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provisioning/docs/src/ai/natural-language-infrastructure.md
Jesús Pérez 27dbc5cd08
chore: review docs from scratch
2026-01-17 03:58:28 +00:00

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Natural Language Infrastructure

Use natural language to describe infrastructure requirements and get automatically generated Nickel configurations and deployment plans.

Overview

Natural Language Infrastructure (NLI) allows requesting infrastructure changes in plain English:

# Instead of writing complex Nickel...
provisioning ai "Deploy a 3-node HA PostgreSQL cluster with automatic backups in AWS"

# Or interactively...
provisioning ai interactive

# Interactive mode guides you through requirements

How It Works

Request Processing Pipeline

User Natural Language Input
    ↓
Intent Recognition
    ├─ Extract resource type (server, database, cluster)
    ├─ Identify constraints (HA, region, size)
    └─ Detect options (monitoring, backup, encryption)
    ↓
RAG Knowledge Retrieval
    ├─ Find similar deployments
    ├─ Retrieve best practices
    └─ Get provider-specific guidance
    ↓
LLM Inference (GPT-4, Claude 3)
    ├─ Generate Nickel schema
    ├─ Calculate resource requirements
    └─ Create deployment plan
    ↓
Configuration Validation
    ├─ Type checking via Nickel compiler
    ├─ Schema validation
    └─ Constraint verification
    ↓
Infrastructure Deployment
    ├─ Dry-run simulation
    ├─ Cost estimation
    └─ User confirmation
    ↓
Execution & Monitoring

Command Usage

Simple Requests

# Web servers with load balancing
provisioning ai "Create 3 web servers with load balancer"

# Database setup
provisioning ai "Deploy PostgreSQL with 2 replicas and daily backups"

# Kubernetes cluster
provisioning ai "Create production Kubernetes cluster with Prometheus monitoring"

Complex Requests

# Multi-cloud deployment
provisioning ai "
  Deploy:
  - 3 HA Kubernetes clusters (AWS, UpCloud, Hetzner)
  - PostgreSQL 15 with synchronous replication
  - Redis cluster for caching
  - ELK stack for logging
  - Prometheus for monitoring
  Constraints:
  - Cross-region high availability
  - Encrypted inter-region communication
  - Auto-scaling based on CPU (70%)
"

# Disaster recovery setup
provisioning ai "
  Set up disaster recovery for production environment:
  - Active-passive failover to secondary region
  - Daily automated backups (30-day retention)
  - Monthly DR tests with automated reports
  - RTO: 4 hours, RPO: 1 hour
  - Test failover every week
"

Interactive Mode

# Start interactive mode
provisioning ai interactive

# System asks clarifying questions:
# Q: What type of infrastructure? (server, database, cluster, other)
# Q: Which cloud provider? (aws, upcloud, hetzner, local)
# Q: Production or development?
# Q: High availability required?
# Q: Expected load? (small, medium, large, enterprise)
# Q: Monitoring and logging?
# Q: Backup strategy?

# Shows generated configuration for approval

Example: Web Application Deployment

Request

provisioning ai "
  Deploy a production web application:
  - Frontend: 3 nginx servers with auto-scaling
  - API: 5 FastAPI instances behind load balancer
  - Database: HA PostgreSQL with read replicas
  - Cache: Redis cluster
  - Monitoring: Prometheus and Grafana
  - Logs: Elasticsearch + Kibana
  Environment: AWS
  Region: us-east-1 (primary), eu-west-1 (DR)
  Budget: $5000/month max
"

Generated Configuration

The system generates:

1. Nickel Infrastructure Definition:

{
  metadata = {
    generated_by = "provisioning-ai"
    timestamp = "2026-01-16T01:47:00Z"
    confidence = 0.94
  }

  infrastructure = {
    frontend = {
      servers = 3
      cpu = 4
      memory = 8
      type = "t3.large"
      auto_scaling = {
        min = 3
        max = 10
        target_cpu = 70
      }
    }

    api = {
      servers = 5
      cpu = 8
      memory = 16
      type = "t3.xlarge"
      load_balancer = {
        type = "application"
        health_check = "/health"
      }
    }

    database = {
      type = "postgresql"
      version = "15"
      replicas = 2
      backup = {
        enabled = true
        frequency = "daily"
        retention_days = 30
      }
    }

    cache = {
      type = "redis"
      nodes = 3
      cluster_mode = true
      replication = true
    }

    monitoring = {
      prometheus = true
      grafana = true
      retention_days = 30
    }

    logging = {
      elasticsearch = true
      kibana = true
      retention_days = 30
    }
  }

  provider = "aws"
  region_primary = "us-east-1"
  region_dr = "eu-west-1"

  cost_estimate = {
    monthly = "$4850"
    breakdown = {
      compute = "$2500"
      database = "$1200"
      cache = "$600"
      monitoring = "$400"
      networking = "$150"
    }
  }
}

2. Deployment Plan:

Deployment Plan: Web Application (Production)

Phase 1: Network & Storage (2-3 hours)
  - Create VPCs in us-east-1 and eu-west-1
  - Set up inter-region VPN
  - Create EBS volumes for database
  - Create EFS for shared storage

Phase 2: Compute Instances (4-5 hours)
  - Launch 3 frontend servers
  - Launch 5 API servers
  - Create load balancers
  - Set up auto-scaling groups

Phase 3: Databases (3-4 hours)
  - Create PostgreSQL primary
  - Create read replicas
  - Configure replication
  - Run initial backup

Phase 4: Cache & Services (2-3 hours)
  - Create Redis cluster
  - Deploy Prometheus
  - Deploy Grafana
  - Deploy Elasticsearch/Kibana

Phase 5: Configuration (2-3 hours)
  - Configure health checks
  - Set up monitoring alerts
  - Configure log shipping
  - Deploy TLS certificates

Total Estimated Time: 13-18 hours

3. Cost Breakdown:

Monthly Cost Estimate: $4,850

Compute               $2,500  (EC2 instances)
Database              $1,200  (RDS PostgreSQL)
Cache                 $600    (ElastiCache Redis)
Monitoring            $400    (CloudWatch + Grafana)
Networking            $150    (NAT Gateway, VPN)

4. Risk Assessment:

Warnings:
- Budget limit reached at $4,850 (max: $5,000)
- Cross-region networking latency: 80-100ms
- Database failover time: 1-2 minutes

Recommendations:
- Implement connection pooling in API
- Use read replicas for analytics queries
- Consider spot instances for non-critical services (30% cost savings)

Output Formats

Get Deployment Script

# Get Bash deployment script
provisioning ai "..." --output bash > deploy.sh

# Get Nushell script
provisioning ai "..." --output nushell > deploy.nu

# Get Terraform
provisioning ai "..." --output terraform > main.tf

# Get Nickel (default)
provisioning ai "..." --output nickel > infrastructure.ncl

Save for Later

# Save configuration for review
provisioning ai "..." --save deployment-plan --review

# Deploy from saved plan
provisioning apply deployment-plan

# Compare with current state
provisioning diff deployment-plan

Configuration

LLM Provider Selection

# Use OpenAI (default)
export PROVISIONING_AI_PROVIDER=openai
export PROVISIONING_AI_MODEL=gpt-4

# Use Anthropic
export PROVISIONING_AI_PROVIDER=anthropic
export PROVISIONING_AI_MODEL=claude-3-opus

# Use local model
export PROVISIONING_AI_PROVIDER=local
export PROVISIONING_AI_MODEL=llama2:70b

Response Options

# ~/.config/provisioning/ai.yaml
natural_language:
  output_format: nickel              # nickel, terraform, bash, nushell
  include_cost_estimate: true
  include_risk_assessment: true
  include_deployment_plan: true
  auto_review: false                 # Require approval before deploy
  dry_run: true                       # Simulate before execution
  confidence_threshold: 0.85          # Reject low-confidence results

  style:
    verbosity: detailed
    include_alternatives: true
    explain_reasoning: true

Advanced Features

Conditional Infrastructure

provisioning ai "
  Deploy web cluster:
  - If environment is production: HA setup with 5 nodes
  - If environment is staging: Standard setup with 2 nodes
  - If environment is dev: Single node with development tools
"

Cost-Optimized Variants

# Generate cost-optimized alternative
provisioning ai "..." --optimize-for cost

# Generate performance-optimized alternative
provisioning ai "..." --optimize-for performance

# Generate high-availability alternative
provisioning ai "..." --optimize-for availability

Template-Based Generation

# Use existing templates as base
provisioning ai "..." --template kubernetes-ha

# List available templates
provisioning ai templates list

Safety & Validation

Review Before Deploy

# Generate and review (no auto-execute)
provisioning ai "..." --review

# Review generated Nickel
cat deployment-plan.ncl

# Validate configuration
provisioning validate deployment-plan.ncl

# Dry-run to see what changes
provisioning apply --dry-run deployment-plan.ncl

# Apply after approval
provisioning apply deployment-plan.ncl

Rollback Support

# Create deployment with automatic rollback
provisioning ai "..." --with-rollback

# Manual rollback if issues
provisioning workflow rollback --to-checkpoint

# View deployment history
provisioning history list --type infrastructure

Limitations

  • Context Window: Very large infrastructure descriptions may exceed LLM limits
  • Ambiguity: Unclear requirements may produce suboptimal configurations
  • Provider Specifics: Some provider-specific features may require manual adjustment
  • Cost: API calls incur per-token charges
  • Latency: Processing takes 2-10 seconds depending on complexity

Related Documentation