elasticsearch搜索建议与上下文提示suggesters

查询建议是什么？

查询建议，为用户提供良好的使用体验。主要包括： 拼写检查； 自动建议查询词（自动补全）

 拼写检查如图：

自动建议查询词（自动补全）：

搜索建议

通过Suggester Api实现

原理是将输入的文本分解为Token，然后在词典中查找类似的Term返回

根据不同场景，ElasticSearch设计了4中类别的Suggesters。

• Term Suggester
• Phrase Suggester
• Complete Suggester
• Context Suggester

Term Suggester

term 词项建议器，对给入的文本进行分词，为每个词进行模糊查询提供词项建议。对于在索引中存在词默认不提供建议词，不存在的词则根据模糊查询结果进行排序后取一定数量的建议词。

类似Google搜索引擎，我给的是一个错误的单词elasticserch，但引擎友好地给出了搜索建议。

 

要实现这个功能，在ElasticSearch中很简单。

创建索引,并写入一些文档

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"}

搜索文档，调用suggest api。有3种Suggestion Mode

1.missing 索引中已经存在，就不提供建议
2.popular 推荐出现频率更加高的词
3.always 无论是否存在，都提供建议
 
POST /articles/_search
{
    "size": 1,
    "query": {
        "match": {
            "body": "elasticserch"
        }
    },
    "suggest": {
        "term-suggestion": {
            "text": "elasticserch",
            "term": {
                "suggest_mode": "missing",
                "field": "body"
            }
        }
    }
}
 
返回结果
 
{
  "took" : 130,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 0,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "suggest" : {
    "term-suggestion" : [
      {
        "text" : "elasticserch",
        "offset" : 0,
        "length" : 12,
        "options" : [
          {
            "text" : "elasticsearch",
            "score" : 0.9166667,
            "freq" : 3
          }
        ]
      }
    ]
  }
}
 
Phrase Suggester
phrase 短语建议，在term的基础上，会考量多个term之间的关系，比如是否同时出现在索引的原文里，相邻程度，以及词频等
其中一些参数
• max_errors 最多可以拼错的terms
• confidence 限制返回结果数，默认1
POST /articles/_search
{
  "suggest": {
    "my-suggestion": {
      "text": "lucne and elasticsear rock hello world ",
      "phrase": {
        "field": "body",
        "max_errors":2,
        "confidence":2,
        "direct_generator":[{
          "field":"body",
          "suggest_mode":"missing"
        }],
        "highlight": {
          "pre_tag": "<em>",
          "post_tag": "</em>"
        }
      }
    }
  }
}
 
结果如下：
 
{
  "took" : 288,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 0,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "suggest" : {
    "my-suggestion" : [
      {
        "text" : "lucne and elasticsear rock hello world ",
        "offset" : 0,
        "length" : 39,
        "options" : [
          {
            "text" : "lucene and elasticsearch rock hello world",
            "highlighted" : "<em>lucene</em> and <em>elasticsearch</em> rock hello world",
            "score" : 1.5788074E-4
          }
        ]
      }
    ]
  }
}
 
Completion Suggester

针对自动补全场景而设计的建议器。此场景下用户每输入一个字符的时候，就需要即时发送一次查询请求到后端查找匹配项，在用户输入速度较高的情况下对后端响应速度要求比较苛刻。因此实现上它和前面两个Suggester采用了不同的数据结构，索引并非通过倒排来完成，而是将analyze过的数据编码成FST和索引一起存放。对于一个open状态的索引，FST会被ES整个装载到内存里的，进行前缀查找速度极快。但是FST只能用于前缀查找，这也是Completion Suggester的局限所在。

类似百度这样的提示功能

在ElasticSearch要实现这样的功能也很简单。

1. 建立索引

PUT titles
{
    "mappings": {
        "properties": {
            "title_completion": {
                "type": "completion"
            }
        }
    }
}

2. 写入文档

POST titles/_bulk
{ "index" : { } }
{ "title_completion": "php是什么"}
{ "index" : { } }
{ "title_completion": "php是世界上最好的语言"}
{ "index" : { } }
{ "title_completion": "php货币"}
{ "index" : { } }
{ "title_completion": "php面试题2019"}

3. 搜索数据

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

3. 返回结果

{
  "took" : 95,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 0,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "suggest" : {
    "article-suggester" : [
      {
        "text" : "php",
        "offset" : 0,
        "length" : 3,
        "options" : [
          {
            "text" : "php是世界上最好的语言",
            "_index" : "titles",
            "_type" : "_doc",
            "_id" : "BhTVQXkBv4MlM6fI82Mo",
            "_score" : 1.0,
            "_source" : {
              "title_completion" : "php是世界上最好的语言"
            }
          },
          {
            "text" : "php是什么",
            "_index" : "titles",
            "_type" : "_doc",
            "_id" : "BRTVQXkBv4MlM6fI82Mo",
            "_score" : 1.0,
            "_source" : {
              "title_completion" : "php是什么"
            }
          },
          {
            "text" : "php货币",
            "_index" : "titles",
            "_type" : "_doc",
            "_id" : "BxTVQXkBv4MlM6fI82Mo",
            "_score" : 1.0,
            "_source" : {
              "title_completion" : "php货币"
            }
          },
          {
            "text" : "php面试题2019",
            "_index" : "titles",
            "_type" : "_doc",
            "_id" : "CBTVQXkBv4MlM6fI82Mo",
            "_score" : 1.0,
            "_source" : {
              "title_completion" : "php面试题2019"
            }
          }
        ]
      }
    ]
  }
}
 
Context Suggester

是Completion Suggester的扩展，加入了上下文信息场景。

例如：

你在电器商城，输入苹果，想要找到的苹果笔记本...
你在水果商城，输入苹果，想要找的是红苹果、绿苹果...

1. 建立索引，定制mapping

PUT comments
{
    "mappings": {
        "properties": {
            "comment_autocomplete": {
                "type": "completion",
                "contexts": [{
                    "type": "category",
                    "name": "comment_category"
                }]
            }
        }
    }
}

2. 并为每个文档加入Context信息

POST comments/_doc
{
    "comment": "苹果电脑",
    "comment_autocomplete": {
        "input": ["苹果电脑"],
        "contexts": {
            "comment_category": "电器商城"
        }
    }
}

POST comments/_doc
{
    "comment": "红红的冰糖心苹果",
    "comment_autocomplete": {
        "input": ["苹果"],
        "contexts": {
            "comment_category": "水果商城"
        }
    }
}

3. 结合Context进行Suggestion查询

POST comments/_search
{
    "suggest": {
        "MY_SUGGESTION": {
            "prefix": "苹",
            "completion": {
                "field": "comment_autocomplete",
                "contexts": {
                    "comment_category": "电器商城"
                }
            }
        }
    }
}

4. 返回结果

{
  "took" : 586,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 0,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "suggest" : {
    "MY_SUGGESTION" : [
      {
        "text" : "苹",
        "offset" : 0,
        "length" : 1,
        "options" : [
          {
            "text" : "苹果电脑",
            "_index" : "comments",
            "_type" : "_doc",
            "_id" : "CRTcQXkBv4MlM6fIDmPo",
            "_score" : 1.0,
            "_source" : {
              "comment" : "苹果电脑",
              "comment_autocomplete" : {
                "input" : [
                  "苹果电脑"
                ],
                "contexts" : {
                  "comment_category" : "电器商城"
                }
              }
            },
            "contexts" : {
              "comment_category" : [
                "电器商城"
              ]
            }
          }
        ]
      }
    ]
  }
}