Vapora/crates/vapora-telemetry/benches/metrics_benchmarks.rs
Jesús Pérez d14150da75 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

149 lines
4.6 KiB
Rust

use criterion::{black_box, criterion_group, criterion_main, Criterion};
use vapora_telemetry::MetricsCollector;
fn metrics_record_task(c: &mut Criterion) {
c.bench_function("record_task_success", |b| {
b.iter(|| {
let collector = MetricsCollector::new();
black_box(collector.record_task_start());
black_box(collector.record_task_success(black_box(1000)));
});
});
}
fn metrics_record_provider_call(c: &mut Criterion) {
c.bench_function("record_provider_call", |b| {
b.iter(|| {
let collector = MetricsCollector::new();
black_box(collector.record_provider_call(
black_box("claude"),
black_box(1000),
black_box(500),
black_box(0.05),
));
});
});
}
fn metrics_get_task_metrics(c: &mut Criterion) {
c.bench_function("get_task_metrics_1000_records", |b| {
b.iter_batched(
|| {
let collector = MetricsCollector::new();
for i in 0..1000 {
collector.record_task_start();
if i % 100 != 0 {
collector.record_task_success(1000 + (i as u64 * 10) % 5000);
} else {
collector.record_task_failure(5000, "timeout");
}
}
collector
},
|collector| {
black_box(collector.get_task_metrics())
},
criterion::BatchSize::SmallInput,
);
});
}
fn metrics_get_provider_metrics(c: &mut Criterion) {
c.bench_function("get_provider_metrics_500_calls", |b| {
b.iter_batched(
|| {
let collector = MetricsCollector::new();
for i in 0..500 {
let provider = match i % 3 {
0 => "claude",
1 => "openai",
_ => "gemini",
};
collector.record_provider_call(
provider,
100 + (i as u64 * 10),
200 + (i as u64 * 20),
0.01 + (i as f64 * 0.001),
);
}
collector
},
|collector| {
black_box(collector.get_provider_metrics())
},
criterion::BatchSize::SmallInput,
);
});
}
fn metrics_get_system_metrics(c: &mut Criterion) {
c.bench_function("get_system_metrics_200_tasks_10_providers", |b| {
b.iter_batched(
|| {
let collector = MetricsCollector::new();
// Record tasks
for i in 0..200 {
collector.record_task_start();
if i % 20 != 0 {
collector.record_task_success(1000 + (i as u64 * 100));
} else {
collector.record_task_failure(5000, "execution_error");
}
}
// Record provider calls
for i in 0..100 {
let provider = match i % 5 {
0 => "claude",
1 => "openai",
2 => "gemini",
3 => "ollama",
_ => "anthropic",
};
collector.record_provider_call(
provider,
100 + (i as u64 * 20),
200 + (i as u64 * 40),
0.01 + (i as f64 * 0.002),
);
}
// Record heartbeats and coalitions
for _ in 0..50 {
collector.record_heartbeat();
}
for _ in 0..10 {
collector.record_coalition();
}
collector
},
|collector| {
black_box(collector.get_system_metrics())
},
criterion::BatchSize::SmallInput,
);
});
}
fn metrics_clone_overhead(c: &mut Criterion) {
c.bench_function("clone_metrics_collector", |b| {
b.iter(|| {
let collector = MetricsCollector::new();
black_box(collector.clone())
});
});
}
criterion_group!(
benches,
metrics_record_task,
metrics_record_provider_call,
metrics_get_task_metrics,
metrics_get_provider_metrics,
metrics_get_system_metrics,
metrics_clone_overhead
);
criterion_main!(benches);