Hongdao Meng

> The new version of Milvus has been released. Now, you can use the hybrid retrieval of bge-m3 following https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py @staoxiao @c121914yu 不好意思打扰了，感谢BGE模型卓越的性能，请问对于[dense,sparse]的混合搜索结果，能否有方法使用大模型的api进行rerank。目前设计的hybrid_earch方法似乎是根据两种向量的相似度得分用RRF进行rerank的，貌似都是基于milvus的向量之间的重排。您觉得对于RRF之后的结果使用大模型再次重排是否有必要。以及能否有机会针对两种向量使用大模型对于语义进行混合的重排，我们之前的RAG模块是用dense向量找到文本对于文本进行大模型的重排序。再次感谢BGE做出的不可替代的贡献。

> 存成dict，参考https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#generate-embedding-for-text 中Sparse Embedding (Lexical Weight) 感谢您的回复，每一个sparse_vector都是一个单独的dict吗

quick question. does colbert still take up a lot of memory during the retrival term. could we do embedding at first and retrival later?

> > quick question. does colbert still take up a lot of memory during the retrival term. could we do embedding at first and retrival later? > > Colbert is...