Troubleshooting Guide

This guide provides diagnostic steps and solutions for common issues encountered when deploying MariaDB AI RAG 1.1 (Beta).

Application Startup Issues

The RAG Application (rag-api) exits immediately

The most common cause for immediate failure is an invalid, expired, or missing MARIADB_LICENSE_KEY. AI RAG performs a mandatory license check at startup and will not run without a validated token.

Action Required: Verify your license key in the config.env.secure file.

Recommended Steps:

1. Check the application logs to confirm the license failure.

2. Ensure the host has outbound access to port 443 to reach the licensing server (https://*.mariadb.com).

docker compose -f docker-compose.dockerhub-dev.yml logs rag-api

Port is already allocated error

Docker will throw this error if a required port (e.g., 8000 or 3306) is already in use by another program on your machine.

Recommended Steps:

1. Identify the conflicting process using the command below.

2. Stop the conflicting program or modify the port mappings in your docker-compose.yml file.

# Example for port 8000
netstat -ano | findstr :8000

Database and Ingestion Issues

Database Connection Errors

While the stack is configured to wait for services, timeouts can occur on slower hardware during the initial startup sequence.

Recommended Steps:

1. Check the status of all containers to ensure mariadb shows as Healthy.

2. Restart the environment to clear the timeout.

Bash

docker compose -f docker-compose.dockerhub-dev.yml stop
docker compose -f docker-compose.dockerhub-dev.yml start

Documents stuck in "pending" status

Asynchronous tasks like document processing are handled by background workers. If documents do not progress past "pending," the task pipeline may be interrupted.

Recommended Steps:

1. Ensure the rag-redis container is running and reachable on port 6379.

2. Verify that the rag-celery-worker is active and picking up tasks from the message broker.

docker compose -f docker-compose.dockerhub-dev.yml logs rag-celery-worker

Document Extraction Issues

Layout-Aware extraction (Docling Ray) failing

Advanced extraction tiers require the dedicated Docling Ray service to be functional and reachable.

Recommended Steps:

1. Confirm the rag-docling-ray container is running and accessible internally on port 8003.

2. Large or complex PDFs may exhaust system memory; ensure the host has at least 8 GB of RAM available.

Diagnostic Procedures

1. Check Overall Service Health

Use Docker Compose to verify that all constituent containers are running correctly: {% code title="Verify status" %}

Bash

docker compose -f docker-compose.dockerhub-dev.yml ps

{% endcode %}

2. Verify Network Connectivity

Ensure the internal services can communicate on their assigned ports:

• Inbound: 8000 (API), 8002 (MCP), 3306 (DB), 8003 (Ray), 6379 (Redis).

• Outbound: 443 (External AI Providers & MariaDB Licensing).

3. Check Clock Synchronization

Ensure the host machine's time is synchronized (e.g., via NTP). Significant time drift can cause authentication failures with external AI providers and licensing servers.