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https://github.com/tencentmusic/supersonic.git
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(improvement)(headless)Refactor the prompts for generating semantic aliases.
This commit is contained in:
jerryjzhang
@@ -26,19 +26,19 @@ import java.util.concurrent.ConcurrentHashMap;
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public class OnePassSCSqlGenStrategy extends SqlGenStrategy {
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private static final String INSTRUCTION = ""
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+ "#Role: You are a data analyst experienced in SQL languages.\n"
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+ "#Task: You will be provided a natural language question asked by users,"
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+ "\n#Role: You are a data analyst experienced in SQL languages."
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+ "#Task: You will be provided with a natural language question asked by users,"
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+ "please convert it to a SQL query so that relevant data could be returned "
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+ "by executing the SQL query against underlying database.\n"
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+ "#Rules:"
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+ "by executing the SQL query against underlying database."
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+ "\n#Rules:"
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+ "1.ALWAYS generate column specified in the `Schema`, DO NOT hallucinate."
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+ "2.ALWAYS specify date filter using `>`,`<`,`>=`,`<=` operator."
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+ "3.ALWAYS calculate the absolute date range by yourself."
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+ "4.DO NOT include date filter in the where clause if not explicitly expressed in the `Question`."
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+ "5.DO NOT miss the AGGREGATE operator of metrics, always add it if needed."
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+ "6.ONLY respond with the converted SQL statement.\n"
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+ "#Exemplars:\n{{exemplar}}"
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+ "#Question:{{question}} #Schema:{{schema}} #SideInfo:{{information}} #SQL:";
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+ "6.ONLY respond with the converted SQL statement."
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+ "\n#Exemplars:\n{{exemplar}}"
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+ "Question:{{question}},Schema:{{schema}},SideInfo:{{information}},SQL:";
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@Override
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public LLMResp generate(LLMReq llmReq) {
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@@ -83,7 +83,7 @@ public class OnePassSCSqlGenStrategy extends SqlGenStrategy {
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private Prompt generatePrompt(LLMReq llmReq, LLMResp llmResp) {
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StringBuilder exemplars = new StringBuilder();
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for (Text2SQLExemplar exemplar : llmReq.getDynamicExemplars()) {
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String exemplarStr = String.format("#Question:%s #Schema:%s #SideInfo:%s #SQL:%s\n",
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String exemplarStr = String.format("Question:%s,Schema:%s,SideInfo:%s,SQL:%s\n",
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exemplar.getQuestion(), exemplar.getDbSchema(),
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exemplar.getSideInfo(), exemplar.getSql());
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exemplars.append(exemplarStr);
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@@ -318,7 +318,7 @@ public class DimensionServiceImpl extends ServiceImpl<DimensionDOMapper, Dimensi
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@Override
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public List<String> mockAlias(DimensionReq dimensionReq, String mockType, User user) {
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String mockAlias = aliasGenerateHelper.generateAlias(mockType, dimensionReq.getName(),
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dimensionReq.getBizName(), "", dimensionReq.getDescription(), false);
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dimensionReq.getBizName(), "", dimensionReq.getDescription());
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String ret = aliasGenerateHelper.extractJsonStringFromAiMessage(mockAlias);
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return JSONObject.parseObject(ret, new TypeReference<List<String>>() {
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});
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@@ -511,7 +511,7 @@ public class MetricServiceImpl extends ServiceImpl<MetricDOMapper, MetricDO>
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public List<String> mockAlias(MetricBaseReq metricReq, String mockType, User user) {
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String mockAlias = aliasGenerateHelper.generateAlias(mockType, metricReq.getName(), metricReq.getBizName(), "",
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metricReq.getDescription(), !"".equals(metricReq.getDataFormatType()));
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metricReq.getDescription());
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String ret = mockAlias.replaceAll("`", "").replace("json", "").replace("\n", "").replace(" ", "");
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return JSONObject.parseObject(ret, new TypeReference<List<String>>() {
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});
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@@ -6,97 +6,89 @@ import com.alibaba.fastjson.JSONException;
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import dev.langchain4j.data.message.AiMessage;
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import dev.langchain4j.data.message.SystemMessage;
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import dev.langchain4j.model.chat.ChatLanguageModel;
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import dev.langchain4j.model.input.Prompt;
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import dev.langchain4j.model.input.PromptTemplate;
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import dev.langchain4j.model.output.Response;
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import dev.langchain4j.provider.ModelProvider;
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import lombok.extern.slf4j.Slf4j;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.stereotype.Component;
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import java.util.HashMap;
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import java.util.Map;
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@Component
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@Slf4j
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public class AliasGenerateHelper {
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public String getChatCompletion(String message) {
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SystemMessage from = SystemMessage.from(message);
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ChatLanguageModel chatLanguageModel = ModelProvider.getChatModel();
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Response<AiMessage> response = chatLanguageModel.generate(from);
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log.info("message:{}\n response:{}", message, response);
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return response.content().text();
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}
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private static final Logger keyPipelineLog = LoggerFactory.getLogger("keyPipeline");
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private static final String NAME_ALIAS_INSTRUCTION = ""
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+ "\n#Role: You are a professional data analyst specializing in metrics and dimensions."
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+ "\n#Task: You will be provided with metadata about a metric or dimension, please help "
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+ "generate a few aliases in the same language as its `fieldName`."
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+ "\n#Rules:"
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+ "1. Please do not generate aliases like xxx1, xxx2, xxx3."
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+ "2. Please do not generate aliases that are the same as the original names of metrics/dimensions."
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+ "3. Please pay attention to the quality of the generated aliases and "
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+ "avoid creating aliases that look like test data."
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+ "4. Please output as a json string array."
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+ "\n#Metadata: {'table':'{{table}}', 'name':'{{name}}', 'type':'{{type}}', "
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+ "'field':'field', 'description':'{{desc}}'}"
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+ "\n#Output:";
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private static final String VALUE_ALIAS_INSTRUCTION = ""
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+ "\n#Role: You are a professional data analyst."
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+ "\n#Task: You will be provided with a json array of dimension values,"
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+ "please help generate a few aliases for each value."
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+ "\n#Rule:"
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+ "1. ALWAYS output json array for each value."
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+ "2. The aliases should be in the same language as its original value."
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+ "\n#Exemplar:"
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+ "Values: [\\\"qq_music\\\",\\\"kugou_music\\\"], "
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+ "Output: {\\\"tran\\\":[\\\"qq音乐\\\",\\\"酷狗音乐\\\"],"
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+ " \\\"alias\\\":{\\\"qq_music\\\":[\\\"q音\\\",\\\"qq音乐\\\"],"
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+ " \\\"kugou_music\\\":[\\\"kugou\\\",\\\"酷狗\\\"]}}"
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+ "\nValues: {{values}}, Output:";
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public String generateAlias(String mockType,
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String name,
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String bizName,
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String table,
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String desc,
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Boolean isPercentage) {
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String msg = "Assuming you are a professional data analyst specializing in metrics and dimensions, "
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+ "you have a vast amount of data analysis metrics content. You are familiar with the basic"
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+ " format of the content,Now, Construct your answer Based on the following json-schema.\n"
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+ "{\n"
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+ "\"$schema\": \"http://json-schema.org/draft-07/schema#\",\n"
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+ "\"type\": \"array\",\n"
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+ "\"minItems\": 2,\n"
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+ "\"maxItems\": 4,\n"
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+ "\"items\": {\n"
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+ "\"type\": \"string\",\n"
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+ "\"description\": \"Assuming you are a data analyst and give a defined "
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+ mockType
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+ " name: "
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+ name + ","
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+ "this "
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+ mockType
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+ " is from database and table: "
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+ table + ",This "
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+ mockType
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+ " calculates the field source: "
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+ bizName
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+ ", The description of this metrics is: "
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+ desc
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+ ", provide some aliases for this, please take chinese or english,"
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+ "You must adhere to the following rules:\n"
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+ "1. Please do not generate aliases like xxx1, xxx2, xxx3.\n"
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+ "2. Please do not generate aliases that are the same as the original names of metrics/dimensions.\n"
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+ "3. Please pay attention to the quality of the generated aliases and "
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+ " avoid creating aliases that look like test data.\n"
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+ "4. Please generate more Chinese aliases."
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+ "},\n"
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+ "\"additionalProperties\":false}\n"
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+ "Please double-check whether the answer conforms to the format described in the JSON-schema.\n"
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+ "回答格式示例:"
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+ "[\n"
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+ " \"人数\",\n"
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+ " \"员工人数\",\n"
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+ " \"员工数量\",\n"
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+ " \"员工总数\"\n"
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+ "]\n"
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+ "请严格按照示例格式进行生成。"
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+ "ANSWER JSON:";
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log.info("msg:{}", msg);
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return getChatCompletion(msg);
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String desc) {
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Map<String, Object> variable = new HashMap<>();
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variable.put("table", table);
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variable.put("name", name);
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variable.put("field", bizName);
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variable.put("type", mockType);
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variable.put("desc", desc);
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Prompt prompt = PromptTemplate.from(NAME_ALIAS_INSTRUCTION).apply(variable);
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keyPipelineLog.info("AliasGenerateHelper.generateNameAlias reqPrompt:{}", prompt.text());
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String response = getChatCompletion(prompt);
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keyPipelineLog.info("AliasGenerateHelper.generateNameAlias modelResp:{}", response);
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return response;
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}
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public String generateDimensionValueAlias(String json) {
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String msg = "Assuming you are a professional data analyst specializing in indicators,for you a json list，"
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+ "the required content to follow is as follows: \n"
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+ "1. The format of JSON,\n"
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+ "2. Only return in JSON format,\n"
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+ "3. the array item > 1 and < 5,more alias,\n"
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+ "for example：\n"
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+ "input:[\"qq_music\",\"kugou_music\"],\n"
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+ "out:{\"tran\":[\"qq音乐\",\"酷狗音乐\"],"
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+ "\"alias\":{\"qq_music\":[\"q音\",\"qq音乐\"],\"kugou_music\":[\"kugou\",\"酷狗\"]}},\n"
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+ "input:[\"qq_music\",\"kugou_music\"],\n"
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+ "out:{\"tran\":[\"qq音乐\",\"酷狗音乐\"],"
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+ "\"alias\":{\"qq_music\":[\"q音\",\"qq音乐\"],\"kugou_music\":[\"kugou\",\"酷狗\"]}},\n"
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+ "input:[\"大专\",\"本科\",\"硕士研究生\"],\n"
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+ "out:{\"tran\":[\"大专\",\"本科\",\"硕士研究生\"],"
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+ "\"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"
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+ "now input: "
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+ json + ",\n"
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+ "answer json:";
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log.info("msg:{}", msg);
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return getChatCompletion(msg);
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Map<String, Object> variable = new HashMap<>();
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variable.put("values", json);
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Prompt prompt = PromptTemplate.from(VALUE_ALIAS_INSTRUCTION).apply(variable);
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keyPipelineLog.info("AliasGenerateHelper.generateValueAlias reqPrompt:{}", prompt.text());
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String response = getChatCompletion(prompt);
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keyPipelineLog.info("AliasGenerateHelper.generateValueAlias modelResp:{}", response);
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return response;
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}
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private String getChatCompletion(Prompt prompt) {
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SystemMessage from = prompt.toSystemMessage();
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ChatLanguageModel chatLanguageModel = ModelProvider.getChatModel();
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Response<AiMessage> response = chatLanguageModel.generate(from);
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return response.content().text();
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}
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private static String extractString(String targetString, String left, String right, Boolean exclusionFlag) {
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