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.
188 lines
5.9 KiB
Rust
188 lines
5.9 KiB
Rust
use std::collections::HashMap;
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use vapora_llm_router::{BudgetManager, RoleBudget};
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fn create_test_budgets() -> HashMap<String, RoleBudget> {
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let mut budgets = HashMap::new();
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budgets.insert(
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"architect".to_string(),
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RoleBudget {
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role: "architect".to_string(),
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monthly_limit_cents: 50000, // $500
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weekly_limit_cents: 12500, // $125
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fallback_provider: "gemini".to_string(),
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alert_threshold: 0.8,
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},
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);
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budgets.insert(
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"developer".to_string(),
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RoleBudget {
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role: "developer".to_string(),
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monthly_limit_cents: 30000,
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weekly_limit_cents: 7500,
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fallback_provider: "ollama".to_string(),
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alert_threshold: 0.8,
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},
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);
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budgets
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}
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#[tokio::test]
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async fn test_budget_initialization() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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let status = manager.check_budget("architect").await.unwrap();
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assert_eq!(status.role, "architect");
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assert_eq!(status.monthly_remaining_cents, 50000);
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assert_eq!(status.monthly_utilization, 0.0);
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assert!(!status.exceeded);
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assert!(!status.near_threshold);
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}
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#[tokio::test]
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async fn test_budget_spending() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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manager.record_spend("developer", 3000).await.unwrap();
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let status = manager.check_budget("developer").await.unwrap();
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assert_eq!(status.monthly_remaining_cents, 27000);
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assert!((status.monthly_utilization - 0.1).abs() < 0.01);
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}
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#[tokio::test]
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async fn test_multiple_spends_accumulate() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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manager.record_spend("developer", 5000).await.unwrap();
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manager.record_spend("developer", 3000).await.unwrap();
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manager.record_spend("developer", 2000).await.unwrap();
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let status = manager.check_budget("developer").await.unwrap();
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assert_eq!(status.monthly_remaining_cents, 20000); // 30000 - 10000
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}
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#[tokio::test]
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async fn test_alert_threshold_near() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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// Spend 81% of weekly budget (12500 * 0.81 = 10125) to trigger near_threshold
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// This keeps us under both monthly and weekly limits while triggering alert
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let spend_amount = (12500.0 * 0.81) as u32; // 10125
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manager.record_spend("architect", spend_amount).await.unwrap();
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let status = manager.check_budget("architect").await.unwrap();
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assert!(!status.exceeded);
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assert!(status.near_threshold);
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}
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#[tokio::test]
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async fn test_budget_exceeded() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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// Spend entire monthly budget
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manager.record_spend("developer", 30000).await.unwrap();
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let status = manager.check_budget("developer").await.unwrap();
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assert!(status.exceeded);
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assert_eq!(status.monthly_remaining_cents, 0);
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}
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#[tokio::test]
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async fn test_budget_overspend() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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// Spend more than budget (overflow protection)
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manager.record_spend("developer", 35000).await.unwrap();
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let status = manager.check_budget("developer").await.unwrap();
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assert!(status.exceeded);
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assert_eq!(status.monthly_remaining_cents, 0); // Saturating subtract
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}
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#[tokio::test]
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async fn test_weekly_budget_independent() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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// Spend 100% of weekly budget but only 25% of monthly
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manager.record_spend("developer", 7500).await.unwrap();
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let status = manager.check_budget("developer").await.unwrap();
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assert_eq!(status.monthly_remaining_cents, 22500);
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assert_eq!(status.weekly_remaining_cents, 0);
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assert!(status.exceeded); // Both budgets checked
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}
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#[tokio::test]
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async fn test_fallback_provider() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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let fallback_dev = manager.get_fallback_provider("developer").await.unwrap();
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assert_eq!(fallback_dev, "ollama");
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let fallback_arch = manager.get_fallback_provider("architect").await.unwrap();
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assert_eq!(fallback_arch, "gemini");
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}
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#[tokio::test]
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async fn test_unknown_role_error() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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let result = manager.check_budget("unknown").await;
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assert!(result.is_err());
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let result = manager.record_spend("unknown", 100).await;
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assert!(result.is_err());
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}
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#[tokio::test]
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async fn test_get_all_budgets() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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manager.record_spend("architect", 5000).await.unwrap();
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manager.record_spend("developer", 3000).await.unwrap();
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let all_statuses = manager.get_all_budgets().await;
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assert_eq!(all_statuses.len(), 2);
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let arch_status = all_statuses
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.iter()
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.find(|s| s.role == "architect")
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.unwrap();
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assert_eq!(arch_status.monthly_remaining_cents, 45000);
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let dev_status = all_statuses
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.iter()
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.find(|s| s.role == "developer")
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.unwrap();
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assert_eq!(dev_status.monthly_remaining_cents, 27000);
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}
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#[tokio::test]
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async fn test_budget_status_comprehensive() {
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let budgets = create_test_budgets();
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let manager = BudgetManager::new(budgets);
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// Spend 6000 cents: keeps us at 12% of monthly and 48% of weekly (both safe)
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manager.record_spend("architect", 6000).await.unwrap();
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let status = manager.check_budget("architect").await.unwrap();
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assert_eq!(status.monthly_remaining_cents, 44000);
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assert!((status.monthly_utilization - 0.12).abs() < 0.01);
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assert!(!status.exceeded);
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assert!(!status.near_threshold); // 12% < 80%
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assert_eq!(status.fallback_provider, "gemini");
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}
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