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.
209 lines
4.4 KiB
Markdown
209 lines
4.4 KiB
Markdown
# VAPORA Kubernetes Manifests
|
|
|
|
Vanilla Kubernetes deployment manifests for VAPORA v1.0 (non-Istio).
|
|
|
|
## Overview
|
|
|
|
These manifests deploy the complete VAPORA stack:
|
|
- SurrealDB (StatefulSet with persistent storage)
|
|
- NATS JetStream (Deployment with ephemeral storage)
|
|
- Backend API (2 replicas)
|
|
- Frontend UI (2 replicas)
|
|
- Agents (3 replicas)
|
|
- MCP Server (1 replica)
|
|
- Ingress (nginx)
|
|
|
|
## Prerequisites
|
|
|
|
1. Kubernetes cluster (1.25+)
|
|
2. kubectl configured
|
|
3. nginx ingress controller installed
|
|
4. Storage class available for PVCs
|
|
5. (Optional) cert-manager for TLS
|
|
|
|
## Quick Deploy
|
|
|
|
```bash
|
|
# 1. Create namespace
|
|
kubectl apply -f 00-namespace.yaml
|
|
|
|
# 2. Update secrets in 03-secrets.yaml
|
|
# Edit the file and replace all CHANGE-ME values
|
|
|
|
# 3. Apply all manifests
|
|
kubectl apply -f .
|
|
|
|
# 4. Wait for all pods to be ready
|
|
kubectl wait --for=condition=ready pod -l app -n vapora --timeout=300s
|
|
|
|
# 5. Get ingress IP/hostname
|
|
kubectl get ingress -n vapora
|
|
```
|
|
|
|
## Manual Deploy (Ordered)
|
|
|
|
```bash
|
|
kubectl apply -f 00-namespace.yaml
|
|
kubectl apply -f 01-surrealdb.yaml
|
|
kubectl apply -f 02-nats.yaml
|
|
kubectl apply -f 03-secrets.yaml
|
|
kubectl apply -f 04-backend.yaml
|
|
kubectl apply -f 05-frontend.yaml
|
|
kubectl apply -f 06-agents.yaml
|
|
kubectl apply -f 07-mcp-server.yaml
|
|
kubectl apply -f 08-ingress.yaml
|
|
```
|
|
|
|
## Secrets Configuration
|
|
|
|
Before deploying, update `03-secrets.yaml` with real credentials:
|
|
|
|
```yaml
|
|
stringData:
|
|
jwt-secret: "$(openssl rand -base64 32)"
|
|
anthropic-api-key: "sk-ant-xxxxx"
|
|
openai-api-key: "sk-xxxxx"
|
|
gemini-api-key: "xxxxx" # Optional
|
|
surrealdb-user: "root"
|
|
surrealdb-pass: "$(openssl rand -base64 32)"
|
|
```
|
|
|
|
## Ingress Configuration
|
|
|
|
Update `08-ingress.yaml` with your domain:
|
|
|
|
```yaml
|
|
rules:
|
|
- host: vapora.yourdomain.com # Change this
|
|
```
|
|
|
|
For TLS with cert-manager:
|
|
|
|
```yaml
|
|
annotations:
|
|
cert-manager.io/cluster-issuer: "letsencrypt-prod"
|
|
tls:
|
|
- hosts:
|
|
- vapora.yourdomain.com
|
|
secretName: vapora-tls
|
|
```
|
|
|
|
## Monitoring
|
|
|
|
```bash
|
|
# Check all pods
|
|
kubectl get pods -n vapora
|
|
|
|
# Check services
|
|
kubectl get svc -n vapora
|
|
|
|
# Check ingress
|
|
kubectl get ingress -n vapora
|
|
|
|
# View logs
|
|
kubectl logs -n vapora -l app=vapora-backend
|
|
kubectl logs -n vapora -l app=vapora-agents
|
|
|
|
# Check health
|
|
kubectl exec -n vapora deploy/vapora-backend -- curl localhost:8080/health
|
|
```
|
|
|
|
## Scaling
|
|
|
|
```bash
|
|
# Scale backend
|
|
kubectl scale deployment vapora-backend -n vapora --replicas=3
|
|
|
|
# Scale agents
|
|
kubectl scale deployment vapora-agents -n vapora --replicas=5
|
|
|
|
# Scale frontend
|
|
kubectl scale deployment vapora-frontend -n vapora --replicas=3
|
|
```
|
|
|
|
## Troubleshooting
|
|
|
|
### Pods not starting
|
|
|
|
```bash
|
|
# Check events
|
|
kubectl get events -n vapora --sort-by='.lastTimestamp'
|
|
|
|
# Describe pod
|
|
kubectl describe pod -n vapora <pod-name>
|
|
|
|
# Check logs
|
|
kubectl logs -n vapora <pod-name>
|
|
```
|
|
|
|
### Database connection issues
|
|
|
|
```bash
|
|
# Check SurrealDB is running
|
|
kubectl get pod -n vapora -l app=surrealdb
|
|
|
|
# Test connection
|
|
kubectl exec -n vapora deploy/vapora-backend -- \
|
|
curl -v http://surrealdb:8000/health
|
|
```
|
|
|
|
### NATS connection issues
|
|
|
|
```bash
|
|
# Check NATS is running
|
|
kubectl get pod -n vapora -l app=nats
|
|
|
|
# Check NATS logs
|
|
kubectl logs -n vapora -l app=nats
|
|
|
|
# Monitor NATS
|
|
kubectl port-forward -n vapora svc/nats 8222:8222
|
|
open http://localhost:8222
|
|
```
|
|
|
|
## Uninstall
|
|
|
|
```bash
|
|
# Delete all resources in namespace
|
|
kubectl delete namespace vapora
|
|
|
|
# Or delete manifests individually
|
|
kubectl delete -f .
|
|
```
|
|
|
|
## Notes
|
|
|
|
- SurrealDB data is persisted in PVC (20Gi)
|
|
- NATS uses ephemeral storage (data lost on pod restart)
|
|
- All images use `latest` tag - update to specific versions for production
|
|
- Default resource limits are conservative - adjust based on load
|
|
- Frontend uses LoadBalancer service type - change to ClusterIP if using Ingress only
|
|
|
|
## Architecture
|
|
|
|
```
|
|
Internet
|
|
↓
|
|
[Ingress: vapora.example.com]
|
|
↓
|
|
├─→ / → [Frontend Service] → [Frontend Pods x2]
|
|
├─→ /api → [Backend Service] → [Backend Pods x2]
|
|
├─→ /ws → [Backend Service] → [Backend Pods x2]
|
|
└─→ /mcp → [MCP Service] → [MCP Server Pod]
|
|
|
|
Internal Services:
|
|
[Backend] ←→ [SurrealDB StatefulSet]
|
|
[Backend] ←→ [NATS]
|
|
[Agents x3] ←→ [NATS]
|
|
```
|
|
|
|
## Next Steps
|
|
|
|
After deployment:
|
|
1. Access UI at https://vapora.example.com
|
|
2. Check health at https://vapora.example.com/api/v1/health
|
|
3. Monitor logs in real-time
|
|
4. Configure external monitoring (Prometheus/Grafana)
|
|
5. Set up backups for SurrealDB PVC
|
|
6. Configure horizontal pod autoscaling (HPA)
|