ColBERT is a neural information retrieval model and dense passage retrieval framework. It functions as a search engine that uses contextual embeddings to index text passages and retrieve relevant documents based on semantic meaning rather than keyword matching.
The system is distinguished by a late interaction architecture that defers the calculation of query and document similarity until the final step. It employs multi-vector indexing to store separate embeddings for every token in a document, enabling granular matching against query terms.
The project covers document indexing, passage retrieval and ranking, and model training using query-passage triples to improve search precision. It also includes a server implementation that provides ranked search results in JSON format for integration with external applications.