compose.yml

services:
  db:
    image: postgres:18-alpine
    restart: unless-stopped
    environment:
      POSTGRES_USER: ${POSTGRES_USER:-postgres}
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-postgres}
      POSTGRES_DB: ${POSTGRES_DB:-dossier}
    ports:
      - "${POSTGRES_PORT:-5432}:5432"
    volumes:
      # Postgres 18+ keeps data in a versioned subdirectory, so mount the parent.
      - pgdata:/var/lib/postgresql
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U $${POSTGRES_USER} -d $${POSTGRES_DB}"]
      interval: 5s
      timeout: 5s
      retries: 10

  app:
    build:
      context: .
      target: dev
    depends_on:
      db:
        condition: service_healthy
    environment:
      # Overrides the localhost URL in .env, which is meant for tools run on the host.
      DATABASE_URL: postgresql://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-dossier}
    ports:
      - "3000:3000"
    volumes:
      - .:/app
      # Keep the container's own (Debian) node_modules and build cache
      # instead of the host's. node_modules is a named volume so installs made
      # through docker/bin/yarn persist, whichever container ran them.
      - node_modules:/app/node_modules
      - /app/.next

  # Prisma Studio on http://localhost:5555. Not started by `docker compose up`;
  # run `docker compose up -d studio` when you want it.
  studio:
    profiles: [tools]
    build:
      context: .
      target: dev
    depends_on:
      db:
        condition: service_healthy
    environment:
      DATABASE_URL: postgresql://${POSTGRES_USER:-postgres}:${POSTGRES_PASSWORD:-postgres}@db:5432/${POSTGRES_DB:-dossier}
    # Studio has no login and can edit every row, so keep it off the network.
    ports:
      - "127.0.0.1:5555:5555"
    volumes:
      - .:/app
      - node_modules:/app/node_modules
    init: true
    command: sh -c "node docker/studio-proxy.mjs & exec node_modules/.bin/prisma studio --port 5555 --browser none"

  # A local model for trying the AI features. Not started by `docker compose
  # up`: run `docker compose up -d ollama`, then pull a model with
  # `docker compose exec ollama ollama pull qwen2.5:3b`. The app reaches it at
  # http://ollama:11434; models are kept in the ollama volume.
  ollama:
    profiles: [ai]
    image: ollama/ollama:0.40.10
    volumes:
      - ollama:/root/.ollama

volumes:
  pgdata:
  node_modules:
  ollama: