相似度账号查询自动判断

long-article-recommend-service/src/main/java/com/tzld/longarticle/recommend/server/model/param/RecommendParam.java

@@ -27,5 +27,6 @@ public class RecommendParam {
     private boolean excludeLog = false;
     private String scene;
     private List<Integer> userGroupIds;
+    private Boolean replaceSimilarityAccount = false;
 }
 

long-article-recommend-service/src/main/java/com/tzld/longarticle/recommend/server/service/recommend/RecommendService.java

@@ -101,6 +101,11 @@ public class RecommendService {
     }
 
     private void setStrategy(RecommendRequest request, RecommendParam param) {
+        int historyCount = articleRepository.countByGhIdAndTypeAndItemIndex(request.getGhId(),
+                ArticleTypeEnum.QUNFA.getVal(), 1);
+        if (historyCount < 10) {
+            param.setReplaceSimilarityAccount(true);
+        }
         // 无限发表，设置为无限发表策略
         if (Objects.equals(request.getPushType(), PushTypeEnum.AUTO_PUBLISH.getVal())
                 || Objects.equals(request.getPushType(), PushTypeEnum.ROBOPOST.getVal())) {
@@ -114,8 +119,6 @@ public class RecommendService {
             param.setStrategy(strategyConfig);
         }
         // 历史群发头条小于10条，且开启配置，则走历史表现随机策略
-        int historyCount = articleRepository.countByGhIdAndTypeAndItemIndex(request.getGhId(),
-                ArticleTypeEnum.QUNFA.getVal(), 1);
         if (historyCount < 10 && accountHisJumpStrategyList.contains(request.getGhId())) {
             param.setStrategy(RankStrategyEnum.HIS_JUMP_STRATEGY.getStrategy());
         }
@@ -258,6 +261,7 @@ public class RecommendService {
         rankParam.setScene(param.getScene());
         rankParam.setUserGroupIds(param.getUserGroupIds());
         rankParam.setType(param.getType());
+        rankParam.setReplaceSimilarityAccount(param.getReplaceSimilarityAccount());
 
         return rankParam;
     }

long-article-recommend-service/src/main/java/com/tzld/longarticle/recommend/server/service/recommend/rank/RankParam.java

@@ -19,5 +19,6 @@ public class RankParam {
     private String scene;
     private List<Integer> userGroupIds;
     private String type;
+    private Boolean replaceSimilarityAccount = false;
 
 }

long-article-recommend-service/src/main/java/com/tzld/longarticle/recommend/server/service/recommend/rank/RankStrategy.java

@@ -64,6 +64,7 @@ public interface RankStrategy {
         scoreParam.setContents(param.getContents());
         scoreParam.setStrategy(param.getStrategy());
         scoreParam.setScene(param.getScene());
+        scoreParam.setReplaceSimilarityAccount(param.getReplaceSimilarityAccount());
         return scoreParam;
     }
 

long-article-recommend-service/src/main/java/com/tzld/longarticle/recommend/server/service/recommend/score/ScoreParam.java

@@ -21,4 +21,5 @@ public class ScoreParam {
     private List<Content> contents;
     private String strategy;
     private String scene;
+    private Boolean replaceSimilarityAccount = false;
 }

long-article-recommend-service/src/main/java/com/tzld/longarticle/recommend/server/service/recommend/score/strategy/SimilarityStrategy.java

@@ -1,7 +1,8 @@
 package com.tzld.longarticle.recommend.server.service.recommend.score.strategy;
 
 import com.ctrip.framework.apollo.spring.annotation.ApolloJsonValue;
-import com.tzld.longarticle.recommend.server.common.enums.recommend.ArticleTypeEnum;
+import com.tzld.longarticle.recommend.server.model.dto.kimi.KimiResult;
+import com.tzld.longarticle.recommend.server.remote.KimiApiService;
 import com.tzld.longarticle.recommend.server.remote.NLPRemoteService;
 import com.tzld.longarticle.recommend.server.repository.crawler.ArticleRepository;
 import com.tzld.longarticle.recommend.server.service.recommend.score.Score;
@@ -9,13 +10,18 @@ import com.tzld.longarticle.recommend.server.service.recommend.score.ScoreParam;
 import com.tzld.longarticle.recommend.server.service.recommend.score.ScoreStrategy;
 import com.tzld.longarticle.recommend.server.util.CommonCollectionUtils;
 import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.collections4.CollectionUtils;
+import org.apache.commons.lang3.RandomUtils;
 import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.data.redis.core.RedisTemplate;
 import org.springframework.stereotype.Component;
-import org.springframework.util.CollectionUtils;
+import org.springframework.util.StringUtils;
 
 import java.util.Collections;
 import java.util.List;
 import java.util.Map;
+import java.util.concurrent.TimeUnit;
 
 /**
  * @author dyp
@@ -28,9 +34,18 @@ public class SimilarityStrategy implements ScoreStrategy {
     private NLPRemoteService nlpRemoteService;
     @Autowired
     private ArticleRepository articleRepository;
+    @Autowired
+    private KimiApiService kimiApiService;
+
+    @Autowired
+    private RedisTemplate<String, String> redisTemplate;
 
     @ApolloJsonValue("${account.score.sim.replace:{}}")
     private Map<String, String> accountSimScoreReplaceMap;
+    @Value("${kimiSimilarityTypePrompt:}")
+    private String kimiSimilarityTypePrompt;
+    @ApolloJsonValue("${similarityTypeAccountMap:}")
+    private Map<String, List<String>> similarityTypeAccountMap;
 
     @Override
     public List<Score> score(ScoreParam param) {
@@ -40,27 +55,40 @@ public class SimilarityStrategy implements ScoreStrategy {
             return Collections.emptyList();
         }
         String ghId = param.getGhId();
-        if (accountSimScoreReplaceMap.containsKey(ghId)) {
-            int historyCount = articleRepository.countByGhIdAndTypeAndItemIndex(ghId,
-                    ArticleTypeEnum.QUNFA.getVal(), 1);
-            if (historyCount < 10) {
+        if (param.getReplaceSimilarityAccount()) {
+            if (accountSimScoreReplaceMap.containsKey(ghId)) {
                 ghId = accountSimScoreReplaceMap.get(ghId);
+            } else {
+                String redisKey = "AccountSimType_" + param.getAccountName();
+                String type = redisTemplate.opsForValue().get(redisKey);
+                if (!StringUtils.hasText(type)) {
+                    String prompt = kimiSimilarityTypePrompt.replace("accountName", param.getAccountName());
+                    // 调用kimi判断账号类型
+                    KimiResult kimiResult = kimiApiService.requestOfficialApi(prompt, null, null);
+                    if (kimiResult.isSuccess()) {
+                        try {
+                            type = kimiResult.getResponse().getChoices().get(0).getMessage().getContent();
+                            log.info("SimilarityStrategy kimiResultType:{}", type);
+                            redisTemplate.opsForValue().set(redisKey, type, 2L, TimeUnit.HOURS);
+                        } catch (Exception e) {
+                            log.error(kimiResult.getResponse().getChoices().get(0).getMessage().getContent());
+                        }
+                    }
+                }
+                // 根据类型随机再类型池中选取账号
+                List<String> ghIds = similarityTypeAccountMap.get(type);
+                if (CollectionUtils.isNotEmpty(ghIds)) {
+                    ghId = ghIds.get(RandomUtils.nextInt(0, ghIds.size()));
+                }
+                log.info("SimilarityStrategy 排序账号：{} 替换账号：{}", param.getGhId(), ghId);
             }
         }
         Map<String, Double> scoreMap = nlpRemoteService.score(ghId, param.getAccountName(), param.getContents());
 
-//        double min = scoreMap.values().stream()
-//                .min(Double::compareTo)
-//                .orElse(0.0);
-//        double max = scoreMap.values().stream()
-//                .max(Double::compareTo)
-//                .orElse(0.0);
-
         List<Score> scores = CommonCollectionUtils.toList(param.getContents(), c -> {
             Score score = new Score();
             score.setContentId(c.getId());
             double val = scoreMap.get(c.getId()) == null ? 0.0 : scoreMap.get(c.getId());
-            // score.setScore(NormalizationUtils.minMax(val, min, max));
             score.setScore(val);
             score.setStrategy(this);
             return score;