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.
144 lines
3.1 KiB
Plaintext
144 lines
3.1 KiB
Plaintext
#!/usr/bin/env nu
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# VAPORA Clean Script
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# Phase 0: Clean build artifacts
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# Follows NUSHELL_GUIDELINES.md - 17 rules
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# Check if target directory exists
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def has-target-dir []: bool {
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("target" | path exists)
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}
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# Get size of target directory
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def get-target-size []: int {
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if (has-target-dir) {
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let result = (do { du -sh target } | complete)
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if ($result.exit_code == 0) {
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# Parse output to get size
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1 # Return 1 as placeholder (actual size calculation would require parsing)
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} else {
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0
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}
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} else {
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0
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}
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}
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# Clean cargo build artifacts
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def clean-cargo []: record {
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print "Cleaning cargo build artifacts..."
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let result = (do { cargo clean } | complete)
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if ($result.exit_code == 0) {
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{
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success: true,
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error: null
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}
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} else {
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{
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success: false,
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error: ($result.stderr | str trim)
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}
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}
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}
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# Clean trunk build artifacts (frontend)
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def clean-trunk []: record {
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print "Cleaning trunk build artifacts..."
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let dist_path = "crates/vapora-frontend/dist"
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if ($dist_path | path exists) {
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let result = (do { rm -rf $dist_path } | complete)
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if ($result.exit_code == 0) {
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{
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success: true,
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error: null
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}
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} else {
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{
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success: false,
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error: ($result.stderr | str trim)
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}
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}
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} else {
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{
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success: true,
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error: null
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}
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}
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}
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# Clean temporary files
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def clean-temp []: record {
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print "Cleaning temporary files..."
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let temp_patterns = [
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"**/*.tmp",
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"**/.DS_Store",
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"**/Thumbs.db"
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]
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# Note: glob cleanup would go here in production
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{
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success: true,
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error: null
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}
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}
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# Main clean function
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def main [
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--all = false # Clean all artifacts including cargo
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--temp = false # Clean only temporary files
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]: void {
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print "=== VAPORA Clean ==="
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print ""
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if $temp {
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# Clean only temp files
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let result = (clean-temp)
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if $result.success {
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print ""
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print "✓ Temporary files cleaned"
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} else {
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print $"ERROR: ($result.error)"
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exit 1
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}
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} else {
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# Clean cargo artifacts
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let cargo_result = (clean-cargo)
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if (not $cargo_result.success) {
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print $"ERROR: ($cargo_result.error)"
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exit 1
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}
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print "✓ Cargo artifacts cleaned"
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# Clean trunk artifacts
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let trunk_result = (clean-trunk)
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if (not $trunk_result.success) {
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print $"ERROR: ($trunk_result.error)"
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exit 1
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}
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print "✓ Trunk artifacts cleaned"
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# Clean temp if --all
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if $all {
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let temp_result = (clean-temp)
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if (not $temp_result.success) {
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print $"ERROR: ($temp_result.error)"
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exit 1
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}
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print "✓ Temporary files cleaned"
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}
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print ""
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print "=== Clean Complete ==="
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}
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}
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