Flowise on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Flowise is an open-source visual AI workflow builder that lets users assemble LangChain and LlamaIndex AI pipelines through a drag-and-drop interface. This lab takes you through the full operational lifecycle of the Flowise on GKE Autopilot module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the GKE module and the Google Cloud platform, not on Flowise product features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

• Deploy the module from the RAD platform and locate the resources it provisions.
• Connect to the GKE cluster and access the running workload.
• Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
• Observe the workload with Cloud Logging and Cloud Monitoring.
• Diagnose and resolve the most common deployment and runtime issues.
• Tear the deployment down cleanly.

Prerequisites

• Services_GCP (provides the VPC, GKE Autopilot cluster, Cloud SQL, Artifact Registry, and shared service accounts this module depends on). You do not need to deploy this yourself first — the platform automatically detects whether it already exists in the target project and provisions it before this module if not (see Task 1).
• A Google Cloud project with billing enabled.
• gcloud CLI and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
• Project Owner (or equivalent) IAM on the project.
• Bringing your own project? Before the first deploy into it, the deployment confirmation dialog asks you to prove you control it (Get verification code, run the commands it shows as a project Owner, then Verify) and to give the RAD deployment service account the Owner role. A project RAD creates for you needs neither.
• Advanced mode for later changes. The create form asks only for the first page of inputs (and, in a project RAD creates for you, little more than the tenant name and region). Every other input in the Configuration Guide — including the scaling and version inputs in the Day-2 tasks — is changed afterwards with Update on the deployment's page after ticking Enable advanced mode, which needs a credit balance that covers the update's estimated build cost (updates never carry a module fee). On a lab environment only an administrator can use Advanced mode.
• RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"           # the region you deploy into

---

Task 1 — Deploy the module [Automated]

1. Open Solutions → Solution Catalog → RAD modules in the RAD platform top navigation, open Flowise (GKE) from the Platform Modules list to start configuration, choose Configuration Form under How would you like to configure this deployment? (the form opens on the Conversational Assistant if you hold purchased credits or are a partner or administrator), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Click Deploy Module, review the estimated cost in the Deployment Confirmation dialog when it appears and click Submit (if the dialog then adds a confirmation step, such as verifying a project you bring, complete it and click Confirm), which opens the deployment status page with real-time logs.

2. The platform deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets, a GCS uploads bucket, builds the container image, and runs a one-shot database-initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

3. Connect to the cluster and discover the namespace with name-agnostic filters:

   CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
   gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"
   
   NS=$(kubectl get ns -o name | grep flowise | head -1 | cut -d/ -f2)
   echo "Cluster: $CLUSTER   Namespace: $NS"
   kubectl get all -n "$NS"

---

Task 2 — Access & verify [Manual]

1. Confirm the workload is running and find its external address:

   kubectl get pods,svc -n "$NS"
   EXTERNAL_IP=$(kubectl get svc -n "$NS" \
     -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
   echo "External IP: $EXTERNAL_IP"
   curl -s "http://${EXTERNAL_IP}:3000/api/v1/ping"   # expect {"pong":true}

2. Retrieve the admin password from Secret Manager and sign in at http://${EXTERNAL_IP}:3000:

   ADMIN_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~flowise AND name~password" --format="value(name)" --limit=1)
   gcloud secrets versions access latest --secret="$ADMIN_SECRET" --project="$PROJECT"

   The admin username defaults to the value of flowise_username set at deploy time (default: admin).

---

Task 3 — Operate & keep it running (Day-2) [Manual]

1. Inspect the workload — deployment, pods, and (if enabled) the horizontal autoscaler and persistent volumes:

   kubectl get deploy,pods,hpa,pvc -n "$NS"
   kubectl describe deploy -n "$NS"

2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply).

3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a rolling update replaces the pods.

4. Manage secrets, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~flowise"
   kubectl get jobs -n "$NS"          # DB-init job

5. Open a database session for inspection or maintenance:

   INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
   # Role and database are tenant-prefixed (e.g. flowisedemo426161cf) — not the bare app name.
   DB_USER=$(gcloud sql users list --instance="$INSTANCE" --project="$PROJECT" \
     --format="value(name)" --filter="name~^flowise" --limit=1)
   gcloud sql connect "$INSTANCE" --user="$DB_USER" --project="$PROJECT"

---

Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from kubectl or the Logs Explorer:

   kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50

   Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. The module also provisions an uptime check (when enabled); review Monitoring → Uptime checks and Alerting → Policies.

---

Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with Flowise releases.

• Pod not Ready / CrashLoopBackOff: inspect events and logs:

  kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
  kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

• Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed.
• Initialisation job failed: inspect the job and its pod logs:

  kubectl get jobs -n "$NS"
  kubectl logs -n "$NS" job/<job-name>

• Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
• Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

---

Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead (from the same Delete dialog) — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the deployment). This removes everything the module created — the Kubernetes workload and namespace, Cloud SQL database, Secret Manager secrets, GCS uploads bucket, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

---

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL, GCS bucket, secrets, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; retrieve admin password and sign in
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources