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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
// Error types for VAPORA v1.0
// Phase 1: Comprehensive error handling with proper conversions
use thiserror::Error;
/// Main error type for VAPORA
#[derive(Error, Debug)]
pub enum VaporaError {
/// Configuration loading or validation error
#[error("Configuration error: {0}")]
ConfigError(String),
/// Database operation error
#[error("Database error: {0}")]
DatabaseError(String),
/// Resource not found error
#[error("Not found: {0}")]
NotFound(String),
/// Invalid input or validation error
#[error("Invalid input: {0}")]
InvalidInput(String),
/// Authentication or authorization error
#[error("Unauthorized: {0}")]
Unauthorized(String),
/// Agent system error
#[error("Agent error: {0}")]
AgentError(String),
/// LLM router error
#[error("LLM router error: {0}")]
LLMRouterError(String),
/// Workflow execution error
#[error("Workflow error: {0}")]
WorkflowError(String),
/// NATS messaging error
#[error("NATS error: {0}")]
NatsError(String),
/// IO operation error
#[error("IO error: {0}")]
IoError(#[from] std::io::Error),
/// Serialization/deserialization error
#[error("Serialization error: {0}")]
SerializationError(#[from] serde_json::Error),
/// TOML parsing error
#[error("TOML error: {0}")]
TomlError(String),
/// Internal server error
#[error("Internal server error: {0}")]
InternalError(String),
}
/// Result type alias using VaporaError
pub type Result<T> = std::result::Result<T, VaporaError>;
// ============================================================================
// Error Conversions
// ============================================================================
#[cfg(feature = "backend")]
impl From<surrealdb::Error> for VaporaError {
fn from(err: surrealdb::Error) -> Self {
VaporaError::DatabaseError(err.to_string())
}
}
impl From<toml::de::Error> for VaporaError {
fn from(err: toml::de::Error) -> Self {
VaporaError::TomlError(err.to_string())
}
}