We are happy to announce the release of Weaviate 1.16, which brings a set of great features, performance and UX improvements, and fixes.

The brief​

If you like your content brief and to the point, here is the TL;DR of this release:

1. New Filter Operators – that allow you to filter data based on null values or array lengths
2. Distributed Backups – an upgrade to the backup functionality, which allows you to backup data distributed across clusters
3. Ref2Vec Centroid Module – a new module that calculates a mean vector of referenced objects
4. Node Status API – to quickly check on the health of your running clusters
5. Support for Azure-issued OIDC tokens – now you can authenticate with Azure, Keycloak, or Dex OIDC tokens
6. Patch releases – ready sooner – starting with Weaviate 1.15, we publish new patch releases as soon as new important fixes are available, so that you get access to all updates as soon as possible

Weaviate has recently unveiled a new module which allows users to easily integrate models from Hugging Face to vectorize their data and incoming queries. Over 700 models (at the time of writing this) that can be easily plugged into Weaviate.

You may ask: Why are there so many models and how do they differ?
And more importantly: How to choose a Sentence Transformer for Semantic Search?

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.