一、什么是搜索建议

image.png

现代的搜索引擎，一般都会提供Suggest as you type的功能
帮助用户在输入搜索的工程中，进行自动补全或者纠错。通过协助用户输入更加精准的关键词，提高后续搜索阶段文档匹配的程度
在google上搜索，一开始会自动补全。当输入到一定长度，如因为单词拼写错误无法补全，就会开始提示相似的词或者句子。

二、Elasticsearch Suggester API

搜索引擎中类似的功能，在Elasticsearch中通过Suggester API 实现的
原理：将输入的文本分解为Token，然后在索引的字典里查找相似的Term并返回
根据不同的使用场景，Elasticsearch 设计了4中类型的Suggesters
Term & Phrase Suggester
Complete & Context Suggester

三、Term Suggester

Suggester就是一种特殊类型的搜索，”text“里是调用时候提供的文本，通常来自于用户界面上用户输入的内容
用户输入的”lucen“是一个错误的拼写
会到指定的字段”body“上搜索，当无法搜索到结果时（missing），返回建议的词

默认使用standard分词器，大写转写，rocks和rock是两个词

DELETE articles
PUT articles
{
  "mappings": {
    "properties": {
      "title_completion":{
        "type": "completion"
      }
    }
  }
}

POST articles/_bulk
{ "index" : { } }
{ "title_completion": "lucene is very cool"}
{ "index" : { } }
{ "title_completion": "Elasticsearch builds on top of lucene"}
{ "index" : { } }
{ "title_completion": "Elasticsearch rocks"}
{ "index" : { } }
{ "title_completion": "elastic is the company behind ELK stack"}
{ "index" : { } }
{ "title_completion": "Elk stack rocks"}
{ "index" : {} }


POST articles/_search?pretty
{
  "size": 0,
  "suggest": {
    "article-suggester": {
      "prefix": "elk ",
      "completion": {
        "field": "title_completion"
      }
    }
  }
}

DELETE articles

POST articles/_bulk
{ "index" : { } }
{ "body": "lucene is very cool"}
{ "index" : { } }
{ "body": "Elasticsearch builds on top of lucene"}
{ "index" : { } }
{ "body": "Elasticsearch rocks"}
{ "index" : { } }
{ "body": "elastic is the company behind ELK stack"}
{ "index" : { } }
{ "body": "Elk stack rocks"}
{ "index" : {} }
{  "body": "elasticsearch is rock solid"}


POST _analyze
{
  "analyzer": "standard",
  "text": ["Elk stack  rocks rock"]
}

POST /articles/_search
{
  "size": 1,
  "query": {
    "match": {
      "body": "lucen rock"
    }
  },
  "suggest": {
    "term-suggestion": {
      "text": "lucen rock",
      "term": {
        "suggest_mode": "missing",
        "field": "body"
      }
    }
  }
}

搜索”lecen rock“

每个建议都包含一个算分，相似性是通过Levenshtein Edit Distance的算法实现的。核心思想就是一个改动多少字符就可以和另外一个词一致。提供了很多可选参数来控制相似性的模糊程度。例如”max_edits“
几种Suggestion Mode
Missing --- 如索引中已经存在，就不提供建议
popular -- 推荐出现频率更加高的词
Always -- 无论是否存在，都提供建议

四、Term Suggester -- Popular Mode

POST /articles/_search
{

  "suggest": {
    "term-suggestion": {
      "text": "lucen rock",
      "term": {
        "suggest_mode": "popular",
        "field": "body"
      }
    }
  }
}

image.png

五、Term Suggester -- Always Mode

POST /articles/_search
{

  "suggest": {
    "term-suggestion": {
      "text": "lucen rock",
      "term": {
        "suggest_mode": "always",
        "field": "body",
      }
    }
  }
}

六、Sorting by Frequency & Prefix Length

POST /articles/_search
{

  "suggest": {
    "term-suggestion": {
      "text": "lucen hocks",
      "term": {
        "suggest_mode": "always",
        "field": "body",
        "prefix_length":0,
        "sort": "frequency"
      }
    }
  }
}

默认按照score排序，也可以按照”frequency“
默认首字母不一致就不会匹配推荐，但是如果将prefix_length设置为0，就会为hock建议rock

七、Phrase Suggester

Phrase Suggester 在Term Suggester上增加了一些额外的逻辑
一些参数
Suggest Mode ：missing, popular, always
Max Errors：最多可以拼错的 Terms 数
Confidence：限制返回结果数，默认为 1

POST /articles/_search
{
  "suggest": {
    "my-suggestion": {
      "text": "lucne and elasticsear rock hello world ",
      "phrase": {
        "field": "body",
        "max_errors":2,
        "confidence":0,
        "direct_generator":[{
          "field":"body",
          "suggest_mode":"always"
        }],
        "highlight": {
          "pre_tag": "<em>",
          "post_tag": "</em>"
        }
      }
    }
  }
}