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.