Question

我想用elasticsearch实现自动完成，我无法做到。我想要这样的问题here。我尝试了建议的答案但是徒劳无功。我希望得到以下内容：

我的索引字符串用于例如：

“Developpeur Java”
“Developpeur C＃”
“Je suis Developpeur”
“Jesuisécrivan”
“Il est developpeur C ++”

对于输入“develop”，我想作为输出：

“Developpeur”
“Developpeur Java”
“Developpeur C＃”
“Developpeur C ++”

对于输入“developpeur”，我想作为输出：

“developpeur Java”
“developpeur C＃”
“developpeur C ++”

输入“suis”，我想作为输出：

“suis developpeur”
“suisécrivan”

我尝试使用完成建议器来实现此目的：

这是我正在使用的弹性搜索：

"number": "6.2.2",
"build_hash": "10b1edd",
"build_date": "2018-02-16T19:01:30.685723Z",
"build_snapshot": false,
"lucene_version": "7.2.1",
"minimum_wire_compatibility_version": "5.6.0",
"minimum_index_compatibility_version": "5.0.0"

映射：

{
"settings": {
    "number_of_shards": "1",
    "analysis": {
        "filter": {
            "prefix_filter": {
                "type": "edge_ngram",
                "min_gram": 1,
                "max_gram": 20
            },
            "ngram_filter": {
                "type": "nGram",
                "min_gram": "3",
                "max_gram": "3"
            },
            "synonym_filter": {
                "type": "synonym",
                "synonyms": [
                    "hackwillbereplacedatindexcreation,hackwillbereplacedatindexcreation"
                ]
            },
            "french_stop": {
                "type": "stop",
                "stopwords": "french"
            }
        },
        "analyzer": {
            "word": {
                "type": "custom",
                "tokenizer": "standard",
                "filter": [
                    "lowercase",
                    "asciifolding",
                    "french_stop"
                ],
                "char_filter": []
            },
            "prefix": {
                "type": "custom",
                "tokenizer": "standard",
                "filter": [
                    "lowercase",
                    "asciifolding",
                    "synonym_filter",
                    "prefix_filter"
                ],
                "char_filter": []
            },
            "ngram_with_synonyms": {
                "type": "custom",
                "tokenizer": "standard",
                "filter": [
                    "lowercase",
                    "asciifolding",
                    "synonym_filter",
                    "ngram_filter"
                ],
                "char_filter": []
            },
            "ngram": {
                "type": "custom",
                "tokenizer": "standard",
                "filter": [
                    "lowercase",
                    "asciifolding",
                    "ngram_filter"
                ],
                "char_filter": []
            }
        }
    }
},
"mappings": {
    "training": {
        "properties": {
            "id": {
                "type": "text",
                "index": false
            },
            "label": {
                "type": "text",
                "index_options": "docs",
                "copy_to": "full_label",
                "analyzer": "word",
                "fields": {
                    "prefix": {
                        "type": "text",
                        "index_options": "docs",
                        "analyzer": "prefix",
                        "search_analyzer": "word"
                    },
                    "ngram": {
                        "type": "text",
                        "index_options": "docs",
                        "analyzer": "ngram_with_synonyms",
                        "search_analyzer": "ngram"
                    }
                }
            },
            "labelSuggest": {
                "type": "completion",
                "analyzer": "word"
            },
        }
    }
}

然后当我用我的数据创建索引时，我这样做（这是对ES api进行put调用的主体，我正在使用pyhon）：

body = {
    "label": r["title"],
    "labelSuggest": {
        "input": r["title"].ngrams()
    },
    "weight": 1.
}

r [“title”]。ngrams（）获取标题的所有ngrams。例如： “发展研究生物技术”将给予：“发展”，“研究”，“生物技术”，“发展研究”，“研究生物技术”和“发展研究生物技术”

然后打电话给suggseter，我这样做：

   POST  http://localhost:9200/training/_search?pretty
{
    "suggest": {
        "labelSuggest": {
            "text": "developpeur",
            "completion": {
                "field": "labelSuggest",
                "skip_duplicates": true

            }
        }
    }
}

结果是：

{
    "text": "développement",
    "_index": "activity_20180518092449",
    "_type": "activity",
    "_id": "2031ce8b-6589-3270-afdf-7901aa21efa1",
    "_score": 1,
    "_source": {
        "id": "2031ce8b-6589-3270-afdf-7901aa21efa1",
        "name": "development research biotech",
        "labelSuggest": [
            "development",
            "research",
            "biotech",
            "development research",
            "research biotech",
            "development research biotech"
        ]
    }

但我想要的东西能给我：“发展”，“发展研究”和“发展研究生物技术”（假设我们只将该文件作为输入）

我正在做的映射/查询有什么问题？这是正确的方法吗？我希望我的问题很明确。我徒劳地搜索了很多。

提前致谢

Answer 1

首先，Ngram不会做你说的话。

这个：

"ngram_filter": {
            "type": "nGram",
            "min_gram": "3",
            "max_gram": "3"
        },

将从＃34; developpeur Java＆＃34; - ＆GT; dev，eve，vel，elo ......等等。

在此处查看文档：{{3}}

第二......对于你想要的结果我只会使用一个带有过滤器的自定义分析器＆＃34; icu_folding＆＃34;和＆＃34; engram＆＃34;和一个空格标记器。现在，我将从2开始，最多20-25。

这将从＆＃34; developpeur Java＆＃34;生成这样的令牌列表。 - ＆GT; de，dev，deve，devel，develo，developp，developpe，devellopeu，developper ......等等。

然后，您在该字段上进行简单的术语搜索。如果它是该自动填充的下拉列表，您将在键入时返回记录。希望我理解你的问题，我希望这会有所帮助。

更新：试试这个：

"suggester": {
"type": "custom",
"tokenizer": "whitespace",
"filter": ["my_ngram_filter", "icu_folding"],
"char_filter": []
}
"my_ngram_filter" is: "my_ngram_filter": {
    "type": "edge_ngram",
    "min_gram": "2",
    "max_gram": "20"
}

然后在该字段上的映射应该看起来像

"labelSuggest": {
            "type": "text",
            "analyzer": "suggester"
        }

然后进行简单的搜索

  {
  "query": {
    "term": {
      "labelSuggest": "dev" 
    }
   }
  }

使用下一个单词建议在Elasticsearch中自动完成

1 个答案: