src/scripts/benchmark_long_context.py

import os
import json
import argparse
from backend.animetix.services import AnimetixService
from core.domain.services.long_context_service import LongContextDiscoveryService

def run_evaluation(max_size=32000):
    """
    Script d'évaluation de la mémoire longue (Needle In A Haystack).
    """
    print("🚀 Starting Long-Context Memory Evaluation (Animetix RULER-lite)...")
    
    animetix = AnimetixService()
    long_ctx_service = LongContextDiscoveryService(inference_engine=animetix.inference_adapter)
    
    sizes = [2000, 8000, 16000, 32000]
    # Filtrer les tailles qui dépassent la limite du modèle actuel
    sizes = [s for s in sizes if s <= max_size]
    
    results = long_ctx_service.benchmark_model_limits(sizes=sizes)
    
    # Calcul des stats
    total = len(results)
    successes = sum(1 for r in results if r['success'])
    avg_latency = sum(r['latency_sec'] for r in results) / total
    
    print("\n" + "="*40)
    print(f"📊 FINAL RESULTS (Max Context: {max_size})")
    print(f"✅ Accuracy: {(successes/total)*100:.2f}% ({successes}/{total})")
    print(f"⏱️  Avg Latency: {avg_latency:.2f}s")
    print("="*40)
    
    # Save to data/mlops
    output_path = "data/mlops/long_context_benchmark.json"
    with open(output_path, 'w', encoding='utf-8') as f:
        json.dump(results, f, indent=2)
    print(f"💾 Report saved to {output_path}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--max-size", type=int, default=32000)
    args = parser.parse_args()
    
    run_evaluation(max_size=args.max_size)