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ElasticSearch搜索建议与上下文提示

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ElasticSearch 搜索建议与上下文提示

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

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。

  1. 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"}

  2. 搜索文档，调用 suggest api。

    当中有 3 种 Suggestion Mode

    • missing 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 

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

  3. 返回结果 

    {
  "took" : 6,
  "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 Suggester 可以在 Term Suggester 上增加一些额外的逻辑

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>"
        }
      }
    }
  }
}

Completion Suggester

自动完成功能，用户每输入一个字符。就需要即时发送一个查询请求到后端查找匹配项。

它对性能要求比较苛刻。

elastic 将 Analyse 的数据编码成 FST 与索引放在一起，它会被整个加载进内存里面，速度非常快

FST 只能支持前缀查找。

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"}
    }
  }
}

  4. 返回结果 

    {
  "took" : 173,
  "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" : "pv8V8WwBISxFcLcZfDXl",
            "_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" : "pf8V8WwBISxFcLcZfDXl",
            "_score" : 1.0,
            "_source" : {"title_completion" : "php 是什么"}
          },
          {
            "text" : "php 货币",
            "_index" : "titles",
            "_type" : "_doc",
            "_id" : "p_8V8WwBISxFcLcZfDXl",
            "_score" : 1.0,
            "_source" : {"title_completion" : "php 货币"}
          },
          {
            "text" : "php 面试题 2019",
            "_index" : "titles",
            "_type" : "_doc",
            "_id" : "qP8V8WwBISxFcLcZfDXl",
            "_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" : 1,
  "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",
            "_index" : "comments",
            "_type" : "_doc",
            "_id" : "qf_s9WwBISxFcLcZszWh",
            "_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"]
            }
          }
        ]
      }
    ]
  }
}

附录

  • suggesters 官方文档
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