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Vapora/.devcontainer/devcontainer.json

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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
{
"name": "VAPORA Development",
"dockerComposeFile": "docker-compose.yml",
"service": "devcontainer",
"workspaceFolder": "/workspace",
"features": {
"ghcr.io/devcontainers/features/rust:1": {
"version": "1.75"
},
"ghcr.io/devcontainers/features/git:1": {
"version": "latest"
}
},
"postCreateCommand": "cargo build --workspace",
"customizations": {
"vscode": {
"extensions": [
"rust-lang.rust-analyzer",
"vadimcn.vscode-lldb",
"serayuzgur.crates",
"tamasfe.even-better-toml",
"esbenp.prettier-vscode"
],
"settings": {
"[rust]": {
"editor.formatOnSave": true,
"editor.defaultFormatter": "rust-lang.rust-analyzer"
},
"rust-analyzer.checkOnSave.command": "clippy",
"rust-analyzer.checkOnSave.extraArgs": ["--all-targets", "--all-features"],
"terminal.integrated.defaultProfile.linux": "bash"
}
}
},
"forwardPorts": [
3000,
8000,
8001,
8002,
4222,
11434
],
"portAttributes": {
"3000": {
"label": "Frontend (Leptos)",
"onAutoForward": "notify"
},
"8000": {
"label": "SurrealDB",
"onAutoForward": "notify"
},
"8001": {
"label": "Backend API",
"onAutoForward": "notify"
},
"8002": {
"label": "Agent Server",
"onAutoForward": "notify"
},
"4222": {
"label": "NATS",
"onAutoForward": "notify"
},
"11434": {
"label": "Ollama",
"onAutoForward": "silent"
}
},
"remoteEnv": {
"SURREAL_URL": "ws://surrealdb:8000",
"SURREAL_USER": "root",
"SURREAL_PASS": "root",
"NATS_URL": "nats://nats:4222",
"OLLAMA_URL": "http://ollama:11434",
"LOG_LEVEL": "debug"
}
}
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