Revolutionizing Semantic Search with Multi-Vector HNSW Indexing in Vespa

Vespa has introduced multi-vector HNSW indexing, a feature that enhances semantic search by allowing multiple vectors per document, thus addressing the limitations of single-vector representation, particularly for lengthy text documents like Wikipedia articles. This advancement in Vespa, available from version 8.144.19, leverages deep learning models to represent data in high-dimensional vector spaces, enabling more efficient and precise nearest neighbor searches. Multi-vector indexing mitigates challenges such as input length limitations of Transformer-based models, which typically necessitate chunking text into smaller segments for effective embedding. By facilitating such indexing, Vespa simplifies the management of complex data structures, supports diverse retrieval tasks across various domains, and enhances search accuracy without requiring intricate relationship modeling. This development is particularly beneficial for applications requiring multi-modal searches, such as e-commerce, where products have evolving metadata and multiple associated vectors like images. The implementation demonstrates minimal performance differences between single and multi-vector indexing, with only slight increases in feed time and query latency, thus offering a scalable and efficient solution for deploying advanced semantic search capabilities.