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ColBERTv2

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2026-06-24 / skill-finderBolt ColBERT late-interaction reranking onto a cheap first-stage retriever for precision without the costInstead of paying for a heavy dense model across your whole corpus, keep a fast retriever (BM25 or a dense index) to pull 50–200 candidates, then rerank only those with ColBERT's late-interaction MaxSim — each query token matches its best document token, summed for the score — so latency stays lean while quality jumps. ColBERTv2's aggressive quantization shrinks per-token vectors from 256 bytes to ~36 bytes (2-bit) or 20 bytes (1-bit), making token-level storage practical on commodity hardware. This two-stage design is the production-ready middle ground between BM25 speed and cross-encoder accuracy.

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