Constella Preview: Swap Query Models Without Re-Embedding - Qdrant

Introduces Constella, a family of embedding models (Stella, Zero, Nano) designed to allow swapping query models without re-embedding documents. Stella encodes documents, while Zero, Nano, or Stella can encode queries, all searching the same Qdrant collection. Zero uses a learned bag of tokens for minimal query compute. Nano uses a smaller transformer (34.5M parameters) to match Stella's embeddings with context. Benchmarks across 15 BEIR datasets show Nano retaining ~91% of full Stella's average score, with significantly lower query latency (0.081ms for Zero, 3.13ms for Nano vs. 38.95ms for full Stella on an M5 Pro CPU). Provides guidance on using Constella with FastEmbed and Qdrant for offline search, high-volume APIs, and hybrid search with BM25.