Vapora/kubernetes/02-nats.yaml

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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
apiVersion: v1
kind: ConfigMap
metadata:
name: nats-config
namespace: vapora
data:
nats.conf: |
port: 4222
http_port: 8222
jetstream {
store_dir: /data
max_memory_store: 256MB
max_file_store: 1GB
}
# Logging
debug: false
trace: false
logtime: true
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: nats
namespace: vapora
labels:
app: nats
component: messaging
spec:
replicas: 1
selector:
matchLabels:
app: nats
template:
metadata:
labels:
app: nats
component: messaging
spec:
containers:
- name: nats
image: nats:latest
ports:
- containerPort: 4222
name: client
protocol: TCP
- containerPort: 8222
name: monitor
protocol: TCP
args:
- "-c"
- "/etc/nats/nats.conf"
volumeMounts:
- name: config
mountPath: /etc/nats
- name: data
mountPath: /data
livenessProbe:
httpGet:
path: /healthz
port: 8222
initialDelaySeconds: 5
periodSeconds: 5
timeoutSeconds: 3
failureThreshold: 3
readinessProbe:
httpGet:
path: /healthz
port: 8222
initialDelaySeconds: 2
periodSeconds: 3
timeoutSeconds: 2
resources:
requests:
cpu: 100m
memory: 128Mi
limits:
cpu: 300m
memory: 512Mi
volumes:
- name: config
configMap:
name: nats-config
- name: data
emptyDir: {}
---
apiVersion: v1
kind: Service
metadata:
name: nats
namespace: vapora
labels:
app: nats
component: messaging
spec:
selector:
app: nats
ports:
- port: 4222
targetPort: 4222
name: client
protocol: TCP
- port: 8222
targetPort: 8222
name: monitor
protocol: TCP
type: ClusterIP