aix.ai_models.manager

Unified model management interface.

Provides a high-level API for managing AI models across providers.

Functions

get_manager([storage_path])

Get a pre-configured model manager.

Classes

ModelManager(*[, storage_path])

Unified interface for AI model management.

class aix.ai_models.manager.ModelManager(*, storage_path=None)

Bases: object

Unified interface for AI model management.

Combines model registry, sources, and connectors into a single facade.

>>> manager = ModelManager()
>>> manager.register_connector(OpenAIConnector())
>>> len(manager.connectors)
1

add_model_tag(model_id, *tags)

Add tags to a model.

Return type:

None

>>> manager = ModelManager()
>>> manager.models["gpt-4"] = Model(id="gpt-4", provider="openai")
>>> manager.add_model_tag("gpt-4", "fast", "expensive")
>>> "fast" in manager.models["gpt-4"].tags
True

property connectors: ConnectorRegistry

Access to connector registry.

create_model_group(group_name, model_ids)

Create a named group of models using tags.

Return type:

None

>>> manager = ModelManager()
>>> manager.models["gpt-4"] = Model(id="gpt-4", provider="openai")
>>> manager.models["claude-3"] = Model(id="claude-3", provider="anthropic")
>>> manager.create_model_group("premium", ["gpt-4", "claude-3"])
>>> premium = manager.list_models(tags=["group:premium"])
>>> len(premium)
2

discover_all(*, auto_register=True)

Discover models from all registered sources.

Return type:

dict[str, list[Model]]

Returns:

Dict mapping source name to list of discovered models

discover_from_source(source_name, *, auto_register=True, verbose=True)

Discover models from a registered source.

Parameters:

• source_name (str) – Name of registered source

• auto_register (bool) – If True, automatically add discovered models to registry

Return type:

list[Model]

Returns:

List of discovered models

get_connector_metadata(model_id, connector_name)

Get formatted metadata for a specific connector.

Parameters:

• model_id (str) – ID of the model

• connector_name (str) – Name of the connector to format for

Return type:

dict[str, Any]

Returns:

Dict with metadata formatted for the specified connector

>>> manager = ModelManager()
>>> manager.models["gpt-4"] = Model(id="gpt-4", provider="openai")
>>> manager.register_connector(OpenAIConnector())
>>> meta = manager.get_connector_metadata("gpt-4", "openai")
>>> meta["model"]
'gpt-4'

get_model(model_id)

Get a specific model by ID.

Return type:

Model

>>> manager = ModelManager()
>>> manager.models["gpt-4"] = Model(id="gpt-4", provider="openai")
>>> model = manager.get_model("gpt-4")
>>> model.id
'gpt-4'

list_model_groups()

List all model groups.

Returns:

‘ prefix)

Return type:

set[str]

list_models(*, provider=None, is_local=None, min_context_size=None, max_context_size=None, has_capabilities=None, tags=None, custom_filter=None)

List models with optional filtering.

Return type:

list[Model]

>>> manager = ModelManager()
>>> manager.models["gpt-4"] = Model(id="gpt-4", provider="openai")
>>> manager.models["llama2"] = Model(id="llama2", provider="ollama", is_local=True)
>>> local = manager.list_models(is_local=True)
>>> len(local)
1
>>> local[0].id
'llama2'

list_sources()

List registered source names.

Return type:

list[str]

property models: ModelRegistry

Access to model registry for direct manipulation.

>>> manager = ModelManager()
>>> manager.models["test"] = Model(id="test", provider="test")
>>> "test" in manager.models
True

register_connector(connector)

Register a model connector.

Return type:

None

>>> manager = ModelManager()
>>> connector = OpenAIConnector()
>>> manager.register_connector(connector)
>>> connector.name in manager.connectors
True

register_source(name, source)

Register a model discovery source.

Return type:

None

>>> manager = ModelManager()
>>> source = OpenRouterSource()
>>> manager.register_source("openrouter", source)
>>> "openrouter" in manager.list_sources()
True

set_custom_metadata(model_id, key, value)

Set custom metadata on a model.

Return type:

None

>>> manager = ModelManager()
>>> manager.models["gpt-4"] = Model(id="gpt-4", provider="openai")
>>> manager.set_custom_metadata("gpt-4", "my_note", "Use for production")
>>> manager.models["gpt-4"].custom_metadata["my_note"]
'Use for production'

aix.ai_models.manager.get_manager(storage_path=None)

Get a pre-configured model manager.

Return type:

ModelManager

>>> manager = get_manager()
>>> len(manager.connectors) > 0
True