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Production Considerations

This guide covers production best practices, security, monitoring, and operational considerations for running AnonDocs in production environments.

Security Best Practices​

Network Security​

Run Behind Reverse Proxy​

Always use a reverse proxy (Nginx, Caddy, Traefik) in front of AnonDocs:

  • ✅ SSL/TLS termination
  • ✅ Rate limiting
  • ✅ DDoS protection
  • ✅ Request logging
  • ✅ IP whitelisting if needed

Network Isolation​

For sensitive deployments:

  • Run in isolated network segments
  • Use VPN or private networks for LLM provider communication
  • Consider air-gapped deployments for maximum security

Firewall Configuration​

# Allow only necessary ports
ufw allow 80/tcp   # HTTP (reverse proxy)
ufw allow 443/tcp # HTTPS (reverse proxy)
ufw deny 3000/tcp # Block direct access to AnonDocs

Authentication & Authorization​

API Authentication​

Implement authentication for API access:

// Example: API key middleware
const authenticate = (req, res, next) => {
  const apiKey = req.headers['x-api-key'];
  if (apiKey !== process.env.API_KEY) {
    return res.status(401).json({ error: 'Unauthorized' });
  }
  next();
};

Rate Limiting​

Configure rate limiting to prevent abuse:

# Nginx rate limiting
limit_req_zone $binary_remote_addr zone=api_limit:10m rate=10r/s;

location /api/ {
    limit_req zone=api_limit burst=20 nodelay;
    proxy_pass http://localhost:3000;
}

IP Whitelisting​

For internal-only deployments:

location / {
    allow 10.0.0.0/8;
    allow 192.168.0.0/16;
    deny all;
    proxy_pass http://localhost:3000;
}

Data Security​

No Data Persistence​

AnonDocs is designed to process data in memory only:

  • ✅ Files are immediately deleted after processing
  • ✅ No database storage
  • ✅ No file system persistence
  • ✅ All processing happens in memory

Secure File Upload Limits​

Configure upload size limits:

client_max_body_size 50M;

Input Validation​

Ensure all inputs are validated:

  • File type validation
  • File size limits
  • Content validation
  • Malicious file detection

Environment Variables Security​

Never Commit Secrets​

# .gitignore
.env
.env.production
*.key
*.pem

Use Secret Management​

For Kubernetes:

apiVersion: v1
kind: Secret
metadata:
  name: anondocs-secrets
type: Opaque
stringData:
  API_KEY: "your-secret-key"

For Docker:

docker run -d \
  --env-file .env.production \
  --env-file .env.secrets \
  anondocs

Rotate Secrets Regularly​

  • Rotate API keys quarterly
  • Update LLM provider credentials
  • Review access logs regularly

Performance Optimization​

Resource Allocation​

CPU and Memory​

# Kubernetes resource requests/limits
resources:
  requests:
    memory: "1Gi"
    cpu: "1000m"
  limits:
    memory: "4Gi"
    cpu: "4000m"

GPU Allocation (if available)​

resources:
  limits:
    nvidia.com/gpu: 1

Caching Strategies​

While AnonDocs doesn't store data, consider:

  • LLM Response Caching: Cache LLM responses for similar inputs (be careful with PII)
  • Model Loading: Keep models in memory for faster inference
  • Connection Pooling: Reuse LLM provider connections

Load Balancing​

For high availability:

upstream anondocs_backend {
    least_conn;
    server anondocs-1:3000;
    server anondocs-2:3000;
    server anondocs-3:3000;
}

server {
    location / {
        proxy_pass http://anondocs_backend;
    }
}

Monitoring & Observability​

Health Monitoring​

Health Check Endpoint​

# Automated health checks
curl -f http://localhost:3000/health || alert

Kubernetes Health Checks​

livenessProbe:
  httpGet:
    path: /health
    port: 3000
  initialDelaySeconds: 30
  periodSeconds: 10
  timeoutSeconds: 5
  failureThreshold: 3

readinessProbe:
  httpGet:
    path: /health
    port: 3000
  initialDelaySeconds: 10
  periodSeconds: 5

Logging​

Structured Logging​

Configure structured logging for better observability:

// Example logging configuration
import winston from 'winston';

const logger = winston.createLogger({
  format: winston.format.json(),
  transports: [
    new winston.transports.File({ filename: 'error.log', level: 'error' }),
    new winston.transports.File({ filename: 'combined.log' })
  ]
});

Log Retention​

  • Keep logs for compliance requirements (e.g., 90 days)
  • Never log sensitive data or PII
  • Use log rotation to manage disk space

Metrics Collection​

Key Metrics to Monitor​

  • Request rate and latency
  • Error rates
  • LLM provider response times
  • Memory and CPU usage
  • Processing queue depth
  • Chunk processing times

Prometheus Integration​

# Example metrics endpoint
/metrics:
  prometheus: true

Backup & Disaster Recovery​

Configuration Backups​

# Backup configuration
cp .env.production .env.production.backup

Infrastructure as Code​

Store deployment configurations in version control:

  • Docker Compose files
  • Kubernetes manifests
  • Infrastructure scripts
  • Configuration templates

Disaster Recovery Plan​

  1. Documentation: Document your deployment architecture
  2. Backup Strategy: Regular backups of configurations
  3. Recovery Testing: Test recovery procedures regularly
  4. Incident Response: Have a plan for security incidents

Compliance Considerations​

GDPR Compliance​

AnonDocs is designed for GDPR compliance:

  • ✅ No data retention
  • ✅ Local processing only
  • ✅ Open source and auditable
  • ✅ No third-party data sharing

HIPAA Considerations​

For healthcare deployments:

  • Deploy in HIPAA-compliant infrastructure
  • Use encrypted connections (TLS)
  • Implement access controls
  • Maintain audit logs
  • Regular security assessments

Regular Security Audits​

  • Review dependencies for vulnerabilities
  • Perform penetration testing
  • Security code reviews
  • Update dependencies regularly

Operational Best Practices​

Update Strategy​

Version Pinning​

FROM node:18-alpine
# Pin specific versions for stability

Update Procedures​

  1. Test updates in staging environment
  2. Review changelog for breaking changes
  3. Backup configuration before updating
  4. Deploy during maintenance windows
  5. Monitor after deployment

Capacity Planning​

Monitor Resource Usage​

  • Track CPU and memory over time
  • Identify peak usage patterns
  • Plan for growth

Scaling Triggers​

Set up alerts for:

  • High CPU usage (>80%)
  • High memory usage (>85%)
  • Increased error rates
  • Slow response times

Documentation​

Maintain documentation for:

  • Deployment architecture
  • Configuration details
  • Emergency contacts
  • Recovery procedures
  • Known issues and workarounds

Troubleshooting Production Issues​

Common Issues​

High Memory Usage​

  • Reduce CHUNK_SIZE
  • Disable ENABLE_PARALLEL_CHUNKS
  • Use smaller LLM models
  • Scale horizontally

Slow Response Times​

  • Check LLM provider performance
  • Review network latency
  • Optimize chunk processing
  • Consider caching strategies

Connection Errors​

  • Verify LLM provider is accessible
  • Check network connectivity
  • Review firewall rules
  • Test health endpoints

Incident Response​

  1. Identify: Determine the scope of the issue
  2. Contain: Prevent further impact
  3. Resolve: Fix the root cause
  4. Learn: Post-mortem and improvements

Next Steps​

For production support, visit our GitHub Discussions or GitHub Issues.

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