nat.test.tool_test_runner#

Attributes#

logger

Classes#

`MockBuilder`	A lightweight mock builder for tool testing that provides minimal dependencies.
`ToolTestRunner`	A test runner that enables isolated testing of NAT tools without requiring

Functions#

with_mocked_dependencies()

Convenience context manager for testing tools with mocked dependencies.

Module Contents#

logger#

class MockBuilder#

Bases: nat.builder.builder.Builder

A lightweight mock builder for tool testing that provides minimal dependencies.

_functions#

_mocks#

property sync_builder: nat.builder.sync_builder.SyncBuilder#

mock_function(name: str, mock_response: Any)#: Add a mock function that returns a fixed response.

mock_function_group(name: str, mock_response: Any)#: Add a mock function group that returns a fixed response.

mock_llm(name: str, mock_response: Any)#: Add a mock LLM that returns a fixed response.

mock_embedder(name: str, mock_response: Any)#: Add a mock embedder that returns a fixed response.

mock_memory_client(name: str, mock_response: Any)#: Add a mock memory client that returns a fixed response.

mock_retriever(name: str, mock_response: Any)#: Add a mock retriever that returns a fixed response.

mock_object_store(name: str, mock_response: Any)#: Add a mock object store that returns a fixed response.

mock_ttc_strategy(name: str, mock_response: Any)#: Add a mock TTC strategy that returns a fixed response.

mock_auth_provider(name: str, mock_response: Any)#: Add a mock auth provider that returns a fixed response.

mock_trainer(name: str, mock_response: Any)#: Add a mock trainer that returns a fixed response.

mock_trainer_adapter(name: str, mock_response: Any)#: Add a mock trainer adapter that returns a fixed response.

mock_trajectory_builder(name: str, mock_response: Any)#: Add a mock trajectory builder that returns a fixed response.

async add_ttc_strategy( name: str, config: nat.data_models.ttc_strategy.TTCStrategyBaseConfig, ) → None#: Mock implementation (no‑op).

async get_ttc_strategy( strategy_name: str, pipeline_type: nat.experimental.test_time_compute.models.stage_enums.PipelineTypeEnum, stage_type: nat.experimental.test_time_compute.models.stage_enums.StageTypeEnum, ) → Any#: Return a mock TTC strategy if one is configured.

async get_ttc_strategy_config( strategy_name: str, pipeline_type: nat.experimental.test_time_compute.models.stage_enums.PipelineTypeEnum, stage_type: nat.experimental.test_time_compute.models.stage_enums.StageTypeEnum, ) → nat.data_models.ttc_strategy.TTCStrategyBaseConfig#: Mock implementation.

async add_auth_provider( name: str, config: nat.data_models.authentication.AuthProviderBaseConfig, ) → None#: Mock implementation (no‑op).

async get_auth_provider( auth_provider_name: str, ) → nat.authentication.interfaces.AuthProviderBase#: Return a mock auth provider if one is configured.

abstractmethod add_function( name: str, config: nat.data_models.function.FunctionBaseConfig, ) → nat.builder.function.Function#

Async:

Mock implementation - not used in tool testing.

async get_function(name: str) → nat.builder.function.Function#: Return a mock function if one is configured.

get_function_config( name: str, ) → nat.data_models.function.FunctionBaseConfig#: Mock implementation.

abstractmethod add_function_group( name: str, config: nat.data_models.function.FunctionGroupBaseConfig, ) → nat.builder.function.FunctionGroup#

Async:

Mock implementation - not used in tool testing.

async get_function_group(name: str) → nat.builder.function.FunctionGroup#: Return a mock function group if one is configured.

get_function_group_config( name: str, ) → nat.data_models.function.FunctionGroupBaseConfig#: Mock implementation.

async set_workflow( config: nat.data_models.function.FunctionBaseConfig, ) → nat.builder.function.Function#: Mock implementation.

get_workflow() → nat.builder.function.Function#: Mock implementation.

get_workflow_config() → nat.data_models.function.FunctionBaseConfig#: Mock implementation.

async get_tools( tool_names: collections.abc.Sequence[str], wrapper_type, ) → list[Any]#: Mock implementation.

async get_tool(fn_name: str, wrapper_type) → Any#: Mock implementation.

async add_llm(name: str, config) → None#: Mock implementation.

async get_llm(llm_name: str, wrapper_type)#: Return a mock LLM if one is configured.

get_llm_config(llm_name: str) → nat.data_models.llm.LLMBaseConfig#: Mock implementation.

async add_embedder(name: str, config) → None#: Mock implementation.

async get_embedder(embedder_name: str, wrapper_type)#: Return a mock embedder if one is configured.

get_embedder_config( embedder_name: str, ) → nat.data_models.embedder.EmbedderBaseConfig#: Mock implementation.

async add_memory_client( name: str, config, ) → nat.memory.interfaces.MemoryEditor#

async get_memory_client( memory_name: str, ) → nat.memory.interfaces.MemoryEditor#: Return a mock memory client if one is configured.

get_memory_client_config( memory_name: str, ) → nat.data_models.memory.MemoryBaseConfig#: Mock implementation.

async add_retriever(name: str, config) → None#: Mock implementation.

async get_retriever(retriever_name: str, wrapper_type=None)#: Return a mock retriever if one is configured.

async get_retriever_config( retriever_name: str, ) → nat.data_models.retriever.RetrieverBaseConfig#: Mock implementation.

async add_object_store( name: str, config: nat.data_models.object_store.ObjectStoreBaseConfig, ) → nat.object_store.interfaces.ObjectStore#: Mock implementation for object store.

async get_object_store_client( object_store_name: str, ) → nat.object_store.interfaces.ObjectStore#: Return a mock object store client if one is configured.

get_object_store_config( object_store_name: str, ) → nat.data_models.object_store.ObjectStoreBaseConfig#: Mock implementation for object store config.

get_user_manager()#: Mock implementation.

get_function_dependencies( fn_name: str, ) → nat.data_models.function_dependencies.FunctionDependencies#: Mock implementation.

get_function_group_dependencies( fn_name: str, ) → nat.data_models.function_dependencies.FunctionDependencies#: Mock implementation.

async get_middleware( middleware_name: str | nat.data_models.component_ref.MiddlewareRef, ) → nat.middleware.FunctionMiddleware#: Mock implementation.

get_middleware_config( middleware_name: str | nat.data_models.component_ref.MiddlewareRef, ) → nat.data_models.middleware.FunctionMiddlewareBaseConfig#: Mock implementation.

async add_middleware( name: str | nat.data_models.component_ref.MiddlewareRef, config: nat.data_models.middleware.FunctionMiddlewareBaseConfig, ) → nat.middleware.FunctionMiddleware#: Mock implementation.

async add_trainer( name: str, config: nat.data_models.finetuning.TrainerConfig, ) → nat.finetuning.interfaces.finetuning_runner.Trainer#: Mock implementation.

async get_trainer( trainer_name: str, trajectory_builder: nat.finetuning.interfaces.trajectory_builder.TrajectoryBuilder, trainer_adapter: nat.finetuning.interfaces.trainer_adapter.TrainerAdapter, ) → nat.finetuning.interfaces.finetuning_runner.Trainer#: Return a mock trainer if one is configured.

async get_trainer_config( trainer_name: str, ) → nat.data_models.finetuning.TrainerConfig#: Mock implementation.

async add_trainer_adapter( name: str, config: nat.data_models.finetuning.TrainerAdapterConfig, ) → nat.finetuning.interfaces.trainer_adapter.TrainerAdapter#: Mock implementation.

async get_trainer_adapter( trainer_adapter_name: str, ) → nat.finetuning.interfaces.trainer_adapter.TrainerAdapter#: Return a mock trainer adapter if one is configured.

async get_trainer_adapter_config( trainer_adapter_name: str, ) → nat.data_models.finetuning.TrainerAdapterConfig#: Mock implementation.

async add_trajectory_builder( name: str, config: nat.data_models.finetuning.TrajectoryBuilderConfig, ) → nat.finetuning.interfaces.trajectory_builder.TrajectoryBuilder#: Mock implementation.

async get_trajectory_builder( trajectory_builder_name: str, ) → nat.finetuning.interfaces.trajectory_builder.TrajectoryBuilder#: Return a mock trajectory builder if one is configured.

async get_trajectory_builder_config( trajectory_builder_name: str, ) → nat.data_models.finetuning.TrajectoryBuilderConfig#: Mock implementation.

class ToolTestRunner#

A test runner that enables isolated testing of NAT tools without requiring full workflow setup, LLMs, or complex dependencies.

Usage: >>> runner = ToolTestRunner() >>> # Test a tool with minimal setup >>> result = await runner.test_tool( … config_type=MyToolConfig, … config_params={“param1”: “value1”}, … input_data=”test input” … ) >>> # Test a tool with mocked dependencies >>> async with runner.with_mocks() as mock_builder: >>> mock_builder.mock_llm(“my_llm”, “mocked response”) >>> result = await runner.test_tool( … config_type=MyToolConfig, … config_params={“llm_name”: “my_llm”}, … input_data=”test input” … )

_ensure_plugins_loaded()#: Ensure all plugins are loaded for tool registration.

async test_tool(config_type: type[nat.data_models.function.FunctionBaseConfig], config_params: dict[str, Any] | None = None, input_data: Any = None, expected_output: Any = None, \*\*kwargs) → Any#

Test a tool in isolation with minimal setup.

Args:: config_type: The tool configuration class config_params: Parameters to pass to the config constructor input_data: Input data to pass to the tool expected_output: Expected output for assertion (optional) **kwargs: Additional parameters
Returns:: The tool’s output
Raises:: AssertionError: If expected_output is provided and doesn’t match ValueError: If tool registration or execution fails

async with_mocks()#

Context manager that provides a mock builder for setting up dependencies.

Usage: >>> async with runner.with_mocks() as mock_builder: >>> mock_builder.mock_llm(“my_llm”, “mocked response”) >>> result = await runner.test_tool_with_builder( … config_type=MyToolConfig, … builder=mock_builder, … input_data=”test input” … )

async test_tool_with_builder( config_type: type[nat.data_models.function.FunctionBaseConfig], builder: MockBuilder, config_params: dict[str, Any] | None = None, input_data: Any = None, expected_output: Any = None, ) → Any#

Test a tool with a pre-configured mock builder.

Args:: config_type: The tool configuration class builder: Pre-configured MockBuilder with mocked dependencies config_params: Parameters to pass to the config constructor input_data: Input data to pass to the tool expected_output: Expected output for assertion (optional)
Returns:: The tool’s output

async with_mocked_dependencies()#

Convenience context manager for testing tools with mocked dependencies.

Usage: >>> async with with_mocked_dependencies() as (runner, mock_builder): >>> mock_builder.mock_llm(“my_llm”, “mocked response”) … result = await runner.test_tool_with_builder( … config_type=MyToolConfig, … builder=mock_builder, … input_data=”test input” … )