For those unfamiliar, is a high-performance, low-latency search engine designed specifically for massive, distributed datasets. Unlike legacy systems (such as Elasticsearch or basic grep commands) that rely on inverted indexes with static scoring, UltraSearch-1.5 utilizes a hybrid architecture combining vector embeddings , probabilistic data structures , and real-time index streaming .
Version 1.0 stored metadata in a raw, uncompressed form for speed. This led to massive RAM requirements. introduces ASC, an on-the-fly compression algorithm that learns the statistical distribution of your specific dataset. It compresses inverted indexes by up to 70% while maintaining sub-millisecond lookup times. This means you can index the entire Library of Congress on a single mid-tier server. ultrasearch-1.5
# Simple keyword query results = client.search("headphones", index="product_catalog") For those unfamiliar