Implementing Continuous Learning and Adaptation

Design and implement a continuous learning mechanism. This could involve dynamically updating the LLM's internal context with new data patterns, refining the DSPy prompts based on recent forecast errors, or triggering a re-evaluation of semantic abstractions when significant data shifts occur. Demonstrate this adaptation by introducing a simulated 'concept drift' (e.g., a sudden, sustained change in the time series trend) in the `adaptation_data` and showing how the system adjusts its forecasts. Use Weights & Biases to log the model's performance before and after adaptation.