feat: 新增检索测试相关接口

- 实现向量 L2 归一化，统一 Milvus/Qdrant/Weaviate 检索评分为 [0, 1] 空间

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/controller/knowledge/KnowledgeFragmentController.java

@@ -8,6 +8,7 @@ import jakarta.validation.constraints.*;
 import cn.dev33.satoken.annotation.SaCheckPermission;
 import org.ruoyi.domain.bo.knowledge.KnowledgeFragmentBo;
 import org.ruoyi.domain.vo.knowledge.KnowledgeFragmentVo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 import org.ruoyi.service.knowledge.IKnowledgeFragmentService;
 import org.springframework.web.bind.annotation.*;
 import org.springframework.validation.annotation.Validated;
@@ -102,4 +103,12 @@ public class KnowledgeFragmentController extends BaseController {
                           @PathVariable Long[] ids) {
         return toAjax(knowledgeFragmentService.deleteWithValidByIds(List.of(ids), true));
     }
+
+    /**
+     * 检索测试
+     */
+    @PostMapping("/retrieval")
+    public R<List<KnowledgeRetrievalVo>> retrieval(@RequestBody KnowledgeFragmentBo bo) {
+        return R.ok(knowledgeFragmentService.retrieval(bo));
+    }
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/knowledge/KnowledgeFragmentBo.java

@@ -49,5 +49,24 @@ public class KnowledgeFragmentBo extends BaseEntity {
      */
     private String remark;
 
+    /**
+     * 知识库ID
+     */
+    private Long knowledgeId;
+
+    /**
+     * 检索内容
+     */
+    private String query;
+
+    /**
+     * 返回条数
+     */
+    private Integer topK;
+
+    /**
+     * 相似度阈值
+     */
+    private Double threshold;
 
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/vo/knowledge/KnowledgeRetrievalVo.java

@@ -0,0 +1,35 @@
+package org.ruoyi.domain.vo.knowledge;
+
+import lombok.Builder;
+import lombok.Data;
+
+import java.io.Serial;
+import java.io.Serializable;
+
+/**
+ * 知识检索测试结果视图对象
+ *
+ * @author RobustH
+ */
+@Data
+@Builder
+public class KnowledgeRetrievalVo implements Serializable {
+
+    @Serial
+    private static final long serialVersionUID = 1L;
+
+    /**
+     * 片段内容
+     */
+    private String content;
+
+    /**
+     * 相似度得分
+     */
+    private Double score;
+
+    /**
+     * 来源文档名称
+     */
+    private String sourceName;
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/chat/impl/ChatServiceFacade.java

@@ -16,6 +16,15 @@ import dev.langchain4j.model.chat.StreamingChatModel;
 import dev.langchain4j.model.chat.response.ChatResponse;
 import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
 import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.rag.AugmentationRequest;
+import dev.langchain4j.rag.AugmentationResult;
+import dev.langchain4j.rag.DefaultRetrievalAugmentor;
+import dev.langchain4j.rag.RetrievalAugmentor;
+import dev.langchain4j.rag.content.aggregator.ContentAggregator;
+import dev.langchain4j.rag.content.aggregator.DefaultContentAggregator;
+import dev.langchain4j.rag.content.aggregator.ReRankingContentAggregator;
+import dev.langchain4j.model.scoring.ScoringModel;
+import dev.langchain4j.rag.query.Metadata;
 import dev.langchain4j.service.tool.ToolProvider;
 import lombok.RequiredArgsConstructor;
 import lombok.SneakyThrows;
@@ -46,6 +55,8 @@ import org.ruoyi.mcp.service.core.ToolProviderFactory;
 import org.ruoyi.service.chat.AbstractChatService;
 import org.ruoyi.service.chat.IChatMessageService;
 import org.ruoyi.service.chat.impl.memory.PersistentChatMemoryStore;
+import org.ruoyi.service.knowledge.retriever.CustomVectorRetriever;
+import org.ruoyi.service.knowledge.rerank.ScoringModelFactory;
 import org.ruoyi.service.knowledge.IKnowledgeInfoService;
 import org.ruoyi.service.vector.VectorStoreService;
 import org.springframework.stereotype.Service;
@@ -89,6 +100,8 @@ public class ChatServiceFacade implements IChatService {
 
     private final ToolProviderFactory toolProviderFactory;
 
+    private final ScoringModelFactory scoringModelFactory;
+
     /**
      * 内存实例缓存，避免同一会话重复创建
      * Key: sessionId, Value: MessageWindowChatMemory实例
@@ -119,7 +132,9 @@ public class ChatServiceFacade implements IChatService {
         // 2. 构建上下文消息列表
         List<ChatMessage> contextMessages = buildContextMessages(chatRequest);
 
-        // 3. 处理特殊聊天模式（工作流、人机交互恢复、思考模式）
+        // 注意：buildContextMessages() 最后返回的列表中，最新的带有增强知识的 UserMessage 在最后。
+        // 对于有些模型API（非langchain4j的代理），它们可能不识别增强后的复杂文本（取决于供应商适配度）
+        // 但是通过标准流，它被解析为 String。
         SseEmitter specialResult = handleSpecialChatModes(chatRequest, contextMessages, chatModelVo, emitter);
         if (specialResult != null) {
             return specialResult;
@@ -346,39 +361,63 @@ public class ChatServiceFacade implements IChatService {
      * @return 上下文消息列表
      */
     private List<ChatMessage> buildContextMessages(ChatRequest chatRequest) {
-        List<ChatMessage> messages  = new ArrayList<>();
+        List<ChatMessage> messages = new ArrayList<>();
-        // 构建用户消息
+
+        // 初始化用户消息
         UserMessage userMessage = UserMessage.userMessage(chatRequest.getContent());
-        messages.add(userMessage);
 
-        // 从向量库查询相关历史消息
+        // 使用 LangChain4j 的 RetrievalAugmentor 进行检索增强
         if (chatRequest.getKnowledgeId() != null) {
-            // 查询知识库信息
             KnowledgeInfoVo knowledgeInfoVo = knowledgeInfoService.queryById(Long.valueOf(chatRequest.getKnowledgeId()));
-            if (knowledgeInfoVo == null) {
+            if (knowledgeInfoVo != null) {
-                log.warn("知识库信息不存在，kid: {}", chatRequest.getKnowledgeId());
+                ChatModelVo chatModel = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModel());
-                return messages;
+                if (chatModel != null) {
-            }
 
-            // 查询向量模型配置信息
+                    // 1. 构建适配器（Retriever）
-            ChatModelVo chatModel = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModel());
+                    CustomVectorRetriever retriever = new CustomVectorRetriever(
-            if (chatModel == null) {
+                            vectorStoreService, knowledgeInfoVo, chatModel);
-                log.warn("向量模型配置不存在，模型名称: {}", knowledgeInfoVo.getEmbeddingModel());
-                return messages;
-            }
 
-            // 构建向量查询参数
+                    // 2. 获取和构建重排模型聚合器（Aggregator）
-            QueryVectorBo queryVectorBo = buildQueryVectorBo(chatRequest, knowledgeInfoVo, chatModel);
+                    // 假设已在 KnowledgeInfoVo 等加入 getRerankModelConfig/getRerankModel 等，这里演示通用逻辑
+                    // 若无重排需求，使用 DefaultContentAggregator 或无 ScoringModel 的聚合器
+                    ContentAggregator contentAggregator;
+                    // TODO: 一旦实体类实现了重排模型的支持，此处可以从数据库读出：
+                    // ChatModelVo scoringModelConfig = chatModelService.selectModelByName(knowledgeInfoVo.getRerankModel());
+                    ChatModelVo scoringModelConfig = null; // 当前暂无对应配置字段
 
-            // 获取向量查询结果
+                    ScoringModel scoringModel = scoringModelFactory.createScoringModel(scoringModelConfig);
-            List<String> nearestList = vectorStoreService.getQueryVector(queryVectorBo);
+                    if (scoringModel != null) {
-            for (String prompt : nearestList) {
+                        contentAggregator = ReRankingContentAggregator.builder()
-                // 知识库内容作为系统上下文添加
+                                .scoringModel(scoringModel)
-                messages.add( new AiMessage(prompt));
+                                // .maxResults(3) 这个数字将来从配置取
+                                .build();
+                    } else {
+                        contentAggregator = new DefaultContentAggregator();
+                    }
+
+                    // 3. 构造流水线
+                    RetrievalAugmentor augmentor = DefaultRetrievalAugmentor.builder()
+                            .contentRetriever(retriever)
+                            .contentAggregator(contentAggregator)
+                            .build();
+
+                    // 4. 执行 Augmentor 增强：将检索到的知识内容编织进 UserMessage 中
+                    Metadata ragMetadata = Metadata.from(userMessage, chatRequest.getSessionId(), new ArrayList<>());
+                    AugmentationRequest augmentationRequest = new AugmentationRequest(userMessage, ragMetadata);
+
+                    AugmentationResult augmentedResult = augmentor.augment(augmentationRequest);
+
+                    ChatMessage augmentedMessage = augmentedResult.chatMessage();
+                    if (augmentedMessage instanceof UserMessage) {
+                         userMessage = (UserMessage) augmentedMessage;
+                    }
+                    log.info("RAG 增强完成: UserMessage 已重构并附加上下文背景。");
+
+                }
             }
         }
 
-        // 从数据库查询历史对话消息
+        // 从数据库查询历史对话消息（历史消息应放在当前提问前）
         if (chatRequest.getSessionId() != null) {
             MessageWindowChatMemory memory = createChatMemory(chatRequest.getSessionId());
             if (memory != null) {
@@ -390,6 +429,9 @@ public class ChatServiceFacade implements IChatService {
             }
         }
 
+        // 注入本次用户提问（经过 RAG 增强后的 UserMessage）
+        messages.add(userMessage);
+
         return messages;
     }
 

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/knowledge/IKnowledgeFragmentService.java

@@ -4,6 +4,7 @@ import org.ruoyi.common.mybatis.core.page.TableDataInfo;
 import org.ruoyi.common.mybatis.core.page.PageQuery;
 import org.ruoyi.domain.bo.knowledge.KnowledgeFragmentBo;
 import org.ruoyi.domain.vo.knowledge.KnowledgeFragmentVo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 
 import java.util.Collection;
 import java.util.List;
@@ -65,4 +66,12 @@ public interface IKnowledgeFragmentService {
      * @return 是否删除成功
      */
     Boolean deleteWithValidByIds(Collection<Long> ids, Boolean isValid);
+
+    /**
+     * 检索测试
+     *
+     * @param bo 检索参数
+     * @return 检索结果
+     */
+    List<KnowledgeRetrievalVo> retrieval(KnowledgeFragmentBo bo);
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/knowledge/impl/KnowledgeFragmentServiceImpl.java

@@ -13,8 +13,17 @@ import org.ruoyi.domain.bo.knowledge.KnowledgeFragmentBo;
 import org.ruoyi.domain.entity.knowledge.KnowledgeFragment;
 import org.ruoyi.domain.vo.knowledge.KnowledgeFragmentVo;
 import org.ruoyi.mapper.knowledge.KnowledgeFragmentMapper;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeInfoVo;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.service.knowledge.IKnowledgeFragmentService;
+import org.ruoyi.service.knowledge.IKnowledgeInfoService;
+import org.ruoyi.common.chat.service.chat.IChatModelService;
+import org.ruoyi.service.vector.VectorStoreService;
 import org.springframework.stereotype.Service;
+import java.util.ArrayList;
+import java.util.stream.Collectors;
 
 import java.util.List;
 import java.util.Map;
@@ -32,6 +41,9 @@ import java.util.Collection;
 public class KnowledgeFragmentServiceImpl implements IKnowledgeFragmentService {
 
     private final KnowledgeFragmentMapper baseMapper;
+    private final IKnowledgeInfoService knowledgeInfoService;
+    private final IChatModelService chatModelService;
+    private final VectorStoreService vectorStoreService;
 
     /**
      * 查询知识片段
@@ -131,4 +143,45 @@ public class KnowledgeFragmentServiceImpl implements IKnowledgeFragmentService {
         }
         return baseMapper.deleteByIds(ids) > 0;
     }
+
+    /**
+     * 检索测试核心实现
+     */
+    @Override
+    public List<KnowledgeRetrievalVo> retrieval(KnowledgeFragmentBo bo) {
+        if (bo.getKnowledgeId() == null || StringUtils.isBlank(bo.getQuery())) {
+            return new ArrayList<>();
+        }
+
+        // 1. 获取知识库及模型配置
+        KnowledgeInfoVo knowledgeInfoVo = knowledgeInfoService.queryById(bo.getKnowledgeId());
+        if (knowledgeInfoVo == null) {
+            return new ArrayList<>();
+        }
+
+        ChatModelVo chatModel = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModel());
+        if (chatModel == null) {
+            log.warn("未找到对应的向量模型配置: {}", knowledgeInfoVo.getEmbeddingModel());
+            return new ArrayList<>();
+        }
+
+        // 2. 构造向量检索参数
+        QueryVectorBo queryVectorBo = new QueryVectorBo();
+        queryVectorBo.setQuery(bo.getQuery());
+        queryVectorBo.setKid(String.valueOf(bo.getKnowledgeId()));
+        queryVectorBo.setMaxResults(bo.getTopK() != null ? bo.getTopK() : knowledgeInfoVo.getRetrieveLimit());
+        queryVectorBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModel());
+        queryVectorBo.setVectorModelName(knowledgeInfoVo.getVectorModel());
+        queryVectorBo.setApiKey(chatModel.getApiKey());
+        queryVectorBo.setBaseUrl(chatModel.getApiHost());
+
+        // 3. 执行物理检索
+        List<KnowledgeRetrievalVo> allResults = vectorStoreService.search(queryVectorBo);
+
+        // 4. 根据阈值过滤 (LangChain4j 结果 score 通常 0-1)
+        double threshold = bo.getThreshold() != null ? bo.getThreshold() : 0.0;
+        return allResults.stream()
+                .filter(res -> res.getScore() >= threshold)
+                .collect(Collectors.toList());
+    }
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/knowledge/rerank/ScoringModelFactory.java

@@ -0,0 +1,37 @@
+package org.ruoyi.service.knowledge.rerank;
+
+import dev.langchain4j.model.scoring.ScoringModel;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.springframework.stereotype.Component;
+
+/**
+ * 重排模型提供商工厂
+ * 用于将来无缝拓展硅基流动、百炼等支持重排的模型厂商
+ *
+ * @author RobustH
+ */
+@Slf4j
+@Component
+public class ScoringModelFactory {
+
+    /**
+     * 根据后台传递的模型配置创建具体的重排模型
+     *
+     * @param rerankModelConfig 重排模型的配置 (例如其 providerCode, apiUrl, apiKey 等)
+     * @return 标准的 LangChain4j ScoringModel
+     */
+    public ScoringModel createScoringModel(ChatModelVo rerankModelConfig) {
+        if (rerankModelConfig == null) {
+            return null;
+        }
+
+        String providerCode = rerankModelConfig.getProviderCode();
+        log.info("初始化重排模型，供应商代码: {}", providerCode);
+
+        // TODO: 在这里通过 switch 或反射具体实例化支持的各种 ScoringModel (例如 CohereScoringModel, DascScope 等)
+        // 目前返回 null 代表暂时没有加载特定的重排底座，这不会影响流程，Aggregator 会忽略它返回原样结果
+
+        return null;
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/knowledge/retriever/CustomVectorRetriever.java

@@ -0,0 +1,54 @@
+package org.ruoyi.service.knowledge.retriever;
+
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.rag.content.Content;
+import dev.langchain4j.rag.content.retriever.ContentRetriever;
+import dev.langchain4j.rag.query.Query;
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeInfoVo;
+import org.ruoyi.service.vector.VectorStoreService;
+
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * 自定义向量检索器：适配 LangChain4j ContentRetriever 接口
+ * 桥接现有的 VectorStoreService 获取检索结果
+ *
+ * @author RobustH
+ */
+@Slf4j
+@RequiredArgsConstructor
+public class CustomVectorRetriever implements ContentRetriever {
+
+    private final VectorStoreService vectorStoreService;
+    private final KnowledgeInfoVo knowledgeInfoVo;
+    private final ChatModelVo chatModelVo;
+
+    @Override
+    public List<Content> retrieve(Query query) {
+        log.info("执行自定义向量检索，关键字: {}", query.text());
+
+        // 构建内部查询参数
+        QueryVectorBo queryVectorBo = new QueryVectorBo();
+        queryVectorBo.setQuery(query.text());
+        queryVectorBo.setKid(String.valueOf(knowledgeInfoVo.getId()));
+        queryVectorBo.setApiKey(chatModelVo.getApiKey());
+        queryVectorBo.setBaseUrl(chatModelVo.getApiHost());
+        queryVectorBo.setVectorModelName(knowledgeInfoVo.getVectorModel());
+        queryVectorBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModel());
+        // 如果接入了重排，这里的 retrieveLimit 也就是 MaxResults 应当被放大，后续留给 Aggregator 截断
+        queryVectorBo.setMaxResults(knowledgeInfoVo.getRetrieveLimit());
+
+        // 执行底层的多种向量库策略检索
+        List<String> nearestList = vectorStoreService.getQueryVector(queryVectorBo);
+
+        // 将结果包装为标准的 Content 返回
+        return nearestList.stream()
+                .map(text -> Content.from(TextSegment.from(text)))
+                .collect(Collectors.toList());
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/VectorStoreService.java

@@ -3,6 +3,7 @@ package org.ruoyi.service.vector;
 import org.ruoyi.common.core.exception.ServiceException;
 import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.domain.bo.vector.StoreEmbeddingBo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 
 import java.util.List;
 
@@ -17,6 +18,11 @@ public interface VectorStoreService {
 
     List<String> getQueryVector(QueryVectorBo queryVectorBo);
 
+    /**
+     * 带分数及元数据的检索（用于测试检索功能）
+     */
+    List<KnowledgeRetrievalVo> search(QueryVectorBo queryVectorBo);
+
     void createSchema(String kid, String embeddingModelName);
 
     void removeById(String id, String modelName) throws ServiceException;

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/impl/AbstractVectorStoreStrategy.java

@@ -37,6 +37,24 @@ public abstract class AbstractVectorStoreStrategy implements VectorStoreService
         return result;
     }
 
+    /**
+     * 向量 L2 归一化 (单位化)
+     */
+    protected static float[] normalize(float[] vector) {
+        if (vector == null) return null;
+        double sum = 0;
+        for (float v : vector) {
+            sum += v * v;
+        }
+        float norm = (float) Math.sqrt(sum);
+        if (norm > 1e-9) {
+            for (int i = 0; i < vector.length; i++) {
+                vector[i] /= norm;
+            }
+        }
+        return vector;
+    }
+
     /**
      * 获取向量模型
      */

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/impl/MilvusVectorStoreStrategy.java

@@ -19,7 +19,11 @@ import org.ruoyi.common.chat.service.chat.IChatModelService;
 import org.ruoyi.config.VectorStoreProperties;
 import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.domain.bo.vector.StoreEmbeddingBo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 import org.ruoyi.factory.EmbeddingModelFactory;
+import org.ruoyi.mapper.knowledge.KnowledgeAttachMapper;
+import org.ruoyi.domain.entity.knowledge.KnowledgeAttach;
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import org.springframework.stereotype.Component;
 
 import java.util.ArrayList;
@@ -32,10 +36,14 @@ import java.util.stream.IntStream;
 @Component
 public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
+    private final KnowledgeAttachMapper knowledgeAttachMapper;
+
     public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties,
                                      IChatModelService chatModelService,
-                                     EmbeddingModelFactory embeddingModelFactory) {
+                                     EmbeddingModelFactory embeddingModelFactory,
+                                     KnowledgeAttachMapper knowledgeAttachMapper) {
         super(vectorStoreProperties, embeddingModelFactory, chatModelService);
+        this.knowledgeAttachMapper = knowledgeAttachMapper;
     }
 
     // 缓存不同集合与 autoFlush 配置的 Milvus 连接
@@ -51,7 +59,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
             .collectionName(collectionName)
             .dimension(dimension)
             .indexType(IndexType.IVF_FLAT)
-            .metricType(MetricType.L2)
+            .metricType(MetricType.COSINE)
             .autoFlushOnInsert(autoFlushOnInsert)
             .idFieldName("id")
             .textFieldName("text")
@@ -104,7 +112,10 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
             TextSegment textSegment = TextSegment.from(text, metadata);
             Embedding embedding = embeddingModel.embed(text).content();
-            embeddingStore.add(embedding, textSegment);
+            // 单位化处理
+            float[] vector = embedding.vector();
+            normalize(vector);
+            embeddingStore.add(Embedding.from(vector), textSegment);
         });
         long endTime = System.currentTimeMillis();
         log.info("Milvus向量存储完成消耗时间：{}秒", (endTime - startTime) / 1000);
@@ -136,6 +147,55 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         return resultList;
     }
 
+    @Override
+    public List<KnowledgeRetrievalVo> search(QueryVectorBo queryVectorBo) {
+        int dimension = getModelDimension(queryVectorBo.getEmbeddingModelName());
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName());
+
+        Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
+        // 查询向量单位化处理
+        float[] queryVector = queryEmbedding.vector();
+        normalize(queryVector);
+
+        String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + queryVectorBo.getKid();
+
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, dimension, true);
+
+        EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
+                .queryEmbedding(Embedding.from(queryVector))
+                .maxResults(queryVectorBo.getMaxResults())
+                .build();
+
+        List<EmbeddingMatch<TextSegment>> matches = embeddingStore.search(request).matches();
+        List<KnowledgeRetrievalVo> resultList = new ArrayList<>();
+
+        for (EmbeddingMatch<TextSegment> match : matches) {
+            TextSegment segment = match.embedded();
+            if (segment == null) continue;
+
+            String docId = segment.metadata().getString("docId");
+            String sourceName = "未知来源";
+            if (docId != null) {
+                KnowledgeAttach attach = knowledgeAttachMapper.selectOne(new LambdaQueryWrapper<KnowledgeAttach>()
+                        .eq(KnowledgeAttach::getDocId, docId)
+                        .last("limit 1"));
+                if (attach != null) {
+                    sourceName = attach.getName();
+                }
+            }
+
+            // 提取内容、评分及来源
+            double score = match.score();
+
+            resultList.add(org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo.builder()
+                    .content(segment.text())
+                    .score(score)
+                    .sourceName(sourceName)
+                    .build());
+        }
+        return resultList;
+    }
+
     @Override
     @SneakyThrows
     public void removeById(String id, String modelName) {

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/impl/QdrantVectorStoreStrategy.java

@@ -24,7 +24,11 @@ import org.ruoyi.common.core.exception.ServiceException;
 import org.ruoyi.config.VectorStoreProperties;
 import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.domain.bo.vector.StoreEmbeddingBo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 import org.ruoyi.factory.EmbeddingModelFactory;
+import org.ruoyi.domain.entity.knowledge.KnowledgeAttach;
+import org.ruoyi.mapper.knowledge.KnowledgeAttachMapper;
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import org.springframework.stereotype.Component;
 
 import static io.qdrant.client.VectorInputFactory.vectorInput;
@@ -47,10 +51,14 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
     private static final String METADATA_KID_KEY    = "kid";
     private static final String METADATA_DOC_ID_KEY = "doc_id";
 
+    private final KnowledgeAttachMapper knowledgeAttachMapper;
+
     public QdrantVectorStoreStrategy(VectorStoreProperties vectorStoreProperties,
                                      IChatModelService chatModelService,
-                                     EmbeddingModelFactory embeddingModelFactory) {
+                                     EmbeddingModelFactory embeddingModelFactory,
+                                     KnowledgeAttachMapper knowledgeAttachMapper) {
         super(vectorStoreProperties, embeddingModelFactory, chatModelService);
+        this.knowledgeAttachMapper = knowledgeAttachMapper;
     }
 
     private EmbeddingStore<TextSegment> getQdrantStore(String collectionName) {
@@ -129,7 +137,10 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
             metadata.put(METADATA_DOC_ID_KEY, docId);
             TextSegment textSegment = TextSegment.from(text, metadata);
             Embedding embedding = embeddingModel.embed(text).content();
-            embeddingStore.add(embedding, textSegment);
+            // 单位化处理
+            float[] vector = embedding.vector();
+            normalize(vector);
+            embeddingStore.add(Embedding.from(vector), textSegment);
         });
 
         long endTime = System.currentTimeMillis();
@@ -140,18 +151,22 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
         EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName());
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
+        // 查询向量单位化处理
+        float[] queryVector = queryEmbedding.vector();
+        normalize(queryVector);
+
         String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + queryVectorBo.getKid();
 
-        List<Float> vector = new ArrayList<>();
+        List<Float> vectorList = new ArrayList<>();
-        for (float f : queryEmbedding.vector()) {
+        for (float f : queryVector) {
-            vector.add(f);
+            vectorList.add(f);
         }
 
         try (QdrantClient client = buildQdrantClient()) {
             QueryPoints request = QueryPoints.newBuilder()
                     .setCollectionName(collectionName)
                     .setQuery(Query.newBuilder()
-                            .setNearest(vectorInput(vector))
+                            .setNearest(vectorInput(vectorList))
                             .build())
                     .setLimit(queryVectorBo.getMaxResults())
                     .setWithPayload(enable(true))
@@ -172,6 +187,69 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
         }
     }
 
+    @Override
+    public List<KnowledgeRetrievalVo> search(QueryVectorBo queryVectorBo) {
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName());
+        Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
+        // 查询向量单位化处理
+        float[] queryVector = queryEmbedding.vector();
+        normalize(queryVector);
+
+        String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + queryVectorBo.getKid();
+
+        List<Float> vectorList = new ArrayList<>();
+        for (float f : queryVector) {
+            vectorList.add(f);
+        }
+
+        try (QdrantClient client = buildQdrantClient()) {
+            QueryPoints request = QueryPoints.newBuilder()
+                    .setCollectionName(collectionName)
+                    .setQuery(Query.newBuilder()
+                            .setNearest(vectorInput(vectorList))
+                            .build())
+                    .setLimit(queryVectorBo.getMaxResults())
+                    .setWithPayload(enable(true))
+                    .build();
+
+            List<ScoredPoint> results = client.queryAsync(request).get();
+            List<org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo> resultList = new ArrayList<>();
+            for (ScoredPoint point : results) {
+                String content = "";
+                JsonWithInt.Value textValue = point.getPayloadMap().get(TEXT_SEGMENT_KEY);
+                if (textValue != null && textValue.hasStringValue()) {
+                    content = textValue.getStringValue();
+                }
+
+                String docId = null;
+                JsonWithInt.Value docIdValue = point.getPayloadMap().get(METADATA_DOC_ID_KEY);
+                if (docIdValue != null && docIdValue.hasStringValue()) {
+                    docId = docIdValue.getStringValue();
+                }
+
+                String sourceName = "未知来源";
+                if (docId != null) {
+                    KnowledgeAttach attach = knowledgeAttachMapper.selectOne(new LambdaQueryWrapper<KnowledgeAttach>()
+                            .eq(KnowledgeAttach::getDocId, docId)
+                            .last("limit 1"));
+                    if (attach != null) {
+                        sourceName = attach.getName();
+                    }
+                }
+
+                resultList.add(org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo.builder()
+                        .content(content)
+                        .score((double) point.getScore())
+                        .sourceName(sourceName)
+                        .build());
+            }
+            return resultList;
+        } catch (Exception e) {
+            log.error("Qdrant检索失败: {}", collectionName, e);
+            throw new ServiceException("Qdrant向量检索失败");
+        }
+    }
+
     @Override
     public void removeById(String id, String modelName) {
         String collectionName = vectorStoreProperties.getQdrant().getCollectionname() + id;

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/impl/VectorStoreServiceImpl.java

@@ -4,6 +4,7 @@ import lombok.RequiredArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.domain.bo.vector.StoreEmbeddingBo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 import org.ruoyi.factory.VectorStoreStrategyFactory;
 import org.ruoyi.service.vector.VectorStoreService;
 import org.springframework.context.annotation.Primary;
@@ -54,6 +55,13 @@ public class VectorStoreServiceImpl implements VectorStoreService {
         return strategy.getQueryVector(queryVectorBo);
     }
 
+    @Override
+    public List<KnowledgeRetrievalVo> search(QueryVectorBo queryVectorBo) {
+        log.info("执行测试搜索: kid={}, query={}", queryVectorBo.getKid(), queryVectorBo.getQuery());
+        VectorStoreService strategy = getCurrentStrategy();
+        return strategy.search(queryVectorBo);
+    }
+
     @Override
     public void removeById(String id, String modelName) {
         log.info("根据ID删除向量数据: id={}, modelName={}", id, modelName);

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/vector/impl/WeaviateVectorStoreStrategy.java

@@ -12,6 +12,7 @@ import org.ruoyi.common.core.exception.ServiceException;
 import org.ruoyi.config.VectorStoreProperties;
 import org.ruoyi.domain.bo.vector.QueryVectorBo;
 import org.ruoyi.domain.bo.vector.StoreEmbeddingBo;
+import org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo;
 import org.ruoyi.factory.EmbeddingModelFactory;
 import org.springframework.stereotype.Component;
 import io.weaviate.client.Config;
@@ -24,6 +25,9 @@ import io.weaviate.client.v1.graphql.model.GraphQLResponse;
 import io.weaviate.client.v1.schema.model.Property;
 import io.weaviate.client.v1.schema.model.Schema;
 import io.weaviate.client.v1.schema.model.WeaviateClass;
+import org.ruoyi.domain.entity.knowledge.KnowledgeAttach;
+import org.ruoyi.mapper.knowledge.KnowledgeAttachMapper;
+import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 
 import java.util.ArrayList;
 import java.util.Collections;
@@ -40,11 +44,14 @@ import java.util.Map;
 public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     private WeaviateClient client;
+    private final KnowledgeAttachMapper knowledgeAttachMapper;
 
     public WeaviateVectorStoreStrategy(VectorStoreProperties vectorStoreProperties,
                                        IChatModelService chatModelService,
-                                       EmbeddingModelFactory embeddingModelFactory) {
+                                       EmbeddingModelFactory embeddingModelFactory,
+                                       KnowledgeAttachMapper knowledgeAttachMapper) {
         super(vectorStoreProperties, embeddingModelFactory,chatModelService);
+        this.knowledgeAttachMapper = knowledgeAttachMapper;
     }
 
     @Override
@@ -110,9 +117,12 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                     "kid", kid,
                     "docId", docId
             );
-            Float[] vector = toObjectArray(embedding.vector());
+            float[] vectorArray = embedding.vector();
+            normalize(vectorArray);
+            Float[] vector = toObjectArray(vectorArray);
+
             client.data().creator()
-                    .withClassName("LocalKnowledge" + kid)
+                    .withClassName(vectorStoreProperties.getWeaviate().getClassname() + kid)
                     .withProperties(properties)
                     .withVector(vector)
                     .run();
@@ -128,6 +138,9 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
         EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName());
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         float[] vector = queryEmbedding.vector();
+        // 查询向量单位化处理
+        normalize(vector);
+
         List<String> vectorStrings = new ArrayList<>();
         for (float v : vector) {
             vectorStrings.add(String.valueOf(v));
@@ -178,6 +191,77 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
         }
     }
 
+    @Override
+    public List<KnowledgeRetrievalVo> search(QueryVectorBo queryVectorBo) {
+        createSchema(queryVectorBo.getKid(), queryVectorBo.getEmbeddingModelName());
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName());
+        Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
+        float[] vector = queryEmbedding.vector();
+        // 查询向量单位化处理
+        normalize(vector);
+        List<String> vectorStrings = new ArrayList<>();
+        for (float v : vector) {
+            vectorStrings.add(String.valueOf(v));
+        }
+        String vectorStr = String.join(",", vectorStrings);
+        String className = vectorStoreProperties.getWeaviate().getClassname();
+
+        String graphQLQuery = String.format(
+                "{\n" +
+                "  Get {\n" +
+                "    %s(nearVector: {vector: [%s]} limit: %d) {\n" +
+                "      text\n" +
+                "      docId\n" +
+                "      _additional {\n" +
+                "        distance\n" +
+                "      }\n" +
+                "    }\n" +
+                "  }\n" +
+                "}",
+                className + queryVectorBo.getKid(),
+                vectorStr,
+                queryVectorBo.getMaxResults()
+        );
+
+        Result<GraphQLResponse> result = client.graphQL().raw().withQuery(graphQLQuery).run();
+        List<org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo> resultList = new ArrayList<>();
+
+        if (result != null && !result.hasErrors()) {
+            Object data = result.getResult().getData();
+            JSONObject entries = new JSONObject(data);
+            Map<String, cn.hutool.json.JSONArray> entriesMap = entries.get("Get", Map.class);
+            cn.hutool.json.JSONArray objects = entriesMap.get(className + queryVectorBo.getKid());
+
+            for (Object obj : objects) {
+                Map<String, Object> map = (Map<String, Object>) obj;
+                String content = (String) map.get("text");
+                String docId = (String) map.get("docId");
+
+                Map<String, Object> additional = (Map<String, Object>) map.get("_additional");
+                Double distance = Double.valueOf(String.valueOf(additional.get("distance")));
+                // 转换距离为得分 (Weaviate 0 是最相近，1 是最远；余弦距离下 1-dist 即为相似度)
+                double score = 1.0 - distance;
+
+                String sourceName = "未知来源";
+                if (docId != null) {
+                    KnowledgeAttach attach = knowledgeAttachMapper.selectOne(new LambdaQueryWrapper<KnowledgeAttach>()
+                            .eq(KnowledgeAttach::getDocId, docId)
+                            .last("limit 1"));
+                    if (attach != null) {
+                        sourceName = attach.getName();
+                    }
+                }
+
+                resultList.add(org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo.builder()
+                        .content(content)
+                        .score(score)
+                        .sourceName(sourceName)
+                        .build());
+            }
+        }
+        return resultList;
+    }
+
     @Override
     @SneakyThrows
     public void removeById(String id, String modelName) {