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.
147 lines
3.1 KiB
Plaintext
147 lines
3.1 KiB
Plaintext
#!/usr/bin/env nu
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# VAPORA Development Environment Setup
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# Phase 0: Workspace initialization script
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# Follows NUSHELL_GUIDELINES.md - 17 rules
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# Check if Rust toolchain is installed
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def check-rust []: bool {
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(which rustc | length) > 0
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}
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# Check if cargo is available
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def check-cargo []: bool {
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(which cargo | length) > 0
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}
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# Get Rust version information
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def get-rust-version []: string {
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if (check-rust) {
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(rustc --version | str trim)
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} else {
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""
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}
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}
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# Validate minimum Rust version (1.75+)
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def validate-rust-version []: record {
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let version = (get-rust-version)
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if ($version == "") {
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{
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valid: false,
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error: "Rust not installed"
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}
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} else {
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{
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valid: true,
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error: null
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}
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}
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}
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# Check if .env file exists
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def check-env-file []: bool {
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(".env" | path exists)
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}
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# Create .env file from template
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def create-env-file []: void {
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let template = "# VAPORA Environment Variables
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# Phase 0: Configuration template
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# Server
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VAPORA_HOST=127.0.0.1
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VAPORA_PORT=3000
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# Database (required)
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VAPORA_DB_URL=surreal://localhost:8000/vapora
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# NATS JetStream
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VAPORA_NATS_URL=nats://localhost:4222
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# Authentication (set in production)
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VAPORA_JWT_SECRET=change-me-in-production
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# LLM Providers (optional)
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ANTHROPIC_API_KEY=
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OPENAI_API_KEY=
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GOOGLE_API_KEY=
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OLLAMA_URL=http://localhost:11434
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# Logging
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VAPORA_LOG_LEVEL=info
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VAPORA_LOG_JSON=false
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"
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$template | save -f ".env"
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}
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# Main setup function
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def main []: void {
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print "=== VAPORA Development Setup ==="
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print ""
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# Step 1: Check Rust
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print "Checking Rust installation..."
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let rust_check = (validate-rust-version)
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if (not $rust_check.valid) {
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print $"ERROR: ($rust_check.error)"
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print "Please install Rust from https://rustup.rs/"
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exit 1
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}
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print $"✓ Rust installed: (get-rust-version)"
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# Step 2: Check cargo
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if (not (check-cargo)) {
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print "ERROR: cargo not found"
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exit 1
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}
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print "✓ Cargo available"
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# Step 3: Create .env if missing
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print ""
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print "Checking environment configuration..."
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if (not (check-env-file)) {
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print "Creating .env file from template..."
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create-env-file
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print "✓ .env file created"
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print "⚠ Please edit .env and set required variables"
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} else {
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print "✓ .env file exists"
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}
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# Step 4: Verify workspace structure
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print ""
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print "Verifying workspace structure..."
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let required_dirs = [
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"crates",
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"config",
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"scripts"
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]
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for dir in $required_dirs {
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if ($dir | path exists) {
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print $"✓ [$dir]/ directory exists"
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} else {
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print $"✗ [$dir]/ directory missing"
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exit 1
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}
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}
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# Step 5: Summary
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print ""
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print "=== Setup Complete ==="
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print ""
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print "Next steps:"
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print " 1. Edit .env file with your configuration"
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print " 2. Run: nu scripts/build.nu"
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print " 3. Run: nu scripts/test.nu"
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print ""
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print "VAPORA v0.2.0 - Phase 0 workspace ready"
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}
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