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docker-compose.yml

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+services:
+  # PostgreSQL Database Service
+  database:
+    image: postgres:16-alpine
+    container_name: dashboard-database
+    restart: unless-stopped
+    environment:
+      - POSTGRES_USER=${DB_USER:-dashboard_user}
+      - POSTGRES_PASSWORD=${DB_PASSWORD:-change_this_password}
+      - POSTGRES_DB=${DB_NAME:-dashboard_db}
+      - PGDATA=/var/lib/postgresql/data/pgdata
+    volumes:
+      # Persist database data
+      - db-data:/var/lib/postgresql/data
+    networks:
+      - dashboard-network
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U ${DB_USER:-dashboard_user} -d ${DB_NAME:-dashboard_db}"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+      start_period: 10s
+    # Server: SSH tunnel / DBeaver via localhost:15432 (avoids host Postgres on 5432).
+    # Mac dev: add docker-compose.override.yml from docker-compose.override.local.example for :5432.
+    ports:
+      - "127.0.0.1:15432:5432"
+
+  # Python RAG Microservice
+  rag:
+    build:
+      context: ./AI Insight
+      dockerfile: Dockerfile
+    container_name: dashboard-rag
+    restart: unless-stopped
+    ports:
+      - "5005:5002"
+    environment:
+      - RAG_PORT=5002
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    networks:
+      - dashboard-network
+
+  # Backend API Service
+  backend:
+    build:
+      context: ./backend
+      dockerfile: Dockerfile
+    container_name: dashboard-backend
+    restart: unless-stopped
+    ports:
+      - "127.0.0.1:15001:5001"
+    environment:
+      - NODE_ENV=production
+      - PORT=5001
+      - DB_HOST=database
+      - DB_PORT=5432
+      - DB_USER=${DB_USER:-dashboard_user}
+      - DB_PASSWORD=${DB_PASSWORD:-change_this_password}
+      - DB_NAME=${DB_NAME:-dashboard_db}
+      - DB_SSL=false
+      - RAG_SERVICE_URL=http://rag:5002
+      - LM_STUDIO_BASE_URL=http://llm:11434
+      - LLM_MODEL_NAME=${LLM_MODEL_NAME:-deepseek-r1:8b}
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    depends_on:
+      database:
+        condition: service_healthy
+      rag:
+        condition: service_started
+      llm:
+        condition: service_started
+    networks:
+      - dashboard-network
+    healthcheck:
+      test: ["CMD", "node", "-e", "require('http').get('http://localhost:5001/health', (r) => {process.exit(r.statusCode === 200 ? 0 : 1)})"]
+      interval: 30s
+      timeout: 3s
+      retries: 3
+      start_period: 40s
+
+  # Frontend Service
+  frontend:
+    build:
+      context: .
+      dockerfile: Dockerfile
+      args:
+        - CHICKEN_COUNTING_API_KEY=${VITE_CHICKEN_COUNTING_API_KEY:-}
+    container_name: dashboard-frontend
+    restart: unless-stopped
+    ports:
+      - "5002:80"
+    depends_on:
+      backend:
+        condition: service_healthy
+    networks:
+      - dashboard-network
+    healthcheck:
+      test: ["CMD", "wget", "--quiet", "--tries=1", "--spider", "http://localhost:80/health"]
+      interval: 30s
+      timeout: 3s
+      retries: 3
+      start_period: 10s
+
+  # Local LLM Server (Ollama)
+  llm:
+    image: ollama/ollama:latest
+    container_name: dashboard-llm
+    restart: unless-stopped
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama-data:/root/.ollama
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    networks:
+      - dashboard-network
+    # Untuk Windows dengan GPU Nvidia (Docker): Install Nvidia Container Toolkit, lalu uncomment baris di bawah:
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: all
+    #           capabilities: [gpu]
+
+  # Sidecar helper to auto-download the LLM model
+  llm-model-downloader:
+    image: curlimages/curl:latest
+    container_name: dashboard-llm-downloader
+    restart: "no"
+    depends_on:
+      - llm
+    environment:
+      - LLM_MODEL_NAME=${LLM_MODEL_NAME:-deepseek-r1:8b}
+    entrypoint: ["/bin/sh", "-c"]
+    command:
+      - |
+        echo "Waiting for Ollama service to start..."
+        until curl -s http://llm:11434/api/tags > /dev/null 2>&1; do
+          sleep 3
+        done
+        echo "Ollama is ready. Checking if model $$LLM_MODEL_NAME is pulled..."
+        if curl -s http://llm:11434/api/tags | grep -q "$$LLM_MODEL_NAME"; then
+          echo "Model $$LLM_MODEL_NAME is already available. Skipping download."
+        else
+          echo "Model $$LLM_MODEL_NAME not found. Initiating auto-pull (this may take a few minutes)..."
+          curl -X POST http://llm:11434/api/pull -d "{\"name\": \"$$LLM_MODEL_NAME\"}"
+          echo "Model $$LLM_MODEL_NAME successfully pulled!"
+        fi
+    networks:
+      - dashboard-network
+
+# Networks
+networks:
+  dashboard-network:
+    driver: bridge
+
+# Volumes
+volumes:
+  db-data:
+    driver: local
+    # Using named volume - Docker will manage it automatically
+    # Data will persist in Docker's volume directory
+  ollama-data:
+    driver: local