Embedding-based Semantic Search for Technical Articles
Find semantically similar tech articles using embedding-based search and cold-start-friendly recommendations.
Try searching with custom input, or explore recommended articles.
Note: This demo is designed to illustrate the behavior and limitations of embedding-based semantic search, including scenarios where similarity scores may be low or unreliable.
This demo is built as a containerized, backend-first system with a strong focus on production readiness, GDPR-aware design, and accessibility-conscious UI. It is designed as a core component of a technical blog platform, providing semantic search and content-based recommendations for technical articles.
What you can explore
- Semantic search for technically related articles
- Content-based recommendations designed for cold-start scenarios
- Similarity scoring and ranking behavior
Engineering focus
- Backend-first system architecture with embedding-based semantic retrieval and light domain adaptation
- Cold-start-friendly recommendations without storing personal user data
- GDPR-aware and accessibility-conscious design, deployed as a cloud-based system
Built with Next.js, FastAPI, PostgreSQL (pgvector), and Sentence Transformers.