Certification track: AI Tooling

RAGFlow on Cloud Run — Lab Guide

Overview

Estimated time: 45–90 minutes

RAGFlow is an open-source document intelligence and Retrieval-Augmented Generation (RAG) platform. It ingests PDFs, Word documents, HTML pages, and other formats, chunks and embeds them, stores vectors in Elasticsearch, and exposes a REST API and a web UI for knowledge base management and enterprise search. This lab takes you through the full operational lifecycle of the RAGFlow on Cloud Run 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 Cloud Run module and the Google Cloud platform, not on RAGFlow 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.
Access and verify the running service.
Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
Observe the service with Cloud Logging and Cloud Monitoring.
Diagnose and resolve the most common deployment and runtime issues.
Tear the deployment down cleanly.

Prerequisites

Services_GCP deployed in the target project (provides the VPC, Cloud SQL, Artifact Registry, and shared service accounts this module depends on).
Elasticsearch_GKE deployed and its elasticsearch_endpoint output available — this is a hard deployment prerequisite; the plan is rejected if elasticsearch_hosts is empty when deploy_application = true.
A Google Cloud project with billing enabled.
gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
Project Owner (or equivalent) IAM on the project.
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]

In the RAD platform, open RAGFlow (Cloud Run), set project_id and elasticsearch_hosts (the elasticsearch_endpoint output from your Elasticsearch_GKE deployment), and review the remaining inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs.
The platform provisions the Cloud Run service, a Cloud SQL (MySQL 8.0) database with its Secret Manager secrets, a Cloud Storage bucket for document artifacts, optional NFS/Redis wiring, builds the container image, and runs a one-shot database-initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

When it completes, discover the resources with name-agnostic filters:

SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~ragflow" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(gcloud run services describe "$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
echo "Service: $SERVICE"
echo "URL:     $SERVICE_URL"

Task 2 — Access & verify [Manual]

Confirm the service is healthy and connected to its dependencies:
```
curl -s "$SERVICE_URL/v1/health"   # expect HTTP 200 with {"code":0}
```
RAGFlow loads embedding models on first boot; if you see a 502 or connection refused, wait a few minutes for the startup probe to complete.
Open $SERVICE_URL in a browser. On first visit RAGFlow presents a registration page — create an admin account with an email and password of your choice and sign in. There is no pre-provisioned admin credential in Secret Manager; the database password secret is for the MySQL backend only.

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

Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (a manual edit would be reverted on the next apply). Note that min_instance_count is hard-capped at 1; scale-to-zero is not supported because RAGFlow loads embedding models at startup.
Update the application version by changing the version input via Update on the deployment details page; a new image builds and a new revision rolls out.

Manage secrets, storage, and jobs:

gcloud secrets list --project="$PROJECT" --filter="name~ragflow"
gcloud storage buckets list --project="$PROJECT"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init and any cron jobs

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud sql connect "$INSTANCE" --user=ragflow --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer:
```
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
```
Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. The module also provisions an uptime check (when enabled); confirm it is green under Monitoring → Uptime checks, and review 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 RAGFlow releases.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved.

gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100

Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the initialisation job completed. RAGFlow requires MySQL 8.0 — verify database_type = MYSQL_8_0.

Initialisation job failed: list executions and read the failed one's logs:

gcloud run jobs list --project="$PROJECT" --region="$REGION" --filter="name~ragflow"
gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"

Documents not processing (stuck in queue): confirm Redis is reachable and redis_host is set — without Redis, RAGFlow's document workers never start.
Elasticsearch errors / failed indexing: verify elasticsearch_hosts points to the correct Elasticsearch endpoint and the cluster health is green.
Image build failed: review Cloud Build history for the failed build's log.
403 / permission errors: verify the runtime service account's IAM roles.

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 — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the project). This removes everything the module created — the Cloud Run service, Cloud SQL (MySQL) database, Secret Manager secrets, Cloud Storage bucket, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here. The Elasticsearch_GKE deployment must also be torn down separately.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run, Cloud SQL (MySQL), Storage, secrets, and runs DB init
2 — Access & verify	Manual	Health check passes; register admin account and sign in
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/storage/jobs, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, init-job, Redis/Elasticsearch, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

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

Task 4 — Observe: Logging & Monitoring [Manual]​

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​