add new chat corrector

在助理最终执行物理SQL前，加入一步LLM优化性能功能

headless/chat/src/main/java/com/tencent/supersonic/headless/chat/corrector/LLMPhysicalSqlCorrector.java

@@ -0,0 +1,97 @@
+package com.tencent.supersonic.headless.chat.corrector;
+
+import com.tencent.supersonic.common.pojo.ChatApp;
+import com.tencent.supersonic.common.pojo.enums.AppModule;
+import com.tencent.supersonic.common.util.ChatAppManager;
+import com.tencent.supersonic.headless.api.pojo.SemanticParseInfo;
+import com.tencent.supersonic.headless.chat.ChatQueryContext;
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import dev.langchain4j.model.input.Prompt;
+import dev.langchain4j.model.input.PromptTemplate;
+import dev.langchain4j.model.output.structured.Description;
+import dev.langchain4j.provider.ModelProvider;
+import dev.langchain4j.service.AiServices;
+import lombok.Data;
+import lombok.ToString;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Objects;
+
+/**
+ * 物理SQL修正器 - 使用LLM优化物理SQL性能
+ */
+@Slf4j
+public class LLMPhysicalSqlCorrector extends BaseSemanticCorrector {
+
+    private static final Logger keyPipelineLog = LoggerFactory.getLogger("keyPipeline");
+
+    public static final String APP_KEY = "PHYSICAL_SQL_CORRECTOR";
+    private static final String INSTRUCTION = ""
+            + "#Role: You are a senior database performance optimization expert experienced in SQL tuning."
+            + "\n\n#Task: You will be provided with a user question and the corresponding physical SQL query,"
+            + " please analyze and optimize this SQL to improve query performance." + "\n\n#Rules:"
+            + "\n1. ALWAYS add appropriate index hints if the database supports them."
+            + "\n2. Optimize JOIN order by placing smaller tables first."
+            + "\n3. Add reasonable query limits to prevent large result sets if no LIMIT exists."
+            + "\n4. Optimize WHERE condition order by placing high-selectivity conditions first."
+            + "\n5. Ensure the optimized SQL is syntactically correct and logically equivalent."
+            + "\n6. If the SQL is already well-optimized, return 'positive'."
+            + "\n\n#Question: {{question}}" + "\n\n#OriginalSQL: {{sql}}" + "\n\n#Response:";
+
+    public LLMPhysicalSqlCorrector() {
+        ChatAppManager.register(APP_KEY, ChatApp.builder().prompt(INSTRUCTION).name("物理SQL修正")
+                .appModule(AppModule.CHAT).description("通过大模型对物理SQL做性能优化").enable(false).build());
+    }
+
+    @Data
+    @ToString
+    static class PhysicalSql {
+        @Description("either positive or negative")
+        private String opinion;
+
+        @Description("optimized sql if negative")
+        private String sql;
+    }
+
+    interface PhysicalSqlExtractor {
+        PhysicalSql generatePhysicalSql(String text);
+    }
+
+    @Override
+    public void doCorrect(ChatQueryContext chatQueryContext, SemanticParseInfo semanticParseInfo) {
+        ChatApp chatApp = chatQueryContext.getRequest().getChatAppConfig().get(APP_KEY);
+        if (!chatQueryContext.getRequest().getText2SQLType().enableLLM() || Objects.isNull(chatApp)
+                || !chatApp.isEnable()) {
+            return;
+        }
+
+        ChatLanguageModel chatLanguageModel =
+                ModelProvider.getChatModel(chatApp.getChatModelConfig());
+        PhysicalSqlExtractor extractor =
+                AiServices.create(PhysicalSqlExtractor.class, chatLanguageModel);
+        Prompt prompt = generatePrompt(chatQueryContext.getRequest().getQueryText(),
+                semanticParseInfo, chatApp.getPrompt());
+        PhysicalSql physicalSql =
+                extractor.generatePhysicalSql(prompt.toUserMessage().singleText());
+        keyPipelineLog.info("LLMPhysicalSqlCorrector modelReq:\n{} \nmodelResp:\n{}", prompt.text(),
+                physicalSql);
+        if ("NEGATIVE".equalsIgnoreCase(physicalSql.getOpinion())
+                && StringUtils.isNotBlank(physicalSql.getSql())) {
+            semanticParseInfo.getSqlInfo().setCorrectedQuerySQL(physicalSql.getSql());
+        }
+    }
+
+    private Prompt generatePrompt(String queryText, SemanticParseInfo semanticParseInfo,
+            String promptTemplate) {
+        Map<String, Object> variable = new HashMap<>();
+        variable.put("question", queryText);
+        variable.put("sql", semanticParseInfo.getSqlInfo().getQuerySQL());
+
+        return PromptTemplate.from(promptTemplate).apply(variable);
+    }
+}

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

@@ -52,7 +52,8 @@ public class PromptHelper {
         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());
+            List<Text2SQLExemplar> subList =
+                    shuffledList.subList(llmReq.getDynamicExemplars().size(), shuffledList.size());
             Collections.shuffle(subList);
             results.add(shuffledList.subList(0, Math.min(shuffledList.size(), fewShotNumber)));
         }