diff --git a/src/components/chat-view/SearchView.tsx b/src/components/chat-view/SearchView.tsx
index 9212039..aa49ee6 100644
--- a/src/components/chat-view/SearchView.tsx
+++ b/src/components/chat-view/SearchView.tsx
@@ -354,8 +354,6 @@ const SearchView = () => {
 		setShowRAGInitConfirm(true)
 	}, [])
 
-
-
 	// 确认初始化 RAG 向量
 	const confirmInitWorkspaceRAG = useCallback(async () => {
 		setShowRAGInitConfirm(false)
diff --git a/src/core/rag/rag-engine.ts b/src/core/rag/rag-engine.ts
index d25b2ae..f4ace14 100644
--- a/src/core/rag/rag-engine.ts
+++ b/src/core/rag/rag-engine.ts
@@ -16,7 +16,7 @@ import { getEmbeddingModel } from './embedding'
 type EmbeddingManager = {
 	modelLoaded: boolean
 	currentModel: string | null
-	loadModel(modelId: string, useGpu: boolean): Promise<any>
+	loadModel(modelId: string, useGpu: boolean): Promise<unknown>
 	embed(text: string): Promise<{ vec: number[] }>
 	embedBatch(texts: string[]): Promise<{ vec: number[] }[]>
 }
@@ -248,7 +248,6 @@ export class RAGEngine {
 
 		if (workspace) {
 			// 获取工作区中的所有文件路径
-			const folders: string[] = []
 			const files: string[] = []
 
 			for (const item of workspace.content) {
diff --git a/src/database/modules/vector/vector-manager.ts b/src/database/modules/vector/vector-manager.ts
index fdb7d83..6b2998b 100644
--- a/src/database/modules/vector/vector-manager.ts
+++ b/src/database/modules/vector/vector-manager.ts
@@ -18,6 +18,7 @@ import { getFilesWithTag } from '../../../utils/glob-utils';
 import { openSettingsModalWithError } from '../../../utils/open-settings-modal';
 import { DBManager } from '../../database-manager';
 import { Workspace } from '../../json/workspace/types';
+import { vectorTables } from '../../schema';
 
 import { VectorRepository } from './vector-repository';
 
@@ -240,7 +241,7 @@ export class VectorManager {
 
 		const embeddingProgress = { completed: 0 }
 		// 减少批量大小以降低内存压力
-		const batchSize = options.batchSize
+		const batchSize = Math.min(options.batchSize, 20) // 限制最大批量大小
 		let batchCount = 0
 
 		try {
@@ -288,9 +289,10 @@ export class VectorManager {
 						},
 					)
 
-					// 立即插入当前批次，避免内存累积
+					// 使用事务批量插入，减少数据库连接压力
 					if (embeddedBatch.length > 0) {
-						await this.repository.insertVectors(embeddedBatch, embeddingModel)
+						await this.insertVectorsWithTransaction(embeddedBatch, embeddingModel)
+						console.log("insert vectors with transaction success, batch size: ", embeddedBatch.length)
 						// 清理批次数据
 						embeddedBatch.length = 0
 					}
@@ -302,12 +304,12 @@ export class VectorManager {
 						totalFiles: filesToIndex.length,
 					})
 
-					// 定期内存清理
-					await this.memoryCleanup(batchCount)
+					// 定期内存清理和延迟
+					await this.memoryCleanupWithDelay(batchCount)
 				}
 			} else {
-				// 不支持批量处理的提供商：使用流式处理逻辑
-				const limit = pLimit(32) // 从50降低到10，减少并发压力
+				// 不支持批量处理的提供商：大幅降低并发度
+				const limit = pLimit(8) // 从32降低到8，大幅减少并发压力
 				const abortController = new AbortController()
 
 				// 流式处理：分批处理并立即插入
@@ -346,24 +348,24 @@ export class VectorManager {
 										embeddedBatch.push(embeddedChunk)
 									},
 									{
-										numOfAttempts: 3, // 减少重试次数
-										startingDelay: 500, // 减少延迟
-										timeMultiple: 1.5,
+										numOfAttempts: 3,
+										startingDelay: 1000, // 增加延迟
+										timeMultiple: 2.0,
 										jitter: 'full',
 									},
 								)
 							} catch (error) {
-								abortController.abort()
-								throw error
+								console.error('Error in embedding task:', error)
+								// 不要立即中止，继续处理其他任务
 							}
 						}),
 					)
 
 					await Promise.all(tasks)
 
-					// 立即插入当前批次
+					// 使用事务批量插入
 					if (embeddedBatch.length > 0) {
-						await this.repository.insertVectors(embeddedBatch, embeddingModel)
+						await this.insertVectorsWithTransaction(embeddedBatch, embeddingModel)
 						// 清理批次数据
 						embeddedBatch.length = 0
 					}
@@ -375,8 +377,8 @@ export class VectorManager {
 						totalFiles: filesToIndex.length,
 					})
 
-					// 定期内存清理
-					await this.memoryCleanup(batchCount)
+					// 定期内存清理和延迟
+					await this.memoryCleanupWithDelay(batchCount)
 				}
 			}
 		} catch (error) {
@@ -523,7 +525,7 @@ export class VectorManager {
 
 		const embeddingProgress = { completed: 0 }
 		// 减少批量大小以降低内存压力
-		const batchSize = options.batchSize
+		const batchSize = Math.min(options.batchSize, 20) // 限制最大批量大小
 		let batchCount = 0
 
 		try {
@@ -564,16 +566,16 @@ export class VectorManager {
 							}
 						},
 						{
-							numOfAttempts: 3, // 减少重试次数
-							startingDelay: 500, // 减少延迟
-							timeMultiple: 1.5,
+							numOfAttempts: 3,
+							startingDelay: 1000, // 增加延迟
+							timeMultiple: 2.0,
 							jitter: 'full',
 						},
 					)
 
-					// 立即插入当前批次，避免内存累积
+					// 使用事务批量插入，减少数据库连接压力
 					if (embeddedBatch.length > 0) {
-						await this.repository.insertVectors(embeddedBatch, embeddingModel)
+						await this.insertVectorsWithTransaction(embeddedBatch, embeddingModel)
 						// 清理批次数据
 						embeddedBatch.length = 0
 					}
@@ -585,12 +587,12 @@ export class VectorManager {
 						totalFiles: filesToIndex.length,
 					})
 
-					// 定期内存清理
-					await this.memoryCleanup(batchCount)
+					// 定期内存清理和延迟
+					await this.memoryCleanupWithDelay(batchCount)
 				}
 			} else {
-				// 不支持批量处理的提供商：使用流式处理逻辑
-				const limit = pLimit(32) // 从50降低到10，减少并发压力
+				// 不支持批量处理的提供商：大幅降低并发度
+				const limit = pLimit(8) // 从32降低到8，大幅减少并发压力
 				const abortController = new AbortController()
 
 				// 流式处理：分批处理并立即插入
@@ -629,24 +631,24 @@ export class VectorManager {
 										embeddedBatch.push(embeddedChunk)
 									},
 									{
-										numOfAttempts: 3, // 减少重试次数
-										startingDelay: 500, // 减少延迟
-										timeMultiple: 1.5,
+										numOfAttempts: 3,
+										startingDelay: 1000, // 增加延迟
+										timeMultiple: 2.0,
 										jitter: 'full',
 									},
 								)
 							} catch (error) {
-								abortController.abort()
-								throw error
+								console.error('Error in embedding task:', error)
+								// 不要立即中止，继续处理其他任务
 							}
 						}),
 					)
 
 					await Promise.all(tasks)
 
-					// 立即插入当前批次
+					// 使用事务批量插入
 					if (embeddedBatch.length > 0) {
-						await this.repository.insertVectors(embeddedBatch, embeddingModel)
+						await this.insertVectorsWithTransaction(embeddedBatch, embeddingModel)
 						// 清理批次数据
 						embeddedBatch.length = 0
 					}
@@ -658,8 +660,8 @@ export class VectorManager {
 						totalFiles: filesToIndex.length,
 					})
 
-					// 定期内存清理
-					await this.memoryCleanup(batchCount)
+					// 定期内存清理和延迟
+					await this.memoryCleanupWithDelay(batchCount)
 				}
 			}
 		} catch (error) {
@@ -1029,4 +1031,56 @@ export class VectorManager {
 			return []
 		}
 	}
+
+	// 增强的内存清理方法，增加延迟
+	private async memoryCleanupWithDelay(batchCount: number) {
+		// 每5批次强制垃圾回收和延迟
+		if (batchCount % 5 === 0) {
+			this.forceGarbageCollection()
+			// 增加延迟让系统有时间处理
+			await new Promise(resolve => setTimeout(resolve, 500))
+		}
+	}
+
+	// 使用事务插入向量的方法
+	private async insertVectorsWithTransaction(
+		data: InsertVector[],
+		embeddingModel: EmbeddingModel,
+	): Promise<void> {
+		const db = this.dbManager.getPgClient()
+		if (!db) {
+			throw new Error('Database not initialized')
+		}
+
+		const tableName = this.getTableName(embeddingModel)
+
+		// 使用 .exec 方法进行批量插入，性能更好
+		const insertStatements = data.map((vector) => {
+			// 转义字符串值以防止SQL注入
+			const escapedPath = vector.path.replace(/'/g, "''")
+			const escapedContent = vector.content.replace(/\0/g, '').replace(/'/g, "''")
+			const embeddingVector = `[${vector.embedding.join(',')}]`
+			const escapedMetadata = JSON.stringify(vector.metadata).replace(/'/g, "''")
+			
+			return `INSERT INTO "${tableName}" (path, mtime, content, embedding, metadata) VALUES ('${escapedPath}', ${vector.mtime}, '${escapedContent}', '${embeddingVector}', '${escapedMetadata}');`
+		}).join('\n')
+
+		// 使用事务包装批量插入
+		const sql = `
+			BEGIN;
+			${insertStatements}
+			COMMIT;
+		`
+
+		await db.exec(sql)
+	}
+
+	// 获取表名的辅助方法
+	private getTableName(embeddingModel: EmbeddingModel): string {
+		const tableDefinition = vectorTables[embeddingModel.dimension]
+		if (!tableDefinition) {
+			throw new Error(`No table definition found for model: ${embeddingModel.id}`)
+		}
+		return tableDefinition.name
+	}
 }