Opening — Why this matters now Search systems have grown fluent, but not necessarily intelligent. As enterprises drown in text—contracts, filings, emails, reports—the gap between what users mean and what systems match has become painfully visible. Keyword search still dominates operational systems, while embedding-based similarity often settles for crude averages. This paper challenges that quiet compromise. ...
Opening — Why this matters now Cosine similarity has enjoyed an unusually long reign. From TF‑IDF vectors to transformer embeddings, it remains the default lens through which we judge “semantic closeness.” Yet the more expressive our embedding models become, the more uncomfortable this default starts to feel. If modern representations are nonlinear, anisotropic, and structurally rich, why are we still evaluating them with a metric that only understands angles? ...
A compact and efficient sentence embedding model from Sentence Transformers, ideal for semantic search, clustering, and sentence similarity tasks.
A high-quality English embedding model from BAAI, optimized for semantic search, retrieval-augmented generation (RAG), and ranking tasks.