aiq.builder.workflow_builder#

Attributes#

logger

Classes#

`ConfiguredExporter`
`ConfiguredFunction`
`ConfiguredLLM`
`ConfiguredEmbedder`
`ConfiguredMemory`
`ConfiguredRetriever`
`WorkflowBuilder`	Helper class that provides a standard way to create an ABC using
`ChildBuilder`	Helper class that provides a standard way to create an ABC using

Module Contents#

logger#

class ConfiguredExporter#

config: aiq.data_models.telemetry_exporter.TelemetryExporterBaseConfig#

instance: opentelemetry.sdk.trace.export.SpanExporter#

class ConfiguredFunction#

config: aiq.data_models.function.FunctionBaseConfig#

instance: aiq.builder.function.Function#

class ConfiguredLLM#

config: aiq.data_models.llm.LLMBaseConfig#

instance: aiq.builder.llm.LLMProviderInfo#

class ConfiguredEmbedder#

config: aiq.data_models.embedder.EmbedderBaseConfig#

instance: aiq.builder.embedder.EmbedderProviderInfo#

class ConfiguredMemory#

config: aiq.data_models.memory.MemoryBaseConfig#

instance: aiq.memory.interfaces.MemoryEditor#

class ConfiguredRetriever#

config: aiq.data_models.retriever.RetrieverBaseConfig#

instance: aiq.builder.retriever.RetrieverProviderInfo#

class WorkflowBuilder( *, general_config: aiq.data_models.config.GeneralConfig | None = None, registry: aiq.cli.type_registry.TypeRegistry | None = None, )#

Bases: aiq.builder.builder.Builder, contextlib.AbstractAsyncContextManager

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

general_config = None#

_registry = None#

_logging_handlers: dict[str, logging.Handler]#

_exporters: dict[str, ConfiguredExporter]#

_functions: dict[str, ConfiguredFunction]#

_workflow: ConfiguredFunction | None = None#

_llms: dict[str, ConfiguredLLM]#

_embedders: dict[str, ConfiguredEmbedder]#

_memory_clients: dict[str, ConfiguredMemory]#

_retrievers: dict[str, ConfiguredRetriever]#

_context_state#

_exit_stack: contextlib.AsyncExitStack | None = None#

function_dependencies: dict[str, aiq.data_models.function_dependencies.FunctionDependencies]#

current_function_building: str | None = None#

build( entry_function: str | None = None, ) → aiq.builder.workflow.Workflow#

Creates an instance of a workflow object using the added components and the desired entry function.

Parameters#

entry_functionstr | None, optional: The function name to use as the entry point for the created workflow. If None, the entry point will be the specified workflow function. By default None

Returns#

Workflow: A created workflow.

Raises#

ValueError: If the workflow has not been set before building.

_get_exit_stack() → contextlib.AsyncExitStack#

async _build_function( name: str, config: aiq.data_models.function.FunctionBaseConfig, ) → ConfiguredFunction#

async add_function( name: str | aiq.data_models.component_ref.FunctionRef, config: aiq.data_models.function.FunctionBaseConfig, ) → aiq.builder.function.Function#

get_function( name: str | aiq.data_models.component_ref.FunctionRef, ) → aiq.builder.function.Function#

get_function_config( name: str | aiq.data_models.component_ref.FunctionRef, ) → aiq.data_models.function.FunctionBaseConfig#

async set_workflow( config: aiq.data_models.function.FunctionBaseConfig, ) → aiq.builder.function.Function#

get_workflow() → aiq.builder.function.Function#

get_workflow_config() → aiq.data_models.function.FunctionBaseConfig#

get_function_dependencies( fn_name: str | aiq.data_models.component_ref.FunctionRef, ) → aiq.data_models.function_dependencies.FunctionDependencies#

get_tool( fn_name: str | aiq.data_models.component_ref.FunctionRef, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str, )#

async add_llm( name: str | aiq.data_models.component_ref.LLMRef, config: aiq.data_models.llm.LLMBaseConfig, )#

async get_llm( llm_name: str | aiq.data_models.component_ref.LLMRef, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str, )#

get_llm_config( llm_name: str | aiq.data_models.component_ref.LLMRef, ) → aiq.data_models.llm.LLMBaseConfig#

async add_embedder( name: str | aiq.data_models.component_ref.EmbedderRef, config: aiq.data_models.embedder.EmbedderBaseConfig, )#

async get_embedder( embedder_name: str | aiq.data_models.component_ref.EmbedderRef, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str, )#

get_embedder_config( embedder_name: str | aiq.data_models.component_ref.EmbedderRef, ) → aiq.data_models.embedder.EmbedderBaseConfig#

async add_memory_client( name: str | aiq.data_models.component_ref.MemoryRef, config: aiq.data_models.memory.MemoryBaseConfig, ) → aiq.memory.interfaces.MemoryEditor#

get_memory_client( memory_name: str | aiq.data_models.component_ref.MemoryRef, ) → aiq.memory.interfaces.MemoryEditor#: Return the instantiated memory client for the given name.

get_memory_client_config( memory_name: str | aiq.data_models.component_ref.MemoryRef, ) → aiq.data_models.memory.MemoryBaseConfig#

async add_retriever( name: str | aiq.data_models.component_ref.RetrieverRef, config: aiq.data_models.retriever.RetrieverBaseConfig, )#

async get_retriever( retriever_name: str | aiq.data_models.component_ref.RetrieverRef, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str | None = None, )#

async get_retriever_config( retriever_name: str | aiq.data_models.component_ref.RetrieverRef, ) → aiq.data_models.retriever.RetrieverBaseConfig#

get_user_manager()#

async populate_builder(config: aiq.data_models.config.AIQConfig)#

classmethod from_config(config: aiq.data_models.config.AIQConfig)#

Async:

class ChildBuilder(workflow_builder: WorkflowBuilder)#

Bases: aiq.builder.builder.Builder

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

_workflow_builder#

_dependencies#

property dependencies: aiq.data_models.function_dependencies.FunctionDependencies#

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

get_function(name: str) → aiq.builder.function.Function#

get_function_config( name: str, ) → aiq.data_models.function.FunctionBaseConfig#

async set_workflow( config: aiq.data_models.function.FunctionBaseConfig, ) → aiq.builder.function.Function#

get_workflow() → aiq.builder.function.Function#

get_workflow_config() → aiq.data_models.function.FunctionBaseConfig#

get_tool( fn_name: str, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str, )#

async add_llm(name: str, config: aiq.data_models.llm.LLMBaseConfig)#

async get_llm( llm_name: str, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str, )#

get_llm_config(llm_name: str) → aiq.data_models.llm.LLMBaseConfig#

async add_embedder( name: str, config: aiq.data_models.embedder.EmbedderBaseConfig, )#

async get_embedder( embedder_name: str, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str, )#

get_embedder_config( embedder_name: str, ) → aiq.data_models.embedder.EmbedderBaseConfig#

async add_memory_client( name: str, config: aiq.data_models.memory.MemoryBaseConfig, ) → aiq.memory.interfaces.MemoryEditor#

get_memory_client( memory_name: str, ) → aiq.memory.interfaces.MemoryEditor#: Return the instantiated memory client for the given name.

get_memory_client_config( memory_name: str, ) → aiq.data_models.memory.MemoryBaseConfig#

async add_retriever( name: str, config: aiq.data_models.retriever.RetrieverBaseConfig, )#

async get_retriever( retriever_name: str, wrapper_type: aiq.builder.framework_enum.LLMFrameworkEnum | str | None = None, )#

async get_retriever_config( retriever_name: str, ) → aiq.data_models.retriever.RetrieverBaseConfig#

get_user_manager() → aiq.builder.builder.UserManagerHolder#

get_function_dependencies( fn_name: str, ) → aiq.data_models.function_dependencies.FunctionDependencies#