ai搜索调整

src/main/java/com/zhiwei/brandkbs2/common/DoubaoAIAccountFactor.java

@@ -16,19 +16,6 @@ import java.util.List;
  */
 public class DoubaoAIAccountFactor {
 
-
-//    public static Account getPersonalAccount() {
-//        String apiKey = "1ff5c1f2-4fa0-4e40-8ea8-3c9674e05a33";
-//        List<AccessModel> modelList = new ArrayList<>();
-//        modelList.add(new AccessModel("ep-20240722180424-45xkt", AccessModel.Model.DOUBAO_PRO_4K));
-//        modelList.add(new AccessModel("ep-20240723162722-7p4xw", AccessModel.Model.DOUBAO_PRO_32K));
-//        modelList.add(new AccessModel("ep-20240723153621-6hd22", AccessModel.Model.DOUBAO_PRO_128K));
-//        modelList.add(new AccessModel("ep-20240723164110-qhtxm", AccessModel.Model.DOUBAO_LITE_4K));
-//        modelList.add(new AccessModel("ep-20240723164127-44ksx", AccessModel.Model.DOUBAO_LITE_32K));
-//        modelList.add(new AccessModel("ep-20240723164141-xxmhs", AccessModel.Model.DOUBAO_LITE_128K));
-//        return new Account(apiKey, modelList);
-//    }
-
     public static Account getCompanyAccount() {
         String apiKey = "607764fc-c9d9-47e4-a673-a310852917a0";
         List<AccessModel> modelList = new ArrayList<>();

src/main/java/com/zhiwei/brandkbs2/controller/app/AppSearchController.java

@@ -353,11 +353,18 @@ public class AppSearchController extends BaseController {
     }
 
     @ApiOperation("搜索-AI搜索")
-    @GetMapping("/ai")
+    @GetMapping("/ai/answer")
     public ResponseResult getAISearchResult(@RequestParam(value = "question") String question) {
         return ResponseResult.success(markDataService.getAISearchResult(question));
     }
 
+    @ApiOperation("搜索-AI参考提问")
+    @GetMapping("/ai/question")
+    public ResponseResult getAIReferenceQuestion(@RequestParam(value = "question") String question,
+                                                 @RequestParam(value = "size") int size) {
+        return ResponseResult.success(markDataService.getAIReferenceQuestion(question, size));
+    }
+
     @ApiOperation("搜索-搜索关键词历史记录")
     @GetMapping("/keyword/cache")
     public ResponseResult getSearchKeywordCache(@ApiParam(name = "searchType",

src/main/java/com/zhiwei/brandkbs2/service/MarkDataService.java

@@ -837,5 +837,17 @@ public interface MarkDataService {
      */
     List<String> expandOriginRange(MarkSearchDTO dto);
 
+    /**
+     * ai搜索-ai参考提问
+     * @param question
+     * @return
+     */
+    List<String> getAIReferenceQuestion(String question, int size);
+
+    /**
+     * ai搜索-搜索结果
+     * @param question
+     * @return
+     */
     JSONObject getAISearchResult(String question);
 }

src/main/java/com/zhiwei/brandkbs2/service/impl/MarkDataServiceImpl.java

@@ -119,7 +119,7 @@ public class MarkDataServiceImpl implements MarkDataService {
             "3.提及到 XX 渠道的定义为渠道要素，条件给到该渠道名。\n" +
             "4.提及到正面、中性、负面的定义为标签要素，条件给到该标签名。\n" +
             "5.提及到针对 XX ，针对 XX 相关或 XX 相关的定义为搜索条件要素（必须包含 针对/相关 字样），条件给到具体值\n" +
-            "5.时间和项目要素为必需要素，若不满足则返回“无法回答”。\n" +
+            "6.时间和项目要素为必需要素，若不满足则返回“无法回答”。\n" +
             "\n" +
             "参考例子：\n" +
             "示例 1：\n" +
@@ -136,9 +136,12 @@ public class MarkDataServiceImpl implements MarkDataService {
             "1 按照指定输出格式输出。\n" +
             "2 严格按照规则进行提炼。\n" +
             "###";
-    private static final String RESULT_PROMPT = "假如你是专业的分析报告人员，你将根据用户提供的内容，给出自己的详细分析和见解。并注明理由(提供对应的数据文本1-{0})" +
+    private static final String RESULT_PROMPT = "假如你是专业的分析报告人员，你将根据用户提供的内容，给出自己提炼的5个的详细分析和见解。并在每点分析后用数字表示注明1-{0}的参考文章，分析结果和参考文章之间严格用”|“作为分隔符进行分隔，并且多个参考文章之间也严格用”|“作为分隔符进行分隔，若没有对应的参考文章则无需返回，示例：分析结果。|1|2|3" +
             "请分析：";
 
+    private static final String REFERENCE_QUESTION_PROMPT = "假如你是专业的问题提出人员，提出自己{0}个关于的{1}参考问题，每个问题给到准确的序号，问题必须包含 针对/相关 字样，每个问题之间严格用”|“作为分隔符进行分隔。" +
+            "请提出：";
+
     @Value("${istarshine.addIStarShineKSData.url}")
     private String addIStarShineKSDataUrl;
 
@@ -3951,6 +3954,22 @@ public class MarkDataServiceImpl implements MarkDataService {
     }
 
     @Override
+    public List<String> getAIReferenceQuestion(String question, int size) {
+        try {
+            // 选用的模型名称
+            String modelName = AccessModel.Model.DOUBAO_PRO_32K.getModelName();
+            String projectName = GlobalPojo.PROJECT_MAP.get(UserThreadLocal.getProjectId()).getProjectName();
+            ChatCompletionResult result = standardRequest(question, modelName, MessageFormat.format(REFERENCE_QUESTION_PROMPT, size, projectName));
+            Object resultContent = result.getChoices().get(0).getMessage().getContent();
+            String[] splits = String.valueOf(resultContent).split("\\|");
+            return new ArrayList<>(Arrays.asList(splits)).stream().filter(StringUtils::isNoneBlank).map(String::trim).collect(Collectors.toList());
+        }catch (Exception e){
+            ExceptionCast.cast(CommonCodeEnum.FAIL, "获取ai参考提问异常-", e);
+        }
+        return null;
+    }
+
+    @Override
     public JSONObject getAISearchResult(String question) {
         JSONObject res = new JSONObject();
         try {
@@ -3973,7 +3992,25 @@ public class MarkDataServiceImpl implements MarkDataService {
             String sbContent = sb.toString();
             result = standardRequest(sbContent, modelName, MessageFormat.format(RESULT_PROMPT, list.size()) + question);
             Object resultContent = result.getChoices().get(0).getMessage().getContent();
-            res.put("result", resultContent);
+            String[] splits = String.valueOf(resultContent).split("\\r?\\n");
+            List<JSONObject> answers = new ArrayList<>();
+            for (int i = 0; i < splits.length; i++) {
+                JSONObject answer = new JSONObject();
+                String[] sonSplit = splits[i].split("\\|");
+                if (0 == sonSplit.length){
+                    continue;
+                }
+                if (i == 0){
+                    answer.put("answer", splits[i].trim());
+                    answers.add(answer);
+                    continue;
+                }
+                answer.put("answer", sonSplit[0]);
+                List<String> sonSplitList = new ArrayList<>(Arrays.asList(sonSplit)).stream().filter(StringUtils::isNoneBlank).skip(1).collect(Collectors.toList());
+                answer.put("referenceArticles", sonSplitList);
+                answers.add(answer);
+            }
+            res.put("answers", answers);
             res.put("articles", articles);
         }catch (Exception e){
             ExceptionCast.cast(CommonCodeEnum.FAIL, "ai搜索异常-", e);
@@ -3986,9 +4023,7 @@ public class MarkDataServiceImpl implements MarkDataService {
     }
 
     private ChatCompletionResult standardRequest(String content, String modelName, String prompt) {
-//        DoubaoAIAccountFactor.Account account = DoubaoAIAccountFactor.getCompanyAccount();
         AccessModel model = DoubaoAIAccountFactor.getCompanyAccount().getModelList().stream().collect(Collectors.toMap(AccessModel::getModelName, m -> m)).get(modelName);
-//        ArkService service = new ArkService(account.getApiKey());
         ChatCompletionResult chatCompletion = null;
         try {
             final List<ChatMessage> messages = new ArrayList<>();
@@ -3998,7 +4033,6 @@ public class MarkDataServiceImpl implements MarkDataService {
             messages.add(userMessage);
             ChatCompletionRequest chatCompletionRequest = ChatCompletionRequest.builder().model(model.getModelId()).messages(messages).build();
             chatCompletion = DoubaoAIAccountFactor.arkService.createChatCompletion(chatCompletionRequest);
-            log.info("chatCompletion-content:{}", JSONObject.toJSONString(chatCompletion.getChoices().get(0).getMessage().getContent()));
             if (chatCompletion.getChoices().size() > 1) {
                 log.error("异常chatCompletion:{}", JSON.toJSONString(chatCompletion));
                 return null;