Accelerating Transformer-based Embedding Retrieval with Vespa

Vespa Blog's post, authored by Chief Scientist Jo Kristian Bergum, explores accelerating transformer-based embedding retrieval by utilizing the Vespa platform, focusing on embedding inference and retrieval with nearest neighbor search. The blog emphasizes the role of text embedding models, particularly encoder-only transformer models like BERT, in mapping text to vector spaces for multilingual retrieval. It discusses the complexity of inference, the importance of sequence length, and the trade-offs between model size and accuracy. Through experiments using Vespa on a laptop, it demonstrates performance improvements with techniques like post-training quantization and approximate nearest neighbor search, leading to enhanced throughput and reduced latency without significantly sacrificing retrieval quality. The article highlights Vespa's ability to streamline embedding inference and retrieval processes, offering a flexible, efficient solution for deploying embedding models across various environments without the need for separate infrastructure.

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