内积(IP) 公式如下: 内积(IP)在你需要比较未归一化的数据或当你关注大小(magnitude)和角度时更为有用。如果你将内积的距离度量应用于归一化的嵌入(embedding),结果将等同于计算这些嵌入之间的余弦相似度。如果向量没有归一化使用内积作为metric type的话,搜索出来的数据是错的。 关于内积的详细说明,可以参考我之前...
"index_type": "IVF_FLAT", # 选择合适的索引类型 "metric_type": "IP", # 选择合适的距离度量方式 "params": {"nlist": 1414} # nlist 是一个影响索引性能的参数,需根据数据量调整 } print(f"Creating new index on '{field_name}'...") Index(collection, field_name, index_params) print("Ne...
index = { "index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128}, } hello_milvus.create_index("embeddings", index) 将集合加载到内存并执行向量相似性搜索 hello_milvus.load() vectors_to_search = entities[-1][-2:] search_params = { "metric_type": "L2", "...
self.milvus = Milvus(host='milvus机器ip', port='19530') self.db_name = 'test01' params = {'collection_name': self.db_name, 'dimension': 512, 'index_file_size': 1024, 'metric_type': MetricType.IP} _, bool = self.milvus.has_collection(self.db_name) if bool == True: self.mil...
In Milvus 2.x, the metric_type is required in create_index and search but is unnecessary in Milvus 1.x. So, defined here as constant. If you want to use other types, please try to use Milvus 2.x API. ''' _METRIC_TYPE = "IP" ...
(host='localhost', port='19530')# 向量个数num_vec =5000# 向量维度vec_dim =768# namecollection_name ="test_collection"# 创建collection,可理解为mongo的collectioncollection_param = {'collection_name': collection_name,'dimension': vec_dim,'index_file_size':32,'metric_type': MetricType.IP# ...
using_database("sample_db") coll_name = 'word_vector' search_params = { "metric_type": 'IP', "offset": 0, "ignore_growing": False, "params": {"nprobe": 16} } collection = Collection(coll_name) collection.load() results = collection.search( data=[np.random.normal(0, 0.1, 768...
.withMetricType(MetricType.IP) .withVectors(vectors) .withVectorFieldName("embedding") .withTopK(topK) .build(); R<SearchResults> searchResultsR = milvusServiceClient.search(searchParam); SearchResults searchResultsRData = searchResultsR.getData(); ...
// 内积 (IP) .withMetricType(MetricType.L2) .withOutFields(Arrays.asList(PushMaterielsConst.Field.CONTENT)) //返回多少条结果 .withTopK(5) //搜索的向量值 .withVectors(list) //搜索的Field .withVectorFieldName(PushMaterielsConst.Field.CONTENT_INTRO) ...
对于binary 类型的索引,HAMMING 和 JACCARD 全部都支持 Range Search,SUBSTRUCTURE/SUPERSTRUCTURE 由于返回值是 true/false,不满足 Range Search 的语义,所以不支持 Range Search。其余 float 类型索引,对于 L2/IP/COSINE 全部支持 Range Search。 下表是所有支持 Range Search 的索引类型和 metric type: ...