Representing BGE embedding models in Vespa using bfloat16

In the blog post, Jo Kristian Bergum explores the integration of BGE (BAAI General Embedding) models into Vespa, highlighting their efficiency on the Massive Text Embedding Benchmark (MTEB) and their performance on the BEIR trec-covid dataset. The post details how Vespa's support for storing vectors using bfloat16 precision reduces memory usage by 50% with minimal impact on retrieval quality. It compares three BGE model variants, emphasizing the balance between accuracy and computational costs, and shows that quantization can enhance CPU inference efficiency. Additionally, the post discusses exporting BGE models to ONNX format for optimized inference and using Vespa's native embedding support for seamless deployment across different environments, eliminating the need for separate systems to manage embedding inference and nearest neighbor search.