(improvement)(headless)Refactor the prompts for generating semantic aliases.

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

@@ -26,19 +26,19 @@ import java.util.concurrent.ConcurrentHashMap;
 public class OnePassSCSqlGenStrategy extends SqlGenStrategy {
 
     private static final String INSTRUCTION = ""
-            + "#Role: You are a data analyst experienced in SQL languages.\n"
+            + "\n#Role: You are a data analyst experienced in SQL languages."
-            + "#Task: You will be provided a natural language question asked by users,"
+            + "#Task: You will be provided with a natural language question asked by users,"
             + "please convert it to a SQL query so that relevant data could be returned "
-            + "by executing the SQL query against underlying database.\n"
+            + "by executing the SQL query against underlying database."
-            + "#Rules:"
+            + "\n#Rules:"
             + "1.ALWAYS generate column specified in the `Schema`, DO NOT hallucinate."
             + "2.ALWAYS specify date filter using `>`,`<`,`>=`,`<=` operator."
             + "3.ALWAYS calculate the absolute date range by yourself."
             + "4.DO NOT include date filter in the where clause if not explicitly expressed in the `Question`."
             + "5.DO NOT miss the AGGREGATE operator of metrics, always add it if needed."
-            + "6.ONLY respond with the converted SQL statement.\n"
+            + "6.ONLY respond with the converted SQL statement."
-            + "#Exemplars:\n{{exemplar}}"
+            + "\n#Exemplars:\n{{exemplar}}"
-            + "#Question:{{question}} #Schema:{{schema}} #SideInfo:{{information}} #SQL:";
+            + "Question:{{question}},Schema:{{schema}},SideInfo:{{information}},SQL:";
 
     @Override
     public LLMResp generate(LLMReq llmReq) {
@@ -83,7 +83,7 @@ public class OnePassSCSqlGenStrategy extends SqlGenStrategy {
     private Prompt generatePrompt(LLMReq llmReq, LLMResp llmResp) {
         StringBuilder exemplars = new StringBuilder();
         for (Text2SQLExemplar exemplar : llmReq.getDynamicExemplars()) {
-            String exemplarStr = String.format("#Question:%s #Schema:%s #SideInfo:%s #SQL:%s\n",
+            String exemplarStr = String.format("Question:%s,Schema:%s,SideInfo:%s,SQL:%s\n",
                     exemplar.getQuestion(), exemplar.getDbSchema(),
                     exemplar.getSideInfo(), exemplar.getSql());
             exemplars.append(exemplarStr);

headless/server/src/main/java/com/tencent/supersonic/headless/server/service/impl/DimensionServiceImpl.java

@@ -318,7 +318,7 @@ public class DimensionServiceImpl extends ServiceImpl<DimensionDOMapper, Dimensi
     @Override
     public List<String> mockAlias(DimensionReq dimensionReq, String mockType, User user) {
         String mockAlias = aliasGenerateHelper.generateAlias(mockType, dimensionReq.getName(),
-                dimensionReq.getBizName(), "", dimensionReq.getDescription(), false);
+                dimensionReq.getBizName(), "", dimensionReq.getDescription());
         String ret = aliasGenerateHelper.extractJsonStringFromAiMessage(mockAlias);
         return JSONObject.parseObject(ret, new TypeReference<List<String>>() {
         });

headless/server/src/main/java/com/tencent/supersonic/headless/server/service/impl/MetricServiceImpl.java

@@ -511,7 +511,7 @@ public class MetricServiceImpl extends ServiceImpl<MetricDOMapper, MetricDO>
     public List<String> mockAlias(MetricBaseReq metricReq, String mockType, User user) {
 
         String mockAlias = aliasGenerateHelper.generateAlias(mockType, metricReq.getName(), metricReq.getBizName(), "",
-                metricReq.getDescription(), !"".equals(metricReq.getDataFormatType()));
+                metricReq.getDescription());
         String ret = mockAlias.replaceAll("`", "").replace("json", "").replace("\n", "").replace(" ", "");
         return JSONObject.parseObject(ret, new TypeReference<List<String>>() {
         });

headless/server/src/main/java/com/tencent/supersonic/headless/server/utils/AliasGenerateHelper.java

@@ -6,97 +6,89 @@ import com.alibaba.fastjson.JSONException;
 import dev.langchain4j.data.message.AiMessage;
 import dev.langchain4j.data.message.SystemMessage;
 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 lombok.extern.slf4j.Slf4j;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 import org.springframework.stereotype.Component;
 
+import java.util.HashMap;
+import java.util.Map;
+
 
 @Component
 @Slf4j
 public class AliasGenerateHelper {
 
-    public String getChatCompletion(String message) {
+    private static final Logger keyPipelineLog = LoggerFactory.getLogger("keyPipeline");
-        SystemMessage from = SystemMessage.from(message);
+
-        ChatLanguageModel chatLanguageModel = ModelProvider.getChatModel();
+    private static final String NAME_ALIAS_INSTRUCTION = ""
-        Response<AiMessage> response = chatLanguageModel.generate(from);
+            + "\n#Role: You are a professional data analyst specializing in metrics and dimensions."
-        log.info("message:{}\n response:{}", message, response);
+            + "\n#Task: You will be provided with metadata about a metric or dimension, please help "
-        return response.content().text();
+            + "generate a few aliases in the same language as its `fieldName`."
-    }
+            + "\n#Rules:"
+            + "1. Please do not generate aliases like xxx1, xxx2, xxx3."
+            + "2. Please do not generate aliases that are the same as the original names of metrics/dimensions."
+            + "3. Please pay attention to the quality of the generated aliases and "
+            + "avoid creating aliases that look like test data."
+            + "4. Please output as a json string array."
+            + "\n#Metadata: {'table':'{{table}}', 'name':'{{name}}', 'type':'{{type}}', "
+            + "'field':'field', 'description':'{{desc}}'}"
+            + "\n#Output:";
+
+    private static final String VALUE_ALIAS_INSTRUCTION = ""
+            + "\n#Role: You are a professional data analyst."
+            + "\n#Task: You will be provided with a json array of dimension values,"
+            + "please help generate a few aliases for each value."
+            + "\n#Rule:"
+            + "1. ALWAYS output json array for each value."
+            + "2. The aliases should be in the same language as its original value."
+            + "\n#Exemplar:"
+            + "Values: [\\\"qq_music\\\",\\\"kugou_music\\\"], "
+            + "Output: {\\\"tran\\\":[\\\"qq音乐\\\",\\\"酷狗音乐\\\"],"
+            + "         \\\"alias\\\":{\\\"qq_music\\\":[\\\"q音\\\",\\\"qq音乐\\\"],"
+            + "         \\\"kugou_music\\\":[\\\"kugou\\\",\\\"酷狗\\\"]}}"
+            + "\nValues: {{values}}, Output:";
 
     public String generateAlias(String mockType,
                                 String name,
                                 String bizName,
                                 String table,
-                                String desc,
+                                String desc) {
-                                Boolean isPercentage) {
+        Map<String, Object> variable = new HashMap<>();
-        String msg = "Assuming you are a professional data analyst specializing in metrics and dimensions, "
+        variable.put("table", table);
-                + "you have a vast amount of data analysis metrics content. You are familiar with the basic"
+        variable.put("name", name);
-                + " format of the content,Now, Construct your answer Based on the following json-schema.\n"
+        variable.put("field", bizName);
-                + "{\n"
+        variable.put("type", mockType);
-                + "\"$schema\": \"http://json-schema.org/draft-07/schema#\",\n"
+        variable.put("desc", desc);
-                + "\"type\": \"array\",\n"
+
-                + "\"minItems\": 2,\n"
+        Prompt prompt = PromptTemplate.from(NAME_ALIAS_INSTRUCTION).apply(variable);
-                + "\"maxItems\": 4,\n"
+        keyPipelineLog.info("AliasGenerateHelper.generateNameAlias reqPrompt:{}", prompt.text());
-                + "\"items\": {\n"
+        String response = getChatCompletion(prompt);
-                + "\"type\": \"string\",\n"
+        keyPipelineLog.info("AliasGenerateHelper.generateNameAlias modelResp:{}", response);
-                + "\"description\": \"Assuming you are a data analyst and give a defined "
+        return response;
-                + mockType
-                + " name: "
-                + name + ","
-                + "this "
-                + mockType
-                + " is from database and table: "
-                + table + ",This "
-                + mockType
-                + " calculates the field source: "
-                + bizName
-                + ", The description of this metrics is: "
-                + desc
-                + ", provide some aliases for this, please take chinese or english,"
-                + "You must adhere to the following rules:\n"
-                + "1. Please do not generate aliases like xxx1, xxx2, xxx3.\n"
-                + "2. Please do not generate aliases that are the same as the original names of metrics/dimensions.\n"
-                + "3. Please pay attention to the quality of the generated aliases and "
-                + "   avoid creating aliases that look like test data.\n"
-                + "4. Please generate more Chinese aliases."
-                + "},\n"
-                + "\"additionalProperties\":false}\n"
-                + "Please double-check whether the answer conforms to the format described in the JSON-schema.\n"
-                + "回答格式示例:"
-                + "[\n"
-                + "  \"人数\",\n"
-                + "  \"员工人数\",\n"
-                + "  \"员工数量\",\n"
-                + "  \"员工总数\"\n"
-                + "]\n"
-                + "请严格按照示例格式进行生成。"
-                + "ANSWER JSON:";
-        log.info("msg:{}", msg);
-        return getChatCompletion(msg);
     }
 
     public String generateDimensionValueAlias(String json) {
-        String msg = "Assuming you are a professional data analyst specializing in indicators,for you a json list，"
+        Map<String, Object> variable = new HashMap<>();
-                + "the required content to follow is as follows: \n"
+        variable.put("values", json);
-                + "1. The format of JSON,\n"
+
-                + "2. Only return in JSON format,\n"
+        Prompt prompt = PromptTemplate.from(VALUE_ALIAS_INSTRUCTION).apply(variable);
-                + "3. the array item > 1 and < 5,more alias,\n"
+        keyPipelineLog.info("AliasGenerateHelper.generateValueAlias reqPrompt:{}", prompt.text());
-                + "for example：\n"
+        String response = getChatCompletion(prompt);
-                + "input:[\"qq_music\",\"kugou_music\"],\n"
+        keyPipelineLog.info("AliasGenerateHelper.generateValueAlias modelResp:{}", response);
-                + "out:{\"tran\":[\"qq音乐\",\"酷狗音乐\"],"
+
-                + "\"alias\":{\"qq_music\":[\"q音\",\"qq音乐\"],\"kugou_music\":[\"kugou\",\"酷狗\"]}},\n"
+        return response;
-                + "input:[\"qq_music\",\"kugou_music\"],\n"
+    }
-                + "out:{\"tran\":[\"qq音乐\",\"酷狗音乐\"],"
+
-                + "\"alias\":{\"qq_music\":[\"q音\",\"qq音乐\"],\"kugou_music\":[\"kugou\",\"酷狗\"]}},\n"
+    private String getChatCompletion(Prompt prompt) {
-                + "input:[\"大专\",\"本科\",\"硕士研究生\"],\n"
+        SystemMessage from = prompt.toSystemMessage();
-                + "out:{\"tran\":[\"大专\",\"本科\",\"硕士研究生\"],"
+        ChatLanguageModel chatLanguageModel = ModelProvider.getChatModel();
-                + "\"alias\":{\"大专\":[\"专科\",\"大学专科\"],\"本科\":[\"学士\",\"本科生\"],\"硕士研究生\":[\"硕士\",\"研究生\"]}},\n"
+        Response<AiMessage> response = chatLanguageModel.generate(from);
-                + "now input: "
+        return response.content().text();
-                + json + ",\n"
-                + "answer json:";
-        log.info("msg:{}", msg);
-        return getChatCompletion(msg);
     }
 
     private static String extractString(String targetString, String left, String right, Boolean exclusionFlag) {