Vapora/docker/vapora-mcp-server.Dockerfile

# Multi-stage build for VAPORA MCP Server
# Build stage
FROM rust:1.75-alpine AS builder

WORKDIR /usr/src/app

# Install build dependencies
RUN apk add --no-cache \
    musl-dev \
    pkgconfig \
    openssl-dev \
    openssl-libs-static

# Copy workspace files
COPY Cargo.toml Cargo.lock ./
COPY crates ./crates

# Build MCP server
RUN cargo build --release -p vapora-mcp-server

# Runtime stage
FROM alpine:latest

RUN apk add --no-cache \
    ca-certificates \
    openssl \
    curl

WORKDIR /app

# Copy binary from builder
COPY --from=builder /usr/src/app/target/release/vapora-mcp-server /app/vapora-mcp-server

# Create non-root user
RUN addgroup -g 1000 vapora && \
    adduser -D -u 1000 -G vapora vapora && \
    chown -R vapora:vapora /app

USER vapora

# Expose port
EXPOSE 3000

# Health check
HEALTHCHECK --interval=10s --timeout=5s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:3000/health || exit 1

# Run
ENTRYPOINT ["/app/vapora-mcp-server"]
CMD ["--port", "3000"]
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: 0.3base + 0.5expertise + 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 MCP Server`
			`# Build stage`
			`FROM rust:1.75-alpine AS builder`

			`WORKDIR /usr/src/app`

			`# Install build dependencies`
			`RUN apk add --no-cache \`
			`musl-dev \`
			`pkgconfig \`
			`openssl-dev \`
			`openssl-libs-static`

			`# Copy workspace files`
			`COPY Cargo.toml Cargo.lock ./`
			`COPY crates ./crates`

			`# Build MCP server`
			`RUN cargo build --release -p vapora-mcp-server`

			`# Runtime stage`
			`FROM alpine:latest`

			`RUN apk add --no-cache \`
			`ca-certificates \`
			`openssl \`
			`curl`

			`WORKDIR /app`

			`# Copy binary from builder`
			`COPY --from=builder /usr/src/app/target/release/vapora-mcp-server /app/vapora-mcp-server`

			`# Create non-root user`
			`RUN addgroup -g 1000 vapora && \`
			`adduser -D -u 1000 -G vapora vapora && \`
			`chown -R vapora:vapora /app`

			`USER vapora`

			`# Expose port`
			`EXPOSE 3000`

			`# Health check`
			`HEALTHCHECK --interval=10s --timeout=5s --start-period=5s --retries=3 \`
			`CMD curl -f http://localhost:3000/health \|\| exit 1`

			`# Run`
			`ENTRYPOINT ["/app/vapora-mcp-server"]`
			`CMD ["--port", "3000"]`