Model Releases
Constella Preview: Swap Query Models Without Re-Embedding
As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means
As query traffic grows, so does the compute bill for embedding it. On a low-power device, a large model may not fit in memory. A smaller query model could reduce that cost, but switching usually means re-embedding the collection. Constella lets you make that switch. It’s a family of models on Hugging Face built around Stella, a 400M-parameter English embedding model. Stella encodes your documents. Zero, Nano, or Stella itself can then encode your queries, all searching the same Qdrant collection.
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Source: Qdrant | 2026-09-29