Vapora/kubernetes/01-surrealdb.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.
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apiVersion: v1
kind: ConfigMap
metadata:
name: surrealdb-config
namespace: vapora
data:
startup.sql: |
-- SurrealDB startup SQL
DEFINE NAMESPACE vapora;
USE NAMESPACE vapora;
DEFINE DATABASE main;
USE DATABASE main;
-- Define scopes for multi-tenancy
DEFINE SCOPE user SESSION 24h;
---
apiVersion: apps/v1
kind: StatefulSet
metadata:
name: surrealdb
namespace: vapora
labels:
app: surrealdb
component: database
spec:
serviceName: surrealdb
replicas: 1
selector:
matchLabels:
app: surrealdb
template:
metadata:
labels:
app: surrealdb
component: database
spec:
containers:
- name: surrealdb
image: surrealdb/surrealdb:latest
args:
- start
- --log
- info
- --user
- root
- --pass
- root
- file:/data
ports:
- containerPort: 8000
name: http
protocol: TCP
env:
- name: SURREAL_PATH
value: "/data"
volumeMounts:
- name: data
mountPath: /data
- name: config
mountPath: /etc/surrealdb
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 10
periodSeconds: 10
timeoutSeconds: 5
failureThreshold: 3
readinessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 5
periodSeconds: 5
timeoutSeconds: 3
failureThreshold: 3
resources:
requests:
cpu: 200m
memory: 256Mi
limits:
cpu: 500m
memory: 512Mi
volumes:
- name: config
configMap:
name: surrealdb-config
volumeClaimTemplates:
- metadata:
name: data
spec:
accessModes: ["ReadWriteOnce"]
resources:
requests:
storage: 20Gi
---
apiVersion: v1
kind: Service
metadata:
name: surrealdb
namespace: vapora
labels:
app: surrealdb
component: database
spec:
clusterIP: None
selector:
app: surrealdb
ports:
- port: 8000
targetPort: 8000
name: http
protocol: TCP