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(improvement)(headless)Remove redundant SqlGenStrategy impl

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This commit is contained in:
jerryjzhang
2024-05-30 15:23:52 +08:00
parent 3b09a5c0ed
commit 18f268f590
13 changed files with 81 additions and 222 deletions
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46
common/src/main/java/com/tencent/supersonic/common/pojo/SysParameter.java
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@@ -48,62 +48,62 @@ public class SysParameter {
admins = Lists.newArrayList("admin");
//detect config
parameters.add(new Parameter("one.detection.size", "8",
parameters.add(new Parameter("s2.one.detection.size", "8",
"一次探测返回结果个数", "在每次探测后, 将前后缀匹配的结果合并, 并根据相似度阈值过滤后的结果个数",
"number", "Mapper相关配置"));
parameters.add(new Parameter("one.detection.max.size", "20",
parameters.add(new Parameter("s2.one.detection.max.size", "20",
"一次探测前后缀匹配结果返回个数", "单次前后缀匹配返回的结果个数", "number", "Mapper相关配置"));
//mapper config
parameters.add(new Parameter("metric.dimension.threshold", "0.3",
parameters.add(new Parameter("s2.metric.dimension.threshold", "0.3",
"指标名、维度名文本相似度阈值", "文本片段和匹配到的指标、维度名计算出来的编辑距离阈值, 若超出该阈值, 则舍弃",
"number", "Mapper相关配置"));
parameters.add(new Parameter("metric.dimension.min.threshold", "0.25",
parameters.add(new Parameter("s2.metric.dimension.min.threshold", "0.25",
"指标名、维度名最小文本相似度阈值", "指标名、维度名相似度阈值在动态调整中的最低值",
"number", "Mapper相关配置"));
parameters.add(new Parameter("dimension.value.threshold", "0.5",
parameters.add(new Parameter("s2.dimension.value.threshold", "0.5",
"维度值文本相似度阈值", "文本片段和匹配到的维度值计算出来的编辑距离阈值, 若超出该阈值, 则舍弃",
"number", "Mapper相关配置"));
parameters.add(new Parameter("dimension.value.min.threshold", "0.3",
parameters.add(new Parameter("s2.dimension.value.min.threshold", "0.3",
"维度值最小文本相似度阈值", "维度值相似度阈值在动态调整中的最低值",
"number", "Mapper相关配置"));
//embedding mapper config
parameters.add(new Parameter("embedding.mapper.word.min",
parameters.add(new Parameter("s2.embedding.mapper.word.min",
"4", "用于向量召回最小的文本长度", "为提高向量召回效率, 小于该长度的文本不进行向量语义召回", "number", "Mapper相关配置"));
parameters.add(new Parameter("embedding.mapper.word.max", "5",
parameters.add(new Parameter("s2.embedding.mapper.word.max", "5",
"用于向量召回最大的文本长度", "为提高向量召回效率, 大于该长度的文本不进行向量语义召回", "number", "Mapper相关配置"));
parameters.add(new Parameter("embedding.mapper.batch", "50",
parameters.add(new Parameter("s2.embedding.mapper.batch", "50",
"批量向量召回文本请求个数", "每次进行向量语义召回的原始文本片段个数", "number", "Mapper相关配置"));
parameters.add(new Parameter("embedding.mapper.number", "5",
parameters.add(new Parameter("s2.embedding.mapper.number", "5",
"批量向量召回文本返回结果个数", "每个文本进行向量语义召回的文本结果个数", "number", "Mapper相关配置"));
parameters.add(new Parameter("embedding.mapper.threshold",
parameters.add(new Parameter("s2.embedding.mapper.threshold",
"0.99", "向量召回相似度阈值", "相似度小于该阈值的则舍弃", "number", "Mapper相关配置"));
parameters.add(new Parameter("embedding.mapper.min.threshold",
parameters.add(new Parameter("s2.embedding.mapper.min.threshold",
"0.9", "向量召回最小相似度阈值", "向量召回相似度阈值在动态调整中的最低值", "number", "Mapper相关配置"));
//parser config
Parameter s2SQLParameter = new Parameter("s2SQL.generation", "TWO_PASS_AUTO_COT",
"S2SQL生成方式", "ONE_PASS_AUTO_COT: 通过思维链方式一步生成sql"
+ "\nONE_PASS_AUTO_COT_SELF_CONSISTENCY: 通过思维链且投票方式一步生成sql"
+ "\nTWO_PASS_AUTO_COT: 通过思维链方式两步生成sql"
Parameter s2SQLParameter = new Parameter("s2.parser.strategy",
"TWO_PASS_AUTO_COT_SELF_CONSISTENCY",
"LLM解析生成S2SQL策略",
"ONE_PASS_AUTO_COT_SELF_CONSISTENCY: 通过思维链且投票方式一步生成sql"
+ "\nTWO_PASS_AUTO_COT_SELF_CONSISTENCY: 通过思维链且投票方式两步生成sql", "list", "Parser相关配置");
s2SQLParameter.setCandidateValues(Lists.newArrayList("ONE_PASS_AUTO_COT", "ONE_PASS_AUTO_COT_SELF_CONSISTENCY",
"TWO_PASS_AUTO_COT", "TWO_PASS_AUTO_COT_SELF_CONSISTENCY"));
s2SQLParameter.setCandidateValues(Lists.newArrayList(
"ONE_PASS_AUTO_COT_SELF_CONSISTENCY", "TWO_PASS_AUTO_COT_SELF_CONSISTENCY"));
parameters.add(s2SQLParameter);
parameters.add(new Parameter("s2SQL.linking.value.switch", "true",
parameters.add(new Parameter("s2.s2SQL.linking.value.switch", "true",
"是否将Mapper探测识别到的维度值提供给大模型", "为了数据安全考虑, 这里可进行开关选择",
"bool", "Parser相关配置"));
parameters.add(new Parameter("query.text.length.threshold", "10",
parameters.add(new Parameter("s2.query.text.length.threshold", "10",
"用户输入文本长短阈值", "文本超过该阈值为长文本", "number", "Parser相关配置"));
parameters.add(new Parameter("short.text.threshold", "0.5",
parameters.add(new Parameter("s2.short.text.threshold", "0.5",
"短文本匹配阈值", "由于请求大模型耗时较长, 因此如果有规则类型的Query得分达到阈值,则跳过大模型的调用,\n如果是短文本, 若query得分/文本长度>该阈值, 则跳过当前parser",
"number", "Parser相关配置"));
parameters.add(new Parameter("long.text.threshold", "0.8",
parameters.add(new Parameter("s2.long.text.threshold", "0.8",
"长文本匹配阈值", "如果是长文本, 若query得分/文本长度>该阈值, 则跳过当前parser",
"number", "Parser相关配置"));
parameters.add(new Parameter("parse.show.count", "3",
parameters.add(new Parameter("s2.parse.show-count", "3",
"解析结果个数", "前端展示的解析个数",
"number", "Parser相关配置"));
}