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(improvement)(common|headless|chat|auth) 鉴权优化与召回优化

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1 修复生成的用户token 一生成就失效的问题
2 如果用户设置的token ，需校验是否数据库存在，因为用户可设置一年的token 有泄露风险
3 结果解析优化， 去除不可以解析的情况，解析问题需要改写后的问，
4 召回样例，用相似度，保住至少有一个样例是高相似度的
5 数据集召回，填加完全匹配格式筛选逻辑
This commit is contained in:
guilinlewis
2025-06-23 09:47:48 +08:00
parent 0721df2e66
commit 7e6639df83
8 changed files with 84 additions and 18 deletions
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38
headless/chat/src/main/java/com/tencent/supersonic/headless/chat/parser/llm/PromptHelper.java
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@@ -14,10 +14,8 @@ import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
import org.springframework.util.CollectionUtils;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.*;
import java.util.stream.Collectors;
import static com.tencent.supersonic.headless.chat.parser.ParserConfig.*;
@@ -51,13 +49,33 @@ public class PromptHelper {
// use random collection of exemplars for each self-consistency inference
for (int i = 0; i < selfConsistencyNumber; i++) {
List<Text2SQLExemplar> shuffledList = new ArrayList<>(exemplars);
// only shuffle the exemplars from config
List<Text2SQLExemplar> subList =
shuffledList.subList(llmReq.getDynamicExemplars().size(), shuffledList.size());
Collections.shuffle(subList);
results.add(shuffledList.subList(0, Math.min(shuffledList.size(), fewShotNumber)));
List<Text2SQLExemplar> same = shuffledList.stream() // 相似度极高的话，先找出来
.filter(e -> e.getSimilarity() > 0.989).collect(Collectors.toList());
List<Text2SQLExemplar> noSame = shuffledList.stream()
.filter(e -> e.getSimilarity() <= 0.989).collect(Collectors.toList());
if ((noSame.size() - same.size()) > fewShotNumber) {// 去除部分最低分
noSame.sort(Comparator.comparingDouble(Text2SQLExemplar::getSimilarity));
noSame = noSame.subList((noSame.size() - fewShotNumber) / 2, noSame.size());
}
Text2SQLExemplar mostSimilar = noSame.get(noSame.size() - 1);
Collections.shuffle(noSame);
List<Text2SQLExemplar> ts;
if (same.size() > 0) {// 一样的话，必须作为提示语
ts = new ArrayList<>();
int needSize = Math.min(noSame.size() + same.size(), fewShotNumber);
if (needSize > same.size()) {
ts.addAll(noSame.subList(0, needSize - same.size()));
}
ts.addAll(same);
} else { // 至少要一个最像的
ts = noSame.subList(0, Math.min(noSame.size(), fewShotNumber));
if (!ts.contains(mostSimilar)) {
ts.remove(ts.size() - 1);
ts.add(mostSimilar);
}
}
results.add(ts);
}
return results;
}