Precision on demand isn’t a rumor—it's here. The Qbit vector search engine from ClickHouse lets you choose precision at query time, flipping the switch between speed and accuracy on the fly [1].
Adjustable Precision at Query Time The knob isn’t cosmetic. It lets teams trade recall for latency as workload realities shift, showing how runtime precision can shape real-world performance [1].
Scale at 1B Vectors Powering AI at Scale: Benchmarking 1B Vectors in YugabyteDB spotlights massive-vector workloads. The benchmark demonstrates that scale-heavy analytics can coexist with vector search, underscoring how modern databases push for both recall and throughput [2].
Domain-Specific Speed: Legal Documents In the legal domain, Adlumal builds fast vector search for documents, illustrating how domain-aware workloads benefit from tuned performance without sacrificing relevance [3].
Rama's Spectrum: Exact to Approximate Diving into Rama: A Clojure LSH Vector Search Experiment maps the spectrum from exact to approximate search, offering tangible lessons on when and how to lean into hashing-based speedups [4].
Closing thought: the trend is clear—precision knobs, scale-ready engines, and domain-aware tweaks are becoming mainstream in vector databases.
References
We built a vector search engine that lets you choose precision at query time
Built vector search engine with adjustable precision; seeks benchmarks comparing against other vector stores.
View sourcePowering AI at Scale: Benchmarking 1B Vectors in YugabyteDB
Evaluates YugabyteDB performance on vector workloads at scale, using one billion vectors to test storage, indexing, and throughput, and scalability.
View sourceI Built Fast Vector Search for Legal Documents
Describes building lightning-fast vector search for legal documents using embeddings.
View sourceDiving into Rama: A Clojure LSH Vector Search Experiment
Explores LSH-based vector search in Clojure using Rama prototype
View source