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.
47 lines
1.2 KiB
TOML
47 lines
1.2 KiB
TOML
# VAPORA Server Configuration
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# Phase 0: Environment-based configuration
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[server]
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# Server will read from environment variables:
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# VAPORA_HOST (default: 127.0.0.1)
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# VAPORA_PORT (default: 3000)
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host = "${VAPORA_HOST:-127.0.0.1}"
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port = ${VAPORA_PORT:-3000}
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[server.tls]
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# TLS configuration (optional)
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# VAPORA_TLS_CERT_PATH
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# VAPORA_TLS_KEY_PATH
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enabled = ${VAPORA_TLS_ENABLED:-false}
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cert_path = "${VAPORA_TLS_CERT_PATH:-}"
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key_path = "${VAPORA_TLS_KEY_PATH:-}"
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[database]
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# Database connection
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# VAPORA_DB_URL (required)
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url = "${VAPORA_DB_URL}"
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max_connections = ${VAPORA_DB_MAX_CONNECTIONS:-10}
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[nats]
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# NATS JetStream configuration
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# VAPORA_NATS_URL (default: nats://localhost:4222)
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url = "${VAPORA_NATS_URL:-nats://localhost:4222}"
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stream_name = "${VAPORA_NATS_STREAM:-vapora-tasks}"
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[auth]
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# Authentication configuration
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# VAPORA_JWT_SECRET (required in production)
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jwt_secret = "${VAPORA_JWT_SECRET}"
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jwt_expiration_hours = ${VAPORA_JWT_EXPIRATION_HOURS:-24}
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[logging]
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# Logging configuration
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# VAPORA_LOG_LEVEL (default: info)
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level = "${VAPORA_LOG_LEVEL:-info}"
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json = ${VAPORA_LOG_JSON:-false}
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[metrics]
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# Metrics configuration
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enabled = ${VAPORA_METRICS_ENABLED:-true}
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port = ${VAPORA_METRICS_PORT:-9090}
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