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🛠️ Infrastructure Setup Guide

This guide explains how to deploy the Automation Center on a Linux server (Oracle Cloud / VPS).

1. Prerequisites​

Ensure the following are available before deployment:

  • Linux server with SSH access (Oracle Cloud, AWS, VPS, etc.)
  • Docker & Docker Compose installed
  • MongoDB Atlas connection string
  • Open ports for HTTP/HTTPS and RabbitMQ (if external)
  • Gemini API Key: Required for the AI Root Cause Analysis feature.

2. Server Configuration (.env)​

The system now supports dynamic environment mapping and AI integration.

# Database & Queue (PaaS Infrastructure)
MONGODB_URL=mongodb+srv://<user>:<pass>@cluster.mongodb.net/automation_platform
RABBITMQ_URL=amqp://automation-rabbitmq
REDIS_URL=redis://automation-redis:6379

# AI Configuration (NEW)
GEMINI_API_KEY=AIzaSy...your_key_here

# Dashboard Defaults (Pre-fills the UI)
DEFAULT_TEST_IMAGE=keinar101/my-automation-tests:latest
DEFAULT_BASE_URL=https://your-app.com
DEFAULT_TEST_FOLDER=all

# --- AGNOSTIC SECRET INJECTION ---
# These variables belong to the CLIENT tests.
# The platform does not interpret them — it only injects them.
ADMIN_USER=admin@example.com
ADMIN_PASS=secure_password
MONGO_URI=mongodb+srv://... (Client DB)

# --- DYNAMIC ENVIRONMENTS ---
# Defining these variables will automatically enable them in the Dashboard UI
DEV_URL=http://localhost:5173
STAGING_URL=https://staging.photographer.keinar.com
PRODUCTION_URL=https://photographer.keinar.com

# WHITE LIST
# Only variables declared here are injected into the test container
INJECT_ENV_VARS=ADMIN_USER,ADMIN_PASS,GEMINI_API_KEY,MONGO_URI

3. The Worker Service Workflow​

The Worker is the heart of the execution engine. Here is the updated lifecycle of a test run:

  1. Pull: Worker pulls the requested Docker image (if not cached).

  2. Run: Executes the container with the secure entrypoint.sh.

  3. Stream: Logs are streamed in real-time to Redis/MongoDB and the Dashboard.

  4. Completion Check:

    • ✅ PASS: Status updated to PASSED.
    • ❌ FAIL: Status updated to ANALYZING.

  5. AI Analysis (If Failed):

    • The Worker gathers the failure logs.
    • It sends them to the Gemini 2.5 Flash model.
    • Generates a "Root Cause Analysis" and "Suggested Fix".
    • Updates the execution record with the analysis report.
    • Final status set to FAILED (or UNSTABLE).

4. Deployment​

Build and start the production stack:

docker compose -f docker-compose.prod.yml up -d --build

Once deployed:

  • The Dashboard becomes accessible via browser.
  • The Producer API handles job requests.
  • The Worker listens for tasks on RabbitMQ.

5. Troubleshooting​

IssueResolution
Tests fail instantlyVerify INJECT_ENV_VARS in .env.
No AI AnalysisCheck if GEMINI_API_KEY is set and valid.
Container exits immediatelyCheck entrypoint.sh permissions.
Status stuck on ANALYZINGCheck Worker logs for timeouts connecting to Google AI.