fix(rag): 完善向量路由批量写入与数据生命周期

docs/script/sql/ruoyi-ai.sql

@@ -1131,7 +1131,8 @@ CREATE TABLE `knowledge_attach`  (
                                      `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键',
                                      `knowledge_id` bigint NOT NULL COMMENT '知识库ID',
                                      `oss_id` bigint NULL DEFAULT NULL COMMENT '对象存储ID',
-                                     `doc_id` varchar(11) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '文档ID',
+                                     `doc_id` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '文档ID',
+                                     `file_hash` varchar(64) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '文件SHA-256摘要',
                                      `name` varchar(500) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '附件名称',
                                      `type` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '附件类型',
                                      `create_dept` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '部门',
@@ -1143,7 +1144,8 @@ CREATE TABLE `knowledge_attach`  (
                                      `tenant_id` bigint NOT NULL DEFAULT 0 COMMENT '租户Id',
                                      `status` tinyint NULL DEFAULT 0 COMMENT '解析状态: 0待解析, 1解析中, 2已解析, 3解析失败',
                                      PRIMARY KEY (`id`) USING BTREE,
-                                     UNIQUE INDEX `idx_kname`(`knowledge_id` ASC, `name` ASC) USING BTREE
+                                     UNIQUE INDEX `idx_kname`(`knowledge_id` ASC, `name` ASC) USING BTREE,
+                                     UNIQUE INDEX `uk_knowledge_file_hash`(`knowledge_id`, `file_hash`) USING BTREE
 ) ENGINE = InnoDB AUTO_INCREMENT = 2033199209203183619 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '知识库附件' ROW_FORMAT = DYNAMIC;
 
 -- ----------------------------
@@ -1156,8 +1158,9 @@ CREATE TABLE `knowledge_attach`  (
 DROP TABLE IF EXISTS `knowledge_fragment`;
 CREATE TABLE `knowledge_fragment`  (
                                        `id` bigint NOT NULL AUTO_INCREMENT COMMENT '主键',
+                                       `fid` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '向量库片段ID',
                                        `idx` int NOT NULL COMMENT '片段索引下标',
-                                       `doc_id` varchar(11) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '文档ID',
+                                       `doc_id` varchar(32) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '文档ID',
                                        `content` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NOT NULL COMMENT '文档内容',
                                        `create_dept` varchar(255) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '部门',
                                        `create_by` varchar(50) CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL DEFAULT NULL COMMENT '创建人',
@@ -1168,6 +1171,9 @@ CREATE TABLE `knowledge_fragment`  (
                                        `tenant_id` bigint NOT NULL DEFAULT 0 COMMENT '租户Id',
                                        `knowledge_id` bigint NULL DEFAULT NULL COMMENT '知识库ID',
                                        PRIMARY KEY (`id`) USING BTREE,
+                                       UNIQUE INDEX `uk_fid`(`fid`) USING BTREE,
+                                       INDEX `idx_doc_id`(`doc_id`) USING BTREE,
+                                       INDEX `idx_knowledge_id`(`knowledge_id`) USING BTREE,
                                        FULLTEXT INDEX `ft_content`(`content`) WITH PARSER `ngram`
 ) ENGINE = InnoDB AUTO_INCREMENT = 2033199209131880451 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '知识片段' ROW_FORMAT = DYNAMIC;
 
@@ -1206,7 +1212,9 @@ CREATE TABLE `knowledge_info`  (
                                    `enable_hybrid` tinyint(1) NULL DEFAULT 0 COMMENT '是否启用混合检索',
                                    `hybrid_alpha` double NULL DEFAULT 0.5 COMMENT '混合检索权重比例 (0.0=纯向量, 1.0=纯关键词)',
                                    `system_prompt` text CHARACTER SET utf8mb4 COLLATE utf8mb4_0900_ai_ci NULL COMMENT '系统提示词',
-                                   PRIMARY KEY (`id`) USING BTREE
+                                   PRIMARY KEY (`id`) USING BTREE,
+                                   INDEX `idx_tenant_user` (`tenant_id`, `user_id`) USING BTREE,
+                                   INDEX `idx_tenant_share` (`tenant_id`, `share`) USING BTREE
 ) ENGINE = InnoDB AUTO_INCREMENT = 2033198818050781187 CHARACTER SET = utf8mb4 COLLATE = utf8mb4_0900_ai_ci COMMENT = '知识库' ROW_FORMAT = DYNAMIC;
 
 -- ----------------------------

docs/script/sql/update/2026-07-20-knowledge-fragment-fid.sql

@@ -0,0 +1,44 @@
+-- RAG metadata migration (MySQL 8). Safe to execute repeatedly.
+ALTER TABLE `knowledge_attach`
+    ADD COLUMN IF NOT EXISTS `file_hash` varchar(64) NULL DEFAULT NULL COMMENT '文件SHA-256摘要' AFTER `doc_id`,
+    MODIFY COLUMN `doc_id` varchar(32) NULL DEFAULT NULL COMMENT '文档ID';
+
+SET @add_file_hash = IF(EXISTS(
+    SELECT 1 FROM information_schema.statistics WHERE table_schema = DATABASE()
+    AND table_name = 'knowledge_attach' AND index_name = 'uk_knowledge_file_hash'),
+    'SELECT 1', 'ALTER TABLE `knowledge_attach` ADD UNIQUE INDEX `uk_knowledge_file_hash` (`knowledge_id`, `file_hash`)');
+PREPARE stmt FROM @add_file_hash; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+ALTER TABLE `knowledge_fragment`
+    ADD COLUMN IF NOT EXISTS `fid` varchar(32) NULL DEFAULT NULL COMMENT '向量库片段ID' AFTER `id`,
+    MODIFY COLUMN `doc_id` varchar(32) NULL DEFAULT NULL COMMENT '文档ID';
+
+UPDATE `knowledge_fragment`
+SET `fid` = LOWER(MD5(CONCAT('knowledge_fragment:', `id`)))
+WHERE `fid` IS NULL OR `fid` = '';
+
+SET @drop_idx_fid = IF(EXISTS(
+    SELECT 1 FROM information_schema.statistics WHERE table_schema = DATABASE()
+    AND table_name = 'knowledge_fragment' AND index_name = 'idx_fid'),
+    'ALTER TABLE `knowledge_fragment` DROP INDEX `idx_fid`', 'SELECT 1');
+PREPARE stmt FROM @drop_idx_fid; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @add_uk_fid = IF(EXISTS(
+    SELECT 1 FROM information_schema.statistics WHERE table_schema = DATABASE()
+    AND table_name = 'knowledge_fragment' AND index_name = 'uk_fid'),
+    'SELECT 1', 'ALTER TABLE `knowledge_fragment` ADD UNIQUE INDEX `uk_fid` (`fid`)');
+PREPARE stmt FROM @add_uk_fid; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+ALTER TABLE `knowledge_fragment` MODIFY COLUMN `fid` varchar(32) NOT NULL COMMENT '向量库片段ID';
+
+SET @add_tenant_user = IF(EXISTS(
+    SELECT 1 FROM information_schema.statistics WHERE table_schema = DATABASE()
+    AND table_name = 'knowledge_info' AND index_name = 'idx_tenant_user'),
+    'SELECT 1', 'ALTER TABLE `knowledge_info` ADD INDEX `idx_tenant_user` (`tenant_id`, `user_id`)');
+PREPARE stmt FROM @add_tenant_user; EXECUTE stmt; DEALLOCATE PREPARE stmt;
+
+SET @add_tenant_share = IF(EXISTS(
+    SELECT 1 FROM information_schema.statistics WHERE table_schema = DATABASE()
+    AND table_name = 'knowledge_info' AND index_name = 'idx_tenant_share'),
+    'SELECT 1', 'ALTER TABLE `knowledge_info` ADD INDEX `idx_tenant_share` (`tenant_id`, `share`)');
+PREPARE stmt FROM @add_tenant_share; EXECUTE stmt; DEALLOCATE PREPARE stmt;

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

@@ -106,7 +106,10 @@ public class KnowledgeAttachController extends BaseController {
 
     /**
      * 上传知识库附件
+     * 注意：multipart 上传不能加 @RepeatSubmit（其参数序列化不支持 MultipartFile）
      */
+    @SaCheckPermission("system:attach:add")
+    @Log(title = "知识库附件", businessType = BusinessType.INSERT)
     @PostMapping(value = "/upload")
     public R<String> upload(KnowledgeInfoUploadBo bo){
         knowledgeAttachService.upload(bo);
@@ -118,7 +121,10 @@ public class KnowledgeAttachController extends BaseController {
      *
      * @param id 附件ID
      */
+    @SaCheckPermission("system:attach:edit")
+    @Log(title = "知识库附件", businessType = BusinessType.UPDATE)
     @PostMapping("/parse/{id}")
+    @RepeatSubmit()
     public R<Void> parse(@PathVariable Long id) {
         knowledgeAttachService.parse(id);
         return R.ok();

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

@@ -107,7 +107,9 @@ public class KnowledgeFragmentController extends BaseController {
     /**
      * 检索测试
      */
+    @SaCheckPermission("system:fragment:list")
     @PostMapping("/retrieval")
+    @RepeatSubmit()
     public R<List<KnowledgeRetrievalVo>> retrieval(@RequestBody KnowledgeFragmentBo bo) {
         return R.ok(knowledgeFragmentService.retrieval(bo));
     }

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

@@ -37,6 +37,9 @@ public class KnowledgeAttach extends BaseEntity {
      */
     private String docId;
 
+    /** SHA-256 content digest used for upload idempotency. */
+    private String fileHash;
+
     /**
      * 附件名称
      */

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

@@ -27,6 +27,11 @@ public class KnowledgeFragment extends BaseEntity {
     @TableId(value = "id")
     private Long id;
 
+    /**
+     * 向量库片段ID（与向量库中的 fid 元数据对应，用于向量定位与混合检索融合）
+     */
+    private String fid;
+
     /**
      * 文档ID-用于关联文本块信息
      */

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

@@ -30,6 +30,11 @@ public class KnowledgeFragmentVo implements Serializable {
     @ExcelProperty(value = "主键")
     private Long id;
 
+    /**
+     * 向量库片段ID
+     */
+    private String fid;
+
     /**
      * 文档ID-用于关联文本块信息
      */

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

@@ -10,6 +10,7 @@ import org.ruoyi.service.vector.impl.MilvusVectorStoreStrategy;
 import org.ruoyi.service.vector.impl.QdrantVectorStoreStrategy;
 import org.ruoyi.service.vector.impl.WeaviateVectorStoreStrategy;
 import org.springframework.stereotype.Component;
+import org.ruoyi.common.core.exception.ServiceException;
 
 import java.util.HashMap;
 import java.util.Map;
@@ -45,14 +46,20 @@ public class VectorStoreStrategyFactory {
      * 获取当前配置的向量库策略
      */
     public VectorStoreService getStrategy() {
-        String vectorStoreType = vectorStoreProperties.getType();
+        return getStrategy(null);
+    }
+
+    public VectorStoreService getStrategy(String requestedType) {
+        String vectorStoreType = requestedType;
+        if (vectorStoreType == null || vectorStoreType.trim().isEmpty()) {
+            vectorStoreType = vectorStoreProperties.getType();
+        }
         if (vectorStoreType == null || vectorStoreType.trim().isEmpty()) {
             vectorStoreType = "weaviate"; // 默认使用weaviate
         }
         VectorStoreService strategy = strategies.get(vectorStoreType.toLowerCase());
         if (strategy == null) {
-            log.warn("未找到向量库策略: {}, 使用默认策略: weaviate", vectorStoreType);
+            throw new ServiceException("不支持的向量库类型: " + vectorStoreType);
-            strategy = strategies.get("weaviate");
         }
         log.debug("使用向量库策略: {}", vectorStoreType);
         return strategy;

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/mapper/knowledge/KnowledgeAttachMapper.java

@@ -6,6 +6,8 @@ import org.apache.ibatis.annotations.Select;
 import org.ruoyi.domain.entity.knowledge.KnowledgeAttach;
 import org.ruoyi.domain.vo.knowledge.KnowledgeAttachVo;
 import org.ruoyi.common.mybatis.core.mapper.BaseMapperPlus;
+import java.util.List;
+import java.util.Map;
 
 /**
  * 知识库附件Mapper接口
@@ -21,4 +23,10 @@ public interface KnowledgeAttachMapper extends BaseMapperPlus<KnowledgeAttach, K
      */
     @Select("SELECT COUNT(*) FROM knowledge_attach WHERE knowledge_id = #{knowledgeId}")
     int countByKnowledgeId(@Param("knowledgeId") Long knowledgeId);
+
+    @Select("<script>SELECT knowledge_id AS knowledgeId, COUNT(*) AS documentCount " +
+            "FROM knowledge_attach WHERE knowledge_id IN " +
+            "<foreach collection='knowledgeIds' item='id' open='(' separator=',' close=')'>#{id}</foreach> " +
+            "GROUP BY knowledge_id</script>")
+    List<Map<String, Object>> countByKnowledgeIds(@Param("knowledgeIds") List<Long> knowledgeIds);
 }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/mapper/knowledge/KnowledgeFragmentMapper.java

@@ -34,7 +34,7 @@ public interface KnowledgeFragmentMapper extends BaseMapperPlus<KnowledgeFragmen
             "</script>")
     List<DocFragmentCountVo> selectFragmentCountByDocIds(@Param("docIds") List<String> docIds);
     @Select("<script>" +
-            "SELECT id, doc_id AS docId, content, idx, knowledge_id AS knowledgeId " +
+            "SELECT id, fid, doc_id AS docId, content, idx, knowledge_id AS knowledgeId " +
             "FROM knowledge_fragment " +
             "WHERE knowledge_id = #{knowledgeId} " +
             "AND MATCH (content) AGAINST (#{query} IN NATURAL LANGUAGE MODE) " +

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

@@ -2,6 +2,7 @@ package org.ruoyi.service.knowledge.impl;
 
 import cn.hutool.core.collection.CollUtil;
 import cn.hutool.core.util.RandomUtil;
+import cn.hutool.crypto.digest.DigestUtil;
 import com.baomidou.mybatisplus.core.conditions.query.LambdaQueryWrapper;
 import com.baomidou.mybatisplus.core.toolkit.Wrappers;
 import com.baomidou.mybatisplus.extension.plugins.pagination.Page;
@@ -11,6 +12,7 @@ import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
 import org.ruoyi.common.chat.service.chat.IChatModelService;
 import org.ruoyi.enums.KnowledgeAttachStatus;
 import org.ruoyi.common.core.domain.dto.OssDTO;
+import org.ruoyi.common.core.exception.ServiceException;
 import org.ruoyi.common.core.service.OssService;
 import org.ruoyi.common.core.utils.MapstructUtils;
 import org.ruoyi.common.core.utils.SpringUtils;
@@ -32,6 +34,7 @@ import org.ruoyi.service.knowledge.IKnowledgeAttachService;
 import org.ruoyi.service.knowledge.IKnowledgeInfoService;
 import org.ruoyi.service.knowledge.ResourceLoader;
 import org.ruoyi.service.vector.VectorStoreService;
+import org.ruoyi.service.retrieval.KnowledgeRetrievalService;
 import org.springframework.scheduling.annotation.Async;
 import org.springframework.stereotype.Service;
 import org.springframework.web.multipart.MultipartFile;
@@ -60,6 +63,7 @@ public class KnowledgeAttachServiceImpl implements IKnowledgeAttachService {
     private final ResourceLoaderFactory resourceLoaderFactory;
     private final VectorStoreService vectorStoreService;
     private final OssService ossService;
+    private final KnowledgeRetrievalService knowledgeRetrievalService;
 
     @Override
     public KnowledgeAttachVo queryById(Long id) {
@@ -126,18 +130,44 @@ public class KnowledgeAttachServiceImpl implements IKnowledgeAttachService {
 
     @Override
     public Boolean deleteWithValidByIds(Collection<Long> ids, Boolean isValid) {
+        // 删除附件前，同步清理其片段记录与向量库中的向量
+        List<KnowledgeAttach> attaches = baseMapper.selectByIds(ids);
+        for (KnowledgeAttach attach : attaches) {
+            String docId = attach.getDocId();
+            String kid = String.valueOf(attach.getKnowledgeId());
+            vectorStoreService.removeByDocId(docId, kid);
+            knowledgeFragmentMapper.delete(
+                Wrappers.<KnowledgeFragment>lambdaQuery().eq(KnowledgeFragment::getDocId, docId));
+            if (attach.getOssId() != null) {
+                ossService.deleteFile(attach.getOssId());
+            }
+            knowledgeRetrievalService.invalidateKnowledge(kid);
+        }
         return baseMapper.deleteByIds(ids) > 0;
     }
 
     @Override
     public void upload(KnowledgeInfoUploadBo bo) {
         MultipartFile file = bo.getFile();
+        final String fileHash;
+        try (InputStream input = file.getInputStream()) {
+            fileHash = DigestUtil.sha256Hex(input);
+        } catch (Exception e) {
+            throw new ServiceException("计算文件摘要失败", e);
+        }
+        boolean duplicate = baseMapper.exists(Wrappers.<KnowledgeAttach>lambdaQuery()
+            .eq(KnowledgeAttach::getKnowledgeId, bo.getKnowledgeId())
+            .eq(KnowledgeAttach::getFileHash, fileHash));
+        if (duplicate) {
+            throw new ServiceException("该文件已上传，请勿重复提交");
+        }
         OssDTO ossDTO = ossService.uploadFile(file);
 
         KnowledgeAttach knowledgeAttach = new KnowledgeAttach();
         knowledgeAttach.setKnowledgeId(bo.getKnowledgeId());
         knowledgeAttach.setOssId(ossDTO.getOssId());
         knowledgeAttach.setDocId(RandomUtil.randomString(10));
+        knowledgeAttach.setFileHash(fileHash);
         knowledgeAttach.setName(ossDTO.getOriginalName());
         knowledgeAttach.setType(ossDTO.getFileSuffix());
         knowledgeAttach.setStatus(KnowledgeAttachStatus.WAITING.getCode()); // 待解析
@@ -166,6 +196,8 @@ public class KnowledgeAttachServiceImpl implements IKnowledgeAttachService {
 
             Long knowledgeId = attach.getKnowledgeId();
             String docId = attach.getDocId();
+            List<KnowledgeFragment> oldFragments = knowledgeFragmentMapper.selectList(
+                    Wrappers.<KnowledgeFragment>lambdaQuery().eq(KnowledgeFragment::getDocId, docId));
 
             // 获取文件信息并下载
             List<OssDTO> ossDTOs = ossService.selectByIds(String.valueOf(attach.getOssId()));
@@ -180,25 +212,25 @@ public class KnowledgeAttachServiceImpl implements IKnowledgeAttachService {
             }
             List<String> chunkList = resourceLoader.getChunkList(content, String.valueOf(knowledgeId));
 
-            List<String> fids = new ArrayList<>();
+            if (CollUtil.isEmpty(chunkList)) {
-            List<KnowledgeFragment> knowledgeFragmentList = new ArrayList<>();
+                throw new RuntimeException("文档分片结果为空，请检查文档内容或分片器是否支持该文件类型");
-            if (CollUtil.isNotEmpty(chunkList)) {
-                for (int i = 0; i < chunkList.size(); i++) {
-                    String fid = RandomUtil.randomString(10);
-                    fids.add(fid);
-                    KnowledgeFragment knowledgeFragment = new KnowledgeFragment();
-                    knowledgeFragment.setKnowledgeId(knowledgeId);
-                    knowledgeFragment.setDocId(docId);
-                    knowledgeFragment.setIdx(i);
-                    knowledgeFragment.setContent(chunkList.get(i));
-                    knowledgeFragment.setCreateTime(new Date());
-                    knowledgeFragmentList.add(knowledgeFragment);
-                }
-                knowledgeFragmentMapper.delete(Wrappers.<KnowledgeFragment>lambdaQuery().eq(KnowledgeFragment::getDocId, docId));
-                knowledgeFragmentMapper.insertBatch(knowledgeFragmentList);
-                log.info("文档切片并入库完成，共计 {} 个片段。id: {}", chunkList.size(), id);
             }
 
+            // 重新解析前先清理旧的向量数据，避免向量重复累积
+            List<String> fids = new ArrayList<>();
+            List<KnowledgeFragment> knowledgeFragmentList = new ArrayList<>();
+            for (int i = 0; i < chunkList.size(); i++) {
+                String fid = RandomUtil.randomString(10);
+                fids.add(fid);
+                KnowledgeFragment knowledgeFragment = new KnowledgeFragment();
+                knowledgeFragment.setKnowledgeId(knowledgeId);
+                knowledgeFragment.setDocId(docId);
+                knowledgeFragment.setFid(fid);
+                knowledgeFragment.setIdx(i);
+                knowledgeFragment.setContent(chunkList.get(i));
+                knowledgeFragment.setCreateTime(new Date());
+                knowledgeFragmentList.add(knowledgeFragment);
+            }
             KnowledgeInfoVo knowledgeInfoVo = knowledgeInfoService.queryById(knowledgeId);
             ChatModelVo chatModelVo = chatModelService.selectModelByName(knowledgeInfoVo.getEmbeddingModel());
 
@@ -211,7 +243,27 @@ public class KnowledgeAttachServiceImpl implements IKnowledgeAttachService {
             storeEmbeddingBo.setEmbeddingModelName(knowledgeInfoVo.getEmbeddingModel());
             storeEmbeddingBo.setApiKey(chatModelVo.getApiKey());
             storeEmbeddingBo.setBaseUrl(chatModelVo.getApiHost());
-            vectorStoreService.storeEmbeddings(storeEmbeddingBo);
+            try {
+                vectorStoreService.storeEmbeddings(storeEmbeddingBo);
+                for (KnowledgeFragment old : oldFragments) {
+                    if (StringUtils.isNotBlank(old.getFid())) {
+                        vectorStoreService.removeByFid(old.getFid(), String.valueOf(knowledgeId));
+                    }
+                }
+            } catch (Exception vectorError) {
+                for (String newFid : fids) {
+                    try {
+                        vectorStoreService.removeByFid(newFid, String.valueOf(knowledgeId));
+                    } catch (Exception cleanupError) {
+                        log.error("补偿删除新向量失败, kid={}, fid={}", knowledgeId, newFid, cleanupError);
+                    }
+                }
+                throw vectorError;
+            }
+
+            knowledgeFragmentMapper.delete(Wrappers.<KnowledgeFragment>lambdaQuery().eq(KnowledgeFragment::getDocId, docId));
+            knowledgeFragmentMapper.insertBatch(knowledgeFragmentList);
+            knowledgeRetrievalService.invalidateKnowledge(String.valueOf(knowledgeId));
 
             attach.setStatus(KnowledgeAttachStatus.COMPLETED.getCode()); // 已完成
             baseMapper.updateById(attach);

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

@@ -40,6 +40,7 @@ public class KnowledgeFragmentServiceImpl implements IKnowledgeFragmentService {
     private final IKnowledgeInfoService knowledgeInfoService;
     private final IChatModelService chatModelService;
     private final KnowledgeRetrievalService knowledgeRetrievalService;
+    private final org.ruoyi.service.vector.VectorStoreService vectorStoreService;
 
     /**
      * 查询知识片段
@@ -114,7 +115,11 @@ public class KnowledgeFragmentServiceImpl implements IKnowledgeFragmentService {
     public Boolean updateByBo(KnowledgeFragmentBo bo) {
         KnowledgeFragment update = MapstructUtils.convert(bo, KnowledgeFragment.class);
         validEntityBeforeSave(update);
-        return baseMapper.updateById(update) > 0;
+        boolean updated = baseMapper.updateById(update) > 0;
+        if (updated && update.getKnowledgeId() != null) {
+            knowledgeRetrievalService.invalidateKnowledge(String.valueOf(update.getKnowledgeId()));
+        }
+        return updated;
     }
 
     /**
@@ -136,6 +141,14 @@ public class KnowledgeFragmentServiceImpl implements IKnowledgeFragmentService {
         if(isValid){
             //TODO 做一些业务上的校验,判断是否需要校验
         }
+        // 删除 DB 片段前，同步删除向量库中对应向量
+        List<KnowledgeFragment> fragments = baseMapper.selectByIds(ids);
+        for (KnowledgeFragment fragment : fragments) {
+            if (StringUtils.isNotBlank(fragment.getFid()) && fragment.getKnowledgeId() != null) {
+                vectorStoreService.removeByFid(fragment.getFid(), String.valueOf(fragment.getKnowledgeId()));
+                knowledgeRetrievalService.invalidateKnowledge(String.valueOf(fragment.getKnowledgeId()));
+            }
+        }
         return baseMapper.deleteByIds(ids) > 0;
     }
 

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

@@ -16,6 +16,9 @@ import org.ruoyi.mapper.knowledge.KnowledgeAttachMapper;
 import org.ruoyi.mapper.knowledge.KnowledgeInfoMapper;
 import org.ruoyi.service.knowledge.IKnowledgeInfoService;
 import org.springframework.stereotype.Service;
+import org.springframework.transaction.annotation.Transactional;
+import org.ruoyi.service.retrieval.KnowledgeRetrievalService;
+import org.ruoyi.common.core.service.OssService;
 
 import java.util.List;
 import java.util.Map;
@@ -36,6 +39,12 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
 
     private final KnowledgeAttachMapper knowledgeAttachMapper;
 
+    private final org.ruoyi.mapper.knowledge.KnowledgeFragmentMapper knowledgeFragmentMapper;
+
+    private final org.ruoyi.service.vector.VectorStoreService vectorStoreService;
+    private final KnowledgeRetrievalService knowledgeRetrievalService;
+    private final OssService ossService;
+
     /**
      * 查询知识库
      *
@@ -97,10 +106,14 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
      */
     private void fillDocumentCount(List<KnowledgeInfoVo> records) {
         if (records == null || records.isEmpty()) return;
-        for (KnowledgeInfoVo vo : records) {
+        List<Long> ids = records.stream().map(KnowledgeInfoVo::getId).toList();
-            int count = knowledgeAttachMapper.countByKnowledgeId(vo.getId());
+        Map<Long, Integer> counts = new java.util.HashMap<>();
-            vo.setDocumentCount(count);
+        for (Map<String, Object> row : knowledgeAttachMapper.countByKnowledgeIds(ids)) {
+            Number kid = (Number) (row.get("knowledgeId") != null ? row.get("knowledgeId") : row.get("knowledgeid"));
+            Number count = (Number) (row.get("documentCount") != null ? row.get("documentCount") : row.get("documentcount"));
+            if (kid != null && count != null) counts.put(kid.longValue(), count.intValue());
         }
+        records.forEach(vo -> vo.setDocumentCount(counts.getOrDefault(vo.getId(), 0)));
     }
 
     /**
@@ -130,7 +143,9 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
     public Boolean updateByBo(KnowledgeInfoBo bo) {
         KnowledgeInfo update = MapstructUtils.convert(bo, KnowledgeInfo.class);
         validEntityBeforeSave(update);
-        return baseMapper.updateById(update) > 0;
+        boolean updated = baseMapper.updateById(update) > 0;
+        if (updated) knowledgeRetrievalService.invalidateKnowledge(String.valueOf(bo.getId()));
+        return updated;
     }
 
     /**
@@ -148,10 +163,33 @@ public class KnowledgeInfoServiceImpl implements IKnowledgeInfoService {
      * @return 是否删除成功
      */
     @Override
+    @Transactional(rollbackFor = Exception.class)
     public Boolean deleteWithValidByIds(Collection<Long> ids, Boolean isValid) {
         if(isValid){
             //TODO 做一些业务上的校验,判断是否需要校验
         }
+        for (Long kid : ids) {
+            KnowledgeInfo info = baseMapper.selectById(kid);
+            // 1. 删除向量库中该知识库的所有向量（按文档逐个清理，三种向量库行为一致）
+            List<org.ruoyi.domain.entity.knowledge.KnowledgeAttach> attaches = knowledgeAttachMapper.selectList(
+                Wrappers.lambdaQuery(org.ruoyi.domain.entity.knowledge.KnowledgeAttach.class)
+                    .eq(org.ruoyi.domain.entity.knowledge.KnowledgeAttach::getKnowledgeId, kid));
+            vectorStoreService.removeById(String.valueOf(kid), info == null ? null : info.getVectorModel());
+            List<Long> ossIds = attaches.stream()
+                    .map(org.ruoyi.domain.entity.knowledge.KnowledgeAttach::getOssId)
+                    .filter(java.util.Objects::nonNull).toList();
+            if (!ossIds.isEmpty()) {
+                for (Long ossId : ossIds) {
+                    ossService.deleteFile(ossId);
+                }
+            }
+            // 2. 删除该知识库下的附件与片段记录
+            knowledgeAttachMapper.delete(Wrappers.lambdaQuery(org.ruoyi.domain.entity.knowledge.KnowledgeAttach.class)
+                .eq(org.ruoyi.domain.entity.knowledge.KnowledgeAttach::getKnowledgeId, kid));
+            knowledgeFragmentMapper.delete(Wrappers.lambdaQuery(org.ruoyi.domain.entity.knowledge.KnowledgeFragment.class)
+                .eq(org.ruoyi.domain.entity.knowledge.KnowledgeFragment::getKnowledgeId, kid));
+            knowledgeRetrievalService.invalidateKnowledge(String.valueOf(kid));
+        }
         return baseMapper.deleteByIds(ids) > 0;
     }
 }

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

@@ -12,6 +12,9 @@ import dev.langchain4j.store.embedding.filter.MetadataFilterBuilder;
 import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
 import io.milvus.param.IndexType;
 import io.milvus.param.MetricType;
+import io.milvus.v2.client.ConnectConfig;
+import io.milvus.v2.client.MilvusClientV2;
+import io.milvus.v2.service.collection.request.DropCollectionReq;
 import lombok.SneakyThrows;
 import lombok.extern.slf4j.Slf4j;
 import org.ruoyi.common.chat.domain.vo.chat.ChatModelVo;
@@ -37,13 +40,16 @@ import java.util.stream.IntStream;
 public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     private final KnowledgeAttachMapper knowledgeAttachMapper;
+    private final org.ruoyi.mapper.knowledge.KnowledgeInfoMapper knowledgeInfoMapper;
 
     public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties,
                                      IChatModelService chatModelService,
                                      EmbeddingModelFactory embeddingModelFactory,
-                                     KnowledgeAttachMapper knowledgeAttachMapper) {
+                                     KnowledgeAttachMapper knowledgeAttachMapper,
+                                     org.ruoyi.mapper.knowledge.KnowledgeInfoMapper knowledgeInfoMapper) {
         super(vectorStoreProperties, embeddingModelFactory, chatModelService);
         this.knowledgeAttachMapper = knowledgeAttachMapper;
+        this.knowledgeInfoMapper = knowledgeInfoMapper;
     }
 
     // 缓存不同集合与 autoFlush 配置的 Milvus 连接
@@ -100,9 +106,11 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         long startTime = System.currentTimeMillis();
 
         // 复用连接，写入场景使用 autoFlush=false 以提升批量插入性能
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, dimension, false);
+        // addAll is already batched; flush before returning so newly parsed fragments are immediately searchable.
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, dimension, true);
 
-        IntStream.range(0, chunkList.size()).forEach(i -> {
+        List<TextSegment> segments = new ArrayList<>(chunkList.size());
+        for (int i = 0; i < chunkList.size(); i++) {
             String text = chunkList.get(i);
             String fid = fidList.get(i);
             Metadata metadata = new Metadata();
@@ -110,13 +118,18 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
             metadata.put("kid", kid);
             metadata.put("docId", docId);
 
-            TextSegment textSegment = TextSegment.from(text, metadata);
+            segments.add(TextSegment.from(text, metadata));
-            Embedding embedding = embeddingModel.embed(text).content();
+        }
+        List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
+        if (embeddings.size() != segments.size()) {
+            throw new org.ruoyi.common.core.exception.ServiceException("Embedding 返回数量与分片数量不一致");
+        }
+        for (Embedding embedding : embeddings) {
             // 单位化处理
             float[] vector = embedding.vector();
             normalize(vector);
-            embeddingStore.add(Embedding.from(vector), textSegment);
+        }
-        });
+        embeddingStore.addAll(embeddings, segments);
         long endTime = System.currentTimeMillis();
         log.info("Milvus向量存储完成消耗时间：{}秒", (endTime - startTime) / 1000);
     }
@@ -174,6 +187,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
             if (segment == null) continue;
 
             String docId = segment.metadata().getString("docId");
+            String fid = segment.metadata().getString("fid");
             String sourceName = "未知来源";
             if (docId != null) {
                 KnowledgeAttach attach = knowledgeAttachMapper.selectOne(new LambdaQueryWrapper<KnowledgeAttach>()
@@ -188,6 +202,8 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
             double score = match.score();
 
             resultList.add(org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo.builder()
+                    .id(fid)
+                    .docId(docId)
                     .content(segment.text())
                     .score(score)
                     .sourceName(sourceName)
@@ -200,16 +216,40 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     @SneakyThrows
     public void removeById(String id, String modelName) {
         // 注意：此处原逻辑使用 collectionname + id，保持现状
-        int dimension = getModelDimension(modelName);
+        String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + id;
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(vectorStoreProperties.getMilvus().getCollectionname() + id, dimension, false);
+        MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
-        embeddingStore.remove(id);
+            .uri(vectorStoreProperties.getMilvus().getUrl()).build());
+        try {
+            client.dropCollection(DropCollectionReq.builder().collectionName(collectionName).build());
+            storeCache.keySet().removeIf(key -> key.startsWith(collectionName));
+            log.info("Milvus collection deleted: {}", collectionName);
+        } catch (Exception e) {
+            log.error("Milvus collection delete failed: {}", collectionName, e);
+            throw new org.ruoyi.common.core.exception.ServiceException("Milvus向量集合删除失败");
+        } finally {
+            client.close();
+        }
+    }
+
+    /**
+     * 根据知识库ID解析其 embedding 模型维度，失败时回退默认 1024
+     */
+    private int getDimensionByKid(String kid) {
+        try {
+            org.ruoyi.domain.entity.knowledge.KnowledgeInfo info = knowledgeInfoMapper.selectById(Long.parseLong(kid));
+            if (info != null && info.getEmbeddingModel() != null) {
+                return getModelDimension(info.getEmbeddingModel());
+            }
+        } catch (Exception e) {
+            log.warn("根据 kid={} 解析向量维度失败，使用默认 1024: {}", kid, e.getMessage());
+        }
+        return 1024;
     }
 
     @Override
     public void removeByDocId(String docId, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        // 使用默认维度，因为删除操作不需要精确的维度信息
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, getDimensionByKid(kid), true);
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, 1024, false);
         Filter filter = MetadataFilterBuilder.metadataKey("docId").isEqualTo(docId);
         embeddingStore.removeAll(filter);
         log.info("Milvus成功删除 docId={} 的所有向量数据", docId);
@@ -218,8 +258,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     @Override
     public void removeByFid(String fid, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        // 使用默认维度，因为删除操作不需要精确的维度信息
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, getDimensionByKid(kid), true);
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, 1024, false);
         Filter filter = MetadataFilterBuilder.metadataKey("fid").isEqualTo(fid);
         embeddingStore.removeAll(filter);
         log.info("Milvus成功删除 fid={} 的所有向量数据", fid);

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

@@ -129,20 +129,26 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
         log.info("Qdrant向量存储条数记录: {}", chunkList.size());
         long startTime = System.currentTimeMillis();
 
-        IntStream.range(0, chunkList.size()).forEach(i -> {
+        List<TextSegment> segments = new ArrayList<>(chunkList.size());
+        for (int i = 0; i < chunkList.size(); i++) {
             String text = chunkList.get(i);
             String fid = fidList.get(i);
             Metadata metadata = new Metadata();
             metadata.put(METADATA_FID_KEY, fid);
             metadata.put(METADATA_KID_KEY, kid);
             metadata.put(METADATA_DOC_ID_KEY, docId);
-            TextSegment textSegment = TextSegment.from(text, metadata);
+            segments.add(TextSegment.from(text, metadata));
-            Embedding embedding = embeddingModel.embed(text).content();
+        }
+        List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
+        if (embeddings.size() != segments.size()) {
+            throw new ServiceException("Embedding 返回数量与分片数量不一致");
+        }
+        for (Embedding embedding : embeddings) {
             // 单位化处理
             float[] vector = embedding.vector();
             normalize(vector);
-            embeddingStore.add(Embedding.from(vector), textSegment);
+        }
-        });
+        embeddingStore.addAll(embeddings, segments);
 
         long endTime = System.currentTimeMillis();
         log.info("Qdrant向量存储完成消耗时间：{}秒", (endTime - startTime) / 1000);
@@ -228,6 +234,12 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
                     docId = docIdValue.getStringValue();
                 }
 
+                String fid = null;
+                JsonWithInt.Value fidValue = point.getPayloadMap().get(METADATA_FID_KEY);
+                if (fidValue != null && fidValue.hasStringValue()) {
+                    fid = fidValue.getStringValue();
+                }
+
                 String sourceName = "未知来源";
                 if (docId != null) {
                     KnowledgeAttach attach = knowledgeAttachMapper.selectOne(new LambdaQueryWrapper<KnowledgeAttach>()
@@ -239,6 +251,8 @@ public class QdrantVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 }
 
                 resultList.add(org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo.builder()
+                        .id(fid)
+                        .docId(docId)
                         .content(content)
                         .score((double) point.getScore())
                         .sourceName(sourceName)

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

@@ -6,6 +6,8 @@ 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.mapper.knowledge.KnowledgeInfoMapper;
+import org.ruoyi.domain.entity.knowledge.KnowledgeInfo;
 import org.ruoyi.service.vector.VectorStoreService;
 import org.springframework.context.annotation.Primary;
 import org.springframework.stereotype.Service;
@@ -24,6 +26,7 @@ import java.util.List;
 public class VectorStoreServiceImpl implements VectorStoreService {
 
     private final VectorStoreStrategyFactory strategyFactory;
+    private final KnowledgeInfoMapper knowledgeInfoMapper;
 
 
     /**
@@ -33,9 +36,23 @@ public class VectorStoreServiceImpl implements VectorStoreService {
         return strategyFactory.getStrategy();
     }
 
+    private VectorStoreService getStrategy(String type) {
+        return strategyFactory.getStrategy(type);
+    }
+
+    private String vectorTypeForKid(String kid) {
+        try {
+            KnowledgeInfo info = knowledgeInfoMapper.selectById(Long.parseLong(kid));
+            return info == null ? null : info.getVectorModel();
+        } catch (Exception e) {
+            log.warn("无法解析知识库向量类型, kid={}", kid, e);
+            return null;
+        }
+    }
+
     @Override
     public void createSchema(String kid, String modelName) {
-        VectorStoreService strategy = getCurrentStrategy();
+        VectorStoreService strategy = getStrategy(vectorTypeForKid(kid));
         strategy.createSchema(kid, modelName);
     }
 
@@ -43,7 +60,7 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
         log.info("存储向量数据: kid={}, docId={}, 数据条数={}",
                 storeEmbeddingBo.getKid(), storeEmbeddingBo.getDocId(), storeEmbeddingBo.getChunkList().size());
-        VectorStoreService strategy = getCurrentStrategy();
+        VectorStoreService strategy = getStrategy(storeEmbeddingBo.getVectorStoreName());
         strategy.storeEmbeddings(storeEmbeddingBo);
     }
 
@@ -51,35 +68,35 @@ public class VectorStoreServiceImpl implements VectorStoreService {
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
         log.info("查询向量数据: kid={}, query={}, maxResults={}",
                 queryVectorBo.getKid(), queryVectorBo.getQuery(), queryVectorBo.getMaxResults());
-        VectorStoreService strategy = getCurrentStrategy();
+        VectorStoreService strategy = getStrategy(queryVectorBo.getVectorModelName());
         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();
+        VectorStoreService strategy = getStrategy(queryVectorBo.getVectorModelName());
         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);
-        VectorStoreService strategy = getCurrentStrategy();
+        VectorStoreService strategy = getStrategy(modelName);
         strategy.removeById(id, modelName);
     }
 
     @Override
     public void removeByDocId(String docId, String kid) {
         log.info("根据docId删除向量数据: docId={}, kid={}", docId, kid);
-        VectorStoreService strategy = getCurrentStrategy();
+        VectorStoreService strategy = getStrategy(vectorTypeForKid(kid));
         strategy.removeByDocId(docId, kid);
     }
 
     @Override
     public void removeByFid(String fid, String kid) {
         log.info("根据fid删除向量数据: fid={}, kid={}", fid, kid);
-        VectorStoreService strategy = getCurrentStrategy();
+        VectorStoreService strategy = getStrategy(vectorTypeForKid(kid));
         strategy.removeByFid(fid, kid);
     }
 }

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

@@ -3,6 +3,7 @@ package org.ruoyi.service.vector.impl;
 import cn.hutool.json.JSONObject;
 import dev.langchain4j.data.embedding.Embedding;
 import dev.langchain4j.model.embedding.EmbeddingModel;
+import dev.langchain4j.data.segment.TextSegment;
 
 import io.weaviate.client.WeaviateClient;
 import lombok.SneakyThrows;
@@ -18,6 +19,8 @@ import org.springframework.stereotype.Component;
 import io.weaviate.client.Config;
 import io.weaviate.client.base.Result;
 import io.weaviate.client.v1.batch.api.ObjectsBatchDeleter;
+import io.weaviate.client.v1.batch.api.ObjectsBatcher;
+import io.weaviate.client.v1.data.model.WeaviateObject;
 import io.weaviate.client.v1.batch.model.BatchDeleteResponse;
 import io.weaviate.client.v1.filters.Operator;
 import io.weaviate.client.v1.filters.WhereFilter;
@@ -43,8 +46,12 @@ import java.util.Map;
 @Component
 public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
-    private WeaviateClient client;
+    private volatile WeaviateClient client;
     private final KnowledgeAttachMapper knowledgeAttachMapper;
+    /**
+     * 已确认存在的 class 缓存，避免每次检索都全量拉取 schema
+     */
+    private final java.util.Set<String> knownClasses = java.util.concurrent.ConcurrentHashMap.newKeySet();
 
     public WeaviateVectorStoreStrategy(VectorStoreProperties vectorStoreProperties,
                                        IChatModelService chatModelService,
@@ -54,6 +61,22 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
         this.knowledgeAttachMapper = knowledgeAttachMapper;
     }
 
+    /**
+     * 懒加载单例客户端，避免 remove 等方法在未调用 createSchema 时 NPE
+     */
+    private WeaviateClient getClient() {
+        if (client == null) {
+            synchronized (this) {
+                if (client == null) {
+                    String protocol = vectorStoreProperties.getWeaviate().getProtocol();
+                    String host = vectorStoreProperties.getWeaviate().getHost();
+                    client = new WeaviateClient(new Config(protocol, host));
+                }
+            }
+        }
+        return client;
+    }
+
     @Override
     public String getVectorStoreType() {
         return "weaviate";
@@ -61,13 +84,12 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public void createSchema(String kid, String embeddingModelName) {
-        String protocol = vectorStoreProperties.getWeaviate().getProtocol();
-        String host = vectorStoreProperties.getWeaviate().getHost();
         String className = vectorStoreProperties.getWeaviate().getClassname() + kid;
-        // 创建 Weaviate 客户端
+        if (knownClasses.contains(className)) {
-        client = new WeaviateClient(new Config(protocol, host));
+            return;
+        }
         // 检查类是否存在，如果不存在就创建 schema
-        Result<Schema> schemaResult = client.schema().getter().run();
+        Result<Schema> schemaResult = getClient().schema().getter().run();
         Schema schema = schemaResult.getResult();
         boolean classExists = false;
         for (WeaviateClass weaviateClass : schema.getClasses()) {
@@ -88,13 +110,15 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                                     Property.builder().name("docId").dataType(Collections.singletonList("text")).build())
                     )
                     .build();
-            Result<Boolean> createResult = client.schema().classCreator().withClass(build).run();
+            Result<Boolean> createResult = getClient().schema().classCreator().withClass(build).run();
             if (createResult.hasErrors()) {
                 log.error("Schema 创建失败: {}", createResult.getError());
+                throw new ServiceException("Weaviate Schema 创建失败: " + createResult.getError());
             } else {
                 log.info("Schema 创建成功: {}", className);
             }
         }
+        knownClasses.add(className);
     }
 
     @Override
@@ -107,10 +131,16 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
         String docId = storeEmbeddingBo.getDocId();
         log.info("向量存储条数记录: {}", chunkList.size());
         long startTime = System.currentTimeMillis();
+        List<TextSegment> segments = chunkList.stream().map(TextSegment::from).toList();
+        List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
+        if (embeddings.size() != chunkList.size()) {
+            throw new ServiceException("Embedding 返回数量与分片数量不一致");
+        }
+        ObjectsBatcher batcher = getClient().batch().objectsBatcher();
         for (int i = 0; i < chunkList.size(); i++) {
             String text = chunkList.get(i);
             String fid = fidList.get(i);
-            Embedding embedding = embeddingModel.embed(text).content();
+            Embedding embedding = embeddings.get(i);
             Map<String, Object> properties = Map.of(
                     "text", text,
                     "fid", fid,
@@ -121,11 +151,13 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
             normalize(vectorArray);
             Float[] vector = toObjectArray(vectorArray);
 
-            client.data().creator()
+            batcher.withObject(WeaviateObject.builder()
-                    .withClassName(vectorStoreProperties.getWeaviate().getClassname() + kid)
+                    .className(vectorStoreProperties.getWeaviate().getClassname() + kid)
-                    .withProperties(properties)
+                    .properties(properties).vector(vector).build());
-                    .withVector(vector)
+        }
-                    .run();
+        Result<?> batchResult = batcher.run();
+        if (batchResult.hasErrors()) {
+            throw new ServiceException("Weaviate 批量写入失败: " + batchResult.getError());
         }
         long endTime = System.currentTimeMillis();
         log.info("向量存储完成消耗时间：" + (endTime - startTime) / 1000 + "秒");
@@ -169,7 +201,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 queryVectorBo.getMaxResults()
         );
 
-        Result<GraphQLResponse> result = client.graphQL().raw().withQuery(graphQLQuery).run();
+        Result<GraphQLResponse> result = getClient().graphQL().raw().withQuery(graphQLQuery).run();
         List<String> resultList = new ArrayList<>();
         if (result != null && !result.hasErrors()) {
             Object data = result.getResult().getData();
@@ -211,6 +243,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 "  Get {\n" +
                 "    %s(nearVector: {vector: [%s]} limit: %d) {\n" +
                 "      text\n" +
+                "      fid\n" +
                 "      docId\n" +
                 "      _additional {\n" +
                 "        distance\n" +
@@ -223,7 +256,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 queryVectorBo.getMaxResults()
         );
 
-        Result<GraphQLResponse> result = client.graphQL().raw().withQuery(graphQLQuery).run();
+        Result<GraphQLResponse> result = getClient().graphQL().raw().withQuery(graphQLQuery).run();
         List<org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo> resultList = new ArrayList<>();
 
         if (result != null && !result.hasErrors()) {
@@ -236,6 +269,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 Map<String, Object> map = (Map<String, Object>) obj;
                 String content = (String) map.get("text");
                 String docId = (String) map.get("docId");
+                String fid = (String) map.get("fid");
 
                 Map<String, Object> additional = (Map<String, Object>) map.get("_additional");
                 Double distance = Double.valueOf(String.valueOf(additional.get("distance")));
@@ -253,6 +287,8 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 }
 
                 resultList.add(org.ruoyi.domain.vo.knowledge.KnowledgeRetrievalVo.builder()
+                        .id(fid)
+                        .docId(docId)
                         .content(content)
                         .score(score)
                         .sourceName(sourceName)
@@ -265,12 +301,10 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
     @Override
     @SneakyThrows
     public void removeById(String id, String modelName) {
-        String protocol = vectorStoreProperties.getWeaviate().getProtocol();
-        String host = vectorStoreProperties.getWeaviate().getHost();
         String className = vectorStoreProperties.getWeaviate().getClassname();
         String finalClassName = className + id;
-        WeaviateClient client = new WeaviateClient(new Config(protocol, host));
+        Result<Boolean> result = getClient().schema().classDeleter().withClassName(finalClassName).run();
-        Result<Boolean> result = client.schema().classDeleter().withClassName(finalClassName).run();
+        knownClasses.remove(finalClassName);
         if (result.hasErrors()) {
             log.error("失败删除向量: " + result.getError());
             throw new ServiceException("失败删除向量数据!");
@@ -288,7 +322,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 .operator(Operator.Equal)
                 .valueText(docId)
                 .build();
-        ObjectsBatchDeleter deleter = client.batch().objectsBatchDeleter();
+        ObjectsBatchDeleter deleter = getClient().batch().objectsBatchDeleter();
         Result<BatchDeleteResponse> result = deleter.withClassName(className)
                 .withWhere(whereFilter)
                 .run();
@@ -308,7 +342,7 @@ public class WeaviateVectorStoreStrategy extends AbstractVectorStoreStrategy {
                 .operator(Operator.Equal)
                 .valueText(fid)
                 .build();
-        ObjectsBatchDeleter deleter = client.batch().objectsBatchDeleter();
+        ObjectsBatchDeleter deleter = getClient().batch().objectsBatchDeleter();
         Result<BatchDeleteResponse> result = deleter.withClassName(className)
                 .withWhere(whereFilter)
                 .run();