📝 Tutorials
· 5 min read

Build an AI Docker Compose Generator — Describe Your Stack, Get Config

Setting up Docker Compose for a new project takes time. You think about services, networks, volumes, environment variables, and dependencies. What if you could describe your stack and get a complete configuration?

In this tutorial, you’ll build a Docker Compose generator that takes natural language descriptions and produces production-ready configurations. No API keys, no cloud calls, everything runs on your machine.

How It Works

  1. You describe your stack: “Node.js app with PostgreSQL and Redis”
  2. AI generates docker-compose.yml with all services, networks, and volumes
  3. You get a working configuration you can run immediately

Prerequisites

  • Ollama installed
  • Python 3.10+

Step 1: Pull the model

ollama pull qwen2.5-coder:7b

Step 2: Create the generator

#!/usr/bin/env python3
"""AI Docker Compose generator using Ollama."""

import subprocess
import sys
import json
import urllib.request

OLLAMA_URL = "http://localhost:11434/api/generate"
MODEL = "qwen2.5-coder:7b"

PROMPT_TEMPLATE = """Generate a docker-compose.yml file based on this description.

Description: {description}

Requirements:
1. Use official Docker images where available
2. Include proper environment variables with placeholders
3. Set up volumes for data persistence
4. Configure networking between services
5. Add health checks where appropriate
6. Use proper resource limits
7. Include comments explaining each service
8. Add a .env.example file with required variables
9. Include a Makefile with common commands
10. Follow Docker Compose best practices

Output format:
- docker-compose.yml
- .env.example
- Makefile with helpful commands

Start with the docker-compose.yml:"""


def generate_docker_compose(description):
    """Generate docker-compose configuration from description."""
    payload = json.dumps({
        "model": MODEL,
        "prompt": PROMPT_TEMPLATE.format(description=description),
        "stream": False,
        "options": {"temperature": 0.3, "num_predict": 3000}
    }).encode()

    req = urllib.request.Request(
        OLLAMA_URL,
        data=payload,
        headers={"Content-Type": "application/json"}
    )

    with urllib.request.urlopen(req, timeout=120) as resp:
        response = json.loads(resp.read())["response"].strip()
        return response


def parse_sections(response):
    """Parse the AI response into sections."""
    sections = {
        "docker-compose.yml": "",
        ".env.example": "",
        "Makefile": ""
    }

    current = None
    lines = response.split("\n")

    for line in lines:
        if "docker-compose.yml" in line.lower() or "docker-compose:" in line.lower():
            current = "docker-compose.yml"
        elif ".env.example" in line.lower() or ".env" in line.lower():
            current = ".env.example"
        elif "makefile" in line.lower() or "make:" in line.lower():
            current = "Makefile"
        elif current:
            sections[current] += line + "\n"

    return sections


def save_files(sections, output_dir="."):
    """Save generated files."""
    for filename, content in sections.items():
        if content.strip():
            filepath = f"{output_dir}/{filename}"
            with open(filepath, "w") as f:
                f.write(content.strip() + "\n")
            print(f"Created: {filepath}")


if __name__ == "__main__":
    description = input("Describe your application stack: ")

    print("\nGenerating Docker Compose configuration...")
    response = generate_docker_compose(description)

    sections = parse_sections(response)

    print("\n" + "=" * 60)
    print("DOCKER-COMPOSE.YML")
    print("=" * 60)
    print(sections["docker-compose.yml"])

    if sections[".env.example"].strip():
        print("\n" + "=" * 60)
        print(".ENV.EXAMPLE")
        print("=" * 60)
        print(sections[".env.example"])

    if sections["Makefile"].strip():
        print("\n" + "=" * 60)
        print("MAKEFILE")
        print("=" * 60)
        print(sections["Makefile"])

    save = input("\nSave files? (y/n): ").strip().lower()
    if save == "y":
        save_files(sections)

Step 3: Run it

python docker_compose_generator.py

Example Session

Describe your application stack: Node.js Express app with PostgreSQL, Redis, and Nginx reverse proxy

Generating Docker Compose configuration...

============================================================
DOCKER-COMPOSE.YML
============================================================
version: '3.8'

services:
  # Node.js Application
  app:
    build: .
    container_name: app
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
      - DATABASE_URL=postgresql://postgres:password@postgres:5432/myapp
      - REDIS_URL=redis://redis:6379
    depends_on:
      postgres:
        condition: service_healthy
      redis:
        condition: service_healthy
    volumes:
      - ./uploads:/app/uploads
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  # PostgreSQL Database
  postgres:
    image: postgres:16-alpine
    container_name: postgres
    environment:
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=myapp
    volumes:
      - postgres_data:/var/lib/postgresql/data
    ports:
      - "5432:5432"
    restart: unless-stopped
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U postgres"]
      interval: 10s
      timeout: 5s
      retries: 5

  # Redis Cache
  redis:
    image: redis:7-alpine
    container_name: redis
    ports:
      - "6379:6379"
    volumes:
      - redis_data:/data
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5

  # Nginx Reverse Proxy
  nginx:
    image: nginx:alpine
    container_name: nginx
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf:ro
      - ./ssl:/etc/nginx/ssl:ro
    depends_on:
      - app
    restart: unless-stopped

volumes:
  postgres_data:
  redis_data:

networks:
  default:
    driver: bridge

Under the Hood

The AI understands common stack patterns:

  • Web apps: Node.js, Python, Ruby with databases
  • Microservices: Multiple services with inter-service communication
  • Data pipelines: Workers, queues, and storage
  • ML/AI stacks: GPUs, Jupyter, model serving

The generated configuration includes:

  • Proper service dependencies with health checks
  • Volume mounts for data persistence
  • Environment variable placeholders
  • Networking configuration
  • Resource limits

Limitations

  • Generated configs need testing before production
  • Custom Dockerfiles need manual creation
  • Complex networking may need adjustment
  • Security defaults should be reviewed

Real-World Use Cases

New project setup. Instead of spending 30 minutes writing Docker configs, describe your stack and get a working setup in seconds. Focus on code, not infrastructure.

Client projects. When starting a new client project, quickly generate a Docker setup to get the team productive on day one.

Microservices architecture. Describe multiple services and their dependencies. The AI generates proper inter-service communication and networking.

Data pipelines. Generate configs for data processing workflows with workers, queues, and storage services.

Development environments. Create consistent development setups that match production configurations.

Tips for Better Configs

  1. Be specific about versions. “PostgreSQL 16” is better than “PostgreSQL.”
  2. Mention dependencies. “App depends on database and cache” helps the AI generate proper depends_on.
  3. Include ports. “App on port 3000, database on 5432” generates correct port mappings.
  4. Specify volumes. “Persist database data” adds named volumes automatically.

Variations

  • Kubernetes: Generate k8s manifests instead
  • Terraform: Generate infrastructure as code
  • CI/CD: Generate GitHub Actions or GitLab CI configs
  • Monitoring: Add Prometheus and Grafana services

My Take

This tool eliminates the boilerplate of Docker Compose setup. Instead of spending 30 minutes writing configs, you get a working setup in seconds. The AI captures the common patterns, and you customize the details.

Rating: 8.5/10 — Saves significant time on new projects. The generated configs are production-ready for most use cases.

FAQ

How accurate is the generated configuration?

Very accurate for standard stacks (Node.js + databases, Python + Redis, etc.). The AI understands common patterns and generates appropriate configurations. For specialized setups, review and adjust.

Can I add custom services?

Yes. The generated config is a starting point. Add your custom services, networks, and volumes as needed.

Does it handle Docker Swarm?

The basic generator produces Docker Compose files. For Docker Swarm, you would need to add swarm-specific configurations.

Can I generate configs for existing projects?

Yes. Describe your existing stack and the AI will generate a compatible docker-compose.yml.

Related: Docker Compose Cheat Sheet · Docker Best Practices · Ollama Complete Guide

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