Zilliz

Milvus 3.0 beta is the biggest architectural upgrade since the project began, introducing native support for indexing and querying vectors directly on data lakes, plus a query engine beyond top-K search; led by core maintainers Li Liu and Jiang Chen, it powers Zilliz Vector Lakebase.

Zilliz 推出 Vector Lakebase 产品，宣称提供湖原生架构支持高并发查询与离线处理，但未披露技术细节。

Milvus 通过鸟类命名其生态系统组件，展示其在向量搜索、存储和监控方面的技术特点。

Milvus 3.0 架构升级，支持向量索引和查询，但内容为宣传稿，信息密度低。

RaBitQ algorithm compresses float32 vectors to 1 bit per dimension through random rotation, significantly reducing RAM and SSD costs for vector search without quality loss. Jiang Chen, Zilliz Head of Developer Relations, shared this at London Unstructured Data Meetup.

Milvus团队在伦敦非结构化数据聚会分享AI Agent构建经验，强调上下文管理、记忆系统与向量检索基础设施的关键作用。