Knowledge Curator

Overview

Knowledge Curator is a comprehensive Plone 6 add-on that transforms traditional content management into an intelligent personal knowledge management system. Built as a full-stack solution combining enterprise-grade Plone architecture with modern AI-powered features, it demonstrates advanced software engineering through containerized microservices, vector database integration, and learning science algorithms.

Target User & Use Cases

Primary Users: Knowledge workers, researchers, academics, and organizations requiring sophisticated content organization with long-term knowledge retention.

Key Use Cases:

Academic Research: Intelligent content curation with automatic relationship discovery across research domains
Professional Knowledge Management: Enterprise-grade personal knowledge libraries with semantic search capabilities
Learning Science Application: Spaced repetition algorithms for optimal knowledge retention using SM-2 scheduling
Content Discovery: AI-powered semantic search revealing hidden connections between concepts
Structured Learning: Progressive disclosure and milestone tracking for complex skill development
External Tool Integration: Seamless import/export with Obsidian, Zotero, and academic reference managers

Technical Architecture & Implementation

Full-Stack Plone Add-on Development

Backend Package: knowledge.curator - Professional Python package with complete Plone integration
Frontend Package: volto-knowledge-curator - Modern React components for Volto framework
Content Types: 6 specialized knowledge content types (Knowledge Items, Learning Goals, Research Notes, Project Logs, Bookmarks)
Behavioral Framework: Plone Dexterity behaviors for extensible knowledge object functionality
REST API: Custom endpoints for AI-powered knowledge operations and vector search

AI-Powered Vector Search Infrastructure

QDrant Vector Database: Production-grade vector storage with semantic similarity search (sub-2-second response times)
Semantic Processing: Sentence-transformers for natural language understanding and content embedding
Advanced Search Interface: Professional academic design with similarity threshold controls and filter combinations
API Integration: Custom /++api++/@vector-search endpoints processing natural language queries
Validated Performance: 100% test success rate across semantic search functionality with comprehensive automation

Containerized Microservices Architecture

Docker Orchestration: 9-service container stack with profile-based management (AI, Web, Integration profiles)
Traefik Reverse Proxy: Host-based routing with SSL termination and load balancing
PostgreSQL Database: Relational storage with proper backup and persistence strategies
Redis Cache: Background task processing and performance optimization
Varnish Caching: Web performance layer with cache purging automation
Network Isolation: Dual-network design separating AI services from web application traffic

Learning Science Integration

SM-2 Spaced Repetition Algorithm: Evidence-based scheduling for optimal knowledge retention
Learning Goal Management: Structured learning objectives with progress analytics
Adaptive Scheduling: Performance-based interval adjustments for personalized learning paths
Progress Analytics: Retention measurement and learning effectiveness tracking
Knowledge Gap Analysis: Automated identification of missing conceptual connections

Production-Ready Implementation

Enterprise Development Practices

Comprehensive Testing Framework: Automated test suites with 100% core functionality validation
CI/CD Pipeline Integration: GitHub Actions workflows for backend and frontend testing
Container Management: 30+ Makefile commands for development workflow automation
Code Quality Standards: Ruff formatting, Pyright type checking, professional linting configuration
Documentation Excellence: Complete API documentation, deployment guides, and troubleshooting resources

Scalable Infrastructure

Cloud-Agnostic Deployment: Docker-based deployment supporting AWS, GCP, Azure, and self-hosted environments
Health Monitoring: Container health checks and restart policies for production reliability
Volume Persistence: Data persistence across container restarts with proper backup strategies
Security Configuration: Network isolation, authentication middleware, and secure inter-service communication
Performance Optimization: Sub-second search capabilities supporting 10,000+ knowledge objects per user

Professional Interface Design

Academic Design Theme: Sophisticated visual identity suitable for scholarly and professional work
Responsive Architecture: Mobile-optimized interface with progressive disclosure for complex features
Accessibility Compliance: Semantic HTML structure with proper ARIA attributes and keyboard navigation
User Experience Excellence: Intuitive learning workflows with clear progress tracking and analytics visualization

Results & Engineering Excellence

Architectural Innovation: Successfully integrated modern AI technologies with enterprise-grade Plone CMS architecture
Vector Search Performance: Achieved 1.06-second average response times with 100% functional test success rate
Container Orchestration: Developed flexible profile-based service management supporting different development workflows
Full-Stack Integration: Seamless backend-frontend integration between Plone REST API and React-based Volto components
Learning Science Implementation: Applied evidence-based SM-2 algorithm for measurable knowledge retention improvement
Production Readiness: Comprehensive testing framework with automated validation and professional deployment strategies

This project demonstrates expertise in enterprise CMS development, AI/ML integration, containerized microservices architecture, full-stack Python/React development, and learning science application - showcasing the ability to build sophisticated, production-ready software systems that bridge traditional enterprise technology with cutting-edge AI capabilities.