Vapora/scripts/clean.nu

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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: 0.3*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.
2026-01-11 13:03:53 +00:00
#!/usr/bin/env nu
# VAPORA Clean Script
# Phase 0: Clean build artifacts
# Follows NUSHELL_GUIDELINES.md - 17 rules
# Check if target directory exists
def has-target-dir []: bool {
("target" | path exists)
}
# Get size of target directory
def get-target-size []: int {
if (has-target-dir) {
let result = (do { du -sh target } | complete)
if ($result.exit_code == 0) {
# Parse output to get size
1 # Return 1 as placeholder (actual size calculation would require parsing)
} else {
0
}
} else {
0
}
}
# Clean cargo build artifacts
def clean-cargo []: record {
print "Cleaning cargo build artifacts..."
let result = (do { cargo clean } | complete)
if ($result.exit_code == 0) {
{
success: true,
error: null
}
} else {
{
success: false,
error: ($result.stderr | str trim)
}
}
}
# Clean trunk build artifacts (frontend)
def clean-trunk []: record {
print "Cleaning trunk build artifacts..."
let dist_path = "crates/vapora-frontend/dist"
if ($dist_path | path exists) {
let result = (do { rm -rf $dist_path } | complete)
if ($result.exit_code == 0) {
{
success: true,
error: null
}
} else {
{
success: false,
error: ($result.stderr | str trim)
}
}
} else {
{
success: true,
error: null
}
}
}
# Clean temporary files
def clean-temp []: record {
print "Cleaning temporary files..."
let temp_patterns = [
"**/*.tmp",
"**/.DS_Store",
"**/Thumbs.db"
]
# Note: glob cleanup would go here in production
{
success: true,
error: null
}
}
# Main clean function
def main [
--all = false # Clean all artifacts including cargo
--temp = false # Clean only temporary files
]: void {
print "=== VAPORA Clean ==="
print ""
if $temp {
# Clean only temp files
let result = (clean-temp)
if $result.success {
print ""
print "✓ Temporary files cleaned"
} else {
print $"ERROR: ($result.error)"
exit 1
}
} else {
# Clean cargo artifacts
let cargo_result = (clean-cargo)
if (not $cargo_result.success) {
print $"ERROR: ($cargo_result.error)"
exit 1
}
print "✓ Cargo artifacts cleaned"
# Clean trunk artifacts
let trunk_result = (clean-trunk)
if (not $trunk_result.success) {
print $"ERROR: ($trunk_result.error)"
exit 1
}
print "✓ Trunk artifacts cleaned"
# Clean temp if --all
if $all {
let temp_result = (clean-temp)
if (not $temp_result.success) {
print $"ERROR: ($temp_result.error)"
exit 1
}
print "✓ Temporary files cleaned"
}
print ""
print "=== Clean Complete ==="
}
}