Matryoshka 🤝 Binary vectors: Slash vector search costs with Vespa

Vespa has introduced support for Matryoshka Representation Learning (MRL) and Binary Quantization Learning (BQL) in its native hugging-face embedder, allowing for significant reductions in vector search costs by encoding text as binary vectors instead of large float vectors. These techniques, which can be applied as post-processing steps after model inference, facilitate the creation of compact text embeddings that reduce storage and computational resources while maintaining about 90% of the accuracy of the original float-based embeddings. The adoption of these methods within Vespa enables cost-effective and scalable vector search solutions, particularly advantageous for unstructured data and scenarios requiring large-scale data processing. This move not only slashes storage costs but also enhances the speed of similarity searches by utilizing efficient distance metrics like Hamming distance for binary vectors, thereby supporting more complex retrieval and ranking tasks without compromising performance.