Dense embeddings are generated from machine learning models like Sparse embeddings are generated from algorithms like BM25 and SPLADE. Sparse vectors have mostly zero values with only a few non-zero values, while dense vectors mostly contain non-zero values. Sparse and dense vectors are calculated with distinct algorithms. In this blog post, you will learn about the implementation of hybrid search in Weaviate and how to use it. It uses sparse and dense vectors to represent the meaning and context of search queries and documents. The Hybrid search feature was introduced in Weaviate 1.17. By leveraging the strengths of different algorithms, it provides a more effective search experience for users. It uses the best features of both keyword-based search algorithms with vector search techniques. Hybrid search is a technique that combines multiple search algorithms to improve the accuracy and relevance of search results.
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