Billion-scale vector search with Vespa - part one

Vespa's blog post introduces a series on billion-scale vector search, focusing on using Vespa to manage and search massive datasets through AI-powered vector representations. It addresses the challenges of working with star-sized data, such as balancing accuracy, latency, and efficiency in approximate nearest neighbor searches. The post delves into the advantages of using compact binary-coded vector representations, which significantly reduce storage requirements compared to continuous vector representations. By utilizing Vespa's tensor field with int8 precision, these binary codes can efficiently perform searches using the hamming distance metric. The post also discusses a two-phase search strategy that combines a coarse-level search using hamming distance with a more refined ranking phase utilizing continuous vector representations. Additionally, Vespa's capabilities in real-time indexing, ranking profiles, and integration with ONNX models for preprocessing are highlighted, setting the stage for future posts that will explore further trade-offs in search accuracy, storage, and latency.