[feature][chat]Introduce LLM-based DataInterpreter. #1828

chat/server/src/main/java/com/tencent/supersonic/chat/server/processor/execute/DataInterpretProcessor.java

@@ -0,0 +1,64 @@
+package com.tencent.supersonic.chat.server.processor.execute;
+
+import com.tencent.supersonic.chat.api.pojo.response.QueryResult;
+import com.tencent.supersonic.chat.server.agent.Agent;
+import com.tencent.supersonic.chat.server.pojo.ExecuteContext;
+import com.tencent.supersonic.common.pojo.ChatApp;
+import com.tencent.supersonic.common.pojo.enums.AppModule;
+import com.tencent.supersonic.common.util.ChatAppManager;
+import dev.langchain4j.data.message.AiMessage;
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import dev.langchain4j.model.input.Prompt;
+import dev.langchain4j.model.input.PromptTemplate;
+import dev.langchain4j.model.output.Response;
+import dev.langchain4j.provider.ModelProvider;
+import org.apache.commons.lang.StringUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Objects;
+
+public class DataInterpretProcessor implements ExecuteResultProcessor {
+
+    private static final Logger keyPipelineLog = LoggerFactory.getLogger("keyPipeline");
+
+    public static final String APP_KEY = "DATA_INTERPRETER";
+    private static final String INSTRUCTION = ""
+            + "#Role: You are a data expert who communicates with business users everyday."
+            + "\n#Task: Your will be provided with a question asked by a user and the relevant "
+            + "result data queried from the databases, please interpret the data and organize a brief answer."
+            + "\n#Rules: " + "\n1.The `#Answer` must use the same language as the `#Question`."
+            + "\n#Question:{{question}} #Data:{{data}} #Answer:";
+
+    public DataInterpretProcessor() {
+        ChatAppManager.register(APP_KEY, ChatApp.builder().prompt(INSTRUCTION).name("结果数据解读")
+                .appModule(AppModule.CHAT).description("通过大模型对结果数据做提炼总结").enable(false).build());
+    }
+
+
+    @Override
+    public void process(ExecuteContext executeContext, QueryResult queryResult) {
+        Agent agent = executeContext.getAgent();
+        ChatApp chatApp = agent.getChatAppConfig().get(APP_KEY);
+        if (Objects.isNull(chatApp) || !chatApp.isEnable()) {
+            return;
+        }
+
+        Map<String, Object> variable = new HashMap<>();
+        variable.put("question", executeContext.getQueryText());
+        variable.put("data", queryResult.getTextResult());
+
+        Prompt prompt = PromptTemplate.from(chatApp.getPrompt()).apply(variable);
+        ChatLanguageModel chatLanguageModel =
+                ModelProvider.getChatModel(chatApp.getChatModelConfig());
+        Response<AiMessage> response = chatLanguageModel.generate(prompt.toUserMessage());
+        String anwser = response.content().text();
+        keyPipelineLog.info("DataInterpretProcessor modelReq:\n{} \nmodelResp:\n{}", prompt.text(),
+                anwser);
+        if (StringUtils.isNotBlank(anwser)) {
+            queryResult.setTextResult(anwser);
+        }
+    }
+}

chat/server/src/main/java/com/tencent/supersonic/chat/server/processor/execute/DimensionRecommendProcessor.java

@@ -11,12 +11,13 @@ import com.tencent.supersonic.headless.api.pojo.SchemaElement;
 import com.tencent.supersonic.headless.api.pojo.SchemaElementType;
 import com.tencent.supersonic.headless.api.pojo.SemanticParseInfo;
 import com.tencent.supersonic.headless.server.facade.service.SemanticLayerService;
+import org.springframework.util.CollectionUtils;
+
 import java.util.Comparator;
 import java.util.List;
 import java.util.Objects;
 import java.util.Optional;
 import java.util.stream.Collectors;
-import org.springframework.util.CollectionUtils;
 
 /**
  * DimensionRecommendProcessor recommend some dimensions related to metrics based on configuration
@@ -37,7 +38,8 @@ public class DimensionRecommendProcessor implements ExecuteResultProcessor {
         if (!firstMetric.isPresent()) {
             return;
         }
-        List<SchemaElement> dimensionRecommended = getDimensions(firstMetric.get().getId(), dataSetId);
+        List<SchemaElement> dimensionRecommended =
+                getDimensions(firstMetric.get().getId(), dataSetId);
         queryResult.setRecommendedDimensions(dimensionRecommended);
     }
 

launchers/chat/src/main/resources/META-INF/spring.factories

@@ -20,7 +20,8 @@ com.tencent.supersonic.chat.server.processor.parse.ParseResultProcessor=\
 com.tencent.supersonic.chat.server.processor.execute.ExecuteResultProcessor=\
     com.tencent.supersonic.chat.server.processor.execute.MetricRecommendProcessor,\
     com.tencent.supersonic.chat.server.processor.execute.DimensionRecommendProcessor,\
-    com.tencent.supersonic.chat.server.processor.execute.MetricRatioProcessor
+    com.tencent.supersonic.chat.server.processor.execute.MetricRatioProcessor,\
+    com.tencent.supersonic.chat.server.processor.execute.DataInterpretProcessor
 
 ### auth-authentication SPIs
 

launchers/standalone/src/main/resources/META-INF/spring.factories

@@ -72,7 +72,8 @@ com.tencent.supersonic.chat.server.processor.parse.ParseResultProcessor=\
 com.tencent.supersonic.chat.server.processor.execute.ExecuteResultProcessor=\
     com.tencent.supersonic.chat.server.processor.execute.MetricRecommendProcessor,\
     com.tencent.supersonic.chat.server.processor.execute.DimensionRecommendProcessor,\
-    com.tencent.supersonic.chat.server.processor.execute.MetricRatioProcessor
+    com.tencent.supersonic.chat.server.processor.execute.MetricRatioProcessor,\
+    com.tencent.supersonic.chat.server.processor.execute.DataInterpretProcessor
 
 ### auth-authentication SPIs
 

launchers/standalone/src/test/java/com/tencent/supersonic/chat/BaseTest.java

@@ -52,7 +52,8 @@ public class BaseTest extends BaseApplication {
         SemanticParseInfo semanticParseInfo = parseResp.getSelectedParses().get(0);
         ChatExecuteReq request = ChatExecuteReq.builder().queryText(parseResp.getQueryText())
                 .user(DataUtils.getUser()).parseId(semanticParseInfo.getId())
-                .queryId(parseResp.getQueryId()).chatId(chatId).saveAnswer(true).build();
+                .queryId(parseResp.getQueryId()).chatId(chatId).agentId(agentId).saveAnswer(true)
+                .build();
         QueryResult queryResult = chatQueryService.execute(request);
         queryResult.setChatContext(semanticParseInfo);
         return queryResult;