最后可以看到vector_store其实就是一个包含文档信息的FAISS对象,其中向量化的过程已经在流程中生成了文件 vector_store = MyFAISS.from_documents(docs, self.embeddings) # docs 为Document列表 class FAISS(VectorStore): """Wrapper around FAISS vector database. To use, you should have the ``faiss`` python...
最后可以看到vector_store其实就是一个包含文档信息的FAISS对象,其中向量化的过程已经在流程中生成了文件 vector_store = MyFAISS.from_documents(docs, self.embeddings) # docs 为Document列表 class FAISS(VectorStore): """Wrapper around FAISS vector database. To use, you should have the ``faiss`` python...
xqfor the query vectors, for which we need to find the nearest neighbors. Its size is nq-by-d. If we have a single query vector, nq=1. In the following examples we are going to work with vectors that are drawn form a uniform distribution in d=64 dimensions. Just for fun, we add...
c_str()); WRITE1 (h); write_index_header (ivpq, f); write_VecProductQuantizer<typename T_IVPQ::VPQ_t>(&ivpq->pq, f); WRITEVECTOR (ivpq->codes); return true; }else{ return false; } } ... } else if(const AbstractIndexVPQ * ivpq = dynamic_cast<const AbstractIndexVPQ *...