🤖 Models Controller Guide#
Dynamic model discovery. Because one size doesn't fit all compliance needs.
The Models Controller lets you discover available AI models and their capabilities. Build adaptive applications that choose the right model for the right task, automatically.🎯 When to Use This Controller#
Dynamic model selection based on capabilities
Cost optimization by choosing appropriate models
Feature discovery for new models
Building model-agnostic applications
Runtime capability detection
🚀 Endpoints#
GET /models#
List all available models with their capabilities.GET /models/{id}#
Get detailed information about a specific model.📋 Response Schema#
Models List Response#
🎨 Real-World Examples#
Dynamic Model Selection#
Model Feature Detection#
UI Feature Detection#
📚 SDK Examples#
Python#
JavaScript#
💡 Best Practices#
1.
Cache model information: Model capabilities don't change frequently, so cache model information to reduce API calls.
🏆 Additional Tips#
Model Selection Strategy#
Create a utility function to choose the right model for each task:Error Handling#
Always implement proper error handling when making API requests:🎁 Pro Tips#
1.
Always check capabilities: Not all models support all features
2.
Plan for deprecation: Models evolve, have migration strategies
3.
Consider multi-model approaches: Use different models for different parts of your pipeline
4.
Monitor pricing changes: Model costs can change over time
5.
Test compliance regularly: Certifications can be added/removed
Model Selection Checklist:Need help choosing the right model? Email us Modificado em 2025-07-16 20:22:53
