Milvus 3.0把排序、聚合、分面、多向量检索都做进引擎，应用端少写不少逻辑，做RAG的值得看看。
Milvus 3.0在向量检索基础上加入更多库内处理能力，支持ORDER BY对过滤结果和ANN候选排序。查询侧聚合提供count、sum、avg、min、max，并按标量字段分组。搜索聚合支持分面检索，返回分桶计数、聚合统计和每桶Top-N样本，但分面计数基于ANN结果故为近似值。新增StructArray让单个实体可包含多个向量与结构化元素，适配文档块、视频帧和ColBERT等交互式模型。稀疏检索增强包括压缩版BM25索引和面向SPLADE学习型稀疏向量的SINDI算法。
Milvus 3.0 further expands what its retrieval engine can handle, with stronger support for in-engine...
Milvus 3.0 further expands what its retrieval engine can handle, with stronger support for in-engine ranking, aggregation, multi-vector search, and sparse retrieval. In production, vector search rarely ends with top-k ANN results. Beyond retrieving candidates, applications often need to sort by freshness, price, rating, inventory, or timestamp; group results by category; return facets; and work with entities that contain many vectors, such as document chunks, video frames, product images, or ColBERT token vectors. Milvus has long supported dense and sparse vector search, hybrid retrieval, and filtering. Milvus 3.0 builds on that foundation by bringing more ranking, grouping, and result-processing work closer to the retrieval path. ORDER BY sorts filtered query results by scalar fields and can also sort ANN candidates within Milvus. Query-side aggregation supports count, sum, avg, min, and max, grouped by scalar fields. Search aggregation adds faceted search over retrieved hits, returning bucket counts, aggregate statistics, and top-N sample hits per bucket. Since it operates on ANN results, facet counts are approximate; use query-side aggregation when exact counts matter. StructArray lets a single entity contain a variable-length list of structured elements and vectors while remaining one row. It fits workloads such as document chunks, video frames, product images, and late-interaction models like ColBERT. Sparse retrieval is also enhanced with compressed BM25 indexes and SINDI, an algorithm designed for learned sparse embeddings such as SPLADE. For retrieval teams, the practical benefit is less over-fetching and less custom application-side logic for ranking, grouping, faceting, multi-vector scoring, and sparse search. More in the Milvus 3.0 launch blog: milvus.io/blog/announcin… 💬 0 🔄 0 ❤️ 1 👀 48 ⚡