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For the last 40 years, BM25 has served as the standard for search engines. It is a simple yet powerful algorithm that has been used by many search engines, including Google, Bing, and Yahoo. Though it seemed that the advent of vector search would diminish its influence, it did so only partially. The current state-of-the-art approach to retrieval nowadays tries to incorporate BM25 along with embedd
It’s been over a year since we published the original article on how to build a hybrid search system with Qdrant. The idea was straightforward: combine the results from different search methods to improve retrieval quality. Back in 2023, you still needed to use an additional service to bring lexical search capabilities and combine all the intermediate results. Things have changed since then. Once
DocumentationConceptsFilteringFilteringWith Qdrant, you can set conditions when searching or retrieving points. For example, you can impose conditions on both the payload and the id of the point. Setting additional conditions is important when it is impossible to express all the features of the object in the embedding. Examples include a variety of business requirements: stock availability, user l
High-Performance Vector Search at ScalePowering the next generation of AI applications with advanced, open-source vector similarity search technology.
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