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.
39 lines
1.6 KiB
XML
39 lines
1.6 KiB
XML
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" width="64" height="64" preserveAspectRatio="xMidYMid meet">
|
|
<defs>
|
|
<!-- Google Fonts import -->
|
|
<style>
|
|
@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@800&display=swap');
|
|
</style>
|
|
<linearGradient id="faviconGrad" x1="0%" y1="0%" x2="100%" y2="0%">
|
|
<stop offset="0%" style="stop-color:#22d3ee;stop-opacity:1"/>
|
|
<stop offset="50%" style="stop-color:#a855f7;stop-opacity:1"/>
|
|
<stop offset="100%" style="stop-color:#ec4899;stop-opacity:1"/>
|
|
</linearGradient>
|
|
</defs>
|
|
|
|
<!-- Fondo -->
|
|
<rect width="64" height="64" fill="#000000"/>
|
|
|
|
<!-- Símbolo simplificado -->
|
|
<g transform="translate(14, 32)">
|
|
<!-- Línea central -->
|
|
<path d="M 0 0 L 0 -12 L 2 -14 L -2 -18 L 2 -22 L 0 -24" stroke="url(#faviconGrad)" stroke-width="1.2" fill="none" stroke-linecap="round" stroke-linejoin="round"/>
|
|
|
|
<!-- Línea izquierda -->
|
|
<path d="M -6 0 L -6 -10 L -5 -14 L -7 -18 L -6 -22" stroke="#22d3ee" stroke-width="0.8" fill="none" stroke-linecap="round" opacity="0.7"/>
|
|
|
|
<!-- Línea derecha -->
|
|
<path d="M 6 0 L 6 -10 L 5 -14 L 7 -18 L 6 -22" stroke="#ec4899" stroke-width="0.8" fill="none" stroke-linecap="round" opacity="0.7"/>
|
|
|
|
<!-- Círculos decorativos -->
|
|
<circle cx="0" cy="-8" r="1" fill="#22d3ee"/>
|
|
<circle cx="0" cy="-14" r="0.8" fill="#a855f7"/>
|
|
</g>
|
|
|
|
<!-- Texto VAPORA simplificado -->
|
|
<text x="36" y="40" font-family="'JetBrains Mono', monospace" font-size="18" font-weight="800" fill="url(#faviconGrad)" letter-spacing="1" text-anchor="middle">
|
|
V
|
|
</text>
|
|
|
|
</svg>
|