Vector Similaruty beyond Search Vector similarity offers a range of powerful functions that go far beyond those available in traditional full-text search engines and the conventional kNN search. We just scratched the surface of the topic but already found a lot of new ways to interact with the data, including: - Dissimilarity search - that can be applied to anomaly detection, mislabeling detection, and data cleaning. - Diversity search - that can be used for giving a better overview of the data, with no query at all. - Recommendations - where we can do beyond the single query vector and use positive and negative examples to find the most relevant items. - Discovery or Exploration - where we can invert the logic behind triplet-loss to provide real-time improvements of the search results. In the article, we are talking about a new toolbox for unstructured data exploration, where the search is just one of the instruments. And maybe you will find there a tool to implement your next big idea 🙂 https://qdrant.tech/articles/vector-similarity-beyond-search/