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elasticsearch 如何在hibernate搜索中使用多个查询字符串检索精确的搜索结果
elasticsearch 如何在hibernate搜索中使用多个查询字符串检索精确的搜索结果
我们正在使用hibernate search orm 5.9.2，希望获得准确的搜索结果，如： 如果用户从
John -> all data with John should display
John Murphy -> all data with John murphy should display
John murphy Columbia -> Only data with John murphy Columbia should display
John murphy Columbia SC -> Only data with John murphy Columbia should display
John murphy Columbia SC 29201 -> Only data with John murphy Columbia SC 29201
29201 -> Only data with 29201 as zipcode should be displayed.
and so on...
bool（）> {
> "_index" : "client_master_index_0300",
> "_type" : "com.csc.pt.svc.data.to.Basclt0300TO",
> "_id" : "518,1",
> "_score" : 4.0615783,
> "_source" : {
> "id" : "518,1",
> "cltseqnum" : 518,
> "addrseqnum" : "1",
> "addrln1" : "Dba",
> "addrln2" : "Betsy Evans",
> "city" : "SDA",
> "state" : "SC",
> "zipcode" : "89756-4531",
> "country" : "USA",
> "basclt0100to" : {
> "cltseqnum" : 518,
> "clientname" : "Betsy Evans",
> "longname" : "Betsy Evans",
> "id" : "518"
> },
> "basclt0900to" : {
> "cltseqnum" : 518,
> "id" : "518"
> }
> }
> }
> {
> "client_master_index_0300" : {
> "aliases" : { },
> "mappings" : {
> "com.csc.pt.svc.data.to.Basclt0300TO" : {
> "dynamic" : "strict",
> "properties" : {
> "addrln1" : {
> "type" : "text",
> "store" : true
> },
> "addrln2" : {
> "type" : "text",
> "store" : true
> },
> "addrln3" : {
> "type" : "text",
> "store" : true
> },
> "addrseqnum" : {
> "type" : "text",
> "store" : true
> },
> "basclt0100to" : {
> "properties" : {
> "clientname" : {
> "type" : "text",
> "store" : true
> },
> "cltseqnum" : {
> "type" : "long",
> "store" : true
> },
> "firstname" : {
> "type" : "text",
> "store" : true
> },
> "id" : {
> "type" : "keyword",
> "store" : true,
> "norms" : true
> },
> "longname" : {
> "type" : "text",
> "store" : true
> },
> "midname" : {
> "type" : "text",
> "store" : true
> }
> }
> },
> "basclt0900to" : {
> "properties" : {
> "cltseqnum" : {
> "type" : "long",
> "store" : true
> },
> "email1" : {
> "type" : "text",
> "store" : true
> },
> "id" : {
> "type" : "keyword",
> "store" : true,
> "norms" : true
> }
> }
> },
> "city" : {
> "type" : "text",
> "store" : true
> },
> "cltseqnum" : {
> "type" : "long",
> "store" : true
> },
> "country" : {
> "type" : "text",
> "store" : true
> },
> "id" : {
> "type" : "keyword",
> "store" : true
> },
> "state" : {
> "type" : "text",
> "store" : true
> },
> "zipcode" : {
> "type" : "text",
> "store" : true
> }
> }
> }
> },
> "settings" : {
> "index" : {
> "creation_date" : "1535607176216",
> "number_of_shards" : "5",
> "number_of_replicas" : "1",
> "uuid" : "x4R71LNCTBSyO9Taf8siOw",
> "version" : {
> "created" : "6030299"
> },
> "provided_name" : "client_master_index_0300"
> }
> }
> }
> }
Query finalQuery = queryBuilder.simpleQueryString()
.onFields("basclt0100to.longname", "addrln1", "addrln2"
,"city","state","zipcode", "country")
.withAndAsDefaultOperator()
.matching(lowerCasedSearchTerm)
.createQuery();
FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery(finalQuery, Basclt0300TO.class);
fullTextQuery.setMaxResults(this.data.getPageSize()).setFirstResult(this.data.getPageSize());
List<String> projectedFields = new ArrayList<String>();
for (String fieldName : projections)
projectedFields.add(fieldName);
@SuppressWarnings("unchecked")
List<Cltj001ElasticSearchResponseTO> results = fullTextQuery.
setProjection(projectedFields.toArray(new String[projectedFields.size()]))
.setResultTransformer( new BasicTransformerAdapter() {
@Override
public Cltj001ElasticSearchResponseTO transformTuple(Object[] tuple, String[] aliases) {
return new Cltj001ElasticSearchResponseTO((String) tuple[0], (long) tuple[1],
(String) tuple[2], (String) tuple[3], (String) tuple[4],
(String) tuple[5],(String) tuple[6], (String) tuple[7], (String) tuple[8]);
}
})
.getResultList();
resultsClt0300MasterIndexList = results;
@AnalyzerDef(name = "withoutEdgeAnalyzerFactory",
// Split input into tokens according to tokenizer
tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
filters = {
@TokenFilterDef(factory = ASCIIFoldingFilterFactory.class),
@TokenFilterDef(factory = LowerCaseFilterFactory.class),
}
/*filters = {
// Normalize token text to lowercase, as the user is unlikely to
// care about casing when searching for matches
@TokenFilterDef(factory = PatternReplaceFilterFactory.class, params = {
@Parameter(name = "pattern", value = "([^a-zA-Z0-9\\.])"),
@Parameter(name = "replacement", value = " "),
@Parameter(name = "replace", value = "all") }),
@TokenFilterDef(factory = LowerCaseFilterFactory.class),
@TokenFilterDef(factory = StopFilterFactory.class) }*/)
希望这些详细信息能有所帮助。最简单的解决方案是在布尔查询中使用
occure.MUSToccure.SHOULD首先，您不需要自己拆分输入字符串；这就是Lucene（和Elasticsearch）的工作，在所谓的“文本分析”中。在开始使用Hibernate搜索之前，您确实应该了解文本分析 简而言之，文本分析是将单个字符串转换为可在全文索引中使用的“标记”（单词）的过程 文本分析在两种情况下执行（我正在简化，但这或多或少是会发生的）：
- 为文档编制索引时，将分析每个字段的内容，Elasticsearch将分析结果（标记列表）存储在索引中
- 查询时，将分析查询字符串，全文引擎将在索引中查找每个生成的标记
- 字符过滤，我不会详细描述它，因为它通常被跳过，所以您可能不需要它
- 标记化，将单个字符串拆分为多个部分，称为“标记”。简而言之，它从字符串中提取单词
- 代币过滤，对代币应用转换，例如将其转换为小写，用更简单的等价物（“é”=>“e”，“á”=>“a”，…）替换变音符号，将代币进一步拆分为ngram（“word”=>[“w”，“wo”，“wor”，“word”]），等等
EdgeNgramTokenFilterEdgeNGramTokenFilter.keyword（）关键字（）Query finalQuery = queryBuilder.simpleQueryString()
.onFields("basclt0100to.longname", "addrln1", "addrln2"
,"city","state","zipcode", "country")
.withAndAsDefaultOperator()
.matching(searchTerms)
.createQuery();
FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery(finalQuery, Basclt0300TO.class);
@Yoann Rodière，请分享你的意见。要理解你的问题到底是什么有点困难。请至少发布您的实体模型和当前查询代码，并解释当前行为的错误，最好使用示例（一组文档、用户输入、实际结果和预期结果）。这将是一个开始。@Yoann Rodière，我已经在我的问题中提供了细节。我会再次尝试：请发布您当前的查询代码，并解释当前行为的错误。最好是举例说明（一套文档、用户输入、实际结果、预期结果）@
Query finalQuery = queryBuilder.simpleQueryString()
.onFields("basclt0100to.longname", "addrln1", "addrln2"
,"city","state","zipcode", "country")
.withAndAsDefaultOperator()
.matching(searchTerms)
.createQuery();
FullTextQuery fullTextQuery = fullTextSession.createFullTextQuery(finalQuery, Basclt0300TO.class);