The Lock Striping pattern is a great way to solve race conditions without sacrificing performance. Lean about Weaviate's improvements.
Learn how to use build an image search application using the Img2vec-neural module in Weaviate.
Vector search on disks: How does Vamana compare to HNSW?
Learn about the various Sentence Transformers from Hugging Face!
Running ML Model Inference in production is hard. You can use Weaviate – a vector database – with Hugging Face Inference module to delegate the heavy lifting.
Two weeks after the 1.15 release, we have a patch (v1.15.1) release for you, which brings 15 bug fixes and 2 UX improvements.
Weaviate 1.15 introduces Cloud-native Backups, Memory Optimizations, faster Filtered Aggregations and Ordered Imports, new Distance Metrics and new Weaviate modules.
Press Release: Pay-as-you-grow comes to Vector Search.
Self-Supervised Retrieval can surpass BM25 and Supervised techniques. This technique also pairs very well alongside BM25 in Hybrid Retrieval. Learn more about it.
Go 1.19 introduced GOMEMLIMIT, which completely changes how you can manage memory limits in Go. Learn how it helps Weaviate be more reliable.
Learn about bi-encoder and cross-encoder machine learning models, and why combining them could improve the vector search experience.
Learn what is new in Weaviate 1.14, the most reliable and observable Weaviate release yet!
