办公网站模板/安卓优化

搜索推荐：Suggest

概述

搜索一般都会要求具有“搜索推荐”或者叫“搜索补全”的功能，即在用户输入搜索的过程中，进行自动补全或者纠错。以此来提高搜索文档的匹配精准度，进而提升用户的搜索体验，这就是Suggest。

四种Suggester

• term suggester：term suggester正如其名，只基于tokenizer之后的单个term去匹配建议词，并不会考虑多个term之间的关系

  POST <index>/_search
  { "suggest": {"<suggest_name>": {"text": "<search_content>","term": {"suggest_mode": "<suggest_mode>","field": "<field_name>"}}}
  }
  

  Options：

  • text：用户搜索的文本
  • field：要从哪个字段选取推荐数据
  • analyzer：使用哪种分词器
  • size：每个建议返回的最大结果数
  • sort：如何按照提示词项排序，参数值只可以是以下两个枚举：
    • score：分数>词频>词项本身
    • frequency：词频>分数>词项本身
  • suggest_mode：搜索推荐的推荐模式，参数值亦是枚举：
    • missing：默认值，仅为不在索引中的词项生成建议词
    • popular：仅返回与搜索词文档词频或文档词频更高的建议词
    • always：根据 建议文本中的词项 推荐 任何匹配的建议词
  • max_edits：可以具有最大偏移距离候选建议以便被认为是建议。只能是1到2之间的值。任何其他值都将导致引发错误的请求错误。默认为2
  • prefix_length：前缀匹配的时候，必须满足的最少字符
  • min_word_length：最少包含的单词数量
  • min_doc_freq：最少的文档频率
  • max_term_freq：最大的词频

• phrase suggester：phrase suggester和term suggester相比，对建议的文本会参考上下文，也就是一个句子的其他token，不只是单纯的token距离匹配，它可以基于共生和频率选出更好的建议。

  注意：purase需要先创建Mapping

  Options：

  • real_word_error_likelihood： 此选项的默认值为 0.95。此选项告诉 Elasticsearch 索引中 5% 的术语拼写错误。这意味着随着这个参数的值越来越低，Elasticsearch 会将越来越多存在于索引中的术语视为拼写错误，即使它们是正确的
  • max_errors：为了形成更正，最多被认为是拼写错误的术语的最大百分比。默认值为 1
  • confidence：默认值为 1.0，最大值也是。该值充当与建议分数相关的阈值。只有得分超过此值的建议才会显示。例如，置信度为 1.0 只会返回得分高于输入短语的建议
  • collate：告诉 Elasticsearch 根据指定的查询检查每个建议，以修剪索引中不存在匹配文档的建议。在这种情况下，它是一个匹配查询。由于此查询是模板查询，因此搜索查询是当前建议，位于查询中的参数下。可以在查询下的“params”对象中添加更多字段。同样，当参数“prune”设置为true时，我们将在响应中增加一个字段“collate_match”，指示建议结果中是否存在所有更正关键字的匹配
  • direct_generator：phrase suggester使用候选生成器生成给定文本中每个项可能的项的列表。单个候选生成器类似于为文本中的每个单独的调用term suggester。生成器的输出随后与建议候选项中的候选项结合打分。目前只支持一种候选生成器，即direct_generator。建议API接受密钥直接生成器下的生成器列表；列表中的每个生成器都按原始文本中的每个项调用。

• completion suggester：自动补全，自动完成，支持三种查询【前缀查询（prefix）模糊查询（fuzzy）正则表达式查询（regex)】 ，主要针对的应用场景就是"Auto Completion"。 此场景下用户每输入一个字符的时候，就需要即时发送一次查询请求到后端查找匹配项，在用户输入速度较高的情况下对后端响应速度要求比较苛刻。因此实现上它和前面两个Suggester采用了不同的数据结构，索引并非通过倒排来完成，而是将analyze过的数据编码成FST和索引一起存放。对于一个open状态的索引，FST会被ES整个装载到内存里的，进行前缀查找速度极快。但是FST只能用于前缀查找，这也是Completion Suggester的局限所在。

  • completion：es的一种特有类型，专门为suggest提供，基于内存，性能很高。
  • prefix query：基于前缀查询的搜索提示，是最常用的一种搜索推荐查询。
    • prefix：客户端搜索词
    • field：建议词字段
    • size：需要返回的建议词数量（默认5）
    • skip_duplicates：是否过滤掉重复建议，默认false
  • fuzzy query
    • fuzziness：允许的偏移量，默认auto
    • transpositions：如果设置为true，则换位计为一次更改而不是两次更改，默认为true。
    • min_length：返回模糊建议之前的最小输入长度，默认 3
    • prefix_length：输入的最小长度（不检查模糊替代项）默认为 1
    • unicode_aware：如果为true，则所有度量（如模糊编辑距离，换位和长度）均以Unicode代码点而不是以字节为单位。这比原始字节略慢，因此默认情况下将其设置为false。
  • regex query：可以用正则表示前缀，不建议使用

• context suggester：完成建议者会考虑索引中的所有文档，但是通常来说，我们在进行智能推荐的时候最好通过某些条件过滤，并且有可能会针对某些特性提升权重。

  • contexts：上下文对象，可以定义多个
    • name：context的名字，用于区分同一个索引中不同的context对象。需要在查询的时候指定当前name
    • type：context对象的类型，目前支持两种：category和geo，分别用于对suggest item分类和指定地理位置。
    • boost：权重值，用于提升排名
  • path：如果没有path，相当于在PUT数据的时候需要指定context.name字段，如果在Mapping中指定了path，在PUT数据的时候就不需要了，因为 Mapping是一次性的，而PUT数据是频繁操作，这样就简化了代码。这段解释有木有很牛逼，网上搜到的都是官方文档的翻译，觉悟雷同。

#term suggestDELETE news
POST _bulk
{ "index" : { "_index" : "news","_id":1 } }
{ "title": "baoqiang bought a new hat with the same color of this font, which is very beautiful baoqiangba baoqiangda baoqiangdada baoqian baoqia"}
{ "index" : { "_index" : "news","_id":2 } }
{ "title": "baoqiangge gave birth to two children, one is upstairs, one is downstairs baoqiangba baoqiangda baoqiangdada baoqian baoqia"}
{ "index" : { "_index" : "news","_id":3} }
{ "title": "baoqiangge 's money was rolled away baoqiangba baoqiangda baoqiangdada baoqian baoqia"}
{ "index" : { "_index" : "news","_id":4} }
{ "title": "baoqiangda baoqiangda baoqiangda baoqiangda baoqiangda baoqian baoqia"}GET news/_mappingPOST _analyze
{"text": ["BaoQiang bought a new hat with the same color of this font, which is very beautiful","BaoQiangGe gave birth to two children, one is upstairs, one is downstairs","BaoQiangGe 's money was rolled away"]
}POST /news/_search
{"suggest": {"my-suggestion": {"text": "baoqing baoqiang","term": {"suggest_mode":"always","field": "title","min_doc_freq": 3}}}
}GET /news/_search
{ "suggest": {"my-suggestion": {"text": "baoqing baoqiang","term": {"suggest_mode": "popular","field": "title"}}}
}GET /news/_search
{ "suggest": {"my-suggestion": {"text": "baoqing baoqiang","term": {"suggest_mode": "popular","field": "title","max_edits":2,"max_term_freq":1}}}
}GET /news/_search
{ "suggest": {"my-suggestion": {"text": "baoqing baoqiang","term": {"suggest_mode": "always","field": "title","max_edits":2}}}
}DELETE news2
POST _bulk
{ "index" : { "_index" : "news2","_id":1 } }
{ "title": "baoqiang4"}
{ "index" : { "_index" : "news2","_id":2 } }
{ "title": "baoqiang4 baoqiang3"}
{ "index" : { "_index" : "news2","_id":3 } }
{ "title": "baoqiang4 baoqiang3 baoqiang2"}
{ "index" : { "_index" : "news2","_id":4 } }
{ "title": "baoqiang4 baoqiang3 baoqiang2  baoqiang"}
POST /news2/_search
{ "suggest": {"second-suggestion": {"text": "baoqian baoqiang baoqiang2 baoqiang3","term": {"suggest_mode": "popular","field": "title"}}}
}

#phrase suggester
DELETE test
PUT test
{"settings": {"index": {"number_of_shards": 1,"number_of_replicas": 0,"analysis": {"analyzer": {"trigram": {"type": "custom","tokenizer": "standard","filter": ["lowercase","shingle"]}},"filter": {"shingle": {"type": "shingle","min_shingle_size": 2,"max_shingle_size": 3}}}}},"mappings": {"properties": {"title": {"type": "text","fields": {"trigram": {"type": "text","analyzer": "trigram"}}}}}
}GET /_analyze
{"tokenizer": "standard","filter": [{"type": "shingle","min_shingle_size": 2,"max_shingle_size": 3}],"text": "lucene and elasticsearch"
}# "min_shingle_size": 2,
# "max_shingle_size": 3
GET test/_analyze
{"analyzer": "trigram", "text" : "lucene and elasticsearch"
}
DELETE test
POST test/_bulk
{ "index" : { "_id":1} }
{"title": "lucene and elasticsearch"}
{ "index" : {"_id":2} }
{"title": "lucene and elasticsearhc"}
{ "index" : { "_id":3} }
{"title": "luceen and elasticsearch"}POST test/_search
GET test/_mapping
POST test/_search
{"suggest": {"text": "Luceen and elasticsearhc","simple_phrase": {"phrase": {"field": "title.trigram","max_errors": 2,"gram_size": 1,"confidence":0,"direct_generator": [{"field": "title.trigram","suggest_mode": "always"}],"highlight": {"pre_tag": "<em>","post_tag": "</em>"}}}}
}

#complate suggester
DELETE suggest_carinfo
PUT suggest_carinfo
{"mappings": {"properties": {"title": {"type": "text","analyzer": "ik_max_word","fields": {"suggest": {"type": "completion","analyzer": "ik_max_word"}}},"content": {"type": "text","analyzer": "ik_max_word"}}}
}POST _bulk
{"index":{"_index":"suggest_carinfo","_id":1}}
{"title":"宝马X5 两万公里准新车","content":"这里是宝马X5图文描述"}
{"index":{"_index":"suggest_carinfo","_id":2}}
{"title":"宝马5系","content":"这里是奥迪A6图文描述"}
{"index":{"_index":"suggest_carinfo","_id":3}}
{"title":"宝马3系","content":"这里是奔驰图文描述"}
{"index":{"_index":"suggest_carinfo","_id":4}}
{"title":"奥迪Q5 两万公里准新车","content":"这里是宝马X5图文描述"}
{"index":{"_index":"suggest_carinfo","_id":5}}
{"title":"奥迪A6 无敌车况","content":"这里是奥迪A6图文描述"}
{"index":{"_index":"suggest_carinfo","_id":6}}
{"title":"奥迪双钻","content":"这里是奔驰图文描述"}
{"index":{"_index":"suggest_carinfo","_id":7}}
{"title":"奔驰AMG 两万公里准新车","content":"这里是宝马X5图文描述"}
{"index":{"_index":"suggest_carinfo","_id":8}}
{"title":"奔驰大G 无敌车况","content":"这里是奥迪A6图文描述"}
{"index":{"_index":"suggest_carinfo","_id":9}}
{"title":"奔驰C260","content":"这里是奔驰图文描述"}
{"index":{"_index":"suggest_carinfo","_id":10}}
{"title":"nir奔驰C260","content":"这里是奔驰图文描述"}GET suggest_carinfo/_search?pretty
{"suggest": {"car_suggest": {"prefix": "奥迪","completion": {"field": "title.suggest"}}}
}#1：内存代价太大，原话是：性能高是通过大量的内存换来的
#2：只能前缀搜索,假如用户输入的不是前缀 召回率可能很低POST suggest_carinfo/_search
{"suggest": {"car_suggest": {"prefix": "宝马5系","completion": {"field": "title.suggest","skip_duplicates":true,"fuzzy": {"fuzziness": 2}}}}
}
GET suggest_carinfo/_doc/10
GET _analyze
{"analyzer": "ik_max_word","text": ["奔驰AMG 两万公里准新车"]
}POST suggest_carinfo/_search
{"suggest": {"car_suggest": {"regex": "nir","completion": {"field": "title.suggest","size": 10}}}
}

# context suggester
# 定义一个名为 place_type 的类别上下文，其中类别必须与建议一起发送。
# 定义一个名为 location 的地理上下文，类别必须与建议一起发送
DELETE place
PUT place
{"mappings": {"properties": {"suggest": {"type": "completion","contexts": [{"name": "place_type","type": "category"},{"name": "location","type": "geo","precision": 4}]}}}
}PUT place/_doc/1
{"suggest": {"input": [ "timmy's", "starbucks", "dunkin donuts" ],"contexts": {"place_type": [ "cafe", "food" ]                    }}
}
PUT place/_doc/2
{"suggest": {"input": [ "monkey", "timmy's", "Lamborghini" ],"contexts": {"place_type": [ "money"]                    }}
}GET place/_search
POST place/_search?pretty
{"suggest": {"place_suggestion": {"prefix": "sta","completion": {"field": "suggest","size": 10,"contexts": {"place_type": [ "cafe", "restaurants" ]}}}}
}
# 某些类别的建议可以比其他类别提升得更高。以下按类别过滤建议，并额外提升与某些类别相关的建议
GET place/_search
POST place/_search?pretty
{"suggest": {"place_suggestion": {"prefix": "tim","completion": {"field": "suggest","contexts": {"place_type": [                             { "context": "cafe" },{ "context": "money", "boost": 2 }]}}}}
}# 地理位置筛选器
PUT place/_doc/3
{"suggest": {"input": "timmy's","contexts": {"location": [{"lat": 43.6624803,"lon": -79.3863353},{"lat": 43.6624718,"lon": -79.3873227}]}}
}
POST place/_search
{"suggest": {"place_suggestion": {"prefix": "tim","completion": {"field": "suggest","contexts": {"location": {"lat": 43.662,"lon": -79.380}}}}}
}# 定义一个名为 place_type 的类别上下文，其中类别是从 cat 字段中读取的。
# 定义一个名为 location 的地理上下文，其中的类别是从 loc 字段中读取的
DELETE place_path_category
PUT place_path_category
{"mappings": {"properties": {"suggest": {"type": "completion","contexts": [{"name": "place_type","type": "category","path": "cat"},{"name": "location","type": "geo","precision": 4,"path": "loc"}]},"loc": {"type": "geo_point"}}}
}
# 如果映射有路径，那么以下索引请求就足以添加类别
# 这些建议将与咖啡馆和食品类别相关联
# 如果上下文映射引用另一个字段并且类别被明确索引，则建议将使用两组类别进行索引
PUT place_path_category/_doc/1
{"suggest": ["timmy's", "starbucks", "dunkin donuts"],"cat": ["cafe", "food"] 
}
POST place_path_category/_search?pretty
{"suggest": {"place_suggestion": {"prefix": "tim","completion": {"field": "suggest","contexts": {"place_type": [                             { "context": "cafe" }]}}}}
}