Merge 0d8ee40d6e into b9dd6bb7c5

headless/chat/pom.xml

@@ -122,6 +122,11 @@
             <version>${mockito-inline.version}</version>
             <scope>test</scope>
         </dependency>
+        <dependency>
+            <groupId>com.huaban</groupId>
+            <artifactId>jieba-analysis</artifactId>
+            <version>${jieba.version}</version>
+        </dependency>
     </dependencies>
 
 </project>

headless/chat/src/main/java/com/tencent/supersonic/headless/chat/parser/llm/HeuristicDataSetResolver.java

@@ -1,98 +1,31 @@
 package com.tencent.supersonic.headless.chat.parser.llm;
 
-import com.tencent.supersonic.headless.api.pojo.SchemaElementMatch;
-import com.tencent.supersonic.headless.api.pojo.SchemaElementType;
-import com.tencent.supersonic.headless.api.pojo.SchemaMapInfo;
-import com.tencent.supersonic.headless.api.pojo.SemanticParseInfo;
+import com.tencent.supersonic.headless.api.pojo.*;
 import com.tencent.supersonic.headless.chat.ChatQueryContext;
 import lombok.extern.slf4j.Slf4j;
-import org.apache.commons.collections.CollectionUtils;
 
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
+import java.util.*;
 import java.util.Map.Entry;
-import java.util.Objects;
-import java.util.Set;
+
+import static com.tencent.supersonic.headless.chat.parser.llm.TextSimilarityCalculation.getDataSetSimilarity;
 
 /**
- * HeuristicDataSetResolver select ONE most suitable data set out of matched data sets. The
- * selection is based on similarity comparison rule and the priority is like: 1.
- * maxSimilarity(matched dataset) 2. maxSimilarity(all matched metrics) 3. totalSimilarity(all
- * matched elements)
+ * HeuristicDataSetResolver select ONE most suitable data set out of data sets. The
+ * selection is based on the cosine similarity directly between the question text and the dataset name
  */
 @Slf4j
 public class HeuristicDataSetResolver implements DataSetResolver {
 
     public Long resolve(ChatQueryContext chatQueryContext, Set<Long> agentDataSetIds) {
-        SchemaMapInfo mapInfo = chatQueryContext.getMapInfo();
-        Set<Long> matchedDataSets = mapInfo.getMatchedDataSetInfos();
-        if (CollectionUtils.isNotEmpty(agentDataSetIds)) {
-            matchedDataSets.retainAll(agentDataSetIds);
+        String  queryText = chatQueryContext.getRequest().getQueryText();
+        List<SchemaElement> dataSets = chatQueryContext.getSemanticSchema().getDataSets();
+        if(dataSets.size() == 1){
+            return dataSets.get(0).getDataSetId();
         }
-        if (matchedDataSets.size() == 1) {
-            return matchedDataSets.stream().findFirst().get();
+        Map<Long,Double> dataSetSimilarity = new LinkedHashMap<>();
+        for (SchemaElement dataSet : dataSets){
+            dataSetSimilarity.put(dataSet.getDataSetId(),getDataSetSimilarity(queryText,dataSet.getDataSetName()));
         }
-        return selectDataSetByMatchSimilarity(mapInfo);
-    }
-
-    protected Long selectDataSetByMatchSimilarity(SchemaMapInfo schemaMap) {
-        Map<Long, SemanticParseInfo.DataSetMatchResult> dataSetMatchRet =
-                getDataSetMatchResult(schemaMap);
-        Entry<Long, SemanticParseInfo.DataSetMatchResult> selectedDataset =
-                dataSetMatchRet.entrySet().stream().sorted((o1, o2) -> {
-                    double difference = o1.getValue().getMaxDatesetSimilarity()
-                            - o2.getValue().getMaxDatesetSimilarity();
-                    if (difference == 0) {
-                        difference = o1.getValue().getMaxMetricSimilarity()
-                                - o2.getValue().getMaxMetricSimilarity();
-                        if (difference == 0) {
-                            difference = o1.getValue().getTotalSimilarity()
-                                    - o2.getValue().getTotalSimilarity();
-                        }
-                        if (difference == 0) {
-                            difference = o1.getValue().getMaxMetricUseCnt()
-                                    - o2.getValue().getMaxMetricUseCnt();
-                        }
-                    }
-                    return difference >= 0 ? -1 : 1;
-                }).findFirst().orElse(null);
-        if (selectedDataset != null) {
-            log.info("selectDataSet with multiple DataSets [{}]", selectedDataset.getKey());
-            return selectedDataset.getKey();
-        }
-
-        return null;
-    }
-
-    protected Map<Long, SemanticParseInfo.DataSetMatchResult> getDataSetMatchResult(
-            SchemaMapInfo schemaMap) {
-        Map<Long, SemanticParseInfo.DataSetMatchResult> dateSetMatchRet = new HashMap<>();
-        for (Entry<Long, List<SchemaElementMatch>> entry : schemaMap.getDataSetElementMatches()
-                .entrySet()) {
-            double maxMetricSimilarity = 0;
-            double maxDatasetSimilarity = 0;
-            double totalSimilarity = 0;
-            long maxMetricUseCnt = 0L;
-            for (SchemaElementMatch match : entry.getValue()) {
-                if (SchemaElementType.DATASET.equals(match.getElement().getType())) {
-                    maxDatasetSimilarity = Math.max(maxDatasetSimilarity, match.getSimilarity());
-                }
-                if (SchemaElementType.METRIC.equals(match.getElement().getType())) {
-                    maxMetricSimilarity = Math.max(maxMetricSimilarity, match.getSimilarity());
-                    if (Objects.nonNull(match.getElement().getUseCnt())) {
-                        maxMetricUseCnt = Math.max(maxMetricUseCnt, match.getElement().getUseCnt());
-                    }
-                }
-                totalSimilarity += match.getSimilarity();
-            }
-            dateSetMatchRet.put(entry.getKey(),
-                    SemanticParseInfo.DataSetMatchResult.builder()
-                            .maxMetricSimilarity(maxMetricSimilarity)
-                            .maxDatesetSimilarity(maxDatasetSimilarity)
-                            .totalSimilarity(totalSimilarity).build());
-        }
-
-        return dateSetMatchRet;
+        return dataSetSimilarity.entrySet().stream().max(Map.Entry.comparingByValue()).get().getKey();
     }
 }

headless/chat/src/main/java/com/tencent/supersonic/headless/chat/parser/llm/TextSimilarityCalculation.java

@@ -0,0 +1,52 @@
+package com.tencent.supersonic.headless.chat.parser.llm;
+
+import com.huaban.analysis.jieba.JiebaSegmenter;
+import lombok.extern.slf4j.Slf4j;
+
+import java.util.*;
+
+@Slf4j
+public class TextSimilarityCalculation {
+    // 生成词频向量
+    private static double[] createVector(List<String> words, List<String> vocabulary) {
+        double[] vector = new double[vocabulary.size()];
+        Map<String, Integer> wordFreq = new HashMap<>();
+        for (String word : words) {
+            wordFreq.put(word, wordFreq.getOrDefault(word, 0) + 1);
+        }
+        for (int i = 0; i < vocabulary.size(); i++) {
+            vector[i] = wordFreq.getOrDefault(vocabulary.get(i), 0);
+        }
+        return vector;
+    }
+    // 余弦相似度计算公式
+    private static double cosineSimilarity(double[] vecA, double[] vecB) {
+        double dotProduct = 0.0;
+        double normA = 0.0;
+        double normB = 0.0;
+        for (int i = 0; i < vecA.length; i++) {
+            dotProduct += vecA[i] * vecB[i];
+            normA += Math.pow(vecA[i], 2);
+            normB += Math.pow(vecB[i], 2);
+        }
+        return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
+    }
+
+    public static double getDataSetSimilarity(String queryText, String datasetName){
+        if(queryText ==null || datasetName == null ){ return 0.0;}
+        JiebaSegmenter segmenter = new JiebaSegmenter();
+
+        // 1.分词
+        List<String> words1 = segmenter.sentenceProcess(queryText);
+        List<String> words2 = segmenter.sentenceProcess(datasetName);
+        // 2. 构建词汇表并生成向量
+        List<String> vocabulary = new ArrayList<>(new HashSet<>(words1));
+        vocabulary.addAll(new HashSet<>(words2));
+
+        double[] vector1 = createVector(words1, vocabulary);
+        double[] vector2 = createVector(words2, vocabulary);
+        // 计算相似度（示例使用简单重叠度计算）
+        double similarity = cosineSimilarity(vector1, vector2);
+        return similarity;
+    }
+}

pom.xml

@@ -82,6 +82,7 @@
         <stax2.version>4.2.2</stax2.version>
         <aws-java-sdk.version>1.12.780</aws-java-sdk.version>
         <jgrapht.version>1.5.2</jgrapht.version>
+        <jieba.version>1.0.2</jieba.version>
     </properties>
 
     <dependencyManagement>
@@ -216,6 +217,11 @@
                 <artifactId>jgrapht-core</artifactId>
                 <version>${jgrapht.version}</version>
             </dependency>
+            <dependency>
+                <groupId>com.huaban</groupId>
+                <artifactId>jieba-analysis</artifactId>
+                <version>${jieba.version}</version>
+            </dependency>
         </dependencies>
     </dependencyManagement>