AI provider consistency

Ensure consistent patterns and configurations across all AI model providers to maintain code maintainability and scalability. This includes providing proper default configurations for all providers and minimizing provider-specific conditional logic that can become unwieldy as more AI models are integrated.

Key practices:

• Always define default model IDs for new AI providers
• Use unified configuration patterns rather than provider-specific if/else chains
• Apply consistent token limit handling across different providers when possible
• Design provider integrations with future model additions in mind

Example of inconsistent approach to avoid:

// Avoid provider-specific logic scattered throughout
const maxTokens = apiConfiguration?.apiProvider === "gemini" 
    ? geminiModels[geminiDefaultModelId].maxTokens
    : anthropicModels["claude-3-7-sonnet-20250219"].maxTokens

// Instead, use a unified approach
case "sambanova":
    return getProviderData(sambanovaModels, sambanovaDefaultModelId) // Always provide default

This approach reduces technical debt and makes the codebase more maintainable as the number of supported AI providers grows.