Skip to content

🎯 Auto-Suggestion Engine

Intelligent Model Recommendations

Find the optimal model for your use case

🤖 How It Works

The Auto-Suggestion Engine analyzes:

  1. Required Capabilities: Match capabilities to models
  2. Cost Constraints: Stay within budget
  3. Performance Metrics: Historical accuracy
  4. Domain Fit: Specialized models for domains

💻 Usage

from llm_evaluation_framework.auto_suggestion_engine import AutoSuggestionEngine

engine = AutoSuggestionEngine(registry)

requirements = {
    "domain": "legal",
    "required_capabilities": ["reasoning", "accuracy"],
    "max_cost_per_call": 0.05,
    "min_accuracy": 0.90
}

suggestions = engine.suggest_models(requirements)

for suggestion in suggestions:
    print(f"Model: {suggestion['model_name']}")
    print(f"Match Score: {suggestion['match_score']:.2%}")

🎯 Scoring Algorithm

Match score = (capability_match × 0.4) + (cost_score × 0.3) + (performance × 0.3)

View Algorithm Details