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(improvement)(chat) Remove the special field 'data date' to avoid generating non-existent time fields. (#1596)

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This commit is contained in:
lexluo09
2024-08-23 13:37:14 +08:00
committed by GitHub
parent afa82bf98d
commit be9a8bbc27
6 changed files with 127 additions and 141 deletions
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10
headless/api/src/main/java/com/tencent/supersonic/headless/api/pojo/DataSetSchema.java
View File

@@ -127,6 +127,16 @@ public class DataSetSchema {
return dimensions.stream().anyMatch(SchemaElement::containsPartitionTime);
}
public SchemaElement getPartitionDimension() {
for (SchemaElement dimension : dimensions) {
String partitionTimeFormat = dimension.getPartitionTimeFormat();
if (StringUtils.isNotBlank(partitionTimeFormat)) {
return dimension;
}
}
return null;
}
public String getPartitionTimeFormat() {
for (SchemaElement dimension : dimensions) {
String partitionTimeFormat = dimension.getPartitionTimeFormat();

14
headless/chat/src/main/java/com/tencent/supersonic/headless/chat/corrector/TimeCorrector.java
View File

@@ -21,6 +21,7 @@ import org.springframework.util.CollectionUtils;
import java.util.HashSet;
import java.util.List;
import java.util.Objects;
import java.util.Set;
/**
@@ -53,19 +54,24 @@ public class TimeCorrector extends BaseSemanticCorrector {
String correctS2SQL = semanticParseInfo.getSqlInfo().getCorrectedS2SQL();
List<String> whereFields = SqlSelectHelper.getWhereFields(correctS2SQL);
Long dataSetId = semanticParseInfo.getDataSetId();
if (CollectionUtils.isEmpty(whereFields) || !TimeDimensionEnum.containsZhTimeDimension(whereFields)) {
DataSetSchema dataSetSchema = chatQueryContext.getSemanticSchema().getDataSetSchemaMap().get(dataSetId);
if (Objects.isNull(dataSetSchema)
|| Objects.isNull(dataSetSchema.getPartitionDimension())
|| Objects.isNull(dataSetSchema.getPartitionDimension().getName())) {
return;
}
String partitionDimension = dataSetSchema.getPartitionDimension().getName();
if (CollectionUtils.isEmpty(whereFields) || !whereFields.contains(partitionDimension)) {
Pair<String, String> startEndDate = S2SqlDateHelper.getStartEndDate(chatQueryContext, dataSetId,
semanticParseInfo.getQueryType());
if (isValidDateRange(startEndDate)) {
correctS2SQL = SqlAddHelper.addParenthesisToWhere(correctS2SQL);
String dateChName = TimeDimensionEnum.DAY.getChName();
String startDateLeft = startEndDate.getLeft();
String endDateRight = startEndDate.getRight();
String condExpr = String.format(" ( %s >= '%s' and %s <= '%s' )",
dateChName, startDateLeft, dateChName, endDateRight);
partitionDimension, startDateLeft, partitionDimension, endDateRight);
correctS2SQL = addConditionToSQL(correctS2SQL, condExpr);
}
}

160
headless/chat/src/main/java/com/tencent/supersonic/headless/chat/parser/llm/LLMRequestService.java
View File

@@ -1,9 +1,8 @@
package com.tencent.supersonic.headless.chat.parser.llm;
import com.tencent.supersonic.common.pojo.enums.DataFormatTypeEnum;
import com.tencent.supersonic.common.pojo.enums.TimeDimensionEnum;
import com.tencent.supersonic.common.util.ContextUtils;
import com.tencent.supersonic.common.util.DateUtils;
import com.tencent.supersonic.headless.api.pojo.DataSetSchema;
import com.tencent.supersonic.headless.api.pojo.SchemaElement;
import com.tencent.supersonic.headless.api.pojo.SchemaElementMatch;
import com.tencent.supersonic.headless.api.pojo.SchemaElementType;
@@ -61,43 +60,24 @@ public class LLMRequestService {
}
public LLMReq getLlmReq(ChatQueryContext queryCtx, Long dataSetId) {
LLMRequestService requestService = ContextUtils.getBean(LLMRequestService.class);
List<LLMReq.ElementValue> linkingValues = requestService.getValues(queryCtx, dataSetId);
SemanticSchema semanticSchema = queryCtx.getSemanticSchema();
Map<Long, String> dataSetIdToName = semanticSchema.getDataSetIdToName();
List<LLMReq.ElementValue> linkingValues = getValues(queryCtx, dataSetId);
Map<Long, String> dataSetIdToName = queryCtx.getSemanticSchema().getDataSetIdToName();
String queryText = queryCtx.getQueryText();
LLMReq llmReq = new LLMReq();
llmReq.setQueryText(queryText);
LLMReq.FilterCondition filterCondition = new LLMReq.FilterCondition();
llmReq.setFilterCondition(filterCondition);
LLMReq.LLMSchema llmSchema = new LLMReq.LLMSchema();
llmSchema.setDataSetId(dataSetId);
llmSchema.setDataSetName(dataSetIdToName.get(dataSetId));
llmSchema.setDomainName(dataSetIdToName.get(dataSetId));
Set<String> fieldNameList = getMatchedFieldNames(queryCtx, dataSetId);
if (Objects.nonNull(semanticSchema.getDataSetSchemaMap())
&& Objects.nonNull(semanticSchema.getDataSetSchemaMap().get(dataSetId))) {
TimeDefaultConfig timeDefaultConfig = semanticSchema.getDataSetSchemaMap()
.get(dataSetId).getTagTypeTimeDefaultConfig();
if (!Objects.equals(timeDefaultConfig.getUnit(), -1)
&& queryCtx.containsPartitionDimensions(dataSetId)) {
// 数据集配置了数据日期字段，并查询设置 时间不为-1时才添加 '数据日期' 字段
fieldNameList.add(TimeDimensionEnum.DAY.getChName());
}
}
llmSchema.setFieldNameList(new ArrayList<>(fieldNameList));
llmSchema.setMetrics(getMatchedMetrics(queryCtx, dataSetId));
llmSchema.setDimensions(getMatchedDimensions(queryCtx, dataSetId));
llmSchema.setTerms(getTerms(queryCtx, dataSetId));
llmReq.setSchema(llmSchema);
String priorExts = getPriorExts(queryCtx, new ArrayList<>(fieldNameList));
llmReq.setPriorExts(priorExts);
String priorKnowledge = getPriorKnowledge(queryCtx, llmSchema);
llmReq.setPriorExts(priorKnowledge);
List<LLMReq.ElementValue> linking = new ArrayList<>();
boolean linkingValueEnabled = Boolean.valueOf(parserConfig.getParameterValue(PARSER_LINKING_VALUE_ENABLE));
@@ -143,12 +123,20 @@ public class LLMRequestService {
}).collect(Collectors.toList());
}
private String getPriorExts(ChatQueryContext queryContext, List<String> fieldNameList) {
StringBuilder extraInfoSb = new StringBuilder();
private String getPriorKnowledge(ChatQueryContext queryContext, LLMReq.LLMSchema llmSchema) {
StringBuilder priorKnowledgeBuilder = new StringBuilder();
SemanticSchema semanticSchema = queryContext.getSemanticSchema();
// 获取字段名到数据格式类型的映射
Map<String, String> fieldNameToDataFormatType = semanticSchema.getMetrics().stream()
appendMetricPriorKnowledge(llmSchema, priorKnowledgeBuilder, semanticSchema);
// 处理维度字段
appendDimensionPriorKnowledge(llmSchema, priorKnowledgeBuilder, semanticSchema);
return priorKnowledgeBuilder.toString();
}
private Map<String, String> getFieldNameToDataFormatTypeMap(SemanticSchema semanticSchema) {
return semanticSchema.getMetrics().stream()
.filter(metric -> Objects.nonNull(metric.getDataFormatType()))
.flatMap(metric -> {
Set<Pair<String, String>> fieldFormatPairs = new HashSet<>();
@@ -161,35 +149,48 @@ public class LLMRequestService {
return fieldFormatPairs.stream();
})
.collect(Collectors.toMap(Pair::getLeft, Pair::getRight, (existing, replacement) -> existing));
}
Map<String, String> fieldNameToDateFormat = semanticSchema.getDimensions().stream()
private void appendMetricPriorKnowledge(LLMReq.LLMSchema llmSchema,
StringBuilder priorKnowledgeBuilder,
SemanticSchema semanticSchema) {
Map<String, String> fieldNameToDataFormatType = getFieldNameToDataFormatTypeMap(semanticSchema);
for (SchemaElement schemaElement : llmSchema.getMetrics()) {
String fieldName = schemaElement.getName();
String dataFormatType = fieldNameToDataFormatType.get(fieldName);
if (DataFormatTypeEnum.DECIMAL.getName().equalsIgnoreCase(dataFormatType)
|| DataFormatTypeEnum.PERCENT.getName().equalsIgnoreCase(dataFormatType)) {
priorKnowledgeBuilder.append(String.format("%s的计量单位是%s; ", fieldName, "小数"));
}
}
}
private Map<String, String> getFieldNameToDateFormatMap(SemanticSchema semanticSchema) {
return semanticSchema.getDimensions().stream()
.filter(dimension -> StringUtils.isNotBlank(dimension.getTimeFormat()))
.collect(Collectors.toMap(
SchemaElement::getName,
value -> Optional.ofNullable(value.getPartitionTimeFormat()).orElse(""),
(k1, k2) -> k1)
);
}
// 构建额外信息字符串
for (String fieldName : fieldNameList) {
String dataFormatType = fieldNameToDataFormatType.get(fieldName);
if (DataFormatTypeEnum.DECIMAL.getName().equalsIgnoreCase(dataFormatType)
|| DataFormatTypeEnum.PERCENT.getName().equalsIgnoreCase(dataFormatType)) {
extraInfoSb.append(String.format("%s的计量单位是%s; ", fieldName, "小数"));
}
}
// 构建日期格式化信息
for (String fieldName : fieldNameList) {
private void appendDimensionPriorKnowledge(LLMReq.LLMSchema llmSchema,
StringBuilder priorKnowledgeBuilder,
SemanticSchema semanticSchema) {
Map<String, String> fieldNameToDateFormat = getFieldNameToDateFormatMap(semanticSchema);
for (SchemaElement schemaElement : llmSchema.getDimensions()) {
String fieldName = schemaElement.getName();
String timeFormat = fieldNameToDateFormat.get(fieldName);
if (StringUtils.isNotBlank(timeFormat)) {
extraInfoSb.append(String.format("%s的日期Format格式是%s; ", fieldName, timeFormat));
priorKnowledgeBuilder.append(String.format("%s是分区时间且格式是%s", fieldName, timeFormat));
}
}
return extraInfoSb.toString();
}
public List<LLMReq.ElementValue> getValues(ChatQueryContext queryCtx, Long dataSetId) {
Map<Long, String> itemIdToName = getItemIdToName(queryCtx, dataSetId);
List<SchemaElementMatch> matchedElements = queryCtx.getMapInfo().getMatchedElements(dataSetId);
if (CollectionUtils.isEmpty(matchedElements)) {
return new ArrayList<>();
@@ -203,20 +204,13 @@ public class LLMRequestService {
})
.map(elementMatch -> {
LLMReq.ElementValue elementValue = new LLMReq.ElementValue();
elementValue.setFieldName(itemIdToName.get(elementMatch.getElement().getId()));
elementValue.setFieldName(elementMatch.getElement().getName());
elementValue.setFieldValue(elementMatch.getWord());
return elementValue;
}).collect(Collectors.toSet());
return new ArrayList<>(valueMatches);
}
protected Map<Long, String> getItemIdToName(ChatQueryContext queryCtx, Long dataSetId) {
SemanticSchema semanticSchema = queryCtx.getSemanticSchema();
List<SchemaElement> elements = semanticSchema.getDimensions(dataSetId);
return elements.stream().collect(
Collectors.toMap(SchemaElement::getId, SchemaElement::getName, (value1, value2) -> value2));
}
protected List<SchemaElement> getMatchedMetrics(ChatQueryContext queryCtx, Long dataSetId) {
List<SchemaElementMatch> matchedElements = queryCtx.getMapInfo().getMatchedElements(dataSetId);
if (CollectionUtils.isEmpty(matchedElements)) {
@@ -235,40 +229,40 @@ public class LLMRequestService {
}
protected List<SchemaElement> getMatchedDimensions(ChatQueryContext queryCtx, Long dataSetId) {
List<SchemaElementMatch> matchedElements = queryCtx.getMapInfo().getMatchedElements(dataSetId);
if (CollectionUtils.isEmpty(matchedElements)) {
return Collections.emptyList();
}
return matchedElements.stream()
.filter(schemaElementMatch -> {
SchemaElementType elementType = schemaElementMatch.getElement().getType();
return SchemaElementType.DIMENSION.equals(elementType);
})
.map(schemaElementMatch -> schemaElementMatch.getElement())
.collect(Collectors.toList());
}
protected Set<String> getMatchedFieldNames(ChatQueryContext queryCtx, Long dataSetId) {
Map<Long, String> itemIdToName = getItemIdToName(queryCtx, dataSetId);
List<SchemaElementMatch> matchedElements = queryCtx.getMapInfo().getMatchedElements(dataSetId);
if (CollectionUtils.isEmpty(matchedElements)) {
return new HashSet<>();
Set<SchemaElement> results = new HashSet<>();
if (!CollectionUtils.isEmpty(matchedElements)) {
results = matchedElements.stream()
.filter(element -> SchemaElementType.DIMENSION.equals(element.getElement().getType()))
.map(SchemaElementMatch::getElement)
.collect(Collectors.toSet());
}
return matchedElements.stream()
.filter(schemaElementMatch -> {
SchemaElementType elementType = schemaElementMatch.getElement().getType();
return SchemaElementType.METRIC.equals(elementType)
|| SchemaElementType.DIMENSION.equals(elementType)
|| SchemaElementType.VALUE.equals(elementType);
})
.map(schemaElementMatch -> {
SchemaElement element = schemaElementMatch.getElement();
SchemaElementType elementType = element.getType();
if (SchemaElementType.VALUE.equals(elementType)) {
return itemIdToName.get(element.getId());
}
return schemaElementMatch.getWord();
})
.collect(Collectors.toSet());
SemanticSchema semanticSchema = queryCtx.getSemanticSchema();
if (semanticSchema == null || semanticSchema.getDataSetSchemaMap() == null) {
return new ArrayList<>(results);
}
DataSetSchema dataSetSchema = semanticSchema.getDataSetSchemaMap().get(dataSetId);
if (dataSetSchema == null) {
return new ArrayList<>(results);
}
TimeDefaultConfig timeDefaultConfig = dataSetSchema.getTagTypeTimeDefaultConfig();
SchemaElement partitionDimension = dataSetSchema.getPartitionDimension();
if (timeDefaultConfig == null || partitionDimension == null) {
return new ArrayList<>(results);
}
if (Objects.equals(timeDefaultConfig.getUnit(), -1)) {
results.remove(partitionDimension);
} else {
results.add(partitionDimension);
}
return new ArrayList<>(results);
}
}

48
headless/chat/src/main/java/com/tencent/supersonic/headless/chat/query/llm/s2sql/LLMReq.java
View File

@@ -7,81 +7,61 @@ import com.tencent.supersonic.common.pojo.ChatModelConfig;
import com.tencent.supersonic.common.pojo.Text2SQLExemplar;
import com.tencent.supersonic.headless.api.pojo.SchemaElement;
import lombok.Data;
import org.apache.commons.collections4.CollectionUtils;
import java.util.ArrayList;
import java.util.List;
import java.util.stream.Collectors;
@Data
public class LLMReq {
private String queryText;
private FilterCondition filterCondition;
private LLMSchema schema;
private List<ElementValue> linking;
private String currentDate;
private String priorExts;
private SqlGenType sqlGenType;
private ChatModelConfig modelConfig;
private PromptConfig promptConfig;
private List<Text2SQLExemplar> dynamicExemplars;
@Data
public static class ElementValue {
private String fieldName;
private String fieldValue;
}
@Data
public static class LLMSchema {
private String domainName;
private String dataSetName;
private Long dataSetId;
private String dataSetName;
private List<String> fieldNameList;
private List<SchemaElement> metrics;
private List<SchemaElement> dimensions;
private List<Term> terms;
}
@Data
public static class FilterCondition {
private String tableName;
public List<String> getFieldNameList() {
List<String> fieldNameList = new ArrayList<>();
if (CollectionUtils.isNotEmpty(metrics)) {
fieldNameList.addAll(metrics.stream().map(metric -> metric.getName()).collect(Collectors.toList()));
}
if (CollectionUtils.isNotEmpty(dimensions)) {
fieldNameList.addAll(dimensions.stream().map(metric -> metric.getName()).collect(Collectors.toList()));
}
return fieldNameList;
}
}
@Data
public static class Term {
private String name;
private String description;
private List<String> alias = Lists.newArrayList();
}
public enum SqlGenType {
ONE_PASS_SELF_CONSISTENCY("1_pass_self_consistency");
private String name;
SqlGenType(String name) {

4
headless/chat/src/test/java/com/tencent/supersonic/headless/chat/corrector/SchemaCorrectorTest.java
View File

@@ -26,11 +26,7 @@ class SchemaCorrectorTest {
+ " \"dataSetId\": 1,\n"
+ " \"llmReq\": {\n"
+ " \"queryText\": \"xxx2024年播放量最高的十首歌\",\n"
+ " \"filterCondition\": {\n"
+ " \"tableName\": null\n"
+ " },\n"
+ " \"schema\": {\n"
+ " \"domainName\": \"歌曲\",\n"
+ " \"dataSetName\": \"歌曲\",\n"
+ " \"fieldNameList\": [\n"
+ " \"商务组\",\n"

32
launchers/standalone/src/main/resources/s2-exemplar.json
View File

@@ -1,50 +1,50 @@
[
{
"question": "比较jackjchen和robinlee今年以来的访问次数",
"sideInfo": "CurrentDate=[2020-12-01],DomainTerms=[<核心用户 COMMENT '核心用户指tom和lucy'>]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>,<访问用户数 ALIAS 'UV,访问人数,' COMMENT '访问的用户个数' AGGREGATE 'COUNT'>,<人均访问次数 ALIAS '平均访问次数,' COMMENT '每个用户平均访问的次数'>], Dimensions=[], Values[<用户='jackjchen'>,<用户='robinlee'>]",
"sideInfo": "CurrentDate=[2020-12-01],DomainTerms=[<核心用户 COMMENT '核心用户指tom和lucy'>],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>,<访问用户数 ALIAS 'UV,访问人数,' COMMENT '访问的用户个数' AGGREGATE 'COUNT'>,<人均访问次数 ALIAS '平均访问次数,' COMMENT '每个用户平均访问的次数'>], Dimensions=[<数据日期>], Values[<用户='jackjchen'>,<用户='robinlee'>]",
"sql": "SELECT 用户, 访问次数 FROM 超音数产品 WHERE 用户 IN ('jackjchen', 'robinlee') AND 数据日期 >= '2020-01-01' AND 数据日期 <= '2020-12-01'"
},
{
"question": "超音数近12个月访问人数 按部门",
"sideInfo": "CurrentDate=[2022-11-06]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>,<访问用户数 ALIAS 'UV,访问人数,' COMMENT '访问的用户个数' AGGREGATE 'COUNT'>,<人均访问次数 ALIAS '平均访问次数,' COMMENT '每个用户平均访问的次数'>], Dimensions=[<部门>], Values=[]",
"sideInfo": "CurrentDate=[2022-11-06],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>,<访问用户数 ALIAS 'UV,访问人数,' COMMENT '访问的用户个数' AGGREGATE 'COUNT'>,<人均访问次数 ALIAS '平均访问次数,' COMMENT '每个用户平均访问的次数'>], Dimensions=[<部门>,<数据日期>], Values=[]",
"sql": "SELECT 部门, 数据日期, 访问人数 FROM 超音数产品 WHERE 数据日期 >= '2021-11-06' AND 数据日期 <= '2022-11-06'"
},
{
"question": "超音数过去90天美术部、技术研发部的访问时长",
"sideInfo": "CurrentDate=[2023-04-21]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问时长 COMMENT '一段时间内用户的访问时长' AGGREGATE 'SUM'>], Dimensions=[], Values=[<部门='美术部'>,<部门='技术研发部'>]",
"sideInfo": "CurrentDate=[2023-04-21],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问时长 COMMENT '一段时间内用户的访问时长' AGGREGATE 'SUM'>], Dimensions=[<数据日期>], Values=[<部门='美术部'>,<部门='技术研发部'>]",
"sql": "SELECT 部门, 访问时长 FROM 超音数产品 WHERE 部门 IN ('美术部', '技术研发部') AND 数据日期 >= '2023-01-20' AND 数据日期 <= '2023-04-21'"
},
{
"question": "超音数访问时长小于1小时，且来自美术部的用户是哪些",
"sideInfo": "CurrentDate=[2023-07-31],DomainTerms=[<核心用户 COMMENT '用户为tom和lucy'>]",
"dbSchema": "Table:[超音数产品], Metrics:[<访问时长 COMMENT '一段时间内用户的访问时长' AGGREGATE 'SUM'>], Dimensions:[<用户>], Values:[<部门='美术部'>]",
"sideInfo": "CurrentDate=[2023-07-31],DomainTerms=[<核心用户 COMMENT '用户为tom和lucy'>],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table:[超音数产品], Metrics:[<访问时长 COMMENT '一段时间内用户的访问时长' AGGREGATE 'SUM'>], Dimensions:[<用户>,<数据日期>], Values:[<部门='美术部'>]",
"sql": "SELECT 用户 FROM 超音数产品 WHERE 部门 = '美术部' AND 访问时长 < 1"
},
{
"question": "超音数本月pv最高的用户有哪些",
"sideInfo": "CurrentDate=[2023-08-31],DomainTerms=[<核心用户 COMMENT '用户为tom和lucy'>]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<用户>], Values=[]",
"sideInfo": "CurrentDate=[2023-08-31],DomainTerms=[<核心用户 COMMENT '用户为tom和lucy'>],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<用户>,<数据日期>], Values=[]",
"sql": "SELECT 用户 FROM 超音数产品 WHERE 数据日期 >= '2023-08-01' AND 数据日期 <= '2023-08-31' ORDER BY 访问次数 DESC LIMIT 1"
},
{
"question": "超音数访问次数大于1k的部门是哪些",
"sideInfo": "CurrentDate=[2023-09-14]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<部门>], Values=[]",
"sideInfo": "CurrentDate=[2023-09-14],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<部门>,<数据日期>], Values=[]",
"sql": "SELECT 部门 FROM 超音数产品 WHERE 访问次数 > 1000"
},
{
"question": "过去半个月核心用户的访问次数",
"sideInfo": "CurrentDate=[2023-09-15],DomainTerms=[<核心用户 COMMENT '用户为alice'>]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<部门>], Values=[]",
"sideInfo": "CurrentDate=[2023-09-15],DomainTerms=[<核心用户 COMMENT '用户为alice'>],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<部门>,<数据日期>], Values=[]",
"sql": "SELECT 用户,SUM(访问次数) FROM 超音数产品 WHERE 用户='alice' AND 数据日期 >= '2023-09-01' AND 数据日期 <= '2023-09-15'"
},
{
"question": "过去半个月忠实用户有哪一些",
"sideInfo": "CurrentDate=[2023-09-15],DomainTerms=[<忠实用户 COMMENT '一段时间内总访问次数大于100的用户'>]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<用户>], Values=[]",
"sideInfo": "CurrentDate=[2023-09-15],DomainTerms=[<忠实用户 COMMENT '一段时间内总访问次数大于100的用户'>],PriorKnowledge=[数据日期 是分区时间且格式是yyyy-MM-dd]",
"dbSchema": "Table=[超音数产品], Metrics=[<访问次数 ALIAS 'pv' COMMENT '一段时间内用户的访问次数' AGGREGATE 'SUM'>], Dimensions=[<用户>,<数据日期>], Values=[]",
"sql": "SELECT 用户 FROM 超音数产品 WHERE 数据日期 >= '2023-09-01' AND 数据日期 <= '2023-09-15' GROUP BY 用户 HAVING SUM(访问次数) > 100"
}
]