Constella Preview: Swap Query Models Without Re-Embedding

Constella is a research-preview family of retrieval models that lets users encode documents once with the 400M-parameter Stella embedding model and then switch among Zero, Nano, or full Stella query encoders without re-embedding a Qdrant collection. Zero uses token lookup and pooling for extremely low-cost queries but lacks word-order awareness, while Nano is a 34.5M-parameter transformer distilled from Stella that adds contextual understanding; across 15 BEIR datasets, Nano retained about 91% of Stella’s average retrieval score, while Zero performed lower overall but was substantially faster and sometimes outperformed Nano. On an Apple M5 Pro CPU, reported warm query encoding times were 38.95 milliseconds for Stella, 3.13 milliseconds for Nano, and 0.081 milliseconds for Zero, excluding vector search and network costs. The approach is intended for offline and low-power search, high-volume APIs, search-as-you-type interfaces, repeated agent retrieval, and hybrid Zero-plus-BM25 search, with models available through Hugging Face and preview FastEmbed support as the project undergoes internal review ahead of release.

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