How to deploy with Helm Chart#

This section shows how to deploy the Visual Search and QA Application using Helm chart.

Prerequisites#

Before you begin, ensure that you have the following:

Kubernetes* cluster set up and running.
The cluster must support dynamic provisioning of Persistent Volumes (PV). Refer to the Kubernetes Dynamic Provisioning Guide for more details.
Install kubectl on your system. See the Installation Guide. Ensure access to the Kubernetes cluster.
Helm chart installed on your system. See the Installation Guide.

Steps to deploy with Helm#

Do the following to deploy VSQA using Helm chart.

Step 1: Clone the Repository#

git clone https://github.com/open-edge-platform/edge-ai-suites.git

Step 2: Change to the Chart Directory#

cd edge-ai-suites/metro-ai-suite/visual-search-question-and-answering/deployment/helm-chart

Step 3: Configure the `values.yaml` File#

Edit the values.yaml file to set the necessary environment variables. At minimum, ensure you set the models, and proxy settings as required.

Settings that must be configured#

Key	Description	Example Value
`global.proxy.http_proxy`	HTTP proxy if required	`http://proxy-example.com:000`
`global.proxy.https_proxy`	HTTPS proxy if required	`http://proxy-example.com:000`
`global.VLM_MODEL_NAME`	VLM model to be used by vlm-openvino-serving	`Qwen/Qwen2.5-VL-7B-Instruct`
`global.EMBEDDING_MODEL_NAME`	Embedding model to be used for feature extraction by multimodal-embedding-serving	`CLIP/clip-vit-h-14`
`global.env.keeppvc`	Set to true to persist the storage. Default is false	false

Step 4: Build Helm Dependencies#

Navigate to the chart directory and build the Helm dependencies using the following command:

helm dependency build

Step 5: Deploy Milvus as the vector DB#

Create a namespace for Milvus

kubectl create namespace milvus

Install Milvus latest helm chart

helm repo add milvus https://zilliztech.github.io/milvus-helm/
helm repo update

Deploy Milvus in a simplified standalone mode

helm install my-milvus milvus/milvus -n milvus --set image.all.tag=v2.6.0   --set cluster.enabled=false --set etcd.replicaCount=1 --set minio.mode=standalone --set pulsar.enabled=false --set pulsarv3.enabled=false

Note: if you need customized settings for Milvus, please refer to the official guide.

Check the pods status with kubectl get po -n milvus. RESTARTS are possible, as long as the 3 pods are stablized after a while, the deployment is successful.

Step 6: Prepare host directories for models and data#

mkdir -p $HOME/data

Make sure the host directories are available to the cluster nodes, and the host-paths under the volumes.hostDataPath section in values.yaml file match the correct directories. Particularly, the default path in values.yaml is /home/user/data, which corresponds to a host username user.

Note: supported media types: jpg, png, mp4

Step 7: Deploy the Application#

Create a namespace for VSQA app

kubectl create namespace vsqa

Install

helm install vsqa . --values values.yaml -n vsqa

Step 8: Verify the Deployment#

Check the status of the deployed resources to ensure everything is running correctly:

kubectl get pods -n vsqa
kubectl get services -n vsqa

Ensure all pods are in the “Running” state before proceeding.

Step 9: Access the application#

For a simpler access, we can do a port forward

kubectl port-forward -n vsqa svc/visual-search-qa-app 17580:17580

Leave the session alive, then access http://localhost:17580 to view the application.

Step 10: Uninstall the Application#

To uninstall, use the following command:

helm uninstall vsqa -n vsqa
helm uninstall my-milvus -n milvus

Verification#

Ensure that all pods are running and the services are accessible.

Troubleshooting#

If you encounter any issues during the deployment process, check the Kubernetes logs for errors:
```
kubectl logs <pod-name> -n <your-namespace>
```