aix.ai_models.sources

Concrete implementations of model sources and connectors.

This module provides ready-to-use sources for discovering models and connectors for formatting metadata for different clients.

Classes

DSPyConnector()	Format metadata for DSPy framework.
LangChainConnector()	Connector for LangChain library.
OllamaConnector()	Format metadata for direct Ollama API calls.
OllamaSource(*[, base_url, timeout])	Discover locally installed Ollama models.
OpenAIConnector()	Format metadata for OpenAI Python client.
OpenRouterConnector()	Connector for OpenRouter API.
OpenRouterSource(*[, timeout])	Discover models from OpenRouter's public API.
ProviderAPISource(provider_name, *[, ...])	Discover models from a provider's API (OpenAI, Anthropic, etc.).

class aix.ai_models.sources.DSPyConnector

Bases: Connector

Format metadata for DSPy framework.

format_metadata(model)

Format for DSPy.

Return type:

dict[str, Any]

>>> connector = DSPyConnector()
>>> model = Model(id="gpt-4", provider="openai")
>>> metadata = connector.format_metadata(model)
>>> "model" in metadata
True

property name: str

Unique identifier for this connector.

class aix.ai_models.sources.LangChainConnector

Bases: Connector

Connector for LangChain library.

format_metadata(model)

Format for LangChain.

Return type:

dict[str, Any]

>>> connector = LangChainConnector()
>>> model = Model(id="gpt-4", provider="openai")
>>> metadata = connector.format_metadata(model)
>>> metadata["model_name"]
'gpt-4'

property name: str

Unique identifier for this connector.

class aix.ai_models.sources.OllamaConnector

Bases: Connector

Format metadata for direct Ollama API calls.

format_metadata(model)

Format for Ollama API.

Return type:

dict[str, Any]

>>> connector = OllamaConnector()
>>> model = Model(id="ollama/llama2", provider="ollama", is_local=True)
>>> model.connector_metadata["ollama"] = {"name": "llama2"}
>>> metadata = connector.format_metadata(model)
>>> metadata["model"]
'llama2'

property name: str

Unique identifier for this connector.

class aix.ai_models.sources.OllamaSource(*, base_url='http://localhost:11434', timeout=10)

Bases: ModelSource

Discover locally installed Ollama models.

Queries the Ollama API for models available on the local machine.

discover_models()

Fetch locally installed Ollama models.

Return type:

Iterable[Model]

>>> source = OllamaSource()
>>> models = list(source.discover_models())  # May be empty if Ollama not running
>>> all(m.is_local for m in models)
True

class aix.ai_models.sources.OpenAIConnector

Bases: Connector

Format metadata for OpenAI Python client.

Provides model metadata in format expected by openai.ChatCompletion.create().

format_metadata(model)

Format model metadata for this connector.

Returns a dict that can be used to instantiate/connect via this connector.

Return type:

dict[str, Any]

property name: str

Unique identifier for this connector.

class aix.ai_models.sources.OpenRouterConnector

Bases: Connector

Connector for OpenRouter API.

format_metadata(model)

Formats metadata for OpenRouter, which uses the model ID directly.

Return type:

dict[str, Any]

property name: str

Unique identifier for this connector.

class aix.ai_models.sources.OpenRouterSource(*, timeout=30)

Bases: ModelSource

Discover models from OpenRouter’s public API.

OpenRouter maintains a comprehensive registry of models across providers.

discover_models()

Fetch models from OpenRouter API.

Return type:

Iterable[Model]

>>> source = OpenRouterSource()
>>> models = list(source.discover_models())
>>> len(models) > 0  # Should find many models
True

class aix.ai_models.sources.ProviderAPISource(provider_name, *, api_key=None, base_url='https://api.openai.com/v1', timeout=30)

Bases: ModelSource

Discover models from a provider’s API (OpenAI, Anthropic, etc.).

Generic source for providers with /v1/models endpoint.

discover_models()

Fetch models from provider API.

Requires valid API key for the provider.

Return type:

Iterable[Model]