Author: Aruna Govindaraju

This blog post introduces a new disk-based vector search approach that allows efficient querying of vectors stored on disk without loading them entirely into memory. By implementing these quantization methods, organizations can achieve compression ratios of up to 64x, enabling cost-effective scaling of vector databases for large-scale AI and machine learning applications.

With the launch of the neural search feature for Amazon OpenSearch Service in OpenSearch 2.9, it’s now effortless to integrate with AI/ML models to power semantic search and other use cases. OpenSearch Service has supported both lexical and vector search since the introduction of its k-nearest neighbor (k-NN) feature in 2020; however, configuring semantic search […]

We often need our text search to be agnostic of accent marks. Accent-insensitive search, also called diacritics-agnostic search, is where search results are the same for queries that may or may not contain Latin characters such as à, è, Ê, ñ, and ç. Diacritics are English letters with an accent to mark a difference in […]