Weaviate 1.31 Release

Weaviate v1.31 introduces several enhancements including MUVERA encoding for multi-vector embeddings, which reduces the size of large embeddings while maintaining precision in searches, albeit with a potential trade-off in search quality. The update offers new BM25 keyword search operators allowing for more customizable queries, such as requiring all or a specific number of terms in a search query to be matched. Users can now add new vectors to existing collections, offering flexibility in adapting to changing needs or incorporating new data types. HNSW snapshotting significantly speeds up start-up times for large indices by creating snapshots rather than rebuilding from logs. The release integrates new models, including Cohere's v3.5 and v4 models, VoyageAI's models, and model2vec, enhancing Weaviate's capabilities for various text and image embedding tasks. These developments are accompanied by substantial performance improvements, all contributing to a faster and more efficient Weaviate experience. The release highlights the contributions from both the core engineering team and community contributors, emphasizing the collaborative nature of the open-source project.

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