Will new vector databases dislodge traditional search engines?

The emergence of vector databases poses questions about their potential to replace traditional search engines, yet these new systems generally lack features such as phrase search and dynamic summaries that are critical for comprehensive search engine implementations. Traditional search engines like Apache Lucene and Vespa have decades of development, offering features like accelerated dynamic pruning algorithms and real-time signal integration, which are challenging to replicate with dense vector calculations alone. While hybrid search models combining sparse and dense vectors prove more effective, integrating dense vector capabilities into traditional engine architectures can lead to increased latency and costs. Vespa, an open-source big data serving engine, presents a viable alternative by implementing mutable HNSW graphs and facilitating efficient hybrid search without the drawbacks of immutable data structures. Vespa's architecture ensures low latency and eliminates the need for full re-indexing when updating data, offering a scalable solution with proven ranking results on extensive open relevancy datasets.