The Excellence
Directory Platform Template

Retrieval paradigm where query and document tokens are encoded separately and interactions computed at search time, combining efficiency of bi-encoders with expressiveness of cross-encoders.

Scoring function used in late interaction models like ColBERT that computes query-document relevance by finding maximum similarity between each query token and document tokens, then summing.

Python library designed to simplify the integration and training of state-of-the-art late-interaction retrieval methods, particularly ColBERT, within RAG pipelines with a modular and user-friendly interface.

Groundbreaking multilingual information retrieval model supporting 89 languages with token-level embeddings and late interaction. Features Matryoshka embeddings for flexible efficiency-precision tradeoffs and 8192 token input context.

Maximum Similarity late interaction function introduced by ColBERT for ranking. Calculates cosine similarity between query and document token embeddings, keeping maximum score per query token for highly effective long-document retrieval.