Sparse Late Interaction Retrieval model that combines the benefits of sparse representations with late interaction mechanisms. Provides efficient storage and fast retrieval while maintaining the accuracy advantages of token-level matching in sparse embedding space.

Advanced multi-vector retrieval model creating token-level embeddings with late interaction mechanism, featuring denoised supervision and improved memory efficiency over original ColBERT.

Specialized retrieval tool for RAG in LLM apps using ColBERT late-interaction for token-level matching, integrable with vector stores like FAISS for high-precision retrieval and reranking.

The first workshop dedicated to late interaction and multi-vector retrieval methods at ECIR 2026, featuring keynote speaker Omar Khattab (ColBERT creator) and focusing on advances in token-level representations, multi-modal retrieval, and long-context search.

Library built on Sentence Transformers for flexible training, inference, and retrieval with state-of-the-art ColBERT models. Features FastPLAID index for efficient multi-vector late interaction retrieval with 10x storage compression and sub-200ms latency.

Late interaction retrieval model that applies the ColBERT token-level embedding approach using the Qwen language model as the base encoder. Provides high-quality semantic search with detailed token-level matching for improved retrieval accuracy.