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DEPLOYMENT.md

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Deployment Guide

This document provides instructions for deploying the OpenAI Assistant application to production.

Prerequisites

  • Node.js v18+
  • PostgreSQL database
  • OpenAI API key
  • (Optional) OAuth credentials for GitHub and Google authentication

Environment Variables

Set up the following environment variables:

Database

  • DATABASE_URL: Full PostgreSQL connection string
  • PGHOST: PostgreSQL host
  • PGPORT: PostgreSQL port
  • PGUSER: PostgreSQL username
  • PGPASSWORD: PostgreSQL password
  • PGDATABASE: PostgreSQL database name

Authentication

  • SESSION_SECRET: Random string for session encryption (use a secure random generator)
  • GITHUB_ID: GitHub OAuth client ID
  • GITHUB_SECRET: GitHub OAuth client secret
  • GITHUB_CALLBACK: GitHub OAuth callback URL
  • GOOGLE_ID: Google OAuth client ID
  • GOOGLE_SECRET: Google OAuth client secret
  • GOOGLE_CALLBACK: Google OAuth callback URL

OpenAI

  • OPENAI_API_KEY: OpenAI API key
  • ASSISTANT_ID: (Optional) Default OpenAI Assistant ID

Email

  • SMTP_RELAY_SERVER: SMTP server for sending emails
  • SMTP_RELAY_PORT: SMTP port
  • SMTP_RELAY_USER: SMTP username
  • SMTP_RELAY_PASSWORD: SMTP password
  • SMTP_FROM: Email sender address

Application

  • NODE_ENV: Set to "production" for production deployments

Deployment Steps

  1. Clone the repository
  2. Install dependencies: 
    npm install
  3. Initialize the database: 
    npm run db:push
  4. Build the application for production: 
    npm run build
  5. Start the application in production mode: 
    npm run start

Health Check

The application provides a health check endpoint at /api/health that returns:

  • System status
  • Runtime environment
  • Memory usage
  • Uptime information
  • Application version

This endpoint can be used with monitoring tools to ensure the application is running correctly.

Production Features

The application has several production-ready features:

  • Security:

    • Helmet middleware for security headers
    • Rate limiting (100 requests per 15 minutes per IP)
    • More aggressive rate limiting for auth endpoints (10 requests per 15 minutes)
    • HTTP-only, secure cookies for sessions
    • Production error handling to prevent leaking sensitive information

  • Performance:

    • Compression middleware for better network performance
    • Response caching for applicable API endpoints
    • Optimized build process with minification

  • Reliability:

    • Graceful shutdown handling for proper cleanup
    • Uncaught exception handling
    • Error boundary for client-side error recovery
    • File upload size limits (50MB)

  • Monitoring:

    • Production-appropriate logging
    • Health check endpoint
    • Memory usage tracking

Deployment Options

Option 1: Traditional Node.js Deployment

Follow the standard deployment steps above. This is suitable for:

  • VPS providers like DigitalOcean, AWS EC2, etc.
  • Bare metal servers
  • Self-hosted environments

Option 2: Containerized Deployment

Build a Docker container:

docker build -t openai-assistant-app .
docker run -p 5000:5000 --env-file .env openai-assistant-app

This approach works well with:

  • Kubernetes
  • Docker Swarm
  • AWS ECS
  • Google Cloud Run

Option 3: Platform as a Service

Deploy directly to a PaaS provider:

  • Heroku
  • Render
  • Railway
  • Fly.io

Most PaaS providers will automatically detect Node.js and run the appropriate build and start commands.

Monitoring & Maintenance

  • Check the log files in the logs directory for application logs
  • Monitor the PostgreSQL database for performance issues
  • Regularly back up the database
  • Set up uptime monitoring using the /api/health endpoint
  • Configure alerts for error rates and performance metrics

Scaling Considerations

  • The application uses in-memory session store by default, which is not suitable for multiple instances. For scale-out deployments:
    • Ensure database connection pooling is properly configured
    • Consider using Redis for session storage in multi-instance deployments
    • Use a load balancer with sticky sessions if needed

Updating

To update the application:

  1. Pull the latest changes
  2. Install dependencies: npm install
  3. Apply database updates: npm run db:push
  4. Build the application: npm run build
  5. Restart the application: npm run start