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
Weaviate 1.15 introduces Cloud-native Backups, Memory Optimizations, faster Filtered Aggregations and Ordered Imports, new Distance Metrics and new Weaviate modules.| weaviate.io