About this demo
A backend-first, containerized system showcasing embedding-based semantic search and cold-start-friendly, content-based recommendations for technical articles.
This demo explores how embedding-based semantic search and content-based recommendations can be built as a backend-first system, with a focus on cold-start scenarios and privacy-aware design.
Instead of relying on user profiles or behavioral data, the system works purely on article content. This makes it suitable for early-stage platforms, internal tools, or privacy-sensitive environments where personalization data is unavailable or intentionally avoided.
The demo is designed as a recommendation service for technical articles, intended to be integrated into a larger content platform in the future. It is intentionally scoped to show how semantic similarity and embedding-based retrieval work in practice, without introducing the complexity of a full CMS or user management system.
What this demo covers
- Embedding-based semantic search for technically related articles
- Content-based recommendations that function in cold-start scenarios
- Similarity scoring, ranking, and transparent inspection of retrieved results
Design principles
- Backend-first architecture with a clean API boundary
- Privacy-aware approach without storing personal user data
- Accessibility-conscious UI and predictable interaction patterns
- Production-oriented packaging (containerized services and deployment-ready setup)
Scope and non-goals
- Not a full blog platform (no authoring workflow or CMS features)
- No user accounts or personalization based on individual user behavior
- Focuses on content understanding via embeddings and retrieval
Built with Next.js, FastAPI, PostgreSQL (pgvector), and Sentence Transformers.