Model Catalog

The model catalog API allows you to discover and understand available AI models.

Basic Usage

using OpenGithubModelsApi

# Create a client (using a placeholder token for documentation)
client = GithubModelsClient("ghp_example_token")

# Get all available models
models = list_models(client)

# Print model information
for model in models
    println("Model: $(model.name) (ID: $(model.id))")
    println("Publisher: $(model.publisher)")
    println("Summary: $(model.summary)")
    println("---")
end

Key Model Properties

• id: Unique model identifier (format: {publisher}/{model_name})
• name: Human-readable model name
• publisher: Organization or individual who published the model
• summary: Brief description of the model's capabilities
• rate_limit_tier: Rate limiting tier for the model
• tags: Categorization tags for the model
• supported_input_modalities: Input formats the model accepts
• supported_output_modalities: Output formats the model produces

Filtering Models

While the API returns all available models, you can filter them in Julia:

# Get only text models
text_models = filter(m -> "text" in m.supported_input_modalities, models)

# Get models from a specific publisher
openai_models = filter(m -> m.publisher == "openai", models)

Model Selection Guidance

When selecting a model, consider:

• The input and output modalities you need
• The model's capabilities as described in the summary
• The rate limit tier for your expected usage volume
• The publisher's reputation and support

Best Practices

• Cache model listings to reduce API calls
• Validate model IDs before using them in requests
• Handle cases where expected models might not be available
• Consider creating a model registry for your application