Vapora/docker/vapora-frontend.Dockerfile

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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: 0.3*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.
2026-01-11 13:03:53 +00:00
# Multi-stage build for VAPORA Frontend (Leptos CSR)
# Build stage
FROM rust:1.75-alpine AS builder
WORKDIR /usr/src/app
# Install build dependencies
RUN apk add --no-cache \
musl-dev \
npm \
pkgconfig \
openssl-dev
# Install trunk for WASM building
RUN cargo install trunk --locked
# Install wasm-bindgen-cli
RUN cargo install wasm-bindgen-cli --locked
# Add wasm32 target
RUN rustup target add wasm32-unknown-unknown
# Copy workspace files
COPY Cargo.toml Cargo.lock ./
COPY crates ./crates
# Build frontend
WORKDIR /usr/src/app/crates/vapora-frontend
RUN trunk build --release
# Runtime stage
FROM nginx:alpine
# Remove default nginx config
RUN rm /etc/nginx/conf.d/default.conf
# Create nginx configuration
RUN cat > /etc/nginx/conf.d/default.conf << 'EOF'
server {
listen 80;
server_name _;
root /usr/share/nginx/html;
index index.html;
# Gzip compression
gzip on;
gzip_types text/plain text/css application/json application/javascript text/xml application/xml application/xml+rss text/javascript application/wasm;
# Frontend static files
location / {
try_files $uri $uri/ /index.html;
add_header Cache-Control "public, max-age=3600";
}
# WASM files need special MIME type
location ~ \.wasm$ {
types {
application/wasm wasm;
}
add_header Cache-Control "public, max-age=86400";
}
# API proxy
location /api/ {
proxy_pass http://vapora-backend:8080/api/;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto $scheme;
}
# WebSocket proxy
location /ws/ {
proxy_pass http://vapora-backend:8080/ws/;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection "upgrade";
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
}
}
EOF
# Copy built frontend from builder
COPY --from=builder /usr/src/app/crates/vapora-frontend/dist /usr/share/nginx/html
# Create simple health check file
RUN echo "OK" > /usr/share/nginx/html/health.html
EXPOSE 80
# Health check
HEALTHCHECK --interval=10s --timeout=5s --start-period=5s --retries=3 \
CMD wget --quiet --tries=1 --spider http://localhost/health.html || exit 1
CMD ["nginx", "-g", "daemon off;"]