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.
140 lines
5.2 KiB
Rust
140 lines
5.2 KiB
Rust
use criterion::{black_box, criterion_group, criterion_main, Criterion};
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use vapora_analytics::{EventPipeline, AgentEvent, AlertLevel};
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use tokio::sync::mpsc;
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fn pipeline_emit_event(c: &mut Criterion) {
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c.bench_function("emit_single_event", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let (alert_tx, _alert_rx) = mpsc::unbounded_channel();
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let (pipeline, _) = EventPipeline::new(alert_tx);
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let event = AgentEvent::new_task_completed(
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black_box("agent-1".to_string()),
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black_box("task-1".to_string()),
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1000,
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100,
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50,
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);
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black_box(pipeline.emit_event(black_box(event)).await)
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});
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});
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}
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fn pipeline_filter_events(c: &mut Criterion) {
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c.bench_function("filter_events_100_events", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter_batched(
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|| {
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let rt = tokio::runtime::Runtime::new().unwrap();
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rt.block_on(async {
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let (alert_tx, _alert_rx) = mpsc::unbounded_channel();
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let (pipeline, _) = EventPipeline::new(alert_tx);
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for i in 0..100 {
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let event = AgentEvent::new_task_completed(
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format!("agent-{}", i % 5),
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format!("task-{}", i),
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1000 + (i as u64 * 100),
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100 + (i as u64 * 10),
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50,
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);
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pipeline.emit_event(event).await.ok();
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}
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pipeline
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})
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},
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|pipeline| async move {
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black_box(
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pipeline
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.filter_events(|e| e.agent_id == "agent-1")
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)
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},
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criterion::BatchSize::SmallInput,
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);
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});
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}
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fn pipeline_get_error_rate(c: &mut Criterion) {
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c.bench_function("get_error_rate_200_events", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter_batched(
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|| {
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let rt = tokio::runtime::Runtime::new().unwrap();
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rt.block_on(async {
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let (alert_tx, _alert_rx) = mpsc::unbounded_channel();
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let (pipeline, _) = EventPipeline::new(alert_tx);
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for i in 0..200 {
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let event = if i % 20 == 0 {
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AgentEvent::new_task_failed(
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format!("agent-{}", i % 5),
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format!("task-{}", i),
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Some("timeout error".to_string()),
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)
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} else {
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AgentEvent::new_task_completed(
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format!("agent-{}", i % 5),
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format!("task-{}", i),
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1000 + (i as u64 * 100),
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100 + (i as u64 * 10),
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50,
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)
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};
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pipeline.emit_event(event).await.ok();
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}
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pipeline
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})
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},
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|pipeline| async move {
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black_box(pipeline.get_error_rate(60))
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},
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criterion::BatchSize::SmallInput,
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);
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});
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}
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fn pipeline_get_top_agents(c: &mut Criterion) {
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c.bench_function("get_top_agents_500_events", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter_batched(
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|| {
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let rt = tokio::runtime::Runtime::new().unwrap();
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rt.block_on(async {
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let (alert_tx, _alert_rx) = mpsc::unbounded_channel();
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let (pipeline, _) = EventPipeline::new(alert_tx);
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for i in 0..500 {
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let event = AgentEvent::new_task_completed(
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format!("agent-{}", i % 10),
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format!("task-{}", i),
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1000 + (i as u64 * 100) % 5000,
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100 + (i as u64 * 10),
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50,
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);
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pipeline.emit_event(event).await.ok();
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}
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pipeline
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})
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},
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|pipeline| async move {
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black_box(pipeline.get_top_agents(60, 5))
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},
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criterion::BatchSize::SmallInput,
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);
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});
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}
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criterion_group!(
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benches,
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pipeline_emit_event,
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pipeline_filter_events,
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pipeline_get_error_rate,
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pipeline_get_top_agents
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);
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criterion_main!(benches);
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