Merge pull request #292 from yangzhen233/feature/rerank-model

Feature/rerank model

docs/script/sql/update/updat-0420.sql

@@ -0,0 +1,46 @@
+/*
+ Navicat Premium Dump SQL
+
+ Source Server         : localhost-mysql
+ Source Server Type    : MySQL
+ Source Server Version : 80045 (8.0.45)
+ Source Host           : localhost:3306
+ Source Schema         : ruoyi-ai
+
+ Target Server Type    : MySQL
+ Target Server Version : 80045 (8.0.45)
+ File Encoding         : 65001
+
+ Date: 20/04/2026 15:30:00
+*/
+
+SET NAMES utf8mb4;
+SET FOREIGN_KEY_CHECKS = 0;
+
+-- ----------------------------
+-- 新增：重排序模型（chat_model）
+-- ----------------------------
+INSERT INTO `chat_model`
+(id, category, model_name, provider_code, model_describe, model_dimension, model_show, api_host, api_key, create_dept, create_by, create_time, update_by, update_time, remark, tenant_id)
+VALUES(2045071617578237953, 'rerank', 'rerank', 'zhipu', '智谱重排序', NULL, 'Y', 'https://open.bigmodel.cn', 'e9xx', 103, 1, '2026-04-17 17:27:24', 1, '2026-04-20 15:21:48', '智谱重排序', 0);
+
+INSERT INTO `chat_model`
+(id, category, model_name, provider_code, model_describe, model_dimension, model_show, api_host, api_key, create_dept, create_by, create_time, update_by, update_time, remark, tenant_id)
+VALUES(2046119803482902530, 'rerank', 'qwen3-rerank', 'qianwen', '千问3重排序', NULL, NULL, 'https://dashscope.aliyuncs.com', 'sk-xx', 103, 1, '2026-04-20 14:52:31', 1, '2026-04-20 15:03:13', '千问3文本重排序', 0);
+
+-- ----------------------------
+-- 新增：字典类型 - 重排序模型分类
+-- ----------------------------
+INSERT INTO `sys_dict_data`
+(dict_code, tenant_id, dict_sort, dict_label, dict_value, dict_type, css_class, list_class, is_default, create_dept, create_by, create_time, update_by, update_time, remark)
+VALUES(2045070879435259905, '000000', 4, '重排序', 'rerank', 'chat_model_category', NULL, '#000000', 'N', 103, 1, '2026-04-17 17:24:28', 1, '2026-04-19 01:02:20', '重排序模型');
+
+-- ----------------------------
+-- 修改表：knowledge_info 增加重排序相关字段
+-- ----------------------------
+ALTER TABLE `knowledge_info` ADD COLUMN `enable_rerank` tinyint DEFAULT 0 NULL COMMENT '是否启用重排序（0否 1是）';
+ALTER TABLE `knowledge_info` ADD COLUMN `rerank_score_threshold` double NULL COMMENT '重排序相关性分数阈值';
+ALTER TABLE `knowledge_info` ADD COLUMN `rerank_top_n` int NULL COMMENT '重排序后返回的文档数量';
+ALTER TABLE `knowledge_info` ADD COLUMN `rerank_model` varchar(100) NULL COMMENT '重排序模型名称';
+
+SET FOREIGN_KEY_CHECKS = 1;

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

@@ -77,10 +77,33 @@ public class KnowledgeInfoBo extends BaseEntity {
      */
     private String embeddingModel;
 
+    /**
+     * 是否启用重排序（0 否 1是）
+     */
+    private Integer enableRerank;
+
+    /**
+     * 重排序模型名称
+     */
+    private String rerankModel;
+
+    /**
+     * 重排序后返回的文档数量
+     */
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     */
+    private Double rerankScoreThreshold;
+
+
     /**
      * 备注
      */
     private String remark;
 
 
+
+
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/rerank/RerankRequest.java

@@ -0,0 +1,44 @@
+package org.ruoyi.domain.bo.rerank;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.util.List;
+
+/**
+ * 重排序请求参数
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class RerankRequest {
+
+    /**
+     * 查询文本
+     */
+    private String query;
+
+    /**
+     * 候选文档列表
+     */
+    private List<String> documents;
+
+    /**
+     * 返回的文档数量（topN）
+     * 如果不指定，默认返回所有文档
+     */
+    private Integer topN;
+
+    /**
+     * 是否返回原始文档内容
+     * 默认为 true
+     */
+    @Builder.Default
+    private Boolean returnDocuments = true;
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/rerank/RerankResult.java

@@ -0,0 +1,72 @@
+package org.ruoyi.domain.bo.rerank;
+
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+
+import java.util.List;
+
+/**
+ * 重排序结果
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Data
+@Builder
+@NoArgsConstructor
+@AllArgsConstructor
+public class RerankResult {
+
+    /**
+     * 重排序后的文档结果列表
+     */
+    private List<RerankDocument> documents;
+
+    /**
+     * 原始请求中的文档总数
+     */
+    private Integer totalDocuments;
+
+    /**
+     * 重排序耗时（毫秒）
+     */
+    private Long durationMs;
+
+    /**
+     * 单个重排序文档结果
+     */
+    @Data
+    @Builder
+    @NoArgsConstructor
+    @AllArgsConstructor
+    public static class RerankDocument {
+
+        /**
+         * 文档在原始列表中的索引位置
+         */
+        private Integer index;
+
+        /**
+         * 相关性分数（通常 0-1 之间，越高越相关）
+         */
+        private Double relevanceScore;
+
+        /**
+         * 文档内容
+         */
+        private String document;
+    }
+
+    /**
+     * 创建空结果
+     */
+    public static RerankResult empty() {
+        return RerankResult.builder()
+                .documents(List.of())
+                .totalDocuments(0)
+                .durationMs(0L)
+                .build();
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/bo/vector/QueryVectorBo.java

@@ -51,4 +51,30 @@ public class QueryVectorBo {
      */
     private String baseUrl;
 
+
+    // ========== 重排序相关参数 ==========
+
+    /**
+     * 是否启用重排序
+     * 默认为 false
+     */
+    private Boolean enableRerank = false;
+
+    /**
+     * 重排序模型名称
+     */
+    private String rerankModelName;
+
+    /**
+     * 重排序后返回的文档数量（topN）
+     * 如果不指定，默认与 maxResults 相同
+     */
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     * 低于此阈值的文档将被过滤
+     */
+    private Double rerankScoreThreshold;
+
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/request/AliBaiLianRerankRequest.java

@@ -0,0 +1,55 @@
+package org.ruoyi.domain.dto.request;
+
+import com.fasterxml.jackson.annotation.JsonInclude;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import java.util.List;
+
+/**
+ * 阿里百炼重排序请求DTO（OpenAI兼容格式）
+ *
+ * @author yang
+ * @date 2026-04-20
+ */
+@JsonInclude(JsonInclude.Include.NON_NULL)
+public record AliBaiLianRerankRequest(
+        String model,
+        List<String> documents,
+        String query,
+        @JsonProperty("top_n")
+        Integer topN,
+        String instruct,
+        @JsonProperty("return_documents")
+        Boolean returnDocuments
+) {
+    private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
+
+    /**
+     * 创建文本重排序请求
+     */
+    public static AliBaiLianRerankRequest create(String modelName, String query,
+                                                  List<String> documents, Integer topN,
+                                                  Boolean returnDocuments) {
+        return new AliBaiLianRerankRequest(
+                modelName,
+                documents,
+                query,
+                topN != null ? topN : documents.size(),
+                null,
+                returnDocuments != null ? returnDocuments : true
+        );
+    }
+
+    /**
+     * 转换为JSON字符串
+     */
+    public String toJson() {
+        try {
+            return OBJECT_MAPPER.writeValueAsString(this);
+        } catch (JsonProcessingException e) {
+            throw new IllegalArgumentException("序列化阿里百炼重排序请求失败", e);
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/request/ZhipuRerankRequest.java

@@ -0,0 +1,48 @@
+package org.ruoyi.domain.dto.request;
+
+import com.fasterxml.jackson.core.JsonProcessingException;
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import java.util.List;
+
+/**
+ * 智谱AI重排序请求DTO
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+public record ZhipuRerankRequest(
+        String model,
+        String query,
+        List<String> documents,
+        Integer top_n,
+        Boolean return_documents
+) {
+    private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();
+
+    /**
+     * 创建智谱重排序请求
+     */
+    public static ZhipuRerankRequest create(String modelName, String query,
+                                            List<String> documents, Integer topN,
+                                            Boolean returnDocuments) {
+        return new ZhipuRerankRequest(
+                modelName,
+                query,
+                documents,
+                topN != null ? topN : documents.size(),
+                returnDocuments != null ? returnDocuments : true
+        );
+    }
+
+    /**
+     * 转换为JSON字符串
+     */
+    public String toJson() {
+        try {
+            return OBJECT_MAPPER.writeValueAsString(this);
+        } catch (JsonProcessingException e) {
+            throw new IllegalArgumentException("序列化智谱重排序请求失败", e);
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/response/AliBaiLianRerankResponse.java

@@ -0,0 +1,81 @@
+package org.ruoyi.domain.dto.response;
+
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.List;
+import java.util.Map;
+import java.util.stream.Collectors;
+
+/**
+ * 阿里百炼重排序响应DTO（OpenAI兼容格式）
+ *
+ * @author yang
+ * @date 2026-04-20
+ */
+@JsonIgnoreProperties(ignoreUnknown = true)
+public record AliBaiLianRerankResponse(
+        String id,
+        String object,
+        List<ResultItem> results,
+        UsageInfo usage
+) {
+    /**
+     * 单个重排序结果项
+     */
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public record ResultItem(
+            Integer index,
+            @JsonProperty("relevance_score")
+            Double relevanceScore,
+            Object document
+    ) {
+        /**
+         * 获取文档文本内容
+         */
+        public String getDocumentText() {
+            if (document == null) return null;
+            if (document instanceof String) return (String) document;
+            if (document instanceof Map) {
+                Object text = ((Map<?, ?>) document).get("text");
+                return text != null ? text.toString() : null;
+            }
+            return document.toString();
+        }
+    }
+
+    /**
+     * Token使用信息
+     */
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public record UsageInfo(
+            @JsonProperty("total_tokens")
+            Integer totalTokens,
+            @JsonProperty("prompt_tokens")
+            Integer promptTokens
+    ) {}
+
+    /**
+     * 转换为通用RerankResult
+     */
+    public RerankResult toRerankResult(int totalDocs, long durationMs) {
+        if (results == null || results.isEmpty()) {
+            return RerankResult.empty();
+        }
+
+        List<RerankResult.RerankDocument> documents = results.stream()
+                .map(item -> RerankResult.RerankDocument.builder()
+                        .index(item.index())
+                        .relevanceScore(item.relevanceScore())
+                        .document(item.getDocumentText())
+                        .build())
+                .collect(Collectors.toList());
+
+        return RerankResult.builder()
+                .documents(documents)
+                .totalDocuments(totalDocs)
+                .durationMs(durationMs)
+                .build();
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/dto/response/ZhipuRerankResponse.java

@@ -0,0 +1,68 @@
+package org.ruoyi.domain.dto.response;
+
+import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * 智谱AI重排序响应DTO
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@JsonIgnoreProperties(ignoreUnknown = true)
+public record ZhipuRerankResponse(
+        String model,
+        String object,
+        List<ResultItem> results,
+        UsageInfo usage
+) {
+    /**
+     * 单个重排序结果项
+     */
+    public record ResultItem(
+            Integer index,
+            @JsonProperty("relevance_score")
+            Double relevanceScore,
+            String document
+    ) {}
+
+    /**
+     * Token使用信息
+     */
+    @JsonIgnoreProperties(ignoreUnknown = true)
+    public record UsageInfo(
+            @JsonProperty("total_tokens")
+            Integer totalTokens,
+            @JsonProperty("input_tokens")
+            Integer inputTokens,
+            @JsonProperty("output_tokens")
+            Integer outputTokens
+    ) {}
+
+    /**
+     * 转换为通用RerankResult
+     */
+    public RerankResult toRerankResult(int totalDocs, long durationMs) {
+        if (results == null || results.isEmpty()) {
+            return RerankResult.empty();
+        }
+
+        List<RerankResult.RerankDocument> documents = results.stream()
+                .map(item -> RerankResult.RerankDocument.builder()
+                        .index(item.index())
+                        .relevanceScore(item.relevanceScore())
+                        .document(item.document())
+                        .build())
+                .collect(Collectors.toList());
+
+        return RerankResult.builder()
+                .documents(documents)
+                .totalDocuments(totalDocs)
+                .durationMs(durationMs)
+                .build();
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/domain/entity/knowledge/KnowledgeInfo.java

@@ -78,6 +78,26 @@ public class KnowledgeInfo extends BaseEntity {
      */
     private String embeddingModel;
 
+    /**
+     * 是否启用重排序（0 否 1是）
+     */
+    private Integer enableRerank;
+
+    /**
+     * 重排序模型名称
+     */
+    private String rerankModel;
+
+    /**
+     * 重排序后返回的文档数量
+     */
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     */
+    private Double rerankScoreThreshold;
+
     /**
      * 备注
      */

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

@@ -94,6 +94,30 @@ public class KnowledgeInfoVo implements Serializable {
     @ExcelProperty(value = "向量模型")
     private String embeddingModel;
 
+    /**
+     * 是否启用重排序（0 否 1是）
+     */
+    @ExcelProperty(value = "是否启用重排序")
+    private Integer enableRerank;
+
+    /**
+     * 重排序模型名称
+     */
+    @ExcelProperty(value = "重排序模型")
+    private String rerankModel;
+
+    /**
+     * 重排序后返回的文档数量
+     */
+    @ExcelProperty(value = "重排序返回数量")
+    private Integer rerankTopN;
+
+    /**
+     * 重排序相关性分数阈值
+     */
+    @ExcelProperty(value = "重排序分数阈值")
+    private Double rerankScoreThreshold;
+
     /**
      * 备注
      */

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/factory/RerankModelFactory.java

@@ -0,0 +1,106 @@
+package org.ruoyi.factory;
+
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.common.chat.service.chat.IChatModelService;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.springframework.beans.factory.NoSuchBeanDefinitionException;
+import org.springframework.context.ApplicationContext;
+import org.springframework.stereotype.Service;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
+import java.util.concurrent.ConcurrentHashMap;
+
+/**
+ * 重排序模型工厂服务类
+ * 参考设计模式：EmbeddingModelFactory
+ * 负责创建和管理重排序模型实例
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Service
+@RequiredArgsConstructor
+@Slf4j
+public class RerankModelFactory {
+
+    private final ApplicationContext applicationContext;
+
+    private final IChatModelService chatModelService;
+
+    /**
+     * 模型缓存，使用ConcurrentHashMap保证线程安全
+     */
+    private final Map<String, RerankModelService> modelCache = new ConcurrentHashMap<>();
+
+    /**
+     * 创建重排序模型实例
+     * 如果模型已存在于缓存中，则直接返回；否则创建新的实例
+     *
+     * @param rerankModelName 重排序模型名称
+     */
+    public RerankModelService createModel(String rerankModelName) {
+        return modelCache.computeIfAbsent(rerankModelName, name -> {
+            ChatModelVo modelConfig = chatModelService.selectModelByName(rerankModelName);
+
+            if (modelConfig == null) {
+                throw new IllegalArgumentException("未找到重排序模型配置，name=" + name);
+            }
+            return createModelInstance(modelConfig.getProviderCode(), modelConfig);
+        });
+    }
+
+    /**
+     * 刷新模型缓存
+     * 根据给定的模型ID从缓存中移除对应的模型
+     *
+     * @param modelId 模型的唯一标识ID
+     */
+    public void refreshModel(Long modelId) {
+        modelCache.remove(modelId);
+    }
+
+    /**
+     * 获取所有支持模型工厂的列表
+     *
+     * @return 支持的模型工厂名称列表
+     */
+    public List<String> getSupportedFactories() {
+        return new ArrayList<>(applicationContext.getBeansOfType(RerankModelService.class)
+                .keySet());
+    }
+
+    /**
+     * 创建具体的模型实例
+     * 根据提供的工厂名称和配置信息创建并配置模型实例
+     *
+     * @param factory 工厂名称，用于标识模型类型（providerCode）
+     * @param config  模型配置信息
+     * @return RerankModelService 配置好的模型实例
+     * @throws IllegalArgumentException 当无法获取指定的模型实例时抛出
+     */
+    private RerankModelService createModelInstance(String factory, ChatModelVo config) {
+        try {
+            // 优先尝试使用 providerCode + "Rerank" 作为 Bean 名称
+            // 例如：zhipu -> zhipuRerank，jina -> jinaRerank
+            String rerankBeanName = factory + "Rerank";
+            RerankModelService model = applicationContext.getBean(rerankBeanName, RerankModelService.class);
+            model.configure(config);
+            log.info("成功创建重排序模型: factory={}, modelName={}", rerankBeanName, config.getModelName());
+            return model;
+        } catch (NoSuchBeanDefinitionException e) {
+            // 如果找不到，尝试使用原始的 providerCode
+            try {
+                RerankModelService model = applicationContext.getBean(factory, RerankModelService.class);
+                model.configure(config);
+                log.info("成功创建重排序模型: factory={}, modelName={}", factory, config.getModelName());
+                return model;
+            } catch (NoSuchBeanDefinitionException ex) {
+                throw new IllegalArgumentException("获取不到重排序模型: " + factory + " 或 " + factory + "Rerank", ex);
+            }
+        }
+    }
+}

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

@@ -54,6 +54,7 @@ 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.IKnowledgeInfoService;
+import org.ruoyi.service.retrieval.KnowledgeRetrievalService;
 import org.ruoyi.service.vector.VectorStoreService;
 import org.springframework.stereotype.Service;
 import org.springframework.web.servlet.mvc.method.annotation.SseEmitter;
@@ -89,6 +90,8 @@ public class ChatServiceFacade implements IChatService {
 
     private final VectorStoreService vectorStoreService;
 
+    private final KnowledgeRetrievalService knowledgeRetrievalService;
+
     private final SseEmitterManager sseEmitterManager;
 
     private final IChatMessageService chatMessageService;
@@ -452,8 +455,8 @@ public class ChatServiceFacade implements IChatService {
             // 构建向量查询参数
             QueryVectorBo queryVectorBo = buildQueryVectorBo(chatRequest, knowledgeInfoVo, chatModel);
 
-            // 获取向量查询结果（知识库内容作为系统上下文，放在历史消息之后）
+            // 使用知识库检索服务（支持重排序）
-            List<String> nearestList = vectorStoreService.getQueryVector(queryVectorBo);
+            List<String> nearestList = knowledgeRetrievalService.retrieveTexts(queryVectorBo);
             for (String prompt : nearestList) {
                 // 知识库内容作为系统上下文添加
                 messages.add(new AiMessage(prompt));
@@ -480,6 +483,13 @@ public class ChatServiceFacade implements IChatService {
         queryVectorBo.setVectorModelName(knowledgeInfoVo.getVectorModel());
         queryVectorBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModel());
         queryVectorBo.setMaxResults(knowledgeInfoVo.getRetrieveLimit());
+
+        // 设置重排序参数
+        queryVectorBo.setEnableRerank(knowledgeInfoVo.getEnableRerank() != null && knowledgeInfoVo.getEnableRerank() == 1);
+        queryVectorBo.setRerankModelName(knowledgeInfoVo.getRerankModel());
+        queryVectorBo.setRerankTopN(knowledgeInfoVo.getRerankTopN());
+        queryVectorBo.setRerankScoreThreshold(knowledgeInfoVo.getRerankScoreThreshold());
+
         return queryVectorBo;
     }
 

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

@@ -0,0 +1,70 @@
+package org.ruoyi.service.rerank;
+
+import dev.langchain4j.data.segment.TextSegment;
+import dev.langchain4j.model.output.Response;
+import dev.langchain4j.model.scoring.ScoringModel;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.List;
+
+/**
+ * 重排序模型服务接口
+ * 继承 langchain4j 的 ScoringModel 接口
+ * 参考设计模式：BaseEmbedModelService
+ *
+ * @author Yzm
+ * @date 2026-04-19
+ */
+public interface RerankModelService extends ScoringModel {
+
+    /**
+     * 根据配置信息配置重排序模型
+     *
+     * @param config 包含模型配置信息的 ChatModelVo 对象
+     */
+    void configure(ChatModelVo config);
+
+    /**
+     * 执行重排序（批量文档）
+     * 这是业务层使用的便捷方法
+     *
+     * @param rerankRequest 重排序请求，包含查询文本和候选文档列表
+     * @return 重排序结果，包含排序后的文档和相关性分数
+     */
+    RerankResult rerank(RerankRequest rerankRequest);
+
+    /**
+     * 实现 ScoringModel 接口的 scoreAll 方法
+     * 将 ScoringModel 的调用转换为重排序调用
+     */
+    @Override
+    default Response<List<Double>> scoreAll(List<TextSegment> segments, String query) {
+        // 将 TextSegment 转换为文档字符串列表
+        List<String> documents = segments.stream()
+                .map(TextSegment::text)
+                .toList();
+
+        RerankRequest request = RerankRequest.builder()
+                .query(query)
+                .documents(documents)
+                .topN(documents.size())
+                .returnDocuments(false)
+                .build();
+
+        RerankResult result = rerank(request);
+
+        // 提取分数列表，按原始顺序排列
+        List<Double> scores = new java.util.ArrayList<>(
+                java.util.Collections.nCopies(documents.size(), 0.0));
+
+        for (RerankResult.RerankDocument doc : result.getDocuments()) {
+            if (doc.getIndex() != null && doc.getIndex() < documents.size()) {
+                scores.set(doc.getIndex(), doc.getRelevanceScore());
+            }
+        }
+
+        return Response.from(scores);
+    }
+}

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

@@ -0,0 +1,115 @@
+package org.ruoyi.service.rerank.impl;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import lombok.extern.slf4j.Slf4j;
+import okhttp3.*;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+import org.ruoyi.domain.dto.request.AliBaiLianRerankRequest;
+import org.ruoyi.domain.dto.response.AliBaiLianRerankResponse;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.springframework.stereotype.Component;
+
+import java.io.IOException;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * 阿里百炼重排序模型实现
+ * 参考设计模式：AliBaiLianMultiEmbeddingProvider
+ *
+ * @author yang
+ * @date 2026-04-20
+ */
+@Slf4j
+@Component("qianwenRerank")
+public class AliBaiLianRerankModelService implements RerankModelService {
+
+    private final OkHttpClient okHttpClient;
+    private final ObjectMapper objectMapper = new ObjectMapper();
+    private ChatModelVo chatModelVo;
+
+    public AliBaiLianRerankModelService() {
+        this.okHttpClient = new OkHttpClient.Builder()
+                .connectTimeout(30, TimeUnit.SECONDS)
+                .readTimeout(60, TimeUnit.SECONDS)
+                .writeTimeout(30, TimeUnit.SECONDS)
+                .build();
+    }
+
+    @Override
+    public void configure(ChatModelVo config) {
+        this.chatModelVo = config;
+    }
+
+    @Override
+    public RerankResult rerank(RerankRequest rerankRequest) {
+        long startTime = System.currentTimeMillis();
+
+        try {
+            // 构建请求
+            AliBaiLianRerankRequest request = buildRequest(rerankRequest);
+            AliBaiLianRerankResponse response = executeRequest(request);
+
+            return response.toRerankResult(
+                    rerankRequest.getDocuments().size(),
+                    System.currentTimeMillis() - startTime
+            );
+
+        } catch (Exception e) {
+            log.error("阿里百炼重排序失败: {}", e.getMessage(), e);
+            throw new RuntimeException("重排序服务调用失败: " + e.getMessage(), e);
+        }
+    }
+
+    /**
+     * 构建请求对象
+     */
+    private AliBaiLianRerankRequest buildRequest(RerankRequest rerankRequest) {
+        return AliBaiLianRerankRequest.create(
+                chatModelVo.getModelName(),
+                rerankRequest.getQuery(),
+                rerankRequest.getDocuments(),
+                rerankRequest.getTopN(),
+                rerankRequest.getReturnDocuments()
+        );
+    }
+
+    /**
+     * 执行HTTP请求并解析响应
+     */
+    private AliBaiLianRerankResponse executeRequest(AliBaiLianRerankRequest request) throws IOException {
+        String jsonBody = request.toJson();
+        RequestBody body = RequestBody.create(jsonBody, MediaType.get("application/json"));
+
+        // 阿里百炼重排序 OpenAI兼容端点
+        String url = chatModelVo.getApiHost() + "/compatible-api/v1/reranks";
+        Request httpRequest = new Request.Builder()
+                .url(url)
+                .addHeader("Authorization", "Bearer " + chatModelVo.getApiKey())
+                .addHeader("Content-Type", "application/json")
+                .post(body)
+                .build();
+
+        try (Response response = okHttpClient.newCall(httpRequest).execute()) {
+            if (!response.isSuccessful()) {
+                String err = response.body() != null ? response.body().string() : "无错误信息";
+                throw new IllegalArgumentException("阿里百炼API调用失败: " + response.code() + " - " + err);
+            }
+
+            ResponseBody responseBody = response.body();
+            if (responseBody == null) {
+                throw new IllegalArgumentException("响应体为空");
+            }
+
+            return parseResponse(responseBody.string());
+        }
+    }
+
+    /**
+     * 解析响应
+     */
+    private AliBaiLianRerankResponse parseResponse(String responseBody) throws IOException {
+        return objectMapper.readValue(responseBody, AliBaiLianRerankResponse.class);
+    }
+}

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

@@ -0,0 +1,163 @@
+package org.ruoyi.service.rerank.impl;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import io.jsonwebtoken.Jwts;
+import io.jsonwebtoken.security.MacAlgorithm;
+import lombok.extern.slf4j.Slf4j;
+import okhttp3.*;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+import org.ruoyi.domain.dto.request.ZhipuRerankRequest;
+import org.ruoyi.domain.dto.response.ZhipuRerankResponse;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.springframework.stereotype.Component;
+
+import javax.crypto.spec.SecretKeySpec;
+import java.io.IOException;
+import java.lang.reflect.Constructor;
+import java.nio.charset.StandardCharsets;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * 智谱AI 重排序模型实现
+ * 参考设计模式：AliBaiLianMultiEmbeddingProvider
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Slf4j
+@Component("zhipuRerank")
+public class ZhiPuRerankModelService implements RerankModelService {
+
+    private final OkHttpClient okHttpClient;
+    private final ObjectMapper objectMapper = new ObjectMapper();
+    private ChatModelVo chatModelVo;
+
+    public ZhiPuRerankModelService() {
+        this.okHttpClient = new OkHttpClient.Builder()
+                .connectTimeout(30, TimeUnit.SECONDS)
+                .readTimeout(60, TimeUnit.SECONDS)
+                .writeTimeout(30, TimeUnit.SECONDS)
+                .build();
+    }
+
+    @Override
+    public void configure(ChatModelVo config) {
+        this.chatModelVo = config;
+    }
+
+    @Override
+    public RerankResult rerank(RerankRequest rerankRequest) {
+        long startTime = System.currentTimeMillis();
+
+        try {
+            // 构建请求
+            ZhipuRerankRequest request = buildRequest(rerankRequest);
+            ZhipuRerankResponse response = executeRequest(request);
+
+            return response.toRerankResult(
+                    rerankRequest.getDocuments().size(),
+                    System.currentTimeMillis() - startTime
+            );
+
+        } catch (Exception e) {
+            log.error("智谱重排序失败: {}", e.getMessage(), e);
+            throw new RuntimeException("重排序服务调用失败: " + e.getMessage(), e);
+        }
+    }
+
+    /**
+     * 构建请求对象
+     */
+    private ZhipuRerankRequest buildRequest(RerankRequest rerankRequest) {
+        return ZhipuRerankRequest.create(
+                chatModelVo.getModelName(),
+                rerankRequest.getQuery(),
+                rerankRequest.getDocuments(),
+                rerankRequest.getTopN(),
+                rerankRequest.getReturnDocuments()
+        );
+    }
+
+    /**
+     * 执行HTTP请求并解析响应
+     */
+    private ZhipuRerankResponse executeRequest(ZhipuRerankRequest request) throws IOException {
+        String jsonBody = request.toJson();
+        RequestBody body = RequestBody.create(jsonBody, MediaType.get("application/json"));
+
+        // 生成智谱认证Token
+        String token = generateToken(chatModelVo.getApiKey());
+
+        // 智谱重排序固定端点路径
+        String url = chatModelVo.getApiHost() + "/api/paas/v4/rerank";
+        Request httpRequest = new Request.Builder()
+                .url(url)
+                .addHeader("Authorization", token)
+                .post(body)
+                .build();
+
+        try (Response response = okHttpClient.newCall(httpRequest).execute()) {
+            if (!response.isSuccessful()) {
+                String err = response.body() != null ? response.body().string() : "无错误信息";
+                throw new IllegalArgumentException("智谱API调用失败: " + response.code() + " - " + err);
+            }
+
+            ResponseBody responseBody = response.body();
+            if (responseBody == null) {
+                throw new IllegalArgumentException("响应体为空");
+            }
+
+            return parseResponse(responseBody.string());
+        }
+    }
+
+    /**
+     * 解析响应
+     */
+    private ZhipuRerankResponse parseResponse(String responseBody) throws IOException {
+        return objectMapper.readValue(responseBody, ZhipuRerankResponse.class);
+    }
+
+    /**
+     * 生成智谱JWT Token
+     */
+    private String generateToken(String apiKey) {
+        try {
+            String[] apiKeyParts = apiKey.split("\\.");
+            String keyId = apiKeyParts[0];
+            String secret = apiKeyParts[1];
+
+            long expireMillis = 1000L * 60 * 30; // 30分钟
+            java.util.Map<String, Object> payload = new java.util.HashMap<>();
+            payload.put("api_key", keyId);
+            payload.put("exp", System.currentTimeMillis() + expireMillis);
+            payload.put("timestamp", System.currentTimeMillis());
+
+            // 使用反射创建 MacAlgorithm（兼容不同版本的 jjwt）
+            MacAlgorithm macAlgorithm;
+            try {
+                Class<?> c = Class.forName("io.jsonwebtoken.impl.security.DefaultMacAlgorithm");
+                Constructor<?> ctor = c.getDeclaredConstructor(String.class, String.class, int.class);
+                ctor.setAccessible(true);
+                macAlgorithm = (MacAlgorithm) ctor.newInstance("HS256", "HmacSHA256", 128);
+            } catch (Exception e) {
+                macAlgorithm = Jwts.SIG.HS256;
+            }
+
+            String token = Jwts.builder()
+                    .header()
+                    .add("alg", "HS256")
+                    .add("sign_type", "SIGN")
+                    .and()
+                    .content(objectMapper.writeValueAsString(payload))
+                    .signWith(new SecretKeySpec(secret.getBytes(StandardCharsets.UTF_8), "HmacSHA256"), macAlgorithm)
+                    .compact();
+
+            return "Bearer " + token;
+        } catch (Exception e) {
+            throw new RuntimeException("生成智谱Token失败: " + e.getMessage(), e);
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/service/retrieval/KnowledgeRetrievalService.java

@@ -0,0 +1,24 @@
+package org.ruoyi.service.retrieval;
+
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
+
+import java.util.List;
+
+/**
+ * 知识库检索服务接口
+ * 整合粗召回（向量检索）和重排序流程
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+public interface KnowledgeRetrievalService {
+
+    /**
+     * 执行知识库检索，返回文本内容
+     * 流程：向量粗召回 -> 重排序（可选） -> 返回结果
+     *
+     * @param queryVectorBo 查询参数
+     * @return 文本内容列表
+     */
+    List<String> retrieveTexts(QueryVectorBo queryVectorBo);
+}

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

@@ -0,0 +1,135 @@
+package org.ruoyi.service.retrieval.impl;
+
+import lombok.RequiredArgsConstructor;
+import lombok.extern.slf4j.Slf4j;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+import org.ruoyi.domain.bo.vector.QueryVectorBo;
+import org.ruoyi.factory.RerankModelFactory;
+import org.ruoyi.service.rerank.RerankModelService;
+import org.ruoyi.service.retrieval.KnowledgeRetrievalService;
+import org.ruoyi.service.vector.VectorStoreService;
+import org.springframework.stereotype.Service;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * 知识库检索服务实现
+ * 整合粗召回（向量检索）和重排序流程
+ *
+ * @author yang
+ * @date 2026-04-19
+ */
+@Slf4j
+@Service
+@RequiredArgsConstructor
+public class KnowledgeRetrievalServiceImpl implements KnowledgeRetrievalService {
+
+    private final VectorStoreService vectorStoreService;
+    private final RerankModelFactory rerankModelFactory;
+
+    /**
+     * 粗召回默认扩大倍数
+     * 如果启用重排序，粗召回会获取更多结果供重排序筛选
+     */
+    private static final int RERANK_EXPANSION_FACTOR = 3;
+
+    @Override
+    public List<String> retrieveTexts(QueryVectorBo queryVectorBo) {
+        log.info("开始知识库检索, kid={}, query={}", queryVectorBo.getKid(), queryVectorBo.getQuery());
+
+        // 1. 粗召回阶段 - 向量检索
+        List<String> coarseResults = coarseRetrieval(queryVectorBo);
+        log.debug("粗召回返回 {} 条结果", coarseResults.size());
+
+        if (coarseResults.isEmpty()) {
+            return coarseResults;
+        }
+
+        // 2. 重排序阶段（可选）
+        if (Boolean.TRUE.equals(queryVectorBo.getEnableRerank()) &&
+                queryVectorBo.getRerankModelName() != null) {
+            return rerank(queryVectorBo, coarseResults);
+        }
+
+        return coarseResults;
+    }
+
+    /**
+     * 粗召回阶段 - 向量检索
+     */
+    private List<String> coarseRetrieval(QueryVectorBo queryVectorBo) {
+        // 如果启用重排序，扩大粗召回数量
+        int originalMaxResults = queryVectorBo.getMaxResults();
+        int expandedResults = originalMaxResults;
+        if (Boolean.TRUE.equals(queryVectorBo.getEnableRerank()) &&
+                queryVectorBo.getRerankModelName() != null) {
+            expandedResults = originalMaxResults * RERANK_EXPANSION_FACTOR;
+            log.debug("启用重排序，粗召回数量从 {} 扩大到 {}", originalMaxResults, expandedResults);
+        }
+
+        // 临时修改查询数量
+        queryVectorBo.setMaxResults(expandedResults);
+        try {
+            return vectorStoreService.getQueryVector(queryVectorBo);
+        } finally {
+            // 恢复原始值
+            queryVectorBo.setMaxResults(originalMaxResults);
+        }
+    }
+
+    /**
+     * 重排序阶段
+     */
+    private List<String> rerank(QueryVectorBo queryVectorBo, List<String> coarseResults) {
+        long startTime = System.currentTimeMillis();
+
+        try {
+            // 1. 通过工厂获取重排序模型
+            RerankModelService rerankModel = rerankModelFactory.createModel(queryVectorBo.getRerankModelName());
+
+            // 2. 构建重排序请求
+            int topN = queryVectorBo.getRerankTopN() != null ?
+                    queryVectorBo.getRerankTopN() : queryVectorBo.getMaxResults();
+
+            RerankRequest rerankRequest = RerankRequest.builder()
+                    .query(queryVectorBo.getQuery())
+                    .documents(coarseResults)
+                    .topN(topN)
+                    .returnDocuments(true)
+                    .build();
+
+            log.info("执行重排序, model={}, documents={}, topN={}",
+                    queryVectorBo.getRerankModelName(), coarseResults.size(), topN);
+
+            // 3. 执行重排序
+            RerankResult rerankResult = rerankModel.rerank(rerankRequest);
+
+            // 4. 转换重排序结果
+            List<String> finalResults = new ArrayList<>();
+            for (RerankResult.RerankDocument doc : rerankResult.getDocuments()) {
+                // 应用分数阈值过滤
+                if (queryVectorBo.getRerankScoreThreshold() != null &&
+                        doc.getRelevanceScore() < queryVectorBo.getRerankScoreThreshold()) {
+                    continue;
+                }
+
+                if (doc.getDocument() != null) {
+                    finalResults.add(doc.getDocument());
+                }
+            }
+
+            long duration = System.currentTimeMillis() - startTime;
+            log.info("重排序完成, 返回 {} 条结果, 耗时 {}ms", finalResults.size(), duration);
+
+            return finalResults;
+
+        } catch (Exception e) {
+            log.error("重排序失败: {}", e.getMessage(), e);
+            // 重排序失败时返回原始粗召回结果（截取到期望数量）
+            int limit = Math.min(queryVectorBo.getMaxResults(), coarseResults.size());
+            return new ArrayList<>(coarseResults.subList(0, limit));
+        }
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/service/rerank/impl/AliBaiLianRerankModelServiceTest.java

@@ -0,0 +1,126 @@
+package org.ruoyi.service.rerank.impl;
+
+import org.junit.jupiter.api.BeforeEach;
+import org.junit.jupiter.api.Test;
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.Arrays;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+/**
+ * 阿里百炼重排序模型测试类
+ * 运行前请设置环境变量 DASHSCOPE_API_KEY 或直接修改 apiKey
+ */
+class AliBaiLianRerankModelServiceTest {
+
+    private AliBaiLianRerankModelService service;
+
+    // 请替换为你的 API Key
+    private static final String API_KEY = System.getenv("DASHSCOPE_API_KEY");
+    private static final String API_HOST = "https://dashscope.aliyuncs.com";
+    private static final String MODEL_NAME = "qwen3-rerank";
+
+    @BeforeEach
+    void setUp() {
+        service = new AliBaiLianRerankModelService();
+    }
+
+    @Test
+    void testConfigure() {
+        ChatModelVo config = createConfig();
+        service.configure(config);
+        assertNotNull(service);
+    }
+
+    @Test
+    void testRerank() {
+        // 跳过测试如果没有配置 API Key
+        if (API_KEY == null || API_KEY.isEmpty()) {
+            System.out.println("跳过测试: 未设置环境变量 DASHSCOPE_API_KEY");
+            return;
+        }
+
+        ChatModelVo config = createConfig();
+        service.configure(config);
+
+        List<String> documents = Arrays.asList(
+                "文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序",
+                "量子计算是计算科学的一个前沿领域",
+                "预训练语言模型的发展给文本排序模型带来了新的进展"
+        );
+
+        RerankRequest request = RerankRequest.builder()
+                .query("什么是文本排序模型")
+                .documents(documents)
+                .topN(2)
+                .returnDocuments(true)
+                .build();
+
+        RerankResult result = service.rerank(request);
+
+        System.out.println("=== 重排序结果 ===");
+        System.out.println("总文档数: " + result.getTotalDocuments());
+        System.out.println("耗时: " + result.getDurationMs() + "ms");
+
+        result.getDocuments().forEach(doc -> {
+            System.out.println("索引: " + doc.getIndex() +
+                    ", 相关性分数: " + doc.getRelevanceScore() +
+                    ", 文档: " + doc.getDocument());
+        });
+
+        assertNotNull(result);
+        assertNotNull(result.getDocuments());
+        assertFalse(result.getDocuments().isEmpty());
+        assertEquals(2, result.getDocuments().size());
+    }
+
+    @Test
+    void testRerankWithFullDocuments() {
+        if (API_KEY == null || API_KEY.isEmpty()) {
+            System.out.println("跳过测试: 未设置环境变量 DASHSCOPE_API_KEY");
+            return;
+        }
+
+        ChatModelVo config = createConfig();
+        service.configure(config);
+
+        List<String> documents = Arrays.asList(
+                "Java是一种广泛使用的编程语言",
+                "Python是人工智能领域最流行的语言",
+                "Go语言由Google开发，适合并发编程"
+        );
+
+        RerankRequest request = RerankRequest.builder()
+                .query("哪种语言适合AI开发")
+                .documents(documents)
+                .build();
+
+        RerankResult result = service.rerank(request);
+
+        System.out.println("=== 重排序结果2 ===");
+        result.getDocuments().forEach(doc -> {
+            System.out.println("索引: " + doc.getIndex() +
+                    ", 分数: " + doc.getRelevanceScore() +
+                    ", 文档: " + doc.getDocument());
+        });
+
+        assertNotNull(result);
+        assertEquals(3, result.getDocuments().size());
+
+        // Python相关文档应该排在前面
+        assertEquals(1, result.getDocuments().get(0).getIndex());
+        assertTrue(result.getDocuments().get(0).getRelevanceScore() > 0.5);
+    }
+
+    private ChatModelVo createConfig() {
+        ChatModelVo config = new ChatModelVo();
+        config.setApiHost(API_HOST);
+        config.setApiKey(API_KEY != null ? API_KEY : "test-api-key");
+        config.setModelName(MODEL_NAME);
+        return config;
+    }
+}

ruoyi-modules/ruoyi-chat/src/test/java/org/ruoyi/service/rerank/impl/AliBaiLianRerankTestMain.java

@@ -0,0 +1,73 @@
+package org.ruoyi.service.rerank.impl;
+
+import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
+import org.ruoyi.domain.bo.rerank.RerankRequest;
+import org.ruoyi.domain.bo.rerank.RerankResult;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * 阿里百炼重排序模型测试 - Main方法直接运行
+ * 运行前请设置 API_KEY
+ */
+public class AliBaiLianRerankTestMain {
+
+    // 请替换为你的 API Key
+    private static final String API_KEY = "sk-your-api-key-here";
+    private static final String API_HOST = "https://dashscope.aliyuncs.com";
+    private static final String MODEL_NAME = "qwen3-rerank";
+
+    public static void main(String[] args) {
+        AliBaiLianRerankModelService service = new AliBaiLianRerankModelService();
+
+        // 配置
+        ChatModelVo config = new ChatModelVo();
+        config.setApiHost(API_HOST);
+        config.setApiKey(API_KEY);
+        config.setModelName(MODEL_NAME);
+        service.configure(config);
+
+        // 测试数据
+        List<String> documents = Arrays.asList(
+                "文本排序模型广泛用于搜索引擎和推荐系统中，它们根据文本相关性对候选文本进行排序",
+                "量子计算是计算科学的一个前沿领域",
+                "预训练语言模型的发展给文本排序模型带来了新的进展"
+        );
+
+        RerankRequest request = RerankRequest.builder()
+                .query("什么是文本排序模型")
+                .documents(documents)
+                .topN(2)
+                .returnDocuments(true)
+                .build();
+
+        System.out.println("=== 开始测试阿里百炼重排序 ===");
+        System.out.println("API Host: " + API_HOST);
+        System.out.println("Model: " + MODEL_NAME);
+        System.out.println("Query: 什么是文本排序模型");
+        System.out.println();
+
+        try {
+            RerankResult result = service.rerank(request);
+
+            System.out.println("=== 重排序结果 ===");
+            System.out.println("总文档数: " + result.getTotalDocuments());
+            System.out.println("耗时: " + result.getDurationMs() + "ms");
+            System.out.println();
+
+            result.getDocuments().forEach(doc -> {
+                System.out.println("索引: " + doc.getIndex());
+                System.out.println("相关性分数: " + doc.getRelevanceScore());
+                System.out.println("文档: " + doc.getDocument());
+                System.out.println("---");
+            });
+
+            System.out.println("=== 测试成功 ===");
+
+        } catch (Exception e) {
+            System.err.println("=== 测试失败 ===");
+            e.printStackTrace();
+        }
+    }
+}