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.
71 lines
1.4 KiB
TOML
71 lines
1.4 KiB
TOML
[package]
|
|
name = "vapora-tracking"
|
|
version.workspace = true
|
|
edition.workspace = true
|
|
authors.workspace = true
|
|
license.workspace = true
|
|
repository.workspace = true
|
|
rust-version.workspace = true
|
|
description = "VAPORA integration adapter for tracking-core library"
|
|
|
|
[lib]
|
|
name = "vapora_tracking"
|
|
path = "src/lib.rs"
|
|
|
|
[dependencies]
|
|
# Core tracking library (standalone, from /Users/Akasha/Tools/tracking-manager)
|
|
tracking-core = { path = "../../../../Tools/tracking-manager/crates/tracking-core" }
|
|
|
|
# VAPORA dependencies
|
|
vapora-shared = { path = "../vapora-shared" }
|
|
|
|
# Serialization
|
|
serde = { workspace = true }
|
|
serde_json = { workspace = true }
|
|
serde_yaml = { workspace = true }
|
|
|
|
# Async runtime
|
|
tokio = { workspace = true }
|
|
futures = { workspace = true }
|
|
|
|
# Logging & observability
|
|
tracing = { workspace = true }
|
|
tracing-subscriber = { workspace = true }
|
|
|
|
# Error handling
|
|
thiserror = { workspace = true }
|
|
anyhow = { workspace = true }
|
|
|
|
# Time and UUID
|
|
chrono = { workspace = true }
|
|
uuid = { workspace = true }
|
|
|
|
# Optional NATS integration for event streaming
|
|
async-nats = { workspace = true, optional = true }
|
|
|
|
[dev-dependencies]
|
|
mockito = { workspace = true }
|
|
criterion = { workspace = true }
|
|
tempfile = { workspace = true }
|
|
|
|
[features]
|
|
default = []
|
|
test-util = []
|
|
|
|
[profile.release]
|
|
opt-level = 3
|
|
lto = true
|
|
codegen-units = 1
|
|
|
|
[profile.bench]
|
|
inherits = "release"
|
|
debug = true
|
|
|
|
[[bench]]
|
|
name = "parser_bench"
|
|
harness = false
|
|
|
|
[[bench]]
|
|
name = "storage_bench"
|
|
harness = false
|