Deploy the assistant on the Google N4A node pool

Add the assistant to the N4A overlay

If you opened a new terminal, return to the source tree and restore the required variables:

    

        
        
source "${HOME}/.storefront-axion-env"
cd "${REPO}"

export FRONTEND_IP="$(kubectl get service frontend-external \
  -o jsonpath='{.status.loadBalancer.ingress[0].ip}')"
export APP_URL="http://${FRONTEND_IP}"

    

The storefront-n4a overlay already places every deployment on N4A. Extend it with the assistant component and the image you pushed. The existing node-selector.yaml patch then applies the same placement to the assistant deployment:

    

        
        
cat <<EOF > kustomize/overlays/storefront-n4a/kustomization.yaml
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
resources:
- ../../base
components:
- ../../components/shopping-assistant
images:
- name: shoppingassistantservice
  newName: ${ASSISTANT_IMAGE_REPO}
  newTag: ${ASSISTANT_IMAGE_TAG}
patches:
- path: node-selector.yaml
  target:
    kind: Deployment
EOF

    

Render the overlay

Review the overlay files and render the manifest:

    

        
        
sed -n '1,120p' kustomize/overlays/storefront-n4a/kustomization.yaml
sed -n '1,120p' kustomize/overlays/storefront-n4a/node-selector.yaml
kubectl kustomize kustomize/overlays/storefront-n4a | sed -n '1,240p'

    

Don’t apply the overlay if the rendered manifest has an empty image name, image tag, or node-pool value.

Apply the overlay

Apply the overlay and wait for the frontend and assistant deployments:

    

        
        
kubectl apply -k kustomize/overlays/storefront-n4a
kubectl rollout status deployment/frontend --timeout=600s
kubectl rollout status deployment/shoppingassistantservice --timeout=1200s

    

Confirm pod placement

Check where the frontend and assistant pods landed:

    

        
        
kubectl get pods -o wide | grep -E 'frontend|shoppingassistantservice'

ASSISTANT_POD="$(kubectl get pod -l app=shoppingassistantservice \
  -o jsonpath='{.items[0].metadata.name}' 2>/dev/null || true)"

echo "${ASSISTANT_POD}"
if [ -n "${ASSISTANT_POD}" ]; then
  kubectl describe pod "${ASSISTANT_POD}" | \
    grep -E 'Node:|Image:|Pulling|Pulled|Ready:' || true
fi

    

The frontend and shoppingassistantservice pods should both be on the N4A node pool.

Check the assistant logs

Inspect both containers in the assistant pod:

    

        
        
kubectl logs deployment/shoppingassistantservice -c server --tail=20
kubectl logs deployment/shoppingassistantservice -c ollama --tail=20

    

The server container serves the assistant API. The ollama container prepares the local model runtime.

Send a request through one assistant session

Open the assistant endpoint and save its HTTP session cookie in /tmp/assistant.cookies. The next request sends the same cookie back, just as a browser would. The assistant can then associate follow-up prompts and cart actions with the same short-lived session.

Send one assistant request through the storefront:

    

        
        
curl --max-time 30 -s -c /tmp/assistant.cookies "${APP_URL}/assistant" >/dev/null

for i in {1..12}; do
  if curl --max-time 120 -s \
    -b /tmp/assistant.cookies \
    -c /tmp/assistant.cookies \
    -H 'Content-Type: application/json' \
    -d '{"message":"Find a mug for a minimalist desk setup."}' \
    "${APP_URL}/bot" | tee /tmp/assistant-response.json | jq .; then
    break
  fi
  sleep 10
done

    

The JSON response includes a non-empty assistant message. The exact product names can vary, but the response is grounded in the live product catalog.

Try the browser flow

Open the storefront URL in your browser. Refresh the page if it was already open before the assistant-enabled frontend rolled out.

Try these prompts in the assistant panel:

    

        
        
Find one desk accessory that looks clean and minimalist for a home office.
Add that item to my cart.
yes
What is in my cart now?

    

The assistant recommends a catalog item and asks for confirmation before changing the cart. It then shows the cart’s contents.

What you’ve accomplished and what’s next

You’ve now deployed the assistant on N4A and validated it through the same storefront path that a shopper uses.

Next, you’ll observe the assistant as a live workload and capture the N4A benchmark baseline.