class AGILargeModelAdaptation:
    """
    The AGILargeModelAdaptation class implements the advanced techniques of Few-Shot learning to improve
    the adaptation capabilities of large language models. This approach enables the model to learn from
    a small number of examples (i.e., a few shots) and generalize from this knowledge to new, unseen tasks.
    """

    def __init__(self, model):
        """
        Initializes a new AGILargeModelAdaptation instance with the given language model.

        Args:
            model (object): The large language model to be adapted.
        """
        self.model = model
        self.shots = None

    def few_shot_learning(self, shots):
        """
        Applies Few-Shot learning by providing the model with a few examples to learn from.

        Args:
            shots (list): List of examples for few-shot learning.
        """
        self.shots = shots

    def adapt_to_task(self, new_task_prompt):
        """
        Adapts the model to a new task using the knowledge gained from few-shot learning.

        Args:
            new_task_prompt (str): The prompt describing the new task.

        Returns:
            str: The model's output for the new task.
        """
        adaptation_prompt = self.create_adaptation_prompt(new_task_prompt)

        # Fine-tuning the model for the new task (not shown here)
        # Assume `adapted_output` is the output generated by the model for the new task
        adapted_output = self.model(adaptation_prompt)

        return adapted_output

    def create_adaptation_prompt(self, new_task_prompt):
        """
        Creates an adaptation prompt by combining the few-shot examples with the new task prompt.

        Args:
            new_task_prompt (str): The prompt describing the new task.

        Returns:
            str: The combined adaptation prompt.
        """
        adaptation_prompt = ""

        if self.shots:
            adaptation_prompt += "\n\n".join(self.shots) + "\n\n"

        return adaptation_prompt + new_task_prompt

# Instantiate the AGILargeModelAdaptation class
large_model = None  # Assume this is the large language model that you want to adapt
agi_adaptation = AGILargeModelAdaptation(large_model)

# Specify few-shot examples
few_shot_examples = [
    "Example 1",
    "Example 2",
    "Example 3"
]
agi_adaptation.few_shot_learning(few_shot_examples)

# Adapt the model to a new task
new_task_prompt = "This is a new task"
adapted_output = agi_adaptation.adapt_to_task(new_task_prompt)

# Print the adapted output
print(adapted_output)
