Elasticsearch Suggester API(自動補全）
1.概念
1.補全api主要分為四類
- Term Suggester（糾錯補全，輸入錯誤的情況下補全正確的單詞）
- Phrase Suggester（自動補全短語，輸入一個單詞補全整個短語）
- Completion Suggester(完成補全單詞，輸出如前半部分，補全整個單詞）
- Context Suggester（上下文補全）
整體效果類似百度搜索，如圖：
2.Term Suggester(糾錯補全）
2.1.api
1.建立索引
PUT /book4 { "mappings": { "english": { "properties": { "passage": { "type": "text" } } } } }
2.插入數據
curl -H "Content-Type: application/json" -XPOST ‘http:localhost:9200/_bulk‘ -d‘ { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Lucene is cool"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Elasticsearch builds on top of lucene"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Elasticsearch rocks"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "Elastic is the company behind ELK stack"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "elk rocks"} { "index" : { "_index" : "book4", "_type" : "english" } } { "passage": "elasticsearch is rock solid"} ‘
3.看下儲存的分詞有哪些
post /_analyze { "text": [ "Lucene is cool", "Elasticsearch builds on top of lucene", "Elasticsearch rocks", "Elastic is the company behind ELK stack", "elk rocks", "elasticsearch is rock solid" ] }
結果：
{ "tokens": [ { "token": "lucene", "start_offset": 0, "end_offset": 6, "type": "<ALPHANUM>", "position": 0 }, { "token": "is", "start_offset": 7, "end_offset": 9, "type": "<ALPHANUM>", "position": 1 }, { "token": "cool", "start_offset": 10, "end_offset": 14, "type": "<ALPHANUM>", "position": 2 }, { "token": "elasticsearch", "start_offset": 15, "end_offset": 28, "type": "<ALPHANUM>", "position": 103 }, { "token": "builds", "start_offset": 29, "end_offset": 35, "type": "<ALPHANUM>", "position": 104 }, { "token": "on", "start_offset": 36, "end_offset": 38, "type": "<ALPHANUM>", "position": 105 }, { "token": "top", "start_offset": 39, "end_offset": 42, "type": "<ALPHANUM>", "position": 106 }, { "token": "of", "start_offset": 43, "end_offset": 45, "type": "<ALPHANUM>", "position": 107 }, { "token": "lucene", "start_offset": 46, "end_offset": 52, "type": "<ALPHANUM>", "position": 108 }, { "token": "elasticsearch", "start_offset": 53, "end_offset": 66, "type": "<ALPHANUM>", "position": 209 }, { "token": "rocks", "start_offset": 67, "end_offset": 72, "type": "<ALPHANUM>", "position": 210 }, { "token": "elastic", "start_offset": 73, "end_offset": 80, "type": "<ALPHANUM>", "position": 311 }, { "token": "is", "start_offset": 81, "end_offset": 83, "type": "<ALPHANUM>", "position": 312 }, { "token": "the", "start_offset": 84, "end_offset": 87, "type": "<ALPHANUM>", "position": 313 }, { "token": "company", "start_offset": 88, "end_offset": 95, "type": "<ALPHANUM>", "position": 314 }, { "token": "behind", "start_offset": 96, "end_offset": 102, "type": "<ALPHANUM>", "position": 315 }, { "token": "elk", "start_offset": 103, "end_offset": 106, "type": "<ALPHANUM>", "position": 316 }, { "token": "stack", "start_offset": 107, "end_offset": 112, "type": "<ALPHANUM>", "position": 317 }, { "token": "elk", "start_offset": 113, "end_offset": 116, "type": "<ALPHANUM>", "position": 418 }, { "token": "rocks", "start_offset": 117, "end_offset": 122, "type": "<ALPHANUM>", "position": 419 }, { "token": "elasticsearch", "start_offset": 123, "end_offset": 136, "type": "<ALPHANUM>", "position": 520 }, { "token": "is", "start_offset": 137, "end_offset": 139, "type": "<ALPHANUM>", "position": 521 }, { "token": "rock", "start_offset": 140, "end_offset": 144, "type": "<ALPHANUM>", "position": 522 }, { "token": "solid", "start_offset": 145, "end_offset": 150, "type": "<ALPHANUM>", "position": 523 } ] }View Code
4.term suggest api(搜索單個字段)
搜索下試試，給出錯誤單詞Elasticsearaach
POST /book4/_search { "suggest" : { "my-suggestion" : { "text" : "Elasticsearaach", "term" : { "field" : "passage"，
"suggest_mode": "popular" } } } }
response:
{ "took": 26, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 0, "max_score": 0, "hits": [] }, "suggest": { "my-suggestion": [ { "text": "elasticsearaach", "offset": 0, "length": 15, "options": [ { "text": "elasticsearch", "score": 0.84615386, "freq": 3 } ] } ] } }
5.搜索多個字段分別給出提示：
POST _search { "suggest": { "my-suggest-1" : { "text" : "tring out Elasticsearch", "term" : { "field" : "message" } }, "my-suggest-2" : { "text" : "kmichy", "term" : { "field" : "user" } } } }
該term建議者提出基於編輯距離條款。在建議術語之前分析提供的建議文本。建議的術語是根據分析的建議文本標記提供的。該term建議者不走查詢到的是是的請求部分。
常見建議選項：
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建議文字。建議文本是必需的選項，需要全局或按建議設置。 |
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從中獲取候選建議的字段。這是一個必需的選項，需要全局設置或根據建議設置。 |
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用於分析建議文本的分析器。默認為建議字段的搜索分析器。 |
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每個建議文本標記返回的最大更正。 |
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定義如何根據建議文本術語對建議進行排序。兩個可能的值：
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建議模式控制包含哪些建議或控制建議的文本術語，建議。可以指定三個可能的值：
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其他術語建議選項：
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在文本分析之後，建議文本術語小寫。 |
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最大編輯距離候選建議可以具有以便被視為建議。只能是介於1和2之間的值。任何其他值都會導致拋出錯誤的請求錯誤。默認為2。 |
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必須匹配的最小前綴字符的數量才是候選建議。默認為1.增加此數字可提高拼寫檢查性能。通常拼寫錯誤不會出現在術語的開頭。（舊名“prefix_len”已棄用） |
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建議文本術語必須具有的最小長度才能包含在內。默認為4.（舊名稱“min_word_len”已棄用） |
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設置從每個單獨分片中檢索的最大建議數。在減少階段，僅根據 |
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用於乘以的因子， |
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建議應出現的文檔數量的最小閾值。可以指定為絕對數字或文檔數量的相對百分比。這可以僅通過建議高頻項來提高質量。默認為0f且未啟用。如果指定的值大於1，則該數字不能是小數。分片級文檔頻率用於此選項。 |
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建議文本令牌可以存在的文檔數量的最大閾值，以便包括在內。可以是表示文檔頻率的相對百分比數（例如0.4）或絕對數。如果指定的值大於1，則不能指定小數。默認為0.01f。這可用於排除高頻術語的拼寫檢查。高頻術語通常拼寫正確，這也提高了拼寫檢查的性能。分片級文檔頻率用於此選項。 |
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用於比較類似建議術語的字符串距離實現。可以指定五個可能的值： |
Elasticsearch Suggester API(自動補全）