MyAIServ MCP Server

# Deployment Architecture ## Container Architecture ```mermaid flowchart TB subgraph LB[Load Balancer] NX[NGINX] end subgraph APP[Application] API1[API 1] API2[API 2] API3[API n...] end subgraph DB[Storage] subgraph ES[Elasticsearch] ES1[(Node 1)] ES2[(Node 2)] ES3[(Node n)] end subgraph RD[Redis] RD1[(Master)] RD2[(Replica 1)] RD3[(Replica n)] end end subgraph MON[Monitoring] PM[Prometheus] GF[Grafana] AE[Alerts] end NX --> API1 NX --> API2 NX --> API3 API1 --> ES1 API1 --> RD1 API2 --> ES1 API2 --> RD1 API3 --> ES1 API3 --> RD1 ES1 <--> ES2 ES1 <--> ES3 ES2 <--> ES3 RD1 --> RD2 RD1 --> RD3 API1 --> PM API2 --> PM API3 --> PM PM --> GF PM --> AE ``` ## Deployment Methods ### Using Docker Compose The project includes a Docker Compose setup for quick deployment: ```bash # Build and start all services docker compose up -d # Scale API servers if needed docker compose up -d --scale web=3 ``` ### Production Deployment with Docker Our Docker image uses Poetry for dependency management, ensuring consistent environments across all deployments: 1. Pull the image: ```bash docker pull myaiserv/api:latest ``` 2. Run with proper environment variables: ```bash docker run -d \ -p 8000:8000 \ -e DATABASE_URL=<db-url> \ -e REDIS_URL=<redis-url> \ -e ES_URL=<elasticsearch-url> \ -v /path/to/logs:/app/logs \ myaiserv/api:latest ``` ## Deployment Configuration ### Container Orchestration - Kubernetes or Docker Swarm for container orchestration - Automatic scaling based on load - Health checks and auto-recovery - Rolling updates with zero downtime ### High Availability - Multiple API server instances - Elasticsearch cluster with 3+ nodes - Redis master-replica setup - Load balancer with SSL termination ### Monitoring & Alerts - Real-time metrics collection - Custom Grafana dashboards - Alert rules for critical conditions - Automated incident response ### Backup & Recovery - Automated Elasticsearch snapshots - Redis persistence and backup - Configuration backups - Disaster recovery procedures ### Security - SSL/TLS encryption - Network isolation - Access control lists - Security monitoring ## Resource Requirements ### Minimum Production Setup - **API Servers**: 2+ instances, 2 CPU, 4GB RAM each - **Elasticsearch**: 3 nodes, 4 CPU, 8GB RAM each - **Redis**: 1 master + 2 replicas, 2 CPU, 4GB RAM each - **Monitoring**: 2 CPU, 4GB RAM - **Storage**: 100GB+ per Elasticsearch node ## CI/CD Pipeline Our CI/CD pipeline uses Poetry to ensure consistent environment across development, testing, and production: 1. **Build Phase**: - Install dependencies with Poetry - Run linters and formatters - Execute tests 2. **Packaging Phase**: - Build Docker image with Poetry dependencies - Push to container registry 3. **Deployment Phase**: - Pull latest image - Deploy with zero downtime - Run health checks ## Environment Variables | Variable | Description | Default | |----------|-------------|---------| | `DATABASE_URL` | PostgreSQL connection URL | `postgresql://user:pass@localhost:5432/myaiserv` | | `REDIS_URL` | Redis connection URL | `redis://localhost:6379/0` | | `ES_URL` | Elasticsearch URL | `http://localhost:9200` | | `LOG_LEVEL` | Application log level | `INFO` | | `DEBUG` | Enable debug mode | `false` | | `PORT` | Application port | `8000` | | `HOST` | Bind address | `0.0.0.0` |