Callbacks

from gepa.core.callbacks import GEPACallback

class MyCallback:
    def on_optimization_start(self, event: OptimizationStartEvent) -> None:
        print(f"Starting optimization with {event['trainset_size']} training examples")

    def on_iteration_end(self, event: IterationEndEvent) -> None:
        status = 'accepted' if event['proposal_accepted'] else 'rejected'
        print(f"Iteration {event['iteration']}: {status}")

from gepa import optimize

result = optimize(
    seed_candidate={"instructions": "..."},
    trainset=data,
    callbacks=[MyCallback()],
    # ... other parameters
)

from gepa.core.callbacks import CompositeCallback

composite = CompositeCallback([callback1, callback2, callback3])

result = optimize(
    seed_candidate={"instructions": "..."},
    trainset=data,
    callbacks=[composite],
)

composite = CompositeCallback()
composite.add(my_callback)
composite.add(another_callback)

from gepa.core.callbacks import (
    OptimizationStartEvent,
    IterationEndEvent,
    OptimizationEndEvent,
)

class ProgressCallback:
    def __init__(self):
        self.best_score = float('-inf')
        self.iterations = 0
    
    def on_optimization_start(self, event: OptimizationStartEvent) -> None:
        print(f"Starting optimization with:")
        print(f"  Training examples: {event['trainset_size']}")
        print(f"  Validation examples: {event['valset_size']}")
    
    def on_iteration_end(self, event: IterationEndEvent) -> None:
        self.iterations += 1
        if event['proposal_accepted']:
            print(f"✓ Iteration {event['iteration']}: New candidate accepted")
        else:
            print(f"✗ Iteration {event['iteration']}: Candidate rejected")
    
    def on_optimization_end(self, event: OptimizationEndEvent) -> None:
        print(f"\nOptimization complete after {event['total_iterations']} iterations")
        print(f"Best candidate: {event['best_candidate_idx']}")
        print(f"Total evaluations: {event['total_metric_calls']}")