from memento import PromptLearner, FeedbackCollector, PromptProcessor

# Initialize components
learner = PromptLearner(model="llama3.2")
collector = FeedbackCollector(model="llama3.2")
processor = PromptProcessor(model="llama3.2")

# Define a problem
problem = {
    "description": "Write a function to find the maximum element in a list",
    "solution": "def find_max(lst): return max(lst) if lst else None"
}

# Run learning cycle
result = learner.evaluate_prompt_performance(
    prompt="You are a Python expert.",
    problem=problem,
    evaluation_criteria=["correctness", "efficiency", "readability"]
)

print(f"Evaluation: {result['evaluation']}")

from memento import PromptLearner
from memento.config import get_settings

# Get settings
settings = get_settings()

# Initialize learner
learner = PromptLearner(
    model=settings.model.model_name,
    storage_path=str(settings.storage.evolution_path)
)

# Run learning cycle
evolution_result = learner.evaluate_prompt_performance(
    prompt="You are an expert programmer.",
    problem={
        "description": "Implement a binary search algorithm",
        "solution": "def binary_search(arr, target): ..."
    },
    evaluation_criteria=["correctness", "efficiency", "readability"]
)

from memento import PromptLearner, FeedbackCollector, PromptProcessor
from memento.utils.logger import setup_logger

# Setup logging
logger = setup_logger("experiment", level="DEBUG")

# Initialize all components
learner = PromptLearner(model="llama3.2")
collector = FeedbackCollector(model="llama3.2")
processor = PromptProcessor(model="llama3.2")

# Run multi-iteration learning
for iteration in range(10):
    logger.info(f"Starting iteration {iteration + 1}")
    
    # Collect feedback
    feedback = collector.collect_solution_feedback(
        problem="Write a sorting algorithm",
        solution="def sort(arr): return sorted(arr)",
        evaluation_criteria=["correctness", "efficiency", "readability"]
    )
    
    # Process feedback and extract insights
    insights = processor.process_feedback()
    
    # Evolve prompt
    updated_prompt = learner.evolve_prompt(
        current_prompt="You are a programmer.",
        lessons=insights
    )
    
    logger.info(f"Iteration {iteration + 1} completed")

from memento.config import ModelConfig, EvaluationConfig, LearningConfig

# Model configuration
model_config = ModelConfig(
    model_type="ollama",
    model_name="llama3.2",
    temperature=0.7,
    max_tokens=2048
)

# Evaluation configuration
eval_config = EvaluationConfig(
    criteria=["correctness", "efficiency", "readability"],
    backend="llm",
    batch_size=10
)

# Learning configuration
learning_config = LearningConfig(
    max_iterations=50,
    convergence_threshold=0.01,
    min_confidence=0.7
)