[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-向量資料庫":3},{"tag":4,"articles":10,"peer_article_count":11},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":9},"f0e1a1d6-eaff-4b63-8dda-9e2a4d9b777c","向量資料庫",3,"向量資料庫用來儲存與檢索高維嵌入，常見於 RAG、語意搜尋與推薦系統。這個主題會關注索引結構、混合搜尋、延遲、召回率、擴充性與成本，也會比較 Qdrant、Milvus、Weaviate、Rust 系方案等實作差異。","Vector databases store and search high-dimensional embeddings for RAG, semantic search, and recommendation systems. This topic covers indexing, hybrid search, latency, recall, scaling, and cost, along with implementation tradeoffs across systems like Qdrant, Milvus, Weaviate, and Rust-based designs.",[],13]