CLI Commands

Command-line tools for service creation, running, and deployment.

Overview

PraisonAI-SVC provides CLI commands for the complete service lifecycle.

praisonai-svc new

Purpose: Create a new service from template

praisonai-svc new my-service
# or with package integration
praisonai-svc new my-service --package praisonaippt

What it creates:

my-service/
├── app.py          # ServiceApp boilerplate
├── .env.example    # Required configuration
├── README.md       # Setup instructions
└── pyproject.toml  # Dependencies

Next steps:

cd my-service
cp .env.example .env
# Edit .env with your Azure credentials
# Edit app.py to implement your logic

praisonai-svc run

Purpose: Run the service locally for development

cd my-service
praisonai-svc run

What it does: 1. Loads environment variables from .env 2. Starts FastAPI server on http://localhost:8080 3. Starts worker process for job processing 4. Enables hot-reload for development

Test it:

# Health check
curl http://localhost:8080/health

# Create a job
curl -X POST http://localhost:8080/jobs \
  -H "Content-Type: application/json" \
  -d '{"payload": {"title": "Test"}}'

praisonai-svc deploy

Purpose: Deploy service to Azure Container Apps

cd my-service
praisonai-svc deploy

What it does: 1. Validates Azure CLI is installed and authenticated 2. Builds Docker image from your service 3. Pushes image to Azure Container Registry 4. Creates/updates Azure Container App 5. Configures environment variables 6. Sets up scaling rules (min 0, max 3 replicas)

Prerequisites: - Azure CLI: brew install azure-cli - Logged in: az login - Resource group and container registry created

praisonai-svc logs

Purpose: View real-time logs from deployed service

cd my-service
praisonai-svc logs

# Follow logs (like tail -f)
praisonai-svc logs --follow

# Show last 100 lines
praisonai-svc logs --tail 100

Useful for: - Debugging production issues - Monitoring job processing - Tracking API requests - Identifying errors