4 posts tagged with "Weaviate"

Vector Search engines use Machine Learning models to offer incredible functionality to operate on your data. We are looking at anything from summarizers (that can summarize any text into a short) sentence), through auto-labelers (that can classify your data tokens), to transformers and vectorizers (that can convert any data – text, image, audio, etc. – into vectors and use that for context-based queries) and many more use cases.

All of these use cases require Machine Learning model inference – a process of running data through an ML model and calculating an output (e.g. take a paragraph, and summarize into to a short sentence) – which is a compute-heavy process.

Whenever I talk about vector search, I like to demonstrate it with an example of a semantic search. To add the wow factor, I like to run my queries on a Wikipedia dataset, which is populated with over 28 million paragraphs sourced from Wikipedia.

We are happy to announce the release of Weaviate 1.15, which is packed with great features, significant performance improvements, new distance metrics and modules, and many smaller improvements and fixes.