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.
88 lines
1.7 KiB
YAML
88 lines
1.7 KiB
YAML
version: '3.9'
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services:
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devcontainer:
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build:
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context: .
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dockerfile: Dockerfile
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volumes:
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- ../:/workspace:cached
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- /var/run/docker.sock:/var/run/docker.sock
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environment:
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- SURREAL_URL=ws://surrealdb:8000
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- SURREAL_USER=root
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- SURREAL_PASS=root
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- NATS_URL=nats://nats:4222
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- OLLAMA_URL=http://ollama:11434
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- LOG_LEVEL=debug
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- RUST_LOG=vapora=debug,info
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command: sleep infinity
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depends_on:
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- surrealdb
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- nats
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- ollama
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networks:
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- vapora-network
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surrealdb:
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image: surrealdb/surrealdb:v2.3
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command: >
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start
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--log trace
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--user root
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--pass root
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memory
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ports:
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- "8000:8000"
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networks:
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- vapora-network
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
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interval: 10s
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timeout: 5s
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retries: 5
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nats:
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image: nats:2.10-alpine
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command: >
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-js
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-sd /data
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--http_port 8222
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ports:
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- "4222:4222"
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- "8222:8222"
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volumes:
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- nats-data:/data
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networks:
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- vapora-network
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healthcheck:
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test: ["CMD", "nats", "server", "ping"]
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interval: 10s
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timeout: 5s
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retries: 5
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ollama:
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image: ollama/ollama:latest
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ports:
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- "11434:11434"
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volumes:
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- ollama-data:/root/.ollama
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environment:
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- OLLAMA_HOST=0.0.0.0:11434
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networks:
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- vapora-network
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 40s
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volumes:
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nats-data:
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ollama-data:
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networks:
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vapora-network:
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driver: bridge
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