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
2.4 KiB
Docker
98 lines
2.4 KiB
Docker
# Multi-stage build for VAPORA Frontend (Leptos CSR)
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# Build stage
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FROM rust:1.75-alpine AS builder
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WORKDIR /usr/src/app
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# Install build dependencies
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RUN apk add --no-cache \
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musl-dev \
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npm \
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pkgconfig \
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openssl-dev
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# Install trunk for WASM building
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RUN cargo install trunk --locked
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# Install wasm-bindgen-cli
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RUN cargo install wasm-bindgen-cli --locked
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# Add wasm32 target
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RUN rustup target add wasm32-unknown-unknown
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# Copy workspace files
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COPY Cargo.toml Cargo.lock ./
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COPY crates ./crates
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# Build frontend
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WORKDIR /usr/src/app/crates/vapora-frontend
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RUN trunk build --release
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# Runtime stage
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FROM nginx:alpine
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# Remove default nginx config
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RUN rm /etc/nginx/conf.d/default.conf
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# Create nginx configuration
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RUN cat > /etc/nginx/conf.d/default.conf << 'EOF'
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server {
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listen 80;
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server_name _;
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root /usr/share/nginx/html;
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index index.html;
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# Gzip compression
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gzip on;
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gzip_types text/plain text/css application/json application/javascript text/xml application/xml application/xml+rss text/javascript application/wasm;
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# Frontend static files
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location / {
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try_files $uri $uri/ /index.html;
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add_header Cache-Control "public, max-age=3600";
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}
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# WASM files need special MIME type
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location ~ \.wasm$ {
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types {
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application/wasm wasm;
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}
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add_header Cache-Control "public, max-age=86400";
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}
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# API proxy
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location /api/ {
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proxy_pass http://vapora-backend:8080/api/;
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proxy_set_header Host $host;
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proxy_set_header X-Real-IP $remote_addr;
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proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
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proxy_set_header X-Forwarded-Proto $scheme;
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}
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# WebSocket proxy
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location /ws/ {
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proxy_pass http://vapora-backend:8080/ws/;
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proxy_http_version 1.1;
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proxy_set_header Upgrade $http_upgrade;
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proxy_set_header Connection "upgrade";
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proxy_set_header Host $host;
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proxy_set_header X-Real-IP $remote_addr;
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}
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}
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EOF
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# Copy built frontend from builder
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COPY --from=builder /usr/src/app/crates/vapora-frontend/dist /usr/share/nginx/html
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# Create simple health check file
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RUN echo "OK" > /usr/share/nginx/html/health.html
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EXPOSE 80
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# Health check
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HEALTHCHECK --interval=10s --timeout=5s --start-period=5s --retries=3 \
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CMD wget --quiet --tries=1 --spider http://localhost/health.html || exit 1
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CMD ["nginx", "-g", "daemon off;"]
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