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.
87 lines
3.7 KiB
Plaintext
87 lines
3.7 KiB
Plaintext
#!/usr/bin/env nu
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# VAPORA Provisioning Integration Validator
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# Validates that Provisioning workspace is properly configured
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# NOTE: Does NOT execute provisioning, only validates configuration
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def main [] {
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print $"(ansi green)🔍 VAPORA Provisioning Integration Validator(ansi reset)"
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print $"(ansi blue)═══════════════════════════════════════════════(ansi reset)"
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print ""
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# Check if provisioning workspace exists
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if not ("provisioning/vapora-wrksp" | path exists) {
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print $"(ansi red)❌ provisioning/vapora-wrksp directory not found(ansi reset)"
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exit 1
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}
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print $"(ansi green)✅ Provisioning workspace exists(ansi reset)"
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# Check workspace.toml
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if ("provisioning/vapora-wrksp/workspace.toml" | path exists) {
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print $"(ansi green)✅ workspace.toml found(ansi reset)"
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} else {
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print $"(ansi yellow)⚠️ workspace.toml not found(ansi reset)"
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}
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# Validate KCL files
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print ""
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print $"(ansi yellow)📝 Checking KCL files...(ansi reset)"
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let kcl_files = (ls provisioning/vapora-wrksp/kcl/**/*.k | get name)
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if ($kcl_files | is-empty) {
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print $"(ansi yellow)⚠️ No KCL files found in provisioning/vapora-wrksp/kcl/(ansi reset)"
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} else {
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print $"(ansi green)✅ Found ($kcl_files | length) KCL file(s)(ansi reset)"
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for file in $kcl_files {
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let rel_path = ($file | str replace $"(pwd)/provisioning/vapora-wrksp/" "")
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print $" • ($rel_path)"
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}
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}
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# Validate taskserv files
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print ""
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print $"(ansi yellow)🛠️ Checking taskserv definitions...(ansi reset)"
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let taskserv_files = (ls provisioning/vapora-wrksp/taskservs/**/*.toml 2>/dev/null | get name)
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if ($taskserv_files | is-empty) {
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print $"(ansi yellow)⚠️ No taskserv files found in provisioning/vapora-wrksp/taskservs/(ansi reset)"
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} else {
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print $"(ansi green)✅ Found ($taskserv_files | length) taskserv definition(s)(ansi reset)"
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for file in $taskserv_files {
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let rel_path = ($file | str replace $"(pwd)/provisioning/vapora-wrksp/" "")
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print $" • ($rel_path)"
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}
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}
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# Validate workflow files
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print ""
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print $"(ansi yellow)🔄 Checking workflow definitions...(ansi reset)"
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let workflow_files = (ls provisioning/vapora-wrksp/workflows/**/*.yaml 2>/dev/null | get name)
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if ($workflow_files | is-empty) {
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print $"(ansi yellow)⚠️ No workflow files found in provisioning/vapora-wrksp/workflows/(ansi reset)"
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} else {
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print $"(ansi green)✅ Found ($workflow_files | length) workflow definition(s)(ansi reset)"
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for file in $workflow_files {
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let rel_path = ($file | str replace $"(pwd)/provisioning/vapora-wrksp/" "")
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print $" • ($rel_path)"
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}
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}
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# Summary
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print ""
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print $"(ansi blue)═══════════════════════════════════════════════(ansi reset)"
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print $"(ansi green)✅ Provisioning integration validated(ansi reset)"
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print ""
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print $"(ansi yellow)📝 NOTE: Provisioning execution deferred for manual deployment(ansi reset)"
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print ""
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print $"(ansi cyan)To deploy using Provisioning:(ansi reset)"
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print " 1. cd provisioning/vapora-wrksp"
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print " 2. provisioning cluster create --config workspace.toml"
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print " 3. provisioning workflow run workflows/deploy-full-stack.yaml"
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print ""
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print $"(ansi cyan)For manual K8s deployment:(ansi reset)"
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print " Use: nu scripts/deploy-k8s.nu"
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}
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