Lab 4.5 Challenge: Professional Model Configuration

Goal

In this lab, you will upgrade your "Support Analyzer" agent to a production-ready state. You will learn how to implement advanced retry logic using a custom Gemini subclass and how to provide a multi-model fallback using LiteLlm.

Prerequisites

1. Install LiteLLM:

   pip install litellm

Lab Tasks

1. Create a Resilient Subclass:

   • In your support_analyzer/agent.py, create a class named ResilientGemini that inherits from Gemini.
   • Override the api_client property.
   • Configure it with a HttpRetryOptions policy: max_delay=10, exp_base=2.0, and jitter=0.5.

2. Implement Multi-Model Fallback:

   • Update your agent's model logic.
   • If the environment variable USE_LOCAL_MODEL is set to "1", use LiteLlm with ollama_chat/mistral.
   • Otherwise, use your new ResilientGemini class.

3. Verify the Configuration:

   • Run the agent using adk run support_analyzer.
   • Verify it works as expected. (Note: You won't "see" the retries unless a network error occurs, but your code is now protected!).

Python Approach (Primary)

Modify agent.py to use the advanced configuration patterns.

import os
from functools import cached_property
from google.adk.agents import LlmAgent
from google.adk.models import Gemini
from google.adk.models.lite_llm import LiteLlm
from google.genai import Client, types

# TODO: Step 1 - Define the ResilientGemini subclass
class ResilientGemini(Gemini):
    @cached_property
    def api_client(self) -> Client:
        # TODO: Implement Client with HttpRetryOptions
        pass

# TODO: Step 2 - Implement the model selection logic
if os.getenv("USE_LOCAL_MODEL") == "1":
    # Use LiteLLM
    model_to_use = ...
else:
    # Use your ResilientGemini
    model_to_use = ...

root_agent = LlmAgent(
    name="support_analyzer_agent",
    model=model_to_use,
    instruction="Analyze customer support issues."
)

Self-Reflection Questions

• Why is "Jitter" important in a retry policy for a high-traffic production application?
• What are the advantages of centralizing model configuration in a subclass instead of passing parameters to every agent instance?
• In which scenario would you prefer using the native Gemini class over the LiteLlm abstraction?

---

🕵️ Hidden Solution 🕵️

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMDQuNS1tdWx0aS1tb2RlbC1saXRlbGxtL2xhYi1zb2x1dGlvbg==

The direct link is: Lab Solution