50 lines
1.8 KiB
Markdown
50 lines
1.8 KiB
Markdown
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## 背景
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向量检索是指用一组数字(向量)来量化一个事物，用大量向量来表示事物集合，用向量计算的方式寻找相似事物的一种检索方式。
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isearch底层采用的向量检索框架为Facebook AI的Faiss，项目地址为：https://github.com/facebookresearch/faiss
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## app_field_define表
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在app_field_define表定义时，vector字段类型需定义好dim、index_type和metric_type三个属性，示例如下：
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```
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{
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"id":3,
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"appId":10065,
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"fieldName":"float_vector",
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"fieldType":15,
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"fieldId":3,
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"IsPrimaryKey":0,
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"indexTag":0,
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"snapshotTag":1,
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"segmentTag":0,
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"segmentFeature":0,
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"unionField":"",
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"createTime":"2021/4/13 15:49:09",
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"dim":128, // 维数
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"index_type": [" PCA80,Flat "], // 索引类型，格式与faiss对外工厂类设置保持一致
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"metric_type": "L2" // 距离计算方式，可选值：InnerProduct、L2
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}
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```
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说明：index_type参考https://github.com/facebookresearch/faiss/wiki/The-index-factory
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## 向量插入示例
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```
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curl -X POST \
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http://127.0.0.1/insert \
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-H 'content-type: application/json' \
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-H 'doc_id: 1' \
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-d '{"appid":10065,"table_content":{"cmd":"add","fields":{"doc_id":"1","random_value":1488981884,"float_vector":[0.005653954876242762, 0.632130963117687, 0.7519577013172226, 0.8568273368123129, 0.2034335192251041, 0.9786219451736441, 0.5948105950093241, 0.9618089054657426]}}}'
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```
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## 向量查询示例
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```
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curl -X POST \
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http://127.0.0.1/search \
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-H 'content-type: application/json' \
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-d '{"appid":10065,"query":{"vector_query":{"float_vector":[0.005653954876242762, 0.632130963117687, 0.7519577013172226, 0.8568273368123129, 0.2034335192251041, 0.9786219451736441, 0.5948105950093241, 0.9618089054657426], "index_type_id":1}} }'
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```
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