colbertv2.0

colbertv2.0 is a high-speed, high-accuracy retrieval model that enables scalable neural search over large text corpora using BERT-based embeddings. It introduces a “late interaction” mechanism where passages and queries are encoded into matrices of token-level embeddings. These are compared efficiently at search time using MaxSim operations, preserving contextual richness without sacrificing speed. Trained on datasets like MS MARCO, it significantly outperforms single-vector retrieval approaches and supports indexing and querying millions of documents with sub-second latency. ColBERTv2 builds on previous ColBERT versions with improved training, lightweight server options, and support for integration into end-to-end LLM pipelines.