d14150da75
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.
60 lines
1.5 KiB
YAML
60 lines
1.5 KiB
YAML
# Ingress for VAPORA (using nginx ingress controller)
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# Change the host to your domain
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apiVersion: networking.k8s.io/v1
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kind: Ingress
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metadata:
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name: vapora
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namespace: vapora
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labels:
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app: vapora
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annotations:
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kubernetes.io/ingress.class: "nginx"
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nginx.ingress.kubernetes.io/ssl-redirect: "true"
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nginx.ingress.kubernetes.io/websocket-services: "vapora-backend"
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nginx.ingress.kubernetes.io/proxy-read-timeout: "3600"
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nginx.ingress.kubernetes.io/proxy-send-timeout: "3600"
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# Optional: cert-manager for TLS
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# cert-manager.io/cluster-issuer: "letsencrypt-prod"
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spec:
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# Uncomment for TLS
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# tls:
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# - hosts:
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# - vapora.example.com
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# secretName: vapora-tls
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rules:
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- host: vapora.example.com
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http:
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paths:
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# API endpoints
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- path: /api
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pathType: Prefix
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backend:
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service:
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name: vapora-backend
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port:
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number: 8080
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# WebSocket endpoints
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- path: /ws
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pathType: Prefix
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backend:
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service:
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name: vapora-backend
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port:
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number: 8080
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# MCP server endpoints
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- path: /mcp
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pathType: Prefix
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backend:
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service:
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name: vapora-mcp-server
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port:
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number: 3000
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# Frontend (must be last to catch all)
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- path: /
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pathType: Prefix
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backend:
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service:
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name: vapora-frontend
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port:
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number: 80
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