标签：多个 控制 bubuko section ati reference col cse open
整体效果类似百度搜索，如图：
1.建立索引
PUT /book4 { "mappings": { "english": { "properties": { "passage": { "type": "text" } } } } }
2.插入数据
curl -H "Content-Type: application/json" -XPOST ‘http:localhost:9200/_bulk‘ -d‘ { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Lucene is cool"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Elasticsearch builds on top of lucene"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Elasticsearch rocks"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Elastic is the company behind ELK stack"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "elk rocks"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "elasticsearch is rock solid"} ‘
3.看下储存的分词有哪些
post /_analyze { "text": [ "Lucene is cool", "Elasticsearch builds on top of lucene", "Elasticsearch rocks", "Elastic is the company behind ELK stack", "elk rocks", "elasticsearch is rock solid" ] }
结果：
{ "tokens": [ { "token": "lucene", "start_offset": 0, "end_offset": 6, "type": "<ALPHANUM>", "position": 0 }, { "token": "is", "start_offset": 7, "end_offset": 9, "type": "<ALPHANUM>", "position": 1 }, { "token": "cool", "start_offset": 10, "end_offset": 14, "type": "<ALPHANUM>", "position": 2 }, { "token": "elasticsearch", "start_offset": 15, "end_offset": 28, "type": "<ALPHANUM>", "position": 103 }, { "token": "builds", "start_offset": 29, "end_offset": 35, "type": "<ALPHANUM>", "position": 104 }, { "token": "on", "start_offset": 36, "end_offset": 38, "type": "<ALPHANUM>", "position": 105 }, { "token": "top", "start_offset": 39, "end_offset": 42, "type": "<ALPHANUM>", "position": 106 }, { "token": "of", "start_offset": 43, "end_offset": 45, "type": "<ALPHANUM>", "position": 107 }, { "token": "lucene", "start_offset": 46, "end_offset": 52, "type": "<ALPHANUM>", "position": 108 }, { "token": "elasticsearch", "start_offset": 53, "end_offset": 66, "type": "<ALPHANUM>", "position": 209 }, { "token": "rocks", "start_offset": 67, "end_offset": 72, "type": "<ALPHANUM>", "position": 210 }, { "token": "elastic", "start_offset": 73, "end_offset": 80, "type": "<ALPHANUM>", "position": 311 }, { "token": "is", "start_offset": 81, "end_offset": 83, "type": "<ALPHANUM>", "position": 312 }, { "token": "the", "start_offset": 84, "end_offset": 87, "type": "<ALPHANUM>", "position": 313 }, { "token": "company", "start_offset": 88, "end_offset": 95, "type": "<ALPHANUM>", "position": 314 }, { "token": "behind", "start_offset": 96, "end_offset": 102, "type": "<ALPHANUM>", "position": 315 }, { "token": "elk", "start_offset": 103, "end_offset": 106, "type": "<ALPHANUM>", "position": 316 }, { "token": "stack", "start_offset": 107, "end_offset": 112, "type": "<ALPHANUM>", "position": 317 }, { "token": "elk", "start_offset": 113, "end_offset": 116, "type": "<ALPHANUM>", "position": 418 }, { "token": "rocks", "start_offset": 117, "end_offset": 122, "type": "<ALPHANUM>", "position": 419 }, { "token": "elasticsearch", "start_offset": 123, "end_offset": 136, "type": "<ALPHANUM>", "position": 520 }, { "token": "is", "start_offset": 137, "end_offset": 139, "type": "<ALPHANUM>", "position": 521 }, { "token": "rock", "start_offset": 140, "end_offset": 144, "type": "<ALPHANUM>", "position": 522 }, { "token": "solid", "start_offset": 145, "end_offset": 150, "type": "<ALPHANUM>", "position": 523 } ] }
4.term suggest api(搜索单个字段)
搜索下试试，给出错误单词Elasticsearaach
POST /book4/_search { "suggest" : { "my-suggestion" : { "text" : "Elasticsearaach", "term" : { "field" : "passage"，
"suggest_mode": "popular" } } } }
response:
{ "took": 26, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 0, "max_score": 0, "hits": [] }, "suggest": { "my-suggestion": [ { "text": "elasticsearaach", "offset": 0, "length": 15, "options": [ { "text": "elasticsearch", "score": 0.84615386, "freq": 3 } ] } ] } }
5.搜索多个字段分别给出提示：
POST _search { "suggest": { "my-suggest-1" : { "text" : "tring out Elasticsearch", "term" : { "field" : "message" } }, "my-suggest-2" : { "text" : "kmichy", "term" : { "field" : "user" } } } }
该term建议者提出基于编辑距离条款。在建议术语之前分析提供的建议文本。建议的术语是根据分析的建议文本标记提供的。该term建议者不走查询到的是是的请求部分。
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建议文字。建议文本是必需的选项，需要全局或按建议设置。 |
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从中获取候选建议的字段。这是一个必需的选项，需要全局设置或根据建议设置。 |
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用于分析建议文本的分析器。默认为建议字段的搜索分析器。 |
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每个建议文本标记返回的最大更正。 |
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定义如何根据建议文本术语对建议进行排序。两个可能的值：
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建议模式控制包含哪些建议或控制建议的文本术语，建议。可以指定三个可能的值：
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在文本分析之后，建议文本术语小写。 |
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最大编辑距离候选建议可以具有以便被视为建议。只能是介于1和2之间的值。任何其他值都会导致抛出错误的请求错误。默认为2。 |
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必须匹配的最小前缀字符的数量才是候选建议。默认为1.增加此数字可提高拼写检查性能。通常拼写错误不会出现在术语的开头。（旧名“prefix_len”已弃用） |
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建议文本术语必须具有的最小长度才能包含在内。默认为4.（旧名称“min_word_len”已弃用） |
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设置从每个单独分片中检索的最大建议数。在减少阶段，仅根据 |
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用于乘以的因子， |
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建议应出现的文档数量的最小阈值。可以指定为绝对数字或文档数量的相对百分比。这可以仅通过建议高频项来提高质量。默认为0f且未启用。如果指定的值大于1，则该数字不能是小数。分片级文档频率用于此选项。 |
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建议文本令牌可以存在的文档数量的最大阈值，以便包括在内。可以是表示文档频率的相对百分比数（例如0.4）或绝对数。如果指定的值大于1，则不能指定小数。默认为0.01f。这可用于排除高频术语的拼写检查。高频术语通常拼写正确，这也提高了拼写检查的性能。分片级文档频率用于此选项。 |
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用于比较类似建议术语的字符串距离实现。可以指定五个可能的值： |
Elasticsearch Suggester API(自动补全）
标签：多个 控制 bubuko section ati reference col cse open
原文地址：https://www.cnblogs.com/wangzhuxing/p/9574630.html
