Traditional vector search methods typically use sentence embeddings, but ColBERT goes further by representing text as token-level multi-vectors instead of a single vector. This approach allows for greater detail and improved search accuracy compared to traditional methods. While token-level late interaction requires more computing power, the combination of sentence-level vector search with token-level rerank can optimize efficiency and search quality. ColBERT is not limited to text retrieval and can also be used for visual document understanding. By leveraging VectorChord and PostgreSQL, users can efficiently store, index, and query data for high-performance vector search. The ColBERT rerank method has shown significant improvements in search results across various datasets. Future work includes exploring further enhancements and extensions for even better performance.
https://blog.vectorchord.ai/supercharge-vector-search-with-colbert-rerank-in-postgresql