"dimension": 512 } } }, "settings": { "index": { "refresh_interval": "60s", "number_of_shards": "3", "knn.space_type": "cosinesimil", "knn": "true", "number_of_replicas": "1" } } } ``` ******ESCloud 数...
"dimension": 512 } } }, "settings": { "index": { "refresh_interval": "60s", "number_of_shards": "3", "knn.space_type": "cosinesimil", "knn": "true", "number_of_replicas": "1" } } } ```...
目标是得到一个能同时表达图片和文字的模型。# ESCloud Mapping 准备```PUT image_search{ "mappings": { "dynamic": "false", "properties": { "photo_id": { "type": "keyword" }, "photo_url": { "type": "keyword" }, "describe": { "type": "text" }, "photo_embedding": { "type": "knn_vector", "dimension": 512 } } }, "settings": { "index": { "refresh_i...
目标是得到一个能同时表达图片和文字的模型。# ESCloud Mapping 准备```PUT image_search{ "mappings": { "dynamic": "false", "properties": { "photo_id": { "type": "keyword" }, "photo_url": { "type": "keyword" }, "describe": { "type": "text" }, "photo_embedding": { "type": "knn_vector", "dimension": 512 } } }, "settings": { "index": { "refresh_i...
分别介绍 HNSW 和 Faiss 的创建索引语法,以及查询方法。 HNSW 语法HNSW 使用时可以定义参数 M 和 EF_CONSTRUCTION 两个参数来在性能和准确度之间做权衡。一般来说 M 越大,EF_CONSTRUCTION 越大,索引构建时间越长,准确度越高,搜索 latency 越高。 SQL INDEX v1 vector TYPE HNSW('DIM=960, METRIC=COSINE, M=32, EF_CONSTRUCTION=512')在创建表时添加索引一个典型的构造 HNSW 索引的语句如下: SQL CREATE TABLE test_ann( `id`...
并为其配置 mappings 和 settings。示例代码如下: HTTP PUT image_search{ "mappings": { "dynamic": "false", "properties": { "photo_id": { "type": "keyword" }, "photo_url": { "type": "keyword" }, "describe": { "type": "text" }, "photo_embedding": { "type": "knn_vector", "dimension": 512 } } }, "settings": { "index": { "refresh_interval": "60s", "number_of_...
相等优先级从左至右计算,小括号()括起来的表达式提升运算优先级计算。 javascript {{ 12 + 22 * 2 / 11 % 3 }} // 22 * 2 -> 44 / 11 -> 4 % 3 = 1, 12 - 2 - 1 = 11;{{ (12 + 22) * 2 / 11 % 3 }} // 12 + 22 ->... 结构内容为 : javascript [name]:valueOfRow举例来说,根据下列表格重复,第二项的取值内容为: index name age 1 Oboo 12 2 Mily 33 javascript $RowData.dimension.name // "Mily"$RowData.measure.age // 333.3 $M...