Vapora/provisioning/vapora-wrksp/taskservs/vapora-frontend.toml

[taskserv]
name = "vapora-frontend"
type = "service"
version = "0.2.0"
description = "VAPORA Frontend (Leptos CSR + UnoCSS)"

[source]
repository = "ssh://git@repo.jesusperez.pro:32225/jesus/Vapora.git"
branch = "main"
path = "vapora-frontend/"

[build]
runtime = "rust"
build_command = "trunk build --release"
artifact_path = "dist/"

[deployment]
namespace = "vapora-system"
replicas = 2
image = "vapora/frontend"
image_tag = "${version}"

[ports]
http = 3000

[resources]
requests = { cpu = "500m", memory = "512Mi" }
limits = { cpu = "1000m", memory = "1Gi" }

[health_check]
type = "http"
path = "/"
initial_delay_secs = 10
interval_secs = 30

[environment]
API_ENDPOINT = "http://vapora-backend.vapora-system:8080"
ENVIRONMENT = "production"

[scaling]
min_replicas = 2
max_replicas = 5
target_cpu = 60

[update_strategy]
type = "RollingUpdate"
max_surge = 1
max_unavailable = 0

[persistence]
enabled = false

[monitoring]
prometheus_metrics = false
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: 0.3base + 0.5expertise + 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			`[taskserv]`
			`name = "vapora-frontend"`
			`type = "service"`
			`version = "0.2.0"`
			`description = "VAPORA Frontend (Leptos CSR + UnoCSS)"`

			`[source]`
			`repository = "ssh://git@repo.jesusperez.pro:32225/jesus/Vapora.git"`
			`branch = "main"`
			`path = "vapora-frontend/"`

			`[build]`
			`runtime = "rust"`
			`build_command = "trunk build --release"`
			`artifact_path = "dist/"`

			`[deployment]`
			`namespace = "vapora-system"`
			`replicas = 2`
			`image = "vapora/frontend"`
			`image_tag = "${version}"`

			`[ports]`
			`http = 3000`

			`[resources]`
			`requests = { cpu = "500m", memory = "512Mi" }`
			`limits = { cpu = "1000m", memory = "1Gi" }`

			`[health_check]`
			`type = "http"`
			`path = "/"`
			`initial_delay_secs = 10`
			`interval_secs = 30`

			`[environment]`
			`API_ENDPOINT = "http://vapora-backend.vapora-system:8080"`
			`ENVIRONMENT = "production"`

			`[scaling]`
			`min_replicas = 2`
			`max_replicas = 5`
			`target_cpu = 60`

			`[update_strategy]`
			`type = "RollingUpdate"`
			`max_surge = 1`
			`max_unavailable = 0`

			`[persistence]`
			`enabled = false`

			`[monitoring]`
			`prometheus_metrics = false`