了解了倒排链及其相关上下文,理解IndexIVFFlat的add就非常容易了,实际就是在一级量化后,每个原始向量找到自己对应的聚类中心,然后插入到这个聚类中心对应的倒排链即可,细节上faiss通过omp实现了高效并行且不上锁的优秀榜样代码: AI检测代码解析 void IndexIVFFlat::add_core( idx_t n, const float* x, const int64...
void IndexIVFPQ::add_core_o (idx_t n, const float * x, const idx_t *xids, float *residuals_2, const idx_t *precomputed_idx) { // ... InterruptCallback::check(); FAISS_THROW_IF_NOT (is_trained); double t0 = getmillisecs (); const idx_t * idx; ScopeDeleter<idx_t> del...
IndexIVF的add比IndexFlat复杂得多,步骤如下: 找到每个点所属的簇 quantizer->assign(n, x, coarse_idx.get()); 将这个点添加到该簇中 IndexIVF::add_core。 将该点插入倒排索引中。 encode_listno做的事情就是返回簇中心的id倒排code,然后再将这个点(就是整个向量)放入对应倒排拉链中,倒排链的数据结构为...
# 创建index对象index = faiss.IndexFlatIP(feature_dim)# 将数据集加载入index中index.add(np.ascontiguousarray(Datasets['feature']))# 获取index中向量的个数print(index.ntotal)# 获取与检索向量最邻近的topk的距离distance与索引值match_idxdistance, match_idx = index.search(feature.reshape(1,-1), top...
addFetchTime(doc, url, page); return doc; } private NutchDocument addFetchTime(NutchDocument doc, String url, WebPage page) { String fetchTime = page.getFetchTime().toString(); LOGGER.info(">>>add fetchtime: " + fetchTime); doc.add("...
Faiss is a standout tool for similarity search, packed with features designed to handle large and diverse datasets effectively. Here’s a closer look at some of the core capabilities that make it a powerful asset for data-intensive tasks. ...
add_core(n, x, xids, nullptr); }void IndexBinaryIVF::add_core( idx_t n, const uint8_t* x, const idx_t* xids, const idx_t* precomputed_idx) { FAISS_THROW_IF_NOT(is_trained); assert(invlists); direct_map.check_can_add(xids);const...
= false; } voidIVFFlat::add_core( idx_t , const float* x, const_t* xids, const idx_t* coarseidx, void* inverted__context) { FAISSTHROW_IF_NOT(is_trained; FAISS_THROW_IF_NOT(coarse_idx; FAISS_THROW_IF_NOT(!by_residual; assert(invlists); direct_...
encoded_data = np.asarray(encoded_data.astype('float32')) index = faiss.IndexIDMap(faiss.IndexFlatIP(768)) ids = np.array(range(0, len(df))) ids = np.asarray(ids.astype('int64')) index.add_with_ids(encoded_data, ids) faiss.write_index(index, 'movie_plot.index')...
index = faiss.IndexIDMap(faiss.IndexFlatIP(768))index.add_with_ids(encoded_data, np.array(range(0, len(data))) 序列化索引 faiss.write_index(index, 'abc_news') 将序列化的索引导出到托管搜索引擎的任何计算机中 反序列化索引 index = faiss.read_index('abc_news') 执行...