# class LearningFramework:
#     def __init__(self, learning_model: Any, learning_service: Any):
#         self.learning_model = learning_model
#         self.learning_service = learning_service

#     async def learn_from_interaction(self, interaction_data: Dict[str, Any]) -> None:
#         """
#         Update the learning model based on interaction data.
#         """
#         # Update the embedded model
#         self.learning_model.update(interaction_data)

#         # Optionally send data to an external learning provider
#         await self.learning_service.send_data_for_learning(interaction_data)

#     async def adapt_response_strategy(self, context: Dict[str, Any]) -> Dict[str, Any]:
#         """
#         Adapt the assistant_response strategy based on learned patterns and context.
#         """
#         # Use the learning model to determine the adaptation strategy
#         adaptation_strategy = self.learning_model.determine_strategy(context)
#         return adaptation_strategy


# class LearningAgent(TaskAgent):
#     def __init__(
#         self, name: Optional[str] = None, learning_model: Optional[Any] = None
#     ):
#         super().__init__(name)
#         self.learning_model = learning_model

#     async def learn_from_interaction(self, interaction_data: Any):
#         """
#         Update the learning model based on interaction data.
#         This method should be implemented to integrate specific learning algorithms.
#         """
#         if self.learning_model:
#             # Placeholder for learning logic. Implement learning model updates based on interaction data.
#             print("Learning from interaction...")

#     async def adapt_response_strategy(self, instruction: Any) -> Any:
#         """
#         Adapt the assistant_response strategy based on what has been learned.
#         Override this method to implement adaptive assistant_response strategies.
#         """
#         # Example implementation. Use the learning model to adapt the assistant_response strategy.
#         print("Adapting assistant_response strategy based on learned patterns...")
#         # Placeholder for demonstration. Implement adaptive assistant_response generation based on the learning model.
#         return "Adapted assistant_response based on learning."

#     async def process_instruction(self, instruction: Any) -> Any:
#         """
#         Process instruction with learning and adaptation.
#         """
#         # Incorporate learning into the instruction processing.
#         response = await super().process_instruction(instruction)
#         await self.learn_from_interaction(instruction)
#         adapted_response = await self.adapt_response_strategy(instruction)
#         return adapted_response
