“你的意思是” 是搜索引擎中一个非常重要的功能，因为它们通过显示建议的术语来帮助用户，以便他可以进行更准确的搜索。比如，在百度中，我们进行搜索时，它通常会显示一些更为常用推荐的搜索选项来供我们选择：

为了创建 “你的意思是”，我们将使用 phrase suggester，因为通过它我们将能够建议句子更正，而不仅仅是术语。在我之前的文章 “Elasticsearch：如何实现短语建议 – phrase suggester”，我有涉及到这个问题。

首先，我们将使用一个 shingle 过滤器，因为它将提供一个分词，短语建议器将使用该标记来进行匹配并返回更正。有关 shingle 过滤器的描述，请阅读之前的文章 “Elasticsearch: Ngrams, edge ngrams, and shingles”。

准备数据

我们首先来定义映射：

PUT movies
{
  "settings": {
    "analysis": {
      "analyzer": {
        "en_analyzer": {
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "stop"
          ]
        },
        "shingle_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "shingle_filter"
          ]
        }
      },
      "filter": {
        "shingle_filter": {
          "type": "shingle",
          "min_shingle_size": 2,
          "max_shingle_size": 3
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "en_analyzer",
        "fields": {
          "suggest": {
            "type": "text",
            "analyzer": "shingle_analyzer"
          }
        }
      },
      "actors": {
        "type": "text",
        "analyzer": "en_analyzer",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "description": {
        "type": "text",
        "analyzer": "en_analyzer",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "director": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "genre": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "metascore": {
        "type": "long"
      },
      "rating": {
        "type": "float"
      },
      "revenue": {
        "type": "float"
      },
      "runtime": {
        "type": "long"
      },
      "votes": {
        "type": "long"
      },
      "year": {
        "type": "long"
      },
      "title_suggest": {
        "type": "completion",
        "analyzer": "simple",
        "preserve_separators": true,
        "preserve_position_increments": true,
        "max_input_length": 50
      }
    }
  }
}

我们接下来使用_bulk命令来写入一些文档到这个索引中去。我们使用这个链接中的内容。我们使用如下的方法：

POST movies/_bulk
{"index": {}}
{"title": "Guardians of the Galaxy", "genre": "Action,Adventure,Sci-Fi", "director": "James Gunn", "actors": "Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana", "description": "A group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe.", "year": 2014, "runtime": 121, "rating": 8.1, "votes": 757074, "revenue": 333.13, "metascore": 76}
{"index": {}}
{"title": "Prometheus", "genre": "Adventure,Mystery,Sci-Fi", "director": "Ridley Scott", "actors": "Noomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron",tgcode "description": "Following clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone.", "year": 2012, "runtime": 124, "rating": 7, "votes": 485820, "revenue": 126.46, "metascore": 65}
 
....

在上面，为了说明的方便，我省去了其它的文档。你需要把整个 movies.txt 的文件拷贝过来，并全部写入到Elasticsearch中。它共有1000 个文档。

搜索数据

现在让我们运行一个基本查询来查看 suggest 的结果：

GET movies/_search?filter_path=suggest
{
  "suggest": {
    "text": "transformers revenge of the falen",
    "did_you_mean": {
      "phrase": {
        "field": "title.suggest",
        "size": 5
      }
    }
  }
}

上面命令显示的结果为：

{
  "suggest": {
    "did_you_mean": [
      {
        "text": "transformers revenge of the falen",
        "offset": 0,
        "length": 33,
        "options": [
          {
            "text": "transformers revenge of the fallen",
            "score": 0.004467494
          },
          {
            "text": "transformers revenge of the fall",
            "score": 0.00020402104
          },
          {
            "text": "transformers revenge of the face",
            "score": 0.00006419608
          }
        ]
      }
    ]
  }
}

请注意，在几行中tgcode你已经获得了一些有希望的结果。

现在让我们通过使用更多短语建议功能来增加我们的查询。让我们使用 max_errors = 2，这样我们希望句子中最多有两个术语。 添加了 highlight 显示以突出​​显示建议的术语。

GET movies/_search?filter_path=suggest
{
  "suggest": {
    "text": "transformer revenge of the falen",
    "did_you_mean": {
      "phrase": {
        "field": "title.suggest",
        "size": 5,
        "confidence": 1,
        "max_errors":2,
        "highlight": {
          "pre_tag": "",
          "post_tag": ""
        }
      }
    }
  }
}

上面命令返回的结果为：

{
  "suggest": {
    "did_you_mean": [tgcode
      {
        "text": "transformer revenge of the falen",
        "offset": 0,
        "length": 32,
        "options": [
          {
            "text": "transformers revenge of the fallen",
            "highlighted": "transformers revenge of the fallen",
            "score": 0.004382903
          },
          {
            "text": "transformers revenge of the fall",
            "highlighted": "transformers revenge of the fall",
            "score": 0.00020015794
          },
          {
            "text": "transformers revenge of the face",
            "highlighted": "transformers revenge of the face",
            "score": 0.00006298054
          },
          {
            "text": "transformers revenge of the falen",
            "highlighted": "transformers revenge of the falen",
            "score": 0.00006159308
          },
          {
            "text": "transformer revenge of the fallen",
            "highlighted": "transformer revenge of the fallen",
            "score": 0.000048000533
          }
        ]
      }
    ]
  }
}

我们再改进一点好吗？ 我们添加了 “collate”，我们可以对每个结果执行查询，改进建议的结果。 我使用了带有 “and” 运算符的匹配项，以便在同一个句子中匹配所有术语。 如果我仍然想要不符合查询条件的结果，我使用 prune = true。

GET movies/_search?filter_path=suggest
{
  "suggest": {
    "text": "transformer revenge of the falen",
    "did_you_mean": {
      "phrase": {
        "field": "title.suggest",
        "size": 5,
        "confidence": 1,
        "max_errors":2,
        "collate": {
          "query": { 
            "source" : {
              "match": {
                "{{field_name}}": {
                  "query": "{{suggestion}}",
                  "operator": "and"
                }
              }
            }
          },
          "params": {"field_name" : "title"}, 
          "prune" :true
        },
        "highlight": {
          "pre_tag": "",
          "post_tag": ""
        }
      }
    }
  }
}

现在的结果是：

请注意，答案已更改，我有一个新字段 “collat​​e_match”，它指示结果中是否匹配整理规则（这是因为 prune = true）。

让我们设置 prune 为 false：

GET movies/_search?filter_path=suggest
{
  "suggest": {
    "text": "transformer revenge of the falen",
    "did_you_mean": {
      "phrase": {
        "field": "title.suggest",
        "size": 5,
        "confidence": 1,
        "max_errors":2,
        "collate": {
          "query": { 
            "source" : {
              "match": {
                "{{field_name}}": {
                  "query": "{{suggestion}}",
                  "operator": "and"
                }
              }
            }
          },
          "params": {"field_name" : "title"}, 
          "prune" :false
        },
        "highlight": {
          "pre_tag": "",
          "post_tag": ""
        }
      }
    }
  }
}

这次我们得到的结果是：

我们可以看到只有一个结果是最相关的建议。