fix(ai-chat): 优化引用文档ID处理支持字符串数组类型

- 将数据库中 referenced_doc_ids 字段从 BIGINT[] 修改为 VARCHAR(255)[]
- 在实体类 AiChatMessage 中将 referencedDocIds 类型改为 String[] 并添加自定义类型处理器
- 新增 PostgresArrayTypeHandler 用于处理 PostgreSQL varchar 数组与 Java String[] 的映射
- 修改查询时 project_id 和 timeline_node_id 的过滤表达式，使用字符串匹配避免类型错误
- AiChatServiceImpl 中保存消息时改用字符串数组保存引用文档ID
- KbDocumentVO 新增 fileUrl 字段映射数据库中对应字段
- 数据库映射文件 AiDocumentMapper.xml 增加 file_url 字段映射

docs/dev-ops/pgsql/sql/weform_run.sql

@@ -1106,7 +1106,7 @@ CREATE TABLE ai_chat_history (
                                  content_embedding vector(1024),
 
     -- 引用的知识库文档
-                                 referenced_doc_ids BIGINT[],
+                                 referenced_doc_ids VARCHAR(255)[],
 
     -- 上下文配置
                                  system_prompt TEXT,

src/main/java/cn/yinlihupo/common/handler/PostgresArrayTypeHandler.java

@@ -0,0 +1,57 @@
+package cn.yinlihupo.common.handler;
+
+import org.apache.ibatis.type.BaseTypeHandler;
+import org.apache.ibatis.type.JdbcType;
+
+import java.sql.Array;
+import java.sql.CallableStatement;
+import java.sql.PreparedStatement;
+import java.sql.ResultSet;
+import java.sql.SQLException;
+
+/**
+ * PostgreSQL 数组类型处理器
+ * 用于处理 Java String[] 与 PostgreSQL varchar[] 类型之间的转换
+ */
+public class PostgresArrayTypeHandler extends BaseTypeHandler<String[]> {
+
+    @Override
+    public void setNonNullParameter(PreparedStatement ps, int i, String[] parameter, JdbcType jdbcType) throws SQLException {
+        // 使用 PostgreSQL 的 createArrayOf 方法创建数组
+        Array array = ps.getConnection().createArrayOf("varchar", parameter);
+        ps.setArray(i, array);
+    }
+
+    @Override
+    public String[] getNullableResult(ResultSet rs, String columnName) throws SQLException {
+        Array array = rs.getArray(columnName);
+        return extractArray(array);
+    }
+
+    @Override
+    public String[] getNullableResult(ResultSet rs, int columnIndex) throws SQLException {
+        Array array = rs.getArray(columnIndex);
+        return extractArray(array);
+    }
+
+    @Override
+    public String[] getNullableResult(CallableStatement cs, int columnIndex) throws SQLException {
+        Array array = cs.getArray(columnIndex);
+        return extractArray(array);
+    }
+
+    private String[] extractArray(Array array) throws SQLException {
+        if (array == null) {
+            return null;
+        }
+        Object[] objArray = (Object[]) array.getArray();
+        if (objArray == null) {
+            return null;
+        }
+        String[] result = new String[objArray.length];
+        for (int i = 0; i < objArray.length; i++) {
+            result[i] = objArray[i] != null ? objArray[i].toString() : null;
+        }
+        return result;
+    }
+}

src/main/java/cn/yinlihupo/domain/entity/AiChatMessage.java

@@ -1,6 +1,7 @@
 package cn.yinlihupo.domain.entity;
 
 import com.baomidou.mybatisplus.annotation.IdType;
+import com.baomidou.mybatisplus.annotation.TableField;
 import com.baomidou.mybatisplus.annotation.TableId;
 import com.baomidou.mybatisplus.annotation.TableName;
 import lombok.Data;
@@ -59,9 +60,10 @@ public class AiChatMessage {
     private String contentEmbedding;
 
     /**
-     * 引用的文档ID列表(JSON数组)
+     * 引用的文档ID列表(字符串数组)
      */
-    private String referencedDocIds;
+    @TableField(typeHandler = cn.yinlihupo.common.handler.PostgresArrayTypeHandler.class)
+    private String[] referencedDocIds;
 
     /**
      * 系统提示词

src/main/java/cn/yinlihupo/domain/vo/KbDocumentVO.java

@@ -45,6 +45,11 @@ public class KbDocumentVO {
      */
     private String filePath;
 
+    /**
+     * 文件URL
+     */
+    private String fileUrl;
+
     /**
      * 来源类型
      */

src/main/java/cn/yinlihupo/service/ai/impl/AiChatServiceImpl.java

@@ -134,9 +134,14 @@ public class AiChatServiceImpl implements AiChatService {
                     () -> {
                         // 保存助手消息
                         int responseTime = (int) (System.currentTimeMillis() - startTime);
+                        // 提取引用的文档ID列表
+                        String[] docIds = retrievedDocs.stream()
+                                .map(doc -> doc.getMetadata().getOrDefault("doc_id", "").toString())
+                                .filter(id -> !id.isEmpty())
+                                .toArray(String[]::new);
                         Long messageId = saveMessage(finalSessionId, userId, request.getProjectId(),
                                 request.getTimelineNodeId(), "assistant",
-                                fullResponse.toString(), JSON.toJSONString(referencedDocs));
+                                fullResponse.toString(), docIds);
 
                         // 发送完成消息
                         sendEvent(emitter, "complete", Map.of(
@@ -334,7 +339,7 @@ public class AiChatServiceImpl implements AiChatService {
      */
     private Long saveMessage(String sessionId, Long userId, Long projectId,
                              Long timelineNodeId, String role, String content,
-                             String referencedDocIds) {
+                             String[] referencedDocIds) {
         // 获取当前最大序号
         Integer maxIndex = chatHistoryMapper.selectMaxMessageIndex(sessionId);
         int nextIndex = (maxIndex != null ? maxIndex : 0) + 1;

src/main/java/cn/yinlihupo/service/ai/rag/RagRetriever.java

@@ -41,10 +41,11 @@ public class RagRetriever {
      */
     public List<Document> vectorSearch(String query, Long projectId, int topK) {
         try {
+            // project_id 在 metadata 中是字符串类型，需要用字符串比较
             SearchRequest searchRequest = SearchRequest.builder()
                     .query(query)
                     .topK(topK)
-                    .filterExpression("project_id == " + projectId + " && status == 'active'")
+                    .filterExpression("project_id == '" + projectId + "' && status == 'active'")
                     .build();
 
             List<Document> results = vectorStore.similaritySearch(searchRequest);
@@ -68,12 +69,13 @@ public class RagRetriever {
     public List<Document> vectorSearchWithTimeline(String query, Long projectId,
                                                     Long timelineNodeId, int topK) {
         try {
+            // project_id 和 timeline_node_id 在 metadata 中是字符串类型
             SearchRequest searchRequest = SearchRequest.builder()
                     .query(query)
                     .topK(topK)
-                    .filterExpression("project_id == " + projectId +
-                            " && timeline_node_id == " + timelineNodeId +
-                            " && status == 'active'")
+                    .filterExpression("project_id == '" + projectId +
+                            "' && timeline_node_id == '" + timelineNodeId +
+                            "' && status == 'active'")
                     .build();
 
             List<Document> results = vectorStore.similaritySearch(searchRequest);

src/main/resources/mapper/AiDocumentMapper.xml

@@ -11,6 +11,7 @@
             vs.file_type as fileType,
             vs.file_size as fileSize,
             vs.file_path as filePath,
+            vs.file_url as fileUrl,
             vs.source_type as sourceType,
             vs.chunk_total as chunkCount,
             vs.status,