Simplify Search with Multilingual Embedding Models

The blog post explores the implementation of multilingual embedding models from the E5 family within Vespa, an open-source engine designed for large-scale data applications. It details how these models transform textual data into a unified vector space, facilitating multilingual information retrieval by allowing queries and documents from different languages to be compared seamlessly. The post highlights the trade-offs between model size, accuracy, and computational costs, noting that larger models increase storage and computational demands linearly, but not necessarily accuracy. It discusses the benefits of using Vespa's vector search and embedding inference capabilities, which enable developers to integrate multilingual semantic search into applications without complex infrastructure management. The evaluation of the small E5 variant on various datasets, including BEIR and MIRACL, shows that while dense embedding models can outperform traditional methods like BM25 in some contexts, they may underperform in low-resource language scenarios due to a lack of pre-training. The article concludes with a look ahead at optimizing embedding inference costs and improving retrieval effectiveness through precision adjustments.