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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.
1.4 KiB
1.4 KiB
Crea un svg para el logo de Vapora Nombre: Vapora Dominio principal: vapora.dev Tagline: "Evaporate complexity. Build in the cloud." Logo concept:
- Ondas de vapor ascendentes
- Gradiente cyan → purple → pink
- Efecto glassmorphism en el texto
- Animación sutil de partículas flotando Color palette: Primary: Cyan (#22d3ee) - vapor frío Secondary: Purple (#a855f7) - transición Accent: Pink (#ec4899) - vapor caliente Background: Deep black (#000000) con gradientes Typography:
- Heading: Monospace futurista (JetBrains Mono, Fira Code)
- Body: Inter o similar sans-serif moderna
💻 Características técnicas:
5 streams de datos ascendentes con diferentes patrones (tipo electrocardiograma/señal digital) Grid técnico de fondo sutil tipo dashboard Nodos de datos brillantes que fluyen hacia arriba Hexágonos técnicos flotando (muy dev/tech) Líneas de conexión horizontales animadas entre streams Indicadores laterales "↑ STREAM" / "↑ DATA" Barras de nivel animadas debajo del tagline Metadata técnica (v4.0.0-dev) en la esquina
🎯 Concepto: Ya no son burbujas de bebida, sino flujos de datos ascendentes de una plataforma cloud. Los streams representan pipelines, deploys, y procesos evaporándose hacia la nube. Mucho más DevOps/Cloud/Tech. 📐 Composición:
Ocupa desde Y=240 hasta Y=120 (mucha más altura vertical) Grid técnico cubre todo el canvas Mucho más elemento visual sin saturar