Weaviate 1.28 Release

Weaviate 1.28 introduces significant enhancements to the vector database, focusing on enterprise-grade security, faster indexing, and improved multilingual support. The release features role-based access control (RBAC) as a technical preview, allowing granular user permissions for better security management, although it's not recommended for production use yet. Async indexing improvements make the system more robust and performant by reducing lock contention and switching to an on-disk queue system, enhancing the handling of large datasets. Conflict resolution strategies for deletions are introduced to maintain data consistency across replicas, offering options like DeleteOnConflict, TimeBasedResolution, and NoAutomatedResolution. The update includes a new Japanese tokenizer, kagome_ja, for enhanced keyword and hybrid search capabilities, contributed by a community member. Experimental features like BlockMax WAND aim to improve the efficiency of BM25 and hybrid searches. Additionally, Weaviate continues to support Voyage AI's Multimodal model and has introduced Weaviate Embeddings for scalable, secure embedding generation. The release is available as open-source on GitHub, with upcoming availability on Weaviate Cloud for various deployment options.