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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
// vapora-backend: WebSocket handler for real-time workflow updates
// Phase 3: Stream workflow progress to connected clients
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use tokio::sync::broadcast;
use tracing::{debug, error};
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct WorkflowUpdate {
pub workflow_id: String,
pub status: String,
pub progress: u32,
pub message: String,
pub timestamp: chrono::DateTime<chrono::Utc>,
}
impl WorkflowUpdate {
pub fn new(workflow_id: String, status: String, progress: u32, message: String) -> Self {
Self {
workflow_id,
status,
progress,
message,
timestamp: chrono::Utc::now(),
}
}
}
/// Broadcaster for workflow updates
pub struct WorkflowBroadcaster {
tx: broadcast::Sender<WorkflowUpdate>,
}
impl WorkflowBroadcaster {
pub fn new() -> Self {
let (tx, _) = broadcast::channel(100);
Self { tx }
}
/// Send workflow update to all subscribers
pub fn send_update(&self, update: WorkflowUpdate) {
debug!(
"Broadcasting update for workflow {}: {} ({}%)",
update.workflow_id, update.message, update.progress
);
if let Err(e) = self.tx.send(update) {
error!("Failed to broadcast update: {}", e);
}
}
/// Subscribe to workflow updates
pub fn subscribe(&self) -> broadcast::Receiver<WorkflowUpdate> {
self.tx.subscribe()
}
/// Get subscriber count
pub fn subscriber_count(&self) -> usize {
self.tx.receiver_count()
}
}
impl Default for WorkflowBroadcaster {
fn default() -> Self {
Self::new()
}
}
impl Clone for WorkflowBroadcaster {
fn clone(&self) -> Self {
Self {
tx: self.tx.clone(),
}
}
}
// Note: WebSocket support requires ws feature in axum
// For Phase 4, we focus on the broadcaster infrastructure
// WebSocket handlers would be added when the ws feature is enabled
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_broadcaster_creation() {
let broadcaster = WorkflowBroadcaster::new();
assert_eq!(broadcaster.subscriber_count(), 0);
}
#[test]
fn test_subscribe() {
let broadcaster = WorkflowBroadcaster::new();
let _rx = broadcaster.subscribe();
assert_eq!(broadcaster.subscriber_count(), 1);
}
#[tokio::test]
async fn test_send_update() {
let broadcaster = WorkflowBroadcaster::new();
let mut rx = broadcaster.subscribe();
let update = WorkflowUpdate::new(
"wf-1".to_string(),
"in_progress".to_string(),
50,
"Step 1 completed".to_string(),
);
broadcaster.send_update(update.clone());
let received = rx.recv().await.unwrap();
assert_eq!(received.workflow_id, "wf-1");
assert_eq!(received.progress, 50);
}
#[tokio::test]
async fn test_multiple_subscribers() {
let broadcaster = WorkflowBroadcaster::new();
let mut rx1 = broadcaster.subscribe();
let mut rx2 = broadcaster.subscribe();
let update = WorkflowUpdate::new(
"wf-1".to_string(),
"completed".to_string(),
100,
"All steps completed".to_string(),
);
broadcaster.send_update(update);
let received1 = rx1.recv().await.unwrap();
let received2 = rx2.recv().await.unwrap();
assert_eq!(received1.workflow_id, received2.workflow_id);
assert_eq!(received1.progress, 100);
assert_eq!(received2.progress, 100);
}
#[test]
fn test_update_serialization() {
let update = WorkflowUpdate::new(
"wf-1".to_string(),
"running".to_string(),
75,
"Almost done".to_string(),
);
let json = serde_json::to_string(&update).unwrap();
let deserialized: WorkflowUpdate = serde_json::from_str(&json).unwrap();
assert_eq!(deserialized.workflow_id, "wf-1");
assert_eq!(deserialized.progress, 75);
}
}