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.
98 lines
2.8 KiB
Plaintext
98 lines
2.8 KiB
Plaintext
#!/usr/bin/env nu
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# VAPORA Docker Build Script
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# Builds all Docker images for VAPORA v2.0
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def main [
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--registry: string = "docker.io" # Docker registry
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--tag: string = "latest" # Image tag
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--push # Push images to registry after build
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--no-cache # Build without cache
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] {
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print $"(ansi green)🐳 VAPORA Docker Build Script(ansi reset)"
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print $"(ansi blue)═══════════════════════════════════════════════(ansi reset)"
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print $"Registry: ($registry)"
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print $"Tag: ($tag)"
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print $"Push: ($push)"
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print ""
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# Define images
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let images = [
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{
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name: "vapora/backend"
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dockerfile: "crates/vapora-backend/Dockerfile"
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context: "."
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}
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{
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name: "vapora/frontend"
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dockerfile: "crates/vapora-frontend/Dockerfile"
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context: "."
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}
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{
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name: "vapora/agents"
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dockerfile: "crates/vapora-agents/Dockerfile"
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context: "."
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}
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{
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name: "vapora/mcp-server"
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dockerfile: "crates/vapora-mcp-server/Dockerfile"
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context: "."
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}
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]
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# Build each image
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for image in $images {
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print $"(ansi yellow)🔨 Building ($image.name):($tag)...(ansi reset)"
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let full_tag = $"($registry)/($image.name):($tag)"
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let build_args = [
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"build"
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"-f" $image.dockerfile
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"-t" $full_tag
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$image.context
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]
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let build_args = if $no_cache {
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$build_args | append ["--no-cache"]
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} else {
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$build_args
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}
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try {
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docker ...$build_args
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print $"(ansi green)✅ Built ($image.name):($tag)(ansi reset)"
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} catch {
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print $"(ansi red)❌ Failed to build ($image.name)(ansi reset)"
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exit 1
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}
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# Push if requested
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if $push {
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print $"(ansi cyan)📤 Pushing ($full_tag)...(ansi reset)"
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try {
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docker push $full_tag
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print $"(ansi green)✅ Pushed ($full_tag)(ansi reset)"
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} catch {
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print $"(ansi red)❌ Failed to push ($full_tag)(ansi reset)"
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exit 1
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}
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}
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print ""
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}
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print $"(ansi green)✅ All images built successfully!(ansi reset)"
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if $push {
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print $"(ansi green)✅ All images pushed to registry!(ansi reset)"
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} else {
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print $"(ansi yellow)💡 Tip: Use --push to push images to registry(ansi reset)"
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}
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print ""
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print $"(ansi cyan)Built images:(ansi reset)"
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for image in $images {
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print $" • ($registry)/($image.name):($tag)"
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}
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}
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