"id": "test-id"}
+
+        mock_consumer = MagicMock()
+        mock_consumer.name = "request"
+
+        mock_flow = MagicMock()
+        mock_flow.name = "test-flow"
+        mock_flow.side_effect = lambda param: {
+            "model": "gpt-4",
+            "temperature": 0.7
+        }.get(param, f"mock-{param}")
+
+        mock_producer = AsyncMock()
+        mock_flow.producer = {"response": mock_producer}
+
+        # Act
+        await service.on_request(mock_message, mock_consumer, mock_flow)
+
+        # Assert
+        # Should have sent error response
+        mock_producer.send.assert_called_once()
+        error_response = mock_producer.send.call_args[0][0]
+
+        assert error_response.error is not None
+        assert error_response.error.type == "llm-error"
+        assert "Test error" in error_response.error.message
+        assert error_response.response is None
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])

tests/unit/test_text_completion/test_parameter_caching.py

@@ -0,0 +1,186 @@
+"""
+Unit tests for Parameter-Based Caching in LLM Processors
+Testing processors that cache based on temperature parameters (Bedrock, GoogleAIStudio)
+"""
+
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from unittest import IsolatedAsyncioTestCase
+
+from trustgraph.model.text_completion.googleaistudio.llm import Processor as GoogleAIProcessor
+from trustgraph.base import LlmResult
+
+
+class TestParameterCaching(IsolatedAsyncioTestCase):
+    """Test parameter-based caching functionality"""
+
+    @patch('trustgraph.model.text_completion.googleaistudio.llm.genai')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_googleai_temperature_cache_keys(self, mock_llm_init, mock_async_init, mock_genai):
+        """Test that GoogleAI processor creates separate cache entries for different temperatures"""
+        # Arrange
+        mock_client = MagicMock()
+        mock_genai.Client.return_value = mock_client
+
+        mock_response = MagicMock()
+        mock_response.text = "Generated response"
+        mock_response.usage_metadata.prompt_token_count = 10
+        mock_response.usage_metadata.candidates_token_count = 5
+        mock_client.models.generate_content.return_value = mock_response
+
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = {
+            'model': 'gemini-2.0-flash-001',
+            'api_key': 'test-api-key',
+            'temperature': 0.0,  # Default temperature
+            'max_output': 1024,
+            'concurrency': 1,
+            'taskgroup': AsyncMock(),
+            'id': 'test-processor'
+        }
+
+        processor = GoogleAIProcessor(**config)
+
+        # Act - Call with different temperatures
+        await processor.generate_content("System", "Prompt 1", model="gemini-2.0-flash-001", temperature=0.0)
+        await processor.generate_content("System", "Prompt 2", model="gemini-2.0-flash-001", temperature=0.5)
+        await processor.generate_content("System", "Prompt 3", model="gemini-2.0-flash-001", temperature=1.0)
+
+        # Assert - Should have 3 different cache entries
+        cache_keys = list(processor.generation_configs.keys())
+
+        assert len(cache_keys) == 3
+        assert "gemini-2.0-flash-001:0.0" in cache_keys
+        assert "gemini-2.0-flash-001:0.5" in cache_keys
+        assert "gemini-2.0-flash-001:1.0" in cache_keys
+
+        # Verify each cached config has the correct temperature
+        assert processor.generation_configs["gemini-2.0-flash-001:0.0"].temperature == 0.0
+        assert processor.generation_configs["gemini-2.0-flash-001:0.5"].temperature == 0.5
+        assert processor.generation_configs["gemini-2.0-flash-001:1.0"].temperature == 1.0
+
+    @patch('trustgraph.model.text_completion.googleaistudio.llm.genai')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_googleai_cache_reuse_same_parameters(self, mock_llm_init, mock_async_init, mock_genai):
+        """Test that GoogleAI processor reuses cache for identical model+temperature combinations"""
+        # Arrange
+        mock_client = MagicMock()
+        mock_genai.Client.return_value = mock_client
+
+        mock_response = MagicMock()
+        mock_response.text = "Generated response"
+        mock_response.usage_metadata.prompt_token_count = 10
+        mock_response.usage_metadata.candidates_token_count = 5
+        mock_client.models.generate_content.return_value = mock_response
+
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = {
+            'model': 'gemini-2.0-flash-001',
+            'api_key': 'test-api-key',
+            'temperature': 0.0,
+            'max_output': 1024,
+            'concurrency': 1,
+            'taskgroup': AsyncMock(),
+            'id': 'test-processor'
+        }
+
+        processor = GoogleAIProcessor(**config)
+
+        # Act - Call multiple times with same parameters
+        await processor.generate_content("System", "Prompt 1", model="gemini-2.0-flash-001", temperature=0.7)
+        await processor.generate_content("System", "Prompt 2", model="gemini-2.0-flash-001", temperature=0.7)
+        await processor.generate_content("System", "Prompt 3", model="gemini-2.0-flash-001", temperature=0.7)
+
+        # Assert - Should have only 1 cache entry for the repeated parameters
+        cache_keys = list(processor.generation_configs.keys())
+        assert len(cache_keys) == 1
+        assert "gemini-2.0-flash-001:0.7" in cache_keys
+
+        # The same config object should be reused
+        config_obj = processor.generation_configs["gemini-2.0-flash-001:0.7"]
+        assert config_obj.temperature == 0.7
+
+    @patch('trustgraph.model.text_completion.googleaistudio.llm.genai')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_googleai_different_models_separate_caches(self, mock_llm_init, mock_async_init, mock_genai):
+        """Test that different models create separate cache entries even with same temperature"""
+        # Arrange
+        mock_client = MagicMock()
+        mock_genai.Client.return_value = mock_client
+
+        mock_response = MagicMock()
+        mock_response.text = "Generated response"
+        mock_response.usage_metadata.prompt_token_count = 10
+        mock_response.usage_metadata.candidates_token_count = 5
+        mock_client.models.generate_content.return_value = mock_response
+
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = {
+            'model': 'gemini-2.0-flash-001',
+            'api_key': 'test-api-key',
+            'temperature': 0.0,
+            'max_output': 1024,
+            'concurrency': 1,
+            'taskgroup': AsyncMock(),
+            'id': 'test-processor'
+        }
+
+        processor = GoogleAIProcessor(**config)
+
+        # Act - Call with different models, same temperature
+        await processor.generate_content("System", "Prompt 1", model="gemini-2.0-flash-001", temperature=0.5)
+        await processor.generate_content("System", "Prompt 2", model="gemini-1.5-flash-001", temperature=0.5)
+
+        # Assert - Should have separate cache entries for different models
+        cache_keys = list(processor.generation_configs.keys())
+        assert len(cache_keys) == 2
+        assert "gemini-2.0-flash-001:0.5" in cache_keys
+        assert "gemini-1.5-flash-001:0.5" in cache_keys
+
+    # Note: Bedrock tests would be similar but testing the Bedrock processor's caching behavior
+    # The Bedrock processor caches model variants with temperature in the cache key
+
+    async def test_bedrock_temperature_cache_keys(self):
+        """Test Bedrock processor temperature-aware caching"""
+        # This would test the Bedrock processor's _get_or_create_variant method
+        # with different temperature values to ensure proper cache key generation
+
+        # Implementation would follow similar pattern to GoogleAI tests above
+        # but using the Bedrock processor and testing model_variants cache
+        pass
+
+    async def test_bedrock_cache_isolation_different_temperatures(self):
+        """Test that Bedrock processor isolates cache entries by temperature"""
+        pass
+
+    async def test_cache_memory_efficiency(self):
+        """Test that caches don't grow unbounded with many different parameter combinations"""
+        # This could test cache size limits or cleanup behavior if implemented
+        pass
+
+
+class TestCachePerformance(IsolatedAsyncioTestCase):
+    """Test caching performance characteristics"""
+
+    async def test_cache_hit_performance(self):
+        """Test that cache hits are faster than cache misses"""
+        # This would measure timing differences between cache hits and misses
+        pass
+
+    async def test_concurrent_cache_access(self):
+        """Test concurrent access to cached configurations"""
+        # This would test thread-safety of cache access
+        pass
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])

tests/unit/test_text_completion/test_vertexai_processor.py

@@ -460,6 +460,180 @@ class TestVertexAIProcessorSimple(IsolatedAsyncioTestCase):
         assert processor.api_params["top_p"] == 1.0
         assert processor.api_params["top_k"] == 32
 
+    @patch('trustgraph.model.text_completion.vertexai.llm.service_account')
+    @patch('trustgraph.model.text_completion.vertexai.llm.vertexai')
+    @patch('trustgraph.model.text_completion.vertexai.llm.GenerativeModel')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_generate_content_temperature_override(self, mock_llm_init, mock_async_init, mock_generative_model, mock_vertexai, mock_service_account):
+        """Test temperature parameter override functionality"""
+        # Arrange
+        mock_credentials = MagicMock()
+        mock_service_account.Credentials.from_service_account_file.return_value = mock_credentials
+
+        mock_model = MagicMock()
+        mock_response = MagicMock()
+        mock_response.text = "Response with custom temperature"
+        mock_response.usage_metadata.prompt_token_count = 20
+        mock_response.usage_metadata.candidates_token_count = 12
+        mock_model.generate_content.return_value = mock_response
+        mock_generative_model.return_value = mock_model
+
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = {
+            'region': 'us-central1',
+            'model': 'gemini-2.0-flash-001',
+            'temperature': 0.0,  # Default temperature
+            'max_output': 8192,
+            'private_key': 'private.json',
+            'concurrency': 1,
+            'taskgroup': AsyncMock(),
+            'id': 'test-processor'
+        }
+
+        processor = Processor(**config)
+
+        # Act - Override temperature at runtime
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model=None,      # Use default model
+            temperature=0.8  # Override temperature
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+        assert result.text == "Response with custom temperature"
+
+        # Verify Gemini API was called with overridden temperature
+        mock_model.generate_content.assert_called_once()
+        call_args = mock_model.generate_content.call_args
+
+        # Check that generation_config has the overridden temperature
+        generation_config = call_args.kwargs['generation_config']
+        assert generation_config.temperature == 0.8  # Should use runtime override
+
+    @patch('trustgraph.model.text_completion.vertexai.llm.service_account')
+    @patch('trustgraph.model.text_completion.vertexai.llm.vertexai')
+    @patch('trustgraph.model.text_completion.vertexai.llm.GenerativeModel')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_generate_content_model_override(self, mock_llm_init, mock_async_init, mock_generative_model, mock_vertexai, mock_service_account):
+        """Test model parameter override functionality"""
+        # Arrange
+        mock_credentials = MagicMock()
+        mock_service_account.Credentials.from_service_account_file.return_value = mock_credentials
+
+        # Mock different models
+        mock_model_default = MagicMock()
+        mock_model_override = MagicMock()
+        mock_response = MagicMock()
+        mock_response.text = "Response with custom model"
+        mock_response.usage_metadata.prompt_token_count = 18
+        mock_response.usage_metadata.candidates_token_count = 14
+        mock_model_override.generate_content.return_value = mock_response
+
+        # GenerativeModel should return different models based on input
+        def model_factory(model_name):
+            if model_name == 'gemini-1.5-pro':
+                return mock_model_override
+            return mock_model_default
+
+        mock_generative_model.side_effect = model_factory
+
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = {
+            'region': 'us-central1',
+            'model': 'gemini-2.0-flash-001',  # Default model
+            'temperature': 0.2,               # Default temperature
+            'max_output': 8192,
+            'private_key': 'private.json',
+            'concurrency': 1,
+            'taskgroup': AsyncMock(),
+            'id': 'test-processor'
+        }
+
+        processor = Processor(**config)
+
+        # Act - Override model at runtime
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model="gemini-1.5-pro",  # Override model
+            temperature=None         # Use default temperature
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+        assert result.text == "Response with custom model"
+
+        # Verify the overridden model was used
+        mock_model_override.generate_content.assert_called_once()
+        # Verify GenerativeModel was called with the override model
+        mock_generative_model.assert_called_with('gemini-1.5-pro')
+
+    @patch('trustgraph.model.text_completion.vertexai.llm.service_account')
+    @patch('trustgraph.model.text_completion.vertexai.llm.vertexai')
+    @patch('trustgraph.model.text_completion.vertexai.llm.GenerativeModel')
+    @patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
+    @patch('trustgraph.base.llm_service.LlmService.__init__')
+    async def test_generate_content_both_parameters_override(self, mock_llm_init, mock_async_init, mock_generative_model, mock_vertexai, mock_service_account):
+        """Test overriding both model and temperature parameters simultaneously"""
+        # Arrange
+        mock_credentials = MagicMock()
+        mock_service_account.Credentials.from_service_account_file.return_value = mock_credentials
+
+        mock_model = MagicMock()
+        mock_response = MagicMock()
+        mock_response.text = "Response with both overrides"
+        mock_response.usage_metadata.prompt_token_count = 22
+        mock_response.usage_metadata.candidates_token_count = 16
+        mock_model.generate_content.return_value = mock_response
+        mock_generative_model.return_value = mock_model
+
+        mock_async_init.return_value = None
+        mock_llm_init.return_value = None
+
+        config = {
+            'region': 'us-central1',
+            'model': 'gemini-2.0-flash-001',  # Default model
+            'temperature': 0.0,               # Default temperature
+            'max_output': 8192,
+            'private_key': 'private.json',
+            'concurrency': 1,
+            'taskgroup': AsyncMock(),
+            'id': 'test-processor'
+        }
+
+        processor = Processor(**config)
+
+        # Act - Override both parameters at runtime
+        result = await processor.generate_content(
+            "System prompt",
+            "User prompt",
+            model="gemini-1.5-flash-001",  # Override model
+            temperature=0.9                # Override temperature
+        )
+
+        # Assert
+        assert isinstance(result, LlmResult)
+        assert result.text == "Response with both overrides"
+
+        # Verify both overrides were used
+        mock_model.generate_content.assert_called_once()
+        call_args = mock_model.generate_content.call_args
+
+        # Verify model override
+        mock_generative_model.assert_called_with('gemini-1.5-flash-001')  # Should use runtime override
+
+        # Verify temperature override
+        generation_config = call_args.kwargs['generation_config']
+        assert generation_config.temperature == 0.9  # Should use runtime override
+
 
 if __name__ == '__main__':
     pytest.main([__file__])