aiq.cli.type_registry#

Attributes#

`logger`
`FrontEndBuildCallableT`
`TelemetryExporterBuildCallableT`
`LoggingMethodBuildCallableT`
`FunctionBuildCallableT`
`LLMProviderBuildCallableT`
`LLMClientBuildCallableT`
`EmbedderProviderBuildCallableT`
`EmbedderClientBuildCallableT`
`EvaluatorBuildCallableT`
`MemoryBuildCallableT`
`RetrieverProviderBuildCallableT`
`RetrieverClientBuildCallableT`
`RegistryHandlerBuildCallableT`
`ToolWrapperBuildCallableT`
`TeleExporterRegisteredCallableT`
`LoggingMethodRegisteredCallableT`
`FrontEndRegisteredCallableT`
`FunctionRegisteredCallableT`
`LLMProviderRegisteredCallableT`
`LLMClientRegisteredCallableT`
`EmbedderProviderRegisteredCallableT`
`EmbedderClientRegisteredCallableT`
`EvaluatorRegisteredCallableT`
`MemoryRegisteredCallableT`
`RetrieverProviderRegisteredCallableT`
`RetrieverClientRegisteredCallableT`
`RegistryHandlerRegisteredCallableT`

Classes#

`RegisteredInfo`
`RegisteredTelemetryExporter`
`RegisteredLoggingMethod`
`RegisteredFrontEndInfo`	Represents a registered front end. Front ends are the entry points to the workflow and are responsible for
`RegisteredFunctionInfo`	Represents a registered function. Functions are the building blocks of the workflow with predefined inputs, outputs,
`RegisteredLLMProviderInfo`	Represents a registered LLM provider. LLM Providers are the operators of the LLMs. i.e. NIMs, OpenAI, Anthropic,
`RegisteredLLMClientInfo`	Represents a registered LLM client. LLM Clients are the clients that interact with the LLM providers and are
`RegisteredEmbedderProviderInfo`	Represents a registered Embedder provider. Embedder Providers are the operators of the Embedder models. i.e. NIMs,
`RegisteredEmbedderClientInfo`	Represents a registered Embedder client. Embedder Clients are the clients that interact with the Embedder providers
`RegisteredEvaluatorInfo`	Represents a registered Evaluator e.g. RagEvaluator, TrajectoryEvaluator, etc.
`RegisteredMemoryInfo`	Represents a registered Memory object which adheres to the memory interface.
`RegisteredToolWrapper`	Represents a registered tool wrapper. Tool wrappers are used to wrap the functions in a particular LLM framework.
`RegisteredRetrieverProviderInfo`	Represents a registered Retriever object which adheres to the retriever interface.
`RegisteredRetrieverClientInfo`	Represents a registered Retriever Client. Retriever Clients are the LLM Framework-specific clients that expose an
`RegisteredRegistryHandlerInfo`	Represents a registered LLM client. LLM Clients are the clients that interact with the LLM providers and are
`RegisteredPackage`
`TypeRegistry`
`GlobalTypeRegistry`

Module Contents#

logger#

FrontEndBuildCallableT#

TelemetryExporterBuildCallableT#

LoggingMethodBuildCallableT#

FunctionBuildCallableT#

LLMProviderBuildCallableT#

LLMClientBuildCallableT#

EmbedderProviderBuildCallableT#

EmbedderClientBuildCallableT#

EvaluatorBuildCallableT#

MemoryBuildCallableT#

RetrieverProviderBuildCallableT#

RetrieverClientBuildCallableT#

RegistryHandlerBuildCallableT#

ToolWrapperBuildCallableT#

TeleExporterRegisteredCallableT#

LoggingMethodRegisteredCallableT#

FrontEndRegisteredCallableT#

FunctionRegisteredCallableT#

LLMProviderRegisteredCallableT#

LLMClientRegisteredCallableT#

EmbedderProviderRegisteredCallableT#

EmbedderClientRegisteredCallableT#

EvaluatorRegisteredCallableT#

MemoryRegisteredCallableT#

RetrieverProviderRegisteredCallableT#

RetrieverClientRegisteredCallableT#

RegistryHandlerRegisteredCallableT#

class RegisteredInfo(/, **data: Any)#

Bases: pydantic.BaseModel, Generic[aiq.data_models.common.TypedBaseModelT]

model_config#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

full_type: str#

config_type: type[aiq.data_models.common.TypedBaseModelT]#

discovery_metadata: aiq.data_models.discovery_metadata.DiscoveryMetadata#

property module_name: str#

property local_name: str#

classmethod validate_full_type(full_type: str) → str#

class RegisteredTelemetryExporter#

Bases: RegisteredInfo[aiq.data_models.telemetry_exporter.TelemetryExporterBaseConfig]

build_fn: TeleExporterRegisteredCallableT = None#

class RegisteredLoggingMethod#

Bases: RegisteredInfo[aiq.data_models.logging.LoggingBaseConfig]

build_fn: LoggingMethodRegisteredCallableT = None#

class RegisteredFrontEndInfo#

Bases: RegisteredInfo[aiq.data_models.front_end.FrontEndBaseConfig]

Represents a registered front end. Front ends are the entry points to the workflow and are responsible for orchestrating the workflow.

build_fn: FrontEndRegisteredCallableT = None#

class RegisteredFunctionInfo#

Bases: RegisteredInfo[aiq.data_models.function.FunctionBaseConfig]

Represents a registered function. Functions are the building blocks of the workflow with predefined inputs, outputs, and a description.

build_fn: FunctionRegisteredCallableT = None#

framework_wrappers: list[str] = None#

class RegisteredLLMProviderInfo#

Bases: RegisteredInfo[aiq.data_models.llm.LLMBaseConfig]

Represents a registered LLM provider. LLM Providers are the operators of the LLMs. i.e. NIMs, OpenAI, Anthropic, etc.

build_fn: LLMProviderRegisteredCallableT = None#

class RegisteredLLMClientInfo#

Bases: RegisteredInfo[aiq.data_models.llm.LLMBaseConfig]

Represents a registered LLM client. LLM Clients are the clients that interact with the LLM providers and are specific to a particular LLM framework.

llm_framework: str#

build_fn: LLMClientRegisteredCallableT = None#

class RegisteredEmbedderProviderInfo#

Bases: RegisteredInfo[aiq.data_models.embedder.EmbedderBaseConfig]

Represents a registered Embedder provider. Embedder Providers are the operators of the Embedder models. i.e. NIMs, OpenAI, Anthropic, etc.

build_fn: EmbedderProviderRegisteredCallableT = None#

class RegisteredEmbedderClientInfo#

Bases: RegisteredInfo[aiq.data_models.embedder.EmbedderBaseConfig]

Represents a registered Embedder client. Embedder Clients are the clients that interact with the Embedder providers and are specific to a particular LLM framework.

llm_framework: str#

build_fn: EmbedderClientRegisteredCallableT = None#

class RegisteredEvaluatorInfo#

Bases: RegisteredInfo[aiq.data_models.evaluator.EvaluatorBaseConfig]

Represents a registered Evaluator e.g. RagEvaluator, TrajectoryEvaluator, etc.

build_fn: EvaluatorRegisteredCallableT = None#

class RegisteredMemoryInfo#

Bases: RegisteredInfo[aiq.data_models.memory.MemoryBaseConfig]

Represents a registered Memory object which adheres to the memory interface.

build_fn: MemoryRegisteredCallableT = None#

class RegisteredToolWrapper(/, **data: Any)#

Bases: pydantic.BaseModel

Represents a registered tool wrapper. Tool wrappers are used to wrap the functions in a particular LLM framework. They do not have their own configuration, but they are used to wrap the functions in a particular LLM framework.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

llm_framework: str#

build_fn: ToolWrapperBuildCallableT = None#

discovery_metadata: aiq.data_models.discovery_metadata.DiscoveryMetadata#

class RegisteredRetrieverProviderInfo#

Bases: RegisteredInfo[aiq.data_models.retriever.RetrieverBaseConfig]

Represents a registered Retriever object which adheres to the retriever interface.

build_fn: RetrieverProviderRegisteredCallableT = None#

class RegisteredRetrieverClientInfo#

Bases: RegisteredInfo[aiq.data_models.retriever.RetrieverBaseConfig]

Represents a registered Retriever Client. Retriever Clients are the LLM Framework-specific clients that expose an interface to the Retriever object.

llm_framework: str | None#

build_fn: RetrieverClientRegisteredCallableT = None#

class RegisteredRegistryHandlerInfo#

Bases: RegisteredInfo[aiq.data_models.registry_handler.RegistryHandlerBaseConfig]

Represents a registered LLM client. LLM Clients are the clients that interact with the LLM providers and are specific to a particular LLM framework.

build_fn: RegistryHandlerRegisteredCallableT = None#

class RegisteredPackage(/, **data: Any)#

Bases: pydantic.BaseModel

package_name: str#

discovery_metadata: aiq.data_models.discovery_metadata.DiscoveryMetadata#

class TypeRegistry#

_registered_telemetry_exporters: dict[type[aiq.data_models.telemetry_exporter.TelemetryExporterBaseConfig], RegisteredTelemetryExporter]#

_registered_logging_methods: dict[type[aiq.data_models.logging.LoggingBaseConfig], RegisteredLoggingMethod]#

_registered_front_end_infos: dict[type[aiq.data_models.front_end.FrontEndBaseConfig], RegisteredFrontEndInfo]#

_registered_functions: dict[type[aiq.data_models.function.FunctionBaseConfig], RegisteredFunctionInfo]#

_registered_llm_provider_infos: dict[type[aiq.data_models.llm.LLMBaseConfig], RegisteredLLMProviderInfo]#

_llm_client_provider_to_framework: dict[type[aiq.data_models.llm.LLMBaseConfig], dict[str, RegisteredLLMClientInfo]]#

_llm_client_framework_to_provider: dict[str, dict[type[aiq.data_models.llm.LLMBaseConfig], RegisteredLLMClientInfo]]#

_registered_embedder_provider_infos: dict[type[aiq.data_models.embedder.EmbedderBaseConfig], RegisteredEmbedderProviderInfo]#

_embedder_client_provider_to_framework: dict[type[aiq.data_models.embedder.EmbedderBaseConfig], dict[str, RegisteredEmbedderClientInfo]]#

_embedder_client_framework_to_provider: dict[str, dict[type[aiq.data_models.embedder.EmbedderBaseConfig], RegisteredEmbedderClientInfo]]#

_registered_evaluator_infos: dict[type[aiq.data_models.evaluator.EvaluatorBaseConfig], RegisteredEvaluatorInfo]#

_registered_memory_infos: dict[type[aiq.data_models.memory.MemoryBaseConfig], RegisteredMemoryInfo]#

_registered_retriever_provider_infos: dict[type[aiq.data_models.retriever.RetrieverBaseConfig], RegisteredRetrieverProviderInfo]#

_retriever_client_provider_to_framework: dict[type[aiq.data_models.retriever.RetrieverBaseConfig], dict[str | None, RegisteredRetrieverClientInfo]]#

_retriever_client_framework_to_provider: dict[str | None, dict[type[aiq.data_models.retriever.RetrieverBaseConfig], RegisteredRetrieverClientInfo]]#

_registered_registry_handler_infos: dict[type[aiq.data_models.registry_handler.RegistryHandlerBaseConfig], RegisteredRegistryHandlerInfo]#

_registered_tool_wrappers: dict[str, RegisteredToolWrapper]#

_registered_packages: dict[str, RegisteredPackage]#

_registration_changed_hooks: list[collections.abc.Callable[[], None]] = []#

_registration_changed_hooks_active: bool = True#

_registered_channel_map#

_registration_changed()#

add_registration_changed_hook( cb: collections.abc.Callable[[], Any], ) → None#

pause_registration_changed_hooks()#

register_telemetry_exporter(registration: RegisteredTelemetryExporter)#

get_telemetry_exporter( config_type: type[aiq.data_models.telemetry_exporter.TelemetryExporterBaseConfig], ) → RegisteredTelemetryExporter#

get_registered_telemetry_exporters() → list[RegisteredInfo[aiq.data_models.telemetry_exporter.TelemetryExporterBaseConfig]]#

register_logging_method(registration: RegisteredLoggingMethod)#

get_logging_method( config_type: type[aiq.data_models.logging.LoggingBaseConfig], ) → RegisteredLoggingMethod#

get_registered_logging_method() → list[RegisteredInfo[aiq.data_models.logging.LoggingBaseConfig]]#

register_front_end(registration: RegisteredFrontEndInfo)#

get_front_end( config_type: type[aiq.data_models.front_end.FrontEndBaseConfig], ) → RegisteredFrontEndInfo#

get_registered_front_ends() → list[RegisteredInfo[aiq.data_models.front_end.FrontEndBaseConfig]]#

register_function(registration: RegisteredFunctionInfo)#

get_function( config_type: type[aiq.data_models.function.FunctionBaseConfig], ) → RegisteredFunctionInfo#

get_registered_functions() → list[RegisteredInfo[aiq.data_models.function.FunctionBaseConfig]]#

register_llm_provider(info: RegisteredLLMProviderInfo)#

get_llm_provider( config_type: type[aiq.data_models.llm.LLMBaseConfig], ) → RegisteredLLMProviderInfo#

get_registered_llm_providers() → list[RegisteredInfo[aiq.data_models.llm.LLMBaseConfig]]#

register_llm_client(info: RegisteredLLMClientInfo)#

get_llm_client( config_type: type[aiq.data_models.llm.LLMBaseConfig], wrapper_type: str, ) → RegisteredLLMClientInfo#

register_embedder_provider(info: RegisteredEmbedderProviderInfo)#

get_embedder_provider( config_type: type[aiq.data_models.embedder.EmbedderBaseConfig], ) → RegisteredEmbedderProviderInfo#

get_registered_embedder_providers() → list[RegisteredInfo[aiq.data_models.embedder.EmbedderBaseConfig]]#

register_embedder_client(info: RegisteredEmbedderClientInfo)#

get_embedder_client( config_type: type[aiq.data_models.embedder.EmbedderBaseConfig], wrapper_type: str, ) → RegisteredEmbedderClientInfo#

register_evaluator(info: RegisteredEvaluatorInfo)#

get_evaluator( config_type: type[aiq.data_models.evaluator.EvaluatorBaseConfig], ) → RegisteredEvaluatorInfo#

get_registered_evaluators() → list[RegisteredInfo[aiq.data_models.evaluator.EvaluatorBaseConfig]]#

register_memory(info: RegisteredMemoryInfo)#

get_memory( config_type: type[aiq.data_models.memory.MemoryBaseConfig], ) → RegisteredMemoryInfo#

get_registered_memorys() → list[RegisteredInfo[aiq.data_models.memory.MemoryBaseConfig]]#

register_retriever_provider(info: RegisteredRetrieverProviderInfo)#

get_retriever_provider( config_type: type[aiq.data_models.retriever.RetrieverBaseConfig], ) → RegisteredRetrieverProviderInfo#

get_registered_retriever_providers() → list[RegisteredInfo[aiq.data_models.retriever.RetrieverBaseConfig]]#

register_retriever_client(info: RegisteredRetrieverClientInfo)#

get_retriever_client( config_type: type[aiq.data_models.retriever.RetrieverBaseConfig], wrapper_type: str | None, ) → RegisteredRetrieverClientInfo#

register_tool_wrapper(registration: RegisteredToolWrapper)#

get_tool_wrapper(llm_framework: str) → RegisteredToolWrapper#

register_registry_handler(info: RegisteredRegistryHandlerInfo)#

get_registry_handler( config_type: type[aiq.data_models.registry_handler.RegistryHandlerBaseConfig], ) → RegisteredRegistryHandlerInfo#

get_registered_registry_handlers() → list[RegisteredInfo[aiq.data_models.registry_handler.RegistryHandlerBaseConfig]]#

register_package( package_name: str, package_version: str | None = None, )#

get_infos_by_type( component_type: aiq.data_models.component.AIQComponentEnum, ) → dict#

get_registered_types_by_component_type( component_type: aiq.data_models.component.AIQComponentEnum, ) → list[str]#

get_registered_channel_info_by_channel_type( channel_type: str, ) → RegisteredRegistryHandlerInfo#

_do_compute_annotation( cls: type[aiq.data_models.common.TypedBaseModelT], registrations: list[RegisteredInfo[aiq.data_models.common.TypedBaseModelT]], )#

compute_annotation(cls: type[aiq.data_models.common.TypedBaseModelT])#

class GlobalTypeRegistry#

_global_registry: TypeRegistry#

static get() → TypeRegistry#

static push()#