nat.builder.builder

Attributes

_current_builder_context

Classes

UserManagerHolder
Builder	Helper class that provides a standard way to create an ABC using
EvalBuilder	Abstract base class for evaluation builder functionality.

Module Contents

_current_builder_context: contextvars.ContextVar[Builder | None]

class UserManagerHolder(context: nat.builder.context.Context)

_context

get_id()

class Builder

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

static current() → Builder

Get the Builder object from the current context.

Returns:

The Builder object stored in the ContextVar, or raises ValueError if not set.

property sync_builder: nat.builder.sync_builder.SyncBuilder

Abstractmethod:

Get the synchronous version of the builder.

Returns:

The SyncBuilder object (synchronous wrapper).

abstractmethod add_function(

name: str | nat.data_models.component_ref.FunctionRef,

config: nat.data_models.function.FunctionBaseConfig,

) → nat.builder.function.Function

Async:

Add a function to the builder.

Args:

name: The name or reference for the function config: The configuration for the function

Returns:

The built function instance

abstractmethod add_function_group(

name: str | nat.data_models.component_ref.FunctionGroupRef,

config: nat.data_models.function.FunctionGroupBaseConfig,

) → nat.builder.function.FunctionGroup

Async:

Add a function group to the builder.

Args:

name: The name or reference for the function group config: The configuration for the function group

Returns:

The built function group instance

abstractmethod get_function(

name: str | nat.data_models.component_ref.FunctionRef,

) → nat.builder.function.Function

Async:

Get a function by name.

Args:

name: The name or reference of the function

Returns:

The built function instance

abstractmethod get_function_group(

name: str | nat.data_models.component_ref.FunctionGroupRef,

) → nat.builder.function.FunctionGroup

Async:

Get a function group by name.

Args:

name: The name or reference of the function group

Returns:

The built function group instance

async get_functions(

function_names: collections.abc.Sequence[str | nat.data_models.component_ref.FunctionRef],

) → list[nat.builder.function.Function]

Get multiple functions by name.

Args:

function_names: The names or references of the functions

Returns:

List of built function instances

async get_function_groups(

function_group_names: collections.abc.Sequence[str | nat.data_models.component_ref.FunctionGroupRef],

) → list[nat.builder.function.FunctionGroup]

Get multiple function groups by name.

Args:

function_group_names: The names or references of the function groups

Returns:

List of built function group instances

abstractmethod get_function_config(

name: str | nat.data_models.component_ref.FunctionRef,

) → nat.data_models.function.FunctionBaseConfig

Get the configuration for a function.

Args:

name: The name or reference of the function

Returns:

The configuration for the function

abstractmethod get_function_group_config(

name: str | nat.data_models.component_ref.FunctionGroupRef,

) → nat.data_models.function.FunctionGroupBaseConfig

Get the configuration for a function group.

Args:

name: The name or reference of the function group

Returns:

The configuration for the function group

abstractmethod set_workflow(

config: nat.data_models.function.FunctionBaseConfig,

) → nat.builder.function.Function

Async:

Set the workflow function.

Args:

config: The configuration for the workflow function

Returns:

The built workflow function instance

abstractmethod get_workflow() → nat.builder.function.Function

Get the workflow function.

Returns:

The workflow function instance

abstractmethod get_workflow_config() → nat.data_models.function.FunctionBaseConfig

Get the configuration for the workflow.

Returns:

The configuration for the workflow function

abstractmethod get_tools(

tool_names: collections.abc.Sequence[str | nat.data_models.component_ref.FunctionRef | nat.data_models.component_ref.FunctionGroupRef],

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → list[Any]

Async:

Get multiple tools by name wrapped in the specified framework type.

Args:

tool_names: The names or references of the tools (functions or function groups) wrapper_type: The LLM framework type to wrap the tools in

Returns:

List of tools wrapped in the specified framework type

abstractmethod get_tool(

fn_name: str | nat.data_models.component_ref.FunctionRef,

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → Any

Async:

Get a tool by name wrapped in the specified framework type.

Args:

fn_name: The name or reference of the tool (function) wrapper_type: The LLM framework type to wrap the tool in

Returns:

The tool wrapped in the specified framework type

abstractmethod add_llm(

name: str | nat.data_models.component_ref.LLMRef,

config: nat.data_models.llm.LLMBaseConfig,

) → Any

Async:

Add an LLM to the builder.

Args:

name: The name or reference for the LLM config: The configuration for the LLM

Returns:

The built LLM instance

abstractmethod get_llm(

llm_name: str | nat.data_models.component_ref.LLMRef,

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → Any

Async:

Get an LLM by name wrapped in the specified framework type.

Args:

llm_name: The name or reference of the LLM wrapper_type: The LLM framework type to wrap the LLM in

Returns:

The LLM wrapped in the specified framework type

async get_llms(

llm_names: collections.abc.Sequence[str | nat.data_models.component_ref.LLMRef],

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → list[Any]

Get multiple LLMs by name wrapped in the specified framework type.

Args:

llm_names: The names or references of the LLMs wrapper_type: The LLM framework type to wrap the LLMs in

Returns:

List of LLMs wrapped in the specified framework type

abstractmethod get_llm_config(

llm_name: str | nat.data_models.component_ref.LLMRef,

) → nat.data_models.llm.LLMBaseConfig

Get the configuration for an LLM.

Args:

llm_name: The name or reference of the LLM

Returns:

The configuration for the LLM

abstractmethod add_auth_provider(

name: str | nat.data_models.component_ref.AuthenticationRef,

config: nat.data_models.authentication.AuthProviderBaseConfig,

) → nat.authentication.interfaces.AuthProviderBase

Async:

Add an authentication provider to the builder.

Args:

name: The name or reference for the authentication provider config: The configuration for the authentication provider

Returns:

The built authentication provider instance

abstractmethod get_auth_provider(

auth_provider_name: str | nat.data_models.component_ref.AuthenticationRef,

) → nat.authentication.interfaces.AuthProviderBase

Async:

Get an authentication provider by name.

Args:

auth_provider_name: The name or reference of the authentication provider

Returns:

The authentication provider instance

async get_auth_providers(

auth_provider_names: list[str | nat.data_models.component_ref.AuthenticationRef],

)

Get multiple authentication providers by name.

Args:

auth_provider_names: The names or references of the authentication providers

Returns:

List of authentication provider instances

abstractmethod add_object_store(

name: str | nat.data_models.component_ref.ObjectStoreRef,

config: nat.data_models.object_store.ObjectStoreBaseConfig,

) → nat.object_store.interfaces.ObjectStore

Async:

Add an object store to the builder.

Args:

name: The name or reference for the object store config: The configuration for the object store

Returns:

The built object store instance

async get_object_store_clients(

object_store_names: collections.abc.Sequence[str | nat.data_models.component_ref.ObjectStoreRef],

) → list[nat.object_store.interfaces.ObjectStore]

Return a list of all object store clients.

abstractmethod get_object_store_client(

object_store_name: str | nat.data_models.component_ref.ObjectStoreRef,

) → nat.object_store.interfaces.ObjectStore

Async:

Get an object store client by name.

Args:

object_store_name: The name or reference of the object store

Returns:

The object store client instance

abstractmethod get_object_store_config(

object_store_name: str | nat.data_models.component_ref.ObjectStoreRef,

) → nat.data_models.object_store.ObjectStoreBaseConfig

Get the configuration for an object store.

Args:

object_store_name: The name or reference of the object store

Returns:

The configuration for the object store

abstractmethod add_embedder(

name: str | nat.data_models.component_ref.EmbedderRef,

config: nat.data_models.embedder.EmbedderBaseConfig,

) → None

Async:

Add an embedder to the builder.

Args:

name: The name or reference for the embedder config: The configuration for the embedder

async get_embedders(

embedder_names: collections.abc.Sequence[str | nat.data_models.component_ref.EmbedderRef],

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → list[Any]

Get multiple embedders by name wrapped in the specified framework type.

Args:

embedder_names: The names or references of the embedders wrapper_type: The LLM framework type to wrap the embedders in

Returns:

List of embedders wrapped in the specified framework type

abstractmethod get_embedder(

embedder_name: str | nat.data_models.component_ref.EmbedderRef,

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → Any

Async:

Get an embedder by name wrapped in the specified framework type.

Args:

embedder_name: The name or reference of the embedder wrapper_type: The LLM framework type to wrap the embedder in

Returns:

The embedder wrapped in the specified framework type

abstractmethod get_embedder_config(

embedder_name: str | nat.data_models.component_ref.EmbedderRef,

) → nat.data_models.embedder.EmbedderBaseConfig

Get the configuration for an embedder.

Args:

embedder_name: The name or reference of the embedder

Returns:

The configuration for the embedder

abstractmethod add_memory_client(

name: str | nat.data_models.component_ref.MemoryRef,

config: nat.data_models.memory.MemoryBaseConfig,

) → nat.memory.interfaces.MemoryEditor

Async:

Add a memory client to the builder.

Args:

name: The name or reference for the memory client config: The configuration for the memory client

Returns:

The built memory client instance

async get_memory_clients(

memory_names: collections.abc.Sequence[str | nat.data_models.component_ref.MemoryRef],

) → list[nat.memory.interfaces.MemoryEditor]

Return a list of memory clients for the specified names.

abstractmethod get_memory_client(

memory_name: str | nat.data_models.component_ref.MemoryRef,

) → nat.memory.interfaces.MemoryEditor

Async:

Return the instantiated memory client for the given name.

abstractmethod get_memory_client_config(

memory_name: str | nat.data_models.component_ref.MemoryRef,

) → nat.data_models.memory.MemoryBaseConfig

Get the configuration for a memory client.

Args:

memory_name: The name or reference of the memory client

Returns:

The configuration for the memory client

abstractmethod add_retriever(

name: str | nat.data_models.component_ref.RetrieverRef,

config: nat.data_models.retriever.RetrieverBaseConfig,

) → None

Async:

Add a retriever to the builder.

Args:

name: The name or reference for the retriever config: The configuration for the retriever

async get_retrievers(

retriever_names: collections.abc.Sequence[str | nat.data_models.component_ref.RetrieverRef],

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str | None = None,

) → list[nat.retriever.interface.Retriever]

Get multiple retrievers by name.

Args:

retriever_names: The names or references of the retrievers wrapper_type: Optional LLM framework type to wrap the retrievers in

Returns:

List of retriever instances

async get_retriever(

retriever_name: str | nat.data_models.component_ref.RetrieverRef,

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → Any

async get_retriever(

retriever_name: str | nat.data_models.component_ref.RetrieverRef,

wrapper_type: None,

) → nat.retriever.interface.Retriever

async get_retriever(

retriever_name: str | nat.data_models.component_ref.RetrieverRef,

) → nat.retriever.interface.Retriever

Get a retriever by name.

Args:

retriever_name: The name or reference of the retriever wrapper_type: Optional LLM framework type to wrap the retriever in

Returns:

The retriever instance, optionally wrapped in the specified framework type

abstractmethod get_retriever_config(

retriever_name: str | nat.data_models.component_ref.RetrieverRef,

) → nat.data_models.retriever.RetrieverBaseConfig

Async:

Get the configuration for a retriever.

Args:

retriever_name: The name or reference of the retriever

Returns:

The configuration for the retriever

abstractmethod add_trainer(

name: str | nat.data_models.component_ref.TrainerRef,

config: nat.data_models.finetuning.TrainerConfig,

) → nat.finetuning.interfaces.finetuning_runner.Trainer

Async:

Add a trainer to the builder.

Args:

name: The name or reference for the trainer config: The configuration for the trainer

Returns:

The built trainer instance

abstractmethod add_trainer_adapter(

name: str | nat.data_models.component_ref.TrainerAdapterRef,

config: nat.data_models.finetuning.TrainerAdapterConfig,

) → nat.finetuning.interfaces.trainer_adapter.TrainerAdapter

Async:

Add a trainer adapter to the builder.

Args:

name: The name or reference for the trainer adapter config: The configuration for the trainer adapter

Returns:

The built trainer adapter instance

abstractmethod add_trajectory_builder(

name: str | nat.data_models.component_ref.TrajectoryBuilderRef,

config: nat.data_models.finetuning.TrajectoryBuilderConfig,

) → nat.finetuning.interfaces.trajectory_builder.TrajectoryBuilder

Async:

Add a trajectory builder to the builder.

Args:

name: The name or reference for the trajectory builder config: The configuration for the trajectory builder

Returns:

The built trajectory builder instance

abstractmethod get_trainer(

trainer_name: str | nat.data_models.component_ref.TrainerRef,

trajectory_builder: nat.finetuning.interfaces.trajectory_builder.TrajectoryBuilder,

trainer_adapter: nat.finetuning.interfaces.trainer_adapter.TrainerAdapter,

) → nat.finetuning.interfaces.finetuning_runner.Trainer

Async:

Get a trainer by name with the specified trajectory builder and trainer adapter.

Args:

trainer_name: The name or reference of the trainer trajectory_builder: The trajectory builder instance trainer_adapter: The trainer adapter instance

Returns:

The trainer instance

abstractmethod get_trainer_adapter(

trainer_adapter_name: str | nat.data_models.component_ref.TrainerAdapterRef,

) → nat.finetuning.interfaces.trainer_adapter.TrainerAdapter

Async:

Get a trainer adapter by name.

Args:

trainer_adapter_name: The name or reference of the trainer adapter

Returns:

The trainer adapter instance

abstractmethod get_trajectory_builder(

trajectory_builder_name: str | nat.data_models.component_ref.TrajectoryBuilderRef,

) → nat.finetuning.interfaces.trajectory_builder.TrajectoryBuilder

Async:

Get a trajectory builder by name.

Args:

trajectory_builder_name: The name or reference of the trajectory builder

Returns:

The trajectory builder instance

abstractmethod get_trainer_config(

trainer_name: str | nat.data_models.component_ref.TrainerRef,

) → nat.data_models.finetuning.TrainerConfig

Async:

Get the configuration for a trainer.

Args:

trainer_name: The name or reference of the trainer

Returns:

The configuration for the trainer

abstractmethod get_trainer_adapter_config(

trainer_adapter_name: str | nat.data_models.component_ref.TrainerAdapterRef,

) → nat.data_models.finetuning.TrainerAdapterConfig

Async:

Get the configuration for a trainer adapter.

Args:

trainer_adapter_name: The name or reference of the trainer adapter

Returns:

The configuration for the trainer adapter

abstractmethod get_trajectory_builder_config(

trajectory_builder_name: str | nat.data_models.component_ref.TrajectoryBuilderRef,

) → nat.data_models.finetuning.TrajectoryBuilderConfig

Async:

Get the configuration for a trajectory builder.

Args:

trajectory_builder_name: The name or reference of the trajectory builder

Returns:

The configuration for the trajectory builder

abstractmethod add_ttc_strategy(

name: str | nat.data_models.component_ref.TTCStrategyRef,

config: nat.data_models.ttc_strategy.TTCStrategyBaseConfig,

)

Async:

Add a test-time compute strategy to the builder.

Args:

name: The name or reference for the TTC strategy config: The configuration for the TTC strategy

abstractmethod get_ttc_strategy(

strategy_name: str | nat.data_models.component_ref.TTCStrategyRef,

pipeline_type: nat.experimental.test_time_compute.models.stage_enums.PipelineTypeEnum,

stage_type: nat.experimental.test_time_compute.models.stage_enums.StageTypeEnum,

) → nat.experimental.test_time_compute.models.strategy_base.StrategyBase

Async:

Get a test-time compute strategy by name.

Args:

strategy_name: The name or reference of the TTC strategy pipeline_type: The pipeline type for the strategy stage_type: The stage type for the strategy

Returns:

The TTC strategy instance

abstractmethod get_ttc_strategy_config(

strategy_name: str | nat.data_models.component_ref.TTCStrategyRef,

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

Async:

Get the configuration for a test-time compute strategy.

Args:

strategy_name: The name or reference of the TTC strategy pipeline_type: The pipeline type for the strategy stage_type: The stage type for the strategy

Returns:

The configuration for the TTC strategy

abstractmethod get_user_manager() → UserManagerHolder

Get the user manager holder.

Returns:

The user manager holder instance

abstractmethod get_function_dependencies(

fn_name: str,

) → nat.data_models.function_dependencies.FunctionDependencies

Get the dependencies for a function.

Args:

fn_name: The name of the function

Returns:

The function dependencies

abstractmethod get_function_group_dependencies(

fn_name: str,

) → nat.data_models.function_dependencies.FunctionDependencies

Get the dependencies for a function group.

Args:

fn_name: The name of the function group

Returns:

The function group dependencies

abstractmethod add_middleware(

name: str | nat.data_models.component_ref.MiddlewareRef,

config: nat.data_models.middleware.MiddlewareBaseConfig,

) → nat.middleware.middleware.Middleware

Async:

Add middleware to the builder.

Args:

name: The name or reference for the middleware config: The configuration for the middleware

Returns:

The built middleware instance

abstractmethod get_middleware(

middleware_name: str | nat.data_models.component_ref.MiddlewareRef,

) → nat.middleware.middleware.Middleware

Async:

Get built middleware by name.

Args:

middleware_name: The name or reference of the middleware

Returns:

The built middleware instance

abstractmethod get_middleware_config(

middleware_name: str | nat.data_models.component_ref.MiddlewareRef,

) → nat.data_models.middleware.MiddlewareBaseConfig

Get the configuration for middleware.

Args:

middleware_name: The name or reference of the middleware

Returns:

The configuration for the middleware

async get_middleware_list(

middleware_names: collections.abc.Sequence[str | nat.data_models.component_ref.MiddlewareRef],

) → list[nat.middleware.middleware.Middleware]

Get multiple middleware by name.

Args:

middleware_names: The names or references of the middleware

Returns:

List of built middleware instances

class EvalBuilder

Bases: abc.ABC

Abstract base class for evaluation builder functionality.

abstractmethod add_evaluator(

name: str,

config: nat.data_models.evaluator.EvaluatorBaseConfig,

)

Async:

Add an evaluator to the builder.

Args:

name: The name for the evaluator config: The configuration for the evaluator

abstractmethod get_evaluator(evaluator_name: str) → Any

Get an evaluator by name.

Args:

evaluator_name: The name of the evaluator

Returns:

The evaluator instance

abstractmethod get_evaluator_config(

evaluator_name: str,

) → nat.data_models.evaluator.EvaluatorBaseConfig

Get the configuration for an evaluator.

Args:

evaluator_name: The name of the evaluator

Returns:

The configuration for the evaluator

abstractmethod get_max_concurrency() → int

Get the maximum concurrency for evaluation.

Returns:

The maximum concurrency value

abstractmethod get_output_dir() → pathlib.Path

Get the output directory for evaluation results.

Returns:

The output directory path

abstractmethod get_all_tools(

wrapper_type: nat.builder.framework_enum.LLMFrameworkEnum | str,

) → list[Any]

Async:

Get all tools wrapped in the specified framework type.

Args:

wrapper_type: The LLM framework type to wrap the tools in

Returns:

List of all tools wrapped in the specified framework type