In-depth technical breakdown of how binary quantization works and how to use it in Weaviate.

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Binary Quantization is a vector compression algorithm that reduces memory requirements. It works by retaining the sign of each dimension in a vector and encoding it as a 1 or 0. Working with Binarized Vectors involves considering the distance between binary vectors and the importance of data distribution. Binary Quantization offers performance improvements in terms of search time, indexing time, and memory footprint. Benchmarking can help determine the optimal compression technique for your data.

32x Reduced Memory Usage With Binary Quantization

📐Details of Working with Binarized Vectors ​

🚀 Performance Improvements with BQ ​

⚖️Comparing Product and Binary Quantization ​

🧑‍💻Benchmarking BQ with your own data ​