Optimize Routing with Ray Tune and Integrate Ellipsis

Set up an experimentation pipeline using Ray Tune to optimize the parameters for your inference routing agent (e.g., thresholds for switching between DeepSeek R1 and simulated Trainium based on prompt length, complexity, or user priority). Concurrently, integrate Ellipsis as a monitoring and control interface, allowing real-time adjustments to routing policies and viewing experiment results. Provide code snippets for defining a Ray Tune experiment and for Ellipsis interaction. ```python
import ray
from ray import tune # Placeholder for your inference routing function that Ray Tune will optimize
def train_router_policy(config): # Simulate routing decisions and measure metrics latency_penalty = config["latency_weight"] * 0.1 cost_penalty = config["cost_weight"] * 0.01 # In a real scenario, this would call your ADK agent and run inferences # Return a metric for Ray Tune to optimize, e.g., 'combined_score' return {"combined_score": -(latency_penalty + cost_penalty)} # Configure Ray Tune experiment
# tune.run(
# train_router_policy,
# config={
# "latency_weight": tune.uniform(0.1, 1.0),
# "cost_weight": tune.uniform(0.1, 1.0),
# },
# num_samples=10,
# ) # Ellipsis integration (conceptual - assumes Ellipsis API or SDK for messaging)
class EllipsisMonitor: def send_alert(self, message: str): print(f"Ellipsis Alert: {message}") def get_user_command(self) -> str: # Simulate getting command from Ellipsis interface return "" # Example usage in the agent system
# monitor = EllipsisMonitor()
# monitor.send_alert("New optimal routing policy identified by Ray Tune!")
```