Learn how Weaviate and NVIDIA enable fast, scalable vector search for agentic AI.
Weaviate `1.31` implements the MUVERA encoding algorithm for multi-vector embeddings. In this blog, we dive the algorithm in detail, including what MUVERA is, how it works, and whether it might make sense for you.
1.31 adds MUVERA for multi-vector embeddings, new BM25 operators, the ability to add new object vectors, and more!
What trends we see arise in Enterprise AI in 2025.
Learn how to build generative AI data pipelines at scale with Weaviate and Databricks
Learn when and how to use GraphRAG and how it can improve on some search tasks
Learn about how you can use our new agentic personalization service to provide user-catered recommendations from Weaviate collections.
Late interaction allow for semantically rich interactions that enable a precise retrieval process across different modalities of unstructured data, including text and images.
Read about BlockMax WAND and multi-vector embeddings in GA, API-based user management, RAG improvements, xAI model support, and more!
Learn how the new Transformation Agent will change the way we manage data. Say goodbye to the tedious tasks of database management!
Agentic workflows give AI agents structure, purpose, and adaptability. This article breaks down their components, patterns, and practical applications.
Learn about the Query Agent, our new agentic search service that redefines how you interact with Weaviate’s database!
