diff --git a/virtual-patient-common/src/main/java/com/supervision/util/SimilarityUtil.java b/virtual-patient-common/src/main/java/com/supervision/util/SimilarityUtil.java
index 741b89d6..490cf53f 100644
--- a/virtual-patient-common/src/main/java/com/supervision/util/SimilarityUtil.java
+++ b/virtual-patient-common/src/main/java/com/supervision/util/SimilarityUtil.java
@@ -1,15 +1,14 @@
 package com.supervision.util;
 
+import cn.hutool.core.collection.CollUtil;
 import com.supervision.domain.QaSimilarityQuestionAnswer;
 import lombok.extern.slf4j.Slf4j;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.vectorstore.RedisVectorStore;
 import org.springframework.ai.vectorstore.SearchRequest;
+import org.springframework.ai.vectorstore.filter.FilterExpressionBuilder;
 
-import java.util.ArrayList;
-import java.util.Comparator;
-import java.util.List;
-import java.util.Optional;
+import java.util.*;
 
 @Slf4j
 public class SimilarityUtil {
@@ -28,18 +27,26 @@ public class SimilarityUtil {
     }
 
     /**
-     * 相似度比较,找出所有的
+     * 相似度比较,找到病历里面配置答案的问题
      *
-     * @param question 问题
-     * @return 相似度最高的top10
+     * @param question              问题
+     * @param libraryQuestionIdList 配置答案的问题ID
+     * @return 匹配到的结果列表
      */
-    public static List<QaSimilarityQuestionAnswer> talkRedisVectorWithScore(String question) {
+    public static List<QaSimilarityQuestionAnswer> talkRedisVectorWithScore(String question, Collection<String> libraryQuestionIdList) {
         log.info("开始调用talkQaSimilarity,问题:{}", question);
         try {
-            // 走Redis向量库进行比较,找出最高的top10
-            List<Document> documents = redisVectorStore.similaritySearch(SearchRequest
+
+            SearchRequest searchRequest = SearchRequest
                     .query(question)
-                    .withTopK(10));
+                    .withTopK(10);
+            // 添加条件,只匹配对应标准问的问题
+            if (CollUtil.isNotEmpty(libraryQuestionIdList)) {
+                FilterExpressionBuilder b = new FilterExpressionBuilder();
+                searchRequest.withFilterExpression(b.in("libraryQuestionId", libraryQuestionIdList).build());
+            }
+            // 走Redis向量库进行比较,找出最高的top10
+            List<Document> documents = redisVectorStore.similaritySearch(searchRequest);
             return documents.stream().map(document -> {
                 QaSimilarityQuestionAnswer qaSimilarityQuestionAnswer = new QaSimilarityQuestionAnswer();
                 qaSimilarityQuestionAnswer.setMatchQuestion(document.getContent());
@@ -59,5 +66,15 @@ public class SimilarityUtil {
         }
     }
 
+    /**
+     * 相似度比较,找出所有的
+     *
+     * @param question 问题
+     * @return 相似度最高的top10
+     */
+    public static List<QaSimilarityQuestionAnswer> talkRedisVectorWithScore(String question) {
+        return talkRedisVectorWithScore(question, null);
+    }
+
 
 }
diff --git a/virtual-patient-web/src/main/java/com/supervision/service/impl/AskServiceImpl.java b/virtual-patient-web/src/main/java/com/supervision/service/impl/AskServiceImpl.java
index 07b74174..046f9212 100644
--- a/virtual-patient-web/src/main/java/com/supervision/service/impl/AskServiceImpl.java
+++ b/virtual-patient-web/src/main/java/com/supervision/service/impl/AskServiceImpl.java
@@ -25,7 +25,9 @@ import org.springframework.web.multipart.MultipartFile;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Optional;
+import java.util.Set;
 import java.util.concurrent.atomic.AtomicInteger;
+import java.util.stream.Collectors;
 
 @Slf4j
 @Service
@@ -92,8 +94,23 @@ public class AskServiceImpl implements AskService {
         // 根据processId找到对应的病人
         Process process = Optional.ofNullable(processService.getById(processId)).orElseThrow(() -> new BusinessException("未找到诊疗进程"));
         MedicalRec medicalRec = medicalRecService.getById(process.getMedicalRecId());
+        // 找到这个病历里面配置的所有配置了答案的问题
+        List<AskPatientAnswer> medicalAnswerList = askPatientAnswerService.lambdaQuery().eq(AskPatientAnswer::getMedicalId, medicalRec.getId()).select(AskPatientAnswer::getLibraryQuestionId).list();
+        if (CollUtil.isEmpty(medicalAnswerList)) {
+            // 记录流转信息
+            circulationList.add(AskCirculationDetail.builder().failInfo("病历里面没有配置任何答案,直接略过相似度匹配,都大模型").build());
+            // 如果没有配置答案,就走大模型
+            String answer = aiService.talk(question, medicalRec.getMedicalRecordAi());
+            // 记录大模型的流转记录
+            buildAiCirculationDetail(circulationList, answer, medicalRec);
+            // 保存消息到记录表
+            saveQaRecord(process.getId(), medicalRec, 2, question, null, answer, circulationList, stopWatch);
+            return answer;
+        }
+        Set<String> libraryQuestionIdSet = medicalAnswerList.stream().map(AskPatientAnswer::getLibraryQuestionId).collect(Collectors.toSet());
+
         // 进行相似度匹配
-        List<QaSimilarityQuestionAnswer> similarityAnswerList = SimilarityUtil.talkRedisVectorWithScore(question);
+        List<QaSimilarityQuestionAnswer> similarityAnswerList = SimilarityUtil.talkRedisVectorWithScore(question, libraryQuestionIdSet);
         Optional<QaSimilarityQuestionAnswer> first = similarityAnswerList.stream().findFirst();
         // 如果匹配度没有匹配到任何数据
         if (first.isEmpty()) {