Deployment Guide¶

This guide provides instructions for deploying EVOSEAL in various environments.

Table of Contents¶

Prerequisites
Local Development
Docker Deployment
Kubernetes Deployment
Serverless Deployment
Configuration Management
Scaling
Monitoring
Backup and Recovery
Security Considerations

Prerequisites¶

Python 3.10+
pip (Python package manager)
Git
(Optional) Docker and Docker Compose
(Optional) Kubernetes cluster (for production)

Systemd Service Setup¶

EVOSEAL can be run as a systemd service for continuous operation. Here's how to set it up:

1. Create Environment File¶

Copy the template environment file and customize it if needed:

cp .evoseal.env.template .evoseal.env
# Edit .evoseal.env to customize settings

2. Install the Service¶

Copy the service file to the systemd directory and enable it:

# Copy service file
sudo cp scripts/evoseal.service /etc/systemd/system/

# Reload systemd
sudo systemctl daemon-reload

# Enable and start the service
sudo systemctl enable --now evoseal.service

3. Verify Service Status¶

Check if the service is running:

sudo systemctl status evoseal.service

4. View Logs¶

To view the logs:

# Follow logs in real-time
sudo journalctl -u evoseal.service -f

# View full logs
sudo journalctl -u evoseal.service --no-pager

Local Development¶

1. Clone the Repository¶

git clone https://github.com/SHA888/EVOSEAL.git
cd EVOSEAL

2. Set Up Virtual Environment¶

python -m venv venv
source .venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies¶

pip install -r requirements.txt

4. Configure Environment Variables¶

Create a .env file in the project root:

# Required
OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key

# Optional
LOG_LEVEL=INFO
CACHE_DIR=./.cache

5. Run the Application¶

python -m evoseal.cli

Docker Deployment¶

1. Build the Docker Image¶

docker build -t evoseal:latest .

2. Run the Container¶

docker run -d \
  --name evoseal \
  -p 8000:8000 \
  --env-file .env \
  evoseal:latest

3. Using Docker Compose¶

Create a docker-compose.yml file:

version: '3.8'

services:
  evoseal:
    build: .
    ports:
      - "8000:8000"
    env_file:
      - .env
    volumes:
      - ./cache:/app/.cache
    restart: unless-stopped

Then run:

docker-compose up -d

Kubernetes Deployment¶

1. Create a Namespace¶

kubectl create namespace evoseal

2. Create a Secret for Environment Variables¶

kubectl create secret generic evoseal-secrets \
  --namespace=evoseal \
  --from-env-file=.env

3. Deploy the Application¶

Create a deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: evoseal
  namespace: evoseal
spec:
  replicas: 3
  selector:
    matchLabels:
      app: evoseal
  template:
    metadata:
      labels:
        app: evoseal
    spec:
      containers:
      - name: evoseal
        image: your-registry/evoseal:latest
        ports:
        - containerPort: 8000
        envFrom:
        - secretRef:
            name: evoseal-secrets
        resources:
          limits:
            cpu: "1"
            memory: "1Gi"
          requests:
            cpu: "500m"
            memory: "512Mi"

4. Create a Service¶

apiVersion: v1
kind: Service
metadata:
  name: evoseal
  namespace: evoseal
spec:
  selector:
    app: evoseal
  ports:
    - protocol: TCP
      port: 80
      targetPort: 8000
  type: LoadBalancer

5. Apply the Configuration¶

kubectl apply -f deployment.yaml
kubectl apply -f service.yaml

Serverless Deployment¶

AWS Lambda¶

Install the AWS SAM CLI
Create a template.yaml
Deploy using SAM

sam build
sam deploy --guided

Configuration Management¶

Environment Variables¶

Variable	Required	Default	Description
`OPENAI_API_KEY`	Yes	-	Your OpenAI API key
`ANTHROPIC_API_KEY`	Yes	-	Your Anthropic API key
`LOG_LEVEL`	No	INFO	Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
`CACHE_DIR`	No	./.cache	Directory to store cache files

Configuration Files¶

EVOSEAL supports configuration through YAML files. The default location is config/config.yaml.

Scaling¶

Horizontal Scaling¶

Use Kubernetes HPA (Horizontal Pod Autoscaler)
Set appropriate resource requests and limits
Use a message queue for task distribution

Caching¶

Enable caching for API responses
Use Redis or Memcached for distributed caching

Monitoring¶

Logging¶

Configure log aggregation (ELK, Loki, etc.)
Set up log rotation

Metrics¶

Expose Prometheus metrics
Set up Grafana dashboards
Monitor error rates and latency

Alerting¶

Set up alerts for errors and performance issues
Use tools like Prometheus Alertmanager

Backup and Recovery¶

Data Backup¶

Regularly back up your database
Test restoration procedures
Store backups in multiple locations

Disaster Recovery¶

Have a disaster recovery plan
Test failover procedures
Document recovery steps

Security Considerations¶

Network Security¶

Use TLS/SSL for all communications
Implement network policies
Use a WAF (Web Application Firewall)

Access Control¶

Implement proper authentication and authorization
Use role-based access control (RBAC)
Rotate API keys regularly

Data Protection¶

Encrypt sensitive data at rest and in transit
Implement proper key management
Follow the principle of least privilege

Upgrading¶

Check the release notes for breaking changes
Backup your data
Test the upgrade in a staging environment
Deploy to production during a maintenance window

Troubleshooting¶

Common Issues¶

API Connection Issues
Check your API keys
Verify network connectivity
Check rate limits
Performance Problems
Check resource utilization
Review query performance
Check for memory leaks
Deployment Failures
Check container logs
Verify configuration
Check resource quotas

Support¶

For additional help, please open an issue.

Last update: 2025-07-22
Created: 2025-06-17