如何在elasticsearch中給出整個單詞的優先級？

Question

Elasticsearch目前對我來說運行良好，但是我想讓整個單詞優先於Ngrams。

我已經試過如下：

client.indices.create index: index, 
         body: { 
          mappings: { 
           search_variable: { 
            properties: { 
             "name" => { 
              "type" => "string", 
              "index" => "not_analyzed" 
             }, 
             "label" => { 
              "type" => "string", 
              "index" => "not_analyzed" 
             }, 
             "value_labels" => { 
              "type" => "string", 
              "index" => "not_analyzed" 
             }, 
             "value_label_search_string" => { 
              "type" => "string", 
              "index" => "not_analyzed" 
             }, 
             "search_text" => { 
              "type" => "multi_field", 
              "fields" => { 
              "whole_words" => {"type" => "string", "analyzer" => "simple"}, 
              "ngram" => {"type" => "string", "analyzer" => "ngram", "search_analyzer" => "ngram_search"} 
              } 
             } 
            } 
           }, 
           settings: { 
            analysis: { 
             filter: { 
              ngram: { 
               type: 'nGram', 
               min_gram: 3, 
               max_gram: 25 
              } 
             }, 
             analyzer: { 
              ngram: { 
               tokenizer: 'whitespace', 
               filter: ['lowercase', 'stop', 'ngram'], 
               type: 'custom' 
              }, 
              ngram_search: { 
               tokenizer: 'whitespace', 
               filter: ['lowercase', 'stop'], 
               type: 'custom' 
              } 
             } 
            } 
           } 
          } 
         } 

這是有關我的全文搜索領域的一部分：我想對整個匹配的項目給予較高的分數

"search_text" => { 
    "type" => "multi_field", 
    "fields" => { 
     "whole_words" => {"type" => "string", "analyzer" => "simple"}, 
     "ngram" => {"type" => "string", "analyzer" => "ngram", "search_analyzer" => "ngram_search"} 
    } 
} 

：search_text搜索文本中的單詞。

[400] {"error":{"root_cause":[{"type":"mapper_parsing_exception","reason":"analyzer [ngram_search] not found for field [ngram]"}],"type":"mapper_parsing_exception","reason":"Failed to parse mapping [search_variable]: analyzer [ngram_search] not found for field [ngram]","caused_by":{"type":"mapper_parsing_exception","reason":"analyzer [ngram_search] not found for field [ngram]"}},"status":400} 

這裏的錯誤： "reason":"analyzer [ngram_search] not found for field [ngram]"

我在做什麼錯？

編輯：

這裏是我的查詢，我就開始想匹配全字現在，我只得到0的結果每一次。

search_query = { 
     index: index, 
     body: { 
      _source: { 
       exclude: ["search_text", "survey_id"] 
      }, 
      query: { 
       :bool => { 
        :filter => { 
         :term => {"survey_id" => 12} 
        }, 
        :must => { 
         :match => { 
          "search_text.whole_words" => {"query" => "BMW", "operator" => "and"} 
         } 
        } 
       } 
      } 
     } 
    } 

    result = client.search(search_query) 

這裏是輸出： curl -XGET localhost:9200/yg_search_variables

{"yg_search_variables":{"aliases":{},"mappings":{"search_variable":{"properties":{"label":{"type":"string","index":"not_analyzed"},"name":{"type":"string","index":"not_analyzed"},"search_text":{"type":"string","index":"no","fields":{"ngram":{"type":"string","analyzer":"ngram","search_analyzer":"ngram_search"},"whole_words":{"type":"string","analyzer":"simple"}}},"value_label_search_string":{"type":"string","index":"not_analyzed"},"value_labels":{"type":"string","index":"not_analyzed"}}},"variables":{"properties":{"category":{"type":"string"},"label":{"type":"string","index":"not_analyzed"},"name":{"type":"string","index":"not_analyzed"},"search_text":{"type":"string","index":"no"},"survey_id":{"type":"long"},"value_label_search_text":{"type":"string"},"value_labels":{"properties":{"0":{"type":"string"},"1":{"type":"string"},"10":{"type":"string"},"100":{"type":"string"},"101":{"type":"string"},"102":{"type":"string"},"103":{"type":"string"},"104":{"type":"string"},"105":{"type":"string"},"106":{"type":"string"},"107":{"type":"string"},"108":{"type":"string"},"109":{"type":"string"},"11":{"type":"string"},"110":{"type":"string"},"1100":{"type":"string"},"1101":{"type":"string"},"1102":{"type":"string"},"1103":{"type":"string"},"1104":{"type":"string"},"1105":{"type":"string"},"1106":{"type":"string"},"1107":{"type":"string"},"1108":{"type":"string"},"1109":{"type":"string"},"111":{"type":"string"},"1110":{"type":"string"},"1111":{"type":"string"},"1112":{"type":"string"},"1113":{"type":"string"},"1114":{"type":"string"},"112":{"type":"string"},"113":{"type":"string"},"114":{"type":"string"},"115":{"type":"string"},"116":{"type":"string"},"117":{"type":"string"},"118":{"type":"string"},"119":{"type":"string"},"12":{"type":"string"},"120":{"type":"string"},"121":{"type":"string"},"122":{"type":"string"},"123":{"type":"string"},"124":{"type":"string"},"125":{"type":"string"},"126":{"type":"string"},"127":{"type":"string"},"128":{"type":"string"},"129":{"type":"string"},"13":{"type":"string"},"130":{"type":"string"},"131":{"type":"string"},"132":{"type":"string"},"133":{"type":"string"},"134":{"type":"string"},"135":{"type":"string"},"136":{"type":"string"},"137":{"type":"string"},"138":{"type":"string"},"139":{"type":"string"},"14":{"type":"string"},"140":{"type":"string"},"141":{"type":"string"},"142":{"type":"string"},"143":{"type":"string"},"144":{"type":"string"},"145":{"type":"string"},"146":{"type":"string"},"147":{"type":"string"},"148":{"type":"string"},"149":{"type":"string"},"15":{"type":"string"},"150":{"type":"string"},"151":{"type":"string"},"152":{"type":"string"},"153":{"type":"string"},"154":{"type":"string"},"155":{"type":"string"},"156":{"type":"string"},"157":{"type":"string"},"158":{"type":"string"},"159":{"type":"string"},"16":{"type":"string"},"160":{"type":"string"},"161":{"type":"string"},"162":{"type":"string"},"163":{"type":"string"},"164":{"type":"string"},"165":{"type":"string"},"166":{"type":"string"},"167":{"type":"string"},"168":{"type":"string"},"169":{"type":"string"},"17":{"type":"string"},"170":{"type":"string"},"171":{"type":"string"},"172":{"type":"string"},"173":{"type":"string"},"174":{"type":"string"},"175":{"type":"string"},"176":{"type":"string"},"177":{"type":"string"},"178":{"type":"string"},"179":{"type":"string"},"18":{"type":"string"},"180":{"type":"string"},"181":{"type":"string"},"182":{"type":"string"},"183":{"type":"string"},"184":{"type":"string"},"185":{"type":"string"},"186":{"type":"string"},"187":{"type":"string"},"188":{"type":"string"},"189":{"type":"string"},"19":{"type":"string"},"190":{"type":"string"},"191":{"type":"string"},"192":{"type":"string"},"193":{"type":"string"},"194":{"type":"string"},"195":{"type":"string"},"196":{"type":"string"},"197":{"type":"string"},"198":{"type":"string"},"199":{"type":"string"},"2":{"type":"string"},"20":{"type":"string"},"200":{"type":"string"},"201":{"type":"string"},"202":{"type":"string"},"203":{"type":"string"},"204":{"type":"string"},"205":{"type":"string"},"206":{"type":"string"},"207":{"type":"string"},"208":{"type":"string"},"209":{"type":"string"},"21":{"type":"string"},"210":{"type":"string"},"211":{"type":"string"},"22":{"type":"string"},"23":{"type":"string"},"24":{"type":"string"},"25":{"type":"string"},"26":{"type":"string"},"27":{"type":"string"},"28":{"type":"string"},"29":{"type":"string"},"3":{"type":"string"},"30":{"type":"string"},"301":{"type":"string"},"302":{"type":"string"},"303":{"type":"string"},"304":{"type":"string"},"305":{"type":"string"},"306":{"type":"string"},"307":{"type":"string"},"308":{"type":"string"},"309":{"type":"string"},"31":{"type":"string"},"310":{"type":"string"},"311":{"type":"string"},"312":{"type":"string"},"313":{"type":"string"},"314":{"type":"string"},"315":{"type":"string"},"316":{"type":"string"},"317":{"type":"string"},"32":{"type":"string"},"33":{"type":"string"},"34":{"type":"string"},"35":{"type":"string"},"36":{"type":"string"},"37":{"type":"string"},"38":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

看來奇怪的是，指數是no：

 "search_text": { 
     "type": "string", 
     "index": "no", 
     "fields": { 
      "ngram": { 
      "type": "string", 
      "analyzer": "ngram", 
      "search_analyzer": "ngram_search" 
      }, 
      "whole_words": { 
      "type": "string", 
      "analyzer": "simple" 
      } 
     } 
     } 

編輯：這是術語「福特樣本匹配文檔「：

{ 
    "name"=>"car_ownership", 
    "label"=>"Customer: Ford", 
    "category"=>["Vehicles", "Passenger Vehicles"], "value"=>nil, 
    "value_labels"=>{"1"=>"Yes", "2"=>"No"}, 
    "node_id"=>14813, 
    "survey_id" => 12, 
    "search_text" => "Customer Ford Vehicles Passenger Vehicles Yes No" 
} 

編輯：我已經添加了一個較小的開始到結束測試用例，可以在這裏找到，它複製了錯誤。

https://www.dropbox.com/s/wwxm3qe0oxc2z5y/Slimmed%20ElasticSearch%20Text%20%281%29.html?dl=0

Answer 1

的第一個問題是，settings不正確的嵌套當您創建索引。 settings和mappings應該處於同一水平。

然後，看着你的保管箱文件，我認爲問題是映射類型被稱爲search_variable，而在你的批量中你正在使用映射類型test_type。因此映射永遠不會被應用。