Introducing support for sparse-dense embeddings for better search result

This post introduces public preview support for sparse-dense embeddings, enabling hybrid search. It details the technical differences between dense and sparse vectors, highlights the flexibility and simplicity of the new approach, and provides guidance on generating and upserting sparse-dense vectors. It also includes performance data comparing hybrid search with different sparse models and provides capacity projections for s1 and p1 pods. The post emphasizes the ability to combine any sparse representation with dense embeddings for improved search relevance.