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.
298 lines
6.1 KiB
Markdown
298 lines
6.1 KiB
Markdown
# VAPORA Workspace for Provisioning
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## Multi-Agent Multi-IA Platform Deployment
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**Version**: 0.2.0
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**Status**: Specification + Structure (Ready for Implementation)
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This workspace contains all configuration for deploying VAPORA using Provisioning.
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---
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## 📁 Structure
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```
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vapora-wrksp/
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├── workspace.toml # Workspace configuration
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├── kcl/ # Infrastructure-as-Code (KCL schemas)
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│ ├── cluster.k # K8s cluster definition
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│ ├── services.k # Microservices
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│ ├── storage.k # Databases + storage
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│ ├── agents.k # Agent pools
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│ └── multi-ia.k # LLM Router configuration
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├── taskservs/ # Taskserv definitions (TOML)
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│ ├── vapora-backend.toml
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│ ├── vapora-frontend.toml
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│ ├── vapora-agents.toml
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│ ├── vapora-mcp-gateway.toml
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│ └── vapora-llm-router.toml
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├── workflows/ # Batch operations
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│ ├── deploy-full-stack.yaml
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│ ├── scale-agents.yaml
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│ ├── upgrade-vapora.yaml
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│ └── disaster-recovery.yaml
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└── README.md # This file
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```
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---
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## 🚀 Quick Start
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### 1. Prerequisites
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```bash
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# Install Provisioning
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./vapora-install.sh
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# Verify installation
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provisioning --version
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# Ensure kubectl access
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kubectl cluster-info
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```
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### 2. Deploy Cluster
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```bash
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cd provisioning/vapora-wrksp
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# Create K8s cluster from KCL
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provisioning cluster create --config kcl/cluster.k
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# This will:
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# - Deploy K3s or RKE2 (auto-detect)
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# - Install Istio service mesh
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# - Install Rook Ceph for storage
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# - Set up monitoring (Prometheus + Grafana)
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```
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### 3. Deploy Full Stack
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```bash
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# Deploy all VAPORA services
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provisioning workflow run workflows/deploy-full-stack.yaml
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# This will:
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# - Deploy SurrealDB (3 replicas)
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# - Deploy NATS JetStream (3 replicas)
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# - Deploy Redis
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# - Deploy Axum backend (3 replicas)
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# - Deploy Leptos frontend (2 replicas)
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# - Deploy Agent Runtime (3 replicas)
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# - Deploy MCP Gateway (2 replicas)
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# - Deploy LLM Router (2 replicas)
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# - Initialize database
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# - Configure ingress
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```
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### 4. Verify Deployment
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```bash
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# Check all services
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provisioning health-check --services all
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# Get status
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provisioning taskserv list
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# View logs
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provisioning logs tail -f vapora-backend
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# Access frontend
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open https://vapora.example.com
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```
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### 5. Scale Agents
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```bash
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# Scale developer agents to 10
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provisioning taskserv scale vapora-agents --agent developer --replicas 10
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# View autoscaling status
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provisioning taskserv autoscale status vapora-agents
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```
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---
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## 📋 Configuration
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### workspace.toml
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Master configuration for entire deployment:
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- Cluster settings
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- Agent pool sizes
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- LLM providers
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- Monitoring & scaling
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- Backup policies
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Edit this file to customize:
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```toml
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[agents]
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developer = { min = 5, max = 20 } # Scale developers to 20 max
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[llm_router]
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warn_threshold_daily = 5000 # Alert if daily LLM cost > $50
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```
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### Taskservs
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Each taskserv (in `taskservs/`) defines:
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- Source repository
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- Build configuration
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- Deployment resources
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- Health checks
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- Scaling policies
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- Environment variables
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- Secrets
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Customize per-service as needed.
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---
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## 🔧 Common Operations
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### Deploy New Version
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```bash
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# Update image version in workspace.toml or taskserv
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# Then upgrade service:
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provisioning taskserv upgrade vapora-backend --image vapora/backend:0.3.0
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# This uses rolling update strategy (no downtime)
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```
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### Rollback Service
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```bash
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provisioning taskserv rollback vapora-backend --to-version 0.2.0
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```
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### View Agent Logs
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```bash
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provisioning logs tail -f vapora-agents --agent developer-001
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```
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### Access Database
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```bash
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# Port-forward to SurrealDB
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kubectl port-forward -n vapora-system svc/surrealdb 8000:8000
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# Then access at http://localhost:8000
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```
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### Monitor Costs
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```bash
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# View LLM usage & costs
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provisioning metrics llm-router --daily
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# Output shows:
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# - Cost per provider
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# - Tokens used per agent
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# - Cost per task type
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```
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---
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## 🎯 Workflows
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Pre-built batch operations in `workflows/`:
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### deploy-full-stack.yaml
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Complete deployment from scratch (1 hour)
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### scale-agents.yaml
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Dynamic agent scaling based on queue depth
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### upgrade-vapora.yaml
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Rolling upgrade of all VAPORA services
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### disaster-recovery.yaml
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Restore from backups and verify
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---
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## 🔒 Security
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VAPORA deployment includes:
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- **mTLS**: Mutual TLS between all services (Istio)
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- **Network Policies**: Namespace isolation
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- **RBAC**: Kubernetes role-based access
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- **Secrets**: RustyVault integration
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- **Audit Logs**: Full audit trail via Cedar
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---
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## 📊 Monitoring
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Post-deployment, access:
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- **Grafana Dashboard**: `https://vapora.example.com/grafana`
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- Agent health & queue depth
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- API latency & error rates
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- LLM costs & usage
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- **Prometheus**: `https://vapora.example.com:9090`
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- Raw metrics scraping
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- **Logs**: `provisioning logs tail vapora-backend`
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---
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## 🆘 Troubleshooting
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### Pods not starting?
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```bash
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kubectl describe pod -n vapora-system <pod-name>
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provisioning logs tail vapora-backend
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```
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### Service unavailable?
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```bash
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# Check health
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provisioning health-check vapora-backend
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# Check ingress
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kubectl get ingress -n vapora-system
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# Check Istio VirtualService
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kubectl get vs -n vapora-system
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```
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### Database connection issues?
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```bash
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# Check SurrealDB
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provisioning logs tail vapora-system surrealdb
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# Port-forward and test
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kubectl port-forward svc/surrealdb 8000:8000
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curl http://localhost:8000/health
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```
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---
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## 📚 References
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- **Workspace Configuration**: `workspace.toml`
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- **Full Architecture**: `../../guides/core/VAPORA-ARCHITECTURE-V2.md`
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- **Provisioning Integration**: `../../guides/integration/PROVISIONING-INTEGRATION.md`
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- **KCL Schemas**: Read `.k` files in `kcl/` directory
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- **Taskserv Format**: Read `.toml` files in `taskservs/` directory
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---
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## 🚀 Next Steps
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1. ✅ Review `workspace.toml` and customize
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2. ✅ Run `provisioning cluster create`
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3. ✅ Run `provisioning workflow run deploy-full-stack.yaml`
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4. ✅ Access VAPORA at `https://vapora.example.com`
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5. ✅ Scale agents as needed for your workload
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---
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**Version**: 0.2.0
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**Status**: Ready for Implementation
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**Maintained**: By VAPORA Team
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