Learn about the different ranking models that are used for better search.| weaviate.io
Get an intuitive understanding of what exactly vector embeddings are, how they're generated, and how they're used in semantic search.| weaviate.io
Learn about the hybrid search feature that enables you to combine dense and sparse vectors to deliver the best of both search methods!| weaviate.io
Learn more about the differences between vector libraries and vector databases!| weaviate.io
Learn about why you need distance metrics in vector search and the metrics implemented in Weaviate (Cosine, Dot Product, L2-Squared, Manhattan, and Hamming).| weaviate.io
Weaviate 1.15 introduces Cloud-native Backups, Memory Optimizations, faster Filtered Aggregations and Ordered Imports, new Distance Metrics and new Weaviate modules.| weaviate.io