Implement intelligent agent learning from Knowledge Graph execution history with per-task-type expertise tracking, recency bias, and learning curves. ## Phase 5.3 Implementation ### Learning Infrastructure (✅ Complete) - LearningProfileService with per-task-type expertise metrics - TaskTypeExpertise model tracking success_rate, confidence, learning curves - Recency bias weighting: recent 7 days weighted 3x higher (exponential decay) - Confidence scoring prevents overfitting: min(1.0, executions / 20) - Learning curves computed from daily execution windows ### Agent Scoring Service (✅ Complete) - Unified AgentScore combining SwarmCoordinator + learning profiles - Scoring formula: 0.3*base + 0.5*expertise + 0.2*confidence - Rank agents by combined score for intelligent assignment - Support for recency-biased scoring (recent_success_rate) - Methods: rank_agents, select_best, rank_agents_with_recency ### KG Integration (✅ Complete) - KGPersistence::get_executions_for_task_type() - query by agent + task type - KGPersistence::get_agent_executions() - all executions for agent - Coordinator::load_learning_profile_from_kg() - core KG→Learning integration - Coordinator::load_all_learning_profiles() - batch load for multiple agents - Convert PersistedExecution → ExecutionData for learning calculations ### Agent Assignment Integration (✅ Complete) - AgentCoordinator uses learning profiles for task assignment - extract_task_type() infers task type from title/description - assign_task() scores candidates using AgentScoringService - Fallback to load-based selection if no learning data available - Learning profiles stored in coordinator.learning_profiles RwLock ### Profile Adapter Enhancements (✅ Complete) - create_learning_profile() - initialize empty profiles - add_task_type_expertise() - set task-type expertise - update_profile_with_learning() - update swarm profiles from learning ## Files Modified ### vapora-knowledge-graph/src/persistence.rs (+30 lines) - get_executions_for_task_type(agent_id, task_type, limit) - get_agent_executions(agent_id, limit) ### vapora-agents/src/coordinator.rs (+100 lines) - load_learning_profile_from_kg() - core KG integration method - load_all_learning_profiles() - batch loading for agents - assign_task() already uses learning-based scoring via AgentScoringService ### Existing Complete Implementation - vapora-knowledge-graph/src/learning.rs - calculation functions - vapora-agents/src/learning_profile.rs - data structures and expertise - vapora-agents/src/scoring.rs - unified scoring service - vapora-agents/src/profile_adapter.rs - adapter methods ## Tests Passing - learning_profile: 7 tests ✅ - scoring: 5 tests ✅ - profile_adapter: 6 tests ✅ - coordinator: learning-specific tests ✅ ## Data Flow 1. Task arrives → AgentCoordinator::assign_task() 2. Extract task_type from description 3. Query KG for task-type executions (load_learning_profile_from_kg) 4. Calculate expertise with recency bias 5. Score candidates (SwarmCoordinator + learning) 6. Assign to top-scored agent 7. Execution result → KG → Update learning profiles ## Key Design Decisions ✅ Recency bias: 7-day half-life with 3x weight for recent performance ✅ Confidence scoring: min(1.0, total_executions / 20) prevents overfitting ✅ Hierarchical scoring: 30% base load, 50% expertise, 20% confidence ✅ KG query limit: 100 recent executions per task-type for performance ✅ Async loading: load_learning_profile_from_kg supports concurrent loads ## Next: Phase 5.4 - Cost Optimization Ready to implement budget enforcement and cost-aware provider selection.
553 lines
13 KiB
Markdown
553 lines
13 KiB
Markdown
# ⚙️ Provisioning Integration
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## Deploying VAPORA via Provisioning Taskservs & KCL
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**Version**: 0.1.0
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**Status**: Specification (VAPORA v1.0 Deployment)
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**Purpose**: How Provisioning creates and manages VAPORA infrastructure
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---
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## 🎯 Objetivo
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Provisioning es el **deployment engine** para VAPORA:
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- Define infraestructura con **KCL schemas** (no Helm)
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- Crea **taskservs** para cada componente VAPORA
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- Ejecuta **batch workflows** para operaciones complejas
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- Escala **agents** dinámicamente
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- Monitorea **health** y triggers **rollback**
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---
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## 📁 VAPORA Workspace Structure
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```
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provisioning/vapora-wrksp/
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├── workspace.toml # Workspace definition
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├── kcl/ # KCL Infrastructure-as-Code
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│ ├── cluster.k # K8s cluster (nodes, networks)
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│ ├── services.k # Microservices (backend, agents)
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│ ├── storage.k # SurrealDB + Rook Ceph
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│ ├── agents.k # Agent pools + scaling
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│ └── multi-ia.k # LLM Router + providers
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├── taskservs/ # Taskserv definitions
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│ ├── vapora-backend.toml # API backend
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│ ├── vapora-frontend.toml # Web UI
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│ ├── vapora-agents.toml # Agent runtime
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│ ├── vapora-mcp-gateway.toml # MCP plugins
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│ └── vapora-llm-router.toml # Multi-IA router
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├── workflows/ # Batch operations
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│ ├── deploy-full-stack.yaml
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│ ├── scale-agents.yaml
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│ ├── upgrade-vapora.yaml
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│ └── disaster-recovery.yaml
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└── README.md # Setup guide
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```
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---
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## 🏗️ KCL Schemas
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### 1. Cluster Definition (cluster.k)
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```kcl
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import kcl_plugin.kubernetes as k
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# VAPORA Cluster
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cluster = k.Cluster {
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name = "vapora-cluster"
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version = "1.30"
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network = {
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cni = "cilium" # Network plugin
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serviceMesh = "istio" # Service mesh
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ingressController = "istio-gateway"
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}
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storage = {
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provider = "rook-ceph"
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replication_factor = 3
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storage_classes = [
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{ name = "ssd", type = "nvme" },
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{ name = "hdd", type = "sata" },
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]
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}
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nodes = [
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# Control plane
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{
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role = "control-plane"
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count = 3
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instance_type = "t3.medium"
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resources = { cpu = "2", memory = "4Gi" }
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},
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# Worker nodes for agents (scalable)
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{
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role = "worker"
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count = 5
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instance_type = "t3.large"
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resources = { cpu = "4", memory = "8Gi" }
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labels = { workload = "agents", tier = "compute" }
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taints = []
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},
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# Worker nodes for data
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{
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role = "worker"
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count = 3
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instance_type = "t3.xlarge"
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resources = { cpu = "8", memory = "16Gi" }
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labels = { workload = "data", tier = "storage" }
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},
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]
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addons = [
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"metrics-server",
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"prometheus",
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"grafana",
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]
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}
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```
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### 2. Services Definition (services.k)
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```kcl
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import kcl_plugin.kubernetes as k
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services = [
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# Backend API
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{
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name = "vapora-backend"
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namespace = "vapora-system"
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replicas = 3
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image = "vapora/backend:0.1.0"
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port = 8080
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resources = {
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requests = { cpu = "1", memory = "2Gi" }
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limits = { cpu = "2", memory = "4Gi" }
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}
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env = [
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{ name = "DATABASE_URL", value = "surrealdb://surreal-0.vapora-system:8000" },
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{ name = "NATS_URL", value = "nats://nats-0.vapora-system:4222" },
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]
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},
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# Frontend
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{
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name = "vapora-frontend"
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namespace = "vapora-system"
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replicas = 2
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image = "vapora/frontend:0.1.0"
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port = 3000
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resources = {
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requests = { cpu = "500m", memory = "512Mi" }
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limits = { cpu = "1", memory = "1Gi" }
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}
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},
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# Agent Runtime
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{
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name = "vapora-agents"
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namespace = "vapora-agents"
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replicas = 3
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image = "vapora/agents:0.1.0"
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port = 8089
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resources = {
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requests = { cpu = "2", memory = "4Gi" }
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limits = { cpu = "4", memory = "8Gi" }
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}
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# Autoscaling
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hpa = {
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min_replicas = 3
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max_replicas = 20
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target_cpu = "70"
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}
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},
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# MCP Gateway
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{
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name = "vapora-mcp-gateway"
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namespace = "vapora-system"
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replicas = 2
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image = "vapora/mcp-gateway:0.1.0"
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port = 8888
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},
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# LLM Router
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{
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name = "vapora-llm-router"
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namespace = "vapora-system"
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replicas = 2
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image = "vapora/llm-router:0.1.0"
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port = 8899
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env = [
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{ name = "CLAUDE_API_KEY", valueFrom = "secret:vapora-secrets:claude-key" },
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{ name = "OPENAI_API_KEY", valueFrom = "secret:vapora-secrets:openai-key" },
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{ name = "GEMINI_API_KEY", valueFrom = "secret:vapora-secrets:gemini-key" },
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]
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},
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]
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```
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### 3. Storage Definition (storage.k)
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```kcl
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import kcl_plugin.kubernetes as k
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storage = {
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# SurrealDB StatefulSet
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surrealdb = {
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name = "surrealdb"
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namespace = "vapora-system"
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replicas = 3
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image = "surrealdb/surrealdb:1.8"
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port = 8000
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storage = {
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size = "50Gi"
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storage_class = "rook-ceph"
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}
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},
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# Redis cache
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redis = {
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name = "redis"
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namespace = "vapora-system"
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replicas = 1
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image = "redis:7-alpine"
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port = 6379
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storage = {
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size = "20Gi"
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storage_class = "ssd"
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}
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},
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# NATS JetStream
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nats = {
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name = "nats"
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namespace = "vapora-system"
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replicas = 3
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image = "nats:2.10-scratch"
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port = 4222
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storage = {
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size = "30Gi"
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storage_class = "rook-ceph"
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}
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},
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}
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```
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### 4. Agent Pools (agents.k)
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```kcl
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agents = {
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architect = {
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role_id = "architect"
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replicas = 2
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max_concurrent = 1
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container = {
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image = "vapora/agents:architect-0.1.0"
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resources = { cpu = "4", memory = "8Gi" }
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}
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},
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developer = {
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role_id = "developer"
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replicas = 5 # Can scale to 20
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max_concurrent = 2
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container = {
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image = "vapora/agents:developer-0.1.0"
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resources = { cpu = "4", memory = "8Gi" }
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}
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hpa = {
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min_replicas = 5
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max_replicas = 20
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target_queue_depth = 10 # Scale when queue > 10
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}
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},
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reviewer = {
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role_id = "code-reviewer"
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replicas = 3
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max_concurrent = 2
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container = {
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image = "vapora/agents:reviewer-0.1.0"
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resources = { cpu = "2", memory = "4Gi" }
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}
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},
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# ... other 9 roles
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}
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```
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---
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## 🛠️ Taskservs Definition
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### Example: Backend Taskserv
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```toml
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# taskservs/vapora-backend.toml
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[taskserv]
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name = "vapora-backend"
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type = "service"
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version = "0.1.0"
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description = "VAPORA REST API backend"
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[source]
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repository = "ssh://git@repo.jesusperez.pro:32225/jesus/Vapora.git"
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branch = "main"
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path = "vapora-backend/"
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[build]
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runtime = "rust"
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build_command = "cargo build --release"
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binary_path = "target/release/vapora-backend"
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dockerfile = "Dockerfile.backend"
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[deployment]
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namespace = "vapora-system"
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replicas = 3
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image = "vapora/backend:${version}"
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image_pull_policy = "Always"
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[ports]
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http = 8080
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metrics = 9090
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[resources]
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requests = { cpu = "1000m", memory = "2Gi" }
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limits = { cpu = "2000m", memory = "4Gi" }
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[health_check]
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path = "/health"
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interval_secs = 10
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timeout_secs = 5
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failure_threshold = 3
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[dependencies]
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- "surrealdb" # Must exist
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- "nats" # Must exist
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- "redis" # Optional
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[scaling]
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min_replicas = 3
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max_replicas = 10
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target_cpu_percent = 70
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target_memory_percent = 80
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[environment]
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DATABASE_URL = "surrealdb://surrealdb-0:8000"
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NATS_URL = "nats://nats-0:4222"
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REDIS_URL = "redis://redis-0:6379"
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RUST_LOG = "debug,vapora=trace"
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[secrets]
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JWT_SECRET = "secret:vapora-secrets:jwt-secret"
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DATABASE_PASSWORD = "secret:vapora-secrets:db-password"
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```
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---
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## 🔄 Workflows (Batch Operations)
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### Deploy Full Stack
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```yaml
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# workflows/deploy-full-stack.yaml
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apiVersion: provisioning/v1
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kind: Workflow
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metadata:
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name: deploy-vapora-full-stack
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namespace: vapora-system
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spec:
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description: "Deploy complete VAPORA stack from scratch"
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steps:
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# Step 1: Create cluster
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- name: create-cluster
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task: provisioning.cluster
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params:
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config: kcl/cluster.k
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timeout: 1h
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on_failure: abort
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# Step 2: Install operators (Istio, Prometheus, Rook)
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- name: install-addons
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task: provisioning.addon
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depends_on: [create-cluster]
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params:
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addons: [istio, prometheus, rook-ceph]
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timeout: 30m
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# Step 3: Deploy data layer
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- name: deploy-data
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task: provisioning.deploy-taskservs
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depends_on: [install-addons]
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params:
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taskservs: [surrealdb, redis, nats]
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timeout: 30m
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# Step 4: Deploy core services
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- name: deploy-core
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task: provisioning.deploy-taskservs
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depends_on: [deploy-data]
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params:
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taskservs: [vapora-backend, vapora-llm-router, vapora-mcp-gateway]
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timeout: 30m
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# Step 5: Deploy frontend
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- name: deploy-frontend
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task: provisioning.deploy-taskservs
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depends_on: [deploy-core]
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params:
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taskservs: [vapora-frontend]
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timeout: 15m
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# Step 6: Deploy agent pools
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- name: deploy-agents
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task: provisioning.deploy-agents
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depends_on: [deploy-core]
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params:
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agents: [architect, developer, reviewer, tester, documenter, devops, monitor, security, pm, decision-maker, orchestrator, presenter]
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initial_replicas: { architect: 2, developer: 5, ... }
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timeout: 30m
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# Step 7: Verify health
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- name: health-check
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task: provisioning.health-check
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depends_on: [deploy-agents, deploy-frontend]
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params:
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services: all
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timeout: 5m
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on_failure: rollback
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# Step 8: Initialize database
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- name: init-database
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task: provisioning.run-migrations
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depends_on: [health-check]
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params:
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sql_files: [migrations/*.surql]
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timeout: 10m
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# Step 9: Configure ingress
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- name: configure-ingress
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task: provisioning.configure-ingress
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depends_on: [init-database]
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params:
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gateway: istio-gateway
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hosts:
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- vapora.example.com
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timeout: 10m
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rollback_on_failure: true
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on_completion:
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- name: notify-slack
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task: notifications.slack
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params:
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webhook: "${SLACK_WEBHOOK}"
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message: "VAPORA deployment completed successfully!"
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```
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### Scale Agents
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```yaml
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# workflows/scale-agents.yaml
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apiVersion: provisioning/v1
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kind: Workflow
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spec:
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description: "Dynamically scale agent pools based on queue depth"
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steps:
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- name: check-queue-depth
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task: provisioning.query
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params:
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query: "SELECT queue_depth FROM agent_health WHERE role = '${AGENT_ROLE}'"
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outputs: [queue_depth]
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- name: decide-scaling
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task: provisioning.evaluate
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params:
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condition: |
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if queue_depth > 10 && current_replicas < max_replicas:
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scale_to = min(current_replicas + 2, max_replicas)
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action = "scale_up"
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elif queue_depth < 2 && current_replicas > min_replicas:
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scale_to = max(current_replicas - 1, min_replicas)
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action = "scale_down"
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else:
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action = "no_change"
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outputs: [action, scale_to]
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- name: execute-scaling
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task: provisioning.scale-taskserv
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when: action != "no_change"
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params:
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taskserv: "vapora-agents-${AGENT_ROLE}"
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replicas: "${scale_to}"
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timeout: 5m
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```
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---
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## 🎯 CLI Usage
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```bash
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cd provisioning/vapora-wrksp
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# 1. Create cluster
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provisioning cluster create --config kcl/cluster.k
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# 2. Deploy full stack
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provisioning workflow run workflows/deploy-full-stack.yaml
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# 3. Check status
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provisioning health-check --services all
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# 4. Scale agents
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provisioning taskserv scale vapora-agents-developer --replicas 10
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# 5. Monitor
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provisioning dashboard open # Grafana dashboard
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provisioning logs tail -f vapora-backend
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# 6. Upgrade
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provisioning taskserv upgrade vapora-backend --image vapora/backend:0.3.0
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# 7. Rollback
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provisioning taskserv rollback vapora-backend --to-version 0.1.0
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```
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---
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## 🎯 Implementation Checklist
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|
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- [ ] KCL schemas (cluster, services, storage, agents)
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|
- [ ] Taskserv definitions (5 services)
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|
- [ ] Workflows (deploy, scale, upgrade, disaster-recovery)
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|
- [ ] Namespace creation + RBAC
|
|
- [ ] PVC provisioning (Rook Ceph)
|
|
- [ ] Service discovery (DNS, load balancing)
|
|
- [ ] Health checks + readiness probes
|
|
- [ ] Logging aggregation (ELK or similar)
|
|
- [ ] Secrets management (RustyVault integration)
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|
- [ ] Monitoring (Prometheus metrics export)
|
|
- [ ] Documentation + runbooks
|
|
|
|
---
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|
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|
## 📊 Success Metrics
|
|
|
|
✅ Full VAPORA deployed < 1 hour
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|
✅ All services healthy post-deployment
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|
✅ Agent pools scale automatically
|
|
✅ Rollback works if deployment fails
|
|
✅ Monitoring captures all metrics
|
|
✅ Scaling decisions in < 1 min
|
|
|
|
---
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|
|
|
**Version**: 0.1.0
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|
**Status**: ✅ Integration Specification Complete
|
|
**Purpose**: Provisioning deployment of VAPORA infrastructure
|