Initialize Google ADK Agent for Inference Routing

Your first task is to set up a Google ADK agent that serves as the primary inference router. Define its initial capabilities and how it will accept incoming inference requests. Integrate a DeepSeek R1 client as a tool the agent can call, and define a simulated 'Trainium' tool for cost-effective inference. The agent should be able to decide which tool (model) to use. ```python
import google.generativeai as genai
from google.generativeai import GenerativeModel
from google.generativeai.types import ChatResponse
from typing import List, Dict, Union # Configure DeepSeek R1 client (placeholder - replace with actual API calls)
class DeepSeekR1Client: def infer(self, prompt: str) -> str: # Simulate DeepSeek R1 API call return f"DeepSeek R1 response for: {prompt}" # Simulated Trainium tool
@genai.tool
def simulated_trainium_infer(prompt: str) -> str: """Performs cost-effective, lower-latency inference for simple tasks.""" return f"Simulated Trainium response for: {prompt}" # Configure the Google ADK model (using a powerful LLM for routing decisions)
# genai.configure(api_key="YOUR_GEMINI_API_KEY")
# routing_model = GenerativeModel('gemini-3-flash') # Or other powerful model # Define the agent function
def inference_router_agent_fn(prompt: str) -> Union[str, genai.tool]: # Agent logic to decide between DeepSeek R1 or simulated_trainium_infer # This should return a tool call or a direct response. if len(prompt.split()) < 10 and "translate" in prompt.lower(): return simulated_trainium_infer.function(prompt) else: # Directly call DeepSeekR1Client or wrap it as a tool for ADK # For this prompt, assume it's integrated as a callable by the agent's logic return DeepSeekR1Client().infer(prompt) # This function would be part of the ADK orchestration
# response = routing_model.generate_content(inference_router_agent_fn("Your prompt"))
```