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.
69 lines
2.8 KiB
Rust
69 lines
2.8 KiB
Rust
use criterion::{black_box, criterion_group, criterion_main, Criterion};
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use vapora_tracking::parsers::{ClaudeTodoParser, MarkdownParser};
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fn markdown_parse_changes_bench(c: &mut Criterion) {
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let content = "---\nproject: vapora\nlast_sync: 2025-11-10T14:30:00Z\n---\n\n\
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## 2025-11-10T14:30:00Z - Implemented WebSocket sync\n\
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**Impact**: backend | **Breaking**: no | **Files**: 5\n\
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Non-blocking async synchronization using tokio channels.\n\n\
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## 2025-11-09T10:15:00Z - Fixed database indices\n\
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**Impact**: performance | **Breaking**: no | **Files**: 2\n\
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Optimized query performance for tracking entries.\n\n\
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## 2025-11-08T16:45:00Z - Added error context\n\
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**Impact**: infrastructure | **Breaking**: no | **Files**: 3\n\
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Improved error messages with structured logging.\n";
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c.bench_function("markdown_parse_changes_small", |b| {
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b.iter(|| MarkdownParser::parse_changes(black_box(content), black_box("/test")))
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});
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}
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fn markdown_parse_todos_bench(c: &mut Criterion) {
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let content = "---\nproject: vapora\nlast_sync: 2025-11-10T14:30:00Z\n---\n\n\
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## [ ] Implement webhook system\n\
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**Priority**: H | **Estimate**: L | **Tags**: #feature #api\n\
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**Created**: 2025-11-10T14:30:00Z | **Due**: 2025-11-15\n\
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Implement bidirectional webhook system for real-time events.\n\n\
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## [>] Refactor database layer\n\
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**Priority**: M | **Estimate**: M | **Tags**: #refactor #database\n\
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**Created**: 2025-11-08T10:00:00Z | **Due**: 2025-11-20\n\
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Improve database abstraction and reduce code duplication.\n\n\
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## [x] Setup CI/CD pipeline\n\
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**Priority**: H | **Estimate**: S | **Tags**: #infrastructure\n\
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**Created**: 2025-11-05T08:00:00Z\n\
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GitHub Actions workflow for automated testing.\n";
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c.bench_function("markdown_parse_todos_small", |b| {
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b.iter(|| MarkdownParser::parse_todos(black_box(content), black_box("/test")))
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});
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}
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fn claude_todo_parser_bench(c: &mut Criterion) {
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let content = r#"[
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{
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"content": "Implement feature X",
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"status": "pending"
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},
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{
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"content": "Fix bug in parser",
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"status": "in_progress"
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},
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{
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"content": "Update documentation",
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"status": "completed"
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}
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]"#;
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c.bench_function("claude_todo_parse_small", |b| {
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b.iter(|| ClaudeTodoParser::parse(black_box(content), black_box("/test")))
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});
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}
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criterion_group!(
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benches,
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markdown_parse_changes_bench,
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markdown_parse_todos_bench,
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claude_todo_parser_bench
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);
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criterion_main!(benches);
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