Milvus 3.0 StructArray 支持视频、文档和产品等多类型数据的多向量搜索，功能强大，值得了解。
Milvus 3.0 StructArray 引入单实体内多向量搜索，视频、文档和产品可保留所有嵌入向量，支持三种检索模式：嵌入列表搜索、个体元素搜索和过滤搜索，实现元素级和实体级结果关联。
Milvus 3.0 StructArray brings multi-vector search inside a single entity. A video can keep all its ...
Milvus 3.0 StructArray brings multi-vector search inside a single entity. A video can keep all its clip embeddings, a document all its passage embeddings, and a product all its image embeddings—without flattening every element into a separate database row. Each element remains attached to its parent while carrying its own vectors and scalar metadata. This enables three retrieval patterns: 𝟭.𝗘𝗻𝘁𝗶𝘁𝘆-𝗹𝗲𝘃𝗲𝗹 𝗺𝘂𝗹𝘁𝗶-𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 EmbeddingList search uses MaxSim-style scoring to compare a multi-vector query with all the vectors stored inside an entity. 𝟮.𝗘𝗹𝗲𝗺𝗲𝗻𝘁-𝗹𝗲𝘃𝗲𝗹 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝗲𝗮𝗿𝗰𝗵 Search individual clips, passages, images, or visual patches. Each result identifies both the parent entity and the matching element’s offset. 𝟯.𝗦𝗮𝗺𝗲-𝗲𝗹𝗲𝗺𝗲𝗻𝘁 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 A filter such as: scene_type = "kitchen" AND confidence > 0.8 must match the same video clip—not two different clips within the video. Together, these capabilities bridge element-level relevance and entity-level results: a single clip, passage, or product image can drive the match, while the application returns the full video, document, or product. StructArray makes that local evidence searchable without losing its parent context. 𝗜𝘁 𝗮𝗹𝘀𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗹𝗮𝘁𝗲-𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗳𝗼𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 𝘀𝘂𝗰𝗵 milvus.io/blog/milvus-3-… �𝗱 𝗖𝗼𝗹𝗣𝗮𝗹𝗶, where one entity is represented by many token or visual-patch vectors. Read the technical deep dive: https://t.co/bXZuMAsX6Q 💬 0 🔄 0 ❤️ 1 👀 32 ⚡