语义检索的效果确实不错，特别是在非关键词命中的情况下。就像我这个案例。通过做数据的文本嵌入，然后用向量的做召回。实现了语义的搜索。虽然我搜索的是“中国的首都”，但是把命中北京的也给我召回了，大大提升了召回效果。
其中我用全文模糊匹配。召回top10，命中的数据完全不相关。但是用语义搜索召回10条数据，有8条都是在描述北京。这大概就是语义检索的魅力。
普通搜索搜索
问题：中国的首都
POST my_index/_search
{
"query": {
"match": {
"title": "中国的首都"
}
},
"_source": ["title"]
}普通搜索的召回
{
"took": 11,
"timed_out": false,
"_shards": {
"total": 2,
"successful": 2,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 52,
"relation": "eq"
},
"max_score": 10.82337,
"hits": [
{
"_index": "my_index",
"_id": "866c3fe0033efed6047ffcc5d0e9eaff",
"_score": 10.82337,
"_source": {
"title": "中国的男人们在哪"
}
},
{
"_index": "my_index",
"_id": "9f0927938ebe9002091ac6b319a7ba2cz1",
"_score": 10.301819,
"_source": {
"title": "英媒：投资中国的时机？“就是当下”"
}
},
{
"_index": "my_index",
"_id": "1d81a61adc99565bec4ef153a6a7d236z1",
"_score": 9.914771,
"_source": {
"title": "研发出属于中国的“黑色黄金”"
}
},
{
"_index": "my_index",
"_id": "9121f2d9959e9063db7515f3f479571bz1",
"_score": 9.914771,
"_source": {
"title": "研发出属于中国的“黑色黄金”"
}
},
{
"_index": "my_index",
"_id": "d36e2da9f11fb764fee66616139e8624",
"_score": 9.718359,
"_source": {
"title": "中国的国鸟、国树、国宝、国花是哪些？"
}
},
{
"_index": "my_index",
"_id": "8145d4e100ec51107620d383370e7bae",
"_score": 9.718359,
"_source": {
"title": "中国的社会公德有哪些 社会公德有哪些"
}
},
{
"_index": "my_index",
"_id": "11308f9cb1a6f86ade85d9a5e3991db6",
"_score": 9.710967,
"_source": {
"title": "中国的崛起之路，又燃又催泪！"
}
},
{
"_index": "my_index",
"_id": "32b77f0c98d224893a9a592c37b3fb28",
"_score": 9.515373,
"_source": {
"title": "特朗普提议大幅提高中国的关税"
}
},
{
"_index": "my_index",
"_id": "a607f403f08233c0eb397242acb438d6z1",
"_score": 8.973172,
"_source": {
"title": "时光穿越50年，画家笔下中国的璀璨岁月"
}
},
{
"_index": "my_index",
"_id": "c51fac40c11bd3fa6aa95ff574444304",
"_score": 8.973172,
"_source": {
"title": "#外国人请走开 #外国人在中国的生活"
}
}
]
}
}使用模型做文本嵌入
该演示模型为m3e。文本嵌入模型。
这里是利用es的机器学习模块，并用eland将模型导入到ES。我已经导入过模型了，在我的其它文章中，已经写过。这里只是用模型做文本嵌入。
POST _ml/trained_models/sentence-transformers__m3e-base/_infer
{
"docs" : [
{"text_field": "中国的首都"}
]
}向量搜索
使用上一个步骤，将问题转向量。然后做搜索
POST my_index/_search
{
"knn": {
"field": "title_embedding.predicted_value",
"query_vector": [
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