Vapora/kubernetes/08-ingress.yaml

# Ingress for VAPORA (using nginx ingress controller)
# Change the host to your domain
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: vapora
  namespace: vapora
  labels:
    app: vapora
  annotations:
    kubernetes.io/ingress.class: "nginx"
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
    nginx.ingress.kubernetes.io/websocket-services: "vapora-backend"
    nginx.ingress.kubernetes.io/proxy-read-timeout: "3600"
    nginx.ingress.kubernetes.io/proxy-send-timeout: "3600"
    # Optional: cert-manager for TLS
    # cert-manager.io/cluster-issuer: "letsencrypt-prod"
spec:
  # Uncomment for TLS
  # tls:
  # - hosts:
  #   - vapora.example.com
  #   secretName: vapora-tls
  rules:
  - host: vapora.example.com
    http:
      paths:
      # API endpoints
      - path: /api
        pathType: Prefix
        backend:
          service:
            name: vapora-backend
            port:
              number: 8080
      # WebSocket endpoints
      - path: /ws
        pathType: Prefix
        backend:
          service:
            name: vapora-backend
            port:
              number: 8080
      # MCP server endpoints
      - path: /mcp
        pathType: Prefix
        backend:
          service:
            name: vapora-mcp-server
            port:
              number: 3000
      # Frontend (must be last to catch all)
      - path: /
        pathType: Prefix
        backend:
          service:
            name: vapora-frontend
            port:
              number: 80
feat: Phase 5.3 - Multi-Agent Learning Infrastructure 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: 0.3base + 0.5expertise + 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. 2026-01-11 13:03:53 +00:00			`# Ingress for VAPORA (using nginx ingress controller)`
			`# Change the host to your domain`
			`apiVersion: networking.k8s.io/v1`
			`kind: Ingress`
			`metadata:`
			`name: vapora`
			`namespace: vapora`
			`labels:`
			`app: vapora`
			`annotations:`
			`kubernetes.io/ingress.class: "nginx"`
			`nginx.ingress.kubernetes.io/ssl-redirect: "true"`
			`nginx.ingress.kubernetes.io/websocket-services: "vapora-backend"`
			`nginx.ingress.kubernetes.io/proxy-read-timeout: "3600"`
			`nginx.ingress.kubernetes.io/proxy-send-timeout: "3600"`
			`# Optional: cert-manager for TLS`
			`# cert-manager.io/cluster-issuer: "letsencrypt-prod"`
			`spec:`
			`# Uncomment for TLS`
			`# tls:`
			`# - hosts:`
			`# - vapora.example.com`
			`# secretName: vapora-tls`
			`rules:`
			`- host: vapora.example.com`
			`http:`
			`paths:`
			`# API endpoints`
			`- path: /api`
			`pathType: Prefix`
			`backend:`
			`service:`
			`name: vapora-backend`
			`port:`
			`number: 8080`
			`# WebSocket endpoints`
			`- path: /ws`
			`pathType: Prefix`
			`backend:`
			`service:`
			`name: vapora-backend`
			`port:`
			`number: 8080`
			`# MCP server endpoints`
			`- path: /mcp`
			`pathType: Prefix`
			`backend:`
			`service:`
			`name: vapora-mcp-server`
			`port:`
			`number: 3000`
			`# Frontend (must be last to catch all)`
			`- path: /`
			`pathType: Prefix`
			`backend:`
			`service:`
			`name: vapora-frontend`
			`port:`
			`number: 80`