标签:ref tac json color ima 包含 trace eai cores
1.orderNum字段相关度 增强 score = math.sqrt(orderNum*0.001)
ScoreFunctionBuilder<?> dateFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("orderNum")
.missing(1d)
.modifier(FieldValueFactorFunction.Modifier.SQRT).factor(0.001f);
2.使用以下设置 如搜索个人所得税 contents 字段包含个人所得税所占相关度约为0.0004
MultiMatchQueryBuilder matchQueryBuilder = QueryBuilders
.multiMatchQuery(text, BwbdType.PROPERTY_NUMBERS
, BwbdType.PROPERTY_TITLES, BwbdType.PROPERTY_CONTENTS).analyzer("ik_smart")
.field(BwbdType.PROPERTY_NUMBERS, 0.01f)
.field(BwbdType.PROPERTY_TITLES, 0.1f)
.field(BwbdType.PROPERTY_CONTENTS, 0.001f)
.minimumShouldMatch(BwbdType.MATCH_LEVEL_THREE);
使用以上设置 两条数据orderNum相差130 对相关度影响是 0.015233534
在不考虑检索相关度情况下只看增强相关度 对最后相关度的影响。
ScoreFunctionBuilder<?> dataTypeFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("dataTypeRelation")
.missing(10d)
.modifier(FieldValueFactorFunction.Modifier.LN1P).factor(1f);
以上配置dataTypeRelation数据类型相关度相差10 相关度相差 0.2795849
增强score = math.log1p(dataTypeRelation*1)
FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[3];
// 时间相关
ScoreFunctionBuilder<?> dateFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("orderNum")
.missing(1d)
.modifier(FieldValueFactorFunction.Modifier.SQRT).factor(0.001f);
FunctionScoreQueryBuilder.FilterFunctionBuilder date = new FunctionScoreQueryBuilder.FilterFunctionBuilder(dateFieldValueScoreFunction);
filterFunctionBuilders[0] = date;
// 类型相关
ScoreFunctionBuilder<?> dataTypeFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("dataTypeRelation")
.missing(10d)
.modifier(FieldValueFactorFunction.Modifier.LN1P).factor(2f);
FunctionScoreQueryBuilder.FilterFunctionBuilder dataType = new FunctionScoreQueryBuilder.FilterFunctionBuilder(dataTypeFieldValueScoreFunction);
filterFunctionBuilders[1] = dataType;
// 来源相关
ScoreFunctionBuilder<?> originFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("originTypeRelation")
.missing(10d)
.modifier(FieldValueFactorFunction.Modifier.LN1P).factor(0.1f);
FunctionScoreQueryBuilder.FilterFunctionBuilder origin = new FunctionScoreQueryBuilder.FilterFunctionBuilder(originFieldValueScoreFunction);
filterFunctionBuilders[2] = origin;
FunctionScoreQueryBuilder query = QueryBuilders.functionScoreQuery(boolQueryBuilder,filterFunctionBuilders)
.boostMode(CombineFunction.SUM)
.scoreMode(FunctionScoreQuery.ScoreMode.SUM);
使用以上代码 多字段配置相关度 最后对相关度的影响
Score = score(相关度)+score(增强相关度1)+score(增强相关度2)+score(增强相关度3)计算方式与
.boostMode(CombineFunction.SUM)
.scoreMode(FunctionScoreQuery.ScoreMode.SUM);
配置有关
总结:多相关度优化方案 主要变更filed值让相关度评分与function_score增强的评分达到一个最优解
另外 也要使用 modifier 和 factor对单个相关度进行调整
最后 贴上该检索方法源码
@Override public SearchDto improveSearch(SearchDto searchDto) { String text = searchDto.getTerm(); String type = searchDto.getType(); HighSearchParam searchParam = searchDto.getSearchParam(); // 搜索请求对象 SearchRequest searchRequest = new SearchRequest(BwbdType.ES_INDEX); // 指定类型 searchRequest.types(BwbdType.ES_TYPE); // 搜索源构建对象 SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder(); // 搜索方式 // 首先构造多关键字查询条件 BoolQueryBuilder boolQueryBuilder = QueryBuilders.boolQuery(); if (StringUtils.isNotEmpty(text)) { text = QueryParser.escape(text); // 主要就是这一句把特殊字符都转义,那么lucene就可以识别 MultiMatchQueryBuilder matchQueryBuilder = QueryBuilders .multiMatchQuery(text, BwbdType.PROPERTY_NUMBERS , BwbdType.PROPERTY_TITLES, BwbdType.PROPERTY_CONTENTS).analyzer("ik_smart") .field(BwbdType.PROPERTY_NUMBERS, 0.01f) .field(BwbdType.PROPERTY_TITLES, 0.1f) .field(BwbdType.PROPERTY_CONTENTS, 0.001f) .minimumShouldMatch(BwbdType.MATCH_LEVEL_THREE); // 添加条件到布尔查询 boolQueryBuilder.must(matchQueryBuilder); } else { if (null == searchDto.getSearchParam() && StringUtils.isEmpty(type)) { searchDto.setType(BwbdType.DATA_TYPE_FG); } } // // 通过布尔查询来构造过滤查询 // boolQueryBuilder.filter(QueryBuilders.matchQuery("economics","L")); if (StringUtils.isNotEmpty(type)) { boolQueryBuilder.filter(QueryBuilders .matchQuery(BwbdType.PROPERTY_DATA_TYPE, type)); } addFilterProperties(text,searchParam, boolQueryBuilder, searchSourceBuilder); FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = buildFilterFunctionBuilders(); FunctionScoreQueryBuilder query = QueryBuilders.functionScoreQuery(boolQueryBuilder,filterFunctionBuilders) .boostMode(CombineFunction.SUM) .scoreMode(FunctionScoreQuery.ScoreMode.SUM); // 将查询条件封装给查询对象 searchSourceBuilder.query(query); if (searchDto.getSize() > 20) { searchDto.setSize(20); } searchSourceBuilder.size(searchDto.getSize()); searchSourceBuilder.from(searchDto.getPage() - 1); // *********************** // 高亮查询 HighlightBuilder highlightBuilder = new HighlightBuilder(); highlightBuilder.preTags(CommonConstraint.LIGHT_TAG_START); // 高亮前缀 highlightBuilder.postTags(CommonConstraint.LIGHT_TAG_END); // 高亮后缀 List<HighlightBuilder.Field> fields = highlightBuilder.fields(); fields.add(new HighlightBuilder .Field(BwbdType.PROPERTY_NUMBERS)); // 高亮字段 fields.add(new HighlightBuilder .Field(BwbdType.PROPERTY_TITLES)); // 高亮字段 fields.add(new HighlightBuilder .Field(BwbdType.PROPERTY_CONTENTS).fragmentSize(100000)); // 高亮字段 // 添加高亮查询条件到搜索源 searchSourceBuilder.highlighter(highlightBuilder); // *********************** // // 设置源字段过虑,第一个参数结果集包括哪些字段,第二个参数表示结果集不包括哪些字段 // searchSourceBuilder.fetchSource(new String[]{"name","studymodel","price","timestamp"},new String[]{}); // 向搜索请求对象中设置搜索源 searchRequest.source(searchSourceBuilder); // 执行搜索,向ES发起http请求 SearchResponse searchResponse = null; try (RestHighLevelClient client = new RestHighLevelClient(restClientBuilder)) { searchResponse = client.search(searchRequest, RequestOptions.DEFAULT); obtainFgType(searchDto, searchResponse); } catch (IOException e) { e.printStackTrace(); } return searchDto; } private FunctionScoreQueryBuilder.FilterFunctionBuilder[] buildFilterFunctionBuilders() { FunctionScoreQueryBuilder.FilterFunctionBuilder[] filterFunctionBuilders = new FunctionScoreQueryBuilder.FilterFunctionBuilder[3]; // 时间相关 ScoreFunctionBuilder<?> dateFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("orderNum") .missing(1d) .modifier(FieldValueFactorFunction.Modifier.SQRT).factor(0.001f); FunctionScoreQueryBuilder.FilterFunctionBuilder date = new FunctionScoreQueryBuilder.FilterFunctionBuilder(dateFieldValueScoreFunction); filterFunctionBuilders[0] = date; // 类型相关 ScoreFunctionBuilder<?> dataTypeFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("dataTypeRelation") .missing(10d) .modifier(FieldValueFactorFunction.Modifier.LN1P).factor(2f); FunctionScoreQueryBuilder.FilterFunctionBuilder dataType = new FunctionScoreQueryBuilder.FilterFunctionBuilder(dataTypeFieldValueScoreFunction); filterFunctionBuilders[1] = dataType; // 来源相关 ScoreFunctionBuilder<?> originFieldValueScoreFunction = ScoreFunctionBuilders.fieldValueFactorFunction("originTypeRelation") .missing(10d) .modifier(FieldValueFactorFunction.Modifier.LN1P).factor(0.1f); FunctionScoreQueryBuilder.FilterFunctionBuilder origin = new FunctionScoreQueryBuilder.FilterFunctionBuilder(originFieldValueScoreFunction); filterFunctionBuilders[2] = origin; return filterFunctionBuilders; } private void addFilterProperties(String text, HighSearchParam searchParam, BoolQueryBuilder boolQueryBuilder, SearchSourceBuilder searchSourceBuilder) { if (null != searchParam) { if (StringUtils.isNotEmpty(searchParam.getYearStr())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery(BwbdType.PROPERTY_YEARS, searchParam.getYearStr())); } if (StringUtils.isNotEmpty(searchParam.getReasonName())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("reasonName", searchParam.getReasonName())); } if (StringUtils.isNotEmpty(searchParam.getCaseType())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("caseType", searchParam.getCaseType())); } if (StringUtils.isNotEmpty(searchParam.getTrialRoundText())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("trialRoundText", searchParam.getTrialRoundText())); } if (StringUtils.isNotEmpty(searchParam.getJudgementType())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("judgementType", searchParam.getJudgementType())); } if (StringUtils.isNotEmpty(searchParam.getAreaCode())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("areaId", searchParam.getAreaCode())); } if (StringUtils.isNotEmpty(searchParam.getIndustry())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("economics", searchParam.getIndustry())); } if (StringUtils.isNotEmpty(searchParam.getTaxType())) { boolQueryBuilder.filter(QueryBuilders.matchPhraseQuery("stypes", searchParam.getTaxType())); } // 排序 // 根据 years 降序排列 if (BwbdType.ORDER_TYPE_DATE.equals(searchParam.getOrderType())) { searchSourceBuilder.sort(new FieldSortBuilder("contentDate").order(SortOrder.DESC)); } } // 如果没有检索内容 默认时间排序 if (StringUtils.isEmpty(text)) { searchSourceBuilder.sort(new FieldSortBuilder("contentDate").order(SortOrder.DESC)); } // 根据分数 _score 降序排列 (默认行为) // searchSourceBuilder.sort(new ScoreSortBuilder().order(SortOrder.DESC)); } private void obtainFgType(AbstractTxjDto searchDto, SearchResponse searchResponse) { // 搜索结果 SearchHits hits = searchResponse.getHits(); // 匹配到的总记录数 long totalHits = hits.getTotalHits(); searchDto.setTotal(totalHits); // 得到匹配度高的文档 SearchHit[] searchHits = hits.getHits(); List<BwbdType> bwbdTypes = new ArrayList<>(); for (SearchHit hit : searchHits) { String content = hit.getSourceAsString();//使用ES的java接口将实体类对应的内容转换为json字符串 BwbdType bwbdType = JSONObject.parseObject(content, BwbdType.class); //生成pojo对象 // 获取高亮查询的内容。如果存在,则替换原来的name Map<String, HighlightField> highlightFields = hit.getHighlightFields(); if (highlightFields != null) { HighlightField nameField = highlightFields.get(bwbdType.PROPERTY_NUMBERS); if (nameField != null) { Text[] fragments = nameField.getFragments(); StringBuffer stringBuffer = new StringBuffer(); for (Text str : fragments) { stringBuffer.append(str.string()); } String numbers = stringBuffer.toString(); bwbdType.setNumbers(numbers); } HighlightField titlesField = highlightFields.get(bwbdType.PROPERTY_TITLES); if (titlesField != null) { Text[] fragments = titlesField.getFragments(); StringBuffer stringBuffer = new StringBuffer(); for (Text str : fragments) { stringBuffer.append(str.string()); } String titles = stringBuffer.toString(); bwbdType.setTitles(titles); } HighlightField contentsField = highlightFields.get(bwbdType.PROPERTY_CONTENTS); if (contentsField != null) { Text[] fragments = contentsField.getFragments(); StringBuffer stringBuffer = new StringBuffer(); for (Text str : fragments) { stringBuffer.append(str.string()); } bwbdType.setContents(stringBuffer.toString()); } // 处理内容 handleResult(bwbdType); } bwbdTypes.add(bwbdType); } searchDto.setRows(bwbdTypes); }
标签:ref tac json color ima 包含 trace eai cores
原文地址:https://www.cnblogs.com/guanxiaohe/p/12936336.html